論文

査読有り 国際誌
2019年

Prediction of Neighbor-Dependent Microbial Interactions From Limited Population Data.

Frontiers in microbiology
  • Joon-Yong Lee
  • ,
  • Shin Haruta
  • ,
  • Souichiro Kato
  • ,
  • Hans C Bernstein
  • ,
  • Stephen R Lindemann
  • ,
  • Dong-Yup Lee
  • ,
  • Jim K Fredrickson
  • ,
  • Hyun-Seob Song

10
開始ページ
3049
終了ページ
3049
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.3389/fmicb.2019.03049

Modulation of interspecies interactions by the presence of neighbor species is a key ecological factor that governs dynamics and function of microbial communities, yet the development of theoretical frameworks explicit for understanding context-dependent interactions are still nascent. In a recent study, we proposed a novel rule-based inference method termed the Minimal Interspecies Interaction Adjustment (MIIA) that predicts the reorganization of interaction networks in response to the addition of new species such that the modulation in interaction coefficients caused by additional members is minimal. While the theoretical basis of MIIA was established through the previous work by assuming the full availability of species abundance data in axenic, binary, and complex communities, its extension to actual microbial ecology can be highly constrained in cases that species have not been cultured axenically (e.g., due to their inability to grow in the absence of specific partnerships) because binary interaction coefficients - basic parameters required for implementing the MIIA - are inestimable without axenic and binary population data. Thus, here we present an alternative formulation based on the following two central ideas. First, in the case where only data from axenic cultures are unavailable, we remove axenic populations from governing equations through appropriate scaling. This allows us to predict neighbor-dependent interactions in a relative sense (i.e., fractional change of interactions between with versus without neighbors). Second, in the case where both axenic and binary populations are missing, we parameterize binary interaction coefficients to determine their values through a sensitivity analysis. Through the case study of two microbial communities with distinct characteristics and complexity (i.e., a three-member community where all members can grow independently, and a four-member community that contains member species whose growth is dependent on other species), we demonstrated that despite data limitation, the proposed new formulation was able to successfully predict interspecies interactions that are consistent with experimentally derived results. Therefore, this technical advancement enhances our ability to predict context-dependent interspecies interactions in a broad range of microbial systems without being limited to specific growth conditions as a pre-requisite.

リンク情報
DOI
https://doi.org/10.3389/fmicb.2019.03049
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/32038529
PubMed Central
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6985286
ID情報
  • DOI : 10.3389/fmicb.2019.03049
  • PubMed ID : 32038529
  • PubMed Central 記事ID : PMC6985286

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