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

Microbe-host interplay in atopic dermatitis and psoriasis

Despite recent advances in understanding microbial diversity in skin homeostasis, the relevance of microbial dysbiosis in inflammatory disease is poorly understood. Here we perform a comparative analysis of skin microbial communities coupled to global patterns of cutaneous gene expression in patients with atopic dermatitis or psoriasis. The skin microbiota is analysed by 16S amplicon or whole genome sequencing and the skin transcriptome by microarrays, followed by integration of the data layers. We find that atopic dermatitis and psoriasis can be classified by distinct microbes, which differ from healthy volunteers microbiome composition. Atopic dermatitis is dominated by a single microbe (Staphylococcus aureus), and associated with a disease relevant host transcriptomic signature enriched for skin barrier function, tryptophan metabolism and immune activation. In contrast, psoriasis is characterized by co-occurring communities of microbes with weak associations with disease related gene expression. Our work provides a basis for biomarker discovery and targeted therapies in skin dysbiosis.Peer reviewe

Methodology for single cell profiling using spatially resolved gene expression data - Case study using a four-stage cancer model

The advent of single-cell transcriptomics has enabled the study of cellular heterogeneity within and among populations. Current methods are only able to process a small number of cells. A promising method for high-throughput spatially resolved gene expression analysis with close to single-cell resolution is currently being developed under the concept of spatial transcriptomics. Work is currently carried out to create bioinformatics tools to enable efficient analysis and integration of the data produced by the method. This thesis describes an automatized pipeline that has been developed for the integration and post-processing of spatial transcriptomics cell line imaging and sequencing data. The pipeline was applied to data from two different cell lines derived from a four-stage cancer model study. Suitable pipeline parameters for the analysis of these cell lines are proposed

Discovering viral genomes in human metagenomic data by predicting unknown protein families

Massive amounts of metagenomics data are currently being produced, and in all such projects a sizeable fraction of the resulting data shows no or little homology to known sequences. It is likely that this fraction contains novel viruses, but identification is challenging since they frequently lack homology to known viruses. To overcome this problem, we developed a strategy to detect ORFan protein families in shotgun metagenomics data, using similarity-based clustering and a set of filters to extract bona fide protein families. We applied this method to 17 virus-enriched libraries originating from human nasopharyngeal aspirates, serum, feces, and cerebrospinal fluid samples. This resulted in 32 predicted putative novel gene families. Some families showed detectable homology to sequences in metagenomics datasets and protein databases after reannotation. Notably, one predicted family matches an ORF from the highly variable Torque Teno virus (TTV). Furthermore, follow-up from a predicted ORFan resulted in the complete reconstruction of a novel circular genome. Its organisation suggests that it most likely corresponds to a novel bacteriophage in the microviridae family, hence it was named bacteriophage HFM.Funding Agencies|Swedish Research Council; Knut and Alice Wallenberg Foundation</p

Microbial and transcriptional differences elucidate atopic dermatitis heterogeneity across skin sites

It is well established that different sites in healthy human skin are colonized by distinct microbial communities due to different physiological conditions. However, few studies have explored microbial heterogeneity between skin sites in diseased skin, such as atopic dermatitis (AD) lesions. To address this issue, we carried out deep analysis of the microbiome and transcriptome in the skin of a large cohort of AD patients and healthy volunteers, comparing two physiologically different sites: upper back and posterior thigh. Microbiome samples and biopsies were obtained from both lesional and nonlesional skin to identify changes related to the disease process. Transcriptome analysis revealed distinct disease-related gene expression profiles depending on anatomical location, with keratinization dominating the transcriptomic signatures in posterior thigh, and lipid metabolism in the upper back. Moreover, we show that relative abundance ofStaphylococcus aureusis associated with disease severity in the posterior thigh, but not in the upper back. Our results suggest that AD may select for similar microbes in different anatomical locations-an "AD-like microbiome," but distinct microbial dynamics can still be observed when comparing posterior thigh to upper back. This study highlights the importance of considering the variability across skin sites when studying the development of skin inflammation.Peer reviewe