Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Integration of multi-omics summary data reveals the role of N6-methyladenosine in neuropsychiatric disorders

Molecular Psychiatry (2024)Cite this article

Subjects

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Flowchart of the analysis pipeline.
Fig. 2: The statistics for the association of m6A with its corresponding gene expression.
Fig. 3: Interaction network of m6A, gene expression and diseases.
Fig. 4: Aassociation results of "chr16_89726971_89727131 - SPATA2L - schizophrenia".
Fig. 5: Functional analysis of the m6A-regulated genes.

Similar content being viewed by others

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

  1. 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.

    Article  CAS  PubMed  Google Scholar 

  2. Geschwind DH, Flint J. Genetics and genomics of psychiatric disease. Science. 2015;349:1489–94.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. 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.

    Article  CAS  PubMed  Google Scholar 

  4. Jansen RC, Nap JP. Genetical genomics: the added value from segregation. Trends Genet. 2001;17:388–91.

    Article  CAS  PubMed  Google Scholar 

  5. 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.

    PubMed  PubMed Central  Google Scholar 

  6. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  9. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  10. 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.

    Article  CAS  PubMed  Google Scholar 

  11. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. 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.

    Article  CAS  PubMed  Google Scholar 

  13. Roundtree IA, Evans ME, Pan T, He C. Dynamic RNA modifications in gene expression regulation. Cell. 2017;169:1187–1200.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. 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.

    Article  CAS  PubMed  Google Scholar 

  15. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  16. 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.

    Article  CAS  PubMed  Google Scholar 

  17. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  18. 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.

    Article  PubMed  Google Scholar 

  19. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Frye M, Harada BT, Behm M, He C. RNA modifications modulate gene expression during development. Science. 2018;361:1346–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  27. 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.

    Article  CAS  PubMed  Google Scholar 

  28. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  29. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Shu L, Huang X, Cheng X, Li X. Emerging roles of N6-methyladenosine modification in neurodevelopment and neurodegeneration. Cells. 2021;10:2694.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Zhang N, Ding C, Zuo Y, Peng Y, Zuo L. N6-methyladenosine and neurological diseases. Mol Neurobiol. 2022;59:1925–37.

    Article  CAS  PubMed  Google Scholar 

  32. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Zhao BS, Roundtree IA, He C. Post-transcriptional gene regulation by mRNA modifications. Nat Rev Mol Cell Biol. 2017;18:31–42.

    Article  CAS  PubMed  Google Scholar 

  35. Meyer KD, Jaffrey SR. The dynamic epitranscriptome: N6-methyladenosine and gene expression control. Nat Rev Mol Cell Biol. 2014;15:313–26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  38. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. 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.

    Article  CAS  PubMed  Google Scholar 

  42. 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.

    Article  Google Scholar 

  43. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Consortium GT. The GTEx consortium atlas of genetic regulatory effects across human tissues. Science. 2020;369:1318–30.

  47. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  50. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. 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.

    Article  CAS  PubMed  Google Scholar 

  52. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  53. 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.

    Article  CAS  PubMed  Google Scholar 

  54. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. 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.

    Article  CAS  PubMed  Google Scholar 

  58. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. He PC, He C. m(6) A RNA methylation: from mechanisms to therapeutic potential. EMBO J. 2021;40:e105977.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Lei C, Wang Q. The progression of N6-methyladenosine study and its role in neuropsychiatric disorders. Int J Mol Sci. 2022;23:5922.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  62. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. 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.

    Article  CAS  PubMed  Google Scholar 

  64. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  66. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Schizophrenia Working Group of the Psychiatric Genomics Consortium Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511:421–7.

    Article  PubMed Central  Google Scholar 

  68. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Conaway RC, Conaway JW. The INO80 chromatin remodeling complex in transcription, replication and repair. Trends Biochem Sci. 2009;34:71–7.

    Article  CAS  PubMed  Google Scholar 

  71. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  73. 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.

    Article  PubMed  Google Scholar 

  74. 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.

    Article  PubMed  Google Scholar 

  75. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. 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.

    Article  CAS  PubMed  Google Scholar 

  78. 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.

    Article  CAS  PubMed  Google Scholar 

  79. Sonawane AR, DeMeo DL, Quackenbush J, Glass K. Constructing gene regulatory networks using epigenetic data. NPJ Syst Biol Appl. 2021;7:45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. 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.

    Article  CAS  PubMed  Google Scholar 

  81. Shlyueva D, Stampfel G, Stark A. Transcriptional enhancers: from properties to genome-wide predictions. Nat Rev Genet. 2014;15:272–86.

    Article  CAS  PubMed  Google Scholar 

  82. Zhang J, Simonti CN, Capra JA. Genome-wide maps of distal gene regulatory enhancers active in the human placenta. PLoS ONE. 2018;13:e0209611.

    Article  PubMed  PubMed Central  Google Scholar 

  83. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. 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.

    Article  CAS  PubMed  Google Scholar 

  85. 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.

    Article  CAS  PubMed  Google Scholar 

  86. Narita T, Weinert BT, Choudhary C. Functions and mechanisms of non-histone protein acetylation. Nat Rev Mol Cell Biol. 2019;20:156–74.

    Article  CAS  PubMed  Google Scholar 

  87. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by grants from the STI2030-Major Projects 2021ZD0200800 and the National Natural Science Foundation of China (32170613 and 31401139).

Author information

Author notes

  1. These authors contributed equally: Chao Liufu, Lingxue Luo.

Authors and Affiliations

  1. Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China

    Chao Liufu, Lingxue Luo, Tao Pang, Haohao Zheng, Li Yang, Lin Lu & Suhua Chang

  2. Research Units of Diagnosis and Treatment of Mood Cognitive Disorder, Chinese Academy of Medical Sciences, Beijing, 100191, China

    Lin Lu & Suhua Chang

Authors
  1. Chao Liufu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lingxue Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tao Pang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Haohao Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Li Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lin Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Suhua Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

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.

Corresponding author

Correspondence to Suhua Chang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41380-024-02574-w

Search

Advanced search

Quick links