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Cited by 285Gut microbiota and healthInfant Nutrition and HealthProbiotics and Fermented Foods

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Bifidobacteria exhibit social behavior through carbohydrate resource sharing in the gut

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Milani, Christian, Lugli, Gabriele Andrea, Duranti, Sabrina, Turroni, Francesca, Mancabelli, Leonardo, Ferrario, Chiara, Mangifesta, Marta, Hevia, Arancha, Viappiani, Alice, Scholz, Matthias, Arioli, Stefania, Sanchez, Borja, Lane, Jonathan, Ward, Doyle V., Hickey, Rita, Mora, Diego, Segata, Nicola, Margolles, Abelardo, van Sinderen, Douwe, Ventura, Marco·2015·Scientific Reports
DOI 10.1038/srep15782PMC PMC4623478
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Abstract

Bifidobacteria are common and frequently dominant members of the gut microbiota of many animals, including mammals and insects. Carbohydrates are considered key carbon sources for the gut microbiota, imposing strong selective pressure on the complex microbial consortium of the gut. Despite its importance, the genetic traits that facilitate carbohydrate utilization by gut microbiota members are still poorly characterized. Here, genome analyses of 47 representative Bifidobacterium (sub)species revealed the genes predicted to be required for the degradation and internalization of a wide range of carbohydrates, outnumbering those found in many other gut microbiota members. The glycan-degrading abilities of bifidobacteria are believed to reflect available carbon sources in the mammalian gut. Furthermore, transcriptome profiling of bifidobacterial genomes supported the involvement of various chromosomal loci in glycan metabolism. The widespread occurrence of bifidobacterial saccharolytic features is in line with metagenomic and metatranscriptomic datasets obtained from human adult/infant faecal samples, thereby supporting the notion that bifidobacteria expand the human glycobiome. This study also underscores the hypothesis of saccharidic resource sharing among bifidobacteria through species-specific metabolic specialization and cross feeding, thereby forging trophic relationships between members of the gut microbiota.

