Twin study reveals non-heritable immune perturbations in multiple sclerosis.
Journal
Nature
ISSN: 1476-4687
Titre abrégé: Nature
Pays: England
ID NLM: 0410462
Informations de publication
Date de publication:
03 2022
03 2022
Historique:
received:
18
03
2021
accepted:
04
01
2022
pubmed:
18
2
2022
medline:
21
4
2022
entrez:
17
2
2022
Statut:
ppublish
Résumé
Multiple sclerosis (MS) is a chronic inflammatory disorder of the central nervous system underpinned by partially understood genetic risk factors and environmental triggers and their undefined interactions
Identifiants
pubmed: 35173329
doi: 10.1038/s41586-022-04419-4
pii: 10.1038/s41586-022-04419-4
pmc: PMC8891021
doi:
Substances chimiques
IL2 protein, human
0
Interleukin-2
0
OX40 Ligand
0
TNFSF4 protein, human
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Twin Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
152-158Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2022. The Author(s).
Références
Wallin, M. T. et al. Global, regional, and national burden of multiple sclerosis 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 18, 269–285 (2019).
doi: 10.1016/S1474-4422(18)30443-5
O’Gorman, C., Lin, R., Stankovich, J. & Broadley, S. A. Modelling genetic susceptibility to multiple sclerosis with family data. Neuroepidemiology 40, 1–12 (2012).
pubmed: 23075677
doi: 10.1159/000341902
Sawcer, S. et al. Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature 476, 214–219 (2011).
pubmed: 21833088
pmcid: 3182531
doi: 10.1038/nature10251
Simpson, S., Blizzard, L., Otahal, P., Van Der Mei, I. & Taylor, B. Latitude is significantly associated with the prevalence of multiple sclerosis: a meta-analysis. J. Neurol. Neurosurg. Psychiatry 82, 1132–1141 (2011).
pubmed: 21478203
doi: 10.1136/jnnp.2011.240432
Nielsen, N. M. et al. Neonatal vitamin D status and risk of multiple sclerosis: a population-based case–control study. Neurology 88, 44–51 (2017).
pubmed: 27903815
pmcid: 5200855
doi: 10.1212/WNL.0000000000003454
Gardener, H. et al. Prenatal and perinatal factors and risk of multiple sclerosis. Epidemiology 20, 611–618 (2009).
pubmed: 19333127
pmcid: 3132937
doi: 10.1097/EDE.0b013e31819ed4b9
Compston, A. & Coles, A. Multiple sclerosis. Lancet 372, 1502–1517 (2008).
pubmed: 18970977
doi: 10.1016/S0140-6736(08)61620-7
Westerlind, H. et al. Modest familial risks for multiple sclerosis: a registry-based study of the population of Sweden. Brain 137, 770–778 (2014).
pubmed: 24441172
pmcid: 3927700
doi: 10.1093/brain/awt356
Patsopoulos, N. A. et al. Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility. Science 365, eaav7188 (2019).
doi: 10.1126/science.aav7188
International Multiple Sclerosis Genetics Consortium, Low-frequency and rare-coding variation contributes to multiple sclerosis risk. Cell 175, 1679–1687.e7 (2018).
doi: 10.1016/j.cell.2018.09.049
Hartmann, F. J. et al. Multiple sclerosis-associated IL2RA polymorphism controls GM-CSF production in human T
pubmed: 25278028
doi: 10.1038/ncomms6056
Smets, I. et al. Multiple sclerosis risk variants alter expression of co-stimulatory genes in B cells. Brain 141, 786–796 (2018).
pubmed: 29361022
pmcid: 5837558
doi: 10.1093/brain/awx372
Jelcic, I. et al. Memory B cells activate brain-homing, autoreactive CD4
pubmed: 30173916
pmcid: 6191934
doi: 10.1016/j.cell.2018.08.011
Galli, E. et al. GM-CSF and CXCR4 define a T helper cell signature in multiple sclerosis. Nat. Med. 25, 1290–1300 (2019).
pubmed: 31332391
pmcid: 6689469
doi: 10.1038/s41591-019-0521-4
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).
pubmed: 18156204
pmcid: 2189615
doi: 10.2353/ajpath.2008.070690
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).
pubmed: 17475834
doi: 10.4049/jimmunol.178.10.6092
Roederer, M. et al. The genetic architecture of the human immune system: a bioresource for autoimmunity and disease pathogenesis. Cell 161, 387–403 (2015).
pubmed: 25772697
pmcid: 4393780
doi: 10.1016/j.cell.2015.02.046
Brodin, P. et al. Variation in the human immune system is largely driven by non-heritable influences. Cell 160, 37–47 (2015).
pubmed: 25594173
pmcid: 4302727
doi: 10.1016/j.cell.2014.12.020
Vogel, D. Y. S. et al. Macrophages in inflammatory multiple sclerosis lesions have an intermediate activation status. J. Neuroinflammation 10, 35 (2013).
