Expertise

Hub members Have many expertise, covering most of the fields in bioinformatics and biostatistics. You'll find below a non-exhaustive list of these expertise

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Searched keyword : Biostatistics

Related people (17)

Christophe BÉCAVIN

Group : GORE - Hub Core

CV Senior Bioinformatician August 2015 – Present : Institut Pasteur, Paris PostDoc fellow 2011 – 2015 : Pascale Cossart’s laboratory, Unité des Interactions Bactéries-Cellules, Institut Pasteur, Paris Phd fellow 2007 – 2010 : Institut des Hautes Etudes Scientifiques, ann Ecole Normale Supérieure, Paris Magister of Science, Theoretical Physics 2003 – 2007 : Dynamical systems and statistics of complex matter, Université Paris 7 and Université Paris 6


Keywords
BiophysicsMachine learningModelingProteomicsBiostatisticsDatabases and ontologiesHost-pathogen interactions
Organisms
ListeriaLeishmania
Projects (12)

Pascal CAMPAGNE

Group : Stats - Hub Core

Initially trained in evolutionary and environmental sciences, I studied population genetics and micro-evolutionary processes in a number of postdoctoral research projects. I recently joined the C3BI-Hub at the Institut Pasteur, where I work on various aspects involving Biostatistics and the analysis of genetic data.


Keywords
Association studiesGenomicsGenotypingBiostatisticsGeneticsEvolutionPopulation genetics
Organisms
BacteriaParasiteHumanInsect or arthropodOther animal
Projects (23)

Freddy CLIQUET


One of my projects consists in developing GRAVITY, a java tool based on Cytoscape to integrate genetic variants within protein-protein interaction networks to allow the visual and statistical interpretation of next-generation sequencing data, ultimately helping geneticists and clinicians to identify causal variants and better diagnose their patients. I’m also involved in several other projects in the lab, taking part in the design of pipelines for the processing and the analysis of genomics data, including SNP arrays, whole-exome and whole-genome sequencing data. This means being confronted to the big data problematic, the unit having to manage hundreds of terabytes of genomics data. Finally, I am now analysing these data in order to identify possible causes for autism, to help clinicians with their diagnosis but also to better understand the biological mechanisms at play in this complex disease. This is done through the project aiming at understanding the genetic architecture of autism in the Faroe Islands, and also with the newly starting IMI2 European project AIMS2-Trials.


Keywords
AlgorithmicsData managementData VisualizationGenomicsMachine learningProteomicsGenome analysisBiostatisticsProgram developmentScientific computingApplication of mathematics in sciencesExploratory data analysisSofware development and engineeringData and text miningGenetics
Organisms

Projects (0)

    Marie-Agnès DILLIES

    Group : HEAD - Hub Core

    I obtained an engineering degree in Biomedical engineering from Université de Technologie de Compiègne (UTC) in 1989, a master degree in Control of Complex Systems from UTC in 1990, a PhD in Control of Complex Systems from UTC in 1993, a University Degree in Human Genetics from The University of Rennes 1 in 2001 and a master degree in Functional Genomics from University Paris Diderot (Paris 7) in 2002. I worked as a statistician at the Transcriptome and Epigenome Platform from 2002 to 2017, where I was responsible for the statistical analyses of the data and had an important training activity (on the campus and outside). Since 2015 I have been co-head of the Bioinformatics and Biostatistics Hub within the Center of Bioinformatics, Biostatistics and Integrative Biology (C3BI). I am co-director of the Pasteur course Introduction to Data Analysis and co-organiser of the sincellTE summer school (a school dedicated to single cell transcriptome and epigenome data analysis). I am also co-managing the StatOmique group which gathers more than 60 statisticians from France.


