Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
Journal of Neuroinflammation
Published in: Journal of Neuroinflammation 1/2023

Open Access 01-12-2023 | Multiple Sclerosis | Research

Prominent epigenetic and transcriptomic changes in CD4+ and CD8+ T cells during and after pregnancy in women with multiple sclerosis and controls

Authors: Alberto Zenere, Sandra Hellberg, Georgia Papapavlou Lingehed, Maria Svenvik, Johan Mellergård, Charlotte Dahle, Magnus Vrethem, Johanna Raffetseder, Mohsen Khademi, Tomas Olsson, Marie Blomberg, Maria C. Jenmalm, Claudio Altafini, Mika Gustafsson, Jan Ernerudh

Published in: Journal of Neuroinflammation | Issue 1/2023

Login to get access

Abstract

Background

Multiple sclerosis (MS) is a neuroinflammatory disease in which pregnancy leads to a temporary amelioration in disease activity as indicated by the profound decrease in relapses rate during the 3rd trimester of pregnancy. CD4+ and CD8+ T cells are implicated in MS pathogenesis as being key regulators of inflammation and brain lesion formation. Although Tcells are prime candidates for the pregnancy-associated improvement of MS, the precise mechanisms are yet unclear, and in particular, a deep characterization of the epigenetic and transcriptomic events that occur in peripheral T cells during pregnancy in MS is lacking.

Methods

Women with MS and healthy controls were longitudinally sampled before, during (1st, 2nd and 3rd trimesters) and after pregnancy. DNA methylation array and RNA sequencing were performed on paired CD4+ and CD8+ T cells samples. Differential analysis and network-based approaches were used to analyze the global dynamics of epigenetic and transcriptomic changes.

Results

Both DNA methylation and RNA sequencing revealed a prominent regulation, mostly peaking in the 3rd trimester and reversing post-partum, thus mirroring the clinical course with improvement followed by a worsening in disease activity. This rebound pattern was found to represent a general adaptation of the maternal immune system, with only minor differences between MS and controls. By using a network-based approach, we highlighted several genes at the core of this pregnancy-induced regulation, which were found to be enriched for genes and pathways previously reported to be involved in MS. Moreover, these pathways were enriched for in vitro stimulated genes and pregnancy hormones targets.

