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Multiple sclerosis patient-derived spontaneous B cells have distinct EBV and host gene expression profiles in active disease

Nature Microbiology volume 9, pages 1540–1554 (2024)Cite this article

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Abstract

Epstein–Barr virus (EBV) is an aetiologic risk factor for the development of multiple sclerosis (MS). However, the role of EBV-infected B cells in the immunopathology of MS is not well understood. Here we characterized spontaneous lymphoblastoid cell lines (SLCLs) isolated from MS patients and healthy controls (HC) ex vivo to study EBV and host gene expression in the context of an individual’s endogenous EBV. SLCLs derived from MS patient B cells during active disease had higher EBV lytic gene expression than SLCLs from MS patients with stable disease or HCs. Host gene expression analysis revealed activation of pathways associated with hypercytokinemia and interferon signalling in MS SLCLs and upregulation of forkhead box protein 1 (FOXP1), which contributes to EBV lytic gene expression. We demonstrate that antiviral approaches targeting EBV replication decreased cytokine production and autologous CD4+ T cell responses in this ex vivo model. These data suggest that dysregulation of intrinsic B cell control of EBV gene expression drives a pro-inflammatory, pathogenic B cell phenotype that can be attenuated by suppressing EBV lytic gene expression.

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Fig. 1: Generation and growth properties of SLCLs from PBMCs of MS patients and healthy controls.
Fig. 2: Whole-genome next generation sequencing of endogenous EBV in SLCLs from AMS, SMS and HC.
Fig. 3: Cell size, surface marker expression and gene expression analysis in SLCLs compared to LCLs.
Fig. 4: Gene expression in SLCLs from AMS, SMS and HC.
Fig. 5: shRNA depletion of FoxP1 or LTA affects EBV latency and SLCL viability.
Fig. 6: TAF decreases EBV lytic activity and inflammation in SLCLs from MS patients.

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Data availability

Sequence data, including quant-seq and DNA-seq studies, are accessible through GEO accessions GSE221624, GSE244312, GSE244313 and GSE244314. No specialty codes were generated for the processing of these data. All Source data are provided with this paper.

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Acknowledgements

We thank the Wistar Core Facilities in Genomics, Bioinformatics and Flow Cytometry for expert assistance. This work was supported by grants from NIH (R01 CA093606, R01 AI153508, R01 DE017336 to P.M.L.), The Wistar Cancer Center Core Grant P30 CA010815, and the DOD (HT9425-23-1-1049 Log#MS220073). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. The Wistar Institute, Philadelphia, PA, USA

    Samantha S. Soldan, Chenhe Su, Leena Yoon, Toshitha Kannan, Urvi Zankharia, Rishi J. Patel, Jayaraju Dheekollu, Olga Vladimirova, Jack W. Dowling, Natalie Brown, Andrew Kossenkov, Daniel E. Schäffer, Noam Auslander & Paul M. Lieberman

  2. Neuroimmunology Branch, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, USA

    Maria Chiara Monaco & Steven Jacobson

  3. Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Jack W. Dowling & Simon Thebault

  4. Neuroimmunology Clinic, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, USA

    Annaliese Clauze, Frances Andrada & Joan Ohayon

  5. Division of Infectious Diseases, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Andries Feder & Paul J. Planet

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Contributions

S.S.S. conceptualized the project, performed experimentation and data analysis, and wrote and edited the manuscript. C.S., M.C.M., U.Z., R.J.P., J.D., J.W.D., S.T. and N.B. conducted experimentation and data analysis. L.Y. and T.K. performed data analysis. O.V. performed experiments. A.C., F.A. and J.O. provided clinical support. A.F., P.J.P., D.E.S. and N.A. performed data analysis and edited the manuscript. A.K. performed data analysis. S.J. conceptualized the project, acquired resources and edited the manuscript. P.M.L. conceptualized the project, acquired resources, and wrote and edited the manuscript.

Corresponding author

Correspondence to Paul M. Lieberman.

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Competing interests

P.M.L. is a founder and advisor to Vironika, LLC, and has served as consultant for GSK and Sanofi. All other authors declare no competing interests.

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Nature Microbiology thanks Marc Horwitz, Jaap Middledorp, Christian Munz, Ludvig Sollid and Lawrence Steinman for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Characterization of SLCLs.