    Keywords
    RNA-seqStatistical inferenceTranscriptomicsBiostatisticsApplication of mathematics in sciencesExploratory data analysisIllumina HiSeqStatistical experiment designSequencing
    Organisms

    Projects (3)

    Amine GHOZLANE

    Group : SINGLE - Hub Core

    After a PhD in informatics on graph analysis (metabolic networks and sRNA-mRNA interaction graphs) at the LaBRI (Université de Bordeaux), I joined the DSIMB team (INTS) for a post-doc on structural modeling. Then, I performed a second post-doc at Metagenopolis – INRA Jouy-en-Josas, where I was initiated to the analysis of metagenomic data. I was recruited at the HUB in 2015, and since I pursue the development of methods dedicated to the treatment of metagenomic data by combining either the treatment of sequencing data, the statistics, the protein structural modeling and the graph analysis.


    Keywords
    AlgorithmicsClusteringGenome assemblyGenomicsMetabolomicsModelingNon coding RNASequence analysisStructural bioinformaticsTargeted metagenomicsDatabaseGenome analysisBiostatisticsProgram developmentScientific computingDatabases and ontologiesExploratory data analysisData and text miningIllumina HiSeqComparative metagenomicsRead mappingIllumina MiSeqSequence homology analysisGene predictionMultidimensional data analysisSequencingShotgun metagenomics
    Organisms

    Projects (28)

    Quentin GIAI

    Group : - Hub Core


    Keywords

    Organisms

    Projects (0)

      Bernd JAGLA

      Group : PLATEFORM - Detached : Biomarker Discovery

      Bernd Jagla received his PhD in bioinformatics (department of Biology, Chemistry, and Parmacy) from the Free University in Berlin, Germany in 1999. Before joining the Institut Pasteur, he worked for almost ten years in New York City, including as an associate research scientist in the Joint Centers for System Biology (Columbia University) and at the Columbia University Screening Center led by Dr J.E. Rothman. He joined the Institut Pasteur in 2009 to take charge of the bioinformatic needs at the Transcriptome et Epigenome platform, focusing on Next Generation Sequencing. As of 2016 he is member of the C3BI – HUB Team detached to the Human immunology center (CIH) and provides support for cytometry, next generation sequencing, and microarray data analysis. His areas of interest include the quality assurance and data analysis and visualization at the facility. He also has strong expertise in developing algorithms for function prediction from sequence data, image analysis, analysis of mass spectrometry data, workflow management systems. While at Pasteur he developed: KNIME extensions for Next Generation Sequencing (Link) Post Alignment Visualization and Characterization of High-Throughput Sequencing Experiments (Link) Post Alignment statistics of Illumina reads (Link)


      Keywords
      AlgorithmicsChIP-seqData managementData VisualizationImage analysisMachine learningSequence analysisDatabaseGenome analysisBiostatisticsProgram developmentScientific computingData and text miningIllumina HiSeqGraphics and Image ProcessingIllumina MiSeqHigh Throughput ScreeningFlow cytometry/cell sortingPac Bio
      Organisms

      Projects (2)

      Thomas OBADIA

      Group : Stats - Hub Core

      Thomas is a biostatistician who holds an engineering degree in Agronomy (Agrocampus Ouest, Rennes, France). He also holds a Ph.D. in biostatistics from Université Pierre et Marie Curie for his work on the spread of nosocomial pathogens on contact networks. During his Ph.D at INSERM, he investigated how high-resolution dynamical contact data could support infection-tracing conducted using more traditional approaches in healthcare settings, e.g. routine swabbing and genetic characterization of strains detected in patients or healthcare workers. He developed a new statistical framework to test the correlation between dynamic close-proximity interaction networks and biological carriage data. While at INSERM, he also developed the R0 package for R that aimed at implementing several computation methods used in estimating reproduction parameters for emerging transmissible diseases. After working as a statistical modeller for a private company in the pharmaceutical industry, he joined the Hub in 2016 as a statistician and is now involved in the projects of the Malaria: parasites and hosts unit headed by Ivo Mueller.


