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

Author: Nicolas Girerd, John Cleland, Stefan D. Anker, William Byra, Carolyn S. P. Lam, David Lapolice, Mandeep R. Mehra, Dirk J. van Veldhuisen, Emmanuel Bresso, Zohra Lamiral, Barry Greenberg, and Faiez Zannad
Subjects: Medicine, Science
Abstract: Abstract 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 0.20). 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
Published: 2022
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2. Investigating ADR mechanisms with Explainable AI: a feasibility study with knowledge graph mining

Author: Emmanuel Bresso, Pierre Monnin, Cédric Bousquet, François-Elie Calvier, Ndeye-Coumba Ndiaye, Nadine Petitpain, Malika Smaïl-Tabbone, and Adrien Coulet
Subjects: Adverse drug reaction, Molecular mechanism, Mechanism of action, Explanation, Data mining, Machine learning, Computer applications to medicine. Medical informatics, R858-859.7
Abstract: Abstract 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 7 3% and 3 8% of the most discriminative features are possibly explanatory for DILI and SCAR, respectively; and partially agreed (2/3) for 9 0% and 7 7% 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.
Published: 2021
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3. Head-to-head comparison of clustering methods for heterogeneous data: a simulation-driven benchmark

Author: Gregoire Preud’homme, Kevin Duarte, Kevin Dalleau, Claire Lacomblez, Emmanuel Bresso, Malika Smaïl-Tabbone, Miguel Couceiro, Marie-Dominique Devignes, Masatake Kobayashi, Olivier Huttin, João Pedro Ferreira, Faiez Zannad, Patrick Rossignol, and Nicolas Girerd
Subjects: Medicine, Science
Abstract: Abstract 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.
Published: 2021
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4. Sex differences in circulating proteins in heart failure with preserved ejection fraction

Author: Susan Stienen, João Pedro Ferreira, Masatake Kobayashi, Gregoire Preud’homme, Daniela Dobre, Jean-Loup Machu, Kevin Duarte, Emmanuel Bresso, Marie-Dominique Devignes, Natalia López Andrés, Nicolas Girerd, Svend Aakhus, Giuseppe Ambrosio, Hans-Peter Brunner-La Rocca, Ricardo Fontes-Carvalho, Alan G. Fraser, Loek van Heerebeek, Gilles de Keulenaer, Paolo Marino, Kenneth McDonald, Alexandre Mebazaa, Zoltàn Papp, Riccardo Raddino, Carsten Tschöpe, Walter J. Paulus, Faiez Zannad, and Patrick Rossignol
Subjects: Medicine, Physiology, QP1-981
Abstract: Abstract 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.
Published: 2020
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5. Computational Strategy for Minimizing Mycotoxins in Cereal Crops: Assessment of the Biological Activity of Compounds Resulting from Virtual Screening

Author: Vessela Atanasova, Emmanuel Bresso, Bernard Maigret, Natalia Florencio Martins, and Florence Richard-Forget
Subjects: Fusarium graminearum, Fusarium culmorum, type B trichothecenes, drug design, experimental validation, Organic chemistry, QD241-441
Abstract: 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
Published: 2022
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6. Large-Scale Virtual Screening Against the MET Kinase Domain Identifies a New Putative Inhibitor Type

Author: Emmanuel Bresso, Alessandro Furlan, Philippe Noel, Vincent Leroux, Flavio Maina, Rosanna Dono, and Bernard Maigret
Subjects: virtual screening, ensemble-docking, structure-based drug design, cross-docking validation, induced fit, conformational selection, met kinase, Organic chemistry, QD241-441
Abstract: 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.
Published: 2020
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7. Meloidogyne incognita PASSE-MURAILLE (MiPM) Gene Encodes a Cell-Penetrating Protein That Interacts With the CSN5 Subunit of the COP9 Signalosome

Author: Caroline Bournaud, François-Xavier Gillet, André M. Murad, Emmanuel Bresso, Erika V. S. Albuquerque, and Maria F. Grossi-de-Sá
Subjects: interactome, root-knot nematode, affinity-purification, hub protein, cell entry, endocytosis, Plant culture, SB1-1110
Abstract: 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.
Published: 2018
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8. Low-Dose Alkylphenol Exposure Promotes Mammary Epithelium Alterations and Transgenerational Developmental Defects, But Does Not Enhance Tumorigenic Behavior of Breast Cancer Cells

Author: Clémence Chamard-Jovenin, Charlène Thiebaut, Amand Chesnel, Emmanuel Bresso, Chloé Morel, Malika Smail-Tabbone, Marie-Dominique Devignes, Taha Boukhobza, and Hélène Dumond
Subjects: mammary gland, alkylphenol mix, cancer, development, estrogen receptor alpha 36, Diseases of the endocrine glands. Clinical endocrinology, RC648-665
Abstract: 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.
Published: 2017
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9. A Chemosensory GPCR as a Potential Target to Control the Root-Knot Nematode Meloidogyne incognita Parasitism in Plants

Author: Emmanuel Bresso, Diana Fernandez, Deisy X. Amora, Philippe Noel, Anne-Sophie Petitot, Maria-Eugênia Lisei de Sa, Erika V. S. Albuquerque, Etienne G. J. Danchin, Bernard Maigret, and Natália F. Martins
Subjects: meloidogyne incognita, homology modelling, molecular dynamics, virtual screening, Organic chemistry, QD241-441
Abstract: 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.
Published: 2019
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10. Tackling scalability issues in mining path patterns from knowledge graphs: a preliminary study.

Author: Pierre Monnin, Emmanuel Bresso, Miguel Couceiro, Malika Smaïl-Tabbone, Amedeo Napoli, and Adrien Coulet
Published: 2020

11. Investigating ADR mechanisms with knowledge graph mining and explainable AI.

Author: Emmanuel Bresso, Pierre Monnin, Cédric Bousquet, François-élie Calvier, Ndeye Coumba Ndiaye, Nadine Petitpain, Malika Smaïl-Tabbone, and Adrien Coulet
Published: 2020

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

Author: Emmanuel Bresso, Joao-Pedro Ferreira, Nicolas Girerd, Masatake Kobayashi, Grégoire Preud'homme, Patrick Rossignol, Fayez Zannad, Marie-Dominique Devignes, and Malika Smaïl-Tabbone
Published: 2022
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13. Discovering associations between adverse drug events using pattern structures and ontologies.

Author: Gabin Personeni, Emmanuel Bresso, Marie-Dominique Devignes, Michel Dumontier, Malika Smaïl-Tabbone, and Adrien Coulet
Published: 2017
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14. GPCRs from fusarium graminearum detection, modeling and virtual screening - the search for new routes to control head blight disease.

Author: Emmanuel Bresso, Roberto C. Togawa, Kim E. Hammond-Kosack, Martin Urban, Bernard Maigret, and Natália Florencio Martins
Published: 2016
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15. Formal Concept Analysis for the Interpretation of Relational Learning Applied on 3D Protein-binding Sites.

Author: Emmanuel Bresso, Renaud Grisoni, Marie-Dominique Devignes, Amedeo Napoli, and Malika Smaïl-Tabbone
Published: 2012

16. ILP Characterization of 3D Protein-Binding Sites and FCA-Based Interpretation.

Author: Emmanuel Bresso, Renaud Grisoni, Marie-Dominique Devignes, Amedeo Napoli, and Malika Smaïl-Tabbone
Published: 2012
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17. Use of Domain Knowledge for Dimension Reduction - Application to Mining of Drug Side Effects.

Author: Emmanuel Bresso, Sidahmed Benabderrahmane, Malika Smaïl-Tabbone, Gino Marchetti, Arnaud Sinan Karaboga, Michel Souchet, Amedeo Napoli, and Marie-Dominique Devignes
Published: 2011

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

Author: Emmanuel Bresso, Pierre Monnin, Cédric Bousquet, François-Elie Calvier, Ndeye-Coumba Ndiaye, Nadine Petitpain, Malika Smaïl-Tabbone, Adrien Coulet, Computational Algorithms for Protein Structures and Interactions (CAPSID), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), Knowledge representation, reasonning (ORPAILLEUR), Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Orange Labs [Belfort] (Orange Labs), France Télécom, Laboratoire d'Informatique Médicale et Ingénierie des Connaissances en e-Santé (LIMICS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Sorbonne Paris Nord, Centre Hospitalier Universitaire de Saint-Etienne [CHU Saint-Etienne] (CHU ST-E), Nutrition-Génétique et Exposition aux Risques Environnementaux (NGERE), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Centre Régional de PharmacoVigilance de Lorraine (CRPV Lorraine), Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), ANR-15-CE23-0028,PractiKPharma,Confrontation entre connaissances de l'état de l'art et connaissances extraites de dossiers patients en pharmacogénomique(2015), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Orange Labs, ANR-15-RHUS-0004,FIGHT-HF,Combattre l'insuffisance cardiaque(2015), Défaillance Cardiovasculaire Aiguë et Chronique (DCAC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Centre Hospitalier Universitaire de Saint-Etienne (CHU de Saint-Etienne), CRHU Nancy, École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP), Monnin, Pierre, Interactions humain-machine, objets connectés, contenus numériques, données massives et connaissance - Confrontation entre connaissances de l'état de l'art et connaissances extraites de dossiers patients en pharmacogénomique - - PractiKPharma2015 - ANR-15-CE23-0028 - AAPG2015 - VALID, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École pratique des hautes études (EPHE), Université de Lorraine (UL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), Coulet, Adrien, and Combattre l'insuffisance cardiaque - - FIGHT-HF2015 - ANR-15-RHUS-0004 - RHUS - VALID
Subjects: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Knowledge graph, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Drug-Related Side Effects and Adverse Reactions, Explanation, Computer applications to medicine. Medical informatics, R858-859.7, Adverse drug reaction, Mechanism of action, Pattern Recognition, Automated, Molecular mechanism, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Pharmacovigilance, Artificial Intelligence, Machine learning, Explainable AI, Adverse Drug Reaction Reporting Systems, Feasibility Studies, Humans, [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], Drug mechanism of action, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Data mining, Research Article, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract: 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. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-021-01518-6.
Published: 2022
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19. Impact of smoking on cardiovascular risk and premature ageing: Findings from the STANISLAS cohort

