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Data integration to close the gap on Prediction of MTB Drug Resistance Mutations

Improved diagnosis and treatment are designated priorities of the World Health Organization and the Centers for Disease Control to address the antimicrobial resistance challenge. These measures rely on an improved understanding of the mechanisms of resistance acquisition in bacteria.
Especially for new anti-tuberculosis drugs, known genetic mutations poorly predict phenotypic resistance testing, yet real-time resistance testing is essentially impossible in the endemic areas most affected. In M. tuberculosis, drug resistance (DR) is caused by protein modifying mutations in
drug targets or in pro-drug to drug converting enzymes. However, the possibility that gene regulation plays an important role in antimicrobial resistance has yet to be systematically studied.
While most mutations have no impact on drug susceptibility, distinguishing those mutated genes that drive DR in a patient is essential to assign an effective treatment regimen, typically involving 5 or more drugs for 9+ months. Knowledge of genes and pathways on which DR mutations operate can also resolve the discordance between genotypic and phenotypic drug susceptibility testing and promises to finally replace the slow, more expensive and biohazardous assay susceptibility testing.
Date:20 Oct 2020 →  20 Oct 2021
Project type:PhD project