      Keywords
      ModelingBiostatisticsScientific computingApplication of mathematics in sciencesClinical researchEpidemiology and public health
      Organisms

      Projects (5)

      Emeline PERTHAME

      Group : Stats - Hub Core

      Since February 2017 Research engineer, Hub of Bioinformatics and Biostatistics of the C3BI, Institut Pasteur 2015-2017 Post doctoral position, team MISTIS, INRIA Grenoble Topic: Robust clustering and robust non linear regression in high dimension. Collaboration with Florence Forbes (INRIA). 2012-2015 PhD thesis in Statistics, Applied Mathematics Department of Agrocampus-Ouest, IRMAR UMR 6625 CNRS, Rennes Topic: Stability of variable selection in regression and classification issues for correlated data in high dimension. Supervisor: David Causeur (Agrocampus-Ouest, IRMAR). Education 2015 PhD thesis in Statistics, Applied Mathematics Department of Agrocampus-Ouest, IRMAR UMR 6625 CNRS, Rennes 2012 ISUP degree (Institut de Statistique de l’UPMC), Université Pierre et Marie Curie, Paris 2012 Master 2 of Statistics, Université Pierre et Marie Curie, Paris


      Keywords
      ClusteringModelingStatistical inferenceTranscriptomicsBiostatisticsExploratory data analysisDimensional reductionStatistical experiment designMultidimensional data analysis
      Organisms

      Projects (22)

      Natalia PIETROSEMOLI

      Group : SysBio - Hub Core

      Dr. Natalia Pietrosemoli is an Engineer with a M. Sc. in Modeling and Simulation of Complex Realities from the International Center for Theoretical Physics, ICTP and the International School of Advanced Studies, SISSA (Triest, Italy). During her M. Sc. internships she mostly worked in modeling, optimization, combinatorics and information theory applied to medical imaging. In 2012 she got a Ph. D in Computational Biology from the School of Bioengineering of Rice University (Houston, TX, US), where she specialized in computational structural biology and functional genomics. Her doctoral thesis “Protein functional features extracted with from primary sequences : a focus on disordered regions”, contributed to a better understanding of the functional and evolutionary role of intrinsic disorder in protein plasticity, complexity and adaptation to stress conditions. As part of her Ph. D., Natalia was a visiting scholar in two labs in Madrid: the Structural Computational Biology Group at the Spanish National Cancer Research Centre (CNIO), where she mainly worked in sequence analysis and the functional-structural relationships of proteins, and the Computational Systems Biology Group at the Spanish National Centre for Biotechnology (CNB-CSIC ), where she studied the functional implications of intrinsically disordered proteins at the genomic level for several organisms, collaborating with different experimental and theoretical groups. In 2013, she joined the Swiss Institute of Bioinformatics as a postdoctoral fellow in the Bioinformactics Core Facility. Her main project consisted in the molecular classification of a rare type of lymphoma, which involved the integration of transcriptomic, clinical and mutational data for the identification of molecular markers for classification, diagnosis and prognosis. This work was performed in collaboration with the Pathology Institute at the University Hospital of Lausanne (CHUV). In November of 2015 Natalia joined the Hub Team @ Pasteur C3BI as a Senior Bioinformatician. Natalia is especially interested in the integrative analysis of different omics data, both at large-scale and for small datasets, and loves collaborating in interdisciplinary environments and having feedback from her fellow experimental colleagues. Currently, she’s coordinating several projects performing functional and pathway analysis at the genomic level. By grouping genes, proteins and other biological molecules into the pathways they are involved in, the complexity of the analyses is significantly reduced, while the explanatory power increases with respect to having a list of differentially expressed genes or proteins.


