Transcriptomics and proteomics reveal a cooperation between interferon and T-helper 17 cells in neuromyelitis optica

Agnieshka M Agasing, Qi Wu, Bhuwan Khatri, Nadja Borisow, Klemens Ruprecht, Alexander Ulrich Brandt, Saurabh Gawde, Gaurav Kumar, James L Quinn, Rose M Ko, Yang Mao-Draayer, Christopher J Lessard, Friedemann Paul, Robert C Axtell, Agnieshka M Agasing, Qi Wu, Bhuwan Khatri, Nadja Borisow, Klemens Ruprecht, Alexander Ulrich Brandt, Saurabh Gawde, Gaurav Kumar, James L Quinn, Rose M Ko, Yang Mao-Draayer, Christopher J Lessard, Friedemann Paul, Robert C Axtell

Abstract

Type I interferon (IFN-I) and T helper 17 (TH17) drive pathology in neuromyelitis optica spectrum disorder (NMOSD) and in TH17-induced experimental autoimmune encephalomyelitis (TH17-EAE). This is paradoxical because the prevalent theory is that IFN-I inhibits TH17 function. Here we report that a cascade involving IFN-I, IL-6 and B cells promotes TH17-mediated neuro-autoimmunity. In NMOSD, elevated IFN-I signatures, IL-6 and IL-17 are associated with severe disability. Furthermore, IL-6 and IL-17 levels are lower in patients on anti-CD20 therapy. In mice, IFN-I elevates IL-6 and exacerbates TH17-EAE. Strikingly, IL-6 blockade attenuates disease only in mice treated with IFN-I. By contrast, B-cell-deficiency attenuates TH17-EAE in the presence or absence of IFN-I treatment. Finally, IFN-I stimulates B cells to produce IL-6 to drive pathogenic TH17 differentiation in vitro. Our data thus provide an explanation for the paradox surrounding IFN-I and TH17 in neuro-autoimmunity, and may have utility in predicting therapeutic response in NMOSD.

Conflict of interest statement

R.C.A. has consulted for Roche, Biogen, and EMD serono. Y.M.-D. has consulted for and/or received grant support from: Acorda, Bayer Pharmaceutical, Biogen Idec, EMD Serono, Genzyme, Novartis, Questor, Genentech, and Teva Neuroscience. F.P. has consulted for and/or received speaker honoraria from Bayer, Teva, Genzyme, Merck, Novartis, and MedImmune. All other authors declare no competing interests.

