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 the rates and spectra of spontaneous mutations. However, demonstrating the role of transition bias in adaptive evolution remains challenging. In particular, it is unclear whether such biases direct the evolution of bacterial pathogens adapting to treatment. We addressed this challenge by analyzing adaptive antibiotic-resistance mutations in the major human pathogen Mycobacterium tuberculosis (MTB). We found strong evidence for transition bias in two independently curated data sets 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 16S ribosomal RNA 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.

Fig 1. Schematic illustration of mutational paths and events.

Four mutational paths that each confer resistance to isoniazid are shown as symbols (see legend at bottom) to the right of a hypothetical phylogenetic tree for 21 MTB strains. Full symbols represent derived genotypes, whereas empty symbols represent ancestral genotypes. The full symbols on the tree represent the reconstruction of the mutational history of the sample. The well-known S315T mutation in katG, encoded by a C > G transversion, is found in eight strains and, in this hypothetical reconstruction, has evolved independently five times. Thus, there are five events for this one mutational path. MTB, M. tuberculosis.

Fig 2. Transition bias in mutational paths and mutational events in the Basel and Manson data sets.

Symbols represent transition:transverion ratios (Basel paths: 74:78, empirical P value = 7.1 × 10⁻⁵; Manson paths: 88:120, empirical P value = 4.2 × 10⁻³; Basel events: 1,755:1,020, empirical P value < 10⁻⁶; Manson events: 1,771:900, empirical P value < 10⁻⁶). Error bars represent 95% binomial confidence intervals. The dashed horizontal line shows the null expectation of the transition:transversion ratio, assuming our default null model that one transition occurs for every two transversions and that all mutations are independent. For additional null models used at the level of events, see the main text. The data visualized in this and all subsequent figures are presented in numerical form in S1 Data.

Fig 3. Relative rates of the six nucleotide pair mutations for mutational paths and events in the Basel and Manson data sets.

Transitions are indicated with bold text. Rates adjusted for GC content (Materials and methods).