Sox9 Accelerates Vascular Aging by Regulating Extracellular Matrix Composition and Stiffness.

BACKGROUND: Vascular calcification and increased extracellular matrix (ECM) stiffness are hallmarks of vascular aging. Sox9 (SRY-box transcription factor 9) has been implicated in vascular smooth muscle cell (VSMC) osteo/chondrogenic conversion; however, its relationship with aging and calcification has not been studied. METHODS: Immunohistochemistry was performed on human aortic samples from young and aged patients. Young and senescent primary human VSMCs were induced to produce ECM, and Sox9 expression was manipulated using adenoviral overexpression and depletion. ECM properties were characterized using atomic force microscopy and proteomics, and VSMC phenotype on hydrogels and the ECM were examined using confocal microscopy. RESULTS: In vivo, Sox9 was not spatially associated with vascular calcification but correlated with the senescence marker p16 (cyclin-dependent kinase inhibitor 2A). In vitro Sox9 showed mechanosensitive responses with increased expression and nuclear translocation in senescent cells and on stiff matrices. Sox9 was found to regulate ECM stiffness and organization by orchestrating changes in collagen (Col) expression and reducing VSMC contractility, leading to the formation of an ECM that mirrored that of senescent cells. These ECM changes promoted phenotypic modulation of VSMCs, whereby senescent cells plated on ECM synthesized from cells depleted of Sox9 returned to a proliferative state, while proliferating cells on a matrix produced by Sox9 expressing cells showed reduced proliferation and increased DNA damage, reiterating features of senescent cells. LH3 (procollagen-lysine, 2-oxoglutarate 5-dioxygenase 3) was identified as an Sox9 target and key regulator of ECM stiffness. LH3 is packaged into extracellular vesicles and Sox9 promotes extracellular vesicle secretion, leading to increased LH3 deposition within the ECM. CONCLUSIONS: These findings highlight the crucial role of ECM structure and composition in regulating VSMC phenotype. We identify a positive feedback cycle, whereby cellular senescence and increased ECM stiffening promote Sox9 expression, which, in turn, drives further ECM modifications to further accelerate stiffening and senescence.

Paracrine signalling by cardiac calcitonin controls atrial fibrogenesis and arrhythmia.

Atrial fibrillation, the most common cardiac arrhythmia, is an important contributor to mortality and morbidity, and particularly to the risk of stroke in humans1. Atrial-tissue fibrosis is a central pathophysiological feature of atrial fibrillation that also hampers its treatment; the underlying molecular mechanisms are poorly understood and warrant investigation given the inadequacy of present therapies2. Here we show that calcitonin, a hormone product of the thyroid gland involved in bone metabolism3, is also produced by atrial cardiomyocytes in substantial quantities and acts as a paracrine signal that affects neighbouring collagen-producing fibroblasts to control their proliferation and secretion of extracellular matrix proteins. Global disruption of calcitonin receptor signalling in mice causes atrial fibrosis and increases susceptibility to atrial fibrillation. In mice in which liver kinase B1 is knocked down specifically in the atria, atrial-specific knockdown of calcitonin promotes atrial fibrosis and increases and prolongs spontaneous episodes of atrial fibrillation, whereas atrial-specific overexpression of calcitonin prevents both atrial fibrosis and fibrillation. Human patients with persistent atrial fibrillation show sixfold lower levels of myocardial calcitonin compared to control individuals with normal heart rhythm, with loss of calcitonin receptors in the fibroblast membrane. Although transcriptome analysis of human atrial fibroblasts reveals little change after exposure to calcitonin, proteomic analysis shows extensive alterations in extracellular matrix proteins and pathways related to fibrogenesis, infection and immune responses, and transcriptional regulation. Strategies to restore disrupted myocardial calcitonin signalling thus may offer therapeutic avenues for patients with atrial fibrillation.

Apolipoprotein Proteomics for Residual Lipid-Related Risk in Coronary Heart Disease.