Figures

Fig. 1. NMOSD patients stratify into two…
Fig. 1. NMOSD patients stratify into two groups based on IFN-I gene expression.
RNA profiles of a untreated patients (NMO-untreated; n = 7), b Rituximab-treated patients (NMO-Ritux; n = 24) and c patients on other therapies (NMO-Other Tx; n = 7) were compared with healthy volunteers (n = 18). d Venn diagram of differentially expressed genes of the NMO-Untreated vs healthy, NMO-Ritux vs healthy, and NMO-Other Tx vs healthy. e Heatmap depicts relative levels of IFN-I genes in NMOSD patients (Red = NMO-Untreated, Yellow = NMO-Other Tx, Green = NMO-Ritux). Patients were stratified into two groups, IFN-low and IFN-high, based on IFN-I gene expression. Yellow represents relative high expression and blue represents relative low expression. f Heatmap depicts the differentially abundant serum proteins in IFN-high NMOSD (N = 16), IFN-low NMOSD (n = 22), and healthy controls (n = 18). Yellow represents relative high serum levels; blue represents relative low serum levels. Comparison of g disability (EDSS), h number of relapses 2 years prior to sample collection, i age, and j autoantibody status of IFN-high and IFN-low NMOSD patients. Two-tailed Student’s t tests and Chi-square tests were used to determine statistical significance. k MCP-3 and l IL-6 levels in NMOSD patients of different EDSS range (EDSS 4–6.5: n = 15, EDSS 2.5–3.5: n = 9, EDSS 0–2: n = 16). P values were determined using two-tailed Kruskal–Wallis tests with multiple comparisons corrected by the Dunn’s method. Bar graphs represent the mean and error bars are the S.E.M. Source data are provided as a Source Data file.
Fig. 2. Correlation between TH17 and disability…
Fig. 2. Correlation between TH17 and disability in NMOSD patients.
Correlations between EDSS and a %TH17, b %TH17.1, c %TH17 + %TH17.1, and d %TH1 cells in NMOSD patients (n = 6). Two-tailed Pearson correlations were used to determine statistical significance. P values < 0.05 were considered significant and P values > 0.05 were not significant.
Fig. 3. Effects of B-cell-depleting therapy (BCDT)…
Fig. 3. Effects of B-cell-depleting therapy (BCDT) in IFN-high and IFN-low NMOSD patients.
a Heatmap depicts the hierarchical clustering of NMOSD patients (Red = NMO-Untreated, Yellow = NMO-Other Tx, Green = NMO-Ritux) based on differentially expressed genes (DEGs) of patients treated with rituximab (n = 24) compared with patients not treated with rituximab (n = 14). Hierarchical clustering of the DEGs grouped genes into four clusters (Gene clusters 1–4). Yellow represents elevated gene expression and blue represents reduced gene expression. b Percent expression of genes in clusters 1–4 per cell type. c Stratification of NMOSD patients into two groups, defined as B-cell-deficient and B-cell-sufficient, based on gene expression and pie chart depicting the distribution of IFN-high and IFN-low NMOSD patients in the B-cell-deficient and B-cell-sufficient groups. d Composite IFN scores (average read count of IFN-I genes) of B-cell-sufficient IFN-high patients (n = 8), B-cell-deficient IFN-high patients (n = 8), B-cell-sufficient IFN-low patients (n = 10), B-cell-deficient IFN-low patients (n = 12) and healthy individuals (n = 18). e Heatmap indicating relative protein levels in IFN-high and IFN-low NMOSD patients from B-cell-sufficient or B-cell-deficient groups. f Serum protein levels of IL-6, IL-17A, and MCP-3 in untreated (n = 7), Other-treated (n = 7), and rituximab-treated NMOSD (n = 24) patients compared with healthy controls (n = 18). P values were determined using two-tailed Kruskal–Wallis tests with multiple comparisons corrected by the Dunn’s method or by a two-tailed Mann–Whitney test indicated with an *.
