Tailoring bioinformatics strategies for the characterization of the human microbiome in health and disease

• 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.

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