BACKGROUND: Recognition of the importance of conventional lipid measures and the advent of novel lipid-lowering medications have prompted the need for more comprehensive lipid panels to guide use of emerging treatments for the prevention of coronary heart disease (CHD). This report assessed the relevance of 13 apolipoproteins measured using a single mass-spectrometry assay for risk of CHD in the PROCARDIS case-control study of CHD (941 cases/975 controls). METHODS: The associations of apolipoproteins with CHD were assessed after adjustment for established risk factors and correction for statin use. Apolipoproteins were grouped into 4 lipid-related classes [lipoprotein(a), low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglycerides] and their associations with CHD were adjusted for established CHD risk factors and conventional lipids. Analyses of these apolipoproteins in a subset of the ASCOT trial (Anglo-Scandinavian Cardiac Outcomes Trial) were used to assess their within-person variability and to estimate a correction for statin use. The findings in the PROCARDIS study were compared with those for incident cardiovascular disease in the Bruneck prospective study (n=688), including new measurements of Apo(a). RESULTS: Triglyceride-carrying apolipoproteins (ApoC1, ApoC3, and ApoE) were most strongly associated with the risk of CHD (2- to 3-fold higher odds ratios for top versus bottom quintile) independent of conventional lipid measures. Likewise, ApoB was independently associated with a 2-fold higher odds ratios of CHD. Lipoprotein(a) was measured using peptides from the Apo(a)-kringle repeat and Apo(a)-constant regions, but neither of these associations differed from the association with conventionally measured lipoprotein(a). Among HDL-related apolipoproteins, ApoA4 and ApoM were inversely related to CHD, independent of conventional lipid measures. The disease associations with all apolipoproteins were directionally consistent in the PROCARDIS and Bruneck studies, with the exception of ApoM. CONCLUSIONS: Apolipoproteins were associated with CHD independent of conventional risk factors and lipids, suggesting apolipoproteins could help to identify patients with residual lipid-related risk and guide personalized approaches to CHD risk reduction.

Proteomic Atlas of Atherosclerosis: The Contribution of Proteoglycans to Sex Differences, Plaque Phenotypes, and Outcomes.

BACKGROUND: Using proteomics, we aimed to reveal molecular types of human atherosclerotic lesions and study their associations with histology, imaging, and cardiovascular outcomes. METHODS: Two hundred nineteen carotid endarterectomy samples were procured from 120 patients. A sequential protein extraction protocol was employed in conjunction with multiplexed, discovery proteomics. To focus on extracellular proteins, parallel reaction monitoring was employed for targeted proteomics. Proteomic signatures were integrated with bulk, single-cell, and spatial RNA-sequencing data, and validated in 200 patients from the Athero-Express Biobank study. RESULTS: This extensive proteomics analysis identified plaque inflammation and calcification signatures, which were inversely correlated and validated using targeted proteomics. The inflammation signature was characterized by the presence of neutrophil-derived proteins, such as S100A8/9 (calprotectin) and myeloperoxidase, whereas the calcification signature included fetuin-A, osteopontin, and gamma-carboxylated proteins. The proteomics data also revealed sex differences in atherosclerosis, with large-aggregating proteoglycans versican and aggrecan being more abundant in females and exhibiting an inverse correlation with estradiol levels. The integration of RNA-sequencing data attributed the inflammation signature predominantly to neutrophils and macrophages, and the calcification and sex signatures to smooth muscle cells, except for certain plasma proteins that were not expressed but retained in plaques, such as fetuin-A. Dimensionality reduction and machine learning techniques were applied to identify 4 distinct plaque phenotypes based on proteomics data. A protein signature of 4 key proteins (calponin, protein C, serpin H1, and versican) predicted future cardiovascular mortality with an area under the curve of 75% and 67.5% in the discovery and validation cohort, respectively, surpassing the prognostic performance of imaging and histology. CONCLUSIONS: Plaque proteomics redefined clinically relevant patient groups with distinct outcomes, identifying subgroups of male and female patients with elevated risk of future cardiovascular events.

Secreted Protein Profiling of Human Aortic Smooth Muscle Cells Identifies Vascular Disease Associations.

