Proteomic profiles of left atrial volume and its influence on response to spironolactone: Findings from the HOMAGE trial and STANISLAS cohort.

AIMS: High left ventricular filling pressure increases left atrial volume and causes myocardial fibrosis, which may decrease with spironolactone. We studied clinical and proteomic characteristics associated with left atrial volume indexed by body surface area (LAVi), and whether LAVi influences the response to spironolactone on biomarker expression and clinical variables. METHODS AND RESULTS: In the HOMAGE trial, where people at risk of heart failure were randomized to spironolactone or control, we analysed 421 participants with available LAVi and 276 proteomic measurements (Olink) at baseline, month 1 and 9 (mean age 73 ± 6 years; women 26%; LAVi 32 ± 9 ml/m2). Circulating proteins associated with LAVi were also assessed in asymptomatic individuals from a population-based cohort (STANISLAS; n = 1640; mean age 49 ± 14 years; women 51%; LAVi 23 ± 7 ml/m2). In both studies, greater LAVi was significantly associated with greater left ventricular masses and volumes. In HOMAGE, after adjustment and correction for multiple testing, greater LAVi was associated with higher concentrations of matrix metallopeptidase-2 (MMP-2), insulin-like growth factor binding protein-2 (IGFBP-2) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) (false discovery rates [FDR] <0.05). These associations were externally replicated in STANISLAS (all FDR <0.05). Among these biomarkers, spironolactone decreased concentrations of MMP-2 and NT-proBNP, regardless of baseline LAVi (pinteraction > 0.10). Spironolactone also significantly reduced LAVi, improved left ventricular ejection fraction, lowered E/e', blood pressure and serum procollagen type I C-terminal propeptide (PICP) concentration, a collagen synthesis marker, regardless of baseline LAVi (pinteraction > 0.10). CONCLUSION: In individuals without heart failure, LAVi was associated with MMP-2, IGFBP-2 and NT-proBNP. Spironolactone reduced these biomarker concentrations as well as LAVi and PICP, irrespective of left atrial size.

Associations of childhood adiposity with adult intima-media thickness and inflammation: a 20-year longitudinal population-based cohort.

BACKGROUND: The associations between childhood adiposity and adult increased carotid intima-media thickness (cIMT) have been well established, which might be corroborated by the association between adiposity in children and inflammation in adults. However, longitudinal data regarding biological pathways associated with childhood adiposity are lacking. METHODS: The current study included participants from the STANISLAS cohort who had adiposity measurements at age 5-18 years [ N  = 519, mean (SD) age, 13.0 (2.9) years; 46.4% male], and who were measured with cIMT, vascular-related and metabolic-related proteins at a median follow-up of 19 ±â€Š2 years. BMI, waist-to-height ratio and waist circumference were converted to age-specific and sex-specific z -scores. RESULTS: A minority of children were overweight/obese (16.2% overweight-BMI z -score >1; 1.3% obesity- z -score >2). Higher BMI, waist-height ratio and waist circumference in children were significantly associated with greater adult cIMT in univariable analysis, although not after adjusting for C-reactive protein. These associations were more pronounced in those with consistently high adiposity status from childhood to middle adulthood. Participants with higher adiposity during childhood (BMI or waist-height ratio) had higher levels of insulin-like growth factor-binding protein-1, protein-2, matrix metalloproteinase-3, osteopontin, hemoglobin and C-reactive protein in adulthood. Network analysis showed that IL-6, insulin-like growth factor-1 and fibronectin were the key proteins associated with childhood adiposity. CONCLUSION: In a population-based cohort followed for 20 years, higher BMI or waist-to-height ratio in childhood was significantly associated with greater cIMT and enhanced levels of proteins reflective of inflammation, supporting the importance of inflammation as progressive atherosclerosis in childhood adiposity.

Inductive database to support iterative data mining: Application to biomarker analysis on patient data in the Fight-HF project.

