Antibody reactivity against EBNA1 and GlialCAM differentiates multiple sclerosis patients from healthy controls

Abstract

Multiple sclerosis (MS) is an autoimmune demyelinating disorder of the central nervous system (CNS), which is linked to Epstein-Barr virus (EBV) infection, preceding the disease. The molecular mechanisms underlying this connection are only partially understood. We previously described molecular mimicry between the EBV transcription factor EBV nuclear antigen 1 (EBNA1) and three human CNS proteins: anoctamin-2 (ANO2), alpha-B crystallin (CRYAB), and glial cellular adhesion molecule (GlialCAM). Here, we investigated antibody responses against EBNA1 and GlialCAM in a large cohort of 650 MS patients and 661 matched population controls and compared them to responses against CRYAB and ANO2. We confirmed that elevated IgG responses against EBNA1 and all three CNS-mimic antigens associate with increased MS risk. Blocking experiments confirmed the presence of cross-reactive antibodies and molecular mimicry between EBNA1 and GlialCAM, and accompanying antibody responses against adjacent peptide regions of GlialCAM suggest epitope spreading. Antibody responses against EBNA1, GlialCAM, CRYAB, and ANO2 are elevated in MS patients carrying the main risk allele HLA-DRB1*15:01, and combinations of HLA-DRB1*15:01 with anti-EBNA1 and anti-GlialCAM antibodies increase MS risk significantly and in an additive fashion. In addition, antibody reactivities against more than one EBNA1 peptide and more than one CNS-mimic increase the MS risk significantly but modestly. Overall, we show that molecular mimicry between EBNA1 and GlialCAM is likely an important molecular mechanism contributing to MS pathology.

Keywords: Epstein–Barr virus; GlialCAM; antibodies; molecular mimicry; multiple sclerosis.

Fig. 1.

Individual antibody reactivities associated with MS risk. ORs of MS vs. controls for the indicated antibody reactivities. All data derived from the bead-based assay measuring mean fluorescence intensities. Data were adjusted for age, gender, batch effects, and PCA1-5. ORs were calculated using logistic regression analysis. ORs ±95% CI are shown.

Fig. 2.

Correlations of antibody reactivities. (Lower Left) Correlation matrix of all tested antigens in all subjects, correlation coefficients of plate-corrected mean fluorescence intensities are shown, calculated using Pearson correlation analysis. (Top Right) Matrix showing the ratio of correlation coefficients of the MS vs. control group. Outliers ≥4 (yellow) and ≤-4 (purple) stem from correlation coefficients close to 0 in both groups and do not reflect relevant differences between the two groups.

Fig. 3.

Cross-blocking of anti-GlialCAM reactivity with EBNA1. (A) Alignment of the epitope regions for the molecular mimics GlialCAM, CRYAB, and ANO2 on the EBNA1 sequence. Identical residues span both sequences. Central epitopes are highlighted in gray. Amino acid colors based on side-chain chemistry. (B–F) Bead-based competitive blocking assay, measuring reactivity to (B) GlialCAM AA370-389, (C) GlialCAM AA370-389 pSer376, (D) EBNA1 AA386-405, and (E) EBNA1 AA1-120 (negative control peptide) after preincubation of selected MS plasma samples (n = 10) with the peptides indicated on the x-axis. Negative fold change of mean fluorescence intensities over samples incubated with BSA indicates increased inhibition. *P < 0.05, **P < 0.005 according to Wilcoxon signed-rank test. (F) Heatmap summarizing (B–E), showing mean negative fold changes over samples blocked with BSA. x-axis: blocking antigen, y-axis: bead-based antigen measurement.

Fig. 4.

Association of HLA-DRB1*15:01 status with anti-EBNA1 and anti-GlialCAM antibody reactivity. (A–C) Association of HLA-DRB1*15:01 with anti-EBNA1 AA381–410 reactivity in (A) all individuals, (B) MS, and (C) population controls. (D–F) Association of HLA-DRB1*15:01 with GlialCAM AA370-389 in (D) all individuals, (E) MS, and (F) healthy controls. Indicated P values according to the Kruskal–Wallis test. (G–I) Plots representing ORs for MS risk for combinations of multiple parameters, including in column 1: positive HLA-DRB1*15:01 status, in column 2: elevated (G and H) anti-EBNA1 AA381-410 and (I) anti-EBNA1 AA421-450 reactivity, and in column 3: (G) anti-GlialCAM AA365-394, (H) anti-CRYAB AA2-33, and (G) anti-ANO2 AA134-153. Data were adjusted for age, gender, plate-based batch effects, and PCA 1-5. ORs were calculated using logistic regression analysis with the group not having any increased antibody level or being HLA*DRB15:01 positive (lowest group in each panel) as a reference. ORs ±95% CI are shown.

Fig. 5.

Cumulative MS risk with multiple combined antibody reactivities against EBNA1 and its three mimics. (A and B) Distribution of individuals with 0 to 3 elevated antibody reactivities to (A) three different EBNA1 peptides (EBNA1 AA386–405, EBNA1 AA401-430, and EBNA1 AA421–450) (Left) and (B) three different CNS-specific antigens (GlialCAM AA370-389, CRYAB AA2-33, and ANO2 AA134-153) (Right). Comparisons between healthy controls and MS are shown. P values according to the Chi-square test. (C) OR showing cumulative effects of 1 to 3 antibody reactivities. Data were adjusted for age, gender, batch effects, and PCA1-5. ORs were calculated using logistic regression analysis. ORs ±95% CI are shown.