Author: Tripti Rastogi, Nicolas Girerd, Zohra Lamiral, Emmanuel Bresso, Erwan Bozec, Jean-Marc Boivin, Patrick Rossignol, Faiez Zannad, João Pedro Ferreira, Défaillance Cardiovasculaire Aiguë et Chronique (DCAC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Centre d'investigation clinique plurithématique Pierre Drouin [Nancy] (CIC-P), Centre d'investigation clinique [Nancy] (CIC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Cardiovascular and Renal Clinical Trialists [Vandoeuvre-les-Nancy] (INI-CRCT), Institut Lorrain du Coeur et des Vaisseaux Louis Mathieu [Nancy], French-Clinical Research Infrastructure Network - F-CRIN [Paris] (Cardiovascular & Renal Clinical Trialists - CRCT ), The fourth examination of the STANISLAS study was sponsored by the Centre Hospitalier Régional Universitaire of Nancy (CHRU) and the French Ministry of Health (Programme Hospitalier de Recherche Clinique Inter‐régional 2013), by the Contrat de Plan Etat‐ Lorraine and 'Fonds Européen de Développement Régional' (FEDER Lorraine), and by a public grant overseen by the French National Research Agency (ANR) as part of the second 'Investissements d'Avenir' program FIGHT‐HF (reference: ANR‐15‐RHU‐0004) and by the French 'Projet investissement d'avenir' (PIA) project 'Lorraine Université d'Excellence' (reference ANR‐15‐IDEX‐04‐LUE)., ANR-15-RHUS-0004,FIGHT-HF,Combattre l'insuffisance cardiaque(2015), ANR-15-IDEX-0004,LUE,Isite LUE(2015), European Project, European Project: FEDER, BOZEC, Erwan, Combattre l'insuffisance cardiaque - - FIGHT-HF2015 - ANR-15-RHUS-0004 - RHUS - VALID, ISITE - Isite LUE - - LUE2015 - ANR-15-IDEX-0004 - IDEX - VALID, Contrat de Plan Etat Lorraine IT2MP - INCOMING, and Fonds Européens de Développement Régional - FEDER - INCOMING
Subjects: Aging, circulating protein profile, premature ageing, Carotid Intima Media Thickness, STANISLAS cohort, Smoking, Pulse Wave Analysis, Carotid Intima-Media Thickness, [SDV.MHEP.CSC] Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, Cardiovascular Diseases, Heart Disease Risk Factors, Risk Factors, Estimated Glomerular, Humans, Cardiology and Cardiovascular Medicine, Biomarkers
Abstract: International audience; 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.
Published: 2022
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20. BioRegistry: Automatic extraction of metadata for biological database retrieval and discovery.

Author: Marie-Dominique Devignes, Philippe Franiatte, Nizar Messai, Emmanuel Bresso, Amedeo Napoli, and Malika Smaïl-Tabbone
Published: 2010
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21. Enhanced clinical phenotyping by mechanistic bioprofiling in heart failure with preserved ejection fraction

Author: Stephane Heymans, Kenneth McDonald, Marie-Dominique Devignes, Alexandre Mebazaa, Paolo Marino, Walter Paulus, Giuseppe Ambrosio, Loek van Heerebeek, Jean-Loup Machu, Carsten Tschöpe, Masatake Kobayashi, Alan G. Fraser, Svend Aakhus, Hans-Peter Brunner-La Rocca, Nicolas Girerd, Ricardo Fontes-Carvalho, Kevin Duarte, João Pedro Ferreira, Susan Stienen, Patrick Rossignol, Gilles W. De Keulenaer, Gregoire Preud'homme, Zoltán Papp, Emmanuel Bresso, Riccardo Raddino, Faiez Zannad, Daniela Dobre, Natalia López, Centre d'investigation clinique plurithématique Pierre Drouin [Nancy] (CIC-P), Centre d'investigation clinique [Nancy] (CIC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Défaillance Cardiovasculaire Aiguë et Chronique (DCAC), Université de Lorraine (UL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), Cardiovascular and Renal Clinical Trialists [Vandoeuvre-les-Nancy] (INI-CRCT), Institut Lorrain du Coeur et des Vaisseaux Louis Mathieu [Nancy], French-Clinical Research Infrastructure Network - F-CRIN [Paris] (Cardiovascular & Renal Clinical Trialists - CRCT ), Department of Physiology and Cardiothoracic Surgery Cardiovascular Research and Development Unit Faculty of Medicine University of Porto, Centre Psychothérapique de Nancy (CPN), Computational Algorithms for Protein Structures and Interactions (CAPSID), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Universidad Pública de Navarra [Espagne] = Public University of Navarra (UPNA), Instituto de Investigacion Sanitaria de Navarra (IdiSNA), Department of Cardiology [Oslo], Oslo University Hospital [Oslo], ISB, Norwegian University of Science and Technology, Trondheim, Division of Cardiology, University of Perugia School of Medicine, Perugia, Department of Cardiology, Maastricht University Medical Center, Department of Surgery and Physiology, Cardiovascular Research Unit (UnIC), Faculty of Medicine, University of Porto, Wales Heart Research Institute [Cardiff] (WHRI), Cardiff University, Department of Cardiology, Onze Lieve Vrouwe Gasthuis, Amsterdam, Department of Cardiology, CARIM School for Cardiovascular Diseases Faculty of Health, Medicine and Life Sciences, Maastricht University, Department of Cardiovascular Sciences, Centre for Molecular and Vascular Biology, KU Leuven, William Harvey Research Institute, Barts Heart Centre, Queen Mary University of London, Laboratory of Physiopharmacology, Antwerp University, and ZNA Hartcentrum, Clinical Cardiology, Università del Piemonte Orientale, Department of Translational Medicine, Azienda Ospedaliero Universitaria 'Maggiore della Carità', St Michael's Hospital Dun Laoghaire Co. Dublin, Service d'Anesthésie-Réanimation [AP-HP Hôpitaux Saint-Louis Lariboisière], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Lariboisière-Fernand-Widal [APHP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université de Paris (UP), Marqueurs cardiovasculaires en situation de stress (MASCOT (UMR_S_942 / U942)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Groupe Hospitalier Saint Louis - Lariboisière - Fernand Widal [Paris], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Université Sorbonne Paris Nord, Division of Clinical Physiology, Department of Cardiology, Faculty of Medicine, University of Debrecen, Department of Cardiology, Spedali Civili di Brescia, Charité Campus Virchow-Klinikum (CVK), Department of Cardiology, Campus Virchow-Klinikum, Charite Universitaetsmedizin Berlin, Berlin Institute of Health – Center for Regenerative Therapies (BIH-BCRT), and the German Center for Cardiovascular Research (DZHK, Berlin partner site), Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers, This study was supported by a grant from the European Union (FP7-HEALTH-2010-MEDIA), by the French Programme Hospitalier de Recherche Clinique (PHRC) and by the RHU Fight-HF, a public grant overseen by the French National Research Agency (ANR) as part of the second 'Investissements d’Avenir' program (reference: ANR-15-RHU-0004), the GEENAGE (ANR-15-IDEX-04-LUE) program, by the Contrat de Plan Etat Région Lorraine and FEDER IT2MP.) N-LA was supported by a Miguel Servet contract CP13/00221 from the 'Instituto de Salud Carlos III-FEDER'. WJP and LvH were supported by CVON, Dutch Heart Foundation, The Hague, The Netherlands (RECONNECT and EARLY-HFpEF projects). AM received speaker's honoraria from Orion, Otsuka, Philips, Roche and Servier. AM received fees as member of advisory board and/or Steering Committee and/or research grant from Adrenomed, Epygon, Neurotronik, Roche, Sanofi and Sphyngotec. AM owns shares in S-Form Pharma. SS acknowledges funding received from the European Society of Cardiology in form of an ESC Research Grant (R-2018-18686). SH was supported by IMI2-CARDIATEAM (N° 821508), research grants from the Netherlands Organization for Scientific Research (NOW, Vidi 91796338), the Netherlands Cardiovascular Research Initiative, an initiative with support of the Dutch Heart Foundation, CVON2016-Early HFPEF, 2015-10, CVON She-PREDICTS, grant 2017-21, CVON Arena-PRIME, 2017-18 and the ERA-Net-CVD project MacroERA, 01KL1706, and FWO G091018N, ANR-15-RHUS-0004,FIGHT-HF,Combattre l'insuffisance cardiaque(2015), ANR-15-IDEX-0004,LUE,Isite LUE(2015), European Project: 261409,EC:FP7:HEALTH,FP7-HEALTH-2010-single-stage,MEDIA(2011), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Università degli Studi di Perugia = University of Perugia (UNIPG), Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), University of Antwerp (UA), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Paris Cité (UPCité), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-Université Sorbonne Paris Nord, BOZEC, Erwan, Combattre l'insuffisance cardiaque - - FIGHT-HF2015 - ANR-15-RHUS-0004 - RHUS - VALID, ISITE - Isite LUE - - LUE2015 - ANR-15-IDEX-0004 - IDEX - VALID, The MEtabolic Road to DIAstolic Heart Failure - MEDIA - - EC:FP7:HEALTH2011-01-01 - 2016-06-30 - 261409 - VALID, Centre Psychothérapique de Nancy [Laxou] (CPN), Academic Medical Center, ACS - Heart failure & arrhythmias, Physiology, Cardiologie, MUMC+: MA Med Staf Spec Cardiologie (9), RS: Carim - H02 Cardiomyopathy, Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)
Subjects: Male, Proteomics, ANGIOPOIETIN RECEPTOR TIE-2, Health, Toxicology and Mutagenesis, Clinical Biochemistry, Disease, 030204 cardiovascular system & hematology, Logistic regression, Bioinformatics, Biochemistry, DISEASE, HFPEF, 0302 clinical medicine, biomarkers, cluster analysis, complex-network analysis, machine learning, phenotype, Pharmacology. Therapy, HEPARIN-BINDING PROTEIN, Diastolic heart failure, Middle Aged, Brain natriuretic peptide, Phenotype, [SDV.MHEP.CSC] Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, 3. Good health, PLASMA ANGIOPOIETIN-1, BRAIN NATRIURETIC PEPTIDE, 030220 oncology & carcinogenesis, L-SELECTIN, Female, Engineering sciences. Technology, EXPRESSION, GROWTH-FACTOR, DATABASE, Diastole, GRANULYSIN, 03 medical and health sciences, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, Diabetes mellitus, medicine, Humans, Biology, Aged, Heart Failure, business.industry, Stroke Volume, medicine.disease, business, Heart failure with preserved ejection fraction
Abstract: 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 significance More 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 metabolism Biomarkers (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.
Published: 2020
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22. Machine Learning-Derived Echocardiographic Phenotypes Predict Heart Failure Incidence in Asymptomatic Individuals

