Download Monsieur Matthieu MILLION Caractérisation des altérations du

AIX-MARSEILLE UNIVERSITE
FACULTE DE MÉDECINE DE MARSEILLE
ECOLE DOCTORALE DES SCIENCES DE LA VIE ET DE LA SANTE
Thèse de Doctorat
Présentée par
Monsieur Matthieu MILLION
En vue de l’obtention du grade de Docteur d’Aix-Marseille Université
Spécialité Maladies Infectieuses
Caractérisation des altérations du microbiote
digestif associées à l’obésité et rôle de la
manipulation du microbiote digestif dans l’obésité
Soutenue le 15 mai 2013
Composition du Jury :
Mr le Professeur Jean-Louis Mège
Mr le Professeur Antoine Andremont
Mr le Professeur Gilbert Greub
Mr le Professeur Didier Raoult
Président du jury
Rapporteur
Rapporteur
Directeur de thèse
Unité de recherche sur les maladies infectieuses et tropicales émergentes,
UMR CNRS 7278
Directeur : Pr. Didier Raoult
SOMMAIRE
AVANT PROPOS
1
RESUME
2
SUMMARY
4
INTRODUCTION
6
Partie I: Identification des altérations du microbiote digestif
associées à l’obésité
9
Article I : REVIEW - Gut bacterial microbiota and obesity
12
Article II : REVIEW - The relationship between gut microbiota
and weight gain in humans
22
Article III : Obesity-associated gut microbiota is enriched in
Lactobacillus reuteri and depleted in Bifidobacterium animalis
and Methanobrevibacter smithii
42
Article IV : Correlation between body mass index and gut
concentrations of Lactobacillus reuteri, Bifidobacterium animalis,
Methanobrevibacter smithii and Escherichia coli
52
Partie II : Le rôle de la manipulation du microbiote digestif
dans l’obésité
60
Article V : REVIEW - The role of the manipulation of the gut
microbiota in obesity
66
Article VI : Comparative meta-analysis of the effect of
Lactobacillus species on weight gain in humans and animals
73
Article VII : Species and strain specificity of Lactobacillus
probiotics effect on weight regulation
83
Article VIII : Publication biases in probiotics
86
Article IX : Lactobacillus rhamnosus bacteremia: an emerging
clinical entity
89
Article X : Occam's razor and probiotics activity on
Listeria monocytogenes
102
Article XI : REVIEW - Human gut microbiota: repertoire
and variations
104
Article XII : Vancomycin-associated gut microbiota
alteration and weight gain in human adults
124
CONCLUSIONS ET PERSPECTIVES
151
REFERENCES
153
ANNEXES
154
Article XIII : Microbial culturomics: Paradigm shift
in the human gut microbiome study
155
Article XIV : Non contiguous finished genome sequence
and description of Bacillus timonensis sp. nov.
165
Article XV : Rapid and accurate bacterial identification
in probiotics and yoghurts by MALDI-TOF mass spectrometry
176
REMERCIEMENTS
182
AVANT-PROPOS
Le format de présentation de cette thèse correspond à une recommandation de la
spécialité Maladies Infectieuses et Microbiologie, à l’intérieur du Master des
Sciences de la Vie et de la Santé qui dépend de l’Ecole Doctorale des Sciences
de la Vie de Marseille.
Le candidat est amené à respecter des règles qui lui sont imposées et qui
comportent un format de thèse utilisé dans le Nord de l’Europe et qui permet un
meilleur rangement que les thèses traditionnelles. Par ailleurs, la partie
introduction et bibliographie est remplacée par une revue envoyée dans un
journal afin de permettre une évaluation extérieure de la qualité de la revue et de
permettre à l’étudiant de commencer le plus tôt possible une bibliographie
exhaustive sur le domaine de cette thèse Par ailleurs, la thèse est présentée sur
article publié, accepté ou soumis associé d’un bref commentaire donnant le sens
général du travail. Cette forme de présentation a paru plus en adéquation avec
les exigences de la compétition internationale et permet de se concentrer sur des
travaux qui bénéficieront d’une diffusion internationale.
Professeur Didier RAOULT
1
RESUME
L’avènement des méthodes de séquençage moléculaire à large échelle a permis
l’identification d’altérations du microbiote digestif spécifiquement associés à l’obésité
notamment un ratio Bacteroidetes/Firmicutes diminué chez les obèses. Depuis, de nombreux
travaux ont décrit de nouvelles altérations associées à l’obésité, notamment une augmentation
des représentants du genre Lactobacillus mais l’ensemble de ces résultats sont souvent l’objet
de controverses. Afin de clarifier si le genre Lactobacillus était associé à l’obésité, nous avons
réalisé deux études cas témoins (la deuxième étant le prolongement de la première avec un
effectif de 263 individus) qui nous ont permis d’identifier que les altérations du microbiote
digestif sont plus reproductibles au niveau de l’espèce. A ce titre nous avons retrouvé une plus
grande concentration de Lactobacillus reuteri dans le microbiote digestif de sujets obèses
alors que les concentrations de Bifidobacterium animalis, Methanobrevibacter smithii et
Escherichia coli étaient diminuées. Nous avons pu établir une relation dose-dépendante entre
la concentration de Lactobacillus reuteri et l’indice de masse corporelle. Par ailleurs, nous
avons réalisé une méta-analyse sur les résultats des études publiées et avons retrouvé une
association entre les genres Bifidobacterium (6 études, 348 individus) et Methanobrevibacter
(3 études, 195 individus) avec l’absence d’obésité.
La manipulation du microbiote digestif par les antibiotiques et les probiotiques,
principalement des Lactobacillus, a été utilisée depuis plus de 50 ans dans l’agriculture pour
un effet promoteur de croissance. Afin de clarifier l’effet des probiotiques contenant des
Lactobacillus sur le poids, nous avons effectué une méta-analyse incluant 17 essais
randomisées chez l’homme, 51 études chez l’animal et 14 études sur des modèles
experimentaux. Lactobacillus acidophilus, Lactobacillus ingluviei et Lactobacillus fermentum
étaient associés à une prise de poids significative chez les animaux. Lactobacillus plantarum
était associé à une perte de poids chez des animaux obèses et Lactobacillus gasseri était
2
associé avec une perte de poids à la fois chez les humains et les animaux en surpoids ou
obèse. L’ensemble de ces résultats suggère que l’effet des probiotiques contenant des
Lactobacillus sur le poids dépend à la fois de l’espèce bactérienne utilisée et de l’hôte. Enfin,
l’administration de vancomycine a été associée à une prise de poids chez les animaux et les
humains mais les modifications du microbiote digestif responsable de cette prise de poids
n’ont pas été élucidées. Dans un travail préliminaire, nous avons retrouvé que l’administration
de vancomycine était associé à l’augmentation des Lactobacillus chez l’homme. Lactobacillus
reuteri, Lactobacillus fermentum et Lactobacillus sakei naturellement résistant à la
vancomycine et identifié comme étant associés à la prise de poids dans d’autres études,
pourrait être des candidats supportant la prise de poids sous vancomycine et des vecteurs
potentiels de l’obésité.
Mots-clé : Obésité, Microbiote digestif, Lactobacillus, Bifidobacterium, Méta-analyse,
MALDI-TOF
3
SUMMARY
The revolution of large scale molecular sequencing methods allowed the identification
of specific alterations in the gut microbiota associated with obesity such as a decreased
Bacteroidetes / Firmicutes ratio in obese individuals. Since then, many studies have described
different alterations associated with obesity, including an increase in members of the
Lactobacillus genus, but results are often controversial. To clarify whether the genus
Lactobacillus was associated with obesity, we conducted two case-control studies (the second
being the follow-up of the first study with a total of 263 individuals) allowing us to
understand that gut microbiota alterations are more reproducible at the species level. We
found a greater concentration of Lactobacillus reuteri in the gut microbiota of obese while
concentrations of Bifidobacterium animalis, Methanobrevibacter smithii and Escherichia coli
were reduced. We were able to establish a dose-dependent relationship between the
concentration of Lactobacillus reuteri and body mass index. In addition, we performed a
meta-analysis on the results of published studies and we found an association between the
Bifidobacterium (6 studies, 348 individuals) and Methanobrevibacter (3 studies, 195
individuals) with absence of obesity.
The manipulation of the gut microbiota by antibiotics and probiotics, mainly
Lactobacillus, has been used for over 50 years in agriculture for its growth promoting effect.
To clarify the effect of probiotics containing Lactobacillus on weight, we performed a metaanalysis including 17 randomized trials in humans, 51 farm animal studies and 14 studies on
experimental models. Lactobacillus acidophilus, Lactobacillus fermentum and Lactobacillus
ingluviei were associated with significant weight gain in animals. Lactobacillus plantarum
was associated with weight loss in obese animals and Lactobacillus gasseri was associated
with weight loss in both overweight or obese humans and animals. Taken together, these
results suggest that the effect of probiotics containing Lactobacillus on weight depends both
4
on the bacterial species and the host. Finally, administration of vancomycin has been
associated with weight gain in animals and humans but changes in digestive microbiota
responsible for this weight gain have not been elucidated. In a preliminary study, we found
that administration of vancomycin was associated with an increase in Lactobacillus in
humans. Lactobacillus reuteri, Lactobacillus fermentum and Lactobacillus sakei naturally
resistant to vancomycin and identified as being associated with weight gain in other studies,
may be candidates for weight gain under vancomycin and are potential vectors of obesity.
Keywords : Obesity, Gut microbiota, Lactobacillus, Bifidobacterium, Meta-analysis,
MALDI-TOF
5
INTRODUCTION
L'obésité est définie par un indice de masse corporelle (IMC) > 30 kg/m2 et une
augmentation de la masse grasse et est associée à une augmentation significative de la
morbidité et de la mortalité incluant notamment les maladies cardio-vasculaires, l’arthrose
mais aussi certains cancers. La fréquence de l'obésité est en augmentation chez les enfants, les
adolescents et les adultes, et a doublé depuis 1980. Selon l'OMS, 65% de la population
mondiale vit dans des pays où l'excès de poids et l'obésité tue plus de gens que l'insuffisance
pondérale, y compris tous les pays à revenu élevé et la plupart des pays à revenu intermédiaire
(www.who.int).
Le microbiome humain est l’ensemble des communautés microbiennes associées au
corps humain dont le nombre d’individus dépasse le nombre des cellules humaines d’au
moins un ordre de grandeur. C’est un écosystème complexe qui se compose de virus,
bactéries, archées, champignons et parasites. Le plus grand nombre de ces micro-organismes
(1010 à 1014 bactéries) réside dans le tube digestif distal où ils synthétisent des acides aminés
essentiels et des vitamines et assurent le métabolisme de nutriments autrement non digestibles
de notre alimentation comme les polysaccharides végétaux. Son rôle dépasse le métabolisme
énergétique et la régulation du stockage des graisses et intervient notamment dans le
développement de l’immunité.
Des entérotypes spécifiques ont été identifiés indépendamment de l’origine ethnique
ou géographique. Ils ont été liés à l'alimentation, et leur modulation induite par les
antibiotiques peut influer le profil métabolique de l'hôte. Parce que l'intestin est un «point
chaud» pour le transfert horizontal de gènes entre un nombre astronomique de bactéries (> 109
/ g), d’archées et de virus, l'analyse au niveau du gène a été jugée la meilleure façon de
caractériser les altérations du microbiote intestinal et leurs corrélations avec l'obésité. A
6
l'inverse, d'autres travaux rapportent que l'analyse sur une base taxonomique reste tout à fait
pertinente.
Une perturbation spécifique au niveau du phylum avec un ratio Bacteroidetes /
Firmicutes diminué a d'abord été montré comme étant associé à l'obésité, mais la distinction
(c’est-à-dire la classification ou le clustering) entre le microbiote intestinal d’individus maigre
et obèses est améliorée lorsque la profondeur de l'analyse taxonomique est augmentée
suggérant qu’une analyse au niveau du genre, de l’espèce voire des souches bactériennes
serait plus pertinente. A ce titre, un travail antérieur à notre thèse réalisé dans notre laboratoire
avait montré une augmentation des représentants du genre Lactobacillus chez les individus
obèses.
Enfin, la manipulation du microbiote digestif est possible par l’alimentation, les
probiotiques et les antibiotiques. De façon parallèle, les antibiotiques puis les probiotiques,
incluant le plus souvent des Lactobacillus, ont été utilisés comme facteur de croissance dans
l’agriculture depuis plus de 50 ans. Il est donc tout à fait plausible que cet effet promoteur de
croissance dépende de la modification du microbiote digestif. A partir de cette observation, il
a été suggéré que les antibiotiques ou les probiotiques pouvaient être associés à l’obésité.
Dans notre laboratoire, l’administration d’une souche de Lactobacillus ingluviei provenant
d’une autruche à des poulets a, de façon tout à fait inattendue, provoqué une prise de poids
massive et cela a été reproductible sur d’autres modèles animaux comme des souris.
Cependant le lien entre l’administration de probiotiques et la modification du poids chez les
animaux et les hommes nécessitait d’être clarifié. Par ailleurs, une étude a montré que des
patients sous vancomycine, antibiotique efficace sur les Firmicutes mais inefficace sur
plusieurs espèces de Lactobacillus, voyaient leur poids augmenter et c’est pourquoi un rôle
des Lactobacillus dans cette prise de poids avait alors été suspecté.
7
Après avoir effectué un travail préliminaire sur la méthode de « microbial
culturomics » (Annexes, Article XIII à XV), l’objectif de notre travail a été d’utiliser des
techniques de culture, de spectrométrie de masse et de biologie moléculaire innovantes pour :
i) Identifier les altérations du microbiote digestif associées à l’obésité (articles I à IV) et ii)
Clarifier le rôle de la manipulation du microbiote digestif sur le poids (Articles V à XII).
8
Partie I :
Identification des altérations du microbiote digestif
associées à l’obésité
9
Avant-propos
Le premier travail rapportant une altération du microbiote digestif associé à l’obésité
chez l’homme a été publié en 2006 par Ley et al.
1
et a montré que les individus obèses
avaient une diminution du pourcentage de séquences correspondant au phylum des
Bacteroidetes par rapport aux sujets de poids normal et que les régimes pauvres en
carbohydrate ou pauvre en matière grasse entrainaient une augmentation du pourcentage de
Bacteroidetes et cette augmentation était d’autant plus importante que l’individu perdait du
poids.
Partant de cette étude, nous avons réalisé une méta-analyse sur les données publiées
(Article II) afin de clarifier quelles étaient les altérations reproductibles au niveau du phylum,
du genre ou de l’espèce. Cette diminution de la proportion des Bacteroidetes n’a pas été
constatée en méta-analyse alors même que nous avons retrouvé la même tendance dans deux
études observationnelles que nous avons réalisé (Article III et IV). Par contre, les altérations
reproductibles et associées à des résultats concordants et significatifs en méta-analyse étaient
une diminution des Bifidobacterium dans 6 études différentes réalisées dans 4 pays différents
à savoir l’Allemagne, la Finlande, l’Espagne et la Chine
et une diminution des
Methanobrevibacter sp. à partir de 3 études réalisées dans 2 pays différents à savoir la France
et l’Allemagne (Article II).
Par ailleurs, une précédente étude du laboratoire a rapporté une augmentation de la
concentration des Lactobacillus chez les obèses 2, c’est pourquoi nous avons étudié par
culture les représentants de ce genre dans le microbiote digestif de patients obèses et
d’individus contrôles. Cependant, nos résultats ont rapidement montré que les Lactobacillus
n’étaient ni plus prévalent ni plus abondant chez les individus obèses (culture sur milieu
LAMVAB). Afin de comprendre cette discordance, nous avons pu montrer que le système
10
d’amorces et de sonde utilisé dans l’étude précédente du laboratoire était significativement
plus sensible pour certaines espèces de Lactobacillus. A partir de là, nous avons conçu
plusieurs systèmes de real-time PCR spécifiques de 8 espèces de Lactobacillus identifiée en
culture ou dans la littérature comme associés à l’obésité ou à un poids normal plus un système
spécifique pour Lactococcus lactis et Bifidobacterium animalis. Lactobacillus reuteri (PCR) a
été associé à l’obésité alors que Lactobacillus plantarum (culture) ou Lactobacillus paracasei
(culture) ont été associés à un poids normal (Article III). De plus nous avons retrouvé de
façon tout à fait inattendue une association extrêmement significative entre Bifidobacterium
animalis et l’absence d’obésité. Nous avons ensuite trouvé que cela était cohérent avec notre
travail de méta-analyse qui retrouvait une association entre le genre Bifidobacterium et
l'absence d’obésité. Nous avons aussi confirmé que Methanobrevibacter smithii,
préalablement associé au microbiote d’individus anorexiques dans une étude de notre
laboratoire 2, était associé au microbiote d’individu de poids normal par rapport à des
individus obèses (Article III).
Enfin, nous avons prolongé cette étude en doublant la taille de l’échantillon et en
incluant des individus en surpoids et des individus anorexiques (Article IV). Dans cette
nouvelle étude, nous avons inclus Escherichia coli, associée par d’autres équipes à l’obésité.
Cela nous a permis d’identifier un effet dose-dépendant avec un coefficient de régression
linéaire positif pour Lactobacillus reuteri (présent en concentration d’autant plus abondante
que l’indice de masse corporelle était élevé), alors que ce coefficient était négatif pour
Bifidobacterium animalis, Methanobrevibacter smithii et Escherichia coli (présents en
concentration d’autant plus faible que l’indice de masse corporelle était élevé).
11
Article I : REVIEW
Gut bacterial microbiota and obesity
Matthieu Million, Jean-Christophe Lagier, Dafna Yahav, Mical Paul
Published in Clinical Microbiology and Infection. Article first published online: 2 MAR 2013.
DOI: 10.1111/1469-0691.12172. (IF 4.54)
12
REVIEW
10.1111/1469-0691.12172
Gut bacterial microbiota and obesity
M. Million1, J.-C. Lagier1, D. Yahav2 and M. Paul2
1) Unite de Recherche sur les Maladies Infectieuses et Tropicales Emergentes, Faculte de Medecine, CNRS UMR 7278, IRD 198, Aix-Marseille Universite,
Marseille, France and 2) Unit of Infectious Diseases, Rabin Medical Centre, Beilinson Hospital and Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
Abstract
Although probiotics and antibiotics have been used for decades as growth promoters in animals, attention has only recently been drawn to
the association between the gut microbiota composition, its manipulation, and obesity. Studies in mice have associated the phylum Firmicutes
with obesity and the phylum Bacteroidetes with weight loss. Proposed mechanisms linking the microbiota to fat content and weight include
differential effects of bacteria on the efficiency of energy extraction from the diet, and changes in host metabolism of absorbed calories. The
independent effect of the microbiota on fat accumulation has been demonstrated in mice, where transplantation of microbiota from obese
mice or mice fed western diets to lean or germ-free mice produced fat accumulation among recipients. The microbiota can be manipulated
by prebiotics, probiotics, and antibiotics. Probiotics affect the microbiota directly by modulating its bacterial content, and indirectly through
bacteriocins produced by the probiotic bacteria. Interestingly, certain probiotics are associated with weight gain both in animals and in
humans. The effects are dependent on the probiotic strain, the host, and specific host characteristics, such as age and baseline nutritional
status. Attention has recently been drawn to the association between antibiotic use and weight gain in children and adults. We herein
review the studies describing the associations between the microbiota composition, its manipulation, and obesity.
Keywords: Fat, growth promoters, microbiota, obesity, probiotics
Clin Microbiol Infect
Corresponding author: M. Paul, Unit of Infectious Diseases,
Rambam Healthcare Campus, Haifa 31096, Israel
E-mail: [email protected]
Introduction
Ten trillion to 100 trillion (1014) microorganisms populate the
adult intestines [1,2]. The vast majority reside in the colon,
where densities approach 1011–1012 cells/mL. Almost all of
these organisms are bacteria, and a minority are archaeons,
eukaryotes, and viruses [3,4]. Bacteria are classified from the
phylum to species level (Table 1). The two most abundant
bacterial phyla in humans and in mice are the Firmicutes (60–
80%) and the Bacteroidetes (20–40%) [1,3,5]. Most of the
representatives of these two phyla do not grow outside of their
host [1]. Babies acquire their initial microbiota from the
surrounding ecosystems, especially the maternal vaginal and
faecal microflora [2,6], and the human gut microbiome is shared
among family members [7,8]. The gut microbiota composition
depends on age, sex, geography, ethnicity, family, and diet, and
can be modulated by prebiotics, probiotics, and antibiotics.
Microbial changes in the human gut were proposed as a
possible cause of obesity [5,9,10]. Certain phyla and classes of
bacteria are associated with improved transfer of calories from
the diet to the host, and with changes in the host metabolism of
absorbed calories [11]. Gut microorganisms ferment dietary
polysaccharides into monosaccharides and short-chain fatty acids,
and thus allow the extraction of calories from indigestible dietary
polysaccharides. One of the ways in which they affect host
metabolism is by suppressing fasting-induced adipocyte factor,
which is a lipoprotein lipase inhibitor, and the suppression of
which contributes to the deposition of triglycerides in adipocytes.
The Association between
Composition and Obesity
Microbiota
Studies in mice have found a higher abundance of Firmicutes in
obese mice and those fed on western diets, concomitant with
ª2013 The Authors
Clinical Microbiology and Infection ª2013 European Society of Clinical Microbiology and Infectious Diseases
2
CMI
Clinical Microbiology and Infection
TABLE 1. Examples of the classification of several common
gut bacteria
Domain
Bacteria
Bacteria
Bacteria
Bacteria
Phylum
Class
Order
Family
Genus
Firmicutes
Clostridia
Clostridiales
Clostridiaceae
Clostridium
Firmicutes
Bacilli
Lactobacillales
Lactobacillaceae
Lactobacillus
Bacteroidetes
Bacteroidetes
Bacteroidiales
Bacteroidiaceae
Bacteroides
Actinobacteria
Actinobacteria
Bifidobacteriales
Bifidobacteriaceae
Bifidobacterium
a decrease in the abundance of Bacteroidetes [1,11]. Within the
phylum Firmicutes, the class Mollicutes was the most common in
obese mice [11]. Bacteroidetes possess fewer genes for
enzymes involved in lipid and carbohydrate metabolism than
Firmicutes [12]. However, within the phylum Bacteroidetes,
Bacteroides thetaiotaomicron was found to improve host nutrient absorption and processing [13].
Studies in humans found various Firmicutes/Bacteroidetes
ratios in obese individuals. Some supported the finding of a
high Firmicutes/Bacteroidetes ratio [5,14–16], some did not find
a correlation between body mass index and the Firmicutes/
Bacteroidetes ratio [3,17], and still others found an opposite
ratio [18,19]. Turnbaugh et al. [7] described a lower proportion of Bacteroidetes and a higher proportion of Actinobacteria in
obese than in lean individuals, with no significant difference in
the proportion of Firmicutes. A significantly higher level of
Lactobacillus species (from the phylum Firmicutes) was found in
obese patients than in lean controls [14]. Specifically, a higher
level of Lactobacillus reuteri and lower levels of Lactobacillus
casei/paracasei and Lactobacillus plantarum were associated with
obesity [15]. Reduced proportions of butyrate-producing
Firmicutes were described in obese subjects on weight loss
diets [20] and their presence was lower in obese subjects as
compared with their blood-related lean family members [8].
Another bacterial genus that has been implicated in obesity
is Bifidobacterium (belonging to the phylum Actinobacteria).
Several studies in humans found an association between lower
levels of bifidobacteria and obesity [15,16,18,19,21]. Bifidobacteria were found at higher levels in the intestinal microbiota
of breast-fed infants than in that of formula-fed infants [12].
The association between bifidobacteria and obesity is probably
also species-specific [22].
At the species level, several studies have investigated the
association between specific bacterial species and obesity in
humans. An association between Staphylococcus aureus and an
overweight state was demonstrated in children and pregnant
women [19,21]. Reduced numbers of Bacteroides and increased
numbers of Staphylococcus, Enterobacteriaceae and Escherichia
coli have been described in overweight as compared with
normal-weight pregnant women [16]. Levels of Faecalibacterium prausnitzii (of the phylum Firmicutes) were significantly
higher in obese than in non-obese children [23]. The
proportions of the Bacteroides–Prevotella group were shown
to increase after weight loss in obese adolescents [24]. The
latter study also revealed a correlation between reductions in
Clostridium histolyticum and Eubacterium rectale–Clostridium coccoides (Firmicutes) proportions and weight loss.
Turnbaugh et al. [25] demonstrated more environmental
gene tags of archaeons in the caecal microbiome of obese mice
than in that of lean mice. Archaeons are methanogenic
organisms that increase the efficiency of bacterial fermentation. The principal methanogenic archaeon in the human gut is
Methanobrevibacter smithii. Studies in mice colonized with this
organism and/or B. thetaiotaomicron revealed that co-colonization increases the efficiency of polysaccharide fermentation,
leading to an increase in adiposity as compared with mice
colonized with either organism alone [25,26]. Zhang et al. [27]
found that Methanobacteriales were present only in obese
individuals, after studying three obese individuals and three
human controls. Several other studies in humans have
demonstrated lower levels of Methanobrevibacter in overweight
and obese human volunteers [14,15,18].
Microbiota Transplantation Studies
The independent contribution of the microbiota to fat
accumulation has been demonstrated in a series of elegant
in vivo studies in mice. Germ-free mice, lacking a microbiota,
have significantly less body fat than normal mice, despite eating
more [10]. Transfer of the microbiota from normal to germfree mice caused a significant increase in body fat content.
Transplanting germ-free mice with the microbiota from obese
mice led to a significantly increased fat content as compared
with transplantation of the microbiota from lean mice, and this
was associated with a greater relative abundance of Firmicutes
in the guts of both the obese donors and their recipients [25].
This was observed in controlled conditions, where both
groups had the same baseline weight and received the same
amount of feeding. Transplanting germ-free mice with the
microbiota from mice raised on a western diet led to
significantly increased body fat as compared with mice
transplanted with the microbiota from donors who had been
fed a lean low-fat diet, rich in structurally complex plant
polysaccharides [11]. Both carbohydrate restriction and fat
restriction from the western diet prevented the increased
accumulation of fat in recipients. This was accompanied by a
decrease in the presence of the Mollicutes lineage (phylum
Firmicutes) and an increase in the relative abundance of
Bacteroidetes. Turnbaugh et al. [28] successfully colonized mice
with human faeces, and fed them with the western diet vs. the
ª2013 The Authors
Clinical Microbiology and Infection ª2013 European Society of Clinical Microbiology and Infectious Diseases, CMI
CMI
Million et al.
low-fat/plant polysaccharide-rich diet for 2 weeks, and then
transplanted their microbota into germ-free mice. Germ-free
mice receiving the microbiota from the obese western-diet-fed
humanized mice gained significantly more adiposity than the
mice receiving the microbiota from the low-fat-fed humanized
mice. Again, this was achieved despite matching of the two
groups of recipients by age, weight, and body fat, similar
feeding of recipients with low-fat/plant polysaccharide-rich
diets, and similar consumption of food. Different strains of
Bifidobacterium (phylum Actinobacteria) from human volunteers’
fresh faeces given to mice produced different effects on body
weight [22]. In all studies, the time to fat changes following
microbiota manipulation was up to 2 weeks.
These studies prove that the microbiota by itself can cause
weight gain. The microbiota derived from genetically obese mice
or mice rendered obese by diet can cause fat accumulation, and
this is not mediated by increased food consumption. The studies
also showed that this is mediated both by improved efficiency of
transfer of calories from the diet to the host and through effects
on host metabolism of the absorbed calories [11].
Microbiota and obesity
3
be responsible for Proteobacteria abundance (order Desulfovibrio), but authors comparing the gut flora of malnourished
children with that of well-nourished children in Bangladesh
found a Bacteroidetes decrease and a Proteobacteria increase,
notably for Escherichia coli and Klebsiella spp. [35].
The Influence of Prebiotics on the Microbiota
Prebiotics are defined as food ingredients that stimulate the
growth of a limited number of microbial genus/species in the
gut microbiota that are hypothesized to confer health benefits
to the host. The administration of oligofructose to high-fat-fed
mice increased the abundance of Bifidobacterium and normalized endotoxaemia and the inflammatory tone associated with
the high-fat diet [36]. The administration of oligofructose to
genetically obese mice induced increases in the levels of
Lactobacillus, Bifidobacterium, and C. coccoides–E. rectale, which
led to a reduction in intestinal permeability and an improvement in tight junction integrity and inflammatory markers, such
as lipopolysaccharides and cytokines [37].
Association between Diet and the Microbiota
Dietary habits constitute a major factor influencing the
diversity of the human gut microbiota [29]. A vegetarian diet
is known to affect the intestinal microbiota by decreasing the
amount and modifying the diversity of Clostridium cluster IV
and Clostridium rRNA clusters XIVa and XVIII [30,31]. Walker
et al. [32] successively tested obese individuals with a control
diet, a diet high in resistant starch or non-starch polysaccharides, and a reduced-carbohydrate weight loss diet. There was
no significant effect of diet on the proportions of the four main
phyla represented in the gut microbiota. E. rectale and
Ruminococcus bromii showed dramatically increased proportions in individuals receiving the resistant starch diet, whereas
the proportion of Collinsella aerofaciens-related sequences was
decreased significantly in those receiving the weight loss diet.
Gut analysis of African children from Burkina Faso showed
specific abundance of Prevotella, Xylanibacter and Treponema
containing bacterial genes for cellulose and xylan hydrolysis,
which are completely absent in European children, and are
probably linked to high intake of fibre, allowing increased
extraction of metabolic energy from the polysaccharides of
ingested plants [33]. Wu et al. [34] found that enterotypes
were strongly associated with long-term diets. After shortterm diet modification, the gut microbiota alteration occurred
rapidly and was quickly reversible [32,34]. Conversely, persistent modifications of individual enterotypes occurred during
long-term dietary interventions [34]. Finally, a high-fat diet can
The Influence of Probiotics on the Microbiota
and Obesity
Probiotics are live bacteria that are thought to be beneficial to the
host. The first food containing probiotics ingested by humans is
breast milk. Two studies showed that Bifidobacterium, Lactobacillus and Enterococcus strains showed identical random amplification of polymorphic DNA profiles in breast milk samples and
faeces of newborns at different sampling times, suggesting vertical
transfer of these bacteria from the mother’s milk to the infant
[38,39]. The influence of probiotics on the intestinal flora is highly
dependent on their adhesion to colonocytes, resistance to acidic
pH, and bile salt tolerance [40]. In an interventional study,
L. reuteri DSM 12246 was associated with excellent adhesion. In
contrast, Lactobacillus delbrueckii ssp. lactis CHCC2329 did not
survive at pH 2.5, and was found in only a few of those to whom it
was administered. It is plausible that probiotics modulate and
shape the digestive microbiota according to the antibiotic
spectrum of their bacteriocins, which are antibiotic-like substances produced by bacteria.
To date, the metagenomic data available from human
intervention studies with probiotics are very limited [41].
Recently, gut analysis was performed by flow cytometry and
fluorescence in situ hybridization in newborns’ faecal samples
after administration of probiotics vs. placebo to mothers for
2 months before delivery up to 2 months after delivery
(during breast-feeding) in a Finish cohort, or to newborns
ª2013 The Authors
Clinical Microbiology and Infection ª2013 European Society of Clinical Microbiology and Infectious Diseases, CMI
4
CMI
Clinical Microbiology and Infection
receiving formula feeding from 1 month of age up to 4 months
of age in a German cohort of newborns [42]. The probiotics
used included Lactobacillus rhamnosus LPR, Lactobacillus paracasei ST11 (only in the Finish cohort), and Bifidobacterium longum
BL999. The combination of L. rhamnosus LPR and Bifidobacterium longum BL999 had the effect of raising the level of
Lactobacillus–Enterococcus and lowering the level of Bifidobacterium in the gut microbiota of the Finnish cohort (whose
mothers were treated), whereas there was no such effect in
the German cohort (where infants were given probiotics). The
authors concluded that probiotic treatment had different
impacts on the gut microbiota composition in Finnish and
German infants, owing to differences in mode of feeding and
the early commensal microbiota. The specificity of the
Lactobacillus species or strain for its effect on the gut
microbiota was demonstrated in this study.
Probiotics, which had also been used for decades in
agriculture for their growth-promoting effects, have undergone
a revival since the ban on antibiotics as growth promoters in
Europe from 1 January 2006 [43]. Probiotics are also frequently
used by people for their proposed health benefits. We
performed a meta-analysis on the effects of probiotics on
weight in humans and animals [44]. The bacteria most
commonly used belonged to the genera Lactobacillus, Bifidobacterium, Enterococcus, and Streptococcus. Lactobacillus acidophilus,
Lactobacillus fermentum and Lactobacillus ingluviei were associated with a weight gain effect in lean individuals, whereas
L. plantarum and Lactobacillus gasseri strains had an anti-obesity
effect in overweight/obese people. It is most likely that this
effect was dependent on the strain used and the metabolic
phenotype of the host, as we found one study linking L. gasseri
ATCC 4962 and ATCC 4963 (formerly named L. acidophilus)
with weight gain in bottle-fed infants, in contrast to the results
observed in overweight/obese people. In a randomized controlled trial, individuals randomized to receive fermented milk
containing L. gasseri showed reductions in abdominal adiposity,
body weight, and other measures [45]. Whether the same
Lactobacillus strains could have a growth-promoting effect in
undernourished individuals and an anti-obesity effect in obese
individuals needs to be clarified. Overall, both the probiotic
bacterial strain and the host are important determinants of the
effects of probiotics on obesity, and it is possible that certain
marketed probiotics favour obesity.
The Influence of Antibiotics on the Microbiota
Oral and intravenous antibiotics have been reported to
decrease the bacterial load in the digestive tract [46,47],
although other studies have found that only the microbiota
composition is changed [48]. For example, metronidazole,
cefoperazone, or vancomycin, in contrast to amoxycillin, led to
alterations in community structure without a significant
decrease in the overall bacterial biomass [49]. Studies
summarizing the effects of antibiotics on the gut microbiota
in animals are summarized in Table 2. Antibiotic effects can be
species-specific. Lactobacillus appears to be particularly
impacted by growth-promoting antibiotics in animal studies
[50]. In contrast, the levels of some bacterial genera seem to
be reduced by the growth-promoting antibiotics, particularly
Proteobacteria (e.g. Salmonella) [51]. Finally, whereas rapid
recovery has been described after short-term antibiotic
TABLE 2. In vivo studies examining the effects of antibiotics on microbiota
Reference
Host
Antibiotics
Microbiota changes with antibiotics
Other effects
Cho, 2012 [50]
Mice
Subtherapeutic antibiotic
treatment with vancomycin,
penicillin, and chlortetracyclines
Proportion of Firmicutes higher vs. controls
Lachnospiraceae family increased
Subtherapeutic antibiotic treatment
altered the gene counts of genes
involvedin the metabolism of
carbohydrates to short-chain fatty acids.
Increases in caecal acetate, butyrate
and propionate have been observed
with STAT
Robinson, 2010 [49]
Mice
Vancomycin
Membrez, 2008 [68]
Looft, 2010 [69]
Mice (obese)
Pigs
Kim, 2010 [70]
Collier, 2003 [71]
Rettedal, 2009 [72]
Pigs
Pigs
Pigs
Norfloxacin and ampicillin
Chlortetracycline–sulphamethazine
and penicillin
Tylosin
Tylosin
Chlortetracycline
Increases in the phyla Proteobacteria and
Tenericutes and the family Lactobacillaceae
Decrease in the family Lachnospiraceae
Decrease in aerobic and anaerobic bacteria
Increase in Proteobacteria, Escherichia coli
Torok, 2011 [73]
Chicken
Avilamycin
Torok, 2011 [74]
Chicken
Avilamycin
Dumonceaux, 2006 [51]
Chicken
Virginiamycin
Guban, 2006 [75]
Chicken
Bacitracin
Lactobacillus and Sporacetigenum increased
Lactobacillus increased
Lactobacillus amylovorus increased
Lactobacillus johnsonii decreased
Lactobacillus crispatus, Lactobacillus reuteri,
Subdoligranulum and Enterobacteriaceae increased
Lachnospiraceae, Ruminococcaceae,
Oxalobacteraceae and L. johnsonii decreased
L. crispatus, Lactobacillus salivarius,
Lactobacillus aviarus, Escherichia coli,
Bacteroides vulgatus or
Faecalibacterium prausnitzii increased
Aerobic bacteria and Lactobacillus,
especially L. crispatus, increased
L. salivarius decreased
ª2013 The Authors
Clinical Microbiology and Infection ª2013 European Society of Clinical Microbiology and Infectious Diseases, CMI
Improved glucose tolerance
Improved feed conversion ratio as
measured by weight gain/amount of feed
consumed
Unspecified
Penicillin, vancomycin, and other
antibiotics, IV, unspecified,
unspecified
Azithromycin, oral, 250 mg daily for
21 months
Azithromycin, 168 days, oral,
250 mg if <40 kg, 500 mg if
>40 kg, 3 days a week
Azithromycin, 168 days, oral,
250 mg if <36 kg, 500 mg if
>36 kg, 3 days a week
Azithromycin, day 168 to day 336
(follow-on study of Saiman [79]),
250 mg if <36 kg, 500 mg if
>36 kg, 3 days a week
Azithromycin, 12 months, oral,
250 mg if <40 kg, 500 mg if
 40 kg
Erythromycin, 10 days or until full
enteral feeding, oral, 50 mg/kg daily
Erythromycin, 14 days, oral, 5 mg/
kg every 6 h
Clarithromycin, 2 weeks, oral,
500 mg twice daily (with ranitidine
bismuth citrate 400 mg twice daily)
Clarithromycin 400 mg twice daily,
amoxycillin 750 mg twice daily,
omeprazole 20 mg twice daily
Oral, 7 days
Minocyclin, period of 3 months with
a broad-spectrum antibiotic
rotation lasting for 2 years
Reference
Trasande, 2012 [65]
Thuny, 2010 [76]
Pirzada, 2003 [77]
Saiman, 2003 [78]
Clement, 2006 [81]
Ng, 2012 [83]
Lane, 2011 [84]
Chlortetracycline 250 mg daily and
procain penicillin, 100 000 units
daily, oral, 7 weeks
Haight, 1955 [59]
Guzman, 1958 [87]
Chlortetracycline, unknown, 50 mg/
kg daily, 12–32 days
Robinson, 1952 [62]
Neonatology
Effect of
antibiotic
prophylaxis
on immune
response
Growth
310 (102 treated with
chlortetracycline, 105 treated with
penicillin, 103 treated with
placebo)18 years, healthy US navy
recruits
H. pylori
eradication in
patients with
peptic ulcer
CF
H. pylori
eradication
Feeding
intolerance
Feeding
intolerance
CF
CF
CF
28 (13 treated), preterm infants (all
<2500 g)
60 (30 treated), preterm infants
predominantly fed with milk
formula
45 (19 treated), very low
birthweight infants (<32 weeks,
<1500 g)
1558 (787 treated), 20–59 years,
Helicobacter pylori-infected
individuals unselected for dyspepsia
150 (50 treated), 23–72 years,
H. pylori-positive population, only
patients with peptic ulcers were
treated
100 patients, unknown, CF of the
pancreas
82 (40 treated), 11
3 years, CF
infected or not with P. aeruginosa
260 (131 treated), 6–18 years, CF
uninfected with Pseudomonas
aeruginosa
146 (77 treated), 6–18 years, CF
uninfected with P. aeruginosa
185 (87 treated), >6 years, CFPA
with FEV1 > 30%
CF with
progressive
pulmonary
disease
CF
Endocarditis
96 (48 treated), 45–75 years,
patients with heart valve disease
40 (20 treated), 18 years, CFPA
Infections
in early life
Indication
11 532 children, <2 years (Avon
Longitudinal Study of Parents and
Children 1991–1992)
Population (no., age,
special population)
WG significant
WG only when minocycline was the
drug used, weight loss when it was
not the drug used. No significance
results available
WG significant
Open case–control study matched
for age and sex (1 : 2 ratio), 2000,
Japan
Unknown
WG significant (chlortetracycline
group vs. placebo group and
penicillin group vs. placebo group)
WG significant
Open randomized placebocontrolled trial, 1996–1999, UK
Antibiotic administered to the
weaker one of twins or the
weakest of triplets, unknown, Israel
Double-blind placebo-controlled
trial, unknown, USA
WG significant
In the placebo–azithromycin group,
the rate of WG trended towards
improvement during the open-label
study as compared with the rate
observed during the placebocontrolled trial. In the
azithromycin–azithromycin group,
the rate of WG remained constant
during the open-label study as
compared with the placebocontrolled trial
WG non-significant (BMI z-score
treatment effect: 0.15
(95% CI !0.03 to 0.34)
WG significant in infants <32 weeks
No effect after 32 weeks
WG significant for patients treated
with azithromycin
WG significant for patients treated
with azithromycin
WG significant at 10, 20 and
38 months when antibiotics were
administered before 6 months of
age
WG significant for all treated
patients and for subgroups treated
with vancomycin
WG not significant for patients
treated with amoxycillin
WG significant for patients treated
with azithromycin (mean duration
of 0.9 months)
Effect on weight
Open randomized trial, 2007–2009,
Taiwan
Multicentre randomized doubleblind placebo-controlled trial,
2001–2003, France
Open prospective randomized
controlled trial, unknown, Egypt
Multicentre randomized doubleblind placebo-controlled trial,
2000–2002, USA
Multicentre randomized doubleblind placebo-controlled trial,
2007–2009, USA–Canada
Multicentre open-label follow-on
study, 2007–2009, USA–Canada
Comparative open-label trial, 1997–
1999, UK
Retrospective analysis in
consecutive adults, 2002–2007,
France
Longitudinal birth cohort study,
1991–1992, UK
Design, year of antibiotic
administration, country
Million et al.
Patterson, 1977 [86]
(abstract only)
Kamada, 2005 [85]
Mansi, 2012 [82]
(abstract only)
Saiman, 2012 [80]
Saiman, 2010 [79]
Antibiotics, duration,
oral or IV, dosage,
frequency
TABLE 3. Studies assessing associations between antibiotics and weight change in humansa
CMI
Microbiota and obesity
5
ª2013 The Authors
Clinical Microbiology and Infection ª2013 European Society of Clinical Microbiology and Infectious Diseases, CMI
BMI, body mass index; CF, cystic fibrosis; CFPA, cystic fibrosis patients colonized by Pseudomonas aeruginosa; FEV1, forced expiratory volume in 1 s; IV, intravenous; WG, weight gain.
a
Studies were searched through PubMed and Google Scholar, unrestricted by language or date, with the following keywords: antibiotics, humans, weight, weight gain, weight loss. Exclusion criteria were comparison of antibiotics without a
control group, animal studies, and studies concerning tuberculosis, because it is known to be associated with important weight loss.
WG non-significant
Open alternate admission, 1951–
1952, USA
WG significant
Malnutrition
Prematurity
72 (38 treated), 2 years, African
undernourished children
113 (57 treated), premature infants
<2500 g
McDougall, 1957 [91]
(abstract only)
Coodin, 1953 [92]
Chlortetracycline, oral, 20 mg daily,
7 months
Chlortetracycline, 37 days
Corbo, 1955 [90]
Heikens, 1993 [88]
(abstract only)
Bethell, 1996 [89]
Terramycin, oral, 25 mg daily,
unknown
WG non-significant
Double-blind placebo-controlled
trial, Italy, 1951–1953
Unknown, Africa, unknown
Poor diet
WG significant
No effect
Prospective cohort study, 1993,
Vietnam
81, malnourished children, 3–
36 months
Children 1–14 years (173 treated
with ciprofloxacin, 153 treatedwith
ofloxacin, 223 healthy untreated
age-matched controls)
338 (181 treated), 6–10 years
Malnutrition
Suspected
typhoid fever
WG significant
Community-based randomized
double-blind placebo-controlled
trial, 1998, Guinea-Bissau
Community-based randomized trial
Co-trimoxazole, <5 years or 18 kg
received paediatric tablets, oral,
7 days
Metronidazole, 20 mg/kg daily,
unknown, 5 days
Ciprofloxacin, 70 mg/kg, 7 days or
ofloxacin, 50 mg/kg, 3–5 days, oral
or IV unknown
Garly, 2006 [67]
Chlortetracycline 50 mg daily,
penicillin 50 mg daily, oral, 2 years
260 (64 treated with penicillin, 92
treated with chlortetracycline, 1
04 not treated), 6–12 years, rural
Guatemalan schoolchildren
84 (46 treated), patients with
measles, 0–25 years
Prophylaxsis
after measles
WG significant for chlortetracycline
Weight loss for penicillin
Open randomized placebocontrolled trial in two villages,
1953–1955, Guatemala
Design, year of antibiotic
administration, country
Indication
Population (no., age,
special population)
Reference
Antibiotics, duration,
oral or IV, dosage,
frequency
Table 3 (Continued)
CMI
Clinical Microbiology and Infection
Effect on weight
6
therapy [49], persistent effects have been described with some
antibiotics, such as quinolones and cefoperazone [49,52], and
recovery may sometimes be incomplete [53].
The influence of antibiotics on obesity
Moore et al. [54] first discovered serendipitously in 1946 that
sulphonamide administration was associated with a two-fold
increase in weight in chicks fed adequate amounts of folic acid,
and he noted that the total gut bacterial count increased, with
coliform counts decreasing and lactobacilli increasing. Stokstad
et al. found that both Streptomyces aureofaciens and its bacteriocin, aureomycin, were associated weight gain, leading to the
proof-of-concept of the growth-promoting effect of both
probiotics and antibiotics [55,56]. Antibiotics, including mainly
tetracycline, glycopeptide, macrolides, and penicillin, have been
used for over 60 years to promote weight gain in animals [54],
with an optimal efficiency in pigs [57], and continue to be
widely used in the USA [58]. From the beginning of their use in
agriculture in the 1950s, a similar growth-promoting effect was
reported in humans [59–62], but this effect seems to have
been overlooked until recently [63–65].
Trasande et al. [65] found that exposure to antibiotics
during the first 6 months of life is associated with consistent
increases in body mass. Exposures later in infancy (6–
14 months and 15–23 months) was not consistently associated with increased body mass. The authors concluded that,
although the effects of early exposure (<6 months) are modest
at the individual level, they could have substantial consequences for population health. Many antibiotics have been
associated with weight gain in children and adults (Table 3).
Shortly after the first animal studies, aureomycin (a tetracycline) was shown to induce weight gain in preterm infants after
10 days [61]. Similar effects of tetracycline were reported in
premature infants, undernourished or rural children, young
recruits of the US navy, and patients with cystic fibrosis and
pancreatic disease. Macrolides, which are widely used in the
animal industry, have been linked with human weight gain,
especially azithromycin in children with cystic fibrosis in
double-blind randomized placebo-controlled studies (Table 2).
A recent meta-analysis confirmed a significant weight gain
effect of macrolides in patients with cystic fibrosis [66].
Clarithromycin was associated with weight gain when used for
the eradication of Helicobacter pylori, with a link to acquired
obesity. Sulphonamides and co-trimoxazole have been linked
to weight gain when used as prophylaxis to prevent pneumonia
and other complications after measles in a community-based
randomized double-blind placebo-controlled trial in GuineaBissau [67]. It is difficult to conclude from these studies
ª2013 The Authors
Clinical Microbiology and Infection ª2013 European Society of Clinical Microbiology and Infectious Diseases, CMI
CMI
Million et al.
FIG. 1. Factors affecting gut microbiota and obesity.
whether antibiotics are associated with weight gain through
their beneficial effects in preventing or treating bacterial
infections, or through their effects on the microbiota. It is
plausible that a mixture of these two mechanisms is present in
different scenarios.
In summary, intriguing data link the microbiota composition
to metabolism, fat accumulation and obesity in animals and
people. Strangely, this was exploited long before the recognition of the mechanism, through the use of probiotics and
antibiotics as growth promoters in animals. Figure 1 summarizes the known data and mechanisms. More precise delineation of the mechanism might lead to tailored interventions or
preventive measures to combat one of the worst enemies of
humanity in the current millennium, obesity.
Transparency Declaration
All authors declare no conflicts of interest.
References
1. Ley RE, Backhed F, Turnbaugh P, Lozupone CA, Knight RD, Gordon JI.
Obesity alters gut microbial ecology. Proc Natl Acad Sci USA 2005; 102:
11070–11075.
Microbiota and obesity
7
2. Ley RE, Peterson DA, Gordon JI. Ecological and evolutionary forces
shaping microbial diversity in the human intestine. Cell 2006; 124: 837–
848.
3. Arumugam M, Raes J, Pelletier E et al. Enterotypes of the human gut
microbiome. Nature 2011; 473: 174–180.
4. Qin J, Li R, Raes J et al. A human gut microbial gene catalogue
established by metagenomic sequencing. Nature 2010; 464: 59–65.
5. Ley RE, Turnbaugh PJ, Klein S, Gordon JI. Microbial ecology: human gut
microbes associated with obesity. Nature 2006; 444: 1022–1023.
6. Reinhardt C, Reigstad CS, Backhed F. Intestinal microbiota during
infancy and its implications for obesity. J Pediatr Gastroenterol Nutr 2009;
48: 249–256.
7. Turnbaugh PJ, Hamady M, Yatsunenko T et al. A core gut microbiome
in obese and lean twins. Nature 2009; 457: 480–484.
8. Elli M, Colombo O, Tagliabue A. A common core microbiota between
obese individuals and their lean relatives? Evaluation of the predisposition to obesity on the basis of the fecal microflora profile. Med
Hypotheses 2010; 75: 350–352.
9. Angelakis E, Armougom F, Million M, Raoult D. The relationship
between gut microbiota and weight gain in humans. Future Microbiol
2012; 7: 91–109.
10. Backhed F, Ding H, Wang T et al. The gut microbiota as an
environmental factor that regulates fat storage. Proc Natl Acad Sci
USA 2004; 101: 15718–15723.
11. Turnbaugh PJ, Backhed F, Fulton L, Gordon JI. Diet-induced obesity is
linked to marked but reversible alterations in the mouse distal gut
microbiome. Cell Host Microbe 2008; 3: 213–223.
12. Kallus SJ, Brandt LJ. The intestinal microbiota and obesity. J Clin
Gastroenterol 2012; 46: 16–24.
13. Hooper LV, Wong MH, Thelin A, Hansson L, Falk PG, Gordon JI.
Molecular analysis of commensal host–microbial relationships in the
intestine. Science 2001; 291: 881–884.
14. Armougom F, Henry M, Vialettes B, Raccah D, Raoult D. Monitoring
bacterial community of human gut microbiota reveals an increase in
Lactobacillus in obese patients and methanogens in anorexic patients.
PLoS ONE 2009; 4: e7125.
15. Million M, Maraninchi M, Henry M et al. Obesity-associated gut microbiota is enriched in Lactobacillus reuteri and depleted in Bifidobacterium
animalis and Methanobrevibacter smithii. Int J Obes 2012; 36: 817–825.
16. Santacruz A, Collado MC, Garcia-Valdes L et al. Gut microbiota
composition is associated with body weight, weight gain and biochemical parameters in pregnant women. Br J Nutr 2010; 104: 83–92.
17. Mai V, McCrary QM, Sinha R, Glei M. Associations between dietary
habits and body mass index with gut microbiota composition and fecal
water genotoxicity: an observational study in African American and
Caucasian American volunteers. Nutr J 2009; 8: 49.
18. Schwiertz A, Taras D, Schafer K et al. Microbiota and SCFA in lean and
overweight healthy subjects. Obesity 2010; 18: 190–195.
19. Collado MC, Isolauri E, Laitinen K, Salminen S. Distinct composition of
gut microbiota during pregnancy in overweight and normal-weight
women. Am J Clin Nutr 2008; 88: 894–899.
20. Duncan SH, Lobley GE, Holtrop G et al. Human colonic microbiota
associated with diet, obesity and weight loss. Int J Obes 2008; 32: 1720–
1724.
21. Kalliomaki M, Collado MC, Salminen S, Isolauri E. Early differences in
fecal microbiota composition in children may predict overweight. Am J
Clin Nutr 2008; 87: 534–538.
22. Yin YN, Yu QF, Fu N, Liu XW, Lu FG. Effects of four bifidobacteria on
obesity in high-fat diet induced rats. World J Gastroenterol 2010; 16:
3394–3401.
23. Balamurugan R, George G, Kabeerdoss J, Hepsiba J, Chandragunasekaran AM, Ramakrishna BS. Quantitative differences in intestinal
Faecalibacterium prausnitzii in obese Indian children. Br J Nutr 2010;
103: 335–338.
ª2013 The Authors
Clinical Microbiology and Infection ª2013 European Society of Clinical Microbiology and Infectious Diseases, CMI
8
CMI
Clinical Microbiology and Infection
24. Nadal I, Santacruz A, Marcos A et al. Shifts in clostridia, bacteroides
and immunoglobulin-coating fecal bacteria associated with weight loss
in obese adolescents. Int J Obes 2009; 33: 758–767.
25. Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI.
An obesity-associated gut microbiome with increased capacity for
energy harvest. Nature 2006; 444: 1027–1031.
26. Samuel BS, Gordon JI. A humanized gnotobiotic mouse model of host–
archaeal–bacterial mutualism. Proc Natl Acad Sci USA 2006; 103: 10011–
10016.
27. Zhang H, DiBaise JK, Zuccolo A et al. Human gut microbiota in obesity
and after gastric bypass. Proc Natl Acad Sci USA 2009; 106: 2365–2370.
28. Turnbaugh PJ, Ridaura VK, Faith JJ, Rey FE, Knight R, Gordon JI. The
effect of diet on the human gut microbiome: a metagenomic analysis in
humanized gnotobiotic mice. Sci Transl Med 2009; 1: 6–14.
29. Backhed F, Ley RE, Sonnenburg JL, Peterson DA, Gordon JI. Host–
bacterial mutualism in the human intestine. Science 2005; 307: 1915–1920.
30. Hayashi H, Sakamoto M, Benno Y. Fecal microbial diversity in a strict
vegetarian as determined by molecular analysis and cultivation.
Microbiol Immunol 2002; 46: 819–831.
31. Liszt K, Zwielehner J, Handschur M, Hippe B, Thaler R, Haslberger AG.
Characterization of bacteria, clostridia and Bacteroides in faeces of
vegetarians using qPCR and PCR-DGGE fingerprinting. Ann Nutr Metab
2009; 54: 253–257.
32. Walker AW, Ince J, Duncan SH et al. Dominant and diet-responsive
groups of bacteria within the human colonic microbiota. ISME J 2011; 5:
220–230.
33. De Filippo C, Cavalieri D, Di Paola M et al. Impact of diet in shaping gut
microbiota revealed by a comparative study in children from Europe
and rural Africa. Proc Natl Acad Sci USA 2010; 107: 14691–14696.
34. Wu GD, Chen J, Hoffmann C et al. Linking long-term dietary patterns
with gut microbial enterotypes. Science 2011; 334: 105–108.
35. Monira S, Nakamura S, Gotoh K et al. Gut microbiota of healthy and
malnourished children in Bangladesh. Front Microbiol 2011; 2: 228.
36. Martin FP, Sprenger N, Yap IK et al. Panorganismal gut microbiome–
host metabolic crosstalk. J Proteome Res 2009; 8: 2090–2105.
37. Cani PD, Neyrinck AM, Fava F et al. Selective increases of bifidobacteria in gut microflora improve high-fat-diet-induced diabetes in mice
through a mechanism associated with endotoxaemia. Diabetologia 2007;
50: 2374–2383.
38. Martin R, Langa S, Reviriego C et al. Human milk is a source of lactic
acid bacteria for the infant gut. J Pediatr 2003; 143: 754–758.
39. Solis G, de Los Reyes-Gavilan CG, Fernandez N, Margolles A,
Gueimonde M. Establishment and development of lactic acid bacteria
and bifidobacteria microbiota in breast-milk and the infant gut.
Anaerobe 2010; 16: 307–310.
40. Jacobsen CN, Rosenfeldt Nielsen V, Hayford AE et al. Screening of
probiotic activities of forty-seven strains of Lactobacillus spp. by in vitro
techniques and evaluation of the colonization ability of five selected
strains in humans. Appl Environ Microbiol 1999; 65: 4949–4956.
41. Gueimonde M, Collado MC. Metagenomics and probiotics. Clin
Microbiol Infect 2012; 18(suppl 4): 32–34.
42. Grzeskowiak L, Gronlund MM, Beckmann C, Salminen S, von Berg A,
Isolauri E. The impact of perinatal probiotic intervention on gut
microbiota: double-blind placebo-controlled trials in Finland and
Germany. Anaerobe 2012; 18: 7–13.
43. EUROPA. Ban on antibiotics as growth promoters in animal feed
enters into effect. 2005. Available from: http://europa.eu/rapid/pressrelease_IP-05-1687_en.htm.
44. Million M, Angelakis E, Paul M, Armougom F, Leibovici L, Raoult D.
Comparative meta-analysis of the effect of Lactobacillus species on
weight gain in humans and animals. Microb Pathog 2012; 53: 100–108.
45. Kondo S, Xiao JZ, Satoh T et al. Antiobesity effects of Bifidobacterium
breve strain B-3 supplementation in a mouse model with high-fat dietinduced obesity. Biosci Biotechnol Biochem 2010; 74: 1656–1661.
46. Bartosch S, Fite A, Macfarlane GT, McMurdo ME. Characterization of
bacterial communities in feces from healthy elderly volunteers and
hospitalized elderly patients by using real-time PCR and effects of
antibiotic treatment on the fecal microbiota. Appl Environ Microbiol
2004; 70: 3575–3581.
47. Palmer C, Bik EM, DiGiulio DB, Relman DA, Brown PO. Development
of the human infant intestinal microbiota. PLoS Biol 2007; 5: e177.
48. Sekirov I, Tam NM, Jogova M et al. Antibiotic-induced perturbations of
the intestinal microbiota alter host susceptibility to enteric infection.
Infect Immun 2008; 76: 4726–4736.
49. Robinson CJ, Young VB. Antibiotic administration alters the community structure of the gastrointestinal microbiota. Gut Microbes 2010; 1:
279–284.
50. Cho I, Yamanishi S, Cox L et al. Antibiotics in early life alter the murine
colonic microbiome and adiposity. Nature 2012; 488: 621–626.
51. Dumonceaux TJ, Hill JE, Hemmingsen SM, Van Kessel AG. Characterization of intestinal microbiota and response to dietary virginiamycin
supplementation in the broiler chicken. Appl Environ Microbiol 2006; 72:
2815–2823.
52. Dethlefsen L, Huse S, Sogin ML, Relman DA. The pervasive effects of
an antibiotic on the human gut microbiota, as revealed by deep 16S
rRNA sequencing. PLoS Biol 2008; 6: e280.
53. Dethlefsen L, Relman DA. Incomplete recovery and individualized
responses of the human distal gut microbiota to repeated antibiotic
perturbation. Proc Natl Acad Sci USA 2011; 108(suppl 1): 4554–4561.
54. Moore PR, Evenson A et al. Use of sulfasuxidine, streptothricin, and
streptomycin in nutritional studies with the chick. J Biol Chem 1946;
165: 437–441.
55. Stokstad EL, Jukes TH et al. The multiple nature of the animal protein
factor. J Biol Chem 1949; 180: 647–654.
56. Duggar BM. Aureomycin; a product of the continuing search for new
antibiotics. Ann N Y Acad Sci 1948; 51: 177–181.
57. Cromwell GL. Why and how antibiotics are used in swine production.
Anim Biotechnol 2002; 13: 7–27.
58. Food and Drug Administration. Withdrawal of Notices of Opportunity
for a Hearing; Penicillin and Tetracycline Used in Animal Feed. Federal
Register [Internet]. 2011; 76(246). Available from: http://www.gpo.gov/
fdsys/pkg/FR-2011-12-22/html/2011-32775.htm.
59. Haight TH, Pierce WE. Effect of prolonged antibiotic administration of
the weight of healthy young males. J Nutr 1955; 56: 151–161.
60. Ozawa E. Studies on growth promotion by antibiotics. II. Results of
aurofac administration to infants. J Antibiot (Tokyo) 1955; 8: 212–214.
61. Perrini F. Aureomycin as a growth factor in premature infants. Boll Soc
Ital Biol Sper 1951; 27: 1151–1152.
62. Robinson P. Controlled trial of aureomycin in premature twins and
triplets. Lancet 1952; 259: 52.
63. Raoult D. Human microbiome: take-home lesson on growth promoters? Nature 2008; 454: 690–691.
64. Ternak G. Antibiotics may act as growth/obesity promoters in humans
as an inadvertent result of antibiotic pollution? Med Hypotheses 2005;
64: 14–16.
65. Trasande L, Blustein J, Liu M, Corwin E, Cox LM, Blaser MJ. Infant
antibiotic exposures and early-life body mass. Int J Obes (Lond) 2013; 37:
16–23.
66. Southern KW, Barker PM, Solis-Moya A, Patel L. Macrolide
antibiotics for cystic fibrosis. Cochrane Database Syst Rev 2011;
CD002203. doi: 10.1002/14651858.CD002203.pub4.
67. Garly ML, Bale C, Martins CL et al. Prophylactic antibiotics to prevent
pneumonia and other complications after measles: community based
randomised double blind placebo controlled trial in Guinea-Bissau. BMJ
2006; 333: 1245–1247.
68. Membrez M, Blancher F, Jaquet M et al. Gut microbiota modulation
with norfloxacin and ampicillin enhances glucose tolerance in mice.
FASEB J 2008; 22: 2416–2426.
ª2013 The Authors
Clinical Microbiology and Infection ª2013 European Society of Clinical Microbiology and Infectious Diseases, CMI
CMI
Million et al.
69. Looft T, Johnson TA, Allen HK et al. In-feed antibiotic effects on the
swine intestinal microbiome. Proc Natl Acad Sci USA 2012; 109: 1691–
1696.
70. Kim HB, Borewicz K, White BA et al. Microbial shifts in the swine distal
gut in response to the treatment with antimicrobial growth promoter,
tylosin. Proc Natl Acad Sci USA 2012; 109: 15485–15490.
71. Collier CT, Smiricky-Tjardes MR, Albin DM et al. Molecular ecological
analysis of porcine ileal microbiota responses to antimicrobial growth
promoters. J Anim Sci 2003; 81: 3035–3045.
72. Rettedal E, Vilain S, Lindblom S et al. Alteration of the ileal microbiota
of weanling piglets by the growth-promoting antibiotic chlortetracycline. Appl Environ Microbiol 2009; 75: 5489–5495.
73. Torok VA, Allison GE, Percy NJ, Ophel-Keller K, Hughes RJ. Influence
of antimicrobial feed additives on broiler commensal posthatch gut
microbiota development and performance. Appl Environ Microbiol 2011;
77: 3380–3390.
74. Torok VA, Hughes RJ, Mikkelsen LL et al. Identification and characterization of potential performance-related gut microbiotas in broiler
chickens across various feeding trials. Appl Environ Microbiol 2011; 77:
5868–5878.
75. Guban J, Korver DR, Allison GE, Tannock GW. Relationship of dietary
antimicrobial drug administration with broiler performance, decreased
population levels of Lactobacillus salivarius, and reduced bile salt
deconjugation in the ileum of broiler chickens. Poult Sci 2006; 85:
2186–2194.
76. Thuny F, Richet H, Casalta JP, Angelakis E, Habib G, Raoult D.
Vancomycin treatment of infective endocarditis is linked with recently
acquired obesity. PLoS ONE 2010; 5: e9074.
77. Pirzada OM, McGaw J, Taylor CJ, Everard ML. Improved lung function
and body mass index associated with long-term use of macrolide
antibiotics. J Cyst Fibros 2003; 2: 69–71.
78. Saiman L, Marshall BC, Mayer-Hamblett N et al. Azithromycin in
patients with cystic fibrosis chronically infected with Pseudomonas
aeruginosa: a randomized controlled trial. JAMA 2003; 290: 1749–
1756.
79. Saiman L, Anstead M, Mayer-Hamblett N et al. Effect of azithromycin
on pulmonary function in patients with cystic fibrosis uninfected with
Pseudomonas aeruginosa: a randomized controlled trial. JAMA 2010; 303:
1707–1715.
Microbiota and obesity
9
80. Saiman L, Mayer-Hamblett N, Anstead M et al. Open-label, follow-on
study of azithromycin in pediatric patients with CF uninfected with
Pseudomonas aeruginosa. Pediatr Pulmonol 2012; 47: 641–648.
81. Clement A, Tamalet A, Leroux E, Ravilly S, Fauroux B, Jais JP. Long
term effects of azithromycin in patients with cystic fibrosis: a double
blind, placebo controlled trial. Thorax 2006; 61: 895–902.
82. Mansi Y, Abdelaziz N, Ezzeldin Z, Ibrahim R. Randomized controlled
trial of a high dose of oral erythromycin for the treatment of feeding
intolerance in preterm infants. Neonatology 2011; 100: 290–294.
83. Ng YY, Su PH, Chen JY et al. Efficacy of intermediate-dose oral
erythromycin on very low birth weight infants with feeding intolerance.
Pediatr Neonatol 2012; 53: 34–40.
84. Lane JA, Murray LJ, Harvey IM, Donovan JL, Nair P, Harvey RF.
Randomised clinical trial: Helicobacter pylori eradication is associated
with a significantly increased body mass index in a placebo-controlled
study. Aliment Pharmacol Ther 2011; 33: 922–929.
85. Kamada T, Hata J, Kusunoki H et al. Eradication of Helicobacter pylori
increases the incidence of hyperlipidaemia and obesity in peptic ulcer
patients. Dig Liver Dis 2005; 37: 39–43.
86. Patterson PR. Minocycline in the antibiotic regimen of cystic fibrosis
patients: weight gain and clinical improvement. Clin Pediatr (Phila) 1977;
16: 60–63.
87. Guzman MA, Scrimshaw NS, Monroe RJ. Growth and development of
Central American children. I. Growth responses of rural Guatemalan
school children to daily administration of penicillin and aureomycin. Am
J Clin Nutr 1958; 6: 430–438.
88. Heikens GT, Schofield WN, Dawson S. The Kingston Project. II. The
effects of high energy supplement and metronidazole on malnourished
children rehabilitated in the community: anthropometry. Eur J Clin Nutr
1993; 47: 160–173.
89. Bethell DB, Hien TT, Phi LT et al. Effects on growth of single short
courses of fluoroquinolones. Arch Dis Child 1996; 74: 44–46.
90. Corbo S, Frontali G, Jolliffe N, Lanciano O, Maggioni G. Effects of
chlortetracycline on weight gain of Italian children ages 6 to 10 on diets
relatively low in animal protein. Antibiot Annu 1955; 3: 19–26.
91. Macdougall LG. The effect of aureomycin on undernourished African
children. J Trop Pediatr 1957; 3: 74–81.
92. Coodin FJ. Studies of tetramycin in premature infants. Pediatrics 1953;
12: 652–656.
ª2013 The Authors
Clinical Microbiology and Infection ª2013 European Society of Clinical Microbiology and Infectious Diseases, CMI
Article II : REVIEW
The relationship between gut microbiota and weight
gain in humans
Emmanouil Angelakis, Fabrice Armougom, Matthieu Million,
Didier Raoult
Published in Future Microbiol. 2012 Jan;7(1):91-109. (IF 3.82)
22
For reprint orders, please contact: [email protected]
Emmanouil Angelakis*, Fabrice Armougom, Matthieu Million & Didier Raoult
Unité des Rickettsies, URMITE -CNRS UMR 6236 IRD 198, IFR 48, Faculté de Médecine, Université de la
Méditerranée, 27 Bd Jean Moulin, 13385 Marseille Cedex 05, France
*Author for correspondence: Tel.: + 33 491 38 55 17 „ Fax: + 33 491 83 03 90 „ [email protected]
The human gut microbiota is a metabolic organ that is determined by a dynamic
process of selection and competition. Age, dietary habits and geographical
origin of people have an important impact on the intestinal microbiota. The role
of the microbiota is still largely unknown, but the bacteria of the gut flora do
contribute enzymes that are absent in humans and play an essential role in the
catabolism of dietary fibers. Germ-free mice provide a complementary approach
for characterizing the properties of the human gut microbiota. Recently, microbial
changes in the human gut were proposed to be one of the possible causes of
obesity. This review summarizes the latest research on the association between
microbial ecology and host weight.
Obesity is a major, public health concern that
affects at least 400 million individuals and is
associated with severe disorders including diabetes and cancers [1] . The causes that drive
obesity appear to be complex, and a consensus
hypothesis is emerging that proposes that obesity is influenced by a mixture of environmental, genetic, neural and endocrine factors [1] .
Infectious agents have also been proposed to be
causes of obesity, and in human obesity, have
been associated with small EDRK-rich factor 1A
(SMAM-1), an avian adenovirus and adenovirus 36 [2] . Human genetics is believed to play a
part in determining body weight [3] . In total,
32 genes were linked to BMI, but their total variance contribution to BMI in the population was
less than 2% [4] . It is believed that other factors
also play a role in obesity, such as the availability of inexpensive, calorically dense foods or the
reduction in physical activity in our daily lives.
Recently, microbial changes in the human gut
was proposed to be another possible cause of
obesity [5] and it was found that the gut microbes
from fecal samples contained 3.3 million nonredundant microbial genes [6] . However, it is still
poorly understood how the dynamics and composition of the intestinal microbiota are affected
by diet or other lifestyle factors. Moreover it has
been difficult to characterize the composition of
the human gut microbiota due to large variations
between individuals.
The human gut microbiota has been also
associated with a number of disease states that
include allergy, inflammatory bowel disease,
cancer and diabetes [7] . Allergy, for example,
has been associated with perturbations in the
10.2217/FMB.11.142 © Raoult D et al.
gastrointestinal microbiota [8] . In addition,
evidence implicating the role of microbiota in
inflammatory bowel disease was supported by a
certain degree of effectiveness of antibiotics in
the prevention and treatment of colonic inflammation in both human patients and animal
models, as well as by the presence of microbes
and microbial components in inflammationinduced colonic lesions [9] . The association of the
gut microbiota with cancer is most commonly
observed with gastrointestinal tumors, although
there are examples of these microbiota modifying the cancer risk to other systems, such as in
breast tumors [7] . Moreover, the notion that gut
microbiota is important in the onset and development of diabetes, cardiovascular dyslipidemia
and metabolic endotoxemia is becoming more
widely accepted as the evidence base grows [7,10] ,
and the beneficial effect of bariatric surgery in
decreasing cardiovascular risk and cancer was
associated with the increase of Enterobacter
hormaechei in the gut microbiota [11] .
The role of the digestive microbiota in the
human body is still largely unknown, but the
bacteria of the gut flora do contribute enzymes
that are absent in humans for food digestion
[12] . Moreover, the link between obesity and the
microbiota is likely to be more sophisticated than
the simple phylum-level Bacteroidetes:Firmicutes
ratio that was initially identified [13] , and it is
likely to involve a microbiota–diet interaction
[14] . Phages have also been proposed to play a
possible role in driving the biodiversity of the gut
flora by their influence on their bacterial hosts
[15] and, recently, a novel pathway that involves
dietary lipid phosphatidylcholine and choline
Future Microbiol. (2012) 7(1), 91–109
Review
Future Microbiology
The relationship between gut
microbiota and weight gain in
humans
Keywords
„
„
gut flora „ microbiota
obesity
part of
ISSN 1746-0913
91
Review
Angelakis, Armougom, Million & Raoult
metabolism, an obligate role for the intestinal
microbial community, and regulation of surface expression levels of macrophage scavenger
receptors that were known to participate in the
atherosclerotic process was proposed [16] . More
subtle alterations in the levels of other bacteria
in the gut may also impact human health. In the
last few years, new technologies have been developed that have allowed researchers to attempt
more systematic studies on intestinal bacterial
flora and have given more realistic information
about its composition (by way of detecting noncultivable species). As a result, an increasing
number of studies have related imbalances in
the composition of the gut microbiota to obesity
and its associated diseases. The approaches used
to characterize the human gut flora vary widely,
and this might explain, in part, why specific
alterations in the microbiota that are associated
with excess body fat or weight loss, can also vary
between studies. This review summarizes the
latest research on the association between the
microbial ecology and host weight.
Human gut microbiota
The gut microbiota harbors large bacterial populations in the intestine and colon, approximately
1011–12 microorganisms per gram of content, and
are comprised of mainly anaerobes (95% of the
total organisms). The initial overview of the composition of the gut microbiota was culture based,
and the predominant cultivable species that were
identified included Bacteroides sp., Eubacterium
sp., Bifidobacterium sp., Peptostreptoccocus sp.,
Fusobacterium sp., Ruminococcus sp., Clostridium
sp. and Lactobacillus spp. [17] . The first, largescale, 16S rDNA sequencing analysis of the gut
microbiota by Eckburg et al. [18] revealed a high
inter-individual variability at the species taxonomic level that was not recovered at the phylum
level, as only nine phyla out of 70 were represented [1] . The overall and individual microbiota
structures were dominated by the Bacteroidetes
and Firmicutes phyla [18] . Finally, three gut microbiota studies [19] assigned 98% of 16S rRNA
sequences to only four bacterial phyla: Firmicutes
(64%), Bacteroidetes (23%), Proteobacteria (8%)
and Actinobacteria (3%). Verrucomicrobia,
Fusobacteria and the TM7 phylum together
accounted for the remaining 2%.
The earliest large-scale, 16S rRNA or metagenomic studies identified Methanobrevibacter
smithii as the dominant, methanogenic archaeon
species in the human gut microbiota [18] .
M. smithii in three healthy individuals comprised up to 11.5% of the gut microorganisms
92
Future Microbiol. (2012) 7(1)
[18] ,
and in a study of 650 individuals, the prevalence of M. smithii was 95.5%, whereas the
prevalence of Methanosphaera stadtmanae was
29.4% in the human gut [20] . Moreover, molecular analyses provided various degrees of evidence
for the presence of groups of archaea, including
Methanosarcina, Thermoplasma, Crenarchaeota
and halophilic archaea in the human gastrointestinal tract, but isolates have not been
obtained [21] .
Age & gut flora modification
During the first days to months of life, the microbiota of the infant gut and the temporal pattern
in which it evolves is remarkably variable from
individual to individual [22] . At birth, humans
are essentially free of bacteria and over time, in a
process of colonization that begins shortly after
delivery and continues through to adulthood,
the body becomes a host to complex microbial
communities. The initial infant gut microbiota
is usually dominated by Bifidobacteria, and
through a series of successions and replacements, it migrates to a more complex, adult pattern [22] . Vael et al. found that the population of
Bacteroides fragilis in the microbiota increased in
infants from the age of 3 weeks until the age of
1 year, whereas the populations of Staphylococcus,
Lactobacillus, Bifidobacterium, Clostridium and
total anaerobes decreased starting at the age of
3 weeks and remained stable until 52 weeks [23] .
Traditionally, it has been thought that
between 1 and 2 years of age, the human gut
microbiota start to resemble that of an adult
[22] . Young children between 1 and 7 years of
age presented higher numbers of enterobacteria
than adults [24] . Moreover, a large-scale study
by Enck et al. found significant shifts in relative
genus abundances during the first 2 years of life
and no noticeable changes in children between
2 and 18 years of age, including stable levels of
Bifidobacterium and Lactobacillus [25] . In a recent
study, the comparison of intestinal microbiota
composition between adolescents and adults
revealed a statistically significantly higher abundance of genera Bifidobacterium and Clostridium
among adolescent samples [26] .