Machine learning is now an essential part of any biomedical study but its integration into real effective Learning Health Systems, including the whole process of Knowledge Discovery from Data (KDD), is not yet realised. We propose an original extension of the KDD process model that involves an inductive database. We designed for the first time a generic model of Inductive Clinical DataBase (ICDB) aimed at hosting both patient data and learned models. We report experiments conducted on patient data in the frame of a project dedicated to fight heart failure. The results show how the ICDB approach allows to identify biomarker combinations, specific and predictive of heart fibrosis phenotype, that put forward hypotheses relative to underlying mechanisms. Two main scenarios were considered, a local-to-global KDD scenario and a trans-cohort alignment scenario. This promising proof of concept enables us to draw the contours of a next-generation Knowledge Discovery Environment (KDE).

Inflammation and remodeling pathways and risk of cardiovascular events in patients with ischemic heart failure and reduced ejection fraction.

Patients with heart failure (HF) and coronary artery disease (CAD) have a high risk for cardiovascular (CV) events including HF hospitalization, stroke, myocardial infarction (MI) and sudden cardiac death (SCD). The present study evaluated associations of proteomic biomarkers with CV outcome in patients with CAD and HF with reduced ejection fraction (HFrEF), shortly after a worsening HF episode. We performed a case-control study within the COMMANDER HF international, double-blind, randomized placebo-controlled trial investigating the effects of the factor-Xa inhibitor rivaroxaban. Patients with the following first clinical events: HF hospitalization, SCD and the composite of MI or stroke were matched with corresponding controls for age, sex and study drug. Plasma concentrations of 276 proteins with known associations with CV and cardiometabolic mechanisms were analyzed. Results were corrected for multiple testing using false discovery rate (FDR). In 485 cases and 455 controls, 49 proteins were significantly associated with clinical events of which seven had an adjusted FDR < 0.001 (NT-proBNP, BNP, T-cell immunoglobulin and mucin domain containing 4 (TIMD4), fibroblast growth factor 23 (FGF-23), growth differentiation factor-15 (GDF-15), pulmonary surfactant-associated protein D (PSP-D) and Spondin-1 (SPON1)). No significant interactions were identified between the type of clinical event (MI/stroke, SCD or HFH) and specific biomarkers (all interaction FDR > 0.20). When adding the biomarkers significantly associated with the above outcome to a clinical model (including NT-proBNP), the C-index increase was 0.057 (0.033-0.082), p < 0.0001 and the net reclassification index was 54.9 (42.5 to 67.3), p < 0.0001. In patients with HFrEF and CAD following HF hospitalization, we found that NT-proBNP, BNP, TIMD4, FGF-23, GDF-15, PSP-D and SPON1, biomarkers broadly associated with inflammation and remodeling mechanistic pathways, were strong but indiscriminate predictors of a variety of individual CV events.

Computational Strategy for Minimizing Mycotoxins in Cereal Crops: Assessment of the Biological Activity of Compounds Resulting from Virtual Screening.

Cereal crops are frequently affected by toxigenic Fusarium species, among which the most common and worrying in Europe are Fusarium graminearum and Fusarium culmorum. These species are the causal agents of grain contamination with type B trichothecene (TCTB) mycotoxins. To help reduce the use of synthetic fungicides while guaranteeing low mycotoxin levels, there is an urgent need to develop new, efficient and environmentally-friendly plant protection solutions. Previously, F. graminearum proteins that could serve as putative targets to block the fungal spread and toxin production were identified and a virtual screening undertaken. Here, two selected compounds, M1 and M2, predicted, respectively, as the top compounds acting on the trichodiene synthase, a key enzyme of TCTB biosynthesis, and the 24-sterol-C-methyltransferase, a protein involved in ergosterol biosynthesis, were submitted for biological tests. Corroborating in silico predictions, M1 was shown to significantly inhibit TCTB yield by a panel of strains. Results were less obvious with M2 that induced only a slight reduction in fungal biomass. To go further, seven M1 analogs were assessed, which allowed evidencing of the physicochemical properties crucial for the anti-mycotoxin activity. Altogether, our results provide the first evidence of the promising potential of computational approaches to discover new anti-mycotoxin solutions.

Impact of smoking on cardiovascular risk and premature ageing: Findings from the STANISLAS cohort.

