GM-CSF and CXCR4 define a T helper cell signature in multiple sclerosis.
Journal
Nature medicine
ISSN: 1546-170X
Titre abrégé: Nat Med
Pays: United States
ID NLM: 9502015
Informations de publication
Date de publication:
08 2019
08 2019
Historique:
received:
31
01
2019
accepted:
11
06
2019
pubmed:
25
7
2019
medline:
7
11
2019
entrez:
24
7
2019
Statut:
ppublish
Résumé
Cytokine dysregulation is a central driver of chronic inflammatory diseases such as multiple sclerosis (MS). Here, we sought to determine the characteristic cellular and cytokine polarization profile in patients with relapsing-remitting multiple sclerosis (RRMS) by high-dimensional single-cell mass cytometry (CyTOF). Using a combination of neural network-based representation learning algorithms, we identified an expanded T helper cell subset in patients with MS, characterized by the expression of granulocyte-macrophage colony-stimulating factor and the C-X-C chemokine receptor type 4. This cellular signature, which includes expression of very late antigen 4 in peripheral blood, was also enriched in the central nervous system of patients with relapsing-remitting multiple sclerosis. In independent validation cohorts, we confirmed that this cell population is increased in patients with MS compared with other inflammatory and non-inflammatory conditions. Lastly, we also found the population to be reduced under effective disease-modifying therapy, suggesting that the identified T cell profile represents a specific therapeutic target in MS.
Identifiants
pubmed: 31332391
doi: 10.1038/s41591-019-0521-4
pii: 10.1038/s41591-019-0521-4
pmc: PMC6689469
mid: EMS83356
doi:
Substances chimiques
CXCR4 protein, human
0
Cytokines
0
Receptors, CXCR4
0
Granulocyte-Macrophage Colony-Stimulating Factor
83869-56-1
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1290-1300Subventions
Organisme : Swiss National Science Foundation
ID : 150768
Pays : Switzerland
Commentaires et corrections
Type : CommentIn
Références
Dendrou, C. A., Fugger, L. & Friese, M. A. Immunopathology of multiple sclerosis. Nat. Rev. Immunol. 15, 545–558 (2015).
doi: 10.1038/nri3871
Krumbholz, M., Derfuss, T., Hohlfeld, R. & Meinl, E. B cells and antibodies in multiple sclerosis pathogenesis and therapy. Nat. Rev. Neurol. 8, 613–623 (2012).
doi: 10.1038/nrneurol.2012.203
Chanvillard, C., Jacolik, R. F., Infante-Duarte, C. & Nayak, R. C. The role of natural killer cells in multiple sclerosis and their therapeutic implications. Front Immunol. 4, 63 (2013).
doi: 10.3389/fimmu.2013.00063
Mishra, M. K. & Yong, V. W. Myeloid cells—targets of medication in multiple sclerosis. Nat. Rev. Neurol. 12, 539–551 (2016).
doi: 10.1038/nrneurol.2016.110
Kleinewietfeld, M. & Hafler, D. A. Regulatory T cells in autoimmune neuroinflammation. Immunol. Rev. 259, 231–244 (2014).
doi: 10.1111/imr.12169
Panitch, H. S., Hirsch, R. L., Haley, A. S. & Johnson, K. P. Exacerbations of multiple sclerosis in patients treated with gamma interferon. Lancet 1, 893–895 (1987).
doi: 10.1016/S0140-6736(87)92863-7
Olsson, T. et al. Autoreactive T lymphocytes in multiple sclerosis determined by antigen-induced secretion of interferon-gamma. J. Clin. Invest. 86, 981–985 (1990).
doi: 10.1172/JCI114800
Tzartos, J. S. et al. Interleukin-17 production in central nervous system-infiltrating T cells and glial cells is associated with active disease in multiple sclerosis. Am. J. Pathol. 172, 146–155 (2008).
doi: 10.2353/ajpath.2008.070690
Noster, R. et al. IL-17 and GM-CSF expression are antagonistically regulated by human T helper cells. Sci. Transl. Med. 6, 241ra80 (2014).
