LAUSR

Background: While sex differences in coronary artery disease (CAD) are widely accepted with women developing more stable atherosclerosis than men, the underlying pathobiology of such differences remains largely unknown. In CAD, recent integrative systems biological studies have inferred gene regulatory networks (GRNs). Within these GRNs, key driver genes have shown great promises but have thus far been unidentified in females. Methods: We generated sex-specific GRNs of the atherosclerotic arterial wall in 160 females and age-matched males in the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) study. We integrated the female GRNs with single-cell RNA-sequencing data of the human atherosclerotic plaque and single-cell RNA-sequencing of advanced atherosclerotic lesions from wildtype and Klf4 knock-out atherosclerotic SMC lineage tracing mice. Results: By comparing sex-specific GRNs, we observed clear sex differences in network activity within the atherosclerotic tissues. Genes more active in females were associated with mesenchymal cells and endothelial cells, whereas genes more active in males were associated with the immune system. We determined that key drivers of GRNs active in female CAD were predominantly found in smooth muscle cells (SMCs) by single-cell sequencing of the human atherosclerotic plaques, as well as higher expressed in female plaque SMCs. To study the functions of these female SMC key drivers in atherosclerosis, we examined single-cell RNA-sequencing of advanced atherosclerotic lesions from wildtype and Klf4 knock-out atherosclerotic SMC lineage tracing mice. The female key drivers were found to be expressed by phenotypically modulated SMCs and affected by Klf4, suggesting that sex differences in atherosclerosis involves phenotypic switching of plaque SMCs. Conclusions: Our systems approach provides novel insights into molecular mechanisms that underlie sex differences in atherosclerosis. To discover sex-specific therapeutic targets for atherosclerosis, an increased emphasis on sex-stratified approaches in the analysis of multi-omics datasets is warranted.