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:

Transcriptomic and epigenomic consequences of heterozygous loss-of-function mutations in AKAP11, a shared risk gene for bipolar disorder and schizophrenia

Molecular Psychiatry (2025)Cite this article

Subjects

Abstract

The gene A-kinase anchoring protein 11 (AKAP11) recently emerged as a shared risk factor between bipolar disorder and schizophrenia, driven by large-effect loss-of-function (LoF) variants. Recent research has uncovered the neurophysiological characteristics and synapse proteomics profile of Akap11-mutant mouse models. Considering the role of AKAP11 in binding cAMP-dependent protein kinase A (PKA) and mediating phosphorylation of numerous substrates, such as transcription factors and epigenetic regulators, and given that chromatin alterations have been implicated in the brains of patients with bipolar disorder and schizophrenia, it is crucial to uncover the transcriptomic and chromatin dysregulations following the heterozygous knockout of AKAP11, particularly in human neurons. This study uses genome-wide approaches to investigate such aberrations in human induced pluripotent stem cell (iPSC)-derived neurons. We show the impact of heterozygous AKAP11 LoF mutations on the gene expression landscape and profile the DNA methylation and histone acetylation modifications. Altogether we highlight the involvement of aberrant activity of intergenic and intronic enhancers, which are enriched in PBX homeobox 2 (PBX2) and Nuclear Factor-1 (NF1) known binding motifs, respectively, in transcription dysregulations of genes mainly involved in DNA-binding transcription factor activity, actin binding and cytoskeleton regulation, and cytokine receptor binding. We also show significant downregulation of pathways related to ribosome structure and function, a pathway also altered in BD and SCZ post-mortem brain tissues and heterozygous Akap11-KO mice synapse proteomics. A better understanding of the dysregulations resulting from haploinsufficiency in AKAP11 improves our knowledge of the biological roots and pathophysiology of BD and SCZ, paving the way for better therapeutic approaches.

Access through your institution
Buy or subscribe

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

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Fig. 1: iPSC differentiation into neurons, validation of Het-AKAP11-KO, and culture viability.
Fig. 2: Identification of DEGs in Het-AKAP11-KO vs. WT and the pathways/GO terms affected.
Fig. 3: DMRs profiling of Het-AKAP11-KO compared to WT human iPSC-derived neurons.
Fig. 4: Intergenic and Intronic differential H3K27ac peaks profiling and investigating the association between H3K27ac level modifications and gene expression changes of target genes.
Fig. 5: DMR status in enhancers with concordant changes in the gene expression of target genes (using H3K27ac, DNA methylation, and gene expression datasets).

Similar content being viewed by others

Data availability

Data are available at NCBI GEO under the accession numbers GSE263850 (WGBS data), GSE263847 (RNA-seq data), and GSE263845 (ChIP-seq data). All other relevant data supporting the key findings of this study are available within the article and its supplementary information files (supplementary information is available at MP’s website) or from the corresponding authors upon request.

References

  1. Merikangas KR, Jin R, He JP, Kessler RC, Lee S, Sampson NA, et al. Prevalence and correlates of bipolar spectrum disorder in the world mental health survey initiative. Arch Gen Psychiatry. 2011;68:241–51.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Yamada Y, Matsumoto M, Iijima K, Sumiyoshi T. Specificity and continuity of schizophrenia and bipolar disorder: relation to biomarkers. Curr Pharm Des. 2020;26:191–200.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. McElroy SL. Diagnosing and treating comorbid (complicated) bipolar disorder. J Clin Psychiatry. 2004;65(Suppl 15):35–44.

    PubMed  CAS  Google Scholar 

  4. Charney AW, Stahl EA, Green EK, Chen CY, Moran JL, Chambert K, et al. Contribution of rare copy number variants to bipolar disorder risk is limited to schizoaffective cases. Biol Psychiatry. 2019;86:110–9.

    Article  PubMed  CAS  Google Scholar 

  5. Ganna A, Satterstrom FK, Zekavat SM, Das I, Kurki MI, Churchhouse C, et al. Quantifying the impact of rare and ultra-rare coding variation across the phenotypic spectrum. The American Journal of Human Genetics. 2018;102:1204–11.