doi: 10.1126/scitranslmed.3008706
Hartmann, F. J. et al. Multiple sclerosis-associated IL2RA polymorphism controls GM-CSF production in human T
doi: 10.1038/ncomms6056
Ornatsky, O. et al. Highly multiparametric analysis by mass cytometry. J. Immunol. Methods 361, 1–20 (2010).
doi: 10.1016/j.jim.2010.07.002
Bendall, S. C., Nolan, G. P., Roederer, M. & Chattopadhyay, P. K. A deep profiler’s guide to cytometry. Trends Immunol. 33, 323–332 (2012).
doi: 10.1016/j.it.2012.02.010
Galli, E. et al. The end of omics? High dimensional single cell analysis in precision medicine. Eur. J. Immunol. 49, 212–220 (2019).
doi: 10.1002/eji.201847758
Qiu, P. et al. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat. Biotechnol. 29, 886–891 (2011).
doi: 10.1038/nbt.1991
Levine, J. H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).
doi: 10.1016/j.cell.2015.05.047
Van Gassen, S. et al. FlowSOM: using self-organizing maps for visualization and interpretation of cytometry data. Cytom. A 87, 636–645 (2015).
doi: 10.1002/cyto.a.22625
Bruggner, R. V., Bodenmiller, B., Dill, D. L., Tibshirani, R. J. & Nolan, G. P. Automated identification of stratifying signatures in cellular subpopulations. Proc. Natl Acad. Sci. USA 111, E2770–E2777 (2014).
doi: 10.1073/pnas.1408792111
Arvaniti, E. & Claassen, M. Sensitive detection of rare disease-associated cell subsets via representation learning. Nat. Commun. 8, 14825 (2017).
doi: 10.1038/ncomms14825
Hartmann, F. J. et al. High-dimensional single-cell analysis reveals the immune signature of narcolepsy. J. Exp. Med. 213, 2621–2633 (2016).
doi: 10.1084/jem.20160897
Rao, D. A. et al. Pathologically expanded peripheral T helper cell subset drives B cells in rheumatoid arthritis. Nature 542, 110–114 (2017).
doi: 10.1038/nature20810
Weber, L. M. & Robinson, M. D. Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data. Cytom. A 89, 1084–1096 (2016).
doi: 10.1002/cyto.a.23030
Pietschmann, P. et al. The effect of age and gender on cytokine production by human peripheral blood mononuclear cells and markers of bone metabolism. Exp. Gerontol. 38, 1119–1127 (2003).
doi: 10.1016/S0531-5565(03)00189-X
Andreakos, E. T., Foxwell, B. M., Brennan, F. M., Maini, R. N. & Feldmann, M. Cytokines and anti-cytokine biologicals in autoimmunity: present and future. Cytokine Growth Factor Rev. 13, 299–313 (2002).
doi: 10.1016/S1359-6101(02)00018-7
Rasouli, J. et al. Expression of GM-CSF in T cells is increased in multiple sclerosis and suppressed by IFN-β therapy. J. Immunol. 194, 5085–5093 (2015).
doi: 10.4049/jimmunol.1403243
Herndler-Brandstetter, D. & Flavell, R. A. Producing GM-CSF: a unique T helper subset? Cell Res. 24, 1379–1380 (2014).
doi: 10.1038/cr.2014.155
Cheng, Y., Wong, M. T., van der Maaten, L. & Newell, E. W. Categorical analysis of human T cell heterogeneity with one-dimensional soli-expression by nonlinear stochastic embedding. J. Immunol. 196, 924–932 (2016).
doi: 10.4049/jimmunol.1501928
O’Gorman, W. E. et al. Single-cell systems-level analysis of human Toll-like receptor activation defines a chemokine signature in patients with systemic lupus erythematosus. J. Allergy Clin. Immunol. 136, 1326–1336 (2015).
doi: 10.1016/j.jaci.2015.04.008
Hauser, S. L. et al. Ocrelizumab versus interferon beta-1a in relapsing multiple sclerosis. N. Engl. J. Med. 376, 221–234 (2017).
doi: 10.1056/NEJMoa1601277
Hauser, S. L. et al. B-cell depletion with rituximab in relapsing–remitting multiple sclerosis. N. Engl. J. Med. 358, 676–688 (2008).
doi: 10.1056/NEJMoa0706383
Rice, G. P., Hartung, H. P. & Calabresi, P. A. Anti-α4 integrin therapy for multiple sclerosis: mechanisms and rationale. Neurology 64, 1336–1342 (2005).
