This collaborative proposal brings together four University of Otago-based tuberculosis (TB) research teams, with expertise covering the areas of microbiology, immunology, bioinformatics, diagnostics, surveillance, public health and social science. Together, these teams will undertake research to decrease transmission of the infectious disease by:
- integrating economic and social network analyses to inform appropriate public health measures to eliminate TB
- improving diagnostics and antimicrobial resistant M. tuberculosis surveillance in the Pacific and understanding links to Aotearoa New Zealand
- improving measurements of protective immunity to infection with M. tuberculosis that will aid in vaccine efficacy testing
- developing an artificial intelligence framework to combat antimicrobial resistance.
The project consists of four parts:
Part A: TB transmission and economic analysis
- in-depth Māori social network research to identify epidemiological patterns of transmission
- economic analysis of the TB elimination approach to inform policy decisions
Part B: TB diagnostics and surveillance in Pacific peoples in the Pacific and Aotearoa New Zealand
- explore TB transmission in the Pacific and how this is linked to TB cases in Māori and Pasifika communities in Aotearoa
- diagnose drug-resistance patterns and lineages of M. tuberculosis in Aotearoa and the Pacific
- develop real time genotypic resistance testing of M. tuberculosis
Part C: Signatures of vaccine-induced protection against TB: a bench-to-bedside approach
- identify BCG vaccine-induced markers of immune protection that can be easily measured in the clinic
- understand how Lineage 2 strains of M. tuberculosis can evade BCG-induced protection
Part D: Developing an Artificial Intelligence framework to combat antimicrobial resistance
- generate an open access repository showing how genes, proteins and metabolites are differentially regulated across drug-susceptible and resistant clinical isolates of M. tuberculosis
- develop a machine-learning-framework to identify therapeutic vulnerabilities in M. tuberculosis strains