Abstract
Schizophrenia (SZ) is a complex and severe psychiatric disorder, which has a global lifetime prevalence of 0.4% and a heritability of around 0.81. A number of epigenome-wide association studies (EWAS) have been carried out for SZ, with discordant results. The main aim of this study was to carry out an integrative in silico analysis of available genome-wide DNA methylation profiles in schizophrenia. In this work, an integration of multiple lines of evidence (top candidate genes from several EWAS and genome-wide expression and association data) was carried out, in order to identify top differentially methylated (DM) genes for SZ. In addition, functional enrichment and protein-protein interaction analyses were carried out. Several top differentially methylated genes, such as APC, CACNB2, and PRKN, were found, and an enrichment of binding sites for brain-expressed transcription factors, such as FOXO1, MYB, and ZIC3, was also observed. Moreover, a protein-protein interaction network showed a central role for DISC1 and ZNF688 genes, and experimentally validated targets of MIR-137, such as and KCNB2, NRXN1, and SYN2, were identified among DM genes. This is the first integrative in silico analysis of available genome-wide DNA methylation profiles in schizophrenia. This work identified novel candidate genes and pathways for SZ and provides the basis to explore their role in the pathogenesis of SZ in future studies.
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Abdolmaleky HM, Gower AC, Wong CK, Cox JW, Zhang X, Thiagalingam A, Shafa R, Sivaraman V, Zhou JR, Thiagalingam S (2019) Aberrant transcriptomes and DNA methylomes define pathways that drive pathogenesis and loss of brain laterality/asymmetry in schizophrenia and bipolar disorder. Am J Med Genet B Neuropsychiatr Genet 180(2):138–149. https://doi.org/10.1002/ajmg.b.32691
Andrews SV, Sheppard B, Windham GC, Schieve LA, Schendel DE, Croen LA, Chopra P, Alisch RS, Newschaffer CJ, Warren ST, Feinberg AP, Fallin MD, Ladd-Acosta C (2018) Case-control meta-analysis of blood DNA methylation and autism spectrum disorder. Mol Autism 9:40. https://doi.org/10.1186/s13229-018-0224-6
Ayalew M, Le-Niculescu H, Levey DF, Jain N, Changala B, Patel SD et al (2012) Convergent functional genomics of schizophrenia: from comprehensive understanding to genetic risk prediction. Mol Psychiatry 17(9):887–905. https://doi.org/10.1038/mp.2012.37
Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, Yefanov A, Lee H, Zhang N, Robertson CL, Serova N, Davis S, Soboleva A (2013) NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res 41(Database issue):D991–D995. https://doi.org/10.1093/nar/gks1193
Bibikova M, Le J, Barnes B, Saedinia-Melnyk S, Zhou L, Shen R et al (2009) Genome-wide DNA methylation profiling using infinium(R) assay. Epigenomics 1(1):177–200. https://doi.org/10.2217/epi.09.14
Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC, Gaasterland T, Glenisson P, Holstege FCP, Kim IF, Markowitz V, Matese JC, Parkinson H, Robinson A, Sarkans U, Schulze-Kremer S, Stewart J, Taylor R, Vilo J, Vingron M (2001) Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet 29(4):365–371. https://doi.org/10.1038/ng1201-365
Chen C, Zhang C, Cheng L, Reilly JL, Bishop JR, Sweeney JA, Chen HY, Gershon ES, Liu C (2014) Correlation between DNA methylation and gene expression in the brains of patients with bipolar disorder and schizophrenia. Bipolar Disord 16(8):790–799. https://doi.org/10.1111/bdi.12255
Choi KH, Elashoff M, Higgs BW, Song J, Kim S, Sabunciyan S, Diglisic S, Yolken RH, Knable MB, Torrey EF, Webster MJ (2008) Putative psychosis genes in the prefrontal cortex: combined analysis of gene expression microarrays. BMC Psychiatry 8:87. https://doi.org/10.1186/1471-244X-8-87
Forero DA (2019) Available software for meta-analyses of genome-wide expression studies. Current Genomics 20:1–6. https://doi.org/10.2174/1389202920666190822113912
Forero DA, Gonzalez-Giraldo Y (2019) Convergent functional genomics of cocaine misuse in humans and animal models. Am J Drug Alcohol Abuse 46:1–9. https://doi.org/10.1080/00952990.2019.1636384
Forero DA, Herteleer L, De Zutter S, Norrback KF, Nilsson LG, Adolfsson R et al (2016a) A network of synaptic genes associated with schizophrenia and bipolar disorder. Schizophr Res 172(1–3):68–74. https://doi.org/10.1016/j.schres.2016.02.012
