DNA methylation changes in glial cells of the normal-appearing white matter in Multiple Sclerosis patients

References

• Frischer JM, Weigand SD, Guo Y, et al. Clinical and pathological insights into the dynamic nature of the white matter multiple sclerosis plaque. Ann Neurol. 2015;78(5):710–721.
   Google Scholar
• Kornek B, Storch MK, Weissert R, et al. Multiple sclerosis and chronic autoimmune encephalomyelitis: a comparative quantitative study of axonal injury in active, inactive, and remyelinated lesions. Am J Pathol. 2000;157(1):267–276.
   Google Scholar
• Frischer JM, Bramow S, Dal-Bianco A, et al. The relation between inflammation and neurodegeneration in multiple sclerosis brains. Brain. 2009;132(5):1175–1189.
   Google Scholar
• Nikic I, Merkler D, Sorbara C, et al. A reversible form of axon damage in experimental autoimmune encephalomyelitis and multiple sclerosis. Nat Med. 2011;17(4):495–499.
   Google Scholar
• Bodini B, Poirion E, Tonietto M, et al. Individual mapping of innate immune cell activation is a candidate marker of patient-specific trajectories of worsening disability in Multiple Sclerosis. J Nucl Med. 2020;61(7):1043–1049.
   Google Scholar
• Yeung MSY, Djelloul M, Steiner E, et al. Dynamics of oligodendrocyte generation in multiple sclerosis. Nature. 2019;566(7745):538–542.
   Google Scholar
• Goldschmidt T, Antel J, Konig FB, et al. Remyelination capacity of the MS brain decreases with disease chronicity. Neurology. 2009;72(22):1914–1921.
   Google Scholar
• Ransohoff RM. How neuroinflammation contributes to neurodegeneration. Science. 2016;353(6301):777–783.
   Google Scholar
• Von Bartheld CS, Bahney J, Herculano-Houzel S. The search for true numbers of neurons and glial cells in the human brain: a review of 150 years of cell counting. J Comp Neurol. 2016;524(18):3865–3895.
   Google Scholar
• Nimmerjahn A, Kirchhoff F, Helmchen F. Resting microglial cells are highly dynamic surveillants of brain parenchyma in vivo. Science. 2005;308(5726):1314–1318.
   Google Scholar
• van der Poel M, Ulas T, Mizee MR, et al. Transcriptional profiling of human microglia reveals grey-white matter heterogeneity and multiple sclerosis-associated changes. Nat Commun. 2019;10(1):1139.
   Google Scholar
• Liddelow SA, Guttenplan KA, Clarke LE, et al. Neurotoxic reactive astrocytes are induced by activated microglia. Nature. 2017;541(7638):481–487.
   Google Scholar
• Schirmer L, Velmeshev D, Holmqvist S, et al. Neuronal vulnerability and multilineage diversity in multiple sclerosis. Nature. 2019;573(7772):75–82.
   Google Scholar
• Jakel S, Agirre E, Mendanha Falcão A, et al. Altered human oligodendrocyte heterogeneity in multiple sclerosis. Nature. 2019;566(7745):543–547.
   Google Scholar
• Masuda T, Sankowski R, Staszewski O, et al. Spatial and temporal heterogeneity of mouse and human microglia at single-cell resolution. Nature. 2019;566(7744):388–392.
   Google Scholar
• Kutzelnigg A, Lucchinetti CF, Stadelmann C, et al. Cortical demyelination and diffuse white matter injury in multiple sclerosis. Brain. 2005;128(11):2705–2712.
   Google Scholar
• de Groot M, Verhaaren BFJ, de Boer R, et al. Changes in normal-appearing white matter precede development of white matter lesions. Stroke. 2013;44(4):1037–1042.
   Google Scholar
• Gallego-Delgado P, James R, Browne E, et al. Neuroinflammation in the normal-appearing white matter (NAWM) of the multiple sclerosis brain causes abnormalities at the nodes of Ranvier. PLoS Biol. 2020;18(12):e3001008.
   Google Scholar
• Moll NM, Rietsch AM, Thomas S, et al. Multiple sclerosis normal-appearing white matter: pathology-imaging correlations. Ann Neurol. 2011;70(5):764–773.
   Google Scholar
• Barnett MH, Prineas JW. Relapsing and remitting multiple sclerosis: pathology of the newly forming lesion. Ann Neurol. 2004;55(4):458–468.
   Google Scholar
• Henderson AP, Barnett MH, Parratt JD, et al. Multiple sclerosis: distribution of inflammatory cells in newly forming lesions. Ann Neurol. 2009;66(6):739–753.
   Google Scholar
• van Horssen J, Singh S, van der Pol S, et al. Clusters of activated microglia in normal-appearing white matter show signs of innate immune activation. J Neuroinflammation. 2012;9(1):156.