BACKGROUND AND AIMS: Smoking may lead to premature ageing, but the impact on the cardiovascular system and circulating proteins needs further investigation. In the present study, we aim to understand the impact of smoking on heart and vessels and circulating biomarkers of multiple domains including cardiovascular damage, premature ageing and cancer-related pathways. METHODS: The STANISLAS Cohort is a longitudinal familial cohort with detailed cardiovascular examination and biomarker assessment. This study included all the participants enrolled in the fourth visit of the STANISLAS Cohort for whom information on smoking habits was available (n = 1696). We assessed pulse wave velocity, intima-media thickness, echocardiographic parameters and a total of 460 proteins to study the association of circulating plasma proteins with smoking status (never vs. past vs. current smoking) while adjusting for potential confounders. RESULTS: Current smokers were approximately 18 years younger but had higher left ventricular mass index (LVMi) and similar pulse wave velocity (PWV), carotid intima media thickness (cIMT), frequency of hypertension, diabetes and carotid plaques compared to the much older never smokers. After multivariate selection, 25 proteins were independently associated with current or past smoking. Current smoking was strongly associated with higher levels of EDIL-3, CCL11, TNFSF13B, KIT, and lower levels of IL-12B and PLTP (p < 0.0001) while past smoking was associated with FGF-21, CHIT1, and lower levels of CXCL10, IL1RL2 and RAGE (p < 0.01). CONCLUSIONS: Current smoking is associated with signs of early onset of cardiovascular ageing and protein biomarkers that regulate inflammation, endothelial function, metabolism, oncological processes and apoptosis.

Machine Learning-Derived Echocardiographic Phenotypes Predict Heart Failure Incidence in Asymptomatic Individuals.

OBJECTIVES: This study sought to identify homogenous echocardiographic phenotypes in community-based cohorts and assess their association with outcomes. BACKGROUND: Asymptomatic cardiac dysfunction leads to a high risk of long-term cardiovascular morbidity and mortality; however, better echocardiographic classification of asymptomatic individuals remains a challenge. METHODS: Echocardiographic phenotypes were identified using K-means clustering in the first generation of the STANISLAS (Yearly non-invasive follow-up of Health status of Lorraine insured inhabitants) cohort (N = 827; mean age: 60 ± 5 years; men: 48%), and their associations with vascular function and circulating biomarkers were also assessed. These phenotypes were externally validated in the Malmö Preventive Project cohort (N = 1,394; mean age: 67 ± 6 years; men: 70%), and their associations with the composite of cardiovascular mortality (CVM) or heart failure hospitalization (HFH) were assessed as well. RESULTS: Three echocardiographic phenotypes were identified as "mostly normal (MN)" (n = 334), "diastolic changes (D)" (n = 323), and "diastolic changes with structural remodeling (D/S)" (n = 170). The D and D/S phenotypes had similar ages, body mass indices, cardiovascular risk factors, vascular impairments, and diastolic function changes. The D phenotype consisted mainly of women and featured increased levels of inflammatory biomarkers, whereas the D/S phenotype, consisted predominantly of men, displayed the highest values of left ventricular mass, volume, and remodeling biomarkers. The phenotypes were predicted based on a simple algorithm including e', left ventricular mass and volume (e'VM algorithm). In the Malmö cohort, subgroups derived from e'VM algorithm were significantly associated with a higher risk of CVM and HFH (adjusted HR in the D phenotype = 1.87; 95% CI: 1.04 to 3.37; adjusted HR in the D/S phenotype = 3.02; 95% CI: 1.71 to 5.34). CONCLUSIONS: Among asymptomatic, middle-aged individuals, echocardiographic data-driven classification based on the simple e'VM algorithm identified profiles with different long-term HF risk. (4th Visit at 17 Years of Cohort STANISLAS-Stanislas Ancillary Study ESCIF [STANISLASV4]; NCT01391442).

Investigating ADR mechanisms with Explainable AI: a feasibility study with knowledge graph mining.

