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Chemical-Mediated Microbial Interactions Can Reduce the Effectiveness of Time-Series-Based Inference of Ecological Interaction Networks
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,...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8834966/ https://www.ncbi.nlm.nih.gov/pubmed/35162258 http://dx.doi.org/10.3390/ijerph19031228 |
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author | Suzuki, Kenta Abe, Masato S. Kumakura, Daiki Nakaoka, Shinji Fujiwara, Fuki Miyamoto, Hirokuni Nakaguma, Teruno Okada, Mashiro Sakurai, Kengo Shimizu, Shohei Iwata, Hiroyoshi Masuya, Hiroshi Nihei, Naoto Ichihashi, Yasunori |
author_facet | Suzuki, Kenta Abe, Masato S. Kumakura, Daiki Nakaoka, Shinji Fujiwara, Fuki Miyamoto, Hirokuni Nakaguma, Teruno Okada, Mashiro Sakurai, Kengo Shimizu, Shohei Iwata, Hiroyoshi Masuya, Hiroshi Nihei, Naoto Ichihashi, Yasunori |
author_sort | Suzuki, Kenta |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8834966 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-88349662022-02-12 Chemical-Mediated Microbial Interactions Can Reduce the Effectiveness of Time-Series-Based Inference of Ecological Interaction Networks Suzuki, Kenta Abe, Masato S. Kumakura, Daiki Nakaoka, Shinji Fujiwara, Fuki Miyamoto, Hirokuni Nakaguma, Teruno Okada, Mashiro Sakurai, Kengo Shimizu, Shohei Iwata, Hiroyoshi Masuya, Hiroshi Nihei, Naoto Ichihashi, Yasunori Int J Environ Res Public Health Article 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. MDPI 2022-01-22 /pmc/articles/PMC8834966/ /pubmed/35162258 http://dx.doi.org/10.3390/ijerph19031228 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Suzuki, Kenta Abe, Masato S. Kumakura, Daiki Nakaoka, Shinji Fujiwara, Fuki Miyamoto, Hirokuni Nakaguma, Teruno Okada, Mashiro Sakurai, Kengo Shimizu, Shohei Iwata, Hiroyoshi Masuya, Hiroshi Nihei, Naoto Ichihashi, Yasunori Chemical-Mediated Microbial Interactions Can Reduce the Effectiveness of Time-Series-Based Inference of Ecological Interaction Networks |
title | Chemical-Mediated Microbial Interactions Can Reduce the Effectiveness of Time-Series-Based Inference of Ecological Interaction Networks |
title_full | Chemical-Mediated Microbial Interactions Can Reduce the Effectiveness of Time-Series-Based Inference of Ecological Interaction Networks |
title_fullStr | Chemical-Mediated Microbial Interactions Can Reduce the Effectiveness of Time-Series-Based Inference of Ecological Interaction Networks |
title_full_unstemmed | Chemical-Mediated Microbial Interactions Can Reduce the Effectiveness of Time-Series-Based Inference of Ecological Interaction Networks |
title_short | Chemical-Mediated Microbial Interactions Can Reduce the Effectiveness of Time-Series-Based Inference of Ecological Interaction Networks |
title_sort | chemical-mediated microbial interactions can reduce the effectiveness of time-series-based inference of ecological interaction networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8834966/ https://www.ncbi.nlm.nih.gov/pubmed/35162258 http://dx.doi.org/10.3390/ijerph19031228 |
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