Introduction: Although multiple efforts have been initiated to shed light into the molecular mechanisms underlying cardiovascular disease, it still remains one of the major causes of death worldwide. Proteomic approaches are unequivocally powerful tools that may provide deeper understanding into the molecular mechanisms associated with cardiovascular disease and improve its management. Areas covered: Cardiovascular proteomics is an emerging field and significant progress has been made during the past few years with the aim of defining novel candidate biomarkers and obtaining insight into molecular pathophysiology. To summarize the recent progress in the field, a literature search was conducted in PubMed and Web of Science. As a result, 704 studies from PubMed and 320 studies from Web of Science were retrieved. Findings from original research articles using proteomics technologies for the discovery of biomarkers for cardiovascular disease in human are summarized in this review. Expert commentary: Proteins associated with cardiovascular disease represent pathways in inflammation, wound healing and coagulation, proteolysis and extracellular matrix organization, handling of cholesterol and LDL. Future research in the field should target to increase proteome coverage as well as integrate proteomics with other omics data to facilitate both drug development as well as clinical implementation of findings.

The increased prevalence of cardiovascular disease (CVD) indicates a demand for novel therapeutic approaches. Proteome analysis of vascular tissues from animal models and humans with CVD could lead to the identification of novel druggable targets. LC-MS/MS analysis of thoracic aortas from three mouse models of non-diabetic and diabetic (streptozotocin (STZ)-induced) atherosclerosis followed by bioinformatics/pathway analysis was performed. Selected findings were confirmed by proteomics analysis of human vessels from patients with CVD as well as in vitro studies (migration, proliferation, angiogenesis assays) using endothelial (HUVEC) cells. Comparative tissue proteomics of low density lipoprotein receptor deficient (Ldlr-/-) and diabetic Ldlr-/- (Ldlr-/-STZ) with wild type (WT) animals led to the identification of 284 differentially expressed proteins in both models. Among them, 177 proteins were also differentially expressed in diabetic apolipoprotein E deficient (ApoE-/-STZ) mice, suggesting expression changes associated with atherosclerosis independent of the model used. These proteins recapitulated the hallmarks of atherosclerosis. Comparison of these findings with differentially expressed proteins in human vessels with CVD enabled shortlisting of six commonly dysregulated proteins. Among them, lysine-specific demethylase 5D (KDM5D) exhibited pronounced overexpression accompanied by a reduction in the protein levels of its substrate, the trimethylated lysine 4 of histone H3 (H3K4me3), in patients with CVD. Functional interference studies applying a KDM5 inhibitor on HUVEC reduced cell proliferation, migration and tube-forming ability in vitro. This high-throughput proteomics strategy identified KDM5 histone demethylases being potentially involved in CVD, possibly by affecting H3K4 methylation. FUND: [SysVasc, HEALTH-2013 603288], [ERA-CVD PROACT: ANR-17-ECVD-0006, 01KL1805], [FRM, DEQ20170336759].

Human mesenchymal stem/stromal cells (MSCs) and their secreted molecules exert beneficial effects in injured tissues by promoting tissue regeneration and angiogenesis and by inhibiting inflammation and fibrosis. We have previously demonstrated that the therapeutic activity of fetal MSCs derived from amniotic fluid (AF-MSCs) and their hepatic progenitor-like cells (HPL) is mediated by paracrine effects in a mouse model of acute hepatic failure (AHF). Herein, we have combined proteomic profiling of the AF-MSCs and HPL cell secretome with ex vivo and in vivo functional studies to identify specific soluble factors, which underpin tissue regeneration in AHF. The anti-inflammatory molecule Annexin-A1 (ANXA1) was detected at high levels in both AF-MSC and HPL cell secretome. Further functional analyses revealed that the shRNA-mediated knock-down of ANXA1 in MSCs (shANXA1-MSCs) decreased their proliferative, clonogenic and migratory potential, as well as their ability to differentiate into HPL cells. Liver progenitors (oval cells) from AHF mice displayed reduced proliferation when cultured ex vivo in the presence of conditioned media from shANXA1-MSCs compared to control MSCs secretome. Intra-hepatic delivery of conditioned media from control MSCs but not shANXA1-MSCs reduced liver damage and circulating levels of pro-inflammatory cytokines in AHF. Collectively, our study uncovers secreted Annexin-A1 as a novel effector of MSCs in liver regeneration and further underscores the potential of cell-free therapeutic strategies for liver diseases. FUND: Fondation Santé, GILEAD Asklipeios Grant, Fellowships of Excellence - Siemens, IKY, Reinforcement of Postdoctoral Researchers, IKY.

Proteomics analysis of bladder cancer invasion: Targeting EIF3D for therapeutic intervention Proteomics analysis of bladder cancer invasion: Targeting EIF3D for therapeutic intervention

(1) Background: Prostate cancer (PCa) is the most frequently diagnosed cancer in men. Wide application of prostate specific antigen test has historically led to over-treatment, starting from excessive biopsies. Risk calculators based on molecular and clinical variables can be of value to determine the risk of PCa and as such, reduce unnecessary and invasive biopsies. Urinary molecular studies have been mostly focusing on sampling after initial intervention (digital rectal examination and/or prostate massage). (2) Methods: Building on previous proteomics studies, in this manuscript, we aimed at developing a biomarker model for PCa detection based on urine sampling without prior intervention. Capillary electrophoresis coupled to mass spectrometry was applied to acquire proteomics profiles from 970 patients from two different clinical centers. (3) Results: A case-control comparison was performed in a training set of 413 patients and 181 significant peptides were subsequently combined by a support vector machine algorithm. Independent validation was initially performed in 272 negative for PCa and 138 biopsy-confirmed PCa, resulting in an AUC of 0.81, outperforming current standards, while a second validation phase included 147 PCa patients. (4) Conclusions: This multi-dimensional biomarker model holds promise to improve the current diagnosis of PCa, by guiding invasive biopsies.