Background: Tissue proteomics holds great promise to unravel the pathophysiological mechanisms of cardiovascular disease (CVD). In this study, through a cross-species proteomics-based integrated approach we intend to shed light on the molecular mechanisms of CVD.
Methods: LC-MS/MS analysis was performed on thoracic aortas from the Ldlr-/- and ApoE-/- mouse models in the absence or presence of STZ-induced diabetes and their wild type (WT) littermates as well as in vessels from patients with CVD and healthy individuals. Key findings were evaluated by western blot and in vitro using the MTS, transwell migration, and tube formation assay.
Results: The proteomic landscape was initially characterized in diabetic and non-diabetic Ldlr-/- atherosclerotic mouse models that led to identification of 284 differentially expressed proteins compared to WT mice. To exclude protein changes specific to the disease background, high-throughput proteomic analysis was also performed in diabetic ApoE-/- mice and 321 proteins were identified differentially expressed in comparison to WT mice. Among them, 177 proteins were common and showed similar expression trend throughout all atherosclerotic models. The high relevance of these proteins with the disease was further supported by in silico analysis. To translate these findings to human disease, LC-MS/MS was subsequently performed in human vessels from patients with CVD and healthy individuals. Through a cross-species comparison overlapping pathways and proteins between mouse and humans were highlighted. The most pronounced overexpression in disease was observed for the KDM5D histone demethylase which was accompanied by a reduction of its substrate -the trimethylated form of H3K4. An increase of this substrate through KDM5 inhibition on human endothelial cells decreased cell proliferation, migration and tube-forming ability in vitro.
Conclusions: This cross-species proteomics approach supports the involvement of the KDM5 epigenetic modulators in CVD progression. Furthermore, the high resolution proteomic datasets hold the promise of unraveling the complexity of CVD mechanisms.