Spatiotemporal specificity of correlated DNA methylation and gene expression pairs across different human tissues and stages of brain development

References

• Barlow DP. Genomic imprinting: a mammalian epigenetic discovery model. Annu Rev Genet. 2011;45(1):379–403.
   Google Scholar
• Jones PA. Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nat Rev Genet. 2012;13(7):484–492.
   Google Scholar
• Reik W. Stability and flexibility of epigenetic gene regulation in mammalian development. Nature. 2007;447(7143):425–432.
   Google Scholar
• Baylin SB, Jones PA. A decade of exploring the cancer epigenome - biological and translational implications. Nat Rev Cancer. 2011;11(10):726–734.
   Google Scholar
• Stadler MB, Murr R, Burger L, et al. DNA-binding factors shape the mouse methylome at distal regulatory regions. Nature. 2011;480(7378):490–495.
   Google Scholar
• Jaenisch R, Bird A. Epigenetic regulation of gene expression: how the genome integrates intrinsic and environmental signals. Nat Genet. 2003;33(S3):245–254. Suppl:245-54.
   Google Scholar
• Hellman A, Chess A. Gene body-specific methylation on the active X chromosome. Science. 2007;315(5815):1141–1143.
   Google Scholar
• Lister R, Pelizzola M, Dowen RH, et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature. 2009;462(7271):315–322.
   Google Scholar
• Olsson AH, Volkov P, Bacos K, et al. Genome-wide associations between genetic and epigenetic variation influence mRNA expression and insulin secretion in human pancreatic islets. PLoS Genet. 2014;10(11):e1004735.
   Google Scholar
• Wagner JR, Busche S, Ge B, et al. The relationship between DNA methylation, genetic and expression inter-individual variation in untransformed human fibroblasts. Genome Biol. 2014;15(2):R37.
   Google Scholar
• Tasaki S, Gaiteri C, Mostafavi S, et al. Multi-omic directed networks describe features of gene regulation in aged brains and expand the set of genes driving cognitive decline. Front Genet. 2018;9:294.
   Google Scholar
• Taylor DL, Jackson AU, Narisu N, et al. Integrative analysis of gene expression, DNA methylation, physiological traits, and genetic variation in human skeletal muscle. Proc Natl Acad Sci U S A. 2019;116(22):10883–10888.
   Google Scholar
• Bell JT, Tsai PC, Yang TP, et al. Epigenome-wide scans identify differentially methylated regions for age and age-related phenotypes in a healthy ageing population. PLoS Genet. 2012;8(4):e1002629.
   Google Scholar
• Varley KE, Gertz J, Bowling KM, et al. Dynamic DNA methylation across diverse human cell lines and tissues. Genome Res. 2013;23(3):555–567.
   Google Scholar
• Smith ZD, Meer A. DNA methylation: roles in mammalian development. Nat Rev Genet. 2013;14(3):204–220.
   Google Scholar
• Liang P, Song F, Ghosh S, et al. Genome-wide survey reveals dynamic widespread tissue-specific changes in DNA methylation during development. BMC Genomics. 2011;12(1):231.
   Google Scholar
• Reynolds LM, Taylor JR, Ding J, et al. Age-related variations in the methylome associated with gene expression in human monocytes and T cells. Nat Commun. 2014;5(1):5366.
   Google Scholar
• Blake LE, Roux J, Hernando-Herraez I, et al. A comparison of gene expression and DNA methylation patterns across tissues and species. Genome Res. 2020;30(2):250–262.
   Google Scholar
• Gutierrez-Arcelus M, Lappalainen T, Montgomery SB, et al. Passive and active DNA methylation and the interplay with genetic variation in gene regulation. Elife. 2013;2:e00523.
   Google Scholar
• Gutierrez-Arcelus M, Ongen H, Lappalainen T, et al. Tissue-specific effects of genetic and epigenetic variation on gene regulation and splicing. PLoS Genet. 2015;11(1):e1004958.