    Article  PubMed  CAS  Google Scholar 

  6. Palmer DS, Howrigan DP, Chapman SB, Adolfsson R, Bass N, Blackwood D, et al. Exome sequencing in bipolar disorder identifies AKAP11 as a risk gene shared with schizophrenia. Nat Genet. 2022;54:541–7.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Bucko PJ, Scott JD. Drugs that regulate local cell signaling: AKAP targeting as a therapeutic option. Annu Rev Pharmacol Toxicol. 2021;61:361–79.

    Article  PubMed  CAS  Google Scholar 

  8. Dodge-Kafka KL, Soughayer J, Pare GC, Carlisle Michel JJ, Langeberg LK, Kapiloff MS, et al. The protein kinase A anchoring protein mAKAP coordinates two integrated cAMP effector pathways. Nature. 2005;437:574–8.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Esseltine JL, Scott JD. AKAP signaling complexes: pointing towards the next generation of therapeutic targets? Trends Pharmacol Sci. 2013;34:648–55.

    Article  PubMed  CAS  Google Scholar 

  10. Wild AR, Dell’Acqua ML. Potential for therapeutic targeting of AKAP signaling complexes in nervous system disorders. Pharmacol Ther. 2018;185:99–121.

    Article  PubMed  CAS  Google Scholar 

  11. Woolfrey KM, Dell’Acqua ML. Coordination of protein phosphorylation and dephosphorylation in synaptic plasticity. J Biol Chem. 2015;290:28604–12.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  Google Scholar 

  13. Schillace RV, Scott JD. Association of the type 1 protein phosphatase PP1 with the A-kinase anchoring protein AKAP220. Curr Biol. 1999;9:321–4.

    Article  PubMed  CAS  Google Scholar 

  14. Whiting JL, Nygren PJ, Tunquist BJ, Langeberg LK, Seternes O-M, Scott JD. Protein kinase A opposes the phosphorylation-dependent recruitment of glycogen synthase Kinase 3β to A-kinase anchoring protein 220 *. Journal of Biological Chemistry. 2015;290:19445–57.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Tanji C, Yamamoto H, Yorioka N, Kohno N, Kikuchi K, Kikuchi A. A-kinase anchoring protein AKAP220 binds to glycogen synthase kinase-3beta (GSK-3beta) and mediates protein kinase A-dependent inhibition of GSK-3beta. J Biol Chem. 2002;277:36955–61.

    Article  PubMed  CAS  Google Scholar 

  16. Freland L, Beaulieu JM. Inhibition of GSK3 by lithium, from single molecules to signaling networks. Front Mol Neurosci. 2012;5:14.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Alda M. Lithium in the treatment of bipolar disorder: pharmacology and pharmacogenetics. Mol Psychiatry. 2015;20:661–70.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Jope RS. Lithium and GSK-3: one inhibitor, two inhibitory actions, multiple outcomes. Trends Pharmacol Sci. 2003;24:441–3.

    Article  PubMed  CAS  Google Scholar 

  19. Logue JS, Whiting JL, Tunquist B, Langeberg LK, Scott JD. Anchored protein kinase A recruitment of active Rac GTPase. J Biol Chem. 2011;286:22113–21.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Steven A, Friedrich M, Jank P, Heimer N, Budczies J, Denkert C, et al. What turns CREB on? And off? And why does it matter? Cell Mol Life Sci. 2020;77:4049–67.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Ha CH, Kim JY, Zhao J, Wang W, Jhun BS, Wong C, et al. PKA phosphorylates histone deacetylase 5 and prevents its nuclear export, leading to the inhibition of gene transcription and cardiomyocyte hypertrophy. Proc Natl Acad Sci USA. 2010;107:15467–72.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Chen S, Owens GC, Makarenkova H, Edelman DB. HDAC6 regulates mitochondrial transport in hippocampal neurons. PLoS One. 2010;5:e10848.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Herzog LE, Wang L, Yu E, Choi S, Farsi Z, Song BJ, et al. Mouse mutants in schizophrenia risk genes GRIN2A and AKAP11 show EEG abnormalities in common with schizophrenia patients. Transl Psychiatry. 2023;13:92.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Tekell JL, Hoffmann R, Hendrickse W, Greene RW, Rush AJ, Armitage R. High frequency EEG activity during sleep: characteristics in schizophrenia and depression. Clin EEG Neurosci. 2005;36:25–35.

