Circulating proteomic signature of early death in heart failure patients with reduced ejection fraction

Biomechanical dysregulation of SGK-1 dependent aortic pathologic markers in hypertension

Introduction

In hypertension (HTN), biomechanical stress may drive matrix remodeling through dysfunctional VSMC activity. Prior evidence has indicated VSMC tension-induced signaling through the serum and glucocorticoid inducible kinase-1 (SGK-1) can impact cytokine abundance. Here, we hypothesize that SGK-1 impacts production of additional aortic pathologic markers (APMs) representing VSMC dysfunction in HTN.

Methods

Aortic VSMC expression of APMs was quantified by QPCR in cyclic biaxial stretch (Stretch) +/- AngiotensinII (AngII). APMs were selected to represent VSMC dedifferentiated transcriptional activity, specifically Interleukin-6 (IL-6), Cathepsin S (CtsS), Cystatin C (CysC), Osteoprotegerin (OPG), and Tenascin C (TNC). To further assess the effect of tension alone, abdominal aortic rings from C57Bl/6 WT mice were held in a myograph at experimentally derived optimal tension (OT) or OT + 30% +/-AngII. Dependence on SGK-1 was assessed by treating with EMD638683 (SGK-1 inhibitor) and APMs were measured by QPCR. Then, WT and smooth muscle cell specific SGK-1 heterozygous knockout (SMC-SGK-1KO+/-) mice had AngII-induced HTN. Systolic blood pressure and mechanical stress parameters were assessed on Day 0 and Day 21. Plasma was analyzed by ELISA to quantify APMs. Statistical analysis was performed by ANOVA.

Results

In cultured aortic VSMCs, expression of all APMs was increased in response to biomechanical stimuli (Stretch +/-AngII,). Integrating the matrix contribution to signal transduction in the aortic rings led to IL-6 and CysC demonstrating SGK-1 dependence in response to elevated tension and interactive effect with concurrent AngII stimulation. CtsS and TNC, on the other hand, primarily responded to AngII, and OPG expression was unaffected in aortic ring experimentation. Both mouse strains had >30% increase in blood pressure with AngII infusion, reduced aortic distensibility and increased PPV, indicating increased aortic stiffness. In WT + AngII mice, IL-6, CtsS, CysC, and TNC plasma levels were significantly elevated, but these APMs were unaffected by HTN in the SMC-SGK-1KO+/- +AngII mice, suggesting SGK-1 plays a major role in VSMC biomechanical signaling to promote dysfunctional production of selected APMs.

Conclusion

In HTN, changes in the plasma levels of markers associated with aortic matrix homeostasis can reflect remodeling driven by mechanobiologic signaling in dysfunctional VSMCs, potentially through the activity of SGK-1. Further defining these pathways may identify therapeutic targets to reduce cardiovascular morbidity and mortality.

Enrichment on steps, not genes, improves inference of differentially expressed pathways

Enrichment analysis is frequently used in combination with differential expression data to investigate potential commonalities amongst lists of genes and generate hypotheses for further experiments. However, current enrichment analysis approaches on pathways ignore the functional relationships between genes in a pathway, particularly OR logic that occurs when a set of proteins can each individually perform the same step in a pathway. As a result, these approaches miss pathways with large or multiple sets because of an inflation of pathway size (when measured as the total gene count) relative to the number of steps. We address this problem by enriching on step-enabling entities in pathways. We treat sets of protein-coding genes as single entities, and we also weight sets to account for the number of genes in them using the multivariate Fisher's noncentral hypergeometric distribution. We then show three examples of pathways that are recovered with this method and find that the results have significant proportions of pathways not found in gene list enrichment analysis.

Proteomics for heart failure risk stratification: a systematic review

BACKGROUND

Heart failure (HF) is a complex clinical syndrome with persistently high mortality. High-throughput proteomic technologies offer new opportunities to improve HF risk stratification, but their contribution remains to be clearly defined. We aimed to systematically review prognostic studies using high-throughput proteomics to identify protein signatures associated with HF mortality.

