Mycobacterium tuberculosis genome mutations and fitness cost: molecular and epidemiological modelling of functional implications

© 2021 Malancha KarmakarIdentification of Mycobacterium tuberculosis (Mtb) increasingly involves characterising large sections of genetic material, such as through whole genome sequencing. While some mutations identified through these techniques are well characterised and strongly associated with anti-tuberculous drug resistance, such molecular methods frequently identify mutations with unknown significance or limited understanding of associated functional biological pathways. In this PhD, I have developed computational protein structural tools and mathematical models of TB transmission, that use genomic data to understand the impact of genomic changes and predict the consequences with regards to transmissibility and drug susceptibility of Mtb. Drug resistant mutations often carry both a selective advantage and a fitness cost, which can be reflected by the changes in protein structure and function. I developed a pipeline that captured the molecular consequences of coding mutations on protein stability, dynamics and interactions. Using my pipeline to evaluate the mechanistic consequences of mutations, I applied it to the real-time genomic analysis of a Victorian tuberculosis patient. The analysis led to identification of a novel resistant strain and altered patient treatment – the first reported use of structural information to guide clinical resistance detection. The information was then used to inform a compartmental epidemiological model of Mtb transmission in order to understand the rise of drug resistance in two high TB-incidence setting. Using a adaptive metropolis algorithm, I estimated drug resistance amplification proportions for two first-line anti-tuberculosis drugs, and explored how structural changes may alter the fitness landscape and transmission dynamics. The work highlighted the power of combining genomic, epidemiological and structural information in the fight against tuberculosis, and presents examples of application across the spectrum from laboratory, clinical and programmatic contexts. This work has further laid the foundation to rapidly apply and translate this approach to other infectious and non-infectious diseases