For nearly 20 years, our lab is focusing on aetiology of a complex disease, rheumatoid arthritis. After a period consisted of 'classical' genetic analyses through linkage and association studies, the GWAS results and conclusion lead us to develop alternative approaches. Instead of considering common genomic variants we decided to focus one rare and specific ones. We also extended our goal of RA biomarkers characterization to a transcriptome level. And we develop a global approach of the disease with mapping all the connections. For the next period of five years, and based to these approaches, we wish to develop our research activity in two main axes: characterization of new RA genomic variants and a computational systems biology approach that involves an interactive map and a dynamical model. Results of the first axe will feed the second project, the purpose of which is to be a tool for identifying new lines in RA drug design.

For my part, I focus my research projects on characterization of biomarkers for RA and its onset using several genomic approaches.

    Sequence variants

    Transcriptome/expression profiling

    Differentially expressed genes in RA

    From 2007, we have started the characterization of RA biomarkers with transcriptome analysis. We have benefited of RA cohort collected by Dr. P. Hilliquin and prepared for storage and sending by Dr Quillet and Dr Lemaire (GenHotel –Rheumatology & Biology Departments, CHSF Corbeil-Essonne). We have then extracted total RNA from blood samples of RA patients (cases). Through another in-house project, we obtained samples for healthy people (controls) (Dr. L Jacq, GenHotel – Cardiology Department, CHSF Corbeil-Essonne).

    Our first goal was to measure expression of RA candidate genes by quantitative PCR (qPCR) and to study relation between level of expression and polymorphism of the candidate gene. We have then studied PRKCH gene (Teixeira et al., 2008a) and CASP7 gene, coding Caspase 7. Caspases are proteases involved in apoptosis mechanisms, which could be deregulated and contributed then to synovioycytes proliferation or to osteoblasts destruction, two physiopathological characteristics in RA. Measurement of expression level for alpha and beta isoforms of Caspase 7 show a significant decrease in cases in comparison with levels in controls, this one being more significant for alpha isoform. Alpha/beta ratio of expression level is then significantly decreased in cases in comparison with controls, suggesting a lower apoptotic activity related to alpha active form of caspase in RA (Teixeira et al., 2008b).

    To extensively benefit of collected samples, we have then developed a collaboration with Dr. R. Olaso (Plateforme de Transcriptome, Centre National de Génotypage, Institut de Génomique, CEA, Evry). We performed whole transcriptome analysis using Illumina microarray technology on peripheral blood mononuclear cells (PBMCs) from RA cases and controls. We identified a remarkably elevated expression of a spectrum of genes involved in Immunity and Defence in PBMCs of RA cases compared to controls. This result is confirmed by GO analysis, suggesting that these genes could be activated systemically in RA (see figure; (Teixeira et al., 2009); PhD : https://www.theses.fr/147412374).

    Among the genes showing the highest expression level change in this study, we have then carried out analysis of those located in a genomic region without copy number variation (CNV) known. Our goal was to analyze relation between expression and SNP and then identify expression Quantitative Trait Loci (eQTL) specific of RA. We have selected genes for which tagSNPs were suitable for our study. We then identify a suggested association of a SNP located in PGLYRP1 with RA but this preliminary result was not replicated in an extended sample of families (Fodil et al., 2015, co-supervising of PhD with Pr A Boudjema, USTO, Oran, Algeria, http://www.theses.fr/2015EVRY0017). Such study, focused on the relation between polymorphisms and expression level in complex diseases, constitutes a field of investigation significant in the determination of regulation regions associated to a specific phenotype.

    Differentially expressed genes in pre-RA states

    RA can be detected years before the first symptoms of the disease, with the development of a systemic autoimmunity. Indeed, auto-antibodies such as the rheumatoid factor and anti-citrullinated peptides antibodies (ACPAs), precede the clinical disease by a median period of at least 5 years.

    Rheumatoid arthritis (RA), once fully developed, is difficult to treat and generally requires lifelong therapy. Treatments in the very early phases of the disease, or ideally before the clinical onset of the disease (= pre-clinical phases), are potentially curative. Several prevention trials for RA are ongoing and may lead to screening and preventive strategies for RA, much as controlling hypertension and reducing high cholesterol is helping to reduce the risk of cardiovascular diseases (Finckh A. et al. 2014) However, before preventing RA can become a reality, the precision of diagnosing preclinical RA will need to be improved. While the hereditability of RA is well established and preclinical stages have been identified, it is currently still impossible to provide patients with an individualized estimate of RA risk. Thus the precise diagnosis of pre-clinical RA has become a major scientific question.

