Virus d'Epstein-Barr

Résumé

Le virus d'Epstein-Barr (aussi appelé EBV) ou virus de l’herpès 4 (HHV-4) est un virus de la famille des Herpesviridae. Il fait partie de la sous-famille des . Le virus d'Epstein-Barr cause plusieurs maladies dont la mononucléose infectieuse et le lymphome de Burkitt. Le virus cible les lymphocytes B humains via leur récepteur CD21. La protéine qu'il produit lors de l'infection de ces cellules, peut recruter des facteurs de transcription (FT) humains et ainsi causer des maladies auto-immunes comme le lupus ou le diabète de type 1 mais aussi la sclérose en plaques. Le laboratoire Moderna a engagé les essais d'un candidat vaccin à ARNm contre ce virus. Histoire Le virus a été découvert en 1964 par l’analyse d’une biopsie d’un spécimen de tumeur en microscopie électronique, il fut nommé ainsi en l’honneur de Michael A. Epstein et de son étudiante Yvonne Barr. En 1968, le Henle et son équipe de recherche établirent un lien entre la mononucléose infectieus

Source officielle

https://fr.wikipedia.org/wiki/Virus_d'Epstein-Barr

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Epstein–Barr virus (EBV) is one of the most common viruses latently infecting humans. Little is known about the impact of human genetic variation on the large inter-individual differences observed in response to EBV infection. To search for a potential imprint of host genomic variation on the EBV sequence, we jointly analyzed paired viral and human genomic data from 268 HIV-coinfected individuals with CD4 + T cell count

2021

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Genome-to-genome analysis of Epstein-Barr virus infection

Jacques Fellay, Christian Hammer

NATURE PUBLISHING GROUP2019

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Contemporary genomic approaches allow us to seek answers to biological questions that were previously out of reach. Genome-wide association studies (GWAS) have identified numerous genetic polymorphisms associated with human diseases, providing new insight into the genes and pathways involved in pathogenesis. The work presented in this thesis aimed to harness the power of large-scale genomics, statistical methods and bioinformatic tools to explore the interactions between persistent infections, chronic low-grade inflammation, and coronary heart disease (CHD). To achieve this, we used data collected from large population-based studies of individuals of European ancestry.To identify human genetic determinants of the humoral immune response against persistent or frequently recurring infections, with a special focus on human polyomaviruses, we performed GWAS and meta-analyses of serostatus and quantitative immunoglobulin G responses. We identified NFKB1 as a common locus associated with antibody response to multiple pathogens. For polyomaviruses, we found strong associations of HLA class II, FUT2, STING1, and MUC1 genetic variants with the intensity of the humoral response. Together, these results demonstrate the modulating contribution of host genetic variation to the individual response against some of the most prevalent human viruses.Even when they are latent, chronic infections can trigger some degree of local or systemic immune response, resulting in low-grade inflammation. In an effort to better understand the variability of humoral immune response and inflammation patterns in response to pathogen exposure, we found evidence for an association between Chlamydia trachomatis and Helicobacter pylori seropositivity, and higher plasma levels of C-reactive protein (CRP), a sensitive indicator of inflammation. High polygenic risk was also significantly associated with CRP levels, confirming that human genetic variation plays a modulating role in systemic inflammation. These results improve our understanding of the relationship between persistent infections and chronic inflammation, an important determinant of long-term morbidity in humans.Finally, we developed statistical models to explore the influences of genetic variation, persistent infections and low-grade inflammation on incident CHD. We identified high polygenic risk and Fusobacterium nucleatum infection as associated with an increased risk of developing CHD, in addition to conventional risk factors. These results confirm that CHD is a multicomponent disease that is caused by demographic, genetic and environmental factors. Moreover, they might allow for better identification of individuals at high risk for CHD and provide a rationale for future anti-infective prevention trials.Together, this research demonstrates that current genomic and serological technology, bioinformatic analysis and functional follow-up studies have the potential to provide new insight into the molecular basis of host-pathogen interactions, and their role in chronic diseases. From a translational perspective, the results of this project include new targets for diagnosis and therapeutic development, new disease biomarkers, and better predictive models. Every advance in the understanding of complex disease has the potential to improve genomic and clinical medicine, and I am pleased to have had the opportunity to act at different levels to participate in this much needed transition to precision medicine.

EPFL2022

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EPFL2022