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Integrative Multiomics Analysis of Human Atherosclerosis Reveals a Serum Response Factor‐driven Network Associated with Intraplaque Hemorrhage by Jin, Han (author);Goossens, Pieter (author);Juhasz, Peter (author);Eijgelaar, Wouter (author);Manca, Marco (author);Karel, Joël M. H. (author);Smirnov, Evgueni (author);Sikkink, Cornelis J. J. M. (author);Mees, Barend M. E. (author);Waring, Olivia (author);van Kuijk, Kim (author);Fazzi, Gregorio E. (author);Gijbels, Marion J. J. (author);Kutmon, Martina (author);Evelo, Chris T. A. (author);Hedin, Ulf (author);Daemen, Mat J. A. P. (author);Sluimer, Judith C. (author);Matic, Ljubica (author);Biessen, Erik A. L. (author) is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Integrative Multiomics Analysis of Human Atherosclerosis Reveals a Serum Response Factor‐driven Network Associated with Intraplaque Hemorrhage about?

## Abstract ## 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_

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Author

Jin, Han (author);Goossens, Pieter (author);Juhasz, Peter (author);Eijgelaar, Wouter (author);Manca, Marco (author);Karel, Joël M. H. (author);Smirnov, Evgueni (author);Sikkink, Cornelis J. J. M. (author);Mees, Barend M. E. (author);Waring, Olivia (author);van Kuijk, Kim (author);Fazzi, Gregorio E. (author);Gijbels, Marion J. J. (author);Kutmon, Martina (author);Evelo, Chris T. A. (author);Hedin, Ulf (author);Daemen, Mat J. A. P. (author);Sluimer, Judith C. (author);Matic, Ljubica (author);Biessen, Erik A. L. (author)

Publisher

Wiley

Published

2021

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)