Multiple routes to tackling drug-resistant tuberculosis: Systems modeling to unravel resistance mechanisms, drug combinations, and novel host-based biomarkers

Abstract

Tuberculosis (TB) has been a major public health problem. The successful treatment of TB requires long-term (6-months) multi-drug chemotherapy, but poor patient compliance and non-adherence lead to the evolution of drug-resistant TB. Several drug resistance mechanisms are known, but there is no clear understanding of whether microbes explore multiple mechanisms simultaneously or if such mechanisms are influenced by each other and lead to global alterations in the cell in a synchronized manner. A systems perspective of drug resistance is necessary to rationally select strategies for tackling resistance by discovering new targets and developing novel drugs to treat drug-resistant TB effectively. In this thesis, we study drug resistance from both the pathogen and the host perspective. The first part is focused on the pathogen where we evolved multi-drug resistant Mycobacterium smegmatis (Msm) strains and performed whole-genome sequencing and transcriptome profiling. Condition-specific networks were then constructed by incorporating gene expression data onto the Msm gene-interaction network. We found redox homeostasis and cell wall synthesis pathways to be predominantly perturbed and can be targeted. Next, we tried to understand the metabolic variations in drug-resistant strains through genome-scale metabolic modeling. We devised a novel bottom-up molecular system-wide approach for identifying metabolites from the genome sequence, utilizing 3D protein structures and ligand binding pocket detection. Our approach is able to identify multiple cell wall-related pathways that are known to be altered in drug-resistant strain, consistent with experimental observation. We identified compounds capable of inhibiting multidrug-resistant strain through the Phenotypic screening of multiple chemical compounds and found a new drug combination (Isoniazid + Vancomycin). This new drug combination showed good efficacy in vitro and in vivo and is a strong candidate for drug-repurposing and targeting multidrug-resistant TB. The second part of the thesis focuses on the host. A major factor that leads to drug resistance is the inability to identify if the first-line therapy is effective. To address this, we built a systems model of the host response to TB treatment and identified a blood-based prognostic marker for TB treatment effectiveness. We devised a new TB treatment response score, R9, which correctly identified drug-resistant cases from a longitudinal cohort of TB patients. Next, we focused on Extra-pulmonary TB (EPTB), which constitutes about 15% of all TB cases, and difficulties in diagnosing combined with drug resistance pose significant challenges to the eradication of TB. We modeled host response to EPTB by using our transcriptome-integrated network approach, combined it with our biomarker discovery pipeline, and developed a blood test to diagnose various forms of EPTB. The signature consisting of 10 genes was validated on independent EPTB samples from India using Nanostring and qRT-PCR and was found to discriminate EPTB from HC with an AUC of 0.97 [95%CI 0.94-1.00]. In conclusion, the thesis's work contributes to tackling the major problem of antimicrobial resistance in TB by identifying a new drug-combination for targeting drug-resistant TB and discovering new biomarker signatures for estimating treatment effectiveness as well as diagnosing extrapulmonary tuberculosis.