Sophia Djebali

French National Centre for Scientific Research, Lutetia Parisorum, Île-de-France, France

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Publications (11)76.38 Total impact

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    ABSTRACT: The primary CD8 T cell immune response constitutes a major mechanism to fight an infection by intra-cellular pathogens. We aim at assessing whether pathogen-specific dynamical parameters of the CD8 T cell response can be identified, based on measurements of CD8 T cell counts, using a modeling approach. We generated experimental data consisting in CD8 T cell counts kinetics during the response to three different live intra-cellular pathogens: two viruses (influenza, vaccinia) injected intranasally, and one bacteria (Listeria monocytogenes) injected intravenously. All pathogens harbor the same antigen (NP68), but differ in their interaction with the host. In parallel, we developed a mathematical model describing the evolution of CD8 T cell counts and pathogen amount during an immune response. This model is characterized by 9 parameters and includes relevant feedback controls. The model outputs were compared with the three data series and an exhaustive estimation of the parameter values was performed. By focusing on the ability of the model to fit experimental data and to produce a CD8 T cell population mainly composed of memory cells at the end of the response, critical parameters were identified. We show that a small number of parameters (2 to 4) define the main features of the CD8 T cell immune response and are characteristic of a given pathogen. Among these parameters, two are related to the effector CD8 T cell mediated control of cell and pathogen death. The parameter associated with memory cell death is shown to play no relevant role during the main phases of the CD8 T cell response, yet it becomes essential when looking at the predictions of the model several months after the infection. Copyright © 2015. Published by Elsevier Ltd.
    Preview · Article · Apr 2015 · Journal of Theoretical Biology
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    ABSTRACT: The inflammasome is a signaling platform that is central to the innate immune responses to bacterial infections. Francisella tularensis is a bacterium replicating within the host cytosol. During F. tularensis subspecies novicida infection, AIM2, an inflammasome receptor sensing cytosolic DNA, activates caspase-1 in an ASC-dependent manner, leading to both pyroptosis and release of the proinflammatory cytokines IL-1β and IL-18. Activation of this canonical inflammasome pathway is key to limit F. novicida infection. In this study, by comparing the immune responses of AIM2 knockout (KO), ASC(KO), and Casp1(KO) mice in response to F. novicida infection, we observed that IFN-γ levels in the serum of Casp1(KO) mice were much higher than the levels observed in AIM2(KO) and ASC(KO) mice. This difference in IFN-γ production was due to a large production of IFN-γ by NK cells in Casp1(KO) mice that was not observed in ASC(KO) mice. The deficit in IFN-γ production observed in ASC(KO) mice was not due to a reduced Dock2 expression or to an intrinsic defect of ASC(KO) NK cells. We demonstrate that in infected Casp1(KO) mice, IFN-γ production is due to an ASC-dependent caspase-1-independent pathway generating IL-18. Furthermore, we present in vitro data suggesting that the recently described AIM2/ASC/caspase-8 noncanonical pathway is responsible for the caspase-1-independent IL-18 releasing activity. To our knowledge, this study is the first in vivo evidence of an alternative pathway able to generate in a caspase-1-independent pathway bioactive IL-18 to boost the production of IFN-γ, a cytokine critical for the host antibacterial response.
    No preview · Article · Aug 2013 · The Journal of Immunology
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    ABSTRACT: The mouse inbred line C57BL/6J is widely used in mouse genetics and its genome has been incorporated into many genetic reference populations. More recently large initiatives such as The International Knockout Mouse Consortium (IKMC) are using the C57BL/6N mouse strain to generate null alleles for all mouse genes. Hence both strains are now widely used in mouse genetics studies. Here we perform a comprehensive genomic and phenotypic analysis of the two strains to identify differences that may influence their underlying genetic mechanisms. We undertake genome sequence comparisons of C57BL/6J and C57BL/6N to identify SNPs, indels and structural variants, with a focus on identifying all coding variants. We annotate 34 SNPs and 2 indels that distinguish C57BL/6J and C57BL/6N coding sequences, as well as 15 structural variants that overlap a gene. In parallel we assess the comparative phenotypes of the two inbred lines utilizing the EMPReSSslim phenotyping pipeline, a broad based assessment encompassing diverse biological systems. We perform additional secondary phenotyping assessments to explore other phenotype domains and to elaborate phenotype differences identified in the primary assessment. We uncover significant phenotypic differences between the two lines, replicated across multiple centers, in a number of physiological, biochemical and behavioral systems. Comparison of C57BL/6J and C57BL/6N demonstrates a range of phenotypic differences that have the potential to impact upon penetrance and expressivity of mutational effects in these strains. Moreover, the sequence variants we identify provide a set of candidate genes for the phenotypic differences observed between the two strains.
    Full-text · Article · Jul 2013 · Genome biology
