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Applications of High Throughput Sequencing for Immunology and Clinical Diagnostics

Creative Commons 'BY' version 4.0 license

High throughput sequencing methods have fundamentally shifted the manner in which biological experiments are performed. In this dissertation, conventional and novel high throughput sequencing and bioinformatics methods are applied to immunology and diagnostics.

In order to study rare subsets of cells, an RNA sequencing method was first optimized for use with minimal levels of RNA and cellular input. The optimized RNA sequencing method was then applied to study the transcriptional differences between subpopulations of T follicular helper cells, which are integral to the adaptive immune response to pathogenic invasion.

In some cases, pathogens have long lasting effects either by integration of viral DNA into the host genome or by immune evasion. Paired-end and mate-pair sequencing are applied to identify the integration of DNA from high risk strains of human papilloma virus, an event that acts as a precursor to the cervical carcinogenesis. Some bacterial pathogens are able to escape the adaptive immune response and antibiotics are necessary to clear infections. However, evolved resistances can nullify the therapeutic benefit of antibiotic treatment. Whole genome sequencing was able to identify the genetic causes of antibiotic-resistance in both clinical isolates and directed evolution studies.

In most cases, the adaptive immune system is able to clear pathogenic invasions without the help of antibiotics. A key player in both adaptive immunity and tissue transplantation is the human leukocyte antigen (HLA). HLA molecules are responsible for surface display of healthy and pathogenic peptides. Knowledge of an individual's HLA types is imperative for successful tissue transplantation and is useful for diagnosis of autoimmune diseases such as type I diabetes, systemic lupus erythematosus and ankylosing spondylitis. Because balancing selection has generated thousands of HLA alleles in the population, identification of an individuals HLA alleles typically requires specialized molecular assays. A novel method is presented that can predict HLA types directly from RNA-seq data without the need for specialized molecular assays.

HLA molecules that display abnormal peptides are recognized by T-cells via their characteristic T-cell receptor. Unlike most protein coding genes, the peptide sequence of the T-cell receptor is not encoded directly in the genome. Instead, a somatic recombination process generates receptors with the ability to bind a wide range of different peptides displayed by HLA molecules. The UCSC Immunobrowser was developed to explore, compare and analyze high throughput T-cell receptor sequencing experiments using interactive visualizations. The public web-based tool can serve as a repository for T-cell receptor sequencing experiments, track blood cancers, identify potential causes of autoimmunity and search the expanse of published literature for studies that have observed similar sequences.

Together, these applications highlight the utility of high throughput sequencing and bioinformatics methods for the study of immunology and the translation of relevant findings to clinical diagnostics.

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