Functional Analysis of Drug Resistance Associated Genetic Variation in Mycobacterium tuberculosis
CitationHicks, Nathan. 2020. Functional Analysis of Drug Resistance Associated Genetic Variation in Mycobacterium tuberculosis. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences.
AbstractTuberculosis (TB) disease, caused by Mycobacterium tuberculosis (Mtb) infection, affects millions of people across the world annually. The best tool for controlling TB in individual patients and preventing transmission in populations is combination antibiotic therapy. Six months of treatment with four first-line antibiotics is sufficient to cure the majority of patients. However, the efficacy of this therapy has been undermined by the evolution of drug resistant Mtb strains, which are able to grow in the presence of clinically utilized antibiotics. The chromosomal mutations that cause drug resistance in Mtb have largely been identified, allowing for rapid genotype-based diagnosis of drug resistance and the selection of appropriate therapy.
Nevertheless, even patients with apparent drug susceptible Mtb have a range of outcomes. Some patients appear durably cured with 4 months of therapy while for others even 6 months is not sufficient to prevent relapse after treatment is completed. Studies of Mtb clinical isolates have found that there are a wider range of antibiotic phenotypes than are captured by standard clinical drug resistance assays, which could help explain this phenomenon. In parallel, genomic association studies have identified mutations that are enriched in drug resistant strains without appearing to cause resistance.
We are interested in connecting these sets of observations via functional analysis of clinically prevalent, drug resistance-associated genetic variation in Mtb. In Chapter 2, we identify genetic variants that are associated with isoniazid resistance in a cohort of 549 strains from China. From these associations, we select prpR for which we construct a series of isogenic mutants. We find prpR mutations confer conditional multidrug tolerance that is readily measured in infection models of TB, but is not captured by standard drug susceptibility testing. In Chapter 3, we focus on genetic associations with resistance to a second-line antibiotic, ethionamide. Here we find the bacterial monooxygenase Rv0565c is a novel activator of ethionamide and mutations reduce its activity. As one out of three known activators, however, mutation of Rv0565c causes a relatively small shift in drug resistance which has not been previously recognized. In Chapter 4, we characterize the functional impact of single-nucleotide polymorphisms in dnaA using precision genome editing of the native locus of dnaA. We find clinically prevalent variants in dnaA increase resistance to the first-line therapeutic isoniazid. As with Rv0565c, the small magnitude of this effect has prevented prior identification of dnaA as contributing to resistance. Together these studies indicate that prevalent, resistance-associated genetic variation can encode drug tolerance or low-level resistance phenotypes. In Chapter 5, we discuss the further work that will help clarify the effect of these mutations on treatment outcomes in patients. We also explore how these methods could be further refined and expanded to address bacterial variation beyond antibiotic responses.
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