Exploring genomic patterns to identify drug-resistant tb: a comprehensive study of age, gender, lineage, and outcome

Anam Tariq; Sahar Fazal

Author(s)

Anam Tariq Capital University of Science and Technology , Pakistan

Sahar Fazal Capital University of Science and Technology , Pakistan

Abstract

Background: Tuberculosis (TB) is a prevalent bacterial infection caused by Mycobacterium tuberculosis (Mtb), primarily affecting the lungs, with rising incidence rates. Drug-resistant TB strains, including multidrug-resistant (MDR) and extensively drug-resistant (XDR) types, pose challenges with poor treatment success rates and increased mortality. Demographic factors and genomic patterns contribute to drug resistance, emphasizing the need for new drugs targeting specific genomic patterns. Methods: This study analyzes the impact of demographics, treatment outcomes, and genomic mutations on drug-resistant TB using pattern identification techniques and whole-genome sequencing to discover therapeutic targets. This study primarily focused on the identification and analysis of MDR and XDR strains, considering various demographic characteristics and treatment outcomes. A dataset comprising 2,602 observations was utilized, and pattern recognition techniques were employed to identify significant features. Additionally, the study employs the whole-genome sequencing (WGS) pipeline to identify single nucleotide polymorphisms (SNPs) that result in new mutations in drug-resistant Mtb strains. Results: The findings of the study revealed that XDR and MDR non-XDR TB were the most prevalent types of drug resistance, and they were associated with unfavorable treatment outcomes, including death or treatment failure. Males exhibited a higher susceptibility to both XDR and MDR non-XDR TB compared to females. The age of onset for both types of resistance was approximately 40 years. Among the observed variants in 31 commonly occurring genes, Conclusion: These findings introduce a novel set of therapeutic targets specific to MDR and XDR Mtb types, which warrant further investigation for potential therapeutic interventions.

Publication Details

Page(s) 346-352

DOI DOI not available

Published Journal: Advancements in life sciences, Volume: 11, Issue: 2, Year: 2024

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Keywords

Drug resistance, gene, mutations, Tuberculosis, Multidrug resistance, Demographics Dataset

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