Characterization and comparison of the eukaryotic virome and of endogenous retroviruses in whole blood RNAseq data of healthy volunteers and patients affected by autoimmune diseases

Type de poste
Niveau d'étude minimal
Durée du poste
Contrat renouvelable
Contrat non renouvelable
Date de prise de fonction
Date de fin de validité de l'annonce

83, boulevard de l'Hôpital
75013 Paris

Signe Hässler
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Autoimmune and autoinflammatory diseases are characterized respectively by an adaptive immune response to self-antigens and by an inflammation without specific recognition of self-antigens. Many diseases present with a mixed spectrum of autoimmune and autoinflammatory components. The Transimmunom study has the goal to better classify diseases within this spectrum and to identify new biomarkers and immunological mechanisms by recruiting patients of 19 diseases within this spectrum as well as healthy controls. In Transimmunom, we apply a systems immunology approach through the collection and integrated analysis of clinical data and of different omic datasets (high dimensional flow cytometry, cytokines, whole blood RNAseq, TCR RepSeq and RNAseq on sorted regulatory and effector T cells, autoantibodies and gut microbiome).
Different eukaryotic viral infections such as cytomegalovirus (CMV), Epstein Barr virus (EBV) and enteroviruses have been associated to autoimmune diseases, but very few systematic studies of the virome have been performed. In order to investigate the hypothesis that an altered susceptibility and/or response to viral infection is a trigger of autoimmunity, we have developed a pipeline to align, classify and quantify eukaryotic viruses as well as endogenous retroviruses on whole blood RNAseq data. The M2 project student will apply the pipeline to the Transimmunom cohort to compare the virome of autoimmune diseases, autoinflammatory diseases and healthy controls included in the study. He will first characterize the spectrum and frequency of viral species actively or latently infecting blood cells at a detectable level. He will correlate the viral counts with blood cell populations measured through flow cytometry and try to distinguish immune response to the virus from viral tropism; the latter will be used to normalize the viral counts. Viral counts will be associated to exposures and clinical data such as smoke, serum vitamin D levels, medications and medical history in order to identify candidate environmental factors influencing susceptibility to certain viral infections or viral reactivation of latent viruses. Viruses and endogenous retroviruses counts will be correlated to search for potential transactivation of endogenous retroviruses by viruses as described in the literature for herpesviruses. Frequency of viral species and of expression of endogenous retrovirus families, and for the most frequent their quantity will be compared between patients and healthy controls to identify patient-specific viral signatures.
In the second part of the internship the student will adapt the pipeline in order to align RNAseq data to the viral transcripts of a single viral species or endogenous retrovirus family and quantify the single transcripts. He will then use this modified pipeline to quantify the transcripts of the most interesting candidate viral species and endogenous retrovirus families identified in the first part of the internship and try to establish by comparison with the published literature if the transcripts differentially expressed between patients and controls are mainly coding for latency proteins or lytic cycle proteins. He will also look for differentially expressed endogenous retroviral transcripts of interest. Expression of these transcripts will be correlated with disease activity scores.
The expected MSc candidate will justify training in Computer Science / Bioinformatics / Biostatistics, with interest for Systems Biology. Experience with R or other programming languages (Python, C++…) are expected. The candidate will benefit from an interdisciplinary environment, including biologists, immunologists, clinicians, computer scientists, and bioinformaticians. This project is part of the laboratory funded projects (LabEx -,

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