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Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis

Author

Listed:
  • Jaeyun Sung

    (Asia Pacific Center for Theoretical Physics
    Center for Computational and Integrative Biology, Massachusetts General Hospital and Harvard Medical School
    Broad Institute of MIT and Harvard)

  • Seunghyeon Kim

    (Asia Pacific Center for Theoretical Physics
    Pohang University of Science and Technology
    The Abdus Salam International Centre for Theoretical Physics)

  • Josephine Jill T. Cabatbat

    (Asia Pacific Center for Theoretical Physics)

  • Sungho Jang

    (Pohang University of Science and Technology)

  • Yong-Su Jin

    (University of Illinois at Urbana-Champaign
    Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign)

  • Gyoo Yeol Jung

    (Pohang University of Science and Technology
    School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology)

  • Nicholas Chia

    (Microbiome Program, Center for Individualized Medicine, Mayo Clinic
    Mayo Clinic
    Mayo College)

  • Pan-Jun Kim

    (Asia Pacific Center for Theoretical Physics
    Pohang University of Science and Technology
    Korea Advanced Institute of Science and Technology)

Abstract

A system-level framework of complex microbe–microbe and host–microbe chemical cross-talk would help elucidate the role of our gut microbiota in health and disease. Here we report a literature-curated interspecies network of the human gut microbiota, called NJS16. This is an extensive data resource composed of ∼570 microbial species and 3 human cell types metabolically interacting through >4,400 small-molecule transport and macromolecule degradation events. Based on the contents of our network, we develop a mathematical approach to elucidate representative microbial and metabolic features of the gut microbial community in a given population, such as a disease cohort. Applying this strategy to microbiome data from type 2 diabetes patients reveals a context-specific infrastructure of the gut microbial ecosystem, core microbial entities with large metabolic influence, and frequently produced metabolic compounds that might indicate relevant community metabolic processes. Our network presents a foundation towards integrative investigations of community-scale microbial activities within the human gut.

Suggested Citation

  • Jaeyun Sung & Seunghyeon Kim & Josephine Jill T. Cabatbat & Sungho Jang & Yong-Su Jin & Gyoo Yeol Jung & Nicholas Chia & Pan-Jun Kim, 2017. "Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis," Nature Communications, Nature, vol. 8(1), pages 1-12, August.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms15393
    DOI: 10.1038/ncomms15393
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    Cited by:

    1. Vanessa R. Marcelino & Caitlin Welsh & Christian Diener & Emily L. Gulliver & Emily L. Rutten & Remy B. Young & Edward M. Giles & Sean M. Gibbons & Chris Greening & Samuel C. Forster, 2023. "Disease-specific loss of microbial cross-feeding interactions in the human gut," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    2. Zihan Wang & Akshit Goyal & Veronika Dubinkina & Ashish B. George & Tong Wang & Yulia Fridman & Sergei Maslov, 2021. "Complementary resource preferences spontaneously emerge in diauxic microbial communities," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    3. Shengbo Wu & Jie Feng & Chunjiang Liu & Hao Wu & Zekai Qiu & Jianjun Ge & Shuyang Sun & Xia Hong & Yukun Li & Xiaona Wang & Aidong Yang & Fei Guo & Jianjun Qiao, 2022. "Machine learning aided construction of the quorum sensing communication network for human gut microbiota," Nature Communications, Nature, vol. 13(1), pages 1-13, December.

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