Tuberculosis Drug Susceptibility Screening Using Single-Sample Melt Analysis

Abstract

Nearly two billion people are infected with Mycobacterium tuberculosis (MTB), with 10.8 million new active cases annually, and tuberculosis (TB) remains the leading infectious disease killer worldwide despite curative therapy existing for decades. Resistance to rifampicin or isoniazid—the most effective first-line anti-TB drugs—can arise from a single nucleotide change, making rapid and accurate susceptibility testing essential to match patients with effective treatment. Delays from slow turnaround, limited access, and reliance on commercial assays that incompletely capture evolving resistance impede timely therapy and worsen outcomes. Current WHO-endorsed molecular diagnostics often trade simplicity, cost, and flexibility for multi-sample comparison, complex chemistries, and proprietary platforms that limit scalability and accessibility. This dissertation develops and provides laboratory-based validation of three complementary single-sample melt analysis platforms—L-DNA–based High-Resolution Melt Analysis (LHRM), Single-sample Melt Analysis for Screening Hotspots (SMASH), and Segmented Screening by Melt Analysis (SSMA)—and applies them to TB drug susceptibility testing, enabling high-resolution screening of clinically actionable resistance-related mutations across both compact and extended genetic regions. LHRM leverages a synthetic susceptible L-DNA comparator to internally calibrate reactions and enable single-sample, single-base resolution in short genetic regions, demonstrated for isoniazid resistance in TB’s katG gene. SMASH extends this architecture using asymmetric PCR with dual internal comparators—an iFRET-reported L-DNA and an unlabeled melt probe—to generate larger mutation-induced melt shifts with reduced post-processing, applied to an emerging mutation hotspot associated with rifampicin resistance in TB’s rpoB gene. SSMA addresses longer genomic regions by segmenting mutation-dense spans, demonstrated with the rifampicin-resistance determining region (RRDR) of rpoB, into multiple probe-defined windows, enabling high-resolution detection using a single intercalating dye with optional internal references for fully single-sample classification. Proof-of-concept studies in synthetic katG and rpoB TB samples demonstrate accurate susceptibility classification at low cost with broad instrument compatibility. Future directions include clinical validation and multiplexed screening of multiple resistance-associated hotspots. Together, these platforms provide a scalable, internally-calibrated framework for TB drug susceptibility screening that can enhance surveillance, enable decentralized diagnostics, and accelerate translation of known TB genetic resistance mutations into actionable clinical care.