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

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.

References

  1. Young, L. S., Yap, L. F. & Murray, P. G. Epstein–Barr virus: more than 50 years old and still providing surprises. Nat. Rev. Cancer 16, 789–802 (2016).

    Article  CAS  PubMed  Google Scholar 

  2. Thorley-Lawson, D. A. EBV persistence–introducing the virus. Curr. Top. Microbiol. Immunol. 390, 151–209 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Taylor, G. S., Long, H. M., Brooks, J. M., Rickinson, A. B. & Hislop, A. D. The immunology of Epstein–Barr virus-induced disease. Annu. Rev. Immunol. 33, 787–821 (2015).

    Article  CAS  PubMed  Google Scholar 

  4. Soldan, S. S. & Lieberman, P. M. Epstein–Barr virus infection in the development of neurological disorders. Drug Discov. Today Dis. Models 32, 35–52 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Soldan, S. S. & Lieberman, P. M. Epstein–Barr virus and multiple sclerosis. Nat. Rev. Microbiol. 21, 51–64 (2023).

  6. Dobson, R. & Giovannoni, G. Multiple sclerosis – a review. Eur. J. Neurol. 26, 27–40 (2019).

    Article  CAS  PubMed  Google Scholar 

  7. Bray, P. F., Bloomer, L. C., Salmon, V. C., Bagley, M. H. & Larsen, P. D. Epstein–Barr virus infection and antibody synthesis in patients with multiple sclerosis. Arch. Neurol. 40, 406–408 (1983).

    Article  CAS  PubMed  Google Scholar 

  8. Bjornevik, K. et al. Longitudinal analysis reveals high prevalence of Epstein–Barr virus associated with multiple sclerosis. Science 375, 296–301 (2022).

    Article  CAS  PubMed  Google Scholar 

  9. Lanz, T. V. et al. Clonally expanded B cells in multiple sclerosis bind EBV EBNA1 and GlialCAM. Nature 603, 321–327 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Sundstrom, P., Nystrom, M., Ruuth, K. & Lundgren, E. Antibodies to specific EBNA-1 domains and HLA DRB1*1501 interact as risk factors for multiple sclerosis. J. Neuroimmunol. 215, 102–107 (2009).

    Article  PubMed  Google Scholar 

  11. Cencioni, M. T., Mattoscio, M., Magliozzi, R., Bar-Or, A. & Muraro, P. A. B cells in multiple sclerosis—from targeted depletion to immune reconstitution therapies. Nat. Rev. Neurol. 17, 399–414 (2021).

    Article  PubMed  Google Scholar 

  12. Enose-Akahata, Y. et al. Immunophenotypic characterization of CSF B cells in virus-associated neuroinflammatory diseases. PLoS Pathog. 14, e1007042 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Li, R. et al. Dimethyl fumarate treatment mediates an anti-inflammatory shift in B cell subsets of patients with multiple sclerosis. J. Immunol. 198, 691–698 (2017).

    Article  CAS  PubMed  Google Scholar 

  14. Matsushita, T. et al. Characteristic cerebrospinal fluid cytokine/chemokine profiles in neuromyelitis optica, relapsing remitting or primary progressive multiple sclerosis. PLoS ONE 8, e61835 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Mandage, R. et al. Genetic factors affecting EBV copy number in lymphoblastoid cell lines derived from the 1000 Genome Project samples. PLoS ONE 12, e0179446 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  16. SoRelle, E. D. et al. Single-cell RNA-seq reveals transcriptomic heterogeneity mediated by host–pathogen dynamics in lymphoblastoid cell lines. Elife https://doi.org/10.7554/eLife.62586 (2021).

  17. Sculley, T. B., Moss, D. J., Hazelton, R. A. & Pope, J. H. Detection of Epstein–Barr virus strain variants in lymphoblastoid cell lines ‘spontaneously’ derived from patients with rheumatoid arthritis, infectious mononucleosis and normal controls. J. Gen. Virol. 68, 2069–2078 (1987).

    Article  PubMed  Google Scholar 

  18. Lewin, N. et al. Characterization of EBV-carrying B-cell populations in healthy seropositive individuals with regard to density, release of transforming virus and spontaneous outgrowth. Int. J. Cancer 39, 472–476 (1987).

    Article  CAS  PubMed  Google Scholar 

  19. Monaco, M. C. G. et al. EBNA1 inhibitors block proliferation of spontaneous lymphoblastoid cell lines from patients with multiple sclerosis and healthy controls. Neurol. Neuroimmunol. Neuroinflamm. https://doi.org/10.1212/NXI.0000000000200149 (2023).

  20. Munch, M. et al. B-lymphoblastoid cell lines from multiple sclerosis patients and a healthy control producing a putative new human retrovirus and Epstein–Barr virus. Mult. Scler. 1, 78–81 (1995).

    Article  CAS  PubMed  Google Scholar 

  21. Christensen, T., Tonjes, R. R., zur Megede, J., Boller, K. & Moller-Larsen, A. Reverse transcriptase activity and particle production in B lymphoblastoid cell lines established from lymphocytes of patients with multiple sclerosis. AIDS Res. Hum. Retroviruses 15, 285–291 (1999).

