Tuberculosis bacteria carry hidden genetic variation in nearly 20% of their genome that standard sequencing routinely misses, and this project will capture that missing data to trace how the disease spreads between people. TB killed 1.3 million people in 2022, and many countries are falling behind global elimination targets. Current whole-genome sequencing struggles with TB because the bacteria are clonal—their genomes look nearly identical, making it hard to tell who infected whom. Long-read, deep sequencing can accurately reconstruct complex genomic regions—repetitive elements, insertions, deletions, and virulence genes—that short-read sequencing cannot assemble. These regions are a rich source of variation between closely related strains. The researcher will analyse roughly 15,000 TB samples from Peru and Moldova using long-read sequencing, plus over 6,000 matched short-read sequences. They will also create a novel dataset from lab-grown mixed TB infections to build tools that detect and assemble multiple bacterial strains within a single patient. The resulting high-resolution transmission networks and predictive models will identify highly transmissible strains and pinpoint who is most at risk. If successful, this could guide targeted, cost-effective treatment and preventative therapy for the people most likely to spread TB, helping countries get back on track toward elimination.
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Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains a major global health concern, causing around 1.3 million deaths in 2022. The World Health Organization's 'End TB Strategy' sets out to reduce TB deaths significantly by 2035 and ultimately eliminate the disease worldwide. Unfortunately, many countries are falling behind global targets for elimination, with efforts also hampered in recent years due to disruptions in diagnosis and treatment during the COVID-19 pandemic. A central pillar of the 'End TB Strategy' is to identify high-risk groups and individuals to ensure early diagnosis of the disease to direct targeted, cost-effective treatment strategies and prioritise contacts for preventative therapy. We can understand factors that contribute to the spread of pathogens to guide control efforts by reconstructing the transmission history of past outbreaks using whole genome sequence (WGS) data. By linking cases with few genomic differences, likely chains of transmission and highly transmissible infections can be identified. WGS can also be used to detect complex disease states such as mixed infection. However, this is not a trivial exercise in clonal organisms like TB where low population-level diversity leads to many seemingly identical sequences, confounding efforts to identify who-infected-whom or fully characterise within-host variation. Long-read, deep sequencing can improve whole genome assemblies by accurately reconstructing genomic features that are routinely ignored in Mtb, such as highly variable genes linked to virulence (PE/PPE genes), repetitive elements, and insertions and deletions. It can be difficult to assemble these features with short-read sequence data as reads are far shorter in length than the target gene. In contrast, long-read sequencing produces reads that span the length of multiple genes to allow for high confidence sequence reconstruction in these complex regions. Previously under-characterised regions comprise almost 20% of the Mtb genome and can be a rich source of added variation between closely related strains. We can use this information to better infer the timing and direction of transmission events between individuals with TB. I will build on our exciting preliminary results in which we can now produce high-quality Mtb whole genome assemblies using long read sequencing to improve TB transmission analysis and better characterise mixed-strain TB infections. I will leverage a comprehensive dataset of ~15,000 Mtb samples with long-read sequencing, along with >6,000 matched, short-read sequences, from Peru and Moldova. The population wide diversity and evolution in under-characterised genomic regions will be explored in high-confidence assemblies, including minor variants and mutations in complex regions. I will use well-validated assembly approaches with long and short-read data and assess assembly quality to guide recommendations for sequencing strategies and analysis pipelines for pathogen transmission investigation. I will produce a novel dataset of long-read sequences from in vitro mixed TB samples to build and validate highly-sensitive tools to detect mixed infection and assemble the underlying constituent strains, addressing previous limitations of these approaches when using short-read sequencing data. These will be applied to our real-world datasets to better understand the transmission of mixed infections. Finally, I will use the added resolution from these high-quality assemblies and include mixed constituent strains to produce high-resolution phylogenetic trees and fully reconstruct transmission networks. These will be used to build predictive models of TB transmission risk and cluster growth. Phylodynamic approaches will identify highly-transmissible TB strains and I will explore genomic associations with transmissibility.
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