Gene evolutionary trajectories in Mycobacterium tuberculosis reveal temporal signs of selection

Genetic differences between different Mycobacterium tuberculosis complex (MTBC) strains determine their ability to transmit within different host populations, their latency times, and their drug resistance profiles. Said differences usually emerge through de novo mutations and are maintained or discarded by the balance of evolutionary forces. Using a dataset of ∼5,000 strains representing global MTBC diversity, we determined the past and present selective forces that have shaped the current variability observed in the pathogen population. We identified regions that have evolved under changing types of selection since the time of the MTBC common ancestor. Our approach highlighted striking differences in the genome regions relevant for host–pathogen interaction and, in particular, suggested an adaptive role for the sensor protein of two-component systems. In addition, we applied our approach to successfully identify potential determinants of resistance to drugs administered as second-line tuberculosis treatments.

The Mycobacterium tuberculosis complex (MTBC) is a genetically monomorphic group of bacteria (1, 2) whose members cause tuberculosis in humans and animals. The MTBC comprises both human-associated (L1, L2, L3, L4, L5, L6, L7, L8, and L9) and animal-associated (A1, A2, A3, and A4) clades (3–7). Due to the absence of horizontal gene transfer, plasmids, and measurable recombination among strains and other species (8–10), chromosomal mutations represent the source of MTBC genetic diversity. The maximum genetic distance between any two MTBC strains is around 2,500 single-nucleotide polymorphisms (SNPs). Strikingly, studies have highlighted large phenotypic differences between strains involving traits like gene expression, drug resistance, transmissibility, and immune response, despite this limited variation. In some cases, the mutations driving phenotypic differences have been identified—for example, nonsynonymous variants in genes, such as rpoB, katG, or gyrA, cause drug-resistant phenotypes (11–13). Furthermore, single mutations in regulatory elements can induce alterations to downstream gene expression, which can foster differential virulence characteristics (14, 15). Finally, specific gene mutations may affect transmission (9), host tropism within the complex (16), and the host immune response (17). However, many of the genomic determinants of these phenotypes remain elusive, despite robust evidence that they are driven by genetic differences between strains (18, 19).Several types of evolutionary forces play crucial roles in the fixation of mutations in bacterial populations. Previous research has provided evidence for the ongoing positive selection of specific genes and regions (9, 20–23), while other studies have reported ongoing purifying selection of specific genomic regions, especially in epitopes and essential genes (24). Additionally, there exists some evidence that genetic drift may have significant functional and evolutionary consequences (25).Detecting selection in MTBC at the genome-wide level remains a challenging task due to limited genetic diversity. The significant accumulation of nonsynonymous substitutions has been previously used to characterize patterns of mutation accumulation in large categories of genes (24, 26); however, these studies employed a limited number of strains. Of note, the number of MTBC sequences has undergone a recent and rapid expansion, with studies involving hundreds to thousands of strains. The large number of available sequences has allowed, for example, the estimation of the ratio of nonsynonymous to synonymous substitutions (dN/dS) signatures in more than 10,000 strains (27), thereby allowing the identification of targets of selection with some probably related to host–pathogen interactions. Host–pathogen interaction signals are specially challenging as they are likely obscured by the force exerted by antimicrobial therapies. Weaker signals are also expected in genes related to second-line drugs related to the relative underuse of related treatments and the low abundance of associated resistant strains in genome databases (28).We reasoned that to detect signs of selection, we should focus on when and/or where they occurred in the phylogenetic tree instead of averaging signs across the phylogeny. In this study, we developed a methodology to study temporal signs of selection in MTBC genes and identified positive selection in a larger number of genes than previously described. This allowed the identification of past and currently unknown players in the MTBC evolution, particularly two-component systems (2CSs), related to host adaptation and second-line drug resistance. This methodology can be applied to other tuberculosis settings to explore signs of selection associated with changing selective pressures and could be extremely useful to unravel hidden details in the evolution of other human pathogens.