Resolving Tuberculosis Transmission Using Long Read And Deep Sequencing Data To Inform Targeted Interventions

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...