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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Publication Details

Journal
Molecular Metabolism
Published
2025-12-26
DOI
https://doi.org/10.1016/j.molmet.2025.102314
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
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article

Deciphering tissue-specific protein regulation for insights into cardiometabolic disease

Mar González-Ramírez, Kristian Hanghøj, Husain A. Talukdar, Johan Björkegren et al.
Molecular Metabolism
Genetic Associations and Epidemiology
article

Deciphering tissue-specific protein regulation for insights into cardiometabolic disease

Mar González-Ramírez, Kristian Hanghøj, Husain A. Talukdar, Johan Björkegren, April Hartley, Arno Ruusalepp, Katyayani Sukhavasi, Ellen Björkegren, Yalda Jamshidi, Sile Hu, Joanna M.M. Howson, Matthew Traylor
article en

Abstract

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.

Molecular MetabolismVol. 104
Karolinska University Hospital (SE), Novo Nordisk (Denmark) (DK), Tartu University Hospital (EE), Novo Nordisk (Finland) (FI), Clinical Gene Networks (Sweden) (SE)
Good health and well-being
Openalex Percentile: Top 25%
Genetic Associations and Epidemiology
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