Download A polyphasic strategy incorporating genomic data for the taxonomic

International Journal of Systematic and Evolutionary Microbiology (2014), 64, 384–391
Review
DOI 10.1099/ijs.0.057091-0
A polyphasic strategy incorporating genomic data
for the taxonomic description of novel bacterial
species
Dhamodharan Ramasamy, Ajay Kumar Mishra, Jean-Christophe Lagier,
Roshan Padhmanabhan, Morgane Rossi, Erwin Sentausa, Didier Raoult
and Pierre-Edouard Fournier
Correspondence
Pierre-Edouard Fournier
pierre-edouard.fournier@
univ-amu.fr
Unité de Recherche sur les Maladies Infectieuses et Tropicales Emergentes URMITE, Institut
Hospitalo-Universitaire Méditerranée-Infection, Aix-Marseille Université, UMR63, CNRS 7278,
IRD 198, INSERM U1095, Faculté de Médecine, 27 Bd Jean Moulin, 13005 Marseille, France
Currently, bacterial taxonomy relies on a polyphasic approach based on the combination of
phenotypic and genotypic characteristics. However, the current situation is paradoxical in that the
genetic criteria that are used, including DNA–DNA hybridization, 16S rRNA gene sequence
nucleotide similarity and phylogeny, and DNA G+C content, have significant limitations, but
genome sequences that contain the whole genetic information of bacterial strains are not used for
taxonomic purposes, despite the decreasing costs of sequencing and the increasing number of
available genomes. Recently, we diversified bacterial culture conditions with the aim of isolating
uncultivated bacteria. To classify the putative novel species that we cultivated, we used a
polyphasic strategy that included phenotypic as well as genomic criteria (genome characteristics
as well as genomic sequence similarity). Herein, we review the pros and cons of genome
sequencing for taxonomy and propose that the incorporation of genome sequences in taxonomic
studies has the advantage of using reliable and reproducible data. This strategy, which we name
taxono-genomics, may contribute to the taxonomic classification of bacteria.
Introduction
Taxonomic information is essential, as it enables scientists
to understand the biodiversity and relationships among
living organisms from different ecosystems (Gevers et al.,
2005). For prokaryotes, taxonomy plays an essential role in
enabling the reliable identification of microbial strains
from clinical or environmental specimens (Moore et al.,
2010). Bacterial taxonomy was initiated in the late 19th
century. Initially, bacteria were classified on the basis of
basic phenotypic markers such as morphology, growth requirements or pathogenic potential (Lehmann & Neumann,
1896). Later, physiological and biochemical properties of
bacteria were also used for this purpose (Orla-Jensen, 1909;
Buchanan, 1955). Between the 1960s and the 1980s,
chemotaxonomy (Minnikin et al., 1975), numerical taxonomy and DNA–DNA hybridization techniques (Brenner
et al., 1969; Johnson, 1991) were used. In the 1980s, the
advent of DNA amplification and sequencing techniques, in
particular of the 16S rRNA gene, constituted a major step
forward by facilitating bacterial classification (Gürtler &
Mayall, 2001; Coenye & Vandamme, 2004; Konstantinidis &
Abbreviations: ANI, average nucleotide identity; DDH, DNA–DNA
hybridization; HGT, horizontal gene transfer.
384
Tiedje, 2007). Then, starting in the mid-1990s, wholegenome sequencing constituted a revolution by giving access
to the complete genetic information of a strain (Janssen et al.,
2003). Despite this tremendous progress and the various
genome-based methods that were developed and proposed
for taxonomic purposes, including multilocus sequence
analysis and average nucleotide identity (ANI), wholegenome analysis (Stackebrandt et al., 2002; Rosselló-Mora,
2005; Goris et al., 2007; Konstantinidis & Tiedje, 2007; Yarza
et al., 2008) has not as yet been accepted as a source of
taxonomic information.
Therefore, currently, routine identification of a bacterial
strain is most often obtained by comparing its phenotypic
and/or molecular characteristics with those of type strains
of previously described species. In the case of an unusual
strain, several minimal standards are used to determine
whether it fulfils the requirements to be assigned to a novel
taxon. However, the validity of this system is debated, as
several of the criteria used were selected empirically, and
some of the methods used are time- and moneyconsuming, are not accessible to all laboratories and lack
intra- and inter-laboratory reproducibility (Stackebrandt &
Ebers, 2006; Rosselló-Mora, 2006).
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Use of genomics in taxonomic classification of bacteria
Current methods used in prokaryote taxonomy
Currently, taxonomy of prokaryotes relies on polyphasic
combinations of phenotypic, chemotaxonomic and genotypic characteristics (Vandamme et al., 1996; Stackebrandt
et al., 2002; Tindall et al., 2010).