BACKGROUND: Adverse drug reactions (ADRs) are statistically characterized within randomized clinical trials and postmarketing pharmacovigilance, but their molecular mechanism remains unknown in most cases. This is true even for hepatic or skin toxicities, which are classically monitored during drug design. Aside from clinical trials, many elements of knowledge about drug ingredients are available in open-access knowledge graphs, such as their properties, interactions, or involvements in pathways. In addition, drug classifications that label drugs as either causative or not for several ADRs, have been established. METHODS: We propose in this paper to mine knowledge graphs for identifying biomolecular features that may enable automatically reproducing expert classifications that distinguish drugs causative or not for a given type of ADR. In an Explainable AI perspective, we explore simple classification techniques such as Decision Trees and Classification Rules because they provide human-readable models, which explain the classification itself, but may also provide elements of explanation for molecular mechanisms behind ADRs. In summary, (1) we mine a knowledge graph for features; (2) we train classifiers at distinguishing, on the basis of extracted features, drugs associated or not with two commonly monitored ADRs: drug-induced liver injuries (DILI) and severe cutaneous adverse reactions (SCAR); (3) we isolate features that are both efficient in reproducing expert classifications and interpretable by experts (i.e., Gene Ontology terms, drug targets, or pathway names); and (4) we manually evaluate in a mini-study how they may be explanatory. RESULTS: Extracted features reproduce with a good fidelity classifications of drugs causative or not for DILI and SCAR (Accuracy = 0.74 and 0.81, respectively). Experts fully agreed that 73% and 38% of the most discriminative features are possibly explanatory for DILI and SCAR, respectively; and partially agreed (2/3) for 90% and 77% of them. CONCLUSION: Knowledge graphs provide sufficiently diverse features to enable simple and explainable models to distinguish between drugs that are causative or not for ADRs. In addition to explaining classifications, most discriminative features appear to be good candidates for investigating ADR mechanisms further.

Head-to-head comparison of clustering methods for heterogeneous data: a simulation-driven benchmark.

The choice of the most appropriate unsupervised machine-learning method for "heterogeneous" or "mixed" data, i.e. with both continuous and categorical variables, can be challenging. Our aim was to examine the performance of various clustering strategies for mixed data using both simulated and real-life data. We conducted a benchmark analysis of "ready-to-use" tools in R comparing 4 model-based (Kamila algorithm, Latent Class Analysis, Latent Class Model [LCM] and Clustering by Mixture Modeling) and 5 distance/dissimilarity-based (Gower distance or Unsupervised Extra Trees dissimilarity followed by hierarchical clustering or Partitioning Around Medoids, K-prototypes) clustering methods. Clustering performances were assessed by Adjusted Rand Index (ARI) on 1000 generated virtual populations consisting of mixed variables using 7 scenarios with varying population sizes, number of clusters, number of continuous and categorical variables, proportions of relevant (non-noisy) variables and degree of variable relevance (low, mild, high). Clustering methods were then applied on the EPHESUS randomized clinical trial data (a heart failure trial evaluating the effect of eplerenone) allowing to illustrate the differences between different clustering techniques. The simulations revealed the dominance of K-prototypes, Kamila and LCM models over all other methods. Overall, methods using dissimilarity matrices in classical algorithms such as Partitioning Around Medoids and Hierarchical Clustering had a lower ARI compared to model-based methods in all scenarios. When applying clustering methods to a real-life clinical dataset, LCM showed promising results with regard to differences in (1) clinical profiles across clusters, (2) prognostic performance (highest C-index) and (3) identification of patient subgroups with substantial treatment benefit. The present findings suggest key differences in clustering performance between the tested algorithms (limited to tools readily available in R). In most of the tested scenarios, model-based methods (in particular the Kamila and LCM packages) and K-prototypes typically performed best in the setting of heterogeneous data.

Sex differences in circulating proteins in heart failure with preserved ejection fraction.