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    ABSTRACT: IL-4 is one of the main cytokines produced during Th2-inducing pathologies. This cytokine has been shown to affect a number of immune processes such as Th differentiation and innate immune responses. However, the impact of IL-4 on CD8 T cell responses remains unclear. In this study, we analyzed the effects of IL-4 on global gene expression profiles of Ag-induced memory CD8 T cells in the mouse. Gene ontology analysis of this signature revealed that IL-4 regulated most importantly genes associated with immune responses. Moreover, this IL-4 signature overlapped with the set of genes preferentially expressed by memory CD8 T cells over naive CD8 T cells. In particular, IL-4 downregulated in vitro and in vivo in a STAT6-dependent manner the memory-specific expression of NKG2D, thereby increasing the activation threshold of memory CD8 T cells. Furthermore, IL-4 impaired activation of memory cells as well as their differentiation into effector cells. This phenomenon could have an important clinical relevance as patients affected by Th2 pathologies such as parasitic infections or atopic dermatitis often suffer from viral-induced complications possibly linked to inefficient CD8 T cell responses.
    Full-text · Article · Aug 2012 · The Journal of Immunology
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    ABSTRACT: The primary immune response mediated by CD8 T cells constitutes a major mechanism to fight an infection by intra-cellular pathogens. This response begins with an expansion phase through a fast increase of CD8 T cell count. Then most of the population dies by apoptosis in a contraction phase, followed by the generation of memory cells. These latter are specific of the antigen and will better control the pathogen in a subsequent infection. We generated experimental data, consisting in CD8 T cell numbers time evolution during the response to three different live intra-cellular pathogens, two viruses (influenza, vaccinia), and one bacteria (Listeria monocytogenes). These pathogens all harbour the same antigen, but differ in their interaction with the host, like the infection route. We are interested in characterizing how such differences translate into differences in the CD8 response. We developed a mathematical model describing the evolution of CD8 T cell count and pathogen amount during the response, including feedback controls that regulate the response. We compared this model with the three data series, and made an exhaustive estimation of the model parameters. We aim at determining not the best parameter which provides a good fit, but a set of values which are suitable for a given parameter. This allows us to be more confident in evaluating the validity of the model and the influence of the parameters. First we discuss how the suitable parameter values are different or not according to the fitted data series, and so according to the nature of the pathogen or the characteristics of the infection it generates. Then we refine the parameter values by discussing supplementary constraints on dynamics of memory cell subpopulation in the model. Finally we discuss how parameter values could be further validated, in particular with supplementary experimental investigations.
    Full-text · Article · Jun 2012
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    ABSTRACT: Besides the classically described subsets of memory CD8 T cells generated under infectious conditions, are T inflammatory memory cells generated under sterile priming conditions, such as sensitization to allergens. Although not fully differentiated as pathogen-induced memory cells, they display memory properties that distinguish them from naive CD8 T cells. Given these memory cells are generated in an antigen-specific context that is devoid of pathogen-derived danger signals and CD4 T cell help, we herein questioned whether they maintained their activation and differentiation potential, could be recruited in an immune response directed against a pathogen expressing their cognate antigen and further differentiate in fully competent secondary memory cells. We show that T inflammatory memory cells can indeed take part to the immune response triggered by a viral infection, differentiate into secondary effectors and further generate typical central memory CD8 T cells and effector memory CD8 T cells. Furthermore, the secondary memory cells they generate display a functional advantage over primary memory cells in their capacity to produce TNF-α and the XCL1 chemokine. These results suggest that cross-reactive stimulations and differentiation of cells directed against allergens or self into fully competent pathogen-induced memory cells might have incidences in inflammatory immuno-pathologies.
    Full-text · Article · Apr 2012 · Immunologic Research
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    Full-text · Dataset · Nov 2009
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    ABSTRACT: The broad aim of biomedical science in the postgenomic era is to link genomic and phenotype information to allow deeper understanding of the processes leading from genomic changes to altered phenotype and disease. The EuroPhenome project (http://www.EuroPhenome.org) is a compre-hensive resource for raw and annotated high-throughput phenotyping data arising from projects such as EUMODIC. EUMODIC is gathering data from the EMPReSSslim pipeline (http://www.empress .har.mrc.ac.uk/) which is performed on inbred mouse strains and knock-out lines arising from the EUCOMM project. The EuroPhenome interface allows the user to access the data via the phenotype or genotype. It also allows the user to access the data in a variety of ways, including graphical display, statistical analysis and access to the raw data via web services. The raw phenotyping data captured in EuroPhenome is annotated by an annotation pipeline which automatically identifies statistically different mutants from the appropriate baseline and assigns ontology terms for that specific test. Mutant phenotypes can be quickly identified using two EuroPhenome tools: PhenoMap, a graphical representation of statistically relevant phenotypes, and mining for a mutant using ontology terms. To assist with data definition and cross-database comparisons, phenotype data is annotated using combinations of terms from biological ontologies.
