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Microbial coexistence through chemical-mediated interactions

Author

Listed:
  • Lori Niehaus

    (Boston College)

  • Ian Boland

    (Boston College)

  • Minghao Liu

    (Boston College)

  • Kevin Chen

    (Boston College)

  • David Fu

    (Boston College)

  • Catherine Henckel

    (Boston College)

  • Kaitlin Chaung

    (Boston College)

  • Suyen Espinoza Miranda

    (Boston College)

  • Samantha Dyckman

    (Boston College)

  • Matthew Crum

    (Boston College)

  • Sandra Dedrick

    (Boston College)

  • Wenying Shou

    (Fred Hutchinson Cancer Research Center)

  • Babak Momeni

    (Boston College)

Abstract

Many microbial functions happen within communities of interacting species. Explaining how species with disparate growth rates can coexist is important for applications such as manipulating host-associated microbiota or engineering industrial communities. Here, we ask how microbes interacting through their chemical environment can achieve coexistence in a continuous growth setup (similar to an industrial bioreactor or gut microbiota) where external resources are being supplied. We formulate and experimentally constrain a model in which mediators of interactions (e.g. metabolites or waste-products) are explicitly incorporated. Our model highlights facilitation and self-restraint as interactions that contribute to coexistence, consistent with our intuition. When interactions are strong, we observe that coexistence is determined primarily by the topology of facilitation and inhibition influences not their strengths. Importantly, we show that consumption or degradation of chemical mediators moderates interaction strengths and promotes coexistence. Our results offer insights into how to build or restructure microbial communities of interest.

Suggested Citation

  • Lori Niehaus & Ian Boland & Minghao Liu & Kevin Chen & David Fu & Catherine Henckel & Kaitlin Chaung & Suyen Espinoza Miranda & Samantha Dyckman & Matthew Crum & Sandra Dedrick & Wenying Shou & Babak , 2019. "Microbial coexistence through chemical-mediated interactions," Nature Communications, Nature, vol. 10(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-10062-x
    DOI: 10.1038/s41467-019-10062-x
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    Cited by:

    1. Kenta Suzuki & Masato S. Abe & Daiki Kumakura & Shinji Nakaoka & Fuki Fujiwara & Hirokuni Miyamoto & Teruno Nakaguma & Mashiro Okada & Kengo Sakurai & Shohei Shimizu & Hiroyoshi Iwata & Hiroshi Masuya, 2022. "Chemical-Mediated Microbial Interactions Can Reduce the Effectiveness of Time-Series-Based Inference of Ecological Interaction Networks," IJERPH, MDPI, vol. 19(3), pages 1-14, January.
    2. Li Xie & Wenying Shou, 2021. "Steering ecological-evolutionary dynamics to improve artificial selection of microbial communities," Nature Communications, Nature, vol. 12(1), pages 1-15, December.

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