    Article  CAS  PubMed  Google Scholar 

  22. Gao, Y., Smith, P. R., Karran, L., Lu, Q. L. & Griffin, B. E. Induction of an exceptionally high-level, nontranslated, Epstein–Barr virus-encoded polyadenylated transcript in the Burkitt’s lymphoma line Daudi. J. Virol. 71, 84–94 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Dheekollu, J. et al. Carcinoma-risk variant of EBNA1 deregulates Epstein–Barr Virus episomal latency. Oncotarget 8, 7248–7264 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Sivachandran, N., Wang, X. & Frappier, L. Functions of the Epstein–Barr virus EBNA1 protein in viral reactivation and lytic infection. J. Virol. 86, 6146–6158 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Mrozek-Gorska, P. et al. Epstein–Barr virus reprograms human B lymphocytes immediately in the prelatent phase of infection. Proc. Natl Acad. Sci. USA 116, 16046–16055 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Fischer, E. M. et al. Expression of CD21 is developmentally regulated during thymic maturation of human T lymphocytes. Int. Immunol. 11, 1841–1849 (1999).

    Article  CAS  PubMed  Google Scholar 

  27. Schneider-Schaulies, J., Dunster, L. M., Kobune, F., Rima, B. & ter Meulen, V. Differential downregulation of CD46 by measles virus strains. J. Virol. 69, 7257–7259 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Santoro, F. et al. CD46 is a cellular receptor for human herpesvirus 6. Cell 99, 817–827 (1999).

    Article  CAS  PubMed  Google Scholar 

  29. Sun, H. et al. Tim3+ Foxp3+ Treg cells are potent inhibitors of effector T cells and are suppressed in rheumatoid arthritis. Inflammation 40, 1342–1350 (2017).

    Article  CAS  PubMed  Google Scholar 

  30. Miteva, L., Trenova, A., Slavov, G. & Stanilova, S. IL12B gene polymorphisms have sex-specific effects in relapsing-remitting multiple sclerosis. Acta Neurol. Belg. 119, 83–93 (2019).

    Article  PubMed  Google Scholar 

  31. Parnell, G. P. et al. The latitude-dependent autoimmune disease risk genes ZMIZ1 and IRF8 regulate mononuclear phagocytic cell differentiation in response to vitamin D. Hum. Mol. Genet. 28, 269–278 (2019).

    CAS  PubMed  Google Scholar 

  32. McWilliam, O., Sellebjerg, F., Marquart, H. V. & von Essen, M. R. B cells from patients with multiple sclerosis have a pathogenic phenotype and increased LTα and TGFβ1 response. J. Neuroimmunol. 324, 157–164 (2018).

    Article  CAS  PubMed  Google Scholar 

  33. Maltby, V. E. et al. Genome-wide DNA methylation changes in CD19+ B cells from relapsing-remitting multiple sclerosis patients. Sci. Rep. 8, 17418 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Thompson, M. P., Aggarwal, B. B., Shishodia, S., Estrov, Z. & Kurzrock, R. Autocrine lymphotoxin production in Epstein–Barr virus-immortalized B cells: induction via NF-kappaB activation mediated by EBV-derived latent membrane protein 1. Leukemia 17, 2196–2201 (2003).

    Article  CAS  PubMed  Google Scholar 

  35. Drosu, N. C., Edelman, E. R. & Housman, D. E. Tenofovir prodrugs potently inhibit Epstein–Barr virus lytic DNA replication by targeting the viral DNA polymerase. Proc. Natl Acad. Sci. USA 117, 12368–12374 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. SoRelle, E. D. et al. Time-resolved transcriptomes reveal diverse B cell fate trajectories in the early response to Epstein–Barr virus infection. Cell Rep. 40, 111286 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Pender, M. P., Csurhes, P. A., Burrows, J. M. & Burrows, S. R. Defective T-cell control of Epstein–Barr virus infection in multiple sclerosis. Clin. Transl. Immunol. 6, e126 (2017).

    Article  Google Scholar 

  38. Angelini, D. F. et al. Increased CD8+ T cell response to Epstein–Barr virus lytic antigens in the active phase of multiple sclerosis. PLoS Pathog. 9, e1003220 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Delecluse, S. et al. Identification and cloning of a new western Epstein–Barr virus strain that efficiently replicates in primary B cells. J. Virol. https://doi.org/10.1128/JVI.01918-19 (2020).

