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Project
Omics Data Integration to Predict Drug Resistance in Mycobacterium tuberculosis (FWOTM1083)
Tuberculosis (TB) remains the deadliest infectious disease
worldwide. Improved diagnosis is essential to address increasing TB
antimicrobial resistance and to assign an effective treatment regimen.
The advent of whole-genome sequencing (WGS) promises to
overcome slow, more expensive, and biohazardous assay
susceptibility testing. However WGS relies on previous
understanding of resistance mechanisms in M. tuberculosis (Mtb). To
date, WGS has poor performance to predict resistance on new, lessstudied drugs like bedaquiline, failing to distinguish innocuous rare
variants from mutations that confer a resistant phenotype in Mtb.
We propose an innovative approach to overcome this gap by
applying an integrative multi-omics analysis in Mtb to determine the
aggregated effect of each mutation on drug resistance, including the
contribution of rare mutations and convergent evolution and using
computational tools like machine learning and Bayesian networks.
Besides improving the accuracy of WGS data interpretation, this
integrative analysis will allow us to explore novel cellular mechanisms
involved in antimicrobial resistance, close the gap between genotypic
and phenotypic drug susceptibility testing, and better understand
resistance evolutionary pathways towards Mtb resistance.
worldwide. Improved diagnosis is essential to address increasing TB
antimicrobial resistance and to assign an effective treatment regimen.
The advent of whole-genome sequencing (WGS) promises to
overcome slow, more expensive, and biohazardous assay
susceptibility testing. However WGS relies on previous
understanding of resistance mechanisms in M. tuberculosis (Mtb). To
date, WGS has poor performance to predict resistance on new, lessstudied drugs like bedaquiline, failing to distinguish innocuous rare
variants from mutations that confer a resistant phenotype in Mtb.
We propose an innovative approach to overcome this gap by
applying an integrative multi-omics analysis in Mtb to determine the
aggregated effect of each mutation on drug resistance, including the
contribution of rare mutations and convergent evolution and using
computational tools like machine learning and Bayesian networks.
Besides improving the accuracy of WGS data interpretation, this
integrative analysis will allow us to explore novel cellular mechanisms
involved in antimicrobial resistance, close the gap between genotypic
and phenotypic drug susceptibility testing, and better understand
resistance evolutionary pathways towards Mtb resistance.
Date:1 Nov 2021 → Today
Keywords:Tuberculosis drug resistance, Whole Genome Sequencing, Multi-omics integration
Disciplines:Analysis of next-generation sequence data, Computational transcriptomics and epigenomics, Infectious diseases, Microbiology not elsewhere classified, Tropical medicine