Background
Evolutionary pressures on bacterial pathogens can result in phenotypic
change including increased virulence, drug resistance, and transmissibility.
Understanding the evolution of these phenotypes in nature and the multiple
genetic changes needed has historically been difficult due to sparse and
contemporaneous sampling. A complete picture of the evolutionary routes
frequently travelled by pathogens would allow us to better understand
bacterial biology and potentially forecast pathogen population
shifts.
Methods
In this work, we develop a phylogeny-based method to assess evolutionary
dependency between mutations. We apply our method to a dataset of
31,428Mycobacterium tuberculosiscomplex
(MTBC) genomes, a globally prevalent bacterial pathogen with increasing
levels of antibiotic resistance.
Results
We find evolutionary dependency within simultaneously- and
sequentially-acquired variation, and identify that genes with dependent
sites are enriched in antibiotic resistance and antigenic function. We
discover 20 mutations that potentiate the development of antibiotic
resistance and 1,003 dependencies that evolve as a consequence antibiotic
resistance. Varying by antibiotic, between 9% and 80% of resistant strains
harbor a dependent mutation acquired after a resistance-conferring variant.
We demonstrate that mutational dependence can not only improve prediction of
phenotype (e.g. antibiotic resistance), but can also detect sequential
environmental pressures on the pathogen (e.g. the pressures imposed by
sequential antibiotic exposure during the course of standard
multi-antibiotic treatment). Taken together, our results demonstrate the
feasibility and utility of detecting dependent events in the evolution of
natural populations.
Data and code available at:https://github.com/farhat-lab/DependentMutations