Author: Kevin Dalleau, Jean-Marc Boivin, Malika Smaïl-Tabbone, Anne-Cecile Huby, Patrick Rossignol, Margret Leosdottir, Marie-Dominique Devignes, Gregoire Preud'homme, Olivier Huttin, Emmanuel Bresso, Martin Magnusson, Faiez Zannad, Kevin Duarte, João Pedro Ferreira, Masatake Kobayashi, Stanislas study, Erwan Bozec, Peter M. Nilsson, Zohra Lamiral, Nicolas Girerd, Défaillance Cardiovasculaire Aiguë et Chronique (DCAC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Centre d'investigation clinique plurithématique Pierre Drouin [Nancy] (CIC-P), Centre d'investigation clinique [Nancy] (CIC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Cardiovascular and Renal Clinical Trialists [Vandoeuvre-les-Nancy] (INI-CRCT), Institut Lorrain du Coeur et des Vaisseaux Louis Mathieu [Nancy], French-Clinical Research Infrastructure Network - F-CRIN [Paris] (Cardiovascular & Renal Clinical Trialists - CRCT ), Lund University [Lund], Skane University Hospital [Malmo], Computational Algorithms for Protein Structures and Interactions (CAPSID), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), The STANISLAS Cohort visit 4 was sponsored by the Nancy CHRU and was funded in part by the Programme Hospitalier de Recherche Clinique Interrégional. Biomarker studies are co-funded by the French National Research Agency Fighting Heart Failure (ANR-15-RHU-0004) and FEDER Lorraine, and all French co-authors are supported by the French Programme d'investissements d'avenir project 'Lorraine Université d’Excellence' GEENAGE (ANR-15-IDEX-04-LUE) programs, and the Contrat de Plan Etat Région Lorraine and FEDER IT2MP. The research leading to these results also received support from the European Union Commission’s Seventh Framework program under grant 305507 (Heart OMics in Aging). Support was also provided from the 'EXPERT' ERA-CVD 2016 and MR-Focus (both grants managed by the French National Research Agency). Drs. Girerd, Rossignol, and Zannad are supported by the French National Research Agency Fighting Heart Failure (ANR-15-RHU-0004), the French PIA project Lorraine Université d’Excellence GEENAGE (ANR-15-IDEX-04-LUE) programs, and the Contrat de Plan Etat Région Lorraine and FEDER IT2MP., ANR-15-RHUS-0004,FIGHT-HF,Combattre l'insuffisance cardiaque(2015), ANR-15-IDEX-0004,LUE,Isite LUE(2015), European Project, European Project: 305507,EC:FP7:HEALTH,FP7-HEALTH-2012-INNOVATION-1,HOMAGE(2013), European Project: ERA-CVD/0001/2016,MINOTAUR, BOZEC, Erwan, Combattre l'insuffisance cardiaque - - FIGHT-HF2015 - ANR-15-RHUS-0004 - RHUS - VALID, ISITE - Isite LUE - - LUE2015 - ANR-15-IDEX-0004 - IDEX - VALID, Entreprises lorraines, parties prenantes et Développement Durable (FEDER) (Région Lorraine) - INCOMING, Heart OMics in AGEing - HOMAGE - - EC:FP7:HEALTH2013-02-01 - 2019-01-31 - 305507 - VALID, Metabolic Therapy for Managing Diastolic Heart Failure - MINOTAUR - ERA-CVD/0001/2016 - INCOMING, and Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: Male, medicine.medical_specialty, heart failure, 030204 cardiovascular system & hematology, Asymptomatic, Ventricular Function, Left, 03 medical and health sciences, 0302 clinical medicine, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, Predictive Value of Tests, Internal medicine, medicine, Humans, Radiology, Nuclear Medicine and imaging, 030212 general & internal medicine, Aged, business.industry, Incidence (epidemiology), Incidence, biomarkers, Stroke Volume, Middle Aged, medicine.disease, Phenotype, echocardiogram, 3. Good health, cardiovascular diseases, [SDV.MHEP.CSC] Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, machine learning, Echocardiography, Heart failure, Female, prognosis, medicine.symptom, Cardiology and Cardiovascular Medicine, business, cluster analysis
Abstract: This study sought to identify homogenous echocardiographic phenotypes in community-based cohorts and assess their association with outcomes.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.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.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).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).
Published: 2021
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23. Head-to-head comparison of clustering methods for heterogeneous data: a simulation-driven benchmark

Author: Marie-Dominique Devignes, Faiez Zannad, Patrick Rossignol, Kevin Dalleau, Gregoire Preud'homme, João Pedro Ferreira, Olivier Huttin, Emmanuel Bresso, Kevin Duarte, Masatake Kobayashi, Miguel Couceiro, Malika Smaïl-Tabbone, Claire Lacomblez, Nicolas Girerd, BOZEC, Erwan, Combattre l'insuffisance cardiaque - - FIGHT-HF2015 - ANR-15-RHUS-0004 - RHUS - VALID, Défaillance Cardiovasculaire Aiguë et Chronique (DCAC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Centre d'investigation clinique plurithématique Pierre Drouin [Nancy] (CIC-P), Centre d'investigation clinique [Nancy] (CIC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Cardiovascular and Renal Clinical Trialists [Vandoeuvre-les-Nancy] (INI-CRCT), Institut Lorrain du Coeur et des Vaisseaux Louis Mathieu [Nancy], French-Clinical Research Infrastructure Network - F-CRIN [Paris] (Cardiovascular & Renal Clinical Trialists - CRCT ), Computational Algorithms for Protein Structures and Interactions (CAPSID), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Knowledge representation, reasonning (ORPAILLEUR), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), This work and the publication of this article were funded by the Agence Nationale de la Recherche (grant number ANR-15-RHUS-0004: RHU FIGHT-HF) and by the CPER IT2MP (Contrat Plan État Région, Innovations Technologiques, Modélisation & Médecine Personnalisée) and FEDER (Fonds Européen de Développement Régional). Kevin Dalleau was recipient of a RHU-Region Lorraine doctoral fellowship, ANR-15-RHUS-0004,FIGHT-HF,Combattre l'insuffisance cardiaque(2015), and Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: 0301 basic medicine, Computer science, Clustering method Q1, Science, Rand index, Population, Merge mode Q3, 030204 cardiovascular system & hematology, Article, Functional clustering, 03 medical and health sciences, 0302 clinical medicine, Distance or transformation Q2, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, Machine learning, Optimization algorithm Numeric Categorical, Cluster analysis, education, Categorical variable, education.field_of_study, Multidisciplinary, business.industry, Pattern recognition, Latent class model, Medoid, Computational biology and bioinformatics, Hierarchical clustering, [SDV.MHEP.CSC] Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, 030104 developmental biology, ComputingMethodologies_PATTERNRECOGNITION, Benchmark (computing), Medicine, Artificial intelligence, business
Abstract: 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.
Published: 2021
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24. A chemosensory GPCR as a potential target to control the Root-Knot Nematode Meloidogyne incognita parasitism in plants

Author: Erika V.S. Albuquerque, Diana Fernandez, Bernard Maigret, Natália F. Martins, Emmanuel Bresso, Maria Eugênia Lisei de Sá, Etienne Danchin, Anne Sophie Petitot, Deisy Xavier Amora, Philippe Noel, EMMANUEL BRESSO, UNIVERSITÉ DE LORRAINE, FRANCE, DEISY X. AMORA, PHILIPPE NOEL, UNIVERSITÉ DE LORRAINE, FRANCE, ANNE-SOPHIE PETITOT, UNIVERSITÉ DE MONTPELLIER, FRANCE, MARIA-EUGÊNIA LISEI DE SA, ERIKA VALERIA SALIBA ALBUQUERQUE FR, Cenargen, ETIENNE G. J. DANCHIN, UNIVERSITÉ CÔTE D’AZUR, FRANCE, BERNARD MAIGRET, UNIVERSITÉ DE LORRAINE, FRANCE, NATALIA FLORENCIO MARTINS, Cenargen., DIANA FERNANDEZ, UNIVERSITÉ DE MONTPELLIER, FRANCE, Maigret, Bernard, Computational Algorithms for Protein Structures and Interactions (CAPSID), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), UMR - Interactions Plantes Microorganismes Environnement (UMR IPME), Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Embrapa Recursos Genéticos e Biotecnologia (CENARGEN), Résistance des plantes aux bio-agresseurs (UMR RPB), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université Montpellier 2 - Sciences et Techniques (UM2), Evolution et Diversité Biologique (EDB), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, CAPES 51/2013, French National Research Agency (ANR) ANR-10-LABX-001-01, Agropolis Fondation (Montpellier, France) AA1402-004, Embrapa Recursos Genéticos e Biotecnologia [Brasília], Université de Montpellier (UM), Université Côte d'Azur (UCA), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD [France-Sud]), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), and Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Virtual screening, Pharmaceutical Science, Computational biology, Molecular dynamics, 01 natural sciences, Genome, Analytical Chemistry, lcsh:QD241-441, 03 medical and health sciences, lcsh:Organic chemistry, Drug Discovery, Meloidogyne incognita, Root-knot nematode, [SDV.MP.PAR]Life Sciences [q-bio]/Microbiology and Parasitology/Parasitology, Homology modeling, Physical and Theoretical Chemistry, Meloidogyne Incognita, homology modelling, 030304 developmental biology, 0303 health sciences, biology, Host (biology), molecular dynamics, virtual screening, screening, Organic Chemistry, biology.organism_classification, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [SDV.BV.PEP]Life Sciences [q-bio]/Vegetal Biology/Phytopathology and phytopharmacy, 0104 chemical sciences, 010404 medicinal & biomolecular chemistry, Nematode, Chemistry (miscellaneous), Molecular Medicine, virtual, Identification (biology), Homology modelling, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [CHIM.CHEM]Chemical Sciences/Cheminformatics
Abstract: International audience; 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.
Published: 2019
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25. Circulating plasma proteins and new-onset diabetes in a population-based study: proteomic and genomic insights from the STANISLAS cohort