BACKGROUND: Many patients with heart failure with preserved ejection fraction (HFpEF) are women. Exploring mechanisms underlying the sex differences may improve our understanding of the pathophysiology of HFpEF. Studies focusing on sex differences in circulating proteins in HFpEF patients are scarce. METHODS: A total of 415 proteins were analyzed in 392 HFpEF patients included in The Metabolic Road to Diastolic Heart Failure: Diastolic Heart Failure study (MEDIA-DHF). Sex differences in these proteins were assessed using adjusted logistic regression analyses. The associations between candidate proteins and cardiovascular (CV) death or CV hospitalization (with sex interaction) were assessed using Cox regression models. RESULTS: We found 9 proteins to be differentially expressed between female and male patients. Women expressed more LPL and PLIN1, which are markers of lipid metabolism; more LHB, IGFBP3, and IL1RL2 as markers of transcriptional regulation; and more Ep-CAM as marker of hemostasis. Women expressed less MMP-3, which is a marker associated with extracellular matrix organization; less NRP1, which is associated with developmental processes; and less ACE2, which is related to metabolism. Sex was not associated with the study outcomes (adj. HR 1.48, 95% CI 0.83-2.63), p = 0.18. CONCLUSION: In chronic HFpEF, assessing sex differences in a wide range of circulating proteins led to the identification of 9 proteins that were differentially expressed between female and male patients. These findings may help further investigations into potential pathophysiological processes contributing to HFpEF.

Circulating plasma proteins and new-onset diabetes in a population-based study: proteomic and genomic insights from the STANISLAS cohort.

OBJECTIVE: Determining the factors associated with new-onset pre-diabetes and type 2 diabetes mellitus (T2D) is important for improving the current prevention strategies and for a better understanding of the disease. DESIGN: To study the factors (clinical, circulating protein and genetic) associated with new onset pre-diabetes and T2D in an initially healthy (without diabetes) populational familial cohort with a long follow-up (STANISLAS cohort). METHODS: A total of 1506 participants attended both the visit 1 and visit 4, separated by ≈20 years. Over 400 proteins, GWAS and genetic associations were studied using models adjusted for potential confounders. Both prospective (V1 to V4) and cross-sectional (V4) analyses were performed. RESULTS: People who developed pre-diabetes (n = 555) and/or T2D (n = 73) were older, had higher BMI, blood pressure, glucose, LDL cholesterol, and lower eGFR. After multivariable selection, PAPP-A (pappalysin-1) was the only circulating protein associated with the onset of both pre-diabetes and T2D with associations persisting at visit 4 (i.e. ≈20 years later). FGF-21 (fibroblast growth factor 21) was a strong prognosticator for incident T2D in the longitudinal analysis, but not in the cross-sectional analysis. The heritability of the circulating PAPP-A was estimated at 44%. In GWAS analysis, the SNP rs634737 was associated with PAPP-A both at V1 and V4. External replication also showed lower levels of PAPP-A in patients with T2D. CONCLUSIONS: The risk of developing pre-diabetes and T2D increases with age and with features of the metabolic syndrome. Circulating PAPP-A, which has an important genetic component, was associated with both the development and presence of pre-diabetes and T2D.

Enhanced clinical phenotyping by mechanistic bioprofiling in heart failure with preserved ejection fraction: insights from the MEDIA-DHF study (The Metabolic Road to Diastolic Heart Failure).

Background: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome for which clear evidence of effective therapies is lacking. Understanding which factors determine this heterogeneity may be helped by better phenotyping. An unsupervised statistical approach applied to a large set of biomarkers may identify distinct HFpEF phenotypes.Methods: Relevant proteomic biomarkers were analyzed in 392 HFpEF patients included in Metabolic Road to Diastolic HF (MEDIA-DHF). We performed an unsupervised cluster analysis to define distinct phenotypes. Cluster characteristics were explored with logistic regression. The association between clusters and 1-year cardiovascular (CV) death and/or CV hospitalization was studied using Cox regression.Results: Based on 415 biomarkers, we identified 2 distinct clusters. Clinical variables associated with cluster 2 were diabetes, impaired renal function, loop diuretics and/or betablockers. In addition, 17 biomarkers were higher expressed in cluster 2 vs. 1. Patients in cluster 2 vs. those in 1 experienced higher rates of CV death/CV hospitalization (adj. HR 1.93, 95% CI 1.12-3.32, p = 0.017). Complex-network analyses linked these biomarkers to immune system activation, signal transduction cascades, cell interactions and metabolism.Conclusion: Unsupervised machine-learning algorithms applied to a wide range of biomarkers identified 2 HFpEF clusters with different CV phenotypes and outcomes. The identified pathways may provide a basis for future research.Clinical significanceMore insight is obtained in the mechanisms related to poor outcome in HFpEF patients since it was demonstrated that biomarkers associated with the high-risk cluster were related to the immune system, signal transduction cascades, cell interactions and metabolismBiomarkers (and pathways) identified in this study may help select high-risk HFpEF patients which could be helpful for the inclusion/exclusion of patients in future trials.Our findings may be the basis of investigating therapies specifically targeting these pathways and the potential use of corresponding markers potentially identifying patients with distinct mechanistic bioprofiles most likely to respond to the selected mechanistically targeted therapies.