    Our work hypothesis is that the asymptomatic, pre-clinical phase of RA can be adequately identified by a combination of biologic markers and clinical risk factors. And the characterization of specific regulation profiles and biological abnormalities lead to the identification of new biomarkers present before the first symptoms appear and predictive of RA onset. Our objective is to establish the mechanisms of disease initiation: identify gene regulations in individuals who develop the disease within a year (pre-RA samples) and specific sequence of biological abnormalities leading to the development of disease. We then will be able to characterize biomarkers predictive of RA onset within one year, to be followed-up for evaluation of the diagnostic value for prognostic or treatment response and to characterize individuals at very high risk of developing RA.

    This project is based on the SCREEN-RA cohort (www.arthritis-checkup.ch) aims to develop and evaluate a screening strategy for the development of RA in individuals genetically at risk, namely first degree relatives of patients with autoimmune diseases. The cohort was established by Pr. Axel Finckh,(HU Geneva, Switzerland), with the help of a previous SNSF grant (SNSF N° 32003B_120639). Since 2010, over 1300 first degree relatives of RA patients have enrolled, given informed consent, answered detailed epidemiological questionnaires and provided biologic samples (serum, DNA, RNA, stool). The study continues to enrol around 200 new participants per year. Only individuals without clinical evidence of RA are enrolled and followed-up yearly to assess incident arthritis or other phases of impending RA. Our project is also based on a similar cohort developed in France by Pr. F Cornélis.

    A preliminary study has been performed with transcriptome analysis through array technology with the collaboration of Dr. R Olaso from CNRGH (Institut François Jacob, CEA/DSV, Evry). Analyses of results are in progress and benefit of the collaboration of Dr. C Dalmasso from LaMME laboratory (UMR8071 CNRS, Evry University).

    Copy Number Variants

    Copy Number Variant (CNV) is a segment of DNA that is 1 kb or larger and present at a variable copy number in comparison with a reference genome (Feuk et al., 2006). CNVs in general are stable and can be inherited. Deletions, duplications, segmental duplications, insertions, inversions and translocations represent some of the processes resulting in CNV. Investigation into the genetic basis of complex diseases without consideration of CNVs will miss important component of the heritability (Beckmann et al., 2007; Manolio et al., 2009).

    Benefiting of our familial samples, we decided to investigate association of CNV gene candidate with RA. First we worked on methodologies for characterization of copy number. Multiplex standard PCR (mPCR) specific for presence and/or absence of gene were used. We then performed quantitative PCR (qPCR) using a specific fluorescent probe for the target gene and a second one for a reference gene without known CNV. Finally we developed a methodology based on droplet digital PCR (ddPCR), a recent technology based on the generation of about 20,000 micro-reactions in droplets from an initial reaction. Each droplet is analyzed regarding fluorescence of the two probes.

    This method gave the highest sensitivity leading to an absolute quantification of copy numbers. Furthermore, in case of two copies identification, it will be able to indicate if the copies are on the same chromosome or not. This particular data is essential in identifying mechanisms causing copy variation such as non-allelic homologous recombination (Gu et al., 2008). We focus the on several genes involved in immunity and stress oxidative pathways. Copy Number genotypes were identified through this analysis of trio families (Ben Kilani et al., 2016; Ben Kilani et al, manuscript in progress; PhD: https://www.theses.fr/185469957).

    Through whole genome sequencing analysis, methodologies of RA specific Copy Number variants are developed in the lab (cf Valerie Chaudru' project). ddPCR methodology will be used for technological validation of identified CNV.

    Our final goal is to characterize a CNV signature specific to RA, which would complete the genomic factors associated to the genetic risk for this disease. Furthermore, it would be of interest to identify specific CNVs related to pre-clinical phenotypes through prospective cohorts of RA relatives mentioned above.

