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An ecological framework to understand the efficacy of fecal microbiota transplantation

Author

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  • Yandong Xiao

    (National University of Defense Technology
    Brigham and Women’s Hospital and Harvard Medical School)

  • Marco Tulio Angulo

    (Universidad Nacional Autónoma de México
    National Council for Science and Technology (CONACyT))

  • Songyang Lao

    (National University of Defense Technology)

  • Scott T. Weiss

    (Brigham and Women’s Hospital and Harvard Medical School)

  • Yang-Yu Liu

    (Brigham and Women’s Hospital and Harvard Medical School
    Dana-Farber Cancer Institute)

Abstract

Human gut microbiota plays critical roles in physiology and disease. Our understanding of ecological principles that govern the dynamics and resilience of this highly complex ecosystem remains rudimentary. This knowledge gap becomes more problematic as new approaches to modifying this ecosystem, such as fecal microbiota transplantation (FMT), are being developed as therapeutic interventions. Here we present an ecological framework to understand the efficacy of FMT in treating conditions associated with a disrupted gut microbiota, using the recurrent Clostridioides difficile infection as a prototype disease. This framework predicts several key factors that determine the efficacy of FMT. Moreover, it offers an efficient algorithm for the rational design of personalized probiotic cocktails to decolonize pathogens. We analyze data from both preclinical mouse experiments and a clinical trial of FMT to validate our theoretical framework. The presented results significantly improve our understanding of the ecological principles of FMT and have a positive translational impact on the rational design of general microbiota-based therapeutics.

Suggested Citation

  • Yandong Xiao & Marco Tulio Angulo & Songyang Lao & Scott T. Weiss & Yang-Yu Liu, 2020. "An ecological framework to understand the efficacy of fecal microbiota transplantation," Nature Communications, Nature, vol. 11(1), pages 1-17, December.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-17180-x
    DOI: 10.1038/s41467-020-17180-x
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    Cited by:

    1. Lu Wu & Xu-Wen Wang & Zining Tao & Tong Wang & Wenlong Zuo & Yu Zeng & Yang-Yu Liu & Lei Dai, 2024. "Data-driven prediction of colonization outcomes for complex microbial communities," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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