Fig. 4. Effects of B-cell depletion on…
Fig. 4. Effects of B-cell depletion on AQP4-Ig + and MOG-Ig + NMOSD.
a Comparison of relapse rates in AQP4-Ig + (n = 12) and MOG-Ig+ NMOSD (n = 5) who are B-cell-sufficient. b Comparison of relapse rates in AQP4-IgG+ (n = 13) and MOG-IgG+ (n = 7) NMOSD who are B-cell-deficient. P values were determined using two-tailed Mann–Whitney tests. Serum c IL-6 and d MCP-3 levels in B cell-sufficient AQP4-IgG+ patients (n = 13), B-cell-deficient AQP4-IgG+ patients (n = 13), B-cell-sufficient MOG-IgG+ patients (n = 5) and B-cell-deficient MOG-IgG+ patients (n = 7) were compared with healthy controls (n = 18). P values were determined using one-way ANOVA tests with multiple comparisons corrected by the Tukey’s method. Error bars indicate the S.E.M. Source data are provided as a Source Data file.
Fig. 5. Type I IFN drives IL-6…
Fig. 5. Type I IFN drives IL-6 production in human memory B cells.
a Correlation between composite IFN scores and serum CXCL11 levels with IL-6 in B cell-sufficient NMOSD patients (n = 18), B-cell-deficient NMOSD patients (n = 20) and healthy volunteers (n = 18). R and p values were determined using two-tailed Pearson correlation coefficient tests. Direct effect of IFN-β on human naive and memory B cells was assessed by stimulating purified human naïve (CD27−) and memory B cells (CD27+) from healthy donors (n = 4) with CD40L, anti-Ig ± IFN-β. Representative flow cytometric plots and frequency of live b naive CD19+ B cells and c memory B cells expressing CD80, CD86, and IL-6 are shown. Statistical significance was determined using two-tailed Student t tests. P values <0.05 were considered significant.
Fig. 6. IFN-β elevates IL-6 and exacerbates…
Fig. 6. IFN-β elevates IL-6 and exacerbates TH17-driven EAE.
MOG-primed TH17 cells were transferred into recipient mice and treated with 1000 U of IFN-β or vehicle every other day from day 0–10 post transfer of cells. a Clinical scores of mice with TH17-EAE treated with vehicle or IFN-β; (TH17-vehicle n = 5, TH17-IFN-β n = 5) and two-tailed Mann–Whitney tests were performed to determine statistical significance (*P < 0.05). Representative of four experiments with similar results. b Spinal cord sections from mice (day 30) were stained with H&E and Luxol fast blue. Representative images of four mice in each group. Scale bar represents 100 µm. c Levels of IL-6 in the sera (measured by ELISA) of TH17-EAE (day 2) were elevated with IFN-β; (TH17-vehicle n = 5, TH17-IFN-β n = 5, healthy n = 3) and a two-tailed Kruskal–Wallis test with multiple comparisons corrected by the Dunn’s method was used to determine statistical significance. d Representative flow cytometry plots and absolute numbers of live CD4+ T cells in the spinal cords of EAE mice, 30 days post transfer. e Representative flow cytometry plots and absolute numbers of CD4+ T cells expressing IL-17, GM-CSF or both in the spinal cords of EAE mice. f Representative flow cytometry plots and absolute numbers of live CD19+ B cells in the spinal cords of EAE mice. (TH17-vehicle n = 5, TH17-IFN-β n = 5) and two-tailed Mann–Whitney tests were performed to determine statistical significance. Error bars indicate the S.E.M. Source data are provided as a Source Data file.
Fig. 7. IL-6 blockade attenuates IFN-β-treated TH17-EAE.
Fig. 7. IL-6 blockade attenuates IFN-β-treated TH17-EAE.