Phenotypic methods applied to prokaryotic taxonomy are not
described in this article, but include a wide array of methods
the aim of which is to describe the main physical characteristics
of micro-organisms, including morphology, staining and
antigenic properties, ultrastructure and chemical composition
of the cell wall and outer membrane, metabolic potential,
protein composition, pathogenesis and habitat (Sneath &
Sokal, 1973; Schleifer & Stackebrandt, 1983; Smibert & Krieg,
1994; Palleroni, 2010; Welker & Moore, 2011).
Among the genotypic criteria, DNA–DNA hybridization
(DDH), DNA G+C content and 16S rRNA gene sequence
analysis have been widely used in bacterial taxonomy (Tindall
et al., 2010; Stackebrandt & Ebers, 2006). Though it was
initially designed empirically, DDH is a widely used technique
to estimate the genetic relatedness between micro-organisms
and is still considered as the ‘gold standard’ criterion for
species delineation of prokaryotes (Wayne et al., 1987). A
DDH value ¡70 % indicates that the tested bacteria belong to
distinct species. However, DDH suffers from various limitations, notably that: (i) the cut-off values are not applicable to
all prokaryote genera [in particular, determining the taxonomic status of an isolate is impossible when the phylogenetically closest species have DDH values .70 %, as is the case
for most species of the genus Rickettsia (Fournier & Raoult,
2009)]; (ii) determining DDH requires special facilities
available in a limited number of laboratories; and (iii) it is a
labour-intensive and expensive method that lacks reproducibility and cannot be used to establish a comparative reference
database incrementally (Tindall et al., 2010). Therefore, in the
current era of genomics, DDH appears to be outdated and
needs to be replaced by easier and more reliable methods.
The 16S rRNA gene is an effective molecular marker for
taxonomic purposes because it is ubiquitous, functionally
stable, highly conserved and poorly subject to horizontal
gene transfer (HGT). 16S rRNA gene sequence variations
observed in archaea and bacteria were used as the backbone
for the classification of prokaryotes (Woese et al., 1990;
Fox et al., 1992; Olsen & Woese 1993; Brenner et al.,
2001; Stackebrandt et al., 2002). Therefore, sequencing
and phylogenetic analysis of the 16S rRNA gene has
been considered as a suitable method for determining the
classification of bacterial isolates at various taxonomic
levels. 16S rRNA gene sequence identity values of 97 and
95 % have been used as cut-offs to classify bacterial isolates
as novel taxa at the genus and species levels, respectively,
when compared with their phylogenetically closest neighbours with validly published names (Stackebrandt et al.,
2002). The former value was increased to 98.7 % in 2006
(Stackebrandt & Ebers, 2006). Such values usually being
correlated with DDH results (Rosselló-Móra, 2006), Stackebrandt and colleagues proposed that 16S rRNA gene
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sequence identity might even replace DDH (Stackebrandt
et al., 2002; Stackebrandt & Ebers, 2006). However, the 16S
rRNA gene also exhibits several limitations as a taxonomic
marker including: (i) its high degree of conservation in
some genera, as is the case for species of the genus Brucella,
which do not differ by more than 1 % (Gándara et al., 2001);
(ii) the presence of nucleotide variations among multiple
rRNA operons in a single genome (Rainey et al., 1996;
Acinas et al., 2004) and (iii) the possibility of 16S rRNA
genes being acquired by HGT that may distort relationships
between taxa in phylogenetic trees (Jain et al., 1999).
The DNA G+C content of prokaryotes is often used to
grossly classify prokaryotes, as is the case for the phylum
Actinobacteria, also referred to as the high-G+C-content
Gram-positive bacteria, and the Firmicutes, referred to as
the low-G+C-content Gram-positive bacteria. Although
intra-genomically variable, due to HGT, differences of .5
and .10 % between strains were used to classify them
within distinct species or genera, respectively (Goodfellow
et al., 1997). However, such values do not apply to all
bacterial genera. As an example, all species within the genus
Rickettsia exhibit less than 5 mol% difference in DNA
G+C content (Fournier & Raoult, 2009).
