Tuberculosis (TB) remains an important public health problem globally, claiming the lives of over a million people annually. Drug resistant TB increasingly threatens the goal to end TB. Rapid and complete identification of a Mycobacterium tuberculosis (Mtb) strain’s drug resistance profile is essential for the effective personalised treatment of patients with multi- and extensively drug resistant TB. Current molecular tools can only detect a limited set of known resistance-conferring mutations. Whole genome sequencing (WGS) could overcome this limitation by investigating the entire genome of Mtb in a single analysis. Unfortunately, the current state-of-the-art culture-based approach to Mtb WGS has a long turnaround time (weeks to months), limiting its application in clinical care. It also results in culture bias, restricting our ability to detect heteroresistance and mixed infections. Increasing the efficiency of the DNA extraction directly from sputum samples requires a better understanding of the amount of DNA lost during the WGS steps and a higher efficiency of mycobacterial lysis. Improved bioinformatic analysis pipelines would further enhance our ability to successfully sequence the low quantities of Mtb DNA in the presence of human, viral and bacterial contaminants in clinical sputum samples. By optimising both the wet lab and bioinformatics steps of culture-free WGS directly on sputum samples, I expect to greatly contribute to translating WGS from bench to bedside.
|Effective start/end date||30/06/21 → …|
IWETO expertise domain