7 results on '"Charbit, Bruno"'
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2. Distinct systemic and mucosal immune responses during acute SARS-CoV-2 infection
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Smith, Nikaïa, Goncalves, Pedro, Charbit, Bruno, Grzelak, Ludivine, Beretta, Maxime, Planchais, Cyril, Bruel, Timothée, Rouilly, Vincent, Bondet, Vincent, Hadjadj, Jérôme, Yatim, Nader, Pere, Helene, Merkling, Sarah H., Ghozlane, Amine, Kernéis, Solen, Rieux-Laucat, Frederic, Terrier, Benjamin, Schwartz, Olivier, Mouquet, Hugo, Duffy, Darragh, Di Santo, James P., Immunologie Translationnelle - Translational Immunology lab, Institut Pasteur [Paris], Immunité Innée - Innate Immunity, Institut Pasteur [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Cytometrie et Biomarqueurs – Cytometry and Biomarkers (UTechS CB), Virus et Immunité - Virus and immunity, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Université de Paris (UP), Immunologie humorale - Humoral Immunology, Datactix, Service de médecine interne et centre de référence des maladies rares [CHU Cochin], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Cochin [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Immunogenetics of pediatric autoimmune diseases (Equipe Inserm U1163), Imagine - Institut des maladies génétiques (IHU) (Imagine - U1163), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), 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 de Paris (UP), Interactions Virus-Insectes - Insect-Virus Interactions (IVI), Centre National de la Recherche Scientifique (CNRS)-Institut Pasteur [Paris], Hub Bioinformatique et Biostatistique - Bioinformatics and Biostatistics HUB, Hôpital Cochin [AP-HP], Infection, Anti-microbiens, Modélisation, Evolution (IAME (UMR_S_1137 / U1137)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP)-Université Sorbonne Paris Nord, Epidémiologie et modélisation de la résistance aux antimicrobiens - Epidemiology and modelling of bacterial escape to antimicrobials (EMAE), This study was supported by grants from the Institut Pasteur (CoVarImm) and from the Agence National de la Recherche (ANR-flash COVID-19, to D.D. and J.P.D.), and by the Laboratoire d’Excellence ‘Milieu Intérieur’ (grant no. ANR-10-LABX-69-01) and the Fonds IMMUNOV, for Innovation in Immunopathology. N.S. is a recipient of the Pasteur-Roux-Cantarini Fellowship., We thank the UTechS CB of the Center for Translational Research, Institut Pasteur for supporting Luminex and Simoa analysis. We thank L. Motreff and L. Ma (Biomics Platform supported by France Génomique, ANR-10-INBS-09-09), IBISA and the Illumina COVID-19 Projects’ offer for microbial sequencing. We thank the Bioinformatics and Biostatistics HUB, Institut Pasteur for the assistance with the 16S rRNA-sequencing data analysis. We acknowledge all health-care workers involved in the diagnosis and treatment of patients in Cochin Hospital, especially C. Azoulay, L. Beaudeau, E. Canoui, P. Cohen, A. Contejean, B. Dunogué, D. Journois, P. Legendre, J. Marey and A. Régent., ANR-20-COVI-0053,CoVarImm,Variation de la réponse immune systémique et muqueuse pendant l'infection par le SRAS-CoV-2 et la convalescence(2020), ANR-10-LABX-0069,MILIEU INTERIEUR,GENETIC & ENVIRONMENTAL CONTROL OF IMMUNE PHENOTYPE VARIANCE: ESTABLISHING A PATH TOWARDS PERSONALIZED MEDICINE(2010), BONDET, Vincent, Variation de la réponse immune systémique et muqueuse pendant l'infection par le SRAS-CoV-2 et la convalescence - - CoVarImm2020 - ANR-20-COVI-0053 - COVID-19 - VALID, Laboratoires d'excellence - GENETIC & ENVIRONMENTAL CONTROL OF IMMUNE PHENOTYPE VARIANCE: ESTABLISHING A PATH TOWARDS PERSONALIZED MEDICINE - - MILIEU INTERIEUR2010 - ANR-10-LABX-0069 - LABX - VALID, Institut Pasteur [Paris] (IP)-Université Paris Cité (UPCité), Institut Pasteur [Paris] (IP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Université Paris Cité (UPCité), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), 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), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité)-Université Sorbonne Paris Nord, Institut Pasteur [Paris] (IP)-Institut National de la Santé et de la Recherche Médicale (INSERM), Virus et Immunité - Virus and immunity (CNRS-UMR3569), and École Pratique des Hautes Études (EPHE)