BACKGROUND: Smooth muscle cells (SMCs), which make up the medial layer of arteries, are key cell types involved in cardiovascular disease, the leading cause of mortality and morbidity worldwide. In response to microenvironment alterations, SMCs dedifferentiate from a contractile to a synthetic phenotype characterized by an increased proliferation, migration, production of ECM (extracellular matrix) components, and decreased expression of SMC-specific contractile markers. These phenotypic changes result in vascular remodeling and contribute to the pathogenesis of cardiovascular disease, including coronary artery disease, stroke, hypertension, and aortic aneurysms. Here, we aim to identify the genetic variants that regulate ECM secretion in SMCs and predict the causal proteins associated with vascular disease-related loci identified in genome-wide association studies. METHODS: Using human aortic SMCs from 123 multiancestry healthy heart transplant donors, we collected the serum-free media in which the cells were cultured for 24 hours and conducted liquid chromatography-tandem mass spectrometry-based proteomic analysis of the conditioned media. RESULTS: We measured the abundance of 270 ECM and related proteins. Next, we performed protein quantitative trait locus mapping and identified 20 loci associated with secreted protein abundance in SMCs. We functionally annotated these loci using a colocalization approach. This approach prioritized the genetic variant rs6739323-A at the 2p22.3 locus, which is associated with lower expression of LTBP1 (latent-transforming growth factor beta-binding protein 1) in SMCs and atherosclerosis-prone areas of the aorta, and increased risk for SMC calcification. We found that LTBP1 expression is abundant in SMCs, and its expression at mRNA and protein levels was reduced in unstable and advanced atherosclerotic plaque lesions. CONCLUSIONS: Our results unravel the SMC proteome signature associated with vascular disorders, which may help identify potential therapeutic targets to accelerate the pathway to translation.

Uncovering Protein Networks in Cardiovascular Proteomics.

Biological networks have been widely used in many different diseases to identify potential biomarkers and design drug targets. In the present review, we describe the main computational techniques for reconstructing and analyzing different types of protein networks and summarize the previous applications of such techniques in cardiovascular diseases. Existing tools are critically compared, discussing when each method is preferred such as the use of co-expression networks for functional annotation of protein clusters and the use of directed networks for inferring regulatory associations. Finally, we are presenting examples of reconstructing protein networks of different types (regulatory, co-expression, and protein-protein interaction networks). We demonstrate the necessity to reconstruct networks separately for each cardiovascular tissue type and disease entity and provide illustrative examples of the importance of taking into consideration relevant post-translational modifications. Finally, we demonstrate and discuss how the findings of protein networks could be interpreted using single-cell RNA-sequencing data.

MEvA-X: a hybrid multiobjective evolutionary tool using an XGBoost classifier for biomarkers discovery on biomedical datasets.

MOTIVATION: Biomarker discovery is one of the most frequent pursuits in bioinformatics and is crucial for precision medicine, disease prognosis, and drug discovery. A common challenge of biomarker discovery applications is the low ratio of samples over features for the selection of a reliable not-redundant subset of features, but despite the development of efficient tree-based classification methods, such as the extreme gradient boosting (XGBoost), this limitation is still relevant. Moreover, existing approaches for optimizing XGBoost do not deal effectively with the class imbalance nature of the biomarker discovery problems, and the presence of multiple conflicting objectives, since they focus on the training of a single-objective model. In the current work, we introduce MEvA-X, a novel hybrid ensemble for feature selection (FS) and classification, combining a niche-based multiobjective evolutionary algorithm (EA) with the XGBoost classifier. MEvA-X deploys a multiobjective EA to optimize the hyperparameters of the classifier and perform FS, identifying a set of Pareto-optimal solutions and optimizing multiple objectives, including classification and model simplicity metrics. RESULTS: The performance of the MEvA-X tool was benchmarked using one omics dataset coming from a microarray gene expression experiment, and one clinical questionnaire-based dataset combined with demographic information. MEvA-X tool outperformed the state-of-the-art methods in the balanced categorization of classes, creating multiple low-complexity models and identifying important nonredundant biomarkers. The best-performing run of MEvA-X for the prediction of weight loss using gene expression data yields a small set of blood circulatory markers which are sufficient for this precision nutrition application but need further validation. AVAILABILITY AND IMPLEMENTATION: https://github.com/PanKonstantinos/MEvA-X.

PCSK9 Activity Is Potentiated Through HDL Binding.