METHODS

We searched four databases and two clinical trial registries for articles published from 2012 to 2023. HF proteomics studies measuring high numbers of proteins using aptamer or antibody-based affinity platforms on human plasma or serum with outcomes of all-cause or cardiovascular death were included. Two reviewers independently screened articles, extracted data, and assessed the risk of bias. A third reviewer resolved conflicts. We assessed the risk of bias using the Risk Of Bias In Non-randomized Studies-of Exposure tool.

RESULTS

Out of 5131 unique articles identified, nine articles were included in the review. The nine studies were observational; three used the aptamer platform, and six used the antibody platform. We found considerable heterogeneity across studies in measurement panels, HF definitions, ejection fraction categorization, follow-up duration, and outcome definitions, and a lack of risk estimates for most protein associations. Hence, we proceeded with a systematic review rather than a meta-analysis. In two comparable aptamer studies in patients with HF with reduced ejection fraction, 21 proteins were identified in common for the association with all-cause death. Among these, one protein, WAP four-disulfide core domain protein 2 was also reported in an antibody study on HFrEF and for the association with CV death. We proposed standardized reporting criteria to facilitate the interpretation of future studies.

CONCLUSIONS

In this systematic review of nine studies evaluating the association of proteomics with mortality in HF, we identified a limited number of proteins common across several studies. Heterogeneity across studies compromised drawing broad inferences, underscoring the importance of standardized approaches to reporting.

Development and Validation of a Protein Risk Score for Mortality in Heart Failure : A Community Cohort Study

BACKGROUND

Heart failure (HF) is a complex clinical syndrome with high mortality. Current risk stratification approaches lack precision. High-throughput proteomics could improve risk prediction. Its use in clinical practice to guide the management of patients with HF depends on validation and evidence of clinical benefit.

OBJECTIVE

To develop and validate a protein risk score for mortality in patients with HF.

DESIGN

Community-based cohort.

SETTING

Southeast Minnesota.

PARTICIPANTS

Patients with HF enrolled between 2003 and 2012 and followed through 2021.

MEASUREMENTS

A total of 7289 plasma proteins in 1351 patients with HF were measured using the SomaScan Assay (SomaLogic). A protein risk score was derived using least absolute shrinkage and selection operator regression and temporal validation in patients enrolled between 2003 and 2007 (development cohort) and 2008 and 2012 (validation cohort). Multivariable Cox regression was used to examine the association between the protein risk score and mortality. The performance of the protein risk score to predict 5-year mortality risk was assessed using calibration plots, decision curves, and relative utility analyses and compared with a clinical model, including the Meta-Analysis Global Group in Chronic Heart Failure mortality risk score and N-terminal pro-B-type natriuretic peptide.

RESULTS

The development (n = 855; median age, 78 years; 50% women; 29% with ejection fraction <40%) and validation cohorts (n = 496; median age, 76 years; 45% women; 33% with ejection fraction <40%) were mostly similar. In the development cohort, 38 unique proteins were selected for the protein risk score. Independent of ejection fraction, the protein risk score demonstrated good calibration, reclassified mortality risk particularly at the extremes of the risk distribution, and showed greater clinical utility compared with the clinical model.

LIMITATION

Participants were predominantly of European ancestry, potentially limiting the generalizability of the findings to different patient populations.

CONCLUSION

Validation of the protein risk score demonstrated good calibration and evidence of predicted benefits to stratify the risk for death in HF superior to that of clinical methods. Further studies are needed to prospectively evaluate the score's performance in diverse populations and determine risk thresholds for interventions.

PRIMARY FUNDING SOURCE

Division of Intramural Research at the National Heart, Lung, and Blood Institute of the National Institutes of Health.

Evaluation of a New Aptamer-Based Array for Soluble Suppressor of Tumorgenicity (ST2) and N-terminal Pro-B-Type Natriuretic Peptide (NTproBNP) in Heart Failure Patients

BACKGROUND

Recent advances in multi-marker platforms offer faster data generation, but the fidelity of these methods compared to the ELISA is not established. We tested the correlation and predictive performance of SOMAscan vs. ELISA methods for NTproBNP and ST2.

METHODS

Patients ≥ 18 years with heart failure and ejection fraction < 50% were enrolled. We tested the correlation between SOMA and ELISA for each biomarker and their association with outcomes.