Forero DA, Prada CF, Perry G (2016b) Functional and genomic features of human genes mutated in neuropsychiatric disorders. Open Neurol J 10:143–148. https://doi.org/10.2174/1874205X01610010143
Forero DA, Guio-Vega GP, Gonzalez-Giraldo Y (2017) A comprehensive regional analysis of genome-wide expression profiles for major depressive disorder. J Affect Disord 218:86–92. https://doi.org/10.1016/j.jad.2017.04.061
Gaspar HA, Breen G (2017) Drug enrichment and discovery from schizophrenia genome-wide association results: an analysis and visualisation approach. Sci Rep 7(1):12460. https://doi.org/10.1038/s41598-017-12325-3
Hall J, Trent S, Thomas KL, O'Donovan MC, Owen MJ (2015) Genetic risk for schizophrenia: convergence on synaptic pathways involved in plasticity. Biol Psychiatry 77(1):52–58. https://doi.org/10.1016/j.biopsych.2014.07.011
Hawrylycz M, Miller JA, Menon V, Feng D, Dolbeare T, Guillozet-Bongaarts AL, Jegga AG, Aronow BJ, Lee CK, Bernard A, Glasser MF, Dierker DL, Menche J, Szafer A, Collman F, Grange P, Berman KA, Mihalas S, Yao Z, Stewart L, Barabási AL, Schulkin J, Phillips J, Ng L, Dang C, Haynor DR, Jones A, van Essen DC, Koch C, Lein E (2015) Canonical genetic signatures of the adult human brain. Nat Neurosci 18(12):1832–1844. https://doi.org/10.1038/nn.4171
Huang da W, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4(1):44–57. https://doi.org/10.1038/nprot.2008.211
Karagkouni D, Paraskevopoulou MD, Chatzopoulos S, Vlachos IS, Tastsoglou S, Kanellos I, Papadimitriou D, Kavakiotis I, Maniou S, Skoufos G, Vergoulis T, Dalamagas T, Hatzigeorgiou AG (2018) DIANA-TarBase v8: a decade-long collection of experimentally supported miRNA-gene interactions. Nucleic Acids Res 46(D1):D239–D245. https://doi.org/10.1093/nar/gkx1141
Kurian SM, Le-Niculescu H, Patel SD, Bertram D, Davis J, Dike C et al (2011) Identification of blood biomarkers for psychosis using convergent functional genomics. Mol Psychiatry 16(1):37–58. https://doi.org/10.1038/mp.2009.117
Lee SA, Huang KC (2016) Epigenetic profiling of human brain differential DNA methylation networks in schizophrenia. BMC Med Genet 9(Suppl 3):68. https://doi.org/10.1186/s12920-016-0229-y
Le-Niculescu H, Balaraman Y, Patel S, Tan J, Sidhu K, Jerome RE et al (2007) Towards understanding the schizophrenia code: an expanded convergent functional genomics approach. Am J Med Genet B Neuropsychiatr Genet 144B(2):129–158. https://doi.org/10.1002/ajmg.b.30481
Lieberman JA, First MB (2018) Psychotic disorders. N Engl J Med 379(3):270–280. https://doi.org/10.1056/NEJMra1801490
Marioni RE, McRae AF, Bressler J, Colicino E, Hannon E, Li S, Prada D, Smith JA, Trevisi L, Tsai PC, Vojinovic D, Simino J, Levy D, Liu C, Mendelson M, Satizabal CL, Yang Q, Jhun MA, Kardia SLR, Zhao W, Bandinelli S, Ferrucci L, Hernandez DG, Singleton AB, Harris SE, Starr JM, Kiel DP, McLean RR, Just AC, Schwartz J, Spiro A III, Vokonas P, Amin N, Ikram MA, Uitterlinden AG, van Meurs JBJ, Spector TD, Steves C, Baccarelli AA, Bell JT, van Duijn CM, Fornage M, Hsu YH, Mill J, Mosley TH, Seshadri S, Deary IJ (2018) Meta-analysis of epigenome-wide association studies of cognitive abilities. Mol Psychiatry 23(11):2133–2144. https://doi.org/10.1038/s41380-017-0008-y
Modai S, Shomron N (2016) Molecular risk factors for schizophrenia. Trends Mol Med 22(3):242–253. https://doi.org/10.1016/j.molmed.2016.01.006
Pardinas AF, Holmans P, Pocklington AJ, Escott-Price V, Ripke S, Carrera N et al (2018) Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat Genet 50(3):381–389. https://doi.org/10.1038/s41588-018-0059-2
Qi X, Guan F, Wen Y, Li P, Ma M, Cheng S, Zhang L, Liang C, Cheng B, Zhang F (2019) Integrating genome-wide association study and methylation functional annotation data identified candidate genes and pathways for schizophrenia. Prog Neuro-Psychopharmacol Biol Psychiatry 96:109736. https://doi.org/10.1016/j.pnpbp.2019.109736
Rolland T, Tasan M, Charloteaux B, Pevzner SJ, Zhong Q, Sahni N et al (2014) A proteome-scale map of the human interactome network. Cell 159(5):1212–1226. https://doi.org/10.1016/j.cell.2014.10.050
