Multi-omics: uncovering mechanisms of diseases and making medicine personalized

Abstract

Multi-omics, meaning “multiple omics,” became accessible because of the revolution in high-throughput technologies. Multi-omics approaches have been integrated and applied by many studies to a wide range of biological problems, including disease mechanism revealing, biomarker identification, and patient classification. The multi-omics involved in this thesis are epigenomics (DNA methylomics), transcriptomics, and proteomics. This thesis divides into two major parts, aiming at using multi-omics to provide a better understanding of autoimmune diseases, including systemic sclerosis (SSc), psoriatic arthritis (PsA), psoriasis (Pso), Ankylosing spondylitis (AS), and rheumatoid arthritis (RA), even to depict a paradigm to study other diseases in general, ultimately facilitating personalized medicine. The first part focuses on using multi-omics to reveal disease mechanisms from the perspective of studying cytokine stimulation of cells. The first part of this thesis provides not only the potential mechanisms of CXCL4 involved in SSc using multi-omics but also an R package called RegEnrich to reveal the mechanisms of other diseases and biological processes. The second part mainly describes the (multi-)omic commonality and the distinction between patients with different autoimmune diseases (PsA, Pso, and AS) or with different subtypes of the same disease (RA). In this part, we have revealed shared serum proteomic signatures between patients with Pso and PsA and divergent multi-omic signatures between responders and non-responders to TNFi using machine learning techniques. All of these findings may ultimately facilitate personalized medicine, either from a better understanding of diseases or simply from providing informative drug response predictors.