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.

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.

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.

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.

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.

Omics-CNN: A comprehensive pipeline for predictive analytics in quantitative omics using one-dimensional convolutional neural networks.

Background and objective: The development of machine learning-based models that can be used for the prediction of severe diseases has been one of the main concerns of the scientific community. The current study seeks to expand a highly sophisticated tool, the Convolutional Neural Networks, making it applicable in multidimensional omics data classification problems and testing the newly introduced method on publicly available transcriptomics and proteomics data. Methods: In this study, we introduce Omics-CNN, a Convolutional Neural Network-based pipeline, which couples Convolutional Neural Networks with dimensionality reduction, preprocessing, clustering, and explainability techniques to make them suitable to build highly accurate and interpretable classification models from high-throughput omics data. The developed tool has the potential to classify patients depending on the expression of genetic and clinical factors and identify features that can act as diagnostic biomarkers. Regarding dimensionality reduction, univariate and multivariate techniques were explored and compared. Gradient Weighted Class Activation Mapping analysis was performed to determine the most important features in the classification of the samples after training the model. Results: The newly introduced pipeline was applied to one transcriptomics and one proteomics dataset for the identification of diagnostic models and biosignatures for Ischemic Stroke (IS) and COVID-19 infection, reporting highly accurate biosignatures with accuracies of 96 % and 95.41 %, respectively. Meanwhile, classification models based solely on a small part of attributes provided lower predictive accuracy, but identified compact transcript biosignature (KRT15, VPRBP, TNFRSF4, GORASP2) for Ischemic Stroke and protein biosignature (ADGRB3, VNN2, AGER, CIAPIN1) for Covid-19 infection diagnosis, respectively. Conclusions: Omics-CNN, overcame the inherent problems of applying Convolutional Neural Networks for the training diagnostic models with quantitative omics data, outperforming previous models of machine learning developed using the same datasets for Ischemic Stroke and Covid-19 infection diagnosis, determining the most contributing biomarkers for both diseases.

Integrative single-cell meta-analysis reveals disease-relevant vascular cell states and markers in human atherosclerosis.

Coronary artery disease (CAD) is characterized by atherosclerotic plaque formation in the arterial wall. CAD progression involves complex interactions and phenotypic plasticity among vascular and immune cell lineages. Single-cell RNA-seq (scRNA-seq) studies have highlighted lineage-specific transcriptomic signatures, but human cell phenotypes remain controversial. Here, we perform an integrated meta-analysis of 22 scRNA-seq libraries to generate a comprehensive map of human atherosclerosis with 118,578 cells. Besides characterizing granular cell-type diversity and communication, we leverage this atlas to provide insights into smooth muscle cell (SMC) modulation. We integrate genome-wide association study data and uncover a critical role for modulated SMC phenotypes in CAD, myocardial infarction, and coronary calcification. Finally, we identify fibromyocyte/fibrochondrogenic SMC markers (LTBP1 and CRTAC1) as proxies of atherosclerosis progression and validate these through omics and spatial imaging analyses. Altogether, we create a unified atlas of human atherosclerosis informing cell state-specific mechanistic and translational studies of cardiovascular diseases.

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 diseases (CVD), 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 extracellular matrix (ECM) components, and decreased expression of SMC-specific contractile markers. These phenotypic changes result in vascular remodeling and contribute to the pathogenesis of CVD, including coronary artery disease (CAD), 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 (GWAS). Methods: Using human aortic SMCs from 123 multi-ancestry 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 (LC-MS/MS)-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 (pQTL) 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 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.

Human model of primary carnitine deficiency cardiomyopathy reveals ferroptosis as a novel mechanism.

Primary carnitine deficiency (PCD) is an autosomal recessive monogenic disorder caused by mutations in SLC22A5. This gene encodes for OCTN2, which transports the essential metabolite carnitine into the cell. PCD patients suffer from muscular weakness and dilated cardiomyopathy. Two OCTN2-defective human induced pluripotent stem cell lines were generated, carrying a full OCTN2 knockout and a homozygous OCTN2 (N32S) loss-of-function mutation. OCTN2-defective genotypes showed lower force development and resting length in engineered heart tissue format compared with isogenic control. Force was sensitive to fatty acid-based media and associated with lipid accumulation, mitochondrial alteration, higher glucose uptake, and metabolic remodeling, replicating findings in animal models. The concordant results of OCTN2 (N32S) and -knockout emphasizes the relevance of OCTN2 for these findings. Importantly, genome-wide analysis and pharmacological inhibitor experiments identified ferroptosis, an iron- and lipid-dependent cell death pathway associated with fibroblast activation as a novel PCD cardiomyopathy disease mechanism.

Human blood vessel organoids reveal a critical role for CTGF in maintaining microvascular integrity.

The microvasculature plays a key role in tissue perfusion and exchange of gases and metabolites. In this study we use human blood vessel organoids (BVOs) as a model of the microvasculature. BVOs fully recapitulate key features of the human microvasculature, including the reliance of mature endothelial cells on glycolytic metabolism, as concluded from metabolic flux assays and mass spectrometry-based metabolomics using stable tracing of 13C-glucose. Pharmacological targeting of PFKFB3, an activator of glycolysis, using two chemical inhibitors results in rapid BVO restructuring, vessel regression with reduced pericyte coverage. PFKFB3 mutant BVOs also display similar structural remodelling. Proteomic analysis of the BVO secretome reveal remodelling of the extracellular matrix and differential expression of paracrine mediators such as CTGF. Treatment with recombinant CTGF recovers microvessel structure. In this work we demonstrate that BVOs rapidly undergo restructuring in response to metabolic changes and identify CTGF as a critical paracrine regulator of microvascular integrity.