Author: Marie-Dominique Devignes, Kevin Duarte, Emmanuel Bresso, Jean-Marc Boivin, Claire Dandine Roulland, Patrick Rossignol, Edith Le Floch, Nicolas Girerd, Faiez Zannad, Bruno Guerci, Zohra Lamiral, Jean-François Deleuze, João Pedro Ferreira, Constance Xhaard, Sandra Wagner, Centre d'investigation clinique plurithématique Pierre Drouin [Nancy] (CIC-P), Centre d'investigation clinique [Nancy] (CIC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Défaillance Cardiovasculaire Aiguë et Chronique (DCAC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Cardiovascular and Renal Clinical Trialists [Vandoeuvre-les-Nancy] (INI-CRCT), Institut Lorrain du Coeur et des Vaisseaux Louis Mathieu [Nancy], French-Clinical Research Infrastructure Network - F-CRIN [Paris] (Cardiovascular & Renal Clinical Trialists - CRCT ), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Centre National de Recherche en Génomique Humaine, Institut de Génomique, CEA, Evry, France, Service d'Endocrinologie - Diabète - Nutrition [CHRU Nancy], Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), The study was funded by the French National Research Agency Fighting Heart Failure (ANR-15-RHU-0004) and FEDER Lorraine, and all coauthors are supported by the French PIA project 'Lorraine Université d’Excellence' GEENAGE (ANR-15-IDEX-04-LUE) programmes, and the Contrat de Plan Etat Région Lorraine and FEDER IT2MP, ANR-15-RHUS-0004,FIGHT-HF,Combattre l'insuffisance cardiaque(2015), ANR-15-IDEX-0004,LUE,Isite LUE(2015), Computational Algorithms for Protein Structures and Interactions (CAPSID), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Oncology, Male, Proteomics, endocrine system diseases, Cross-sectional study, Endocrinology, Diabetes and Metabolism, Genome-wide association study, Cohort Studies, 0302 clinical medicine, Endocrinology, Risk Factors, MESH: Risk Factors, MESH: Blood Proteins, Prospective Studies, Prospective cohort study, MESH: Cohort Studies, MESH: Genomics, MESH: Proteomics, General Medicine, Blood Proteins, Genomics, 3. Good health, MESH: Young Adult, 030220 oncology & carcinogenesis, Cohort, diabetes mellitus, Female, Cohort study, MESH: Diabetes Mellitus, Type 2, Adult, medicine.medical_specialty, 030209 endocrinology & metabolism, MESH: Prediabetic State, Prediabetic State, 03 medical and health sciences, Young Adult, MESH: Cross-Sectional Studies, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, pre-diabetes, Diabetes mellitus, Internal medicine, medicine, cohort study, Humans, MESH: Humans, business.industry, genetic variants, Type 2 Diabetes Mellitus, nutritional and metabolic diseases, MESH: Adult, medicine.disease, MESH: Male, MESH: Prospective Studies, Cross-Sectional Studies, Diabetes Mellitus, Type 2, Metabolic syndrome, business, MESH: Female, pappalysin-1
Abstract: 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.
Published: 2020
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26. Insulin-like growth factor binding protein 2: A prognostic biomarker for heart failure hardly redundant with natriuretic peptides

Author: Marie-Dominique Devignes, Nicolas Girerd, Patrick Rossignol, Emmanuel Bresso, Centre d'investigation clinique plurithématique Pierre Drouin [Nancy] (CIC-P), Centre d'investigation clinique [Nancy] (CIC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Défaillance Cardiovasculaire Aiguë et Chronique (DCAC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Cardiovascular and Renal Clinical Trialists [Vandoeuvre-les-Nancy] (INI-CRCT), Institut Lorrain du Coeur et des Vaisseaux Louis Mathieu [Nancy], French-Clinical Research Infrastructure Network - F-CRIN [Paris] (Cardiovascular & Renal Clinical Trialists - CRCT ), Computational Algorithms for Protein Structures and Interactions (CAPSID), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Contrat de Plan Etat Lorraine IT2MP and FEDER Lorraine, IMPACT GEENAGE, ANR-15-CE14-0032,MR-focus,Régulation, Diagnostique et Thérapeutique ciblée du récepteur minéralocorticoïde dans le remodelage cardiaque(2015), ANR-16-ECVD-0002,EXPERT,Exploring new pathways in age-related heart diseases(2016), ANR-15-IDEX-0004,LUE,Isite LUE(2015), ANR-15-RHUS-0004,FIGHT-HF,Combattre l'insuffisance cardiaque(2015), DE CARVALHO, Philippe, Régulation, Diagnostique et Thérapeutique ciblée du récepteur minéralocorticoïde dans le remodelage cardiaque - - MR-focus2015 - ANR-15-CE14-0032 - AAPG2015 - VALID, Exploring new pathways in age-related heart diseases - - EXPERT2016 - ANR-16-ECVD-0002 - ERA-CVD - VALID, ISITE - Isite LUE - - LUE2015 - ANR-15-IDEX-0004 - IDEX - VALID, Combattre l'insuffisance cardiaque - - FIGHT-HF2015 - ANR-15-RHUS-0004 - RHUS - VALID, Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)
Subjects: [SDV]Life Sciences [q-bio], Heart failure, 030204 cardiovascular system & hematology, Insulin-like growth factor-binding protein, MESH: Prognosis, 03 medical and health sciences, 0302 clinical medicine, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, Medicine, Prognostic biomarker, 030212 general & internal medicine, Natriuretic peptides, MESH: Natriuretic Peptides, ComputingMilieux_MISCELLANEOUS, MESH: Humans, [SDV.MHEP] Life Sciences [q-bio]/Human health and pathology, biology, business.industry, MESH: Insulin-Like Growth Factor Binding Protein 2, Biomarker, medicine.disease, Insulin-like Growth Factor Binding Protein 2 (IGFBP2), 3. Good health, [SDV.MHEP.CSC] Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, [SDV] Life Sciences [q-bio], MESH: Heart Failure, Cancer research, biology.protein, MESH: Biomarkers, Biomarker (medicine), Cardiology and Cardiovascular Medicine, business, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology
Abstract: International audience
Published: 2020
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27. Large-Scale Virtual Screening Against the MET Kinase Domain Identifies a New Putative Inhibitor Type

Author: Flavio Maina, Vincent Leroux, Bernard Maigret, Alessandro Furlan, Philippe Noel, Emmanuel Bresso, Rosanna Dono, Computational Algorithms for Protein Structures and Interactions (CAPSID), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Institut de Biologie du Développement de Marseille (IBDM), Aix Marseille Université (AMU)-Collège de France (CdF (institution))-Centre National de la Recherche Scientifique (CNRS), Université de Lille, Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Aix Marseille Université (AMU), Service Informatique de Soutien à la Recherche (SISR), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique des Lasers, Atomes et Molécules - UMR 8523 (PhLAM), and Université de Lille-Centre National de la Recherche Scientifique (CNRS)
Subjects: conformational selection, In silico, [SDV]Life Sciences [q-bio], Pharmaceutical Science, Computational biology, Molecular Dynamics Simulation, Article, Analytical Chemistry, lcsh:QD241-441, MET kinase, 03 medical and health sciences, 0302 clinical medicine, lcsh:Organic chemistry, Drug Discovery, [CHIM]Chemical Sciences, Humans, Physical and Theoretical Chemistry, Binding site, Protein Kinase Inhibitors, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, 0303 health sciences, Virtual screening, Chemistry, Kinase, Organic Chemistry, cross-dockingvalidation, ensemble-docking, cross-docking validation, Proto-Oncogene Proteins c-met, virtual screening, 3. Good health, Molecular Docking Simulation, Drug development, Protein kinase domain, Chemistry (miscellaneous), Docking (molecular), 030220 oncology & carcinogenesis, Molecular Medicine, structure-based drug design, Kinase binding, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], induced fit
Abstract: 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&mdash, thanks to the molecular docking results&mdash, 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 &mu, 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.
Published: 2020
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28. Plasma protein biomarkers and their association with mutually exclusive cardiovascular phenotypes: the FIBRO‑TARGETS case–control analyses