pubmed: 23452918
pmcid: 3610294
doi: 10.1186/1742-2094-10-35
Frischer, J. M. et al. The relation between inflammation and neurodegeneration in multiple sclerosis brains. Brain 132, 1175–1189 (2009).
pubmed: 19339255
pmcid: 2677799
doi: 10.1093/brain/awp070
Mildner, A. et al. CCR2
pubmed: 19531531
doi: 10.1093/brain/awp144
King, I. L., Dickendesher, T. L. & Segal, B. M. Circulating Ly-6C
pubmed: 19196868
pmcid: 2665891
doi: 10.1182/blood-2008-07-168575
Croxford, A. L. et al. The cytokine GM-CSF drives the inflammatory signature of CCR2
pubmed: 26341401
doi: 10.1016/j.immuni.2015.08.010
Dutertre, C. A. et al. Single-cell analysis of human mononuclear phagocytes reveals subset-defining markers and identifies circulating inflammatory dendritic cells. Immunity 51, 573–589.e8 (2019).
pubmed: 31474513
doi: 10.1016/j.immuni.2019.08.008
Becht, E. et al. InfinityFlow: high-throughput single-cell quantification of 100s of proteins using conventional flow cytometry and machine learning. Preprint at https://doi.org/10.1101/2020.06.17.152926 (2020).
Marrie, R. A. & Rudick, R. A. Drug insight: interferon treatment in multiple sclerosis. Nat. Clin. Pract. Neurol. 2, 34–44 (2006).
pubmed: 16932519
doi: 10.1038/ncpneuro0088
The International Multiple Sclerosis Genetics Consortium. Risk alleles for multiple sclerosis identified by a genomewide study. N. Engl. J. Med. 357, 851–862 (2007).
doi: 10.1056/NEJMoa073493
Ahsan, M. K. et al. Loss of interleukin-2-dependency in HTLV-I-infected T cells on gene silencing of thioredoxin-binding protein-2. Oncogene 25, 2181–2191 (2006).
pubmed: 16314839
doi: 10.1038/sj.onc.1209256
Boyman, O. & Sprent, J. The role of interleukin-2 during homeostasis and activation of the immune system. Nat. Rev. Immunol. 12, 180–190 (2012).
pubmed: 22343569
doi: 10.1038/nri3156
Lee, P. W., Xin, M. K., Pei, W., Yang, Y. & Lovett-Racke, A. E. IL-3 is a marker of encephalitogenic T cells, but not essential for CNS autoimmunity. Front. Immunol. 9, 1255 (2018).
pubmed: 29915594
pmcid: 5994593
doi: 10.3389/fimmu.2018.01255
Cao, Y. et al. Functional inflammatory profiles distinguish myelin-reactive T cells from patients with multiple sclerosis. Sci. Transl. Med. 7, 287ra74 (2015).
pubmed: 25972006
pmcid: 4497538
doi: 10.1126/scitranslmed.aaa8038
Ruan, Q. et al. The Th17 immune response is controlled by the Rel–RORγ–RORγT transcriptional axis. J. Exp. Med. 208, 2321–2333 (2011).
pubmed: 22006976
pmcid: 3201209
doi: 10.1084/jem.20110462
Hussman, J. P. et al. GWAS analysis implicates NF-κB-mediated induction of inflammatory T cells in multiple sclerosis. Genes Immun. 208, 2321–2333 (2016).
Chitnis, T. et al. Effect of targeted disruption of STAT4 and STAT6 on the induction of experimental autoimmune encephalomyelitis. J. Clin. Invest. 108, 739–747 (2001).
pubmed: 11544280
pmcid: 209380
doi: 10.1172/JCI200112563
Schneider-Hohendorf, T. et al. VLA-4 blockade promotes differential routes into human CNS involving PSGL-1 rolling of T cells and MCAM-adhesion of TH17 cells. J. Exp. Med. 211, 1833–1846 (2014).
pubmed: 25135296
pmcid: 4144733
doi: 10.1084/jem.20140540
Rudick, R. A. et al. Natalizumab plus Interferon beta-1a for relapsing multiple sclerosis. N. Engl. J. Med. 354, 911–923 (2006).
pubmed: 16510745
doi: 10.1056/NEJMoa044396
de Craen, A. J. M. et al. Heritability estimates of innate immunity: an extended twin study. Genes Immun. 6, 167–170 (2005).
pubmed: 15674372
doi: 10.1038/sj.gene.6364162
Gerdes, L. A. et al. Immune signatures of prodromal multiple sclerosis in monozygotic twins. Proc. Natl Acad. Sci. USA 117, 21546–21556 (2020).
pubmed: 32817525
pmcid: 7474627
doi: 10.1073/pnas.2003339117
Chuluundorj, D., Harding, S. A., Abernethy, D. & La Flamme, A. C. Expansion and preferential activation of the CD14
pubmed: 24638064
doi: 10.1038/icb.2014.15
Gjelstrup, M. C. et al. Subsets of activated monocytes and markers of inflammation in incipient and progressed multiple sclerosis. Immunol. Cell Biol. 96, 160–174 (2018).