RESULTS

There was good correlation of SOMA vs. ELISA for ST2 (ρ = 0.71) and excellent correlation for NTproBNP (ρ = 0.94). The two versions of both markers were not significantly different regarding survival association. The two ST2 assays and NTproBNP assays were similarly associated with all-cause mortality and cardiovascular mortality. These associations remained statistically significant when adjusted for MAGGIC risk score (all p < 0.05).

CONCLUSION

SOMAscan quantifications of ST2 and NTproBNP correlate to ELISA versions and carry similar prognosis.

Machine learning-based biomarker profile derived from 4210 serially measured proteins predicts clinical outcome of patients with heart failure

Aims

Risk assessment tools are needed for timely identification of patients with heart failure (HF) with reduced ejection fraction (HFrEF) who are at high risk of adverse events. In this study, we aim to derive a small set out of 4210 repeatedly measured proteins, which, along with clinical characteristics and established biomarkers, carry optimal prognostic capacity for adverse events, in patients with HFrEF.

Methods and results

In 382 patients, we performed repeated blood sampling (median follow-up: 2.1 years) and applied an aptamer-based multiplex proteomic approach. We used machine learning to select the optimal set of predictors for the primary endpoint (PEP: composite of cardiovascular death, heart transplantation, left ventricular assist device implantation, and HF hospitalization). The association between repeated measures of selected proteins and PEP was investigated by multivariable joint models. Internal validation (cross-validated c-index) and external validation (Henry Ford HF PharmacoGenomic Registry cohort) were performed. Nine proteins were selected in addition to the MAGGIC risk score, N-terminal pro-hormone B-type natriuretic peptide, and troponin T: suppression of tumourigenicity 2, tryptophanyl-tRNA synthetase cytoplasmic, histone H2A Type 3, angiotensinogen, deltex-1, thrombospondin-4, ADAMTS-like protein 2, anthrax toxin receptor 1, and cathepsin D. N-terminal pro-hormone B-type natriuretic peptide and angiotensinogen showed the strongest associations [hazard ratio (95% confidence interval): 1.96 (1.17-3.40) and 0.66 (0.49-0.88), respectively]. The multivariable model yielded a c-index of 0.85 upon internal validation and c-indices up to 0.80 upon external validation. The c-index was higher than that of a model containing established risk factors (P = 0.021).

Conclusion

Nine serially measured proteins captured the most essential prognostic information for the occurrence of adverse events in patients with HFrEF, and provided incremental value for HF prognostication beyond established risk factors. These proteins could be used for dynamic, individual risk assessment in a prospective setting. These findings also illustrate the potential value of relatively 'novel' biomarkers for prognostication.

Clinical Trial Registration

https://clinicaltrials.gov/ct2/show/NCT01851538?term=nCT01851538&draw=2&rank=1 24.

Effects of Cathepsin S Inhibition in the Age-Related Dry Eye Phenotype

Purpose

Aged C57BL/6J (B6) mice have increased levels of cathepsin S, and aged cathepsin S (Ctss-/-) knockout mice are resistant to age-related dry eye. This study investigated the effects of cathepsin S inhibition on age-related dry eye disease.

Methods

Female B6 mice aged 15.5 to 17 months were randomized to receive a medicated diet formulated by mixing the RO5461111 cathepsin S inhibitor or a standard diet for at least 12 weeks. Cornea mechanosensitivity was measured with a Cochet-Bonnet esthesiometer. Ocular draining lymph nodes and lacrimal glands (LGs) were excised and prepared for histology or assayed by flow cytometry to quantify infiltrating immune cells. The inflammatory foci (>50 cells) were counted under a 10× microscope lens and quantified using the focus score. Goblet cell density was investigated in periodic acid-Schiff stained sections. Ctss-/- mice were compared to age-matched wild-type mice.

Results

Aged mice subjected to cathepsin S inhibition or Ctss-/- mice showed improved conjunctival goblet cell density and cornea mechanosensitivity. There was no change in total LG focus score in the diet or Ctss-/- mice, but there was a lower frequency of CD4+IFN-γ+ cell infiltration in the LGs. Furthermore, aged Ctss-/- LGs had an increase in T central memory, higher numbers of CD19+B220-, and fewer CD19+B220+ cells than wild-type LGs.