Author: Emmanuel Bresso, João Pedro Ferreira, Arantxa González, Jean-Loup Machu, Céline Leroy, Stephane Heymans, Hans-Peter Brunner-La-Rocca, Marie-Dominique Devignes, Nicolas Girerd, Faiez Zannad, Javier Díez, Patrick Rossignol, Anne Pizard, Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), Computational Algorithms for Protein Structures and Interactions (CAPSID), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Cardiovascular Research Institute Maastricht (CARIM), Maastricht University [Maastricht], Universidad de Navarra [Pamplona] (UNAV), Centre National de la Recherche Scientifique (CNRS), Contrat de Plan Etat Région Lorraine and FEDER IT2MP, Netherlands Cardiovascular Research Initiative, Dutch Heart Foundation, Support from the Netherlands Cardiovascular Research Initiative, an initiative with support of the Dutch Heart Foundation, CVON2016-Early HFPEF, 2015-10, and CVON She-PREDICTS, 2017-21, Co-financed as a PPP-allowance Research and Innovation by the Ministry of Economic Affairs within Top Sector Life sciences & Health in the Netherlands, Capsid, IMPACT GEENAGE, ANR-15-IDEX-04-LUE,LUE,Lorraine Université d'Excellence(2016), ANR-15-RHU-0004,FIGHT-HF ,Fighting Heart Failure, European Project: 602904,EC:FP7:HEALTH,FP7-HEALTH-2013-INNOVATION-1,FIBRO-TARGETS(2013), European Project: 261409,EC:FP7:HEALTH,FP7-HEALTH-2010-single-stage,MEDIA(2011), Défaillance Cardiovasculaire Aiguë et Chronique (DCAC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Centre d'investigation clinique plurithématique Pierre Drouin [Nancy] (CIC-P), Centre d'investigation clinique [Nancy] (CIC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Cardiovascular and Renal Clinical Trialists [Vandoeuvre-les-Nancy] (INI-CRCT), Institut Lorrain du Coeur et des Vaisseaux Louis Mathieu [Nancy], French-Clinical Research Infrastructure Network - F-CRIN [Paris] (Cardiovascular & Renal Clinical Trialists - CRCT ), Universidade do Porto, ANR-15-IDEX-0004,LUE,Isite LUE(2015), ANR-15-RHUS-0004,FIGHT-HF,Combattre l'insuffisance cardiaque(2015), Universidade do Porto = University of Porto, Cardiologie, MUMC+: MA Med Staf Spec Cardiologie (9), and RS: Carim - H02 Cardiomyopathy
Subjects: Male, Proteomics, Cardiac fibrosis, VARIANT, RATIONALE, BLOOD-PRESSURE, Disease, LASSO, 030204 cardiovascular system & hematology, Bioinformatics, DISEASE, 0302 clinical medicine, Fibrosis, Risk Factors, Decision tree, 030212 general & internal medicine, STEM-CELL FACTOR, RISK, General Medicine, Blood Proteins, Middle Aged, Phenotype, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], 3. Good health, Phenotypes, Cardiovascular diseases, Hypertension, Cardiology, Female, Cardiology and Cardiovascular Medicine, Adult, medicine.medical_specialty, DATABASE, Complex networks, 03 medical and health sciences, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, Internal medicine, Diabetes mellitus, medicine, Diabetes Mellitus, Humans, Obesity, Aged, business.industry, DIABETES-MELLITUS, medicine.disease, Blood pressure, Heart failure, Case-Control Studies, UPDATE, business, Biomarkers, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, TASK-FORCE
Abstract: 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.Graphic abstractPlasma 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.[GRAPHICS].
Published: 2020
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29. Sex differences in circulating proteins in heart failure with preserved ejection fraction

Author: Loek van Heerebeek, Kevin Duarte, Gregoire Preud'homme, Paolo Marino, Riccardo Raddino, Nicolas Girerd, Faiez Zannad, Emmanuel Bresso, Giuseppe Ambrosio, Zoltán Papp, Alexandre Mebazaa, Gilles W. De Keulenaer, Masatake Kobayashi, Alan G. Fraser, Daniela Dobre, Marie-Dominique Devignes, Ricardo Fontes-Carvalho, Carsten Tschöpe, Patrick Rossignol, Hans-Peter Brunner-La Rocca, João Pedro Ferreira, Natalia López Andrés, Susan Stienen, Svend Aakhus, Jean Loup Machu, Kenneth McDonald, Walter Paulus, Centre d'investigation clinique plurithématique Pierre Drouin [Nancy] (CIC-P), Centre d'investigation clinique [Nancy] (CIC), Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM), Défaillance Cardiovasculaire Aiguë et Chronique (DCAC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), French-Clinical Research Infrastructure Network - F-CRIN [Paris] (Cardiovascular & Renal Clinical Trialists - CRCT ), Cardiovascular and Renal Clinical Trialists [Vandoeuvre-les-Nancy] (INI-CRCT), Institut Lorrain du Coeur et des Vaisseaux Louis Mathieu [Nancy], Universidade do Porto, Centre Psychothérapique de Nancy (CPN), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Universidad Pública de Navarra [Espagne] = Public University of Navarra (UPNA), Oslo University Hospital [Oslo], Norwegian University of Science and Technology [Trondheim] (NTNU), Norwegian University of Science and Technology (NTNU), Department of Medicine [Perugia, Italy], Università degli Studi di Perugia (UNIPG), Department of Cardiology, Maastricht University Medical Center, Department of Surgery and Physiology, Cardiovascular Research Unit (UnIC), Faculty of Medicine, University of Porto, Wales Heart Research Institute [Cardiff] (WHRI), Cardiff University, Department of Cardiology, Onze Lieve Vrouwe Gasthuis, Amsterdam, Laboratory of Physiopharmacology, Antwerp University, and ZNA Hartcentrum, Università degli Studi del Piemonte Orientale - Amedeo Avogadro (UPO), St Michael's Hospital Dun Laoghaire Co. Dublin, Marqueurs cardiovasculaires en situation de stress (MASCOT (UMR_S_942 / U942)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Groupe Hospitalier Saint Louis - Lariboisière - Fernand Widal [Paris], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Université Sorbonne Paris Nord, Service d'Anesthésie-Réanimation [AP-HP Hôpitaux Saint-Louis Lariboisière], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Lariboisière-Fernand-Widal [APHP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université de Paris (UP), Division of Clinical Physiology, Department of Cardiology, Faculty of Medicine, University of Debrecen, Department of Cardiology, Spedali Civili di Brescia, Charité Campus Virchow-Klinikum (CVK), Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers, This study was supported by a grant from the European Union (FP7-HEALTH2010-MEDIA), by the French Programme Hospitalier de Recherche Clinique (PHRC), and by the RHU Fight-HF, a public grant overseen by the French National Research Agency (ANR) as part of the second Investissements d’Avenir' program (reference: ANR-15-RHU-0004), the GEENAGE (ANR-15-IDEX-04-LUE) program, by the Contrat de Plan Etat Région Lorraine and FEDER IT2MP.N-LA was supported by a Miguel Servet contract CP13/00221 from the 'Instituto de Salud Carlos III-FEDER'. WJP and LvH were supported by VON,Dutch Heart Foundation, The Hague, The Netherlands (RECONNECT and EARLY-HFpEF projects). AM received speaker’s honoraria from Orion, Otsuka, Philips, Roche, and Servier. AM received fees as a member of the advisory board and/or Steering Committee and/or research grant from Adrenomed, Epygon, Neurotronik, Roche, Sanofi, and Sphyngotec. AM owns shares in SForm Pharma. SS acknowledges funding received from the European Society of Cardiology in the form of an ESC Research Grant (R-2018-18686)., ANR-15-RHUS-0004,FIGHT-HF,Combattre l'insuffisance cardiaque(2015), ANR-15-IDEX-0004,LUE,Isite LUE(2015), European Project: 261409,EC:FP7:HEALTH,FP7-HEALTH-2010-single-stage,MEDIA(2011), RS: Carim - H02 Cardiomyopathy, MUMC+: MA Med Staf Spec Cardiologie (9), Cardiologie, Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Universidade do Porto = University of Porto, Centre Psychothérapique de Nancy [Laxou] (CPN), Computational Algorithms for Protein Structures and Interactions (CAPSID), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Università degli Studi di Perugia = University of Perugia (UNIPG), University of Antwerp (UA), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-Université Sorbonne Paris Nord, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Paris Cité (UPCité), Physiology, and ACS - Heart failure & arrhythmias
Subjects: Male, 0301 basic medicine, ACUTE MYOCARDIAL-INFARCTION, MATRIX METALLOPROTEINASES, DATABASE, [SDV]Life Sciences [q-bio], IGFBP3, Physiology, lcsh:Medicine, LIPOPROTEIN-LIPASE, lcsh:Physiology, Gender Studies, 03 medical and health sciences, Sex Factors, 0302 clinical medicine, Endocrinology, LEPTIN, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, Humans, Medicine, Myocardial infarction, Aged, Aged, 80 and over, Heart Failure, GENDER-DIFFERENCES, lcsh:QP1-981, business.industry, Proportional hazards model, Research, Leptin, MORTALITY, lcsh:R, Diastolic heart failure, WOMEN, Stroke Volume, MEN, medicine.disease, Pathophysiology, 030104 developmental biology, Gene Expression Regulation, ATHEROSCLEROSIS, Female, Human medicine, business, Heart failure with preserved ejection fraction, Biomarkers, 030217 neurology & neurosurgery, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, Extracellular matrix organization
Abstract: 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.
Published: 2020
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30. Bioprofiles and mechanistic pathways associated with Cheyne-Stokes respiration: insights from the SERVE-HF trial