Rationale: Proprotein convertase subtilisin/kexin type 9 (PCSK9) circulates in a free and lipoprotein-bound form, yet the functional consequence of the association between PCSK9 and high-density lipoprotein (HDL) remains unexplored. Objective: This study sought to interrogate the novel relationship between PCSK9 and HDL in humans. Methods and Results: Comparing lipoprotein and apolipoprotein profiles by nuclear magnetic resonance and targeted mass spectrometry measurements with PCSK9 levels in the community-based Bruneck (n=656) study revealed a positive association of plasma PCSK9 with small HDL, alongside a highly significant positive correlation between plasma levels of PCSK9 and apolipoprotein-C3, an inhibitor of lipoprotein lipase. The latter association was replicated in an independent cohort, the SAPHIR study (n=270). Thus, PCSK9-HDL association was determined during the postprandial response in two dietary studies (n=20 participants each, 8 times points). Peak triglyceride levels coincided with an attenuation of the PCSK9-HDL association, a loss of apolipoprotein-C3 from HDL and lower levels of small HDL as measured by nuclear magnetic resonance. Crosslinking mass spectrometry (XLMS) upon isolated HDL identified PCSK9 as a potential HDL-binding partner. PCSK9 association with HDL was confirmed through size-exclusion chromatography and immuno-isolation. Quantitative proteomics upon HDL isolated from patients with coronary artery disease (n=172) returned PCSK9 as a core member of the HDL proteome. Combined interrogation of the HDL proteome and lipidome revealed a distinct cluster of PCSK9, phospholipid transfer protein, clusterin and apolipoprotein-E within the HDL proteome, that was altered by sex and positively correlated with sphingomyelin content. Mechanistically, HDL facilitated PCSK9-mediated low-density lipoprotein receptor degradation and reduced low-density lipoprotein uptake through the modulation of PCSK9 internalisation and multimerisation. Conclusions: This study reports HDL as a binder of PCSK9 and regulator of its function. The combination of -omic technologies revealed postprandial lipaemia as a driver of PCSK9 and apolipoprotein-C3 release from HDL.

Neutrophil-Derived Protein S100A8/A9 Alters the Platelet Proteome in Acute Myocardial Infarction and Is Associated With Changes in Platelet Reactivity.

OBJECTIVE: Platelets are central to acute myocardial infarction (MI). How the platelet proteome is altered during MI is unknown. We sought to describe changes in the platelet proteome during MI and identify corresponding functional consequences. Approach and Results: Platelets from patients experiencing ST-segment-elevation MI (STEMI) before and 3 days after treatment (n=30) and matched patients with severe stable coronary artery disease before and 3 days after coronary artery bypass grafting (n=25) underwent quantitative proteomic analysis. Elevations in the proteins S100A8 and S100A9 were detected at the time of STEMI compared with stable coronary artery disease (S100A8: FC, 2.00; false discovery rate, 0.05; S100A9: FC, 2.28; false discovery rate, 0.005). During STEMI, only S100A8 mRNA and protein levels were correlated in platelets (R=0.46, P=0.012). To determine whether de novo protein synthesis occurs, activated platelets were incubated with 13C-labeled amino acids for 24 hours and analyzed by mass spectrometry. No incorporation was confidently detected. Platelet S100A8 and S100A9 was strongly correlated with neutrophil abundance at the time of STEMI. When isolated platelets and neutrophils were coincubated under quiescent and activated conditions, release of S100A8 from neutrophils resulted in uptake of S100A8 by platelets. Neutrophils released S100A8/A9 as free heterodimer, rather than in vesicles or extracellular traps. In the community-based Bruneck study (n=338), plasma S100A8/A9 was inversely associated with platelet reactivity-an effect abrogated by aspirin. CONCLUSIONS: Leukocyte-to-platelet protein transfer may occur in a thromboinflammatory environment such as STEMI. Plasma S100A8/A9 was negatively associated with platelet reactivity. These findings highlight neutrophils as potential modifiers for thrombotic therapies in coronary artery disease.

Extracellular Matrix in Heart Failure: Role of ADAMTS5 in Proteoglycan Remodeling.

