# In Silico Design of gRNA for CRISPR System for Detection of Multidrug Resistant Tuberculosis Using Indian Mycobacterium tuberculosis Genomes: A Computational Study

## TL;DR

This study designs CRISPR-based guide RNAs to detect drug-resistant tuberculosis in India using computational methods.

## Contribution

The study provides a tailored set of gRNAs for CRISPR diagnostics targeting MDR-TB mutations prevalent in India.

## Key findings

- Six key drug resistance-associated mutations in M. tuberculosis were identified and targeted with gRNAs.
- Top-ranked gRNAs showed high cleavage efficiency and zero off-target activity.
- Multiple candidate gRNAs were designed for each resistance locus suitable for CRISPR-based assays.

## Abstract

Background

Multidrug-resistant tuberculosis (MDR-TB) continues to pose a major challenge to TB elimination in India, where drug resistance and delayed diagnosis contribute significantly to ongoing transmission. Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) based diagnostics have emerged as versatile tools, compared to GeneXpert, capable of detecting resistance-associated mutations with rapid turnaround and high accuracy. This study aimed to design and in silico validate Clustered Regularly Interspaced Short Palindromic Repeats-CRISPR-associated protein (CRISPR-Cas)-based guide RNAs (gRNAs) targeting major drug-resistance mutations in Indian Mycobacterium tuberculosis (M. tuberculosis) isolates.

Methods

Whole-genome mutation profiles were analyzed using TBProfiler, and gRNAs were designed using CHOPCHOP. Off-target evaluation was performed using Cas-OFFinder and Basic Local Alignment Search Tool (BLAST). High-confidence mutations in gyrA, rpoB, katG, rpsL, embB, and ethA were selected based on prevalence in Indian isolates and WHO-defined resistance markers.

Results

Numerous drug resistance-associated mutations were identified in the drug-resistant tuberculosis genome isolates. The study identified six key genetic mutations identified in MTB isolates that are associated with phenotypic drug resistance, including gyrA (Asp94Gly), rpoB (Ser450Leu), and katG (Ser315Thr). For each of the six genes, the chromosome position, locus ID, mutation type, and affected amino acids were identified, and tailored guide RNAs were designed in silico. Top-ranked gRNAs demonstrated optimal GC content, high predicted cleavage efficiency, and zero off-target activity. Each resistance locus yielded multiple candidate gRNAs suitable for CRISPR-based assays.

Conclusions

This computational in silico analysis provides a robust panel of mutation-targeted gRNAs tailored to Indian MDR-TB genomic profiles. These findings lay a strong foundation for developing rapid, affordable CRISPR diagnostics for point-of-care detection of drug resistance. Future laboratory validation and clinical testing are essential for translation into diagnostic practice.