    Full-text · Dataset · Nov 2009
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    ABSTRACT: The broad aim of biomedical science in the postgenomic era is to link genomic and phenotype information to allow deeper understanding of the processes leading from genomic changes to altered phenotype and disease. The EuroPhenome project (http://www.EuroPhenome.org) is a comprehensive resource for raw and annotated high-throughput phenotyping data arising from projects such as EUMODIC. EUMODIC is gathering data from the EMPReSSslim pipeline (http://www.empress.har.mrc.ac.uk/) which is performed on inbred mouse strains and knock-out lines arising from the EUCOMM project. The EuroPhenome interface allows the user to access the data via the phenotype or genotype. It also allows the user to access the data in a variety of ways, including graphical display, statistical analysis and access to the raw data via web services. The raw phenotyping data captured in EuroPhenome is annotated by an annotation pipeline which automatically identifies statistically different mutants from the appropriate baseline and assigns ontology terms for that specific test. Mutant phenotypes can be quickly identified using two EuroPhenome tools: PhenoMap, a graphical representation of statistically relevant phenotypes, and mining for a mutant using ontology terms. To assist with data definition and cross-database comparisons, phenotype data is annotated using combinations of terms from biological ontologies.
    Full-text · Article · Nov 2009 · Nucleic Acids Research
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    ABSTRACT: Most memory CD8 T cell subsets that have been hitherto defined are generated in response to infectious pathogens. In this study, we have characterized the CD8 T cells that survive priming conditions, devoid of pathogen-derived danger signals. In both a TCR-transgenic model and a model of contact hypersensitivity, we show that the priming of naive CD8 T cells under sterile inflammatory conditions generates memory. The corresponding memory CD8 T cells can be identified by their intermediate expression levels of CD44 and CD122. We also show that CD44/122(int) memory CD8 T cells spontaneously develop in wild type mice and that they display intermediate levels of several other memory traits including functional (IFN-gamma secretion capacity, CCL5 messenger stores), phenotypic, and molecular (T-bet and eomesodermin expression levels) features. We finally show that they correspond to an early differentiation stage and can further differentiate in CD44/122(high) memory T cells. Altogether, our results identify a new memory CD8 T cell subset that is generated under sterile inflammatory conditions and involved in the recall contact hypersensitivity reactions that are responsible for allergic contact dermatitis.
    Full-text · Article · Apr 2009 · The Journal of Immunology
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    ABSTRACT: IL-17A is a T cell-specific cytokine that is involved in chronic inflammations, such as Mycobacterium infection, Crohn's disease, rheumatoid arthritis and multiple sclerosis. Mouse models have explained the molecular basis of IL-17A production and have shown that IL-17A has a positive effect not only on granuloma formation and neurodegeneration through unknown mechanisms, but also on bone resorption through Receptor activator of NF-kappaB ligand (RANKL) induction in osteoblasts. Langerhans cell histiocytosis (LCH) is a rare disease of unknown etiology, lacking an animal model, that cumulates symptoms that are found separately in various IL-17A-related diseases, such as aggressive chronic granuloma formation, bone resorption and soft tissue lesions with occasional neurodegeneration. We examined IL-17A in the context of LCH and found that there were high serum levels of IL-17A during active LCH and unexpected IL-17A synthesis by dendritic cells (DCs), the major cell type in LCH lesions. We also found an IL-17A-dependent pathway for DC fusion, which was highly potentiated by IFN-gamma and led to giant cells expressing three major tissue-destructive enzymes: tartrate resistant acidic phosphatase and matrix metalloproteinases 9 and 12. IFN-gamma expression has been previously documented in LCH and observed in IL-17A-related diseases. Notably, serum IL-17A-dependent fusion activity correlates with LCH activity. Thus, IL-17A and IL-17A-stimulated DCs represent targets that may have clinical value in the treatment of LCH and other IL-17A-related inflammatory disorders.
    Full-text · Article · Feb 2008 · Nature medicine

Publication Stats

274 Citations
76.38 Total Impact Points

Institutions

  • 2013-2015
    • French National Centre for Scientific Research
      Lutetia Parisorum, Île-de-France, France
  • 2012
    • University of Lyon
      Lyons, Rhône-Alpes, France
  • 2009
    • French Institute of Health and Medical Research
      Lutetia Parisorum, Île-de-France, France
  • 2008
    • Claude Bernard University Lyon 1
      Villeurbanne, Rhône-Alpes, France