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Introduction: Proprotein convertase subtilisin/kexins (PCSKs) constitute a family of 7 related proteases (PCSK1-7) and 2 distant ones (MBTPS1, PCSK9), with unexplored role in cardiovascular disease (CVD), apart from PCSK9 in lipid metabolism. Here, we aimed to investigate the expression landscape and therapeutic targeting potential of the entire PCSK family for ameliorating CVD. Methods: An integrative approach was applied with genetic, transcriptomic and proteomic data mining from public databases and three independent vascular biobanks comprising carotid atherosclerosis, thoracic and abdominal aneurysms. Omics analyses were followed by gene expression association with patient clinical parameters and immunohistochemistry in vascular biopsies. Results: Genetic studies revealed that, apart from PCSK4 , all PCSK family members lie in regions containing variants associated with human cardiovascular traits. Transcriptomic analyses showed that FURIN, PCSK5, MBTPS1 were downregulated, while PCSK6/7 were upregulated in atherosclerotic plaques vs. normal arteries. In abdominal aneurysms, FURIN, PCSK5, PCSK7, MBTPS1 were downregulated, while PCSK6 was enriched in diseased media. In thoracic aneurysms, only FURIN was significantly upregulated. To understand the mechanistic relationships of this protein family with the disease, network analyses of the upstream and downstream pathways related to PCSKs were done on the omics data from vascular biopsies. Immunohistochemistry indicated that PCSK protein levels correspond to the mRNA expression, except in the case of PCSK9 protein that was abundant in vascular biopsies. Correlation to clinical parameters in a carotid endarterectomy cohort revealed positive associations for PCSK5/6/7 with adverse cardiovascular events. Conclusions: Our results show that PCSK6 is the most enriched PCSK in plaques and abdominal aneurysms, while FURIN upregulation is characteristic for thoracic aneurysms. PCSK9 protein but not the transcript, was present in vascular lesions, suggesting its accumulation from circulation. Overall evaluation revealed that PCSK6 is the most attractive protease from this family to target for drug development in CVD.

AimsTo identify novel risk markers for incident heart failure using proteomic profiling of 80 proteins previously associated with cardiovascular pathology.Methods and resultsProteomic profiling (pr ...

Background and Purpose— Emerging technologies have made it possible to simultaneously evaluate a large number of circulating proteins as potential new stroke risk markers. Methods— We explored associations between 85 cardiovascular proteins, assessed by a proteomics chip, and incident ischemic stroke in 2 independent cohorts of elderly (Prospective Investigation of the Vasculature in Uppsala Seniors [PIVUS]: n=977; 50% women, mean age=70.1 years, 71 fatal/nonfatal ischemic stroke events during 10.0 years; and Uppsala Longitudinal Study in Adult Men [ULSAM]: n=720, mean age=77.5 years, 75 ischemic stroke events during 9.5 years). The proteomics chip uses 2 antibodies for each protein and a polymerase chain reaction step to achieve a high-specific binding and the possibility to measure multiple proteins in parallel, but gives no absolute concentrations. Results— In PIVUS, 16 proteins were related to incident ischemic stroke using a false discovery rate of 5%. Of these, N-terminal pro-B-type natriuretic peptide ( P =0.0032), adrenomedullin ( P =0.018), and eosinophil cationic protein ( P =0.0071) were replicated in ULSAM after adjustment for established stroke risk factors. In predefined secondary meta-analyses of individual data, interleukin-27 subunit α, growth/differentiation factor 15, urokinase plasminogen activator surface receptor, tumor necrosis factor receptor superfamily member 6, macrophage colony-stimulating factor 1, and matrix metalloproteinase-7 were also potential risk markers for ischemic stroke after adjustment for multiple comparisons ( P P =0.001). Conclusions— Our data suggest that large-scale proteomics analysis is a promising way of discovering novel biomarkers that could substantially improve the prediction of ischemic stroke.