Conclusions

Our results indicate that therapies aimed at decreasing cathepsin S can ameliorate age-related dry eye disease with a highly beneficial impact on the ocular surface. Further studies are needed to investigate the role of cathepsin S during aging.

Identification of patient subtypes based on protein expression for prediction of heart failure after myocardial infarction

This study investigates the ability of high-throughput aptamer-based platform to identify circulating biomarkers able to predict occurrence of heart failure (HF), in blood samples collected during hospitalization of patients suffering from a first myocardial infarction (MI). REVE-1 (derivation) and REVE-2 (validation) cohorts included respectively 254 and 238 patients, followed up respectively 9 · 2 ± 4 · 8 and 7 · 6 ± 3 · 0 years. A blood sample collected during hospitalization was used for quantifying 4,668 proteins. Fifty proteins were significantly associated with long-term occurrence of HF with all-cause death as the competing event. k-means, an unsupervised clustering method, identified two groups of patients based on expression levels of the 50 proteins. Group 2 was significantly associated with a higher risk of HF in both cohorts. These results showed that a subset of 50 selected proteins quantified during hospitalization of MI patients is able to stratify and predict the long-term occurrence of HF.

Cathepsin S (CTSS) activity in health and disease - A treasure trove of untapped clinical potential

Amongst the lysosomal cysteine cathepsin family of proteases, cathepsin S (CTSS) holds particular interest due to distinctive properties including a normal restricted expression profile, inducible upregulation and activity at a broad pH range. Consequently, while CTSS is well-established as a member of the proteolytic cocktail within the lysosome, degrading unwanted and damaged proteins, it has increasingly been shown to mediate a number of distinct, more selective roles including antigen processing and antigen presentation, and cleavage of substrates both intra and extracellularly. Increasingly, aberrant CTSS expression has been demonstrated in a variety of conditions and disease states, marking it out as both a biomarker and potential therapeutic target. This review seeks to contextualise CTSS within the cysteine cathepsin family before providing an overview of the broad range of pathologies in which roles for CTSS have been identified. Additionally, current clinical progress towards specific inhibitors is detailed, updating the position of the field in exploiting this most unique of proteases.

Rapid evidence synthesis to enable innovation and adoption in health and social care

BACKGROUND

The rapid identification and adoption of effective innovations in healthcare is a known challenge. The strongest evidence base for innovations can be provided by evidence synthesis, but this is frequently a lengthy process and even rapid versions of this can be time-consuming and complex. In the UK, the Accelerated Access Review and Academic Health Science Network (AHSN) have provided the impetus to develop a consistently rapid process to support the identification and adoption of high-value innovations in the English NHS.

METHODS

The Greater Manchester Applied Research Collaboration (ARC-GM) developed a framework for a rapid evidence synthesis (RES) approach, which is highly integrated within the innovation process of the Greater Manchester AHSN and the associated healthcare and research ecosystem. The RES uses evidence synthesis approaches and draws on the GRADE Evidence to Decision framework to provide rapid assessments of the existing evidence and its relevance to specific decision problems. We implemented this in a real-time context of decision-making around adoption of innovative health technologies.

RESULTS

Key stakeholders in the Greater Manchester decision-making process for healthcare innovations have found that our approach is both timely and flexible; it is valued for its combination of rigour and speed. Our RES approach rapidly and systematically identifies, appraises and contextualises relevant evidence, which can then be transparently incorporated into decisions about the wider adoption of innovations. The RES also identifies limitations in existing evidence for innovations and this can inform subsequent evaluations. There is substantial interest from other ARCs and AHSNs in implementing a similar process. We are currently exploring methods to make completed RES publicly available. We are also exploring methods to evaluate the impact of using RES as more implementation decisions are made.

CONCLUSIONS

The RES framework we have implemented combines transparency and consistency with flexibility and rapidity. It therefore maximises utility in a real-time decision-making context for healthcare innovations.

Unique and shared systemic biomarkers for emphysema in Alpha-1 Antitrypsin deficiency and chronic obstructive pulmonary disease

BACKGROUND

Alpha-1 Antitrypsin (AAT) deficiency (AATD), the most common genetic cause of emphysema presents with unexplained phenotypic heterogeneity in affected subjects. Our objectives to identify unique and shared AATD plasma biomarkers with chronic obstructive pulmonary disease (COPD) may explain AATD phenotypic heterogeneity.