The adult intestinal microbiota has been
shown to be relatively stable over time [27] and
is sufficiently similar between individuals. This
observation allowed for identification of a core
microbiome that was comprised of 66 dominant,
operational, taxonomic units that corresponded
to 38% of the sequence reads from 17 individuals [28] . Turroni et al. found that Bifidobacterium
pseudolongum and Bifidobacterium bifidum, are
future science group
Gut flora & weight gain
exclusively dominant in the adult bifidobacterial population, whereas Bifidobacterium longum,
Bifidobacterium breve, Bifidobacterium pseudocatenulatum and Bifidobacterium adolescentis, were
found to be widely distributed, irrespective of
host age [29] .
In the elderly, both Bacteroides numbers and
species diversity is declined [30,31] . The analyses
of fecal samples collected from subjects from
four European study groups indicated higher
proportions of enterobacteria in all elderly volunteers [32] . Zwielehner et al., found that the
proportion of Bacteroidetes in the fecal microbiota of 17 institutionalized, elderly subjects
was significantly higher than in younger adults,
but these patients had lower proportions of
Bifidobacterium and Clostridium cluster IV [33] .
Analysis of the core microbiota in the elderly
showed a clear shift to a more Clostridium
cluster IV-dominated community [34] .
Several host factors have been correlated with
methanogenic archaea carriage, and it has been
proposed that the acquisition of methanogenic
archaea occurs by environmental contamination.
Additionally, it has been hypothesized that once
methanogenic archaea find favorable physicochemical conditions and available substrates in
the gut, stable colonization is established [21] .
archaea were not detected in children who were
younger than 27 months, but it has been shown
that carriage increases with age, up to 60% in
5-year-old children. Moreover, it is possible that
an adult diet may create an intestinal microbiota
that is favorable for the implantation of methanogenic archaea [35] . A possible direct, motherto-child route of transmission has also been proposed because archaea have been detected in the
vaginal flora of pregnant women [21] .
Gut flora variations among different
populations
It is not yet completely understood how the
different environments and wide range of diets
that modern humans around the world experience has affected the microbial ecology of the
human gut. Certain lifestyles of a person may
have an impact on the composition of his/her
gut microbiota (FIGURE 1) , but these impacts are
currently poorly understood. Qin et al., in the
largest study to date, found that only one-third
of the bacterial gene clusters that were conserved
across individuals of all 124 European (Nordic
and Mediterranean) origins could be associated
with a broad functional assignment [6] . Nearly
40% of the genes from each individual were
shared with at least half of the individuals of
future science group
Review
the cohort. Of these, 99.1% of the genes had
bacterial origin, and the remainder was mostly
archaeal, with only 0.1% of eukaryotic or viral
origins [6] . Therefore, it seems that important
variations in the gut flora between close countries do not exist. As a result, Dicksved et al.
did not observe differences between fecal samples collected from children from Germany,
Switzerland and Sweden by the use of terminal restriction fragment length polymorphism
[36] . Lay et al., when testing the composition
of the fecal microbiota assessed by FISH combined with flow cytometry, also did not find a
significant correlation between the microbial
compositions, with regard to age, geographical
origin, or gender, among subjects from France,
Denmark, Germany, the Netherlands and the
UK [37] . However, 16S rDNA pyrosequencing
ana lysis revealed that geographical origin has
an important impact on the intestinal microbiota. As a result, differences in the gut microbiota have been observed between people living
in northern and southern European countries.
For instance, Fallani et al. observed that human
infants from northern European countries were
associated with higher Bifidobacteria in their
gut microbiota, whereas infants with higher
Bacteroides and lactobacilli were characteristic
of southern countries [38] . Mueller et al. found
that the proportion of Bifidobacteria was twoto three-fold higher in Italians than in the
French, Germans or Swedes [32] . A bigger difference has been observed between European
and Africans, and De Filippo et al. found that
children from a rural African village presented
more Actinobacteria and Bacteroidetes but
less Firmicutes and Proteobacteria in their gut
flora than European children [39] . Moreover,
African children presented significantly more
short-chain fatty acids in their gut flora than
European children [39] . Li et al. found that there
were distinct microbiota profiles at the species
level between a Chinese family and American
volunteers. Moreover, they identified a higher
proportion of Bacteroidetes thetaiotaomicron in
males than in females [40] . Finally, Arumugam
et al., by combining 22 sequenced, fecal metagenomes of individuals from four countries, identified three enterotype clusters that were not
nation- or continent-specific [41] . Enterotype 1
was enriched in Bacteroides and seemed to derive
energy primarily from carbohydrates and proteins through fermentation. Enterotype 2 was
enriched in Prevotella and Desulfovibrio, which
can act in synergy to degrade mucin glycoproteins that are present in the mucosal layer of the
www.futuremedicine.com
93
Review
Angelakis, Armougom, Million & Raoult
Age
Dietary habits
First days of life
Vegetarian diet
Mostly Bifidobacteria
1. Increase Bacteroidetes
2. Decrease Clostridia
Human microbiota
Stable:
Firmicutes (64%)
Bacteroidetes (23%)
Proteobacteria (8%)
Actinobacteria (3%)
Adults
1. Increase Bacteroidetes Elderly
2. Decrease Bifidobacteria
Different species level
Increase Bifidobacteria
Southern vs northern
Europeans
Chinese vs Americans
Origin
1. Increase Actinobacteria
and Bacteroidetes
2. Decrease Firmicutes
and Proteobacteria
Europeans vs Africans
Figure 1. Impact factors for the composition of the human gut microbiota.
gut. Enterotype 3 was the most frequent and
was enriched in Ruminococcus and Akkermansia,
which degrade mucins [41] . Moreover, enterotypes 1 and 2 were capable of biosynthesis of
different vitamins. The authors proposed that
these three enterotypes used different routes
to generate energy from fermentable substrates
that were available in the colon, reminiscent of
a potential specialization in ecological niches or
guilds [41] .
Effect of the alimentation on human
gut flora
Dietary habits are considered to be one of the
main factors that contribute to the diversity of
the human gut microbiota [42] , and the pattern
of variation in copy number of the human salivary amylase gene is consistent with a history of
diet-related selection pressures, demonstrating
the importance of starchy foods in human evolution [43] . Prevotella, Xylanibacter and Treponema
were present in the gut flora of children from
a rural African village but not from Europe,
and the authors of this study hypothesized that
the presence of these three genera could be a
consequence of high fiber intake, maximizing metabolic energy extraction from ingested
plant polysaccharides [39] . These bacteria could
ferment both xylan and cellulose through carbohydrate-active enzymes, such as xylanase,
94
Future Microbiol. (2012) 7(1)
carboxymethylcellulase and endoglucanase [39] .
Moreover, Bacteroides and Faecalibacterium species and particularly Faecalibacterium prausnitzii, which were found in both children populations, could generally indicate the importance
of maintaining a microflora with potential antiinflammatory capability [39,44] . Liszt et al. found
that a vegetarian diet affected the intestinal
microbiota, especially by decreasing the amount
and changing the diversity of Clostridium cluster IV [45] . Similar results found by Hayashi
et al., who based their studies on RFLP ana lysis,
revealed that the major composition of the vegetarian gut microbiota consisted of Clostridium
rRNA subcluster XIVa and Clostridium rRNA
cluster XVIII [46] . Recently, Walker et al. tested
overweight men with a control diet, diet high
in resistant starch or nonstarch polysaccharides
and a reduced carbohydrate weight loss diet, over
10 weeks and they found no significant effect
of diet upon the proportions of Bacteroidetes,
Firmicutes, Actinobacteria or Proteobacteria
within the fecal microbiota [47] . However, two
individual phylotypes, Eubacterium rectale and
Ruminococcus bromii, showed increased proportions on the resistant starch diet while Collinsella
aerofaciens showed decreased proportions on the
weight loss diet [47] . Finally, Wu et al. analyzed
the fecal samples from 98 individuals and found
that fecal communities clustered into enterotypes
future science group
future science group
www.futuremedicine.com
N
Ob
†
Significant increase in Methanobacteriales in Ob subjects
Ob microbiota somewhat enriched in the Coriobacteriaceae family of Actinobacteria
Proteobacteria
Actinobacteria
Verrucomicrobia
Fusobacteria
Ob microbiota were significantly enriched in Prevotellaceae
More Bacteroidetes in Ob subjects (not significant)
Correlation between excessive weight gain and high Bacteroides levels
Higher numbers of Bacteroides and Staphylococcus aureus in Ov pregnant women
Significant diet-dependent reduction in Eubacterium rectale/Roseburia levels in Ob subjects
No difference in Bacteroidetes levels, independent of diet, in Ob versus N subjects
No significant difference in Bacteroidetes levels between Ob and N subjects
Bacteroidetes
Firmicutes
Staphylococcus aureus
Bifidobacteria
Bacteroidetes
Eubacterium rectale/
Clostridium coccoides
Bacteroidetes
Firmicutes
Bacteroidetes
Staphylococcus aureus†
Clostridia
Indicates that the difference is significant.
FCM: Flow cytometry; N: Normal weight; Ob: Obese; Ov: Overweight; Pyro: Pyrosequencing; qPCR: Quantitative real-time PCR.
†
16S Pyro
Zhang et al.
N pregnant
FCM-FISH and
qPCR
N
FISH
qPCR
N
Ob
FISH
Ob
N children
Lactobacilli
Lower number of bifidobacteria and greater number of Staphylococcus aureus predict Ob/Ov
phenotype
Bifidobacteria†
FISH
Bifidobacteria†
N
Ob/Ov
children
Decrease in Firmicutes
Significant decrease in Bifidobacteria and Methanobrevibacter sp. in Ob subjects
Bacteroidetes†
Significantly reduced levels of Clostridium perfringens and Bacteroides in the Ob population
Ov
Bacteroidetes
Firmicutes
Correlation between an increase in Bacteroidetes and weight loss in Ob subjects
Significantly reduced level of Bacteroidetes in Ob subjects
Major finding
Significant increase in Bacteroidetes in Ob subjects
qPCR
Culture
Bacteroidetes
Firmicutes
Community measured
Firmicutes
Ob
N
Ob
16S clonal
sequencing
Method
Collado et al. Ob pregnant
Duncan
et al.
Mai et al.
Kalliomäki
et al.
Schwiertz
et al.
Zuo et al.
Ob
Ley et al.
N
Sample
category
Study
Table 1. Weight gain-associated bacterial population shifts in human gut microbiota.
[53]
[52]
[59]
[57]
[58]
[51]
[54]
[5]
Ref.
Gut flora & weight gain
Review
95
96
Ob, N twins
and mother
Turnbaugh
et al.
Future Microbiol. (2012) 7(1)
Ov
adolescents
Santacruz
et al.
qPCR
FISH
qPCR
qPCR
16S Pyro of V2
clonal Sanger
sequencing
16S Pyro of V6
Method
Ob microbiome enriched in genes that belong to Actinobacteria and Firmicutes
Nearly half of the lean-enriched genes were from Bacteroidetes
Actinobacteria
Proteobacteria
†
Significant reduction of Eubacterium rectale, Clostridium coccoides and Clostridium histolyticum
Correlation with weight
Significant increase in Bacteroides/Prevotella
Bifidobacterium
Clostridium hystolyticum
Eubacterium rectale/
Clostridium coccoides
Significant reduction in C. coccoides
Increase in the Bacteroides fragilis and Lactobacillus groups
Lactobacillus
Clostridium coccoides
Total bacteria
Escherichia coli
Bifidobacterium
Clostridium leptum
Present after an Ob group submitted to a weight program lost >4 kg
Bacteroides fragilis
Enteric group
Lactobacillus/Enteroccocus
Greater weight loss after a multidisciplinary treatment program associated with:
No difference in dietary intake
Significantly higher levels of Lactobacillus
Significantly reduced levels of Bacteroidetes in Ob versus N subjects
No significant difference in Bacteroidetes and Bifidobacterium levels between Ob and N subjects
Bacteroidetes/Prevotella†
Methanobrevibacter smithii
Lactobacillus
†
Bacteroidetes
Firmicutes
Faecalibacterium prausnitzii†
Eubacterium rectale
Lactobacillus acidophilus
Bifidobacterium
Significant increase of Faecalibacterium prausntzi levels (belonging to Firmicutes) in Ob subjects
Significant increase in Actinobacteria levels in Ob versus N subjects
Bacteroidetes
Bacteroidetes
Significantly reduced levels of Bacteroidetes in Ob versus N subjects
Major finding
Firmicutes
Community measured
Indicates that the difference is significant.
FCM: Flow cytometry; N: Normal weight; Ob: Obese; Ov: Overweight; Pyro: Pyrosequencing; qPCR: Quantitative real-time PCR.
†
Ob
Anorexic
N
Ob
Nadal et al.
Armougom
et al.
N
Balamurugan Ob
et al.
Sample
category
Study
Table 1. Weight gain-associated bacterial population shifts in human gut microbiota (cont.).
[56]
[55]
[49]
[60]
[13]
Ref.
Review
Angelakis, Armougom, Million & Raoult
future science group
Total bacteria
Staphylococcus†
Escherichia coli†
Bacteroides†
Clostridium leptum
Indicates that the difference is significant.
FCM: Flow cytometry; N: Normal weight; Ob: Obese; Ov: Overweight; Pyro: Pyrosequencing; qPCR: Quantitative real-time PCR.
†
Ov pregnant
Bacteria species & obesity
The Bacteroidetes phylum
Clostridium coccoides
Lactobacillus group
Significantly reduced Bifidobacterium and Bacteroides and increased Staphylococcus and
Escherichia coli levels in Ov pregnant women
Bifidobacterium†
qPCR
Ob pregnant
Santacruz
et al.
future science group
Review
distinguished primarily by levels of Bacteroides
and Prevotella [48] . They also found that longterm diet, particularly protein and animal fat
versus carbohydrate diet were strongly associated with enterotype partitioning. Moreover, in
a controlled-feeding study authors found that
the microbiome composition changed detectably within 24 h of initiating a high-fat/low-fiber
or low-fat/high-fiber diet, but that enterotype
identity remained stable [48] .
[62]
Major finding
Community measured
Method
Sample
category
Study
Table 1. Weight gain-associated bacterial population shifts in human gut microbiota (cont.).
Ref.
Gut flora & weight gain
Armougom et al. found a significant reduction
of Bacteroidetes proportions in obese, compared
with lean and anorexic, individuals [49] and
reported lower Bacteroidetes concentrations in
obese subjects (TABLE 1) [50] . Moreover, the ana lysis
of 16S rDNA sequences from 154 individuals
indicated that the microbiota of obese subjects
was associated with a decrease in the diversity
level and was composed of significantly fewer
Bacteroidetes [13] . On the other hand, Schwiertz
et al. quantified bacterial communities in overweight, obese and lean individuals and found
a significant increase in the proportions of
Bacteroidetes in obese and overweight groups [51] .
Likewise, before pregnancy, overweight women
have a higher number of Bacteroidetes than
women of normal weight, and excessive weight
gain during pregnancy is associated with an
increase in Bacteroidetes numbers [52] . Assuming
that Type 2 diabetes and reduced glucose tolerance is linked to obesity, Larsen and colleagues
also found higher levels of Bacteroidetes in diabetic patients than in control patients [10] . Using
16S rDNA pyrosequencing, Zhang et al. studied
the composition of the gut microbiota in morbidly obese, normal-weight and post-gastricbypass subjects [53] . Their results indicated that
the obese microbiota is significantly enriched in
Prevotellaceae, a subgroup of Bacteroidetes [53] .
Zuo et al., using culture methods for organisms
found in the feces of obese and normal weight
participants, found that obese people had fewer
cultivable Bacteroides than control individuals
[54] . Moreover, they found that obese individuals
with a Pro/Ala genotype of the nuclear hormone
receptor peroxisome proliferator-activated receptor J2, which modulates cellular differentiation
and lipid accumulation during adipogenesis, had
lower levels of Bacteroides than obese participants with a Pro/Pro genotype [54] . Interestingly,
the monitoring of the proportions of two major
bacterial communities in obese participants during a weight loss program resulted in linking an
www.futuremedicine.com
97
Review
Angelakis, Armougom, Million & Raoult
increase in levels of Bacteroidetes to weight loss,
independent of energy intake [5] . The impact
of an obesity treatment program, including
a calorie-restricted diet and increase of physical activity on gut microbiota composition in
overweight and obese adolescents was reported
[55,56] . The FISH method indicated that a significant increase in the ratio of Bacteroides and
Prevotella correlated to weight loss in the adolescent group that exhibited the highest weight
loss [55] . Using the same population, the results
obtained by FISH [55] were verified by a quantitative PCR (qPCR) method, which detected a
notable increase in Bacteroides fragilis after the
weight loss program [56] . Lastly, Vael et al. found
that high intestinal Bacteroides fragilis concentrations and low Staphylococcus concentrations in
infants between the ages of 3 weeks and 1 year
were associated with a higher BMI in preschool
children [23] .
Group by
phyla
Study (year)
Subgroup within study
Others studies have not found any correlation between the proportions of Bacteroidetes
and obesity or type of diet. Both qPCR and
FISH methods have been applied to subsets of
lean and obese subjects, and both have failed
to associate a reduced level of Bacteroidetes to
obesity [57] . In an attempt to study whether the
composition of early gut microbiota can affect
weight development throughout early childhood, Kalliomäki et al. monitored weight,
height and bacterial community abundances in
children of 6 months, 12 months and 7 years of
age. Children who became overweight or obese
at 7 years did not present any significant reduction in the proportion of Bacteroides-Prevotella,
compared with those maintaining a normal
weight [58] . The relationships between weight
loss and Bacteroidetes abundance were examined
in adults, but no difference between obese and
nonobese subjects was observed [59] .
Sample size
SDM and 95% CI
Ow/obese Control
Ley et al. (2006)
16S clonal sequencing
,
ancestry
Turnbaugh et al. (2009) V2 pyrosequencing African
Turnbaugh et al. (2009) V2 pyrosequencing European
,
ancestry
Pyrosequencing
Zhang et al. (2009)
Bacteroidetes relative count (% of total sequences)
Collado et al. (2008)
FCM-FISH
Armougom et al. (2009) qPCR
qPCR
Schwiertz et al. (2010)
qPCR
Million et al. (2011)
Bacteroidetes absolute count (log cells or copies of DNA)
Ley et al. (2006)
16S clonal sequencing
Turnbaugh et al. (2009) V2 pyrosequencing, African ancestry
Turnbaugh et al. (2009) V2 pyrosequencing, European ancestry
Firmicutes relative count (% of total sequences)
qPCR
Armougom et al. (2009)
qPCR
Schwiertz et al. (2010)
qPCR
Million et al. (2011)
Firmicutes absolute count (log copies DNA)
12
62
42
3
2
8
26
3
18
20
33
53
36
20
30
39
12
62
42
2
8
26
20
33
53
20
30
39
-2.00
-1.00
Lean status
0.00
1.00
2.00
Ow/obese
Figure 2. Meta-analysis of the obesity-associated gut microbiota alterations at the phylum level (Bacteroidetes and
Firmicutes) comparing the absolute (abs) or relative (percentage of total sequences) number of sequences (generated by
quantitative PCR or cloning/sequencing or pyrosequencing) or cells (flow cytometry-FISH). Meta-analysis was performed with
the comprehensive meta-analysis software version 2 [93,94] . Each line represents a comparison between an obese group (right) and a
control group (left). The first reported alteration [5] was a decrease in the relative proportion of Bacteroidetes (percentage decrease)
represented by a deviation of the square (standardized difference in the means) to the left. The size of the square represents the relative
weight of each comparison (random model). The length of the horizontal line represents the 95% CI and the diamond represents the
summarized effect. The presence of a square to the right and left of the midline means studies with conflicting results corresponding to a
substantial heterogeneity (I2 >50%). Here, the only reproducible and significant alteration at the phylum level is the decrease in the
absolute number of sequences of Firmicutes in obese subjects. Relative count of Bacteroidetes (n = 4; SDM = -0.51; 95% CI = -1.7–0.67;
p = 0.40 [I2 = 81%]); absolute count of Bacteroidetes (n = 4; SDM = -0.07; 95% CI = -0.78–0.65; p = 0.86 [I2 = 85]); relative count of
Firmicutes (n = 3; SDM = 0.88; 95% CI = -0.21–1.97; p = 0.11 [I2 = 79%]); absolute count of Firmicutes (n = 3; SDM = -0.43;
95% CI = -0.72 to -0.15; p = 0.003 [I2 = 0%]).
FCM: Flow cytometry; Ow: Overweight; qPCR: Quantitative PCR; SDM: Standardized difference in the means.
98
Future Microbiol. (2012) 7(1)
future science group
Gut flora & weight gain
Group by
genus
Study (year)
Sample size
Review
SDM and 95% CI
Obese Control
Collado et al. (2008)
18
36
Kalliomäki et al. (2008)
25
24
Schwiertz et al. (2009)
33
30
Balamurugan et al. (2010)
15
13
Santacruz et al. (2010)
16
34
Zuo et al. (2011)
52
52
Armougom et al. (2009)
20
20
Zuo et al. (2011)
52
52
Million et al. (2011)
53
39
Bifidobacterium (log copies DNA/ml)
Lactobacillus (log copies DNA/ml)
-2.00
-1.99
Lean status
0.00
1.00
2.00
Ow/obese
Figure 3. Meta-analysis of the obesity-associated gut microbiota alterations at the genus
level for Bifidobacteria and Lactobacilli comparing the absolute number of sequences
generated by genus-specific quantitative PCR. For Bifidobacteria, a consistent difference was
found by our meta-analysis between 159 obese subjects and 189 controls from six published studies
showing that the digestive microbiota of the obese group was significantly depleted in Bifidobacteria.
Low heterogeneity (I2 = 17%) shows that this result is very robust. Additional tests have shown that
there was no small studies bias (Egger’s regression intercept test, p = 0.92; no change after Duval
and Tweedie’s trim and fill). For Lactobacilli, no consistent and significant summary effect was found
comparing 127 obese subjects and 110 controls from three studies. Bifidobacterium sp. (n = 6;
SDM = -0.45; 95% CI = -0.69 to -0.20; p < 0.001 [I2 = 17%]); Lactobacillus spp. (n = 3; SDM = 0.29;
95% CI = -0.31–0.90; p = 0.34 [I2 = 80%]).
Ow: Overweight; SDM: Standardized difference in the means.
Meta-analysis of the obesity-associated
gut microbiota alteration at the phylum level
(Bacteroidetes) comparing the absolute (abs) or
relative (percentage of total sequences) number
of sequences (generated by qPCR or cloning/
sequencing or pyrosequencing) or cells (flow
cytometry [FCM]-FISH) was performed for the
seven studies [5,13,49–53] . These studies revealed
no difference in the Bacteroidetes concentrations
between obese people and people of normal
weight (FIGURE 2) .
The Firmicutes phylum
Ley et al. reported that the reduced level of
Bacteroidetes found in obese humans was counterbalanced by a proportional increase in Firmicutes
[5] . The greater Firmicutes proportion tended
to decrease when patients were submitted to a
weight-loss program [5] . These results were in
agreement with other works, which found that
significantly reduced levels of Clostridium hystoliticum, Eubacterium rectale and Clostridium
coccoides correlated to weight loss in an obese,
adolescent population [55,56] . Moreover, obese,
future science group
Indian children presented significantly higher
levels of Faecalibacterium prauznitzii but no
difference between the levels of Bacteroides and
that of Prevotella, Bifidobacterium species, the
Lactobacillus acidophilus group or Eubacterium
rectal, compared with lean children [60] . Duncan
et al. identified a significant, diet-dependent
reduction in levels of Roseburia-E. rectale, a
group of butyrate-producing Firmicutes, for obese
patients that were on a weight-loss diet [59] . Zuo
et al. found a lower amount of C. perfringens and
a higher proportion of Enterococci in obese subjects when compared with normal-weight individuals [54] . Finally, Schwiertz et al. found that
overweight and obese volunteers exhibited lower
cell numbers of the Ruminococcus flavefaciens
subgroup [51] .
Meta-analysis of the obesity associated gut
microbiota alteration at the phylum level
(Firmicutes) comparing the absolute (abs) or
relative (percentage of total sequences) number
of sequences (generated by qPCR or cloning/
sequencing or pyrosequencing) or cells (FCMFISH) was performed for the five studies
www.futuremedicine.com
99
Review
Angelakis, Armougom, Million & Raoult
Study (year)
Subgroup within study
Sample size
SDM and 95% CI
Ow/obese Control
20
20
Schwiertz et al. (2010) Methanobrevibacter sp. qPCR 33
30
Million et al. (2011)
39
Armougorn et al. (2009) M. smithii specific qPCR
M. smithii specific qPCR
53
-2.00
-1.00
0.00
Lean status
1.00
2.00
Ow/obese
Figure 4. Meta-analysis of the obesity-associated gut microbiota alterations for archaea
representatives comparing the absolute number of archaeal sequences generated by
quantitative PCR. One study, focused on the Methanobacteriales order level, comparing only three
obese subjects and three controls, found an increase of this bacterial group in the obese group [53]
(square deviated to the right) instead of the three other studies. Our meta-analysis showed, by
observing the funnel plot, that this study was an outlier that was subsequently excluded. The
comparison of 106 obese subjects and 89 controls including analysis at the Methanobrevibacter
genus level by Schwiertz et al. [51] and at the Methanobrevibacter smithii species level [49,50] is
justified because it shows a consistent and reproducible effect with a significant reduction of
Methanobrevibacter sp. in obese subjects (Egger’s regression intercept test, p value = 0.39; and
Duval’s and Tweedie’s trim and fill did not change these results). Methanobrevibacter sp. (n = 3;
SDM = -0.51; 95% CI: -0.79 to -0.22; p = 0.001 [I2 = 0%]).
Ow: Overweight; SDM: Standardized difference in the means.
100
[5,13,49–51] . The only reproducible and significant
The Actinobacteria phylum
alteration at the phylum level is the decrease in
the absolute number of sequences of Firmicutes
in obese (n = 3; standardized difference in the
means [SDM] = -0.43; 95% CI = -0.72 to
-0.15; p = 0.003 [I2 = 0%]) (FIGURE 2) .
Recent studies suggest a role for Lactobacillus
spp. in weight changes, and the quantification
of Lactobacillus species in lean, anorexic and
obese subjects revealed significantly higher
Lactobacillus concentrations in nearly half of
the obese population [49] . Obese Type 2 diabetic patients displayed significantly higher
levels of Bacilli and Lactobacillus spp. in their
gut microbiota [10] . However, an increase in
Lactobacillus number in an obese, adolescent
group after a weight-loss program was also
reported [56] . Thuny et al. reported significant
weight gain in patients with infected endocarditis after treatment with high doses of
vancomycin and proposed that Lactobacillus
spp. that were resistant to vancomycin were
responsible for this weight gain [61] . Similarly,
Million et al. found that L. reuteri was associated with obesity [50] . Meta-ana lysis of the
obesity associated gut microbiota alteration at
the genus level for lactobacilli comparing the
absolute number of sequences generated by
genus-specific qPCR revealed a nonsignificant
summary effect in Lactobacillus spp. levels in
obese subjects (FIGURE 3) .
Recent gut microbiota studies that have been
associated with obesity have focused on shifts
in Firmicutes and Bacteroidetes populations.
However, the Actinobacteria phylum, which is
comprised of the Bifidobacterium genus as well
as other genera, has also been linked to weight
gain. Indeed, in an investigation of gut microbial communities of 18 lean or obese twins and
their mothers, the obese subjects showed higher
levels of Actinobacteria [13] . Interestingly, most
of the obesity related genes were found to be
from Actinobacteria (75%), and many of the
obesity associated genes that were identified
were involved in carbohydrate, lipid and amino
acid processing [13] . In addition, the sequencing
ana lysis by Zhang and colleagues revealed that
the Coriobacteriaceae family of Actinobacteria
was enriched in the obese microbiota [53] .
The fecal concentration of the Bifidobacterium
genus was reported to be significantly lower in
obese subjects when compared with lean subjects [51,52,58,62] . Moreover, Santacruz et al. found
significantly lower Bifidobacteria counts in obese
subjects after they had been subjected to a dietary
program [56] . Furthermore, Zuo et al. found a
nonsignificant decrease in the concentration of
bifidobacteria between obese and normal weight
humans [54] . Meta-analysis of the obesity-associated gut microbiota alteration at the genus
level for bifidobacteria comparing the absolute
Future Microbiol. (2012) 7(1)
future science group
Gut flora & weight gain
number of sequences generated by genus specific
qPCR revealed that the obese group was consistently and significantly depleted in Bifidobacteria
(n = 6; SDM = -0.45; 95% CI = -0.69 to -0.20;
p < 0.001 (I2 = 17%) (FIGURE 3) . This is extremely
important because bifidobacteria depletion
seems to be the more reproducible alteration in
obese gut microbiota and the best candidate to
have an antiobesity effect.
Archaea & obesity
Using the data of Armougom et al., but calculating means of log10 copies DNA/ml of
M. smithii, we found, contrary to Armougom
et al., that there was a decrease in the M. smithii
load in the obese group, compared with the
normal group [49] . Correspondingly, Zhang
et al. found more M. smithii in obese individuals than in lean controls [53] , and Schwiertz
et al. identified lower levels of M. smithii in
obese subjects compared with lean subjects [51] .
However, Million et al. recently found higher
concentrations of M. smithii in nonobese subjects [50] . Overall, methanogenic archaea could
indirectly promote caloric intake by the colon
and further fat accumulation-related obesity
in individuals who were on a high-fiber diet
[21] . During the fermentation process, the
accumulation of excess H2 reduces the yield of
ATP, which leads to a gradual decrease in the
Review
fermentation efficiency [21] . The importance of
methanogenic Archaea to humans lies in their
ability to improve fermentation efficiency by
removing H 2 from the gut [21] . It has been
speculated that the coexistence of Prevotellaceae
with methanogenic Archaea species in the obese
gut allows for greater efficiency of dietary polysaccharide fermentation and therefore increases
their conversion into short-chain fatty acids,
resulting in their excessive storage [53] .
Meta-analysis of the obesity-associated gut
microbiota alteration at the genus level for
Methanobrevibacter spp., main representative
of Archaea known in the digestive microbiota,
comparing the absolute number of sequences
generated by qPCR revealed that obese subjects presented less Methanobrevibacter than
nonobese subjects (FIGURE 4) . However, the reasons linking methanogens to weight gain still
remain unclear. To date, Methanobrevibacter is
the main representative of archaea in the gut
microbiota but archaea could not be extrapolated from Methanobrevibacter assessment. This
is extremely important since domain-level and
genus-level could lead to very different results.
Ability to process polysaccharides
The gut microbiome is also involved in the
complex carbohydrate metabolism of food
owing to its ability to process indigestible
Major carbon sources of the gut microbiota
Nondigestible food components
Starch, pectin, cellulose, mucilage
xylan, inulin, fructans
Substrates
Processes
Anaerobic fermentation
–
Intermediate
products
Succinate, formate… Lactate
Major end
products
Butyrate
+
Acetate
Propionate
H2 excess
CO2
SO42-
Short-chain fatty acids
Acetate
Colonocyste
Lipid metabolism
Methane
SH2
H2 removal mechanisms
Bacteroides as major propionate producers
Bacteroides and Firmicutes as major acetate producers
Firmicutes as major butyrate producers
Lactobacillus, Bifidobacterium and Streptococcus as major lactate producers
Desulfovibrio as major sulfato-reducers
Methanobrevibacter smithii archaeon species as major methane producers
Figure 5. Outline of carbohydrate fermentation by gut microbiota.
future science group
www.futuremedicine.com
101
Review
Angelakis, Armougom, Million & Raoult
components of diets, such as plant polysaccharides [6,13,63] . The human gut microbiome plays
an essential role in the catabolism of dietary
fibers, the part of plant material in the human
diet that is not metabolized by the upper
digestive tract, because the human genome
does not encode for an adequate carbohydrate
active enzyme (CAZymes) (FIGUR E 5) . Dietary
fibers are the components of vegetables, cereals, leguminous seeds, and fruits that are not
digested in the stomach or in the small intestine. Instead, they are fermented in the colon
by the gut microbiome and/or excreted in the
feces. Additionally, dietary fibers have been
identified as strong, positive dietary factors in
the prevention of obesity [64] . The human gut
bacteria produce a huge panel of CAZymes,
with widely different substrate specificities, to
degrade these compounds into metabolizable
monosaccharides and disaccharides. The array
of CAZymes in gut microbes is highly diverse,
exemplified by Bacteroides thetaiotaomicron,
which contains 261 glycoside hydrolases and
polysaccharide lyases, as well as 208 homologs
of susC and susD genes, which code for two
outer membrane proteins that are involved in
starch utilization [65,66] . The CAZymes represent, on average, 2.6% of the sequenced genes
in each microbiome [13] . As the human genome
encodes, at best, 20–25 digestive enzymes from
CAZyme families (i.e., GH1 [lactase], GH13
[D-amylase] and GH31 [maltase, isomaltase
and sucrase]), the ability to digest dietary plant
carbohydrates resides entirely in gut microbiomes [67] . The CAZymes represented in different human populations that consume different diets may be influenced by their varied
cultural traditions. Hehemann et al. found that
porphyranase and agarase genes are specifically
encountered in Japanese gut bacteria and are
Table 2. Major bacteria and archaea in the human gut microbiota and their possible association with obesity.
Representative phyla
Class
Genera
Proven association with obesity
Ref.
Clostridia
Clostridium
Yes
[54,55,56]
Eubacterium
Yes
[55,59]
Faecalibacterium
Yes
[60]
Roseburia
Yes
[59]
Lactobacillus
Yes
[10,49]
Enterococcus
Yes
[54]
Staphylococcus
Yes
[52,58,62]
Bacteroides
Yes
[52,54–56,62]
Yes
[62]
Yes
[51,52,58,62]
Yes
[49–51,53]
Bacteria
Firmicutes
Peptostreptococcus
Ruminococcus
Bacilli
Bacteroidetes
Bacteroidia
Prevotella
Xylanibacter
Proteobacteria
Deltaproteobacteria
Desulfovibrio
Gammaproteobacteria
Escherichia
Epsilonproteobacteria
Helicobacter
Actinobacteria
Actinobacteria
Bifidobacterium
Fusobacteria
Fusobacteria
Fusobacterium
Synergistetes
Synergistia
Synergistes
Spirochaetes
Spirochaetes
Treponema
Methanobacteria
Methanobrevibacter
Methanobacteria
Methanosphaera
Verrucomicrobia
Cyanobacteria
Archaea
Euryarchaeota
102
Future Microbiol. (2012) 7(1)
future science group
Gut flora & weight gain
probably absent in the microbiome of western
individuals [68] . The authors proposed that consumption of sushi that contains algae from the
genus Porphyramay, which is associated with
the marine bacteria Zobellia galactanivorans
and Bacteroides plebeius, has been the route
through which these CAZymes were acquired
in human gut bacteria [68,69] .
Recently, Benjdia et al. hypothesized that
sulfatases are critical, evolved fitness factors
[70] . To be active, sulfatases must undergo a
critical post-translational modification that is
catalyzed in anaerobic bacteria by the radical
AdoMet enzyme, anaerobic sulfatase-maturating enzyme (anSME). They found that human
gut Bacteroidetes possessed an anSME gene, and
several genes that encoded sulfatases were present within many species, including B. fragilis,
Bacteroides dorei or Parabacteroides distasonis
[70] . On the other hand, Firmicutes did not possess genes encoding predicted sulfatases, and
it was proposed that this demonstrated that
sulfatases were an important and evolutionary conserved feature among Bacteroidetes that
inhabited the human digestive tract [70,71] .
Gut flora of twins
Turnbaugh et al. compared the fecal microbial
communities of young, adult female monozygotic and dizygotic twin pairs, who were either
lean or obese, along with those of their mothers, to assess the gut microbiota relationship to
host weight. Comparisons between all participants showed that obesity was associated with
reduced bacterial diversity and a reduced representation of the Bacteroidetes [13] . In a more
recent study, they found that the majority of
species-level phylotypes were shared between
deeply sampled monozygotic twins, despite
large variations in the abundance of each phylotype [72] . From the gene clusters present in
their microbiome bins, only 17% were shared
between the two co-twins. Bins exhibited
differences in their degree of sequence variation, gene content, including the repertoire of
carbohydrate active enzymes present within,
and between twins (e.g., predicted cellulases,
dockerins) and transcriptional activities [72] .
Gnotobiotic mice for the analysis of
human gut microbes
Germ-free mice provide a complementary
approach for characterizing the properties of
the human gut microbiome. Backhed et al.
found that young, conventionally reared mice
have a 40% higher body fat content and 47%
future science group
Review
Coriobacteriaceae
Lactobacillus
Enterococcus
Faecalibacterium prausnitzii
Obese individuals
Prevotella
Clostridium
Eubacterium
Roseburia
Staphylococcus
Escherichia coli
Methanobrevibacter
Treponema
Xylanibacter
Lean individuals
Bacteroides
Bifidobacterium
Figure 6. Population of bacteria found to increase in obese and lean
individuals.
higher gonadal fat content than germ-free mice,
even though they consumed less food than their
germ-free counterparts [73] . When the microbiota of normal mice were transplanted into gnotobiotic mice, there was a 60% increase in body
fat within 2 weeks without any increase in food
consumption or obvious differences in energy
expenditure [73] . Moreover, in a separate study
using genetically modified (fasting-induced
adipocyte factor [Fiaf ]) knockout mice, the
same authors showed that gut microbes suppress intestinal Fiaf. Fiaf suppression resulted
in increased lipoprotein lipase activity in adipocytes and promoted storage of calories as fat.
These findings suggested that the gut microbiota could affect both sides of the energy balance equation, influencing energy harvest from
dietary substances (Fiaf ) and affecting genes
that regulate how energy is expended and stored
[74] . Turnbaugh et al. were the first to determine
that differences in the microbial community
could be a factor for obesity [75] . They found
that transfer of the gut microbiota from obese
(ob/ob) mice to germ-free, wild-type recipients
led to an increase in fat mass in the recipients.
This led to speculation that the gut microbiota
promoted obesity by increasing the capacity
of the host to extract energy (calories) from
ingested food [75] . Controlled diet manipulation in gnotobiotic mice, which were colonized
with a complete human gut (fecal) microbiota,
www.futuremedicine.com
103
Review
Angelakis, Armougom, Million & Raoult
revealed that the composition of their human
gut microbial communities changed dramatically within a single day after the animals were
switched from a plant polysaccharide-rich
chow to a high-fat, high-sugar ‘‘western’’ diet
[14] . Goodman et al. developed an approach
called insertion-sequencing (INSeq), which
is based on a mutagenic transposon that captures adjacent chromosomal DNA to define its
genomic location [76] . In this approach, complex
populations of tens of thousands of transposon
mutants are simultaneously introduced into
wild-type or genetically manipulated, germfree mice in the presence or absence of other
microbes. Using this assay, they discovered that
B. thetaiotaomicron employed the products of
five adjacent genes (BT1957–49) in response to
variations in vitamin B12 levels [76] . Moreover,
mice colonized with complete or cultured fecal
communities from two human donors displayed
significantly greater fat pad to body weight
ratios than germ-free controls [77] . Notably,
18 species-level phylotypes were significantly
affected when these gnotobiotic mice received
a western diet for 2 weeks. Specifically, the relative proportion of representatives of one class of
Firmicutes (the Erysipilotrichi) was increased,
and the relative proportion of the Bacteroidia
class was decreased [77] . Hildebrandt et al. found
that both wild-type and RELME knockout
mice were lean on a standard chow diet, but
upon switching to a high-fat diet, the wild-type
mice became obese, whereas RELME knockout
mice remained comparatively lean [78] . After the
switch to the high-fat diet, the proportions of
Proteobacteria, Firmicutes and Actinobacteria
increased, whereas the levels of Bacteriodetes
decreased [78] . When adult, germ-free, male
mice were colonized with Marvinbryantella formatexigens and B. thetaiotaomicron, it was found
that B. hydrogenotrophica targeted aliphatic and
aromatic amino acids and increased the efficiency of fermentation by consuming reducing
equivalents, thereby maintaining a high NAD +/
NADH ratio and boosting acetate production
[79] . By contrast, M. formatexigens consumed oligosaccharides, did not impact the redox state
of the gut and boosted the yield of succinate
[79] . Normalized RNA-Seq counts, generated
from the cecal contents and fecal samples of
the mice revealed that prophages in M. formatexigens were completely activated and that
two gene pairs were constitutively expressed
in all fecal and cecal samples [80] . The authors
proposed that a prophage might be liberated
from its host cell when that cell is present in
104
Future Microbiol. (2012) 7(1)
a fecal community [80] . Colonization of germfree mice that consumed a plant polysacchariderich or a simple sugar diet with wild-type or
anSME-deficient strains revealed that active
sulfatase production by B. thetaiotaomicron
was essential for competitive colonization of the
gut, especially when the organism was forced
to adaptively forage on host mucosal glycans
because complex dietary polysaccharides were
not available [70] . The authors proposed that
anSME activity and the subsequent activation
of sulfatases represented an important pathway
that allowed this Bacteroidetes species to adapt
to life in the gut [70] . Fleissner et al. showed
that changes in energy expenditure rather than
“energy harvest” were responsible for changes
in fat deposition and weight gain in mice as
they found no difference in body weight gain
between germ-free and conventional mice fed
a semi-synthetic low-fat diet [81] . By contrast,
germ-free mice gained more body weight and
body fat than conventional mice on a highfat diet. Moreover they found that the proportion of Firmicutes increased in both mice
high-fat and on a western diet. This increase
was mainly due to the proliferation of the
Erysipelotrichaceae [81] . Murphy et al. treated
ob/ob mice with a low-fat diet and wild-type
mice with either a low-fat diet or a high-fat diet
and found that the proportions of Firmicutes,
Bacteroidetes and Actinobacteria did not correlate with energy harvesting markers [82] . Higher
concentrations of taurine-conjugated bile acids
were identified in the livers and intestines of
germ-free mice [83] and in those colonized by
human baby microbiota [84] compared with conventional animals. Historically, bile acids have
been primarily viewed as detergent molecules
important for the absorption of dietary fats and
lipidsoluble vitamins in the small intestine and
the maintenance of cholesterol homeostasis in
the liver [83] .
Conclusion
Obese and lean subjects presented increased
levels of different bacterial populations (TABLE 2
& FIGURE 6) . In addition, a caloric diet restriction
impacted the composition of the gut microbiota
in obese/overweight individuals and weight loss
[5,55,56] . Interestingly, the initial microbiota of
overweight adolescents, before any treatment,
drove the efficiency of weight loss [56] , and
differences in the gut composition at infancy
could lead to weight gain [23,58] . Studies using
gnotobiotic mice have shown that the gut
microbiota was critical for normal digestion of
future science group
Gut flora & weight gain
nutrients [74] . It was proposed that the metabolic activities of the gut microbiota facilitated
the extraction of calories from ingested dietary
substances, helped to store these calories in host
adipose tissue for later use and provided energy
and nutrients for microbial growth and proliferation [85] . A more recent hypothesis is based on
data from vegetarian human populations who
presented bacteria that were commonly found
in plants, like B. thetaiotaomicron, which produced CAZymes and metabolized monosaccharides and disaccharides [6,13,62] . Moreover, it was
predicted that other unknown factors in the
microbiota and, recently, the manipulation of
gut microbial with probiotics, prebiotics, antibiotics or other interventions, were factors for
weight gain and obesity [1,86,87] , which should
be investigated more [88,89] . These results suggest that manipulating the composition of the
gut microbiota may prevent weight gain or
facilitate weight loss in humans.
Future perspective
During the last few years, an increasing number of studies have related imbalances in the
composition of the gut microbiota to obesity.
Many studies have reported shifts in the relative
abundances of bacterial communities in the gut
microbiota of obese relative to normal-weight
individuals, and each study has attempted to
link obesity with a species- or genus-specific
composition profile of the gut microbiota.
However, it is possible that the design and/or
interpretation of the results has been affected
by a conflict of interest of each team. It has
recently been shown that published papers in
nutrition and obesity research in which the
authors were funded by industry were more
likely than other papers to contain results or
interpretations that favored the industry or
company that was producing the product or
service that was being studied [90] . Moreover,
the heterogeneous methods that were utilized
in individual microbiota studies to estimate
bacterial proportions prevented rational comparisons of results [12] . Notably, 16S rRNA
sequencing-based methods are biased by the
heterogeneity of the copy number of the 16S
rRNA gene that is present in an individual bacterial genome [91] and can lead to an overestimation of bacterial proportions. However, it is
noteworthy that the current 16S rDNA pyrosequencing [53] , as well as clonal, Sanger sequencing, studies [5] of gut microbiota within obese
populations were not able to detect bacterial
concentrations that were below 107 organisms
future science group
Review
per gram of feces [49] . Indeed, the characterization of the 1011 bacterial copies per gram of feces
that was used in these studies remains superficial. The use of FISH and qPCR methods were
dependent on both sensitivity and specificity
of the targeted bacterial group. Additionally,
the Bac303 probe, which was used in most of
the FISH- and qPCR-based studies [55,57–59] ,
underestimated the Bacteroidetes proportions
because the probe targeted only the BacteroidesPrevotella groups, and it was inadequately sensitive to the Prevotella group [92] . Ley et al. suggested that it will be interesting to study and
compare the effects of these molecular methods
using the same sample stool [12] . An integration
of mechanistically based investigations and
microbial ecology studies using high-throughput sequencing will provide insights into how
to best reshape host–microbial interactions to
promote weight loss.
Food is a source of bacteria and viruses,
and changes in patterns of food consumption
results in differences in human gut flora among
different groups of people. A question being
investigated is whether it is important to identify the source of the gut microorganisms as
the most are ingested with food, drinks, and in
the course of physical contact and interhuman
relationships. Data from agriculture, laboratory
animals and humans show that manipulating
gut microbiota results in weight modifications
and, recently, it was proposed that is necessary to further investigate the effects of routinely adding high amounts of bacteria to food
[1,86,87] . In the last few years, the number of
published descriptions of the organisms and
genes that comprise and manipulate the gut
microbiota is increasing dramatically, but these
studies have so far been limited to fairly small
populations. Moreover, little effort has been
made to standardize the microbiota ana lysis
methodology and different sample collection,
storage and ana lysis methods have only been
superficially investigated in human studies.
This makes it almost impossible to directly
compare findings from different groups, limiting our ability to generalize findings. Further
well-designed studies should be conducted
into how gut microbial communities normally
operate, how they shape host physiology, and
how they may be altered by probiotic, prebiotic, antibiotic or other interventions. For that
reason, massive parallel sequencing technologies and the necessary bioinformatics tools to
handle the resulting large datasets should be
adapted for human microbiota ana lysis.
www.futuremedicine.com
105
Review
Angelakis, Armougom, Million & Raoult
Financial & competing interests disclosure
The authors have no relevant affiliations or financial
involvement with any organization or entity with a
financial interest in or financial conflict with the subject matter or materials discussed in the manuscript.
This includes employment, consultancies, honoraria,
stock ownership or options, expert testimony, grants or
patents received or pending, or royalties.
No writing assistance was utilized in the production
of this manuscript.
Executive summary
Human gut microbiota
The gut microbiota harbors approximately 1011–12 microorganisms per gram of content.
„ At birth, humans are essentially free of bacteria and over time, in a process of colonization that begins shortly after delivery, the body
becomes a host to complex microbial communities.
„ 16S rDNA pyrosequencing analysis revealed that geographical origin has an important impact on the intestinal microbiota.
„ Dietary habits are considered to be one of the main factors that contribute to the diversity of the human gut microbiota.
„
Bacteria species & obesity
Meta-analyses revealed no difference in the Bacteroidetes concentrations between obese and humans of normal weight.
„ Meta-analyses revealed that obese subjects present less Firmicutes than nonobese subjects in their gut flora.
„ Meta-analyses revealed that obese subjects presented less Bifidobacteria than nonobese subjects.
„ Meta-analyses revealed that obese subjects presented less Methanobrevibacter spp. than nonobese subjects.
„
Ability to process polysaccharides
The gut microbiota plays an essential role in the catabolism of dietary fibers into metabolizable monosaccharides and disaccharides by
adequate carbohydrate active enzymes.
„ Dietary fibers have been identified as strong, positive dietary factors in the prevention of obesity.
„ The human gut bacteria produce a huge panel of carbohydrate active enzymes to degrade dietary fibers into metabolizable
monosaccharides and disaccharides.
„
Gnotobiotic mice for the analysis of human gut microbes
Germ-free mice provide a complementary approach for characterizing the properties of the human gut microbiota.
„ It was first demonstrated in experimental mice models that that differences in the gut microbiota could be a factor for obesity.
„
Conclusion
„ Microbial changes in the human gut are one of the possible causes of obesity.
metagenomic sequencing. Nature 464(7285),
59–65 (2010).
References
Papers of special note have been highlighted as:
„ of interest
„„ of considerable interest
7.
Holmes E, Li JV, Athanasiou T, Ashrafian H,
Nicholson JK. Understanding the role of gut
microbiome-host metabolic signal disruption
in health and disease. Trends Microbiol.
19(7), 349–359 (2011).
1.
Raoult D. Obesity pandemics and the
modification of digestive bacterial flora. Eur.
J. Clin. Microbiol. Infect. Dis. 27(8), 631–634
(2008).
8.
2.
Vasilakopoulou A, le Roux CW. Could a virus
contribute to weight gain? Int. J. Obes.
(Lond.). 31(9), 1350–1356 (2007).
Sekirov I, Russell SL, Antunes LC, Finlay
BB. Gut microbiota in health and disease.
Physiol. Rev. 90(3), 859–904 (2010).
9.
Barnich N, Darfeuille-Michaud A. Role of
bacteria in the etiopathogenesis of
inflammatory bowel disease. World
J. Gastroenterol. 13(42), 5571–5576 (2007).
3.
Farooqi S, O’Rahilly S. Genetics of obesity in
humans. Endocr. Rev. 27(7), 710–718 (2006).
4.
Speliotes EK, Willer CJ, Berndt SI et al.
Association analyses of 249,796 individuals
reveal 18 new loci associated with body mass
index. Nat. Genet. 42(11), 937–948 (2010).
5.
„„
6.
Ley RE, Turnbaugh PJ, Klein S, Gordon JI.
Microbial ecology: human gut microbes
associated with obesity. Nature 444(7122),
1022–1023 (2006).
Pioneering study linking obesity and gut
microbiota.
Qin J, Li R, Raes J et al. A human gut
microbial gene catalogue established by
106
13. Turnbaugh PJ, Hamady M, Yatsunenko T
et al. A core gut microbiome in obese and
lean twins. Nature 457(7228), 480–484
(2009).
14.
Turnbaugh PJ, Ridaura VK, Faith JJ, Rey FE,
Knight R, Gordon JI. The effect of diet on
the human gut microbiome: a metagenomic
ana lysis in humanized gnotobiotic mice. Sci.
Transl. Med. 1(6), 6ra14 (2009).
„„
One of the studies showing that gut
transplantation can lead to increased
adiposity, establishing the causal link
between gut microbiota and obesity.
15.
Ventura M, Sozzi T, Turroni F, Matteuzzi D,
van SD. The impact of bacteriophages on
probiotic bacteria and gut microbiota
diversity. Genes Nutr. 6(3), 205–207 (2010).
10. Larsen N, Vogensen FK, van den Berg FW
et al. Gut Microbiota in human adults with
Type 2 diabetes differs from non-diabetic
adults. PLoS ONE 5(2), e9085 (2010).
11.
Li JV, Ashrafian H, Bueter M et al. Metabolic
surgery profoundly influences gut microbialhost metabolic cross-talk. Gut 60(9),
1214–1223 (2011).
12. Ley RE. Obesity and the human microbiome.
Curr. Opin. Gastroenterol. 26(1), 5–11
(2010).
Future Microbiol. (2012) 7(1)
16. Wang Z, Klipfell E, Bennett BJ et al. Gut
flora metabolism of phosphatidylcholine
promotes cardiovascular disease. Nature
472(7341), 57–63 (2011).
17.
Moore WE , Holdeman LV. Human fecal
flora: the normal flora of 20 JapaneseHawaiians. Appl. Microbiol. 27(5), 961–979
(1974).
future science group
Gut flora & weight gain
18. Eckburg PB, Bik EM, Bernstein CN et al.
31.
Diversity of the human intestinal microbial
flora. Science 308(5728), 1635–1638 (2005).
19.
Frank DN, St Amand AL, Feldman RA,
Boedeker EC, Harpaz N, Pace NR.
Molecular-phylogenetic characterization of
microbial community imbalances in human
inflammatory bowel diseases. Proc. Natl Acad.
Sci. USA 104(34), 13780–13785 (2007).
21.
Differences in fecal microbiota in different
European study populations in relation to age,
gender, and country: a cross-sectional study.
Appl. Environ. Microbiol. 72(2), 1027–1033
(2006).
Lapin A, Haslberger AG. Combined
PCR-DGGE fingerprinting and quantitativePCR indicates shifts in fecal population sizes
and diversity of Bacteroides, bifidobacteria and
Clostridium cluster IV in institutionalized
elderly. Exp. Gerontol. 44(6–7), 440–446
(2009).
22. Palmer C, Bik EM, Digiulio DB, Relman DA,
34. Claesson MJ, Cusack S, O’Sullivan O et al.
Composition, variability, and temporal
stability of the intestinal microbiota of the
elderly. Proc. Natl Acad. Sci. USA
108(Suppl. 1), S4586–S4591 (2011).
23. Vael C, Verhulst SL, Nelen V, Goossens H,
Desager KN. Intestinal microflora and body
mass index during the first three years of life:
an observational study. Gut Pathog. 3(1), 8
(2011).
35.
24. Hopkins MJ, Sharp R, Macfarlane GT. Age
and disease related changes in intestinal
bacterial populations assessed by cell culture,
16S rRNA abundance, and community
cellular fatty acid profiles. Gut 48(2),
198–205 (2001).
26. Agans R, Rigsbee L, Kenche H, Michail S,
Khamis HJ, Paliy O. Distal gut microbiota of
adolescent children is different from that of
adults. FEMS Microbiol. Ecol. 77(2), 404–412
(2011).
27.
Zoetendal EG, Akkermans AD, de Vos WM.
Temperature gradient gel electrophoresis
ana lysis of 16S rRNA from human fecal
samples reveals stable and host-specific
communities of active bacteria. Appl. Environ.
Microbiol. 64(10), 3854–3859 (1998).
28. Tap J, Mondot S, Levenez F et al. Towards the
the diversity of the bifidobacterial population
in the human intestinal tract. Appl. Environ.
Microbiol. 75(6), 1534–1545 (2009).
30. Woodmansey EJ. Intestinal bacteria and
ageing. J. Appl. Microbiol. 102(5), 1178–1186
(2007).
future science group
44. Sokol H, Pigneur B, Watterlot L et al.
Faecalibacterium prausnitzii is an antiinflammatory commensal bacterium
identified by gut microbiota ana lysis of
Crohn disease patients. Proc. Natl Acad. Sci.
USA 105(43), 16731–16736 (2008).
45.
37.
Lay C, Rigottier-Gois L, Holmstrom K et al.
Colonic microbiota signatures across five
northern European countries. Appl. Environ.
Microbiol. 71(7), 4153–4155 (2005).
microbial diversity in a strict vegetarian as
determined by molecular ana lysis and
cultivation. Microbiol. Immunol. 46(12),
819–831 (2002).
47.
39.
De Filippo C, Cavalieri D, Di PM et al.
Impact of diet in shaping gut microbiota
revealed by a comparative study in children
from Europe and rural Africa. Proc. Natl Acad.
Sci.USA 107(33), 14691–14696 (2010).
40. Li M, Wang B, Zhang M et al. Symbiotic gut
microbes modulate human metabolic
phenotypes. Proc. Natl Acad. Sci.USA 105(6),
2117–2122 (2008).
41.
Arumugam M, Raes J, Pelletier E et al.
Enterotypes of the human gut microbiome.
Nature 473(7346), 174–180 (2011).
42. Backhed F, Ley RE, Sonnenburg JL, Peterson
DA, Gordon JI. Host-bacterial mutualism in
the human intestine. Science 307(5717),
1915–1920 (2005).
www.futuremedicine.com
Walker AW, Ince J, Duncan SH et al.
Dominant and diet-responsive groups of
bacteria within the human colonic
microbiota. ISME J. 5(2), 220–230 (2011).
48. Wu GD, Chen J, Hoffmann C et al. Linking
long-term dietary patterns with gut
microbial enterotypes. Science 334(6052),
105–108 (2011).
49.
Armougom F, Henry M, Vialettes B, Raccah
D, Raoult D. Monitoring bacterial
community of human gut microbiota reveals
an increase in Lactobacillus in obese patients
and Methanogens in anorexic patients. PLoS
ONE 4(9), e7125 (2009).
50. Million M, Maraninchi M, Henry M,
Armougom F, Raoult D. Obesity-associated
Gut microbiota is enriched in Lactobacillus
reuteri and depleted in Bifidobacterium
animalis and Methanobrevibacter smithii. Int.
J. Obesity doi:10.1038/ijo.2011.153 (2011)
(Epub ahead of print).
38. Fallani M, Amarri S, Uusijarvi A et al.
Determinants of the human infant intestinal
microbiota after introduction of first
complementary foods in five European centres.
Microbiology 157(Pt 5),1385–1392 (2011).
Liszt K, Zwielehner J, Handschur M, Hippe
B, Thaler R, Haslberger AG.
Characterization of bacteria, clostridia and
Bacteroides in faeces of vegetarians using
qPCR and PCR-DGGE fingerprinting. Ann.
Nutr. Metab. 54(4), 253–257 (2009).
46. Hayashi H, Sakamoto M, Benno Y. Fecal
Molecular fingerprinting of the fecal
microbiota of children raised according to
different lifestyles. Appl. Environ. Microbiol.
73(7), 2284–2289 (2007).
human intestinal microbiota phylogenetic
core. Environ. Microbiol. 11(10), 2574–2584
(2009).
29. Turroni F, Foroni E, Pizzetti P et al. Exploring
Rutili A, Canzi E, Brusa T, Ferrari A.
Intestinal methanogenic bacteria in children of
different ages. New Microbiol. 19(3), 227–243
(1996).
36. Dicksved J, Floistrup H, Bergstrom A et al.
25. Enck P, Zimmermann K, Rusch K, Schwiertz
A, Klosterhalfen S, Frick JS. The effects of
maturation on the colonic microflora in
infancy and childhood. Gastroenterol. Res.
Pract. 752401 (2009).
and the evolution of human amylase gene
copy number variation. Nat. Genet. 39(10),
1256–1260 (2007).
33. Zwielehner J, Liszt K, Handschur M, Lassl C,
Dridi B, Raoult D, Drancourt M. Archaea as
emerging organisms in complex human
microbiomes. Anaerobe 17(2), 56-63 (2011).
Brown PO. Development of the human infant
intestinal microbiota. PLoS Biol. 5(7), e177
(2007).
43. Perry GH, Dominy NJ, Claw KG et al. Diet
32. Mueller S, Saunier K, Hanisch C et al.
20. Dridi B, Henry M, El Khechine A, Raoult D,
Drancourt M. High prevalence of
Methanobrevibacter smithii and
Methanosphaera stadtmanae detected in the
human gut using an improved DNA detection
protocol. PLoS ONE 4(9), e7063 (2009).
Woodmansey EJ, McMurdo ME, Macfarlane
GT, Macfarlane S. Comparison of
compositions and metabolic activities of fecal
microbiotas in young adults and in antibiotictreated and non-antibiotic-treated elderly
subjects. Appl. Environ. Microbiol. 70(10),
6113–6122 (2004).
Review
51.
Schwiertz A, Taras D, Schafer K et al.
Microbiota and SCFA in lean and
overweight healthy subjects. Obesity (Silver
Spring) 18(1), 190–195 (2010).
52. Collado MC, Isolauri E, Laitinen K,
Salminen S. Distinct composition of gut
microbiota during pregnancy in overweight
and normal-weight women. Am. J. Clin.
Nutr. 88(4), 894–899 (2008).
53. Zhang H, DiBaise JK, Zuccolo A et al.
Human gut microbiota in obesity and after
gastric bypass. Proc. Natl Acad. Sci. USA
106(7), 2365–2370 (2009).
54. Zuo HJ, Xie ZM, Zhang WW et al.
Gut bacteria alteration in obese people and
its relationship with gene polymorphism.
World J. Gastroenterol. 17(8), 1076–1081
(2011).
107
Review
55.
Angelakis, Armougom, Million & Raoult
Nadal I, Santacruz A, Marcos A et al. Shifts
in clostridia, bacteroides and
immunoglobulin-coating fecal bacteria
associated with weight loss in obese
adolescents. Int. J. Obes. (Lond.) 33(7),
758–767 (2009).
56. Santacruz A, Marcos A, Warnberg J et al.
65.
66. Martens EC, Koropatkin NM, Smith TJ,
Interplay between weight loss and gut
microbiota composition in overweight
adolescents. Obesity (Silver Spring) 17(10),
1906–1915 (2009).
57.
Mai V, McCrary QM, Sinha R, Glei M.
Associations between dietary habits and
body mass index with gut microbiota
composition and fecal water genotoxicity: an
observational study in African American and
Caucasian American volunteers. Nutr. J. 8,
49 (2009).
Gordon JI. Complex glycan catabolism by
the human gut microbiota: the Bacteroidetes
Sus-like paradigm. J. Biol. Chem. 284(37),
24673–24677 (2009).
67.
58. Kalliomäki M, Collado MC, Salminen S,
Isolauri E. Early differences in fecal
microbiota composition in children may
predict overweight. Am. J. Clin. Nutr. 87(3),
534–538 (2008).
„„
59.
Bifidobacteria protect children from
becoming overweight. This is confirmed
by the fact that our meta-analysis found
that the obesity-associated gut microbiota
are depleted in Bifidobacteria.
Bifidobacteria and Lactobacillus strains
are currently main candidates for
antiobesity probiotics.
Hepsiba J, Chandragunasekaran AM,
Ramakrishna BS. Quantitative differences in
intestinal Faecalibacterium prausnitzii in
obese Indian children. Br. J. Nutr. 103(3),
335–338 (2010).
61.
Thuny F, Richet H, Casalta JP, Angelakis E,
Habib G, Raoult D. Vancomycin treatment
of infective endocarditis is linked with
recently acquired obesity. PLoS ONE 5(2),
e9074 (2010).
Helbert W, Czjzek M, Michel G. Transfer of
carbohydrate-active enzymes from marine
bacteria to Japanese gut microbiota. Nature
464(7290), 908–912 (2010).
69.
O. Sulfatases and a radical AdoMet enzyme
are key for mucosal glycan foraging and
fitness of a prominent human gut.
Bacteroides. J. Biol. Chem. 286(29),
25973–25982 (2011).
71.
Organismal, genetic, and transcriptional
variation in the deeply sequenced gut
microbiomes of identical twins. Proc. Natl
Acad. Sci. USA 107(16), 7503–7508 (2010).
73. Backhed F, Ding H, Wang T et al. The gut
microbiota as an environmental factor that
regulates fat storage. Proc. Natl Acad. Sci.
USA 101(44), 15718–15723 (2004).
74.
108
Backhed F, Manchester JK, Semenkovich CF,
Gordon JI. Mechanisms underlying the
resistance to diet-induced obesity in
germ-free mice. Proc. Natl Acad. Sci. USA
104(3), 979–984 (2007).
75. Turnbaugh PJ, Ley RE, Mahowald MA,
Magrini V, Mardis ER, Gordon JI. An
obesity-associated gut microbiome with
increased capacity for energy harvest. Nature
444(7122), 1027–1031 (2006).
64. Grabitske HA , Slavin JL. Low-digestible
carbohydrates in practice. J. Am. Diet. Assoc.
108(10), 1677–1681 (2008).
Berteau O, Guillot A, Benjdia A, Rabot S. A
new type of bacterial sulfatase reveals a novel
maturation pathway in prokaryotes. J. Biol.
Chem. 281(32), 22464–22470 (2006).
72. Turnbaugh PJ, Quince C, Faith JJ et al.
63. Gloux K, Berteau O, El OH, Beguet F,
Leclerc M, Dore J. A metagenomic
E-glucuronidase uncovers a core adaptive
function of the human intestinal
microbiome. Proc. Natl Acad. Sci. USA
108(Suppl. 1), S4539–S4546 (2011).
Kurokawa K, Itoh T, Kuwahara T et al.
Comparative metagenomics revealed
commonly enriched gene sets in human gut
microbiomes. DNA Res. 14(4), 169–181
(2007).
that is one of the putative mechanisms for
gut microbiota-associated obesity.
76. Goodman AL, McNulty NP, Zhao Y et al.
Identifying genetic determinants needed to
establish a human gut symbiont in its habitat.
Cell Host Microbe 6(3), 279–289 (2009).
77.
„
Pioneering study linking gut microbiota
and increased capacity for energy harvest
Future Microbiol. (2012) 7(1)
Goodman AL, Kallstrom G, Faith JJ et al.
Extensive personal human gut microbiota
culture collections characterized and
manipulated in gnotobiotic mice. Proc. Natl
Acad. Sci. USA 108(15), 6252–6257 (2011).
78. Hildebrandt MA, Hoffmann C, Sherrill-Mix
SA et al. High-fat diet determines the
composition of the murine gut microbiome
independently of obesity. Gastroenterology
137(5), 1716–1724 (2009).
„
Proved that diet modifies gut microbiota
independently of obesity.
79. Rey FE, Faith JJ, Bain J et al. Dissecting the
in vivo metabolic potential of two human gut
acetogens. J. Biol. Chem. 285(29),
22082–22090 (2010).
80. Reyes A, Haynes M, Hanson N et al. Viruses
in the faecal microbiota of monozygotic twins
and their mothers. Nature 466(7304),
334–338 (2010).
81.
Fleissner CK, Huebel N, Abd El-Bary MM,
Loh G, Klaus S, Blaut M. Absence of
intestinal microbiota does not protect mice
from diet-induced obesity. Br. J. Nutr. 104(6),
919–929 (2010).
„
Showed that modification of gut
microbiota is not the only way for diet to
induce obesity since germ-free mice are not
protected against diet-induced obesity.
Diet, gut microbiota and obesity are
associated by a triangular causal link.
70. Benjdia A, Martens EC, Gordon JI, Berteau
62. Santacruz A, Collado MC, Garcia-Valdez L
et al. Gut microbiota composition is
associated with body weight, weight gain and
biochemical parameters in pregnant women.
Br. J. Nutr. 104, 83–92 (2010).
Turnbaugh PJ, Henrissat B, Gordon JI.
Viewing the human microbiome through
three-dimensional glasses: integrating
structural and functional studies to better
define the properties of myriad carbohydrateactive enzymes. Acta Crystallogr. Sect. F.
Struct. Biol. Cryst. Commun. 66(Pt 10),
1261–1264 (2010).
68. Hehemann JH, Correc G, Barbeyron T,
Duncan SH, Lobley GE, Holtrop G et al.
Human colonic microbiota associated with
diet, obesity and weight loss. Int. J. Obes.
(Lond.) 32(11), 1720–1724 (2008).
60. Balamurugan R, George G, Kabeerdoss J,
Cantarel BL, Coutinho PM, Rancurel C,
Bernard T, Lombard V, Henrissat B. The
carbohydrate-active enzymes database
(CAZy): an expert resource for
glycogenomics. Nucleic Acids Res.
37(Database issue), D233–D238 (2009).
82. Murphy EF, Cotter PD, Healy S et al.
Composition and energy harvesting capacity
of the gut microbiota: relationship to diet,
obesity and time in mouse models. Gut
59(12), 1635–1642 (2010).
83. Swann JR, Tuohy KM, Lindfors P et al.
Variation in antibiotic-induced microbial
recolonization impacts on the host metabolic
phenotypes of rats. J. Proteome Res. 10(8),
3590–3603 (2011).
84. Martin FP, Dumas ME, Wang Y et al. A
top-down systems biology view of
microbiome-mammalian metabolic
interactions in a mouse model. Mol. Syst. Biol.
3, 112 (2007).
85. DiBaise JK, Zhang H, Crowell MD,
Krajmalnik-Brown R, Decker GA, Rittmann
BE. Gut microbiota and its possible
relationship with obesity. Mayo Clin. Proc.
83(4), 460–469 (2008).
86. Raoult D. Probiotics and obesity: a link? Nat.
Rev. Microbiol. 7, 619 (2009).
future science group
Gut flora & weight gain
87.
Raoult D. Human microbiome: take-home
lesson on growth promoters? Nature
454(7205), 690–691 (2008).
90. Thomas O, Thabane L, Douketis J, Chu R,
Westfall AO, Allison DB. Industry funding
and the reporting quality of large long-term
weight loss trials. Int. J. Obes. (Lond.)
32(10), 1531–1536 (2008).
88. Gordon JI , Klaenhammer TR. A rendezvous
with our microbes. Proc. Natl Acad. Sci. USA
108(Suppl. 1), S4513–S4515 (2011).
89. Khoruts A , Sadowsky MJ. Therapeutic
transplantation of the distal gut microbiota.
Mucosal. Immunol. 4(1), 4–7 (2011).
future science group
91.
Hattori M , Taylor TD. The human
intestinal microbiome: a new frontier of
human biology. DNA Res. 16(1), 1–12
(2009).
www.futuremedicine.com
Review
92. Hoyles L, McCartney AL. What do we mean
when we refer to Bacteroidetes populations in
the human gastrointestinal microbiota? FEMS
Microbiol. Lett. 299(2), 175–183 (2009).
93. Borenstein M, Hedges L, Higgins J, Rothstein
H. Comprehensive Meta Analysis Version 2.
Biostat, Englewood, NJ, USA (2005).
94. Borenstein M, Hedges L, Higgins J,
Rothstein H. Introduction to Meta-Analysis.
Wiley, Hoboken, NJ, USA (2009).
109
Article III :
Obesity-associated gut microbiota is enriched in
Lactobacillus reuteri and depleted in Bifidobacterium
animalis and Methanobrevibacter smithii
Matthieu Million, Marie Maraninchi, Mireille Henry, Fabrice
Armougom, Hervé Richet, Patrizia Carrieri, René Valero, Denis
Raccah, Bernard Vialettes, Didier Raoult
Published in . Int J Obes (Lond). 2012 Jun;36(6):817-25. (IF 4.69)
42
International Journal of Obesity (2012) 36, 817–825
& 2012 Macmillan Publishers Limited All rights reserved 0307-0565/12
www.nature.com/ijo
ORIGINAL ARTICLE
Obesity-associated gut microbiota is enriched in
Lactobacillus reuteri and depleted in Bifidobacterium
animalis and Methanobrevibacter smithii
M Million1, M Maraninchi2, M Henry1, F Armougom1, H Richet1, P Carrieri3,4,5, R Valero2, D Raccah6,
B Vialettes2 and D Raoult1
1
URMITE -CNRS UMR 6236 IRD 198, IFR 48, Faculté de Médecine, Université de la Méditerranée, Marseille, France;
Service de Nutrition, Maladies Métaboliques et Endocrinologie, UMR-INRA U1260, CHU de la Timone, Marseille, France;
3
INSERM, U912(SE4S), Marseille, France; 4Université Aix Marseille, IRD, UMR-S912, Marseille, France; 5ORS PACA,
Observatoire Régional de la Santé Provence Alpes Côte d’Azur, Marseille, France and 6Service de Nutrition et Diabétologie,
CHU Sainte Marguerite, Marseille, France
2
Background: Obesity is associated with increased health risk and has been associated with alterations in bacterial gut
microbiota, with mainly a reduction in Bacteroidetes, but few data exist at the genus and species level. It has been reported that
the Lactobacillus and Bifidobacterium genus representatives may have a critical role in weight regulation as an anti-obesity effect
in experimental models and humans, or as a growth-promoter effect in agriculture depending on the strains.
Objectives and methods: To confirm reported gut alterations and test whether Lactobacillus or Bifidobacterium species found
in the human gut are associated with obesity or lean status, we analyzed the stools of 68 obese and 47 controls targeting
Firmicutes, Bacteroidetes, Methanobrevibacter smithii, Lactococcus lactis, Bifidobacterium animalis and seven species of Lactobacillus
by quantitative PCR (qPCR) and culture on a Lactobacillus-selective medium.
Findings: In qPCR, B. animalis (odds ratio (OR) ¼ 0.63; 95% confidence interval (CI) 0.39–1.01; P ¼ 0.056) and M. smithii
(OR ¼ 0.76; 95% CI 0.59–0.97; P ¼ 0.03) were associated with normal weight whereas Lactobacillus reuteri (OR ¼ 1.79; 95% CI
1.03–3.10; P ¼ 0.04) was associated with obesity.
Conclusion: The gut microbiota associated with human obesity is depleted in M. smithii. Some Bifidobacterium or Lactobacillus
species were associated with normal weight (B. animalis) while others (L. reuteri) were associated with obesity. Therefore, gut
microbiota composition at the species level is related to body weight and obesity, which might be of relevance for further studies
and the management of obesity. These results must be considered cautiously because it is the first study to date that links
specific species of Lactobacillus with obesity in humans.
International Journal of Obesity (2012) 36, 817 – 825; doi:10.1038/ijo.2011.153; published online 9 August 2011
Keywords: gut microbiota; Methanobrevibacter smithii; Lactobacillus reuteri; Bifidobacterium animalis
Introduction
Obesity, defined as a body mass index (BMI) over 30 kg mÿ2 (ref. 1)
and a massive expansion of fat, is related to a significantly
increased mortality and is a risk factor for many diseases,
including diabetes mellitus, hypertension, respiratory disorders,
ischemic heart disease, stroke and cancer.2,3 Obesity can be
considered as a transmissible disease because maternal obesity
Correspondence: Professor D Raoult, Unité des Rickettsies, URMITE -CNRS
UMR 6236 IRD 198, IFR 48, Faculté de Médecine, Université de la
Méditerranée, 27 Bd Jean Moulin, Marseille, 13005 France.
E-mail: [email protected]
Received 24 November 2010; revised 27 June 2011; accepted 2 July 2011;
published online 9 August 2011
predisposes children to adulthood obesity.4 Its prevalence is
increasing steadily among adults, adolescents and children,
and has doubled since 1960; and obesity is now considered
a worldwide epidemic as, for example, over 30% of the
population of North America is obese. The WHO data
indicate that obesity currently affects at least 400 million
people worldwide and 1.6 billion are overweight. The WHO
further projects that by 2015, B2.3 billion adults will be
overweight and more than 700 million will be obese.5
The causes behind the obesity epidemic appear to be
complex and involve environmental, genetic, neural and
endocrine origins.6
More recently, obesity has been associated with a specific
profile of the bacterial gut microbiota, including a decrease
Gut microbiota and obesity
M Million et al
818
in the Bacteroidetes/Firmicutes ratio7–10 and a decrease in
Methanobrevibacter smithii, the leading representative of the
gut microbiota archaea.11 Since these pioneering studies,
significant associations were found between the increase of
some bacterial groups and obesity (Lactobacillus,12 Staphylococcus aureus,13–15 Escherichia coli,15 Faecalibacterium prausnitzii16). Conversely, other groups have been associated with
lean status, mainly belonging to the Bifidobacterium
genus.11,13–16 To date, controversial studies make it clear
that the connection between the microbiome and excess
weight is complex.17
As many probiotic strains of Lactobacillus and Bifidobacterium
are marketed in products for human consumption, altering
the intestinal flora18 and stimulating indigenous lactobacilli
and bifidobacteria strains,19 we hypothesized that widespread ingestion of probiotics may promote obesity by
altering the intestinal flora.20–22 However, this remains
controversial.23,24 In a first step to elucidate the interactions
between probiotics for human consumption and obesity,
only a few studies have compared the obese and lean subjects
by focusing on the Lactobacillus and Bifidobacterium genera
at the species level13,16 and they have not been able to
demonstrate significant differences probably because of a
too small sample size. As a result, by increasing the sample
size, we analyze the composition of the digestive microbiota
for Firmicutes, Bacteroidetes, the archaea M. smithii,
Lactobacillus genus, L. lactis, and explore the relationships
between seven selected species of Lactobacillus and one
species of Bifidobacterium, used elsewhere in marketed
probiotics for human consumption and obesity.