   Google Scholar
• Burm SM, Peferoen LAN, Zuiderwijk-Sick EA, et al. Expression of IL-1beta in rhesus EAE and MS lesions is mainly induced in the CNS itself. J Neuroinflammation. 2016;13(1):138.
   Google Scholar
• Sharma R, Fischer M-T, Bauer J, et al. Inflammation induced by innate immunity in the central nervous system leads to primary astrocyte dysfunction followed by demyelination. Acta Neuropathol. 2010;120(2):223–236.
   Google Scholar
• Luchicchi A, Hart B, Frigerio I, et al. Axon-Myelin unit blistering as early event in MS normal appearing white matter. Ann Neurol. 2021;89(4):711–725.
   Google Scholar
• Werring DJ, Brassat, D, Droogan, AG, et al. The pathogenesis of lesions and normal-appearing white matter changes in multiple sclerosis: a serial diffusion MRI study. Brain. 2000;123(Pt 8):1667–1676.
   Google Scholar
• Elkjaer ML, Frisch T, Reynolds R, et al. Molecular signature of different lesion types in the brain white matter of patients with progressive multiple sclerosis. Acta Neuropathol Commun. 2019;7(1):205.
   Google Scholar
• Ouellette R, Mangeat G, Polyak I, et al. Validation of rapid magnetic resonance myelin imaging in Multiple Sclerosis. Ann Neurol. 2020;87(5):710–724.
   Google Scholar
• Kiljan S, Preziosa P, Jonkman LE, et al. Cortical axonal loss is associated with both gray matter demyelination and white matter tract pathology in progressive multiple sclerosis: evidence from a combined MRI-histopathology study. Mult Scler. 2021;27(3):380–390.
   Google Scholar
• Kular L, Jagodic M. Epigenetic insights into multiple sclerosis disease progression. J Intern Med. 2020;288(1):82–102.
   Google Scholar
• Chen ZX, Riggs AD. DNA methylation and demethylation in mammals. J Biol Chem. 2011;286(21):18347–18353.
   Google Scholar
• Neri F, Rapelli S, Krepelova A, et al. Intragenic DNA methylation prevents spurious transcription initiation. Nature. 2017;543(7643):72–77.
   Google Scholar
• Chomyk AM, Volsko C, Tripathi A, et al. DNA methylation in demyelinated multiple sclerosis hippocampus. Sci Rep. 2017;7(1):8696.
   Google Scholar
• Huynh JL, Garg P, Thin TH, et al. Epigenome-wide differences in pathology-free regions of multiple sclerosis-affected brains. Nat Neurosci. 2014;17(1):121–130.
   Google Scholar
• Kular L, Needhamsen M, Adzemovic MZ, et al. Neuronal methylome reveals CREB-associated neuro-axonal impairment in multiple sclerosis. Clin Epigenetics. 2019;11(1):86.
   Google Scholar
• Morris TJ, Butcher LM, Feber A, et al. ChAMP: 450k chip analysis methylation pipeline. Bioinformatics. 2014;30(3):428–430.
   Google Scholar
• Aryee MJ, Jaffe AE, Corrada-Bravo H, et al. Minfi: a flexible and comprehensive bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics. 2014;30(10):1363–1369.
   Google Scholar
• McCartney DL, Walker RM, Morris SW, et al. Identification of polymorphic and off-target probe binding sites on the Illumina Infinium MethylationEPIC BeadChip. Genom Data. 2016;9:22–24.
   Google Scholar
• Pidsley R, Zotenko E, Peters TJ, et al. Critical evaluation of the illumina methylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biol. 2016;17(1):208.
   Google Scholar
• Nordlund J, Bäcklin CL, Wahlberg P, et al. Genome-wide signatures of differential DNA methylation in pediatric acute lymphoblastic leukemia. Genome Biol. 2013;14(9):r105.
   Google Scholar
• Chen YA, Lemire M, Choufani S, et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics. 2013;8(2):203–209.
   Google Scholar
• Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8(1):118–127.
   Google Scholar
• Houseman EA, Molitor J, Marsit CJ. Reference-free cell mixture adjustments in analysis of DNA methylation data. Bioinformatics. 2014;30(10):1431–1439.
   Google Scholar
• Ritchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47.
   Google Scholar
• Du P, Zhang X, Huang -C-C, et al. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics. 2010;11(1):587.
   Google Scholar
• Peters TJ, Buckley MJ, Statham AL, et al. De novo identification of differentially methylated regions in the human genome. Epigenetics Chromatin. 2015;8(1):6.
   Google Scholar
• Ng B, White CC, Klein H-U, et al. An xQTL map integrates the genetic architecture of the human brain’s transcriptome and epigenome. Nat Neurosci. 2017;20(10):1418–1426.