      Keywords
      AlgorithmicsData managementGenomicsImage analysisMachine learningModelingProteomicsSequence analysisStructural bioinformaticsTranscriptomicsDatabaseGenome analysisBiostatisticsScientific computingDatabases and ontologiesApplication of mathematics in sciencesData and text miningGeneticsGraphics and Image ProcessingBiosensors and biomarkersClinical researchCell biology and developmental biologyInteractomicsBioimage analysis
      Organisms

      Projects (32)

      Hugo VARET

      Group : Stats - Detached : Metabolomics Core Facility

      Hugo Varet is a biostatistician engineer from the Ensai (Ecole Nationale de la Statistique et de l’Analyse de l’Information) and has been recruited in 2013 by the Transcriptome & Epigenome Platform of the Biomics Pole. Late 2014 he obtained a permanent position at the Bioinformatics & Biostatistics Hub and has been detached to the platform to continue the statistical analyses of RNA-Seq data and develop R pipelines and Shiny applications that help in this task. One of them is named SARTools and is available on GitHub: https://github.com/PF2-pasteur-fr/SARTools. In December 2019 he left the Biomics Platform and joined the Bioinformatics & Biostatistics Hub as a core-member.


      Keywords
      MetabolomicsModelingSequence analysisStatistical inferenceTranscriptomicsBiostatisticsScientific computingApplication of mathematics in sciencesExploratory data analysisHigh Throughput ScreeningClinical research
      Organisms

      Projects (28)

      Stevenn VOLANT

      Group : Stats - Hub Core

      After a diploma of statistician engineer from the Ensai (Ecole Nationale de la Statistique et de l’Analyse de l’Information) and a Ph.D in applied mathematics in the Statistics & Genome lab (AgroParisTech), I worked as a developer for the XLSTAT software. I have implemented some statistical methods such as mixture models, log-linear regression, mood test, bayesian hierarchical modeling CBC/HB, … Then I worked as a head teacher in statistics for one year. I was recruited in the Bioinformatic and biostatistic hub of the C3BI (Center of Bioinformatics, Biostatistics and Integrative Biology) in 2014, I am in charge of the statistical analysis and the development of R/R shiny pipelines.


      Keywords
      Machine learningStatistical inferenceTargeted metagenomicsBiostatisticsApplication of mathematics in sciencesStatistical experiment design
      Organisms

      Projects (34)

      Vincent LAVILLE


      NA


      Keywords
      Biostatistics
      Organisms
      Human
      Projects (0)

        Related projects (41)

        Insight into the Immune System: A bioresource and data-sharing platform to study chronic inflammatory diseases (IsIShare)

        Chronic inflammatory systemic diseases (CIDs) are a burden to humans because of life-long debilitating illness, increased mortality and high therapy costs. CIDs’ increasing prevalence in western countries has indeed placed them at the third rank of morbi-mortality causes. Unfortunately, available treatments are poorly targeted and non-curative. That is partly linked to a complex and largely ununderstood pathophysiology. Genetic susceptibility clearly plays a role. Genes linked to the immune system have been identified, but causal genes remain mostly unknown and other factors such as intestinal microbiota have also been implicated. The complexity of CIDs’ pathophysiology suggests that a holistic approach is the most susceptible to help make significant progress. Our project intends to take advantage of recent technical progress and development of informatics tools to set up a transversal approach. High-resolution sequencing technology indeed quickly produces large amounts of accurate data. Besides, new integrative informatics tools allowing storage and integrative analysis of this resulting high amount of data are now available. We intend to set-up a CID’s network allowing the gathering and extensive analysis of data related to immuno-genetic determinants, immune repertoire and microbiota from individuals suffering from one of the three major interlinked CIDs, namely Hidradenitis Suppurativa (HS), Crohn’s disease (CD) and Spondyloarthropathy (SpA) as compared to healthy volunteers.



