A look into our


The Big Picture

Common metabolic disorders such as coronary artery disease (CAD), type 2 diabetes (T2D), non-alcoholic fatty liver disease (NAFLD), and obesity represent top health concerns worldwide, with CAD and T2D among the top ten leading causes of death in western societies and obesity being the risk factor for CAD, T2D, and NAFLD. These diseases are not only highly interconnected but also linked to various other diseases such as neurodegenerative diseases and cancers. We hypothesize that the complex interactions between genetic and environmental risk factors perturb specific gene networks which in turn induce variations in disease susceptibility and therapeutic response. Research in our lab employs integrative genomics and systems biology approaches that leverage various genome-wide multi-omics molecular datasets, such as genetic, transcriptional, epigenomic, proteomic, microbiomics, and relevant phenotypic data from human and rodent populations to identify causal molecular alterations ad the subsequently perturbed molecular networks that contribute to the development of metabolic diseases. We are also exploring the connection between metabolic disorders with neuroplasticity and brain disorders. The candidate causal components and networks identified will serve as the basis for therapeutic target and biomarker discovery as well as for patient stratification with respect to clinical outcome or pharmacological response.


We hypothesize that genes, rather than acting in isolation, are organized into well-structured scale-free networks. Common complex diseases/phenotypes conferred by genetic variations and environmental perturbations are mediated through specific parts of gene networks, termed subnetworks. Perturbation of a particular subnetwork may result in a specific disease or disease subtype and can be treated with a drug targeting the same subnetwork.

Figure credit: Montgomery Blencowe. Citation: Yang X. Multi-tissue Multi-omics Systems Biology of Complex Diseases. Trends in Molecular Medicine, 26:718-728, 2020. [link]


Multiple layers of tissue-specific genomic and epigenomic information such as DNA genotyping, mRNA expression, and DNA methylation as well as phenotypic data are first collected from human or mouse populations in which variations in disease phenotypes are demonstrated. We then apply multiple integrative genomics approaches such as causality testing, network modeling, and functional genomics to sort out the relationships between DNA variations, gene expression alterations, epigenomic changes, and phenotypic variations. Based on these relationships, we attempt to map the complex regulatory network structure underlying complex metabolic disorders.

Figure credit: VP Maekinen. Citation:  Meng et al. Current Cardiovascular Risk Reports. 7:73-83, 2013 [link]

Ongoing research directions

1. Identification of genetically-driven gene networks underlying individual metabolic diseases and across diseases. This is achieved by comprehensive integration of genetic, gene expression, epigenetic, metabolite, and proteomic profiles of cells, tissues, and compound treatments relevant to metabolic disorders in animal models and human populations.

2. Identification of environmentally-driven gene networks underlying metabolic diseases. This is achieve by examining how environmental factors (eg., nutritional, obesogens) modulate the epigenome, transcriptome, and  microbiome, and how they interact with genetic factors to affect metabolic disorders.

3. Investigation of the molecular networks that connect metabolic diseases with brain diseases. This is achieved by systems level integration of genetic and environmental risks that affect shared pathways and networks between diseases.

4. Development of integrative genomics approaches for big data integration. This is achieved by designing and implementing bioinformatics tools that effectively integrate multi-dimensional omics data (genetic, transcriptomic, epigenetic, metabolic, proteomic, etc) and model molecular networks and regulators of pathophysiology.