Conclusion

This study represents, to our knowledge, the first in-depth investigation of the methylation and expression changes in peripheral CD4+ and CD8+ T cells during pregnancy in MS. Our findings indicate that pregnancy induces profound changes in peripheral T cells, in both MS and healthy controls, which are associated with the modulation of inflammation and MS activity.
Appendix
Available only for authorised users
Literature
1.
Dendrou CA, Fugger L, Friese MA. Immunopathology of multiple sclerosis. Nat Rev Immunol. 2015;15:545–58.PubMedCrossRef
2.
Confavreux C, Hutchinson M, Hours MM, Cortinovis-Tourniaire P, Moreau T. Rate of pregnancy-related relapse in multiple sclerosis. N Engl J Med. 1998;339:285–91.PubMedCrossRef
3.
Finkelsztejn A, Brooks J, Paschoal F Jr, Fragoso Y. What can we really tell women with multiple sclerosis regarding pregnancy? A systematic review and meta-analysis of the literature. BJOG Int J Obstet Gynaecol. 2011;118:790–7.CrossRef
4.
Mor G, Aldo P, Alvero AB. The unique immunological and microbial aspects of pregnancy. Nat Rev Immunol. 2017;17:469–82.PubMedCrossRef
5.
6.
Ysrraelit MC, Correale J. Impact of sex hormones on immune function and multiple sclerosis development. Immunology. 2019;156:9–22.PubMedCrossRef
7.
Shah NM, Lai PF, Imami N, Johnson MR. Progesterone-related immune modulation of pregnancy and labor. Front Endocrinol. 2019;10:198.CrossRef
8.
9.
Lissauer D, Kilby MD, Moss P. Maternal effector T cells within decidua: the adaptive immune response to pregnancy? Placenta. 2017;60:140–4.PubMedCrossRef
10.
Attfield KE, Jensen LT, Kaufmann M, Friese MA, Fugger L. The immunology of multiple sclerosis. Nat Rev Immunol. 2022;22:734–50.
11.
Saligrama N, Zhao F, Sikora MJ, Serratelli WS, Fernandes RA, Louis DM, et al. Opposing T cell responses in experimental autoimmune encephalomyelitis. Nature. 2019;572:481–7.PubMedPubMedCentralCrossRef
12.
Badam TV, Hellberg S, Mehta RB, Lechner-Scott J, Lea RA, Tost J, et al. CD4+ T-cell DNA methylation changes during pregnancy significantly correlate with disease-associated methylation changes in autoimmune diseases. Epigenetics. 2022;17:1040–55.
13.
Ramien C, Yusko EC, Engler JB, Gamradt S, Patas K, Schweingruber N, et al. T cell repertoire dynamics during pregnancy in multiple sclerosis. Cell Rep. 2019;29:810-815.e4.PubMedCrossRef
14.
Spadaro M, Martire S, Marozio L, Mastromauro D, Montanari E, Perga S, et al. Immunomodulatory effect of pregnancy on leukocyte populations in patients with multiple sclerosis: a comparison of peripheral blood and decidual placental tissue. Front Immunol. 2019;10:1935.PubMedPubMedCentralCrossRef
15.
16.
Engler JB, Heckmann NF, Jäger J, Gold SM, Friese MA. Pregnancy enables expansion of disease-specific regulatory T cells in an animal model of multiple sclerosis. J Immunol. 2019;203:1743–52.PubMedCrossRef
17.
Langer-Gould A, Gupta R, Huang S, Hagan A, Atkuri K, Leimpeter AD, et al. Interferon-gamma-producing T cells, pregnancy, and postpartum relapses of multiple sclerosis. Arch Neurol. 2010;67:51–7.PubMedPubMedCentralCrossRef
18.
Neuteboom RF, Verbraak E, Wierenga-Wolf AF, van Meurs M, Steegers EA, de Groot CJ, et al. Pregnancy-induced fluctuations in functional T-cell subsets in multiple sclerosis patients. Mult Scler J. 2010;16:1073–8.CrossRef
19.
Gilli F, Lindberg RLP, Valentino P, Marnetto F, Malucchi S, Sala A, et al. Learning from nature: pregnancy changes the expression of inflammation-related genes in patients with multiple sclerosis. PLoS ONE. 2010;5: e8962.PubMedPubMedCentralCrossRef
20.
Airas L, Nikula T, Huang Y-H, Lahesmaa R, Wiendl H. Postpartum-activation of multiple sclerosis is associated with down-regulation of tolerogenic HLA-G. J Neuroimmunol. 2007;187:205–11.PubMedCrossRef
21.
22.
