Competition and cooperation: The plasticity of bacterial interactions across environments.

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
PLoS Computational Biology Pub Date : 2025-07-24 eCollection Date: 2025-07-01 DOI:10.1371/journal.pcbi.1013213
Josephine Solowiej-Wedderburn, Jennifer T Pentz, Ludvig Lizana, Bjoern O Schroeder, Peter A Lind, Eric Libby
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引用次数: 0

Abstract

Bacteria live in diverse communities, forming complex networks of interacting species. A central question in bacterial ecology is whether species engage in cooperative or competitive interactions. But this question often neglects the role of the environment. Here, we use genome-scale metabolic networks from two different open-access collections (AGORA and CarveMe) to assess pairwise interactions of different microbes in varying environmental conditions (provision of different environmental compounds). By computationally simulating thousands of environments for 10,000 pairs of bacteria from each collection, we found that most pairs were able to both compete and cooperate depending on the availability of environmental resources. This modeling approach allowed us to determine commonalities between environments that could facilitate the potential for cooperation or competition between a pair of species. Namely, cooperative interactions, especially obligate, were most common in less diverse environments. Further, as compounds were removed from the environment, we found interactions tended to degrade towards obligacy. However, we also found that on average at least one compound could be removed from an environment to switch the interaction from competition to facultative cooperation or vice versa. Together our approach indicates a high degree of plasticity in microbial interactions in response to the availability of environmental resources.

竞争与合作:细菌跨环境相互作用的可塑性。
细菌生活在不同的群落中,形成相互作用的复杂物种网络。细菌生态学的一个核心问题是物种之间的相互作用是合作的还是竞争的。但这个问题往往忽略了环境的作用。在这里,我们使用来自两个不同的开放获取集合(AGORA和CarveMe)的基因组尺度代谢网络来评估不同微生物在不同环境条件下的成对相互作用(提供不同的环境化合物)。通过计算模拟来自每个集合的10,000对细菌的数千种环境,我们发现大多数细菌对能够既竞争又合作,这取决于环境资源的可用性。这种建模方法使我们能够确定环境之间的共性,这些共性可以促进一对物种之间的合作或竞争。也就是说,合作互动,特别是义务互动,在多样性较低的环境中最常见。此外,随着化合物从环境中移除,我们发现相互作用倾向于向义务性降解。然而,我们还发现,平均而言,至少有一种化合物可以从环境中去除,从而将相互作用从竞争转变为兼性合作,反之亦然。总之,我们的方法表明,微生物相互作用对环境资源可用性的反应具有高度的可塑性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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