7226 Leveraging Inter-Individual Transcriptional Correlation Structure To Infer Discrete Signaling Mechanisms Across Metabolic Tissues

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Abstract Disclosure: M. Zhou: None. I. Tamburini: None. C. Van: None. J. Molendijk: None. L.M. Velez: None. C. Nguyen: None. R. Yeo: None. C. Filho: None. A.L. Hevener: None. J. Justice: None. L.M. Sparks: None. E.E. Kershaw: None. D. Nicholas: None. M. Seldin: None. Inter-organ communication is a vital process to maintain physiologic homeostasis, and its dysregulation contributes to many human diseases. Given that circulating bioactive factors are stable in serum, occur naturally, and are easily assayed from blood, they present obvious focal molecules for therapeutic intervention and biomarker development. A major obstacle in the characterization of such soluble factors is that defining their tissues and pathways of action requires extensive experimental testing in cells and animal models. Recently, studies have shown that secreted proteins mediating inter-tissue signaling could be identified by “brute-force” surveys of all genes within RNA-sequencing measures across tissues within a population. Expanding on this intuition, we reasoned those parallel strategies could be leveraged to understand how individual genes mediate signaling across metabolic tissues through correlative analyses of genetic gene variation between individuals. Thus, genetics comparison of quantitative levels of gene expression relationships between organs in a population could aid in understanding cross-organ signaling by adopting a gene-centric approach. Here, we surveyed gene-gene genetic correlation structure for ∼6.1x10^12 gene pairs across 18 metabolic tissues in 310 human individuals and 7 tissues in 103 diverse strains of mice fed a normal chow or HFHS diet. where the variation of genes such as FGF21, ADIPOQ, GCG, and IL6 showed enrichments that recapitulate experimental observations. Further, similar analyses were applied to explore both local within-tissue signaling mechanisms (liver PCSK9) as well as genes encoding enzymes producing metabolites (adipose PNPLA2), where genetic inter-individual correlation structure aligned with known roles for these critical metabolic pathways. Examination of sex hormone receptor correlations in mice highlighted the difference in tissue-specific variation in relationships with metabolic traits. Finally, we utilized this resource to suggest new functions for metabolic coordination between organs. For example, we prioritized key proteins for putative signaling between skeletal muscle and hippocampus, and further suggest colon as a central coordinator for systemic circadian clocks. We refer to this resource as Genetically-Derived Correlations Across Tissues (GD-CAT) where all tools and data are built into a web portal enabling users to perform these analyses without a single line of code (gdcat.org). This resource enables the querying of any gene in any tissue to find genetic coregulation correlated patterns of genes, cell types, pathways, and network architectures across metabolic organs. Presentation: 6/1/2024

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