Genome-Wide Study of Drug Resistant Mycobacterium tuberculosis and Its Intra-Host Evolution during Treatment

Abstract

The emergence of drug resistant Mycobacterium tuberculosis (MTB) strains has become a global public health problem, while, at the same time, there has been development of new antimicrobial agents. The main goals of this study were to determine new variants associated with drug resistance in MTB and to observe which polymorphisms emerge in MTB genomes after anti-tuberculosis treatment. We performed whole-genome sequencing of 152 MTB isolates including 70 isolates as 32 series of pre- and post-treatment MTB. Based on genotypes and phenotypic drug susceptibility, we conducted phylogenetic convergence-based genome-wide association study (GWAS) with streptomycin-, isoniazid-, rifampicin-, ethambutol-, fluoroquinolones-, and aminoglycosides-resistant MTB against susceptible ones. GWAS revealed statistically significant associations of SNPs within Rv2820c, cyp123 and indels in Rv1269c, Rv1907c, Rv1883c, Rv2407, Rv3785 genes with resistant MTB phenotypes. Comparisons of serial isolates showed that treatment induced different patterns of intra-host evolution. We found indels within Rv1435c and ppsA that were not lineage-specific. In addition, Beijing-specific polymorphisms within Rv0036c, Rv0678, Rv3433c, and dop genes were detected in post-treatment isolates. The appearance of Rv3785 frameshift insertion in 2 post-treatment strains compared to pre-treatment was also observed. We propose that the insertion within Rv3785, which was a GWAS hit, might affect cell wall biosynthesis and probably mediates a compensatory mechanism in response to treatment. These results may shed light on the mechanisms of MTB adaptation to chemotherapy and drug resistance formation.

Vaccines against Tuberculosis: Where Are We Now?

“…Thus, it is difficult for researchers to correlate the results obtained from challenge with the CDC1551 strain or the Erdman strain (also known as “laboratory strains”) and compare them with clinical strains. Altogether, if such information is available to researchers, it will help them to design clinical trials and implementation strategies with precision [ 109 , 112 , 113 , 114 ].…”