doi: 10.1212/01.WNL.0000158329.30470.D0
Gold, R. et al. Placebo-controlled phase 3 study of oral BG-12 for relapsing multiple sclerosis. N. Engl. J. Med. 367, 1098–1107 (2012).
doi: 10.1056/NEJMoa1114287
Fox, R. J. et al. Placebo-controlled phase 3 study of oral BG-12 or glatiramer in multiple sclerosis. N. Engl. J. Med. 367, 1087–1097 (2012).
doi: 10.1056/NEJMoa1206328
Spencer, C. M., Crabtree-Hartman, E. C., Lehmann-Horn, K., Cree, B. A. & Zamvil, S. S. Reduction of CD8
doi: 10.1212/NXI.0000000000000076
Gross, C. C. et al. Dimethyl fumarate treatment alters circulating T helper cell subsets in multiple sclerosis. Neurol. Neuroimmunol. Neuroinflamm. 3, e183 (2016).
doi: 10.1212/NXI.0000000000000183
Wu, Q. et al. Dimethyl fumarate selectively reduces memory T cells and shifts the balance between T
doi: 10.4049/jimmunol.1601532
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).
doi: 10.4049/jimmunol.1601649
Diebold, M. et al. Dimethyl fumarate influences innate and adaptive immunity in multiple sclerosis. J. Autoimmun. 86, 39–50 (2018).
doi: 10.1016/j.jaut.2017.09.009
McCandless, E. E. et al. Pathological expression of CXCL12 at the blood–brain barrier correlates with severity of multiple sclerosis. Am. J. Pathol. 172, 799–808 (2008).
doi: 10.2353/ajpath.2008.070918
Holman, D. W., Klein, R. S. & Ransohoff, R. M. The blood–brain barrier, chemokines and multiple sclerosis. Biochim. Biophys. Acta 1812, 220–230 (2011).
doi: 10.1016/j.bbadis.2010.07.019
Kowarik, M. C. et al. Differential effects of fingolimod (FTY720) on immune cells in the CSF and blood of patients with MS. Neurology 76, 1214–1221 (2011).
doi: 10.1212/WNL.0b013e3182143564
Kowarik, M. C. et al. Immune cell subtyping in the cerebrospinal fluid of patients with neurological diseases. J. Neurol. 261, 130–143 (2014).
doi: 10.1007/s00415-013-7145-2
Spitzer, M. H. et al. Immunology. An interactive reference framework for modeling a dynamic immune system. Science 349, 1259425 (2015).
doi: 10.1126/science.1259425
Croxford, A. L. et al. The cytokine GM-CSF drives the inflammatory signature of CCR2
doi: 10.1016/j.immuni.2015.08.010
Croxford, A. L., Spath, S. & Becher, B. GM-CSF in neuroinflammation: licensing myeloid cells for tissue damage. Trends Immunol. 36, 651–662 (2015).
doi: 10.1016/j.it.2015.08.004
Komuczki, J. et al. Fate-mapping of GM-CSF expression identifies a discrete subset of inflammation-driving T helper cells regulated by cytokines IL-23 and IL-1β. Immunity 50, 1289–1304.e6 (2019).
doi: 10.1016/j.immuni.2019.04.006
Spath, S. et al. Dysregulation of the cytokine GM-CSF induces spontaneous phagocyte invasion and immunopathology in the central nervous system. Immunity 46, 245–260 (2017).
doi: 10.1016/j.immuni.2017.01.007
Codarri, L. et al. RORγt drives production of the cytokine GM-CSF in helper T cells, which is essential for the effector phase of autoimmune neuroinflammation. Nat. Immunol. 12, 560–567 (2011).
doi: 10.1038/ni.2027
Imitola, J. et al. Elevated expression of granulocyte–macrophage colony-stimulating factor receptor in multiple sclerosis lesions. J. Neuroimmunol. 317, 45–54 (2018).
doi: 10.1016/j.jneuroim.2017.12.017
Sheng, W. et al. STAT5 programs a distinct subset of GM-CSF-producing T helper cells that is essential for autoimmune neuroinflammation. Cell Res. 24, 1387–1402 (2014).