BACKGROUND: Remodeling of the extracellular matrix (ECM) is a hallmark of heart failure (HF). Our previous analysis of the secretome of murine cardiac fibroblasts returned ADAMTS5 (a disintegrin and metalloproteinase with thrombospondin motifs 5) as one of the most abundant proteases. ADAMTS5 cleaves chondroitin sulfate proteoglycans such as versican. The contribution of ADAMTS5 and its substrate versican to HF is unknown. METHODS: Versican remodeling was assessed in mice lacking the catalytic domain of ADAMTS5 (Adamts5ΔCat). Proteomics was applied to study ECM remodeling in left ventricular samples from patients with HF, with a particular focus on the effects of common medications used for the treatment of HF. RESULTS: Versican and versikine, an ADAMTS-specific versican cleavage product, accumulated in patients with ischemic HF. Versikine was also elevated in a porcine model of cardiac ischemia/reperfusion injury and in murine hearts after angiotensin II infusion. In Adamts5ΔCat mice, angiotensin II infusion resulted in an aggravated versican build-up and hyaluronic acid disarrangement, accompanied by reduced levels of integrin ß1, filamin A, and connexin 43. Echocardiographic assessment of Adamts5ΔCat mice revealed a reduced ejection fraction and an impaired global longitudinal strain on angiotensin II infusion. Cardiac hypertrophy and collagen deposition were similar to littermate controls. In a proteomics analysis of a larger cohort of cardiac explants from patients with ischemic HF (n=65), the use of ß-blockers was associated with a reduction in ECM deposition, with versican being among the most pronounced changes. Subsequent experiments in cardiac fibroblasts confirmed that ß1-adrenergic receptor stimulation increased versican expression. Despite similar clinical characteristics, patients with HF treated with ß-blockers had a distinct cardiac ECM profile. CONCLUSIONS: Our results in animal models and patients suggest that ADAMTS proteases are critical for versican degradation in the heart and that versican accumulation is associated with impaired cardiac function. A comprehensive characterization of the cardiac ECM in patients with ischemic HF revealed that ß-blockers may have a previously unrecognized beneficial effect on cardiac chondroitin sulfate proteoglycan content.

Pharmacoepigenomics circuits induced by a novel retinoid-polyamine conjugate in human immortalized keratinocytes.

Retinoids are widely used in diseases spanning from dermatological lesions to cancer, but exhibit severe adverse effects. A novel all-trans-Retinoic Acid (atRA)-spermine conjugate (termed RASP) has shown previously optimal in vitro and in vivo anti-inflammatory and anticancer efficacy, with undetectable teratogenic and toxic side-effects. To get insights, we treated HaCaT cells which resemble human epidermis with IC50 concentration of RASP and analyzed their miRNA expression profile. Gene ontology analysis of their predicted targets indicated dynamic networks involved in cell proliferation, signal transduction and apoptosis. Furthermore, DNA microarrays analysis verified that RASP affects the expression of the same categories of genes. A protein-protein interaction map produced using the most significant common genes, revealed hub genes of nodal functions. We conclude that RASP is a synthetic retinoid derivative with improved properties, which possess the beneficial effects of retinoids without exhibiting side-effects and with potential beneficial effects against skin diseases including skin cancer.

Comparative Analysis of Circulating Noncoding RNAs Versus Protein Biomarkers in the Detection of Myocardial Injury.

RATIONALE: Noncoding RNAs (ncRNAs), including microRNAs (miRNAs), circular RNAs (circRNAs), and long noncoding RNAs (lncRNAs), are proposed novel biomarkers of myocardial injury. Their release kinetics have not been explored without confounding by heparin nor has their relationship to myocardial protein biomarkers. OBJECTIVE: To compare ncRNA types in heparinase-treated samples with established and emerging protein biomarkers for myocardial injury. METHODS AND RESULTS: Screening of 158 circRNAs and 21 lncRNAs in human cardiac tissue identified 12 circRNAs and 11 lncRNAs as potential biomarkers with cardiac origin. Eleven miRNAs were included. At low spike-in concentrations of myocardial tissue, significantly higher regression coefficients were observed across ncRNA types compared with cardiac troponins and cMyBP-C (cardiac myosin-binding protein C). Heparinase treatment of serial plasma and serum samples of patients undergoing transcoronary ablation of septal hypertrophy removed spurious correlations between miRNAs in non-heparinase-treated samples. After transcoronary ablation of septal hypertrophy, muscle-enriched miRNAs (miR-1 and miR-133a) showed a steeper and earlier increase than cardiac-enriched miRNAs (miR-499 and miR-208b). Putative cardiac lncRNAs, including LIPCAR (long intergenic noncoding RNA predicting cardiac remodeling and survival), did not rise, refuting a predominant cardiac origin. Cardiac circRNAs remained largely undetectable. In a validation cohort of acute myocardial infarction, receiver operating characteristic curve analysis revealed noninferiority of cardiac-enriched miRNAs, but miRNAs failed to identify cases presenting with low troponin values. cMyBP-C was validated as a biomarker with highly sensitive properties, and the combination of muscle-enriched miRNAs with high-sensitive cardiac troponin T and cMyBP-C returned the highest area under the curve values. CONCLUSIONS: In a comparative assessment of ncRNAs and protein biomarkers for myocardial injury, cMyBP-C showed properties as the most sensitive cardiac biomarker while miRNAs emerged as promising candidates to integrate ncRNAs with protein biomarkers. Sensitivity of current miRNA detection is inferior to cardiac proteins but a multibiomarker combination of muscle-enriched miRNAs with cMyBP-C and cardiac troponins could open a new path of integrating complementary characteristics of different biomarker types.