    Article  PubMed  Google Scholar 

  25. Aryal S, Bonanno K, Song B, Mani DR, Keshishian H, Carr SA, et al. Deep proteomics identifies shared molecular pathway alterations in synapses of patients with schizophrenia and bipolar disorder and mouse model. Cell Rep. 2023;42:112497.

    Article  PubMed  CAS  Google Scholar 

  26. Stern S, Santos R, Marchetto MC, Mendes APD, Rouleau GA, Biesmans S, et al. Neurons derived from patients with bipolar disorder divide into intrinsically different sub-populations of neurons, predicting the patients’ responsiveness to lithium. Mol Psychiatry. 2018;23:1453–65.

    Article  PubMed  CAS  Google Scholar 

  27. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102:15545–50.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Whiting JL, Nygren PJ, Tunquist BJ, Langeberg LK, Seternes OM, Scott JD. Protein kinase A opposes the phosphorylation-dependent recruitment of glycogen synthase kinase 3beta to A-kinase anchoring protein 220. J Biol Chem. 2015;290:19445–57.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21.

    Article  PubMed  CAS  Google Scholar 

  31. Anders S, Pyl PT, Huber W. HTSeq-a python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31:166–9.

    Article  PubMed  CAS  Google Scholar 

  32. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.

    Article  PubMed  CAS  Google Scholar 

  33. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Gu Z. Complex heatmap visualization. iMeta. 2022;1:e43.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Ge SX, Jung D, Yao R. ShinyGO: a graphical gene-set enrichment tool for animals and plants. Bioinformatics. 2020;36:2628–9.

    Article  PubMed  CAS  Google Scholar 

  36. Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28:882–3.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Krueger F, Andrews SR. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics. 2011;27:1571–2.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Feng H, Wu H. Differential methylation analysis for bisulfite sequencing using DSS. Quant Biol. 2019;7:327–34.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell. 2010;38:576–89.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754–60.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 2008;9:R137.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Ramirez F, Dundar F, Diehl S, Gruning BA, Manke T. deepTools: a flexible platform for exploring deep-sequencing data. Nucleic Acids Res. 2014;42:W187–191.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Naqvi S, Martin KJ, Arthur JS. CREB phosphorylation at Ser133 regulates transcription via distinct mechanisms downstream of cAMP and MAPK signalling. Biochem J. 2014;458:469–79.

    Article  PubMed  CAS  Google Scholar 

  44. Hunt GJ, Freytag S, Bahlo M, Gagnon-Bartsch JA. dtangle: accurate and robust cell type deconvolution. Bioinformatics. 2019;35:2093–9.

    Article  PubMed  CAS  Google Scholar 

  45. Sutton GJ, Poppe D, Simmons RK, Walsh K, Nawaz U, Lister R, et al. Comprehensive evaluation of deconvolution methods for human brain gene expression. Nat Commun. 2022;13:1358.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Gillespie M, Jassal B, Stephan R, Milacic M, Rothfels K, Senff-Ribeiro A, et al. The reactome pathway knowledgebase 2022. Nucleic Acids Res. 2022;50:D687–D692.

    Article  PubMed  CAS  Google Scholar 

  47. Fabregat A, Sidiropoulos K, Viteri G, Forner O, Marin-Garcia P, Arnau V, et al. Reactome pathway analysis: a high-performance in-memory approach. BMC Bioinformatics. 2017;18:142.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Moller TC, Hottin J, Clerte C, Zwier JM, Durroux T, Rondard P, et al. Oligomerization of a G protein-coupled receptor in neurons controlled by its structural dynamics. Sci Rep. 2018;8:10414.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25:25–29.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Gene Ontology C, Aleksander SA, Balhoff J, Carbon S, Cherry JM, Drabkin HJ, et al. The gene ontology knowledgebase in 2023. Genetics. 2023;224:iyad031.