Materials and methods
Ethics, participants and samples
All aspects of the study were approved by the local ethics
committee ‘Comité d’éthique de l’IFR 48, Service de
Médecine Légale’ (Faculté de Médecine, Marseille, France)
under the accession number 10–002, 2010. Only verbal
consent was necessary from patients for this study. This is
according to the French bioethics decree Number 2007–
1220, published in the official journal of the French
Republic. Obese patients, as defined by a BMI430 kg mÿ2
(BMI: weight over height squared (kg mÿ2)), were selected
from two endocrinology units (Hopital La Timone and
Hopital Sainte Marguerite, Marseilles, France) from a group
of patients attending the clinic for excessive body weight.
BMI provides the most useful population-level measure of
overweight and obese, as it is the same for both sexes and for
all ages of adults.5 However, it may not correspond to the
same degree of fatness in different individuals (The Y-Y
paradox).25 Control subjects were healthy volunteers over 18
years of age with BMIs between 19 and 25 kg mÿ2. Only a few
patients had participated in the previous study conducted by
our laboratory.12 The control subjects were predominantly
International Journal of Obesity
Caucasian and were approached in different geographical
locations using a snowball approach. This approach was
helpful in making the period of recruitment of cases and
controls comparable. The exclusion criteria included the
following: non-assessable BMI value, BMIo19 kg mÿ2,
BMI425 kg mÿ2 and o30 kg mÿ2, gastric bypass, history of
colon cancer, bowel inflammatory diseases, acute or chronic
diarrhea in the previous 4 weeks and antibiotic administration o1 month before stool collection. Clinical data (gender,
date of birth, clinical history, weight, height and antibiotic
use) were recorded using a standardized questionnaire. The
samples, collected using sterile plastic containers, were
transported as soon as possible to the laboratory and frozen
immediately at ÿ80 1C for later analysis. For Firmicutes,
Bacteroidetes, M. smithii and Lactobacillus species, analyses
were first performed on the whole population and then after
exclusion of common subjects with our previous study.12
Analysis of gut microbiota
Culture on specific Lactobacillus medium (LAMVAB
medium). After thawing at room temperature, 100 mg of
stool was suspended in 900 ml of cysteine–peptone–water
solution26 and homogenized. A serial dilution was undertaken in phosphate buffered saline. Samples diluted to 1/10
and 1/1000 were inoculated using a 10 ml inoculation loop
on LAMVAB medium.27 After a 72-hour incubation in jars
(AnaeroPack, Mitsubishi Gas Chemical America, Inc., New
York, NY, USA) in an anaerobic atmosphere (GasPak EZ
Anaerobe, Becton Dickinson, Heidelberg, Germany) at 37 1C,
the number of morphotypes were identified and 1–4 colonies
per morphotype were placed on four spots of an MTP 384
Target plate made of polished steel (Bruker Daltonics GmbH,
Bremen, Germany) and stored in trypticase cases in soy
culture medium (AES, Bruz, France).
Lactobacillus strain collection and MALDI-TOF spectra
database. The Lactobacillus strain collection of our laboratory has been completed by the strains from the Pasteur and
DSMZ collections, and reference spectra have been created
from those missing in the Bruker database. Bacterial identification was undertaken with an Autoflex II mass spectrometer
(Bruker Daltonik GmbH). Data were automatically acquired
using Flex control 3.0 and Maldi Biotyper Automation
Control 2.0. (Bruker Daltonics GmbH). Raw spectra, obtained for each isolate, were analyzed by standard pattern
matching (with default parameter settings) against the
spectra of species used as a reference database. An isolate
was regarded as correctly identified at the species level when
at least one spectrum had a score X1.9, and one spectrum
had a score X1.7.28 The reproducibility of the method was
evaluated by the duplicate analysis of 10 samples.
Quantitative real-time PCR for M. smithii, Bacteroidetes,
Firmicutes and Lactobacillus genus. DNA was isolated from
stools as described in Dridi et al.29 The purified DNA samples
Gut microbiota and obesity
M Million et al
819
were eluted to a final volume of 100 ml and stored at ÿ80 1C
until analysis. Real-time PCR was performed on a Stratagene
MX3000 system (Agilent, Santa Clara, CA, USA) using
QuantiTect PCR mix (Qiagen, Courtaboeuf, France) as
described previously.12
Quantitative real-time PCR specific for Lactococcus lactis,
Bifidobacterium animalis and seven Lactobacillus species. The
primer and probe sequences were located on the Tuf
(elongation factor Tu) gene. The Tuf gene from the
Lactobacillus strains, reported in Supplementary Table 1, were
sequenced and compared, where possible, to the sequence
reported in Genbank as described in Supplementary Text 1.
All of these sequences were compared by ClustalX (1.8;
http://www.clustal.org) using global-multiple sequence
alignment by the progressive method. A distance is calculated between every pair of sequences and these are used to
construct the phylogenetic tree, which guides the final
multiple alignment. The scores are calculated from separate
pairwise alignments using the dynamic programming
method. A consensus sequence was obtained and compared
with the Tuf sequences of Lactobacillus acidophilus, Lactobacillus casei-paracasei, Lactobacillus plantarum, Lactobacillus
reuteri, Lactobacillus gasseri, Lactobacillus fermentum and
Lactobacillus rhamnosus, Bifidobacterium animalis and Lactococcus lactis, and sequences of primers and probes of highly
specific real-time PCR were established. The primer and
probe sequences are reported in Supplementary Table 2. The
Lactobacillus strain-specific detection proceeded in duplex
real-time PCR: L. acidophilus (FAM) and L. casei/paracasei
(VIC), L. plantarum (FAM) and L. reuteri (VIC), L. gasseri (FAM)
and fermentum (VIC), and L. rhamnosus (FAM). B. animalis
(VIC) and Lactococcus lactis (FAM) detection utilized simplex
real-time PCR. The duplex real-time PCR was executed as
described above and in Armougom et al.12 The specificity was
tested on the DNA of the reference strains reported in
Supplementary table 1. The stool-purified DNA was analyzed
in samples that were pure, diluted at 1/10, and diluted at 1/
100 to confirm the absence of inhibitors. Negative controls
were included on each plate. The different lactobacilli, B.
animalis and Lactococcus lactis were quantified using a
plasmid standard curve from 107–10 copies per assay.
account possible confounders like age or gender, a logistic
regression model was used. Variables with a liberal Po0.20 in
the univariate logistic regression analysis were considered
eligible for the multiple logistic regression analyses.30 A
secondary analysis based on logistic regression analysis was
used to identify which culture variables (Lactobacillus species
concentration) where associated with obesity. Data analyses
were conducted using SPSS v.9.0 (SPSS Inc., Chicago, IL, USA).
Results
Patients
In total, 115 subjects (68 obese patients and 47 controls)
were included. Thirteen obese subjects and nine controls
were part of the previous study conducted in our laboratory.12 The two populations were homogeneous in sex and
height, but not in age (Table 1).
Culture
In total, 68 obese and 44 controls samples were analyzed.
The number of positive samples was greater among the
controls vs obese (32/44 vs 30/68, Fisher’s exact test,
P ¼ 0.002). For positive samples, the concentration was not
significantly different between obese subjects and controls,
respectively (median 4.15 (interquartile range 4–6) vs 5.2
(4–6) log10 CFU mlÿ1, Mann–Whitney test, P ¼ 0.93). The
proportion (Table 2) and non-parametric quantitative comparison of the concentration of Lactobacillus species between
obese subjects and controls has been achieved for the species
present in at least six individuals. L. paracasei was found
more frequently in controls (17/44 vs 10/68, Fisher’s exact
test, P ¼ 0.004). L. reuteri was found more frequently in obese
patients (6/68 vs 1/44, Fisher’s exact test, P ¼ 0.15), although
this was not significant. L. plantarum was found only in
Table 1
Age
Male sex
Body mass index
a
Statistical Analysis
First, the results of Lactobacillus-specific culture and quantitative PCR (qPCR) were compared in the two groups (obese
and control group) using the Fisher’s exact test when
comparing proportions, and the Mann–Whitney test when
comparing bacterial concentrations. A difference was considered statistically significant when Po0.05. In order to
identify which qPCR bacterial groups (Bacteroidetes, B. animalis,
Lactococcus lactis, L. acidophilus, L. casei/paracasei, L. fermentum,
L. gasseri, L. plantarum, L. reuteri, L. rhamnosus) was most
associated with the likelihood of being obese while taking into
Baseline characteristics
Obese (n ¼ 68)
Controls (n ¼ 47)
P (obese vs controls)
50.5±14.4
31 (45.6%)
43.6±7.8
42.6±17.5
21 (51.2%)
22.1±1.8
0.01a
0.35b
o0.0001a
Mann–Whitney test. bFisher’s exact test
Table 2 Results of Lactobacillus-specific culture
Obese (n ¼ 68)
L.
L.
L.
L.
L.
L.
a
paracasei
plantarum
reuteri
rhamnosus
ruminis
salivarius
10
0
6
3
3
5
(14.7%)
(0%)
(8.8%)
(4.4%)
(4.4%)
(7.4%)
Controls (n ¼ 44)
17
8
1
4
4
2
(38.6%)
(18.2%)
(2.3%)
(9.1%)
(9.1%)
(4.5%)
P-valuea
0.004
0.0004
0.16
0.27
0.27
0.43
Species present in at least six individuals. Fisher’s exact test.
International Journal of Obesity
Gut microbiota and obesity
M Million et al
820
Figure 1 Quantification of L. paracasei, L. plantarum and L. reuteri in culture (LAMVAB medium) ÿlog (colony forming units per ml of feces)FMann–Whitney test.
Table 3 Results of Bacteroidetes, Firmicutes, Methanobrevibacter smithii and
Lactobacillus genus quantitative PCR
Obese (n ¼ 67)
Controls (n ¼ 45)
P
a
Presence of phyla, genus or species
Bacteroidetes
41
Firmicutes
67
Lactobacillus
23
Methanobrevibacter smithii
50
(61.2%)
(100%)
(34.3%)
(74.6%)
27
45
8
40
(60%)
(100%)
(17.8%)
(88.9%)
0.52
F
0.04
0.05
(0–6.37)
(5.86–7.21)
(0–0)
(1.71–5.30)
0.25
0.30
0.039
0.002
ÿ1 b
Quantitative comparison (log copies DNA ml )
Bacteroidetes
4.26 (0–5.82)
Firmicutes
6.43 (5.32–7.29)
Lactobacillus
0 (0–3.31)
Methanobrevibacter smithii 2.31 (0–3.51)
5.65
6.62
0
3.78
a
Values noted as number (percentage), Fisher’s exact test. bValues noted as
log copies DNA mlÿ1, median (interquartile range), Mann–Whitney test.
controls (8/44 vs 0/68, Fisher’s exact test, P ¼ 0.0004).
Quantitative comparison found higher levels of L. paracasei
and L. plantarum in controls (Mann–Whitney test, P ¼ 0.005
and P ¼ 0.0004, respectively), while L. reuteri was higher in
the obese subjects; however, this was not significant (Mann–
Whitney test, P ¼ 0.14) (Figure 1). Variables eligible for the
final logistic regression model were L. paracasei, L. reuteri,
L. plantarum, L. brevis, L. fermentum and age. The final
multiple logistic regression model showed that after adjustment for age, only L. paracasei was significantly associated
with lean status (odds ratio ¼ 0.79; 95% confidence interval
0.64–0.97; P ¼ 0.03).
Firmicutes, Bacteroidetes, M. smithii and Lactobacillus
species-specific qPCR
M. smithii was found more frequently in controls (40/
45(89%) vs 50/67(75%), Fisher’s exact test, P ¼ 0.05). The
analysis did find a lower concentration of M. smithii in
obese subjects (Mann–Whitney test, P ¼ 0.002; Table 3) and
a higher concentration of Lactobacillus (Mann–Whitney
test, P ¼ 0.04). Bacteroidetes was found in lower concentration in obese, but this result was not significant (Mann–
Whitney test, P ¼ 0.25) (Figure 2). The same results were
International Journal of Obesity
found after the exclusion of the common subjects from
our previous study (Mann–Whitney test; higher level of
Lactobacillus genus in obese people, P ¼ 0.026; lower level
of M. smithii, P ¼ 0.008 and lower level of Bacteroidetes,
P ¼ 0.09).
Bifidobacterium–Lactococcus–Lactobacillus species-specific
qPCR
The different Bifidobacterium–Lactococcus–Lactobacillus species-specific real-time PCRs were tested for their specificity
against purified DNA of the strains reported in Supplementary Table 1. The different real-time PCR systems were tested
for their sensitivity and we obtained a cycle threshold of
about 35 for 10 copies of DNA per 5 ml of sample. All of these
real-time PCRs have good sensitivity and specificity (Supplementary Table 3). In total, 64 obese samples and 43 control
samples were analyzed. The presence of B. animalis was
associated with normal weight (Table 4, Fisher’s exact test,
P ¼ 0.007), and L. reuteri was associated with obesity (Fisher’s
exact test, P ¼ 0.03). Comparison using non-parametric
statistics found that levels of B. animalis were lower
(Mann–Whitney test, P ¼ 0.004) and that of L. reuteri were
higher in obese people (Mann–Whitney test, P ¼ 0.02)
(Figure 3). By comparing the culture and the Lactobacillus
species-specific PCR, the sensitivity was higher for all seven
tested species by PCR vs culture except for L. acidophilus,
which was not found by culture or species-specific PCR.
Overall, results of culture and PCR were consistent for the
presence of L. casei/paracasei (Fisher’s exact test, P ¼ 0,017),
L. plantarum (Fisher’s exact test, P ¼ 0,05) and L. reuteri
(Fisher’s exact test, P ¼ 0,00001).
Logistic regression analysis
The results of the logistic regression analysis on the qPCR
results are presented in Table 5. Variables eligible for the final
model were L. casei/paracasei, L. reuteri, L. gasseri, B. animalis,
M. smithii and age. The final multiple logistic regression
model showed that after adjustment for age, L. reuteri,
B. animalis and M. smithii were significantly associated with
Gut microbiota and obesity
M Million et al
821
Figure 2 Quantification of Bacteroidetes, Firmicutes, M. smithii and Lactobacillus genus by qPCRFMann–Whitney test.
Table 4 Results of Bifidobacterium animalis, Lactococcus lactis and seven
Lactobacillus species-specific quantitative PCR
Obese (n ¼ 64)
Presence of targeted taxaa
L. acidophilus
L. casei/paracasei
L. fermentum
L. gasseri
L. plantarum
L. reuteri
L. rhamnosus
Lactococcus lactis
Bifidobacterium animalis
0
24
11
21
14
16
11
55
1
(0%)
(37.5%)
(17.2%)
(32.8%)
(21.9%)
(25.0%)
(17.2%)
(85.9%)
(1.6%)
Controls (n ¼ 43)
0
24
9
9
12
4
9
34
7
(0%)
(55.8%)
(20.9%)
(20.9%)
(27.9%)
(9.3%)
(20.9%)
(79.1%)
(16.3%)
P
F
0.047
0.40
0.13
0.31
0.03
0.40
0.25
0.007
a
Values expressed as number (percentage). Fisher’s exact test.
obesity. L. reuteri was the only one which showed higher
levels in obese individuals while B. animalis and M. smithii
were found at greater levels in non-obese subjects.
Discussion
To our knowledge, we report the largest case–control study
comparing human obese gut microbiota to controls focusing
on Archaea, Bacteroidetes, Firmicutes, Lactobacillus genus,
Lactococcus lactis and B. animalis and, for the first time, we
used a culture-dependent and culture-independent method
to compare the Lactobacillus population at the species level
between obese and normal-weighted humans. Our results
confirm global alteration in obese gut microbiota with a
lower level of M. smithii as already reported in the
literature,11 and newly report lower levels of B. animalis,
L. paracasei, L. plantarum and higher levels of L. reuteri in
obese gut microbiota.
The qPCR system used in this study to detect and quantify
Bacteroidetes, Firmicutes, Lactobacillus genus and M. smithii in
human feces has already been evaluated and validated.12,29
LAMVAB-selective media has also been used successfully to
International Journal of Obesity
Gut microbiota and obesity
M Million et al
822
Figure 3 Quantification of B. animalis, L. casei/paracasei, L. plantarum and L. reuteri by qPCRFMann–Whitney test.
Table 5 Factors associated with obesity based on multiple logistic regression
(qPCR results, logistic regression analysis, n ¼ 107)
OR (95%CI)
Lactobacillus reuteri
Bifidobacterium animalis
Methanobrevibacter smithii
Age
1.79
0.63
0.76
1.05
(1.03–3.10)
(0.39–1.01)
(0.59–0.97)
(1.01–1.08)
P-value
0.04
0.056
0.03
0.006
Abbreviations: CI, confidence interval; OR, odds ratio; qPCR, quantitative PCR.
identify and enumerate lactobacilli from human feces.27 As
in our previous study,12 we found an increase in Lactobacillus
in obese patients using the same Lactobacillus genus-specific
PCR system. However, we found that its sensitivity profile
was heterogeneous among the Lactobacillus species found in
human feces by culture (data not shown). We subsequently
developed a novel Lactobacillus species-specific qPCR system
International Journal of Obesity
targeting species associated with obesity or normal weight in
our preliminary culture study, and targeting other species
present in marketed probiotics products as Lactococcus lactis
and B. animalis. Species-specific Lactobacillus PCR based
on the Tuf gene and designed for this new study showed
good reproducibility, sensitivity and specificity. However,
we found significant discrepancies between culture and
Lactobacillus species-specific PCR species. First, L. gasseri
and L. acidophilus could not be identified in culture due
to the presence of vancomycin in the LAMVAB medium.
Conversely, although qPCR was much more sensitive than
culture to detect selected species of Lactobacillus, we showed
that the two methods were consistent for L. casei/paracasei,
L. plantarum and L. reuteri. For these three Lactobacillus
species, both techniques resulted in the same effect direction
with human obesity gut microbiota enriched in L. reuteri,
and depleted in L. casei/paracasei and L. plantarum.
Gut microbiota and obesity
M Million et al
823
The decrease of Bacteroidetes was historically the first
alteration significantly associated with obesity as reported
by Ley and Turnbaugh,8 in mice and in North American
individuals,7,9 and by Santacruz et al.,15 who observed
overweight pregnant women in Spain. We found the same
correlation in our previous study,12 and the same effect
direction in the present study with the same PCR system on
the whole population and after the exclusion of common
subjects. Schwiertz et al.11 reported opposite results, but the
methodology was objectionable because the Bacteroidetes
proportion was obtained by summing Bacteroides and
Prevotella genera. Other studies found no interaction
between the relative or absolute abundance of Bacteroidetes
and obesity.31–33
In our previous study,12 abundance of M. smithii was
significantly higher in patients with anorexia but not in
lean controls. In this new study, we found that M. smithii
was less frequent and significantly less abundant in obese
patients on the whole population and after the exclusion of
common subjects. Schwiertz et al.11 using a specific qPCR for
Methanobrevibacter species, found similar results in a German
population. These results are in contradiction to those of
Zhang et al.33 who found that Methanobacteriales was present
only in obese individuals using a qPCR but only three obese
vs three controls were compared.
In this study, we report an association between lower levels
of B. animalis and obesity for the first time. Five studies
reported a decreased number of Bifidobacterium representatives
in the feces of obese subjects at the genus level.11,13–16 At the
species level, Kalliomaki et al.13 using a Bifidobacterium speciesspecific PCR, found that Bifidobacterium longum and Bifidobacterium breve were higher in normal weight controls, but this
result was not significant probably because of a small sample
size. Experimental data report that administration of a B. breve
strain to mice with high-fat diet-induced obesity led to a
significant weight decrease.34 Administering four different
Bifidobacterium strains to high-fat diet induced obese rats, Yin
et al.35 reported that one strain increased body weight gain,
another induced a decrease and the two other strains lead to no
significant change in body weight but species were not
mentioned in this study. In this way, Cani et al.36 reported
that high-fat feeding was associated with higher endotoxaemia
and lower Bifidobacterium species cecal content in mice. The
selective increase of bifidobacteria by oligofructose, improving
mucosal barrier function, significantly and positively correlated
with improved glucose tolerance, glucose-induced insulin
secretion and decreased endotoxaemia.
L. plantarum and L. paracasei were associated with normal
weight in culture, consistent with experimental models in
the literature reporting an anti-obesity effect of L. plantarum
in mice.37 Other Lactobacillus strains have shown an antiobesity effect in animals and humans similar to the L. gasseri
SBT2055 (LG2055) strain in lean Zucker rats38 and in
humans.39 This anti-obesity effect may be linked to the
production of specific molecules that can interfere with
host metabolism, such as conjugated linoleic acid (CLA) for
L. plantarum or L. rhamnosus.37,40 In vivo and in vitro analyses
of physiological modifications imparted by CLA on protein
and gene expression suggest that CLA exerts its delipidating
effects by modulating energy expenditure, apoptosis, fatty
acid oxidation, lipolysis, stromal vascular cell differentiation and lipogenesis.37 Authors who have investigated the
mechanisms linking conjugated linoleic acid and antiobesity effects have reported the upregulated expression of
genes encoding uncoupling proteins (UCP-2), which could
be a primary mechanism through which CLA increases
energy expenditure and produces an anti-obesity effect.40
L. reuteri has been associated here with obesity. L. reuteri
has been one of the most studied probiotic species especially
for its ability to inhibit the growth of other potentially
pathogenic microorganisms by secreting antibiotic substances such as reuterin.41 When introduced in pigs, turkeys
and rats, L. reuteri led to a significant weight gain and was
isolated in higher concentrations from feces after probiotic
administration.42–44 The mechanism by which L. reuteri is
able to support the healthy growth of these animals is not
entirely understood. It is possible that L. reuteri simply serves
to protect livestock against illness caused by Salmonella
typhimurium and other pathogens. However, other studies
have revealed that L. reuteri can also help when the growth
depression is caused entirely by a lack of dietary protein and
not by contagious disease.45 This raises the possibility that
L. reuteri somehow improves the intestines’ ability to absorb
and process nutrients, and increase food conversion.46
As a theoretical basis for the causal link between the
gut microbiota alterations and obesity, several mechanisms
have been suggested. First, the gut microbiota could interact with weight regulation by hydrolysis of indigestible
polysaccharides to monosaccharides easily absorbable activating lipoprotein lipase. Consequently, glucose is rapidly
absorbed producing substantial elevations in serum glucose
and insulin, both factors that trigger lipogenesis and fatty
acids excessively stored with de novo synthesis of triglycerides
derived from liver, these two phenomena causing weight
gain.47 Second, the composition of gut microbiota has been
shown to selectively suppress the angiopoietin-like protein
4/fasting-induced adipose factor in the intestinal epithelium,
known as a circulating lipoprotein lipase inhibitor and
regulator of peripheral lipid and glucose metabolism.48
Third, it has been suggested that bacterial isolates of gut
microbiota may have pro- or anti-inflammatory properties,
impacting weight as obesity, having been associated with a
low-grade systemic inflammation corresponding to higher
plasma endotoxin lipopolysaccharide concentrations defined
as metabolic endotoxaemia.49–52 Fourth, extracting crude fat
in feed and excreta, Nahashon et al.53 reported that feeding
laying Leghorn with Lactobacillus improved significantly
retention of fat with increased cellularity of the Peyer’s
patches of the ileum, which indicated ileal immune response.
Conversely, Bifidobacterium and Lactobacillus species have
been cited to deconjugate bile acids, which may decrease
fat absorption.54
International Journal of Obesity
Gut microbiota and obesity
M Million et al
824
Finally, specific strains of Lactobacillus and Bifidobacterium
fed to farm animals have been shown to increase daily
weight gain,55 and this fact has been used for decades in
agriculture to increase feed conversion. In this context, one
cannot exclude that the ‘growth promoter’ effect in animals
associated with oral administration of specific probiotics
strains is similar to the mechanisms involved in human
obesity. For instance, Abdulrahim et al.56 reported that
L. acidophilus significantly increased abdominal fat deposition in female chickens when administered alone and up to
31% when it was associated with zinc bacitracin. Further
studies are therefore mandatory in exploring the interactions
between probiotics and weight regulation.
Conclusion
In conclusion, reduced levels of M. smithii has been
confirmed as being associated with obesity. In addition,
higher levels of B. animalis, L. paracasei or L. plantarum were
associated with a normal weight whereas higher levels of
L. reuteri were associated with obesity, suggesting a
possible interrelationship between certain probiotic species,
marketed elsewhere for human consumption, and obesity.
These results must be considered cautiously because it is the
first study to date that links specific species of Lactobacillus
with obesity in humans. This issue will be of critical
importance in the management of the twenty-first century
worldwide epidemic that is obesity and especially considering the booming market of probiotics.
Conflict of interest
The authors declare no conflict of interest.
Acknowledgements
We thank all the volunteers without whom this study would
not have been possible.
Author contributions
Conceived and designed the experiments: DR. Performed the
clinical study: MM, MM, RV, BV and DR. Performed the
experiments: MM and MH. Analyzed the data: FA, HR and
PC. Wrote the paper: MM, MH, HR and DR.
Disclaimer
The funders had no role in study design, data collection and
analysis, decision to publish or preparation of the manuscript
International Journal of Obesity
References
1 Appropriate body-mass index for Asian populations and its
implications for policy and intervention strategies. Lancet 2004;
363: 157–163.
2 Whitlock G, Lewington S, Sherliker P, Clarke R, Emberson J,
Halsey J et al. Body-mass index and cause-specific mortality in
900 000 adults: collaborative analyses of 57 prospective studies.
Lancet 2009; 373: 1083–1096.
3 Yanovski SZ, Yanovski JA. Obesity. N Engl J Med 2002; 346:
591–602.
4 Lawlor DA, Smith GD, O’Callaghan M, Alati R, Mamun AA,
Williams GM et al. Epidemiologic evidence for the fetal overnutrition hypothesis: findings from the mater-university study of
pregnancy and its outcomes. Am J Epidemiol 2007; 165: 418–424.
5 World health organization. Obesity and overweight. Fact sheet
N1311. 2011.
6 Tilg H, Moschen AR, Kaser A. Obesity and the microbiota.
Gastroenterology 2009; 136: 1476–1483.
7 Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A,
Ley RE et al. A core gut microbiome in obese and lean twins.
Nature 2009; 457: 480–484.
8 Ley RE, Backhed F, Turnbaugh P, Lozupone CA, Knight RD,
Gordon JI. Obesity alters gut microbial ecology. Proc Natl Acad Sci
USA 2005; 102: 11070–11075.
9 Ley RE, Turnbaugh PJ, Klein S, Gordon JI. Microbial ecology:
human gut microbes associated with obesity. Nature 2006; 444:
1022–1023.
10 Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER,
Gordon JI. An obesity-associated gut microbiome with increased
capacity for energy harvest. Nature 2006; 444: 1027–1031.
11 Schwiertz A, Taras D, Schafer K, Beijer S, Bos NA, Donus C et al.
Microbiota and SCFA in lean and overweight healthy subjects.
Obesity (Silver Spring) 2010; 18: 190–195.
12 Armougom F, Henry M, Vialettes B, Raccah D, Raoult D.
Monitoring bacterial community of human gut microbiota
reveals an increase in Lactobacillus in obese patients and
Methanogens in anorexic patients. PLoS One 2009; 4: e7125.
13 Kalliomaki M, Collado MC, Salminen S, Isolauri E. Early
differences in fecal microbiota composition in children may
predict overweight. Am J Clin Nutr 2008; 87: 534–538.
14 Collado MC, Isolauri E, Laitinen K, Salminen S. Distinct
composition of gut microbiota during pregnancy in overweight
and normal-weight women. Am J Clin Nutr 2008; 88: 894–899.
15 Santacruz A, Collado MC, Garcia-Valdes L, Segura MT, MartinLagos JA, Anjos T et al. Gut microbiota composition is associated
with body weight, weight gain and biochemical parameters in
pregnant women. Br J Nutr 2010; 104: 83–92.
16 Balamurugan R, George G, Kabeerdoss J, Hepsiba J, Chandragunasekaran AM, Ramakrishna BS. Quantitative differences in
intestinal Faecalibacterium prausnitzii in obese Indian children.
Br J Nutr 2010; 103: 335–338.
17 Pennisi E. Microbiology. girth and the gut (bacteria). Science 2011;
332: 32–33.
18 Fujimoto J, Matsuki T, Sasamoto M, Tomii Y, Watanabe K.
Identification and quantification of Lactobacillus casei strain
Shirota in human feces with strain-specific primers derived from
randomly amplified polymorphic DNA. Int J Food Microbiol 2008;
126: 210–215.
19 Ohashi Y, Inoue R, Tanaka K, Matsuki T, Umesaki Y, Ushida K.
Lactobacillus casei strain Shirota-fermented milk stimulates
indigenous lactobacilli in the pig intestine. J Nutr Sci Vitaminol
(Tokyo) 2001; 47: 172–176.
20 Raoult D. Obesity pandemics and the modification of digestive
bacterial flora. Eur J Clin Microbiol Infect Dis 2008; 27: 631–634.
21 Raoult D. Human microbiome: take-home lesson on growth
promoters? Nature 2008; 454: 690–691.
22 Raoult D. Probiotics and obesity: a link? Nat Rev Microbiol 2009;
7: 616.
Gut microbiota and obesity
M Million et al
825
23 Delzenne N, Reid G. No causal link between obesity and
probiotics. Nat Rev Microbiol 2009; 7: 901.
24 Ehrlich SD. ProbioticsFlittle evidence for a link to obesity. Nat
Rev Microbiol 2009; 7: 901.
25 Yajnik CS, Yudkin JS. The Y-Y paradox. Lancet 2004; 363: 163.
26 Jackson MS, Bird AR, McOrist AL. Comparison of two selective
media for the detection and enumeration of lactobacilli in
human faeces. J Microbiol Methods 2002; 51: 313–321.
27 Hartemink R, Domenech VR, Rombouts FM. LAMVAB-A new
selective medium for the isolation of lactobacilli from faeces.
J Microbiol Methods 1997; 29: 77–84.
28 Seng P, Drancourt M, Gouriet F, La Scola B, Fournier PE, Rolain JM
et al. Ongoing revolution in bacteriology: routine identification
of bacteria by matrix-assisted laser desorption ionization time-offlight mass spectrometry. Clin Infect Dis 2009; 49: 543–551.
29 Dridi B, Henry M, El Khechine A, Raoult D, Drancourt M. High
prevalence of Methanobrevibacter smithii and Methanosphaera
stadtmanae detected in the human gut using an improved DNA
detection protocol. PLoS One 2009; 4: e7063.
30 Hosmer DW, Lemeshow S. Applied Logistic Regression 2nd edn.
Wiley: New York, 2000.
31 Mai V, McCrary QM, Sinha R, Glei M. Associations between
dietary habits and body mass index with gut microbiota
composition and fecal water genotoxicity: an observational study
in African American and Caucasian American volunteers. Nutr J
2009; 8: 49.
32 Duncan SH, Lobley GE, Holtrop G, Ince J, Johnstone AM, Louis P
et al. Human colonic microbiota associated with diet, obesity and
weight loss. Int J Obes (Lond) 2008; 32: 1720–1724.
33 Zhang H, DiBaise JK, Zuccolo A, Kudrna D, Braidotti M, Yu Y et al.
Human gut microbiota in obesity and after gastric bypass. Proc
Natl Acad Sci USA 2009; 106: 2365–2370.
34 Kondo S, Xiao JZ, Satoh T, Odamaki T, Takahashi S, Sugahara H
et al. Antiobesity effects of Bifidobacterium breve strain B-3
supplementation in a mouse model with high-fat diet-induced
obesity. Biosci Biotechnol Biochem 2010; 74: 1656–1661.
35 Yin YN, Yu QF, Fu N, Liu XW, Lu FG. Effects of four bifidobacteria
on obesity in high-fat diet induced rats. World J Gastroenterol
2010; 16: 3394–3401.
36 Cani PD, Neyrinck AM, Fava F, Knauf C, Burcelin RG, Tuohy KM
et al. Selective increases of bifidobacteria in gut microflora
improve high-fat-diet-induced diabetes in mice through a
mechanism associated with endotoxaemia. Diabetologia 2007;
50: 2374–2383.
37 Lee K, Paek K, Lee HY, Park JH, Lee Y. Antiobesity effect of trans10, cis-12-conjugated linoleic acid-producing Lactobacillus plantarum PL62 on diet-induced obese mice. J Appl Microbiol 2007;
103: 1140–1146.
38 Hamad EM, Sato M, Uzu K, Yoshida T, Higashi S, Kawakami H
et al. Milk fermented by Lactobacillus gasseri SBT2055 influences
adipocyte size via inhibition of dietary fat absorption in Zucker
rats. Br J Nutr 2009; 101: 1–9.
39 Kadooka Y, Sato M, Imaizumi K, Ogawa A, Ikuyama K, Akai Y et al.
Regulation of abdominal adiposity by probiotics (Lactobacillus
gasseri SBT2055) in adults with obese tendencies in a randomized
controlled trial. Eur J Clin Nutr 2010; 64: 636–643.
40 Lee HY, Park JH, Seok SH, Baek MW, Kim DJ, Lee KE et al. Human
originated bacteria, Lactobacillus rhamnosus PL60, produce
conjugated linoleic acid and show anti-obesity effects in dietinduced obese mice. Biochim Biophys Acta 2006; 1761: 736–744.
41 Talarico TL, Casas IA, Chung TC, Dobrogosz WJ. Production and
isolation of reuterin, a growth inhibitor produced by Lactobacillus reuteri. Antimicrob Agents Chemother 1988; 32: 1854–1858.
42 Chang YH, Kim JK, Kim HJ, Kim WY, Kim YB, Park YH. Selection
of a potential probiotic Lactobacillus strain and subsequent in
vivo studies. Antonie Van Leeuwenhoek 2001; 80: 193–199.
43 Lu YC, Yin LT, Chang WT, Huang JS. Effect of Lactobacillus
reuteri GMNL-263 treatment on renal fibrosis in diabetic rats.
J Biosci Bioeng 2010; 110: 709–715.
44 England JA, Watkins SE, Saleh E, Waldroup PW. Effects of
Lactobacillus reuteri on live performance and intestinal development of male turkeys. J Appl Poultry Sci 1996; 5: 311–324.
45 Dunham HJ, Casas IA, Edens FW, Parkhurst CR, Garlich JD,
Dobrogosz WJ. Avian growth depression in chickens induced by
environmental, microbiological, or nutritional stress is moderated by probiotic administrations of Lactobacillus reuteri. Biosc
Microflor 1998; 17: 133–139.
46 Casas IA, Dobrogosz WJ. Validation of the probiotic concept:
Lactobacillus reuteri confers broad-spectrum protection against
disease in humans and animals. Microb Ecol Health Dis 2000; 12:
247–285.
47 Backhed F, Manchester JK, Semenkovich CF, Gordon JI. Mechanisms
underlying the resistance to diet-induced obesity in germ-free mice.
Proc Natl Acad Sci USA 2007; 104: 979–984.
48 Backhed F, Ding H, Wang T, Hooper LV, Koh GY, Nagy A et al.
The gut microbiota as an environmental factor that regulates
fat storage. Proc Natl Acad Sci USA 2004; 101: 15718–15723.
49 Bastard JP, Maachi M, Lagathu C, Kim MJ, Caron M, Vidal H
et al. Recent advances in the relationship between obesity,
inflammation, and insulin resistance. Eur Cytokine Netw 2006;
17: 4–12.
50 Hotamisligil GS. Inflammation and metabolic disorders. Nature
2006; 444: 860–867.
51 Sbarbati A, Osculati F, Silvagni D, Benati D, Galie M, Camoglio FS
et al. Obesity and inflammation: evidence for an elementary
lesion. Pediatrics 2006; 117: 220–223.
52 Fogarty AW, Glancy C, Jones S, Lewis SA, McKeever TM,
Britton JR. A prospective study of weight change and systemic
inflammation over 9 y. Am J Clin Nutr 2008; 87: 30–35.
53 Nahashon SN, Nakaue HS, Snyder SP, Mirosh LW. Performance of
single comb White Leghorn layers fed corn-soybean meal and
barley-corn-soybean meal diets supplemented with a direct-fed
microbial. Poult Sci 1994; 73: 1712–1723.
54 Shimada K, Bricknell KS, Finegold SM. Deconjugation of bile
acids by intestinal bacteria: review of literature and additional
studies. J Infect Dis 1969; 119: 73–81.
55 Fuller R. Probiotics in man and animals. J Appl Bacteriol 1989; 66:
365–378.
56 Abdulrahim SM, Haddadin MS, Odetallah NH, Robinson RK.
Effect of Lactobacillus acidophilus and zinc bacitracin as
dietary additives for broiler chickens. Br Poult Sci 1999; 40:
91–94.
This work is licensed under the Creative
Commons Attribution-NonCommercial-No
Derivative Works 3.0 Unported License. To view a copy
of this license, visit http://creativecommons.org/
licenses/by-nc-nd/3.0/
Supplementary Information accompanies the paper on International Journal of Obesity website (http://www.nature.com/ijo)
International Journal of Obesity
Article IV :
Correlation between body mass index and gut
concentrations of Lactobacillus reuteri,
Bifidobacterium animalis, Methanobrevibacter smithii
and Escherichia coli
Matthieu Million, Emmanouil Angelakis, Marie Maraninchi, Mireille
Henry, Roch Giorgi, René Valero, Bernard Vialettes, Didier Raoult
Published in Int J Obes (Lond). 2013 Mar 5. doi: 10.1038/ijo.2013.20. [Epub ahead of print]
(IF 4.69)
52
OPEN
International Journal of Obesity (2013), 1–7
& 2013 Macmillan Publishers Limited All rights reserved 0307-0565/13
www.nature.com/ijo
ORIGINAL ARTICLE
Correlation between body mass index and gut concentrations
of Lactobacillus reuteri, Bifidobacterium animalis,
Methanobrevibacter smithii and Escherichia coli
M Million1,2,7, E Angelakis1,7, M Maraninchi3, M Henry1, R Giorgi4,5, R Valero3,6, B Vialettes6 and D Raoult1,2
BACKGROUND: Genus and species level analysis is the best way to characterize alterations in the human gut microbiota that are
associated with obesity, because the clustering of obese and lean microbiotas increases with the taxonomic depth of the analysis.
Bifidobacterium genus members have been associated with a lean status, whereas different Lactobacillus species are associated
both with a lean and an obese status.
OBJECTIVES AND METHODS: We analyzed the fecal concentrations of Bacteroidetes, Firmicutes, Methanobrevibacter smithii, the
genus Lactobacillus, five other Lactobacillus species previously linked with lean or obese populations, Escherichia coli and
Bifidobacterium animalis in 263 individuals, including 134 obese, 38 overweight, 76 lean and 15 anorexic subjects to test for the
correlation between bacterial concentration and body mass index (BMI). Of these subjects, 137 were used in our previous study.
FINDINGS: Firmicutes were found in 498.5%, Bacteroidetes in 67%, M. smithii in 64%, E. coli in 51%, Lactobacillus species between
17 and 25% and B. animalis in 11% of individuals. The fecal concentration of Lactobacillus reuteri was positively correlated with BMI
(coefficient ¼ 0.85; 95% confidence interval (CI) 0.12–0.58; P ¼ 0.02) in agreement with what was reported for Lactobacillus sakei. As
reported, B. animalis (coefficient ¼ ÿ 0.84; 95% CI ÿ 1.61 to ÿ 0.07; P ¼ 0.03) and M. smithii (coefficient ¼ ÿ 0.43, 95% CI ÿ 0.90 to
0.05; P ¼ 0.08) were negatively associated with the BMI. Unexpectedly, E. coli was found here for the first time to negatively
correlate with the BMI (coefficient ¼ ÿ 1.05; 95% CI ÿ 1.60 to ÿ 0.50; Po0.001).
CONCLUSION: Our findings confirm the specificity of the obese microbiota and emphasize the correlation between the
concentration of certain Lactobacillus species and obesity.
International Journal of Obesity advance online publication, 5 March 2013; doi:10.1038/ijo.2013.20
Keywords: body mass index; Lactobacillus; Bifidobacterium; probiotics; Methanobrevibacter smithii; Escherichia coli
INTRODUCTION
Obesity is defined by a body mass index (BMI)430 kg m ÿ 2 (ref. 1)
and a massive expansion of fat and is associated with a significant
increase in morbidity and mortality.2,3 The frequency of obesity is
rising among children, adolescents and adults and has doubled
since 1980. According to the WHO, 65% of the world’s population
lives in countries where excess weight and obesity kills more
people than underweight conditions, including all high-income
and most middle-income countries (www.who.int).
The digestive microbiota is a complex ecosystem that consists
of viruses, bacteria, archaea, fungi and parasites. Specific
enterotypes have been identified regardless of ethnic or
geographical origins.4 They have been linked to diet,5 and their
antibiotic-mediated modulation can impact the metabolic profile
of the host.6 Because the gut is a ‘hot spot’ for horizontal gene
transfer between an astronomical number of bacteria (4109 g ÿ 1),
archaea and viruses,7 analysis at the gene level was found to be
the best way to characterize gut microbiota alteration and its
correlation with obesity.8 Conversely, we and others have found
1
that analysis on a taxonomic basis remains fully relevant,
specifically at the species level.9
A decreased Bacteroidetes/Firmicutes ratio was initially shown
to be associated with obesity,10 but the discrimination between
lean and obese gut microbiota is improved when the taxonomic
depth of the analysis is increased.11 For instance, the
Bifidobacterium genus was associated with lean humans in a
meta-analysis, including studies from Finland, Germany, Spain and
China.12 Conversely, we showed that among Lactobacillus
species previously associated with obesity13,14 and diabetes,15
some have also been associated with weight gain9,16 while others
have more of a protective effect.16–18 Other bacterial species, such
as Tropheryma whipplei, have been associated with acquired
obesity.19 Finally, Karlsson et al.20,21linked the Enterobacteriaceae
and specifically Escherichia coli to overweight and obesity.
Here, we looked at the inter-relationships among E. coli,
one of the main representatives of the Enterobacteriaceae,
Methanobrevibacter smithii, a leading representative of the gut
archaea,22 Bifidobacterium animalis and 5 Lactobacillus species
URMITE, UM63, CNRS 7278, IRD 198, Inserm 1095, Aix Marseille Université, Marseille, France; 2APHM, CHU Timone, Pôle Infectieux, Marseille, France; 3INSERM UMR1062, INRA
UMR1260, Faculté de Médecine, Aix-Marseille Université, Marseille, France; 4INSERM, IRD, SESSTIM UMR S 912, Aix-Marseille Université, Marseille, France; 5Service de Santé
Publique et d’Information Médicale, CHU de la Timone, APHM, Marseille, France and 6Service de Nutrition, Maladies Métaboliques et Endocrinologie, CHU de la Timone, APHM,
Marseille, France. Correspondence: Professor D Raoult, URMITE–CNRS UMR 7278, INSERM U1095, IRD 198, Faculté de Médecine, Aix-Marseille Université, CNRS, 27 Bd Jean Moulin,
Marseille 13385, France.
E-mail: [email protected]
7
These authors contributed equally to this work.
Received 24 October 2012; revised 14 January 2013; accepted 28 January 2013
Gut microbiota is linked to the body mass index
M Million et al
2
(Lactobacillus reuteri, Lactobacillus plantarum, Lactobacillus
rhamnosus, Lactobacillus fermentum, Lactobacillus acidophilus). All
of the above species have been associated with weight in
previous studies.9,16 Based on our previous case-control study,9
we have more than doubled the sample size, having included
both anorexic and overweight patients in this study, and finally
we have analyzed the correlations between the BMI and the
considered taxa.
METHODS
Patients
This study was approved by the local ethics committee (accession number
10-002, 2010). Fecal samples were obtained from hospitalized patients and
outpatients at the Nutrition Unit (Hopital La Timone, Marseille, France) who
were overweight, obese or anorexic. The controls were healthy individuals
recruited based on a snowball approach and included subjects of our
previous study and outpatients who were not treated with antibiotics at
the infectious disease unit (Hopital La Timone, Marseille, France). Anorexic
subjects met the DSM-IV criteria (Diagnostic and Statistical Manual of
Mental Disorders, Fourth Edition) for anorexia nervosa. The inclusion
criteria were adults for whom the BMI value and a fecal sample were
readily available. The exclusion criteria were patients o18 years of age, a
history of colon cancer, the presence of an inflammatory bowel disease, an
acute or a chronic diarrhea in the previous 4 weeks and an antibiotic
administration o6 months before the fecal sampling. Clinical data (gender,
date of birth, clinical history, weight, height and antibiotic use) were
recorded using a standardized questionnaire. Other factors, such as yogurt
(pro- and prebiotics) intake, vegetarian habits, ethnicity or familial obesity,
were not taken into consideration in the analysis of the data. Four groups
were identified as follows: group I: obese subjects (BMI430 kg m ÿ 2),
group II: overweight subjects (BMI425 and o30 kg m ÿ 2), group III: lean
subjects (BMI419 and o25 kg m ÿ 2) and group IV: anorexic subjects
(BMIo19 kg m ÿ 2). A total of 137 patients from our previous study9 and
126 new subjects were included, of whom 15 were anorexic, 30 were lean
controls, 21 were overweight and 60 were obese. Data from our
previous study were also included, and most samples from that study
were analyzed further for the presence of E. coli. All new samples were
also analyzed for the presence of Bacteroidetes, Firmicutes, genus
Lactobacillus, E. coli, M. smithii, L. reuteri, L. plantarum, L. rhamnosus,
L. fermentum and L. acidophilus.
PCR
PCR analysis was performed as previously described9 except for E. coli,
for which the protocol was the same, but the primers and probes were
the following: Forward, 50 -GCTGCGCGTGCAAATGCG-30 ; Reverse, 50 -CATGGT
CATCGCTTCGGTCT-30 ; and probe, 50 -CATCAGAAACTGAACACCAC-30 . The
primers for L. reuteri were evaluated in our previous study9 and have a
very high specificity at the species level with a low cross-reactivity
(cycle threshold 435 for DNA extracted from pure culture) with
Lactobacillus oris and Lactobacillus pontis. However, it cannot be
excluded that the detection of L. oris, exceptionally present in the
human gut,23 could have yielded false-positive results. Conversely, L. pontis
has never been reported in the human gut. The results in this study are
depicted as log10 DNA copies ml ÿ 1.
Statistical analysis
As an exploratory step, a principal component analysis was performed,
including BMI and the concentrations of all taxa present at the phylum and
species levels.
Table 1.
Initially, we tested whether the bacterial prevalence was different
between each BMI group using the bilateral Pearson Chi-square test. A
bilateral Barnard exact test24 was used when the Pearson Chi-square test
was not applicable. Because it is unknown whether overweight individuals
should be considered as individuals with a disease or controls, all the
groups were compared either with group I (obese subjects who were
considered as cases) or with group III (lean subjects who were used as
controls). A logistic regression using the ascendant maximum likelihood
model was used to identify bacteria whose presence was associated with
the BMI groups in a multivariate analysis. Three models were used as
follows: considering age, sex, Bacteroidetes, Firmicutes and M. smithii
(phylum level); considering age, sex and Lactobacillus (genus level); or
considering age, sex, M. smithii, E. coli, B. animalis, L. reuteri, L. plantarum,
L. fermentum and L. rhamnosus (species level).
As a second step, we tested whether the bacterial concentrations were
different according to the BMI groups. Because of a generally non-Gaussian
distribution, comparisons were performed using the Kruskal–Wallis test.
Following that step, we tested for the correlation between each bacterial
concentration and BMI. As most of the bacterial clades were present
in a minority of individuals, a dose-dependent relationship (BMI vs
bacterial load) was explored graphically, and the correlation was tested
using the Spearman method only on patients harboring each of the
clades considered (carriers). Linear regression was used to identify
bacteria whose concentrations were correlated with BMI on the whole
population. Three models were used as follows: considering age, sex,
Bacteroidetes, Firmicutes and M. smithii (phylum level); considering age,
sex and Lactobacillus (genus level); or considering age, sex, M. smithii,
E. coli, B. animalis, L. reuteri, L. plantarum, L. fermentum and L. rhamnosus
(species level).
M. smithii is the major representative of the gut archaeal phylum
Euryarchaeota21 and has been included in the analyses both at the phylum
and at the species level. All the tests were bilateral and considered
significant when Po0.05. The analyses were performed using the SPSS
v20.0 (IBM, Paris, France), R version 2.14.0 (R-foundation, Vienna, Austria)
and XLSTAT v12 (Addinsoft, Paris, France) software.
RESULTS
Of the 263 patients enrolled in this study, there were 134 obese, 38
overweight, 76 lean and 15 anorexic subjects (Table 1). The average
age was 50±s.d. 17 years, and 138 (52.5%) of them were males.
As was expected, anorexic patients were more frequently found to
be younger women. PCR detection and quantification was
performed on 262 individuals to study the levels of Bacteroidetes,
Firmicutes, M. smithii and the Lactobacillus genus; on 219
individuals to study each Lactobacillus species and B. animalis;
and on 165 individuals to investigate the levels of E. coli.
The prevalences of each bacterial clade were heterogeneous.
Firmicutes was found in all the individuals (498.5%), whereas
Bacteroidetes was detected in only 67% (Supplementary Table S1).
At the species level, B. animalis was found to be the rarest species
(11%), whereas M. smithii (64%) was shown to be more prevalent
than E. coli (51%). Lactobacillus genus was found in only one-third
of the subjects (28%), with different species ranging from 17 to
25% in frequency. In agreement with our previous study,8
L. acidophilus was not detected by our system in any sample,
despite the positive amplification of the type strain L. acidophilus
CIP7613 in our in silico study.
When present, the Firmicutes (109 DNA copies ml ÿ 1) was the
most abundant clade before the Bacteroidetes (108). At the
Population characteristics
Age (mean±s.d.)
Male sex (n (%))
BMI (median, IQR)
Anorexic subjects (n ¼ 15)
Lean subjects (n ¼ 76)
Overweight subjects (n ¼ 38)
Obese subjects (n ¼ 134)
P-valuea
27.3±10.8
1 (7%)
13.5 (11.7–14.6)
49.5±18.6b
40 (57%)
22.4 (20.7–23.7)
54.1±17.8
32 (84%)
27.1 (25.9–28.6)
51.8±14.7
65 (49%)
40.0 (36.4–46.8)
o0.0001
o0.0001
o0.0001
Abbreviations: BMI, body mass index; IQR, interquartile range. aMann–Whitney U test for age and BMI, Pearson chi-square for sex. bData unavailable for seven
patients.
International Journal of Obesity (2013) 1 – 7
& 2013 Macmillan Publishers Limited
Gut microbiota is linked to the body mass index
M Million et al
3
species level, when found, E. coli (107) was 10 times more
abundant than M. smithii (106), whereas Lactobacillus species were
present at much lower concentrations (104–105 DNA copies ml ÿ 1;
Po0.0001 when comparing Lactobacillus with E. coli).
Preliminary analysis by principal component analysis and
density plots suggested that some bacterial species or phyla
were differentially distributed according to the BMI (Figure 1 and
Supplementary Figure S1) and this was confirmed by further
analyses (Figures 2 and 3).
Bacterial clades associated with obesity
Genus Lactobacillus. There was a trend towards a higher
prevalence of Lactobacillus in obese compared with lean patients
(32 vs 20%; P ¼ 0.06) and a higher frequency of Lactobacillus in
patients with BMIs425 vs BMIso25 kg m ÿ 2 (32 vs 20.8%;
P ¼ 0.06, Supplementary Table S1 and Supplementary Figure S2).
In a logistic regression, the presence of Lactobacillus was not
associated with any BMI group (Supplementary Table S2).
The Lactobacillus concentration was higher in obese patients
compared with lean patients (Po0.05) and in individuals with
BMIs425 kg m ÿ 2 vs individuals with BMIso25 kg m ÿ 2 (Po0.05,
Figure 2). We also found a positive correlation between the
concentration of Lactobacillus and BMI in the carriers (patients
positive for the genus Lactobacillus, correlation coefficient 0.25;
P ¼ 0.03). No significant result was found in a linear regression.
L. reuteri. There was a threefold increase in the L. reuteri
occurrence in obese patients compared with lean subjects
(22 vs 8%; P ¼ 0.01), a fourfold increase between overweight
patients and lean subjects (34 vs 8%; P ¼ 0.001) and a threefold
increase between individuals with BMIs425 kg m ÿ 2 compared
with individuals with BMIso25 kg m ÿ 2 (20 vs 7%; P ¼ 0.001,
Supplementary Figure S2). In a logistic regression, the presence
of L. reuteri was associated with obesity (odds ratio (OR) ¼ 5.31;
95% confidence interval (CI) 1.04–27.1; P ¼ 0.04), overweight
(OR ¼ 2.8 107; 95% CI 6.9–1014; P ¼ 0.03) or BMI425 kg m ÿ 2
(OR ¼ 8.07; 95% CI 2.06–31.5; P ¼ 0.003).
The L. reuteri concentration was greater in obese vs lean
individuals (Po0.05), in overweight vs lean individuals (Po0.005)
and in individuals with BMIs425 kg m ÿ 2 compared with individuals with BMIso25 kg m ÿ 2 (Po0.005, Figure 3). Furthermore,
we found a positive correlation between the concentration of
L. reuteri and BMI (patients positive for L. reuteri, coefficient
correlation 0.44; P ¼ 0.004, Figure 4). In a linear regression, a
higher concentration of L. reuteri was associated with a higher
BMI (Table 2).
Bacterial clades associated with lean status
Bacteroidetes. The difference in the occurrence of Bacteroidetes
between the obese and the lean groups was not significant (60 vs
70%, respectively; P ¼ 0.18); however, we found a decreased
frequency of Bacteroidetes in obese compared with non-obese
individuals (60 vs 74%; P ¼ 0.02). Moreover, prevalence was
increased in overweight compared with obese subjects (84 vs
60%; P ¼ 0.008, Supplementary Figure S2). In a logistic regression,
the presence of Bacteroidetes was associated with the absence of
obesity (OR ¼ 0.51; 95% CI 0.30–0.87; P ¼ 0.01) or overweight
individuals when compared with obese population (OR ¼ 0.28;
0.11–0.74; P ¼ 0.01, Supplementary Table S2).
Finally, we found a trend towards decreased concentrations of
Bacteroidetes in obese patients compared with lean controls
(P ¼ 0.054), and this decrease in Bacteroidetes concentration was
significant when comparing obese with non-obese (P ¼ 0.01) or
with overweight individuals (P ¼ 0.017, Figure 2). No correlation
was found between the Bacteroidetes concentration and BMI in
the carrier subgroup. In a linear regression, Bacteroidetes
concentration was not correlated with BMI.
M. smithii. There was a trend towards an increased prevalence of
M. smithii in lean compared with obese individuals (72 vs 60%;
P ¼ 0.07), and this frequency difference was significant when
individuals with BMIso25 kg m ÿ 2 were compared with individuals with BMIs425 kg m ÿ 2 (72 vs 60%; P ¼ 0.04, Supplementary
Figure S2). In a logistic regression, the presence of M. smithii was
not associated with the absence of obesity but was associated
with lean compared with overweight individuals (OR ¼ 0.001; 95%
CI 0–0.98; P ¼ 0.049, Supplementary Table S2).
The M. smithii concentration was lower in obese compared
with either lean (P ¼ 0.008) or non-obese individuals (P ¼ 0.01).
Moreover, the M. smithii concentration was higher in patients
having BMIso25 kg m ÿ 2 compared with patients having
BMIs425 kg m ÿ 2 (P ¼ 0.005, Figure 2). We also found a negative
correlation between the BMI values and M. smithii concentration in
patients harboring M. smithii (correlation coefficient ÿ 0.20;
P ¼ 0.01, Figure 4). In a linear regression, M. smithii was not
associated with BMI when analyzed at the phylum level as
the M. smithii phylum is the leading representative of the
Euryarchaeota in the gut microbiota. Conversely, there was a
trend towards a correlation between a higher BMI and a lower
M. smithii concentration at the species level (P ¼ 0.08, Table 2).
B. animalis. The prevalence of B. animalis was very low in our
population, between 6 and 15%, but there was a trend towards a
significantly lower occurrence in obese compared with lean
Body mass index
Firmicutes
Body mass index
Bacteroidetes
2nd component (16%)
2nd Component (25%)
Euryarchaeota
L. reuteri
L. rhamnosus
L. plantarum
B. animalis
L. fermentum
M. smithii
E. coli
1st Component (33%)
1st Component (20%)
Figure 1. Primary component analysis associating the gut microbial phylum and species to the BMI. Principal component analysis, including
(a) BMI and phylum or (b) species found in the gut microbiota (Lactobacillus acidophilus was not included because it was not found by our
quantitative PCR system). The preliminary analyses shown in this figure were performed on the whole population.
& 2013 Macmillan Publishers Limited
International Journal of Obesity (2013) 1 – 7
Gut microbiota is linked to the body mass index
M Million et al
4
Figure 2. Scatter plots at the phylum and genus levels. Methanobrevibacter smithii is considered to be the leading representative of the
Euryarchaeota phylum. *Po0.05, **Po0.005. The medians and the interquartile ranges are shown.
Figure 3.
Scatter plots at the species level. *Po0.05, **Po0.005. The medians and the interquartile ranges are shown.
International Journal of Obesity (2013) 1 – 7
& 2013 Macmillan Publishers Limited
Gut microbiota is linked to the body mass index
M Million et al
5
Methanobrevibacter smithii
Escherichia coli
9
log10 copies DNA/ml
log10 copies DNA/ml
10
8
6
4
8
7
6
5
2
20
40
60
80
10
20
30
2
kg/m
kg/m
Bifidobacterium animalis
50
60
70
2
Lactobacillus reuteri
9
9
8
8
log10 copies DNA/ml
log10 copies DNA/ml
40
7
6
5
4
7
6
5
4
3
3
20
30
40
50
kg/m2
10
20
30
40
50
kg/m2
Figure 4. Correlation between the BMI and specific bacterial clades. Plots represent analyses performed only on the carriers for each bacterial
clade studied. Spearman correlation test: Methanobrevibacter smithii r ¼ ÿ 0.20, P ¼ 0.01. Lactobacillus reuteri r ¼ 0.44, P ¼ 0.004. No correlation
was found in the patients positive for E. coli (P ¼ 0.80) or Bifidobacterium animalis (P ¼ 0.99).
Table 2.
BMI linear regression according to each bacterial clade
Speciesa
Methanobrevibacter smithii
Escherichia colib
Bifidobacterium animalis
Lactobacillus reuteri
Coefficient (95% CI)
ÿ 0.43
ÿ 1.05
ÿ 0.84
0.85
( ÿ 0.90 to 0.05)
( ÿ 1.60 to ÿ 0.50)
( ÿ 1.61 to ÿ 0.07)
(0.12 to 1.58)
P-value
0.08
o0.001
0.03
0.02
Abbreviations: BMI, body mass index; CI, confidence interval. aLinear
regression, adjusted by age and sex, was performed on 218 patients for
whom data for M. smithii, B. animalis, L. reuteri, L. plantarum, L. fermentum
and L. rhamnosus were available. bE. coli concentration was available only
for 133 of these patients and was replaced by the mean for the 85 lacking
data.
individuals (6 vs 15%; P ¼ 0.052) and a significant decrease in the
incidence of B. animalis in obese compared with non-obese
individuals (6 vs 15%; P ¼ 0.04, Supplementary Figure S2). Using a
logistic regression, there was a trend towards an association
between the presence of B. animalis and lean compared with
obese individuals (OR ¼ 0.22; 95% CI 0.05–1.03; P ¼ 0.054).
Furthermore, the presence of B. animalis was associated with lean
individuals when compared with overweight subjects (OR ¼ 0;
95% CI 0–0.76; P ¼ 0.045).
The concentration of B. animalis was significantly lower in obese
population compared with lean (P ¼ 0.045) and non-obese
populations (P ¼ 0.03, Figure 3) but no correlation was found
between the B. animalis concentration and BMI when we
performed a univariate analysis (Figure 4). In a linear regression,
& 2013 Macmillan Publishers Limited
a higher concentration of B. animalis was associated with a lower
BMI (P ¼ 0.03, Table 2).
E. coli. The prevalence of E. coli was lower in obese compared
with lean (36 vs 60%; P ¼ 0.006), overweight (36 vs 75%; P ¼ 0.004)
and non-obese individuals (36 vs 47%; Po0.001, Supplementary
Figure S2). The prevalence was also significantly lower in
individuals with BMIs425 kg m ÿ 2 compared with those with
BMIso25 kg m ÿ 2 (31 vs 51%; P ¼ 0.004). In a logistic regression,
the presence of E. coli was associated with the absence of
obesity (OR ¼ 0.25; 95% CI 0.1–0.5; Po0.001, Supplementary
Table S2), with lean when compared with obese individuals
(OR ¼ 0.3; 95% CI 0.1–0.8; P ¼ 0.01), with overweight when
compared with obese individuals (OR ¼ 0.15; 95% CI 0.03–0.9;
P ¼ 0.01) and with individuals with BMIso25 kg m ÿ 2 vs individuals
with BMIs425 kg m ÿ 2 (OR ¼ 0.3; 95% CI 0.1–0.6; P ¼ 0.002).
A lower concentration of E. coli was found in obese vs anorexic
(P ¼ 0.001), lean (P ¼ 0.02), overweight individuals (P ¼ 0.012) and
in individuals with BMIs425 vso25 kg m ÿ 2 (P ¼ 0.02). Moreover, a
lower concentration of E. coli was found when comparing obese
with non-obese individuals (P ¼ 0.001, Figure 3). No correlation was
found in the subgroup of individuals positive for E. coli (correlation
coefficient 0.03, P ¼ 0.8, Figure 4). In a linear regression, a higher
concentration of E. coli was associated with a lower BMI (Table 2).
DISCUSSION
In this study, we found a relatively low prevalence of Lactobacillus
species because it was detected in only 30% of the individuals, but
International Journal of Obesity (2013) 1 – 7
Gut microbiota is linked to the body mass index
M Million et al
6
L. reuteri was detected in 20% of the study population with
occurrence increasing along with BMI values (7, 8, 34 and 22% for
anorexic, lean, overweight and obese individuals, respectively).
Lactobacillus species, and specifically L. reuteri, have been
previously associated with obesity as it has been reported in our
previous case-control studies.9,13 However, this is the first time
that a correlation between the bacterial loads of this species and
BMI is reported. To our knowledge, only one other previous study
identified a correlation between the Lactobacillus species, and
specifically Lactobacillus sakei, and BMI.14
Other prokaryotes have been associated with a lower BMI, as
has been previously reported in other publications, such as
Bacteroidetes,5,10,13,25 B. animalis9,12,26,27 and the archeal species
M. smithii.28 In contrast to previous studies,29 we found a lower
frequency and lower bacterial loads for E. coli in obese individuals
with a strong statistical significance. This finding demands a word
of caution and requires further confirmation. Moreover, our results
suggest a ‘dose-dependent’ relationship between certain species
of bacteria and archaea in the human gut and BMI.
A limitation of our study, as it is for most studies, is that the
analysis of the digestive microbiota associated with obesity was
performed by analyzing stool samples.5,25 However, as 95% of fat
is absorbed before the cecum,30 the proximal gut microbiota may
be critical for the analysis of factors associated with obesity and
diabetes.31–33 The analysis of the fecal microbiota reflects only
indirectly the upper intestinal flora. Indeed, several studies have
shown a significant difference in the gut microbiota composition
according to the gut section in animals34 and humans35 with a
proximal (small bowel) enrichment in aerobic Firmicutes
(Streptococcaceae and Lactobacillaceae) and Actinobacteria.34,35
Finally, obesity is a multifactorial disease. The causes that drive
obesity appear to be influenced by a mixture of environmental,
genetic, neural and endocrine factors along with microbes that are
also thought to have a role in weight gain.12,36 Accumulating data
has shown that the gut microbiota is associated with both obesity
and diet, and there is evidence that modulation of the gut flora by
antibiotics,6,37 during pregnancy38 or by probiotics16,36 causes
weight gain. The repertoire of bacteria, and especially Lactobacillus
species, that protect or result in weight gain should be
determined at the strain level as the genomic variations within a
single species of Lactobacillus may be dramatic (only 64% of
protein genes are common between Lactobacillus johnsonii FI9785
and L. johnsonii NCC 53339).
CONCLUSION
This work confirms the link between the microbiota and obesity.
This link appears to be the result of both diet5 and the cause of
the weight gain as demonstrated by microbiota transplantation
from obese individuals or pregnant women to axenic animals.8,38
CONFLICT OF INTEREST
The authors declare no conflict of interest.
ACKNOWLEDGEMENTS
We thank all the volunteers because without them, this study would not have been
possible. The sponsors took no part in the design of the study, data collection and
analysis, decision to publish or preparation of the manuscript.
AUTHOR CONTRIBUTIONS
DR conceived and designed the experiments. MM, EA, MM, RV, BV and DR
performed the clinical study. MM, EA and MH performed the experiments.
MM and RG analyzed the data. MM, EA and DR wrote the manuscript.
International Journal of Obesity (2013) 1 – 7
REFERENCES
1 WHO Expert Consultation. Appropriate body-mass index for Asian populations
and its implications for policy and intervention strategies. Lancet 2004; 363:
157–163.
2 Whitlock G, Lewington S, Sherliker P, Clarke R, Emberson J, Halsey J et al.
Body-mass index and cause-specific mortality in 900 000 adults: collaborative
analyses of 57 prospective studies. Lancet 2009; 373: 1083–1096.
3 Yanovski SZ, Yanovski JA. Obesity. N Engl J Med 2002; 346: 591–602.
4 Arumugam M, Raes J, Pelletier E, Le Paslier D, Yamada T, Mende DR et al.
Enterotypes of the human gut microbiome. Nature 2011; 473: 174–180.
5 Ley RE, Turnbaugh PJ, Klein S, Gordon JI. Microbial ecology: human gut microbes
associated with obesity. Nature 2006; 444: 1022–1023.
6 Cho I, Yamanishi S, Cox L, Methe BA, Zavadil J, Li K et al. Antibiotics in
early life alter the murine colonic microbiome and adiposity. Nature 2012; 488:
621–626.
7 Kurokawa K, Itoh T, Kuwahara T, Oshima K, Toh H, Toyoda A et al. Comparative
metagenomics revealed commonly enriched gene sets in human gut
microbiomes. DNA Res 2007; 14: 169–181.
8 Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesityassociated gut microbiome with increased capacity for energy harvest. Nature
2006; 444: 1027–1031.
9 Million M, Maraninchi M, Henry M, Armougom F, Richet H, Carrieri P et al. Obesityassociated gut microbiota is enriched in Lactobacillus reuteri and depleted in
Bifidobacterium animalis and Methanobrevibacter smithii. Int J Obes (Lond) 2012;
36: 817–825.
10 Ley RE, Backhed F, Turnbaugh P, Lozupone CA, Knight RD, Gordon JI.
Obesity alters gut microbial ecology. Proc Natl Acad Sci USA 2005; 102:
11070–11075.
11 Murphy EF, Cotter PD, Hogan A, O’Sullivan O, Joyce A, Fouhy F et al. Divergent
metabolic outcomes arising from targeted manipulation of the gut microbiota in
diet-induced obesity. Gut 2012; 62: 220–226.
12 Angelakis E, Armougom F, Million M, Raoult D. The relationship between gut
microbiota and weight gain in humans. Future Microbiol 2012; 7: 91–109.
13 Armougom F, Henry M, Vialettes B, Raccah D, Raoult D. Monitoring bacterial
community of human gut microbiota reveals an increase in Lactobacillus in obese
patients and Methanogens in anorexic patients. PLoS One 2009; 4: e7125.
14 Stsepetova J, Sepp E, Kolk H, Loivukene K, Songisepp E, Mikelsaar M. Diversity and
metabolic impact of intestinal Lactobacillus species in healthy adults and the
elderly. Br J Nutr 2011; 105: 1235–1244.
15 Larsen N, Vogensen FK, van den Berg FW, Nielsen DS, Andreasen AS, Pedersen BK
et al. Gut microbiota in human adults with type 2 diabetes differs from nondiabetic adults. PLoS One 2010; 5: e9085.
16 Million M, Angelakis E, Paul M, Armougom F, Leibovici L, Raoult D. Comparative
meta-analysis of the effect of Lactobacillus species on weight gain in humans and
animals. Microb Pathog 2012; 53: 100–108.
17 Luoto R, Kalliomaki M, Laitinen K, Isolauri E. The impact of perinatal probiotic
intervention on the development of overweight and obesity: follow-up study
from birth to 10 years. Int J Obes (Lond) 2010; 34: 1531–1537.
18 Kadooka Y, Sato M, Imaizumi K, Ogawa A, Ikuyama K, Akai Y et al. Regulation
of abdominal adiposity by probiotics (Lactobacillus gasseri SBT2055) in adults
with obese tendencies in a randomized controlled trial. Eur J Clin Nutr 2010; 64:
636–643.
19 Fenollar F, Nicoli F, Paquet C, Lepidi H, Cozzone P, Antoine JC et al. Progressive
dementia associated with ataxia or obesity in patients with Tropheryma whipplei
encephalitis. BMC Infect Dis 2011; 11: 171.
20 Karlsson CL, Molin G, Fak F, Johansson Hagslatt ML, Jakesevic M, Hakansson A
et al. Effects on weight gain and gut microbiota in rats given bacterial supplements and a high-energy-dense diet from fetal life through to 6 months of age.
Br J Nutr 2011; 106: 887–895.
21 Karlsson CL, Onnerfält J, Xu J, Molin G, Ahrné S, Thorngren-Jerneck K. The
microbiota of the gut in preschool children with normal and excessive body
weight. Obesity (Silver Spring) 2012; 20: 2257–2261.
22 Dridi B, Henry M, El Khechine A, Raoult D, Drancourt M. High prevalence of
Methanobrevibacter smithii and Methanosphaera stadtmanae detected in the
human gut using an improved DNA detection protocol. PLoS One 2009; 4: e7063.
23 Larsen N, Vogensen FK, Gobel R, Michaelsen KF, Abu Al-Soud W, Sorensen SJ et al.
Predominant genera of fecal microbiota in children with atopic dermatitis are not
altered by intake of probiotic bacteria Lactobacillus acidophilus NCFM and
Bifidobacterium animalis subsp. lactis Bi-07. FEMS Microbiol Ecol 2011; 75: 482–496.
24 Barnard GAA. New Test for 2 2 Tables. Nature 1945; 156: 177.
25 Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, Ley RE et al.
A core gut microbiome in obese and lean twins. Nature 2009; 457: 480–484.
26 Waldram A, Holmes E, Wang Y, Rantalainen M, Wilson ID, Tuohy KM et al.
Top-down systems biology modeling of host metabotype-microbiome associations in obese rodents. J Proteome Res 2009; 8: 2361–2375.
& 2013 Macmillan Publishers Limited
Gut microbiota is linked to the body mass index
M Million et al
7
27 Kalliomaki M, Collado MC, Salminen S, Isolauri E. Early differences in fecal
microbiota composition in children may predict overweight. Am J Clin Nutr 2008;
87: 534–538.
28 Schwiertz A, Taras D, Schafer K, Beijer S, Bos NA, Donus C et al. Microbiota and
SCFA in lean and overweight healthy subjects. Obesity (Silver Spring) 2010; 18:
190–195.
29 Santacruz A, Collado MC, Garcia-Valdes L, Segura MT, Martin-Lagos JA, Anjos T
et al. Gut microbiota composition is associated with body weight, weight gain
and biochemical parameters in pregnant women. Br J Nutr 2010; 104: 83–92.
30 Carriere F, Renou C, Ransac S, Lopez V, De Caro J, Ferrato F et al. Inhibition of
gastrointestinal lipolysis by Orlistat during digestion of test meals in healthy
volunteers. Am J Physiol Gastrointest Liver Physiol 2001; 281: G16–G28.
31 Zhang H, DiBaise JK, Zuccolo A, Kudrna D, Braidotti M, Yu Y et al. Human gut
microbiota in obesity and after gastric bypass. Proc Natl Acad Sci USA 2009; 106:
2365–2370.
32 Rubino F, Forgione A, Cummings DE, Vix M, Gnuli D, Mingrone G et al.
The mechanism of diabetes control after gastrointestinal bypass surgery reveals
a role of the proximal small intestine in the pathophysiology of type 2 diabetes.
Ann Surg 2006; 244: 741–749.
33 Kremen AJ, Linner JH, Nelson CH. An experimental evaluation of the nutritional
importance of proximal and distal small intestine. Ann Surg 1954; 140: 439–448.
34 Torok VA, Allison GE, Percy NJ, Ophel-Keller K, Hughes RJ. Influence of
antimicrobial feed additives on broiler commensal posthatch gut microbiota
development and performance. Appl Environ Microbiol 2011; 77: 3380–3390.
35 Frank St DN, Amand AL, Feldman RA, Boedeker EC, Harpaz N, Pace NR. Molecularphylogenetic characterization of microbial community imbalances in human
inflammatory bowel diseases. Proc Natl Acad Sci USA 2007; 104: 13780–13785.
36 Million M, Raoult D. The role of the manipulation of the gut microbiota in obesity.
Curr Infect Dis Rep 2012; 15: 25–30.
37 Trasande L, Blustein J, Liu M, Corwin E, Cox LM, Blaser MJ. Infant antibiotic
exposures and early-life body mass. Int J Obes (Lond) 2012; 37: 16–23.
38 Koren O, Goodrich JK, Cullender TC, Spor A, Laitinen K, Backhed HK et al. Host
remodeling of the gut microbiome and metabolic changes during pregnancy. Cell
2012; 150: 470–480.
39 Lukjancenko O, Ussery DW, Wassenaar TM. Comparative genomics of
Bifidobacterium, Lactobacillus and related probiotic genera. Microb Ecol 2012; 63:
651–673.
This work is licensed under a Creative Commons AttributionNonCommercial-NoDerivs 3.0 Unported License. To view a copy of
this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/
Supplementary Information accompanies this paper on International Journal of Obesity website (http://www.nature.com/ijo)
& 2013 Macmillan Publishers Limited
International Journal of Obesity (2013) 1 – 7
Partie II :
Le rôle de la manipulation du microbiote digestif
dans l’obésité
60
Avant- propos
Elie Metchnikoff est considéré comme le pionnier des probiotiques modernes
évoquant un lien entre la consommation régulière de bactéries lactiques et la longévité des
populations paysannes bulgares ("La prolongation de la vie ", publié en 1907). En 1935,
Minoru Shirota a découvert une souche de Lactobacillus casei baptisée Shirota à l’origine
d’une boisson probiotique commercialisée, Yakult®, associée à un succès commercial encore
actuel. Depuis, le marché des probiotiques est en expansion exponentielle. Bien que la
définition actuellement acceptée des probiotiques est la suivante : « micro-organismes vivants
qui, lorsqu'ils sont administrés en quantités adéquates, sont associés à un bénéfice pour la
santé de l'hôte », les probiotiques sont souvent associés à des propriétés non démontrées.