METHODS

The plasma or serum of 5,924 subjects from four AATD and COPD cohorts were analyzed on SomaScan V4.0 platform. Using multivariable linear regression, inverse variance random-effects meta-analysis, and Least Absolute Shrinkage and Selection Operator (LASSO) regression we tested the association between 4,720 individual proteins or combined in a protein score with emphysema measured by 15th percentile lung density (PD15) or diffusion capacity (DLCO) in distinct AATD genotypes (Pi*ZZ, Pi*SZ, Pi*MZ) and non-AATD, PiMM COPD subjects. AAT SOMAmer accuracy for identifying AATD was tested using receiver operating characteristic curve analysis.

FINDINGS

In PiZZ AATD subjects, 2 unique proteins were associated with PD15 and 98 proteins with DLCO. Of those, 68 were also associated with DLCO in COPD also and enriched for three cellular component pathways: insulin-like growth factor, lipid droplet, and myosin complex. PiMZ AATD subjects shared similar proteins associated with DLCO as COPD subjects. Our emphysema protein score included 262 SOMAmers and predicted emphysema in AATD and COPD subjects. SOMAmer AAT level <7.99 relative fluorescence unit (RFU) had 100% sensitivity and specificity for identifying Pi*ZZ, but it was lower for other AATD genotypes.

INTERPRETATION

Using SomaScan, we identified unique and shared plasma biomarkers between AATD and COPD subjects and generated a protein score that strongly associates with emphysema in COPD and AATD. Furthermore, we discovered unique biomarkers associated with DLCO and emphysema in PiZZ AATD.

FUNDING

This work was supported by a grant from the Alpha-1 Foundation to RPB. COPDGene was supported by Award U01 HL089897 and U01 HL089856 from the National Heart, Lung, and Blood Institute. Proteomics for COPDGene was supported by NIH 1R01HL137995. GRADS was supported by Award U01HL112707, U01 HL112695 from the National Heart, Lung, and Blood Institute, and UL1TRR002535 to CCTSI; QUANTUM-1 was supported by the National Heart Lung and Blood Institute, the Office of Rare Diseases through the Rare Lung Disease Clinical Research Network (1 U54 RR019498-01, Trapnell PI), and the Alpha-1 Foundation. COPDGene is also supported by the COPD Foundation through contributions made to an Industry Advisory Board that has included AstraZeneca, Bayer Pharmaceuticals, Boehringer-Ingelheim, Genentech, GlaxoSmithKline, Novartis, Pfizer, and Sunovion.

The complement C3-complement factor D-C3a receptor signalling axis regulates cardiac remodelling in right ventricular failure

Failure of the right ventricle plays a critical role in any type of heart failure. However, the mechanism remains unclear, and there is no specific therapy. Here, we show that the right ventricle predominantly expresses alternative complement pathway-related genes, including Cfd and C3aR1. Complement 3 (C3)-knockout attenuates right ventricular dysfunction and fibrosis in a mouse model of right ventricular failure. C3a is produced from C3 by the C3 convertase complex, which includes the essential component complement factor D (Cfd). Cfd-knockout mice also show attenuation of right ventricular failure. Moreover, the plasma concentration of CFD correlates with the severity of right ventricular failure in patients with chronic right ventricular failure. A C3a receptor (C3aR) antagonist dramatically improves right ventricular dysfunction in mice. In summary, we demonstrate the crucial role of the C3-Cfd-C3aR axis in right ventricular failure and highlight potential therapeutic targets for right ventricular failure.

Right ventricular (RV) failure is clinically crucial, but there is no specific therapy. Here, the authors show that the complement alternative pathway is activated in RV failure and that blockade of the pathway ameliorates RV failure in mice.