Large-Scale Virtual Screening Against the MET Kinase Domain Identifies a New Putative Inhibitor Type.

By using an ensemble-docking strategy, we undertook a large-scale virtual screening campaign in order to identify new putative hits against the MET kinase target. Following a large molecular dynamics sampling of its conformational space, a set of 45 conformers of the kinase was retained as docking targets to take into account the flexibility of the binding site moieties. Our screening funnel started from about 80,000 chemical compounds to be tested in silico for their potential affinities towards the kinase binding site. The top 100 molecules selected-thanks to the molecular docking results-were further analyzed for their interactions, and 25 of the most promising ligands were tested for their ability to inhibit MET activity in cells. F0514-4011 compound was the most efficient and impaired this scattering response to HGF (Hepatocyte Growth Factor) with an IC 50 of 7.2 µ M. Interestingly, careful docking analysis of this molecule with MET suggests a possible conformation halfway between classical type-I and type-II MET inhibitors, with an additional region of interaction. This compound could therefore be an innovative seed to be repositioned from its initial antiviral purpose towards the field of MET inhibitors. Altogether, these results validate our ensemble docking strategy as a cost-effective functional method for drug development.

Plasma protein biomarkers and their association with mutually exclusive cardiovascular phenotypes: the FIBRO-TARGETS case-control analyses.

BACKGROUND: Hypertension, obesity and diabetes are major and potentially modifiable "risk factors" for cardiovascular diseases. Identification of biomarkers specific to these risk factors may help understanding the underlying pathophysiological pathways, and developing individual treatment. METHODS: The FIBRO-TARGETS (targeting cardiac fibrosis for heart failure treatment) consortium has merged data from 12 patient cohorts in 1 common database of > 12,000 patients. Three mutually exclusive main phenotypic groups were identified ("cases"): (1) "hypertensive"; (2) "obese"; and (3) "diabetic"; age-sex matched in a 1:2 proportion with "healthy controls" without any of these phenotypes. Proteomic associations were studied using a biostatistical method based on LASSO and confronted with machine-learning and complex network approaches. RESULTS: The case:control distribution by each cardiovascular phenotype was hypertension (50:100), obesity (50:98), and diabetes (36:72). Of the 86 studied proteins, 4 were found to be independently associated with hypertension: GDF-15, LEP, SORT-1 and FABP-2; 3 with obesity: CEACAM-8, LEP and PRELP; and 4 with diabetes: GDF-15, REN, CXCL-1 and SCF. GDF-15 (hypertension + diabetes) and LEP (hypertension + obesity) are shared by 2 different phenotypes. A machine-learning approach confirmed GDF-15, LEP and SORT-1 as discriminant biomarkers for the hypertension group, and LEP plus PRELP for the obesity group. Complex network analyses provided insight on the mechanisms underlying these disease phenotypes where fibrosis may play a central role. CONCLUSION: Patients with "mutually exclusive" phenotypes display distinct bioprofiles that might underpin different biological pathways, potentially leading to fibrosis. Plasma protein biomarkers and their association with mutually exclusive cardiovascular phenotypes: the FIBRO-TARGETS case-control analyses. Patients with "mutually exclusive" phenotypes (blue: obesity, hypertension and diabetes) display distinct protein bioprofiles (green: decreased expression; red: increased expression) that might underpin different biological pathways (orange arrow), potentially leading to fibrosis.

Bioprofiles and mechanistic pathways associated with Cheyne-Stokes respiration: insights from the SERVE-HF trial.

