๐Ÿ‡บ๐Ÿ‡ธMulti-Omic Integration Reveals Cell-Type-Specific Regulatory Networks of Insulin Resistance

Minghui Wang, Ph.D., Icahn School of Medicine at Mount Sinai

Presented at the 2023 BayesiaLab Spring Conference.


Understanding the molecular regulatory networks underlying insulin resistance (IR) is crucial to preventing type 2 diabetes and related metabolic disorders. However, our knowledge of the landscape of IR-related transcriptomic regulation in glucose-responsive tissues and its cell-type specificity regulatory mechanisms remains incomplete. To provide a comprehensive population-level understanding of the organizations and cell-type-specific regulations of gene expressions underlying IR,

we employed an integrative network biology approach to integrate the multi-omics and phenotypic data from well-powered African American (AA) and European ancestry (EA) cohorts. By integrating the state-of-art single-cell sequencing data analyses with bulk-tissue expression quantitative trait loci (eats), and coexpression and Bayesian causal networks, we presented trans-ethnic and cross-tissue results of IR in adipose and muscle tissues. We identified ethnically conserved cell-type signatures and gene modules associated with insulin sensitivity responses. We further prioritized modules enriched for cis-eQTL genes and predicted network driver genes for experimental validations. Together, this study revealed the cell-type-specific transcriptomic networks and signaling maps underlying insulin resistance in major glucose-responsive tissues.

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About the Presenter

Dr. Minghui Wang is an Associate Professor in the Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai. Dr. Wang was trained in statistical and quantitative genetics during his graduate studies at Fudan University and postdoctoral research at the University of Birmingham. At Icahn School of Medicine at Mount Sinai, his research focused on developing novel integrative network models that combine multi-layers of bulk-tissue and/or single-cell functional genomics data to uncover the cellular changes and hidden regulatory relationships among a large network of genes/proteins in disease-relevant tissues for human aging and human complex disorders like Alzheimerโ€™s disease (AD), cancers, and diabetes, etc.

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