PPM1-1220-150017

Recent advances in sequencing technologies, collectively referred to as Next Generation Sequencing (NGS), allow unprecedented opportunities to gain a deeper understanding of human biology and fundamental processes of life. One of these important advances is our ability to determine any individual’s genome sequence in a matter of hours and at a fraction of the cost in comparison to the first human genomes that had been completely sequenced ~15 years ago (2015 marks the 25th anniversary of the initiation of the Human Genome project which took ~10 years for completion by conventional Sanger sequencing). This has spurred the initiation of various population-based sequencing projects, starting with the 1000 Genomes Project (www.1000genomes.org), all aimed at revealing variations in the human DNA sequence between individuals of similar or different ethnic background, and including patients with rare inborn errors as well as with malignancies due to somatic mutations (e.g. the 100,000 Genomes Project). In total over 88 million variants were revealed by the 1000 Genomes Project for which, with the completion of the final phase 3, the genomes of 2,504 individuals from 26 populations were reconstructed. This analysis did not include populations with Arab ancestry. Thus, the recently launched Qatar genome project, including Qatari nationals and long-term residents will undoubtedly provide important information about human genetic variation among Arab populations in particular. A critical step in translating knowledge of human genetic variants into clinical decision-making (i.e. personalized medicine) is to establish causal relationships between genotypes and clinical phenotypes. Despite the breadth of clinical and demographic information collected alongside human genome sequencing data, this goal has remained highly challenging and therefore has (mostly) yet to influence routine clinical practice. A major obstacle to knowledge translation is to identify the specific variants (e.g. gain- or loss-of-function mutations) that play an etiological role in disease among the much larger number of human genetic variants that have no clinical relevance. This is further complicated by the multifactorial nature of many common human diseases that have their origin long before the onset of clinical symptoms. Indeed, clinical disease is often preceded by changes in the homeostasis of the immune system, including adaptive immune responses to harmless or self-antigens (such as in the case of allergy or autoimmune diseases, respectively), which in turn is highly dependent on environmental triggers, including common infections, dietary composition, pollutants, or a combination thereof. Here we propose to establish a platform for large-scale serological profiling for antiviral as well as autoantibodies present in the Qatari population, in order to link individual genome and immune phenotype variation in health and disease. The platform has been previously validated and takes advantage of a novel high-throughput technology, utilizing two bacteriophage libraries for peptide display, one library comprising the human virome, the other the human peptidome. Specific antibody detection is achieved by immunoprecipitation and high-throughput NGS, and can reveal peptide-antibody interactions down to the epitope, requiring less than 1 μl of blood. This unique and powerful approach can reveal the history of each individual’s past viral exposure, as well as perturbations in the repertoire of autoantibodies even in the absence of clinical disease. In addition, we will further enrich the generated datasets by conventional enzyme-linked immunosorbent assays (ELISA) for validation purposes and to test for antibody specificities to common allergens. The overall goal of this research proposal is to enhance the Qatar Genome Project pilot sequencing effort by building a comprehensive database of peptide-antibody interactions that comprise each individual’s unique repertoire of auto-, antiviral- and a selection of allergen-specific antibodies. This specific ‘antibody fingerprint’ can be linked directly to the individual data of human genetic variants. The peptide-antibody interactions will include specificities found among the general population (which will serve as a reference serological profile for the Qatari population complementary to the reference genome that will be built through the Qatar Genome Project), as well as antibody specificities associated with disease. By involving Dr. Stephen Elledge and his research group in this collaborative study, who provided proof-of-principle for this new technology, we are convinced that we will be able: (i) to enhance research infrastructure in Qatar by building a high-throughput platform that can be utilized for the analysis of serum samples collected as part of the Qatar Biobank; (ii) to reveal important insights into the epidemiology and seroprevalence of viral infections, autoimmune diseases and allergies at the population level; and (iii) to enable personalized genome interpretation which will ultimately help to bring about the long-desired personalization of medicine.