Genomics and taxonomy
As early as 1999, Fitz Gibbon & House (1999) proposed
that the presence or absence of genes within genomes
might be used to assess taxonomic relationships among
prokaryotes. Later studies also suggested that genomic
sequences may represent a source of taxonomic parameters
including chromosomal gene order and metabolic pathways (Snel et al., 1999; Huson & Steel, 2004), comparison
of orthologous genes (Coenye & Vandamme, 2003) and the
presence of indels or single nucleotide polymorphisms
(SNPs) in conserved genes (Gupta, 2001). Phylogenetic
studies based on the comparison of orthologous genes and
the presence or absence of genes further demonstrated
good congruence with studies built by comparison of 16S
rRNA gene sequences (Zhi et al., 2012).
However, in the decade following the pioneering sequencing of the genome of Haemophilus influenzae in 1995
(Fleischmann et al., 1995), genome sequencing remained
labour- and money-consuming and thus poorly adapted to
routine use. The decreasing costs and high throughput of
next-generation sequencing methods have enabled thousands of genomes to be sequenced (Soon et al., 2013). As
of 30 July 2013, more than 6000 prokaryotic genome sequences were available in public databases [National Center
for Biotechnology Information (http://www.ncbi.nlm.nih.
gov/genome), European Molecular Biology Laboratory
(http://www.ebi.ac.uk/genomes) and Genomes Online Database (http://www.genomesonline.org)]. Such a large number
of available genomes facilitates whole-genome sequence
comparison of new isolates (http://img.jgi.doe.gov/cgi-bin/
w/main.cgi; Markowitz et al., 2012).
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385
D. Ramasamy and others
Over the past few years, several authors have suggested that
genome sequences may be used as a source of taxonomic
information. In particular, some of them studied the
correlation between the percentage of nucleotide sequence
similarity at the core genome level and DDH results.
Konstantinidis and colleagues suggested that the ANI, i.e.
the mean nucleotide sequence identity of shared genes
between two strains, was a valid alternative to DDH
(Konstantinidis & Tiedje, 2005; Goris et al., 2007). These
authors even proposed that an ANI value ¢95 % between
genomes corresponded to a DDH value of ¢70 % (Goris
et al., 2007). In 2009, Richter & Rosselló-Móra (2009)
proposed that ANI might be used as an alternative to DDH
for species delineation, and might be obtained from
comparison of only 20 % of any given genome sequence.
To date, the ANI method has been used to describe several
novel bacterial genera and/or species, including Dehalococcoides mccartyi (Loffler et al., 2012), Sphaerochaeta
globosa and S. pleomorpha (Ritalahti et al., 2012) and Vibrio
caribbeanicus (Hoffmann et al., 2012). However, Ozen et al.
(2012) have argued that ANI results are not systematically
consistent with current taxonomy and thus should not be
used as a single tool for prokaryote classification.
Other parameters based on genome sequences have been
proposed to discriminate species, such as the maximal
unique matches index (genomic distance index based on
DNA conservation of the core genome and the proportion
shared DNA by two genomes; Deloger et al., 2009), tetranucleotide regression [differences between observed and
expected values of the frequencies of all 256 possible
tetranucleotide (A, T, G, C) combinations; Richter &
Rosselló-Móra, 2009] and genome-to-genome comparison
[total length of all high-scoring segment pairs (HSPs)
identified by a BLAST search; Auch et al., 2010]. The latter
parameter may be calculated using the genome-to-genome
distance calculator (GGDC) available online (http://ggdc.
dsmz.de/distcalc2.php).
However, several authors have pointed out drawbacks of
genomics for taxonomic purposes. In 2010, Klenk & Göker
(2010) argued that sequenced genomes were lacking for
many major lineages of prokaryotes, but the rapidly
increasing number of available genome sequences, doubling almost every 2 years, should overcome this limitation.
Zhi et al. (2012) pointed out that the increasing number of
sequenced genomes remains biased towards prokaryotes of
medical or biotechnological importance, and Tindall et al.
(2010) observed that the genome sequences of species of
interest are not always those of type strains. Ricker et al.
(2012) also contested the quality of genome sequences
available in public databases, which may be available as
complete sequences, draft assemblies, scaffolds or contigs.
Klassen & Currie (2012) argued that draft genomes may be
less informative than complete genomes for taxonomic
purposes, and that there is a need to define minimal
Bacterial isolate
If score lower than 2 with species available in the database
MALDI-TOF MS
If nucleotide sequence identity is lower than 98.7% with
phylogenetically closest species with validly published name
AND
This value is lower than or equal to the highest similarity
observed among species of the same genus with standing in nomenclature
Comparison of phenotypic
16S rRNA gene sequence analysis
Putative novel bacterial species
Genome sequencing and annotation
and chemotaxonomic characteristics
Gram staining, cell size and shape, motility, sporulation,
oxygen requirement, biochemical characteristics,
MALDI-TOF spectrum
Determination of AGIOS values between
the new isolate and other closely related species
Comparison of genome
characteristics with closest
phylogenetic neighbours for
which genomes are available
Description of novel bacterial species
If in the range of those observed
among closely related species
Deposit
in two culture collections
Fig. 1. Polyphasic strategy used in our laboratory for the taxonomic classification of bacterial isolates.
