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The small intestine, as the primary site of nutrient absorption, integrates signals from dietary components and gut microbiota to coordinate systemic energy balance, metabolism, and glucose homeostasis alongside other tissues such as the pancreas, liver, and brain. Nuclear receptors represent one mechanism by which the intestinal epithelium senses and respond to dietary signals via transcriptional regulation of metabolic programs. The Peroxisome Proliferator Activated Receptors (PPARs), including PPARα, PPARδ, and PPARγ, are lipid-responsive nuclear hormone receptors. PPAR transcriptional activity is highly context-dependent, shaped by the availability and affinity of their lipid ligands and co-regulators as well as by cell and tissue types. Whereas PPARγ and PPARδ are established regulators of glucose homeostasis, acting in white adipose tissue, skeletal muscle, and liver to improve insulin sensitivity, PPARα has been primarily associated with hepatic lipid metabolism, where it promotes fatty acid transport, β-oxidation, and ketogenesis. Due to their roles in lipid metabolism and anti-inflammatory processes, selective PPAR agonists are being actively pursued as therapies in clinical and pre-clinical studies for metabolic diseases. Thus, it is critical to improve our understanding of the context-specific and tissue-specific effects of PPAR signaling.

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Deciphering tissue-specific protein regulation for insights into cardiometabolic disease

April E. Hartley, Katyayani Sukhavasi, Sile Hu, Matthew Traylor, ... Yalda Jamshidi

Deciphering tissue-specific protein regulation for insights into cardiometabolic disease

Understanding tissue-specific mechanisms of protein regulation gives crucial insights into cardiometabolic disease and informs drug discovery. Most proteomic studies have primarily concentrated on plasma, overlooking tissue-specific effects. Utilizing Olink technology, we assessed relative protein levels across plasma and tissue (aortic wall, mammary artery, liver, and skeletal muscle) from the STARNET cohort: 284 individuals with a high prevalence of coronary artery disease (CAD). We identified 608 cis protein quantitative trait loci (pQTLs), primarily in plasma, reflecting greater protein variability. Of 190 proteins with cis-pQTLs in non-plasma tissues, 50% also had plasma pQTLs, validating Olink technology in these tissues while reinforcing the relevance of plasma data for understanding protein regulation. To identify potential mechanistic pathways linking genetic variants to clinical traits, we performed Bayesian colocalization and Mendelian randomization. These analyses revealed shared genetic regulation between tissues at the gene expression and protein level, and key cardiometabolic traits including low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglycerides. Notably, analyses provide further support to SORT1 and PSRC1 gene and protein expression having liver-specific influences on CAD risk and lipid profiles. We also observed distinct genetic regulation of gene expression and protein within the same tissues, underscoring the value of tissue proteomics for therapeutic insights.

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Deciphering tissue-specific protein regulation for insights into cardiometabolic disease

April E. Hartley, Katyayani Sukhavasi, Sile Hu, Matthew Traylor, ... Yalda Jamshidi

Deciphering tissue-specific protein regulation for insights into cardiometabolic disease

Understanding tissue-specific mechanisms of protein regulation gives crucial insights into cardiometabolic disease and informs drug discovery. Most proteomic studies have primarily concentrated on plasma, overlooking tissue-specific effects. Utilizing Olink technology, we assessed relative protein levels across plasma and tissue (aortic wall, mammary artery, liver, and skeletal muscle) from the STARNET cohort: 284 individuals with a high prevalence of coronary artery disease (CAD). We identified 608 cis protein quantitative trait loci (pQTLs), primarily in plasma, reflecting greater protein variability. Of 190 proteins with cis-pQTLs in non-plasma tissues, 50% also had plasma pQTLs, validating Olink technology in these tissues while reinforcing the relevance of plasma data for understanding protein regulation. To identify potential mechanistic pathways linking genetic variants to clinical traits, we performed Bayesian colocalization and Mendelian randomization. These analyses revealed shared genetic regulation between tissues at the gene expression and protein level, and key cardiometabolic traits including low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglycerides. Notably, analyses provide further support to SORT1 and PSRC1 gene and protein expression having liver-specific influences on CAD risk and lipid profiles. We also observed distinct genetic regulation of gene expression and protein within the same tissues, underscoring the value of tissue proteomics for therapeutic insights.

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