Les premiers paramètres reconnus pour influencer le microbiote digestif ont été
l’alimentation, les antibiotiques et les probiotiques. Le rôle de ces derniers sur le poids a été
démontré dès les années 1950, quand Jukes et Stokstad
3
ont associé l’administration d’une
souche bactérienne, Streptomyces aureofaciens, a un effet promoteur de croissance chez
l’animal. Ils ont par la suite démontré que cet effet passait par la chlortetracycline,
bactériocine naturellement secrétée par cette bactérie. Leurs travaux pionniers ont été à
l’origine de l’utilisation massive des antibiotiques et des probiotiques dans l’agriculture.
Partant de là, un lien entre la consommation des probiotiques et l’obésité a été proposé 4.
Afin de clarifier l’effet des probiotiques contenant des Lactobacillus sur le poids, nous
avons effectué une méta-analyse incluant 17 essais randomisées chez l’homme, 51 études
chez l’animal et 14 études sur des modèles experimentaux (Article VI). Lactobacillus
acidophilus, Lactobacillus ingluviei et Lactobacillus fermentum étaient associés à une prise de
poids significative chez les animaux. Lactobacillus plantarum était associé à une perte de
poids chez des animaux obèses et Lactobacillus gasseri était associé avec une perte de poids à
61
la fois chez les humains et les animaux en surpoids ou obèse. L’ensemble de ces résultats
suggère que l’effet des probiotiques contenant des Lactobacillus sur le poids dépend à la fois
de l’espèce bactérienne utilisée et de l’hôte. Cela a été confirmé par une autre équipe qui a
retrouvé une relation linéaire entre la concentration de Lactobacillus sakei et l’indice de
masse corporelle 5.
Notre travail initial incluait une étude publiée en 1952 par Robinson et al. 6 qui avait
montré un effet significatif de l’administration d’une souche identifiée comme Lactobacillus
acidophilus sur la prise de poids chez les nouveaux-nés surtout s’ils étaient nourris au biberon
et non pas au sein. Nous avons reçu un commentaire mentionnant que cette souche avait été
reclassée comme Lactobacillus gasseri
7
. A la suite de cela, nous avons revérifié
l’identification de l’intégralité des souches des études inclues dans notre méta-analyse et,
excluant cette étude, nous avons confirmé que l’effet des probiotiques contenant des
Lactobacillus sur le poids dépend à la fois de l’espèce bactérienne utilisée et de l’hôte (Article
VII). Quoiqu’il en soit, cette étude
6
est la seule, à notre connaissance, a clairement lier
l’administration d’une souche de Lactobacillus à la prise de poids chez des nouveaux nés
humains et c’est pourquoi nous pensons que la manipulation du microbiote juste après la
naissance est la plus à même d’être responsable d’obésité acquise. De façon similaire, il a été
montré que l’administration d’antibiotiques pendant cette période était associée à une obésité
acquise 8.
Dans un deuxième travail (Article VIII), nous avons examiné le biais de publication
des études sur l'administration de probiotiques contenant des Lactobacillus chez les animaux
de ferme à partir de notre premier travail sur ce sujet (Article VI). Après élimination des
valeurs aberrantes correspondant à 3 publications, nous avons constaté une persistance du
biais de publication significative en observant le funnel plot et par le test de régression
d'Egger (intercept 1,05, p-value < 10-6). C’est-à-dire qu’il est très probable que des études
62
ayant trouvé une absence de prise de poids des animaux de ferme à qui il avait été administré
des probiotiques contenant des Lactobacillus n’aient pas été publiées. Et ce biais de
publication était constaté dans deux autres études de la littérature sur d’autres possibles effets
bénéfiques des probiotiques
(Article VIII). L’effet à long terme des probiotiques chez
l'homme devrait être analysé par une recherche indépendante afin d'éviter le même
désagrément déjà signalé pour l'usage délétère des probiotiques chez les personnes souffrant
de pancréatite 9.
A ce titre, nous avons contribué à la publication d’une série de cas rapportant
des bactériémies à Lactobacillus rhamnosus (Article IX), confirmant que ces prétendues
bactéries bénéfiques pouvaient être délétères.
Jukes et Stokstad
3
avait bien montré dès les années 1950 que l’effet promoteur de
croissance de Streptomyces aureofaciens était lié à la production d’une bactériocine,
l’auréomycine plus connue sous le nom de chlortetracycline. Ils ont donc fait le lien entre
probiotiques, antibiotiques et prise de poids. Plus récemment, Murphy et al.
10
ont montré
qu’une bactériocine produite par un probiotique pouvait altérer de manière significative la
composition du microbiote digestif in vivo alors que la même souche bactérienne sans la
bactériocine n’entrainait aucune altération. Dans ce sens, nous avons lu avec intérêt un article
publié par Archambaud et al.
11
qui rapportait que l’antagonisme entre deux souches de
Lactobacillus, Lactobacillus casei BL23 et Lactobacillus paracasei CNCM I-3689 et Listeria
monocytogenes passait par une modulation du transcriptome de l’hôte et de L.
monocytogenes. En utilisant les bases de données de bactériocines Bagel et Bactibase pour
analyser les souches utilisées dans cette étude, nous avons pu trouver une prébacteriocine
(GenBank ID: YP_001988475) dans L. casei BL23, et une Lactocin-705 (GenBank:
LC70_LACPA) parmi les génomes disponibles de L. paracasei (Article X). Prises ensemble,
ces données suggèrent que l'impact d'une souche de Lactobacillus sur le microbiote digestif
63
est principalement déterminé par ses activités directes antibiotiques, y compris les
bactériocines.
Enfin, de nombreux antibiotiques continuent à être utilisés comme facteur de
croissance dans l’agriculture notamment aux Etats-unis alors que leur utilisation a été interdite
en France depuis quelques années. Dans un modèle animal, l'administration d’antibiotiques à
doses infrathérapeutiques dès le sevrage augmente l’adiposité et les hormones liées au
métabolisme 12. Chez ces animaux ont été observé des changements taxonomiques importants
dans le microbiome, des changements dans le nombre de copies de gènes clés impliqués dans
le métabolisme des glucides en acide gras à chaîne courte (SCFA pour short chain fatty acid
principalement représentés par le propionate, butyrate et acétate), l'augmentation des acides
gars à chaines courtes dans le colon, et des changements dans la régulation du métabolisme
hépatique des lipides et du cholestérol.
Dans cette dernière partie, nous avons résumé dans la tableau 3 de l’article I les études
ayant évalué la modification du poids sous antibiotiques chez les humains. Plusieurs études
ont mis en relation la prise d’antibiotiques chez l’homme avec une prise de poids voire une
augmentation du risque d’obésité quand les antibiotiques sont administrés dans les premiers
mois de vie 8. De plus, nous avons résumé dans le tableau 1 de l’article XI, les modifications
du microbiote digestif associées à chaque classe d’antibiotiques.
Enfin, une étude antérieure du laboratoire a associé une prise de poids et une obésité
acquise à l’administration de vancomycine intraveineuse pendant 4 à 6 semaines chez des
patients traités pour une endocardite
13
. Afin de prolonger cette étude et de clarifier les
changements du microbiote pouvant être responsable de cette prise de poids, nous avons
effectué une étude observationnelle sur le poids de 98 patients dont 41 était sous vancomycine
et 57 étaient sous amoxicilline (Article XII), Nous avons retrouvé, comme Thuny et al.
13
dans l’étude antérieure du laboratoire, une augmentation de la fréquence des patients ayant
64
une augmentation de l’indice de masse corporelle de plus de 10% à un an et de la fréquence
d’obésité acquise chez les patients sous vancomycine. Analysant 192 échantillons de selles
dont 83 avaient été prélevés sous amoxicilline, 67 sous vancomycine et 42 avaient été
prélevés chez des contrôles, nous avons retrouvé une augmentation dans la concentration en
Firmicutes et Lactobacillus et une diminutions de la concentration en Methanobrevibacter
smithii dans les échantillons prélevés chez des patients sous vancomycine. Bien que
l’augmentation des Lactobacillus puissant être une consequence des antibiotiques sans lien
avec la prise de poids importante chez certains patients, l’existence de nombreuse donnés de
la littérature et certaines de nos études (Article III, IV et VI) sont en faveur du rôle clé de
certaines espèces de Lactobacillus sur la prise de poids, comme Lactobacillus reuteri,
Lactobacillus fermentum et Lactobacillus sakei, espèces naturellement résistantes à la
vancomycine, retrouvés dans le microbiote digestif humain et associées à la prise de poids ou
à l’obésité.
65
Article V : REVIEW
The role of the manipulation of the gut microbiota in
obesity
Matthieu Million, Didier Raoult
Published in Curr Infect Dis Rep. 2013 Feb;15(1):25-30. (IF ND)
66
Article VI :
Comparative meta-analysis of the effect of
Lactobacillus species on weight gain in humans and
animals.
Matthieu Million, Emmanouil Angelakis, Mical Paul, Fabrice
Armougom, Leonard Leibovici, Didier Raoult
Published in Microb Pathog. 2012 Aug;53(2):100-8. (IF 1.94)
73
Microbial Pathogenesis 53 (2012) 100e108
Contents lists available at SciVerse ScienceDirect
Microbial Pathogenesis
journal homepage: www.elsevier.com/locate/micpath
Comparative meta-analysis of the effect of Lactobacillus species on weight gain
in humans and animals
Matthieu Million a,1, Emmanouil Angelakis a,1, Mical Paul b, Fabrice Armougom a, Leonard Leibovici c,
Didier Raoult a, *
a
b
c
URMITE-CNRS UMR 7278 IRD 198, IFR 48, Faculté de Médecine, Université de la Méditerranée, 27 bd jean moulin, Marseille, France
Sackler Faculty of Medicine, Tel-Aviv University’ Tel-Aviv, Israel
Rabin Medical Center, Beilinson Hospital, Petah-Tiqva, Israel
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 27 April 2012
Received in revised form
11 May 2012
Accepted 16 May 2012
Available online 24 May 2012
Background: Obesity is associated with alteration of the gut microbiota. In order to clarify the effect of
Lactobacillus-containing probiotics (LCP) on weight we performed a meta-analysis of clinical studies and
experimental models. We intended to assess effects by Lactobacillus species.
Methods: A broad search with no date or language restriction was performed. We included randomized
controlled trials (RCTs) and comparative clinical studies in humans and animals or experimental models
assessing the effect of Lactobacillus-containing probiotics on weight. We primarily attempted to extract
and use change from baseline values. Data were extracted independently by two authors. Results were
pooled by host and by Lactobacillus species and are summarized in a meta-analysis of standardized
difference in means (SMDs).
Results: We identified and included 17 RCTs in humans, 51 studies on farm animals and 14 experimental
models. Lactobacillus acidophilus administration resulted in significant weight gain in humans and in
animals (SMD 0.15; 95% confidence intervals 0.05e0.25). Results were consistent in humans and animals.
Lactobacillus fermentum and Lactobacillus ingluviei were associated with weight gain in animals.
Lactobacillus plantarum was associated with weight loss in animals and Lactobacillus gasseri was associated with weight loss both in obese humans and in animals.
Conclusions: Different Lactobacillus species are associated different effects on weight change that are
host-specific. Further studies are needed to clarify the role of Lactobacillus species in the human energy
harvest and weight regulation. Attention should be drawn to the potential effects of commonly marketed
lactobacillus-containing probiotics on weight gain.
 2012 Elsevier Ltd. All rights reserved.
Keywords:
Probiotics
Lactobacillus
Weight
Obesity
Meta-analysis
1. Introduction
The prevalence of obesity is increasing steadily among adults,
adolescents and children and is now considered a worldwide
epidemic [1]. The causes driving the obesity appear to be complex
and include environmental, genetic, neural and endocrine factors
[2] but infectious agents have also been proposed [3]. More recently
obesity was associated with a specific profile of the bacterial gut
microbiota [4] and was shown to be a transmissible phenotype by
microbiota transplantation [5]. First studies on obesity reported
a decrease in the Bacteroidetes/Firmicutes ratio [4] and a decrease in
the archae Methanobrevibacter smithii [6]. Since these pioneering
studies, significant associations were found between the increase of
* Corresponding author. Tel.: þ33 491 38 55 17; fax: þ33 491 83 03 90.
E-mail address: [email protected] (D. Raoult).
1
These authors contributed equally to this work.
0882-4010/$ e see front matter  2012 Elsevier Ltd. All rights reserved.
doi:10.1016/j.micpath.2012.05.007
some bacterial groups and human obesity (Lactobacillus [7],
Staphylococcus aureus [8e10], Escherichia coli [10] and Faecalibacterium prausnitzii [11]). Conversely, other bacterial groups have
been associated with lean status, mainly belonging to the Bifidobacterium genus [6,8e11]. We found recently that different Lactobacillus species may have a paradoxical effect with higher levels of
Lactobacillus reuteri and lower levels of Lactobacillus plantarum and
paracasei in obese gut microbiota [12]. In contrast, symbiotics (the
combination of prebiotics and probiotics) have been proposed in
the management of malnutrition with promising results on
mortality [13].
As many probiotic strains of Lactobacillus and Bifidobacterium
are marketed in products for human consumption, altering the
intestinal flora [14], we hypothesized that widespread ingestion of
probiotics may promote obesity by altering the intestinal flora [15].
However, this remains controversial [16]. On the other hand,
manipulation of the gut microbiota by probiotics has been used for
M. Million et al. / Microbial Pathogenesis 53 (2012) 100e108
growth promotion in farm animals for at least 30 years [17].
Indeed, Lactobacillus acidophilus, L. plantarum, Lactobacillus casei,
Lactobacillus fermentum, L. reuteri are the most commonly used
Lactobacillus spp. in agriculture [18]. All these data strongly suggest
that Lactobacillus containing probiotics (LCP) may impact the
weight regulation in humans and animals.
Many studies have reported the effects of Lactobacillus containing probiotics (LCP) on weight but according to recent data [12],
this effect is at least species dependent. To our knowledge, no metaanalysis has been performed to confirm this difference among
Lactobacillus containing probiotics. For this purpose, we pooled
data from animal and human studies to obtain sufficient power to
detect a significant effect at the species level.
2. Methods
2.1. Data sources
According to PRISMA 2009 guidelines [19] (Table A.1), PubMed,
Medline, ISI Web of knowledge, Google scholar, Google, Cochrane
Central Register of Controlled Trials (www.cochrane.org), metaRegister of Controlled Trials (www.controlledtrials.com/mrct),
clinicaltrials.gov and a recent evidence report/technology assessment [20] were searched for articles, unrestricted by language,
from 1950 to August 2011. Search terms included: probiotics,
Lactobacillus, weight, weight gain, weight loss, weight change,
growth, performance, randomized controlled trials, placebocontrolled and associated author names.
101
2.4. Statistical analysis and heterogeneity investigation
We used RevMan v5.1 [22] to carry out meta-analysis of the
standardized difference in means (SMD) with 95% confidence
interval for weight change after probiotics administration. When
means were available and the p-value was described as > 0.05 (or
“not significant”), a two-tailed p-value of 0.9 was attributed in
order to increase the sensitivity of this pioneering work in this area.
Each trial could contribute more than one comparison but
comparisons were pooled for each study only if experimental
conditions were similar. Heterogeneity was assessed by the
I-squared value, 50% being considered as substantial. Summary
measures were determined by a random effect model assuming
significant clinical heterogeneity regardless of the I-squared value.
We primarily investigated heterogeneity by stratifying results by
Lactobacillus species including comparisons with only one Lactobacillus species in the probiotic product. In addition, subgroup
analyses were planned a priori to discern weight changes by host
category; overweight/obese animals or humans; and very low
weight birth (VLWB) newborns. The effect of studies’ risk of bias
was assessed through sensitivity analysis. Funnel plot was used to
identify outliers subsequently excluded and to assess small studies
and publication bias. Classic fail-safe N, Egger’s test for asymmetry
were also used to assess small studies bias and Duval and Tweedie’s
Trim and Fill adjustment with random effects model was used to
provide an estimate of the unbiased effect size. A standardized
difference in means > 0.10 was considered clinically relevant as it
correspond to a 1 kg weight difference for a 70 kg man based on
data from a sample of 5000 healthy human individuals [23].
2.2. Study selection and data extraction
3. Results
We retrieved the full text of studies including Lactobacillus-containing probiotics and looked for weight assessment as primary or
secondary outcome. Inclusion was limited to experimental studies
and randomized controlled trials in farm animals, experimental
models and healthy humans. Authors were contacted when published data were incomplete. Exclusion criteria included hosts with
underlying diseases (except for obesity) or pregnant women, probiotics given only to the mother, symbiotics (probiotics associated
with prebiotics), other nutrients given exclusively to the intervention
group, non-direct fed microbials (probiotics in silage), non viable
probiotic administration, recombinant probiotics, hosts challenged
prior to probiotic administration by viruses or bacteria, hosts with
diarrhoea or colitis, before-after intervention studies, inappropriate
control group (prebiotics, probiotics or antibiotics administration e
traditional yogurt including Lactobacillus delbrueckii subsp. bulgaricus and Streptococcus thermophilus was accepted as control intervention), unavailable statistical data and double publications. Data
were extracted independently by two authors (MM, EA).
The search yielded 200 studies of which 118 were excluded
because of probiotic or host group definitions, study design or
missing outcome data (Fig. 1). 82 studies, involving 153 comparisons, were included in the quantitative synthesis. Included human
studies involved 15 double-blind randomized controlled trials and
two open-labelled randomized trials (Table 1). Included animal
studies involved 14 studies on experimental models and 51 on farm
animals. After exclusion of two studies with high risk of bias
(weight significantly different at baseline, open label design), LCPs
were not associated with significant weight change in human
adults (SMD ¼ 0.18; 95%CI ( 0.43e0.79)), infants (SMD ¼ 0.004;
95%CI ( 0.20e0.21)), or preterm newborn infants (SMD ¼ 0.10;
95%CI ( 0.32e0.12)). Meta-analysis of all comparisons in healthy
humans and animals (134 comparisons, overweight and VLWB
newborns excluded) resulted in weight gain but significant
heterogeneity (SMD ¼ 0.15; 95%CI (0.12e0.18); p < 0.001; I2 ¼ 85%)
and thus we proceeded directly to the subgroup analyses, primarily
assessing effects by species.
2.3. Risk of bias assessment and outcome measures
3.1. Lactobacillus acidophilus
The Jadad score [21] was used for the assessment of bias in evaluating human trials to determine studies to exclude and allowing
sensitivity analysis based on this quality score. In animals, all studies
were included except those with major methodological concerns,
and a score was calculated with one point for each of these terms:
dropouts mentioned, dropouts < 10%, outcome expressed as a weight
difference (and not weight at the end) and absence of other risk of
bias. Studies with a score > 2 were considered at low risk for bias.
The primary outcome was the effect on weight. Weight change
from baseline, weight at the end of the study, daily weight
change, weight/age ratio, delta-BMI (Body Mass Index) and weight
percentile were considered as outcome measures. We primarily
attempted to extract and use change from baseline values.
The meta-analysis of 13 studies and 18 comparisons including
3307 subjects (879 humans) on L. acidophilus administration
showed a significant weight gain effect (SMD ¼ 0.15; 95%CI
(0.05e0.25); p ¼ 0.005; I2 ¼ 42%) (Fig. 2). Using classic fail-safe N,
34 unpublished studies would have been necessary to bring
p-value > 0.05, Duval and Tweedies’s trim and fill did not change
this result, and Egger’s asymmetry test was not significant (twotailed p-value ¼ 0.66) making this summary effect robust and
publication bias unlikely. Direction of effect favouring weight gain
was consistent in humans and animals. A sensitivity analysis
including only studies with a quality score > 2 reduced heterogeneity and found a consistent and significant result (I2 ¼ 28%,
102
M. Million et al. / Microbial Pathogenesis 53 (2012) 100e108
p ¼ 0.03; I2 ¼ 74%) (Fig. 3b). No human studies included
L. plantarum. Small studies bias was unlikely because 19 unpublished studies would have been necessary to bring the p-value
to > 0,05, Duval and Tweedies’s trim and fill did not change this
result and Egger’s asymmetry test was not significant (two-tailed
p-value ¼ 0.52). All these comparisons in experimental animals had
a medium to low risk of bias.
3.5. Lactobacillus gasseri
Fig. 1. Studies flow through meta-analysis according to PRISMA guidelines [19].
L. gasseri was associated with a trend for weight loss in lean
animals in three studies and four comparisons including 48 pigs
and rats (p ¼ 0.09) (Fig. 3a). In obese animals and humans (Fig. 3b),
three studies and three comparisons including 87 humans and 36
rats found an anti-obesity effect (SMD ¼ 0.67; 95%CI ( 1.17
to 0.16); p ¼ 0.009; I2 ¼ 29%). Using classic fail-safe N, six
unpublished studies would have been necessary to bring the
p-value to > 0,05, Duval and Tweedies’s trim and fill did not change
this result and Egger’s asymmetry test was not significant (twotailed p-value ¼ 0.88) making this summary effect robust and small
studies bias unlikely. This effect was consistent between humans
and animals. Two L. gasseri strains (SBT2055 [25e27] and BNR17
[28]) have a significant anti-obesity effects in individual studies. All
these studies had a medium to low risk of bias. In obese individuals,
the difference (SMD ¼ 0.57) was clinically relevant since it
correspond to a weight loss of 6 kg in humans.
3.6. Other species
p ¼ 0.005). The difference (SMD ¼ 0.15) was clinically relevant as it
corresponds to a weight gain of 1.5 kg for a 70 kg man.
3.2. Lactobacillus fermentum
The meta-analysis of 3 studies and 12 comparisons including
598 chicks, pigs and ducks but no humans on L. fermentum found
a significant weight increase (SMD ¼ 0.81; 95%CI (0.12e1.50);
p ¼ 0.02; I2 ¼ 90%). After exclusion of one outlier, 34 unpublished
studies would have been necessary to bring p-value > 0,05 using
classic fail-safe N, Duval and Tweedies’s trim and fill one study but
found a consistent result (SMD ¼ 0.53; 95% CI (0.18e0.87)) and
Egger’s asymmetry test was not significant (two-tailed pvalue ¼ 0.28) making this summary effect robust. All these studies
have a quality score > 2.
3.3. Lactobacillus ingluviei
The meta-analysis of three studies and 11 comparisons
including 198 chicks, ducks and mice on one L. ingluviei strain
isolated from an ostrich found a significant weight increase effect
(SMD ¼ 0.97; 95%CI (0.49e1.45); p < 0.001; I2 ¼ 59%). After
exclusion of one outlier [24], 27 unpublished studies would have
been necessary to bring a p-value > 0.05 using classic fail-safe N,
Egger’s asymmetry test was not significant (p ¼ 0.59) and Duval
and Tweedies’s trim and fill give a similar significant result
(SMD ¼ 0.76; 95% CI (0.48e1.03)). All these studies had a quality
score > 2. No human trials included L. ingluviei.
Other species (L. reuteri, L. casei, Lactobacillus rhamnosus and
Lactobacillus sporogenes) were not associated with significant and
consistent effects. Only L. delbrueckii was significantly associated
with weight gain (five comparisons; I2 ¼ 0%; SMD ¼ 0.39; 95%CI
(0.06e0.71); p ¼ 0.02) but this effect was summarized from only
two different studies in chicks and rats.
4. Discussion
4.1. Significant impact of Lactobacillus-containing probiotics on
weight
In this meta-analysis, we showed that some Lactobacillus species
were significantly associated with weight modifications in human
and animals: L. acidophilus, L. ingluviei, L. fermentum were linked to
weight gain whereas L. gasseri and L. plantarum were linked to
weight loss or an anti-obesity effect. The latter effect seemed
particularly evident in overweight or obese individuals. Wide
variation in response was explained by probiotic species and host.
Stratification only by probiotic species revealed significant and
consistent results. In a second step, we showed that the host was
a covariate explaining part of the heterogeneity found for a specific
probiotic species (Table A.2). The differences found were clinically
relevant as they correspond to a weight change that ranged from
1.5 kg gain in leans for L. acidophilus or 6 kg loss in overweight for
L. gasseri based on the statistics of a population of >600 healthy
men with an average weight of 70 kg (standard deviation of 9.8 kg)
[23].
3.4. Lactobacillus plantarum
4.2. Lactobacillus species associated with weight gain
Pooled analysis of three studies, three comparisons including
335 lean chicks, rats and mice on L.plantarum showed a weight loss
effect direction but result was not significant (Fig. 3a). However, L.
plantarum was associated with significant weight loss effect in
overweight/obese animals in four studies and five comparisons
including 64 mice and rats (SMD ¼ 1.33; 95%CI ( 2.50 to 0.16);
With the results of our meta-analysis, bacteria candidates for
increasing energy efficiency in humans are L. acidophilus and
L. fermentum. To our knowledge, L. ingluviei was not identified in the
human digestive microbiota but only in the intestinal tract of
pigeons, chickens and ostrich and is not contained in probiotics
Location; period of inclusion;
mono or multicentric; study
design; risk of bias (Jadad score)
Spain; period of inclusion not
mentioned; monocentric;
prospective, double-blind,
placebo controlled, randomized
trial; medium (3)
USA; period of inclusion not
mentioned; two centers;
prospective, randomized trial
(no mentioned blinding); high
(1)
USA; 2006e2007; multicentric;
prospective, double-blind,
randomized trial; low (4)
Finland; 2002; multicentric;
prospective, double-blind,
randomized trial; medium (3)
Israel; 2006; monocentric;
prospective, double-blind,
randomized trial; low (4)
Maldonado et al., 2010
Robinson et al.1952
Scalabrin et al., 2009
Vendt et al., 2006
Weizman et al., 2006
Lactobacillus probiotics in infants (<2 years)
Chouraqui et al., 2008
France; 2004e2005;
multicentric (n ¼ 5);
prospective, double-blind,
reference controlled, parallelgroup, randomized trial; low
(5)
Study source
Table 1
Characteristics of included human studies.
Healthy infants from 0 to 2
months on formula at least half
of their daily feedings; M/
F ¼ 60/60; (120, 15, 105);
treated/control: 51/54
Full term healthy infants aged
3e65 days solely formula fed;
M/F ¼ 26/13; (39; 7;32);
treated/control: 16/17.
w800 enrolled newborns,
number of dropped out not
mentioned, four groups
(treated/control): Completely
bottle fed (124/123), partially
breast fed infants (79/69),
completely bottle fed with folic
acid (134/129), partially breast
fed with folic acid (60/83) e sex
ratio not mentioned
Healthy term infants (birth
weight 2500 g) enrolled at
14 d of age, solely formula fed;
M/F ¼ 94/94; (188, 55, 133) e
one group using different
casein formula was excluded;
treated/control: 63/70
Full term, singletons,
exclusively formula fed healthy
infants, with weight between
2500 and 4500 g; <15 d of age
(284, 57, 227) e 2/4 groups
using prebiotics were excluded
from this meta-analysis; two
comparisons: boys: 29/25, girls:
30/28
Healthy breast-fed infants fed
exclusively with formula at the
moment of recruitment, sixth
month of life; boys 39, girls 41;
(80, 0, 80); treated/control:
40/ 40
Subjects included; age and sex;
sample size (subjects enrolled,
dropped out, used); no of
treated /control subjects
Underlying disease or
congenital malformation,
formula intolerance, weight at
14 d of age " 98% of birth
weight, large for gestational age
born from a mother diabetic at
childbirth, immunodeficiency,
fever, antibiotic within 7 d,
systemic steroid since birth,
LGG-suppl diet since birth,
diarrhoea within 24 h
Not mentioned (reasons for
discontinuation: colic pain,
cow’s milk protein intolerance,
constipation, diarrhoea,
excessive breastfeeding)
<36 wks gestation, birth
weight < 2500 g, congenital
anomalies, chronic disease,
failure to thrive (weight loss
of > 2 percentiles), allergy or
atopic disease and recent (less
Frequent gastrointestinal
disorders (frequent diarrheal,
constipation episodes,
gastroesophageal reflux),
gastrointestinal surgery, cow’s
milk protein allergy, metabolic
disease (diabetes or lactose
intolerance), antibiotic
treatment during the trial or
within the preceding 3 wk
Infants who were obviously ill
in the hospital, who had
congenital irregularities or
those found to have been ill and
that did not gain at least 16
ounces during the first month
Major deformities or
cardiovascular, GI, renal,
neurologic, or metabolic
illnesses, intensive care for 3
days, mother with diabetes,
parents having difficulties
complying the feeding regimen
Exclusion criteria
L. reuteri ATCC55730 (BioGAIA
AB, Sweden) (1 ! 108 cfu); 4
weeks
L. rhamnosus strain GG
ATCC53103 (1 ! 107 cfu); till
the age of 6 months
Extensively hydrolysed casein
formula supplemented or not
with L. rhamnosus strain GG
(108 cfu/g of formula powder);
120 days
L. acidophilus ATCC4962 and
ATCC4963 (>5 ! 108 cfu), 1 ml
to each quart of formula; from
birth until hospital discharge
(1e6 days)
L. salivarius CECT5713
(2 ! 106 cfu/g) on formula; 6
months
B. longum BL999 (1.3 ! 108 cfu
per 100 mL of reconstituted
formula) and L. rhamnosus LPR
(6.45 ! 108 cfu per 100 mL of
reconstituted formula) in
powdered starter formula; 4
months
Probiotic (dose); duration of
treatment
(continued on next page)
Growth parameters, daily
characteristics of feeding,
stooling and behaviour and side
effects.
Growthb and fecal flora on 6
months
Growth and tolerance; weight
gain (g/d) on 120 d
Weight gain; weight gain at one
montha
Multiple outcomes : antibiotic
susceptibility of the strain, AEs’,
growth parameters, intestinal
microbiota; weight gain on 6
months (g)
Weight gain; daily weight gain
on 4 months (g/d)
Outcomes (primary in first);
weight change assessment
(unit)
M. Million et al. / Microbial Pathogenesis 53 (2012) 100e108
103
Location; period of inclusion;
mono or multicentric; study
design; risk of bias (Jadad score)
Iran; period of inclusion not
mentioned; prospective,
double-blind, randomized trial;
medium (3)
Sadrzadeh-Yeganeh
et al., 2010
b
Healthy weight-stable
overweight and obese
(25 < BMI<37.5 kg/m2); mean
38 years, M/F ¼ 4/12, 5/9 and 4/
10; (73,3,70); treated
L. acidophilus 16/L. rhamnosus
14/control 14
Healthy adults with body mass
index (BMI) between 24.2 and
30.7 kg/m2, abdominal visceral
fat area between 81.2 and
178.5 cm2 aged 33e63 years,
M/F ¼ 59/28; (87,0,87); treated/
control: 43/44
Morbidly obese patients
undergoing Roux-en-Y gastric
bypass (RNYGB) (BMI w 45 kg/
m2); Age 40e50 yrs, M/F ¼ 5/
36; (44, 8, 35);
treated/control:15/20
Healthy adults women
(cholesterol < 6.2 mmol/l,
TAG < 2.3 mmol/l, BMI < 30 kg/
m2) (90,1,89) e one group
excluded (no yoghurt)
Treated/control: 30/29
Healthy adults women (BMI:
21  3 kg/m2) e 22e29 years
(33, 1, 32); treated/control :
16/16
Healthy adults between 18 and
65 years; BMI 24  3, at least
50% of the enrolled volunteers
had serum cholesterol levels
over 5 mmol/L, M/F ¼ 22/56;
(85,7,78); treated/control:
39/39
Subjects included; age and sex;
sample size (subjects enrolled,
dropped out, used); no of
treated /control subjects
Abdominal adiposity and body
weight; weight change (kg)
Bacterial overgrowth, weight
loss, quality of life; percent
excess weight loss (%)
L. gasseri SBT2055 (5 ! 1010 cfu/
100 g)e200 g/day; 12 weeks
Puritan’s Pride (2.4 ! 109 cfu/
pill) one pill a day e no
characterization of the
Lactobacillus strains; 6 months
Serious disorders, including
internal organ diseases,
diabetes and hypersensitivity to
dairy products.
No exclusion criteria
mentioned
Lipid profile and body weight;
weight change (kg)
2 strains of L. acidophilus
(2 ! 107/ml) and 1 strain of
S. thermophilus (10 ! 107/ml); 2
strains of S. thermophilus
(8 ! 108/ml) and 1 strain of
L. rhamnosus (2 ! 108/ml)
Lipid profile; weight change
(kg)
Antioxidants and oxidant
parameters in plasma; weight
change (kg)b
Actimel (L. paracasei subsp.
Paracasei (L. casei DN-114 001)
(3.6 ! 108 cfu/g)); 4 weeks
L. acidophilus La1 and
Bifidobacterium. lactis Bb12
(4 ! 107 cfu); 6 weeks
Lipid profile and body weight
change; weight change
difference (kg)
Outcomes (primary in first);
weight change assessment
(unit)
L. acidophilus L-1 (5 ! 109 to
3 ! 1010 cfu d)
Probiotic (dose); duration of
treatment
Diabetes, kidney or liver
disease, high blood pressure,
pregnancy, breastfeeding, elite
athlete, chronic ethylism
Heart disease, diabetes, liver or
kidney disease, medications
known to affect blood lipid
metabolism, serum total
cholesterol concentration
higher than 8 mmol/L or
a triacylglycerol concentration
higher than 4 mmol/L
Smoking,
hypercholesterolemia,
pregnancy, overweight or
metabolic disease, allergies or
intolerance, regular
medications except oral
contraceptive
Smoking, kidney, liver or
inflammatory intestinal
disease, thyroid disorders,
diabetes, immunodeficiency,
lactose intolerance; taking
supplements or medication,
probiotic consumption in the
last 2 months, elite athletes,
pregnant or breastfeeding
women
than four weeks) exposure to
probiotics or antibiotics.
Exclusion criteria
g calculated from ounce, !28.35.
Data given by the authors under request e in this study, weight of treated group was significantly lower at baseline.
USA; 2006e2007; monocentric;
prospective, double-blind,
randomized trial; low (5)
Woodard et al., 2009
a
Japan; 2008; multicentric
(n ¼ 10); prospective, doubleblind, randomized trial;
medium (3)
Kadooka et al., 2010
Lactobacillus probiotics in overweight/obese adults
Agerholm-Larsen
Denmark; period of inclusion
et al., 2000
not mentioned; monocentric;
prospective, double-blind,
randomized trial; low (4)
Austrich; period of inclusion
not mentioned; monocentric;
prospective, randomized trial
(blinding not mentioned); high
(2)
Fabian et al., 2007
Lactobacillus probiotics in lean adults
De Roos et al., 1999
The Netherlands; period of
inclusion not mentioned;
monocentric; prospective,
double-blind, randomized trial;
medium (3)
Study source
Table 1 (continued )
104
M. Million et al. / Microbial Pathogenesis 53 (2012) 100e108
M. Million et al. / Microbial Pathogenesis 53 (2012) 100e108
105
Fig. 2. Forest plot of three Lactobacillus species associated with weight gain in humans and animals (a) L. acidophilus, (b) L. fermentum, (c) L. ingluviei (strain isolated from an ostrich
[24]). A weight gain effect is represented by a deviation of the square (standardized difference in the means) to the right. The length of the horizontal line represents the 95%
confidence interval and the diamond represents the summarized effect. Substantial heterogeneity was cancelled after sensitivity analysis for L. acidophilus. L. acidophilus and
L. fermentum, when administered in overweight/obese humans or animals didn’t have a significant anti-obesity effect (data not shown).
marketed for humans. The L. ingluviei comparisons included in this
analysis involved only one strain, isolated from an ostrich gut,
showing an astonishing weight gain effect both in farm animals and
experimental models [24,29]. One candidate for the transmission of
the obese phenotype, L. acidophilus, is widely present in many products for human consumption as the “acidophilus milk”, traditionally
consumed in the United States or in other formulations such as freezedried products sold without any regulation on the internet. The
consumption of this species is particularly prevalent in the United
States [30] where the prevalence of obesity is particularly important.
effect was observed with two species having a significant antiobesity effect; L. gasseri and L. plantarum (Fig. 3b). This antiobesity effect was consistent with absence of significant weightgain effect in lean individuals (Fig. 3a). A recent study confirmed
our results showing a 6-months weight-loss effect of L. plantarum
DSM 15313 in high-energy-dense diet rats when administered to
mother and offspring [31]. This anti-obesity effect could be an
important adjunct in the treatment of obesity, since, apart from
surgery, no medical treatment can support efficiently the fight
against obesity to date.
4.3. Lactobacillus species associated with anti-obesity effect
4.4. Limitations
On the other hand, many bacteria appear to be protective against
obesity. In our study, a strong species dependent anti-obesity
However, the paucity of data in individual hosts impelling us to
the pool animal and human data limits the generalization of these
106
M. Million et al. / Microbial Pathogenesis 53 (2012) 100e108
Fig. 3. Forest plot of three Lactobacillus species associated with an anti-obesity effect in humans and animals. Lactobacillus plantarum and L. gasseri and weight changes in (a) lean
(b) and overweight/obese humans and animals. Weight loss effect is represented by a deviation of the square (standardized difference in the means) to the left. The length of the
horizontal line represents the 95% confidence interval and the diamond represents the summarized effect. Anti-obesity effect was consistent for these two species.
data to humans. Moreover, effect size and standard deviation are
probably very different in experimental models and in the general
human population. Only few clinical studies have been conducted
to test a weight gain effect assessing only one Lactobacillus species
because, unlike animal studies, this effect was generally not sought
in humans. L. acidophilus increased weight gain both in bottle-fed
and breast-fed newborns but this effect was stronger in bottlefed newborns [32], L. gasseri SBT2055 resulted in significant
weight loss in human adults with obese tendencies [27].
4.5. Conflict of interest in nutrition and obesity research
Finally, it is possible that the design and/or interpretation of the
results of each individual study had been affected by a conflict of
interest of each team. It has recently been shown that published
papers in nutrition and obesity research in which the authors were
funded by industry were more likely than other papers to contain
results or an interpretation that favored the industry or company
that was producing the product or service that was being studied
[33]. Furthermore, while a comprehensive search was performed
using several strategies, we cannot be sure that all studies examining the effects of LCPs on weight have been identified.
4.6. Perspectives
In the next future, new double-blind randomized human trials
should assess long-term growth in newborn infants receiving
Lactobacillus-containing probiotics. A critical point is to stratify
according to the initial weight [34]. For species associated here with
a significant weight change and used for human consumption as
M. Million et al. / Microbial Pathogenesis 53 (2012) 100e108
L. acidophilus, L. fermentum, L. plantarum and L. gasseri, trials evaluating weight gain as a primary outcome measure will be needed.
The long-term evaluation to at least 3e5 years of age will be critical
to identify a difference that could have been undetected by shorter
studies [35]. According to the register Clinicaltrials.gov, at least one
current study is testing the average weight gain as a primary
endpoint among newborns receiving a probiotic containing
L. fermentum (trial number NCT01346644). This bacterium has been
associated with obesity in our study. In addition, caseecontrol
studies comparing obese and lean children according to their
consumption of Lactobacillus-containing probiotics in the first
weeks of life should be carried out.
107
Funding
URMITE -CNRS UMR 6236 IRD 198.
Ethical approval
Not required.
Acknowledgements
We thanks all authors that kindly answer and send us requested
data and especially Asal Ataie, Wageha Awad, Jonna Aaltonen, Elisabeth Fabian, Kirsi Laitinen, Kostas Mountzouris, Denis Roy, Nancy
Szabo, Neve Vendt and Christina West.
5. Conclusion
Appendix A. Supplementary material
Food is a source of bacteria and viruses and changes in patterns
of food consumption result in differences in human gut flora among
different groups of people [36,37]. As a result, it is necessary to
further investigate the effects of routinely adding high amounts of
bacteria in food [38]. Our systematic analysis found that the
manipulation of the gut microbiota by L. acidophilus, L. ingluviei or
L. fermentum results in weight gain whereas specific strains of
L. gasseri and L. plantarum used as food supplements presented an
anti-obesity effect. Only two studies including these species were
available in humans, one showing a significant weight gain effect of
L. acidophilus in newborns whereas L. gasseri was found to have
a significant anti-obesity effect in the first and only well-designed
study to date assessing the impact of Lactobacillus-containing
probiotics on overweight humans. L. acidophilus and L. gasseri were
associated with the same effect direction both in animals and
humans. L. fermentum and L. ingluviei were associated with an
astonishing weight gain effect in ducks, chicks and mice but have
never been studied in humans. Next-generation human probiotic
species should contain Lactobacillus spp. that are not associated
with weight gain in humans. Of note, on 24 August 2007, the FDA
issued regulations that require current good manufacturing practice for dietary supplements to be phased in over the next few years
[39]. These regulations should focus on weight assessment
outcome according to probiotic species and strains. Finally, selection of specific Lactobacillus containing probiotics could take part in
the future management of the two major health problems in the
21st century, malnutrition and obesity.
Competing interest statement
All authors have completed the Unified Competing Interest form
at www.icmje.org/coi_disclosure.pdf (available on request from the
corresponding author) and declare that (1) no authors have support
from probiotics companies for the submitted work; (2) no authors
have relationships with probiotics companies that might have an
interest in the submitted work in the previous 3 years; (3) their
spouses, partners, or children have not financial relationships that
may be relevant to the submitted work; and (4) no authors have
non-financial interests that may be relevant to the submitted work.
Contributors
DR conceived and designed the study, MM & EA extracted the
data, MM, FA, MP, LL analysed the data. MM, EA and DR wrote the
manuscript. MP & LL revised the paper. MM had full access to all of
the data in the study and takes responsibility for the integrity of the
data and the accuracy of the data analysis and is guarantor. MM and
EA contributed equally to this work.
Supplementary material associated with this article can be
found, in the online version, at doi:10.1016/j.micpath.2012.05.007.
References
[1] Yanovski SZ, Yanovski JA. Obesity. N Engl J Med 2002;346:591e602.
[2] Tilg H, Moschen AR, Kaser A. Obesity and the microbiota. Gastroenterology
2009;136:1476e83.
[3] Dhurandhar NV. A framework for identification of infections that contribute to
human obesity. Lancet Infect Dis 2011;11:963e9.
[4] Ley RE, Turnbaugh PJ, Klein S, Gordon JI. Microbial ecology: human gut
microbes associated with obesity. Nature 2006;444:1022e3.
[5] Turnbaugh PJ, Backhed F, Fulton L, Gordon JI. Diet-induced obesity is linked to
marked but reversible alterations in the mouse distal gut microbiome. Cell
Host Microbe 2008;3:213e23.
[6] Schwiertz A, Taras D, Schafer K, Beijer S, Bos NA, Donus C, et al. Microbiota and
SCFA in lean and overweight healthy subjects. Obesity (Silver Spring) 2010;
18:190e5.
[7] Armougom F, Henry M, Vialettes B, Raccah D, Raoult D. Monitoring bacterial
community of human gut microbiota reveals an increase in Lactobacillus in
obese patients and Methanogens in anorexic patients. PLoS One 2009;4:e7125.
[8] Kalliomaki M, Collado MC, Salminen S, Isolauri E. Early differences in fecal
microbiota composition in children may predict overweight. Am J Clin Nutr
2008;87:534e8.
[9] Collado MC, Isolauri E, Laitinen K, Salminen S. Distinct composition of gut
microbiota during pregnancy in overweight and normal-weight women. Am J
Clin Nutr 2008;88:894e9.
[10] Santacruz A, Collado MC, Garcia-Valdes L, Segura MT, Martín-Lagos JA, Anjos T,
et al. Gut microbiota composition is associated with body weight, weight gain
and biochemical parameters in pregnant women. Br J Nutr 2010;104:83e92.
[11] Balamurugan R, George G, Kabeerdoss J, Hepsiba J, Chandragunasekaran AM,
Ramakrishna BS. Quantitative differences in intestinal Faecalibacterium
prausnitzii in obese Indian children. Br J Nutr 2010;103:335e8.
[12] Million M, Maraninchi M, Henry M, Armougom F, Richet H, Carrieri P, et al.
Obesity-associated gut microbiota is enriched in Lactobacillus reuteri and
depleted in Bifidobacterium animalis and Methanobrevibacter smithii. Int J Obes
(Lond); 2011. doi:10.1038/ijo.2011.153 [Epub ahead of print].
[13] Kerac M, Bunn J, Seal A, Thindwa M, Tomkins A, Sadler K, et al. Probiotics and
prebiotics for severe acute malnutrition (PRONUT study): a double-blind
efficacy randomised controlled trial in Malawi. Lancet 2009;374:136e44.
[14] Fujimoto J, Matsuki T, Sasamoto M, Tomii Y, Watanabe K. Identification and
quantification of Lactobacillus casei strain Shirota in human feces with strainspecific primers derived from randomly amplified polymorphic DNA. Int J
Food Microbiol 2008;126:210e5.
[15] Raoult D. Human microbiome: take-home lesson on growth promoters?
Nature 2008;454:690e1.
[16] Delzenne N, Reid G. No causal link between obesity and probiotics. Nat Rev
Microbiol 2009;7:901.
[17] Fuller R. Probiotics in man and animals. J Appl Bacteriol 1989;66:365e78.
[18] Anadon A, Martinez-Larranaga MR, Aranzazu MM. Probiotics for animal
nutrition in the European Union. Regulation and safety assessment. Regul
Toxicol Pharmacol 2006;45:91e5.
[19] Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JP, et al. The
PRISMA statement for reporting systematic reviews and meta-analyses of
studies that evaluate health care interventions: explanation and elaboration.
PLoS Med 2009;6:e1000100.
[20] Hempel S, Newberry S, Ruelaz A, Wang Z, Miles JNV, Suttorp MJ, et al. Safety of
probiotics to reduce risk and prevent or treat disease. Evidence report/technology assessment no. 200. Agency for Healthcare Research and Quality
[21] Jadad AR, Moore RA, Carroll D, Jenkinson C, Reynolds DJ, Gavaghan DJ, et al.
Assessing the quality of reports of randomized clinical trials: is blinding necessary? Control Clin Trials 1996;17:1e12.
108
M. Million et al. / Microbial Pathogenesis 53 (2012) 100e108
[22] The Cochrane Collaboration. Review Manager (RevMan) [computer program].
Version 5.1. Copenhagen: The Nordic Cochrane Centre; 2011.
[23] Khosla T, Lowe C. Indices of obesity derived from body weight and height. Br J
Prev Soc Med 1967;21:122e8.
[24] Khan M, Raoult D, Richet H, Lepidi H, La Scola B. Growth-promoting effects of
single-dose intragastrically administered probiotics in chickens. Br Poult Sci
2007;48:732e5.
[25] Hamad EM, Sato M, Uzu K, Yoshida T, Higashi S, Kawakami H, et al.
Milk fermented by Lactobacillus gasseri SBT2055 influences adipocyte
size via inhibition of dietary fat absorption in Zucker rats. Br J Nutr 2009;101:
1e9.
[26] Sato M, Uzu K, Yoshida T, Hamad EM, Kawakami H, Matsuyama H, et al. Effects
of milk fermented by Lactobacillus gasseri SBT2055 on adipocyte size in rats.
Br J Nutr 2008;99:1013e7.
[27] Kadooka Y, Sato M, Imaizumi K, Ogawa A, Ikuyama K, Akai Y, et al. Regulation
of abdominal adiposity by probiotics (Lactobacillus gasseri SBT2055) in adults
with obese tendencies in a randomized controlled trial. Eur J Clin Nutr 2010;
64:636e43.
[28] Kang JH, Yun SI, Park HO. Effects of Lactobacillus gasseri BNR17 on body weight
and adipose tissue mass in diet-induced overweight rats. J Microbiol 2010;48:
712e4.
[29] Angelakis E, Raoult D. The increase of Lactobacillus species in the gut flora of
newborn broiler chicks and ducks is associated with weight gain. PLoS ONE
2010;5:e10463.
[30] Chandan RC. History and consumption trends. In: Chandan RC, White CH,
Kilara A, Hui YH, editors. Manufacturing Yogurt and Fermented Milks. Ames:
Blackwell Publishing; 2006. p. 3e15.
[31] Karlsson C, Molin G, Fak F, Johansson Hagslätt ML, Jakesevic M, Håkansson Å,
et al. Effects on weight gain and gut microbiota in rats given bacterial
supplements and a high-energy-dense diet from fetal life through to 6 months
of age. Br J Nutr 2011;106:887e95.
[32] Robinson EL, Thompson WL. Effect on weight gain of the addition of Lactobacillus acidophilus to the formula of newborn infants. J Pediatr 1952;41:
395e8.
[33] Thomas O, Thabane L, Douketis J, Chu R, Westfall AO, Allison DB. Industry
funding and the reporting quality of large long-term weight loss trials. Int J
Obes (Lond) 2008;32:1531e6.
[34] Vendt N, Grunberg H, Tuure T, Malminiemi O, Wuolijoki E, Tillmann V, et al.
Growth during the first 6 months of life in infants using formula enriched
with Lactobacillus rhamnosus GG: double-blind, randomized trial. J Hum Nutr
Diet 2006;19:51e8.
[35] Chouraqui JP, Grathwohl D, Labaune JM, Hascoet JM, de Montgolfier I,
Leclaire M, et al. Assessment of the safety, tolerance, and protective effect
against diarrhea of infant formulas containing mixtures of probiotics or probiotics and prebiotics in a randomized controlled trial. Am J Clin Nutr 2008;
87:1365e73.
[36] Raoult D. The globalization of intestinal microbiota. Eur J Clin Microbiol Infect
Dis 2010;29:1049e50.
[37] Gordon JI, Klaenhammer TR. A rendezvous with our microbes. Proc Natl Acad
Sci U S A 2011;108(Suppl. 1):4513e5.
[38] Raoult D. Obesity pandemics and the modification of digestive bacterial flora.
Eur J Clin Microbiol Infect Dis 2008;27:631e4.
[39] Sanders ME. Probiotics: definition, sources, selection, and uses. Clin Infect Dis
2008;46(Suppl. 2):S58e61.
Glossary
Probiotics: Probiotics are defined as “Live microorganisms which when administered in adequate amounts confer a health benefit on the host.” According to
FAO/WHO.
Obesity: According to the WHO, Obesity is defined by a BMI > 30 kg/m2 and
a massive expansion of fat, and is associated with a significant increase in
morbidity and mortality.
Lactobacillus: Lactobacillus is a genus of Gram-positive facultative anaerobic or
microaerophilic rod-shaped bacteria. They are a major part of the lactic acid
bacteria group, named as such because most of its members convert lactose and
other sugars to lactic acid. In humans they are present in the vagina and the
gastrointestinal tract. They are largely present in food products for human and
animal consumption as probiotics.
Meta-analysis with random effect model: A meta-analysis combines the results of
several studies by identification of a common measure of effect size, of which
a weighted average might be calculated. A random effect model assumes that
heterogeneity is due at least in part to the different experimental conditions
between individual studies.
Article VII :
Species and strain specificity of Lactobacillus
probiotics effect on weight regulation
Matthieu Million, Didier Raoult
Published in Microb Pathog. 2013 Feb;55:52-4. (IF 1.94)
83
Microbial Pathogenesis 55 (2013) 52–54
Contents lists available at SciVerse ScienceDirect
Microbial Pathogenesis
journal homepage: www.elsevier.com/locate/micpath
Letter to the Editor
Species and strain specificity of Lactobacillus probiotics effect on weight regulation
a b s t r a c t
Keywords:
Obesity
Probiotics
Meta-analysis
Lactobacillus
Weight
Certain strains of Lactobacillus appear to have a reproducible effect on weight as a weight-gain effect in
lean humans and animals or a weight-loss effect in overweight/obese humans and animals. These results
are completely sufficient to capture the attention of the scientific community to assess the effect on the
weight of Lactobacillus-containing probiotics sold for human consumption.
 2012 Elsevier Ltd. All rights reserved.
Dear Editor,
described in three separate studies on chicks (broiler chicks
Ross308, Hubbard JV, Cobb500) and ducks (Hybrid PKB) [12–14]
and has been confirmed by another study not included in our
meta-analysis with a significant 5% weight-gain in chickens [15],
whereas this effect was less intense (2%) and was not significant
for Enterococcus faecium M74. The L. fermentum CCM7158 strain is
widely marketed under the trade name PROPOUL in order to
fatten chickens. Another L. fermentum strain, not deposited but
typed through standard morphological, biochemical, physiological
tests and 16S rRNA gene by sequence analysis by the China General
Microbiology Culture Collection Center [16] was associated with
a weight-gain of 9–20% among weaned pigs and these results
were significant. The latter study was conducted at the National
Key Lab of Animal Nutrition, Beijing China so that consistent results
were obtained from 3 different countries (Slovaky, France and
China) using three different strains of L. fermentum.
For L. gasseri, studies showing a protective effect against obesity in
humans and animals correspond to two strains SBT2055 [17–19]
and BNR17 [20], which correspond to recent articles and
therefore are most probably correctly identified at the species level.
For L. plantarum, Karlsson [21], who published in British Journal of
Nutrition, used the DSM15313 strain with a 11% decrease in weight
at 6 months compared with control rats fed a high-energy-dense
diet. Lee et al. [22] used a strain isolated from human feces by the
authors identified as L. plantarum PL62 sequencing the 16S gene but
authors did not gave the obtained sequence. Takemura [23] used
a L. plantarum strain (strain 14) for which we could not obtain the
identification techniques.
Overall, even taking into account the taxonomic corrections
mentioned by Dr. Morelli, the main message of our work is essentially the same: that certain strains of Lactobacillus appear to have
a reproducible effect on weight as a weight-gain effect in lean
humans and animals or a weight-loss effect in overweight/obese
humans and animals. These results are completely sufficient to
capture the attention of the scientific community to assess the
effect on the weight of Lactobacillus-containing probiotics sold for
human consumption.
The fact that two strains of the same species can have contradictory effects is not impossible as it has been shown that different
We read with interest the comments of Dr. Morelli regarding our
article published in Microbial pathogenesis entitled “Comparative
meta-analysis of the effect of Lactobacillus species on weight-gain
in humans and animals.”
Dr. Morelli pointed out the fact that the Lactobacillus acidophilus
group includes several Lactobacillus species usually susceptible to
vancomycin and whose taxonomy has been difficult to clarify
because even until very recently strains identified as L. acidophilus
are being reclassified in other species [1]. Taxonomy of this group
identified 3 clusters by the DNA homology with L. acidophilus,
Lactobacillus gasseri and Lactobacillus johnsonii [2]. Indeed, according to data from the American Type Culture Collection, the strains
(ATCC 4962 and 4963) used by Robinson et al. [3] in 1952 identified
initially as L. acidophilus corresponds in realty to L. gasseri. In addition, the NP51 strain identified as L. acidophilus used in the article of
Elam et al. [4] in 2003 has been reclassified as Lactobacillus animalis
[5]. However in this study we couldn’t verify if the other strain used
in combination (LA45 deposited in ATCC as PTA-6749) corresponds
to the species L. acidophilus or not. These discrepancies pointed out
the importance of characterizing each strain using the most recent
taxonomic means with the deposition of discriminating DNA
sequences in international database, and we highlight this critical
point in a recent article on probiotics for human consumption [6].
Nevertheless, taking into account the remarks of Dr. Morelli, we
repeated the meta-analysis on L. acidophilus excluding studies cited
by Dr. Morelli namely Robinson, 1952 [3] – Elam, 2003 [4] – Brashears, 2003 [7] – Peterson, 2007 [8]. Even after exclusion of these
studies, the weight-gain effect of L. acidophilus remains significant
(Fig. 1 (random model, I2 ¼ 46%, p-value for overall effect ¼ 0.01)).
In addition, Dr. Morelli seems to completely neglect the consistent results obtained for Lactobacillus fermentum and Lactobacillus
ingluviei in lean animals with a weight-gain effect and consistent
results for an anti-obesity effect of L. gasseri and Lactobacillus plantarum found specifically in overweight/obese animals.
In our laboratory, three studies have found a similar weight-gain
effect with the same strain isolated from an ostrich [9–11]. Outside
our laboratory, the weight-gain effect of L. fermentum CCM7158 was
0882-4010/$ – see front matter  2012 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.micpath.2012.09.013
Letter to the Editor / Microbial Pathogenesis 55 (2013) 52–54
53
step alerting the scientific community to clarify the effect Lactobacillus-containing probiotics on weight. This effect is not limited to
the inhibition of pathogenic bacteria since this weight-gain effect
after Lactobacillus probiotic administration is present when the
loss depends only on a protein deficit [32], improving the intestines’ ability to absorb and process nutrients [33] and significantly
increasing retention of fat [34].
Finally, it may be noticed that our works enrage mainly people
having direct links with the food industry such as Ehrlich [35].
References
Fig. 1. Forest plot of three Lactobacillus species associated with weight gain in animals
(one study by de Roos et al. focused on Lactobacillus acidophilus included humans with
a non significant weight gain effect). A weight gain effect is represented by a deviation
of the square (standardized difference in the mean) to the right. The length of the horizontal line represents the 95% confidence interval and the diamond represents the
summarized effect.
Bifidobacterium strains might improve (strain M13-4) or decrease
body weight (strain L66-5) of high-fat diet obese rats [24], in addition the whole genome content of two strains of the same species
(L. johnsonii NCC 533 and FI9785) share only 64% of the proteins
showing high intra-species variability [25].
As the global probiotic market generated US $15.9 billion in
2008 (http://www.marketsandmarkets.com) and the global probiotic market is expected to exceed US $28.8 billion by 2015, according to a report by Global Industry Analysts, Inc. (http://www.prweb.
com), the risk of conflict of interest seems extremely important
[26]. Indeed, we recently published a work identifying significant
publication biases in the literature about probiotics and beneficial
results in human health and probiotics and growth-promoting
results in farm industry [36]. This is why we caution researchers
not to eliminate a priori the hypothesis that probiotics may be associated with weight-gain in humans. It is interesting to note that the
first article published by Dr. Morelli available on PubMed [27] was
funded by Bracco Spa. which owns patents on Lactobacillus-containing probiotics as Lactobacillus paracasei CNCM 1-1390 and
that Dr. Morelli participated in a workshop of a food industry
marketing several probiotics products [28].
The hypothesis that probiotics may be linked to human obesity
must be tested scientifically with a maximum accuracy on the strain
identification and the maximum power in order not to neglect such
an effect. For instance, after one of us (DR) pointed out that both probiotics and antibiotics could be linked with a weight-gain in humans
[29], the effect of antibiotics used as growth-promoter in agriculture
for 60 years has been recognized very recently in humans when they
are administered in first months of life [30] and in an animal model
as promoting adiposity [31].
The same paradigm has to be clarified for probiotics and we
think our work, even if comporting some limitations inevitably
linked with the difficult taxonomy of Lactobacillus genus, is a first
[1] Voronina OL, Kunda MS, Bondarenko VM, Shabanova NA, Lunin VG. Refinement of taxonomic position of Lactobacillus genus probiotic strains by 16S
rDNA and rpoA gene sequencing. Zh Mikrobiol Epidemiol Immunobiol
2012;3:18–24.
[2] Johnson JL, Phelps CF, Cummins CS, London J, Gasser F. Taxonomy of the Lactobacillus acidophilus group. Int J Syst Bacteriol 1980;30:53–68.
[3] Robinson EL, Thompson WL. Effect on weight gain of the addition of Lactobacillus acidophilus to the formula of newborn infants. J Pediatr 1952;41:395–8.
[4] Elam NA, Gleghorn JF, Rivera JD, Galyean ML, Defoor PJ, Brashears MM, et al.
Effects of live cultures of Lactobacillus acidophilus (strains NP45 and NP51) and
Propionibacterium freudenreichii on performance, carcass, and intestinal characteristics, and Escherichia coli strain O157 shedding of finishing beef steers. J
Anim Sci 2003;81:2686–98.
[5] Randhawa S, Brashears MM, McMahon KW, Fokar M, Karunasena E. Comparison of phenotypic and genotypic methods used for the species identification
of Lactobacillus NP51 and development of a strain-specific PCR assay. Probiotics Antimicrob Prot 2010;2:274–83.
[6] Angelakis E, Million M, Henry M, Raoult D. Rapid and accurate bacterial identification in probiotics and yoghurts by MALDI-TOF mass spectrometry. J Food
Sci 2010;76:M568–72.
[7] Brashears MM, Galyean ML, Loneragan GH, Mann JE, Killinger-Mann K. Prevalence of Escherichia coli O157:H7 and performance by beef feedlot cattle
given Lactobacillus direct-fed microbials. J Food Prot 2003;66:748–54.
[8] Peterson RE, Klopfenstein TJ, Erickson GE, Folmer J, Hinkley S, Moxley RA, et al.
Effect of Lactobacillus acidophilus strain NP51 on Escherichia coli O157:H7 fecal
shedding and finishing performance in beef feedlot cattle. J Food Prot 2007;
70:287–91.
[9] Khan M, Raoult D, Richet H, Lepidi H, La Scola B. Growth-promoting effects of
single-dose intragastrically administered probiotics in chickens. Br Poult Sci
2007;48:732–5.
[10] Angelakis E, Bastelica D, Ben AA, El Filali A, Dutour A, Mege JL, et al. An evaluation of the effects of Lactobacillus ingluviei on body weight, the intestinal
microbiome and metabolism in mice. Microb Pathog 2012;52:61–8.
[11] Angelakis E, Raoult D. The increase of Lactobacillus species in the gut flora of
newborn broiler chicks and ducks is associated with weight gain. PLoS One
2010;5:e10463.
[12] Weis J, Baranska B, Pal G, Hrncar C. Performance of the broiler duck males
after application of two different probiotic preparations. Anim Sci Biotechnologies 2010;43:300–2.
[13] Weis J, Hrncar C, Mindek S. Effect of probiotic preparates with different
strain on meat production of broiler ducks. Zootehnie si Biotehnologii
2008;41:717–20.
[14] Weis J, Hrncar C, Pal G, Baranska B, Bujko J, Policka M, et al. Effect of probiotic
strain Lactobacillus fermentum CCM7158 supplement on performance and
carcass characteristics of broiler chickens. Acta fytotechnica et zootechnica
2010;4:96–8.
[15] Capcarova M, Weiss J, Hrncar C, Kolesarova A, Pal G. Effect of Lactobacillus fermentum and Enterococcus faecium strains on internal milieu, antioxidant
status and body weight of broiler chickens. J Anim Physiol Anim Nutr (Berl)
2010;94:e215–24.
[16] Yu HF, Wang AN, Li XJ, Qiao SY. Effect of viable Lactobacillus fermentum on the
growth performance, nutrient digestibility and immunity of weaned pigs. J
Anim Feed Sci 2008;17:61–9.
[17] Sato M, Uzu K, Yoshida T, Hamad EM, Kawakami H, Matsuyama H, et al. Effects
of milk fermented by Lactobacillus gasseri SBT2055 on adipocyte size in rats.
Br J Nutr 2008;99:1013–7.
[18] Hamad EM, Sato M, Uzu K, Yoshida T, Higashi S, Kawakami H, et al. Milk fermented by Lactobacillus gasseri SBT2055 influences adipocyte size via inhibition of dietary fat absorption in Zucker rats. Br J Nutr 2009;101:716–24.
[19] Kadooka Y, Sato M, Imaizumi K, Ogawa A, Ikuyama K, Akai Y, et al. Regulation of
abdominal adiposity by probiotics (Lactobacillus gasseri SBT2055) in adults with
obese tendencies in a randomized controlled trial. Eur J Clin Nutr 2010;64:636–43.
[20] Kang JH, Yun SI, Park HO. Effects of Lactobacillus gasseri BNR17 on body
weight and adipose tissue mass in diet-induced overweight rats. J Microbiol
2010;48:712–4.
[21] Karlsson CL, Molin G, Fak F, Johansson Hagslatt ML, Jakesevic M, Hakansson A,
et al. Effects on weight gain and gut microbiota in rats given bacterial supplements and a high-energy-dense diet from fetal life through to 6 months of
age. Br J Nutr 2011;106:887–95.
54
Letter to the Editor / Microbial Pathogenesis 55 (2013) 52–54
[22] Lee K, Paek K, Lee HY, Park JH, Lee Y. Antiobesity effect of trans-10, cis-12conjugated linoleic acid-producing Lactobacillus plantarum PL62 on dietinduced obese mice. J Appl Microbiol 2007;103:1140–6.
[23] Takemura N, Okubo T, Sonoyama K. Lactobacillus plantarum strain no. 14
reduces adipocyte size in mice fed high-fat diet. Exp Biol Med (Maywood )
2010;235:849–56.
[24] Yin YN, Yu QF, Fu N, Liu XW, Lu FG. Effects of four Bifidobacteria on obesity in
high-fat diet induced rats. World J Gastroenterol 2010;16:3394–401.
[25] Lukjancenko O, Ussery DW, Wassenaar TM. Comparative genomics of Bifidobacterium, Lactobacillus and related probiotic genera. Microb Ecol 2012;63:651–73.
[26] Thomas O, Thabane L, Douketis J, Chu R, Westfall AO, Allison DB. Industry
funding and the reporting quality of large long-term weight loss trials. Int J
Obes (Lond) 2008;32:1531–6.
[27] Morelli L, Zonenschain D, Callegari ML, Grossi E, Maisano F, Fusillo M. Assessment of a new synbiotic preparation in healthy volunteers: survival, persistence
of probiotic strains and its effect on the indigenous flora. Nutr J 2003;2:11.
[28] Morelli L. The microbiological risk. In: Nestle Nutr workshop ser pediatr
program, vol. 60; 2007. p. 79–90.
[29] Raoult D. Human microbiome: take-home lesson on growth promoters?
Nature 2008;454:690–1.
[30] Trasande L, Blustein J, Liu M, Corwin E, Cox LM, Blaser MJ. Infant antibiotic
exposures and early-life body mass. Int J Obes (Lond) 2012 Aug 21. http://
dx.doi.org/10.1038/ijo.2012.132 [Epub ahead of print].
[31] Cho I, Yamanishi S, Cox L, Methe BA, Zavadil J, Li K, et al. Antibiotics in early life
alter the murine colonic microbiome and adiposity. Nature 2012;488:621–6.
[32] Dunham HJ, Casas IA, Edens FW, Parkhurst CR, Garlich JD, Dobrogosz WJ. Avian
growth depression in chickens induced by environmental, microbiological, or
[33]
[34]
[35]
[36]
nutritional stress is moderated by probiotic administrations of Lactobacillus
reuteri. Biosc Microflor 1998;17:133–9.
Casas IA, Dobrogosz WJ. Validation of the probiotic concept: Lactobacillus reuteri confers broad-spectrum protection against disease in humans and
animals. Microb Ecol Health Dis 2000;12:247–85.
Nahashon SN, Nakaue HS, Snyder SP, Mirosh LW. Performance of single
comb white leghorn layers fed corn-soybean meal and barley-cornsoybean meal diets supplemented with a direct-fed microbial. Poult Sci
1994;73:1712–23.
Ehrlich SD. Probiotics – little evidence for a link to obesity. Nat Rev Microbiol
2009 Dec;7:901.
Million M, Raoult D. Publication Biases in Probiotics. Eur J Epidemiol, in press.
Matthieu Million, Didier Raoult*
Unité de Recherche sur les Maladies Infectieuses et Tropicales
Emergentes, Faculté de Médecine, CNRS UMR 7278, IRD 198,
Aix-Marseille Université, Marseille, France
* Corresponding author. Tel.: þ33 491 38 55 17;
fax: þ33 491 83 03 90.
E-mail address: [email protected] (D. Raoult)
Available online 26 October 2012
Article VIII :
Publication biases in probiotics
Matthieu Million, Didier Raoult
Published in Eur J Epidemiol. 2012 Nov;27(11):885-6. (IF 4.71)
86
LETTER
Occam’s razor and probiotics activity
on Listeria monocytogenes
We read with interest the article by Archambaud et al. (1) on
Lactobacillus casei BL23 and Lactobacillus paracasei CNCM
I-3689, which were able to limit the Listeria monocytogenes
dissemination in a gnotobiotic humanized mouse model. The
authors suggested that changes in the expression of IFN-stimulated genes and of mi-RNA, together with the L. monocytogenes
metabolism redirection by Lactobacillus strains, may explain the
modulation of the infection. However, according to the Occam’s
razor principle postulating that a simpler explanation is more
likely to be true, we believe that the role of bacteriocins is critical
in this instance.
Bacteriocins have been used as bio-preservatives, especially
against L. monocytogenes contamination in vegetable food
matrices, for ∼20 y. The ability of Lactobacillus to inhibit
pathogens in vitro is well documented. In one study, all of the
L. casei and L. paracasei strains inhibited L. monocytogenes
growth (2). The Lactocin 705 produced by L. casei CRL705 is
bacteriostatic on L. monocytogenes (3), whereas a strain of
L. paracasei subsp. paracasei has been shown to produce another
substance inhibiting L. monocytogenes growth and leading to
cellular lysis (4). Using the Bagel and Bactibase bacteriocin
databases to analyze the strains used in this study, we were able
to find one prebacteriocin (GenBank ID: YP_001988475) in
www.pnas.org/cgi/doi/10.1073/pnas.1218418110
L. casei BL23, and one Lactocin-705 (GenBank: LC70_LACPA)
among available L. paracasei genomes.
Taken together, these data suggest that the impact of a
Lactobacillus strain on the microbiota flora is mainly determined
by its direct antibiotic activities, including bacteriocins, as
recently rediscovered (5).
Matthieu Million1, Emmanouil Angelakis1, Fatima Drissi, and
Didier Raoult2
Unité de Recherche sur les Maladies Infectieuses et Tropicales
Emergentes, Unité Mixte de Recherche (UMR) Centre National de
la Recherche Scientifique (CNRS) 7278, Institut de Recherche pour
le Développement (IRD) 198, Institut National de la Santé et de la
Recherche Médicale (INSERM) 1095, Faculté de Médecine, Aix
Marseille Université, 13005 Marseille, France
1. Archambaud C, et al. (2012) Impact of lactobacilli on orally acquired listeriosis. Proc
Natl Acad Sci USA 109(41):16684–16689.
2. Jacobsen CN, et al. (1999) Screening of probiotic activities of forty-seven strains of
Lactobacillus spp. by in vitro techniques and evaluation of the colonization ability of
five selected strains in humans. Appl Environ Microbiol 65(11):4949–4956.
3. Vignolo G, Fadda S, de Kairuz MN, de Ruiz Holgado AA, Oliver G (1996) Control of
Listeria monocytogenes in ground beef by ‘Lactocin 705’, a bacteriocin produced by
Lactobacillus casei CRL 705). Int J Food Microbiol 29(2-3):397–402.
4. Bendali F, Gaillard-Martinie B, Hebraud M, Sadoun D (2008) Kinetic of production and
mode of action of the Lactobacillus paracasei subsp. paracasei anti-listerial bacteriocin,
an Algerian isolate. LWT–Food Science and Technology 41(10):1784–1792.
5. Kim HB, et al. (2012) Microbial shifts in the swine distal gut in response to the treatment
with antimicrobial growth promoter, tylosin. Proc Natl Acad Sci USA 109(38):15485–15490.
Author contributions: D.R. designed research; M.M., E.A., and F.D. performed research;
and M.M., E.A., and D.R. wrote the paper.
The authors declare no conflict of interest.
1
M.M. and E.A. contributed equally to this work.
2
To whom correspondence should be addressed. E-mail: [email protected].
PNAS | January 2, 2013 | vol. 110 | no. 1 | E1
Article IX :
Lactobacillus rhamnosus bacteremia: an emerging
clinical entity
Frédérique Gouriet, Matthieu Million, Mireille Henri, Pierre-Edouard
Fournier, Didier Raoult
Published in Eur J Clin Microbiol Infect Dis. 2012 Apr 28 (IF 2.86)
89
Eur J Clin Microbiol Infect Dis (2012) 31:2469–2480
DOI 10.1007/s10096-012-1599-5
ARTICLE
Lactobacillus rhamnosus bacteremia: an emerging
clinical entity
F. Gouriet & M. Million & M. Henri & P.-E. Fournier &
D. Raoult
Received: 20 November 2011 / Accepted: 29 February 2012 / Published online: 28 April 2012
# Springer-Verlag 2012
Abstract Lactobacillus spp. are ubiquitous commensals of
the normal human flora that are only occasionally found in
clinical infections. Their role in human disease is established
for infectious endocarditis but is controversial for other
infections. We sought to characterize clinically associated
Lactobacillus spp. We conducted a retrospective study,
which consisted of the screening of Lactobacillus isolates
obtained in our laboratory from January 2004 to December
2009. The polymerase chain reaction (PCR) assay was
selected as the gold standard method. The isolates were first
identified using API Coryne strips, matrix-assisted laser
desorption/ionization time-of-flight mass spectrometry
(MALDI-TOF MS), and 16S rRNA gene sequencing.
Lactobacillus tuf gene-based identification was used when
the 16S rRNA results were inconclusive. Among the 60
strains of Lactobacillus spp. obtained in our laboratory, L.
rhamnosus was the most commonly isolated species and
was found in blood cultures from 16 patients. Combined
with 45 patients reported in the literature, we found that
patients presenting with L. rhamnosus bacteremia experienced nosocomial infections associated with both immunosuppression (66 %) and catheters (83 %).
Introduction
Microbial identification methods using 16S rRNA gene
sequencing, which have increased by 456 % in 15 years,
F. Gouriet : M. Million : M. Henri : P.-E. Fournier : D. Raoult (*)
Unité des Rickettsies, CNRS UMR 6236, IRD 198, Faculté de
Médecine, Université de la Méditerranée,
27 Bd. Jean Moulin,
13385 Marseille Cedex 05, France
e-mail: [email protected]
have enhanced the recognition of new bacterial species
[1]. These methods have also improved the discrimination between bacterial species, allowing studies of
the emerging clinical importance of microorganisms
that have been previously misidentified or unidentified. More recently, matrix-assisted laser desorption/
ionization time-of-flight mass spectrometry (MALDITOF MS) has been added as a rapid identification tool
[2, 3].
Lactobacillus spp. have occasionally been associated
with serious infections, especially among immunocompromised patients and those suffering from endocarditis
[4]. The risk of infection due to lactobacilli and bifidobacteria is extremely rare and is estimated to represent 0.05–0.4 % of cases of infective endocarditis and
bacteremia [5]. Meanwhile, historically, Lactobacillus
spp. found in food have been considered to be insignificant [6], with little clinical significance, and they
are often regarded as contaminants when isolated from
patient samples. Their role as commensals in the mammalian flora and their established safety in various
foods and probiotics support this conclusion. However,
hazardous adverse effects of probiotics have been
reported [7], such as acute pancreatitis, in which probiotic prophylaxis was associated with an increased risk
of mortality [8].
In this work, we analyzed Lactobacillus spp. isolated under pathological conditions over a five-year period. L. rhamnosus was the most commonly isolated
species. Our aim was to characterize the clinical entity
associated with this particular Lactobacillus species.
We also reviewed the literature in order to identify
specific clinical conditions associated with L. rhamnosus bacteremia.
2470
Materials and methods
Bacterial strain isolation
Our laboratory serves a university-affiliated tertiary care
institution (3,820 beds) and routinely processes human samples for the culture-based diagnosis of bacterial infectious
diseases, including those due to anaerobes, aerobes, mycobacteria, and spirochetes. The blood-culture vials used for
aerobic and anaerobic cultures (BACTEC; Becton
Dickinson, Sparks, MD) were incubated for five days.
After the incubation period, Gram staining was performed,
and the samples were cultured on 5 % sheep blood and
chocolate agar at 37 °C under aerobic and anaerobic atmospheric conditions for all positive blood cultures. Lung
abscess biopsy specimens were cultured on chocolate agar
and 5 % sheep blood with or without nalidixic acid and
colistin at 37 °C under aerobic and anaerobic atmospheric
conditions for 10 days. Urine specimens were cultured on
5 % sheep blood agar for 24 h, and pleural effusions were
cultured on chocolate agar at 37 °C under the appropriate
atmospheric conditions for 10 days.
