Download Supplementary Text Cross-feeding of bifidobacteria. The

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Supplementary Text
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Cross-feeding of bifidobacteria. The transcriptomic data analyzed here suggests the occurrence
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of cross-feeding interactions between particular human gut bifidobacteria in their ecological
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niche, which is ultimately expected to allow an expansion of carbohydrate metabolism by
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bifidobacterial communities in the human intestine. In this context, the up-regulation of
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BBPR_1793 and BBPR_1794 by strain PRL2010, or Blon_2348 by strain ATCC15697, or
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B12L_0154 by strain 12L, which all encode putative exo-sialidases may be crucial in releasing
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sialic acid from glycoconjugates such as mucin or Human Milk Oligosaccharides (HMOs). Such
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glycoproteins are present in the human gut (Turroni et al 2014, Ventura et al 2012) and represent
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direct or indirect carbon sources for various members of the gut microbiota. Interestingly,
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Blon_0646 and BBPR_1794, which are predicted to encode sialidases, are highly transcribed
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when B. longum subsp. infantis ATCC15697 and B. bifidum PRL2010, respectively, are in bi-
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association with B. adolescentis 22L, which does not possess any predicted sialidase-encoding
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gene in its genome. In this context, sialic acid could be further metabolized by bifidobacteria
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through the sialic acid metabolic pathway consisting of N-acetylneuraminate lyase (nanA), N-
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acetylmannosamine kinase (nanK), N-acetylmannosamine-6-phosphate epimerase (nanE), whose
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produced metabolic end products enter the amino sugar degradation pathway (Egan et al 2014).
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Notably, nan genes encoded by B. breve 12L are highly expressed (≥ 2 fold) when B. breve 12L
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is present in bi-association with B. longum subsp. infantis ATCC15697, and when it is in
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multiple association with the other three strains (Fig. S2). In addition, nan genes are present on
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the chromosome of B. longum subsp. infantis ATCC15697 (Sela et al 2008), and these genes are
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transcribed (especially of nanK and nanE) in all tested conditions (Fig. S2). This finding
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supports the notion that the sialic acid released by the exosialidases of PRL2010 strain would be
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accessed by the products of the nan genes of 12L and ATCC15697, and shunted in the glycolytic
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pathway of these two strains in a similar fashion to what previously described under in vitro
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conditions for B. breve UCC2003 (Egan et al 2014).
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16S rRNA microbial profiling. The number of reads generated on the Ion Torrent PGM
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machine for each conditions was greater than 104493 (Table S4), supporting the notion that a
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large part of the microbial diversity residing in the analysed samples had been captured as was
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obvious from the decrease in the rate of phylotype detection and the plateauing of the Chao1 and
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Shannon diversity indexes for the different samples analysed (Fig. S5). When the composition of
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the different samples was investigated and compared between the different samples analysed
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(Fig. S6), we observed the existence of differences that are linked to the occurrence of a specific
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bifidobacterial strain. In this context, the heat map representation of differences at genus level
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between bi-association sample profiles and the composition profiles of the respective mono-
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associations clearly highlights a reduction in the unclassified members of the S24-7 family when
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B. adolescentis 22L is present in bi-association, followed by an increase in unclassified members
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of Lachnospiraceae family (Fig. S6). Focusing on B. breve 12L, during T1 and T2 time points
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we observed a reduction in unclassified members of the S24-7 and Lachnospiraceae family,
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followed by an increase in unclassified members of Rikenellaceae family (Fig. S6). Interestingly,
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the discontinuation of B. breve 12L administration to mice causes a reduction in the relative
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abundance of unclassified members of Clostridiales order in the murine gut microbiota
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especially during time point T3. Furthermore, it provokes an increase in unclassified members of
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Lachnospiraceae family and a decrease in unclassified members of Rikenellaceae family in time
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point T4 (Fig. S6). The presence of B. bifidum PRL2010 in bi-associations induces an overall
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reduction in unclassified members of the Rikenellaceae family in the murine gut microbiota
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across various time points. In contrast, murine cecal presence of B. longum subsp. infantis
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ATCC15697 in bi-associations provokes an opposite effect on the gut microbiota as obvious
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from an increase of unclassified members of the Rikenellaceae family (Fig. S6).