doi: 10.1038/cr.2014.154
Constantinescu, C. S. et al. Randomized phase 1b trial of MOR103, a human antibody to GM-CSF, in multiple sclerosis. Neurol. Neuroimmunol. Neuroinflamm. 2, e117 (2015).
doi: 10.1212/NXI.0000000000000117
Barr, T. A. et al. B cell depletion therapy ameliorates autoimmune disease through ablation of IL-6-producing B cells. J. Exp. Med. 209, 1001–1010 (2012).
doi: 10.1084/jem.20111675
Duddy, M. et al. Distinct effector cytokine profiles of memory and naive human B cell subsets and implication in multiple sclerosis. J. Immunol. 178, 6092–6099 (2007).
doi: 10.4049/jimmunol.178.10.6092
Jelcic, I. et al. Memory B cells activate brain-homing, autoreactive CD4
doi: 10.1016/j.cell.2018.08.011
Krumbholz, M. et al. Chemokines in multiple sclerosis: CXCL12 and CXCL13 up-regulation is differentially linked to CNS immune cell recruitment. Brain 129, 200–211 (2006).
doi: 10.1093/brain/awh680
Giunti, D. et al. Phenotypic and functional analysis of T cells homing into the CSF of subjects with inflammatory diseases of the CNS. J. Leukoc. Biol. 73, 584–590 (2003).
doi: 10.1189/jlb.1202598
Calderon, T. M. et al. A role for CXCL12 (SDF-1α) in the pathogenesis of multiple sclerosis: regulation of CXCL12 expression in astrocytes by soluble myelin basic protein. J. Neuroimmunol. 177, 27–39 (2006).
doi: 10.1016/j.jneuroim.2006.05.003
Restorick, S. M. et al. CCR6
doi: 10.1016/j.bbi.2017.03.008
Brucklacher-Waldert, V., Stuerner, K., Kolster, M., Wolthausen, J. & Tolosa, E. Phenotypical and functional characterization of T helper 17 cells in multiple sclerosis. Brain 132, 3329–3341 (2009).
doi: 10.1093/brain/awp289
Kornberg, M. D. et al. Dimethyl fumarate targets GAPDH and aerobic glycolysis to modulate immunity. Science 360, 449–453 (2018).
doi: 10.1126/science.aan4665
Polman, C. H. et al. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann. Neurol. 69, 292–302 (2011).
doi: 10.1002/ana.22366
Teunissen, C. et al. Consensus definitions and application guidelines for control groups in cerebrospinal fluid biomarker studies in multiple sclerosis. Mult. Scler. 19, 1802–1809 (2013).
doi: 10.1177/1352458513488232
Thompson, A. J. et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 17, 162–173 (2018).
doi: 10.1016/S1474-4422(17)30470-2
Mei, H. E., Leipold, M. D., Schulz, A. R., Chester, C. & Maecker, H. T. Barcoding of live human peripheral blood mononuclear cells for multiplexed mass cytometry. J. Immunol. 194, 2022–2031 (2015).
doi: 10.4049/jimmunol.1402661
Zunder, E. R. et al. Palladium-based mass tag cell barcoding with a doublet-filtering scheme and single-cell deconvolution algorithm. Nat. Protoc. 10, 316–333 (2015).
doi: 10.1038/nprot.2015.020
Finck, R. et al. Normalization of mass cytometry data with bead standards. Cytom. A 83, 483–494 (2013).
doi: 10.1002/cyto.a.22271
R Development Core Team R: A language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2010).
Spitzer, M. H. et al. Systemic immunity is required for effective cancer immunotherapy. Cell 168, 487–502.e15 (2017).
doi: 10.1016/j.cell.2016.12.022
Poznansky, M. C. et al. Active movement of T cells away from a chemokine. Nat. Med. 6, 543–548 (2000).
doi: 10.1038/75022
Noble, W. S. How does multiple testing correction work? Nat. Biotechnol. 27, 1135–1137 (2009).
doi: 10.1038/nbt1209-1135
McDonald, J. Handbook of Biological Statistics 3rd edn (Sparky House Publishing, 2014).
Youden, W. J. Index for rating diagnostic tests. Cancer 3, 32–35 (1950).
doi: 10.1002/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3