TH17-EAE mice, either treated with IFN-β or vehicle, were also treated with either anti-IL-6R or an isotype control every 5 days from days 1–11. a Effect of treatment with anti-IL-6R (n = 10) or isotype (n = 10) control on the clinical scores of vehicle-treated TH17-EAE. Data were pooled from two independent experiments. b Number of CD4+ T cells that express GM-CSF and IL-17 in spinal cords of vehicle-treated mice (day 35). c Effect of anti-IL-6R (n = 8) or isotype (n = 10) treatment on the clinical scores of IFN-β-treated TH17-EAE. Data were pooled from two independent experiments. Vehicle/isotype treated mice were also plotted for reference. d Number of CD4+ T cells that express GM-CSF and IL-17 in the spinal cords of IFN-β-treated EAE mice (day 35). Statistical analysis was performed using Mann–Whitney tests (P < 0.05). Error bars indicate the S.E.M. Results are compiled from two independent experiments.
Fig. 8. B cell-deficiency attenuates IFN-β-treated TH17-EAE.
Fig. 8. B cell-deficiency attenuates IFN-β-treated TH17-EAE.
TH17-EAE was induced in either C57BL/6 or µMT mice and treated with IFN-β or vehicle. a Clinical scores of vehicle-treated C57BL/6 (n = 10) and µMT (n = 10) mice with TH17-EAE. Data were pooled from two independent experiments. Mann–Whitney tests were performed to determine statistical significance (P < 0.05). b Number of CD4+ T cells that express GM-CSF and IL-17 in spinal cords of vehicle-treated mice (day 29). c Clinical scores of IFN-β-treated C57BL/6 (n = 10) and µMT (n = 11) mice with TH17-EAE. Data were pooled from two independent experiments. Mann–Whitney tests were performed to determine statistical significance (P < 0.05). d Number of CD4+ T cells that express GM-CSF and IL-17 in spinal cords of IFN-β-treated mice (day 29). Statistical analysis was performed using two-tailed Mann–Whitney tests. Error bars indicate the S.E.M. Results are compiled from two independent experiments. Source data are provided as a Source Data file.
Fig. 9. Effect of IFN-β-stimulated B cells…
Fig. 9. Effect of IFN-β-stimulated B cells on TH17 cells.
Purified B cells from spleens of healthy or EAE mice (n = 5) were stimulated with anti-CD40 ± IFN-β for 3 days. Following stimulation, B cells were washed and co-cultured with CD4+ T cells from 2D2 mice in the presence of MOG35–55 antigen. Following IFN-β stimulation, B-cell phenotype and cytokine production were assessed by a, b FACS and c ELISA, respectively. Stimulated B cells were washed and co-cultured with antigen-specific 2D2 T-helper cells in the presence of MOG35–55 antigen. Representative flow cytometry plots of Ki-67 staining of 2D2 CD4+ T cells cultured with B cells from d healthy (n = 3) or e EAE mice (n = 3). f The cytokines IL-17A, GM-CSF, and IL-10 from the co-culture supernatants were analyzed by ELISA (n = 3 per group). Statistical significance was determined using paired one-way ANOVA tests with multiple comparison corrections using the Holm-Sidak’s method. P values < 0.05 were considered significant. Error bars indicate SEM. Source data are provided as a Source Data file.
Fig. 10. IFN-I indirectly promotes TH17 pathogenicity.
Fig. 10. IFN-I indirectly promotes TH17 pathogenicity.
Data from Fig. 9 indicate that IFN-I stimulates the expression of IL-6 and IL-12p40 from activated B cells which, in the context of auto-antigen, supports the proliferation of inflammatory TH17 cells. In contrast, IFN-I stimulation of naive B cells elevates IL-10 and not IL-6 and does not efficiently promote inflammatory TH17 cell proliferation.

