From Genetics to Transcriptomics to Unravel the Mechanisms Behind a Poor Outcome in Multiple Sclerosis

Not Applicable

Recruiting

• Conditions: Multiple Sclerosis

• Registration Number: NCT04873492
• Lead Sponsor: Nantes University Hospital

Brief Summary

MS is a heterogeneous disease either in its response to treatment or clinical manifestation. Indeed, the natural history of MS is varying from a benign condition to a devastating and rapidly incapacitating disease. Clinical heterogeneity could also be cellular and / or molecular. The aim is to identify from OMIC analyses, at the early stage of the disease, differentially expressed molecules and / or cell subpopulations derived from CD8 + T lymphocytes and / or CD4 + T lymphocytes and / or B lymphocytes and monocytes from patients with aggressive versus non-aggressive, compared to a cohort of healthy controls

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2021-04-30
• Study First Submitted QC: 2021-04-30
• Study First Posted: 2021-05-05
• Study Start: 2022-01-24
• Status Verified: 2023-01
• Last Update Submitted: 2023-01-27
• Last Update Posted: 2023-01-30
• Primary Completion: 2023-07-24
• Study Completion: 2025-07-24

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 130

Inclusion Criteria

Not provided

Exclusion Criteria

Not provided

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Primary Outcome Measures

Name	Time	Method
Bulk RNA-sequencing	Blood sample collection within 6 months after first inflammatory event for MS patients and at inclusion for healthy volunteers.	Transcriptional profile of T and B cells in aggressive and non-aggressive MS and healthy volunteers. Measurement of gene expression of naïve and memory CD4+ and CD8+ T and B cell. Comparison of these expression level between MS patients with aggressive and non-aggressive form and healthy volunteers.

Secondary Outcome Measures

Name	Time	Method
Association of genetic sequence variation from whole genome sequencing with gene expression profile via Bulk RNA-seq	Blood sample collection within 6 months after first inflammatory event.	Add genetic variant analyzes to RNA seq analyses related to MS 1) Identify eQTL. 2 Impute SNPs result to calculate MS Genetic Burden (MSGB) a polygenic risk score of MS computed based on a weighted scoring algorithm using independent MS-SNPs.
Single RNA sequencing	Blood sample collection within 6 months after first inflammatory event.	Single cell transcriptomics of T and B cells in order to identify by clustering, sub populations within these cells based on gene expression and associated to poor pronostic.
Association of transcriptomic variation with DNA methylation	Blood sample collection within 6 months after first inflammatory event.	Add Analyzes of gene expression regulation throughout DNA methylation of CpG sites to RNA seq analyses related to MS.
OMIC integration	Blood sample collection within 6 months after first inflammatory event.	Developing machine learning method to combine genomic, epigenomic transcriptomic and clinical data to pinpoint genes of interest particularly involved in aggressive MS outcomes.

Trial Locations

Locations (1)

Nantes University Hospital; 🇫🇷 Nantes, Loire-Atlantique, France