386
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Table 1. Novel species identified and described using our polyphasic approach incorporating genome analysis
Species
Phylum Proteobacteria
‘Enterobacter massiliensis’
‘Herbaspirillum massiliense’
Oxygen requirement
Genome size
(Mb)
DNA G+C
content (mol%)
Accession no.
Genome
16S rRNA gene
Reference
ACS-065V-Col13
MM10403188
phR
JC122
JC66
ph5
ph1
JC140
JC401
JC6
JC13
AP8
ph2
Anaerobic
Aerobic
Anaerobic
Anaerobic
Facultatively anaerobic
Anaerobic
Anaerobic
Anaerobic
Anaerobic
Aerobic
Anaerobic
Aerobic
Anaerobic
1.79
4.632
5.05
3.89
5.58
2.1
1.77
1.84
1.75
4.98
3.57
4.61
1.77
28.56
37.3
53.1
26.8
48.2
33.9
30.01
32.2
30.7
37.6
40
34.1
51.4
AEXM00000000
CAET00000000
CAGW00000000
CAEV00000000
CAES00000000
CAGX00000000
CAHB00000000
CAEL00000000
CAEL00000000
CAHJ00000000
CAEN00000000
CAPG00000000
CAHC00000000
JF824805
JF824810
JN837488
JF824801
JF824808
JN837491
JN837495
JF824803
JN657222
JF824800
JF824807
JX101689
JN837487
Lagier et al. (2012b)
Kokcha et al. (2012a)
Hugon et al. (2013a)
Mishra et al. (2012b)
Mishra et al. (2012d)
Mishra et al. (2012e)
Mishra et al. (2013a)
Mishra et al. (2013b)
Mishra et al. (2012c)
Ramasamy et al. (2013a)
Ramasamy et al. (2013b)
Mishra et al. (2013d)
Hugon et al. (2013c)
ph8
JC50
JC136
Anaerobic
Anaerobic
Anaerobic
3.16
4.01
3.49
58.6
58.4
58.82
CAHA00000000
CAHI00000000
CAEG00000000
JN837494
JF824804
JF824799
Hugon et al. (2013b)
Mishra et al. (2012a)
Lagier et al. (2012c)
JC14
JC43
JC225
JC110
phI
JC301
Aerobic
Aerobic
Aerobic
Anaerobic
Anaerobic
Primarily aerobic,
facultatively anaerobic
3.32
3.42
3.4
2.83
2.28
3.01
72.49
70
71.22
85.7
62
61.4
CAHG00000000
CAHK00000000
CAHD00000000
CAEM00000000
CAGZ00000000
CAHH00000000
JF824798
JF824806
JN657218
JF824809
JN837493
JN657220
Ramasamy et al. (2012)
Kokcha et al. (2012b)
Lagier et al. (2012e)
Lagier et al. (2013a)
Mishra et al. (2013c)
Mishra et al. (2013d)
JC163
JC206
Aerobic
Aerobic
4.92
4.18
55.1
59.73
CAEO00000000
CAHF00000000
JN657217
JN657219
Lagier et al. (2013b)
Lagier et al. (2012d)
387
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Use of genomics in taxonomic classification of bacteria
Phylum Firmicutes
‘Anaerococcus senegalensis’
‘Bacillus timonensis’
Brevibacillus massiliensis
‘Clostridium senegalense’
‘Paenibacillus senegalensis’
‘Peptoniphilus grossensis’
‘Peptoniphilus obesi’
‘Peptoniphilus senegalensis’
‘Peptoniphilus timonensis’
‘Bacillus massiliosenegalensis’
‘Dielma fastidiosa’
‘Bacillus massilioanorexius’
‘Kallipyga massiliensis’
Phylum Bacteroidetes
‘Alistipes obesi’
‘Alistipes senegalensis’
‘Alistipes timonensis’
Phylum Actinobacteria
‘Aeromicrobium massiliense’
Brevibacterium senegalense
‘Cellulomonas massiliensis’
‘Senegalemassilia anaerobia’
‘Enorma massiliensis’
‘Timonella senegalensis’
Type strain
D. Ramasamy and others
sequencing quality for genomes to be included in taxonomic analyses.