References

    1. Isaacs A, Lindenmann J. Virus interference. I. The interferon. Proc. R. Soc. Lond. B Biol. Sci. 1957;147:258–267.
    1. Moschos S, Varanasi S, Kirkwood JM. Interferons in the treatment of solid tumors. Cancer Treat. Res. 2005;126:207–241.
    1. Paty DW, Li DK. Interferon beta-1b is effective in relapsing-remitting multiple sclerosis. II. MRI analysis results of a multicenter, randomized, double-blind, placebo-controlled trial. UBC MS/MRI Study Group and the IFNB Multiple Sclerosis Study Group. Neurology. 1993;43:662–667.
    1. Palace J, Leite MI, Nairne A, Vincent A. Interferon Beta treatment in neuromyelitis optica: increase in relapses and aquaporin 4 antibody titers. Arch. Neurol. 2010;67:1016–1017.
    1. Uzawa A, Mori M, Hayakawa S, Masuda S, Kuwabara S. Different responses to interferon beta-1b treatment in patients with neuromyelitis optica and multiple sclerosis. Eur. J. Neurol. 2010;17:672–676.
    1. Borisow N, Mori M, Kuwabara S, Scheel M, Paul F. Diagnosis and treatment of NMO spectrum disorder and MOG-encephalomyelitis. Front Neurol. 2018;9:888.
    1. Narayan R, et al. MOG antibody disease: a review of MOG antibody seropositive neuromyelitis optica spectrum disorder. Mult. Scler. Relat. Disord. 2018;25:66–72.
    1. Matsushita T, et al. Characteristic cerebrospinal fluid cytokine/chemokine profiles in neuromyelitis optica, relapsing remitting or primary progressive multiple sclerosis. PloS ONE. 2013;8:e61835.
    1. Herges K, et al. Protective effect of an elastase inhibitor in a neuromyelitis optica-like disease driven by a peptide of myelin oligodendroglial glycoprotein. Mult. Scler. J. 2012;18:398–408.
    1. Varrin-Doyer M, et al. Aquaporin 4-specific T cells in neuromyelitis optica exhibit a Th17 bias and recognize Clostridium ABC transporter. Ann. Neurol. 2012;72:53–64.
    1. Shimizu J, et al. IFNbeta-1b may severely exacerbate Japanese optic-spinal MS in neuromyelitis optica spectrum. Neurology. 2010;75:1423–1427.
    1. Jarius S, et al. MOG-IgG in NMO and related disorders: a multicenter study of 50 patients. Part 2: epidemiology, clinical presentation, radiological and laboratory features, treatment responses, and long-term outcome. J. Neuroinflamm. 2016;13:280.
    1. Kroenke MA, Carlson TJ, Andjelkovic AV, Segal BM. IL-12- and IL-23-modulated T cells induce distinct types of EAE based on histology, CNS chemokine profile, and response to cytokine inhibition. J. Exp. Med. 2008;205:1535–1541.
    1. Axtell RC, et al. T helper type 1 and 17 cells determine efficacy of interferon-beta in multiple sclerosis and experimental encephalomyelitis. Nat. Med. 2010;16:406–412.
    1. Stromnes IM, Cerretti LM, Liggitt D, Harris RA, Goverman JM. Differential regulation of central nervous system autoimmunity by T(H)1 and T(H)17 cells. Nat. Med. 2008;14:337–342.
    1. Pennell LM, Fish EN. Immunoregulatory effects of interferon-beta in suppression of Th17 cells. J. Interferon Cytokine Res. 2014;34:330–341.
    1. Ramgolam VS, Sha Y, Jin J, Zhang X, Markovic-Plese S. IFN-beta inhibits human Th17 cell differentiation. J. Immunol. 2009;183:5418–5427.
    1. Durelli L, et al. T-helper 17 cells expand in multiple sclerosis and are inhibited by interferon-beta. Ann. Neurol. 2009;65:499–509.
    1. Rusinova I, et al. Interferome v2.0: an updated database of annotated interferon-regulated genes. Nucleic Acids Res. 2013;41:D1040–D1046.
    1. El-Sherbiny YM, et al. A novel two-score system for interferon status segregates autoimmune diseases and correlates with clinical features. Sci. Rep. 2018;8:5793.
    1. Shaygannejad V, et al. Long-term tolerability, safety and efficacy of rituximab in neuromyelitis optica spectrum disorder: a prospective study. J. Neurol. 2019;266:642–650.
    1. Thul, P. J. et al. A subcellular map of the human proteome. Science356, eaal3321 (2017).
    1. Durozard P, et al. Comparison of the response to rituximab between myelin oligodendrocyte glycoprotein and aquaporin-4 antibody diseases. Ann. Neurol. 2020;87:256–266.
    1. Heink S, et al. Trans-presentation of IL-6 by dendritic cells is required for the priming of pathogenic TH17 cells. Nat. Immunol. 2017;18:74–85.
    1. Veldhoen M, Hocking RJ, Atkins CJ, Locksley RM, Stockinger B. TGFbeta in the context of an inflammatory cytokine milieu supports de novo differentiation of IL-17-producing T cells. Immunity. 2006;24:179–189.
