Title: Incorporating whole genome diversity into the clinical epidemiology of Mycobacterium tuberculosis
Funder: The Academy of Medical Sciences

Abstract: Tuberculosis (TB), caused by the bacterium Mycobacterium tuberculosis, is a leading Global Health Challenge with 1.6M deaths every year. Tracking M. tuberculosis transmission is key to clinical case reduction (currently over 27,000 new cases a day worldwide), and whole genome sequencing (WGS) is the recommended approach for this process. M. tuberculosis WGS relies on determining DNA mutations by comparing isolates to a single reference genome. Although M. tuberculosis consists of nine lineages (L1-9), all are mapped against H37Rv, an L4 strain. I have shown that for L5, this L4-centric mapping approach creates incorrect transmission clusters, resulting in inaccurate public health data. This is due to the differences in gene content both between and within lineages. This calls the entire H37Rv-centric pipeline into question, especially in West Africa and South-East Asia where non-L4 lineages dominate. In this project, I will rectify this clinical workflow inaccuracy. First, I will map out the entire M. tuberculosis gene content diversity, particularly within lineages, to evaluate the true extent of this problem. From this, a lightweight computational pipeline will be constructed to better handle this diversity, ensuring easy implementation in clinical settings. The pipeline will also be applied to numerous multi-country, multi-lineage clinical datasets. I aim to improve public health interventions by incorporating the entire genomic diversity of M. tuberculosis into clinical pipelines. This will allow for accurate recovery of transmission clusters and hotspots of this deadly pathogen in all settings, increasing the efficacy of public health interventions and reducing the number of new infections.

Lay abstract: One of the best ways to rid the globe of a deadly pathogen is to quickly find new cases and treat them before they spread (track and trace). The most effective way to do this is to compare the DNA sequences of bacteria taken from two patients to see if one person infected another (transmission). This process, known as genomic epidemiology, relies on accurate detection of DNA mutations, no matter the country, especially for global pandemics. Daily, over 27,000 people fall ill with tuberculosis (TB), a lung infection caused by a bacterium, Mycobacterium tuberculosis. This results in over 1.6 million deaths a year. Genomic epidemiology is at the heart of tracking this pathogen, but recent work by us and others have revealed flaws in the transmission tracing workflows. These flaws are due to DNA differences between Mycobacterium tuberculosis lineages not being captured sufficiently. This results in inaccurate public health data, especially in West Africa and South-East Asia, which are regions of high TB burden. In this project I will rectify this clinical problem and improve global accuracy tracking and tracing of TB. We will investigate the total DNA diversity and construct improved tools for better track and trace procedures. These new approaches will be fully evaluated using a wide variety of DNA datasets from around the world. With this new approach we will be one step closer to identifying previously unrecognized transmission hotspots, thus quickly reducing the number of TB transmissions and removing this devastating pathogen from our society.

Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS;2.2 FACTORS RELATING TO PHYSICAL ENVIRONMENT
Health categories: INFECTION