        Project status : Closed

        Identification of immune response signatures that correlate with therapeutic responses to TNF inhibitors using machine-learning algorithms

        Anti-TNF therapy has revolutionized treatment of many chronic inflammatory diseases, including rheumatoid arthritis, Crohn’s disease and spondyloarthritis (SpA). However, clinical efficacy of TNF-inhibitors (TNFi) is limited by a high rate of non-responsiveness (30-40%) both in SpA and other diseases, exposing a substantial fraction of patients to important side-effects without any clinical benefit. Despite the extensive use of TNFi since many years, it is still not possible to determine which patients will respond to TNFi before treatment initiation. In this study, we have tested the hypothesis that the functional analysis of immune responses may not only improve our understanding of the molecular mechanisms of TNF-blocker activity, but also identify correlates of therapeutic responses in SpA patients. To facilitate the potential translation of our findings into a clinical setting, we have used standardized whole-blood stimulation assays (“TruCulture” assays, Duffy et al., Immunity 2014), and have minimized sources of pre-analytical variability, implementing a highly sensitive and robust pipeline to assess immune functions in patients. To investigate the concept that the immune status of a patient will define their response to TNFi treatment, we have used machine-learning algorithms to identify, in whole-blood stimulation assays, immunological transcripts that correlate with clinical efficacy of TNFi. Our results obtained with a cohort of 67 SpA patients demonstrate that high expression, before treatment initiation, of molecules associated with leukocyte invasion/migration and inflammatory processes predisposes to favorable outcome of anti-TNF therapy, while high-level expression of cytotoxic molecules was associated with poor therapeutic responses to TNF-blockers. These findings may suggest that SpA patients whose immune response is characterized by strong, NF-kB-mediated inflammation are more likely to benefit from TNFi treatment than patients with an active T/NK-cell component. Unfortunately our manuscript describing these results has been rejected by Nature Medicine. However, in her letter the editor mentioned, “Should future experimental data allow you to demonstrate that the identified gene signatures predict response to treatment and outperform previously reported approaches in an independent cohort, we would be happy to look at a new submission…”. We have recruited additional SpA patients over the summer and we are currently in the process of performing the gene expression analysis. The goal of this bioinformatic analysis will be to identify transcripts in stimulated immune cells that predict therapeutic outcome in a training set of patients using machine-learning algorithms and validate the findings in a replication cohort.



        Project status : Closed

        High content screening of mitochondrial morphology defects in mitochondrial genetic diseases

        Mitochondria are double-membrane bound organelles that are essential in every tissue of the body. They are metabolic hubs and signalling platforms that are deeply integrated into cellular homeostasis. The functions of mitochondria are intimately linked to their form, which is regulated by a balance of membrane fusion and fission: dynamin-like GTPases OPA1 and MFN1/2 perform membrane fusion and DRP1 regulates membrane fission. Mutations in mitochondrial genes cause a pleiotropic spectrum of clinical disorders whose underlying genetic, morphological and biochemical defects can be easily studied in skin fibroblasts generated from patient biopsies. The morphology of mitochondria is inextricably linked to its many essential functions in the cell and we are interested in understanding the relationship between mitochondrial shape changes and metabolism in the context of acquired and inborn human diseases. Balanced fusion and fission events shape mitochondria to meet metabolic demands and to ensure removal of damaged organelles. Mitochondrial fragmentation occurs in response to nutrient excess and cellular dysfunction and has been observed in mitochondrial genetic diseases and is thought to play an important role in the development of disease. The physiological relevance of mitochondrial morphology and the mechanisms that regulate mitochondrial dynamics are incomplete and so we have set out to find ways to rebalance mitochondrial dynamics in genetic diseases. We recently developed imaging and informatics pipelines to allow for the automated, rapid, reliable quantification of mitochondrial morphology in human fibroblasts. We applied this new technology in the context of genome-wide siRNA screens in immortalized fibroblasts from an OPA1 patient with dominant optic atrophy and control fibroblasts to identify candidate genes able to reverse the mitochondrial fragmentation phenotype associated with mitochondrial dysfunction in patient cells. We have performed the same genome-wide screen in healthy immortalized control fibroblasts. Together, these studies will help us identify lists of genetic modifiers and therapeutic targets that can be investigated further using cell biology and biochemical tools in the lab.



        Project status : Closed

        Impact des contraintes biomécanistiques sur la dynamique des macro-ouverture induits par l'EDIN de Staphylococcus aureus.