    Collaborations

    • A Boudjema: USTO (Oran, Algérie)
    • R Olsao, JF Deleuze : CNRGH (Institut François Jacob, CEA/DSV, Evry)
    • C Dalmasso : LaMME (UMR8071 CNRS, Evry University)
    • A Finckh : HU Geneva (Switzerland)
    • F Cornélis : CHU Auvergne, Clermont-Ferrand Auvergne University

    Interactive Knowledge Base

    Discrete Modeling

    Integration, analysis and modelling of cellular networks for complex human diseases. Connecting the pieces of the etiopathogenesis puzzle.

    Biological processes rely on the concerted interactions and regulations of thousands of molecules that form complex molecular and signaling networks. The analysis of their structure and organization can reveal interesting topological properties that shed light onto the basic mechanisms that control normal cellular processes. Disruption and dysregulation of these complex molecular and signaling networks can lead to disease. Therefore, the mapping and accurate representation of pathways implicated is a primary but essential step for elucidating the mechanisms underlying disease pathogenesis.

    High-throughput experiments and their subsequent bioinformatics analysis provide us typically with a vast amount of information concerning genes and proteins that are dysregulated. In order to make sense of this data, novel integrative approaches are needed in order to place these genes and their products within their functional biological context.

    However, as all living systems are dynamic in nature, static representations of molecular networks can provide useful but relatively limited understanding. A dynamical study can reveal information about the system's behavior under different conditions by in silico simulations, perturbations, hypotheses testing and predictions. Quantitative kinetic modelling approaches using differential or stochastic equations can provide a detailed analysis of a network's dynamics, but the large number of parameters required make them less appropriate for large scale networks. In order to address the lack of kinetic data, discrete logical modelling can be used as an alternative way to study the system's qualitative dynamic behavior.

    My main research interests focus on biological networks, efforts for accurate pathway mapping and pathway representation, network construction and inference, topological analysis and the passage from network static representations and structural analysis to executable models and qualitative dynamical studies, using as examples two ongoing research projects concerning Rheumatoid Arthritis and Glioblastoma.

    Integrative Modelling and Analysis of Molecular Pathways deregulated in Rheumatoid Arthritis

    Rheumatoid arthritis (RA) is a multifaceted autoimmune disease that causes chronic inflammation of the joints. Until know, the etiology of the disease remains unclear. Patients with autoimmune diseases have antibodies and immune cells in their blood that target their own body tissues, where they can be associated with inflammation. While inflammation of the tissue around the joints and inflammatory arthritis are characteristic features of rheumatoid arthritis, the disease can also cause inflammation and injury in other organs in the body. Because it can affect multiple other organs of the body, rheumatoid arthritis is referred to as a systemic illness and is sometimes called rheumatoid disease. Highly complex molecular networks, which play fundamental roles in almost all cellular processes, are known to be deregulated in a number of diseases. As a consequence, there is a critical need to develop practical methodologies for constructing and analyzing molecular networks at a systems level. Mathematical models built with continuous differential equations are an ideal methodology because they can provide a detailed picture of a network's dynamics. To be predictive, however, differential equation models require that numerous parameters be known a priori and this information is almost never available. An alternative dynamical approach is the use of discrete logic-based models that can provide a good approximation of the qualitative behavior of a biochemical system without the burden of a large parameter space. The objectives of the project are first, the construction of a well annotated, comprehensive molecular map integrating pathways involved in the pathogenesis of rheumatoid arthritis and second, the building of a dynamical logical model in order to study the dynamics of the system, focused on fibroblasts activation.

    Collaborations

    • Marek Ostaszewsky, Luxembourg Centre for Systems Biomedicine, Université du Luxembourg, Esch-sur-Alzette, Luxembourg
    • George Kalliolias, Arthritis & Tissue Degeneration Program, Hospital for Special Surgery, New York, USA, Department of Medicine, Weill Cornell Medical College, New York City, USA
    • Gilles Chiocchia, Faculty of Health Sciences Simone Veil, INSERM U1173, University of Versailles Saint-Quentin-en-Yvelines Montigny-le-Bretonneux, France
    • Robert Olasso, Centre National de Recherche en Génomique Humaine (CNRGH), CEA, Evry, France
    • Tomas Helikar, Department of Biochemistry, University of Nebraska-Lincoln Lincoln, NE, USA
    • Sylvain Solyman, Inria Saclay-Île-de-France - Équipe Lifeware, France