   Google Scholar
• Do C, Lang C, Lin J, et al. Mechanisms and disease associations of haplotype-dependent allele-specific DNA methylation. Am J Hum Genet. 2016;98(5):934–955.
   Google Scholar
• Gatev E, Inkster AM, Negri GL, et al. Autosomal sex-associated co-methylated regions predict biological sex from DNA methylation. Nucleic Acids Res. 2021;49(16):9097–9116.
   Google Scholar
• Darmanis S, Sloan SA, Zhang Y, et al. A survey of human brain transcriptome diversity at the single cell level. Proc Natl Acad Sci U S A. 2015;112(23):7285–7290.
   Google Scholar
• Zhang Y, Sloan S, Clarke L, et al. Purification and characterization of progenitor and mature human astrocytes reveals transcriptional and functional differences with mouse. Neuron. 2016;89(1):37–53.
   Google Scholar
• Absinta M, Maric D, Gharagozloo M, et al. A lymphocyte-microglia-astrocyte axis in chronic active multiple sclerosis. Nature. 2021;597(7878):709–714.
   Google Scholar
• Zhang B, Kirov S, Snoddy J. WebGestalt: an integrated system for exploring gene sets in various biological contexts. Nucleic Acids Res. 2005;33:W741–8.
   Google Scholar
• Supek F, Bosnjak M, Skunca N, et al. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS One. 2011;6(7):e21800.
   Google Scholar
• Ewing E, Planell-Picola N, Jagodic M, et al. GeneSetCluster: a tool for summarizing and integrating gene-set analysis results. BMC Bioinformatics. 2020;21(1):443.
   Google Scholar
• Frohman EM, Racke MK, Raine CS. Multiple sclerosis–the plaque and its pathogenesis. N Engl J Med. 2006;354(9):942–955.
   Google Scholar
• Lucchinetti CF, Bruck W, Rodriguez M, et al. Distinct patterns of multiple sclerosis pathology indicates heterogeneity on pathogenesis. Brain Pathol. 1996;6(3):259–274.
   Google Scholar
• Davalos D, Grutzendler J, Yang G, et al. ATP mediates rapid microglial response to local brain injury in vivo. Nat Neurosci. 2005;8(6):752–758.
   Google Scholar
• Skripuletz T, Hackstette D, Bauer K, et al. Astrocytes regulate myelin clearance through recruitment of microglia during cuprizone-induced demyelination. Brain. 2013;136(1):147–167.
   Google Scholar
• Plemel JR, Manesh SB, Sparling JS, et al. Myelin inhibits oligodendroglial maturation and regulates oligodendrocytic transcription factor expression. Glia. 2013;61(9):1471–1487.
   Google Scholar
• Thomason EJ, Escalante M, Osterhout DJ, et al. The oligodendrocyte growth cone and its actin cytoskeleton: a fundamental element for progenitor cell migration and CNS myelination. Glia. 2020;68(7):1329–1346.
   Google Scholar
• Fancy SP, Baranzini, SE, Zhao, C, et al. Dysregulation of the Wnt pathway inhibits timely myelination and remyelination in the mammalian CNS. Genes Dev. 2009;23(13):1571–1585.
   Google Scholar
• Lee HK, Chaboub L, Zhu W, et al. Daam2-PIP5K is a regulatory pathway for Wnt signaling and therapeutic target for remyelination in the CNS. Neuron. 2015;85(6):1227–1243.
   Google Scholar
• Lund H, Pieber, M, Parsa, R, et al. Fatal demyelinating disease is induced by monocyte-derived macrophages in the absence of TGF-beta signaling. Nat Immunol. 2018;19:1–7.
   Google Scholar
• Niu J, Tsai -H-H, Hoi KK, et al. Aberrant oligodendroglial-vascular interactions disrupt the blood-brain barrier, triggering CNS inflammation. Nat Neurosci. 2019;22(5):709–718.
   Google Scholar
• Lengfeld JE, Lutz SE, Smith JR, et al. Endothelial Wnt/beta-catenin signaling reduces immune cell infiltration in multiple sclerosis. Proc Natl Acad Sci U S A. 2017;114(7):E1168–E1177.
   Google Scholar
• Robinson KF, Narasipura SD, Wallace J, et al. beta-Catenin and TCFs/LEF signaling discordantly regulate IL-6 expression in astrocytes. Cell Commun Signal. 2020;18(1):93.
   Google Scholar
• Nagy C, Suderman M, Yang J, et al. Astrocytic abnormalities and global DNA methylation patterns in depression and suicide. Mol Psychiatry. 2015;20(3):320–328.