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Bacterial strains and culture conditions. Bifidobacterial cultures were incubated in an
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anaerobic atmosphere (2.99 % H2, 17.01 % CO2 and 80 % N2) in a chamber (Concept 400,
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Ruskin) in the Man-Rogosa-Sharp (MRS) (Scharlau Chemie, Barcelona, Spain) supplemented
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with 0.05 % (w/v) L-cysteine hydrochloride and incubated at 37°C for 16 h.
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Serial dilutions of the murine fecal samples were pour-plated onto Bifidobacterium selective agar
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(BSM) for selective outgrowth of bifidobacteria. The BSM selective medium was prepared by
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the addition to MRS agar (Scharlau Chemie, Barcelona, Spain) of 0.05 % (w/v) L-Cysteine
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hydrochloride and 50 mg mupirocin (Delchimica, Italy) per liter of MRS as described previously
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(Serafini et al 2011, Simpson et al 2004). Agar plates were incubated anaerobically at 37°C for
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72 h. Bacterial colonies were molecular identified by direct sequencing of their 16S rRNA genes.
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In vitro co-cultivation. Approximately 108 cell/ml of each of the following strains: B. bifidum
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PRL2010, B. breve 12L, B. adolescentis 22L or B. longum subsp. infantis ATCC15697, or these
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strains in co-cultivation were inoculated in 6 ml of MRS without carbohydrates (Scharlau
17
Chemie, Barcelona, Spain) supplemented with 1 % of starch or xylan (Sigma Aldrich) as unique
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carbon sources. Cell suspensions were mixed and incubated at 37°C for 24 h in anaerobic
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conditions.
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Detection of bifidobacteria by quantitative-real time PCR (qRT-PCR). Evaluation of the
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presence of bifidobacteria in the murine fecal samples prior supplementation of bifidobacteria
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was performed by quantitative real-time qRT-PCR using previously described genus-specific
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primers (Gueimonde et al 2004)(Matsuki et al 2002). qRT-PCR reactions were performed on
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1
MicroAmp optical plates sealed with MicroAmp optical caps (Applied Biosystems, Foster City,
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CA) and amplifications were carried out in CFX96 (Biorad) using SYBR Green PCR Master
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Mix (Applied Biosystems). Thermal cycling consisted of an initial cycle of 95˚C for 10 min
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followed by 40 cycles of 95˚C for 15 sec and 60˚C for 1 min. DNA extracts from cultures of the
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strain B. longum subsp. longum NCIMB 8809 were used for standard curves. Samples were
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analysed by duplicate in at least two independent PCR runs.
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Murine exposure to bifidobacteria. All animals used in this study were cared for in compliance
8
with guidelines established by the Italian Ministry of Health. All procedures were approved by
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the University of Parma, as executed by the Institutional Animal Care and Use Committee
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(Dipartimento per la Sanità Pubblica Veterinaria, la Nutrizione e la Sicurezza degli Alimenti
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Direzione Generale della Sanità Animale e del Farmaco Veterinario). Eleven groups, each
12
containing five animals of 3 months old female BALB/c mice, were orally inoculated with only
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one bifidobacterial strain (B. breve 12L or B. adolescentis 22L or B. longum subsp. infantis
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ATCC15697 or B. bifidum PRL2010), or with two bifidobacterial strains (B. adolescentis 22L
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and B. bifidum PRL2010 or B. adolescentis 22L and B. breve 12L, or B. adolescentis 22L and B.
