Pro-inflammatory c-Met+ CD4 T cells in multiple sclerosis

Objective Hepatocyte growth factor (HGF) binds exclusively the c-Met surface receptor, and the HGF/c-Met axis regulates T-cell function in autoimmune diseases. We analyzed c-Met expression on human CD4 T cells in the blood and cerebrospinal fluid (CSF) from patients with multiple sclerosis (MS) versus non-inflammatory neurological disease (NIND), to better understand the role of CD4 T cells in MS. Methods We recruited 34 untreated MS patients (aged 28–43 years) and 10 NIND (aged 34–51 years) who underwent paired blood and CSF sampling at the time of diagnosis work-up. Phenotypic and functional CD4 T cells characterization was determined by flow cytometry and bulk RNA sequencing. Adhesion and transmigration capacities were studied to further characterize the function of c-Met+ CD4 T cells. Results c-Met+ memory CD4 T cells were detected at higher levels in both blood (median of 1.98%) and CSF (5.88%) in MS compared to NIND (0.37% and 0.68% respectively) (p<0.0001). Ex vivo c-Met+ CD4 T cells exhibited higher levels of GM-CSF, IL-17, IFN-γ and double positive IL-17+IFN-γ+ expression, compared with c-Met- CD4 T cells. c-Met+ CD4 T cells expressed increased levels of integrins – Itgα4β1 (VLA-4) and ItgαLβ2 (LFA-1) – compared with c-Met- CD4 T cells. Anti-Itgα4 (natalizumab) and anti-ItgαLβ2 (odulimomab) inhibited CD4 T cell transmigration with predominant inhibition of CD4 T cells expressing c-Met. Interpretation These results emphasize c-Met as an immune marker of highly pro-inflammatory and migratory CD4 T lymphocytes in both the periphery and central nervous system of MS patients. FACS-sorted T cell subsets were washed with PBS and resuspended in RNA lysis solution with 1% DTT. Total RNA was extracted using the Nucleospin RNA kit (Macherey-Nagel). Complementary DNA was synthesized from total RNA using a reaction mix containing Tris-aminoMethane (200 mM), KCl (500 mM), MgCl2 (0.2 M; Sigma-Aldrich), DTT (100 mM; Invitrogen), random hexamers (50 mM; Invitrogen), oligo(dT) 15 primer (100 mg/ml; Promega), dNTP mix (10 mM; Promega), RNAsin (40 U/ml; Promega) and SuperScriptTM II (200 U/ml; Invitrogen). After incubation at 42°C for 50 min and inactivation at 99°C for 3 min, cDNA was diluted and stored at -20°C until use. On the FACS-sorted c-Met⁻ and c-Met⁺ CD4 T cells, we performed bulk-RNA sequencing. The SMART-Seq v4 kit from Clontech was used for the reverse transcription and cDNA amplification according to the manufacturer’s specifications, starting with 1 ng of total RNA as input. Two hundred picograms of cDNA were used for library preparation using the Nextera XT kit from Illumina. Library molarity and quality were assessed with the Qubit and Tapestation using a DNA High Sensitivity chip (Agilent Technologies). Libraries were sequenced on a NovaSeq 6000 Illumina sequencer for SR100 reads, generating an average of around 35,000,000 reads per sample. We employed GSEA to identify enriched biological themes within our dataset. Initially, differentially expressed genes (DEGs) were identified based on specific criteria (e.g., fold-change and p-value thresholds) and ranked based on their expression levels. Using the ranked list, GSEA was performed to determine whether sets of genes associated with specific biological processes, molecular functions, or cellular components were statistically overrepresented. We utilized the Molecular Signatures Database (MSigDB) for gene set annotations, and enrichment scores, nominal p-values, and false discovery rate (FDR) were calculated to assess the significance of gene set enrichment.