An Adaptive Pipeline To Maximize Isobaric Tagging Data in Large-Scale MS-Based Proteomics.

Isobaric tagging is the method of choice in mass-spectrometry-based proteomics for comparing several conditions at a time. Despite its multiplexing capabilities, some drawbacks appear when multiple experiments are merged for comparison in large sample-size studies due to the presence of missing values, which result from the stochastic nature of the data-dependent acquisition mode. Another indirect cause of data incompleteness might derive from the proteomic-typical data-processing workflow that first identifies proteins in individual experiments and then only quantifies those identified proteins, leaving a large number of unmatched spectra with quantitative information unexploited. Inspired by untargeted metabolomic and label-free proteomic workflows, we developed a quantification-driven bioinformatic pipeline (Quantify then Identify (QtI)) that optimizes the processing of isobaric tandem mass tag (TMT) data from large-scale studies. This pipeline includes innovative features, such as peak filtering with a self-adaptive preprocessing pipeline optimization method, Peptide Match Rescue, and Optimized Post-Translational Modification. QtI outperforms a classical benchmark workflow in terms of quantification and identification rates, significantly reducing missing data while preserving unmatched features for quantitative comparison. The number of unexploited tandem mass spectra was reduced by 77 and 62% for two human cerebrospinal fluid and plasma data sets, respectively.

Systemic mechanisms and effects of ionizing radiation: A new 'old' paradigm of how the bystanders and distant can become the players.

Exposure of cells to any form of ionizing radiation (IR) is expected to induce a variety of DNA lesions, including double strand breaks (DSBs), single strand breaks (SSBs) and oxidized bases, as well as loss of bases, i.e., abasic sites. The damaging potential of IR is primarily related to the generation of electrons, which through their interaction with water produce free radicals. In their turn, free radicals attack DNA, proteins and lipids. Damage is induced also through direct deposition of energy. These types of IR interactions with biological materials are collectively called 'targeted effects', since they refer only to the irradiated cells. Earlier and sometimes 'anecdotal' findings were pointing to the possibility of IR actions unrelated to the irradiated cells or area, i.e., a type of systemic response with unknown mechanistic basis. Over the last years, significant experimental evidence has accumulated, showing a variety of radiation effects for 'out-of-field' areas (non-targeted effects-NTE). The NTE involve the release of chemical and biological mediators from the 'in-field' area and thus the communication of the radiation insult via the so called 'danger' signals. The NTE can be separated in two major groups: bystander and distant (systemic). In this review, we have collected a detailed list of proteins implicated in either bystander or systemic effects, including the clinically relevant abscopal phenomenon, using improved text-mining and bioinformatics tools from the literature. We have identified which of these genes belong to the DNA damage response and repair pathway (DDR/R) and made protein-protein interaction (PPi) networks. Our analysis supports that the apoptosis, TLR-like and NOD-like receptor signaling pathways are the main pathways participating in NTE. Based on this analysis, we formulate a biophysical hypothesis for the regulation of NTE, based on DNA damage and apoptosis gradients between the irradiation point and various distances corresponding to bystander (5mm) or distant effects (5cm). Last but not least, in order to provide a more realistic support for our model, we calculate the expected DSB and non-DSB clusters along the central axis of a representative 200.6MeV pencil beam calculated using Monte Carlo DNA damage simulation software (MCDS) based on the actual beam energy-to-depth curves used in therapy.

EnsembleGASVR: a novel ensemble method for classifying missense single nucleotide polymorphisms.