   Google Scholar
• Bonder MJ, Kasela S, Kals M, et al. Genetic and epigenetic regulation of gene expression in fetal and adult human livers. BMC Genomics. 2014;15(1):860.
   Google Scholar
• Schubeler D. Function and information content of DNA methylation. Nature. 2015;517(7534):321–326.
   Google Scholar
• Zhu H, Wang G, Qian J. Transcription factors as readers and effectors of DNA methylation. Nat Rev Genet. 2016;17(9):551–565.
   Google Scholar
• Yin Y, Morgunova E, Jolma A, et al. Impact of cytosine methylation on DNA binding specificities of human transcription factors. Science. 2017;356(6337).
   Google Scholar
• Wang G, Luo X, Wang J, et al. MeDReaders: a database for transcription factors that bind to methylated DNA. Nucleic Acids Res. 2018;46:D146–D51.
   Google Scholar
• Roadmap Epigenomics C, Kundaje A, Meuleman W, et al. Integrative analysis of 111 reference human epigenomes. Nature. 2015;518(7539):317–330.
   Google Scholar
• Lovkvist C, Sneppen K, Haerter JO. Exploring the link between nucleosome occupancy and DNA methylation. Front Genet. 2017;8:232.
   Google Scholar
• Spektor R, Tippens ND, Mimoso CA, et al. methyl-ATAC-seq measures DNA methylation at accessible chromatin. Genome Res. 2019;29(6):969–977.
   Google Scholar
• Collings CK, Anderson JN. Links between DNA methylation and nucleosome occupancy in the human genome. Epigenetics Chromatin. 2017;10(1):18.
   Google Scholar
• Cedar H, Bergman Y. Linking DNA methylation and histone modification: patterns and paradigms. Nat Rev Genet. 2009;10(5):295–304.
   Google Scholar
• Du J, Johnson LM, Jacobsen SE, et al. DNA methylation pathways and their crosstalk with histone methylation. Nat Rev Mol Cell Biol. 2015;16(9):519–532.
   Google Scholar
• Bonder MJ, Luijk R, Zhernakova DV, et al. Disease variants alter transcription factor levels and methylation of their binding sites. Nat Genet. 2017;49(1):131–138.
   Google Scholar
• Liu XS, Wu H, Ji X, et al. Editing DNA methylation in the mammalian genome. Cell. 2016;167(1):233–47 e17.
   Google Scholar
• Kang HJ, Kawasawa YI, Cheng F, et al. Spatio-temporal transcriptome of the human brain. Nature. 2011;478(7370):483–489.
   Google Scholar
• Numata S, Ye T, Hyde TM, et al. DNA methylation signatures in development and aging of the human prefrontal cortex. Am J Hum Genet. 2012;90(2):260–272.
   Google Scholar
• De Jager PL, Srivastava G, Lunnon K, et al. Alzheimer’s disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci. Nat Neurosci. 2014;17(9):1156–1163.
   Google Scholar
• Horvath S, Erhart W, Brosch M, et al. Obesity accelerates epigenetic aging of human liver. Proc Natl Acad Sci U S A. 2014;111(43):15538–15543.
   Google Scholar
• Jaffe AE, Gao Y, Deep-Soboslay A, et al. Mapping DNA methylation across development, genotype and schizophrenia in the human frontal cortex. Nat Neurosci. 2016;19(1):40–47.
   Google Scholar
• Pujol J, Vendrell P, Junque C, et al. When does human brain development end? Evidence of corpus callosum growth up to adulthood. Ann Neurol. 1993;34(1):71–75.
   Google Scholar
• Zhou W, Laird PW, Shen H. Comprehensive characterization, annotation and innovative use of infinium DNA methylation BeadChip probes. Nucleic Acids Res. 2017;45:e22.
   Google Scholar
• Nordlund J, Backlin 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
• Teschendorff AE, Marabita F, Lechner M, et al. A beta-mixture quantile normalization method for correcting probe design bias in Illumina infinium 450 k DNA methylation data. Bioinformatics. 2013;29(2):189–196.
