Inferring strain-level mutational drivers of phage-bacteria interaction phenotypes.
Inferring strain-level mutational drivers of phage-bacteria interaction phenotypes.
复制标题
推断噬菌体-细菌相互作用表型的菌株水平突变驱动因素。
DOI：
10.1101/2024.01.08.574707
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
Weitz,JoshuaS
中科院分区：
文献类型：
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作者：
Lucia-Sanz,Adriana;Peng,Shengyun;Leung,ChungYinJoey;Gupta,Animesh;Meyer,JustinR;Weitz,JoshuaS
噬菌体及其细菌宿主的巨大多样性对预测哪种噬菌体感染细菌焦点组提出了重大挑战。感染在很大程度上是由吸附、注射、细胞接管和溶解的互补遗传学决定的，而且很大程度上是不确定的。在这里，我们提出了一种机器学习方法来预测噬菌体-细菌相互作用，该方法在实验室条件下共同进化37天的51种大肠杆菌菌株和45种噬菌体λ菌株的基因组序列和表型相互作用上进行训练。该框架利用多种推理策略，在没有驱动突变的优先知识的情况下，预测了谁感染了谁，以及在一套2,295种潜在相互作用中感染的定量水平。我们发现，最有效的方法从噬菌体和细菌突变的独立贡献推断相互作用表型，准确预测86%的相互作用，同时将感染表型估计强度的相对误差降低40%。特征选择揭示了对噬菌体-细菌相互作用的结果具有显著影响的关键噬菌体λ和土方chia共突变，证实了先前已知影响噬菌体λ感染的位点，以及鉴定了先前未显示影响细菌耐药性的未知功能基因中的突变。该方法的成功重演菌株水平的感染结果在共同进化动力学过程中产生的，也可能有助于通知通用的方法，在复杂的噬菌体和细菌群落的相互作用表型的遗传驱动程序。
The enormous diversity of bacteriophages and their bacterial hosts presents a significant challenge to predict which phages infect a focal set of bacteria. Infection is largely determined by complementary—and largely uncharacterized—genetics of adsorption, injection, cell take-over, and lysis. Here we present a machine learning approach to predict phage–bacteria interactions trained on genome sequences of and phenotypic interactions among 51Escherichia colistrains and 45 phage λ strains that coevolved in laboratory conditions for 37 days. Leveraging multiple inference strategies and withouta prioriknowledge of driver mutations, this framework predicts both who infects whom and the quantitative levels of infections across a suite of 2,295 potential interactions. We found that the most effective approach inferred interaction phenotypes from independent contributions from phage and bacteria mutations, accurately predicting 86% of interactions while reducing the relative error in the estimated strength of the infection phenotype by 40%. Feature selection revealed key phage λ andEscherchia colimutations that have a significant influence on the outcome of phage–bacteria interactions, corroborating sites previously known to affect phage λ infections, as well as identifying mutations in genes of unknown function not previously shown to influence bacterial resistance. The method’s success in recapitulating strain-level infection outcomes arising during coevolutionary dynamics may also help inform generalized approaches for imputing genetic drivers of interaction phenotypes in complex communities of phage and bacteria.