Patients
From January 2004 to December 2009, a retrospective study
including the screening of Lactobacillus strains was conducted. We collected information for each patient on age,
sex, admitting hospital department, type of Lactobacillus
infection, and source of Lactobacillus isolate. We focused
on patients with bacteremia due to L. rhamnosus, which was
the most commonly identified species, and reviewed the
clinical data, including underlying disease, possible predisposing factors, and antibiotic treatment.
Lactobacillus identification
From January 2004 to September 2008, isolates were identified using API Coryne strips (bioMérieux, Marcy l’Etoile,
France). The identification was considered to be satisfactory
when the score was >80 %. From September 2008 to
December 2009, MALDI-TOF MS was also used for the
identification [2]. Measurements were performed using an
Autoflex II mass spectrometer (Bruker Daltonics, Bremen,
Germany) equipped with a 337-nm nitrogen laser. For each
spectrum, a maximum of 100 peaks was considered, and
these peaks were compared with peaks in the database. The
15 bacterial species exhibiting the most similar protein
patterns compared to each isolate were ranked by their
identification scores. For the MALDI-TOF MS analysis,
we adapted the score values proposed by Seng et al. [2, 3].
More specifically, an isolate was considered to be correctly
identified by MALDI-TOF MS when ≥2/4 spectra had a
Eur J Clin Microbiol Infect Dis (2012) 31:2469–2480
score ≥1.9 or 4/4 had a score ≥1.2. For each product, the
colony-forming unit (CFU) and MALDI-TOF MS analyses
were conducted three times independently. For all
Lactobacillus spp. obtained from the clinical specimens,
16S rRNA gene-based identifications were performed as
previously described [9]. The sequences determined were
compared with those available in the GenBank database
using BlastN software (http://www.ncbi.nlm.nih.gov/
BLAST/). Only one isolate per patient was further identified
by 16S rRNA gene sequence analysis.
PCR and sequencing
When the 16S rRNA gene sequence analysis was not conclusive, we used Lactobacillus tuf gene-based identification.
Primer sequences Lac_tuf for: AYGGATGGTGC
KATCTTART and Lac_tuf rev: TCAGTGGTGTGG
AAGTAGAA were used. A negative control was introduced
for each assay. The polymerase chain reaction (PCR) program
was: 15 min at 95 °C, followed by 40 cycles of 95 °C for 30 s,
45 s at 55 °C, 72 °C for 1 min using HotStar polymerase
(Qiagen). After analysis on agarose gel electrophoresis, the
PCR products were purified and sequenced by using the
BigDye Terminator 1.1 Cycle Sequencing kit (Applied
Biosystems, Courtaboeuf, France) and the 3130 Genetic
Analyzer (Applied Biosystems). The sequences were analyzed
by the SeqScape program (Applied Biosystems) and their
similarity with previously published sequences was determined
using the online BLAST program at the NCBI and analyzed in
a phylogenetic tree
Statistical analyses
For data comparison, we used EpiInfo version 6.0 software
(Centers for Disease Control and Prevention [CDC], Atlanta,
GA). A p-value <0.05 was considered to be significant.
Literature review
Cases reports related to L. rhamnosus or Lactobacillus GG
were identified through a MEDLINE (http://www.ncbi.nlm.
nih.gov/sites/entrez) search for these terms, which was limited
to the English language. Additional cases were identified from
the references cited in the case reports. An attempt was made
to obtain the original publication in each case. In situations
where the original publication was either unavailable via
interlibrary loan or written in a non-English language, information involving the case was based on information in the
article(s) referenced by the case report.
The following data elements were extracted from each
case: patient age, patient gender, patient comorbidity, type
of Lactobacillus infection, source of Lactobacillus isolate,
species of Lactobacillus recovered, treatment regimen, and
Eur J Clin Microbiol Infect Dis (2012) 31:2469–2480
overall mortality. The case was included in our study if it
contained three or more of these elements. Microbiological
methods used to identify the Lactobacillus organism in each
case were not evaluated. Additionally, the distinction between community-acquired infections and nosocomial
infections was not examined.
Results
Identification methods
PCR methods were chosen as the gold standard. A total of
60 Lactobacillus strains were isolated from 60 patients in
our laboratory between January 2004 and December 2009.
From 2004 to 2008, 48 Lactobacillus spp. were isolated and
50 % (24) of the isolates were identified at the genus level
using the phenotypic API Coryne strip system. In 2009, 12
Lactobacillus spp. were isolated and 41 % (5) of the isolates
were identified at the species level using MALDI-TOF MS.
From this five-year period, all 60 isolates were also identified prospectively using 16S rRNA sequencing: 75 % (44/
60) were identified at the species level. For the five isolates
identified by MALDI-TOF MS, the 16S rRNA results were
consistent in only two of the cases. In 16 cases, the 16S
rRNA sequence analyses could not discriminate between L.
rhamnosus and L. casei. We retrospectively identified these
strains using Lactobacillus tuf gene-based identification, but
only 14 (13L. rhamnosus and one L. casei) were available.
Patient demographics
All patient characteristics are listed in Tables 1 and 2. The
sex ratio was equivalent (M/F029/31) and the patient age
range was 8–92 years. Lactobacillus spp. were isolated from
various sites, but most of the strains (28 strains) were
isolated from blood. The hospitalization departments of the
patients included the critical care unit (14 cases), oncology
(nine cases), and internal medicine (eight cases).
Lactobacillus spp. were associated with other microorganisms in only three cases (5 %) as follows: one ascites fluid
sample, one pleural effusion sample, and one pacemaker
device sample.
Bacteremia
In 2004 and 2005, the blood cultures positive with
Lactobacillus represent 0.06 % (3/3,146) and 0.11 % (4/
3,267) in 2006, 0.14 % (4/3,589) and 0.15 % (5/3,195) in
2007, 0.08 % (3/3,864) in 2008, and 0.24 % in 2009 (9/
3,632). The number of Lactobacillus spp. obtained from
blood cultures in 2009 (9/3,632) increased significantly
compared to the previous five years (15/17,061; p <
2471
Table 1 Patient characteristics and species isolated overall and in
bacteremia
Characteristic
All cases (n060)
Bacteremia (n028)
55.87
8–92
4
57
8–89
1
29
31
16
10
Age (years)
Median
Range
Unknown
Sex
Male
Female
Species
Lactobacillus
Lactobacillus
Lactobacillus
Lactobacillus
Lactobacillus
rhamnosus
gasseri
casei
delbrueckii
fermentum
25
12
7
3
3
16
1
4
1
1
Lactobacillus
Lactobacillus
Lactobacillus
Lactobacillus
Lactobacillus
Lactobacillus
murinus
minutus
plantarum
zeae
reuteri
spp.
3
1
1
2
1
2
0
1
1
1
1
1
0.0004). During this period, only one death associated with
Lactobacillus bacteremia was noted in our study. The most
commonly isolated species were as follows: L. rhamnosus (16
isolates); L. casei (four isolates); L. gasseri, L. delbrueckii, L.
fermentum, L. minutus, L. zeae, L. murinus, L. plantarum, and
L. reuteri (one isolate each).
Most L. rhamnosus isolates, 16/25 (64 %), were obtained
from the blood (Table 3). These patients were hospitalized
in the oncology department (seven patients), the critical care
unit (five patients), and the emergency department (one
patient). The most common predisposing factor was immunosuppressive therapy in patients with cancer (n06), hepatic
or lung transplantation (n02), and skin allograft (n01).
Only one patient was not undergoing immunosuppressive
therapy. In 91 % of cases, the patients had a central venous
catheter (11/12 patients; for four patients, the information
was not available), and the bacteremia was nosocomial in
those cases (Table 3). In nine cases, L. rhamnosus was
isolated from various sites, including the lungs (two cases),
pleural effusion (two cases), as well as sputum, mediastinal
abscess, pharynx, intracardiac device, and bile (one case
each). None of the patients were undergoing probiotic
therapy.
Literature review
We found 45 reported cases of L. rhamnosus infection in the
literature [10–45]. Most occurred in patients with the
Sex
M
M
F
M
M
F
M
M
M
F
M
M
M
M
M
F
M
M
M
M
M
F
M
M
F
F
F
F
F
M
F
Patient
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
63
78
8
89
65
57
43
31
44
16
45
64
84
53
88
33
35
61
57
60
38
64
64
36
67
92
83
Age (years)
Blood culture 2/2
Blood culture 1/3
Bone
Urine
Urine
Urine
Pharynx
Left kidney
Bone abscess
Blood culture 1/3
Renal lithiasis
Lung abscess
Blood culture 1/1
Defibrillator
Eye
Cervical abscess
Blood culture
Bile
Blood culture
Peritoneal abscess
Blood culture
Blood culture 2/2
Blood culture
Pleural effusion
Blood culture
Kidney
Mediastinal abscess
Blood culture
Pleural effusion
Lung abscess
Ascites
Sample
Oncology
Nephrology
Orthopedic surgery
Infectious disease
Internal medicine
Emergency
Oncology
Urology
Orthopedic
Infectious disease
Urology
Critical care unit
Critical care unit
Cardiology
Ophthalmology
ORL
Emergency
Oncology
Urology
Gastroenterology
Emergency
Oncology
Oncology
Thoracic surgery
Oncology
Critical care unit
Thoracic surgery
Internal medicine
Critical care unit
Internal medicine
Critical care unit
Department
BGP vancomycin R
BGP vancomycin R
BGP vancomycin R
BGP vancomycin R
Lactobacillus spp.
Lactobacillus spp.,
Propionibacterium acnes,
Staphylococcus epidermidis
BGP vancomycin R
BGP vancomycin R
BGP vancomycin R
BGP vancomycin R
BGP vancomycin R
BGP vancomycin R
BGP vancomycin R
BGP vancomycin R
Lactobacillus spp.
Lactobacillus spp.
Lactobacillus spp.
Lactobacillus spp.
Lactobacillus spp.
Lactobacillus spp.
Lactobacillus spp.,
Escherichia coli
Lactobacillus spp
Lactobacillus spp.
Lactobacillus spp.
Lactobacillus spp.
Lactobacillus spp.
Lactobacillus spp.
Lactobacillus spp.,
Enterococcus faecalis,
Candida glabrata
BGP vancomycin R
L. gasseri
L. fermentum
Phenotypic identification
Table 2 Patient characteristics and Lactobacillus species isolated overall and by type of infection in the present series
casei
fermentum.
rhamnosus/casei
rhamnosus/casei
rhamnosus/casei
gasseri
rhamnosus/casei
Lactobacillus spp.
L. casei/L. rhamnosus
L. murinus
L. delbrueckii
L. gasseri
L. delbrueckii
L. casei
L. gasseri
L. casei
L. reuteri
L. gasseri
Lactobacillus spp.
Lactobacillus spp.
L. casei/L. rhamnosus
L. gasseri
L. gasseri
L. fermentum
L. murinus
L. casei
L. gasseri
L. rhamnosus/casei
L. casei
L. rhamnosus/casei
Lactobacillus spp.
L.
L.
L.
L.
L.
L.
L.
16S rRNA-based identification
99
99
99
99
99
99
99
99
98
99
99
99
99
99
99
99
99
99
99
99
99
99
99
99
99
99
99
99
99
Similarity (%)
L. rhamnosus
NA
L. rhamnosus
L. rhamnosus
L. rhamnosus
L. rhamnosus
L. rhamnosus
L. rhamnosus
NA
L. rhamnosus
L. casei
L. rhamnosus
tuf gene-based
identification
2472
Eur J Clin Microbiol Infect Dis (2012) 31:2469–2480
M
F
F
F
M
M
F
F
F
M
F
F
F
F
F
M
F
F
M
F
M
F
F
F
M
M
M
F
M
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
71
58
22
54
82
54
83
50
70
69
83
47
8
11
14
49
49
56
66
56
60
62
83
43
84
NA
65
76
90
Age (years)
NA: not available; R resistant
Sex
Patient
Table 2 (continued)
Blood
Blood
Blood
Blood
Blood
Blood
Bile
Blood
Lung
Blood
1/1
2/2
1/2
3/3
2/6
culture 1/1
culture 1/2
culture
culture
culture
culture
culture
culture
Blood culture
Sputum
Sputum
Kidney abscess
Abdominal abscess
Blood culture
Blood culture
ORL
Blood culture
Bone biopsy
Blood culture
Blood culture 1/3
Urine
Blood culture
Blood culture 2
Blood culture
Arm abscess
Urine
Urine
Sample
Urology
Respiratory care unit
Critical care unit
Oncology
Internal medicine
Gastroenterology
Gastroenterology
Oncology
Respiratory care unit
Internal medicine
Oncology
Critical care unit
Oncology
Critical care unit
Gastroenterology
Critical care unit
Critical care unit
ORL
Critical care unit
Orthopedic surgery
Respiratory care unit
Nephrology
Emergency
Critical care unit
Critical care unit
Gastroenterology
Emergency
Emergency
Emergency
Department
spp.
spp.
spp.
spp.
No
No
No
No
No
No
No
No
No
No
identification
identification
identification
identification
identification
identification
identification
identification
identification
identification
No identification
Lactobacillus spp.
Lactobacillus spp.
Lactobacillus spp.
L. casei
L. casei
L. casei
Lactobacillus
Lactobacillus
Lactobacillus
Lactobacillus
BGP vancomycin R
Lactobacillus spp.
Lactobacillus spp.
Lactobacillus spp.
Lactobacillus spp.
Lactobacillus spp.
Lactobacillus spp.
Lactobacillus spp.
Phenotypic identification
zeae
gasseri
rhamnosus/casei
rhamnosus/casei
rhamnosus
gasseri
gasseri
gasseri
L.
L.
L.
L.
L.
L.
L.
L.
L.
L.
delbrueckii
rhamnosus
rhamnosus
rhamnosus
minutus
gasseri
rhamnosus
rhamnosus
rhamnosus
plantarum
L. rhamnosus
L. rhamnosus
L. casei/rhamnosus
L. murinus
Lactobacillus spp. (rhamnosus
casei zeae)
L. fermentum
L. rhamnosus
L. casei
L. zeae
L. rhamnosus
L. rhamnosus
L.
L.
L.
L.
L.
L.
L.
L.
16S rRNA-based identification
99.7
99.9
99.9
99.6
99.2
99.9
100
99.9
99.9
99.9
99
100
99
99
99
99.9
100
100
99
99
99
99
99
100
99
99
99
100
99
Similarity (%)
L. rhamnosus
L. rhamnosus
L. rhamnosus
L. rhamnosus
tuf gene-based
identification
Eur J Clin Microbiol Infect Dis (2012) 31:2469–2480
2473
M
M
F
M
F
F
M
M
F
F
F
M
M
F
F
M
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
50
54
83
58
22
49
49
60
8
43
84
16
35
57
Age
(years)
NA: not available
Sex
Patient
culture 2/2
culture
culture 2/2
culture
culture
culture
Blood culture 1/2
Blood culture 1/1
Blood culture
Blood culture 2/6
Blood culture
Blood culture
Blood culture
Blood
Blood
Blood
Blood
Blood
Blood
Blood culture 1/1
Blood culture
Blood culture
Sample
Oncology
Oncology
Oncology
Respiratory care unit
Critical care unit
Critical care unit
Critical care unit
Oncology
Critical care unit
Critical care unit
Gastroenterology
Critical care unit
Respiratory care unit
Critical care unit
Emergency
Oncology
Department
Yes
No
No
No
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Nosocomial
Radiotherapy,
chemotherapy
Chemotherapy
No
Chemotherapy
Immunosuppressor
Na
Immunosuppressor
Chemotherapy
Na
No
Yes
Yes
No
No
Na
Radiotherapy and
chemotherapy
Immunosuppression
Table 3 Data of patients with Lactobacillus rhamnosus bacteremia in the present series
Fracture and luxation of
dorsal rachis
Medulloblastoma
Na
Skin allograft, burn
Gastric carcinoma
Pharynx carcinoma
Dilatation of bronchi,
pulmonary fibrosis
Na
Hepatic transplantation
amylose
Gastric adenocarcinoma
Pleural mesothelioma
Pulmonary transplantation,
cystic fibrosis
Jugular epidermoid
carcinoma
Laryngeal epidermoid
carcinoma
Na
Lingual epidermoid
carcinoma
Predisposing factors
Central
Central
Central
Central
Central
Na
Na
Central
Na
Central
Central
Central
No
Central
Na
Central
Intravenous
catheter
Amoxicillin acid, clavulanic
acid, metronidazole, and
piperacillin + tazobactam
Na
Ceftriaxone and ciprofloxacin
Na
Na
Na
NA
Vancomycin and ceftazidime
Na
Na
Na
Na
Na
Na
Amoxicillin acid, clavulanic
acid, ciprofloxacin,
fluconazole, and vancomycin
Na
Antibiotics
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Death
2474
Eur J Clin Microbiol Infect Dis (2012) 31:2469–2480
Eur J Clin Microbiol Infect Dis (2012) 31:2469–2480
following clinical characteristics: immunosuppressive therapy (n02), dialysis (n02), transplantation (n04), cancer
(n08), diabetes mellitus (n03), intravenous catheters (n0
12), prior surgical interventions (n02), and prior digestive
disorders (n08) (Table 4). Seventeen patients without any
predisposing factors other than valvulopathy had L. rhamnosus infectious endocarditis. Six patients were undergoing
probiotic therapy. Antibiotics treatment was available for 21
patients. The antibiotic treatment was appropriate in 17
cases including aminopenicillin or carbapenem.
Discussion
Lactobacillus spp. are commensals found in the gastrointestinal tract, oral cavity, and female urogenital tract [46]. L.
rhamnosus specifically belongs to the normal flora of
healthy human rectal, oral [46], and vaginal mucosa [47].
Because most (47 %) Lactobacillus spp. were isolated from
blood cultures, our findings confirm prior observations that
Lactobacillus spp. can exhibit virulence. These species are
used worldwide as starter cultures for the production of
fermented dairy products, and they are among the most
commonly used microorganisms in probiotics for human
consumption. For example, the L. rhamnosus strain, GG,
has been used in the treatment of infantile diarrhea [48],
antibiotic-associated diarrhea [49], and candidal vaginitis
[50]. Lactobacillus spp. are occasionally found in human
clinical infections and are often considered as contaminants
or opportunistic pathogens. Because of their low significance and special growth requirements, they are often overlooked or incorrectly identified. Furthermore, identification
at the species level is often difficult. Over the past decade,
the taxonomy has changed, and accurate Lactobacillus spp.
identification has, indeed, improved with molecular analysis
[51]. However, in most microbiology laboratories, commercial systems routinely employed for Gram-positive rod identification are inefficient, and only 30–50 % of isolates are
correctly identified [5]. In the current study, we identified
only 41 % of the isolates using this routine method. In
contrast, all of the isolates in the present study were identified at the species level using molecular techniques: 76 % of
the isolates were identified using 16S rRNA gene sequencing and 100 % by tuf gene-based identification.
At our institution, Lactobacillus spp. have been uncommon clinical isolates, identified in only 0.05 % of positive
blood cultures; over a five-year period, the identification of
the most common species, L. rhamnosus, increased from 11/
17,068 cases to 5/3,632 cases, which represents an increase
to 0.13 % of the positive blood cultures. The increase of
Lactobacillus spp. isolates over this five-year period may be
due to the recent incorporation of a more sensitive molecular
technique. The most common species recovered in our study
2475
were L. rhamnosus (41 %), L. gasseri (19 %), and L. casei
(11 %). Cannon et al. [4] reported 200 cases of
Lactobacillus spp.-associated infections from 1950 to
2003. Similar to our study and other works, L. casei
(35.7 %) and L. rhamnosus (22.9 %) were the most commonly isolated species from blood cultures. Additionally,
the antimicrobial pressure link to the increased use of vancomycin to which lactobacilli are resistant may also have
contributed to the increasing isolation of colonizing lactobacilli. Several reports of L. rhamnosus-related infectious
endocarditis [52, 53] and bacteremia have been published
[5, 54–56]. In one review [4], 73 cases of infectious endocarditis were reported. L. rhamnosus and L. casei were the
most commonly isolated species in these cases. We received
267 endocarditis cases during this five-year period; despite
the fact that we are a referral center for the diagnosis of
infectious endocarditis, bacteremia was more common than
infectious endocarditis (16 vs. 1) in the current study. We
found 45 cases of L. rhamnosus infections reported in the
literature (Table 4). Upon combining published data with
those from our study, we identified 47 patients with L.
rhamnosus bacteremia without infectious endocarditis.
Thirty-one (66 %) presented with cancer leukemia or had
received a transplant, and 33/40 (82 %) may have had a
possible catheter-related infection. Indeed, the 18 patients
with L. rhamnosus infectious endocarditis had significantly
fewer catheters (2/17) and did not undergo immunosuppression (0/16; p<0.004). In contrast, underlying cardiac lesions
(12/17, 68 %) were more common in infectious endocarditis
compared to isolated bacteremia (1/50, 2 %; p<0.0004). In
patients with L. rhamnosus-associated bacteremia, the
reported mortality rate ranges from 12 to 48 %, depending
on the treatment [54, 55]. Documented cases of systemic
infections associated with the consumption of lactic acid
bacteria are extremely rare, with only nine published cases.
However, adverse events are not systematically assessed or
reported in clinical trials [57]. Probiotic supplementation
must be used with caution in individuals who may be at
risk for the dissemination of these live microbes to sterile
sites [58].
In conclusion, the frequency of L. rhamnosus isolation
from blood cultures has increased. Because few cases of L.
rhamnosus bacteremia are reported in the literature compared to infectious endocarditis, we suspect that bacteremia
cases are underestimated compared to L. rhamnosus-associated infectious endocarditis. We believe that, when >50
cases of infection are reportedly caused by a specific species, it is useful to describe its clinical entity. The increase in
reported Lactobacillus spp.-associated infections raises
questions. It is important for clinicians to be aware of the
potential risks of microbial therapy. Our analysis found that
L. rhamnosus bacteremia occurs in 66 % of immunosuppressed patients and 82.5 % of catheterized patients.
Age
(years*)
79
74
74
5
57
35
70
59
32
61
73
56
73
Na
Na
Na
Na
Na
43
73
42
6
Na
11 months
Case
report
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Na
Na
F
F
F
M
Na
Na
Na
Na
Na
F
M
M
F
M
M
F
F
M
M
F
M
M
Sex
Blood culture
Blood culture
Blood culture
Blood culture,
central line
Blood culture
Blood culture
Blood culture
Blood culture
Blood culture,
bronchoalveolar
lavage
Blood culture
Blood culture
Purulent material
Pleural fluid
Sputum
Sputum
Lymph nodes
Peritoneal fluid
Angiocholitis pus,
blood culture
Pericardial effusion
and blood culture
Peritoneal dialysate
culture
Pus
Blood culture, pus,
and gallbladder
Pus
Pleural effusion
Sample
Short gut syndrome, probiotic
therapy
Sjogren’s syndrome,
immunosuppressive therapy,
diarrhea with vancomycin
Cerebral palsy, microcephaly,
mental retardation,
gastrojejunostomy feeding,
probiotic therapy for diarrhea
Depressed immune status
Diabetes mellitus
Ulcerative colitis
Acute leukemia, concomitant
popular skin rash
Acute leukemia, concomitant
popular skin rash
Lung transplantation
Gore-Tex patch in the inferior
vena cava
Acute leukemia
Double lung transplantation
HIV
Chronic cholecystis
Emphysema
Myeloid leukemia
Continuous ambulatory
peritoneal dialysis
Erysipeloid
Hypothyroid
Na
Tonsillar carcinoma, Mirizzi
syndrome, diabetes mellitus
Drinks containing L.
rhamnosus GG
Bone marrow transplant
for aplastic anemia
Peritoneal dialysis
COPD, diabetes mellitus
Predisposing factor
Yes
No
No
Yes
Na
Yes
No
Na
Na
Na
Yes
Yes
Yes
Yes
Na
Na
Yes
No
Na
Yes
No
No
Yes
Yes
No
No
No
Intravenous
catheter
No
No
No
Yes
Yes
Yes
Na
No
No
Yes
No
No
No
No
No
Yes
No
Yes
No
Neoplasia
Catheter-related bacteremia
Bacteremia
Bacteremia
Na
Liver abscess and bacteremia
Bacteremia
Catheter-related bacteremia
Bacteremia
Bacteremia
Bacteremia, pneumonia
Bacteremia
Empyema gallbladder
Pleuritis
Pneumonia/lung abscess
Chest infection
Adenitis
Peritonitis
Pancreatitis with necrosis and
bacteremia
Angiocholitis and pancreatitis
Peritonitis
Pericardial infection
Liver abscesses
Liver abscess
Lung abscess pleuritis
Diagnosis
Ampicillin + gentamicin
Na
Vancomycin + ceftazidime,
amoxicillin
Tazocin + gentamicin +
vancomycin, rifampicin
Ampicillin + gentamicin
Na
Na
Imipenem + erythromycin
Imipenem + erythromycin
Imipenem + erythromycin
Na
Na
Ampicillin sulbactam
Na
Na
Ciprofloxacin + imipenem,
amoxicillin + rifampicin,
surgery
Amoxicillin + clavulanic
acid, ofloxacin,
amoxicillin + rifampicin,
surgery
Vancomycin, gentamicin,
erythromycin
Na
Na
Na
Na
Na
Na
Treatment
Table 4 Review of reported clinical cases involving Lactobacillus rhamnosus infection and data from patients with L. rhamnosus infection in the present series
[15]
Cured
Na
Na
Cured
Died
Cured
Na
Na
Na
Na
Na
Na
Cured
Na
Na
Died
[28]
[27]
[26]
[25]
[24]
[23]
[22]
[21]
[21]
[21]
[20]
[42]
[44]
[19]
[18]
[17]
[16]
[15]
Cured
Cured
[12]
[11]
[10]
[9]
[8]
Reference
Cured
Cured
Cured
Cured
Cured
Outcome
2476
Eur J Clin Microbiol Infect Dis (2012) 31:2469–2480
36 weeks’
gestation
34 week’
gestation
25
Na
14
35
57
16
8
43
84
49
49
83
58
22
54
50
66
Na
60
66
29
73
65
6 weeks old
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
26
Age
(years*)
Case
report
Table 4 (continued)
M
M
M
M
M
F
M
M
M
F
F
M
M
F
F
F
F
M
F
M
M
Na
Na
M
M
Sex
Blood culture
Blood culture
Blood culture
Blood culture
Blood culture
Blood culture
Blood culture
Blood culture
Blood culture
Blood culture
Blood culture
Blood culture
Blood culture
Blood culture
Blood culture
Blood culture 2
Blood culture
Blood culture 2/2
Blood culture 1/1
Blood culture
Blood culture
Blood culture,
cerebrospinal
fluid
Blood cultures
Blood culture
Blood culture
Sample
Double outlet right ventricle
and pulmonary stenosis
Dairy product consumption,
colonoscopy
Prosthetic aortic valve
Prolapse of mitral valve
Mitral
Dilatation of bronchi
Pharynx carcinoma
Pleural mesothelioma,
chemotherapy
Pulmonary transplantation,
cystic fibrosis,
immunosuppressor
Jugular epidermoid carcinoma,
chemotherapy
Laryngeal epidermoid
carcinoma, radiotherapy,
chemotherapy
Gastric neoplasia
Hepatic transplantation,
amylase, immunosuppressor
Gastric adenocarcinoma
Na
Skin allograft, burn
Na
Fracture and luxation of dorsal
rachis
Medulloblastoma, chemotherapy
Lingual epidermoid carcinoma,
radiotherapy and chemotherapy
Na
Parenteral nutrition short residual
intestine, probiotic therapy
Gastroschisis, gastrostomy,
and jejunostomy for bowel
infarction
Allogeneic hematopoietic stem
cell transplantation for acute
leukemia
Acute myeloid leukemia
Predisposing factor
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Na
No
No
Na
Na
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Na
Na
Yes
Na
Yes
Yes
Yes
Yes
Yes
No
Na
Yes
Na
Yes
No
Yes
No
Na
Yes
Yes
Yes
Na
Na
Intravenous
catheter
Na
Na
Neoplasia
Endocarditis
Endocarditis
Endocarditis
Endocarditis
Endocarditis
Bacteremia
Bacteremia
Bacteremia
Bacteremia
Bacteremia
Bacteremia
Bacteremia
Bacteremia
Bacteremia
Bacteremia
Bacteremia
Bacteremia
Bacteremia
Bacteremia
Bacteremia
Bacteremia
Bacteremia
Meningitis
Bacteremia
Bacteremia
Diagnosis
Vancomycin + gentamicin,
amoxicillin + rifampicin,
surgery
Penicillin + gentamicin and
ceftriaxone + clindamycin
+ ciprofloxacin
Penicillin G + gentamicin
Penicillin + gentamicin, surgery
Na
Na
Na
Amoxicillin, clavulanic acid,
metronidazole, and
piperacillin + tazobactam
Na
Na
Na
Na
Ceftriaxone and ciprofloxacin
NA
Na
Na
Na
Vancomycin and ceftazidime
Amoxicillin, clavulanic acid,
ciprofloxacin, fluconazole,
and vancomycin
Na
Na
Na
Na
Ceftriaxone ampicillin
Ceftriaxone ampicillin
Treatment
[26]
[35]
Cured
Cured
[34]
[33]
[32]
Present report
Present report
Cured
Cured
Na
Cured
Cured
Present report
Present report
Cured
Cured
Present report
Present report
Present report
Present report
Present report
Present report
Present report
Present report
Present report
Present report
Present report
Present report
[31]
[30]
[29]
[29]
Reference
Cured
Cured
Cured
Cured
Cured
Cured
Cured
Cured
Cured
Cured
Cured
Cured
Cured
Na
Na
Na
Outcome
Eur J Clin Microbiol Infect Dis (2012) 31:2469–2480
2477
Na
31
17
36
6
7
68
71
45
Na
67
24
50
51
52
53
54
55
56
57
58
59
60
61
F
M
M
M
F
M
F
F
F
F
M
Na
Sex
Na: not available
*Unless otherwise stated
Age
(years*)
Case
report
Table 4 (continued)
Blood culture
Blood culture
Blood culture
Blood culture
Pus covering
aortic graft
Blood culture
Blood culture
Blood culture
Blood culture
Blood culture
Blood culture
Blood culture
Sample
Probiotic tabs consumption,
mitral regurgitation
Aortic mechanical valve
previous1 endocarditis,
probiotic tabs with
L. rhamnosus
Probiotic yogurt consumption
Heart disease aortic valve
replaced
Dental surgery
Severe atrial disease
Tricuspid atresia carious teeth
Coarctation of the aorta +
aneurysm aorta ascending
Tooth extraction
Marfan syndrome
No
Yes
No
Na
Na
Na
Na
No
No
Na
Na
Na
Na
Intravenous
catheter
No
No
No
No
No
No
No
No
No
No
Na
surgery, probiotic therapy
for diarrhea
Na
Rheumatic valvular disease
Neoplasia
Predisposing factor
Endocarditis
Endocarditis
and septic
arthritis
Endocarditis
Endocarditis
Endocarditis
Endocarditis
Endocarditis
Endocarditis
Endocarditis
Endocarditis
Endocarditis
Endocarditis
Diagnosis
Na
Ampicillin + gentamicin
Na
Na
Na
Na
Na
Na
Na
Na
Na
Na
Treatment
Cured
Cured
Na
Na
Na
Na
Na
Na
Cured
Cured
Cured
Na
Outcome
[45]
[43]
[41]
[40]
[39]
[39]
[38]
[37]
[17]
[17]
[17]
[36]
Reference
2478
Eur J Clin Microbiol Infect Dis (2012) 31:2469–2480
Eur J Clin Microbiol Infect Dis (2012) 31:2469–2480
References
1. Janda JM, Abbott SL (2007) 16S rRNA gene sequencing for
bacterial identification in the diagnostic laboratory: pluses, perils,
and pitfalls. J Clin Microbiol 45:2761–2764
2. Seng P, Drancourt M, Gouriet F et al (2009) Ongoing revolution in
bacteriology: routine identification of bacteria by matrix-assisted
laser desorption ionization time-of-flight mass spectrometry. Clin
Infect Dis 49:543–551
3. Seng P, Rolain JM, Fournier PE et al (2010) MALDI-TOF-mass
spectrometry applications in clinical microbiology. Future Microbiol 5:1733–1754
4. Cannon JP, Lee TA, Bolanos JT et al (2005) Pathogenic relevance
of Lactobacillus: a retrospective review of over 200 cases. Eur J
Clin Microbiol Infect Dis 24:31–40
5. Salminen MK, Tynkkynen S, Rautelin H et al (2002) Lactobacillus
bacteremia during a rapid increase in probiotic use of Lactobacillus rhamnosus GG in Finland. Clin Infect Dis 35:1155–1160
6. Adams MR, Marteau P (1995) On the safety of lactic acid bacteria
from food. Int J Food Microbiol 27:263–264
7. Bernardeau M, Vernoux JP, Henri-Dubernet S et al (2008) Safety
assessment of dairy microorganisms: the Lactobacillus genus. Int J
Food Microbiol 126:278–285
8. Besselink MG, van Santvoort HC, Buskens E et al (2008) Probiotic prophylaxis in predicted severe acute pancreatitis: a randomised, double-blind, placebo-controlled trial. Lancet 371:651–659
9. Drancourt M, Bollet C, Carlioz A et al (2000) 16S ribosomal DNA
sequence analysis of a large collection of environmental and clinical unidentifiable bacterial isolates. J Clin Microbiol 38:3623–
3630
10. Shoji H, Yoshida K, Niki Y (2010) Lung abscess and pleuritis
caused by Lactobacillus rhamnosus in an immunocompetent patient. J Infect Chemother 16:45–48
11. Chan JF, Lau SK, Woo PC et al (2010) Lactobacillus rhamnosus
hepatic abscess associated with Mirizzi syndrome: a case report
and review of the literature. Diagn Microbiol Infect Dis 66:94–97
12. Rautio M, Jousimies-Somer H, Kauma H et al (1999) Liver abscess due to a Lactobacillus rhamnosus strain indistinguishable
from L. rhamnosus strain GG. Clin Infect Dis 28:1159–1160
13. Kalima P, Masterton RG, Roddie PH et al (1996) Lactobacillus
rhamnosus infection in a child following bone marrow transplant. J
Infect 32:165–167
14. Klein G, Zill E, Schindler R et al (1998) Peritonitis associated with
vancomycin-resistant Lactobacillus rhamnosus in a continuous
ambulatory peritoneal dialysis patient: organism identification,
antibiotic therapy, and case report. J Clin Microbiol 36:1781–1783
15. Brahimi M, Mathern P, Fascia P et al (2008) Two cases of Lactobacillus rhamnosus infection and pancreatitis. Med Mal Infect
38:29–31
16. Sanyal D, Bhandari S (1992) CAPD peritonitis caused by Lactobacillus rhamnosus. J Hosp Infect 22:325–327
17. Sharpe ME, Hill LR, Lapage SP (1973) Pathogenic lactobacilli. J
Med Microbiol 6:281–286
18. Rahman M (1982) Chest infection caused by Lactobacillus casei
ss rhamnosus. Br Med J (Clin Res Ed) 284:471–472
19. Namnyak SS, Blair AL, Hughes DF et al (1992) Fatal lung abscess
due to Lactobacillus casei ss rhamnosus. Thorax 47:666–667
20. Jureen R, Søndenaa K, Høiby EA et al (2002) Lactobacillus
rhamnosus septicaemia in a patient with a graft in the inferior vena
cava. Scand J Infect Dis 34:135–136
21. Chomarat M, Espinouse D (1991) Lactobacillus rhamnosus septicemia in patients with prolonged aplasia receiving ceftazidime–
vancomycin. Eur J Clin Microbiol Infect Dis 10:44
2479
22. Carretto E, Barbarini D, Marzani FC et al (2001) Catheter-related
bacteremia due to Lactobacillus rhamnosus in a single-lung transplant recipient. Scand J Infect Dis 33:780–782
23. Farina C, Arosio M, Mangia M et al (2001) Lactobacillus casei
subsp. rhamnosus sepsis in a patient with ulcerative colitis. J Clin
Gastroenterol 33:251–252
24. Notario R, Leardini N, Borda N et al (2003) Hepatic abscess and
bacteremia due to Lactobacillus rhamnosus. Rev Argent Microbiol
35:100–101
25. MacGregor G, Smith AJ, Thakker B et al (2002) Yoghurt biotherapy: contraindicated in immunosuppressed patients? Postgrad Med
J 78:366–367
26. Land MH, Rouster-Stevens K, Woods CR et al (2005) Lactobacillus
sepsis associated with probiotic therapy. Pediatrics 115:178–181
27. Arpi M, Vancanneyt M, Swings J et al (2003) Six cases of Lactobacillus bacteraemia: identification of organisms and antibiotic
susceptibility and therapy. Scand J Infect Dis 35:404–408
28. De Groote MA, Frank DN, Dowell E et al (2005) Lactobacillus
rhamnosus GG bacteremia associated with probiotic use in a child
with short gut syndrome. Pediatr Infect Dis J 24:278–280
29. Kunz AN, Noel JM, Fairchok MP (2004) Two cases of Lactobacillus bacteremia during probiotic treatment of short gut syndrome.
J Pediatr Gastroenterol Nutr 38:457–458
30. Robin F, Paillard C, Marchandin H et al (2010) Lactobacillus
rhamnosus meningitis following recurrent episodes of bacteremia
in a child undergoing allogeneic hematopoietic stem cell transplantation. J Clin Microbiol 48:4317–4319
31. Majcher-Peszynska J, Heine W, Richter I et al (1999) Persistent
Lactobacillus casei subspecies rhamnosus bacteremia in a 14 year
old girl with acute myeloid leukemia. A case report. Klin Padiatr
211:53–56
32. Naudé WD, Swanepoel A, Böhmer RH et al (1988) Endocarditis
caused by Lactobacillus casei subspecies rhamnosus. A case report. S Afr Med J 73:612–614
33. Monterisi A, Dain AA, Suárez de Basnec MC et al (1996) Nativevalve endocarditis produced by Lactobacillus casei sub. rhamnosus refractory to antimicrobial therapy. Medicina (B Aires)
56:284–286
34. Wallet F, Dessein R, Armand S et al (2002) Molecular diagnosis of
endocarditis due to Lactobacillus casei subsp. rhamnosus. Clin
Infect Dis 35:e117–e119
35. Avlami A, Kordossis T, Vrizidis N et al (2001) Lactobacillus
rhamnosus endocarditis complicating colonoscopy. J Infect
42:283–285
36. Golledge C (1988) Vancomycin resistant lactobacilli. J Hosp Infect
11:292
37. Fritsche D, Mennicken U, Vielhaber K (1973) Endocarditis caused
by diphtheroids and lactobacilli (author’s transl). Dtsch Med
Wochenschr 98:2239–2242
38. Tornos MP, Perez-Soler R, Fernandez-Perez R (1980) Lactobacillus casei endocarditis in tricuspid atresia. Chest 77:713
39. Holliman RE, Bone GP (1988) Vancomycin resistance of clinical
isolates of lactobacilli. J Infect 16:279–283
40. Griffiths JK, Daly JS, Dodge RA (1992) Two cases of endocarditis
due to Lactobacillus species: antimicrobial susceptibility, review,
and discussion of therapy. Clin Infect Dis 15:250–255
41. Presterl E, Kneifel W, Mayer HK et al (2001) Endocarditis by
Lactobacillus rhamnosus due to yogurt ingestion? Scand J Infect
Dis 33:710–714
42. Allison D, Galloway A (1988) Empyema of the gall-bladder due to
Lactobacillus casei. J Infect 17:191
43. Mackay AD, Taylor MB, Kibbler CC et al (1999) Lactobacillus
endocarditis caused by a probiotic organism. Clin Microbiol Infect
5:290–292
2480
44. Luong ML, Sareyyupoglu B, Nguyen MH et al (2010) Lactobacillus probiotic use in cardiothoracic transplant recipients: a link to
invasive Lactobacillus infection? Transpl Infect Dis 12:561–564
45. Kochan P, Chmielarczyk A, Szymaniak L et al (2011) Lactobacillus rhamnosus administration causes sepsis in a cardiosurgical
patient—is the time right to revise probiotic safety guidelines?
Clin Microbiol Infect 17:1589–1592
46. Ahrné S, Nobaek S, Jeppsson B et al (1998) The normal Lactobacillus flora of healthy human rectal and oral mucosa. J Appl
Microbiol 85:88–94
47. Kiss H, Kögler B, Petricevic L et al (2007) Vaginal Lactobacillus
microbiota of healthy women in the late first trimester of pregnancy. BJOG 114:1402–1407
48. Szajewska H, Kotowska M, Mrukowicz JZ et al (2001) Efficacy of
Lactobacillus GG in prevention of nosocomial diarrhea in infants.
J Pediatr 138:361–365
49. Biller JA, Katz AJ, Flores AF et al (1995) Treatment of recurrent v
colitis with Lactobacillus GG. J Pediatr Gastroenterol Nutr
21:224–226
50. Falagas ME, Betsi GI, Athanasiou S (2007) Probiotics for the
treatment of women with bacterial vaginosis. Clin Microbiol Infect
13:657–664
51. Sheu SJ, Hwang WZ, Chen HC et al (2009) Development and use of
tuf gene-based primers for the multiplex PCR detection of
Eur J Clin Microbiol Infect Dis (2012) 31:2469–2480
52.
53.
54.
55.
56.
57.
58.
Lactobacillus acidophilus, Lactobacillus casei group, Lactobacillus
delbrueckii, and Bifidobacterium longum in commercial dairy products. J Food Prot 72:93–100
Salvana EM, Frank M (2006) Lactobacillus endocarditis: case
report and review of cases reported since 1992. J Infect 53:e5–e10
Yagi S, Akaike M, Fujimura M et al (2008) Infective endocarditis
caused by Lactobacillus. Intern Med 47:1113–1116
Salminen MK, Rautelin H, Tynkkynen S et al (2004) Lactobacillus
bacteremia, clinical significance, and patient outcome, with special
focus on probiotic L. rhamnosus GG. Clin Infect Dis 38:62–69
Salminen MK, Rautelin H, Tynkkynen S et al (2006) Lactobacillus
bacteremia, species identification, and antimicrobial susceptibility
of 85 blood isolates. Clin Infect Dis 42:e35–e44
Ouwehand AC, Saxelin M, Salminen S (2004) Phenotypic differences between commercial Lactobacillus rhamnosus GG and L.
rhamnosus strains recovered from blood. Clin Infect Dis 39:1858–
1860
Hempel S, Newberry S, Ruelaz A, Wang Z, Miles JNV, Suttorp
MJ, Johnsen B, Shanman R, Slusser W, Fu N, Smith A, Roth B,
Polak J, Motala A, Perry T, Shekelle PG (2011) Safety of probiotics to reduce risk and prevent or treat disease. Agency for
Healthcare Research and Quality (US), Rockville
Sanders ME, Akkermans LM, Haller D et al (2010) Safety assessment of probiotics for human use. Gut Microbes 1:164–185
Article X :
Occam's razor and probiotics activity on Listeria
monocytogenes
Matthieu Million, Emmanouil Angelakis, Fatima Drissi, Didier Raoult
Published in Proc Natl Acad Sci U S A. 2013 Jan 2;110(1):E1. (IF 9.77)
102
LETTER
Occam’s razor and probiotics activity
on Listeria monocytogenes
We read with interest the article by Archambaud et al. (1) on
Lactobacillus casei BL23 and Lactobacillus paracasei CNCM
I-3689, which were able to limit the Listeria monocytogenes
dissemination in a gnotobiotic humanized mouse model. The
authors suggested that changes in the expression of IFN-stimulated genes and of mi-RNA, together with the L. monocytogenes
metabolism redirection by Lactobacillus strains, may explain the
modulation of the infection. However, according to the Occam’s
razor principle postulating that a simpler explanation is more
likely to be true, we believe that the role of bacteriocins is critical
in this instance.
Bacteriocins have been used as bio-preservatives, especially
against L. monocytogenes contamination in vegetable food
matrices, for ∼20 y. The ability of Lactobacillus to inhibit
pathogens in vitro is well documented. In one study, all of the
L. casei and L. paracasei strains inhibited L. monocytogenes
growth (2). The Lactocin 705 produced by L. casei CRL705 is
bacteriostatic on L. monocytogenes (3), whereas a strain of
L. paracasei subsp. paracasei has been shown to produce another
substance inhibiting L. monocytogenes growth and leading to
cellular lysis (4). Using the Bagel and Bactibase bacteriocin
databases to analyze the strains used in this study, we were able
to find one prebacteriocin (GenBank ID: YP_001988475) in
www.pnas.org/cgi/doi/10.1073/pnas.1218418110
L. casei BL23, and one Lactocin-705 (GenBank: LC70_LACPA)
among available L. paracasei genomes.
Taken together, these data suggest that the impact of a
Lactobacillus strain on the microbiota flora is mainly determined
by its direct antibiotic activities, including bacteriocins, as
recently rediscovered (5).
Matthieu Million1, Emmanouil Angelakis1, Fatima Drissi, and
Didier Raoult2
Unité de Recherche sur les Maladies Infectieuses et Tropicales
Emergentes, Unité Mixte de Recherche (UMR) Centre National de
la Recherche Scientifique (CNRS) 7278, Institut de Recherche pour
le Développement (IRD) 198, Institut National de la Santé et de la
Recherche Médicale (INSERM) 1095, Faculté de Médecine, Aix
Marseille Université, 13005 Marseille, France
1. Archambaud C, et al. (2012) Impact of lactobacilli on orally acquired listeriosis. Proc
Natl Acad Sci USA 109(41):16684–16689.
2. Jacobsen CN, et al. (1999) Screening of probiotic activities of forty-seven strains of
Lactobacillus spp. by in vitro techniques and evaluation of the colonization ability of
five selected strains in humans. Appl Environ Microbiol 65(11):4949–4956.
3. Vignolo G, Fadda S, de Kairuz MN, de Ruiz Holgado AA, Oliver G (1996) Control of
Listeria monocytogenes in ground beef by ‘Lactocin 705’, a bacteriocin produced by
Lactobacillus casei CRL 705). Int J Food Microbiol 29(2-3):397–402.
4. Bendali F, Gaillard-Martinie B, Hebraud M, Sadoun D (2008) Kinetic of production and
mode of action of the Lactobacillus paracasei subsp. paracasei anti-listerial bacteriocin,
an Algerian isolate. LWT–Food Science and Technology 41(10):1784–1792.
5. Kim HB, et al. (2012) Microbial shifts in the swine distal gut in response to the treatment
with antimicrobial growth promoter, tylosin. Proc Natl Acad Sci USA 109(38):15485–15490.
Author contributions: D.R. designed research; M.M., E.A., and F.D. performed research;
and M.M., E.A., and D.R. wrote the paper.
The authors declare no conflict of interest.
1
M.M. and E.A. contributed equally to this work.
2
To whom correspondence should be addressed. E-mail: [email protected].
PNAS | January 2, 2013 | vol. 110 | no. 1 | E1
Article XI : REVIEW
Human gut microbiota: repertoire and variations
Jean-Christophe Lagier, Matthieu Million, Perrine Hugon, Fabrice
Armougom, Didier Raoult
Published in Front Cell Infect Microbiol. 2012;2:136 (IF: ND)
104
REVIEW ARTICLE
published: 02 November 2012
doi: 10.3389/fcimb.2012.00136
CELLULAR AND INFECTION MICROBIOLOGY
Human gut microbiota: repertoire and variations
Jean-Christophe Lagier , Matthieu Million, Perrine Hugon, Fabrice Armougom and Didier Raoult *
URMITE, UM63, CNRS 7278, L’Institut de Recherche pour le Développement 198, INSERM 1095, Aix-Marseille Université, Marseille, France
Edited by:
Lorenza Putignani, Children’s Hospital
and Research Institute Bambino
Gesù, Italy
Reviewed by:
Nikhil Thomas, Dalhousie University,
Canada
Jun Lin, The University of Tennessee,
USA
*Correspondence:
Didier Raoult , URMITE, UMR CNRS
7278, L’Institut de Recherche pour le
Développement 198, INSERM U1095,
Faculté de Médecine, Aix-Marseille
Université, 27 Boulevard Jean Moulin,
13385 Marseille Cedex 5, France.
e-mail: [email protected]
The composition of human gut microbiota and their relationship with the host and, consequently, with human health and disease, presents several challenges to microbiologists.
Originally dominated by culture-dependent methods for exploring this ecosystem, the
advent of molecular tools has revolutionized our ability to investigate these relationships.
However, many biases that have led to contradictory results have been identified. Microbial
culturomics, a recent concept based on a use of several culture conditions with identification by MALDI-TOF followed by the genome sequencing of the new species cultured had
allowed a complementarity with metagenomics. Culturomics allowed to isolate 31 new
bacterial species, the largest human virus, the largest bacteria, and the largest Archaea
from human. Moreover, some members of this ecosystem, such as Eukaryotes, giant
viruses, Archaea, and Planctomycetes, have been neglected by the majority of studies.
In addition, numerous factors, such as age, geographic provenance, dietary habits, antibiotics, or probiotics, can influence the composition of the microbiota. Finally, in addition
to the countless biases associated with the study techniques, a considerable limitation to
the interpretation of studies of human gut microbiota is associated with funding sources
and transparency disclosures. In the future, studies independent of food industry funding
and using complementary methods from a broad range of both culture-based and molecular tools will increase our knowledge of the repertoire of this complex ecosystem and
host-microbiota mutualism.
Keywords: gut microbiota, culturomics, metagenomics, archaea, transparency disclosures, antibiotics
INTRODUCTION
The exhaustive description of human microbiota and their relationship with health and disease are major challenges in the
twenty-first century (Turnbaugh et al., 2007). To assess the importance of this challenge, we used the ISI Web of Knowledge to
demonstrate the dramatically renewed interest of scientists in this
subject. To extend the chart presented by Sekirov et al. (2010);
Marchesi (2011), which lists the number of publications per year
involving human gut microbiota, we found that in 2011, there
were more than 4 times as many citations referencing human
gut microbiota than in 2005 (Figure 1A), when Eckburg et al.
(2005) published the seminal large-scale gut metagenomics study.
In addition, in 2011, there were approximately as many published
items investigating human gut microbiota than during the 10 years
between 1993 and 2002 (Figure 1B).
The human gut microbiota is composed of approximately
1011–12 microorganisms per gram of content, including diverse
populations of bacteria, mainly anaerobes (95% of the total),
which is 10 times higher than the total number of human cells (Ley
et al., 2006a). In the study of human gut microbiota, two major
technological periods can be distinguished: schematic microscopic
observation and culture-based methods before 1995 followed by
the advent of culture-independent methods. This technologydriven progress led to suggest relationships between gut microbiota composition and diverse diseases, such as irritable bowel
syndrome (Kassinen et al., 2007), polyposis or colorectal cancer (Scanlan et al., 2008), necrotizing enterocolitis (Siggers et al.,
Frontiers in Cellular and Infection Microbiology
2008), Crohn’s disease (De Hertogh et al., 2006; Manichanh et al.,
2006; Scanlan et al., 2006), and metabolic diseases such as type II
diabetes (Larsen et al., 2010) and obesity (Ley et al., 2006b; Turnbaugh et al., 2006, 2009; Armougom et al., 2009; Santacruz et al.,
2009).
Based on these early data and to complete the description of the
human gut composition, considerable funds have been granted.
Among the projects pursuing this line of research, the human
microbiome project is an international consortium with the aim of
sequencing 1,000 bacterial genomes and multiplication by metagenomic analysis to characterize the complexity of microbial communities at several body sites, including the human gut, to determine whether there is a core microbiome (Turnbaugh et al., 2007).
Despite these advances in knowledge of gut microbiota composition, the relationships of the microbiota with their host and,
consequently, with health and disease are still largely unknown,
as reflected in several contradictory results (Sekirov et al., 2010).
Moreover, molecular tools and by extension, experimental models, often reflect a reductionist approach as opposed to a holistic
approach (Fang and Casadevall, 2011). Nevertheless, an appealing
approach that was recently applied to the study of oral microbiota
will allow us to detect the minor bacterial populations, which are
usually neglected, using dilution to obtain a threshold below 106
bacteria per ml or DNA >1 pg per µl (Biesbroek et al., 2012).
We propose here an inventory of current knowledge regarding
gut microbiota composition, the techniques used for this study
and the relationships with the host. Finally, further research on
www.frontiersin.org
November 2012 | Volume 2 | Article 136 | 1
Lagier et al.
Human gut microbiota
Citaons in each year
A
20000
15000
10000
5000
0
1993 1995 1997 1999 2001 2003 2005 2007 2009 2011
Cita ons in each year
P bli h d it
Published
h year
item iin each
B
500
400
300
200
100
0
1993 1995 1997 1999 2001 2003 2005 2007 2009 2011
Published item in each year
FIGURE 1 | Using the key words “human gut microbiota” or “human
fecal flora” and using the ISI Web of Knowledge database, (A) shows
citations in each year regarding this subject, and (B) shows the
number of published items each year, both between 1993 and 2011.
human gut microbiota is the subject of considerable funding by
the food industry. Consequently, to perform an efficient analysis of
this subject, the design and/or interpretation of the results of each
study can be associated with a conflict of interest. For example, it
has recently been shown that published papers in obesity research
in which the authors were funded by the food industry were more
likely than other papers to contain results or an interpretation that
favored the industry or company that was producing the product
or service that was being studied (Thomas et al., 2008).
REPERTOIRE
CULTURE
Culturing has been the first method used to characterize a bacterial ecosystem (Finegold et al., 1974, 1977; Moore and Holdeman, 1974a). Gut composition was first studied by microscopic
observation and axenic culture. Gram staining has been widely
used by microbiologists to describe stool composition. Using this
technique, gram-positive bacteria accounted for only 2–45% of
the cells observed (Gossling and Slack, 1974). However, a discrepancy arises because culture counts reveal a predominance of
gram-positive bacteria in human feces. Indeed, one of the first
culture studies of human stools showed that anaerobes always
constitute the major component of the culturable flora of children and adults (Mata et al., 1969), with a predominance of
gram-positive cells. Moore and Holdeman (1974a), in a study
of 20 individuals, revealed 113 different bacteria, including more
gram-positive bacteria (Bifidobacterium, Eubacterium, Peptostreptococcus, Ruminococcus, Lactobacillus, and Clostridium genera)
than gram-negative bacteria (Bacteroides, Fusobacteria genera;).
Frontiers in Cellular and Infection Microbiology
Nevertheless, these studies attempted especially to culture anaerobic bacterial species whereas some gut bacteria preferentially
grown in microaerophilic conditions.
Among other unique problems associated with bacterial culture, Moore have also observed a major discordance between the
culture counts and the microscopic counts of species (Moore and
Holdeman, 1974b); these discrepancies have been named by Staley
and Konopka (1985) as the “great plate count anomaly”. Indeed, it
is generally accepted that only 1% of bacteria can be easily grown
in vitro (Vartoukian et al., 2010). Consequently, the major population easily isolated from stools is composed of bacteria that grow
quickly in classical high-nutrient growth media, with the usual carbon or electron sources at mesophilic temperatures (Hugenholtz,
2002), and this constitutes the most studied bacteria. It is estimated
that approximately 75% of published studies by microbiologists
before the advent of molecular tools focused on only nine bacterial
genera among four phyla (Actinobacteria, Proteobacteria, Firmicutes, Bacteroidetes; Galvez et al., 1998), whereas we know now
that more than 30 different phyla compose the gut microbiota
(Figure 2; Rajilic-Stojanovic et al., 2007). Nevertheless, studies of
these fast-growing and easily cultured bacteria neglect the minority bacterial populations, including potentially pathogenic bacteria
such as Salmonella typhi.
Finally, considering the main first culture-based studies the
number of bacterial species was estimated at approximately 400–
500 (Mata et al., 1969; Moore and Holdeman, 1974a; Finegold
et al., 1977). In addition to the necessary use of stringent anaerobic
conditions to culture bacteria from human stools, the usual phenotypic identification methods are time consuming and expensive
(Seng et al., 2009). Indeed, the exponential technological advances
in molecular tools led microbiologists to progressively abandon
the culture-based approach for studies of the gut microbiota
ecosystem.
METAGENOMICS AND PYROSEQUENCING
As often occurs during scientific progress, technological advances
in microbiology allowed scientists to revisit the knowledge base
(Rajilic-Stojanovic et al., 2007). Since 2000, large-scale 16S rRNA
or metagenomic studies have allowed scientists to dramatically
expand the known diversity of the human gut microbiome, illuminating new ways (Eckburg et al., 2005; Andersson et al., 2008).
It is now commonly accepted that approximately 80% of the bacteria found by molecular tools in the human gut are uncultured,
and hence can be characterized only by metagenomic studies (Eckburg et al., 2005). Whereas the number of species was limited in the
seminal studies using culture-based methods (Finegold et al., 1974;
Moore and Holdeman, 1974b), Turnbaugh et al. (2010) estimated
473 phylotypes using V2 pyrosequencing. There is a significant
discrepancy between bacterial observations with a microscope and
most of the molecular studies, which observe a striking dominance
of gram-positive bacteria (Eckburg et al., 2005; Andersson et al.,
2008; Turnbaugh et al., 2010; Figure 2).
Indeed, these recent methods generate contradictory results
reflecting the biases in every step of the Polymerase Chain Reaction procedure. A dramatic divergence in the proportion of the
different phyla was observed depending of the type of extraction
kit used, notably for the Fusobacteria (2–40%) and Bacteroidetes
www.frontiersin.org
November 2012 | Volume 2 | Article 136 | 2
Lagier et al.
Human gut microbiota
100%
80%
60%
G ram -
40%
G ram +
20%
0%
G ram
ME
P yros équenç age
Phylum
Gram staining (×100 oil immersion)
53% bacteria Gram-positive
Gram positive
47% bacteria Gram-negative
Electron microscopy (×7100)
60% bacteria Gram-positive
Gram positive
40% bacteria Gram-negative
Reads
%
Firmicutes
44769
71.12
Actinobacteria
5797
9.21
Other
6627
10.53
Bacteroidetes
3983
6.33
Proteobacteria
1747
2.78
Cyanobacteria
21
0.03
Verrucomicrobia
4
0.01
Total
62948
100
Pyrosequencing
80% bacteria Gram-positive
9% bacteria Gram-negative
11% not available
FIGURE 2 | A comparison of Gram staining, electron microscopy, and pyrosequencing to determine the proportion of Gram-positive/Gram-negative
bacteria in the same stool sample (personal data).
(40–60%) phyla (Wu et al., 2010). In addition, the relative abundance of a phylum depends significantly on the 16S hypervariable
region, independent of pyrosequencing chemistry. For example,
the 454 titanium and Illumina next-generation sequencing (NGS)
methods reveal a dominance of the Bacteroidetes phylum using 16S
rDNA v4v5 region primers, whereas Firmicutes was predominant
using v3v4 primers on the same gut microbiota (Claesson et al.,
2010). Using 454 titanium, Ralstonia genera have been detected
only by V4/V5 primers, whereas Bifidobacteria have been detected
only by V3/V4 primers (Turnbaugh et al., 2010). In parallel, Hong
et al. (2009) have described that the rRNA approach misses half of
the bacteria in environmental microbiology.
Although controversial, the higher taxonomic level analyses (as
phylum level) have suggested an association between obesity and
Firmicutes/Bacteroidetes proportion (Ley et al., 2005). The genuslevel analysis has allowed to hypothesize specific enterotypes compositions despite controversies (Arumugam et al., 2011). In addition, Murphy et al. (2012) has recently observed in a study from the
manipulation of the mice gut microbiota in diet-induced obesity
that a better separation of lean and diet-induced obese mice was
observed at the family and genus-level than at the phylum level.
However, the large inter-individual variability leads the analysis of
lower taxonomic-level to complex results because of small number
of samples. Finally, the optimization of primers able to detect genera often misdetected by pyrosequencing, as Bifidobacteria (Sim
et al., 2012), and technology progress in pyrosequencing, will allow
Frontiers in Cellular and Infection Microbiology
to more quickly analyze longer reads sequenced to study larger
cohort samples in low taxonomic level.
Finally, molecular methods detected bacteria present at concentrations greater than approximately 106 and neglected minority
populations. Among these neglected populations are potentially
pathogenic bacteria such as S. typhi, Yersinia enterocolitica, and
Tropheryma whipplei, which may be present in human stools at
concentrations below 105 cfu per ml (Raoult et al., 2010), the current threshold of the latest NGS method (Turnbaugh et al., 2010;
Lagier et al., 2012a; Figure 3). The depth is directly correlated with
the number of generated sequences, and no plateau was obtained
in the number of phylotypes observed, although close to 1,000,000
16S rRNA gene amplicons have been sequenced by Turnbaugh
et al. (2010).
VIRUSES
Research in the human gut has been focused on bacterial composition (Walker, 2010). Early studies suggested that most DNA
viruses found in the intestine were phages and that most RNA
viruses were plant viruses (Breitbart et al., 2008). Nevertheless,
a recent metagenomic study carried out over 1 year, with three
stools analyzed from each monozygotic adult twin and their
mother, revolutionized virome knowledge (Reyes et al., 2010).
The authors carried out shotgun pyrosequencing to generate over
280 Mb of sequence and, at the same time, a pyrosequencing of
16S rRNA genes to identify the bacterial species. Approximately
www.frontiersin.org
November 2012 | Volume 2 | Article 136 | 3
Lagier et al.
Human gut microbiota
Microorganisms per gram of feces
1012
Ley et al.
2006
SANGER
1011
Andersson
et al. 2008
GS 20
Qin et al.
2010
SOLEXA
Turnbaugh
et al.
2010
0 0
1010
GS FLX
109
108
107
106
Bacteroides spp.
Eubacterium spp.
spp
Clostridium spp.
Peptostreptococcus spp.
Bifidobacterium spp.
Vibrio cholerae
E. coli O157
Shigella dysenteriae
Enterotoxigenic E. coli
Streptococcus spp.
Aeromonas hydrophila
Clostridium difficile
Campylobacter jejuni
Lactobacillus spp.
pp
105
104
Salmonella Typhimurium
Tropheryma whipplei
Yersinia enterocolitica
103
Salmonella Typhi
102
101
FIGURE 3 | The statistical detection thresholds of metagenomic methods. The statistical detection thresholds of metagenomic methods are correlated with
the number of bacteria in the ecosystem studied by the number of sequences generated.
80% of sequencing reads did not match any known viruses in
the database corresponding to prophages or temperate phages.
These populations were persistent in each individual, with no
significant clustering between co-twins or between twins and
their mothers, contrasting with the bacterial similarity between
twins (Turnbaugh et al., 2009). In addition, Minot et al. (2011)
observed that a change of diet is associated with a change in virome
composition.
CULTUROMICS
There has been a renewed interest in culture methods for these
“non-cultivable” species (Vartoukian et al., 2010). Initially, environmental microbiologists were confronted with the fact that the
majority of bacteria do not grow in classical Petri dishes. These
first studies used prolonged incubation and stringent anaerobic
conditions, notably, diffusion chambers (Kaeberlein et al., 2002;
Bollmann et al., 2007), with the aim of simulating the natural
environment of these “uncultivable” microorganisms (Kaeberlein
et al., 2002). This technique enlarged the diversity of the environmental microorganisms that were isolated (Epstein, 2009). In
parallel, a recently published study proposed an anaerobic culture
of a single stool sample to complement 16S rRNA sequencing,
using rumen fluid or an extract of fresh stools to mimic the natural environment of the gut bacteria. Goodman et al. (2011) have
recovered 36 cultured species: four uncultured described species
and 53 unknown isolates with different v2 sequences. However,
these authors used the most probable number (MPC) technique
Frontiers in Cellular and Infection Microbiology
for creating arrayed species collections that do not detect minority
populations.
In addition to the stringent culture conditions, some of the difficulties linked to culture include the cost and the amount of time
required for bacterial identification (Seng et al., 2009). These difficulties have recently been overcome by mass spectrometry, which
enables quick and effective identifications in routine bacteriology
(Seng et al., 2009, 2010) and allow the researcher to quickly check
the major population and to concentrate interest on the minority population. We have recently reported a breakthrough in this
field of research with the microbial culturomics concept (Lagier
et al., 2012a). We applied 212 different culture conditions in two
African stools and a French obese stool samples, including enrichment techniques, Escherichia coli phage cleaning, and innovative
conditions (using rumen fluid, sterile human stools). We analyzed
32,500 colonies by MALDI-TOF, allowed us to culture 340 different bacterial species among seven phyla and 117 genera. This
included 174 species never described in the human gut. Moreover 31 new species were found, including five new genera, as well
two species from rare phyla (Deinococcus-Thermus and Synergistetes). Genome sequencing and description of each new species
is in progress (Kokcha et al., 2012; Lagier et al., 2012b,c; Mishra
et al., 2012a,b,c,d). By comparison, pyrosequencing of 16S rDNA
amplicons from the three stools noted a dramatic discrepancy with
culturomics as only 51 species identified by 16S rDNA amplification and sequencing were also found among the 340 cultured
species highlighting the renewed interest for the culture in the gut
www.frontiersin.org
November 2012 | Volume 2 | Article 136 | 4
Lagier et al.
Human gut microbiota
Gram+ Gram- No staining
LPS absent
Pepdoglycan absent
Phylum present in human
Verrucomicrobia
SR1
Sphaerobacter thermophilus
Bacteroidetes
Chrysiogenetes
Thermobaculum terrenum
Spirochaetes
Thermotogae
Chloroflexi
Nitrospira
Cyanobacteria
Chloroflexis,
Herpe"sphon
Dehalococcoides
Acidobacteria
Unculvated phyla
-OP1, OP3, OP8, OP9,OP11
-BRC1
BRC1
-OD1
-ABY1 SR1
-TM6
-TM7
-WS2, WS3, WS5, WS6
-…
Len"sphaerae
Chlorobi
Dictyoglomi
Caldiserica
C
ldi i
(OP5)
Deferribacteres
Elusimicrobia (Termite
Group 1)
Fusobacteria
Deinococcus-Thermus
Proteobacteria
Fibrobacteres
Synergistetes
Arma"monadetes (OP10)
Gemma"monadetes
Aquificae
Planctomycetes
Firmicutes
Chlamydiae
Tenericutes
Acidaminococcus
Eubacterium
Oscillibacter
Ac!nobacteria
Mycobacterium (triple
layered structure)
FIGURE 4 | A non-exhaustive representation of different bacterial
phyla found in culture (outer star in blue) or phyla with no
representative in culture (inner star in gray). Gram-positive bacteria
are colored in green, and Gram-negative bacteria are colored in white.
Bacteria with an atypical cell wall (triple-layered structure of
microbiota study. Culturomics allowed us break several “records”
with the largest number of bacteria cultured from a single stool
(219 species), the first bacteria from Deinococcus-Thermus phylum isolated from human, the largest human virus and the largest
bacteria from human (Lagier et al., 2012a).
COMPARISON OF THE TECHNIQUES
There are currently no rational explanations for the typical
observed proportions of gram-positive/negative bacteria, which
are highly divergent microscopically (Turnbaugh et al., 2007) with
culture, (Gossling and Slack, 1974) and the proportions obtained
by sequence detection (Eckburg et al., 2005; Figures 2 and 4). In
2002, Hayashi compared the digestive microbiota of three individuals by cloning/sequencing and anaerobic culture using the
“plate-in-bottle” method. These researchers isolated between 48
and 65 phylotypes in the cloning of individuals and 48 species,
of which three individuals were potentially three new species
(Hayashi et al., 2002b). In light of the phylogenetic tree described
in this publication, these authors found significant discrepancies
between these two techniques, which were somewhat surprising
given the low number of species and phylotypes identified. Several species in culture had no equivalent in cloning. A previous
study compared these same techniques, but the number of species
and phylotypes was even lower (Wilson and Blitchington, 1996).
In this study, of 48 species, 25 were detected only by cloning, nine
were common to both techniques, and 14 were identified only by
culture. In addition, in our microbial culturomics study, by comparison with the 340 bacteria cultured, pyrosequencing of 16S
Frontiers in Cellular and Infection Microbiology
Mycobacterium) or without a cell wall (Tenericutes) have abnormal
Gram staining and are shown in pink. The purple triangle represents the
absence of lipopolysaccharide in the outer membrane of Gram-negative
bacteria. The red square symbolizes phyla that do not have a
peptidoglycan structure.
rDNA amplicons from the three stools identified 698 phylotypes
including 282 phylotypes of known bacterial species and 416 phylotypes of uncultured bacteria. We noted a dramatic discrepancy
with culturomics as only 51 species identified by 16S rDNA amplification and sequencing were also found among the 340 cultured
species. Consequently, microbial culturomics increased by 30% the
microbial repertoire of the human gut studied by pyrosequencing
(Lagier et al., 2012a).
GAPS IN KNOWLEDGE
In addition to the bias previously described, some components of
human gut microbiota have been partially neglected by the current
tools (Figure 5).
EUKARYOTES
Eukaryotes are an important part of the human gut microbiome
and play different beneficial or harmful roles. Some species may be
commensal or mutualistic, whereas others may be opportunistic
or parasitic (Parfrey et al., 2011). The eukaryotic component of the
human gut microbiome remains unexplored because these organisms are of limited interest (Marchesi, 2010). Culture-dependent
techniques and microscopy-based approaches have been mainly
used to explore eukaryotes in the human gut, and identification has
frequently been based on morphological and physiological techniques with numerous biases. Moreover, this approach detects only
a small fraction of microorganisms, including Candida and Saccharomyces spp., but the growth requirements for many eukaryotic
species remain unknown.
www.frontiersin.org
November 2012 | Volume 2 | Article 136 | 5
Lagier et al.
Human gut microbiota
FIGURE 5 | A non exhaustive overview of human gut microorganisms among bacterial, Archaea, viral, and Eukaryota domains.
Using culture-independent methods, Scupham et al. (2006)
have identified a large number of fungi, including Ascomycota,
Basidiomycota, Chytridiomycota, and Zygomycota phyla, in studies of mouse feces. Furthermore, Scanlan and Marchesi (2008),
studying the human distal gut, have shown that the diversity and
abundance of eukaryotes is low relative to those of bacteria. Only
members of the genera Gloeotinia/Paecilomyces and Galactomyces
have been identified as the most abundant. Nevertheless, we have
shown that due to a large variety of primers used, the human gut
contains a broader eukaryotic diversity than predicted (Hamad
et al., 2012). In parallel, applying traditional and modern laboratory techniques (using intergenic spacers for 18S rDNA), the
repertoire of intensive care unit pneumonial microbiota has been
considerably extended, notably regarding fungal microbiota and
plants (Bousbia et al., 2012).
Giant viruses
Giant viruses growing in amoebae have previously been isolated
in the environment, e.g., in the water of cooling towers, in rivers
and lakes, in seawater, in decorative fountains, and in soil (Pagnier et al., 2008). Mimivirus DNA has been obtained from the
bronchoalveolar lavage of patients (Raoult et al., 2007; Lysholm
et al., 2012), and a laboratory infection was documented by serology (Raoult et al., 2006). In addition, Lysholm et al. (2012), in a
viral microbiome metagenomic study performed in 210 children
and adults with lower respiratory infections, recently identified
Mimivirus. Because the authors used two pools and filtered with
0.22 and 0.45 µm pore-size disk filters, they were able to isolate a
Frontiers in Cellular and Infection Microbiology
giant virus that is frequently missed by large-scale virome metagenomics studies that use only 0.22 µm filters, making giant virus
detection unlikely (Willner et al., 2009; Reyes et al., 2010).
In our laboratory, in an effort to obtain fastidious bacteria from
an African stool sample by amoeba (Acanthamoeba polyphaga) coculture, we obtained a new giant virus strain named Senegal virus
(Lagier et al., 2012a), which we sequenced (Genbank JF909596–
JF909602). These findings indicate that giant viruses may be a
part of the gut microbiota and that virome metagenomic studies
should use different filter sizes. Because the potential pathology
of the giant viruses is currently unknown, it is unreasonable to
neglect them (Boyer et al., 2009).
Archaea
Nottingham and Hungate (1968) isolated a previously unidentified methanogenic Archaea from human feces using a nonselective medium and a stringent anaerobic atmosphere composed
of 80% H2 and 20% CO2 . Miller et al. (1982) isolated Methanobrevibacter smithii from human stool specimens from four healthy
adults using anaerobic cultures enriched with the same H2 –CO2
anaerobic atmosphere pressurized to two bars. Illustrating the
technical limitations of the fastidious Archaea culture, in our laboratory, we have recently achieved the isolation of the fourth
methanogenic Archaea species in humans and the first cultured
representative of a new order of Archaea (Methanomassiliicoccus
luminyensis) after a 16-month tentative culturing procedure. We
obtained this strain after subtle modifications in the composition
of the culture medium (enzyme co-factors) and adaptation of the
www.frontiersin.org
November 2012 | Volume 2 | Article 136 | 6
Lagier et al.
Human gut microbiota
atmospheric pressure (the culture medium is patented; Dridi et al.,
2012a). In addition, the genome sequencing of this new species
represents the largest genome of a methanogenic euryarchaeota
isolated from humans (Gorlas et al., 2012).
In addition, recent molecular studies indicated that human
Archaea constitute an expanding world (Dridi et al., 2011). Using
16S rDNA sequencing, many studies confirmed the presence of M.
smithii and M. stadtmanae in the human gut, with variable and
low prevalence (Dridi et al., 2009). Nevertheless, in our study, our
new Archaea was detected in stools in 4% of individuals, and its
prevalence increases with age, although its role in human health is
unknown (Dridi et al., 2012b). Regarding the influence of Archaea
on human health, a recent meta-analysis compared the number of
sequences of Methanobrevibacter spp. in stools. Obese individuals
had fewer Methanobrevibacter genera by quantitative polymerase
chain reaction (qPCR) than non-obese subjects (Angelakis et al.,
2012a). Previous studies had reported discordant results concerning the levels of detection of M. smithii in the obese gut (Zhang
et al., 2009; Schwiertz et al., 2010; Million et al., 2011). In addition,
the detection of Archaea in the vaginal flora of pregnant women
allowed us to hypothesize a possible mother-to-child transmission
(Dridi et al., 2011).
Planctomycetes
The phylum Planctomycetes, phylogenetically closely related to
Verrucomicrobia and Chlamydiae, is composed of environmental microorganisms characterized by a peptidoglycan-free cell wall
and cell compartmentalization (Fuerst and Sagulenko, 2011). The
culture is fastidious and requires the addition of appropriate
antibiotics (peptidoglycan synthesis inhibitors) and amphotericin
B to prevent contamination of the culture medium. Undetected by
conventional 16S rRNA PCR or standard culture techniques, this
phylum has been reported in black-and-white colobus monkey
stools (Yildirim et al., 2010) and, in one instance, in the human
gut microbiota, using metagenomics (De Filippo et al., 2010). In
our laboratory, preliminary results (unpublished data) confirmed
the presence of specific Planctomycetes DNA in human stools.
Several species of Planctomycetes and, more generally, of species
including the superphyla Verrucomicrobia, Planctomycetes, and
Chlamydiae, are undergoing genome sequencing. It is expected
that this sequencing will increase our knowledge of this specific
branch of the tree of bacterial life (Wagner and Horn, 2006).
The variability depending of the gut samples
“The gut microbiota is non-homogenous with a progressive
increase of bacterial concentration from the stomach (approximately 103 bacteria per gram to the colon (approximately 1011 bacteria per gram; O’Hara and Shanahan, 2006). Nevertheless, most
of studies explored stools samples reflecting mainly the colonic
composition. However, differences in compositions have been
reported between small intestine biopsies (most of Streptococcaceae belonging to Firmicutes phylum and Actinomycinaeae and
Corynebacteriaceae belonging to Actinobacteria phylum) whereas
colonic biopsies were enriched by Bacteroidetes phylum and Lachnospiraceae among the Firmicutes phylum (Frank et al., 2007).