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longum subsp. infantis ATCC15697 or B. bifidum and B. breve 12L, or B. bifidum PRL2010 and
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B. longum subsp. infantis ATCC15697, or B. breve 12L and B. longum subsp. infantis
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ATCC15697) or with four bifidobacterial strains (B. adolescentis 22L, and B. bifidum PRL2010,
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as well as B. breve 12L and B. longum subsp. infantis ATCC15697). Balb/C female mice were
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purchesed from Charles River Italia (Calco, Italy). Bacterial presence in the murine gut was
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established by five consecutive daily administrations whereby each animal received 20 µl of 109
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cells using a micropipette tip placed immediately behind the incisors. Bacterial inocula were
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prepared by growing bifidobacterial cells, anaerobically overnight at 37°C in MRS broth.
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Cultures were harvested by centrifugation (3,000 rpm for 8 minutes), washed and resuspended in
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100 µL of water. The viable count of each inoculum was determined by retrospective plating on
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MRS.
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In order to estimate the level of bifidobacterial transient-presence in each animal, a qRT-PCR
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approach using strain specific primers based on strain-specific genes were used. For B. bifidum
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PRL2010 were used primers Bbif_0282Fw (5’-GCGAACAATGATGGCACCTA-3’) and
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Bbif_0282Rv (5’-GTCGAACACCACGACGATGT-3’), for B. adolescentis 22L were used
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primers
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TTGGTGGCCTTGTAGTAGCC-3’), for B. breve 12L were used primers Br12L_105Fw (5’-
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CGAAGTTCCAGTTCACCAT-3’) and Br12L_105Rv (5’-GTTCTTGGCGTTCCAGATGT),
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for
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ATGGATGGAGACCAGTTT-3’)
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Animals were sacrificed and their individual cecum tracts were removed and then used for RNA
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extraction.
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RNA isolation. Total RNA was isolated using a previously described method (Turroni et al.,
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2013, PNAS). Briefly, tissue materials were resuspended in 1 ml of QUIAZOL (Qiagen, UK)
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and placed in a tube containing 0.8 g of glass beads (diameter, 106 μm; Sigma). The cells were
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lysed by shaking the mix on a BioSpec homogenizer at 4°C for 2 min (maximum setting). The
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mixture was then centrifuged at 12,000 rpm for 15 min, and the upper phase containing the
20
RNA-containing sample was recovered. The RNA sample was further purified by phenol
21
extraction and ethanol precipitation according to an established method (Sambrook and Russell
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2001). The quality of the RNA was checked by analysing the integrity of rRNA molecules by
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Experion (BioRad).
B.
BAD1574Fw
longum
(5’-GACCAAGCCAACCAGTTCAT-3’)
subsp.
infantis
ATCC15697
and
Binf_uFw
were
used
and
BAD1574Rv
primers
Binf_uRv
(5’-
(5’-
(5’-ATCAGTTCGGAGGTGATG-3’).
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1
Microarray, description, labelling and hybridizations. Microarray analysis was performed
2
with an oligonucleotide array based on the B. adolescentis 22L, B. bifidum PRL2010, B. breve
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12L and B. longum subsp. infantis ATCC15697 genomes. A total of 45,220 oligonucleotide
4
probes of 60 bp in length were designed based on 7679 identified ORFs using eArray5.0 (Agilent
5
Technologies). 5 Oligos were designed for each gene on a 4x44k Agilent Microarrays (Agilent
6
Technologies, Santa Clara, CA, USA). Replicates were distributed on the chip at random, non-
7
adjacent positions. A set of 152 negative control probes designed on phage and plant sequences
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were also included on the chip. Construction of the custom array was performed using the
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Agilent eArray software (Agilent Technologies, Santa Clara, CA, USA).
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Reverse transcription and amplification of 500 ng of total RNA was performed with ImProm-
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IITM Reverse Transcriptase (Promega, Madison, USA) according to the manufacturer’s
12
instructions. Five µg of cDNA was then labeled with ULS Labeling kit with Cy5 or Cy3
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(Kreatech, The Netherlands).