pubmed: 29363161
doi: 10.1111/imcb.1025
Waschbisch, A. et al. Pivotal role for CD16
pubmed: 26746191
doi: 10.4049/jimmunol.1501960
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).
pubmed: 28228281
doi: 10.1016/j.immuni.2017.01.007
Weber, F. et al. IL2RA and IL7RA genes confer susceptibility for multiple sclerosis in two independent European populations. Genes Immun. 9, 259–263 (2008).
pubmed: 18354419
doi: 10.1038/gene.2008.14
Dendrou, C. A. et al. Cell-specific protein phenotypes for the autoimmune locus IL2RA using a genotype-selectable human bioresource. Nat. Genet. 41, 1011–1015 (2009).
pubmed: 19701192
pmcid: 2749506
doi: 10.1038/ng.434
Codarri, L. et al. RORγ3T 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).
pubmed: 21516112
doi: 10.1038/ni.2027
Noster, R. et al. IL-17 and GM-CSF expression are antagonistically regulated by human T helper cells. Sci. Transl. Med. 6, 241ra80 (2014).
pubmed: 24944195
doi: 10.1126/scitranslmed.3008706
Pekalski, M. L. et al. Postthymic expansion in human CD4 naive T cells defined by expression of functional high-affinity IL-2 receptors. J. Immunol. 190, 2554–2566 (2013).
pubmed: 23418630
doi: 10.4049/jimmunol.1202914
Thompson, A. J. et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 17, 162–173 (2018).
pubmed: 29275977
doi: 10.1016/S1474-4422(17)30470-2
Polman, C. H. et al. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann. Neurol. 69, 292–302 (2011).
pubmed: 21387374
pmcid: 3084507
doi: 10.1002/ana.22366
Bushnik, T. in Encyclopedia of Clinical Neuropsychology (eds Kreutzer, J. S., DeLuca, J. & Caplan, B.) https://doi.org/10.1007/978-3-319-57111-9_1805 (Springer, 2018).
Hartmann, F. J. et al. High-dimensional single-cell analysis reveals the immune signature of narcolepsy. J. Exp. Med. 213, 2621–2633 (2016).
pubmed: 27821550
pmcid: 5110028
doi: 10.1084/jem.20160897
Finck, R. et al. Normalization of mass cytometry data with bead standards. Cytom. Part A 83, 483–494 (2013).
doi: 10.1002/cyto.a.22271
McInnes, L., Healy, J., Saul, N. & Großberger, L. UMAP: uniform manifold approximation and projection. J. Open Source Softw. 3, 861 (2018).
doi: 10.21105/joss.00861
Van Gassen, S. et al. FlowSOM: using self-organizing maps for visualization and interpretation of cytometry data. Cytom. Part A 87, 636–645 (2015).
doi: 10.1002/cyto.a.22625
Ingelfinger, F. et al. Single-cell profiling of myasthenia gravis identifies a pathogenic T cell signature. Acta Neuropathol. 141, 901–915 (2021).
pubmed: 33774709
pmcid: 8113175
doi: 10.1007/s00401-021-02299-y
Weber, L. M., Nowicka, M., Soneson, C. & Robinson, M. D. diffcyt: Differential discovery in high-dimensional cytometry via high-resolution clustering. Commun. Biol. 2, 183 (2019).
pubmed: 31098416
pmcid: 6517415
doi: 10.1038/s42003-019-0415-5
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289–300 (1995).
Spitzer, M. H. et al. An interactive reference framework for modeling a dynamic immune system. Science 349, 1259425 (2015).
pubmed: 26160952
pmcid: 4537647
doi: 10.1126/science.1259425
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).
pubmed: 34062119
pmcid: 8238499
doi: 10.1016/j.cell.2021.04.048
Garcia-Alonso, L., Holland, C. H., Ibrahim, M. M., Turei, D. & Saez-Rodriguez, J. Benchmark and integration of resources for the estimation of human transcription factor activities. Genome Res. 29, 1363–1375 (2019).
pubmed: 31340985
pmcid: 6673718
doi: 10.1101/gr.240663.118
Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).
pubmed: 24658644
pmcid: 4122333
doi: 10.1038/nbt.2859
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
pubmed: 29409532
pmcid: 5802054
doi: 10.1186/s13059-017-1382-0
Bates, T. C., Maes, H. & Neale, M. C. Umx: twin and path-based structural equation modeling in R. Twin Res. Hum. Genet. 22, 27–41 (2019).
pubmed: 30944056
doi: 10.1017/thg.2019.2
Verweij, K. J. H., Mosing, M. A., Zietsch, B. P. & Medland, S. E. Estimating heritability from twin studies. Methods Mol. Biol. 850, 151–170 (2012).
pubmed: 22307698
doi: 10.1007/978-1-61779-555-8_9