MOTIVATION: Single nucleotide polymorphisms (SNPs) are considered the most frequently occurring DNA sequence variations. Several computational methods have been proposed for the classification of missense SNPs to neutral and disease associated. However, existing computational approaches fail to select relevant features by choosing them arbitrarily without sufficient documentation. Moreover, they are limited to the problem of missing values, imbalance between the learning datasets and most of them do not support their predictions with confidence scores. RESULTS: To overcome these limitations, a novel ensemble computational methodology is proposed. EnsembleGASVR facilitates a two-step algorithm, which in its first step applies a novel evolutionary embedded algorithm to locate close to optimal Support Vector Regression models. In its second step, these models are combined to extract a universal predictor, which is less prone to overfitting issues, systematizes the rebalancing of the learning sets and uses an internal approach for solving the missing values problem without loss of information. Confidence scores support all the predictions and the model becomes tunable by modifying the classification thresholds. An extensive study was performed for collecting the most relevant features for the problem of classifying SNPs, and a superset of 88 features was constructed. Experimental results show that the proposed framework outperforms well-known algorithms in terms of classification performance in the examined datasets. Finally, the proposed algorithmic framework was able to uncover the significant role of certain features such as the solvent accessibility feature, and the top-scored predictions were further validated by linking them with disease phenotypes. AVAILABILITY AND IMPLEMENTATION: Datasets and codes are freely available on the Web at http://prlab.ceid.upatras.gr/EnsembleGASVR/dataset-codes.zip. All the required information about the article is available through http://prlab.ceid.upatras.gr/EnsembleGASVR/site.html.

Serum metabolomics improves risk stratification for incident heart failure.

AIMS: Prediction and early detection of heart failure (HF) is crucial to mitigate its impact on quality of life, survival, and healthcare expenditure. Here, we explored the predictive value of serum metabolomics (168 metabolites detected by proton nuclear magnetic resonance [1H-NMR] spectroscopy) for incident HF. METHODS AND RESULTS: Leveraging data of 68 311 individuals and >0.8 million person-years of follow-up from the UK Biobank cohort, we (i) fitted per-metabolite Cox proportional hazards models to assess individual metabolite associations, and (ii) trained and validated elastic net models to predict incident HF using the serum metabolome. We benchmarked discriminative performance against a comprehensive, well-validated clinical risk score (Pooled Cohort Equations to Prevent HF [PCP-HF]). During a median follow-up of ≈12.3 years, several metabolites showed independent association with incident HF (90/168 adjusting for age and sex, 48/168 adjusting for PCP-HF). Performance-optimized risk models effectively retained key predictors representing highly correlated clusters (≈80% feature reduction). Adding metabolomics to PCP-HF improved predictive performance (Harrel's C: 0.768 vs. 0.755, ΔC = 0.013, [95% confidence interval [CI] 0.004-0.022], continuous net reclassification improvement [NRI]: 0.287 [95% CI 0.200-0.367], relative integrated discrimination improvement [IDI]: 17.47% [95% CI 9.463-27.825]). Models including age, sex and metabolomics performed almost as well as PCP-HF (Harrel's C: 0.745 vs. 0.755, ΔC = 0.010 [95% CI -0.004 to 0.027], continuous NRI: 0.097 [95% CI -0.025 to 0.217], relative IDI: 13.445% [95% CI -10.608 to 41.454]). Risk and survival stratification was improved by integrating metabolomics. CONCLUSION: Serum metabolomics improves incident HF risk prediction over PCP-HF. Scores based on age, sex and metabolomics exhibit similar predictive power to clinically-based models, potentially offering a cost-effective, standardizable, and scalable single-domain alternative.

Predicting multiple taste sensations with a multiobjective machine learning method.

