Suppr超能文献

预测随机组装群落中进化的初始步骤

Predicting the First Steps of Evolution in Randomly Assembled Communities.

作者信息

McEnany John, Good Benjamin H

机构信息

Biophysics Program, Stanford University, Stanford, CA 94305, USA.

Department of Applied Physics, Stanford University, Stanford, CA 94305, USA.

出版信息

bioRxiv. 2024 Jun 14:2023.12.15.571925. doi: 10.1101/2023.12.15.571925.

Abstract

Microbial communities can self-assemble into highly diverse states with predictable statistical properties. However, these initial states can be disrupted by rapid evolution of the resident strains. When a new mutation arises, it competes for resources with its parent strain and with the other species in the community. This interplay between ecology and evolution is difficult to capture with existing community assembly theory. Here, we introduce a mathematical framework for predicting the first steps of evolution in large randomly assembled communities that compete for substitutable resources. We show how the fitness effects of new mutations and the probability that they coexist with their parent depends on the size of the community, the saturation of its niches, and the metabolic overlap between its members. We find that successful mutations are often able to coexist with their parent strains, even in saturated communities with low niche availability. At the same time, these invading mutants often cause extinctions of metabolically distant species. Our results suggest that even small amounts of evolution can produce distinct genetic signatures in natural microbial communities.

摘要

微生物群落能够自我组装成具有可预测统计特性的高度多样化状态。然而,这些初始状态可能会因群落中常驻菌株的快速进化而受到干扰。当一个新突变出现时,它会与其亲本菌株以及群落中的其他物种竞争资源。生态与进化之间的这种相互作用很难用现有的群落组装理论来描述。在此,我们引入一个数学框架,用于预测在竞争可替代资源的大型随机组装群落中进化的最初步骤。我们展示了新突变的适应度效应以及它们与其亲本共存的概率如何取决于群落的大小、其生态位的饱和度以及其成员之间的代谢重叠。我们发现,成功的突变通常能够与其亲本菌株共存,即使在生态位可用性较低的饱和群落中也是如此。与此同时,这些入侵的突变体往往会导致代谢距离较远的物种灭绝。我们的结果表明,即使是少量的进化也能在自然微生物群落中产生独特的遗传特征。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db86/11181387/77a718021f87/nihpp-2023.12.15.571925v2-f0001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验