Uncovering the molecular networks of metabolic diseases using systems biology
- Author(s): Shu, Le
- Advisor(s): Yang, Xia
- et al.
The past few decades have seen dramatic increase in the prevalence of metabolic diseases (MetDs) including obesity, type 2 diabetes (T2D) and cardiovascular disease (CVD), imposing unprecedented burden on public health worldwide. MetDs stem from a complex interplay of multiple genes and cumulative exposure to environmental risk factors, yet the exact etiology remains elusive. To address this challenge, I embarked interdisciplinary systems biology studies encompassing the development of a multi-omics integration tool, elucidation of genetically perturbed tissue networks shared by T2D and CVD, and examination of environmentally perturbed gene networks by a prevalent endocrine disrupting chemical (EDC). First, I developed a multi-omics integration pipeline named Mergeomics, which consists of independent modules that 1) leverage multi-omics association data to identify biological processes that are perturbed in disease, and 2) overlay the disease-associated processes onto molecular interaction networks to pinpoint hubs as potential key regulators. Unlike existing tools that are mostly dedicated to specific data type or settings, the Mergeomics pipeline accepts and integrates datasets across platforms, data types, and species. The performance of Mergeomics was demonstrated by both simulation and case studies that include genome-wide, epigenome-wide, and transcriptome-wide datasets of total cholesterol and fasting glucose. I then applied Mergeomics to identify the shared gene networks between CVD and T2D through a comprehensive integrative analysis driven by five multi-ethnic genome-wide association studies (GWAS) for CVD and T2D, expression quantitative trait loci (eQTLs), ENCODE, and tissue-specific gene network models from CVD and T2D relevant tissues. The shared networks captured both known and novel processes underlying CVD and T2D. I also predicted 15 key drivers for the shared gene networks and cross-validated the regulatory role of top key drivers using in vitro siRNA knockdown, in vivo gene knockout, and two Hybrid Mouse Diversity Panels each comprised of >100 strains. Lastly, I leveraged systems biology approaches to assess the target tissues, molecular pathways, and gene regulatory networks associated with a developmental exposure to the model EDC Bisphenol A (BPA). Prenatal BPA exposure was found to cause transcriptomic and methylomic alterations in the adipose, hypothalamus, and liver tissues in mouse offspring, with cross-tissue perturbations in lipid metabolism as well as tissue-specific alterations in histone subunits, glucose metabolism and extracellular matrix. Network modeling prioritized main molecular targets of BPA, including Pparg, Hnf4a, Esr1, and Fasn. Moreover, integrative analyses identified the association of BPA molecular signatures with MetDs phenotypes in mouse and human. In summary, I presented the community a flexible and robust computational pipeline for multidimensional data integration, and offered mechanistic insights into the genetic and environmental underpinnings of MetDs by exploiting the power of systems biology through both computational and experimental approaches.