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Labeled cDNA was hybridized using the Agilent Gene Transcription hybridization kit (part
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number 5188-5242) as described in the Agilent Two-Color Microarray-Based Gene
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Transcription Analysis v4.0 manual (G4140-90050). Following hybridization, microarrays were
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washed in accordance with Agilent's standard procedures.
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Differential expression of genes was confirmed by real-time quantitative PCR (qRT-PCR). De
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novo cDNAs were prepared as described above. The mRNA expression levels were analyzed
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with SYBR Green technology in qRT-PCR using SoFast EvaGreen Supermix (Bio-Rad) on a
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Bio-Rad CFX96 system according to the manufacturer’s instructions (Bio-Rad). The primers
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used are indicated in Table S3. Quantitative RT-PCR was carried out according to the following
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cycle: initial hold at 96 °C for 30 s and then 40 cycles at 96 °C for 2 s and 60 °C for 5 s. Gene
6
1
expression was normalized relative to a housekeeping gene as previously described by Turroni et
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al. (Turroni et al 2011). The amount of template cDNA used for each sample was 12.5 ng.
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Microarray data acquisition and treatment. Fluorescence scanning was performed on an
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InnoScan 710 microarray scanner (Innopsys, France). Signal intensities for each spot were
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determined using Mapix5.5 software (Innopsys, Carbonne, France). Differential transcription
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tests were performed with the Cyber-T implementation of a variant of the t-test, as described
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previously (Baldi and Long 2001). A gene was considered differentially expressed between a test
8
condition and a control when an expression ratio of 2 or 0.5 relative to the result for the control
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was obtained with a corresponding P-value ≤ 0.001.
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Cross-talk index for each bi-association was calculated as the ratio between the over-expressed
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genes respect the total gene repertoire for each genome pair, expressed as percentage.
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RNAseq analyses. Two hundred ng of total RNA was used as the starting input for RNA-Seq
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library preparation. Briefly, 200 ng of total RNA was treated with RiboMinus Eukaryote System
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v2, with bacterial probe supplied by Life Technologies instead eukaryote probe(Ambion) to
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remove rRNA according to the supplier’s instructions. The efficacy of rRNA depletion was
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checked by a Tape Station (Agilent Technologies), after which rRNA-depleted RNA samples
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were fragmented using RNaseIII (Life Technologies, USA) followed by size evaluation using a
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Tape Station (Agilent Technologies). Whole transcriptome libraries were constructed using the
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TruSeq Stranded mRNA Sample Preparation Guide (Illumina, USA). Indexed libraries were
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quantified by Qubit (Life Technologies).The samples were loaded on MiSeq® Reagent Kit v3
21
(150 cycle) and sequenced by means of a MiSeq instrument (Illumina, USA). Sequencing reads
22
were depleted of adapters, quality filtered (with overall quality, quality window and length
23
filters) and aligned to the reference genomes through BWA (Li and Durbin 2009). Counting of
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1
the number of reads that corresponded to annotated ORFs was performed using HTSeq
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(http://www-huber.embl.de/users/anders/HTSeq/doc/overview.html) and analysis of the count
3
data was performed using the R package DESeq (Anders and Huber 2010).
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Microbiota identification by 16S rRNA gene- amplification, -sequencing and data analysis.
5
DNA was extracted from murine fecal samples using the QIAamp DNA Stool Mini kit following
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the manufacturer’s instructions (Qiagen Ltd., Strasse, Germany). Partial 16S rRNA gene
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sequences were amplified from extracted DNA using primer pair Probio_Uni and /Probio_Rev,
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which target the V3 region of the 16S rRNA gene sequence (Milani et al 2013). 16S rRNA gene
9
amplification and amplicon checks were carried out as previously described (Milani et al 2013).
