The within-host population dynamics of Mycobacterium tuberculosis vary with treatment efficacy

Abstract

BackgroundCombination therapy is one of the most effective tools for limiting the emergence of drug resistance in pathogens. Despite the widespread adoption of combination therapy across diseases, drug resistance rates continue to rise, leading to failing treatment regimens. The mechanisms underlying treatment failure are well studied, but the processes governing successful combination therapy are poorly understood. We address this question by studying the population dynamics of Mycobacterium tuberculosis within tuberculosis patients undergoing treatment with different combinations of antibiotics.ResultsBy combining very deep whole genome sequencing (~1000-fold genome-wide coverage) with sequential sputum sampling, we were able to detect transient genetic diversity driven by the apparently continuous turnover of minor alleles, which could serve as the source of drug-resistant bacteria. However, we report that treatment efficacy has a clear impact on the population dynamics: sufficient drug pressure bears a clear signature of purifying selection leading to apparent genetic stability. In contrast, M. tuberculosis populations subject to less drug pressure show markedly different dynamics, including cases of acquisition of additional drug resistance.ConclusionsOur findings show that for a pathogen like M. tuberculosis, which is well adapted to the human host, purifying selection constrains the evolutionary trajectory to resistance in effectively treated individuals. Nonetheless, we also report a continuous turnover of minor variants, which could give rise to the emergence of drug resistance in cases of drug pressure weakening. Monitoring bacterial population dynamics could therefore provide an informative metric for assessing the efficacy of novel drug combinations.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-017-1196-0) contains supplementary material, which is available to authorized users.

In-host population dynamics ofM. tuberculosisduring treatment failure

“…The underlying mechanism 299 explaining these observations in Mtb have included purifying pressure on synonymous variants 300 and oxidative DNA damage respectively 33,35 . Overall the observed diversity spared the CD4 + and 301 CD8 + T cell epitope encoding regions of the genome, consistent with prior studies 8, 10,47 and 302 adding to the existing literature describing that host adaptive immunity does not drive directional 303 selection in Mtb genomes. Diversity was concentrated in both antibiotic resistance regions and to 304 an even larger extent in PE/PPE genes.…”

“…To understand how different classes of proteins evolve in-host, we separated Mtb genes 180 into five non-redundant categories (Methods): Antibiotic resistance -genes as defined above 7 , 181 PE/PPE -gene family unique to pathogenic mycobacteria, thought to influence 182 immunopathogenicity and is characterized by conserved proline-glutamate (PE) and proline-183 proline-glutamate (PPE) motifs at the N protein termini 10,34,37 , Antigen -genes encoding a CD4 + 184 or CD8 + T-cell epitope 8,10 (excluding PE/PPE genes), Essential -genes required for growth in 185 vitro and in vivo 10,38,39 , and Non-Essential -genes not categorized into one of the aforementioned 186 categories. The vast majority of genes in each category did not vary within subject (Fig.…”

“…Every gene on H37Rv was classified into one of six non-redundant gene categories according to 427 the following criteria: (i) genes identified as belonging to the PE/PPE family of genes 10,37 were 428 classified as PE/PPE (n = 167), (ii) genes flagged as being associated with antibiotic resistance 429 were classified into the Antibiotic Resistance category (n = 28), (iii) genes encoding a T cell 430 epitope (but not already classified as a PE/PPE or Antibiotic Resistance gene) were classified as 431…”

Phase variation inMycobacterium tuberculosis glpKproduces transiently heritable drug tolerance

“…We suspect that it is possible that PI treatment provided a strong selection for the emergence of glpK mutants in these mice. Variants with glpK frameshift mutations are detectable in sputum samples from many human TB patients and, furthermore, these glpK mutants were unstable (47,48).…”

Contaminant DNA in bacterial sequencing experiments is a major source of false genetic variability

“…The datasets comprised 8 MTB studies and 12 studies of other 13 relevant pathogenic species. Nineteen of these datasets were publicly available beforehand (22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40). To include a dataset generated in our laboratory, we sequenced 138 MTB samples from Mozambique in the Illumina MiSeq platform (Supplementary Methods 1).…”