Molecular Detection Of Gene Mutations Linked To Drug Resistance In Mycobacterium Tuberculosis: A Review

Abstract

Tuberculosis (TB) remains a major global health threat, with an estimated 10.8 million new cases in 2024. The rise of multidrug-resistant (MDR‑TB), pre-extensively drug-resistant (pre‑XDR‑TB), and extensively drug-resistant (XDR‑TB) strains severely undermines TB control. Rapid, accurate molecular detection of resistance mutations in Mycobacterium tuberculosis is essential for guiding effective therapy and limiting transmission.

This review synthesizes current evidence on the molecular mechanisms of resistance to first‑line, second‑line, and novel anti‑TB drugs; evaluates the diagnostic performance of WHO‑endorsed and emerging molecular platforms; and identifies key challenges and future directions. A systematic literature search (2015–2025) in PubMed, Google Scholar, ScienceDirect, and the WHO database included studies reporting sensitivity, specificity, and mutation prevalence.

Rifampicin resistance is largely driven by the rpoB S531L mutation (74.2% of resistant isolates). Isoniazid resistance involves katG S315T (89.2%) for high‑level resistance and inhA promoter C‑15T (77.5%) for low‑level resistance. Fluoroquinolone resistance is primarily mediated by gyrA D94G, while injectable drug resistance commonly involves rrs A1401G (74%). Bedaquiline resistance is linked to mmpR (Rv0678) and atpE mutations. Among molecular platforms, targeted next‑generation sequencing (tNGS) shows 88–98% sensitivity and 95–100% specificity across 13–16 resistance genes, receiving WHO endorsement in 2025. Xpert MTB/XDR delivers results for isoniazid, fluoroquinolones, and injectables within 90 minutes. Whole‑genome sequencing (WGS) remains the gold standard but is limited by cost ($75–150/sample) and turnaround time (5–14 days). Major challenges include detecting borderline/uncommon mutations, heteroresistance (up to 32.5% for isoniazid), and low sensitivity for ethambutol and pyrazinamide resistance (58–79%).

In conclusion, molecular resistance detection has transformed TB diagnostics, reducing turnaround from weeks to hours. Integrating tNGS and WGS within a tiered, WHO‑guided framework adapted to local epidemiology is critical. Continued innovation in point‑of‑care testing, AI‑based resistance prediction, and expanded mutation catalogs remains essential to meet the WHO 2030 End TB targets.