Association of Matrix Metalloproteinases with Coronary Artery Calcification in Patients with CHD

This work is aimed at studying the relationship of matrix metalloproteinases with calcification of the coronary arteries. The study included 78 people with coronary heart disease (CHD) and 36 without CHD. Blood and samples of coronary arteries obtained as a result of endarterectomy were examined. Serum levels of metalloproteinases (MMP) MMP-1, MMP-2, MMP-3, MMP-7, MMP-9, MMP-10, MMP-12, and MMP-13 were determined by multiplex analysis. In blood vessel samples, MMP-1, MMP-3, MMP-7, and MMP-9 were determined by enzyme immunoassay; MMP-9 expression was evaluated by immunohistochemistry. Patients with CHD had higher serum levels of MMP-1, MMP-7, and MMP-12. Blood levels of MMP-1 and MMP-3 were associated with calcium levels, MMP-9 with osteoprotegerin and osteonectin, MMP-7 and MMP-10 with osteoprotegerin, MMP-12 with osteocalcin, and MMP-13 with osteopontin. Calcified plaques had higher levels of MMP-1 and MMP-9 compared to plaques without calcification. The relative risk of coronary arteries calcification was associated with MMP-9, which is confirmed by the results of immunohistochemistry. The results obtained indicate the participation of some MMPs, and especially MMP-9, in the calcification processes. The study can serve as a basis for the further study of the possibility of using MMP-1, MMP-7 and MMP-12 as potential biomarkers of CHD.

Plasma Proteomic Profile Predicts Survival in Heart Failure With Reduced Ejection Fraction

BACKGROUND

It remains unclear whether the plasma proteome adds value to established predictors in heart failure (HF) with reduced ejection fraction (HFrEF). We sought to derive and validate a plasma proteomic risk score (PRS) for survival in patients with HFrEF (HFrEF-PRS).

METHODS

Patients meeting Framingham criteria for HF with EF<50% were enrolled (N=1017) and plasma underwent SOMAscan profiling (4453 targets). Patients were randomly divided 2:1 into derivation and validation cohorts. The HFrEF-PRS was derived using Cox regression of all-cause mortality adjusted for clinical score and NT-proBNP (N-terminal pro-B-type natriuretic peptide), then was tested in the validation cohort. Risk stratification improvement was evaluated by C statistic, integrated discrimination index, continuous net reclassification index, and median improvement in risk score for 1-year and 3-year mortality.

RESULTS

Participants' mean age was 68 years, 48% identified as Black, 35% were female, and 296 deaths occurred. In derivation (n=681), 128 proteins associated with mortality, 8 comprising the optimized HFrEF-PRS. In validation (n=336) the HFrEF-PRS associated with mortality (hazard ratio, 2.27 [95% CI, 1.84-2.82], P=6.3×10^-14), Kaplan-Meier curves differed significantly between HFrEF-PRS quartiles (P=2.2×10^-6), and it remained significant after adjustment for clinical score and NT-proBNP (hazard ratio, 1.37 [95% CI, 1.05-1.79], P=0.021). The HFrEF-PRS improved metrics of risk stratification (C statistic change, 0.009, P=0.612; integrated discrimination index, 0.041, P=0.010; net reclassification index=0.391, P=0.078; median improvement in risk score=0.039, P=0.016) and associated with cardiovascular death and HF phenotypes (eg, 6-minute walk distance, EF change). Most HFrEF-PRS proteins had little known connection to HFrEF.

CONCLUSIONS

A plasma multiprotein score improved risk stratification in patients with HFrEF and identified novel candidates.

Epidemiology of Heart Failure: A Contemporary Perspective

Designated as an emerging epidemic in 1997, heart failure (HF) remains a major clinical and public health problem. This review focuses on the most recent studies identified by searching the Medline database for publications with the subject headings HF, epidemiology, prevalence, incidence, trends between 2010 and present. Publications relevant to epidemiology and population sciences were retained for discussion in this review after reviewing abstracts for relevance to these topics. Studies of the epidemiology of HF over the past decade have improved our understanding of the HF syndrome and of its complexity. Data suggest that the incidence of HF is mostly flat or declining but that the burden of mortality and hospitalization remains mostly unabated despite significant ongoing efforts to treat and manage HF. The evolution of the case mix of HF continues to be characterized by an increasing proportion of cases with preserved ejection fraction, for which established effective treatments are mostly lacking. Major disparities in the occurrence, presentation, and outcome of HF persist particularly among younger Black men and women. These disturbing trends reflect the complexity of the HF syndrome, the insufficient mechanistic understanding of its various manifestations and presentations and the challenges of its management as a chronic disease, often integrated within a context of aging and multimorbidity. Emerging risk factors including omics science offer the promise of discovering new mechanistic pathways that lead to HF. Holistic management approaches must recognize HF as a syndemic and foster the implementation of multidisciplinary approaches to address major contributors to the persisting burden of HF including multimorbidity, aging, and social determinants of health.