10
16S rRNA gene sequencing was performed using a Personal Genome Machine platform (Life
11
Technologies) at the DNA sequencing facility of GenProbio srl (www.genprobio.com) according
12
to the protocol previously reported (Milani et al 2013). Following sequencing, the obtained
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individual sequence reads were filtered by the PGM software to remove low quality and
14
polyclonal sequences. Sequences matching the PGM 3’ adaptor were also automatically
15
trimmed. All PGM quality-approved, trimmed and filtered data were exported as fastq files. The
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fastq files were processed using QIIME (Caporaso et al 2010). Quality control retained
17
sequences with a length between 140 and 400 bp, mean sequence quality score >15, with
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truncation of a sequence at the first base if a low quality rolling 10 bp window was found.
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Presence of homopolymers >7 bp, and sequences with mismatched primers were omitted. In
20
order to calculate downstream diversity measures (alpha and beta diversity indices, Unifrac
21
analysis), 16S rRNA Operational Taxonomic Units (OTUs) were defined at ≥ 97 % sequence
22
homology. All reads were classified to the lowest possible taxonomic rank (Genus level) using
23
QIIME and a reference dataset from the Ribosomal Database Project (Cole et al 2009). OTUs
8
1
were assigned using uclust (Edgar 2010). Similarities between samples were calculated by
2
Weighted uniFrac (Lozupone and Knight 2005). The range of similarities is calculated between
3
the values 0 and 1. Analysis of results was initially performed at genus level to obtain an
4
overview of microbiota taxonomy in the analyzed samples. Subsequently, analysis of OTUs was
5
also carried out in order to detect detailed changes in microbiota composition at phylotype level.
6
Co-occurrence and co-exclusion analysis. The Kendall tau rank correlation was used to
7
evaluate the co-occurrence and co-exclusion between the genus Bifidobacterium and the
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principal genera found in the samples (genera that showed relative abundance > 1 % in at least
9
one sample was considered), as well as metabolites levels obtained from metabolic profiles and
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genes involved in SCFA production. The Kendall tau rank was calculated with the IBM SPSS
11
Statistics software v. 22.
12
Shotgun metagenomics analysis. DNA was extracted from murine fecal samples using the
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QIAamp DNA Stool Mini kit following the manufacturer’s instructions (Qiagen Ltd., Strasse,
14
Germany) and subsequently fragmented to 550-650 bp using a BioRuptor machine (Diagenodo,
15
Belgium). Samples were prepared following the TruSeq DNA PCR-free Sample Preparation
16
Guide. Sequencing was performed using an Illumina MiSeq sequencer with MiSeq Reagent Kit
17
v3 chemicals. The reads contained in the fastq files were mapped using the BWA software (Li
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and Durbin 2010) on the EggNog database, the CAZy database and a custom database containing
19
genes involved in SCFA production. Additionally, BWA software (Li and Durbin 2010) was
20
used for evaluation multibifido-array probe specificity by mapping the DNA sequences of the
21
probes on reads constituting shotgun metagenomics datasets. BWA software was employed for
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the screening of the occurrence of putative bifidobacteria in fecal samples of mice prior the
23
administration of bifidobacteria through mapping of shotgun reads on core genome sequences of
9
1
the genus Bifidobacterium (Milani et al 2014). Reads counts were subsequently normalized by
2
total read number of each sample, allowing cross-comparisons.
3
Fecal metabolome and data analysis. Fecal samples were treated by the addition of 800 µl of
4
water to 0.1 gr of faeces. Fecal material lysis was achieved by mechanical treatment placing the
5
faecal suspension in a tube containing 0.8 g of glass beads (diameter, 106 μm; Sigma). Samples
6
were lysed by shaking the mix on a BioSpec homogenizer at 4°C for 2 min (maximum setting).
