Paper of the Semester Award
Gene evolutionary trajectories in Mycobacterium tuberculosis reveal temporal signs of selection
Published in PNAS
Álvaro Chiner-Oms, Mariana G. López, Miguel Moreno-Molina, Victoria Furió and Iñaki Comas
Genetic differences between different Mycobacterium tuberculosis complex (MTBC) strains are likely associated with different disease and epidemiological phenotypes. Said differences usually emerge through de novo mutations and are maintained or discarded by a balance of evolutionary forces which includes different types of selection and random population processes. Purifying selection is thought to be weak in MTBC but dominant. Only around 10% of the genes have evidence of genetic drift or positive selection, the latter mainly associated with drug resistance. Using a dataset of ∼5,000 strains representing global MTBC diversity and a methodology that reconstructs the evolutionary trajectory of each gene since the emergence of the MTBC, we have determined the action of past and present selective forces through time, for every single gene.
Almost half of the genes seem to have been under positive selection and/or genetic drift at some point in time, in contrast with previous estimates of 10% or less.
Temporal signals identify genes under positive selection in the past but highly conserved in the present. This includes epitopes that tend to accumulate older mutations, suggesting very early adaptation to host populations.
Temporal signals identify genes that were conserved in the past but under positive selection in the present. Beyond drug-resistant genes, we detected several sensor proteins of two-component systems and toxin-antitoxin systems.
When applied to an enriched drug resistance dataset from high-burden countries our approach correctly identifies changing selection patterns linked to first-line drug use and reveals candidate genes associated with resistance to second-line drugs. We functionally validated one of these genes, Rv1830.
In conclusion, our novel approach allows to incorporate the joint analysis of past and present evolutionary dynamics. Our results reveal hidden signals of the action of evolutionary forces and can be adapted to identify genes involved in different selective pressures.