MethylDetectR: a software for methylation-based health profiling

DNA methylation is an important biological process that involves the reversible addition of chemical tags called methyl groups to DNA and affects whether genes are active or inactive. Individual methylation profiles are determined by both genetic and environmental influences. Inter-individual variation in DNA methylation profiles can be exploited to estimate or predict a wide variety of human characteristics and disease risk profiles. Indeed, a number of methylation-based predictors of human traits have been developed and linked to important health outcomes. However, there is an unmet need to communicate the applicability and limitations of state-of-the-art methylation-based predictors to the wider community. To address this need, we have created a secure, web-based interactive platform called 'MethylDetectR' which automates the calculation of estimated values or scores for a variety of human traits using blood methylation data. These traits include age, lifestyle traits and high-density lipoprotein cholesterol. Methylation-based predictors often return scores on arbitrary scales. To provide meaning to these scores, users can interactively view how estimated trait scores for a given individual compare against other individuals in the sample. Users can optionally upload binary phenotypes and investigate how estimated traits vary according to case vs. control status for these phenotypes. Users can also view how different methylation-based predictors correlate with one another, and with phenotypic values for corresponding traits in a large reference sample (n = 4,450; Generation Scotland). The 'MethylDetectR' platform allows for the fast and secure calculation of DNA methylation-derived estimates for several human traits. This platform also helps to show the correlations between methylation-based scores and corresponding traits at the level of a sample, report estimated health profiles at an individual level, demonstrate how scores relate to important binary outcomes of interest and highlight the current limitations of molecular health predictors.

MethylDetectR: a software for methylation-based health profiling

DNA methylation is an important biological process which involves the reversible addition of chemical tags called methyl groups to DNA and affects whether genes are active or inactive. Individual methylation profiles are determined by both genetic and environmental influences. Inter-individual variation in DNA methylation profiles can be exploited to estimate or predict a wide variety of human characteristics and disease risk profiles. Indeed, a number of methylation-based predictors of human traits have been developed and linked to important health outcomes. However, there is an unmet need to communicate the applicability and limitations of state-of-the-art methylation-based predictors to the wider community. To address this, we created a secure, web-based interactive platform called 'MethylDetectR' which calculates estimated values or scores for a variety of human traits using blood methylation data. These traits include age, lifestyle traits, high-density lipoprotein cholesterol and the levels of 27 blood proteins related to inflammatory and neurological processes and disease. Methylation-based predictors often return scores on arbitrary scales. To provide meaning to these scores, users can interactively view how estimated trait scores for a given individual compare against other individuals in the sample. Users can optionally upload binary phenotypes and investigate how estimated traits vary according to case vs. control status for these phenotypes. Users can also view how different methylation-based predictors correlate with one another, and with phenotypic values for corresponding traits in a large reference sample (n = 4,450; Generation Scotland). The 'MethylDetectR' platform allows for the fast and secure calculation of DNA methylation-derived estimates for many human traits. This platform also helps to show the correlations between methylation-based scores and corresponding traits at the level of a sample, report estimated health profiles at an individual level, demonstrate how scores relate to important binary outcomes of interest and highlight the current limitations of molecular health predictors.

