Variation of the Mtb-Host Interactome & Tuberculosis Pathogenesis (TBRU)

Funder
National Institute of Health

Principal Investigator
Associate Professor Nguyen Thuy Thuong Thuong

Duration
2021 – 2026

This project aims to use genetic, genomic, proteomic and bioinformatics strategies to discover host and pathogen variants of genes and gene products that are associated with TB clinical outcomes and to determine how these variants interact to regulate molecular, cellular, and in vivo functions.

Background

The heterogeneity of human responses and outcomes after Mycobacterium tuberculosis (Mtb) exposure is a central conundrum that has spanned history with thousands of years of co-evolution of the pathogen and its host.

Why do some individuals appear to resist infection despite multiple exposures?

Why do most infected individuals remain asymptomatic over their lifetime?

Why do others suffer devastating outcomes from disseminated disease?

Despite over 130 years since the discovery of the Mtb bacillus, we still do not know the answers to these simple questions. This broad clinical spectrum also presents a unique opportunity for understanding the biological mechanisms that control TB pathogenesis. The discovery of these mechanisms may uncover new host and bacterial pathways that influence the clinical trajectory of Mtb host interactions and provide new insights into treatments and prevention strategies.

Objectives

The overall objective is to use genetic, genomic, proteomic and bioinformatics strategies

  • To discover host and pathogen variants of genes and gene products that are associated with TB clinical outcomes;
  • To determine how these variants interact to regulate molecular, cellular, and in vivo functions.

Study design

Our strategy is using two cohorts in Vietnam and Uganda that capture the full spectrum of resistance to traditional LTBI (latent TB infection), LTBI, pulmonary TB disease, and TB meningitis.

Then we use pathway-driven and novel bioinformatics approaches to integrate the genetic results from the two cohorts above with the multiple large-scale and diverse datasets (proteomics, Path-Seq, RNAseq) to identify and prioritize pathways and protein networks for functional testing.

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