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Figure 1 Predicted glycobiome of the Bifidobacterium genus and some additional members of the Bifidobacteriaceae family. GH families and carbohydrate-utilization pathways profiles, based on CAZy database and Pathway-tools software, respectively, were used to construct a hierarchical clustering of all tested species of the Bifidobacterium genus and additional members of the Bifidobacteriaceae family. This clustering highlights the presence of three distinct clusters named GHP/A, GHP/B and GHP/C that display a different repertoire of GHs as well as a different repertoire of plant carbohydrate degradation pathways. GH arsenal prediction for every analyzed Bifidobacteriaceae species is represented by a bar plot. The presence of pathways for degradation of simple or complex carbohydrates is represented by the red color in the heat map and the GH index (the number of GHs predicted in each genome normalized by genome size expressed as Mbp) is illustrated as an orange bar plot. Pathways denominations are indicated as follows: 1 Bifidobacterium shunt, 2 galactose degradation I (Leloir pathway), 3 melibiose degradation, 4 ribose degradation, 5 lactose degradation III, 6 glycogen degradation I, 7 glycogen degradation II, 8 sucrose degradation IV, 9 L-arabinose degradation I, 10 xylose degradation I, 11 D-mannose degradation, 12 (1,4)-ß-xylan degradation, 13 starch degradation V, 14 chitin degradation (chitinase), 15 trehalose degradation IV, 16 Pectin (homogalacturonan) degradation, 17 2'-deoxy-a-D-ribose 1-phosphate degradation, 18 trehalose degradation I (low osmolarity), 19 L-rhamnose degradation II.
Figure 1 Predicted glycobiome of the Bifidobacterium genus and some additional members of the Bifidobacteriaceae family. GH families and carbohydrate-utilization pathways profiles, based on CAZy database and Pathway-tools software, respectively, were used to construct a hierarchical clustering of all tested species of the Bifidobacterium genus and additional members of the Bifidobacteriaceae family. This clustering highlights the presence of three distinct clusters named GHP/A, GHP/B and GHP/C that display a different repertoire of GHs as well as a different repertoire of plant carbohydrate degradation pathways. GH arsenal prediction for every analyzed Bifidobacteriaceae species is represented by a bar plot. The presence of pathways for degradation of simple or complex carbohydrates is represented by the red color in the heat map and the GH index (the number of GHs predicted in each genome normalized by genome size expressed as Mbp) is illustrated as an orange bar plot. Pathways denominations are indicated as follows: 1 Bifidobacterium shunt, 2 galactose degradation I (Leloir pathway), 3 melibiose degradation, 4 ribose degradation, 5 lactose degradation III, 6 glycogen degradation I, 7 glycogen degradation II, 8 sucrose degradation IV, 9 L-arabinose degradation I, 10 xylose degradation I, 11 D-mannose degradation, 12 (1,4)-ß-xylan degradation, 13 starch degradation V, 14 chitin degradation (chitinase), 15 trehalose degradation IV, 16 Pectin (homogalacturonan) degradation, 17 2'-deoxy-a-D-ribose 1-phosphate degradation, 18 trehalose degradation I (low osmolarity), 19 L-rhamnose degradation II.CC BY · PubMed Central
Figure 2 Comparative analysis of bifidobacterial GHs against other gut bacteria. The central heat map shows GH prediction data of 2721 sequenced bacterial strains belonging to bacterial orders residing in the human gut, identified by different color codes as explained in the underlying table. The four heat map rows, situated above the main heat map, represent an enlarged view of the GH51, GH3, GH43 and GH13 content. Data regarding Bifidobacteriales are highlighted in blue. Data regarding Clostridiales and Bacteroidales are highlighted in green.
Figure 2 Comparative analysis of bifidobacterial GHs against other gut bacteria. The central heat map shows GH prediction data of 2721 sequenced bacterial strains belonging to bacterial orders residing in the human gut, identified by different color codes as explained in the underlying table. The four heat map rows, situated above the main heat map, represent an enlarged view of the GH51, GH3, GH43 and GH13 content. Data regarding Bifidobacteriales are highlighted in blue. Data regarding Clostridiales and Bacteroidales are highlighted in green.CC BY · PubMed Central
Figure 3 Evaluation of possible bifidobacterial cross-feeding by a transcriptomics approach. (Panel a) reports the abundance, observed through quantitative qRT-PCR, of eight bifidobacterial species cultivated in MRS supplemented with four different carbohydrates. These species were either grown on their own (mono-association) or in the presence of another bifidobacterial strain (bi-associations) sharing the same ecological niche. The five case studies analysed are named progressively with letters from A to D, corresponding to: B. cuniculi and B. magnum grown on starch (A), B. cuniculi and B. magnum grown on xylan (B), B. biavatii and B. stellenboschense grown on glycogen (C) and B. thermacidophilum subsp. porcinum and B. longum subsp. suis grown on starch (D). (Panel b) shows the transcriptional fold change of genes encoding enzymes in the breakdown of glycans observed in the five case studies, named progressively with letters from A to D. Functional annotation of enzymes are indicated in orange while the functional annotation of transporter encoding genes and the predicted glycan specificity is highlighted in green.
Figure 3 Evaluation of possible bifidobacterial cross-feeding by a transcriptomics approach. (Panel a) reports the abundance, observed through quantitative qRT-PCR, of eight bifidobacterial species cultivated in MRS supplemented with four different carbohydrates. These species were either grown on their own (mono-association) or in the presence of another bifidobacterial strain (bi-associations) sharing the same ecological niche. The five case studies analysed are named progressively with letters from A to D, corresponding to: B. cuniculi and B. magnum grown on starch (A), B. cuniculi and B. magnum grown on xylan (B), B. biavatii and B. stellenboschense grown on glycogen (C) and B. thermacidophilum subsp. porcinum and B. longum subsp. suis grown on starch (D). (Panel b) shows the transcriptional fold change of genes encoding enzymes in the breakdown of glycans observed in the five case studies, named progressively with letters from A to D. Functional annotation of enzymes are indicated in orange while the functional annotation of transporter encoding genes and the predicted glycan specificity is highlighted in green.CC BY · PubMed Central
Figure 4 Data mining for bifidobacterial GH genes and bifidobacterial pathways for carbohydrate degradation in adult and infant fecal metagenome data sets and an infant fecal metatranscriptome data set. Bar plots above the heatmaps show the relative abundance of bifidobacteria in the analysed samples. Heatmaps in the upper part depict the coverage obtained by alignment of adult and infant fecal metagenomic data sets, or infant metatranscriptome data sets to predicted bifidobacterial GH-encoding genes. In order to compare results for datasets with different sizes, all coverage values were normalized as obtained from a 10 million read dataset. Heatmaps in the lower part of the image represent the coverage obtained by alignment of the same datasets to genes constituting the bifidobacterial pathways for carbohydrate degradation. Relevant GH genes and pathways involved in the metabolism of glycans are highlighted in red. Pathways designations are identical to those indicated in Fig. 1.
Figure 4 Data mining for bifidobacterial GH genes and bifidobacterial pathways for carbohydrate degradation in adult and infant fecal metagenome data sets and an infant fecal metatranscriptome data set. Bar plots above the heatmaps show the relative abundance of bifidobacteria in the analysed samples. Heatmaps in the upper part depict the coverage obtained by alignment of adult and infant fecal metagenomic data sets, or infant metatranscriptome data sets to predicted bifidobacterial GH-encoding genes. In order to compare results for datasets with different sizes, all coverage values were normalized as obtained from a 10 million read dataset. Heatmaps in the lower part of the image represent the coverage obtained by alignment of the same datasets to genes constituting the bifidobacterial pathways for carbohydrate degradation. Relevant GH genes and pathways involved in the metabolism of glycans are highlighted in red. Pathways designations are identical to those indicated in Fig. 1.CC BY · PubMed Central