        Project status : In Progress

        Afribiota-Neuro

        Globally one out of four children under 5 years is affected by linear growth delay (stunting). This syndrome has severe long-term sequelae including increased risk of illness and mortality and delayed psychomotor development. Stunting is a syndrome that is linked to poor nutrition and repeated infections. To date, the treatment of stunted children is challenging as the underlying etiology and pathophysiological mechanisms remain elusive. We hypothesize that pediatric environmental enteropathy (PEE), a chronic inflammation of the small intestine, plays a major role in the pathophysiology of stunting, failure of nutritional interventions and diminished response to oral vaccines, potentially via changes in the composition of the pro- and eukaryotic intestinal communities. The main objective of AFRIBIOTA is to describe the intestinal dysbiosis observed in the context of stunting and to link it to PEE. Secondary objectives include the identification of the broader socio-economic environment and biological and environmental risk factors for stunting and PEE as well as the testing of a set of easy-to-use candidate biomarkers for PEE. We also assess host outcomes including mucosal and systemic immunity and psychomotor development. AFRIBIOTA is a case-control study for stunting recruiting children in Bangui, Central African Republic and in Antananarivo, Madagascar. In each country, 460 children aged 2–5 years with no overt signs of gastrointestinal disease are recruited (260 with no growth delay, 100 moderately stunted and 100 severely stunted). We compare the intestinal microbiota composition (gastric and small intestinal aspirates; feces), the mucosal and systemic immune status and the psychomotor development of children with stunting and/or PEE compared to non-stunted controls. We also perform anthropological and epidemiological investigations of the children’s broader living conditions and assess risk factors using a standardized questionnaire. To date, the pathophysiology and risk factors of stunting and PEE have been insufficiently investigated. AFRIBIOTA will add new insights into the pathophysiology underlying stunting and PEE and in doing so will enable implementation of new biomarkers and design of evidence-based treatment strategies for these two syndromes. These analyses comprise the child development aspects of AFRIBIOTA.



        Project status : Closed

        Hamper cell-to-cell variation to enhance drug-mediated killing

        Without new treatment development tuberculosis could cause about 70 million deaths by 2050, mostly due to the spread of multidrug-resistant strains. The standard drug regimen still builds on the first drugs introduced decades ago, and takes 6 months in the case of drug-sensitive tuberculosis, and up to 2 years in the case of drug-resistant tuberculosis, with heavy side effects. This long therapeutic regimen often results in patients not being able to follow it or complete it correctly, which promotes the chronicity of the infection and ultimately the onset of drug resistance. Although a few new molecules have been discovered, improving both the quality and the duration of tuberculosis chemotherapy remain pressing needs. Furthermore, the failure of chemotherapy is not only due to genetic resistance, which takes relatively long to occur, but also to the intrinsic ability of mycobacteria to diversify in discrete phenotypic states, which can endure drugs even in the absence of genetic mutations. This phenomenon, known as persistence, can eventually favor the onset of resistance, with major repercussions on disease control. In sum, tuberculosis therapy presents many challenges and in our view it is critical to study the ability of a drug or a drug combination to sterilize discrete subpopulations, which may either pre-exist in the population or result from adaptive processes. We found that, prior to drug exposure, phenotypically distinct subpopulations exist that display different drug susceptibility. In light of this, we hypothesized that phenotypic variation from cell to cell favors persistence and can consequently bring about treatment failure. Here we aim to identify molecules that reduce phenotypic variation, making the population more uniformly and rapidly susceptible to standard treatment. To this end, we developed a microfluidic system that allows us to track single cells by live imaging and to carry out a screening at the single-cell level, looking for molecules that homogenize the bacterial population and enhance the effectiveness of the standard treatment. Our approach could ultimately offer original therapeutic strategies towards better control of tuberculosis.