  40. Weisel, N. M. et al. Comprehensive analyses of B-cell compartments across the human body reveal novel subsets and a gut-resident memory phenotype. Blood 136, 2774–2785 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. SoRelle, E. D., Reinoso-Vizcaino, N. M., Horn, G. Q. & Luftig, M. A. Epstein–Barr virus perpetuates B cell germinal center dynamics and generation of autoimmune-associated phenotypes in vitro. Front. Immunol. 13, 1001145 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Yang, R. et al. Human T-bet governs the generation of a distinct subset of CD11chighCD21low B cells. Sci. Immunol. 7, eabq3277 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Mouat, I. C. et al. Gammaherpesvirus infection drives age-associated B cells toward pathogenicity in EAE and MS. Sci. Adv. 8, eade6844 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Veroni, C., Serafini, B., Rosicarelli, B., Fagnani, C. & Aloisi, F. Transcriptional profile and Epstein–Barr virus infection status of laser-cut immune infiltrates from the brain of patients with progressive multiple sclerosis. J. Neuroinflammation 15, 18 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Moreno, M. A. et al. Molecular signature of Epstein–Barr virus infection in MS brain lesions. Neurol. Neuroimmunol. Neuroinflamm. 5, e466 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Hong, T. et al. Epstein–Barr virus nuclear antigen 2 extensively rewires the human chromatin landscape at autoimmune risk loci. Genome Res. 31, 2185–2198 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Harley, J. B. et al. Transcription factors operate across disease loci, with EBNA2 implicated in autoimmunity. Nat. Genet. 50, 699–707 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Ramasamy, R., Mohammed, F. & Meier, U. C. HLA DR2b-binding peptides from human endogenous retrovirus envelope, Epstein–Barr virus and brain proteins in the context of molecular mimicry in multiple sclerosis. Immunol. Lett. 217, 15–24 (2020).

    Article  CAS  PubMed  Google Scholar 

  49. Mansouri, S., Pan, Q., Blencowe, B. J., Claycomb, J. M. & Frappier, L. Epstein–Barr virus EBNA1 protein regulates viral latency through effects on let-7 microRNA and dicer. J. Virol. 88, 11166–11177 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Sagardoy, A. et al. Downregulation of FOXP1 is required during germinal center B-cell function. Blood 121, 4311–4320 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Patzelt, T. et al. Foxp1 controls mature B cell survival and the development of follicular and B-1 B cells. Proc. Natl Acad. Sci. USA 115, 3120–3125 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Wang, J. et al. EBV miRNAs BART11 and BART17-3p promote immune escape through the enhancer-mediated transcription of PD-L1. Nat. Commun. 13, 866 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Torkildsen, Ø., Myhr, K. M., Skogen, V., Steffensen, L. H. & Bjørnevik, K. Tenofovir as a treatment option for multiple sclerosis. Mult. Scler. Relat. Disord. 46, 102569 (2020).

    Article  PubMed  Google Scholar 

  54. Latifi, T., Zebardast, A. & Marashi, S. M. The role of human endogenous retroviruses (HERVs) in multiple sclerosis and the plausible interplay between HERVs, Epstein–Barr virus infection, and vitamin D. Mult. Scler. Relat. Disord. 57, 103318 (2022).

    Article  CAS  PubMed  Google Scholar 

  55. Kubuschok, B. et al. Gene-modified spontaneous Epstein–Barr virus-transformed lymphoblastoid cell lines as autologous cancer vaccines: mutated p21 ras oncogene as a model. Cancer Gene Ther. 7, 1231–1240 (2000).

    Article  CAS  PubMed  Google Scholar 

  56. Hindson, B. J. et al. High-throughput droplet digital PCR system for absolute quantitation of DNA copy number. Anal. Chem. 83, 8604–8610 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Lin, C. T., Leibovitch, E. C., Almira-Suarez, M. I. & Jacobson, S. Human herpesvirus multiplex ddPCR detection in brain tissue from low- and high-grade astrocytoma cases and controls. Infect. Agent Cancer 11, 32 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Čejková, D., Strouhal, M., Norris, S. J., Weinstock, G. M. & Šmajs, D. A retrospective study on genetic heterogeneity within Treponema strains: subpopulations are genetically distinct in a limited number of positions. PLoS Negl. Trop. Dis. 9, e0004110 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience https://doi.org/10.1093/gigascience/giab008 (2021).

  61. Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).

    Article  CAS  PubMed  Google Scholar 

  63. Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49, W293–W296 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Felsenstein, J. Confidence limits on phylogenies: an approach using the bootstrap. Evolution 39, 783–791 (1985).

    Article  PubMed  Google Scholar 

  65. Zanella, L. et al. A reliable Epstein–Barr Virus classification based on phylogenomic and population analyses. Sci. Rep. 9, 9829 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Argimon, S. et al. Microreact: visualizing and sharing data for genomic epidemiology and phylogeography. Microb. Genom. 2, e000093 (2016).

    PubMed  PubMed Central  Google Scholar 

  67. Messick, T. E. et al. Structure-based design of small-molecule inhibitors of EBNA1 DNA binding blocks Epstein–Barr virus latent infection and tumor growth. Sci. Transl. Med. https://doi.org/10.1126/scitranslmed.aau5612 (2019).

  68. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Zhao, T. & Wang, Z. GraphBio: a shiny web app to easily perform popular visualization analysis for omics data. Front. Genet. 13, 957317 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Afrasiabi, A. et al. Genetic and transcriptomic analyses support a switch to lytic phase in Epstein Barr virus infection as an important driver in developing Systemic Lupus Erythematosus. J. Autoimmun. 127, 102781 (2022).

    Article  CAS  PubMed  Google Scholar 

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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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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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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.

Source data

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.

Source data

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.

Source data

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.

(ac) 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. (ej) 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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