Open AccessIntegrative multiomics analysis of human atherosclerosis reveals a serum response factor‐driven network associated with intraplaque hemorrhage
Author(s) -
Jin Han,
Goossens Pieter,
Juhasz Peter,
Eijgelaar Wouter,
Manca Marco,
Karel Joël M. H.,
Smirnov Evgueni,
Sikkink Cornelis J. J. M.,
Mees Barend M. E.,
Waring Olivia,
van Kuijk Kim,
Fazzi Gregorio E.,
Gijbels Marion J. J.,
Kutmon Martina,
Evelo Chris T. A.,
Hedin Ulf,
Daemen Mat J. A. P.,
Sluimer Judith C.,
Matic Ljubica,
Biessen Erik A. L.
Publication year - 2021
Publication title -
clinical and translational medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.125
H-Index - 1
ISSN - 2001-1326
DOI - 10.1002/ctm2.458
Subject(s) - medicine , transcriptome , pathology , bioinformatics , gene expression , computational biology , biology , gene , genetics
Background While single‐omics analyses on human atherosclerotic plaque have been very useful to map stage‐ or disease‐related differences in expression, they only partly capture the array of changes in this tissue and suffer from scale‐intrinsic limitations. In order to better identify processes associated with intraplaque hemorrhage and plaque instability, we therefore combined multiple omics into an integrated model. Methods In this study, we compared protein and gene makeup of low‐ versus high‐risk atherosclerotic lesion segments from carotid endarterectomy patients, as judged from the absence or presence of intraplaque hemorrhage, respectively. Transcriptomic, proteomic, and peptidomic data of this plaque cohort were aggregated and analyzed by DIABLO, an integrative multivariate classification and feature selection method. Results We identified a protein‐gene associated multiomics model able to segregate stable, nonhemorrhaged from vulnerable, hemorrhaged lesions at high predictive performance (AUC >0.95). The dominant component of this model correlated with αSMA − PDGFRα + fibroblast‐like cell content ( p = 2.4E‐05) and Arg1 + macrophage content ( p = 2.2E‐04) and was driven by serum response factor (SRF), possibly in a megakaryoblastic leukemia‐1/2 (MKL1/2) dependent manner. Gene set overrepresentation analysis on the selected key features of this model pointed to a clear cardiovascular disease signature, with overrepresentation of extracellular matrix synthesis and organization, focal adhesion, and cholesterol metabolism terms, suggestive of the model's relevance for the plaque vulnerability. Finally, we were able to corroborate the predictive power of the selected features in several independent mRNA and proteomic plaque cohorts. Conclusions In conclusion, our integrative omics study has identified an intraplaque hemorrhage‐associated cardiovascular signature that provides excellent stratification of low‐ from high‐risk carotid artery plaques in several independent cohorts. Further study revealed suppression of an SRF‐regulated disease network, controlling lesion stability, in vulnerable plaque, which can serve as a scaffold for the design of targeted intervention in plaque destabilization.