Chemical-Mediated Microbial Interactions Can Reduce the Effectiveness of Time-Series-Based Inference of Ecological Interaction Networks

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, Naoto Nihei and Yasunori Ichihashi
Additional contact information
Kenta Suzuki: BioResource Research Center, RIKEN, Tsukuba 305-0074, Japan
Masato S. Abe: Center for Advanced Intelligence Project, RIKEN, Chuo-ku, Tokyo 103-0027, Japan
Daiki Kumakura: Graduate School of Life Science, Hokkaido University, Sapporo 060-0810, Japan
Shinji Nakaoka: Graduate School of Life Science, Hokkaido University, Sapporo 060-0810, Japan
Fuki Fujiwara: Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Tokyo 113–8657, Japan
Hirokuni Miyamoto: Graduate School of Horticulture, Chiba University, Matsudo 271-8501, Japan
Teruno Nakaguma: Graduate School of Horticulture, Chiba University, Matsudo 271-8501, Japan
Mashiro Okada: Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Tokyo 113–8657, Japan
Kengo Sakurai: Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Tokyo 113–8657, Japan
Shohei Shimizu: Center for Advanced Intelligence Project, RIKEN, Chuo-ku, Tokyo 103-0027, Japan
Hiroyoshi Iwata: Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Tokyo 113–8657, Japan
Hiroshi Masuya: BioResource Research Center, RIKEN, Tsukuba 305-0074, Japan
Naoto Nihei: Faculty of Food and Agricultural Sciences, Fukushima University, Fukushima 960-1296, Japan
Yasunori Ichihashi: BioResource Research Center, RIKEN, Tsukuba 305-0074, Japan

IJERPH, 2022, vol. 19, issue 3, 1-14

Abstract: Network-based assessments are important for disentangling complex microbial and microbial–host interactions and can provide the basis for microbial engineering. There is a growing recognition that chemical-mediated interactions are important for the coexistence of microbial species. However, so far, the methods used to infer microbial interactions have been validated with models assuming direct species-species interactions, such as generalized Lotka–Volterra models. Therefore, it is unclear how effective existing approaches are in detecting chemical-mediated interactions. In this paper, we used time series of simulated microbial dynamics to benchmark five major/state-of-the-art methods. We found that only two methods (CCM and LIMITS) were capable of detecting interactions. While LIMITS performed better than CCM, it was less robust to the presence of chemical-mediated interactions, and the presence of trophic competition was essential for the interactions to be detectable. We show that the existence of chemical-mediated interactions among microbial species poses a new challenge to overcome for the development of a network-based understanding of microbiomes and their interactions with hosts and the environment.

Keywords: chemical-mediated interactions; ecological interaction network; microbiome; exometabolome; mediator-explicit model; interaction network inference; microbial time series (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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