7
Supernatant was collected and freeze-dried. Dried fecal extract samples were dissolved in 600 µl
8
of 99.9 % D2O (Goss Scientific Instruments), vortexed for 1 min. Samples stood for 20 min at
9
room temperature to allow dry particles to thoroughly dissolve and were vortexed again,
10
followed by centrifuging at 18,000 x g for 10 min at 4 °C. A total of 540 µl of supernatant was
11
transferred into a 1.5 mL tube containing 60 µl of 1.5 M potassium phosphate buffer in D2O,
12
pH=7.4, 0.1 % 3-(trimethylsilyl)-[2,2,3,3-2H4]propionic acid sodium salt (TSP) and 2 mM
13
sodium azide. The resulting mixture was then vortex and 580 µl was transferred into an NMR
14
tube with an outer diameter of 5 mm. 1H NMR spectra were acquired using a Bruker 600 MHz
15
spectrometer (Bruker, Rheinstetten, Germany) at the operating 1H frequency of 600.13 MHz at a
16
temperature of 300 K. One-dimensional Car-Purcel-Meiboom-Gill (CPMG) spectra were
17
acquired using a spin-echo pulse sequence: [recycle delay-90°-(-180°-)n-acquire free
18
induction decay]. The spin relaxation delay (2n) is 160 ms. A 90 degree pulse was adjusted to
19
10 µs. A total of 32 scans were collected into 64 k data points with a spectral width of 20 ppm.
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Automatic phasing, baseline correction and reference to TSP signal at 1H 0.00 were performed
21
on the 1H NMR CPMG spectra. The processed NMR spectral data (1H 0.25 to 10) were
22
imported to MATLAB (R2012a, 7.14.0.739, MathWorks) and digitized into approximately 20 k
23
data points with a resolution of 0.0005 ppm. The water peak region (1H 4.74-4.85) were
10
1
removed due to its disordered peak shape caused by water suppression. Probabilistic quotient
2
normalization was applied to the remaining spectral data. Principal component analysis (PCA)
3
was carried out with the unit variance scaling method in SIMCA (P+13.0). The relative
4
concentrations of selected metabolites were calculated based on the integrals of chosen peaks.
5
Statistical analysis. Statistical significance between means was analyzed using the unpaired
6
Student's t test with a threshold P value of <0.05. In this context the statistical analyses involving
7
comparison of the multiple-associations vs. mono-associations growth was performed at T4
8
points. the Values are expressed as the means ± the standard errors of the mean of three
9
experiments. Multiple comparisons are analyzed using One-way ANOVA and Bonferroni tests.
10
Statistical calculations were performed using the software
11
program GraphPad Prism 5 (La Jolla, CA, USA).
12
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References
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Supplementary figures
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6
Figure S1. Schematic representation of the design of the murine trial (panel a). Population sizes
7
of mono-association of bifidobacterial strains (panel b), multiple-association of bifidobacterial
8
strains (panel c) and bi-association of bifidobacterial strains (panel d) transiently present in the
9
intestine of BALB/c mice. In panels a-d each point represents the average of the log-transformed
10
population size ± standard deviation for five mice. Panel e displays the hybridization profile of
11
the multibifido array to complementary DNA targets prepared from animals administered a
12
single bacterial species (mono-association). In the final column, hybridization of the multibifido
13
array with fecal DNA samples extracted from a mix of all T0 samples is represented.
14
15
Figure S2. Transcriptome of genes predicted to be involved in the breakdown of complex
16
carbohydrates by B. breve 12L, B. longum subsp. infantis ATCC15697, B. adolescentis 22L and
17
B. bifidum PRL2010 in response to murine mono- bi- or multiple-association (m.a.). Panel a,
18
shows the transcriptional profiling of genes predicted to be involved in sialic acid metabolism.
19
Panel b displays the transcriptional profiling of genes predicted to be involved in xylan
20
breakdown. Panel c depicts the transcriptional profiling of genes predicted to be involved in
21
starch hydrolysis. Colours (black to green) represent the average signal intensity.