INTRODUCTION: The SERVE-HF trial included patients with heart failure and reduced ejection fraction (HFrEF) with sleep-disordered breathing, randomly assigned to treatment with Adaptive-Servo Ventilation (ASV) or control. The primary outcome was the first event of death from any cause, lifesaving cardiovascular intervention, or unplanned hospitalization for worsening heart failure. A subgroup analysis of the SERVE-HF trial suggested that patients with Cheyne-Stokes respiration (CSR) < 20% (low CSR) experienced a beneficial effect from ASV, whereas in patients with CSR ≥ 20% ASV might have been harmful. Identifying the proteomic signatures and the underlying mechanistic pathways expressed in patients with CSR could help generating hypothesis for future research. METHODS: Using a large set of circulating protein-biomarkers (n = 276, available in 749 patients; 57% of the SERVE-HF population) we sought to investigate the proteins associated with CSR and to study the underlying mechanisms that these circulating proteins might represent. RESULTS: The mean age was 69 ± 10 years and > 90% were male. Patients with CSR < 20% (n = 139) had less apnoea-hypopnea index (AHI) events per hour and less oxygen desaturation. Patients with CSR < 20% might have experienced a beneficial effect of ASV treatment (primary outcome HR [95% CI] = 0.55 [0.34-0.88]; p = 0.012), whereas those with CSR ≥ 20% might have experienced a detrimental effect of ASV treatment (primary outcome HR [95% CI] = 1.39 [1.09-1.76]; p = 0.008); p for interaction = 0.001. Of the 276 studied biomarkers, 8 were associated with CSR (after adjustment and with a FDR1%-corrected p value). For example, higher PAR-1 and ITGB2 levels were associated with higher odds of having CSR < 20%, whereas higher LOX-1 levels were associated with higher odds of CSR ≥ 20%. Signalling, metabolic, haemostatic and immunologic pathways underlie the expression of these biomarkers. CONCLUSION: We identified proteomic signatures that may represent underlying mechanistic pathways associated with patterns of CSR in HFrEF. These hypothesis-generating findings require further investigation towards better understanding of CSR in HFrEF. SUMMARY OF THE FINDINGS: PAR-1 proteinase-activated receptor 1, ADM adrenomedullin, HSP-27 heat shock protein-27, ITGB2 integrin beta 2, GLO1 glyoxalase 1, ENRAGE/S100A12 S100 calcium-binding protein A12, LOX-1 lectin-like LDL receptor 1, ADAM-TS13 disintegrin and metalloproteinase with a thrombospondin type 1 motif, member13 also known as von Willebrand factor-cleaving protease.

A Chemosensory GPCR as a Potential Target to Control the Root-Knot Nematode Meloidogyne incognita Parasitism in Plants.

Root-knot nematodes (RKN), from the Meloidogyne genus, have a worldwide distribution and cause severe economic damage to many life-sustaining crops. Because of their lack of specificity and danger to the environment, most chemical nematicides have been banned from use. Thus, there is a great need for new and safe compounds to control RKN. Such research involves identifying beforehand the nematode proteins essential to the invasion. Since G protein-coupled receptors GPCRs are the target of a large number of drugs, we have focused our research on the identification of putative nematode GPCRs such as those capable of controlling the movement of the parasite towards (or within) its host. A datamining procedure applied to the genome of Meloidogyne incognita allowed us to identify a GPCR, belonging to the neuropeptide GPCR family that can serve as a target to carry out a virtual screening campaign. We reconstructed a 3D model of this receptor by homology modeling and validated it through extensive molecular dynamics simulations. This model was used for large scale molecular dockings which produced a filtered limited set of putative antagonists for this GPCR. Preliminary experiments using these selected molecules allowed the identification of an active compound, namely C260-2124, from the ChemDiv provider, which can serve as a starting point for further investigations.

Meloidogyne incognita PASSE-MURAILLE (MiPM) Gene Encodes a Cell-Penetrating Protein That Interacts With the CSN5 Subunit of the COP9 Signalosome.

