Transition bias influences the evolution of antibiotic resistance in Mycobacterium tuberculosis

Abstract

Transition bias, an overabundance of transitions relative to transversions, has been widely reported among studies of mutations spreading under relaxed selection. However, demonstrating the role of transition bias in adaptive evolution remains challenging. We addressed this challenge by analyzing adaptive antibiotic-resistance mutations in the major human pathogen Mycobacterium tuberculosis. We found strong evidence for transition bias in two independently curated datasets comprising 152 and 208 antibiotic resistance mutations. This was true at the level of mutational paths (distinct, adaptive DNA sequence changes) and events (individual instances of the adaptive DNA sequence changes), and across different genes and gene promoters conferring resistance to a diversity of antibiotics. It was also true for mutations that do not code for amino acid changes (in gene promoters and the ribosmal gene rrs), and for mutations that are synonymous to each other and are therefore likely to have similar fitness effects, suggesting that transition bias can be caused by a bias in mutation supply. These results point to a central role for transition bias in determining which mutations drive adaptive antibiotic resistance evolution in a key pathogen.\n\nSignificance statementWhether and how transition bias influences adaptive evolution remain open questions. We studied 296 DNA mutations that confer antibiotic resistance to the human pathogen Mycobacterium tuberculosis. We uncovered strong transition bias among these mutations and also among the number of times each mutation has evolved in different strains or geographic locations, demonstrating that transition bias can influence adaptive evolution. For a subset of mutations, we were able to rule out an alternative selection-based hypothesis for this bias, indicating that transition bias can be caused by a biased mutation supply. By revealing this bias among M. Tuberculosis resistance mutations, our findings improve our ability to predict the mutational pathways by which pathogens overcome treatment.