Abstract
N6-methyladenosine (m6A) methylation regulates gene expression/protein by influencing numerous aspects of mRNA metabolism and contributes to neuropsychiatric diseases. Here, we integrated multi-omics data and genome-wide association study summary data of schizophrenia (SCZ), bipolar disorder (BP), attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), major depressive disorder (MDD), Alzheimer’s disease (AD), and Parkinson’s disease (PD) to reveal the role of m6A in neuropsychiatric disorders by using transcriptome-wide association study (TWAS) tool and Summary-data-based Mendelian randomization (SMR). Our investigation identified 86 m6A sites associated with seven neuropsychiatric diseases and then revealed 7881 associations between m6A sites and gene expressions. Based on these results, we discovered 916 significant m6A–gene associations involving 82 disease-related m6A sites and 606 genes. Further integrating the 58 disease-related genes from TWAS and SMR analysis, we obtained 61, 8, 7, 3, and 2 associations linking m6A-disease, m6A–gene, and gene-disease for SCZ, BP, AD, MDD, and PD separately. Functional analysis showed the m6A mapped genes were enriched in “response to stimulus” pathway. In addition, we also analyzed the effect of gene expression on m6A and the post-transcription effect of m6A on protein. Our study provided new insights into the genetic component of m6A in neuropsychiatric disorders and unveiled potential pathogenic mechanisms where m6A exerts influences on disease through gene expression/protein regulation.
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Data availability
The summary data of the seven GWAS are available from their respective original articles. The m6A phenotype file are requested from the original paper and the genotype data for these samples were requested from dbGaP (phs000424.v9.p2 under project ID 31888). The eQTL summary dataset of the eQTLGen Consortium is available at https://www.eqtlgen.org/cis-eqtls.html. The eQTL summary datasets of GTEx v8 whole blood, lung and muscle are available at https://www.gtexportal.org/home/downloads/adult-gtex and the weight file of GTEx v8 whole blood is available at http://gusevlab.org/projects/fusion/. The eQTL data of PsychENCODE is available at http://resource.psychencode.org/. Protein weights and the pQTL summary statistics are available at https://www.synapse.org/#!Synapse:syn23191787.
References
Polderman TJC, Benyamin B, de Leeuw CA, Sullivan PF, van Bochoven A, Visscher PM, et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat Genet. 2015;47:702–9.
Geschwind DH, Flint J. Genetics and genomics of psychiatric disease. Science. 2015;349:1489–94.
Qi X, Wang S, Zhang L, Liu L, Wen Y, Ma M, et al. An integrative analysis of transcriptome-wide association study and mRNA expression profile identified candidate genes for attention-deficit/hyperactivity disorder. Psychiatry Res. 2019;282:112639.
Jansen RC, Nap JP. Genetical genomics: the added value from segregation. Trends Genet. 2001;17:388–91.
Veturi Y, Ritchie MD. How powerful are summary-based methods for identifying expression-trait associations under different genetic architectures? Pac Symp Biocomput. 2018;23:228–39.
Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx BW, et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet. 2016;48:245–52.
Gamazon ER, Wheeler HE, Shah KP, Mozaffari SV, Aquino-Michaels K, Carroll RJ, et al. A gene-based association method for mapping traits using reference transcriptome data. Nat Genet. 2015;47:1091–8.
Barbeira AN, Dickinson SP, Bonazzola R, Zheng J, Wheeler HE, Torres JM, et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat Commun. 2018;9:1825.
Barfield R, Feng H, Gusev A, Wu L, Zheng W, Pasaniuc B, et al. Transcriptome-wide association studies accounting for colocalization using Egger regression. Genet Epidemiol. 2018;42:418–33.
Meng TG, Lu X, Guo L, Hou GM, Ma XS, Li QN, et al. Mettl14 is required for mouse postimplantation development by facilitating epiblast maturation. FASEB J. 2019;33:1179–87.