   Google Scholar
• Harrow J, Frankish A, Gonzalez JM, et al. GENCODE: the reference human genome annotation for the ENCODE project. Genome Res. 2012;22(9):1760–1774.
   Google Scholar
• Martin N, Maes H. Multivariate analysis. London: Academic press; 1979.
   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
• Leek JT, Storey JD. Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet. 2007;3(9):1724–1735.
   Google Scholar
• Li J, Bushel PR, Chu TM, et al. Principal variance components analysis: estimating batch effects in microarray gene expression data. Batch Effects and Noise in Microarray Experiments: Sources and Solutions. 2009: 141–154.
   Google Scholar
• Wang J, Vasaikar S, Shi Z, et al. WebGestalt 2017: a more comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit. Nucleic Acids Res. 2017;45:W130–W7.
   Google Scholar
• Watanabe K, Taskesen E, van Bochoven A, et al. Functional mapping and annotation of genetic associations with FUMA. Nat Commun. 2017;8(1):1826.
   Google Scholar
• Battram T, Yousefi P, Crawford G, et al. The EWAS catalog: a database of epigenome-wide association studies. OSF Preprints. 2021; February 4. DOI: 10.31219/osf.io/837wn.
   Google Scholar
• Saffari A, Silver MJ, Zavattari P, et al. Estimation of a significance threshold for epigenome-wide association studies. Genet Epidemiol. 2018;42(1):20–33.
   Google Scholar
• Consortium EP. The ENCODE (ENCyclopedia Of DNA Elements) Project. Science. 2004;306(5696):636–640.
   Google Scholar
• Sheffield NC, Bock C. LOLA: enrichment analysis for genomic region sets and regulatory elements in R and bioconductor. Bioinformatics. 2015;32(4):587–589.
   Google Scholar
• Dixon JR, Selvaraj S, Yue F, et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature. 2012;485(7398):376–380.
   Google Scholar
• Ho JW, Jung YL, Liu T, et al. Comparative analysis of metazoan chromatin organization. Nature. 2014;512(7515):449–452.
   Google Scholar
• Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010;26(6):841–842.
   Google Scholar
• Vorontsov IE, Fedorova AD, Yevshin IS, et al. Genome-wide map of human and mouse transcription factor binding sites aggregated from ChIP-Seq data. BMC Res Notes. 2018;11(1):756.
   Google Scholar
• Acemel RD, Maeso I, Gomez-Skarmeta JL. Topologically associated domains: a successful scaffold for the evolution of gene regulation in animals. WIRES Dev Biol. 2017;6(3):e265.
   Google Scholar
• Ng B, White CC, Klein HU, 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
• Wu G, Fang YZ, Yang S, et al. Glutathione metabolism and its implications for health. J Nutr. 2004;134(3):489–492.
   Google Scholar
• Forman HJ, Zhang H, Rinna A. Glutathione: overview of its protective roles, measurement, and biosynthesis. Mol Aspects Med. 2009;30(1–2):1–12.
   Google Scholar
• Lertratanangkoon K, Wu CJ, Savaraj N, et al. Alterations of DNA methylation by glutathione depletion. Cancer Lett. 1997;120(2):149–156.
   Google Scholar
• Maurano MT, Wang H, John S, et al. Role of DNA methylation in modulating transcription factor occupancy. Cell Rep. 2015;12(7):1184–1195.
   Google Scholar
• Ong CT, Corces VG. CTCF: an architectural protein bridging genome topology and function. Nat Rev Genet. 2014;15(4):234–246.
   Google Scholar
• Li G, Margueron R, Ku M, et al. Jarid2 and PRC2, partners in regulating gene expression. Genes Dev. 2010;24(4):368–380.
   Google Scholar
• Pasini D, Cloos PA, Walfridsson J, et al. JARID2 regulates binding of the Polycomb repressive complex 2 to target genes in ES cells. Nature. 2010;464(7286):306–310.