Author: João Pedro Ferreira, Anita K. Simonds, Marie Pia d'Ortho, Martin R. Cowie, Faiez Zannad, Patrick Rossignol, Christiane E. Angermann, Helmut Teschler, Patrick Levy, Erland Erdmann, Emmanuel Bresso, Virend K. Somers, Karl Wegscheider, Holger Woehrle, Marie Dominique-Devignes, Kevin Duarte, Wolfgang Koenig, Centre d'investigation clinique plurithématique Pierre Drouin (CIC-P), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Biology, genetics and statistics (BIGS), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Élie Cartan de Lorraine (IECL), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), ResMed Science Center, Clinical Cardiology, Imperial College London, Comprehensive Heart Failure Center, Julius-Maximilians-Universität Würzburg [Wurtzbourg, Allemagne] (JMU)-University Hospital of Würzburg-Rudolf Virchow Center for Experimental Biomedicine, Julius-Maximilians-Universität Würzburg [Wurtzbourg, Allemagne] (JMU), Department of Internal Medicine III, University of Cologne, Hypoxie : Physiopathologie Respiratoire et Cardiovasculaire (HP2), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA), Royal Brompton Hospital, Mayo Clinic [Rochester], Universitätsklinikum Essen [Universität Duisburg-Essen] (Uniklinik Essen), Department of Medical Biometry and Epidemiology [Universitaetsklinikum Hamburg-Eppendorf], Universitaetsklinikum Hamburg-Eppendorf = University Medical Center Hamburg-Eppendorf [Hamburg] (UKE), Knowledge representation, reasonning (ORPAILLEUR), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Center for Molecular and Vascular Biology, Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Deutsches Herzzentrum München [Munich, Allemagne] (DHM), Technische Universität München [München] (TUM), CIC-Nancy, Institut Lorrain du Coeur et des Vaisseaux Louis Mathieu [Nancy]-Institut National de la Santé et de la Recherche Médicale (INSERM), Contrat de Plan Etat-Lorraine and FEDER Lorraine, IMPACT GEENAGE, ANR-15-IDEX-04-LUE,LUE,Lorraine Université d'Excellence(2016), ANR-15-RHU-0004,FIGHT-HF ,Fighting Heart Failure, Centre d'investigation clinique plurithématique Pierre Drouin [Nancy] (CIC-P), Centre d'investigation clinique [Nancy] (CIC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Institut Lorrain du Coeur et des Vaisseaux Louis Mathieu [Nancy], Défaillance Cardiovasculaire Aiguë et Chronique (DCAC), Cardiovascular and Renal Clinical Trialists [Vandoeuvre-les-Nancy] (INI-CRCT), French-Clinical Research Infrastructure Network - F-CRIN [Paris] (Cardiovascular & Renal Clinical Trialists - CRCT ), University of Würzburg = Universität Würzburg, AP-HP - Hôpital Bichat - Claude Bernard [Paris], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Université Sorbonne Paris Cité (USPC), Hypoxie : Physiopathologie Respiratoire et Cardiovasculaire (HP2 ), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Computational Algorithms for Protein Structures and Interactions (CAPSID), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), ANR-15-IDEX-0004,LUE,Isite LUE(2015), ANR-15-RHUS-0004,FIGHT-HF,Combattre l'insuffisance cardiaque(2015), and Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)
Subjects: Male, Proteomics, medicine.medical_specialty, Circulating biomarkers, Adaptive servo-ventilation, [SDV]Life Sciences [q-bio], Medizin, Subgroup analysis, chemical and pharmacologic phenomena, 030204 cardiovascular system & hematology, Cheyne–Stokes respiration, 03 medical and health sciences, Ventricular Dysfunction, Left, 0302 clinical medicine, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, Internal medicine, Respiration, medicine, Humans, 030212 general & internal medicine, Cheyne-Stokes Respiration, Receptor, Aged, Heart Failure, Ejection fraction, business.industry, General Medicine, Middle Aged, medicine.disease, Respiration, Artificial, 3. Good health, Adrenomedullin, Treatment Outcome, Heart failure, LDL receptor, Cardiology, Female, medicine.symptom, Cardiology and Cardiovascular Medicine, business, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, Biomarkers
Abstract: International audience; 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) 90% were male. Patients with CSR
Published: 2019
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31. Integrative relational Machine-Learning Approach for Understanding Drug Side-Effect Profiles.

Author: Emmanuel Bresso, Renaud Grisoni, Gino Marchetti, Arnaud Sinan Karaboga, Michel Souchet, Marie-Dominique Devignes, and Malika Smaïl-Tabbone
Published: 2013
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32. Antimicrobial properties of two novel peptides derived from Theobroma cacao osmotin

Author: Natália F. Martins, Lucilia Helena Marcellino, Loeni Ludke Falcão, Magali A. Rodrigues, Marcelo P. Bemquerer, Joseilde Oliveira Silva-Werneck, Alessandra de Rezende Ramos, and Emmanuel Bresso
Subjects: Models, Molecular, 0106 biological sciences, 0301 basic medicine, Antifungal Agents, Physiology, Antimicrobial peptides, Peptide, Microbial Sensitivity Tests, Saccharomyces cerevisiae, 01 natural sciences, Biochemistry, Pichia, Moniliophthora perniciosa, Pichia pastoris, Microbiology, 03 medical and health sciences, Cellular and Molecular Neuroscience, Endocrinology, Fusarium, Protein Domains, Colletotrichum, Spore germination, Amino Acid Sequence, Peptide sequence, Plant Proteins, chemistry.chemical_classification, Cacao, Mycelium, biology, Basidiomycota, fungi, biology.organism_classification, Antimicrobial, 030104 developmental biology, chemistry, Antimicrobial Cationic Peptides, 010606 plant biology & botany
Abstract: The osmotin proteins of several plants display antifungal activity, which can play an important role in plant defense against diseases. Thus, this protein can be useful as a source for biotechnological strategies aiming to combat fungal diseases. In this work, we analyzed the antifungal activity of a cacao osmotin-like protein (TcOsm1) and of two osmotin-derived synthetic peptides with antimicrobial features, differing by five amino acids residues at the N-terminus. Antimicrobial tests showed that TcOsm1 expressed in Escherichia coli inhibits the growth of Moniliophthora perniciosa mycelium and Pichia pastoris X-33 in vitro. The TcOsm1-derived peptides, named Osm-pepA (H-RRLDRGGVWNLNVNPGTTGARVWARTK-NH2), located at R23-K49, and Osm-pepB (H-GGVWNLNVNPGTTGARVWARTK-NH2), located at G28-K49, inhibited growth of yeasts (Saccharomyces cerevisiae S288C and Pichia pastoris X-33) and spore germination of the phytopathogenic fungi Fusarium f. sp. glycines and Colletotrichum gossypi. Osm-pepA was more efficient than Osm-pepB for S. cerevisiae (MIC=40μM and MIC=127μM, respectively), as well as for P. pastoris (MIC=20μM and MIC=127μM, respectively). Furthermore, the peptides presented a biphasic performance, promoting S. cerevisiae growth in doses around 5μM and inhibiting it at higher doses. The structural model for these peptides showed that the five amino acids residues, RRLDR at Osm-pepA N-terminus, significantly affect the tertiary structure, indicating that this structure is important for the peptide antimicrobial potency. This is the first report of development of antimicrobial peptides from T. cacao. Taken together, the results indicate that the cacao osmotin and its derived peptides, herein studied, are good candidates for developing biotechnological tools aiming to control phytopathogenic fungi.
Published: 2016
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33. A Chemosensory GPCR as a Potential Target to Control the Root-Knot Nematode

Author: Emmanuel, Bresso, Diana, Fernandez, Deisy X, Amora, Philippe, Noel, Anne-Sophie, Petitot, Maria-Eugênia Lisei, de Sa, Erika V S, Albuquerque, Etienne G J, Danchin, Bernard, Maigret, and Natália F, Martins
Subjects: Genome, Helminth, Antinematodal Agents, Helminth Proteins, Molecular Dynamics Simulation, virtual screening, Plant Roots, Protein Structure, Secondary, Article, Meloidogyne incognita, molecular dynamics, Host-Parasite Interactions, Receptors, G-Protein-Coupled, Solanum lycopersicum, Animals, Tylenchoidea, Phylogeny, homology modelling, Plant Diseases
Abstract: 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.
Published: 2018

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

Author: Emmanuel Bresso, Caroline Bournaud, André M. Murad, Maria Fatima Grossi-de-Sa, Erika V.S. Albuquerque, and François-Xavier Gillet
Subjects: 0301 basic medicine, root-knot nematode, Protein subunit, parasitism, Context (language use), interactome, Plant Science, lcsh:Plant culture, Interactome, 03 medical and health sciences, Bimolecular fluorescence complementation, Ubiquitin, Meloidogyne incognita, endocytosis, lcsh:SB1-1110, COP9 signalosome, affinity-purification, cell entry, Original Research, biology, food and beverages, biology.organism_classification, Cell biology, 030104 developmental biology, Secretory protein, hub protein, biology.protein
Abstract: 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.
Published: 2018
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35. Extended spectrum of MBD5 mutations in neurodevelopmental disorders

Author: Emmanuel Bresso, Bruno Leheup, Sarah Lejczak, Charlène Vigouroux, Joris Andrieux, Irina Giurgea, Asma A. Khan, Anne Moncla, Philippe Jonveaux, Marie-Dominique Devignes, Mylène Beri, Bénédicte Deemer, Céline Bonnet, Christophe Philippe, Service de Génétique [CHRU Nancy], Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), Nutrition-Génétique et Exposition aux Risques Environnementaux (NGERE), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Déficiences Mentales et Anomalies de Structure du Génome (DMASG), Université Henri Poincaré - Nancy 1 (UHP), Knowledge representation, reasonning (ORPAILLEUR), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Pédiatrie, CHU Amiens, Service de Génétique Médicale [Lille], Institut de génétique médicale-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Département de génétique médicale [Hôpital de la Timone - APHM], Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)- Hôpital de la Timone [CHU - APHM] (TIMONE)-Institut National de la Santé et de la Recherche Médicale (INSERM), Service de Biochimie [Mondor], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Henri Mondor-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), Computational Algorithms for Protein Structures and Interactions (CAPSID), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Service de Médecine Infantile III et Génétique Clinique [CHRU Nancy], and UL, NGERE
Subjects: Male, Developmental Disabilities, [SDV]Life Sciences [q-bio], Nonsense mutation, Short Report, Biology, medicine.disease_cause, 03 medical and health sciences, Exon, Neurodevelopmental disorder, Genes, Duplicate, Intellectual Disability, Genetics, medicine, Humans, Abnormalities, Multiple, Child, Gene, Genetics (clinical), 030304 developmental biology, 0303 health sciences, Mutation, 030305 genetics & heredity, Microdeletion syndrome, medicine.disease, Phenotype, 3. Good health, [SDV] Life Sciences [q-bio], DNA-Binding Proteins, Codon, Nonsense, Child, Preschool, Female, Haploinsufficiency
Abstract: International audience; Intellectual disability (ID) is a clinical sign reflecting diverse neurodevelopmental disorders that are genetically and phenotypically heterogeneous. Just recently, partial or complete deletion of methyl-CpG-binding domain 5 (MBD5) gene has been implicated as causative in the phenotype associated with 2q23.1 microdeletion syndrome. In the course of systematic whole-genome screening of individuals with unexplained ID by array-based comparative genomic hybridization, we identified de novo intragenic deletions of MBD5 in three patients leading, as previously documented, to haploinsufficiency of MBD5. In addition, we described a patient with an unreported de novo MBD5 intragenic duplication. Reverse transcriptase-PCR and sequencing analyses showed the presence of numerous aberrant transcripts leading to premature termination codon. To further elucidate the involvement of MBD5 in ID, we sequenced ten coding, five non-coding exons and an evolutionary conserved region in intron 2, in a selected cohort of 78 subjects with a phenotype reminiscent of 2q23.1 microdeletion syndrome. Besides variants most often inherited from an healthy parent, we identified for the first time a de novo nonsense mutation associated with a much more damaging phenotype. Taken together, these results extend the mutation spectrum in MBD5 gene and contribute to refine the associated phenotype of neurodevelopmental disorder.
Published: 2013
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36. GPCRs from fusarium graminearum detection, modeling and virtual screening - the search for new routes to control head blight disease