Meyer KD, Saletore Y, Zumbo P, Elemento O, Mason CE, Jaffrey SR. Comprehensive analysis of mRNA methylation reveals enrichment in 3’ UTRs and near stop codons. Cell. 2012;149:1635–46.
Fu Y, Dominissini D, Rechavi G, He C. Gene expression regulation mediated through reversible m(6)A RNA methylation. Nat Rev Genet. 2014;15:293–306.
Roundtree IA, Evans ME, Pan T, He C. Dynamic RNA modifications in gene expression regulation. Cell. 2017;169:1187–1200.
Xiao W, Adhikari S, Dahal U, Chen Y-S, Hao Y-J, Sun B-F, et al. Nuclear m(6)A reader YTHDC1 regulates mRNA splicing. Mol Cell. 2016;61:507–19.
Kasowitz SD, Ma J, Anderson SJ, Leu NA, Xu Y, Gregory BD, et al. Nuclear m6A reader YTHDC1 regulates alternative polyadenylation and splicing during mouse oocyte development. PLoS Genet. 2018;14:e1007412.
Louloupi A, Ntini E, Conrad T, Ørom UAV. Transient N-6-methyladenosine transcriptome sequencing reveals a regulatory role of m6A in splicing efficiency. Cell Rep. 2018;23:3429–37.
Roundtree IA, Luo G-Z, Zhang Z, Wang X, Zhou T, Cui Y, et al. YTHDC1 mediates nuclear export of N6-methyladenosine methylated mRNAs. eLife. 2017;6:e31311.
Wang X, Lu Z, Gomez A, Hon GC, Yue Y, Han D, et al. N6-methyladenosine-dependent regulation of messenger RNA stability. Nature. 2014;505:117–20.
Wang X, Zhao BS, Roundtree IA, Lu Z, Han D, Ma H, et al. N(6)-methyladenosine modulates messenger RNA translation efficiency. Cell. 2015;161:1388–99.
Zhou J, Wan J, Gao X, Zhang X, Jaffrey SR, Qian S-B. Dynamic m(6)A mRNA methylation directs translational control of heat shock response. Nature. 2015;526:591–4.
Shi H, Zhang X, Weng Y-L, Lu Z, Liu Y, Lu Z, et al. m6A facilitates hippocampus-dependent learning and memory through YTHDF1. Nature. 2018;563:249–53.
Li A, Chen Y-S, Ping X-L, Yang X, Xiao W, Yang Y, et al. Cytoplasmic m6A reader YTHDF3 promotes mRNA translation. Cell Res. 2017;27:444–7.
Shi H, Wang X, Lu Z, Zhao BS, Ma H, Hsu PJ, et al. YTHDF3 facilitates translation and decay of N(6)-methyladenosine-modified RNA. Cell Res. 2017;27:315–28.
Frye M, Harada BT, Behm M, He C. RNA modifications modulate gene expression during development. Science. 2018;361:1346–9.
Wang Y, Li Y, Yue M, Wang J, Kumar S, Wechsler-Reya RJ, et al. N(6)-methyladenosine RNA modification regulates embryonic neural stem cell self-renewal through histone modifications. Nat Neurosci. 2018;21:195–206.
Chang M, Lv H, Zhang W, Ma C, He X, Zhao S, et al. Region-specific RNA m6A methylation represents a new layer of control in the gene regulatory network in the mouse brain. Open Biol. 2017;7:170166.
Dominissini D, Moshitch-Moshkovitz S, Schwartz S, Salmon-Divon M, Ungar L, Osenberg S, et al. Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq. Nature. 2012;485:201–6.
Ma C, Chang M, Lv H, Zhang Z-W, Zhang W, He X, et al. RNA m6A methylation participates in regulation of postnatal development of the mouse cerebellum. Genome Biol. 2018;19:68.
Yoon K-J, Ringeling FR, Vissers C, Jacob F, Pokrass M, Jimenez-Cyrus D, et al. Temporal control of mammalian cortical neurogenesis by m6A methylation. Cell. 2017;171:877–89.e817.