Intestinal analysis of tiered samples will allow to exhaustively
describe the gut composition.”
Frontiers in Cellular and Infection Microbiology
COMPOSITION
The composition of the human gut ecosystem is influenced by
multiple and diverse factors, some physiological (age, origin, environment) and others linked to external factors, such as dietary
habits, antibiotics, and probiotics (Figure 6).
AGE
In a pioneering study using microarrays to detect small rRNAs,
Palmer followed a cohort of newborns, including a pair of twins,
during the first year of life. It was shown that despite considerable
temporal variations and environmental influences, the composition of the intestinal ecosystem tended to be characteristic of
adulthood at the end of this period (Palmer et al., 2007). The proportion of Bacteroides fragilis increased from 1 month to 1 year
(Vael et al., 2011). In a 2.5-year case study, Koenig analyzed
>300,000 16S rRNAs from 60 fecal samples from healthy children
and showed that infant gut variation is associated with life events.
The phylogenetic diversity of the microbiome increased gradually
over time with progressive temporal changes but, inversely, the
major phyla, genera, and species composition showed rough shifts
in abundance corresponding to modifications in diet or health
(Koenig et al., 2011).
Nevertheless, using microbiota array to analyze gut microbiota
composition in adolescent subjects, Agans et al. (2011) found
a statistically significantly higher abundance of Bifidobacterium
and Clostridium genera, contrary to current knowledge, suggesting that the gut microbiome of adolescents is different from that
of adults. At the other extreme of life, using pyrosequencing of 16S
rRNA gene V4 region amplicons, the gut microbiota composition
of elderly subjects was distinct from that of younger adults, with
a greater temporal stability over a limited time, particularly in the
proportion of Bacteroides spp. (Claesson et al., 2011).
GEOGRAPHICAL PROVENANCE AND ENVIRONMENT
Discordant results have also been published regarding a geographic
signature of the gut microbiota depending on the technique used.
To investigate the hypothetical association between gut composition and cancer, early culture-dependent studies compared populations at high-risk (western countries) and at low risk (Japan,
Uganda, India) and reported different compositions of microbiota
(Hill et al., 1971; Drasar et al., 1973; Finegold et al., 1974). The
high-risk population had a microbiota composed primarily of
Bacteroidetes, and there were specific differences, including patterns of food consumption, between western countries and Asian
or African populations, although multiculturalism and population
exchanges have reduced these differences. Only a few large-scale
molecular studies have used stool samples collected from Asia or
Africa (De Filippo et al., 2010; Lee et al., 2011), where approximately 75% of the population of the world lives; nevertheless,
the findings have suggested a possible signature of biogeography
(Lee et al., 2011). Indeed, most of the large-scale metagenomic or
pyrosequencing studies used stools collected from American or
European individuals (Ley et al., 2006b; Turnbaugh et al., 2006;
Claesson et al., 2011).
Lay, characterizing 91 European gut microbiota using FISH
combined with flow cytometry, did not observe a significant
grouping with regard to country of origin (France, Netherlands,
www.frontiersin.org
November 2012 | Volume 2 | Article 136 | 7
Lagier et al.
Human gut microbiota
Asian vs American
European vs African
Southern vs Northern Europeans
Geographic
provenance
Age
First month of life : Mostly Bifidobacteria
Differences in species level but
Contradictory results
1 month to 1 year of life : Increase Bacteroides fragilis
Adolescent : contradictory results
Dietary Habits
Vegetarians :
Increase Bacteroidetes
Decrease Clostridia
Human gut
composion
Increase Lactobacillus spp. ?
Adult: Stable:
Firmicutes > Bacteroidetes
> Proteobacteria> Ac"nobacteria
Probiocs
Elderly:
Increase Bacteroidetes
Decrease Bifidobacteria
Malnurished children :
Increase Proteobacteria
Decrease Bacteroidetes
Decrease bacterial load ?
?
p
Alteraon of composion
Malnutrion
Anbiocs
FIGURE 6 | The influence of external factors determining the composition of the human gut microbiota.
Denmark, UK, and Germany; Lay et al., 2005). With the same technology, Mueller et al. (2006) found differences in Bifidobacteria
species between European individuals. Grzeskowiak et al. (2012),
using flow cytometry-FISH and qPCR, have shown that several species (Bifidobacterium adolescentis, Staphylococcus aureus,
and Clostridium perfringens) were absent in Malawian children
but present in 6-month-old Finnish infants. Fallani comparing
infants living in northern or southern European countries by 16S
rDNA pyrosequencing, have found that geographical provenance
is important, with a higher proportion of Bifidobacteria in northern infants and more Bacteroidetes and Lactobacilli in southern
European countries.
Finally, Arumugam studied 22 fecal metagenomes of individuals from four different countries and identified three different
enterotype clusters, which were independent of geographic provenance. The three different enterotypes were, respectively, richer in
Bacteroides, in Prevotella, and in Ruminococcus for the Enterotype
3. Arumugam suggested that each enterotype used a different route
to generate energy (Arumugam et al., 2011).
DIETARY HABITS
Dietary habits are thought to be a major factor contributing to
the diversity of the human digestive microbiota (Backhed et al.,
2005). Part of the geographic diversity of the gut microbiota
seems to be explained by differences in diet. For example, African
children from a rural area in Burkina Faso showed a specific
abundance of Prevotella and Xylanibacter, known to contain a
set of bacterial genes for cellulose and xylan hydrolysis, completely lacking in European children (De Filippo et al., 2010).
Frontiers in Cellular and Infection Microbiology
The authors hypothesized that the abundance of these genera
could be a consequence of the high intake of fiber, similar to
the diet of early human settlements at the time of the birth of
agriculture, maximizing the extraction of metabolic energy from
the polysaccharides of ingested plants (De Filippo et al., 2010).
A vegetarian diet affects the intestinal microbiota, specifically by
decreasing the amount and modifying the diversity of Clostridium cluster IV (Liszt et al., 2009). Based their studies on RFLP
analysis, Hayashi et al. (2002a) Hayashi found that the digestive
microbiota of vegetarians harbored Clostridium rRNA clusters
XIVa and XVIII. Recently, Walker et al. (2011) tested overweight
people successively with a control diet, a diet high in resistant
starch (RS) or non-starch polysaccharides (NSP) and a reduced
carbohydrate weight loss (WL) diet for 10 weeks by two different
methods: large-scale sequencing and quantitative PCR. No significant effect was observed at the phylum level, but at finer taxonomic
level, Eubacterium rectale and Ruminococcus bromii showed significant and dramatic (fourfold) increased proportions in the RS diet,
whereas the proportion of Collinsella aerofaciens-related sequences
was decreased significantly on the WL diet (Walker et al., 2011).
In this study, reproducible changes were found only at the phylotype level, whereas no differences were significant at a broader
taxonomic level (the phylum or family Ruminococcaceae level),
and the analysis suggested that the amplified 16S rRNA sequence
clustered more strongly by individuals than by diet. These changes
are entirely in agreement with studies using RNA-based stable isotope probing, which showed that R. bromii was the first starch
degrader in the human gut (Kovatcheva-Datchary et al., 2009).
Wu analyzed stool samples from 98 individuals and found that
www.frontiersin.org
November 2012 | Volume 2 | Article 136 | 8
Lagier et al.
Human gut microbiota
enterotypes were strongly associated with long-term diets, especially for animal fat and protein (Bacteroides) vs. carbohydrate
(Prevotella). Changes in gut microbiota related to short-term diet
modifications occurred rapidly, were detectable within 3–4 days,
and were rapidly reversed (Walker et al., 2011; Wu et al., 2011).
Conversely, Wu et al. (2011) suggested that long-term dietary
interventions might allow the pervasive modulation of an individual’s enterotype to improve health. In animal models of obesity
induced by diet (DIO), Turnbaugh et al. (2008) showed that a
high-fat diet could significantly alter the intestinal flora of experimental models with a bloom in a single uncultured clade within
the Mollicutes class of the Firmicutes. Hildebrandt et al. (2009)
suggested that a high-fat diet altered the intestinal flora regardless
of weight change. These authors observed a bloom of Clostridia
and Proteobacteria associated with the high-fat diet. The major
group of Proteobacteria that increased in abundance was the DeltaProteobacteria phylum, order Desulfovibrio. Finally, Monira et al.
(2011) have recently published a study comparing the gut flora
of malnourished children with that of well-nourished children in
Bangladesh and found a decrease in Bacteroidetes and an increase
in Proteobacteria phyla, including E. coli and Klebsiella spp.
OBESITY AND GNOTOBIOTIC MICE
Beginning in 2005, obesity has been associated with a specific
profile of bacterial gut microbiota, including a decrease in the
Bacteroidetes/Firmicutes ratio (Ley et al., 2005, 2006b; Turnbaugh
et al., 2006, 2009) and decreased bacterial diversity (Turnbaugh
et al., 2009). Since these pioneering studies, significant associations have been found between obesity and an increase in some
bacterial groups, including Lactobacillus, S. aureus, E. coli, and Faecalibacterium prausnitzii (Collado et al., 2008; Kalliomaki et al.,
2008; Armougom et al., 2009; Santacruz et al., 2009; Balamurugan et al., 2010). In a recent review, we found no reproducible and
significant alteration linking obesity and gut microbiota at the phylum level (Angelakis et al., 2012a). Conversely, meta-analysis at the
genus-level found decreased levels of bifidobacteria (Collado et al.,
2008; Kalliomaki et al., 2008; Santacruz et al., 2009; Balamurugan
et al., 2010; Schwiertz et al., 2010) and Methanobrevibacter spp.
(Armougom et al., 2009; Schwiertz et al., 2010; Million et al., 2011),
the leading known representative of Archaea in the human gut, in
overweight/obese people. To date, controversial studies show that
the connection between the microbiome and excess weight is complex (Pennisi, 2011). We found a difference at the species level, with
L. reuteri enriched in obese gut microbiota, whereas L. plantarum
was increased in lean individuals (Million et al., 2011). At the
gene level, obesity has been associated with an altered representation of bacterial genes and metabolic pathways. Turnbaugh et al.
(2009) showed that diversity of organismal assemblages yields a
core microbiome at a functional level and that deviations from this
core are associated with different physiological states (obese compared with lean), with obese gut microbiota having an increased
capacity for energy harvest.
As a theoretical basis for the causal link between alterations in
the gut microbiota and obesity, several mechanisms have been
suggested. First, the gut microbiota may interact with weight
regulation by hydrolyzing indigestible polysaccharides to easily
absorbable monosaccharides and by activating lipoprotein lipase.
Frontiers in Cellular and Infection Microbiology
Consequently, glucose is rapidly absorbed, producing substantial
elevations in serum glucose and insulin, both factors that trigger
lipogenesis. In addition, fatty acids are stored excessively, with de
novo synthesis of triglycerides derived from the liver. Together,
these phenomena cause weight gain (Backhed et al., 2007). Using
Fasting-Induced Adipocyte Factor (Fiaf) knockout mice, Backhed
et al. (2007) showed that gut microbiota suppressed intestinal Fiaf,
consequently increasing the storage of calories.
Second, the composition of gut microbiota has been shown
to selectively suppress angiopoietin-like protein 4/fasting-induced
adipose factor in the intestinal epithelium. This molecule is a circulating lipoprotein lipase inhibitor and a regulator of peripheral
lipid and glucose metabolism (Backhed et al., 2004). Backhed et al.
(2004) showed that when the microbiota of normal mice were
transplanted into germ-free mice, after 2 weeks, body fat increased
by 60% without increased food consumption, modifications of
energy expenditure, or relative insulin resistance, and there was a
2.3-fold higher production of triglycerides in the liver, suggesting
that the gut operates in host energy homeostasis and adiposity.
Third, it has been suggested that bacterial isolates of gut microbiota may have pro- or anti-inflammatory properties, impacting weight. Obesity has been associated with a low-grade systemic inflammation corresponding to higher plasma endotoxin
lipopolysaccharide (LPS) concentrations, defined as metabolic
endotoxemia (Bastard et al., 2006; Hotamisligil, 2006; Sbarbati
et al., 2006; Fogarty et al., 2008). Cani et al. (2008) showed that
antibiotics can lower LPS levels in mice fed a high-fat diet and
in ob/ob mice and, consequently, can reduce glucose intolerance,
body weight gain, and fat mass. Conversely, some Bifidobacterium
and Lactobacillus species have been reported to deconjugate bile
acids, which may decrease fat absorption (Shimada et al., 1969).
ANTIBIOTICS AND PROBIOTICS
Antibiotics and total bacterial count
According to the literature, oral or intravenous antibiotics tend
to decrease the bacterial load in the digestive tracts of infants
(Palmer et al., 2007) and elderly patients (Bartosch et al., 2004).
However, other studies reported that only the microbiota composition is altered, and the total biomass is not modified by antibiotics
(Sekirov et al., 2008). In contrast to amoxicillin and metronidazole
or cefoperazone, Robinson noted that the alterations in community structure associated with vancomycin specifically occurred
without a significant decrease in the overall bacterial biomass
(Robinson and Young, 2010).
Structural disruption
Antibiotic administration has a reproducible effect on the community structure of the indigenous gastrointestinal microbiota
in mice (Robinson and Young, 2010). A very recent study found
that the administration of a commercial growth-promoting antibiotic combination (ASP250: chlortetracycline-sulfamethazine and
penicillin) entailed a reproducible bloom in proteobacteria (1–
11%) in swine gut microbiota (Looft et al., 2012). This shift was
driven by an increase in E. coli populations. In humans, analysis of the fecal microbial populations of infants after antibiotic
therapy showed a major alteration as measured by SSU rDNA
microarray analysis (Palmer et al., 2007) or culture-based methods
www.frontiersin.org
November 2012 | Volume 2 | Article 136 | 9
Lagier et al.
Human gut microbiota
(Savino et al., 2011). In adults, the same dramatic shift has been
reported, depending on the antibiotic. Clindamycin (Donskey
et al., 2003; Jernberg et al., 2005a) has the strongest effect compared to oral cephalosporin, which is responsible for minor or no
changes (Swedish Study Group, 1991a,b). Of note, the extremely
moderate effect of cephalosporin on gut microbiota (Donskey
et al., 2003) has been linked with the low activity of this molecule on intestinal anaerobes. Moreover, the fecal elimination of
carbapenems is very limited, explaining why changes in the intestinal microflora are only moderate, whereas these agents have the
broadest spectra of the beta-lactam antibacterial agents (Sullivan
et al., 2001). The characterization of gut microbiota alteration
by metagenomic analysis of the v3–v6 region has been studied in three patients on ciprofloxacin (Dethlefsen et al., 2008).
Ciprofloxacin decreased to one-third the abundance of taxa [number of ref Operational taxonomic units (OTU)], their diversity
and distribution. However, comparing gut microbiota alterations
by DGGE analysis, the rate of similarity with the pre-treatment
profile was 73% with ciprofloxacin but only 11–18% with clindamycin (Donskey et al., 2003). In addition, ciprofloxacin has been
reported to have little or no impact on anaerobic intestinal microbiota (Nord, 1995; Edlund and Nord, 1999b). Glycopeptides, used
widely in agriculture as growth promoters, are associated with
natural resistance of most of the lactobacilli and have no effect
on gram-negative bacteria, including Enterobacteria (Barna and
Williams, 1984). Analyzing vancomycin-associated gut microbiota
alterations in mice by cloning sequencing, Robinson found that
vancomycin increased members of the Proteobacteria and Tenericutes phyla and the Lactobacillaceae family, whereas members
of the Lachnospiraceae family decreased (Robinson and Young,
2010). Using a continuous-culture colonic model system, Maccaferri et al. (2010) demonstrated that rifaximin, reported to induce
clinical remission of active Crohn’s disease while not altering
the overall structure of the human colonic microbiota, increased
Bifidobacterium, Atopobium, and F. prausnitzii and led to a variation of metabolic profiles associated with potential beneficial
effects on the host. The effects of tetracycline on gut microbiota in humans are of particular interest because this antibiotic
is commonly used in poultry production as a growth promoter,
suggesting dramatic changes in intestinal microbial populations.
One notable effect of tetracycline is a decrease in bifidobacteria (Nord et al., 2006; Saarela et al., 2007). Overall, specific
gut microbiota changes are associated with specific antibiotics
(Table 1).
The effects of three growth-promoting antibiotics (avilamycin,
zinc bacitracin, and flavomycin) on broiler gut microbial community colonization and bird performance were investigated
(Torok et al., 2011). OTU linked to changes in gut microbiota in
birds on antimicrobial-supplemented diets were characterized and
identified. Lachnospiraceae, L. johnsonii, Ruminococcaceae, and
Oxalobacteraceae genera were less prevalent in the guts of chicks
fed antimicrobial-supplemented diets. L. crispatus, L. reuteri, Subdoligranulum, and Enterobacteriaceae were more prevalent in the
guts of chicks raised on the antimicrobial diet (Torok et al., 2011).
These results suggest that antibiotic effects on gut microbiota
may be relevant at the species level because different Lactobacillus
species-related OTUs showed paradoxical changes.
Frontiers in Cellular and Infection Microbiology
The reversibility of structural gut microbiota modification
The recovery of the gut community toward baseline after shortterm antibiotic therapy has been reported in animal models
(Robinson and Young, 2010), but pervasive disturbance to the
community has been observed several weeks after withdrawal of
certain antibiotics, including cefoperazone (Robinson and Young,
2010) and quinolones (Dethlefsen et al., 2008). Changing the
intestinal microbiota of termites with antibiotics offers a privileged
experimental model and has shown that prolonged antibiotic
treatment with rifampicin has an irreversible effect not only
on microbial diversity but also on longevity, fecundity and the
weight (weight gain compared to controls) of two termite species,
Zootermopsis angusticollis and Reticulitermes flavipes.
Probiotics
Probiotics were initially used in agriculture to prevent diarrhea in
poultry because they reduce intestinal colonization by Salmonella
spp. and C. perfringens (Angelakis and Raoult, 2010), but the use
of probiotics such as Lactobacillus spp. can led to a rapid weight
increase in chickens (Angelakis and Raoult, 2010). L. acidophilus,
L. plantarum, L. casei, L. fermentum, and L. reuteri are the most
commonly used Lactobacillus species in agriculture (Anadon et al.,
2006). The inoculation of L. ingluviei in mice is responsible for
gut flora alterations associated with an increase in weight gain and
liver enlargement (Angelakis et al., 2012b). In parallel, probiotics
are increasingly used in human foods, notably in the milk industry (Raoult, 2008). Although the mechanisms are not yet known,
many studies suggest that probiotics function through direct or
indirect impacts on colonizing microbiota of the gut (Sanders,
2011). Million et al. (2011) recently found that different Lactobacillus species may have a paradoxical effect, with higher levels
of L. reuteri and lower levels of L. plantarum and L. paracasei in
obese gut microbiota. A recent systematic meta-analysis reported
that the administration of L. acidophilus is responsible of weight
gain in human and animals and that the use of L. fermentum and L.
ingluviei resulted of weight gain in animals (Million et al., 2012).
Thuny et al. (2010) observed a weight gain in patients treated
with vancomycin and hypothesized that the gain was induced by
the growth-promoting effect of Lactobacillus spp., as these species
are resistant to glycopeptides. In contrast, symbiotics (the combination of prebiotics and probiotics) have been proposed for the
management of malnutrition, with promising results on mortality
(Kerac et al., 2009). After gavage of gnotobiotic mice with a combination of bacteria, including B. animalis subsp. lactis, L. delbrueckii
subsp. bulgaricus, Lactococcus lactis subsp. cremoris, and Streptococcus thermophilus, only anecdotal changes were noted in microbiota
composition, whereas significant changes were observed in the
expression of microbiome-encoded enzymes involved in metabolic pathways, notably, carbohydrate metabolism (McNulty et al.,
2011). However, these suggestions of a relationship between probiotics and obesity remain controversial (Delzenne and Reid, 2009).
In addition, the reports of the anti-diabetic and anti-inflammatory
effects of Lactobacilli should be considered cautiously because the
translation of findings based on animal models to humans is hazardous (Kootte et al., 2012). Finally, all these results should be
interpreted with caution in view of the substantial funding of
obesity research by the food industry, creating conflicts of interest.
www.frontiersin.org
November 2012 | Volume 2 | Article 136 | 10
Lagier et al.
Human gut microbiota
Table 1 | Modifications of gut flora linked to antibiotics.
Antibiotic
Method
References
Decrease in enterococci
Cultivation
Black et al. (1991)
Decrease in streptococci
Cultivation
Black et al. (1991)
Decrease in E. coli strains
Cultivation
Black et al. (1991)
Slight decrease in anaerobic Gram-positive bacteria
Cultivation
Black et al. (1991)
Cultivation
Brismar et al. (1993), Floor et al. (1994), Stark et al. (1996)
PENICILLINS
Ampicillin
Amoxicillin
Increase in aerobic Gram-negative rods, such as
enterobacteria, other than E. coli (Klebsiella, Enterobacter )
Increase in anaerobic Gram-positive rods
Cultivation
Swedish Study Group (1991b)
Increase in Bacteroides
Cultivation
Swedish Study Group (1991b)
Decrease in streptococci and Staphylococci
Cultivation
Brismar et al. (1993)
Decrease in anaerobic Gram-positive cocci, such as
Cultivation
Brismar et al. (1993), Stark et al. (1996)
Increase in enterococci and E. coli
Cultivation
Lode et al. (2001)
Decrease in lactobacilli, clostridia, bifidobacteria
Cultivation
Lode et al. (2001)
Disappearance of Clostridium cluster XIVa
Cloning/sequencing
Young and Schmidt (2004)
Cloning/sequencing
Young and Schmidt (2004)
Decrease in enterobacteria
Cultivation
Nord et al. (1993)
Decrease in bifidobacteria, eubacteria, lactobacilli
Cultivation
Nord et al. (1993)
Decrease in anaerobic Gram-positive cocci like clostridia
Cultivation
Nord et al. (1993)
Decrease in E. coli and bifidobacteria
Cultivation
Bacher et al. (1992)
Increase in clostridia and Bacteroides
Cultivation
Bacher et al. (1992)
Decrease in the total numbers of anaerobes
Cultivation
Welling et al. (1991)
Dramatic decrease in clostridia, lactobacilli, bifidobacteria
Cultivation
Vogel et al. (2001)
Dramatic decrease in Gram-negative rods (enterobacteria)
Cultivation
Cavallaro et al. (1992), Vogel et al. (2001), Welling et al. (1991)
Increase in enterococci
Cultivation
Vogel et al. (2001), Welling et al. (1991)
Decrease in enterobacteria and streptococci
Cultivation
Bergan et al. (1991)
Increase in enterococci
Cultivation
Bergan et al. (1991)
Decrease in clostridia, Gram-negative cocci, and bacteroides
Cultivation
Bergan et al. (1991)
Cultivation
Bergan et al. (1986), Borzio et al. (1997), Brismar et al.
eubacteria
Amoxicillin/clavulanic acid
(cloning/sequencing)
Decrease in Faecalibacterium spp.
Piperacillin/tazobactam*
CEPHALOSPORINS
Cefepime
Ceftriaxone
Carbapenems
Meropenem
FLUOROQUINOLONES
Ciprofloxacin
Dramatic decrease in enterobacteria
(1990), Brumfitt et al. (1984), Enzensberger et al. (1985),
Esposito et al. (1987), Holt et al. (1986), Krueger et al.
(1997), Ljungberg et al. (1990), Rozenberg-Arska et al.
(1985), Van Saene et al. (1986), Wistrom et al. (1992)
Decrease in aerobic Gram-positive cocci
Cultivation
Bergan et al. (1986), Brismar et al. (1990), Brumfitt et al.
Decrease in streptococci
Cultivation
Brismar et al. (1990), Brumfitt et al. (1984), Ljungberg et al.
Decrease in enterococci
Cultivation
Bergan et al. (1986), Brismar et al. (1990), Ljungberg et al.
(1984), Ljungberg et al. (1990), Van Saene et al. (1986)
(1990)
(1990), Van Saene et al. (1986)
(Continued)
Frontiers in Cellular and Infection Microbiology
www.frontiersin.org
November 2012 | Volume 2 | Article 136 | 11
Lagier et al.
Human gut microbiota
Table 1 | Continued
Antibiotic
Method
References
Increase in enterococci
Cultivation
Borzio et al. (1997)
Decrease in anaerobic bacteria
Cultivation
Bergan et al. (1986), Brismar et al. (1990), Rozenberg-Arska
Suppression of Bacteroides putredinis, Ruminococcus
DGGE
Donskey et al. (2003)
Dramatic decrease in enterobacteria
Cultivation
de Vries-Hospers et al. (1985), Edlund et al. (1987), Leigh
Decrease in aerobic Gram-positive cocci
Cultivation
de Vries-Hospers et al. (1985), Pecquet et al. (1986)
Decrease in streptococci
Cultivation
Pecquet et al. (1986)
Decrease in enterococci
Cultivation
de Vries-Hospers et al. (1985)
Dramatic decrease in enterobacteria
Cultivation
Edlund et al. (1988), Edlund et al. (1997b), Pecquet et al.
Decrease in aerobic Gram-positive cocci
Cultivation
Edlund et al. (1988), Edlund et al. (1997b), Pecquet et al.
Decrease in enterococci
Cultivation
Edlund et al. (1988), Pecquet et al. (1987)
Decrease in lactobacilli, bifidobacteria, eubacteria
Cultivation
Edlund et al. (1988)
Decrease in anaerobic bacteria
Cultivation
Edlund et al. (1988)
Decrease in Veillonella and Bacteroides spp.
Cultivation
et al. (1985)
torques
Norfloxacin
et al. (1985), Pecquet et al. (1986)
Ofloxacin
(1987)
(1987)
Levofloxacin, Gatifloxacin, Trovafloxacin, Moxifloxacin
Dramatic decrease in enterobacteria
Cultivation
Edlund et al. (1997b), Edlund and Nord (1999a), van Nispen
Strong decrease in aerobic Gram-positive cocci
Cultivation
Edlund et al. (1997b), Edlund and Nord (1999a), van Nispen
Levofloxacin, gatifloxacin: decrease in clostridia
Cultivation
Edlund et al. (1997b), Edlund and Nord (1999a)
Gatifloxacin: decrease in fusobacteria
Cultivation
Edlund and Nord (1999a)
et al. (1998)
et al. (1998)
GLYCOPEPTIDS
Oral vancomycin
Decrease in enterococci
Cultivation
Edlund et al. (1997a), Lund et al. (2000)
Decrease in staphylococci
Cultivation
Van der Auwera et al. (1996)
Overgrowth of lactobacilli (and pediococci)
Cultivation
Edlund et al. (1997a), Lund et al. (2000), Van der Auwera
Strong suppression or elimination of bacteroides
Cultivation
Edlund et al. (1997a), Lund et al. (2000)
Decrease in clostridia and bifidobacteria
Cultivation
Lund et al. (2000)
Cultivation
Van der Auwera et al. (1996)
Cultivation
Van der Auwera et al. (1996)
et al. (1996)
Oral teicoplanin
Increase in Gram-negative aerobic rods and total numbers
of aerobes
Increase in lactobacilli and pediococci
LINEZOLID
Reduction of enterococci
Cultivation
Lode et al. (2001)
Reduction of bifidobacteria, lactobacilli, clostridia, and
Cultivation
Lode et al. (2001)
Cultivation
Lode et al. (2001)
Cultivation
Saarela et al. (2007)
bacteroides
Increase in Klebsiella
TETRACYCLINES
Doxycycline
Decrease in bifidobacteria
Tigecycline
Decrease in enterococci
Cultivation
Nord et al. (2006)
Decrease in E. coli
Cultivation
Nord et al. (2006)
Increase of other enterobacteria (Klebsiella and
Cultivation
Nord et al. (2006)
Enterobacter spp.)
(Continued)
Frontiers in Cellular and Infection Microbiology
www.frontiersin.org
November 2012 | Volume 2 | Article 136 | 12
Lagier et al.
Human gut microbiota
Table 1 | Continued
Antibiotic
Method
References
Marked reduction of lactobacilli and bifidobacteria
Cultivation
Nord et al. (2006)
Increase in yeasts
Cultivation
Nord et al. (2006)
Dramatic decrease in streptococci and enterobacteria
Cultivation
Brismar et al. (1991)
Decrease in clostridia, lactobacilli, bifidobacteria, and
Cultivation
Brismar et al. (1991)
Reduction of enterobacteria, E. coli, and streptococci
Cultivation
Brismar et al. (1991), Edlund et al. (2000b)
Dramatic decrease in clostridia, and bacteroides
Cultivation
Brismar et al. (1991)
Reduction of lactobacilli and bifidobacteria
Cultivation
Brismar et al. (1991), Edlund et al. (2000b)
Cultivation
Edlund et al. (2000a)
Cultivation
Edlund et al. (2000a)
Increase in enterobacteria
T-RFLP
Jernberg et al. (2005b)
Decrease in total anaerobic bacteria
Cultivation
Nord et al. (1997)
Decrease in lactobacilli and Bacteroides
Cultivation
Nord et al. (1997), Sullivan et al. (2003)
Decrease in clostridia
Cultivation
Nord et al. (1997)
Disappearance of bifidobacteria
Cultivation
Jernberg et al. (2005b), Nord et al. (1997)
Dramatic decrease in Bifidobacterium, Clostridium
T-RFLP
Jernberg et al. (2005b)
DGGE
Donskey et al. (2003)
DGGE
Donskey et al. (2003)
MACROLIDES, LINCOSAMIDES, SYNERGISTINS
Erythromycin
bacteroides
Clarithromycin
Telithromycin
Decrease in E. coli but overgrowth of non-E. coli
enterobacteria
Reduction of lactobacilli and bifidobacteria
Clindamycin
(particularly C. coccoides subgroup as Eubacterium) and
Bacteroides
Suppression of B. vulgatus, B. acidofasciens, F. prausnitzii,
C. indolis, and C. leptum cluster
No change in B. thetaiotaomicron and B. uniformis
Streptogramins: Quinupristin/dalfopristin
Decrease in anaerobic Gram-negative bacteria
Cultivation
Scanvic-Hameg et al. (2002)
Increase in enterococci and enterobacteria
Cultivation
Scanvic-Hameg et al. (2002)
Cultivation
Mavromanolakis et al. (1997)
Cultivation
Sullivan et al. (2001)
Cultivation
Mavromanolakis et al. (1997)
OTHERS
Cotrimoxazole
Suppression of Enterobacteriaceae
Metronidazole
No significant change but not enough data available
Nitrofurantoin
No impact on intestinal microflora
REDUCTIONIST APPROACH AND BIASES
In their attempts to reduce ignorance and in contrast to the
holistic approach based on the combination of conventional techniques and technology-driven methods, which enable researchers
to study and make sense of a complex ecosystem, diverse studies based on experimental models have induced reductionism
in the understanding of human microbiota and have generated
contradictory results (Raoult, 2010; Fang and Casadevall, 2011).
HYPOTHESIS-DRIVEN RESEARCH VERSUS HOLISTIC-DRIVEN
RESEARCH
Because it has been suggested that gut microbiota play a role in
health and disease, it has been attractive to find a stable model
to help scientists to understand host-gut microbiota mutualism,
but this relationship is very complex and involves control diets,
Frontiers in Cellular and Infection Microbiology
genetics, and environmental conditions. The first germ-free model
was used by Pasteur in 1885. Since that time, various models have
been used to study gut microbiota, including germ-free neonatal pigs (Meurens et al., 2007), zebrafish (Rawls et al., 2004), and
gnotobiotic mice, which is the most effective tool (Backhed et al.,
2007; Goodman et al., 2011).
Conversely, based on observations and not supported by any
preconceived hypothesis, several findings by different research
teams have shown a significant reduction in Bacteroidetes proportions in obese patients (Armougom et al., 2009; Turnbaugh et al.,
2009; Million et al., 2011). Comparing the composition of the
gut microbiota between young adult female monozygotic or dizygotic twins who are obese or lean and their mothers, Turnbaugh
found that obesity was associated with reduced bacterial diversity
and, notably, a reduced proportion of Bacteroidetes. Moreover, the
www.frontiersin.org
November 2012 | Volume 2 | Article 136 | 13
Lagier et al.
Human gut microbiota
genes over-represented within obese individuals exclusively belong
to the Firmicutes phylum (Turnbaugh et al., 2009). Nevertheless,
previous investigations based on genomic data and experimental
models have shown that co-colonization of B. thetaiotaomicron
and M. smithii in the digestive tract of xenobiotic mice was
responsible for significant weight gain, with discordance between
sequence analysis results and the initial hypothesis (Xu et al., 2003;
Samuel and Gordon, 2006).
CONFLICTS OF INTEREST
67% were supported by the food industry. Moreover, the results
of industry-supported trials were significantly associated with a
higher quality of reporting score associated with long-term WL.
Moreover, compounding this problem, some scientists do not
declare their conflicts of interest. Based on these data, and the
considerable financial involvement associated with human gut
microbiota research, notably in obesity, we regret that there is not
more public funding (Smith, 2005), and that conflict of interest
with food industry are not actively required as for pharmaceutical
industry.
Finally, to exhaustively review the topic of human gut microbiota
composition and mutualism with the host, it would be morally
objectionable not to address the central influence of funding
sources and transparency disclosures (Million and Raoult, 2012).
A transparency declaration of conflict of interest is important
for publication in the medical literature. Lundh et al. (2010), based
on the articles published in six of the most prestigious medical
journals, showed that the publication of studies financed by industries was associated with an increase in the impact factor of the
journal. Regarding the economic aspect and the payment of physicians by five manufacturers of hip and knee prostheses, a recent
study confirmed that only approximately 80% of direct payments
and 50% of indirect payments to physicians have been disclosed
(Okike et al., 2009). Some authors even consider that publications
in medical journals are a marketing tool for the pharmaceutical
industry (Smith, 2005). In the beverage and food industry, Levine
studied the financial relationships between industry and authors
who have published research on alimentary substitutes. Classifying
these publications as neutral, critical, or supportive toward the alimentary substitutes, the authors suggested a significant association
between the authors who support the efficiency of the substitute
and the authors with financial relationships with the industrial
company (Levine et al., 2003).
Finally, Thomas et al. (2008) has recently shown that of
63 randomized trials published regarding nutrition and obesity,
CONCLUDING REMARKS
Factors affecting the composition of the gut microbiota and the
relationship with the hosts are of considerable complexity. Both
physiological and external factors are often unstable over time,
influencing the gut microbiota. Despite the contribution of recent
technologies, the repertoire of this ecosystem remains incomplete.
As a striking example, despite the dramatic increase in the number of publications regarding gut microbiota, simplistic anomalies
persist, such as the discordance among microscopic observation,
pyrosequencing, and culture results. We regret that fewer studies
are based on observation and description in opposition to studies
performed to confirm a hypothesis. Indeed, it is paradoxical to
design experiments and models to confirm a hypothesis because
the ecosystem is only partially described. Finally, the central problem of funding sources and transparency declarations lead us to
hope that public funding will develop food-industry-independent
research to increase confidence in the results.
In the future, we think that culturomics followed by the highthroughput genome sequencing and its applications as the exploration of host-pathogen interactions will allows to capture the relationships in the gut microbiota. In addition, technology advances
in pyrosequencing with higher reads fragment analysis, may facilitate the analysis to low taxonomic level (genera, species) reducing
consequently the depth bias.
REFERENCES
Agans, R., Rigsbee, L., Kenche, H.,
Michail, S., Khamis, H. J., and Paliy,
O. (2011). Distal gut microbiota of
adolescent children is different from
that of adults. FEMS Microbiol. Ecol.
77, 404–412.
Anadon, A., Martinez-Larranaga, M.
R., and Aranzazu, M. M. (2006).
Probiotics for animal nutrition in
the European Union. Regulation
and safety assessment. Regul. Toxicol.
Pharmacol. 45, 91–95.
Andersson, A. F., Lindberg, M., Jakobsson, H., Backhed, F., Nyren, P.,
and Engstrand, L. (2008). Comparative analysis of human gut
microbiota by barcoded pyrosequencing. PLoS ONE 3, e2836.
doi:10.1371/journal.pone.0002836
Angelakis, E., Armougom, F., Million,
M., and Raoult, D. (2012a). The
relationship between gut microbiota
and weight gain in humans. Future
Microbiol. 7, 91–109.
Angelakis, E., Bastelica, D., Ben, A. A., El
Filali, A., Dutour, A., Mege, J. L., et al.
(2012b). An evaluation of the effects
of Lactobacillus ingluviei on body
weight, the intestinal microbiome
and metabolism in mice. Microb.
Pathog. 52, 61–68.
Angelakis, E., and Raoult, D. (2010).
The increase of Lactobacillus species
in the gut flora of newborn broiler
chicks and ducks is associated with
weight gain. PLoS ONE 5, e10463.
doi:10.1371/journal.pone.0010463
Armougom, F., Henry, M., Vialettes,
B., Raccah, D., and Raoult, D.
(2009). Monitoring bacterial community of human gut microbiota
reveals an increase in Lactobacillus
in obese patients and Methanogens
in anorexic patients. PLoS ONE
4, e7125. doi:10.1371/journal.pone.
0007125
Arumugam, M., Raes, J., Pelletier, E.,
Le, P. D., Yamada, T., Mende, D.
R., et al. (2011). Enterotypes of the
Frontiers in Cellular and Infection Microbiology
human gut microbiome. Nature 473,
174–180.
Bacher, K., Schaeffer, M., Lode, H.,
Nord, C. E., Borner, K., and
Koeppe, P. (1992). Multiple dose
pharmacokinetics,
safety,
and
effects on faecal microflora, of
cefepime in healthy volunteers.
J. Antimicrob. Chemother. 30,
365–375.
Backhed, F., Ding, H., Wang, T., Hooper,
L. V., Koh, G. Y., Nagy, A., et al.
(2004). The gut microbiota as an
environmental factor that regulates
fat storage. Proc. Natl. Acad. Sci.
U.S.A. 101, 15718–15723.
Backhed, F., Ley, R. E., Sonnenburg, J.
L., Peterson, D. A., and Gordon, J.
I. (2005). Host-bacterial mutualism
in the human intestine. Science 307,
1915–1920.
Backhed, F., Manchester, J. K.,
Semenkovich, C. F., and Gordon, J. I.
(2007). Mechanisms underlying the
resistance to diet-induced obesity
www.frontiersin.org
in germ-free mice. Proc. Natl. Acad.
Sci. U.S.A. 104, 979–984.
Balamurugan, R., George, G., Kabeerdoss, J., Hepsiba, J., Chandragunasekaran, A. M., and Ramakrishna, B. S. (2010). Quantitative differences in intestinal Faecalibacterium prausnitzii in obese
Indian children. Br. J. Nutr. 103,
335–338.
Barna, J. C., and Williams, D. H. (1984).
The structure and mode of action of
glycopeptide antibiotics of the vancomycin group. Annu. Rev. Microbiol. 38, 339–357.
Bartosch, S., Fite, A., Macfarlane, G.
T., and McMurdo, M. E. (2004).
Characterization of bacterial communities in feces from healthy
elderly volunteers and hospitalized elderly patients by using realtime PCR and effects of antibiotic treatment on the fecal microbiota. Appl. Environ. Microbiol. 70,
3575–3581.
November 2012 | Volume 2 | Article 136 | 14
Lagier et al.
Bastard, J. P., Maachi, M., Lagathu, C.,
Kim, M. J., Caron, M., Vidal, H., et al.
(2006). Recent advances in the relationship between obesity, inflammation, and insulin resistance. Eur.
Cytokine Netw. 17, 4–12.
Bergan, T., Delin, C., Johansen, S.,
Kolstad, I. M., Nord, C. E.,
and Thorsteinsson, S. B. (1986).
Pharmacokinetics of ciprofloxacin
and effect of repeated dosage
on salivary and fecal microflora.
Antimicrob. Agents Chemother. 29,
298–302.
Bergan, T., Nord, C. E., and Thorsteinsson, S. B. (1991). Effect of
meropenem on the intestinal
microflora. Eur. J. Clin. Microbiol.
Infect. Dis. 10, 524–527.
Biesbroek, G., Sanders, E. A., Roeselers, G., Wang, X., Caspers, M. P.,
Trzcinski, K., et al. (2012). Deep
sequencing analyses of low density
microbial communities: working at
the boundary of accurate microbiota
detection. PLoS ONE 7, e32942.
doi:10.1371/journal.pone.0032942
Black, F., Einarsson, K., Lidbeck, A.,
Orrhage, K., and Nord, C. E.
(1991). Effect of lactic acid producing bacteria on the human intestinal microflora during ampicillin
treatment. Scand. J. Infect. Dis. 23,
247–254.
Bollmann, A., Lewis, K., and Epstein,
S. S. (2007). Incubation of environmental samples in a diffusion chamber increases the diversity of recovered isolates. Appl. Environ. Microbiol. 73, 6386–6390.
Borzio, M., Salerno, F., Saudelli, M.,
Galvagno, D., Piantoni, L., and Fragiacomo, L. (1997). Efficacy of oral
ciprofloxacin as selective intestinal
decontaminant in cirrhosis. Ital. J.
Gastroenterol. Hepatol. 29, 262–266.
Bousbia, S., Papazian, L., Saux, P.,
Forel, J. M., Auffray, J. P., Martin, C., et al. (2012). Repertoire
of intensive care unit pneumonia
microbiota. PLoS ONE 7, e32486.
doi:10.1371/journal.pone.0032486
Boyer, M., Yutin, N., Pagnier, I., Barrassi,
L., Fournous, G., Espinosa, L., et al.
(2009). Giant Marseillevirus highlights the role of amoebae as a melting pot in emergence of chimeric
microorganisms. Proc. Natl. Acad.
Sci. U.S.A. 106, 21848–21853.
Breitbart, M., Haynes, M., Kelley, S.,
Angly, F., Edwards, R. A., Felts, B.,
et al. (2008). Viral diversity and
dynamics in an infant gut. Res.
Microbiol. 159, 367–373.
Brismar, B., Edlund, C., Malmborg,
A. S., and Nord, C. E. (1990).
Ciprofloxacin concentrations and
impact of the colon microflora
Human gut microbiota
in patients undergoing colorectal surgery. Antimicrob. Agents
Chemother. 34, 481–483.
Brismar, B., Edlund, C., and Nord, C. E.
(1991). Comparative effects of clarithromycin and erythromycin on the
normal intestinal microflora. Scand.
J. Infect. Dis. 23, 635–642.
Brismar, B., Edlund, C., and Nord, C.
E. (1993). Impact of cefpodoxime
proxetil and amoxicillin on the normal oral and intestinal microflora.
Eur. J. Clin. Microbiol. Infect. Dis. 12,
714–719.
Brumfitt, W., Franklin, I., Grady, D.,
Hamilton-Miller, J. M., and Iliffe, A.
(1984). Changes in the pharmacokinetics of ciprofloxacin and fecal
flora during administration of a 7day course to human volunteers.
Antimicrob. Agents Chemother. 26,
757–761.
Cani, P. D., Bibiloni, R., Knauf,
C., Waget, A., Neyrinck, A. M.,
Delzenne, N. M., et al. (2008).
Changes in gut microbiota control
metabolic
endotoxemia-induced
inflammation in high-fat dietinduced obesity and diabetes in
mice. Diabetes 57, 1470–1481.
Cavallaro, V., Catania, V., Bonaccorso,
R., Mazzone, S., Speciale, A., Di
Marco, R., et al. (1992). Effect of
a broad-spectrum cephalosporin on
the oral and intestinal microflora
in patients undergoing colorectal
surgery. J. Chemother. 4, 82–87.
Claesson, M. J., Cusack, S., O’Sullivan,
O., Greene-Diniz, R., de Weerd, H.,
and Flannery, E. (2011). Composition, variability, and temporal stability of the intestinal microbiota of the
elderly. Proc. Natl. Acad. Sci. U.S.A.
108(Suppl. 1), 4586–4591.
Claesson, M. J.,Wang, Q., O’Sullivan, O.,
Greene-Diniz, R., Cole, J. R., Ross, R.
P., et al. (2010). Comparison of two
next-generation sequencing technologies for resolving highly complex microbiota composition using
tandem variable 16S rRNA gene
regions. Nucleic Acids Res. 38, e200.
Collado, M. C., Isolauri, E., Laitinen,
K., and Salminen, S. (2008). Distinct composition of gut microbiota
during pregnancy in overweight and
normal-weight women. Am. J. Clin.
Nutr. 88, 894–899.
De Filippo, C., Cavalieri, D., Di Paola,
M., Ramazzotti, M., Poullet, J. B.,
Massart, S., et al. (2010). Impact
of diet in shaping gut microbiota
revealed by a comparative study
in children from Europe and rural
Africa. Proc. Natl. Acad. Sci. U.S.A.
107, 14691–14696.
De Hertogh, G., Aerssens, J., De Hoogt,
R., Peeters, P., Verhasselt, P., Van
Frontiers in Cellular and Infection Microbiology
Eyken, P., et al. (2006). Validation of
16S rDNA sequencing in microdissected bowel biopsies from Crohn’s
disease patients to assess bacterial flora diversity. J. Pathol. 209,
532–539.
de Vries-Hospers, H. G., Welling, G. W.,
and van der Waaij, D. (1985). Norfloxacin for selective decontamination: a study in human volunteers.
Prog. Clin. Biol. Res. 181, 259–262.
Delzenne, N., and Reid, G. (2009). No
causal link between obesity and probiotics. Nat. Rev. Microbiol. 7, 901.
Dethlefsen, L., Huse, S., Sogin, M.
L., and Relman, D. A. (2008).
The pervasive effects of an antibiotic on the human gut microbiota, as revealed by deep 16S
rRNA sequencing. PLoS Biol. 6, e280.
doi:10.1371/journal.pbio.0060280
Donskey, C. J., Hujer, A. M., Das, S. M.,
Pultz, N. J., Bonomo, R. A., and Rice,
L. B. (2003). Use of denaturing gradient gel electrophoresis for analysis
of the stool microbiota of hospitalized patients. J. Microbiol. Methods
54, 249–256.
Drasar, B. S., Crowther, J. S., Goddard,
P., Hawksworth, G., Hill, M. J., Peach,
S., et al. (1973). The relation between
diet and the gut microflora in man.
Proc. Nutr. Soc. 32, 49–52.
Dridi, B., Fardeau, M. L., Ollivier,
B., Raoult, D., and Drancourt,
M. (2012a). Methanomassiliicoccus
luminyensis, gen. nov., sp. nov., a
novel methanogenic Archaea isolated from human feces. Int. J. Syst.
Evol. Microbiol. 62, 1902–1907.
Dridi, B., Henry, M., Richet, H.,
Raoult, D., and Drancourt, M.
(2012b). Age-related prevalence of
Methanomassiliicoccus luminyensis
in the human gut microbiome.
APMIS 120, 773–777.
Dridi, B., Henry, M., El Khéchine,
A., Raoult, D., and Drancourt,
M. (2009). High prevalence of
Methanobrevibacter smithii and
Methanosphaera
stadtmanae
detected in the human gut using
an improved DNA detection
protocol. PLoS ONE 4, e7063.
doi:10.1371/journal.pone.0007063
Dridi, B., Raoult, D., and Drancourt, M.
(2011). Archaea as emerging organisms in complex human microbiomes. Anaerobe 17, 56–63.
Eckburg, P. B., Bik, E. M., Bernstein, C.
N., Purdom, E., Dethlefsen, L., Sargent, M., et al. (2005). Diversity of
the human intestinal microbial flora.
Science 308, 1635–1638.
Edlund, C., Alvan, G., Barkholt, L.,
Vacheron, F., and Nord, C. E.
(2000a). Pharmacokinetics and
comparative effects of telithromycin
www.frontiersin.org
(HMR 3647) and clarithromycin
on the oropharyngeal and intestinal microflora. J. Antimicrob.
Chemother. 46, 741–749.
Edlund, C., Beyer, G., Hiemer-Bau, M.,
Ziege, S., Lode, H., and Nord, C.
E. (2000b). Comparative effects of
moxifloxacin and clarithromycin on
the normal intestinal microflora.
Scand. J. Infect. Dis. 32, 81–85.
Edlund, C., Barkholt, L., OlssonLiljequist, B., and Nord, C. E.
(1997a). Effect of vancomycin on
intestinal flora of patients who previously received antimicrobial therapy. Clin. Infect. Dis. 25, 729–732.
Edlund, C., Sjostedt, S., and Nord,
C. E. (1997b). Comparative effects
of levofloxacin and ofloxacin on
the normal oral and intestinal
microflora. Scand. J. Infect. Dis. 29,
383–386.
Edlund, C., Bergan, T., Josefsson,
K., Solberg, R., and Nord, C. E.
(1987). Effect of norfloxacin on
human oropharyngeal and colonic
microflora and multiple-dose pharmacokinetics. Scand. J. Infect. Dis.
19, 113–121.
Edlund, C., Kager, L., Malmborg, A.
S., Sjostedt, S., and Nord, C. E.
(1988). Effect of ofloxacin on oral
and gastrointestinal microflora in
patients undergoing gastric surgery.
Eur. J. Clin. Microbiol. Infect. Dis. 7,
135–143.
Edlund, C., and Nord, C. E. (1999a).
Ecological effect of gatifloxacin
on the normal human intestinal
microflora. J. Chemother. 11, 50–53.
Edlund, C., and Nord, C. E. (1999b).
Effect of quinolones on intestinal
ecology. Drugs 58(Suppl. 2), 65–70.
Enzensberger, R., Shah, P. M., and
Knothe, H. (1985). Impact of oral
ciprofloxacin on the faecal flora
of healthy volunteers. Infection 13,
273–275.
Epstein, S. S. (2009). Microbial awakenings. Nature 457, 1083.
Esposito, S., Barba, D., Galante,
D., Gaeta, G. B., and Laghezza,
O. (1987). Intestinal microflora
changes induced by ciprofloxacin
and treatment of portal-systemic
encephalopathy (PSE). Drugs Exp.
Clin. Res. 13, 641–646.
Fang, F. C., and Casadevall, A. (2011).
Reductionistic and holistic science.
Infect. Immun. 79, 1401–1404.
Finegold, S. M., Attebery, H. R., and
Sutter, V. L. (1974). Effect of diet
on human fecal flora: comparison of
Japanese and American diets. Am. J.
Clin. Nutr. 27, 1456–1469.
Finegold, S. M., Sutter, V. L., Sugihara, P.
T., Elder, H. A., Lehmann, S. M., and
Phillips, R. L. (1977). Fecal microbial
November 2012 | Volume 2 | Article 136 | 15
Lagier et al.
flora in seventh day adventist populations and control subjects. Am. J.
Clin. Nutr. 30, 1781–1792.
Floor, M., van Akkeren, F., RozenbergArska, M., Visser, M., Kolsters, A.,
Beumer, H., et al. (1994). Effect
of loracarbef and amoxicillin on
the oropharyngeal and intestinal
microflora of patients with bronchitis. Scand. J. Infect. Dis. 26,
191–197.
Fogarty, A. W., Glancy, C., Jones, S.,
Lewis, S. A., McKeever, T. M., and
Britton, J. R. (2008). A prospective
study of weight change and systemic
inflammation over 9 y. Am. J. Clin.
Nutr. 87, 30–35.
Frank, D. N., St Amand, A. L.,
Feldman, R. A., Boedeker, E. C.,
Harpaz, N., and Pace, N. R. (2007).
Molecular-phylogenetic characterization of microbial community
imbalances in human inflammatory
bowel diseases. Proc. Natl. Acad. Sci.
U.S.A. 104, 13780–13785.
Fuerst, J. A., and Sagulenko, E. (2011).
Beyond the bacterium: planctomycetes challenge our concepts of
microbial structure and function.
Nat. Rev. Microbiol. 9, 403–413.
Galvez, A., Maqueda, M., MartinezBueno, M., and Valdivia, E. (1998).
Publication rates reveal trends in
microbiological research. ASM News
64, 269–275.
Goodman, A. L., Kallstrom, G., Faith,
J. J., Reyes, A., Moore, A., Dantas, G., et al. (2011). Extensive personal human gut microbiota culture collections characterized and
manipulated in gnotobiotic mice.
Proc. Natl. Acad. Sci. U.S.A. 108,
6252–6257.
Gorlas, A., Robert, C., Gimenez,
G., Drancourt, M., and Raoult,
D. (2012). Complete genome
sequence of Methanomassiliicoccus
luminyensis, the largest genome of a
human-associated Archaea species.
J. Bacteriol. 194, 4745.
Gossling, J., and Slack, J. M. (1974).
Predominant gram-positive bacteria
in human feces: numbers, variety,
and persistence. Infect. Immun. 9,
719–729.
Grzeskowiak, L., Collado, M. C., Mangani, C., Maleta, K., Laitinen, K.,
and Ashorn, P. (2012). Distinct gut
microbiota in south eastern African
and northern European infants.
J. Pediatr. Gastroenterol. Nutr. 54,
812–816.
Hamad, I., Sokhna, C., Raoult, D.,
and Bittar, F. (2012). Molecular detection of eukaryotes in a
single human stool sample from
Senegal. PLos ONE 7, e40888.
doi:10.1371/journal.pone.0040888
Human gut microbiota
Hayashi, H., Sakamoto, M., and Benno,
Y. (2002a). Fecal microbial diversity in a strict vegetarian as determined by molecular analysis and
cultivation. Microbiol. Immunol. 46,
819–831.
Hayashi, H., Sakamoto, M., and Benno,
Y. (2002b). Phylogenetic analysis of the human gut microbiota
using 16S rDNA clone libraries
and strictly anaerobic culture-based
methods. Microbiol. Immunol. 46,
535–548.
Hildebrandt, M. A., Hoffmann, C.,
Sherrill-Mix, S. A., Keilbaugh, S. A.,
Hamady, M., and Chen, Y. Y. (2009).
High-fat diet determines the composition of the murine gut microbiome
independently of obesity. Gastroenterology 137, 1716–1724.
Hill, M. J., Goddard, P., and Williams,
R. E. (1971). Gut bacteria and aetiology of cancer of the breast. Lancet
2, 472–473.
Holt, H. A., Lewis, D. A., White, L. O.,
Bastable, S. Y., and Reeves, D. S.
(1986). Effect of oral ciprofloxacin
on the faecal flora of healthy
volunteers. Eur. J. Clin. Microbiol. 5,
201–205.
Hong, S., Bunge, J., Leslin, C., Jeon, S.,
and Epstein, S. S. (2009). Polymerase
chain reaction primers miss half of
rRNA microbial diversity. ISME J. 3,
1365–1373.
Hotamisligil, G. S. (2006). Inflammation and metabolic disorders. Nature
444, 860–867.
Hugenholtz, P. (2002). Exploring prokaryotic diversity in the
genomic era. Genome Biol. 3,
reviews0003.1–reviews0003.8.
Jernberg, C., Sullivan, A., Edlund, C.,
and Jansson, J. K. (2005a). Monitoring of antibiotic-induced alterations in the human intestinal
microflora and detection of probiotic strains by use of terminal restriction fragment length polymorphism. Appl. Environ. Microbiol. 71,
501–506.
Jernberg, C., Sullivan, A., Edlund, C.,
and Jansson, J. K. (2005b). Monitoring of antibiotic-induced alterations
in the human intestinal microflora
and detection of probiotic strains
by use of terminal restriction fragment length polymorphism. Appl.
Environ. Microbiol. 71, 501–506.
Kaeberlein, T., Lewis, K., and Epstein,
S. S. (2002). Isolating “uncultivable”
microorganisms in pure culture in
a simulated natural environment.
Science 296, 1127–1129.
Kalliomaki, M., Collado, M. C., Salminen, S., and Isolauri, E. (2008). Early
differences in fecal microbiota composition in children may predict
Frontiers in Cellular and Infection Microbiology
overweight. Am. J. Clin. Nutr. 87,
534–538.
Kassinen, A., Krogius-Kurikka, L.,
Makivuokko, H., Rinttila, T., Paulin,
L., and Corander, J. (2007). The fecal
microbiota of irritable bowel syndrome patients differs significantly
from that of healthy subjects. Gastroenterology 133, 24–33.
Kerac, M., Bunn, J., Seal, A., Thindwa,
M., Tomkins, A., and Sadler, K.
(2009). Probiotics and prebiotics
for severe acute malnutrition
(PRONUT study): a double-blind
efficacy randomised controlled trial
in Malawi. Lancet 374, 136–144.
Koenig, J. E., Spor, A., Scalfone, N.,
Fricker, A. D., Stombaugh, J., and
Knight, R. (2011). Succession of
microbial consortia in the developing infant gut microbiome. Proc.
Natl. Acad. Sci. U.S.A. 108(Suppl. 1),
4578–4585.
Kokcha, S., Mishra, A. K., Lagier, J. C.,
Million, M., Leroy, Q., Raoult, D., et
al. (2012). Non contiguous finished
genome sequence and description of
Bacillus timonensis sp. nov. Stand.
Genomic Sci. 6, 346–355.
Kootte, R. S., Vrieze, A., Holleman, F.,
Dallinga-Thie, G. M., Zoetendal, E.
G., and de Vos, W. M. (2012). The
therapeutic potential of manipulating gut microbiota in obesity and
type 2 diabetes mellitus. Diabetes
Obes. Metab. 14, 112–120.
Kovatcheva-Datchary, P., Zoetendal, E.
G., Venema, K., de Vos, W. M., and
Smidt, H. (2009). Tools for the tract:
understanding the functionality of
the gastrointestinal tract. Therap.
Adv. Gastroenterol. 2, 9–22.
Krueger, W. A., Ruckdeschel, G., and
Unertl, K. (1997). Influence of intravenously administered ciprofloxacin
on aerobic intestinal microflora and
fecal drug levels when administered simultaneously with sucralfate.
Antimicrob. Agents Chemother. 41,
1725–1730.
Lagier, J. C., Armougom, F., Million,
M., Hugon, P., Pagnier, I., Robert,
C., et al. (2012a). Microbial culturomics: a paradigm shift in the
human gut microbiome study. Clin.
Microbiol. Infect. PMID: 23033984.
[Epub ahead of print].
Lagier, J. C., Armougom, F., Mishra, A.
K., Nguyen, T. T., Raoult, D., and
Fournier, P. E. (2012b). Non contiguous finished genome sequence
and description of Alistipes timonensis sp. nov. Stand. Genomic Sci. 6,
315–324.
Lagier, J. C., El Karkouri, K., Nguyen,
T. T., Armougom, F., Raoult,
D., and Fournier, P. E. (2012c).
Non-contiguous finished genome
www.frontiersin.org
sequence and description of Anaerococcus senegalensis sp. nov. Stand.
Genomic Sci. 6, 116–125.
Larsen, N., Vogensen, F. K., van den
Berg, F. W., Nielsen, D. S., Andreasen,
A. S., and Pedersen, B. K. (2010). Gut
microbiota in human adults with
type 2 diabetes differs from nondiabetic adults. PLoS ONE 5, e9085.
doi:10.1371/journal.pone.0009085
Lay, C., Rigottier-Gois, L., Holmstrom,
K., Rajilic, M., Vaughan, E. E., and de
Vos, W. M. (2005). Colonic microbiota signatures across five northern
European countries. Appl. Environ.
Microbiol. 71, 4153–4155.
Lee, S., Sung, J., Lee, J., and Ko, G. (2011).
Comparison of the gut microbiotas
of healthy adult twins living in South
Korea and the United States. Appl.
Environ. Microbiol. 77, 7433–7437.
Leigh, D. A., Emmanuel, F. X. S., and
Tighe, C. (1985). “Pharmacokinetic
studies of norfloxacin in healthy volunteers, and effect on the fecal flora,”
in Proceedings of the 14th International Congress of Chemotherapy,
1835–1836, Kyoto.
Levine, J., Gussow, J. D., Hastings,
D., and Eccher, A. (2003) Authors’
financial relationships with the food
and beverage industry and their
published positions on the fat substitute olestra. Am. J. Public Health
93, 664–669.
Ley, R. E., Backhed, F., Turnbaugh, P.,
Lozupone, C. A., Knight, R. D., and
Gordon, J. I. (2005). Obesity alters
gut microbial ecology. Proc. Natl.
Acad. Sci. U.S.A. 102, 11070–11075.
Ley, R. E., Peterson, D. A., and Gordon, J.
I. (2006a). Ecological and evolutionary forces shaping microbial diversity in the human intestine. Cell 124,
837–848.
Ley, R. E., Turnbaugh, P. J., Klein, S.,
and Gordon, J. I. (2006b). Microbial ecology: human gut microbes
associated with obesity. Nature 444,
1022–1023.
Liszt, K., Zwielehner, J., Handschur,
M., Hippe, B., Thaler, R., and
Haslberger, A. G. (2009). Characterization of bacteria, clostridia and
Bacteroides in faeces of vegetarians
using qPCR and PCR-DGGE fingerprinting. Ann. Nutr. Metab. 54,
253–257.
Ljungberg, B., Nilsson-Ehle, I., Edlund,
C., and Nord, C. E. (1990). Influence of ciprofloxacin on the colonic
microflora in young and elderly volunteers: no impact of the altered
drug absorption. Scand. J. Infect. Dis.
22, 205–208.
Lode, H., Von der, H. N., Ziege, S.,
Borner, K., and Nord, C. E. (2001).
Ecological effects of linezolid versus
November 2012 | Volume 2 | Article 136 | 16
Lagier et al.
amoxicillin/clavulanic acid on the
normal intestinal microflora. Scand.
J. Infect. Dis. 33, 899–903.
Looft, T., Johnson, T. A., Allen, H. K.,
Bayles, D. O., Alt, D. P., Stedtfeld, R.
D. (2012). In-feed antibiotic effects
on the swine intestinal microbiome.
Proc. Natl. Acad. Sci. U.S.A. 109,
1691–1696.
Lund, B., Edlund, C., Barkholt, L., Nord,
C. E., Tvede, M., and Poulsen, R. L.
(2000). Impact on human intestinal microflora of an Enterococcus
faecium probiotic and vancomycin.
Scand. J. Infect. Dis. 32, 627–632.
Lundh, A., Barbateskovic, M., Hrobjartsson, A., and Gotzsche, P.
C. (2010). Conflicts of interest
at medical journals: the influence of industry-supported randomised trials on journal impact
factors and revenue – cohort
study. PLoS Med. 7, e1000354.
doi:10.1371/journal.pmed.1000354
Lysholm, F., Wetterbom, A., Lindau, C., Darban, H., Bjerkner,
A., and Fahlander, K. (2012).
Characterization of the viral
microbiome in patients with
severe lower respiratory tract
infections, using metagenomic
sequencing. PLoS ONE 7: e30875.
doi:10.1371/journal.pone.0030875
Maccaferri, S., Vitali, B., Klinder, A.,
Kolida, S., Ndagijimana, M., and
Laghi, L. (2010). Rifaximin modulates the colonic microbiota of
patients with Crohn’s disease: an
in vitro approach using a continuous culture colonic model system. J. Antimicrob. Chemother. 65,
2556–2565.
Manichanh, C., Rigottier-Gois, L., Bonnaud, E., Gloux, K., Pelletier, E., and
Frangeul, L. (2006). Reduced diversity of faecal microbiota in Crohn’s
disease revealed by a metagenomic
approach. Gut 55, 205–211.
Marchesi, J. R. (2010). Prokaryotic and
eukaryotic diversity of the human
gut. Adv. Appl. Microbiol. 72, 43–62.
Marchesi, J. R. (2011). Human distal gut
microbiome. Environ. Microbiol. 13,
3088–3102.
Mata, L. J., Carrillo, C., and Villatoro,
E. (1969). Fecal microflora in health
persons in a preindustrial region.
Appl. Microbiol. 17, 596–602.
Mavromanolakis, E., Maraki, S., Samonis, G., Tselentis, Y., and Cranidis,
A. (1997). Effect of norfloxacin,
trimethoprim-sulfamethoxazole
and nitrofurantoin on fecal flora of
women with recurrent urinary tract
infections. J. Chemother. 9, 203–207.
McNulty, N. P., Yatsunenko, T., Hsiao,
A., Faith, J. J., Muegge, B. D., and
Goodman, A. L. (2011). The impact
Human gut microbiota
of a consortium of fermented milk
strains on the gut microbiome of
gnotobiotic mice and monozygotic
twins. Sci. Transl. Med. 3, 106ra106.
Meurens, F., Berri, M., Siggers,
R. H., Willing, B. P., Salmon,
H., Van Kessel, A. G., et al.
(2007). Commensal bacteria and
expression of two major intestinal chemokines, TECK/CCL25
and MEC/CCL28, and their
receptors. PLoS ONE 2, e677.
doi:10.1371/journal.pone.0000677
Miller, T. L., Wolin, M. J., Conway
de Macario, E., and Macario, A. J.
(1982). Isolation of Methanobrevibacter smithii from human
feces. Appl. Environ. Microbiol. 43,
227–232.
Million, M., Angelakis, E., Paul, M.,
Armougom, F., Leibovici, L., and
Raoult, D. (2012). Comparative
meta-analysis of the effect of Lactobacillus species on weight gain
in humans and animals. Microb.
Pathog. 53, 100–108.
Million, M., Maraninchi, M., Henry, M.,
Armougom, F., Richet, H., and Carrieri, P. (2011). Obesity-associated
gut microbiota is enriched in
Lactobacillus reuteri and depleted
in Bifidobacterium animalis and
Methanobrevibacter smithii. Int. J.
Obes. (Lond.) 36, 817–825.
Million, M., and Raoult, D. (2012). Publication biases in probiotics. Eur. J.
Epidemiol. PMID: 23086285. [Epub
ahead of print].
Minot, S., Sinha, R., Chen, J., Li, H., Keilbaugh, S. A., and Wu, G. D. (2011).
The human gut virome: interindividual variation and dynamic
response to diet. Genome Res. 21,
1616–1625.
Mishra, A. K., Gimenez, G., Lagier, J. C.,
Robert, C., Raoult, D., and Fournier,
P. E. (2012a). Non contiguous finished genome sequence and description of Alistipes senegalensis sp. nov.
Stand. Genomic Sci. 6, 304–314.
Mishra, A. K., Lagier, J. C., Robert,
C., Raoult, D., and Fournier, P.
E. (2012b). Non contiguous finished genome sequence and description of Clostridium senegalense
sp. nov. Stand. Genomic Sci. 6,
386–395.
Mishra, A. K., Lagier, J. C., Robert,
C., Raoult, D., and Fournier, P. E.
(2012c). Non contiguous finished
genome sequence and description
of Peptoniphilus timonensis sp. nov.
Stand. Genomic Sci. 7. [Epub ahead
of print]
Mishra, A. K., Lagier, J. C., Rivet,
R., Raoult, D., and Fournier, P. E.
(2012d). Non contiguous finished
genome sequence and description
Frontiers in Cellular and Infection Microbiology
of Paenibacillus senegalensis sp. nov.
Stand. Genomic Sci. 7. [Epub ahead
of print]
Monira, S., Nakamura, S., Gotoh,
K., Izutsu, K., Watanabe, H.,
and Alam, N. H. (2011).
Gut
microbiota
of
healthy
and malnourished children in
Bangladesh. Front. Microbiol. 2:228.
doi:10.3389/fmicb.2011.00228
Moore, W. E., and Holdeman, L. V.
(1974a). Human fecal flora: the normal flora of 20 Japanese-Hawaiians.
Appl. Microbiol. 27, 961–979.
Moore, W. E., and Holdeman, L. V.
(1974b). Special problems associated with the isolation and identification of intestinal bacteria in fecal
flora studies. Am. J. Clin. Nutr. 27,
1450–1455.
Mueller, S., Saunier, K., Hanisch, C.,
Norin, E., Alm, L., and Midtvedt, T.
(2006). Differences in fecal microbiota in different European study
populations in relation to age, gender, and country: a cross-sectional
study. Appl. Environ. Microbiol. 72,
1027–1033.
Murphy, E. F., Cotter, P. D., Hogan,
A., O’Sullivan, O., Joyce, A., Fouhy,
F., et al. (2012). Divergent metabolic outcomes arising from targeted
manipulation of the gut microbiota in diet-induced obesity. Gut.
PMID:22345653. [Epub ahead of
print].
Nord, C. E. (1995). Effect of quinolones
on the human intestinal microflora.
Drugs 49(Suppl. 2), 81–85.
Nord, C. E., Brismar, B., KasholmTengve, B., and Tunevall, G. (1993).
Effect of piperacillin/tazobactam
treatment on human bowel
microflora.
J.
Antimicrob.
Chemother. 31(Suppl. A), 61–65.
Nord, C. E., Lidbeck, A., Orrhage,
K., and Sjostedt, S. (1997). Oral
supplementation with lactic acidproducing bacteria during intake of
clindamycin. Clin. Microbiol. Infect.
3, 124–132.
Nord, C. E., Sillerstrom, E., and
Wahlund, E. (2006). Effect of
tigecycline on normal oropharyngeal and intestinal microflora.
Antimicrob. Agents Chemother. 50,
3375–3380.
Nottingham, P. M., and Hungate, R. E.
(1968). Isolation of methanogenic
bacteria from feces of man. J. Bacteriol. 96, 2178–2179.
O’Hara, A. M., and Shanahan, F. (2006).
The gut flora as a forgotten organ.
EMBO Rep. 7, 688–693.
Okike, K., Kocher, M. S., Wei, E.
X., Mehlman, C. T., and Bhandari,
M. (2009). Accuracy of conflictof-interest disclosures reported by
www.frontiersin.org
physicians. N. Engl. J. Med. 361,
1466–1474.
Pagnier, I., Raoult, D., and La Scola,
B. (2008). Isolation and identification of amoeba-resisting bacteria from water in human environment by using an Acanthamoeba
polyphaga co-culture procedure.
Environ. Microbiol. 10, 1135–1144.
Palmer, C., Bik, E. M., Di Giulio, D.
B., Relman, D. A., and Brown,
P. O. (2007). Development of
the human infant intestinal
microbiota. PLoS Biol. 5, e177.
doi:10.1371/journal.pbio.0050177
Parfrey, L. W., Walters, W. A., and
Knight, R. (2011). Microbial eukaryotes in the human microbiome:
ecology, evolution, and future
directions. Front. Microbiol. 2:153.
doi:10.3389/fmicb.2011.00153
Pecquet, S., Andremont, A., and Tancrede, C. (1986). Selective antimicrobial modulation of the intestinal tract by norfloxacin in human
volunteers and in gnotobiotic mice
associated with a human fecal flora.
Antimicrob. Agents Chemother. 29,
1047–1052.
Pecquet, S., Andremont, A., and
Tancrede, C. (1987). Effect of
oral ofloxacin on fecal bacteria
in human volunteers. Antimicrob.
Agents Chemother. 31, 124–125.
Pennisi, E. (2011). Microbiology. Girth
and the gut (bacteria). Science 332,
32–33.
Rajilic-Stojanovic, M., Smidt, H., and de
Vos, W. M. (2007). Diversity of the
human gastrointestinal tract microbiota revisited. Environ. Microbiol. 9,
2125–2136.
Raoult, D. (2008). Obesity pandemics
and the modification of digestive
bacterial flora. Eur. J. Clin. Microbiol.
Infect. Dis. 27, 631–634.
Raoult, D. (2010). Technology-driven
research will dominate hypothesisdriven research: the future of
microbiology. Future Microbiol. 5,
135–137.
Raoult, D., Fenollar, F., Rolain, J. M.,
Minodier, P., Bosdure, E., and Li, W.
(2010). Tropheryma whipplei in children with gastroenteritis. Emerging
Infect. Dis. 16, 776–782.
Raoult, D., La Scola, B., and Birtles, R.
(2007). The discovery and characterization of Mimivirus, the largest
known virus and putative pneumonia agent. Clin. Infect. Dis. 45,
95–102.
Raoult, D., Renesto, P., and Brouqui,
P. (2006). Laboratory infection of
a technician by mimivirus. Ann.
Intern. Med. 144, 702–703.
Rawls, J. F., Samuel, B. S., and
Gordon, J. I. (2004). Gnotobiotic
November 2012 | Volume 2 | Article 136 | 17
Lagier et al.
zebrafish reveal evolutionarily conserved responses to the gut microbiota. Proc. Natl. Acad. Sci. U.S.A.
101, 4596–4601.
Reyes, A., Haynes, M., Hanson, N.,
Angly, F. E., Heath, A. C., Rohwer,
F., et al. (2010). Viruses in the
faecal microbiota of monozygotic
twins and their mothers. Nature 466,
334–338.
Robinson, C. J., and Young, V. B.
(2010). Antibiotic administration
alters the community structure of
the gastrointestinal micobiota. Gut
Microbes 1, 279–284.
Rozenberg-Arska, M., Dekker, A. W.,
and Verhoef, J. (1985). Ciprofloxacin
for selective decontamination of
the alimentary tract in patients
with acute leukemia during remission induction treatment: the effect
on fecal flora. J. Infect. Dis. 152,
104–107.
Saarela, M., Maukonen, J., von Wright,
A., Vilpponen-Salmela, T., Patterson,
A. J., and Scott, K. P. (2007). Tetracycline susceptibility of the ingested
Lactobacillus acidophilus LaCH-5
and Bifidobacterium animalis subsp.
lactis Bb-12 strains during antibiotic/probiotic intervention. Int. J.
Antimicrob. Agents 29, 271–280.
Samuel, B. S., and Gordon, J. I. (2006).
A humanized gnotobiotic mouse
model of host-archaeal-bacterial
mutualism. Proc. Natl. Acad. Sci.
U.S.A. 103, 10011–10016.
Sanders, M. E. (2011). Impact of probiotics on colonizing microbiota
of the gut. J. Clin. Gastroenterol.
45(Suppl.), S115–S119.
Santacruz, A., Marcos, A., Warnberg,
J., Marti, A., Martin-Matillas, M.,
and Campoy, C. (2009). Interplay
between weight loss and gut microbiota composition in overweight
adolescents. Obesity (Silver Spring)
17, 1906–1915.
Savino, F., Roana, J., Mandras, N.,
Tarasco, V., Locatelli, E., and Tullio, V. (2011). Faecal microbiota
in breast-fed infants after antibiotic
therapy. Acta Paediatr. 100, 75–78.
Sbarbati, A., Osculati, F., Silvagni, D.,
Benati, D., Galie, M., and Camoglio,
F. S. (2006). Obesity and inflammation: evidence for an elementary
lesion. Pediatrics 117, 220–223.
Scanlan, P. D., and Marchesi, J. R.
(2008). Micro-eukaryotic diversity
of the human distal gut microbiota:
qualitative assessment using culturedependent and -independent analysis of faeces. ISME J. 2, 1183–1193.
Scanlan, P. D., Shanahan, F., Clune,
Y., Collins, J. K., O’Sullivan, G. C.,
and O’Riordan, M. (2008). Cultureindependent analysis of the gut
Human gut microbiota
microbiota in colorectal cancer and
polyposis. Environ. Microbiol. 10,
789–798.
Scanlan, P. D., Shanahan, F., O’Mahony,
C., and Marchesi, J. R. (2006).
Culture-independent analyses of
temporal variation of the dominant
fecal microbiota and targeted bacterial subgroups in Crohn’s disease. J.
Clin. Microbiol. 44, 3980–3988.
Scanvic-Hameg, A., Chachaty, E., Rey, J.,
Pousson, C., Ozoux, M. L., Brunel,
E., et al. (2002). Impact of quinupristin/dalfopristin (RP59500) on
the faecal microflora in healthy volunteers. J. Antimicrob. Chemother.
49, 135–139.
