Toxin-Antitoxin Systems Reflect Community Interactions Through Horizontal Gene Transfer.

IF 11 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Molecular biology and evolution Pub Date : 2024-10-04 DOI:10.1093/molbev/msae206
Jonathan H Bethke, Jeffrey Kimbrel, Yongqin Jiao, Dante Ricci
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Abstract

Bacterial evolution through horizontal gene transfer (HGT) reflects their community interactions. In this way, HGT networks do well at mapping community interactions, but offer little toward controlling them-an important step in the translation of synthetic strains into natural contexts. Toxin-antitoxin (TA) systems serve as ubiquitous and diverse agents of selection; however, their utility is limited by their erratic distribution in hosts. Here we examine the heterogeneous distribution of TAs as a consequence of their mobility. By systematically mapping TA systems across a 10,000 plasmid network, we find HGT communities have unique and predictable TA signatures. We propose these TA signatures arise from plasmid competition and have further potential to signal the degree to which plasmids, hosts, and phage interact. To emphasize these relationships, we construct an HGT network based solely on TA similarity, framing specific selection markers in the broader context of bacterial communities. This work both clarifies the evolution of TA systems and unlocks a common framework for manipulating community interactions through TA compatibility.

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毒素-抗毒素系统通过水平基因转移反映了群落间的相互作用。
细菌通过水平基因转移(HGT)实现的进化反映了其群落间的相互作用。因此,HGT 网络能很好地绘制群落相互作用的图谱,但在控制群落相互作用方面却无能为力--而这正是将合成菌株转化为自然菌株的重要一步。毒素-抗毒素(TA)系统是无处不在、多种多样的选择媒介;然而,由于它们在宿主体内的分布不稳定,它们的作用受到了限制。在这里,我们研究了TA的异质性分布是其流动性的结果。通过系统地绘制 10,000 个质粒网络中的 TA 系统图,我们发现 HGT 群体具有独特且可预测的 TA 特征。我们认为这些TA特征源于质粒竞争,并有可能进一步表明质粒、宿主和噬菌体之间的相互作用程度。为了强调这些关系,我们仅根据 TA 相似性构建了一个 HGT 网络,在细菌群落的大背景下构建了特定的选择标记。这项工作既阐明了TA系统的进化,又为通过TA兼容性操纵群落相互作用打开了一个共同的框架。
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来源期刊
Molecular biology and evolution
Molecular biology and evolution 生物-进化生物学
CiteScore
19.70
自引率
3.70%
发文量
257
审稿时长
1 months
期刊介绍: Molecular Biology and Evolution Journal Overview: Publishes research at the interface of molecular (including genomics) and evolutionary biology Considers manuscripts containing patterns, processes, and predictions at all levels of organization: population, taxonomic, functional, and phenotypic Interested in fundamental discoveries, new and improved methods, resources, technologies, and theories advancing evolutionary research Publishes balanced reviews of recent developments in genome evolution and forward-looking perspectives suggesting future directions in molecular evolution applications.
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