Background Apart from several established clinical risk factors for atrial fibrillation (AF), a number of biomarkers have also been identified as potential risk factors for AF. None of these have so far been adopted in clinical practice. Objective To use a novel custom-made proteomics chip to discover new prognostic biomarkers for AF risk. Methods In two independent community-based cohorts (Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) study (978 participants without AF, mean age 70.1 years, 50% women, median follow-up 10.0 years) and Uppsala Longitudinal Study of Adult Men (ULSAM) (n=725, mean age 77.5 years, median follow-up 7.9 years)), ninety-two plasma proteins were assessed at baseline by a proximity extension assay (PEA) chip. Of those, 85 proteins showed a call rate >70% in both cohorts. Results Thirteen proteins were related to incident AF in PIVUS (148 events) using a false discovery rate of 5%. Of those, five were replicated in ULSAM at nominal multivariable p value (123 events, N-terminal pro-B-type natriuretic peptide (NT-pro-BNP), fibroblast growth factor 23 (FGF-23), fatty acid-binding protein 4 (FABP4), growth differentiation factor 15 (GDF-15) and interleukin-6 (IL-6)). Of those, NT-pro-BNP and FGF-23 were also associated with AF after adjusting for established AF risk factors. In a prespecified secondary analysis pooling the two data sets, T-cell immunoglobulin and mucin domain 1 (TIM-1) and adrenomedullin (AM) were also significantly related to incident AF in addition to the aforementioned five proteins (Bonferroni-adjustment). The addition of NT-pro-BNP to a model with established risk factors increased the C-statistic from 0.605 to 0.676 (p Conclusions Using a novel proteomics approach, we confirmed the previously reported association between NT-pro-BNP, FGF-23, GDF-15 and incident AF, and also discovered four proteins (FABP4, IL-6, TIM-1 and AM) that could be of importance in the development of AF.

Background The underlying mechanisms for the development of albuminuria and the increased cardiovascular risk in patients with elevated albuminuria levels are incompletely understood. We therefore investigated the associations between 80 cardiovascular proteins and the urinary albumin to creatinine ratio (ACR). Methods We used a discovery/replication approach in two independent community-based cohorts of elderly patients: the Uppsala Longitudinal Study of Adult Men ( n = 662; mean age 78 years) and the Prospective Investigation of the Vasculature in Uppsala Seniors ( n = 757; mean age 75 years; 51% women). A proteomic chip with a panel of 80 plasma proteins associated with different aspects of cardiovascular disease was analysed. In the discovery cohort, we used a false discovery rate of 5% to take into account the multiple statistical testing. Nominal p values were used in the replication. Results Higher levels of T-cell immunoglobulin mucin-1, placenta growth factor, growth/differentiation factor-15, urokinase plasminogen activator surface receptor and kallikrein-11 were robustly associated with a higher ACR in both cohorts in multivariable linear regression models adjusted for sex, established cardiovascular risk factors, antihypertensive treatment, prevalent cardiovascular disease and glomerular filtration rate ( p 0.02 for all). All associations were also significant in separate analyses of patients without diabetes. Conclusions We discovered and replicated associations between ACR and five cardiovascular proteins involved in tubular injury, atherosclerosis, endothelial function, heart failure, inflammation, glomerulosclerosis and podocyte injury. Our findings put forward multiplex proteomics as a promising approach to explore novel aspects of the complex detrimental interplay between kidney function and the cardiovascular system.

Non-targeted metabolomic profiling is used to simultaneously assess a large part of the metabolome in a biological sample. Here, we describe both the analytical and computational methods used to analyze a large UPLC–Q-TOF MS-based metabolomic profiling effort using plasma and serum samples from participants in three Swedish population-based studies of middle-aged and older human subjects: TwinGene, ULSAM and PIVUS. At present, more than 200 metabolites have been manually annotated in more than 3600 participants using an in-house library of standards and publically available spectral databases. Data available at the metabolights repository include individual raw unprocessed data, processed data, basic demographic variables and spectra of annotated metabolites. Additional phenotypical and genetic data is available upon request to cohort steering committees. These studies represent a unique resource to explore and evaluate how metabolic variability across individuals affects human diseases.

Protein biomarkers have the potential to improve diagnosis, stratification of patients into treatment cohorts, follow disease progression and treatment response. One distinct group of potential biomarkers comprises proteins which have been linked to cancer, known as cancer associated proteins (CAPs). We determined the normal variation of 86 CAPs in 72 individual plasma samples collected from ten individuals using SRM mass spectrometry. Samples were collected weekly during 5 weeks from ten volunteers and over one day at nine fixed time points from three volunteers. We determined the degree of the normal variation depending on interpersonal variation, variation due to time of day, and variation over weeks and observed that the variation dependent on the time of day appeared to be the most important. Subdivision of the proteins resulted in two predominant protein groups containing 21 proteins with relatively high variation in all three factors (day, week and individual), and 22 proteins with relatively low variation in all factors. We present a strategy for prioritizing biomarker candidates for future studies based on stratification over their normal variation and have made all data publicly available. Our findings can be used to improve selection of biomarker candidates in future studies and to determine which proteins are most suitable depending on study design.