Author: Roberto C. Togawa, Natália F. Martins, Emmanuel Bresso, Bernard Maigret, Kim E. Hammond-Kosack, Martin Urban, Computational Algorithms for Protein Structures and Interactions (CAPSID), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Embrapa Uva e Vinho (BRAZIL), Rothamsted Research, Universidade Federal do Ceará = Federal University of Ceará (UFC), EMBRAPA, CAPES, CNPq (Brazil), BBSRC (UK), EMBRAPA (Br), LORIA (Fr), Rothamsted Research (UK), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), and Biotechnology and Biological Sciences Research Council (BBSRC)
Subjects: 0301 basic medicine, 030103 biophysics, In silico, Genomics, Computational biology, Biology, Molecular Dynamics Simulation, Biochemistry, Chemical library, Receptors, G-Protein-Coupled, Fungal Proteins, 03 medical and health sciences, Structural bioinformatics, chemistry.chemical_compound, Fusarium, Structural Biology, Homology modeling, Molecular Biology, Plant Diseases, 2. Zero hunger, Fungal protein, Virtual screening, business.industry, Applied Mathematics, Research, food and beverages, Computer Science Applications, Biotechnology, Fusarium graminearum, 030104 developmental biology, G-protein coupled receptors, Fusarium head blight, chemistry, 13. Climate action, Docking (molecular), [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, Signal Transduction
Abstract: Backgound Fusarium graminearum (FG) is one of the major cereal infecting pathogens causing high economic losses worldwide and resulting in adverse effects on human and animal health. Therefore, the development of new fungicides against FG is an important issue to reduce cereal infection and economic impact. In the strategy for developing new fungicides, a critical step is the identification of new targets against which innovative chemicals weapons can be designed. As several G-protein coupled receptors (GPCRs) are implicated in signaling pathways critical for the fungi development and survival, such proteins could be valuable efficient targets to reduce Fusarium growth and therefore to prevent food contamination. Results In this study, GPCRs were predicted in the FG proteome using a manually curated pipeline dedicated to the identification of GPCRs. Based on several successive filters, the most appropriate GPCR candidate target for developing new fungicides was selected. Searching for new compounds blocking this particular target requires the knowledge of its 3D-structure. As no experimental X-Ray structure of the selected protein was available, a 3D model was built by homology modeling. The model quality and stability was checked by 100 ns of molecular dynamics simulations. Two stable conformations representative of the conformational families of the protein were extracted from the 100 ns simulation and were used for an ensemble docking campaign. The model quality and stability was checked by 100 ns of molecular dynamics simulations previously to the virtual screening step. The virtual screening step comprised the exploration of a chemical library with 11,000 compounds that were docked to the GPCR model. Among these compounds, we selected the ten top-ranked nontoxic molecules proposed to be experimentally tested to validate the in silico simulation. Conclusions This study provides an integrated process merging genomics, structural bioinformatics and drug design for proposing innovative solutions to a world wide threat to grain producers and consumers. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1342-9) contains supplementary material, which is available to authorized users.
Published: 2016
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37. Discovering associations between adverse drug events using pattern structures and ontologies

Author: Gabin, Personeni, Emmanuel, Bresso, Marie-Dominique, Devignes, Michel, Dumontier, Malika, Smaïl-Tabbone, and Adrien, Coulet
Subjects: Patient data, Pharmacovigilance, Phenotype, Biological Ontologies, Drug-Related Side Effects and Adverse Reactions, Research, Ontologies, Electronic Health Records, Humans, Pattern structures, Association rules, Adverse drug event, Pattern Recognition, Automated
Abstract: 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.
Published: 2016

38. Structure-based virtual screening of hypothetical inhibitors of the enzyme longiborneal synthase - a potential target to reduce Fusarium head blight disease

Author: Natália F. Martins, Emmanuel Bresso, Martin Urban, Bernard Maigret, Vincent Leroux, Kim E. Hammond-Kosack, Embrapa Solos, Ministério da Agricultura, Computational Algorithms for Protein Structures and Interactions (CAPSID), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Rothamsted Research, and Biotechnology and Biological Sciences Research Council (BBSRC)
Subjects: Models, Molecular, 0301 basic medicine, Fusarium, 030106 microbiology, Virulence, Computational biology, Catalysis, Fungal Proteins, Ligases, Inorganic Chemistry, 03 medical and health sciences, chemistry.chemical_compound, Protein Domains, Amino Acid Sequence, Homology modeling, Enzyme Inhibitors, Physical and Theoretical Chemistry, Mycotoxin, Phylogeny, Plant Diseases, 2. Zero hunger, chemistry.chemical_classification, Virtual screening, Molecular Structure, Sequence Homology, Amino Acid, biology, ATP synthase, business.industry, Organic Chemistry, food and beverages, biology.organism_classification, High-Throughput Screening Assays, Computer Science Applications, Biotechnology, 030104 developmental biology, Enzyme, Computational Theory and Mathematics, chemistry, Docking (molecular), biology.protein, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Agrochemicals, business, Sesquiterpenes, Protein Binding
Abstract: International audience; Fusarium head blight (FHB) is one of the most destructive diseases of wheat and other cereals worldwide. During infection, the Fusarium fungi produce mycotoxins that represent a high risk to human and animal health. Developing small-molecule inhibitors to specifically reduce mycotoxin levels would be highly beneficial since current treatments unspecifically target the Fusarium pathogen. Culmorin possesses a well-known important synergistically virulence role among mycotoxins, and longiborneol synthase appears to be a key enzyme for its synthesis, thus making longiborneol synthase a particularly interesting target. This study aims to discover potent and less toxic agrochemicals against FHB. These compounds would hamper culmorin synthesis by inhibiting longiborneol synthase. In order to select starting molecules for further investigation, we have conducted a structure-based virtual screening investigation. A longiborneol synthase structural model is first built using homology modeling, followed by molecular dynamics simulations that provided the required input for a protein-ligand ensemble docking procedure. From this strategy, the three most interesting compounds (hits) were selected among the 25 top-ranked docked compounds from a library of 15,000 drug-like compounds. These putative inhibitors of longiborneol synthase provide a sound starting point for further studies involving molecular modeling coupled to biochemical experiments. This process could eventually lead to the development of novel approaches to reduce mycotoxin contamination in harvested grain
Published: 2016
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39. Searching for novel targets to control wheat head blight disease—I-Protein identification,3D modeling and virtual screening

Author: Kim E. Hammond-Kosack, Natália F. Martins, John F. Antoniw, Bernard Maigret, Emmanuel Bresso, Martin Urban, Roberto C. Togawa, Embrapa Uva e Vinho (BRAZIL), Rothamsted Research, Computational Algorithms for Protein Structures and Interactions (CAPSID), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Brésil (CNPq), Biotechnology and Biological Sciences Research Council (BBSRC), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)
Subjects: 0301 basic medicine, 2. Zero hunger, Virtual screening, biology, Trichodiene synthase, Target Selection, food and beverages, 3D Protein Modeling, General Medicine, Disease, Computational biology, Bioinformatics, Genome, Fungicide Development, Crop protection, Fungicide, 03 medical and health sciences, Fusarium graminearum, 030104 developmental biology, Docking (molecular), biology.protein, Homology modeling, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract: Fusarium head blight (FHB) is a destructive disease of wheat and other cereals. FHB occurs in Europe, North America and around the world causing significant losses in production and endangers human and animal health. In this article, we provide the strategic steps for the specific target selection for the phytopathogen system wheat-Fusarium graminearum. The economic impact of FHB leads to the need for innovation. Currently used fungicides have been shown to be effective over the years, but recently cereal infecting Fusaria have developed resistance. Our work presents a new perspective on target selection to allow the development of new fungicides. We developed an innovative approach combining both genomic analysis and molecular modeling to increase the discovery for new chemical compounds with both safety and low environmental impact. Our protein targets selection revealed 13 candidates with high specificity, essentiality and potentially assayable with a favorable accessibility to drug activity. Among them, three proteins: trichodiene synthase, endoglucanase-5 and ERG6 were selected for deeper structural analyses to identify new putative fungicides. Overall, the bioinformatics filtering for novel protein targets applied for agricultural purposes is a response to the demand for chemical crop protection. The availability of the genome, secretome and PHI-base allowed the enrichment of the search that combined experimental data in planta. The homology modeling and molecular dynamics simulations allowed the acquisition of three robust and stable conformers. From this step, approximately ten thousand compounds have been virtually screened against three candidates. Forty-five top-ranked compounds were selected from docking results as presenting better interactions and energy at the binding pockets and no toxicity. These compounds may act as inhibitors and lead to the development of new fungicides.
Published: 2016
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40. Integrative relational machine-learning for understanding drug side-effect profiles