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.

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.

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.

Optimisation Models for Pathway Activity Inference in Cancer.

BACKGROUND: With advances in high-throughput technologies, there has been an enormous increase in data related to profiling the activity of molecules in disease. While such data provide more comprehensive information on cellular actions, their large volume and complexity pose difficulty in accurate classification of disease phenotypes. Therefore, novel modelling methods that can improve accuracy while offering interpretable means of analysis are required. Biological pathways can be used to incorporate a priori knowledge of biological interactions to decrease data dimensionality and increase the biological interpretability of machine learning models. METHODOLOGY: A mathematical optimisation model is proposed for pathway activity inference towards precise disease phenotype prediction and is applied to RNA-Seq datasets. The model is based on mixed-integer linear programming (MILP) mathematical optimisation principles and infers pathway activity as the linear combination of pathway member gene expression, multiplying expression values with model-determined gene weights that are optimised to maximise discrimination of phenotype classes and minimise incorrect sample allocation. RESULTS: The model is evaluated on the transcriptome of breast and colorectal cancer, and exhibits solution results of good optimality as well as good prediction performance on related cancer subtypes. Two baseline pathway activity inference methods and three advanced methods are used for comparison. Sample prediction accuracy, robustness against noise expression data, and survival analysis suggest competitive prediction performance of our model while providing interpretability and insight on key pathways and genes. Overall, our work demonstrates that the flexible nature of mathematical programming lends itself well to developing efficient computational strategies for pathway activity inference and disease subtype prediction.

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.

Toward a general and interpretable umami taste predictor using a multi-objective machine learning approach.

The umami taste is one of the five basic taste modalities normally linked to the protein content in food. The implementation of fast and cost-effective tools for the prediction of the umami taste of a molecule remains extremely interesting to understand the molecular basis of this taste and to effectively rationalise the production and consumption of specific foods and ingredients. However, the only examples of umami predictors available in the literature rely on the amino acid sequence of the analysed peptides, limiting the applicability of the models. In the present study, we developed a novel ML-based algorithm, named VirtuousUmami, able to predict the umami taste of a query compound starting from its SMILES representation, thus opening up the possibility of potentially using such a model on any database through a standard and more general molecular description. Herein, we have tested our model on five databases related to foods or natural compounds. The proposed tool will pave the way toward the rationalisation of the molecular features underlying the umami taste and toward the design of specific peptide-inspired compounds with specific taste properties.

Reduced secretion of neuronal growth regulator 1 contributes to impaired adipose-neuronal crosstalk in obesity.

While the endocrine function of white adipose tissue has been extensively explored, comparatively little is known about the secretory activity of less-investigated fat depots. Here, we use proteomics to compare the secretory profiles of male murine perivascular depots with those of canonical white and brown fat. Perivascular secretomes show enrichment for neuronal cell-adhesion molecules, reflecting a higher content of intra-parenchymal sympathetic projections compared to other adipose depots. The sympathetic innervation is reduced in the perivascular fat of obese (ob/ob) male mice, as well as in the epicardial fat of patients with obesity. Degeneration of sympathetic neurites is observed in presence of conditioned media of fat explants from ob/ob mice, that show reduced secretion of neuronal growth regulator 1. Supplementation of neuronal growth regulator 1 reverses this neurodegenerative effect, unveiling a neurotrophic role for this protein previously identified as a locus associated with human obesity. As sympathetic stimulation triggers energy-consuming processes in adipose tissue, an impaired adipose-neuronal crosstalk is likely to contribute to the disrupted metabolic homeostasis characterising obesity.

Serial measurements of protein and microRNA biomarkers to specify myocardial infarction subtypes.

Background: While cardiac-specific troponin (cTn) allows for rapid diagnosis of acute type 1 myocardial infarction (T1MI), its performance to differentiate acute myocardial injury (AI) or type 2 myocardial infarction (T2MI) is limited. The objective was to combine biomarkers to improve discrimination of different myocardial infarction (MI) aetiologies. Methods: We determined levels of cardiac troponin T and I (cTnT, cTnI), cardiac myosin-binding protein C (cMyBP-C), NT-proBNP and ten miRNAs, known to be associated with cardiac pathology in a total of n = 495 serial plasma samples at three time points (on admission, after 1 h and 3 h) from 57 NSTEMI (non-ST-elevation myocardial infarction), 18 AI, and 31 STEMI patients, as defined by fourth universal definition of MI (UDMI4) and 59 control individuals. We then applied linear mixed effects model to compare the kinetics of all molecules in these MI sub-types. Results: Established (cTnT, cTnI) and novel (cMyBP-C) cardiac necrosis markers failed in differentiating T1MI vs T2MI at early time points. All cardiac necrosis markers were higher in T1MI than in T2MI at 3 h after admission. Muscle-enriched miRNAs (miR-1 and miR-133a) were correlated with cardiac necrosis protein markers and did not offer better discrimination. Established cardiac strain marker NT-proBNP differentiated AI and T1MI at all time points but failed to discriminate T2MI from T1MI. However, the combination of NT-proBNP and cTnT along with age returned an overall AUC of 0.76 [95 % CI 0.67-0.84] for differentiating T1MI, T2MI and AI. Conclusions: Rather than using single biomarkers of myocardial necrosis, a combination of clinical biomarkers for cardiac necrosis (troponin) and cardiac strain (NT-proBNP) might aid in differentiating T1MI, T2MI and AI.