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Metadata

Authors
Milani, C., Lugli, G. A., Duranti, S., Turroni, F., Mancabelli, L., Ferrario, C., Mangifesta, M., Hevia, A., Viappiani, A., Scholz, M., Arioli, S., Sanchez, B., Lane, J., Ward, D. V., Hickey, R., Mora, D., Segata, N., Margolles, A., van Sinderen, D., & Ventura, M.
Affiliations
  • University of Parma (IT)
  • National University of Ireland (IE)
  • Consejo Superior de Investigaciones Científicas (ES)
  • University of Trento (IT)
  • Teagasc - The Irish Agriculture and Food Development Authority (IE)
  • Broad Institute (US)
  • University of Milan (IT)
Corresponding author
Ventura, Marco
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2015-10-28
Volume
5
Issue
1
Pages
15782-15782
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/srep15782
PMC
PMC4623478
OpenAlex
W2302783498
Cited by
284
References
44
Citations by year
2026: 8 · 2025: 19 · 2024: 20 · 2023: 25 · 2022: 33 · 2021: 25 · 2020: 49 · 2019: 39 · 2018: 22 · 2017: 27 · 2016: 15 · 2015: 1
FWCI
9.40
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, other-oa)
  • DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion, cc-by)
  • Institutional Research Information System (Università degli Studi di Trento) (submittedVersion)
Funders
  • U.S. Department of Health and Human Services (HHSN272200900018C)
  • European Commission (618833)
  • Science Foundation Ireland (SFI/12/RC/, 12/RC/2273, 12/RC/, SFI/12/RC/2273., 08/SRC/B1393)
  • Ministero dell’Istruzione, dell’Università e della Ricerca
  • Fondazione Cassa Di Risparmio Di Trento E Rovereto
  • Alimentary Glycoscience Research Cluster (08/SRC/B1393)
  • National Institutes of Health (HHSN272200900018C)
  • APC Microbiome Institute (SFI/12/RC/2273)
  • National Institute of Allergy and Infectious Diseases (HHSN272200900018C)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
  • Health Sciences › Nursing › Nutrition and Dietetics › Infant Nutrition and Health
  • Life Sciences › Agricultural and Biological Sciences › Food Science › Probiotics and Fermented Foods
Keywords
Bifidobacterium, gut microbiota, carbohydrate utilization, glycan degradation, cross-feeding, resource sharing, gene expression profiling, human faecal microbiota, insect gut microbiota, metabolic specialization
MeSH terms
Adult, Bifidobacterium, Carbohydrates, Feces, Humans, Infant, Gastrointestinal Tract, Carbohydrate Metabolism

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