Piñero J, Saüch J, Sanz F, Furlong LI. The DisGeNET cytoscape app: exploring and visualizing disease genomics data. Comput Struct Biotechnol J. 2021;19:2960–7.PubMedPubMedCentralCrossRef
23.
International multiple sclerosis genetics consortium. Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility. Science. 2019;365:eaav7188.PubMedCentralCrossRef
24.
Ghiassian SD, Menche J, Barabási A-L. A DIseAse MOdule Detection (DIAMOnD) algorithm derived from a systematic analysis of connectivity patterns of disease proteins in the human interactome. PLOS Comput Biol. 2015;11: e1004120.PubMedPubMedCentralCrossRef
25.
de Weerd HA, Badam TVS, Martínez-Enguita D, Åkesson J, Muthas D, Gustafsson M, et al. MODifieR: an Ensemble R Package for inference of disease modules from transcriptomics networks. Bioinformatics. 2020;36:3918–9.PubMedCrossRef
26.
Hellberg S, Raffetseder J, Rundquist O, Magnusson R, Papapavlou G, Jenmalm MC, et al. Progesterone dampens immune responses in in vitro activated CD4+ T cells and affects genes associated with autoimmune diseases that improve during pregnancy. Front Immunol. 2021;12: 672168.PubMedPubMedCentralCrossRef
27.
Ghaemi MS, DiGiulio DB, Contrepois K, Callahan B, Ngo TTM, Lee-McMullen B, et al. Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy. Bioinformatics. 2019;35:95–103.PubMedCrossRef
28.
29.
30.
Mittal A, Pachter L, Nelson JL, Kjærgaard H, Smed MK, Gildengorin VL, et al. Pregnancy-induced changes in systemic gene expression among healthy women and women with rheumatoid arthritis. PLoS ONE. 2015;10: e0145204.PubMedPubMedCentralCrossRef
31.
Erlebacher A. Mechanisms of T cell tolerance towards the allogeneic fetus. Nat Rev Immunol. 2013;13:23–33.PubMedCrossRef
32.
33.
Chen D, Wang W, Wu L, Liang L, Wang S, Cheng Y, et al. Single-cell atlas of peripheral blood mononuclear cells from pregnant women. Clin Transl Med. 2022;12: e821.PubMedPubMedCentralCrossRef
34.
Hellberg S, Eklund D, Gawel DR, Köpsén M, Zhang H, Nestor CE, et al. Dynamic response genes in CD4+ T cells reveal a network of interactive proteins that classifies disease activity in multiple sclerosis. Cell Rep. 2016;16:2928–39.PubMedCrossRef
35.
Cappelletti C, Eriksson A, Brorson IS, Leikfoss IS, Kråbøl O, Høgestøl EA, et al. Quantitative proteomics reveals protein dysregulation during T cell activation in multiple sclerosis patients compared to healthy controls. Clin Proteomics. 2022;19:23.PubMedPubMedCentralCrossRef
36.
Rundquist O, Nestor CE, Jenmalm MC, Hellberg S, Gustafsson M. Progesterone inhibits the establishment of activation-associated chromatin during TH1 differentiation. Front Immunol. 2022;13: 835625.PubMedPubMedCentralCrossRef
37.
Papapavlou G, Hellberg S, Raffetseder J, Brynhildsen J, Gustafsson M, Jenmalm MC, et al. Differential effects of estradiol and progesterone on human T cell activation in vitro. Eur J Immunol. 2021;51:2430–40.PubMedCrossRef
38.
Hughes GC, Clark EA, Wong AH. The intracellular progesterone receptor regulates CD4+ T cells and T cell-dependent antibody responses. J Leukoc Biol. 2013;93:369–75.PubMedPubMedCentralCrossRef
39.
Chien EJ, Chang C-P, Lee W-F, Su T-H, Wu C-H. Non-genomic immunosuppressive actions of progesterone inhibits PHA-induced alkalinization and activation in T cells. J Cell Biochem. 2006;99:292–304.PubMedCrossRef
40.
Mohammad I, Starskaia I, Nagy T, Guo J, Yatkin E, Väänänen K, et al. Estrogen receptor α contributes to T cell-mediated autoimmune inflammation by promoting T cell activation and proliferation. Sci Signal. 2018;11:eaap9415.PubMedCrossRef
41.
Goodman WA, Bedoyan SM, Havran HL, Richardson B, Cameron MJ, Pizarro TT. Impaired estrogen signaling underlies regulatory T cell loss-of-function in the chronically inflamed intestine. Proc Natl Acad Sci. 2020;117:17166–76.PubMedPubMedCentralCrossRef
