Omic Network Modules in Complex diseases

Abstract: Biological systems encompass various molecular entities such as genes, proteins, and other biological molecules, including interactions among those components. Understanding a given phenotype, the functioning of a cell or tissue, aetiology of disease, or cellular organization, requires accurate measurements of the abundance profiles of these molecular entities in the form of biomedical data. The analysis of the interplay between these different entities at various levels represented in the form of biological network provides a mechanistic understanding of the observed phenotype. In order to study this interplay, there is a requirement of a conceptual and intuitive framework which can model multiple omics such as genome, transcriptome, or a proteome. This can be addressed by application of network-based strategies.Translational bioinformatics deals with the development of analytic and interpretive methods to optimize the transformation of different omics and clinical data to understanding of complex diseases and improving human health. Complex diseases such as multiple sclerosis (MS), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and non-small cell lung cancer (NSCLC) etc., are hypothesized to be a result of a disturbance in the omic networks rendering the healthy cells to be in a state of malfunction. Even though there are numerous methods to layout the relation of the interactions among omics in complex diseases, the output network modules were not clearly interpreted.In this PhD thesis, we showed how different omic data such as transcriptome and methylome can be mapped to the network of interactions to extract highly interconnected gene sets relevant to the disease, so called disease modules. First, we selected common module identification methods and assembled them into a unified framework of the methods implemented in an Rpackage MODifieR (Paper I). Secondly, we showed that the concept of the network modules can be applied on the whole genome sequencing data for developing a tested model for predicting myelosuppressive toxicity (Paper II).Furthermore, we demonstrated that network modules extracted using the methylome data helped identifying several genes that were associated with pregnancy-induced pathways and were enriched for disease-associated methylation changes that were also shared by three auto-immune and inflammatory diseases, namely MS, RA, and SLE (Paper III). Remarkably, those methylation changes correlated with the expected outcome from clinical experience in those diseases. Last, we benchmarked the omic network modules on 19 different complex diseases using both transcriptomic and methylomic data. This led to the identification of a multi-omic MS module that was highly enriched disease-associated genes identified by genome-wide association studies, but also genes associated with the most common environmental risk factors of MS (Paper IV).The application of the network modules concept on different omics is the centrepiece of the research presented in this PhD thesis. The thesis represents the application of omic network modules in complex diseases and how these modules should be integrated and interpreted. In particular, it aimed to show the importance of networks owing to the incomplete knowledge of the genes dysregulated in complex diseases and the contribution of this thesis that provides tools and benchmarks for the methods as well as insights into how a network module can be extracted and interpreted from the omic data in complex diseases.