Metabolic complexity drives divergence in microbial communities.
Metabolic complexity drives divergence in microbial communities.
复制标题
代谢的复杂性导致微生物群落的分化。
DOI：
10.1101/2023.08.03.551516
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Segrè,Daniel
中科院分区：
文献类型：
--
作者：
Silverstein,Michael;Bhatnagar,JenniferM;Segrè,Daniel
微生物群落是由环境代谢物形成的，但在任何给定条件下，控制不同群落是否会收敛或发散的原则仍然未知，这对微生物组工程的可行性提出了根本性问题。在这里，我们研究了一组自然微生物群落在实验室条件下生长的代谢复杂性增加的纵向组装动力学。我们发现，当在代谢简单的条件下生长时，不同的微生物群落往往会变得彼此相似，但随着环境代谢复杂性的增加，它们的组成会出现分歧，我们将这种现象称为分歧-复杂性效应。对这些群落的比较分析表明，这种差异是由群落多样性和能够降解复杂代谢物的专业分类群的分类所驱动的。群落动态的生态模型表明，代谢本身的层次结构，复杂的分子被酶降解成逐渐简单的，然后参与社区成员之间的交叉喂养，是必要的，足以概括我们的实验观察。除了帮助理解环境在群落组装中的作用外，趋异-复杂性效应还可以提供对哪些环境支持多个群落状态的洞察，从而能够在微生物组工程应用中搜索所需的生态系统功能。
Microbial communities are shaped by environmental metabolites, but the principles that govern whether different communities will converge or diverge in any given condition remain unknown, posing fundamental questions about the feasibility of microbiome engineering. Here we studied the longitudinal assembly dynamics of a set of natural microbial communities grown in laboratory conditions of increasing metabolic complexity. We found that different microbial communities tend to become similar to each other when grown in metabolically simple conditions, but they diverge in composition as the metabolic complexity of the environment increases, a phenomenon we refer to as the divergence-complexity effect. A comparative analysis of these communities revealed that this divergence is driven by community diversity and by the assortment of specialist taxa capable of degrading complex metabolites. An ecological model of community dynamics indicates that the hierarchical structure of metabolism itself, where complex molecules are enzymatically degraded into progressively simpler ones that then participate in cross-feeding between community members, is necessary and sufficient to recapitulate our experimental observations. In addition to helping understand the role of the environment in community assembly, the divergence-complexity effect can provide insight into which environments support multiple community states, enabling the search for desired ecosystem functions towards microbiome engineering applications.