Taxono-genomics
In our laboratory, in order to isolate bacteria that were
unable to grow under routine culture conditions, Lagier
et al. (2012a) implemented a strategy named ‘culturomics’,
in which they diversified culture conditions (culture
medium composition, temperature, atmosphere, incubation
length). This strategy enabled the identification of 31 putative
novel species, for which genome sequences were systematically determined. In this context, we proposed to use a
polyphasic approach (Fig. 1) to describe these novel bacterial
species using both phenotypic characteristics such as habitat,
Gram-stain reaction, culture and metabolic characteristics,
proteic spectrum and, when applicable, pathogenicity, and
their genomic characteristics.
In this polyphasic approach, bacterial isolates are first
identified using MALDI-TOF MS as described previously
(Seng et al., 2009) (Fig. 1). For all isolates that exhibit an
MS score ,2 with the reference spectra of species whose
names have standing in nomenclature, we perform amplification and sequencing of the complete 16S rRNA gene.
The resulting sequences are then compared to GenBank
(http://www.ncbi.nlm.nih.gov/genbank/), and their closest
phylogenetic neighbours identified. Among those, we select
only the species and/or genera that have validly published
names, and retrieve the 16S rRNA gene sequences for the
corresponding type strains from the List of Prokaryotic
Names with Standing in Nomenclature website (http://
www.bacterio.net/index.html). Percentages of 16S rRNA
gene sequence identity are then calculated between all pairs
of studied taxa. However, rather than using only a universal
16S rRNA gene sequence similarity cut-off (Stackebrandt &
Ebers, 2006), we also consider the variability of conservation
of this gene among genera. Therefore, isolates that exhibit
16S rRNA gene sequence identity ,98.7 % with their closest
phylogenetic neighbour, as recommended by Stackebrandt
& Ebers (2006), are considered to represent putative novel
species if this identity value is equal to or lower than the
highest identity observed among species with names with
standing in nomenclature within the same genus. The next
step consists of characterizing the strains phenotypically and
chemotaxonomically using laboratory tools that are widely
available in microbiology laboratories and may be easily
used to reproduce the data, including staining properties,
cell shape and size, motility, sporulation, oxygen and other
growth requirements, biochemical characteristics using
commercially available strips and MALDI-TOF mass spectra.
Their genome sequence is obtained by using a 454 GS-FLX
Titanium platform (Roche Diagnostics) and annotated.
Genome sequences of the strains of interest and their
phylogenetically closest neighbours are then compared in
terms of size, DNA G+C content, percentage of coding
sequences, gene content, gene distribution in COG categories
(Tatusov et al., 2001), presence of mobile genetic elements,
388
numbers of RNA genes, signal peptides and transmembrane
helices. In addition, we determine the average of genomic
identity of orthologous gene sequences (AGIOS) of studied
strains by comparison with their closest phylogenetic
neighbours as well as between members of the genus of
interest. The AGIOS parameter is obtained by first identifying with BLASTP the set of orthologous proteins between
pairs of compared genomes using minimal coverage of 50 %
and a degree of amino acid identity of 30 %. We then
determine the mean percentage of nucleotide sequence
identity between these orthologous genes. The AGIOS and
ANI parameters differ from each other in that, for the latter,
orthologous genes are identified using BLASTN, which is less
sensitive for this purpose than BLASTP. We acknowledge the
fact that a drawback of our taxono-genomics method is that
genome sequences from some strains from species with validly
published names are currently not available, and our results
may be modified slightly when these data become available.
Type strains are also deposited in two reference culture
collections. To date, we have described 24 novel species using
this polyphasic method (Table 1), including Brevibacterium
senegalense (Kokcha et al., 2012b, 2013) and Brevibacillus
massiliensis (Hugon et al., 2013a, d), the names of which were
validly published by inclusion in Validation List no. 153.
Conclusion
With the decreasing costs of high-throughput sequencing
and the increasing number of prokaryotic genomes that are
sequenced, and given the drawbacks of the current genetic
gold standards that are used as taxonomic tools, should
genomic sequences be excluded from taxonomic descriptions? We strongly believe that genomic information
should be included among taxonomic criteria in addition
to, but not instead of, phenotypic and chemotaxonomic
parameters. We also suggest that putative novel taxa should
always be compared with their phylogenetic neighbours
with validly published names, and the genetic parameters
obtained among members of the closest genus or family
should be taken into account in the description of the
novel species, rather than trying to define universal cut-offs
that may be applicable to only a limited proportion of
prokaryotes. However, we agree that there is no current
consensus on the way genomic data should be integrated
into the taxonomic classification of prokaryotes, but such a
discussion should take place.
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