42.
Langer-Gould A. Sex hormones and multiple sclerosis: another informative failure. Lancet Neurol. 2016;15:22–3.PubMedCrossRef
43.
Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21.PubMedCrossRef
44.
Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods. 2017;14:417–9.PubMedPubMedCentralCrossRef
45.
Tian Y, Morris TJ, Webster AP, Yang Z, Beck S, Feber A, et al. ChAMP: updated methylation analysis pipeline for Illumina BeadChips. Bioinformatics. 2017;33:3982–4.PubMedPubMedCentralCrossRef
46.
Zhou W, Laird PW, Shen H. Comprehensive characterization, annotation and innovative use of Infinium DNA methylation BeadChip probes. Nucleic Acids Res. 2017;45: e22.PubMed
47.
Teschendorff AE, Marabita F, Lechner M, Bartlett T, Tegner J, Gomez-Cabrero D, et al. A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinforma Oxf Engl. 2013;29:189–96.CrossRef
48.
Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostat Oxf Engl. 2007;8:118–27.
49.
Zhang Y, Parmigiani G, Johnson WE. ComBat-seq: batch effect adjustment for RNA-seq count data. NAR Genomics Bioinforma. 2020;2:lqaa078.CrossRef
50.
Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.PubMedCrossRef
51.
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43: e47.PubMedPubMedCentralCrossRef
52.
Du P, Zhang X, Huang C-C, Jafari N, Kibbe WA, Hou L, et al. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics. 2010;11:587.PubMedPubMedCentralCrossRef
53.
Komori HK, Hart T, LaMere SA, Chew PV, Salomon DR. Defining CD4 T cell memory by the epigenetic landscape of CpG DNA methylation. J Immunol Baltim Md. 1950;2015(194):1565–79.
54.
Durek P, Nordström K, Gasparoni G, Salhab A, Kressler C, de Almeida M, et al. Epigenomic profiling of human CD4+ T cells supports a linear differentiation model and highlights molecular regulators of memory development. Immunity. 2016;45:1148–61.PubMedCrossRef
55.
56.
Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021;49:D605–12.PubMedCrossRef
57.
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.PubMedPubMedCentralCrossRef
58.
Yu G, Wang L-G, Han Y, He Q-Y. clusterProfiler: an R Package for comparing biological themes among gene clusters. OMICS J Integr Biol. 2012;16:284–7.CrossRef
59.
Yu G, Wang L-G, He Q-Y. ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics. 2015;31:2382–3.PubMedCrossRef
Metadata
Title
Prominent epigenetic and transcriptomic changes in CD4+ and CD8+ T cells during and after pregnancy in women with multiple sclerosis and controls
Authors
Alberto Zenere
Sandra Hellberg
Georgia Papapavlou Lingehed
Maria Svenvik
Johan Mellergård
Charlotte Dahle
Magnus Vrethem
Johanna Raffetseder
Mohsen Khademi
Tomas Olsson
Marie Blomberg
Maria C. Jenmalm
Claudio Altafini
Mika Gustafsson
Jan Ernerudh
Publication date
01-12-2023
Publisher
BioMed Central
Published in
Journal of Neuroinflammation / Issue 1/2023
Electronic ISSN: 1742-2094
DOI
https://doi.org/10.1186/s12974-023-02781-2

Other articles of this Issue 1/2023

Journal of Neuroinflammation 1/2023 Go to the issue

Review

Exercise suppresses neuroinflammation for alleviating Alzheimer’s disease

Research

A role for P-selectin and complement in the pathological sequelae of germinal matrix hemorrhage

Correction

Correction: Caffeine blocks disruption of blood brain barrier in a rabbit model of Alzheimer's disease

Research

TNEA therapy promotes the autophagic degradation of NLRP3 inflammasome in a transgenic mouse model of Alzheimer’s disease via TFEB/TFE3 activation

Research

NLRP3–GABA signaling pathway contributes to the pathogenesis of impulsive-like behaviors and cognitive deficits in aged mice

Research

Microglia-derived TNF-α contributes to RVLM neuronal mitochondrial dysfunction via blocking the AMPK–Sirt3 pathway in stress-induced hypertension