Reactivation of Human Herpesvirus 6 and Epstein-Barr Virus in relapsing remitting multiple sclerosis: association with disabilities, disease progression, and inflammatory processes.
Almulla, A. F.; Vojdani, A.; Zhang, Y.; Vojdani, E.; Maes, M. F.
Show abstract
BackgroundMultiple sclerosis (MS) is a chronic autoimmune disorder affecting the central nervous system (CNS). Reactivation of Human herpesvirus 6 (HHV-6) and Epstein-Barr virus (EBV) is observed in MS. ObjectivesThis study investigates immunoglobulins (Ig)G, IgM, and IgA directed against EBV nuclear antigen EBNA-366-406, HHV-6 and EBV deoxyuridine-triphosphatase (dUTPase), and different immune profiles in 58 patients with relapsing remitting MS (RRMS) compared to 60 healthy controls. MethodsWe employed enzyme-linked immunosorbent assays (ELISA) to measure the immunoglobulins to viral antigens. Multiplex immunoassays were used to measure cytokines, chemokines and growth factor levels that were used to compute immune profiles, including M1 macrophage, T helper (Th)-1, Th-17, and overall immune activation. We assessed disabilities using the Expanded Disability Status Scale (EDSS) and disease progression using the Multiple Sclerosis Severity Score (MSSS). ResultsIgG/IgA/IgM directed to the three viral antigens were significantly higher in RRMS than in controls. RRMS was significantly discriminated from controls by using IgG and IgM against HHV-6 dUTPase, yielding an accuracy of 91.5% (sensitivity=87.3% and specificity=95.2%). Neural network analysis showed that using IgG to EBV-dUTPase, IgM to EBV-dUTPase, and immune profiles yielded an area under the ROC curve of 1 and a predictive accuracy of 97.1%. There were strong associations between IgG/IgM responses to HHV-6 and EBV-dUTPases and the EDSS/MSSS scores and aberrations in M1, Th-17, profiles, and overall immune activation. ConclusionsHHV-6 and EBV reactivation play a key role in RRMS and these effects are mediated by activation of cytokine profiles.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Frequency and Potential Risk Factors Associated with the Development of Asymptomatic T2 Hyperintense Cervical Spine Lesions on MRI in Patients with Relapsing-Remitting Multiple Sclerosis 96%
- Tissue damage detected by quantitative gradient echo MRI correlates with clinical progression in non-relapsing progressive MS 95%
- Choroid plexus volume is enlarged in clinically isolated syndrome patients with optic neuritis. 95%
Similar papers in this journal
- COVID-19 is associated with multiple sclerosis exacerbations that are prevented by disease modifying therapies 95%
- Humoral and cellular immune responses to SARS CoV-2 vaccination in Persons with Multiple Sclerosis and NMOSD patients receiving immunomodulatory treatments 95%
- Rituximab in the treatment of multiple sclerosis in the Hospital District of Southwest Finland 93%
Similar papers in this journal
- CSF of SARS-CoV-2 patients with neurological syndromes reveals hints to understand pathophysiology 96%
- Dynamics of spinal fluid immune cell alterations following cladribine tablet treatment in multiple sclerosis 95%
- Evolution of Chronic Lesion Tissue in RRMS patients: An association with disease progression 94%
Similar papers in this journal
- Low memory T cells blood counts and high naive regulatory T cells percentage at relapsing remitting multiple sclerosis diagnosis 97%
- Cross-sectional analysis of the humoral response after SARS-CoV-2 vaccination in Sardinian Multiple Sclerosis patients, a follow-up study 96%
- Low-Density Granulocytes are a novel immunopathological feature in both Multiple Sclerosis and Neuromyelitis optica spectrum disorder. 95%
Similar papers in this journal
- Genetic subtypes predict multiple sclerosis severity and response to treatment 94%
- Diffusivity anisotropy signature of the slowly expanding lesions predicts progression independent of relapse activity in multiple sclerosis 91%
- Neuroinflammation predicts disease progression in progressive supranuclear palsy 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.