From data to science: a multi-Omics analysis of the pathobiome

Humans represent a complex ecosystem colonized not only by our cells but trillions of other microbes such as bacteria, archaea, fungi, and viruses. This microbiome gains increasing interest due to its involvement in human health and disease. While we live in symbiosis with most of these travellers, dysbiosis can lead to the growth of pathogens. Pathobionts are commensal microbes and harmless in healthy individuals until specific circumstances occur. There is increasing interest in studying this pathobiome due to the rise in infections with high mortality rates and stagnant treatment options. Due to the complexity of possible interactions between the host and microbes, studies on microbial interactions are conducted at varying scales. In this thesis, we start to study interactions in small, well-controlled model systems in vitro and then at the community level in vivo. The key technology used to identify, quantify, and characterize microbes and study host- microbe interactions throughout my studies is whole-genome and transcriptome sequencing. While an extensive body of work has focused on understanding the virulence factors of common pathogens, such as Aspergillus and Candida species, very little work has been done on understanding the interplay of those pathogens with the host’s symbionts or other pathogens at the start of my Ph.D. In my Ph.D. project, I used next- generation sequencing, advanced statistical approaches, and machine learning to significantly expanded our knowledge of the life of pathogens from an ecological point of view