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Title: Prediction of Neighbor-Dependent Microbial Interactions From Limited Population Data

Abstract

Modulation of interspecies interactions in 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 that enables predicting the effect of new species on pairwise interactions among existing members and termed it the Minimal Interspecies Interaction Adjustment (MIIA). The theoretical basis for MIIA has been validated in the condition where all member species can grow solely as well as with partners. While useful, this development becomes highly constrained in cases that species have not been mono-cultured (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 axenic data 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, inmore » the case where both axenic and binary populations are missing, we parameterize binary interaction coefficients to determine their values through a sensitivity analysis. In the case study of a four-member, multispecies community that provides data with respect to each case mentioned, we were 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.« less

Authors:
; ; ; ; ; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI Identifier:
1592781
Alternate Identifier(s):
OSTI ID: 1600521
Report Number(s):
PNNL-SA-144026
Journal ID: ISSN 1664-302X; 3049
Grant/Contract Number:  
AC05-76RL01830; 18K19364; PJ01334605
Resource Type:
Published Article
Journal Name:
Frontiers in Microbiology
Additional Journal Information:
Journal Name: Frontiers in Microbiology Journal Volume: 10; Journal ID: ISSN 1664-302X
Publisher:
Frontiers Research Foundation
Country of Publication:
Switzerland
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; microbial communities; microbial ecology; context dependence; network inference; interspecies interactions

Citation Formats

Lee, Joon-Yong, Haruta, Shin, Kato, Souichiro, Bernstein, Hans C., Lindemann, Stephen R., Lee, Dong-Yup, Fredrickson, Jim K., and Song, Hyun-Seob. Prediction of Neighbor-Dependent Microbial Interactions From Limited Population Data. Switzerland: N. p., 2020. Web. doi:10.3389/fmicb.2019.03049.
Lee, Joon-Yong, Haruta, Shin, Kato, Souichiro, Bernstein, Hans C., Lindemann, Stephen R., Lee, Dong-Yup, Fredrickson, Jim K., & Song, Hyun-Seob. Prediction of Neighbor-Dependent Microbial Interactions From Limited Population Data. Switzerland. doi:10.3389/fmicb.2019.03049.
Lee, Joon-Yong, Haruta, Shin, Kato, Souichiro, Bernstein, Hans C., Lindemann, Stephen R., Lee, Dong-Yup, Fredrickson, Jim K., and Song, Hyun-Seob. Tue . "Prediction of Neighbor-Dependent Microbial Interactions From Limited Population Data". Switzerland. doi:10.3389/fmicb.2019.03049.
@article{osti_1592781,
title = {Prediction of Neighbor-Dependent Microbial Interactions From Limited Population Data},
author = {Lee, Joon-Yong and Haruta, Shin and Kato, Souichiro and Bernstein, Hans C. and Lindemann, Stephen R. and Lee, Dong-Yup and Fredrickson, Jim K. and Song, Hyun-Seob},
abstractNote = {Modulation of interspecies interactions in 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 that enables predicting the effect of new species on pairwise interactions among existing members and termed it the Minimal Interspecies Interaction Adjustment (MIIA). The theoretical basis for MIIA has been validated in the condition where all member species can grow solely as well as with partners. While useful, this development becomes highly constrained in cases that species have not been mono-cultured (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 axenic data 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. In the case study of a four-member, multispecies community that provides data with respect to each case mentioned, we were 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.},
doi = {10.3389/fmicb.2019.03049},
journal = {Frontiers in Microbiology},
number = ,
volume = 10,
place = {Switzerland},
year = {2020},
month = {1}
}

Journal Article:
Free Publicly Available Full Text
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DOI: 10.3389/fmicb.2019.03049

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