Russ AP, Lampel S (2005) The druggable genome: an update. Drug Discov Today 10(23–24):1607–1610. https://doi.org/10.1016/S1359-6446(05)03666-4
Saha S, Chant D, Welham J, McGrath J (2005) A systematic review of the prevalence of schizophrenia. PLoS Med 2(5):e141. https://doi.org/10.1371/journal.pmed.0020141
Sakamoto K, Crowley JJ (2018) A comprehensive review of the genetic and biological evidence supports a role for MicroRNA-137 in the etiology of schizophrenia. Am J Med Genet B Neuropsychiatr Genet 177(2):242–256. https://doi.org/10.1002/ajmg.b.32554
Sandoval J, Heyn H, Moran S, Serra-Musach J, Pujana MA, Bibikova M, Esteller M (2011) Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome. Epigenetics 6(6):692–702. https://doi.org/10.4161/epi.6.6.16196
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504. https://doi.org/10.1101/gr.1239303
Snijders C, Bassil KC, de Nijs L (2018) Methodologies of neuroepigenetic research: background, challenges and future perspectives. Prog Mol Biol Transl Sci 158:15–27. https://doi.org/10.1016/bs.pmbts.2018.04.009
Story Jovanova O, Nedeljkovic I, Spieler D, Walker RM, Liu C, Luciano M, Bressler J, Brody J, Drake AJ, Evans KL, Gondalia R, Kunze S, Kuhnel B, Lahti J, Lemaitre RN, Marioni RE, Swenson B, Himali JJ, Wu H, Li Y, McRae AF, Russ TC, Stewart J, Wang Z, Zhang G, Ladwig KH, Uitterlinden AG, Guo X, Peters A, Räikkönen K, Starr JM, Waldenberger M, Wray NR, Whitsel EA, Sotoodehnia N, Seshadri S, Porteous DJ, van Meurs J, Mosley TH, McIntosh AM, Mendelson MM, Levy D, Hou L, Eriksson JG, Fornage M, Deary IJ, Baccarelli A, Tiemeier H, Amin N (2018) DNA methylation signatures of depressive symptoms in middle-aged and elderly persons: meta-analysis of multiethnic Epigenome-wide studies. JAMA Psychiatry 75(9):949–959. https://doi.org/10.1001/jamapsychiatry.2018.1725
Sullivan PF, Kendler KS, Neale MC (2003) Schizophrenia as a complex trait: evidence from a meta-analysis of twin studies. Arch Gen Psychiatry 60(12):1187–1192. https://doi.org/10.1001/archpsyc.60.12.1187
Teroganova N, Girshkin L, Suter CM, Green MJ (2016) DNA methylation in peripheral tissue of schizophrenia and bipolar disorder: a systematic review. BMC Genet 17:27. https://doi.org/10.1186/s12863-016-0332-2
Tomoda T, Hikida T, Sakurai T (2017) Role of DISC1 in neuronal trafficking and its implication in neuropsychiatric manifestation and neurotherapeutics. Neurotherapeutics 14(3):623–629. https://doi.org/10.1007/s13311-017-0556-5
Viana J, Hannon E, Dempster E, Pidsley R, Macdonald R, Knox O, Spiers H, Troakes C, al-Saraj S, Turecki G, Schalkwyk LC, Mill J (2017) Schizophrenia-associated methylomic variation: molecular signatures of disease and polygenic risk burden across multiple brain regions. Hum Mol Genet 26(1):210–225. https://doi.org/10.1093/hmg/ddw373
Wockner LF, Noble EP, Lawford BR, Young RM, Morris CP, Whitehall VL et al (2014) Genome-wide DNA methylation analysis of human brain tissue from schizophrenia patients. Transl Psychiatry 4:e339. https://doi.org/10.1038/tp.2013.111
Zhao Y, Liang X, Zhu F, Wen Y, Xu J, Yang J, Ding M, Cheng B, Ma M, Zhang L, Cheng S, Wu C, Wang S, Wang X, Ning Y, Guo X, Zhang F (2018) A large-scale integrative analysis of GWAS and common meQTLs across whole life course identifies genes, pathways and tissue/cell types for three major psychiatric disorders. Neurosci Biobehav Rev 95:347–352. https://doi.org/10.1016/j.neubiorev.2018.10.005
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YG-G was previously supported by a PhD fellowship from Centro de Estudios Interdisciplinarios Básicos y Aplicados CEIBA (Rodolfo Llinás Program). DAF was previously supported by research grants from Colciencias.
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All authors contributed to the study conception and design. Data collection and analysis were performed by DAF and YG-G. The first draft of the manuscript was written by DAF and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Forero, D.A., González-Giraldo, Y. Integrative In Silico Analysis of Genome-Wide DNA Methylation Profiles in Schizophrenia. J Mol Neurosci 70, 1887–1893 (2020). https://doi.org/10.1007/s12031-020-01585-w
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DOI: https://doi.org/10.1007/s12031-020-01585-w