        Project status : Pending

        3D PATH

        Complex chronic diseases are caused by the accumulation of genetic, microbial and lifestyle factors. The number and complexity of such factors makes prediction of pathogenesis and therapy particularly difficult. Although a single factor is rarely sufficient to trigger pathology, genetic and environmental factors have so far been studied in isolation. Nevertheless, a substantial number of genetic variants have been associated with disease risk and the concomitant lifestyle shift and excessive hygiene are thought to contribute to the increased incidence in inflammatory diseases in industrialized countries. Moreover, clinical and experimental observations suggest a strong impact of gut microbiota on susceptibility to inflammatory diseases. The aim of 3D PATH is to explore the multiplicity and complexity of genetic, microbial and lifestyle factors associated with vulnerability to inflammatory pathology, using mice of the Collaborative Cross (CC), that model human genetic variability. Quantitative trait loci analyses, as well as integrative data analyses on metabolic and inflammatory outcomes will reveal new genetic variants and combinations of variants associated with disease susceptibility, and whether alterations of the gut microbiota in genetically susceptible mouse strains can trigger the phenotype. Moreover, the longitudinal design of experiments will allow us to identify early biomarkers that predict the pathology later in life. In a second step, validation of the identified risk factor combinations and exploration of the underlying molecular mechanisms will be performed taking advantage of the mouse model.



        Project status : Closed

        Transcriptional profiling of the innate immune response of human fibroblasts of the LabEx Milieu interieur collection: interindividual variability and response to infection

        The LabEx Milieu interieur (MI) project is a clinical study aiming to define the natural variability of the human immune response. Fibroblasts were prepared from skin biopsies of 300 of the 1000 healthy donors (30 men and 30 women in each of 5 decades from 20 to 69 years) by Genethon. Each primary line is annotated by metadata derived from the systematic genotypic-phenotypic analysis Labex-MI (serology, genomic analyzes, proteomics, transcriptome, microbiota, clinical data and epidemiological selection criteria). The objective is to characterize the innate immune response of fibroblasts from healthy donors. We have developed approaches to obtain standardized measures of the immune response and to identify interindividual variance (Chansard et al., 2021) on a subset of sixteen primary fibroblast lines.. We observed that cell responses to LPS stimulation were distributed between “low” and “high” responders, while inter-donor variability of the response to poly I: C was very low. Poly I: C and LPS activate pathways from TLR3 and TLR4 receptors, respectively. We set up a real-time quantitative PCR assay of the 16 cells in parallel using the BioMark automated PCR system (Fluidigm) for comparative quantification of mRNAs baseline expression in the donors’ fibroblasts to check if low responses to LPS were due to a different expression of TLR4 and other factors of the pathway. We will extend the analysis to stimulated cells by Poly I: C ,LPS and other agonists of the innate immune response and also in parallel to cells infected by Leptospira interrogans.



        Project status : Pending

        Clinical and biological characterisation of auto-antibodies to the nicotinic receptors in major psychiatric disorder patient

        In recent years, immune dysfunctions, including auto-immune mechanisms and peripheral inflammation, have been clearly associated with severe neuropsychiatric disorders like bipolar disorders (BD) and schizophrenia (SZ). These findings are supported by an extensive litterature highlighting a higher risk to develop neuropsychiatric disorders in patients suffering from auto-immune diseases and also the presence of low-grade inflammation and abnormal immunoglobulin rates in patients with psychosis. It is now well-established, since the description of paraneoplastic and auto-immune encephalitis, that antibodies targeting self-antigens such as membrane receptors or intracellular proteins could trigger psychiatric symptoms and severe inflammation. Therefore, an in-depth characterisation of circulating auto-antibodies and their clinical and/or biological implications has become a critical issue to stratify specific subgroups of patients with auto-immune psychosis in order to adjust and adapt the treatment and care, and develop novel approaches. Nicotinic acetylcholine receptors (nAChRs) have been linked to severe neuropsychiatric disorders by clinical and genome-wide association studies (GWAS). In addition to their key roles in neuronal function, nAChRs are also involved in the complex regulation of immuno-inflammatory processes both in the brain and the periphery, making them prime candidates to study the link between inflammation and major psychiatric disorders. Furthermore, nAChRs have already been identified as the target of autoimmune mechanisms leading to the destruction of the neuromuscular junction in the well-described autoimmune disease, myasthenia gravis. Based on these findings, we have started to dissect auto-immune mechanisms against nAChRs involved in SZ and BD. In brief, anti-nAChR auto-antibodies contribute to cognitive dysfunction and psychotic symptoms through peripheral inflammation. Here, our goals are (1) to extend the current knowledge by dissecting the relationship between clinical features and peripheral inflammation (cytokines, chemokines, ...) (2) to stratify patients with anti-nAChR auto-immune psychosis by using cluster analyse