Shu L, Huang X, Cheng X, Li X. Emerging roles of N6-methyladenosine modification in neurodevelopment and neurodegeneration. Cells. 2021;10:2694.
Zhang N, Ding C, Zuo Y, Peng Y, Zuo L. N6-methyladenosine and neurological diseases. Mol Neurobiol. 2022;59:1925–37.
Zhang Z, Luo K, Zou Z, Qiu M, Tian J, Sieh L, et al. Genetic analyses support the contribution of mRNA N(6)-methyladenosine (m(6)A) modification to human disease heritability. Nat Genet. 2020;52:939–49.
Xiong X, Hou L, Park YP, Molinie B, Gtex C, Ardlie KG, et al. Genetic drivers of m6A methylation in human brain, lung, heart and muscle. Nat Genet. 2021;53:1156–65.
Zhao BS, Roundtree IA, He C. Post-transcriptional gene regulation by mRNA modifications. Nat Rev Mol Cell Biol. 2017;18:31–42.
Meyer KD, Jaffrey SR. The dynamic epitranscriptome: N6-methyladenosine and gene expression control. Nat Rev Mol Cell Biol. 2014;15:313–26.
Shafik AM, Zhang F, Guo Z, Dai Q, Pajdzik K, Li Y, et al. N6-methyladenosine dynamics in neurodevelopment and aging, and its potential role in Alzheimer’s disease. Genome Biol. 2021;22:17.
Wu Y, Zeng J, Zhang F, Zhu Z, Qi T, Zheng Z, et al. Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits. Nat Commun. 2018;9:918.
Trubetskoy V, Pardiñas AF, Qi T, Panagiotaropoulou G, Awasthi S, Bigdeli TB, et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature. 2022;604:502–8.
Mullins N, Forstner AJ, O’Connell KS, Coombes B, Coleman JRI, Qiao Z, et al. Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology. Nat Genet. 2021;53:817–29.
Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet. 2018;50:668–81.
Demontis D, Walters RK, Martin J, Mattheisen M, Als TD, Agerbo E, et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat Genet. 2019;51:63–75.
The ASDW. Meta-analysis of GWAS of over 16,000 individuals with autism spectrum disorder highlights a novel locus at 10q24.32 and a significant overlap with schizophrenia. Mol Autism. 2017;8:21.
Jansen IE, Savage JE, Watanabe K, Bryois J, Williams DM, Steinberg S, et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat Genet. 2019;51:404–13.
Nalls MA, Blauwendraat C, Vallerga CL, Heilbron K, Bandres-Ciga S, Chang D, et al. Identification of novel risk loci, causal insights, and heritable risk for Parkinson’s disease: a meta-analysis of genome-wide association studies. Lancet Neurol. 2019;18:1091–102.
Võsa U, Claringbould A, Westra H-J, Bonder MJ, Deelen P, Zeng B, et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat Genet. 2021;53:1300–10.
Consortium GT. The GTEx consortium atlas of genetic regulatory effects across human tissues. Science. 2020;369:1318–30.
Gandal MJ, Zhang P, Hadjimichael E, Walker RL, Chen C, Liu S, et al. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science. 2018;362:eaat8127.
Wingo AP, Liu Y, Gerasimov ES, Gockley J, Logsdon BA, Duong DM, et al. Integrating human brain proteomes with genome-wide association data implicates new proteins in Alzheimer’s disease pathogenesis. Nat Genet. 2021;53:143–6.
Giambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 2014;10:e1004383.
Liu B, Gloudemans MJ, Rao AS, Ingelsson E, Montgomery SB. Abundant associations with gene expression complicate GWAS follow-up. Nat Genet. 2019;51:768–9.
Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016;48:481–7.
Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10:1523.
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:D605–612.
Wainberg M, Sinnott-Armstrong N, Mancuso N, Barbeira AN, Knowles DA, Golan D, et al. Opportunities and challenges for transcriptome-wide association studies. Nat Genet. 2019;51:592–9.
