Dooley Keven D, Bergelson Joy
Committee on Microbiology, University of Chicago, Chicago, IL 60637, USA.
Center for Genomics and System Biology, Department of Biology, New York University, New York, NY 10003, USA.
iScience. 2023 Dec 7;27(1):108654. doi: 10.1016/j.isci.2023.108654. eCollection 2024 Jan 19.
Pairwise interactions are often used to predict features of complex microbial communities due to the challenge of measuring multi-species interactions in high dimensional contexts. This assumes that interactions are unaffected by community context. Here, we used synthetic bacterial communities to investigate that assumption by observing how interactions varied across contexts. Interactions were most often weakly negative and showed a phylogenetic signal among genera. Community richness and total density emerged as strong predictors of interaction strength and contributed to an attenuation of interactions as richness increased. Population level and per-capita measures of interactions both displayed such attenuation, suggesting factors beyond systematic changes in population size were involved; namely, changes to the interactions themselves. Nevertheless, pairwise interactions retained some explanatory power across contexts, provided those contexts were not substantially divergent in richness. These results suggest that understanding the emergent properties of microbial interactions can improve our ability to predict the features of microbial communities.
由于在高维环境中测量多物种相互作用具有挑战性,成对相互作用常被用于预测复杂微生物群落的特征。这假定相互作用不受群落背景的影响。在此,我们通过观察相互作用如何随背景变化,利用合成细菌群落来研究这一假设。相互作用最常呈弱负相关,并且在属之间显示出系统发育信号。群落丰富度和总密度成为相互作用强度的有力预测指标,并随着丰富度增加导致相互作用减弱。相互作用的种群水平和人均指标均表现出这种减弱,表明涉及种群大小系统变化之外的因素;即相互作用本身的变化。然而,成对相互作用在不同背景下仍保留了一定的解释力,前提是这些背景在丰富度上没有显著差异。这些结果表明,理解微生物相互作用的涌现特性可以提高我们预测微生物群落特征的能力。