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Adult ,Male ,Adolescent ,viruses ,[SDV]Life Sciences [q-bio] ,Adaptive immunity ,Antibodies, Viral ,Article ,Cohort Studies ,Young Adult ,Nasopharynx ,Humans ,Immunity, Mucosal ,Aged ,Bacteria ,SARS-CoV-2 ,Microbiota ,COVID-19 ,Middle Aged ,Viral Load ,Gastrointestinal Microbiome ,Immunity, Humoral ,[SDV] Life Sciences [q-bio] ,Viral infection ,Acute Disease ,Spike Glycoprotein, Coronavirus ,Cytokines ,Mucosal immunology ,Female ,Interferons - Abstract
Coordinated local mucosal and systemic immune responses following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection either protect against coronavirus disease 2019 (COVID-19) pathologies or fail, leading to severe clinical outcomes. To understand this process, we performed an integrated analysis of SARS-CoV-2 spike-specific antibodies, cytokines, viral load and bacterial communities in paired nasopharyngeal swabs and plasma samples from a cohort of clinically distinct patients with COVID-19 during acute infection. Plasma viral load was associated with systemic inflammatory cytokines that were elevated in severe COVID-19, and also with spike-specific neutralizing antibodies. By contrast, nasopharyngeal viral load correlated with SARS-CoV-2 humoral responses but inversely with interferon responses, the latter associating with protective microbial communities. Potential pathogenic microorganisms, often implicated in secondary respiratory infections, were associated with mucosal inflammation and elevated in severe COVID-19. Our results demonstrate distinct tissue compartmentalization of SARS-CoV-2 immune responses and highlight a role for the nasopharyngeal microbiome in regulating local and systemic immunity that determines COVID-19 clinical outcomes., Mucosal surfaces of the respiratory tract are the first sites of entry and defense against SARS-CoV-2. Di Santo and colleagues perform paired analysis of the nasopharyngeal and systemic immune responses of SARS-CoV-2-infected patients and demonstrate distinct compartmentalization of immunity and shifts in the microbiome.
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- 2021
3. Additional file 1 of Platelet activation in critically ill COVID-19 patients
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Yatim, Nader, Boussier, Jeremy, Chocron, Richard, Hadjadj, Jérôme, Philippe, Aurélien, Gendron, Nicolas, Barnabei, Laura, Charbit, Bruno, Szwebel, Tali-Anne, Carlier, Nicolas, Pène, Frédéric, Azoulay, Célia, Khider, Lina, Mirault, Tristan, Diehl, Jean-Luc, Guerin, Coralie L., Rieux-Laucat, Frédéric, Duffy, Darragh, Kernéis, Solen, Smadja, David M., and Terrier, Benjamin
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Additional file 1: Figure S1. sP-selectin as a marker of later requirement for mechanical ventilation or in-hospital mortality in mild-to-moderate and severe COVID-19 patients sP-selectin normalized to platelet counts as a predictor of intubation (left) or death (right). Each dot represents one patient (upper panel). ROC curves with area under the curve (AUC) and associated p values are shown (lower panel). Groups: no intubation (n = 31), intubation (n = 14), no death (n = 39), death (n = 6).
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- 2021
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4. Additional file 2 of Platelet activation in critically ill COVID-19 patients
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Yatim, Nader, Boussier, Jeremy, Chocron, Richard, Hadjadj, Jérôme, Philippe, Aurélien, Gendron, Nicolas, Barnabei, Laura, Charbit, Bruno, Szwebel, Tali-Anne, Carlier, Nicolas, Pène, Frédéric, Azoulay, Célia, Khider, Lina, Mirault, Tristan, Diehl, Jean-Luc, Guerin, Coralie L., Rieux-Laucat, Frédéric, Duffy, Darragh, Kernéis, Solen, Smadja, David M., and Terrier, Benjamin
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Additional file 2: Figure S2. sP-selectin is not associated with death in non-COVID-19 septic ICU patients. sP-selectin normalized to platelet counts as a predictor of intubation (left) or death (right). Each dot represents one patient (upper panel). ROC curves with area under the curve (AUC) and associated p values are shown (lower panel). Groups: no death (n = 13), death (n = 16).