Schwartz S, Mumbach MR, Jovanovic M, Wang T, Maciag K, Bushkin GG, et al. Perturbation of m6A writers reveals two distinct classes of mRNA methylation at internal and 5’ sites. Cell Rep. 2014;8:284–96.
Hsu PJ, Zhu Y, Ma H, Guo Y, Shi X, Liu Y, et al. Ythdc2 is an N(6)-methyladenosine binding protein that regulates mammalian spermatogenesis. Cell Res. 2017;27:1115–27.
Arguello AE, DeLiberto AN, Kleiner RE. RNA chemical proteomics reveals the N(6)-methyladenosine (m(6)A)-regulated protein-RNA interactome. J Am Chem Soc. 2017;139:17249–52.
Friedrichs F, Zugck C, Rauch GJ, Ivandic B, Weichenhan D, Muller-Bardorff M, et al. HBEGF, SRA1, and IK: three cosegregating genes as determinants of cardiomyopathy. Genome Res. 2009;19:395–403.
He PC, He C. m(6) A RNA methylation: from mechanisms to therapeutic potential. EMBO J. 2021;40:e105977.
Lei C, Wang Q. The progression of N6-methyladenosine study and its role in neuropsychiatric disorders. Int J Mol Sci. 2022;23:5922.
Yang S, Wei J, Cui YH, Park G, Shah P, Deng Y, et al. m(6)A mRNA demethylase FTO regulates melanoma tumorigenicity and response to anti-PD-1 blockade. Nat Commun. 2019;10:2782.
Boissel S, Reish O, Proulx K, Kawagoe-Takaki H, Sedgwick B, Yeo GS, et al. Loss-of-function mutation in the dioxygenase-encoding FTO gene causes severe growth retardation and multiple malformations. Am J Hum Genet. 2009;85:106–11.
Daoud H, Zhang D, McMurray F, Yu A, Luco SM, Vanstone J, et al. Identification of a pathogenic FTO mutation by next-generation sequencing in a newborn with growth retardation and developmental delay. J Med Genet. 2016;53:200–7.
Pain O, Pocklington AJ, Holmans PA, Bray NJ, O’Brien HE, Hall LS, et al. Novel insight into the etiology of autism spectrum disorder gained by integrating expression data with genome-wide association statistics. Biol Psychiatry. 2019;86:265–73.
Ni J, Wang P, Yin KJ, Yang XK, Cen H, Sui C, et al. Novel insight into the aetiology of rheumatoid arthritis gained by a cross-tissue transcriptome-wide association study. RMD Open. 2022;8:e002529.
Liu E, Lv L, Zhan Y, Ma Y, Feng J, He Y, et al. METTL3/N6-methyladenosine/ miR-21-5p promotes obstructive renal fibrosis by regulating inflammation through SPRY1/ERK/NF-kappaB pathway activation. J Cell Mol Med. 2021;25:7660–74.
Schizophrenia Working Group of the Psychiatric Genomics Consortium Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511:421–7.
Aberg KA, Liu Y, Bukszar J, McClay JL, Khachane AN, Andreassen OA, et al. A comprehensive family-based replication study of schizophrenia genes. JAMA Psychiatry. 2013;70:573–81.
Ayala R, Willhoft O, Aramayo RJ, Wilkinson M, McCormack EA, Ocloo L, et al. Structure and regulation of the human INO80-nucleosome complex. Nature. 2018;556:391–5.
Conaway RC, Conaway JW. The INO80 chromatin remodeling complex in transcription, replication and repair. Trends Biochem Sci. 2009;34:71–7.
Vysotskiy M, Zhong X, Miller-Fleming TW, Zhou D, Cox NJ, Weiss LA. Integration of genetic, transcriptomic, and clinical data provides insight into 16p11.2 and 22q11.2 CNV genes. Genome Med. 2021;13:172.
Liu H, Sun Y, Zhang X, Li S, Hu D, Xiao L, et al. Integrated analysis of summary statistics to identify pleiotropic genes and pathways for the comorbidity of schizophrenia and cardiometabolic disease. Front Psychiatry. 2020;11:256.