The pathogenicity of phytonematodes relies on secreted virulence factors to rewire host cellular pathways for the benefits of the nematode. In the root-knot nematode (RKN) Meloidogyne incognita, thousands of predicted secreted proteins have been identified and are expected to interact with host proteins at different developmental stages of the parasite. Identifying the host targets will provide compelling evidence about the biological significance and molecular function of the predicted proteins. Here, we have focused on the hub protein CSN5, the fifth subunit of the pleiotropic and eukaryotic conserved COP9 signalosome (CSN), which is a regulatory component of the ubiquitin/proteasome system. We used affinity purification-mass spectrometry (AP-MS) to generate the interaction network of CSN5 in M. incognita-infected roots. We identified the complete CSN complex and other known CSN5 interaction partners in addition to unknown plant and M. incognita proteins. Among these, we described M. incognita PASSE-MURAILLE (MiPM), a small pioneer protein predicted to contain a secretory peptide that is up-regulated mostly in the J2 parasitic stage. We confirmed the CSN5-MiPM interaction, which occurs in the nucleus, by bimolecular fluorescence complementation (BiFC). Using MiPM as bait, a GST pull-down assay coupled with MS revealed some common protein partners between CSN5 and MiPM. We further showed by in silico and microscopic analyses that the recombinant purified MiPM protein enters the cells of Arabidopsis root tips in a non-infectious context. In further detail, the supercharged N-terminal tail of MiPM (NTT-MiPM) triggers an unknown host endocytosis pathway to penetrate the cell. The functional meaning of the CSN5-MiPM interaction in the M. incognita parasitism is discussed. Moreover, we propose that the cell-penetrating properties of some M. incognita secreted proteins might be a non-negligible mechanism for cell uptake, especially during the steps preceding the sedentary parasitic phase.

Low-Dose Alkylphenol Exposure Promotes Mammary Epithelium Alterations and Transgenerational Developmental Defects, But Does Not Enhance Tumorigenic Behavior of Breast Cancer Cells.

Fetal and neonatal exposure to long-chain alkylphenols has been suspected to promote breast developmental disorders and consequently to increase breast cancer risk. However, disease predisposition from developmental exposures remains unclear. In this work, human MCF-10A mammary epithelial cells were exposed in vitro to a low dose of a realistic (4-nonylphenol + 4-tert-octylphenol) mixture. Transcriptome and cell-phenotype analyses combined to functional and signaling network modeling indicated that long-chain alkylphenols triggered enhanced proliferation, migration ability, and apoptosis resistance and shed light on the underlying molecular mechanisms which involved the human estrogen receptor alpha 36 (ERα36) variant. A male mouse-inherited transgenerational model of exposure to three environmentally relevant doses of the alkylphenol mix was set up in order to determine whether and how it would impact on mammary gland architecture. Mammary glands from F3 progeny obtained after intrabuccal chronic exposure of C57BL/6J P0 pregnant mice followed by F1-F3 male inheritance displayed an altered histology which correlated with the phenotypes observed in vitro in human mammary epithelial cells. Since cellular phenotypes are similar in vivo and in vitro and involve the unique ERα36 human variant, such consequences of alkylphenol exposure could be extrapolated from mouse model to human. However, transient alkylphenol treatments combined to ERα36 overexpression in mammary epithelial cells were not sufficient to trigger tumorigenesis in xenografted Nude mice. Therefore, it remains to be determined if low-dose alkylphenol transgenerational exposure and subsequent abnormal mammary gland development could account for an increased breast cancer susceptibility.

Discovering associations between adverse drug events using pattern structures and ontologies.

BACKGROUND: Patient data, such as electronic health records or adverse event reporting systems, constitute an essential resource for studying Adverse Drug Events (ADEs). We explore an original approach to identify frequently associated ADEs in subgroups of patients. RESULTS: Because ADEs have complex manifestations, we use formal concept analysis and its pattern structures, a mathematical framework that allows generalization using domain knowledge formalized in medical ontologies. Results obtained with three different settings and two different datasets show that this approach is flexible and allows extraction of association rules at various levels of generalization. CONCLUSIONS: The chosen approach permits an expressive representation of a patient ADEs. Extracted association rules point to distinct ADEs that occur in a same group of patients, and could serve as a basis for a recommandation system. The proposed representation is flexible and can be extended to make use of additional ontologies and various patient records.