(a) Proliferation index in SLCLs and LCLs (B95.8), and EBV− BJAB cells measured by CFSE (one-way ANOVA followed by Tukey’s multiple comparison test: (HC SLCL, n = 4; SMS SLCL, n = 6; AMS SLCL n = 5; HC LCL, n = 8 MS LCL, n = 7). (b) Viability of long-term culture of AMS SLCLs compared to HC and SMS SLCLs, LCLs (B95.8), and EBV− BJAB cells (Log-rank Mantel-Cox test). (HC SLCL, n = 4; SMS SLCL, n = 6; AMS SLCL n = 5; HC LCL, n = 8 MS LCL, n = 8). (c) Flow cytometry analysis of EA-D and Zta expression in AMS and SMS and (d) quantitation of flow cytometry using FlowJo software (one-way ANOVA followed by Tukey’s multiple comparison test (HC SLCL, n = 4; SMS SLCL, n = 6; AMS SLCL n = 5; LCL, n = 8). (e) Western blot of EBV latent (EBNA1, EBNA2, LMP1, EBNA3C) and lytic (Zta and Ea-D) genes relative to β-actin in EBV B95.8 strain transformed LCLs (HC LCL n = 4 MS LCL n = 4). (f) RNA-seq summary heatmap showing top EBV lytic genes that are upregulated (red) or downregulated in AMS SLCLs compared to those from HC or SMS SLCLs. (g) RT-qPCR analysis of EBNA1 and LMP1 gene expression in SLCLs compared to LCLs (one-way ANOVA followed by Tukey’s multiple comparison test: (HC SLCL, n = 4; SMS SLCL, n = 6; AMS SLCL n = 5). All data points represent distinct samples tested in triplicate. Data are mean ± SD (DSeq2 Wald Test).

Source data

Extended Data Fig. 2 Overlap between population and phylogenetic groups of masked genomes.

(a) The top phylogenetic tree is midpoint rooted and ignores branch lengths. (b) The bottom tree is unrooted, emphasizing branch lengths. Branches are colored according to geographic isolation. (HC SLCL, n = 4; SMS SLCL, n = 6; AMS SLCL n = 5). All data points represent distinct samples.

Source data

Extended Data Fig. 3 Host protein and gene expression in SLCLs compared to LCLs.

(a) Flow cytometry analysis of CD20, Ki67, HLA Class I, and CD45 expression in SLCL HC, SMS, AMS, LCL (B95.8), LCL (Mutu-I), and BJAB. (HC SLCL, n = 4; SMS SLCL, n = 6; AMS SLCL n = 5, LCL n = 16). (b) Principal Component Analysis (PCA) of RNA-Seq comparing SLCLs (green) and LCLs(orange). (c) Volcano plot comparing host gene expression in LCLs vs SLCLs. (d) Gene expression (normalized counts) in LCLs (green) vs SLCLs (red) (DESeq Wald test) (HC SLCL, n = 4; SMS SLCL, n = 6; AMS SLCL, n = 5; LCL n = 15). All data points represent distinct samples. Stattistical analysis performed using DSeq2 Wald test. Data are mean ± SD.

Source data

Extended Data Fig. 4 Host gene expression comparing SLCLs between MS and HCs.

(a) Heat map analysis of RNA-seq showing top cellular genes that are upregulated (red) or downregulated (blue) in HC, SMS, and AMS SLCLs (HC SLCL, n = 4; SMS SLCL, n = 6; AMS SLCL, n = 5,). (b) IPA showing top pathways that are activated (red) or inactivated (blue) in MS SLCLs compared to HC SLCLs (HC SLCL, n = 4; SMS SLCL, n = 6; AMS SLCL, n = 5,). (c) Volcano plot highlighting differentially regulated host genes in MS SLCLs vs HC SLCLs (HC SLCL, n = 4; SMS SLCL, n = 6; AMS SLCL n = 5). (d) IPA showing top regulators that are activated (red) or inactivated (blue) in MS SLCLs compared to HC LCLs (HC SLCL, n = 4; SMS SLCL, n = 6; AMS SLCL n = 5,). Statistical analysis performed using DSeq2. All data points represent distinct samples.

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Extended Data Fig. 5 Host gene expression comparing Active MS SLCLs to Non-Active SLCLs (SMS SLCLs + HC SLCLs).

(a) Volcano plot comparing top differentially regulated genes in AMS SLCLs vs non-active SLCLs(HC SLCL, n = 4; SMS SLCL, n = 6; AMS SLCL, n = 5) DSeq2 Wald Test. (b) Ingenuity pathway analysis showing top pathways that are activated (red) or inactivated (blue) in MS SLCLs compared to HC LCLs (HC SLCL, n = 4; SMS SLCL, n = 6; AMS SLCL n = 5). (c) IPA showing top regulators that are activated (red) or inactivated (blue) in AMS SLCLs vs non-active SLCLs (HC SLCL, n = 4; SMS SLCL, n = 6; AMS SLCL, n = 5). (d) Gene expression (normalized counts) in AMS (red), SMS (green) and HC (blue) SLCLs for IL12B and ZMIZ1 (DESeq Wald test) (HC SLCL, n = 4; SMS SLCL, n = 6; AMS SLCL n = 5). Stattistical analysis performed using DSeq2 Wald Test. All data points represent distinct samples tested. Data for (d) are mean ± SD.