        Project status : In Progress

        CORSER-4 Cohort Study: Assessment of the humoral immune response to COVID-19 vaccination in each subpopulation defined by type of vaccination regimen, at 1-3-6-12-24 months

        Vaccines against COVID-19 have been developed for use as homologous two-dose regimens and several of them have demonstrated efficacy. There is limited data on the clinical and the immune response induced by heterologous vaccination regimens (HVR) using alternate vaccine modalities. CORSER-4 is a Viro-Immunology cohort promoted by the Institut Pasteur that will follow-up subjects from the time they receive a prime vaccine dose or a boosting dose. We determined subjects sub-groups of interest defined by the type of vaccine immunization they receive: (i) Heterologous two-dose vaccine regimen, the less frequently used at the moment, (ii) Homologous two-dose vaccine regimen, the more frequently used; (iii) One-dose vaccine regimen and (iv) Infectious-prime vaccine-boost immunization in COVID-19-experienced patients. The subject will be enrolled on the planed last boost injection visit and 5 Follow-up visits will be further conducted (Month1, M3, M6, M12 and M24). We will collect longitudinal clinical data and biological samples. We will explore Viral and Immune Response in sera, nasopharyngeal secretions and in saliva samples. We will assess the immune response obtained by various vaccination regimen against the reference strains and the variants of concern, and the durability of the humoral and cellular response. In addition, we will document any occurring acute infection episode with a supplemental unscheduled visit in order to detect an emerging variant viral strain. Altogether, the CORSER-4 project constitutes an opportunity to evaluate early anti-SARS-CoV-2 immunity in heterologous (and others) vaccine regimens and to estimate their efficacy and their sustainable (M24) protection against circulating virus strains (reference and variants).



        Project status : In Progress

        A genome-wide RNAi screening for mitochondrial fission factors

        Mitochondria are dynamic organelles that undergo constant morphological changes, resulting from fusion and fission events. Mitochondrial fission is crucial for mitochondrial function, apoptosis, mitophagy, and mitochondrial segregation during mitosis. While core mitochondrial fission factors have been elucidated and characterized, it is unclear if additional molecules participate or are main players of the fission process. To solve this question, we setup a genome wide, high content imaging (HCI) screening to identify suppressors of mitochondrial fragmentation in Opa1-/- cells. The principle is that using cells deficient for mitochondrial fusion (ablated for the core fusion protein Opa1), we may identify fission factors by screening for genes for which the loss of function is able to complement Opa1-/- phenotype. This was validated in preliminary experiments of silencing of the core fission protein Drp1, where confocal and electron microscopy confirmed that ablation of Drp1 resulted in mitochondrial elongation in Opa1-/- cells without causing mitochondrial fusion in a classic polyethylene glycol fusion assay. Following miniaturization of the assay, we set up an efficient pipeline to perform an automated HCI screening in Opa1-/- MEFs transfected with a pooled siRNAs library targeting >19,000 genes. Automated imaging and high content image quantification allowed us to generate a list of potential hits, that we aim to process in collaboration with the lab of Timothy Wai and the C3BI HUB, in order to identify promising genes that will be validated and investigated in the future.



        Project status : Closed