   Google Scholar
• John A, Ng-Cordell E, Hanna N, et al. The neurodevelopmental spectrum of synaptic vesicle cycling disorders. J Neurochem. 2020;157(2):208–228.
   Google Scholar
• Beckner ME. A roadmap for potassium buffering/dispersion via the glial network of the CNS. Neurochem Int. 2020;136:104727.
   Google Scholar
• Menichella DM, Majdan, M, Awatramani, R, et al. Genetic and physiological evidence that oligodendrocyte gap junctions contribute to spatial buffering of potassium released during neuronal activity. J Neurosci. 2006;26(43):10984–10991.
   Google Scholar
• Brasko C, Hawkins V, De La Rocha IC, et al. Expression of Kir4.1 and Kir5.1 inwardly rectifying potassium channels in oligodendrocytes, the myelinating cells of the CNS. Brain Struct Funct. 2017;222(1):41–59.
   Google Scholar
• Battefeld A, Klooster J, Kole MH. Myelinating satellite oligodendrocytes are integrated in a glial syncytium constraining neuronal high-frequency activity. Nat Commun. 2016;7(1):11298.
   Google Scholar
• Perea G, Navarrete M, Araque A. Tripartite synapses: astrocytes process and control synaptic information. Trends Neurosci. 2009;32(8):421–431.
   Google Scholar
• Bernardinelli Y, Randall, J, Janett, E, et al. Activity-dependent structural plasticity of perisynaptic astrocytic domains promotes excitatory synapse stability. Curr Biol. 2014;24(15):1679–1688.
   Google Scholar
• Sanchez-Mut JV, Heyn H, Vidal E, et al. Whole genome grey and white matter DNA methylation profiles in dorsolateral prefrontal cortex. Synapse. 2017;71(6):e21959.
   Google Scholar
• Davies MN, Volta, M, Pidsley, R, et al. Functional annotation of the human brain methylome identifies tissue-specific epigenetic variation across brain and blood. Genome Biol. 2012;13(6):R43.
   Google Scholar
• Kim YS, Harry, GJ, Kang, HS, et al. Altered cerebellar development in nuclear receptor TAK1/ TR4 null mice is associated with deficits in GLAST(+) glia, alterations in social behavior, motor learning, startle reactivity, and microglia. Cerebellum. 2010;9(3):310–323.
   Google Scholar
• Zhang Y, Chen Y-T, Xie S, et al. Loss of testicular orphan receptor 4 impairs normal myelination in mouse forebrain. Mol Endocrinol. 2007;21(4):908–920.
   Google Scholar
• Yoshizawa T, Karim M, Sato Y, et al. SIRT7 controls hepatic lipid metabolism by regulating the ubiquitin-proteasome pathway. Cell Metab. 2014;19(4):712–721.
   Google Scholar
• Peng XP, Huang L, Liu ZH. miRNA-133a attenuates lipid accumulation via TR4-CD36 pathway in macrophages. Biochimie. 2016;127:79–85.
   Google Scholar
• Xu M, Qin J, Wang L, et al. Nuclear receptors regulate alternative lengthening of telomeres through a novel noncanonical FANCD2 pathway. Sci Adv. 2019;5(10):eaax6366.
   Google Scholar
• Marzec P, Armenise C, Pérot G, et al. Nuclear-receptor-mediated telomere insertion leads to genome instability in ALT cancers. Cell. 2015;160(5):913–927.
   Google Scholar
• Kornmann G, Curtin F. Temelimab, an IgG4 anti-human endogenous retrovirus monoclonal antibody: an early development safety review. Drug Saf. 2020;43(12):1287–1296.
   Google Scholar
• Dolei A, Ibba G, Piu C, et al. Expression of HERV genes as possible biomarker and target in neurodegenerative diseases. Int J Mol Sci. 2019;20(15):3706.
   Google Scholar
• Gottle P, Förster, M, Gruchot, J, et al. Rescuing the negative impact of human endogenous retrovirus envelope protein on oligodendroglial differentiation and myelination. Glia. 2019;67(1):160–170.
   Google Scholar
• Rasmussen HB, Heltberg A, Lisby G, et al. Three allelic forms of the human endogenous retrovirus, ERV3, and their frequencies in multiple sclerosis patients and healthy individuals. Autoimmunity. 1996;23(2):111–117.
   Google Scholar
• Rasmussen HB, Geny C, Deforges L, et al. Expression of endogenous retroviruses in blood mononuclear cells and brain tissue from multiple sclerosis patients. Mult Scler. 1995;1(2):82–87.
   Google Scholar
• Bustamante Rivera YY, Brutting C, Schmidt C, et al. Endogenous Retrovirus 3 - history, physiology, and pathology. Front Microbiol. 2017;8:2691.
   Google Scholar