Schwiertz, A., Taras, D., Schafer, K., Beijer, S., Bos, N. A., Donus, C., et
al. (2010). Microbiota and SCFA in
lean and overweight healthy subjects. Obesity (Silver Spring) 18,
190–195.
Scupham, A. J., Presley, L. L., Wei, B.,
Bent, E., Griffith, N., and McPherson, M. (2006). Abundant and
diverse fungal microbiota in the
murine intestine. Appl. Environ.
Microbiol. 72, 793–801.
Sekirov, I., Russell, S. L., Antunes, L. C.,
and Finlay, B. B. (2010). Gut microbiota in health and disease. Physiol.
Rev. 90, 859–904.
Sekirov, I., Tam, N. M., Jogova, M.,
Robertson, M. L., Li, Y., Lupp, C., et
al. (2008). Antibiotic-induced perturbations of the intestinal microbiota alter host susceptibility to
enteric infection. Infect. Immun. 76,
4726–4736.
Seng, P., Drancourt, M., Gouriet, F., La
Scola, B., Fournier, P. E., Rolain, J. M.,
et al. (2009). Ongoing revolution in
bacteriology: routine identification
of bacteria by matrix-assisted laser
desorption ionization time-of-flight
mass spectrometry. Clin. Infect. Dis.
49, 543–551.
Seng, P., Rolain, J. M., Fournier, P. E.,
La Scola, B., Drancourt, M., and
Raoult, D. (2010). MALDI-TOFmass spectrometry applications in
clinical microbiology. Future Microbiol. 5, 1733–1754.
Shimada, K., Bricknell, K. S., and Finegold, S. M. (1969). Deconjugation
of bile acids by intestinal bacteria: review of literature and additional studies. J. Infect. Dis. 119,
273–281.
Siggers, R. H., Siggers, J., Boye, M., Thymann, T., Molbak, L., and Leser, T.
(2008). Early administration of probiotics alters bacterial colonization
and limits diet-induced gut dysfunction and severity of necrotizing enterocolitis in preterm pigs. J. Nutr. 138,
1437–1444.
Frontiers in Cellular and Infection Microbiology
Sim, K., Cox, M. J., Wopereis, H.,
Martin, R., Knol, J., Li, M. S.,
et al. (2012). Improved detection of bifidobacteria with optimised 16S rRNA-gene based pyrosequencing. PLos ONE 7, e32543.
doi:10.1371/journal.pone.0032543
Smith, R. (2005). Medical journals
are an extension of the marketing arm of pharmaceutical
companies. PLoS Med. 2, e138.
doi:10.1371/journal.pmed.0020138
Staley, J. T., and Konopka, A. (1985).
Measurement of in situ activities of nonphotosynthetic microorganisms in aquatic and terrestrial
habitats. Annu. Rev. Microbiol. 39,
321–346.
Stark, C. A., Adamsson, I., Edlund, C.,
Sjosted, S., Seensalu, R., Wikstrom,
B., et al. (1996). Effects of omeprazole and amoxycillin on the human
oral and gastrointestinal microflora
in patients with Helicobacter pylori
infection. J. Antimicrob. Chemother.
38, 927–939.
Sullivan, A., Barkholt, L., and Nord,
C. E. (2003). Lactobacillus acidophilus, Bifidobacterium lactis and
Lactobacillus F19 prevent antibioticassociated ecological disturbances of
Bacteroides fragilis in the intestine. J.
Antimicrob. Chemother. 52, 308–311.
Sullivan, A., Edlund, C., and Nord,
C. E. (2001). Effect of antimicrobial agents on the ecological balance
of human microflora. Lancet Infect.
Dis. 1, 101–114.
Swedish Study Group. (1991a). A
randomized multicenter trial to
compare the influence of cefaclor
and amoxycillin on the colonization
resistance of the digestive tract in
patients with lower respiratory tract
infection. Infection 19, 208–215.
Swedish Study Group. (1991b). A
randomized multicenter trial to
compare the influence of cefaclor
and amoxycillin on the colonization
resistance of the digestive tract in
patients with lower respiratory tract
infection. Infection 19, 208–215.
Thomas, O., Thabane, L., Douketis, J.,
Chu, R., Westfall, A. O., and Allison,
D. B. (2008). Industry funding and
the reporting quality of large longterm weight loss trials. Int. J. Obes.
(Lond.) 32, 1531–1536.
Thuny, F., Richet, H., Casalta, J.
P., Angelakis, E., Habib, G., and
Raoult, D. (2010). Vancomycin
treatment of infective endocarditis is linked with recently acquired
obesity. PLoS ONE 5, e9074.
doi:10.1371/journal.pone.0009074
Torok, V. A., Hughes, R. J., Mikkelsen,
L. L., Perez-Maldonado, R., Balding, K., and MacAlpine, R. (2011).
www.frontiersin.org
Identification and characterization
of potential performance-related
gut microbiotas in broiler chickens across various feeding trials. Appl. Environ. Microbiol. 77,
5868–5878.
Turnbaugh, P. J., Backhed, F., Fulton, L., and Gordon, J. I. (2008).
Diet-induced obesity is linked to
marked but reversible alterations in
the mouse distal gut microbiome.
Cell Host Microbe 3, 213–223.
Turnbaugh, P. J., Hamady, M., Yatsunenko, T., Cantarel, B. L., Duncan,
A., and Ley, R. E. (2009). A core gut
microbiome in obese and lean twins.
Nature 457, 480–484.
Turnbaugh, P. J., Ley, R. E., Hamady,
M., Fraser-Liggett, C. M., Knight,
R., and Gordon, J. I. (2007). The
human microbiome project. Nature
449, 804–810.
Turnbaugh, P. J., Ley, R. E., Mahowald,
M. A., Magrini, V., Mardis, E. R.,
and Gordon, J. I. (2006). An obesityassociated gut microbiome with
increased capacity for energy harvest. Nature 444, 1027–1031.
Turnbaugh, P. J., Quince, C., Faith, J. J.,
McHardy, A. C., Yatsunenko, T., and
Niazi, F. (2010). Organismal, genetic,
and transcriptional variation in the
deeply sequenced gut microbiomes
of identical twins. Proc. Natl. Acad.
Sci. U.S.A. 107, 7503–7508.
Vael, C., Verhulst, S. L., Nelen, V.,
Goossens, H., and Desager, K. N.
(2011). Intestinal microflora and
body mass index during the first
three years of life: an observational
study. Gut Pathog. 3, 8.
Van der Auwera, P., Pensart, N.,
Korten, V., Murray, B. E., and
Leclercq, R. (1996). Influence of
oral glycopeptides on the fecal
flora of human volunteers: selection of highly glycopeptide-resistant
enterococci. J. Infect. Dis. 173,
1129–1136.
van Nispen, C. H., Hoepelman, A. I.,
Rozenberg-Arska, M., Verhoef, J.,
Purkins, L., and Willavize, S. A.
(1998). A double-blind, placebocontrolled, parallel group study
of oral trovafloxacin on bowel
microflora in healthy male volunteers. Am. J. Surg. 176, 27S–31S.
Van Saene, J. J., Van Saene, H. K.,
Geitz, J. N., Tarko-Smit, N. J., and
Lerk, C. F. (1986). Quinolones and
colonization resistance in human
volunteers. Pharm. Weekbl. Sci. 8,
67–71.
Vartoukian, S. R., Palmer, R. M., and
Wade, W. G. (2010). Strategies
for culture of “unculturable” bacteria. FEMS Microbiol. Lett. 309,
1–7.
November 2012 | Volume 2 | Article 136 | 18
Lagier et al.
Vogel, F., Ochs, H. R.,Wettich, K., Kalich,
S., Nilsson-Ehle, I., Odenholt, I., et
al. (2001). Effect of step-down therapy of ceftriaxone plus loracarbef
versus parenteral therapy of ceftriaxone on the intestinal microflora in
patients with community-acquired
pneumonia. Clin. Microbiol. Infect. 7,
376–379.
Wagner, M., and Horn, M. (2006).
The planctomycetes, verrucomicrobia, chlamydiae and sister phyla
comprise a superphylum with
biotechnological and medical relevance. Curr. Opin. Biotechnol. 17,
241–249.
Walker, A. (2010). Gut metagenomics
goes viral. Nat. Rev. Microbiol. 8,
841.
Walker, A. W., Ince, J., Duncan, S.
H., Webster, L. M., Holtrop, G.,
and Ze, X. (2011). Dominant and
diet-responsive groups of bacteria
within the human colonic microbiota. ISME J. 5, 220–230.
Welling, G. W., Meijer-Severs, G. J.,
Helmus, G., van Santen, E., Tonk,
R. H., de Vries-Hospers, H. G., et
al. (1991). The effect of ceftriaxone
on the anaerobic bacterial flora and
Human gut microbiota
the bacterial enzymatic activity in
the intestinal tract. Infection 19,
313–316.
Willner, D., Furlan, M., Haynes,
M., Schmieder, R., Angly, F. E.,
and Silva, J. (2009). Metagenomic analysis of respiratory tract
DNA viral communities in cystic
fibrosis and non-cystic fibrosis
individuals. PLoS ONE 4, e7370.
doi:10.1371/journal.pone.0007370
Wilson, K. H., and Blitchington, R. B.
(1996). Human colonic biota studied by ribosomal DNA sequence
analysis. Appl. Environ. Microbiol.
62, 2273–2278.
Wistrom, J., Gentry, L. O., Palmgren, A.
C., Price, M., Nord, C. E., Ljungh,
A., et al. (1992). Ecological effects of
short-term ciprofloxacin treatment
of travellers’ diarrhoea. J. Antimicrob. Chemother. 30, 693–706.
Wu, G. D., Chen, J., Hoffmann, C.,
Bittinger, K., Chen, Y. Y., and Keilbaugh, S. A. (2011). Linking longterm dietary patterns with gut
microbial enterotypes. Science 334,
105–108.
Wu, J. Y., Jiang, X. T., Jiang, Y. X.,
Lu, S. Y., Zou, F., and Zhou, H.
Frontiers in Cellular and Infection Microbiology
W. (2010). Effects of polymerase,
template dilution and cycle number
on PCR based 16 S rRNA diversity
analysis using the deep sequencing
method. BMC Microbiol. 10, 255.
doi:10.1186/1471-2180-10-255
Xu, J., Bjursell, M. K., Himrod, J., Deng,
S., Carmichael, L. K., and Chiang,
H. C. (2003). A genomic view
of the human-Bacteroides thetaiotaomicron symbiosis. Science 299,
2074–2076.
Yildirim, S., Yeoman, C. J., Sipos,
M., Torralba, M., Wilson, B. A.,
and Goldberg, T. L. (2010). Characterization of the fecal microbiome from non-human wild primates reveals species specific microbial communities. PLoS ONE 5,
e13963. doi:10.1371/journal.pone.
0013963
Young, V. B., and Schmidt, T. M.
(2004). Antibiotic-associated diarrhea accompanied by large-scale
alterations in the composition of the
fecal microbiota. J. Clin. Microbiol.
42, 1203–1206.
Zhang, H., Dibaise, J. K., Zuccolo, A.,
Kudrna, D., Braidotti, M., and Yu,
Y. (2009). Human gut microbiota
www.frontiersin.org
in obesity and after gastric bypass.
Proc. Natl. Acad. Sci. U.S.A. 106,
2365–2370.
Conflict of Interest Statement: The
authors declare that the research was
conducted in the absence of any commercial or financial relationships that
could be construed as a potential conflict of interest.
Received: 30 August 2012; paper pending
published: 22 September 2012; accepted:
16 October 2012; published online: 02
November 2012.
Citation: Lagier J-C, Million M, Hugon
P, Armougom F and Raoult D (2012)
Human gut microbiota: repertoire and
variations. Front. Cell. Inf. Microbio.
2:136. doi: 10.3389/fcimb.2012.00136
Copyright © 2012 Lagier, Million,
Hugon, Armougom and Raoult . This is
an open-access article distributed under
the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums,
provided the original authors and source
are credited and subject to any copyright notices concerning any third-party
graphics etc.
November 2012 | Volume 2 | Article 136 | 19
Article XII :
Vancomycin-associated Gut Microbiota Alteration
and Weight Gain in Human Adults
Matthieu Million, Franck Thuny, Manolis Angelakis, Jean-Paul
Casalta, Gilbert Habib, Didier Raoult
Work in progress
124
1
Vancomycin-associated Gut Microbiota Alteration and Weight Gain in Human Adults
2
Matthieu MILLION1, Franck THUNY 1, 2, Manolis ANGELAKIS 1, Jean-Paul CASALTA3,
3
Gilbert HABIB2, Didier RAOULT 1,3*
4
1.
Unité de Recherche sur les Maladies Infectieuses et Tropicales Emergentes, Faculté de
5
Médecine, CNRS UMR 7278, IRD 198, Aix-Marseille Université, 27 Bd Jean Moulin, 13005
6
Marseille, France.
7
2. Service de Cardiologie, Hôpital de la Timone, Marseille, France,
8
3. Pôle de Maladies Infectieuses, Hôpital de la Timone, Marseille, France
9
10
*Corresponding author.
11
Phone: (33) 491 38 55 17
12
Fax: (33) 491 83 03 90
13
Email: [email protected]
14
Abstract: XXX
15
Text word count: 2,921
16
17
18
19
20
21
22
23
24
25
Keywords: Antibiotics, weight gain, obesity, gut microbiota
1
26
Introduction
27
Antibiotics, including mainly glycopeptide, tetracycline, macrolides and penicillin,
28
have been used for over 60 years to promote weight gain in animals (1) and continue to be
29
widely used in the USA (2). A report (3) estimated that 1600 tons of antibiotic were used for
30
their growth-promoter effect in European agriculture in 1997 before their ban (4) due to
31
bacterial resistance issues that represent a threat for human health. From the beginning of their
32
use in agriculture in the 50s, a similar effect on growth was reported in humans (5-7) but
33
seems to have been neglected until very recently. We and others stress the need to assess
34
carefully the impact of prolonged antibiotics on weight in humans (8-10) while antibiotics in
35
early infancy have been linked very recently to excess risk of being overweight later in
36
childhood (10).
Among the many antibiotics used for their growth factor effect, it was observed that
37
38
the glycopeptides showed the same effect in animals and humans. Indeed, avoparcin,
39
originally isolated from Streptomyces candidus (11), was associated with weight gain in farm
40
animals (12) while vancomycin isolated from Amycolatopsis orientalis (formerly named
41
Streptomyces orientalis) (13) was associated with significant weight gain and acquired obesity
42
in humans (14). Both these glycopeptides are known to be active only on Gram positive
43
bacteria with the notable exception of most of Pediococcous and Lactobacillus. As an
44
exception, members of the Lactobacillus acidophilus group (L. acidophilus, L. gasseri, L.
45
crispatus, L. johnsonii and L. delbruecki) have been found to be susceptible to glycopeptides
46
(15).
47
It has been shown using axenic animals that the weight gain of antibiotic growth
48
promoter (AGP) was linked to changes in the intestinal flora (16;17). Many studies have
49
reported the modification of digestive microbiota after administration of AGP in animals (18-
50
21) and some studies from the 1950s’ reported an E. coli decrease by culture in bottle-fed
2
51
infant gaining weight after chlortetracycline administration (22). To our knowledge, no recent
52
study has linked the modification of the intestinal flora and weight gain in humans by
53
molecular methods.
54
In a former work, we found that antibiotics (ampicillin, vancomycin and gentamycin)
55
were linked with increased body weight but gut flora was not explored (14). In this work, we
56
have tested the weight gain effect associated with vancomycin in a new series in humans and
57
compared the change in digestive microbiota in patients receiving vancomycin or amoxicillin
58
by molecular techniques.
59
60
Methods
61
Ethics Committee
62
Written consent was obtained from each participant and approved by the ethics committee of
63
the Faculty of Medicine La Timone, Marseille, France under number 08–002.
64
Patients
65
We retrospectively included all IE patients treated in the departments of Cardiology, Hospital
66
de la Timone, Marseille, France from January 2008 until January 2011. Endocarditis was
67
defined by the modified Duke criteria (23). Patients were treated with amoxicillin or
68
vancomycin for at least 4 weeks initially associated or not to gentamycin. For each case of
69
endocarditis, the decision for surgery was made by a multidisciplinary discussion following
70
our protocol (24). The use of another antibiotic than amoxicillin, vancomycin or gentamycin
71
for more than 7 days was an exclusion criterion. A control group included 42 controls for
72
whom the stools were analysed in a previous study (25).
73
Analysis of weight change
74
A standardized questionnaire was used to collect demographic, clinical and therapeutic data in
75
all patients treated by antibiotics. The baseline weight (one month before the onset of the
3
76
disease), weight at one year and height were collected prospectively or retrospectively for
77
each patient based on clinical records, systematic follow-up consultations or phone call.
78
Analysis of intestinal flora
79
From April 2009, all patients with a diagnosis of endocarditis had a stools sample analyzed at
80
diagnosis, and repeated as possible once a week during 4-6 weeks of treatment. DNA was
81
isolated from the stool as described in Dridi et al. (26). The purified DNA was eluted from
82
samples to a final volume of 100 µl and stored at -80 ° C until analysis. The real-time PCR
83
was performed on a Stratagene MX3000 (Agilent, Santa Clara, CA, United States) using the
84
Quantitect PCR mix (Qiagen, Courtaboeuf, France) as described previously (27) targeting the
85
Bacteroidetes, Firmicutes, Lactobacillus, Methanobrevibacter smithii. Quantification of all
86
bacteria was performed as previously described (26). A second real-time PCR was performed
87
on BioRad FLX96 targeting Lactobacillus reuteri, Lactobacillus plantarum, Lactobacillus
88
rhamnosus, Bifidobacterium animalis as previously reported (25). Escherichia coli was
89
quantified using an in-house PCR system using the following primers (ECOmpGMGBAluId
90
GCTGCGCGTGCAAATGCG, ECOmpGMGBAluIr CATGGTCATCGCTTCGGTCT) and a
91
MGB probe (ECOmpGMGB 6FAM-CATCAGAAACTGAACACCAC ) yielding a 100 bp
92
amplicons with the previously reported PCR protocol (25).
93
94
Statistical Analysis
95
The baseline BMI was that obtained one month before the first symptoms. First, the changes
96
of BMI at one year were compared between the two groups. Proportions were compared using
97
the Fisher bilateral exact test. For the continuous variables, the student t test or Mann-
98
Whitney test were used for comparison between the two groups according to the distribution
99
assessed by Kolmogorov-Smirnov test. Logistic regression was used to determine the
100
predictors of an increase of BMI ≥10% at one year. The following variables were tested as
4
101
potential predictors: age, sex, cardiac surgery within the year after admission, and the type of
102
antibiotic used in association with gentamycin (i.e., vancomycin or amoxicillin). In a second
103
analysis, concentration of Bacteroidetes, Firmicutes, Methanobrevibacter smithii and
104
Lactobacillus were compared between intervention groups (vancomycin, amoxicillin,
105
controls). In a third analysis, specific bacterial species present in the first stool sample
106
collected before 7 days of treatment were tested as potential predictors of weight change at
107
one year. P<0.05 was considered significant and a Bonferroni's correction was used in case of
108
multiple comparisons. All statistical analyses were performed using EpiInfo software version
109
3.4.1 (Centers for Disease Control and Prevention, Atlanta, GA, USA). To confirm data on a
110
broader group we later combine this study with our former work (14).
111
112
Results
113
Characteristics of patients
114
98 patients were included in the weight study, 57 patients were included in the amoxicillin
115
group and 41 in the vancomycin group (Table 1). Sex, Age and BMI were not significantly
116
different at baseline. Surgery was more frequent in the amoxicillin group; 34/54(62%), than in
117
the vancomycin group; 18/38(47%) but this was not significant (p=0.13). In the second part of
118
the study, comparing bacterial concentration between intervention groups, 192 stool samples
119
were analyzed for Bacteroidetes, Firmicutes, Lactobacillus or M. smithii (67 during
120
vancomycin treatment, 83 during amoxicillin treatment, 42 without antibiotics) corresponding
121
to 80 patients including 35 patients on vancomycin and 45 patients on amoxicillin and 42
122
controls. For the third part of our study, 53 samples were available for the analysis of the
123
initial gut microbiota targeting different Lactobacillus species, Bifidobacterium animalis and
124
E. coli corresponding to 53 patients.
125
5
126
Weight change
127
Globally, there was no significant difference between vancomycin and amoxicillin groups in
128
BMI change over a year and after stratification by age, sex, initial weight or surgery (Table 2).
129
However, the proportion of patients with an increase in BMI greater than 10% was higher in
130
the vancomycin group (5 / 41 (12.2%) compared to 1 / 57 (1.7%) for the amoxicillin group,
131
Barnard bilateral exact test, p = 0.038). It was not significant for an increase in BMI greater
132
than 5% (9/41 in the vancomycin group vs 7/57 in the amoxicillin group (p=0.18). Again in
133
logistic regression model including age, sex, surgery, initial weight category (lean,
134
overweight, obese), vancomycin was associated with an increase in BMI greater than 10%
135
(adjusted OR = 0.54 (95%CI 0.003 – 1.074); p = 0.049). Women of less than 60 years seemed
136
to be more at risk, indeed 2 out of 4 women of less than 60 years treated by vancomycin
137
showed an increase in BMI greater than 10% with acquired obesity in one case (Initial BMI at
138
25.7, BMI at one year 30.6). This initially lean woman gain 11 kg in one year. When we
139
combined this study with our first study, we did not observe any significant change in weight
140
for all individuals nor for an increase in BMI of 5%. Conversely, there was a significant
141
increase in the number of patients with increased BMI of over 10% (12/52 vs 8/84, p = 0.034)
142
and the number of patients with acquired obesity (7/52 vs. 2/84, p = 0.011). However, even
143
after stratification for age, sex, surgery or initial BMI, it was not possible to identify a
144
subgroup with a significant weight gain.
145
146
Gut microbiota alteration following antibiotic treatment
147
192 stool samples were analyzed including 83 during amoxicillin treatment, 67 during
148
vancomycin treatment and 42 in the control group. The number of sample per patient was not
149
significantly different between the 2 antibiotic groups (p=0.47) so samples were pooled by
150
antibiotic. The amount of Firmicutes were significantly increased in the vancomycin group
6
151
compared to the amoxicillin group (p = 0.027) and controls (p = 0.005) while the amoxicillin
152
group showed no difference for Firmicutes compared to the control group (p = 0.66) (figure
153
1). The amount of Bacteroidetes was also increased in the vancomycin groups but only
154
compared to controls (p <0.0001). Similarly the amount of Bacteroidetes was increased in the
155
amoxicillin group (p = 0.002). Conversely, the amount of Methanobrevibacter smithii was
156
significantly decreased in both groups of patients on antibiotics compared to controls (p =
157
0.013 and p = 0.011 respectively for vancomycin and amoxicillin). Conversely, there was no
158
significant difference for M. smithii between patients receiving vancomycin and amoxicillin
159
(p = 0.86). Lactobacillus were significantly increased in the vancomycin group both
160
compared to the amoxicillin group and controls (p = 0.0009 and p = 0.037 respectively).
161
There was no significant difference for Lactobacillus bacterial count between the amoxicillin
162
group and the control group. Finally, the total number of bacteria was increased in both
163
groups of patients receiving antibiotics compared to controls (p <0.0001 and p = 0.002 for
164
vancomycin and amoxicillin, respectively). Similarly, the total number of bacteria was higher
165
in the vancomycin group compared to the amoxicillin group (p = 0.04).
166
167
Testing bacterial predictors for weight changes after antibiotic treatment
168
Analysing the presence of specific species previously related to obese or lean human status
169
(B. animalis, L. plantarum, L. reuteri, L. rhamnosus, E. coli), we found no significant results
170
for an association with BMI change. Considering presence or absence of each analysed
171
species by individuals and by weight change category (>1%, >5%, >10% BMI increase),
172
consistent effect direction but not significant was noted with Bifidobacterium animalis,
173
Lactobacillus plantarum and Escherichia coli associated with a protective effect against
174
weight gain (Odds ratio < 1) and Lactobacillus reuteri favoring weight gain (Odds ratio > 1)
175
(Figure 2). E. coli, reported elsewhere as associated with obesity (27), unexpectedly show
7
176
here a protective effect (OR < 1 for each category of weight gain) even if result was not
177
significant. The woman with acquired obesity who gained 11kg after vancomycin treatment
178
harboured Lactobacillus reuteri in her gut microbiota, identified previously having a pro-
179
obesity link (2).
180
Finally, we didn’t found any statistical significant antagonism between any Lactobacillus
181
species or Bifidobacterium animalis and E. coli (data not shown).
182
183
184
Discussion
We found in this work an increased frequency of patients with acquired obesity and
185
BMI increase over 10% after prolonged treatment with vancomycin and gentamycin for
186
endocarditis compared with amoxicillin and gentamycin treatment, taken here as a control.
187
We found that vancomycin and amoxicillin were associated with an increase in Bacteroidetes
188
and total bacterial concentration and a decrease of Methanobrevibacter smithii. Furthermore,
189
we found a significant increase in Firmicutes, Lactobacillus and global bacterial
190
concentration in vancomycin-treated patients gut microbiota compared with patients treated
191
by amoxicillin. Finally, we found, even if no significant results were obtain, that pre-existing
192
profile of the gut microbiota could predict weight gain after prolonged antibiotic treatment.
193
Weight gain associated with antibiotics have been reported since the 1940s’ in animals
194
(28) and since the 1950s in humans (5-7)(Table 3). Avoparcin, a glycopeptides bacteriocin
195
first isolated from a Streptomyces sp. (11), have been used for decades as a growth-promoter
196
in the farm industry (12). In humans, Thuny et al. (14) first found that another glycopeptide,
197
vancomycin, was associated with significant weight gain, increased frequency of acquired
198
obesity and of BMI increase over 10% in patients treated for infective endocarditis.
199
Amoxicillin was also associated with weight gain over 10% compared to controls without
200
antibiotics. In this study, we confirm an association between vancomycin and an increased
8
201
frequency of acquired obesity and BMI increase over 10%. Amoxicillin has not been retested
202
but experimental studies found a similar effect with a growth-promoting effect of penicillin
203
(29).
204
Independently of weight gain, we recently reviewed the reported effects of antibiotics
205
on gut microbiota (30). Here, we found significant alterations in vancomycin treated humans
206
gut microbiota with an increase in Bacteroidetes, Firmicutes and Lactobacillus and a decrease
207
in M. smithii. In the literature (Table 4), the effects of vancomycin and amoxicillin on the
208
digestive flora are completely different. The most striking differences is an increase in
209
Lactobacillacaeae under vancomycin while they are reduced as amoxicillin. Searching for
210
association between gut bacteria, antibiotics and weight change, penicillin have been shown
211
to increased weight gain in conventional animals whereas germ free animals did not respond
212
to the antibiotic (17). Clostridium perfringens when implanted in the gut of germ-free chicks,
213
caused growth depression reversible by penicillin (31). Enterococcus faecalis or Clostridium
214
perfringens have been linked with decreased fat absorption in gnotobiotic animals, whereas
215
Lactobacillus didn’t impact the fat absorption (32). Torok et al. found certain Lactobacillus
216
species linked to weight gain in the proximal gut of chicks whereas others were specifically
217
associated with non responding birds (20). In one of the most recent studies on antibiotics
218
generating adiposity including vancomycin and penicillin, Cho et al. (29) reported an increase
219
in the number of sequences of the Lachnospiraceae but also Lactobacillaceae. The authors
220
found a significant change in the proportion of sequences at the phylum level only for
221
vancomycin with a decrease in Bacteroidetes and a Firmicutes increased.
222
In our study, we found that the total bacterial amount is surprisingly increased under
223
antibiotic and is significantly increased in the vancomycin group as compared with penicillin.
224
Jukes noted in 1955 that when the usual “feed” antibiotics are given to animals, there is
225
typically an increase in the number of intestinal bacteria that can be plated out, and the new
9
226
population is predominantly antibiotic-tolerant or resistant (33). Cho et al. (29) reported
227
similar results. Indeed, in their study, vancomycin and vancomycin associated with penicillin
228
were associated with a non-significant increase in the total number of such sequences
229
suggesting an increase of total gut bacteria with antibiotics. Conversely, Vijay-Kumar et al.
230
(34) reported that a very broad spectrum antibiotics regimen including ampicillin and
231
neomycin reduces the gut total bacterial load by 90% and corrects the metabolic syndrome in
232
mice experimentally induced TLR5-KO. These results suggest that the metabolic syndrome is
233
linked with gut bacteria. The gut microbiota seems to be only structurally altered but not
234
quantitatively reduced by antibiotics with limited anti-bacterial spectra and this alteration
235
could lead to increased adiposity and weight gain.
236
Coates et al. (17) reported a decreased weight gain in germ-free animals fed a standard
237
diet whereas Backed et al. (35) found that germ-free mice were resistant to diet-induced
238
obesity. Whatever, as a putative explanation, it is plausible that changes in the microbiota
239
following antibiotic treatment could reproduce the alterations observed in the digestive
240
microbiota of obese namely a decrease in Bifidobacterium in animals treated by antibiotics
241
(29) or in obese (36), an increase in Lactobacillus under antibiotics (present study, (37)) and
242
obese patients (25;27;38), an increase in Staphylococcaceae under antibiotics (29) and in
243
obese patients (36;39), an increase of Lachnospiraceae on antibiotics (29) and obese-induced
244
animals (40). Finally, antibiotics were linked here with a Methanobrevibacter smithii
245
decrease, previously found in obese gut microbiota (25;36). Conversely, Membrez et al. (41)
246
reported that administration of ampicillin and norfloxacin in ob/ob and diet-induced obese and
247
insulin-resistant mice were linked with a non-significant weight loss, a significant total fat pad
248
weight decrease and a significant improvement in fasting glycemia and oral glucose tolerance.
249
More generally, to our knowledge, fluoroquinolones had never been linked to human or
250
animal weight gain in the literature (42) but a weight gain suppression (43). These results
10
251
suggest that antibiotics, and especially vancomycin, can enhance weight gain through gut
252
microbiota manipulation decreasing bacteria associated with lower energy harvest and fat
253
absorption (Clostridium perfringens, Enterococcus sp. usually susceptible to vancomycin)
254
while favoring bacteria promoting the absorption and accumulation of energy (carbohydrate
255
and lipid) by the host (Lactobacillus).
256
Finally, our work suggests that the structure of the microbiota before antibiotic
257
administration predict which individuals are more likely to gain weight and to present an
258
acquired obesity as a result of treatment with a specific antibiotic. In this sense, even if not
259
significant, Lactobacillus reuteri, linked with obesity elsewhere (25) has the same effect and
260
predict a BMI increase > 10%, while Bifidobacterium animalis and Lactobacillus plantarum
261
linked elsewhere with lean status (25) have the opposite effect and could prevent weight gain
262
after antibiotic administration.
263
264
Conclusion
265
Our work confirms that vancomycin is associated with weight gain in humans in relation to a
266
specific gut alteration including a Firmicutes and Lactobacillus increase and
267
Methanobrevibacter smithii decrease. It appears that antibiotics modulate the digestive
268
microbiota depending on the pre-existent gut microbiota and the spectrum of the antibiotic
269
administered. This work extends the previous findings showing that vancomycin led to
270
dramatic weight gain in certain individuals. More generally, in view of our results, it seems
271
necessary to inform the patient of the risk of weight gain and obesity acquired during long-
272
term antibiotic treatment especially vancomycin. Further studies are needed to identify which
273
individuals are more susceptible to have this weight gain side effect characterizing their
274
digestive microbiota prior to antibiotics.
275
11
276
Acknowledgement
277
We thank the cardiology department for their helpful help in the recruitment of the IE
278
patients.
279
280
Funding source
281
No funding source
282
283
284
285
286
287
288
289
290
291
292
293
294
295
12
296
Reference List
297
298
299
(1) OZAWA E. Studies on growth promotion by antibiotics. I. Effects of chlortetracycline on
growth. J Antibiot (Tokyo) 1955 Dec;8(6):205-11.
300
301
302
(2) Food and drug administration. Withdrawal of Notices of Opportunity for a Hearing;
Penicillin and Tetracycline Used in Animal Feed. 79697-79701. 22-12-2011.
303
304
(3) Harvey J, Mason L. The Use and Misuse of Antibiotics in UK agriculture. 1998.
305
306
(4) Ban on antibiotics as growth promoters in animal feed enters into effect. 22-12-2005.
307
308
(5) HAIGHT TH, PIERCE WE. Effect of prolonged antibiotic administration of the weight of
healthy young males. J Nutr 1955 May 10;56(1):151-61.
309
310
(6) OZAWA E. Studies on growth promotion by antibiotics. II. Results of aurofac administration
to infants. J Antibiot (Tokyo) 1955 Dec;8(6):212-4.
311
312
(7) Robinson P. Controlled trial of aureomycin in premature twins and triplets. Lancet 1952 Jan
5;259(6697):52.
313
314
(8) Ternak G. Antibiotics may act as growth/obesity promoters in humans as an inadvertent
result of antibiotic pollution? Med Hypotheses 2005;64(1):14-6.
315
316
(9) Raoult D. Human microbiome: take-home lesson on growth promoters? Nature 2008 Aug
7;454(7205):690-1.
317
318
(10) Trasande L, Blustein J, Liu M, Corwin E, Cox LM, Blaser MJ. Infant antibiotic exposures and
early-life body mass. Int J Obes (Lond) 2012 Aug 21.
319
320
321
(11) Michel KH, Shah RM, Hamill RL. A35512, a complex of new antibacterial antibiotics
produced by Streptomyces candidus. I. Isolation and characterization. J Antibiot (Tokyo)
1980 Dec;33(12):1397-406.
322
323
324
(12) Feighner SD, Dashkevicz MP. Subtherapeutic levels of antibiotics in poultry feeds and their
effects on weight gain, feed efficiency, and bacterial cholyltaurine hydrolase activity. Appl
Environ Microbiol 1987 Feb;53(2):331-6.
325
326
(13) MCCORMICK MH, MCGUIRE JM, PITTENGER GE, PITTENGER RC, STARK WM. Vancomycin, a
new antibiotic. I. Chemical and biologic properties. Antibiot Annu 1955;3:606-11.
327
328
(14) Thuny F, Richet H, Casalta JP, Angelakis E, Habib G, Raoult D. Vancomycin treatment of
infective endocarditis is linked with recently acquired obesity. PLoS One 2010;5(2):e9074.
329
330
331
(15) Klare I, Konstabel C, Werner G, Huys G, Vankerckhoven V, Kahlmeter G, et al. Antimicrobial
susceptibilities of Lactobacillus, Pediococcus and Lactococcus human isolates and cultures
intended for probiotic or nutritional use. J Antimicrob Chemother 2007 May;59(5):900-12.
13
332
333
(16) Gaskins HR, Collier CT, Anderson DB. Antibiotics as growth promotants: mode of action.
Anim Biotechnol 2002 May;13(1):29-42.
334
335
336
(17) COATES ME, Fuller R, HARRISON GF, LEV M, SUFFOLK SF. A comparison of the growth of
chicks in the Gustafsson germ-free apparatus and in a conventional environment, with and
without dietary supplements of penicillin. Br J Nutr 1963;17:141-50.
337
338
339
(18) Looft T, Johnson TA, Allen HK, Bayles DO, Alt DP, Stedtfeld RD, et al. In-feed antibiotic
effects on the swine intestinal microbiome. Proc Natl Acad Sci U S A 2012 Jan
31;109(5):1691-6.
340
341
342
(19) Kim HB, Borewicz K, White BA, Singer RS, Sreevatsan S, Tu ZJ, et al. Microbial shifts in the
swine distal gut in response to the treatment with antimicrobial growth promoter, tylosin.
Proc Natl Acad Sci U S A 2012 Sep 18;109(38):15485-90.
343
344
345
346
(20) Torok VA, Hughes RJ, Mikkelsen LL, Perez-Maldonado R, Balding K, MacAlpine R, et al.
Identification and characterization of potential performance-related gut microbiotas in
broiler chickens across various feeding trials. Appl Environ Microbiol 2011 Sep;77(17):586878.
347
348
349
(21) Torok VA, Allison GE, Percy NJ, Ophel-Keller K, Hughes RJ. Influence of antimicrobial feed
additives on broiler commensal posthatch gut microbiota development and performance.
Appl Environ Microbiol 2011 May;77(10):3380-90.
350
351
(22) OZAWA E. Studies on growth promotion by antibiotics. II. Results of aurofac administration
to infants. J Antibiot (Tokyo) 1955 Dec;8(6):212-4.
352
353
354
(23) Li JS, Sexton DJ, Mick N, Nettles R, Fowler VG, Jr., Ryan T, et al. Proposed modifications to
the Duke criteria for the diagnosis of infective endocarditis. Clin Infect Dis 2000
Apr;30(4):633-8.
355
356
357
(24) Botelho-Nevers E, Thuny F, Casalta JP, Richet H, Gouriet F, Collart F, et al. Dramatic
reduction in infective endocarditis-related mortality with a management-based approach.
Arch Intern Med 2009 Jul 27;169(14):1290-8.
358
359
360
361
(25) Million M, Maraninchi M, Henry M, Armougom F, Richet H, Carrieri P, et al. Obesityassociated gut microbiota is enriched in Lactobacillus reuteri and depleted in
Bifidobacterium animalis and Methanobrevibacter smithii. Int J Obes (Lond) 2012
Jun;36(6):817-25.
362
363
364
(26) Dridi B, Henry M, El KA, Raoult D, Drancourt M. High prevalence of Methanobrevibacter
smithii and Methanosphaera stadtmanae detected in the human gut using an improved
DNA detection protocol. PLoS One 2009;4(9):e7063.
365
366
367
(27) Armougom F, Henry M, Vialettes B, Raccah D, Raoult D. Monitoring bacterial community of
human gut microbiota reveals an increase in Lactobacillus in obese patients and
Methanogens in anorexic patients. PLoS One 2009;4(9):e7125.
368
369
370
(28) Moore PR, Evenson A, Luckey TD, McCoy E, Elvehjem CA, Hart EB. Use of sulfasuxidine,
streptothricin, and streptomycin in the nutritional studies with the chick. J Biol Chem
1946;165:437-41.
14
371
372
(29) Cho I, Yamanishi S, Cox L, Methe BA, Zavadil J, Li K, et al. Antibiotics in early life alter the
murine colonic microbiome and adiposity. Nature 2012 Aug 30;488(7413):621-6.
373
374
(30) Lagier J-C, Million M, Hugon P, Armougom F, Raoult D. Human gut microbiota: repertoire
and variations. Front Cell Inf Microbio. In press 2012.
375
376
(31) LEV M, FORBES M. Growth response to dietary penicillin of germ-free chicks and of chicks
with a defined intestinal flora. Br J Nutr 1959;13(1):78-84.
377
378
(32) Cole JR, Jr., Boyd FM. Fat absorption from the small intestine of gnotobiotic chicks. Appl
Microbiol 1967 Sep;15(5):1229-34.
379
380
(33) Jukes TH. Antibiotics in Animal Feeds and Animal Production. BioScience 1972;22(9):52634.
381
382
383
(34) Vijay-Kumar M, Aitken JD, Carvalho FA, Cullender TC, Mwangi S, Srinivasan S, et al.
Metabolic syndrome and altered gut microbiota in mice lacking Toll-like receptor 5. Science
2010 Apr 9;328(5975):228-31.
384
385
386
(35) Backhed F, Manchester JK, Semenkovich CF, Gordon JI. Mechanisms underlying the
resistance to diet-induced obesity in germ-free mice. Proc Natl Acad Sci U S A 2007 Jan
16;104(3):979-84.
387
388
(36) Angelakis E, Armougom F, Million M, Raoult D. The relationship between gut microbiota
and weight gain in humans. Future Microbiol 2012 Jan;7(1):91-109.
389
390
(37) Robinson CJ, Young VB. Antibiotic administration alters the community structure of the
gastrointestinal micobiota. Gut Microbes 2010 Jul;1(4):279-84.
391
392
393
(38) Stsepetova J, Sepp E, Kolk H, Loivukene K, Songisepp E, Mikelsaar M. Diversity and
metabolic impact of intestinal Lactobacillus species in healthy adults and the elderly. Br J
Nutr 2011 Apr;105(8):1235-44.
394
395
396
(39) Santacruz A, Collado MC, Garcia-Valdes L, Segura MT, Martin-Lagos JA, Anjos T, et al. Gut
microbiota composition is associated with body weight, weight gain and biochemical
parameters in pregnant women. Br J Nutr 2010 Jul;104(1):83-92.
397
398
399
(40) Hildebrandt MA, Hoffmann C, Sherrill-Mix SA, Keilbaugh SA, Hamady M, Chen YY, et al.
High-fat diet determines the composition of the murine gut microbiome independently of
obesity. Gastroenterology 2009 Nov;137(5):1716-24.
400
401
402
(41) Membrez M, Blancher F, Jaquet M, Bibiloni R, Cani PD, Burcelin RG, et al. Gut microbiota
modulation with norfloxacin and ampicillin enhances glucose tolerance in mice. FASEB J
2008 Jul;22(7):2416-26.
403
404
(42) Bethell DB, Hien TT, Phi LT, Day NP, Vinh H, Duong NM, et al. Effects on growth of single
short courses of fluoroquinolones. Arch Dis Child 1996 Jan;74(1):44-6.
405
406
407
(43) Takizawa T, Hasimoto K, Itoh N, Yamashita S, Owen K. A comparative study of the repeat
dose toxicity of grepafloxacin and a number of other fluoroquinolones in rats. Hum Exp
Toxicol 1999 Jan;18(1):38-45.
15
408
409
410
(44) Dubray C, Ibrahim SA, Abdelmutalib M, Guerin PJ, Dantoine F, Belanger F, et al. Treatment
of severe malnutrition with 2-day intramuscular ceftriaxone vs 5-day amoxicillin. Ann Trop
Paediatr 2008 Mar;28(1):13-22.
411
412
(45) Pirzada OM, McGaw J, Taylor CJ, Everard ML. Improved lung function and body mass index
associated with long-term use of Macrolide antibiotics. J Cyst Fibros 2003 Jun;2(2):69-71.
413
414
415
(46) Saiman L, Marshall BC, Mayer-Hamblett N, Burns JL, Quittner AL, Cibene DA, et al.
Azithromycin in patients with cystic fibrosis chronically infected with Pseudomonas
aeruginosa: a randomized controlled trial. JAMA 2003 Oct 1;290(13):1749-56.
416
417
418
(47) Saiman L, Mayer-Hamblett N, Anstead M, Lands LC, Kloster M, Goss CH, et al. Open-label,
follow-on study of azithromycin in pediatric patients with CF uninfected with Pseudomonas
aeruginosa. Pediatr Pulmonol 2012 Jul;47(7):641-8.
419
420
421
422
(48) Saiman L, Anstead M, Mayer-Hamblett N, Lands LC, Kloster M, Hocevar-Trnka J, et al. Effect
of azithromycin on pulmonary function in patients with cystic fibrosis uninfected with
Pseudomonas aeruginosa: a randomized controlled trial. JAMA 2010 May 5;303(17):170715.
423
424
425
(49) Clement A, Tamalet A, Leroux E, Ravilly S, Fauroux B, Jais JP. Long term effects of
azithromycin in patients with cystic fibrosis: A double blind, placebo controlled trial. Thorax
2006 Oct;61(10):895-902.
426
427
(50) Southern KW, Barker PM, Solis-Moya A, Patel L. Macrolide antibiotics for cystic fibrosis.
Cochrane Database Syst Rev 2011;(12):CD002203.
428
429
430
(51) Mansi Y, Abdelaziz N, Ezzeldin Z, Ibrahim R. Randomized controlled trial of a high dose of
oral erythromycin for the treatment of feeding intolerance in preterm infants. Neonatology
2011;100(3):290-4.
431
432
433
(52) Lane JA, Murray LJ, Harvey IM, Donovan JL, Nair P, Harvey RF. Randomised clinical trial:
Helicobacter pylori eradication is associated with a significantly increased body mass index
in a placebo-controlled study. Aliment Pharmacol Ther 2011 Apr;33(8):922-9.
434
435
436
(53) Kamada T, Hata J, Kusunoki H, Ito M, Tanaka S, Kawamura Y, et al. Eradication of
Helicobacter pylori increases the incidence of hyperlipidaemia and obesity in peptic ulcer
patients. Dig Liver Dis 2005 Jan;37(1):39-43.
437
438
439
(54) Azuma T, Suto H, Ito Y, Muramatsu A, Ohtani M, Dojo M, et al. Eradication of Helicobacter
pylori infection induces an increase in body mass index. Aliment Pharmacol Ther 2002
Apr;16 Suppl 2:240-4.
440
441
(55) Patterson PR. Minocycline in the antibiotic regimen of cystic fibrosis patients: weight gain
and clinical improvement. Clin Pediatr (Phila) 1977 Jan;16(1):60-3.
442
443
444
(56) Edlund C, Barkholt L, Olsson-Liljequist B, Nord CE. Effect of vancomycin on intestinal flora
of patients who previously received antimicrobial therapy. Clin Infect Dis 1997
Sep;25(3):729-32.
445
446
447
(57) Lund B, Edlund C, Barkholt L, Nord CE, Tvede M, Poulsen RL. Impact on human intestinal
microflora of an Enterococcus faecium probiotic and vancomycin. Scand J Infect Dis
2000;32(6):627-32.
16
448
449
450
(58) Van der Auwera P, Pensart N, Korten V, Murray BE, Leclercq R. Influence of oral
glycopeptides on the fecal flora of human volunteers: selection of highly glycopeptideresistant enterococci. J Infect Dis 1996 May;173(5):1129-36.
451
452
453
(59) Yap IK, Li JV, Saric J, Martin FP, Davies H, Wang Y, et al. Metabonomic and microbiological
analysis of the dynamic effect of vancomycin-induced gut microbiota modification in the
mouse. J Proteome Res 2008 Sep;7(9):3718-28.
454
455
456
(60) Swedish Study Group. A randomized multicenter trial to compare the influence of cefaclor
and amoxycillin on the colonization resistance of the digestive tract in patients with lower
respiratory tract infection. Infection 1991 Jul;19(4):208-15.
457
458
(61) Brismar B, Edlund C, Nord CE. Impact of cefpodoxime proxetil and amoxicillin on the
normal oral and intestinal microflora. Eur J Clin Microbiol Infect Dis 1993 Sep;12(9):714-9.
459
460
461
(62) Floor M, van AF, Rozenberg-Arska M, Visser M, Kolsters A, Beumer H, et al. Effect of
loracarbef and amoxicillin on the oropharyngeal and intestinal microflora of patients with
bronchitis. Scand J Infect Dis 1994;26(2):191-7.
462
463
464
(63) Stark CA, Adamsson I, Edlund C, Sjosted S, Seensalu R, Wikstrom B, et al. Effects of
omeprazole and amoxycillin on the human oral and gastrointestinal microflora in patients
with Helicobacter pylori infection. J Antimicrob Chemother 1996 Dec;38(6):927-39.
465
466
467
(64) Schumann A, Nutten S, Donnicola D, Comelli EM, Mansourian R, Cherbut C, et al. Neonatal
antibiotic treatment alters gastrointestinal tract developmental gene expression and
intestinal barrier transcriptome. Physiol Genomics 2005 Oct 17;23(2):235-45.
468
469
470
471
472
(65) Million M, Angelakis E, Paul M, Armougom F, Leibovici L, Raoult D. Comparative metaanalysis of the effect of Lactobacillus species on weight gain in humans and animals.
Microb Pathog 2012 Aug;53(2):100-8.
473
17
474
475
Table 1. Baseline characteristics of patients and results of the weight change study
Amoxicillin (n=57)
Vancomycin (n=41)
P-value (test)
Sexe (Male)
46/57
28/41
0.24 (Fisher)
Age
61.9 ± 12.1
65.3±12.8
0.24 (Student)
Surgery*
34/54
18/38
0.20 (Fisher)
Baseline BMI
26.8 ± 5.1
26.2 ± 4.8
0.53 (Student)
* Data unavailable for 6 patients
476
477
478
479
480
18
481
7/11
3/11
BMI10%
Acquired obesity
5/41
4/41
BMI10%
Acquired obesity
12/52
7/52
BMI10%
Acquired obesity
2/85
8/85
0.56 (-4.49 to 2.63)
0/57
1/57
-0.48 (-4.82 to -0.48)
2/28
7/28
1.31 (-1.33 to 8)
Amoxicillin
proportions (b) median (IQR) - calculated with epi-info v7
(a) Mann-Withney test for continuous data, Fisher bilateral exact test for
0.52 (-5.15 to 7.5)
%deltaBMI
All (n = 137)
0 (-5.15 to 3.17)
%deltaBMI
Our study (n=98)
15.5 (0 to 17.7)
%deltaBMI(b)
Former study (n=39) (20)
Vanco
Table 2. Weight change and acquired obesity according to antibiotics
0.0159
0.03
0.28
0.029
0.047
0.35
0.12
0.03
0.15
P-value (a)
6.37 (1.27 - 32.00)
2.85 (1.07 - 7.54)
undefined
7.63 (0.85 - 68.10)
4.87 (0.68 - 34.50)
5.25 (1.17 - 23.46)
OR
19
482
Malnutrition
Ceftriaxone
Cystic fibrosis
Neonatology
Eradication of Helicobacter pylori
Azithromycin
Erythromycin
Clarithromycin
Macrolides
Malnutrition
Amoxicillin
Betalactamines
Vancomycin
Endocarditis
Infections in the early life
All
Glycopeptides
Indication
Antibiotics
Lane, Aliment Pharmacol Ther, 2011 (52)
Mansi, Neonatology, 2012 (51)
Southern, Cochrane, 2011 (50)
Clement, Thorax, 2006 (49)
Saiman, Pediatr Pulmonol, 2012 (46-48)
Saiman, JAMA, 2003 – Saiman, JAMA, 2010 -
Pirzada, J Cystic fibrosis, 2003 (45)
Dubray, Ann Trop Paediatr, 2008 (44)
Dubray, Ann Trop Paediatr, 2008 (44)
Thuny, PlosOne, 2010 (14)
Trasande, Int J Obesity, 2012 (10)
Reference
Table 3. Studies reporting a significant weight gain associated with antibiotic administration in humans
20
484
483
Neonatology
Antibiotic prophylaxis on
Chlortetracycline
Chlortetracycline
immune response
Cystic fibrosis
Minocyclin
Tetracyclines
Haight, J Nutr, 1955 (5)
Robinson, Lancet, 1952 (7)
Patterson, Clin Pediatr (Phila), 1977 (55)
amoxicillin) (54)
Azuma, Aliment Pharmacol Therap, 2002 (with
Kamada, Dig Liver Dis, 2005 (53)
21
485
Humans
Humans
Humans
Humans
Humans
Humans
Mouse
Increase of Lactobacillaceae (a)
Increase of Proteobacteria (a)
Decrease of enterococci
Decrease of staphylococci
Strong suppression or elimination of Bacteroides (b)
Decrease of clostridia and bifidobacteria (b)
Decrease of Firmicutes, especially C. leptum, C. coccoides, C. symbosium
Cultivation
Humans
Humans
Increase of Bacteroides (b)
Cultivation
Cultivation
Humans
PCR-DGGE
Cultivation
Cultivation
Cultivation
Cultivation
Cloning
Cloning
Cultivation
Method
Increase of aerobic Gram-negative rods like enterobacteria other than E. coli
(Klebsiella, Enterobacter)
Increase of anaerobic Gram-positive rods
Amoxicillin
Humans
Overgrowth of lactobacilli (a) (and pediococci)
Vancomycin
Host
Table 4. Effect of vancomycin and amoxicillin on gut microbiota in the literature
(60)
(60)
(60-63)
(59)
(57)
(56;57)
(58)
(56;57)
(37)
(37)
(56-58)
Ref.
22
Rat pups
Rat pups
Depletion of enterococci
Depletion of enterobacteriacae (b)
Cultivation
Cultivation
Cultivation
(64)
(64)
(64)
(61)
23
and obesity whereas Bacteroidetes, Bacteroides and bifidobacteria have been linked to a statistical anti-obesity effect (36).
488
489
associated with weight gain (65). (b) Specific Proteobacteria, enterobacteriaceae and Escherichia coli have been linked with weight gain
(a) Specific lactobacilli (members of the Lactobacillaceae family) have been associated with obesity (25), correlated with BMI (38) and
Rat pups
Almost suppression of lactobacillus (a)
Cultivation
487
486
Humans
Decrease of streptococci and staphylococci
490
Figure legends
491
Figure 1. Global modification of the gut microbiota durting protracted amoxicillin or
492
vancomycin treatment
493
* p < 0.05, **p < 0.005, ***p < 0.0005 compared to controls
494
Figure 2. Principal component analysis identifying potential bacterial predictors of weight
495
gain after amoxicillin or vancomycin treatment
496
Analysis was done with XLSTAT 2012 software (Addinsoft, paris, France)
497
24
498
Figure 1.
499
500
25
501
Figure 2.
502
503
-
26
Conclusion Générale et Perspectives
Notre travail a permis, pour la première fois à notre connaissance, d’identifier quelles
étaient les altérations du microbiote digestif associées à l’obésité de façon reproductible
indépendamment des études et des pays par la méthode de méta-analyse. De plus, nous avons
confirmé que l’analyse au niveau de l’espèce est primordial dans l’identification de altérations
associées à l’obésité et avons retrouvé une espèce de Lactobacillus associée de manière dosedépendante à l’indice de masse corporelle. Cela a déjà été confirmé par une autre équipe pour
une autre espèce de Lactobacillus. Cependant, il n’est pas possible d’éliminer à ce jour un
facteur confondant comme le régime qui pourrait être responsable à la fois de la prise de poids
et de la modulation du microbiote digestif incluant l’augmentation de la concentration de
certains Lactobacillus. Dans le futur, d’autres études observationnelles associant des
approches de culture et de biologie moléculaire ciblant des genres d’intérêt mais discriminatif
au niveau de l’espèce voire de la souche permettront de confirmer la reproductibilité de nos
résultats et d’identifier de nouveaux candidats microbiens pour l’obésité.
Enfin, notre travail a permis d’éclaircir le rôle de la manipulation du microbiote
digestif sur l’obésité. Nous avons montré que l’effet des probiotiques contenant des
Lactobacillus sur le poids dépendait de l’espèce et de l’hôte. Basé sur la littérature, nous
émettons l’hypothèse que l’administration des probiotiques a un impact majeur sur le poids à
l’âge adulte quand ils sont administrés dans les premiers mois de vie pendant la formation du
microbiote digestif, et cela pourrait être associée à une obésité acquise comme cela a été
montré pour les antibiotiques. Cependant, afin de confirmer un rôle causal chez l’homme,
suggéré par les expériences de transplantation de microbiote chez l’animal, de nouvelles
études sont nécessaires. Enfin, devant toutes ces données évoquant un rôle causal de certaines
souches de probiotiques sur l’obésité, nous suggérons que le poids soit systématiquement
151
évalué dans les études à venir sur les probiotiques afin de ne pas négliger une augmentation
de l’obésité associée à la commercialisation massive de ces produits. En conséquence, les
études sur les probiotiques sont sujettes à des biais de publication importants, c’est pourquoi
une politique active sur les conflits d'intérêts des études sur les probiotiques devrait être
encouragée.
152
References
1. Ley RE, Turnbaugh PJ, Klein S, Gordon JI. Microbial ecology: human gut microbes
associated with obesity. Nature 2006;444(7122):1022-1023.
2. Armougom F, Henry M, Vialettes B, Raccah D, Raoult D. Monitoring bacterial
community of human gut microbiota reveals an increase in Lactobacillus in obese
patients and Methanogens in anorexic patients. PLoS One 2009;4(9):e7125.
3. Stokstad EL, Jukes TH, . The multiple nature of the animal protein factor. J Biol Chem
1949;180(2):647-654.
4. Raoult D. Human microbiome: take-home lesson on growth promoters? Nature
2008;454(7205):690-691.
5. Stsepetova J, Sepp E, Kolk H, Loivukene K, Songisepp E, Mikelsaar M. Diversity and
metabolic impact of intestinal Lactobacillus species in healthy adults and the elderly.
Br J Nutr 2011;105(8):1235-1244.
6. Robinson EL, Thompson WL. Effect on weight gain of the addition of Lactobacillus
acidophilus to the formula of newborn infants. J Pediatr 1952;41(4):395-398.
7. Morelli L. Million et al. "Comparative meta-analysis of the effect of Lactobacillus
species on weight gain in humans and animals." Letter to editors. Microb Pathog
2013;55:51.
8. Trasande L, Blustein J, Liu M, Corwin E, Cox LM, Blaser MJ. Infant antibiotic
exposures and early-life body mass. Int J Obes (Lond) 2013;37(1):16-23.
9. Besselink MG, van Santvoort HC, Buskens E et al. Probiotic prophylaxis in predicted
severe acute pancreatitis: a randomised, double-blind, placebo-controlled trial. Lancet
2008;371(9613):651-659.
10. Murphy EF, Cotter PD, Hogan A et al. Divergent metabolic outcomes arising from
targeted manipulation of the gut microbiota in diet-induced obesity. Gut
2013;62(2):220-226.
11. Archambaud C, Nahori MA, Soubigou G et al. Impact of lactobacilli on orally
acquired listeriosis. Proc Natl Acad Sci U S A 2012;109(41):16684-16689.
12. Cho I, Yamanishi S, Cox L et al. Antibiotics in early life alter the murine colonic
microbiome and adiposity. Nature 2012;488(7413):621-626.
13. Thuny F, Richet H, Casalta JP, Angelakis E, Habib G, Raoult D. Vancomycin
treatment of infective endocarditis is linked with recently acquired obesity. PLoS One
2010;5(2):e9074.
153
ANNEXES
154
Article XIII :
Microbial Culturomics: Paradigm shift in the human
gut microbiome study
Jean-Christophe Lagier, Fabrice Armougom, Matthieu Million,
Perrine Hugon, Isabelle Pagnier, Catherine Robert, Fadi Bittar,
Ghislain Fournous, Gregory Gimenez, Marie Maraninchi, JeanFrançois Trape, EugeneV. Koonin, Bernard La Scola, Didier Raoult.
Published in Clin Microbiol Infect 2012 Dec;18(12):1185-93. (IF 4.54)
155
ORIGINAL ARTICLE
BACTERIOLOGY
Microbial culturomics: paradigm shift in the human gut microbiome
study
J.-C. Lagier1,*, F. Armougom1,*, M. Million1, P. Hugon1, I. Pagnier1, C. Robert1, F. Bittar1, G. Fournous1, G. Gimenez1,
M. Maraninchi2, J.-F. Trape3, E. V. Koonin4, B. La Scola1 and D. Raoult1
1) Aix Marseille Université, URMITE, UM63, CNRS 7278, IRD 198, INSERM 1095, 2) Service de Nutrition, Maladies Métaboliques et Endocrinologie,
UMR-INRA U1260, CHU de la Timone, Marseille, France, 3) IRD, UMR CNRS 7278-IRD 198, Route des Pères Maristes, Dakar, Sénégal and 4) National
Centre for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
Abstract
Comprehensive determination of the microbial composition of the gut microbiota and the relationships with health and disease are
major challenges in the 21st century. Metagenomic analysis of the human gut microbiota detects mostly uncultured bacteria. We studied
stools from two lean Africans and one obese European, using 212 different culture conditions (microbial culturomics), and tested the
colonies by using mass spectrometry and 16S rRNA amplification and sequencing. In parallel, we analysed the same three samples by
pyrosequencing 16S rRNA amplicons targeting the V6 region. The 32 500 colonies obtained by culturomics have yielded 340 species of
bacteria from seven phyla and 117 genera, including two species from rare phyla (Deinococcus-Thermus and Synergistetes, five fungi, and a
giant virus (Senegalvirus). The microbiome identified by culturomics included 174 species never described previously in the human gut,
including 31 new species and genera for which the genomes were sequenced, generating c. 10 000 new unknown genes (ORFans), which
will help in future molecular studies. Among these, the new species Microvirga massiliensis has the largest bacterial genome so far
obtained from a human, and Senegalvirus is the largest virus reported in the human gut. Concurrent metagenomic analysis of the same
samples produced 698 phylotypes, including 282 known species, 51 of which overlapped with the microbiome identified by culturomics.
Thus, culturomics complements metagenomics by overcoming the depth bias inherent in metagenomic approaches.
Keywords: Culturomics, gut microbiota, MALDI-TOF MS, metagenomic analysis, uncultured bacteria
Original Submission: 20 August 2012; Revised Submission: 28 August 2012; Accepted: 29 August 2012
Editor: G. Greub
Article published online: 4 September 2012
Clin Microbiol Infect 2012; 18: 1185–1193
10.1111/1469-0691.12023
Corresponding author: D. Raoult, Aix-Marseille Université, URMITE, UMR CNRS 7278, IRD 198, INSERM U1095, Faculté de Médecine, 27 Bd Jean Moulin, Cedex 5, 13385 Marseille, France
E-mail: [email protected]
*These authors contributed equally to this work.
Introduction
The composition of the human gut microbiome, the determination of which represents a major challenge in the 21st century
[1], has been studied with different tools, leading to increasingly
complex results [2–6]. The first approach used to study the gut
microbiota employed microbial culture [2]. Subsequent studies
that involved amplification and sequencing of 16S rRNA and later
metagenomic analysis have dramatically expanded the known
diversity of the human gut microbiome [4,5,7,8]. It is commonly
accepted that c. 80% of the bacterial species found by molecular
tools in the human gut are uncultured or even unculturable [1].
However, several drawbacks of the current metagenomic
approaches, including major discrepancies among different
studies, apparently reflect biases of the employed techniques. In
particular, sequence-based techniques miss clinically relevant
minority populations, including potentially pathogenic bacteria,
such as Salmonella Typhi, Tropheryma whipplei, and Yersinia
enterocolitica, that may be present at concentrations lower than
105/mL; this major problem is known as the depth bias.
Recently, there has been a renewed interest in culture
methods for ‘non-cultivable’ species [3,9]. One of the gridlocks of the traditional bacteriological culture methods has
ª2012 The Authors
Clinical Microbiology and Infection ª2012 European Society of Clinical Microbiology and Infectious Diseases
1186
Clinical Microbiology and Infection, Volume 18 Number 12, December 2012
been recently overcome by advances in mass spectrometry
(MS) techniques, which can accurately and rapidly identify
microorganisms with matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF), allowing rapid screening of
large numbers of colonies [10]. In this study, we combined
the MS approach with extensive sequencing and exploration
of the potential of numerous known and new culture methods to introduce culturomics as a major complement to metagenomics in the study of the human gut microbiome.
Materials and Methods
All of the data are detailed in the Supporting information. We
used two African stools, both from healthy young males living in
rural Senegal, and a stool from a French obese individual with a
body mass index of 48.2 kg/m2. Each patient’s consent was
obtained, and the study was approved by the local ethics committee of IFR48 (agreement number 09-022; Marseille, France). We
designed 212 culture conditions, using variable physicochemical
conditions, pre-incubation in blood culture bottles, rumen fluid
CMI
and sterile stool extract to mimic the natural environment [9].
Moreover, with the aim of selecting a minority population, we
used antibiotics, both active and passive filtration, and bacteriophages. We used MALDI-TOF MS to quickly identify a maximum
of colonies. When the strains remained unrecognized, the 16S
rRNA gene was sequenced. As previously described, a threshold
similarity of >98.7% was chosen to define a new bacterial species
[11]. The same three stool samples were tested by pyrosequencing of a 16S rRNA amplicon targeting the V6 region, the most
variable region, as previously described [7] NCBI accession number=SRA049748. The new bacterial genera and species were
sequenced with a paired-end strategy for high-throughput
pyrosequencing on the 454-Titanium instrument. Senegalvirus
was sequenced with the Roche 454 FLX-Titanium platform.
Results
Proof-of-concept
We studied stool samples from two young lean Africans
from a rural environment in Senegal (Fig. 1) and one obese
(d)
(a)
(b)
(c)
FIG. 1. The source of material for culturomics and the record-breaking virus and bacterium from the human gut. (a) The geographical locations
of the Dielmo and N’diop villages (Sources: Wikitravel.org and Google Earth) from which the two African stool samples analysed in this work
were obtained. (b) Electronmicrograph of the giant Senegalvirus, which was isolated from a stool sample of an individual from N’Diop. (c)
Comparison of the Senegalvirus genome with the genomes of related giant viruses, Marseillevirus and Lausannevirus. (d) Electronmicrograph of
Microvirga massiliensis (the bacterium with the largest genome ever isolated from humans), which was isolated from the Dielmo stool sample.
ª2012 The Authors
Clinical Microbiology and Infection ª2012 European Society of Clinical Microbiology and Infectious Diseases, CMI, 18, 1185–1193
Lagier et al.
CMI
French individual, using 212 different conditions, including
amoebal co-culture. For the first African sample, 56 different
culture methods were applied, including different physicochemical conditions and the addition of specific nutrients or
inhibitors (Table S1). Using these approaches, we isolated
3000 colonies, which were subjected to MALDI-TOF MS
analysis for rapid identification of microbial species [10]. This
analysis resulted in the identification of 99 bacterial species,
42 of which had never been found in the human gut
(Fig. S1), and two of which had never been described
(Table 1 Fig. 2).
Culturomics revolutionizes gut
1187
With the two other stool samples, only those culture
conditions that proved to be efficient with the first sample
were used again, and many additional culture conditions
were applied to maximize the chance of isolation of new
species (Table S1). This optimized approach yielded 191 distinct bacterial isolates, including two new genera and six new
species from the obese individual’s stool sample; the largest
number of bacteria ever identified in a single stool (219 bacteria and five fungi, including three new genera and 18 new
bacterial species) were isolated from the second African
sample (Tables 1 and 2; Fig. 2; Table S2) [12–16].
TABLE 1. Characteristics of the 23 new bacterial species and genera cultured from the Senegalese stools [12–16]
N’Diop stool sample
New species
Oceanobacillus massiliensis
Bacillus timonensis
Dielmo stool sample
New species
Kurthia massiliensis
Kurthia senegalensis
Phylum
Initial culture conditions
Firmicutes
Filtration brain–heart infusion 5% sheep
blood 0.45-lm aerobe, 37°C
Brain–heart infusion + sheep blood 5%,
aerobe, 37°C
Firmicutes
Firmicutes
Firmicutes
Kurthia timonensis
Anaerococcus senegalensis
Paenibacillus senegalensis
Firmicutes
Firmicutes
Firmicutes
Bacillus massiliosenegalensis
Clostridium senegalense
Firmicutes
Firmicutes
Peptoniphilus senegalensis
Firmicutes
Peptoniphilus timonensis
Firmicutes
Ruminococcus massiliensisa
Firmicutes
Alistipes senegalensis
Bacteroidetes
Alistipes timonensis
Bacteroidetes
Cellulomonas massiliensis
Actinobacteria
Aeromicrobium massiliense
Brevibacterium senegalense
Enterobacter massiliensis
Actinobacteria
Actinobacteria
Proteobacteria
Herbaspirillum massiliense
Proteobacteria
Microvirga massiliensis
New genera
Dielma fastidiosa
Proteobacteria
Firmicutes
Senegalemassilia anaerobia
Actinobacteria
Timonella senegalensis
Actinobacteria
CNA aerobe 2.5% CO2, 37°C
Filtration 5% sheep blood agar 1.2-lm
aerobe, 37°C
HTM, aerobe, 2.5% CO2, 37°C
Brucella anaerobe, 37°C
Schaedler kanamycin vancomycin, aerobe,
37°C
5% sheep blood agar, aerobe, 28°C
Inoculation in blood culture bottle for
5 days with 5 mL of sheep blood, 5%
sheep blood agar, anaerobe, 37°C
Inoculation in blood culture bottle for
10 days with 5 mL of sheep blood,
5% sheep blood agar, anaerobe, 37°C
Inoculation in blood culture bottle anaerobe
for 14 days with 8 mL of rumen fluid, 5%
sheep blood agar, anaerobe, 37°C
Inoculation in blood culture bottle anaerobe
for 14 days with 8 mL of rumen fluid 5%
sheep blood agar, anaerobe, 37°C
Schaedler kanamycin vancomycin, anaerobe,
37°C
Inoculation in blood culture bottle anaerobe
for 5 days, Schaedler kanamycin
vancomycin, anaerobe 37°C
Passive filtration with Leptospira broth, 5%
sheep blood agar, aerobic atmosphere,
37°C
5% sheep blood agar, aerobe, 37°C
Brucella, aerobe, 37°C
Phage T1 + T4, then 5% sheep blood agar,
aerobe, 37°C
Passive filtration with Leptospira broth, 5%
sheep blood agar, aerobic atmosphere,
37°C
MOD 2, aerobe, 37°C
Inoculation in blood culture bottle anaerobe
for 10 days, brain–heart infusion, anaerobe,
37°C
Inoculation in blood culture bottle anaerobe
for 5 days. 5% sheep blood aga,r anaerobe,
37°C
Inoculation in blood culture bottle anaerobe
for 14 days with 8 mL of rumen fluid, 5%
sheep blood agar, anaerobe, 37°C
Diameter
(lm) (EM)
Genome size
estimate (Mb)
ORFan
(%)
Estimated GC
content (%)
Genbank
no.
0.70
3.6
5.6
41
HQ586877
0.66
4.7
6.8
38.3
JF824810
1.08
1.03
3.3
2.9
11.9
11.3
39.7
39.6
JF824795
JF824796
0.94
0.68
0.66
4.1
1.8
5.7
16.2
3
10.7
39
28.5
48.3
JF824797
JF824805
JF824808
0.64
1.05
4.9
3.9
7.7
11.5
37.7
29.3
JF824800
JF824801
0.64
1.8
3.9
32.5
JF824803
0.91
1.7
9.3
31
JN657222
0.96
5.1
25
57
JN657221
0.53
4
3.8
58.3
JF824804
0.62
3.5
2.9
58.8
JF824799
0.48
3.4
7.9
73.9
JN657218
1.04
0.68
1.02
3.3
3.4
4.9
10.5
9.6
3
72.6
69.9
55.4
JF824798
JF824806
JN657217
0.44
4.2
8.1
59.7
JN657219
2.28
9.35
24.1
59.2
JF824802
0.59
3.6
10.5
40
JF824807
0.70
2.3
6.3
61.8
JF824809
0.59
3
11.9
61.3
JN657220
EM : Electron Microscopy.
The characterization of this bacterial species was not performed, because the impossibility of subculture.
a
ª2012 The Authors
Clinical Microbiology and Infection ª2012 European Society of Clinical Microbiology and Infectious Diseases, CMI, 18, 1185–1193
1188
Clinical Microbiology and Infection, Volume 18 Number 12, December 2012
CMI
FIG. 2. Phylogenetic tree representing the new bacterial species and genera obtained by culturomics. Red labels indicate the new species found
in the Senegalese patients and obese patient. Dark labels indicate the closest neighbour species defined as Isolates and Type in the RDP-II
database. Tree branches in red, dark green, purple and blue represent the phyla Bacteroidetes, Proteobacteria, Actinobacteria, and Firmicutes,
respectively. Green squares denote new species found in the obese patient. Dark circles indicate that the genome sequence is available for the
closest neighbour species.
TABLE 2. Characteristics of the eight new bacterial species and genera cultured from the stools of the obese individual
Phylum
Initial culture conditions
New species
Anaerococcus obesiensis
Firmicutes
Brevibacillus massiliensis
Peptoniphilus grossensis
Firmicutes
Firmicutes
Peptoniphilus obesiensis
Firmicutes
Alistipes obesiensis
Bacteroidetes
Actinomyces grossensis
Actinobacteria
Inoculation in blood culture bottle with
thioglycolate for 4 days, 5% sheep blood
agar, anaerobe, 37°C
M17, aerobe, 37°C
Inoculation in blood culture bottle for
26 days with rumen and sheep blood, 5%
sheep blood agar, anaerobe, 37°C
Inoculation in blood culture bottle for
26 days with rumen and sheep blood, 5%
sheep blood agar, anaerobe, 37°C
Inoculation in blood culture bottle for
11 days with rumen, 5% sheep blood agar,
anaerobe, 37°C
Inoculation in blood culture bottle with
thioglycolate for 4 days, 5% sheep blood
agar, anaerobe, 37°C
New genera
Enorma massiliensis
Kallipyga massiliensis
Actinobacteria
Firmicutes
Inoculation in blood culture bottle with
thioglycolate for 4 days, 5% sheep blood
agar, anaerobe, 37°C
Inoculation in blood culture bottle for
26 days with rumen and sheep blood, 5%
sheep blood agar, anaerobe, 37°C
Diameter
(lm) (EM)
Genome size
estimate (Mb)
ORFan
(%)
Estimated GC
content (%)
Genbank
no.
0.71
2.05
3.7
30.1
JN837490
0.73
0.77
5.1
2.1
7.2
5.5
53
34.5
JN837488
JN837491
0.85
1.77
4.7
30.4
JN837495
0.61
3.1
7.3
58.5
JN837494
0.49
1.87
5.2
56
JN837492
0.57
2.3
7.9
61.8
JN837493
0.67
1.77
6.3
51.4
JN837487
EM : Electron Microscopy.
ª2012 The Authors
Clinical Microbiology and Infection ª2012 European Society of Clinical Microbiology and Infectious Diseases, CMI, 18, 1185–1193
Lagier et al.
CMI
Optimization of rapid screening by MALDI-TOF MS
With a low level of operator training, MALDI-TOF MS allows
rapid discrimination between identified bacteria (present in the
current data bank) and unidentified bacteria. Indeed, subsequent
to the analysis of the first sample, with the two other tested
samples we were able to identify a significantly greater number
of colonies: without automatic colony picking, we tested a total
of 29 500 different colonies from these two samples, as compared with the 3000 colonies tested from the first sample. In
addition to the better training of the operators, as and when
necessary, each previously unknown bacterial spectrum was
added to our data bank, facilitating the screening for further
studies. MS, which obviates the need for both the time-consuming Gram-staining procedure and the usual biochemical tests,
seems to be the current method of choice for the identification
of microorganisms, and has the potential to supplant traditional
microbiological methods [10]. Indeed, with the third stool sample, we tested, under eight different culture conditions, 50–100
colonies that were indistinguishable in appearance. This experiment allowed us to identify several species, notably those from
the genus Enterococcus, for which identification in routine bacteriology is mainly based on colony morphology.
Eliminating the predominant population
The high concentration of bacteria in the human gut (1012 to
1011 bacteria per gram of stools) [1] hampers non-selective
culture analysis. Therefore, we used antibiotics in culture
media to eliminate sensitive organisms and thus facilitate the
identification of resistant ones. We developed different strategies to extend the use of ‘classic selective media’. First, to
identify new proteobacteria, we had to develop alternative
strategies, because Escherichia coli is the overwhelmingly dominant bacterial species in the human gut under aerobic conditions. We used a cocktail of E. coli lytic bacteriophages [17]
that allowed us to clear the culture of E. coli and to identify
an unknown enterobacterial species (Enterobacter massiliensis)
that was not detected by classic axenic culture.
Otherwise, an effective method to remove the major bacterial population was active filtration with successive membranes (from 5 to 0.2 lm); this procedure allowed us to
identify eight new bacterial species. Finally, using the physical
characteristics of certain bacteria, we applied passive filtration,
which resulted in the identification, in the second African
stool sample, of three motile bacteria that have not been previously detected in the human gut, including two new species
(Table 1 and Table S1).
