Tuberculosis: new era for diagnosis and surveillance using whole-genome sequencing-based approaches

Abstract

Tuberculosis (TB) has been declared as a global public health emergency by the WHO since 1993. It still accounts for almost 2 million deaths each year, making it the ninth leading cause of death worldwide. The major obstacle for an effective TB control is antimicrobial resistance, thus, to be successful, new strategies must be addressed, for instance, the implementation of new rapid TB diagnostic technologies that could translate into early treatment initiation and blocking of transmission chains. Considering the major constraints regarding the isolation and time of growth of M. tuberculosis strains, the main goal of this PhD dissertation was to acknowledge the potential of the use of WGS-based methodologies for routine diagnostic and epidemiological surveillance. We evaluated several software for in silico prediction of antibiotic resistance and developed bioinformatics pipelines for surveillance purposes, in particular for the identification of transmission chains. As they revealed high sensitivity, these approaches are already implemented in the routine of the Portuguese National Reference Laboratory (NRL). We also recognised the possibility to use these same approaches directly to samples collected from TB patients, lowering the time-to-results, for a complete drug resistance pattern and phylogeny analysis, for five to eight days. The validation of this methodology is ongoing and will be implemented in a near future. Additionally, and according to the new recommendations for TB treatment, we have initiated studies to identify new mutations associated with resistance to the recently adopted drugs, in order to enrich the available databases and improve the performance of the genotypic diagnostics pipelines. This PhD dissertation highlights WGS-based methodologies as powerful tools to surpass the difficulties of phenotypic TB diagnosis and surveillance and to provide a much more rapid information regarding resistance prediction and eventual transmission chains. It also supported the technological transition performed at the NRL for TB surveillance.