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Extended Data Fig. 6 Longitudinal analysis and overall comparison of Active MS SLCLs (AMS) with Stable SLCLs (SMS).

(a) Comparison of viral DNA load by ddPCR and (b) EBV LF3 transcript levels by RT-qPCR for SMS2 vs AMS5 (Patient A) and SMS5 vs AS2 (Patient B) (two patients, two timepoints per patient). (c) Heat map comparing all AMS vs SMS showing top 930 differentially regulated genes with p < .05. The gene expression values were log2 normalized and mean-centered to highlight relative changes (SMS n = 6; AMS n = 5). (d) Gene expression (normalized counts) for all AMS (red) or SMS (green) SLCLs for light chain/immunoglobulin κ, MHC class II DP, MHC class II DR, and IL-6 (SMS n = 6; AMS n = 5, DESeq Wald test). All data points represent distinct samples tested from paired-end RNA-seq data set. Data for (d) are mean ± SD.

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Extended Data Fig. 7 FOXP1 knockdown downregulates EBV lytic gene expression in AMS SLCLs and Mutu-I Burkitt’s lymphoma cells.

(a) RT-qPCR expression of FOXP1 expression relative to GUSB in AMS4 SLCLs treated with 3 individual shRNAs specific for FOXP1 and control shRNA (n = 3 per siRNA treatment). (b) Western blot showing of EBV latent (EBNA1 and LMP1) and lytic (EA-D and Zta) genes, and FOXP1 in shRNA control and FOXP1 shRNA treated cells (n = 3 per siRNA treatment, TTEST). (c) Western blot showing three biological replicates of shFOXP1 and shCtrl treated Mutu-I cells probed for expression of FOXP1 and EBV lytic gene Zta and EA-D (n = 3 per siRNA treatment, TTEST). (d) Zta expression (mean fluorescence intensity) in Mutu-I cells treated with shFOXP1 or shCtrl ((n = 3 per siRNA treatment, one-way ANOVA followed by Tukey’s multiple comparison test). All data points represent distinct samples tested in triplicate. Data are mean ± SD.

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Extended Data Fig. 8 LTA knockdown decreases viability and increases EBV lytic gene expression in SLCLs.

(a–c) AMS2 SLCLs were treated with 3 individual shRNAs specific for LTA or control shRNA (pLKO) and assayed for (a) fold change in LTA RNA expression by RT-qPCR, (b) expression of Zta or (c) EA-D by RT-qPCR, and (d) cell viability. (e–j) HC SLCLs (n = 2, triplicate samples per treatment for each cell line); SMS SLCLs (n = 2, triplicate samples per treatment for each cell line; and AMS SLCLs (n = 2); triplicate samples per treatment for each cell line) treated with shLTA1.1 or shCtrl and assayed for (e) LTA RNA expression by RT-qPCR, (f) LTA/TNFβ protein expression in cell supernatant by ELISA (g) Zta expression by RT-pPCR (h) EA-D expression by RT-qPCR, (i) EBV DNA copies per cell by ddPCR, and (j) cell viability by CellTitreGlo. BJAB used as negative control cell line. (one-way ANOVA followed by Tukey’s multiple comparison test). All data points represent distinct samples tested in triplicate. Data are mean ± SD.

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Extended Data Fig. 9 Mixed lymphocyte reaction analysis.

(a) IFNγ expression (EliSpot) in CD4+ T cells (1HC SLCL, 1 SMS SLCL, 1AMS SLCL, n = 3 for each treatment group) co cultured with autologous SLCLs treated with GCV or DMSO. (one-way ANOVA followed by Tukey’s multiple comparison test). (b) EliSpot analysis of cytokine production during a mixed lymphocyte reaction with three different donor T cells incubated with HC1 or AMS4 n = 3 for each treatment group, one-way ANOVA followed by Tukey’s multiple comparison test). All data points represent distinct samples tested in triplicate. Data are mean ± SD.

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Supplementary information

Supplementary Information

Original western blot data for Figs. 1f and 5c, and Extended Data Figs. 1e and 7c,b. Source Data Fig. 1 Unprocessed western blots for Fig. 1f. Source Data Fig. 4 Flow cytometry data relating to Fig. 4a. Source Data Fig. 5 Unprocessed western blots for Fig. 5c. Source Data Extended Data Fig. 1 Unprocessed western blots for Fig. 1f. Source Data Extended Data Fig. 7 Unprocessed western blots for Fig. 7b,c.

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Supplementary Table 1.

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Soldan, S.S., Su, C., Monaco, M.C. et al. Multiple sclerosis patient-derived spontaneous B cells have distinct EBV and host gene expression profiles in active disease. Nat Microbiol 9, 1540–1554 (2024). https://doi.org/10.1038/s41564-024-01699-6

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