Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Center for Computational Biomedicine, Harvard Medical School, Boston, MA, USA.
Mol Biol Evol. 2023 Jun 1;40(6). doi: 10.1093/molbev/msad131.
Pathogenic microorganisms are in a perpetual struggle for survival in changing host environments, where host pressures necessitate changes in pathogen virulence, antibiotic resistance, or transmissibility. The genetic basis of phenotypic adaptation by pathogens is difficult to study in vivo. In this work, we develop a phylogenetic method to detect genetic dependencies that promote pathogen adaptation using 31,428 in vivo sampled Mycobacterium tuberculosis genomes, a globally prevalent bacterial pathogen with increasing levels of antibiotic resistance. We find that dependencies between mutations are enriched in antigenic and antibiotic resistance functions and discover 23 mutations that potentiate the development of antibiotic resistance. Between 11% and 92% of resistant strains harbor a dependent mutation acquired after a resistance-conferring variant. We demonstrate the pervasiveness of genetic dependency in adaptation of naturally evolving populations and the utility of the proposed computational approach.
病原体在宿主环境不断变化的情况下，为了生存而持续斗争。宿主压力迫使病原体的毒力、抗生素耐药性或传染性发生变化。病原体表型适应性的遗传基础很难在体内进行研究。在这项工作中，我们开发了一种系统发育方法，利用 31428 个体内采样的结核分枝杆菌基因组，检测促进病原体适应的遗传依赖性，结核分枝杆菌是一种具有日益增加抗生素耐药性的全球流行细菌病原体。我们发现，突变之间的依赖性在抗原和抗生素耐药性功能中富集，并发现了 23 个促进抗生素耐药性发展的突变。在 11%到 92%的耐药菌株中，存在一种在赋予耐药性的变异体之后获得的依赖性突变。我们证明了遗传依赖性在自然进化群体适应中的普遍性，以及所提出的计算方法的实用性。
