Nature MicrobiologyMicrobiologyPeer-reviewed journal article
Integrative genomic reconstruction reveals heterogeneity in carbohydrate utilization across human gut bifidobacteria
Aleksandr A. ArzamasoviD, Dmitry A. RodionoviD, Matthew Charles HibberdiD, Janaki L. Guruge, James E. KentiD, Marat D. KazanoviD, Semen A. LeyniD, Marinela L. ElaneiD, Kristija SejaneiD, Annalee FürstiD, Lars BodeiD, Michael J. BarrattiD, Jeffrey I. GordoniD, Andrei L. OstermaniD
Abstract
Bifidobacteria are among the earliest colonizers of the human gut and are widely used as probiotics for their health-promoting properties. However, individual responses to probiotic supplementation may vary with strain type(s), microbiota composition, diet, or lifestyle conditions, highlighting the need for strain-level insights into the bifidobacterial metabolism of dietary and host glycans. Here, we systematically reconstructed 68 pathways involved in the utilization of mono-, di-, oligo-, and polysaccharides by analyzing the distribution of 589 curated metabolic functional roles (catabolic enzymes, transporters, transcriptional regulators) in 3083 non-redundant cultured Bifidobacterium isolates and metagenome-assembled genomes (MAGs) of human origin. Our analysis uncovered extensive inter- and intraspecies heterogeneity, including a distinct clade within the Bifidobacterium longum species capable of metabolizing -glucans. We also identified isolates of Bangladeshi origin that harbor unique gene clusters implicated in the breakdown of xyloglucan and human milk oligosaccharides. Thirty-eight predicted carbohydrate utilization phenotypes were experimentally validated in 30 geographically diverse Bifidobacterium isolates in vitro. Our large-scale genomic compendium expands the knowledge of bifidobacterial carbohydrate metabolism and can inform the rational design of probiotic and synbiotic formulations tailored to strain-specific nutrient preferences.
- organism: human
- assay: whole-genome sequencing
- research focus: gene regulatory network inference
- research focus: clustering
- approach: statistical model
- approach: experimental assay
- Citations
- 56
- FWCI
- 13.42
- Field percentile
- top 1%
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