Transforming clinical mycobacteriology with modern molecular methodology

Abstract

Whole genome sequencing (WGS) is an attractive approach for mycobacteria diagnosis and epidemiological studies. It provides the potential for a rapid method that produces detailed information and could theoretically be used as a routine tool in clinical settings. This thesis focuses on the benefits and challenges involved in transforming molecular approaches into practical clinical mycobacteriology in general, and in particular WGS, as well as examining how it might be implemented. We first set out to improve the quantification of viable mycobacteria cells in vitro and make the molecular bacterial load assay (MBLA) sensitive enough to use in future clinical trials that monitor treatment response. The results showed the assay is rapid and accurate in its detection and count of viable bacteria. WGS was tested with different types of mycobacteria species to address different epidemiological questions. WGS not only provides a higher resolution result than traditional epidemiological methods but it can rapidly identify an outbreak, thus simplifying the investigation and reducing the cost. WGS accurately identified the sources of TB recurrence and could therefore have a potential role in determining the endpoints for clinical trials. Rapid genotyping of species in this way has been demonstrated in our studies. In addition, WGS has the ability to, in most circumstances, predict TB drug resistance. This could also prove very beneficial from a clinical standpoint. We used different approaches in our studies; for example, single nucleotide polymorphism threshold methods and the creation of a putative outbreak reference genome, which can be used in future outbreak investigations. WGS is a cost-effective, high-resolution method with a short turnaround. This makes it potentially usable as a routine tool in clinical settings and reference laboratories. Future studies are needed to improve the mycobacterial genome sequencing procedure, analysis and bioinformatics in order to implement WGS in clinical practice.