   Google Scholar
• Peng JC, Valouev A, Swigut T, et al. Jarid2/Jumonji coordinates control of PRC2 enzymatic activity and target gene occupancy in pluripotent cells. Cell. 2009;139(7):1290–1302.
   Google Scholar
• Dixon G, Pan H, Yang D, et al. QSER1 protects DNA methylation valleys from de novo methylation. Science. 2021;372(6538). DOI:10.1126/science.abd0875.
   Google Scholar
• Laugesen A, Hojfeldt JW, Helin K. Molecular mechanisms directing PRC2 recruitment and H3K27 methylation. Mol Cell. 2019;74(1):8–18.
   Google Scholar
• Bartke T, Vermeulen M, Xhemalce B, et al. Nucleosome-interacting proteins regulated by DNA and histone methylation. Cell. 2010;143(3):470–484.
   Google Scholar
• Jermann P, Hoerner L, Burger L, et al. Short sequences can efficiently recruit histone H3 lysine 27 trimethylation in the absence of enhancer activity and DNA methylation. Proc Natl Acad Sci U S A. 2014;111(33):E3415–21.
   Google Scholar
• Wachter E, Quante T, Merusi C, et al. Synthetic CpG islands reveal DNA sequence determinants of chromatin structure. Elife. 2014;3:e03397.
   Google Scholar
• Wu Y, Yao YG, Luo XJ. SZDB: a database for Schizophrenia genetic research. Schizophr Bull. 2017;43:459–471.
   Google Scholar
• Gandal MJ, Haney JR, Parikshak NN, et al. Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science. 2018;359(6376):693–697.
   Google Scholar
• Gandal MJ, Zhang P, Hadjimichael E, et al. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science. 2018;362(6420). 10.1126/science.aat8127.
   Google Scholar
• Walker RL, Ramaswami G, Hartl C, et al. Genetic control of expression and splicing in developing human brain informs disease mechanisms. Cell. 2019;179(3):750–71 e22.
   Google Scholar
• Birnbaum R, Jaffe AE, Chen Q, et al. Investigating the neuroimmunogenic architecture of schizophrenia. Mol Psychiatry. 2018;23(5):1251–1260.
   Google Scholar
• Hartwig FP, Borges MC, Horta BL, et al. Inflammatory biomarkers and risk of Schizophrenia: a 2-sample Mendelian randomization study. JAMA Psychiatry. 2017;74(12):1226–1233.
   Google Scholar
• Sharabi A, Tsokos GC. T cell metabolism: new insights in systemic lupus erythematosus pathogenesis and therapy. Nat Rev Rheumatol. 2020;16(2):100–112.
   Google Scholar
• Robertson KD. DNA methylation and human disease. Nat Rev Genet. 2005;6(8):597–610.
   Google Scholar
• Rakyan VK, Down TA, Balding DJ, et al. Epigenome-wide association studies for common human diseases. Nat Rev Genet. 2011;12(8):529–541.
   Google Scholar
• Chen C, Zhang C, Cheng L, et al. Correlation between DNA methylation and gene expression in the brains of patients with bipolar disorder and schizophrenia. Bipolar Disord. 2014;16(8):790–799.
   Google Scholar
• Paul DS, Beck S. Advances in epigenome-wide association studies for common diseases. Trends Mol Med. 2014;20(10):541–543.
   Google Scholar
• Li M, Zou D, Li Z, et al. EWAS Atlas: a curated knowledgebase of epigenome-wide association studies. Nucleic Acids Res. 2019;47(D1):D983–D8.
   Google Scholar
• Semick SA, Bharadwaj RA, Collado-Torres L, et al. Integrated DNA methylation and gene expression profiling across multiple brain regions implicate novel genes in Alzheimer’s disease. Acta Neuropathol. 2019;137(4):557–569.
   Google Scholar
• Goodwin LD, Leech NL. Understanding correlation: factors that affect the size of r. The Journal of Experimental Education. 2006;74(3):249–266
   Google Scholar