Li X, Shen A, Zhao Y, Xia J. Mendelian randomization using the druggable genome reveals genetically supported drug targets for psychiatric disorders. Schizophr Bull. 2023;49:1305–15.
Liu J, Cheng Y, Li M, Zhang Z, Li T, Luo XJ. Genome-wide Mendelian randomization identifies actionable novel drug targets for psychiatric disorders. Neuropsychopharmacology. 2023;48:270–80.
Fernandes BS, Dai Y, Jia P, Zhao Z. Charting the proteome landscape in major psychiatric disorders: From biomarkers to biological pathways towards drug discovery. Eur Neuropsychopharmacol. 2022;61:43–59.
Tanaka H, Kondo K, Chen X, Homma H, Tagawa K, Kerever A, et al. The intellectual disability gene PQBP1 rescues Alzheimer’s disease pathology. Mol Psychiatry. 2018;23:2090–110.
Kang JH, Hwang SM, Chung IY. S100A8, S100A9 and S100A12 activate airway epithelial cells to produce MUC5AC via extracellular signal-regulated kinase and nuclear factor-kappaB pathways. Immunology. 2015;144:79–90.
Lavoie H, Gagnon J, Therrien M. ERK signalling: a master regulator of cell behaviour, life and fate. Nat Rev Mol Cell Biol. 2020;21:607–32.
Sonawane AR, DeMeo DL, Quackenbush J, Glass K. Constructing gene regulatory networks using epigenetic data. NPJ Syst Biol Appl. 2021;7:45.
Liu Y, You Y, Lu Z, Yang J, Li P, Liu L, et al. N (6)-methyladenosine RNA modification-mediated cellular metabolism rewiring inhibits viral replication. Science. 2019;365:1171–6.
Shlyueva D, Stampfel G, Stark A. Transcriptional enhancers: from properties to genome-wide predictions. Nat Rev Genet. 2014;15:272–86.
Zhang J, Simonti CN, Capra JA. Genome-wide maps of distal gene regulatory enhancers active in the human placenta. PLoS ONE. 2018;13:e0209611.
Vance KW, Sansom SN, Lee S, Chalei V, Kong L, Cooper SE, et al. The long non-coding RNA Paupar regulates the expression of both local and distal genes. EMBO J. 2014;33:296–311.
Liu J, Yue Y, Han D, Wang X, Fu Y, Zhang L, et al. A METTL3-METTL14 complex mediates mammalian nuclear RNA N6-adenosine methylation. Nat Chem Biol. 2014;10:93–5.
Demetriadou C, Raoukka A, Charidemou E, Mylonas C, Michael C, Parekh S, et al. Histone N-terminal acetyltransferase NAA40 links one-carbon metabolism to chemoresistance. Oncogene. 2022;41:571–85.
Narita T, Weinert BT, Choudhary C. Functions and mechanisms of non-histone protein acetylation. Nat Rev Mol Cell Biol. 2019;20:156–74.
Godavarthi JD, Polk S, Nunez L, Shivachar A, Glenn GNL, Matin A. Deficiency of splicing factor 1 (SF1) reduces intestinal polyp incidence in Apc(Min/)(+) mice. Biology. 2020;9:398.
Acknowledgements
This work was supported by grants from the STI2030-Major Projects 2021ZD0200800 and the National Natural Science Foundation of China (32170613 and 31401139).
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SC conceived, designed, and supervised the study. CL, LL, TP, and HZ participated in the data acquisition and data analysis. LY and LL contributed to the results interpretation. CL and LL wrote the manuscript. All authors revised the manuscript critically and approved the final version.
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Liufu, C., Luo, L., Pang, T. et al. Integration of multi-omics summary data reveals the role of N6-methyladenosine in neuropsychiatric disorders. Mol Psychiatry (2024). https://doi.org/10.1038/s41380-024-02574-w
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DOI: https://doi.org/10.1038/s41380-024-02574-w