Metalloproteinases and Their Inhibitors: Potential for the Development of New Therapeutics

The metalloproteinase (MP) family of zinc-dependent proteases, including matrix metalloproteinases (MMPs), a disintegrin and metalloproteases (ADAMs), and a disintegrin and metalloproteinase with thrombospondin motifs (ADAMTSs) plays a crucial role in the extracellular matrix (ECM) remodeling and degradation activities. A wide range of substrates of the MP family includes ECM components, chemokines, cell receptors, and growth factors. Metalloproteinases activities are tightly regulated by proteolytic activation and inhibition via their natural inhibitors, tissue inhibitors of metalloproteinases (TIMPs), and the imbalance of the activation and inhibition is responsible in progression or inhibition of several diseases, e.g., cancer, neurological disorders, and cardiovascular diseases. We provide an overview of the structure, function, and the multifaceted role of MMPs, ADAMs, and TIMPs in several diseases via their cellular functions such as proteolysis of other cell signaling factors, degradation and remodeling of the ECM, and other essential protease-independent interactions in the ECM. The significance of MP inhibitors targeting specific MMP or ADAMs with high selectivity is also discussed. Recent advances and techniques used in developing novel MP inhibitors and MP responsive drug delivery tools are also reviewed.

Pulmonary hypertension: Proteins in the blood

The plasma proteome is rich in information. It comprises proteins that are secreted or lost from cells as they respond to their local environment. Changes in the constitution of the plasma proteome offer a relatively non-invasive report on the health of tissues. This is particularly true of the lung in pulmonary hypertension, given the large surface area of the pulmonary vasculature in direct communication with blood. So far, this is relatively untapped; we have relied on proteins released from the heart, specifically brain natriuretic peptide and troponin, to inform clinical management. New technology allows the measurement of a larger number of proteins that cover a broad range of molecular pathways in a single small aliquot. The emerging data will yield more than just new biomarkers of pulmonary hypertension for clinical use. Integrated with genomics and with the help of new bioinformatic tools, the plasma proteome can provide insight into the causative drivers of pulmonary vascular disease and guide drug development.

Elevated circulating metalloproteinase 7 predicts recurrent cardiovascular events in patients with carotid stenosis: a prospective cohort study

BACKGROUND

Major adverse cardiovascular events are the main cause of morbidity and mortality over the long term in patients undergoing carotid endarterectomy. There are few reports assessing the prognostic value of markers of inflammation in relation to the risk of cardiovascular disease after carotid endarterectomy. Here, we aimed to determine whether matrix metalloproteinases (MMP-1, MMP-2, MMP-7, MMP-9 and MMP-10), tissue inhibitor of MMPs (TIMP-1) and in vivo inflammation studied by ¹⁸F-FDG-PET/CT predict recurrent cardiovascular events in patients with carotid stenosis who underwent endarterectomy.

METHODS

This prospective cohort study was carried out on 31 consecutive patients with symptomatic (23/31) or asymptomatic (8/31) severe (> 70%) carotid stenosis who were scheduled for carotid endarterectomy between July 2013 and March 2016. In addition, 26 healthy controls were included in the study. Plasma and serum samples were collected 2 days prior to surgery and tested for MMP-1, MMP-2, MMP-7, MMP-9, MMP-10, TIMP-1, high-density lipoprotein, low-density lipoprotein, high-sensitivity C-reactive protein and erythrocyte sedimentation rate. ¹⁸F-FDG-PET/CT focusing on several territories' vascular wall metabolism was performed on 29 of the patients because of no presurgical availability in 2 symptomatic patients. Histological and immunohistochemical studies were performed with antibodies targeting MMP-10, MMP-9, TIMP-1 and CD68.

RESULTS

The patients with carotid stenosis had significantly more circulating MMP-1, MMP-7 and MMP-10 than the healthy controls. Intraplaque TIMP-1 was correlated with its plasma level (r = 0.42 P = .02) and with ¹⁸F-FDG uptake (r = 0.38 P = .05). We did not find any correlation between circulating MMPs and in vivo carotid plaque metabolism assessed by ¹⁸F-FDG-PET. After a median follow-up of 1077 days, 4 cerebrovascular, 7 cardiovascular and 11 peripheral vascular events requiring hospitalization were registered. Circulating MMP-7 was capable of predicting events over and above the traditional risk factors (HR = 1.15 P = .006). When the model was associated with the variables of interest, the risk predicted by ¹⁸F-FDG-PET was not significant.

CONCLUSIONS

Circulating MMP-7 may represent a novel marker for recurrent cardiovascular events in patients with moderate to severe carotid stenosis. MMP-7 may reflect the atherosclerotic burden but not plaque inflammation in this specific vascular territory.