Abstract
The human microbiome is a very active area of research due to its potential to explain
health and disease. Advances in high throughput DNA sequencing in the last decade have
catalyzed the growth of microbiome research; DNA sequencing allows for a cost-effective
method to characterize entire microbial communities directly, including unculturable
microbes which were previously difficult to study. 16S rRNA sequencing and shotgun
metagenomics, coupled with bioinformatics methods have powered the characterization of
the human microbiome in different parts of the body. This has led to the discovery of novel
links between the microbiome and diseases such as allergies, cancer, and autoimmune
diseases.
This thesis focuses on the application of both 16S rRNA sequencing and shotgun
metagenomics for the characterization of the human microbiome and its relationship with
health and disease. We established two methodologies to address these questions. The first
methodology is a bench-to-bioinformatics pipeline to discover putative viral pathogens
involved in disease using shotgun metagenomics technology. In paper I, we apply the
proposed pipeline to explore the hypothesis of viral infection as a putative cause of
childhood Acute Lymphoblastic Leukemia. In paper II, we propose a complementary
method to the pipeline to improve the detection of unknown viruses, especially those with
little or no homology to currently known viruses. We applied this method on a collection of
viral-enriched libraries which resulted in the characterization of a new viral-like genome.
The second methodology was developed to explore and generate hypothesis from a human
skin microbiome dataset of Psoriasis and Atopic Dermatitis patients. The results of the
analysis are presented in Paper III and Paper IV. Paper III is a pure data-driven exploration
of the dataset to discover different aspects on how the microbiome is linked to both
diseases. Paper IV follows up from the results of paper III but focuses on characterizing
the skin site microbiome variability in Atopic Dermatitis.