Enrichment of samples in blood culture bottles
Incubation of clinical samples in blood culture bottles is
known to promote the growth of Kingella kingae in osteoar-
Culturomics revolutionizes gut
1189
ticular infections. Therefore, for the second and the third
samples, we developed an enrichment culture technique
involving several days of direct pre-incubation of stools in an
aerobic or anaerobic blood culture bottle, allowing the
growth of 29 bacterial species that were not detected by
standard axenic culture (including 24 anaerobic species). This
approach yielded three new genera and three new species.
Addition of sheep blood to the blood culture bottle allowed
us to identify three additional species.
New culture conditions
To increase the growth of bacteria under culture conditions
that mimic their natural environment, and drawing from previous studies on environmental bacteria [9], we used sterile
rumen fluid [3] (Fig. S2) and sterile fresh human stools with
or without pre-incubation in blood culture bottles. This
approach allowed us to isolate 17 strains that were not
recovered in classic axenic conditions, including two new
genera, three new species, and one species of the Deinococcus-Thermus phylum that has not been previously cultured
from human clinical samples [18].
Finally, in an effort to obtain fastidious bacteria by amoebal co-culture with Acanthamoeba polyphaga [19], we identified from the three stool samples four additional bacterial
species that have not been detected by axenic culture. Serendipitously, we also isolated, from the first African stool, a
new giant virus strain, which we named Senegalvirus and that
has the largest genome among the viruses isolated from
humans (Figs 1 and S3), with the exception of two reports
of mimivirus detection [19]. The isolation of a non-filterable
giant virus from a human stool indicates that giant viruses
could constitute a component of the gut microbiome that is
missed by metagenomic studies with 0.22-lm filters [6,20].
Microbial culturomics: a general perspective
Only 45 bacterial species obtained in culture in the present
work were common to all three analysed stool samples and
could be named ‘the culturomics core microbiome’. The
majority of the isolated species (63.6%) were cultured from
only one stool sample, indicative of large inter-individual
diversity of the human gut microbiome (Fig. S4). Thus,
microbial culturomics allowed the detection of numerous
new bacteria from each tested sample (Tables 1 and 2).
Although we used a total of 212 different culture conditions for the three stool samples, 100% of the species
grew under only 70 culture conditions, and 73% of the
species were identified with only 20 conditions (Fig. S5;
Table S3). These results provide guidance for future culturomics studies, which will benefit from using the set of
conditions shown to be efficient in this study before
ª2012 The Authors
Clinical Microbiology and Infection ª2012 European Society of Clinical Microbiology and Infectious Diseases, CMI, 18, 1185–1193
1190
Clinical Microbiology and Infection, Volume 18 Number 12, December 2012
developing new culture approaches. Although a limited
number of culture conditions allowed us to grow the
majority of the common bacterial species, a more thorough approach that used many ‘exotic’ culture conditions
substantially expanded the repertoire by allowing the isolation of less abundant bacteria.
CMI
ria ranged from 1.7 to 9.35 Mb. Among the predicted gene
products of the new genomes, 2.9–24.1% had no readily
detectable homologues, i.e. they represented ORFans. Altogether, the present study yielded c. 10 000 previously
unknown genes.
Comparison with 16S rRNA sequencing
‘Giant’ bacteria and giant virus
The typical diameter of the isolated bacteria ranged from 0.5
to 1.5 lm [21]. However, the largest isolated bacterium, Microvirga massiliensis, reached 2.28 lm according to transmission electron microscopy, and was also shown to possess
the largest genome (9.35 Mb) of any bacterium previously
obtained from a human sample (Tables 1 and 2; Fig. 1). The
giant (194 nm in diameter) Senegalvirus isolated by amoebal
co-culture is the first giant virus ever isolated from the
human gut. The genome of Senegalvirus (Genbank JF909596–
JF909602) is closely related to those of Marseillevirus (96%
identity) and Lausannevirus [22] (Fig. S3), suggesting that this
is a new strain of Marseillevirus. Preliminary work indicates
the presence of antibodies against this virus in the serum
and stool of the subject.
Genome sequencing of new bacteria
The genomes of all 31 new bacterial species and genera
(Tables 1 and 2; Fig. 2) isolated from the two African stool
samples and the French stool sample were sequenced, generating a total of 110.4 Mb of unique sequence, and are freely
available in the EMBL database (http://www.ebi.ac.uk/embl/
Submission/index.html). The genome sizes of the new bacte-
(a)
The pyrosequencing that was performed as part of this
study identified 126, 138 and 157 phylotypes corresponding to known species from the three stools, respectively
(Tables S4, S5 and S6). For the three stools taken
together, the microbial culturomics approach yielded 340
bacterial species from seven phyla and 117 genera,
whereas pyrosequencing identified 282 species from six
phyla and 91 genera. However, a dramatic difference was
observed with culturomics: only 51 phylotypes were common between the two approaches (15% of the culturomics
set) (Fig. 3a). Among the ‘culturomics core microbiome’ of
45 cultured species, only 12 (26%) were detected by pyrosequencing. Similarly, only 44 genera (38% of the culturomics set) were shared between the two approaches
(Fig. 3b). Altogether, 416 phylotypes of previously uncultured bacteria were identified. Notably, the sequence from
a new genus cultured here (Senegalemassilia anaerobia) had
been previously identified as an uncultured bacterium by
metagenomics, demonstrating the capacity of culturomics
to grow such supposedly ‘unculturable’ microorganisms
[23–27]. Finally, the molecular techniques did not identify
pathogenic bacteria, such as Salmonella, that were detected
by culturomics (Fig. 4).
(b)
FIG. 3. Identification of bacteria in the human gut by culturomics and metagenomics. (a) The two ‘icebergs’ represent the 340 cultured bacterial
species and the 698 phylotypes identified by pyrosequencing. The overlap between the two sets of species, i.e. the 51 species detected by both
approaches, is shown in purple. Below the ‘sea level’ is the projected unknown part of the human gut microbiome. (b) Taxonomic distribution
of organisms identified by culturomics and pyrosequencing. The ovoid shape denoted ‘Culture’ indicates all of the bacterial and fungal genera
identified by culturomics from the three stool samples. The ovoid shape denoted ‘Pyro’ indicates genera identified by 16S amplicon pyrosequencing of the three stool samples. The dashed coloured lines show the phylum membership of the respective genus node. The two shapes with
dashed lines at left and right represent the bacterial and fungal genera identified by only one technique (pyrosequencing or culturomics), whereas
the shape in the middle represents the genera identified by both culturomics and pyrosequencing.
ª2012 The Authors
Clinical Microbiology and Infection ª2012 European Society of Clinical Microbiology and Infectious Diseases, CMI, 18, 1185–1193
Lagier et al.
CMI
S
A
N
G
E
R
G
S
G
S
2
0
F
L
X
S
O
L
E
X
A
Culturomics revolutionizes gut
1191
S. Aureus
G
S
G
S
G
S
F
L
X
F
L
X
F
L
X
P
C
C
U
R
L
T
U
R
35%
E
Bacteroides,
Eubacterium,
Clostridium
Peptostreptococcus
Bifidobacterium
Vibrio cholerae
E. coli O157
Shigella dysenteriae
Enterotoxigenic E. coli
Streptococcus
Aeromonas hydrophila
Clostridium difficile
Campylobacter jejuni
Salmonella Typhimurium
65%
Tropheryma whipplei
Yersinia enterocolitica
Salmonella Typhi
FIG. 4. The detection thresholds of metagenomic and culturomic approaches. The detection threshold of metagenomic methods correlates with
the concentration of bacteria in the investigated sample divided by the number of generated sequences. The blue pointed shapes show the detection depth of different published metagenomic analyses of the human gut microbiome. The upper dotted red line shows the detection threshold
of the most powerful available metagenomic methods, the middle line shows the detection threshold of PCR, and the lower line shows the detection threshold of culturomics. The latter two thresholds were determined by detection of Staphylococcus aureus that was added to the samples in
varying concentrations (indicated by green pointed shapes). Among the 340 cultivated bacterial species, 29 were identified only after several days
of incubation in an anaerobic blood culture bottle, so their concentrations in the original samples could not be estimated. Among the remaining
311 bacteria, 203 (65%) were found at concentrations of <106 CFU/g of stool, i.e. below the detection threshold of metagenomic methods.
Discussion
Metagenomics is currently thought of as the mainstream of
microbiome studies, in particular as applied to the human
gut. Unexpectedly, however, in a direct comparison, we
described more known bacterial species by systematically
applying a large sample of culture conditions (the approach
we denoted culturomics) than by pyrosequencing (Fig. 3a).
Moreover, we found a dramatic divergence between the sets
of bacteria identified by the two approaches at the level of
both species and genera. The detection by culturomics of
numerous bacteria that go undetected in genomic and metagenomic studies is far from being trivial, even if most of
these microorganisms are of low abundance. Undoubtedly, a
minority population, as in the famous short story [28], can
have a substantial effect on the ecology of the gut microbiota
and on human health. Indeed, c. 65% of bacterial species
from the three samples were detected at concentrations
between 103 and 106 CFU/mL, which are below the
detection thresholds of large-scale molecular studies, demonstrating the major ‘depth bias’ that is characteristic of the
metagenomic approaches (Fig. 4). In support of this conclusion,
culture methods allowed the detection of Staphylococcus aureus that was added to the stools at a concentration that was
100 times lower than the concentration detectable by molecular tools (Fig. 4).
The paradigm shift in microbiome study that seems to be
brought about by culturomics became possible thanks to the
breakthrough in the application of MALDI-TOF MS [10]. In
comparison with the most rapid conventional phenotypic
identification method for bacteria (Vitek System; Biomerieux,
Marcy I’Etoile, France), MALDI-TOF MS reduces by c. 55-fold
the time to bacterial identification and reduces the costs by
at least a factor of 5 [10]. Therefore, MALDI-TOF MS is currently the most time-effective and cost-effective identification
method available for culture-based microbiota studies.
In the present culturomics study, the actual analysis of
microbial cultures involved only three students (JCL,MM,PH)
that performed the experience on the three stools during
2 years in a single laboratory. Nevertheless, this limited effort
yielded 174 bacterial species that have not been previously
reported from the human gut microbiota. Genome sequencing
of these bacteria would increase by c. 18% the number of
ª2012 The Authors
Clinical Microbiology and Infection ª2012 European Society of Clinical Microbiology and Infectious Diseases, CMI, 18, 1185–1193
1192
Clinical Microbiology and Infection, Volume 18 Number 12, December 2012
sequenced bacterial genomes from the human gut (975) that
have been identified by several laboratories within the human
microbiome project [29].
The present limited culturomics study shows that microbial
biodiversity in the human gut is substantially broader than predicted on the basis of genomic and metagenomic analyses
[27,30]. Interestingly, culturomics also ‘broke the records’ for
the largest bacterium and virus isolated from humans so far.
By using different atmospheres, temperatures, pH, nutrients,
minerals, antibiotics or phages, ‘microbial culturomics’ provides comprehensive culture conditions simulating, reproducing or mimicking the entirety of selective constraints that have
shaped the gut microbiota for millions of years. In fact, each
isolated microorganism is one among the possible viable solutions to the evolutionary equation whose constants are the
selective constraints of the environment, corresponding here
to the human gut. This is why microbial culturomics is the best
way to capture the functional and viable gut microbiota biodiversity of each human individual through large-scale isolation,
and to capture the deepest informational genetic gut biodiversity by sequencing the complete genomes of the previously isolated microorganisms. In the future, the use of the most
effective conditions and automatic colony picking will further
deepen this field of research.
Acknowledgements
The authors wish to thank B. Davoust for the sheep rumen
collection, R. Rivet for his technical assistance, and I. Combe
for her administrative assistance.
Author Contributions
Conception and and design of the experiments: D. Raoult. Performance of the experiments: J. C. Lagier, M. Million, P. Hugon,
I. Pagnier, C. Robert, and B. La Scola. Analysis of the data: J. C.
Lagier, F. Armougom, P. Hugon, I. Pagnier, F. Bittar, G. Fournous, G. Gimenez, E. V. Koonin, B. La Scola, and D. Raoult.
Contribution of reagents/material/analysis tools: J. C. Lagier, F.
Armougom, P. Hugon, I. Pagnier, F. Bittar, G. Fournous, G.
Gimenez, M. Million, B. La Scola, and D. Raoult. Writing of the
paper: J. C. Lagier, F. Armougom, E. V. Koonin, and D. Raoult.
Transparency Declaration
This work was funded by the CNRS, Centre National de la
Recherche Scientifique, the IRD (Institut de Recherche et
CMI
Développement), and Aix-Marseille Université (crédits
recurrents). The authors have declared that no competing
interests exist.
Supporting Information
Additional Supporting Information may be found in the
online version of this article:
Figure S1. A comparison of the species identified from
the cultures of the different samples.
Figure S2. The protocol used for the rumen fluid preparation.
Figure S3. A genomic comparison between Marseillevirus, Senegalvirus and Lausannevirus.
Figure S4. A Venn diagram representing the number of
species cultivated from each of the stool samples.
Figure S5. The number of species that were cultivated in
10–70 conditions out of the 212 tested culture conditions.
Figure S6. The percentage of new species and of the
total number of species cultured that grew in only one culture condition or in multiple culture conditions.
Table S1. Culture conditions for microbial culturomics
characterization from the stool samples of the N’Diop and
Dielmo individuals and the obese French individual.
Table S2. The 345 bacterial and fungal species cultured
from the N’Diop, Dielmo and obese patient stool samples.
Table S3. The 20 best culture conditions, which facilitated the identification of 73% of the bacterial species.
Table S4. Cultivated species identified in a Senegalese
stool sample from Dielmo village.
Table S5. Cultivated phylotypes identified in the stool
sample from an obese patient.
Table S6. Cultivated phylotypes identified in a Senegalese
stool sample from N’Diop village.
Please note: Wiley-Blackwell are not responsible for the
content or functionality of any supporting materials supplied
by the authors. Any queries (other than missing material)
should be directed to the corresponding author for the article.
References
1. Turnbaugh PJ, Ley RE, Hamady M, Fraser-Liggett CM, Knight R,
Gordon JI. The human microbiome project. Nature 2007; 449:
804–810.
2. Finegold SM, Attebery HR, Sutter VL. Effect of diet on human fecal
flora: comparison of Japanese and American diets. Am J Clin Nutr
1974; 27: 1456–1469.
3. Goodman AL, Kallstrom G, Faith JJ et al. Extensive personal human
gut microbiota culture collections characterized and manipulated in
gnotobiotic mice. Proc Natl Acad Sci USA 2011; 108: 6252–6257.
ª2012 The Authors
Clinical Microbiology and Infection ª2012 European Society of Clinical Microbiology and Infectious Diseases, CMI, 18, 1185–1193
Lagier et al.
CMI
4. Andersson AF, Lindberg M, Jakobsson H, Backhed F, Nyren P, Engstrand L. Comparative analysis of human gut microbiota by barcoded
pyrosequencing. PLoS ONE 2008; 3: e2836.
5. Turnbaugh PJ, Quince C, Faith JJ et al. Organismal, genetic, and transcriptional variation in the deeply sequenced gut microbiomes of
identical twins. Proc Natl Acad Sci USA 2010; 107: 7503–7508.
6. Reyes A, Haynes M, Hanson N et al. Viruses in the faecal microbiota
of monozygotic twins and their mothers. Nature 2010; 466: 334–338.
7. Claesson MJ, Wang Q, O’Sullivan O et al. Comparison of two nextgeneration sequencing technologies for resolving highly complex microbiota composition using tandem variable 16S rRNA gene regions.
Nucleic Acids Res 2010; 38: e200.
8. Wu GD, Lewis JD, Hoffmann C et al. Sampling and pyrosequencing
methods for characterizing bacterial communities in the human gut
using 16S sequence tags. BMC Microbiol 2010; 10: 206.
9. Kaeberlein T, Lewis K, Epstein SS. Isolating ‘uncultivable’ microorganisms in pure culture in a simulated natural environment. Science 2002;
296: 1127–1129.
10. Seng P, Drancourt M, Gouriet F et al. Ongoing revolution in bacteriology: routine identification of bacteria by matrix-assisted laser
desorption ionization time-of-flight mass spectrometry. Clin Infect Dis
2009; 49: 543–551.
11. Stackebrandt E, Ebers J. Taxonomic parameters revisited: tarnished
gold standards. Microbiol Today 2006; 33: 152–155.
12. Lagier JC, El Karkouri K, Nguyen TT, Armougom F, Raoult D, Fournier
PE. Non-contiguous finished genome sequence and description of Anaerococcus senegalensis sp. nov. Stand Genomic Sci 2012; 6: 116–125.
13. Lagier JC, Armougom F, Mishra AK, Nguyen TT, Raoult D, Fournier
PE. Non-contiguous finished genome sequence and description of Alistipes timonensis sp. nov. Stand Genomic Sci 2012; 6: 315–324.
14. Mishra AK, Gimenez G, Lagier JC, Robert C, Raoult D, Fournier PE.
Non-contiguous finished genome sequence and description of Alistipes
senegalensis sp. nov. Stand Genomic Sci 2012; 6: 304–314.
15. Kokcha S, Mishra AK, Lagier JC et al. Non-contiguous finished genome sequence and description of Bacillus timonensis sp. nov. Stand
Genomic Sci 2012; 6: 346–355.
Culturomics revolutionizes gut
1193
16. Mishra AK, Lagier JC, Robert C, Raoult D, Fournier PE. Non-contiguous finished genome sequence and description of Clostridium senegalense sp. nov. Stand Genomic Sci 2012; 6: 386–395.
17. Sillankorva S, Oliveira D, Moura A et al. Efficacy of a broad host
range lytic bacteriophage against E. coli adhered to urothelium. Curr
Microbiol 2010; 68: 1128–1132.
18. Tian B, Hua Y. Carotenoid biosynthesis in extremophilic DeinococcusThermus bacteria. Trends Microbiol 2010; 18: 512–520.
19. Pagnier I, Raoult D, La Scola B. Isolation and identification of
amoeba-resisting bacteria from water in human environment by using
an Acanthamoeba polyphaga co-culture procedure. Environ Microbiol
2008; 10: 1135–1144.
20. Willner D, Furlan M, Haynes M et al. Metagenomic analysis of respiratory tract DNA viral communities in cystic fibrosis and non-cystic
fibrosis individuals. PLoS ONE 2009; 4: e7370.
21. Dworkin M, Falkow S. The prokariotes, 3rd edn. NY: springer, 2006.
22. Thomas V, Bertelli C, Collyn F et al. Lausannevirus, a giant amoebal
virus encoding histone doublets. Environ Microbiol 2011; 13: 1454–
1466.
23. Qin J, Li R, Raes J et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 2010; 464: 59–65.
24. De Filippo C, Cavalieri D, Di Paola M et al. Impact of diet in shaping
gut microbiota revealed by a comparative study in children from Europe and rural Africa. Proc Natl Acad Sci USA 2010; 107: 14691–14696.
25. Ley RE, Turnbaugh PJ, Klein S, Gordon JI. Microbial ecology: human
gut microbes associated with obesity. Nature 2006; 444: 1022–1023.
26. Turnbaugh PJ, Hamady M, Yatsunenko T et al. A core gut microbiome in obese and lean twins. Nature 2009; 457: 480–484.
27. Arumugam M, Raes J, Pelletier E et al. Enterotypes of the human gut
microbiome. Nature 2011; 473: 174–180.
28. Dick PK. The minority report. NY: Pantheon, 1956.
29. Human microbiome project catalogue. Available at: http://www.hmpdacc-resources.org/cgi-bin/hmp_catalog/main.cgi?section=HmpSummary&page=showSummary (last accessed 1 November 2011).
30. Fournier PE, Drancourt M, Raoult D. Bacterial genome sequencing
and its use in infectious diseases. Lancet Infect Dis 2007; 7: 711–723.
ª2012 The Authors
Clinical Microbiology and Infection ª2012 European Society of Clinical Microbiology and Infectious Diseases, CMI, 18, 1185–1193
Article XIV :
Non contiguous finished genome sequence and
description of Bacillus timonensis sp. nov.
Sahare Kokcha, Jean-Christophe Lagier, Matthieu Million, Rivet R,
Grégory Gimenez, Didier Raoult, Pierre-Edouard Fournier
Published in Stand. Genomic Sci. 2012;6:346-355. (IF 1.62)
165
Standards in Genomic Sciences (2012) 6:346-355
DOI:10.4056/sigs.2776064
Non contiguous-finished genome sequence and
description of Bacillus timonensis sp. nov.
Sahare Kokcha1, Ajay Kumar Mishra1, Jean-Christophe Lagier1, Matthieu Million1, Quentin
Leroy1, Didier Raoult1 and Pierre-Edouard Fournier1*
1
Unité de Recherche sur les Maladies Infectieuses et Tropicales Emergentes, UMR CNRS
6236 – IRD 198, Faculté de médecine, Aix-Marseille Université
* Corresponding author: Pierre-Edouard Fourner ([email protected])
Key words: Bacillus timonensis, genome
Bacillus timonensis strain MM10403188T sp. nov. is the type strain of a proposed new species
within the genus Bacillus. This strain, whose genome is described here, was isolated from the
fecal flora of a healthy patient. B. timonensis is an aerobic Gram-negative rod shaped bacterium. Here we describe the features of this organism, together with the complete genome sequence and annotation. The 4,632,049 bp long genome (1 chromosome but no plasmid)
contains 4,610 protein-coding and 74 RNA genes, including 5 rRNA genes.
Introduction
Bacillus timonensis strain MM10403188T (= CSUR
P162 = DSM 25372) is designated as the type
strain of B. timonensis, a new Gram-negative aerobic, indole-positive bacillus that was isolated from
the stool of a healthy Senegalese patient as part of
a “culturomics” study aiming at cultivating individually all species within human feces.
To date, DNA-DNA hybridization and G+C content
determination [1] remain the gold standard methods for the definition of bacterial species, despite
the development of 16S rRNA PCR and sequencing which have deeply changed bacterial taxonomy [2]. Over recent years, high throughput genome sequencing provided a wealth of genetic
information [3]. In an effort to include genomic
data in bacterial taxonomy we recently used a
polyphasic approach [4] that includes genomic
data, MALDI-TOF spectrum and main phenotypic
characteristics to describe new bacterial species
[5,6] .
Here we present a summary classification and a
set of features for B. timonensis sp. nov. strain
MM10403188T together with the description of
the complete genomic sequencing and annotation.
These characteristics support the circumscription
of the species B. timonensis.
The genus Bacillus (Cohn 1872) was created in
1872 [6]. To date, this genus, mostly comprised of
Gram-positive, motile, and spore-forming bacteria,
is made of 256 species and 7 subspecies with validly published names [7]. Members of the genus
Bacillus are ubiquitous bacteria isolated from various environments including soil, fresh and sea
water, food, and occasionally from humans in
whom they are either pathogens, such as B.
anthracis and B. cereus, or opportunists in
immunocompromised patients [7]. Apart from
anthrax, caused by B. anthracis [8], and toxiinfections caused by B. cereus, Bacillus species
may be involved in a variety of aspecific human
infections, including cutaneous, ocular, central
nervous system or bone infections, pneumonia,
endocarditis and bacteremia [9].
Classification and features
A stool sample was collected from a healthy 16year-old male Senegalese volunteer patient living
in Dielmo (a rural village in the Guinean-Sudanian
zone in Senegal), who was included in a research
protocol. The patient gave an informed and signed
consent, and the agreement of the National Ethics
Committee of Senegal and the local ethics committee of the IFR48 (Marseille, France) was obtained
under agreements 09-022 and 11-017). The fecal
specimen was preserved at -80°C after collection
and sent to Marseille. Strain MM10403188 (Table
1) was isolated in June 2011 by cultivation on 5%
The Genomic Standards Consortium
Kokcha et al.
sheep blood-enriched Brain Heart Infusion agar
with (Becton Dickinson, Heidelberg, Germany).
This strain exhibited a 98.2% nucleotide sequence
similarity with Bacillus humi, the phylogenetically
closest validated Bacillus species (Figure 1). This
value was lower than the 98.7% 16S rRNA gene
sequence
threshold
recommended
by
Stackebrandt and Ebers to delineate a new species
without carrying out DNA-DNA hybridization [2].
Table 1. Classification and general features of Bacillus timonensis strain MM10403188T
MIGS ID
Property
Term
Evidence codea
Current classification
Domain Bacteria
TAS [10]
Phylum Firmicutes
TAS [11-13]
Class Bacilli
TAS [14,15]
Order Bacillales
TAS [16,17]
Family Bacillaceae
TAS [16,18]
Genus Bacillus
TAS [16,19,20]
Species Bacillus timonensis
IDA
T
IDA
Type strain MM10403188
Gram stain
negative
IDA
Cell shape
rod
IDA
Motility
motile
IDA
Sporulation
sporulating
IDA
Temperature range
mesophile
IDA
Optimum temperature
37°C
IDA
MIGS-6.3
Salinity
growth in BHI medium + 5% NaCl
IDA
MIGS-22
Oxygen requirement
aerobic
IDA
Carbon source
unknown
NAS
Energy source
unknown
NAS
MIGS-6
Habitat
human gut
IDA
MIGS-15
Biotic relationship
Free living
IDA
MIGS-14
Pathogenicity
Unknown
NAS
Biosafety level
2
Isolation
human feces
MIGS-4
Geographic location
Senegal
IDA
MIGS-5
Sample collection time
September 2010
IDA
MIGS-4.1
Latitude
13.7167
IDA
MIGS-4.1
Longitude
-16.4167
IDA
MIGS-4.3
Depth
Surface
IDA
MIGS-4.4
Altitude
51 m above sea level
IDA
Evidence codes - IDA: Inferred from Direct Assay; TAS: Traceable Author Statement (i.e., a direct report
exists in the literature); NAS: Non-traceable Author Statement (i.e., not directly observed for the living, isolated sample, but based on a generally accepted property for the species, or anecdotal evidence). These
evidence codes are from the Gene Ontology project [21]. If the evidence is IDA, then the property was directly observed for a live isolate by one of the authors or an expert mentioned in the acknowledgements.
http://standardsingenomics.org
347
Bacillus timonensis sp. nov.
Figure 1. Phylogenetic tree highlighting the position of Bacillus timonensis strain MM10403188T relative to other
type strains within the Bacillus genus. GenBank accession numbers are indicated in parentheses. Sequences were
aligned using CLUSTALW, and phylogenetic inferences obtained using the maximum-likelihood method within
the MEGA software. Numbers at the nodes are percentages of bootstrap values obtained by repeating the analysis
500 times to generate a majority consensus tree. Clostridium botulinum was used as an outgroup. The scale bar
represents a 2% nucleotide sequence divergence.
Different growth temperatures (25, 30, 37, 45°C)
were tested. Growth occurred at all tested temperatures, but optimal growth occurred between 30 and
37°C. Colonies were 3 mm in diameter on bloodenriched BHI agar. Growth of the strain was tested
under anaerobic and microaerophilic conditions
using GENbag anaer and GENbag microaer systems,
respectively (BioMérieux), and in aerobic conditions,
with or without 5% CO2. Growth was achieved in
aerobic (with and without CO2) and microaerophilic
conditions. No growth was observed in anaerobic
conditions. Gram staining showed Gram negative
bacilli (Figure 2). A motility test was positive. Cells
grown on agar are sporulated and have a mean diameter of 0.66 µm (Figure 3).
348
Strain MM10403188T exhibited oxidase activity but
not catalase activity, and was positive for indole.
Using API 50CH, a positive reaction was obtained for
L-arabinose, D-lactose, D-melibiose, D-trehalose, Dsaccharose, and D-turanose fermentation. A weak
reaction was obtained for aesculin. Other tests were
negative. Using API-ZYM, positive reactions were
obtained for esterƒ•‡ǡ Ƚ-…Š‹‘–”›’•‹‡ǡ Ⱦ‰Ž—…‘”‹‹†ƒ•‡ǡ ƒ† Ƚ- ƒ† Ⱦ-glucosinidase. B.
timonensis was susceptible to penicillin G, amoxicillin,
vancomycin,
gentamicin,
erythromycin,
doxycyclin, rifampicin, and ciprofloxacin but resistant to trimethoprim/sulfamethoxazole.
Standards in Genomic Sciences
Kokcha et al.
Figure 2. Gram staining of B. timonensis strain MM10403188T
Figure 3. Transmission electron microscopy of B. timonensis strain MM10403188T, using a Morgani 268D (Philips) at an operating voltage of 60kV. The scale bar represents
900 nm.
http://standardsingenomics.org
349
Bacillus timonensis sp. nov.
Intens. [a.u.]
By comparison with B. humi, B. timonensis differed
in Gram staining, in culture atmosphere, as B. humi
was able to grow anaerobically, in catalase activity,
in spore forming capacity, in indole production, and
in carbohydrate metabolism, notably for arbutin,
salicin, L-arabinose, melibiose, turanose, and trehalose [22].
Matrix-assisted laser-desorption/ionization timeof-flight (MALDI-TOF) MS protein analysis was
carried out as previously described [23]. Briefly, a
pipette tip was used to pick one isolated bacterial
colony from a culture agar plate, and to spread it as
a thin film on a MTP 384 MALDI-TOF target plate
(Bruker Daltonics, Leipzig, Germany). Four distinct
deposits were done for strain MM10403188 from
four isolated colonies. Each smear was overlaid
with 2µL of matrix solution (saturated solution of
alpha-cyano-4-hydroxycinnamic acid) in 50% acetonitrile, 2.5% tri-fluoracetic-acid, and allowed to
dry for five minutes. Measurements were performed with a Microflex spectrometer (Bruker).
Spectra were recorded in the positive linear mode
for the mass range of 2,000 to 20,000 Da (parameter settings: ion source 1 (IS1), 20 kV; IS2, 18.5 kV;
lens, 7 kV). A spectrum was obtained after 675
shots at a variable laser power. The time of acquisition was between 30 seconds and 1 minute per
spot. The four MM10403188 spectra were imported into the MALDI BioTyper software (version 2.0,
Bruker) and analyzed by standard pattern matching (with default parameter settings) against the
main spectra of 3,769 bacteria including 129 spectra from 98 Bacillus species, notably B. humi, used
as reference data, in the BioTyper database. The
method of identification included the m/z from
3,000 to 15,000 Da. For every spectrum, 100 peaks
at most were taken into account and compared
with spectra in the database. A score enabled the
presumptive identification and discrimination of
the tested species from those in the database: a
score > 2 with a validated species enabled the identification at the species level, a score > 1.7 but < 2
enabled the identification at the genus level; and a
score < 1.7 did not enable any identification. For
strain MM10403188T, the obtained score was 1.2,
thus suggesting that our isolate was not a member
of a known species. We incremented our database
with the spectrum from strain MM10403188 (Figure 4). The spectrum was made available online in
our free-access URMS database [24].
8000
6000
4000
2000
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
m/z
Figure 4. Reference mass spectrum from B. timonensis strain MM10403188 . Spectra from 12 individual colonies
were compared and a reference spectrum was generated.
T
350
Standards in Genomic Sciences
Kokcha et al.
Genome sequencing information
Genome project history
The organism was selected for sequencing on the
basis of its phylogenetic position and 16S rRNA similarity to other members of the genus Bacillus, and is
part of a “culturomics” study of the human digestive
flora aiming at isolating all bacterial species within
human feces. It was the 60th genome of a Bacillus
species and the first genome of Bacillus timonensis
sp. nov. A summary of the project information is
shown in Table 2. The Genbank accession number is
CAET00000000 and consists of 146 contigs.
Growth conditions and DNA isolation
B. timonensis sp. nov. strain MM10403188T, CSUR
P162, DSM 25372, was grown aerobically on 5%
sheep blood-enriched BHI agar at 37°. Four petri
dishes were spread and growth from the plates
was resuspended in 3x500µl of TE buffer and
stored at 80°C. Then, 500µl of this suspension
were thawed, centrifuged 3 minutes at 10,000
rpm and resuspended in 3x100µL of G2 buffer
(EZ1 DNA Tissue kit, Qiagen). A first mechanical
lysis was performed by glass powder on the
Fastprep-24 device (Sample Preparation system,
MP Biomedicals, USA) using 2x20 seconds cycles.
DNA was then treated with 2.5µg/µL lysozyme (30
minutes at 37°C) and extracted using the BioRobot
EZ1 Advanced XL (Qiagen). The DNA was then
concentrated and purified using the Qiamp kit
(Qiagen). The yield and the concentration was
measured by the Quant-it Picogreen kit (Invitrogen) on the Genios Tecan fluorometer at 50ng/µl.
Genome sequencing and assembly
DNA (5 µg) was mechanically fragmented on a
Hydroshear device (Digilab, Holliston, MA,USA) with
an enrichment size at 3-4kb. The DNA fragmentation
was visualized through the Agilent 2100 BioAnalyzer
on a DNA labchip 7500 with an optimal size of
3.345kb. The library was constructed according to the
454 GS FLX Titanium paired-end protocol. Circularization and nebulization were performed and generated a pattern with an optimum at 492 bp. After PCR
amplification through 15 cycles followed by double
size selection, the single stranded paired end library
was then quantified on the Quant-it Ribogreen kit
(Invitrogen) on the Genios Tecan fluorometer at 339
pg/µL. The library concentration equivalence was
calculated as 12,6E+08 molecules/µL. The library was
stored at -20°C until further use.
The shotgun library was clonally amplified with 3cpb
and the paired-end library was amplified with lower
cpb (1 cpb) in 4 emPCR reactions with the GS Titanium SV emPCR Kit (Lib-L) v2 (Roche). The yields of the
emPCR was 5.97% for the shotgun and 15.92% for
the paired end as expected by the range of 5 to 20%
from the Roche procedure.
Approximately 790,000 beads for a 1/4 region and
340,000 beads for a 1/8 region were loaded on the GS
Titanium PicoTiterPlate PTP Kit 70×75 and sequenced with the GS FLX Titanium Sequencing Kit
XLR70 (Roche). The run was performed overnight
and then analyzed on the cluster through the
gsRunBrowser and Newbler assembler (Roche). For
the shotgun sequencing, 112,962 passed filter wells
were obtained and generated 34.48Mb with a length
average of 322 bp. For the shotgun sequencing,
213,882 passed filter wells were obtained and generated 50.6 Mb with a length average of 236 bp. The
passed filter sequences were assembled Using
Newbler with 90% identity and 40bp as overlap. The
final assembly identified 11 scaffolds and 89 contigs
(>1500bp) generating a genome size of 4.6 Mb.
Table 2. Project information
MIGS ID
MIGS-31
MIGS-28
MIGS-29
MIGS-31.2
MIGS-30
MIGS-32
MIGS-13
Property
Finishing quality
Libraries used
Sequencing platform
Sequencing coverage
Assemblers
Gene calling method
INSDC ID
Genbank Date of Release
Gold ID
NCBI project ID
Project relevance
http://standardsingenomics.org
Term
High-quality draft
454 GS shotgun and paired-end 3- kb libraries
454 GS FLX Titanium
19×
Newbler version 2.5.3
PRODIGAL
112529
February 28 , 2012
Gi13534
CAET00000000
Study of the human gut microbiome
th
351
Bacillus timonensis sp. nov.
Genome annotation
Open Reading Frames (ORFs) were predicted using Prodigal [25] with default parameters but the
predicted ORFs were excluded if they were spanning a sequencing gap region. The predicted bacterial protein sequences were searched against
the GenBank database [26] and the Clusters of
Orthologous Groups (COG) databases using
BLASTP. The tRNAScanSE tool [27] was used to
find tRNA genes, whereas ribosomal RNAs were
found by using RNAmmer [28] and BLASTn
against the GenBank database. ORFans were identified if their BLASTP E-value was lower than 1e03 for alignment length greater than 80 amino
acids. If alignment lengths were smaller than 80
amino acids, we used an E-value of 1e-05. Such
parameter thresholds have already been used in
previous works to define ORFans.
To estimate the mean level of nucleotide sequence
similarity at the genome level between Bacillus
species, we compared the ORFs only using
BLASTN and the following parameters: a query
coverage of t 70% and a minimum nucleotide
length of 100 bp.
Genome properties
The genome is 4,632,049 bp long (1 chromosome,
but no plasmid) with a 37.30% GC content (Figure
5 and Table 3). Of the 4,684 predicted genes, 4,610
were protein-coding genes and 74 were RNAs. A
total of 3,399 genes (75.56%) were assigned a
putative function. Three hundred forty genes were
identified as ORFans (7.4%). The remaining genes
were annotated as hypothetical proteins. The
properties and the statistics of the genome are
summarized in Tables 3. The distribution of genes
into COGs functional categories is presented in
Table 4.
Comparison with the genomes from
other Bacillus species
Genome sequences are currently available for
more than 25 validly named Bacillus species. Here
we compared the genome sequence of B.
timonensis strain MM10403188T with that of B.
licheniformis strain ATCC 14580, the most closely
related phylogenetic neighbor for which the genome sequence is available. The draft genome
sequence of B. timonensis is larger than B.
licheniformis (4.6 Mb and 4.2 Mb, respectively) but
its G+C content is lower (37.30 and 46.19%, respectively). B. timonensis has more predicted
352
genes than B. licheniformis (4,684 and 4,356, respectively), and more genes assigned to COGs
(3,399 and 3,130, respectively). However, the distribution of genes into COG categories (Table 4)
was highly similar in both genomes. In addition, B.
timonensis shared a mean 86.10% (range 76.493%) sequence similarity with B. licheniformis at
the genome level.
Although the degree of 16S rRNA similarity was
elevated (98.2%) between strain MM10403188
and B. humi strain DSM 16318, both strains exhibited several phenotypic and genomic differences,
and we formally propose the creation of Bacillus
timonensis sp. nov. that contains the strain
MM10403188T. This strain has been found in Senegal.
Description of Bacillus timonensis sp. nov.
Bacillus timonensis (tim.on.en´sis. L. gen. masc. n.
timonensis, of Timone, the name of the hospital
where strain MM10403188T was cultivated.) Isolated from stool from an asymptomatic Senegalese
patient. B. timonensis is an aerobic Gram-negative
bacterium. Grows on axenic medium at 37°C in an
aerobic atmosphere. Colonies were 3 mm in diameter on blood-enriched BHI agar. Cells grown on
agar are sporulated and have a mean diameter of
0.66 µm. A positive reaction was obtained for Larabinose, D-lactose, D-melibiose, D-trehalose, Dsaccharose, and D-turanose fermentation. Positive
reactions were obtained for oxidase, esterƒ•‡ǡ Ƚ…Š‹‘–”›’•‹‡ǡ Ⱦ-glucorinidase, and Ƚ- ƒ† Ⱦglucosinidase activity. No catalase activity was
exhibited. Positive for indole. By comparison with
B. humi, B. timonensis differs in Gram staining, in
culture atmosphere, as B. humi grows anaerobically, in catalase activity, in spore forming capacity, in
indole production, and in carbohydrate metabolism, notably for arbutin, salicin, L-arabinose,
melibiose, turanose, and trehalose. B. timonensis is
susceptible to penicillin G, amoxicillin, vancomycin, gentamicin, erythromycin, doxycyclin, rifampicin, and ciprofloxacin but resistant to trimethoprim/sulfamethoxazole. Motile. The G+C content of the genome is 37.30%. The 16S rRNA and
genome sequences are deposited in GenBank under
accession
numbers
JF824810
and
CAET00000000, respectively. The type strain
MM10403188T (= CSUR P162 = DSM 253720) was
isolated from the fecal flora of a healthy patient
from Senegal.
Standards in Genomic Sciences
Kokcha et al.
Figure 5. Graphical circular map of the chromosome. From outside to the center: Genes on the forward
strand (colored by COG categories), genes on the reverse strand (colored by COG categories), RNA
genes (tRNAs green, rRNAs red), GC content, and GC skew.
Table 3. Nucleotide content and gene count levels of the genome
Attribute
Value % of totala
Genome size (bp)
4,632,049
DNA Coding region (bp)
3,959,694
85.48
DNA G+C content (bp)
1,727,754
37.3
Total genes
4,684
100
RNA genes
74
1.58
Protein-coding genes
4,610
98.42
Genes with function prediction
3,643
77.75
Genes assigned to COGs
3,399
75.56
Genes with peptide signals
Genes with transmembrane helices
189
4.03
1,261
26.92
a
The total is based on either the size of the genome in base pairs or
the total number of protein coding genes in the annotated genome
http://standardsingenomics.org
353
Bacillus timonensis sp. nov.
Table 4. Number of genes associated with the 25 general COG functional categories
Code Value % agea Description
a
J
181
3.93
Translation, ribosomal structure and biogenesis
A
0
0
K
310
6.72
Transcription
L
169
3.67
Replication, recombination and repair
B
1
0.02
Chromatin structure and dynamics
D
39
0.85
Cell cycle control, mitosis and meiosis
Y
0
0
V
71
1.54
RNA processing and modification
Nuclear structure
Defense mechanisms
T
193
4.19
Signal transduction mechanisms
M
197
4.27
Cell wall/membrane biogenesis
N
67
1.45
Cell motility
Z
0
0
Cytoskeleton
W
0
0
Extracellular structures
U
49
1.06
Intracellular trafficking and secretion
O
114
2.47
Posttranslational modification, protein turnover, chaperones
C
184
3.99
Energy production and conversion
G
349
7.57
Carbohydrate transport and metabolism
E
412
8.94
Amino acid transport and metabolism
F
97
2.10
Nucleotide transport and metabolism
H
121
2.62
Coenzyme transport and metabolism
I
150
3.25
Lipid transport and metabolism
P
245
5.31
Inorganic ion transport and metabolism
Q
100
2.17
Secondary metabolites biosynthesis, transport and catabolism
R
594
12.89
S
361
7.83
-
606
13.15
General function prediction only
Function unknown
Not in COGs
The total is based on the total number of protein coding genes in the annotated genome
References
1.
Rossello-Mora R. DNA-DNA Reassociation Methods Applied to Microbial Taxonomy and Their Critical Evaluation. In: Stackebrandt E (ed), Molecular
Identification, Systematics, and population Structure
of Prokaryotes. Springer, Berlin, 2006, p. 23-50.
2.
Stackebrandt E, Ebers J. Taxonomic parameters revisited: tarnished gold standards. Microbiol Today
2006; 33:152-155.
3.
Welker M, Moore ER. Applications of whole-cell
matrix-assisted laser-desorption/ionization time-offlight mass spectrometry in systematic microbiology.
Syst Appl Microbiol 2011; 34:2-11. PubMed
http://dx.doi.org/10.1016/j.syapm.2010.11.013
4.
Tindall BJ, Rosselló-Móra R, Busse HJ, Ludwig W,
Kämpfer P. Notes on the characterization of prokaryote strains for taxonomic purposes. Int J Syst Evol
354
Microbiol 2010; 60:249-266. PubMed
http://dx.doi.org/10.1099/ijs.0.016949-0
5.
Lagier JC, El Karkouri K, Nguyen TT, Armougom F,
Raoult D, Fournier PE. Non-contiguous finished genome sequence and description of Anaerococcus
senegalensis sp. nov. Stand Genomic Sci 2012;
6:116-125. PubMed
http://dx.doi.org/10.4056/sigs.2415480
6.
Mishra AK, Lagier JC, Robert C, Raoult D, Fournier
PE. 2012. Non-contiguous finished genome sequence and description of Clostridium senegalense
sp. nov. Stand.Genomic.Sci. In press</jrn>5. Cohn
F. Untersuchungen über Bakterien. Beitrage zur
Biologie der Pflanzen Heft 1872; 1:127-224.
7.
Jernigan JA, Stephens DS, Ashford DA, Omenaca C,
Topiel MS, Galbraith M, Tapper M, Fisk TL, Zaki S,
Standards in Genomic Sciences
Kokcha et al.
Popovic T, et al. Bioterrorism-related inhalational
anthrax: the first 10 cases reported in the United
States. Emerg Infect Dis 2001; 7:933-944. PubMed
http://dx.doi.org/10.3201/eid0706.010604
8.
Bottone EJ. Bacillus cereus, a volatile human pathogen. Clin Microbiol Rev 2010; 23:382-398. PubMed
http://dx.doi.org/10.1128/CMR.00073-09
9.
Woese CR, Kandler O, Wheelis ML. Towards a
natural system of organisms: proposal for the domains Archae, Bacteria, and Eukarya. Proc Natl
Acad Sci USA 1990; 87:4576-4579. PubMed
http://dx.doi.org/10.1073/pnas.87.12.4576
10. Gibbons NE, Murray RGE. Proposals Concerning
the Higher Taxa of Bacteria. Int J Syst Bacteriol
1978; 28:1-6. http://dx.doi.org/10.1099/0020771328-1-1
11. Garrity GM, Holt JG. The Road Map to the Manual.
In: Garrity GM, Boone DR, Castenholz RW (eds),
Bergey's Manual of Systematic Bacteriology, Second
Edition, Volume 1, Springer, New York, 2001, p.
119-169.
12. Murray RGE. The Higher Taxa, or, a Place for Everything...? In: Holt JG (ed), Bergey's Manual of Systematic Bacteriology, First Edition, Volume 1, The
Williams and Wilkins Co., Baltimore, 1984, p. 3134.
13. List Editor. List of new names and new combinations
previously effectively, but not validly, published. List
no. 132. Int J Syst Evol Microbiol 2010; 60:469-472.
http://dx.doi.org/10.1099/ijs.0.022855-0
14. Ludwig W, Schleifer KH, Whitman WB. Class I.
Bacilli class nov. In: De Vos P, Garrity G, Jones D,
Krieg NR, Ludwig W, Rainey FA, Schleifer KH,
Whitman WB (eds), Bergey's Manual of Systematic
Bacteriology, Second Edition, Volume 3, SpringerVerlag, New York, 2009, p. 19-20.
15. Skerman VBD, Sneath PHA. Approved list of bacterial names. Int J Syst Bact 1980; 30:225-420.
http://dx.doi.org/10.1099/00207713-30-1-225
16. Prevot AR. Dictionnaire des bactéries pathogens. In:
Hauduroy P, Ehringer G, Guillot G, Magrou J,
Prevot AR, Rosset, Urbain A (eds). Paris, Masson,
1953, p.1-692.
17. Fischer A. Untersuchungen über bakterien.
Jahrbücher für Wissenschaftliche Botanik 1895;
27:1-163.
18. Gibson T, Gordon RE. Genus I. Bacillus Cohn 1872,
174; Nom. gen. cons. Nomencl. Comm. Intern. Soc.
Microbiol. 1937, 28; Opin. A. Jud. Comm. 1955,
39. In: Buchanan RE, Gibbons NE (eds), Bergey's
http://standardsingenomics.org
Manual of Determinative Bacteriology, Eighth Edition, The Williams and Wilkins Co., Baltimore,
1974, p. 529-550.
19. Mathews WC, Caperna J, Toerner JG, Barber RE,
Morgenstern H. Neutropenia is a risk factor for
Gram-negative Bacillus bacteremia in human immunodeficiency virus-infected patients: results of a
nested case-control study. Am J Epidemiol 1998;
148:1175-1183. PubMed
http://dx.doi.org/10.1093/oxfordjournals.aje.a00960
6
20. Ashburner M, Ball CA, Blake JA, Botstein D, Butler
H, Cherry JM, Davis AP, Dolinski K, Dwight SS,
Eppig JT, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.
Nat Genet 2000; 25:25-29. PubMed
http://dx.doi.org/10.1038/75556
21. Stackebrandt E, Ebers J. Taxonomic parameters revisited: tarnished gold standards. Microbiol Today
2006; 33:152-155.
22. Heyrman J, Rodriguez-Diaz M, Devos J, Felske A,
Logan NA, De Vos P. Bacillus arenosi sp. nov., Bacillus arvi sp. nov., Bacillus humi sp. nov., isolated
from soil. Int J Syst Evol Microbiol 2005; 55:111117. PubMed http://dx.doi.org/10.1099/ijs.0.632400
23. Seng P, Drancourt M, Gouriet F, La SB, Fournier PE,
Rolain JM, Raoult D. Ongoing revolution in bacteriology: routine identification of bacteria by matrixassisted laser desorption ionization time-of-flight
mass spectrometry. Clin Infect Dis 2009; 49:543551. PubMed http://dx.doi.org/10.1086/600885
24. URMS database. http://ifr48.timone.univmrs.fr/portail2/index.php?option=com_content&task
=view
25. Prodigal. http://prodigal.ornl.gov
26. Benson DA, Karsch-Mizrachi I, Clark K, Lipman DJ,
Ostell J, Sayers EW. GenBank. Nucleic Acids Res
2012; 40:D48-D53. PubMed
http://dx.doi.org/10.1093/nar/gkr1202
27. Lowe TM, Eddy SR. t-RNAscan-SE: a program for
imroved detection of transfer RNA gene in genomic
sequence. Nucleic Acids Res 1997; 25:955-964.
PubMed
28. Lagesen K, Hallin P, Rodland EA, Staerfeldt HH,
Rognes T, Ussery DW. RNAmmer: consistent and
rapid annotation of ribosomal RNA genes. Nucleic
Acids Res 2007; 35:3100-3108. PubMed
http://dx.doi.org/10.1093/nar/gkm160
355
Article XV :
Rapid and accurate bacterial identification in
probiotics and yoghurts by MALDI-TOF mass
spectrometry
Emmanouil Angelakis, Matthieu Million, Mireille Henry,
Didier Raoult
Published in J Food Sci. 2011 Oct;76(8):M568-72. (IF 1.66) + photo de couverture
176
Rapid and Accurate Bacterial Identification
in Probiotics and Yoghurts by MALDI-TOF Mass
Spectrometry
Emmanouil Angelakis, Matthieu Million, Mireille Henry, and Didier Raoult
Probiotic food is manufactured by adding probiotic strains simultaneously with starter cultures in fermentation
tanks. Here, we investigate the accuracy and feasibility of matrix-assisted laser desorption/ionisation time-of-flight mass
spectrometry (MALDI-TOF MS) for bacterial identification at the species level in probiotic food and yoghurts. Probiotic
food and yoghurts were cultured in Columbia and Lactobacillus specific agar and tested by quantitative real-time PCR
(qPCR) for the detection and quantification of Lactobacillus sp. Bacterial identification was performed by MALDI-TOF
analysis and by amplification and sequencing of tuf and 16S rDNA genes. We tested 13 probiotic food and yoghurts
and we identified by qPCR that they presented 106 to 107 copies of Lactobacillus spp. DNA/g. All products contained
very large numbers of living bacteria varying from 106 to 109 colony forming units/g. These bacteria were identified as
Lactobacillus casei, Lactococcus lactis, Bifidobacterium animalis, Lactobacillus delbrueckii, and Streptococcus thermophilus. MALDITOF MS presented 92% specificity compared to the molecular assays. In one product we found L. lactis, instead of
Bifidus spp. which was mentioned on the label and for another L. delbrueckii and S. thermophilus instead of Bifidus spp.
MALDI-TOF MS allows a rapid and accurate bacterial identification at the species level in probiotic food and yoghurts.
Although the safety and functionality of probiotics are species and strain dependent, we found a discrepancy between the
bacterial strain announced on the label and the strain identified.
Abstract:
M: Food Microbiology
& Safety
Keywords: functional food, Lactobacillus species, MALDI-TOF MS, probiotics, yoghurts
MALDI-TOF MS is rapid and specific for the identification of bacteria in probiotic food and
yoghurts. Although the safety and functionality of probiotics are species and strain dependent, we found a discrepancy
between the bacterial strain announced on the label and the strain identified.
Practical Application:
Introduction
The international endorsed definition for probiotics is live microorganisms that, when administered in adequate amounts, confer a health benefit on the host (Sanders 2008). Probiotic food
are manufactured by adding probiotic strains simultaneously with
starter cultures in fermentation tanks (Saxelin 2008). Humans,
particularly children, have been consuming probiotic especially in
fermented dairy products (Raoult 2008). Manufacturers promote
fermented food with probiotics under the pretense of having positive effects on immunological, digestive, and respiratory functions
but in vivo evidences of these health benefits are unclear except for
reducing infectious diarrhea (Reid 1999). Probiotics are regulated
by the Food and Drug Administration (FDA) in the United States
and by the European Commission in Europe (Anadon and others
2006). The essential characteristics for bacteria to be used as probiotics during manufacturing include (i) recognition as safe (GRAS;
generally recognized as safe), (ii) viability during processing and
storage, (iii) antagonistic effects against pathogens; (iv) tolerance to
MS 20110320 Submitted 3/14/2011, Accepted 7/18/2011. Authors are with
Unité des Rickettsies, CNRS UMR 6020, IFR 48, Faculté de Médecine, Univ.
de la Méditerranée, 27 Bd Jean Moulin, 13385 Marseille Cedex 05, France. Direct
inquiries to author Raoult (E-mail: [email protected]).
M568
Journal of Food Science r Vol. 76, Nr. 8, 2011
bile acid challenge, and (v) adherence to the intestinal epithelium
of the host (Perea and others 2007).
Food is a major source of bacteria and viruses, and it modifies
the microbial balance in the intestine (Raoult 2010). Probiotic
bacterial strains used in dairy products are mostly lactic acid bacteria (LAB) and in the United States, probiotic-containing products contain Bifidobacterium spp., Lactobacillus reuteri, Lactobacillus
rhamnosus, Lactobacillus casei, Lactobacillus bulgaricus, and Lactobacillus
acidophilus (Saulnier and others 2009). Other probiotic species include the genera Pediococcus, Enterococcus, and Streptococcus for LAB
and Propionibacteria, Bacillus spp. for bacteria that do not produce
lactic acid (Huys and others 2006). Probiotic properties have been
shown to be species specific and some authors reported that in
vitro studies showed different features at the strain level for adhesion, autoaggregation, and immunomodulatory effect (Reid 1999;
Kotzamanidis and others 2010). Identification of bacterial species
and strains from commercialized probiotics has been conducted
mostly using molecular methods (Holzapfel and others 2001;
Schillinger and others 2003; Huys and others 2006; Perea and
others 2007; Marcobal and others 2008; Sheu and others 2009).
Our objective was to assess the accuracy and feasibility of matrixassisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS) for bacterial identification at the species
level in various fermented dairy foods and to compare our results
to species announced on each.
R
°
C 2011 Institute of Food Technologists°
doi: 10.1111/j.1750-3841.2011.02369.x
Further reproduction without permission is prohibited
Lactobacillus spp. in food. . .
Eight functional foods (4 probiotic drinks and 4 probiotic yoghurts) and 5 yoghurts from 5 different brands that are sold in
France were used in this study. All products were bought from local supermarkets in Marseille. Labeled bacterial species were noted
if mentioned on the product.
tra in the Bruker database from May 2010, and 850 bacteria in
the local database. The Lactobacillus strain collection of our laboratory has been completed by strains from the Pasteur and DSMZ
collections and reference spectra have been created from those
missing in the Bruker database. The 15 bacterial species exhibiting the most similar peptide pattern with the isolate were ranked by
their identification score. For MALDI-TOF analysis, we adapted
the score values proposed by Seng and others ( 2009). That is,
an isolate was considered correctly identified by MALDI-TOF
when ≥2/4 spectra had a score ≥ 1.9. Using these criteria, analysis of spectra from 19 Lactobacillus strains from our laboratory
already present in the Bruker Database showed good identification at the species level (Supplementary table 1). For each product, CFU-enumeration and MALDI-TOF analyses were repeated
3 times independently.
Culture
To determine whether products contained live bacteria and to
enumerate the number of colony forming units (CFU), we diluted
100 µL of each tested sample in 1 mL of sterile water (Figure 1).
Serial dilutions were plated on Columbia 5% sheep blood agar
(BioMerieux, Marcy l’Etoile, France) or Lactobacillus agar according to De Man, Rogosa, and Sharpe (Merck, Darmstadt,
Germany) (De Man and others 1960) in 3 cultivation conditions:
(1) anaerobic at 37 ◦ C, (2) 5% CO2 at 37 ◦ C, and (3) 5% CO2
at 30 ◦ C. After 72-h inoculation colonies obtained on each agar
plate were counted, and the bacteria from 5 independent colonies Molecular assays
DNA was extracted from 100 µg of each product by a
were identified by mass spectrometry.
NucleoSpin°R Tissue Mini Kit (Macherey Nagel, Hoerdt, France)
Mass spectrometry
according to the manufacturer’s procedure. DNA was eluted in
Measurements were performed with an Autoflex II mass spec- 100 µL of elution buffer and stored at −20 ◦ C until used. An
trometer (Bruker Daltonics, Billerica, Mass., U.S.A.) equipped extraction-negative control with 100 µL of sterile water was introwith a 337-nm nitrogen laser. Data were automatically acquired duced in each series of DNA extractions. Real-time quantitative
using Flex control 3.0 and Maldi Biotyper Automation Control PCR (qPCR) reactions were performed in an MX3000TM system
2.0. software (Bruker Daltonics GmbH, Bremen, Germany). The (Stratagene Europe, Amsterdam, the Netherlands). The detection
method of identification included the m/z from 3 to 15 kDa. For and quantification of Lactobacillus sp. was performed as reported
each spectrum, a maximum of 100 peaks were taken into account, by Menard and others (2008). Lactobacillus spp. were quantified
and these peaks were compared with peaks in the database. The using a quantification plasmid that was constructed as previously
database contains 3290 organisms including 219 Lactobacillus spec- described by Carcopino and others ( 2006). PCR amplification
Figure 1− Dilution procedure for the identification of live bacteria contained in diary products.
Vol. 76, Nr. 8, 2011 r Journal of Food Science M569
M: Food Microbiology
& Safety
Materials
Lactobacillus spp. in food. . .
and sequencing targeting the 16S ribosomal RNA (16S rRNA)
and tuf genes were used for the identification of LAB at the species
level, as previously described (Chavagnat and others 2002; Ventura
and others 2003).
Statistical analysis
We compared the CFU/g obtained by the culture of products on Columbia and on Lactobacillus agar. In addition, we
compared the number of Lactobacillus spp. DNA copies/g obtained by qPCR and the CFU/g. For data comparison, we
used EpiInfo version 6.0 software (Centers for Disease Control
and Prevention, Atlanta, Ga., U.S.A.). A P value < 0.05 was
considered significant.
Results and Discussion
M: Food Microbiology
& Safety
Overall, qPCR was positive in all the samples and based on the
plasmid quantification we determined that all products contained
large numbers of Lactobacillus spp. DNA copies. As a result, all
products contained 106 to 107 copies of Lactobacillus spp. DNA/g
(Table 1).
Both Columbia and Lactobacillus agar cultures were positive
for all products. No significant difference was observed in the
CFU/g obtained when products were cultured on Columbia or
on Lactobacillus agar (P = 0.80) (Table 1). As a result, probiotic
drinks contained 107 to 3 × 108 CFU/g, probiotic yoghurts contained 107 to 8 × 107 CFU/g, and yoghurts contained 9 × 106
to 2 × 109 CFU/g. The number of Lactobacillus spp. DNA copies/g
of food was comparable to the CFU/g (P = 0.44). Live bacteria
were identified by mass spectrometry. We identified the presence
of L. casei, Lactococcus lactis, and Bifidobacterium animalis in probiotic
drinks, L. delbrueckii in probiotic yoghurts and L. delbrueckii and
Streptococcus thermophilus in yoghurts (Table 1). The 92% of products were correctly identified by MALDI-TOF MS compared to
the CFU-enumeration and qPCR results as only in one product
we identified L. paracasei instead L. casei. In 2 products, we identified a discrepancy between the bacterial strain announced on the
label and the strain identified. For the probiotic drink 3, we found
by both techniques the presence of L. lactis, instead of Bifidus spp.
which was mentioned on the label. Similarly for yoghurt 1, we
identified L. delbrueckii and S. thermophilus instead of Bifidus spp..
For this study, our results were validated by independent methods based on molecular assays, on agar dilution assays, and on
mass spectrometry. The accuracy of standard quantification of our
qPCR assay was assessed on the basis of the linearity of DNA
amplification of samples with known concentrations and the reproducibility of the quantification in each PCR run (Menard and
others 2008). PCR amplification and sequencing of tuf and 16S
rDNA genes was also validated for the identification of LAB at
the species level (Chavagnat and others 2002; Ventura and others
2003).
Mass spectrometry has been used to control quality of dairy probiotics determining a change in milk protein profile (Fedele and
others 1999a; 1999b) or chemical fingerprinting (Liu and others
2010). In the last decade, mass spectrometry has been successfully employed to achieve the characterization of different bacteria
(Claydon and others 1996) and allow qualitative characterization
of bacterial strains to be used in the production of yoghurt (Fedele
and others 1999b). However, to our knowledge, there is no report
of bacterial identification from commercialized probiotic products
by this method. MALDI-TOF MS presented high specificity at
bacteria species level in the fermented dairy food, and only in
one product we identified L. paracasei instead of L. casei. This discordant in results can probably be explained by the fact that the
L. casei group includes a number of species (L. casei, L. paracasei,
L. rhamnosus, L. zeae) which cannot be distinguished by conventional phenotypic properties (Klein and others 1998; Holzapfel
and others 2001). These difficulties in the correct identification
of probiotic lactobacilli have led to the frequent misclassification
of Lactobacillus strains (Schillinger and others 2003). In addition
to phenotypic identification limitations, taxonomic controversy
about to reject (Dicks and others 1996) or to retain (Dellaglio
and others 2002) the species name L. paracasei may lead to different denomination for the same probiotic strain (Huys and others
2006).
Functional food and yoghurts contained very large numbers
of living bacteria varying from 106 to 109 CFU/g (Figure 2).
These concentrations were higher than the 105 CFU/g found
by Schillinger when tested probiotics yoghurts by MRS agar
(Schillinger 1999). Probably the fact that we used 2 different agars
(Columbia and Lactobacillus specific agar) under various temperature conditions was the reason why we found higher CFU/g.
Moreover, in 2 products we found a discrepancy between the
bacteria we identified and the the microorganisms stated on
the label. Numerous surveys have revealed deficiencies in the
Table 1− Molecular and MALDI-TOF identification of lactic acid bacteria in functional foods and yogurts.
CFU/mL
Product
Lactobacillus sp. Columbia Lactobacillus
Trademark DNA copies/g
agar
agar
P. drink 1
P. drink 2
P. drink 3
P. drink 4
P. yoghurt 1
P. yoghurt 2
P. yoghurt 3
P. yoghurt 4
Yoghurt 1
1
2
1
2
1
2
2
2
1
1 × 107
3 × 106
4 × 106
1 × 106
106
2 × 106
1 × 106
1 × 107
2 × 106
1 × 108
1 × 107
1 × 107
1 × 107
1 × 107
3 × 107
5 × 107
6 × 107
1 × 107
3 × 108
2 × 107
8 × 106
9 × 106
1 × 107
3 × 107
4 × 107
8 × 107
9 × 106
Yoghurt 2
1
5 × 107
2 × 109
109
Yoghurt 3
Yoghurt 4
Yoghurt 5
3
4
5
1 × 107
1 × 106
2 ×107
1 × 107
9 × 107
3 × 108
4 × 107
2 × 108
4 × 108
P: Probiotic.
∗
Discordance between the labeled and identified bacteria.
M570 Journal of Food Science r Vol. 76, Nr. 8, 2011
PCR amplification and sequencing
MALDI-TOF MS
L. paracasei
L. casei
L. lactis
B. animalis
L. delbrueckii
L. delbrueckii
L. delbrueckii
L. delbrueckii
L. delbrueckii,
S. thermophilus
S. thermophilus,
L. delbrueckii
L. delbrueckii
L. delbrueckii
L. delbrueckii
tuf gene
L. casei
L. casei
L. lactis
B. animalis
L. delbrueckii
L. delbrueckii
L. delbrueckii
L. delbrueckii
L. delbrueckii,
S. thermophilus
S. thermophilus,
L. delbrueckii
L. delbrueckii
L. delbrueckii
L. delbrueckii
16S rDNA gene
L. casei
L. casei
L. lactis
B. animalis
L. delbrueckii
L. delbrueckii
L. delbrueckii
L. delbrueckii
L. delbrueckii,
S. thermophilus
S. thermophilus,
L. delbrueckii
L. delbrueckii
L. delbrueckii
L. delbrueckii
Labeled
strain
L. casei
L. casei
Bifidus sp.∗
Bifidus sp.
Not labeled
Not labeled
Not labeled
Not labeled
Bifidus sp.∗
Not labeled
Not labeled
Not labeled
Not labeled
M: Food Microbiology
& Safety
Lactobacillus spp. in food. . .
Figure 2− Gram stain of a yoghurt.
microbiological quality and labeling of probiotic products (Yeung and others 2002; Fasoli and others 2003; Temmerman and
others 2003a, 2003b; Drisko and others 2005; Masco and others 2005) and the label information of the probiotics purchased
products was rather vague, and often did not indicate the full scientific name of the probiotic microorganism present in the product
(Perea and others 2007). Many probiotic products contain unadvertized additional lactobacilli and bifidobacteria, whereas others are
missing species listed on the product label (Marcobal and others
2008). Theunissen found that only 54.5% of the probiotic yoghurts
contained the microorganisms stated on the label (Theunissen and
others 2005). Moreover, some Bifidobacterium spp. were incorrectly
identified and various microorganisms were detected without being on the label (Theunissen and others 2005). In the EU-funded
project, Huys and others (2006) collected 213 cultures of LAB
intended for probiotic or nutritional use and identified 46 cases of
misidentification at the genus level or species level.
Conclusion
Functional food contains high concentrations of bacteria such
as L. casei and B. animalis. Functional foods have no standardized
species or concentrations of bacteria (Scharl and others 2011),
and in some products, we found a discrepancy between the bacteria we identified and the microorganisms stated on the label.
Probiotics are promoted as “live microorganisms which when administered in adequate number confer a health benefit on the
host” (FAO/WHO 2001) (Anadon and others 2006). However,
only manufacturers determine whether the supplement presents a
potential health risk or not (Vanderhoof and Young 2008). The
safety and functionality of probiotics are species and strain dependent (Anadon and others 2006), and manufactures should clearly
characterize the species and the concentrations of the bacteria presented in their products. We proved that in functional food and
yoghurts MALDI-TOF MS could be a useful tool for bacteria
identification.
References
Anadon A, Martinez-Larranaga MR, Aranzazu MM. 2006. Probiotics for animal nutrition in
the European Union. Regulation and safety assessment. Regul Toxicol Pharmacol 45:91–5.
Carcopino X, Henry M, Benmoura D, Fallabregues AS, Richet H, Boubli L, Tamalet C. 2006.
Determination of HPV type 16 and 18 viral load in cervical smears of women referred to
colposcopy. J Med Virol 78:1131–40.
Chavagnat F, Haueter M, Jimeno J, Casey MG. 2002. Comparison of partial tuf gene sequences
for the identification of lactobacilli. FEMS Microbiol Lett 217:177–83.
Claydon MA, Davey SN, Edwards-Jones V, Gordon DB. 1996. The rapid identification of intact
microorganisms using mass spectrometry. Nat Biotechnol 14:1584–86.
De Man JC, Rogosa M, Sharpe ME. 1960. A medium for the cultivation of lactobacilli. J Appl
Bacteriol 23:130–5.
Dellaglio F, Felis GE, Torriani S. 2002. The status of the species Lactobacillus casei (Orla-Jensen
1916) Hansen and Lessel 1971 and Lactobacillus paracasei Collins et al. 1989. Request for an
opinion. Int J Syst Evol Microbiol 52:285–7.
Dicks LM, Du Plessis EM, Dellaglio F, Lauer E. 1996. Reclassification of Lactobacillus casei subsp.
casei ATCC 393 and Lactobacillus rhamnosus ATCC 15820 as Lactobacillus zeae nom. rev.,
designation of ATCC 334 as the neotype of L. casei subsp. casei, and rejection of the name
Lactobacillus paracasei. Int J Syst Bacteriol 46:337–40.
Drisko J, Bischoff B, Giles C, Adelson M, Rao RV, McCallum R. 2005. Evaluation of five
probiotic products for label claims by DNA extraction and polymerase chain reaction analysis.
Dig Dis Sci 50:1113–7.
Fasoli S, Marzotto M, Rizzotti L, Rossi F, Dellaglio F, Torriani S. 2003. Bacterial composition
of commercial probiotic products as evaluated by PCR-DGGE analysis. Int J Food Microbiol
82:59–70.
Fedele L, Seraglia R, Battistotti B, Pinelli C, Traldi P. 1999a. Matrix-assisted laser desorption/ionization mass spectrometry for monitoring bacterial protein digestion in yogurt production. J Mass Spectrom 34:1338–45.
Fedele L, Seraglia R, Battistotti B, Pinelli C, Traldi P. 1999b. Qualitative characterization
of bacterial strains employed in the production of yogurt by matrix-assisted laser desorption/ionization mass spectrometry. J Mass Spectrom 34:1385–8.
Vol. 76, Nr. 8, 2011 r Journal of Food Science M571
Lactobacillus spp. in food. . .
M: Food Microbiology
& Safety
Holzapfel WH, Haberer P, Geisen R, Bjorkroth J, Schillinger U. 2001. Taxonomy and important features of probiotic microorganisms in food and nutrition. Am J Clin Nutr 73:
365S-73S.
Huys G, Vancanneyt M, D’Haene K, Vankerckhoven V, Goossens H, Swings J. 2006. Accuracy
of species identity of commercial bacterial cultures intended for probiotic or nutritional use.
Res Microbiol 157:803–10.
Klein G, Pack A, Bonaparte C, Reuter G. 1998. Taxonomy and physiology of probiotic lactic
acid bacteria. Int J Food Microbiol 41:103–25.
Kotzamanidis C, Kourelis A, Litopoulou-Tzanetaki E, Tzanetakis N, Yiangou M. 2010. Evaluation of adhesion capacity, cell surface traits and immunomodulatory activity of presumptive
probiotic Lactobacillus strains. Int J Food Microbiol 140:154–63.
Liu J, Qiu B, Luo H. 2010. Fingerprinting of yogurt products by laser desorption spray postionization mass spectrometry. Rapid Commun Mass Spectrom 24:1365–70.
Marcobal A, Underwood MA, Mills DA. 2008. Rapid determination of the bacterial composition
of commercial probiotic products by terminal restriction fragment length polymorphism
analysis. J Pediatr Gastroenterol Nutr 46:608–11.
Masco L, Huys G, De BE, Temmerman R, Swings J. 2005. Culture-dependent and cultureindependent qualitative analysis of probiotic products claimed to contain bifidobacteria. Int J
Food Microbiol 102:221–30.
Menard JP, Fenollar F, Henry M, Bretelle F, Raoult D. 2008. Molecular quantification of
Gardnerella vaginalis and Atopobium vaginae loads to predict bacterial vaginosis. Clin Infect Dis
47:33–43.
Perea VM, Hermans K, Verhoeven TL, Lebeer SE, Vanderleyden J, De Keersmaecker SC.
2007. Identification and characterization of starter lactic acid bacteria and probiotics from
Columbian dairy products. J Appl Microbiol 103:666–74.
Raoult D. 2008. Human microbiome: take-home lesson on growth promoters? Nature
454:690–1.
Raoult D. 2010. The globalization of intestinal microbiota. Eur J Clin Microbiol Infect Dis
Reid G. 1999. The scientific basis for probiotic strains of Lactobacillus. Appl Environ Microbiol
65:3763–6.
Sanders ME. 2008. Probiotics: definition, sources, selection, and uses. Clin Infect Dis 46
(Suppl 2):S58-61.
Saulnier DM, Spinler JK, Gibson GR, Versalovic J. 2009. Mechanisms of probiosis and prebiosis:
considerations for enhanced functional foods. Curr Opin Biotechnol 20:135–41.
Saxelin M. 2008. Probiotic formulations and applications, the current probiotics market,
and changes in the marketplace: a European perspective. Clin Infect Dis 46 (Suppl 2):
S76-9.
Scharl M, Geisel S, Vavricka SR, Rogler G. 2011. Dying in yoghurt: the number of living bacteria
in probiotic yoghurt decreases under exposure to room temperature. Digestion 83:13–7.
Schillinger U. 1999. Isolation and identification of lactobacilli from novel-type probiotic and
mild yoghurts and their stability during refrigerated storage. Int J Food Microbiol 47:79–87.
M572 Journal of Food Science r Vol. 76, Nr. 8, 2011
Schillinger U, Yousif NM, Sesar L, Franz CM. 2003. Use of group-specific and RAPD-PCR
analyses for rapid differentiation of Lactobacillus strains from probiotic yogurts. Curr Microbiol
47:453–6.
Seng P, Drancourt M, Gouriet F, La SB, Fournier PE, Rolain JM, Raoult D. 2009. Ongoing
revolution in bacteriology: routine identification of bacteria by matrix-assisted laser desorption
ionization time-of-flight mass spectrometry. Clin Infect Dis 49:543–51.
Sheu SJ, Hwang WZ, Chen HC, Chiang YC, Tsen HY. 2009. Development and use of tuf
gene-based primers for the multiplex PCR detection of Lactobacillus acidophilus, Lactobacillus
casei group, Lactobacillus delbrueckii, and Bifidobacterium longum in commercial dairy products.
J Food Prot 72:93–100.
Temmerman R, Pot B, Huys G, Swings J. 2003a. Identification and antibiotic susceptibility of
bacterial isolates from probiotic products. Int J Food Microbiol 81:1–10.
Temmerman R, Scheirlinck I, Huys G, Swings J. 2003b. Culture-independent analysis of
probiotic products by denaturing gradient gel electrophoresis. Appl Environ Microbiol
69:220–6.
Theunissen J, Britz TJ, Torriani S, Witthuhn RC. 2005. Identification of probiotic microorganisms in South African products using PCR-based DGGE analysis. Int J Food Microbiol
98:11–21.
Vanderhoof JA, Young R. 2008. Probiotics in the United States. Clin Infect Dis 46 (Suppl 2):
S67–72.
Ventura M, Canchaya C, Meylan V, Klaenhammer TR, Zink R. 2003. Analysis, characterization,
and loci of the tuf genes in Lactobacillus and Bifidobacterium species and their direct application
for species identification. Appl Environ Microbiol 69, 6908–22.
Yeung PS, Sanders ME, Kitts CL, Cano R, Tong PS. 2002. Species-specific identification of
commercial probiotic strains. J Dairy Sci 85:1039–51.
Supporting Information
The following supporting information is available for this article.
Supplementary Table 1.
Please note: Wiley-Blackwell is not responsible for the content or
functionality of any supporting materials supplied by the authors.
Any queries (other than missing material) should be directed to
the corresponding author for the article.
REMERCIEMENTS
A ma femme, Estelle, qui est ma bonne étoile,
A Eva Catalina et Satine Alizée, mes amours qui ont changé ma vie,
A mes parents, Claire et Claude, qui m’ont transmis le goût de la Science.
Au Professeur Didier Raoult,
Qui m’a fait confiance
Qui m’a réellement appris l’esprit de la recherche scientifique
Qui m’a donné les moyens d’aller jusqu’au bout de ces travaux
Et que je remercie profondément pour m’avoir accueilli dans son équipe.
Je lui exprime ici mon plus grand respect
et combien j’apprécie ses idées et ses méthodes souvent originales, parfois
paradoxales et toujours si efficaces.
J’exprime également mes remerciements au Professeur Jean-Louis Mège qui a
accepté de présider le Jury de cette thèse.
Ma gratitude s’adresse aussi à mes rapporteurs qui ont bien voulu juger ce travail.
Je remercie l’équipe de recherche URMITE et l’équipe du service de Nutrition,
Maladies Métaboliques et Endocrinologie du CHU de la Timone et notamment le
Professeur Vialettes, le Professeur Valero et Marie Maraninchi.
Un grand merci à Sophie, Jean-Christophe, Fadi, Isabelle, Manolis, Fabrice,
Caroline, Stéphanie, Aurélie, Annick, Alpha, Véronique, Mical, Roch, Hervé, Patrizia,
Mireille, Juline, Franck, Jean-Marie sans qui cela n’aurait pas été possible.
182