22
Panel d exhibits the transcriptomic data obtained for in vitro co-cultures of PRL2010-12L-22L-
23
ATCC15697 cells grown on xylan compared to the mono-associations cultivated on the same
24
substrates. The heat maps illustrate the RPKM values detected for genes involved in xylan
25
breakdown.
13
1
2
Figure S3. Utilization of simple carbohydrates by 12L, 22L, ATCC15697 and PRL2010 in
3
mono-, bi-and multiple-associations. Average expression levels for five cecal samples are shown
4
for genes encoding enzymes that shunt the indicated monosaccharides into the glycolytic or
5
pentose phosphate pathways. Bars are coloured according to the different presence of
6
bifidobacterial strains as indicated in the legend on the left side. Significant differences in
7
expression levels are indicated with asterisk (p≤ 0.001). The x axis indicates the gene function
8
and the locus tag in the corresponding genome. On the y axis is represented the average signal.
9
10
Figure S4. Signal intensity level of the predicted carbohydrate transporter-encoding genes
11
identified in the genomes of 12L, 22L, ATCC15697 and PRL2010 when present in bi- and
12
multiple-association within a murine model. Only transporters that appear to be modulated in at
13
least one condition are shown. Colours (black to green) represent the average signal intensity.
14
15
Figure S5. Rarefaction curves generated for 16S rRNA gene sequences obtained from fecal
16
murine samples taken at different time points following administration of mice with 12L, 22L,
17
ATCC15697 and PRL2010 strains on their own, or as different combinations. Panel a represents
18
the rarefaction curves using the Chao index. Panel b displays rarefaction curves using the
19
Shannon index.
20
21
Figure S6. Evaluation of changes in murine fecal microbiota composition upon bifidobacterial
22
exposure at genus level. Panel a displays the aggregate microbiota composition determined by
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16S microbial profiling analyses at family level from samples taken from the different
14
1
bifidobacterial associations (mono-, bi- and multiple- association) and at different time points
2
from inner (T0) to outer circle (T4) (see Figure S1). Panel b displays the effect of bi- and
3
multiple- associations with respect to mono-associations in terms of enhancing (in green) or
4
reducing (in red) the main groups of murine gut bacteria. Only genera with at least a variation >
5
1% are shown.
6
7
Figure S7. Evaluation of changes in murine fecal microbiota composition upon bifidobacterial
8
exposure at phylotype level. The heat map represents modulation (changes in relative
9
abundance) of the 100 OTUs with higher average abundance in the bi- and multiple-associations
10
with respect to mono-associations. Colors in the first column on the left indicate taxonomic
11
classification of corresponding OTU at genus level. For each OTU the taxonomy at phylum and
12
family level is also indicated on the right side of the heat map.
13
14
Figure S8. Impact of bifidobacterial administration on the glycobiome of fecal mice microbiota.
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Panel a shows a bar plot representation of the COG functional families’ average relative
16
abundance predicted at time point T0 and in the mono-, bi- and multiple-associations at time
17
points T2 and T4, based on shotgun metagenomics analyses. Panel b represents the average
18
relative abundance of the COG functional families corresponding to carbohydrate transport and
19
metabolism, and amino acid transport and metabolism, based on clustering groups defined in
20
Figure 4. Panel c displays a heat map representing the average relative abundance variation of
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GH encoding genes predicted in the shotgun metagenomics datasets grouped by mono-, bi- and
22
multiple-association, and by T2 and T4 time points, with respect to the T0 time point (murine
23
sample obtained prior to bifidobacterial administration).
15
1
2
Figure S9. Simple-carbohydrate profiles of murine fecal samples identified by metabolomic
3
analyses. The bar plot representation shows the concentration, expressed in p.p.m., of alpha- and
4
beta-glucose, as well as alpha- and beta-galactose, at time point T0 and in all the mono-, bi- and
5
multiple associations.
6
7
Figure S10. Schematic representation of deduced molecular interactions toward glycans of
8
PRL2010, 12L, 22L and ATCC15697 strains.
16