Taste perception plays a pivotal role in guiding nutrient intake and aiding in the avoidance of potentially harmful substances through five basic tastes - sweet, bitter, umami, salty, and sour. Taste perception originates from molecular interactions in the oral cavity between taste receptors and chemical tastants. Hence, the recognition of taste receptors and the subsequent perception of taste heavily rely on the physicochemical properties of food ingredients. In recent years, several advances have been made towards the development of machine learning-based algorithms to classify chemical compounds' tastes using their molecular structures. Despite the great efforts, there remains significant room for improvement in developing multi-class models to predict the entire spectrum of basic tastes. Here, we present a multi-class predictor aimed at distinguishing bitter, sweet, and umami, from other taste sensations. The development of a multi-class taste predictor paves the way for a comprehensive understanding of the chemical attributes associated with each fundamental taste. It also opens the potential for integration into the evolving realm of multi-sensory perception, which encompasses visual, tactile, and olfactory sensations to holistically characterize flavour perception. This concept holds promise for introducing innovative methodologies in the rational design of foods, including pre-determining specific tastes and engineering complementary diets to augment traditional pharmacological treatments.

Oxidative DNA damage promotes vascular aging associated with changes in extracellular matrix-regulating proteins.

AIMS: Vascular aging is characterized by vessel stiffening, with increased deposition of extracellular matrix (ECM) proteins including collagens. Oxidative DNA damage occurs in vascular aging, but how it regulates ECM proteins and vascular stiffening is unknown. We sought to determine the relationship between oxidative DNA damage and ECM regulatory proteins in vascular aging. METHODS AND RESULTS: We examined oxidative DNA damage, the major base excision repair (BER) enzyme 8-Oxoguanine DNA Glycosylase (Ogg1) and its regulators, multiple physiological markers of aging, and ECM proteomics in mice from 22-72w. Vascular aging was associated with increased oxidative DNA damage, and decreased expression of Ogg1, its active acetylated form, its acetylation regulatory proteins P300 and CBP, and the transcription factor Foxo3a. Vascular stiffness was examined in vivo in control, Ogg1-/-, or mice with vascular smooth muscle cell-specific expression of Ogg1+ (Ogg1) or an inactive mutation (Ogg1KR). Ogg1-/- and Ogg1KR mice showed reduced arterial compliance and distensibility, and increased stiffness and pulse pressure, whereas Ogg1 expression normalised all parameters to 72w. ECM proteomics identified major changes in collagens with aging, and downregulation of the ECM regulatory proteins Protein 6-lysyl oxidase (LOX) and WNT1-inducible-signaling pathway protein 2 (WISP2). Ogg1 overexpression upregulated LOX and WISP2 both in vitro and in vivo, and downregulated Transforming growth factor ß1 (TGFb1) and Collagen 4α1 in vivo compared with Ogg1KR. Foxo3a activation induced Lox, while Wnt3 induction of Wisp2 also upregulated LOX and Foxo3a, and downregulated TGFß1 and fibronectin 1. In humans, 8-oxo-G increased with vascular stiffness, while active OGG1 reduced with both age and stiffness. CONCLUSIONS: Vascular aging is associated with oxidative DNA damage, downregulation of major BER proteins, and changes in multiple ECM structural and regulatory proteins. Ogg1 protects against vascular aging, associated with changes in ECM regulatory proteins including LOX and WISP2.

VirtuousPocketome: a computational tool for screening protein-ligand complexes to identify similar binding sites.

Protein residues within binding pockets play a critical role in determining the range of ligands that can interact with a protein, influencing its structure and function. Identifying structural similarities in proteins offers valuable insights into their function and activation mechanisms, aiding in predicting protein-ligand interactions, anticipating off-target effects, and facilitating the development of therapeutic agents. Numerous computational methods assessing global or local similarity in protein cavities have emerged, but their utilization is impeded by complexity, impractical automation for amino acid pattern searches, and an inability to evaluate the dynamics of scrutinized protein-ligand systems. Here, we present a general, automatic and unbiased computational pipeline, named VirtuousPocketome, aimed at screening huge databases of proteins for similar binding pockets starting from an interested protein-ligand complex. We demonstrate the pipeline's potential by exploring a recently-solved human bitter taste receptor, i.e. the TAS2R46, complexed with strychnine. We pinpointed 145 proteins sharing similar binding sites compared to the analysed bitter taste receptor and the enrichment analysis highlighted the related biological processes, molecular functions and cellular components. This work represents the foundation for future studies aimed at understanding the effective role of tastants outside the gustatory system: this could pave the way towards the rationalization of the diet as a supplement to standard pharmacological treatments and the design of novel tastants-inspired compounds to target other proteins involved in specific diseases or disorders. The proposed pipeline is publicly accessible, can be applied to any protein-ligand complex, and could be expanded to screen any database of protein structures.