Prediction of Neighbor-Dependent Microbial Interactions From Limited Population Data

Frontiers in Microbiology

Hyun-Seob Song¹  Joon-Yong Lee²  Jim K. Fredrickson²  Shin Haruta⁴  Souichiro Kato⁷  Dong-Yup Lee⁸  Hans C. Bernstein⁹  Stephen R. Lindemann^1,10 

[1] 0Nebraska Food for Health Center, Department of Food Science and Technology, University of Nebraska-Lincoln, Lincoln, NE, United States;Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, United States;Bioprocessing Technology Institute, Agency for Science, Technology and Research, Singapore, Singapore;Department of Biological Sciences, Tokyo Metropolitan University, Hachioji, Japan;Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States;Faculty of Biosciences, Fisheries and Economics, UiT – The Arctic University of Norway, Tromsø, Norway;National Institute of Advanced Industrial Science and Technology, Sapporo, Japan;School of Chemical Engineering, Sungkyunkwan University, Seoul, South Korea;The Arctic Centre for Sustainable Energy, UiT – The Arctic University of Norway, Tromsø, Norway;Whistler Center for Carbohydrate Research, Department of Food Science, Purdue University, West Lafayette, IN, United States;

关键词: microbial communities;    microbial ecology;    context dependence;    network inference;    interspecies interactions;   

DOI  :  10.3389/fmicb.2019.03049

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown