Genomic and phylogenetic analysis of infectious diseases

Abstract

Infectious diseases pose an unsustainable human and economic burden, and despite control efforts they remain a leading cause of mortality and morbidity, last evidenced by the COVID-19 pandemic and its devastating impact all over the world. Effective control of infectious diseases requires the continuous monitoring and surveillance of outbreaks of emerging, re-emerging or endemic pathogens. Disease prevention strategies are further challenged by the increasing incidence of antimicrobial resistance, which has emerged as a risk to global health and threatens the effectiveness of public health interventions. Advances in our understanding of pathogen biology and the development of clinical microbiology methodologies are necessary to tackle the many challenges posed by infectious diseases. Recent years have witnessed rapid advancements in pathogen genomics that have transformed clinical microbiology, patient care and disease control efforts. Despite this, genome sequencing has yet to be completely implemented in routine microbiology.

This thesis aims to extend and facilitate the use of genomics in clinical microbiology, as well as to improve our knowledge of pathogen biology and evolution. First, I present an approach to reduce mapping bias in short-read sequencing. The proposed methodology increases accuracy in hypervariable genomic regions and reduces inaccuracies due to reference bias. Next, I define the concept of pre-resistance to describe the genetic predisposition of drug susceptible pathogens to acquire drug resistance in the future. I then show evidence of pre-resistance in Mycobacterium tuberculosis, demonstrating a differential risk of drug resistance acquisition among lineages and in mono-resistance strains. Furthermore, I identify candidate loci and genomic polymorphisms associated with a higher risk of resistance acquisition. Identifying markers of future antibiotic resistance could enable targeted therapy to prevent resistance emergence in M. tuberculosis and other pathogens. Finally, I describe the effects of within-host diversity in phylogenetic and transmission inference. Using simulations and data from SARS-CoV-2 hospital outbreaks, I show that using within-host diversity improves phylogenetic inference and our understanding of who infected whom.

The results of this thesis show the potential of pathogen genomics within clinical microbiology, and highlight the opportunities to improve patient care and public health measures to reduce the human and social burden of infectious diseases.