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- 2021
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5. Additional file 2: of Human genetic variants and age are the strongest predictors of humoral immune responses to common pathogens and vaccines
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Scepanovic, Petar, CĂŠcile Alanio, Hammer, Christian, Hodel, Flavia, Bergstedt, Jacob, Patin, Etienne, Thorball, Christian, Nimisha Chaturvedi, Charbit, Bruno, Abel, Laurent, Quintana-Murci, Lluis, Duffy, Darragh, Albert, Matthew, and Fellay, Jacques
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Figure S1. Principal Component Analysis. Figure S2. Distribution of serological variables, and clinical thresholds. Figure S3. Seroprevalence data. Figure S4. Impact of non-genetic factors on serostatus. Figure S5. Evolution of serostatus with age and sex. Figure S6. Correlations between age and IgG specific to Rubella and T. gondii. Figure S7. QQ plots for logistic regressions preformed in the study. Figure S8. QQ plots for linear regressions preformed on total Ig levels. Figure S9. QQ plots for linear regressions preformed for pathogen-specific IgG levels. Figure S10. QQ plots for burden testing analyses preformed for all binary phenotypes. Figure S11. QQ plots for burden testing analyses preformed for total Ig levels. Figure S12. QQ plots for burden testing analyses preformed for pathogen-specific IgG levels. (DOCX 89996 kb)
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- 2018
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6. Systems Biology in 874 Patients with Primary Sjogren's Syndrome Indicates the Predominant Role of Interferon Alpha Compared to Interferon Gamma, Its Association with Systemic Complications, and a New Aspect of the Genetic Contribution of HLA
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Bost, Pierre, Mariette, Xavier, Bondet, Vincent, Llibre, Alba, Posseme, Celine, Charbit, Bruno, Thorball, Christian, Jonsson, Roland, Lessard, Chris, Felten, Renaud, Ng, Fai, Silvis, K., Chatenoud, Lucienne, Dumortier, Helene, Jacques Fellay, Brostadt, Karl A., Appel, Silke, Tarn, Jessica R., Quintana-Murci, Lluis, Minguenau, Michael, Meyer, Nicolas, Duffy, Darragh, Schwikowski, Benno, and Gottenberg, Jacques-Eric
7. Variability of Primary Sjogren's Syndrome Is Driven by Interferon alpha and Interferon alpha Blood Levels Are Associated With the Class II HLA-DQ Locus
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Trutschel, Diana, Bost, Pierre, Mariette, Xavier, Bondet, Vincent, Llibre, Alba, Posseme, Celine, Charbit, Bruno, Thorball, Christian W., Jonsson, Roland, Lessard, Christopher J., Felten, Renaud, Ng, Wan Fai, Chatenoud, Lucienne, Dumortier, Helene, Sibilia, Jean, Fellay, Jacques, Brokstad, Karl A., Appel, Silke, Tarn, Jessica R., Quintana-Murci, Lluis, Mingueneau, Michael, Meyer, Nicolas, Duffy, Darragh, Schwikowski, Benno, and Gottenberg, Jacques Eric
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disease-activity ,signatures ,pathways ,innate ,cells ,autoantibody production ,clinical phenotypes - Abstract
Objective Primary Sjogren's syndrome (SS) is the second most frequent systemic autoimmune disease, affecting 0.1% of the general population. To characterize the molecular and clinical variabilities among patients with primary SS, we integrated transcriptomic, proteomic, cellular, and genetic data with clinical phenotypes in a cohort of 351 patients with primary SS. Methods We analyzed blood transcriptomes and genotypes of 351 patients with primary SS who were participants in a multicenter prospective clinical cohort. We replicated the transcriptome analysis in 3 independent cohorts (n = 462 patients). We determined circulating interferon-alpha (IFN alpha) and IFN gamma protein concentrations using digital single molecular arrays (Simoa). Results Transcriptome analysis of the prospective cohort showed a strong IFN gene signature in more than half of the patients; this finding was replicated in the 3 independent cohorts. Because gene expression analysis did not discriminate between type I IFN and type II IFN, we used Simoa to demonstrate that the IFN transcriptomic signature was driven by circulating IFN alpha and not by IFN gamma protein levels. IFN alpha protein levels, detectable in 75% of patients, were significantly associated with clinical and immunologic features of primary SS disease activity at enrollment and with increased frequency of systemic complications over the 5-year follow-up. Genetic analysis revealed a significant association between IFN alpha protein levels, a major histocompatibility (MHC) class II haplotype, and anti-SSA antibody. Additional cellular analysis revealed that an MHC class II HLA-DQ locus acts through up-regulation of HLA class II molecules on conventional dendritic cells. Conclusion We identified the predominance of IFN alpha as a driver of primary SS variability, with IFN alpha demonstrating an association with HLA gene polymorphisms.
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