Author: Michel Souchet, Gino Marchetti, Malika Smaïl-Tabbone, Emmanuel Bresso, Marie-Dominique Devignes, Arnaud Sinan Karaboga, Renaud Grisoni, Knowledge representation, reasonning (ORPAILLEUR), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Harmonic Phama, Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)
Subjects: Drug, Drug-Related Side Effects and Adverse Reactions, Side effect, Databases, Pharmaceutical, Computer science, media_common.quotation_subject, Statistical relational learning, Decision tree, Machine learning, computer.software_genre, 01 natural sciences, Biochemistry, 03 medical and health sciences, Side effect (computer science), Semantic similarity, Artificial Intelligence, Structural Biology, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Molecule, Data mining, Molecular Biology, 030304 developmental biology, media_common, 0303 health sciences, Drug discovery, business.industry, Applied Mathematics, Decision Trees, Computational Biology, Reproducibility of Results, Relational machine learning, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], 0104 chemical sciences, Computer Science Applications, Clinical trial, 010404 medicinal & biomolecular chemistry, Identification (information), Drug development, Drug side-effects, Table (database), Data integration, Artificial intelligence, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], DNA microarray, business, computer, DrugBank, Research Article
Abstract: International audience; BackgroundDrug side effects represent a common reason for stopping drug development during clinical trials. Improving our ability to understand drug side effects is necessary to reduce attrition rates during drug development as well as the risk of discovering novel side effects in available drugs. Today, most investigations deal with isolated side effects and overlook possible redundancy and their frequent co-occurrence.ResultsIn this work, drug annotations are collected from SIDER and DrugBank databases. Terms describing individual side effects reported in SIDER are clustered with a semantic similarity measure into term clusters (TCs). Maximal frequent itemsets are extracted from the resulting drug x TC binary table, leading to the identification of what we call side-effect profiles (SEPs). A SEP is defined as the longest combination of TCs which are shared by a significant number of drugs. Frequent SEPs are explored on the basis of integrated drug and target descriptors using two machine learning methods: decision-trees and inductive-logic programming. Although both methods yield explicit models, inductive-logic programming method performs relational learning and is able to exploit not only drug properties but also background knowledge. Learning efficiency is evaluated by cross-validation and direct testing with new molecules. Comparison of the two machine-learning methods shows that the inductive-logic-programming method displays a greater sensitivity than decision trees and successfully exploit background knowledge such as functional annotations and pathways of drug targets, thereby producing rich and expressive rules. All models and theories are available on a dedicated web site.ConclusionsSide effect profiles covering significant number of drugs have been extracted from a drug ×side-effect association table. Integration of background knowledge concerning both chemical and biological spaces has been combined with a relational learning method for discovering rules which explicitly characterize drug-SEP associations. These rules are successfully used for predicting SEPs associated with new drugs.
Published: 2013
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41. ILP Characterization of 3D Protein-Binding Sites and FCA-Based Interpretation

Author: Amedeo Napoli, Marie-Dominique Devignes, Renaud Grisoni, Malika Smaïl-Tabbone, and Emmanuel Bresso
Subjects: Interpretation (logic), business.industry, Computer science, Heuristic, Statistical relational learning, Machine learning, computer.software_genre, Knowledge extraction, Inductive logic programming, Formal concept analysis, Domain knowledge, Artificial intelligence, Explicit knowledge, business, computer
Abstract: Life sciences are continuously producing large amounts of complex data that require relational learning to facilitate knowledge discovery. Inductive Logic Programming (ILP) is a powerful method which allows expressive representation of the data and produces explicit knowledge. However, ILP systems return variable theories depending on heuristic user-choices of various parameters and may miss potentially relevant rules. Accordingly, we propose an original approach based on post-ILP propositionalization of the examples and Formal Concept Analysis for effective interpretation of reached rules with the possibility of adding domain knowledge. Our approach is applied to the characterization of three-dimensional (3D) protein-binding sites which are protein portions on which interactions with other proteins take place. We define a relational representation of protein 3D patches and formalize the problem as a concept learning problem using ILP. We report here the results we obtained on particular protein-binding sites namely phosphorylation sites using ILP followed by FCA-based interpretation.
Published: 2013
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42. BioRegistry: Automatic extraction of metadata for biological database retrieval and discovery

Author: Emmanuel Bresso, Philippe Franiatte, Amedeo Napoli, Marie-Dominique Devignes, Malika Smaïl-Tabbone, Nizar Messai, Knowledge representation, reasonning (ORPAILLEUR), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: semantic similarity, Computer science, Semantic analysis (machine learning), Biological database, metadata extraction, Directory, Library and Information Sciences, Semantics, 03 medical and health sciences, formal concept analysis, Semantic similarity, information retrieval, semantics, 030304 developmental biology, resource discovery, FCA, 0303 health sciences, Thesaurus (information retrieval), Information retrieval, 05 social sciences, bioinformatics, biological data sources, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], biomedical thesaurus, Computer Science Applications, Metadata, classification, 0509 other social sciences, biological databases, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], 050904 information & library sciences, Precision and recall, Information Systems
Abstract: International audience; The need for a well-maintained searchable directory is an important issue with regard to the numerous biological databases produced by genomic and post-genomic research. The BioRegistry repository aims to associate content metadata belonging to a biomedical thesaurus with biological databases in view of retrieval or discovery. It is automatically generated from a publicly available list of biological databases. The querying modalities include a search by semantic similarity. The system performance is evaluated in terms of precision and recall on a collection test. A classification method is proposed for browsing and discovering databases through the BioRegistry.
Published: 2010
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43. 490: Long chain alkylphenol mixture promotes mammary epithelial cell metaplastic phenotype through an estrogen receptor alpha 36 mediated mechanism

Author: Taha Boukhobza, Marie-Dominique Devignes, Emmanuel Bresso, Malika Smaïl-Tabbone, Clémence Chamard, Hélène Dumond, and Amand Chesnel
Subjects: 0303 health sciences, Cancer Research, Alkylphenol, Mechanism (biology), Chemistry, Phenotype, Epithelium, Cell biology, 03 medical and health sciences, 0302 clinical medicine, medicine.anatomical_structure, Oncology, Biochemistry, 030220 oncology & carcinogenesis, medicine, Long chain, Estrogen receptor alpha, 030304 developmental biology
Published: 2014
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44. Découverte d'associations entre Evénements Indésirables Médicamenteux par les structures de patrons et les ontologies

Author: Gabin Personeni, Emmanuel Bresso, Marie-Dominique Devignes, Michel Dumontier, Malika Smaïl-Tabbone, Adrien Coulet, Knowledge representation, reasonning (ORPAILLEUR), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Stanford Center for BioMedical Informatics Research (BMIR), Stanford University, Maastricht University [Maastricht], Inria@SiliconValley, Snowball Inria Associate Team, Snowflake Inria Associate Team, ANR-15-CE23-0028,PractiKPharma,Confrontation entre connaissances de l'état de l'art et connaissances extraites de dossiers patients en pharmacogénomique(2015), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)
Subjects: Evénements Indésirables Médicamenteux, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Structures de Patrons, Ontologies, Analyse Formelle de Concepts, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
Abstract: National audience; On présente dans cet article une méthode d'extraction d'associations entre événements indé-sirables médicamenteux (EIM) utilisant les structures de patrons. Cette méthode permet une comparaison d'EIM fondée sur des ontologies biomédicales et a déjà été présentée avec une application sur des dossiers médicaux électroniques. Une application sur un autre type de données utilisant d'autres ontologies biomédi-cales est présentée ici, ainsi qu'une comparaison et une évaluation plus détaillée des règles extraites. Cette méthode se révèle flexible puisque pouvant être appliquée à divers jeux de données et ontologies, et capable d'extraire des règles d'associations entre EIM avec une représentation expressive de ces EIM.

45. Providing Molecular Characterization for Unexplained Adverse Drug Reactions

Author: François-Élie Calvier, Pierre Monnin, Miguel Boland, Patryk Jarnot, Emmanuel Bresso, Malika Smaïl-Tabbone, Adrien Coulet, and Cedric Bousquet

46. Tackling scalability issues in mining path patterns from knowledge graphs: a preliminary study

Author: Pierre Monnin, Emmanuel Bresso, Miguel Couceiro, Malika Smaïl-Tabbone, Amedeo Napoli, Adrien Coulet, Knowledge representation, reasonning (ORPAILLEUR), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Computational Algorithms for Protein Structures and Interactions (CAPSID), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Supported by the PractiKPharma project, founded by the French National Research Agency (ANR) under Grant ANR15-CE23-0028, and by the Snowball Inria Associate Team., Snowball Inria Associate Team, and ANR-15-CE23-0028,PractiKPharma,Confrontation entre connaissances de l'état de l'art et connaissances extraites de dossiers patients en pharmacogénomique(2015)
Subjects: FOS: Computer and information sciences, Artificial Intelligence (cs.AI), [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Computer Science - Databases, Knowledge Graph, Computer Science - Artificial Intelligence, Ontology, Path Pattern, Scalability, Databases (cs.DB), Path, MathematicsofComputing_DISCRETEMATHEMATICS, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract: International audience; Features mined from knowledge graphs are widely used within multiple knowledge discovery tasks such as classification or fact-checking. Here, we consider a given set of vertices, called seed vertices, and focus on mining their associated neighboring vertices, paths, and, more generally, path patterns that involve classes of ontologies linked with knowledge graphs. Due to the combinatorial nature and the increasing size of real-world knowledge graphs, the task of mining these patterns immediately entails scalability issues. In this paper, we address these issues by proposing a pattern mining approach that relies on a set of constraints (e.g., support or degree thresholds) and the monotonicity property. As our motivation comes from the mining of real-world knowledge graphs, we illustrate our approach with PGxLOD, a biomedical knowledge graph.

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