    1. Serada S, et al. IL-6 blockade inhibits the induction of myelin antigen-specific Th17 cells and Th1 cells in experimental autoimmune encephalomyelitis. Proc. Natl Acad. Sci. USA. 2008;105:9041–9046.
    1. Okuda Y, et al. IL-6 plays a crucial role in the induction phase of myelin oligodendrocyte glucoprotein 35-55 induced experimental autoimmune encephalomyelitis. J. Neuroimmunol. 1999;101:188–196.
    1. Pfeifle R, et al. Regulation of autoantibody activity by the IL-23-TH17 axis determines the onset of autoimmune disease. Nat. Immunol. 2017;18:104–113.
    1. Mitsdoerffer M, et al. Proinflammatory T helper type 17 cells are effective B-cell helpers. Proc. Natl Acad. Sci. USA. 2010;107:14292–14297.
    1. Quinn JL, Kumar G, Agasing A, Ko RM, Axtell RC. Role of TFH cells in promoting T helper 17-Induced Neuroinflammation. Front. Immunol. 2018;9:382.
    1. Bettelli E, et al. Myelin oligodendrocyte glycoprotein-specific T cell receptor transgenic mice develop spontaneous autoimmune optic neuritis. J. Exp. Med. 2003;197:1073–1081.
    1. Axtell RC, Raman C. Janus-like effects of type I interferon in autoimmune diseases. Immunol. Rev. 2012;248:23–35.
    1. Kothur K, et al. B cell, Th17, and neutrophil related cerebrospinal fluid cytokine/chemokines are eevated in MOG antibody associated demyelination. PloS ONE. 2016;11:e0149411.
    1. Asgari N, et al. Interferon alpha association with neuromyelitis optica. Clin. Dev. Immunol. 2013;2013:713519.
    1. Feng X, et al. Type I interferon signature is high in lupus and neuromyelitis optica but low in multiple sclerosis. J. Neurol. Sci. 2012;313:48–53.
    1. Barr TA, et al. B cell depletion therapy ameliorates autoimmune disease through ablation of IL-6-producing B cells. J. Exp. Med. 2012;209:1001–1010.
    1. Zamvil SS, Slavin AJ. Does MOG Ig-positive AQP4-seronegative opticospinal inflammatory disease justify a diagnosis of NMO spectrum disorder? Neurol. Neuroimmunol. Neuroinflamm. 2015;2:e62.
    1. Yamamura T, et al. Trial of satralizumab in neuromyelitis optica spectrum disorder. N. Engl. J. Med. 2019;381:2114–2124.
    1. Paul F, Murphy O, Pardo S, Levy M. Investigational drugs in development to prevent neuromyelitis optica relapses. Expert Opin. Investig. Drugs. 2018;27:265–271.
    1. Molnarfi N, et al. MHC class II-dependent B cell APC function is required for induction of CNS autoimmunity independent of myelin-specific antibodies. J. Exp. Med. 2013;210:2921–2937.
    1. Parker Harp CR, et al. B cell antigen presentation is sufficient to drive neuroinflammation in an animal model of multiple sclerosis. J. Immunol. 2015;194:5077–5084.
    1. Sagan SA, et al. Tolerance checkpoint bypass permits emergence of pathogenic T cells to neuromyelitis optica autoantigen aquaporin-4. Proc. Natl. Acad. Sci. USA. 2016;113:14781–14786.
    1. Hegen H, et al. Cytokine profiles show heterogeneity of interferon-beta response in multiple sclerosis patients. Neurol. Neuroimmunol. Neuroinflamm. 2016;3:e202.
    1. Wingerchuk DM, et al. International consensus diagnostic criteria for neuromyelitis optica spectrum disorders. Neurology. 2015;85:177–189.
    1. Mader S, et al. Complement activating antibodies to myelin oligodendrocyte glycoprotein in neuromyelitis optica and related disorders. J. Neuroinflamm. 2011;8:184.
    1. Jarius S, et al. Standardized method for the detection of antibodies to aquaporin-4 based on a highly sensitive immunofluorescence assay employing recombinant target antigen. J. Neurol. Sci. 2010;291:52–56.
    1. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–2120.
    1. Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods. 2015;12:357–360.
    1. Li H, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–2079.
    1. Wang L, Wang S, Li W. RSeQC: quality control of RNA-seq experiments. Bioinformatics. 2012;28:2184–2185.
    1. Kauffmann A, Gentleman R, Huber W. arrayQualityMetrics–a bioconductor package for quality assessment of microarray data. Bioinformatics. 2009;25:415–416.
    1. Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30:923–930.
    1. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.
    1. Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl Acad. Sci. USA. 1998;95:14863–14868.

Source: PubMed

3
Abonnieren