    Article  Google Scholar 

  51. Creyghton MP, Cheng AW, Welstead GG, Kooistra T, Carey BW, Steine EJ, et al. Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proc Natl Acad Sci USA. 2010;107:21931–6.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Rada-Iglesias A, Bajpai R, Swigut T, Brugmann SA, Flynn RA, Wysocka J. A unique chromatin signature uncovers early developmental enhancers in humans. Nature. 2011;470:279–83.

    Article  PubMed  CAS  Google Scholar 

  53. Melgar MF, Collins FS, Sethupathy P. Discovery of active enhancers through bidirectional expression of short transcripts. Genome Biol. 2011;12:R113.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Zerbino DR, Johnson N, Juettemann T, Wilder SP, Flicek P. WiggleTools: parallel processing of large collections of genome-wide datasets for visualization and statistical analysis. Bioinformatics. 2014;30:1008–9.

    Article  PubMed  CAS  Google Scholar 

  55. Zhang T, Zhang Z, Dong Q, Xiong J, Zhu B. Histone H3K27 acetylation is dispensable for enhancer activity in mouse embryonic stem cells. Genome Biol. 2020;21:45.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Hartl D, Krebs AR, Juttner J, Roska B, Schubeler D. Cis-regulatory landscapes of four cell types of the retina. Nucleic Acids Res. 2017;45:11607–21.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Ziller MJ, Gu H, Muller F, Donaghey J, Tsai LT, Kohlbacher O, et al. Charting a dynamic DNA methylation landscape of the human genome. Nature. 2013;500:477–81.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Hodges E, Molaro A, Dos Santos CO, Thekkat P, Song Q, Uren PJ, et al. Directional DNA methylation changes and complex intermediate states accompany lineage specificity in the adult hematopoietic compartment. Mol Cell. 2011;44:17–28.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Stadler MB, Murr R, Burger L, Ivanek R, Lienert F, Scholer A, et al. DNA-binding factors shape the mouse methylome at distal regulatory regions. Nature. 2011;480:490–5.

    Article  PubMed  CAS  Google Scholar 

  60. Kreibich E, Kleinendorst R, Barzaghi G, Kaspar S, Krebs AR. Single-molecule footprinting identifies context-dependent regulation of enhancers by DNA methylation. Mol Cell. 2023;83:787–802 e789.

    Article  PubMed  CAS  Google Scholar 

  61. Mayr B, Montminy M. Transcriptional regulation by the phosphorylation-dependent factor CREB. Nat Rev Mol Cell Biol. 2001;2:599–609.

    Article  PubMed  CAS  Google Scholar 

  62. Marmorstein R, Zhou MM. Writers and readers of histone acetylation: structure, mechanism, and inhibition. Cold Spring Harb Perspect Biol. 2014;6:a018762.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Jin Q, Yu LR, Wang L, Zhang Z, Kasper LH, Lee JE, et al. Distinct roles of GCN5/PCAF-mediated H3K9ac and CBP/p300-mediated H3K18/27ac in nuclear receptor transactivation. EMBO J. 2011;30:249–62.

    Article  PubMed  CAS  Google Scholar 

  64. Bartal G, Yitzhaky A, Segev A, Hertzberg L. Multiple genes encoding mitochondrial ribosomes are downregulated in brain and blood samples of individuals with schizophrenia. World J Biol Psychiatry. 2023;24:829–37.

    Article  PubMed  CAS  Google Scholar 

  65. Iadevaia V, Liu R, Proud CG. mTORC1 signaling controls multiple steps in ribosome biogenesis. Semin Cell Dev Biol. 2014;36:113–20.

    Article  PubMed  CAS  Google Scholar 

  66. Huang G, Li H, Zhang H. Abnormal expression of mitochondrial ribosomal proteins and their encoding genes with cell apoptosis and diseases. Int J Mol Sci. 2020;21:8879.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  67. Deng Z, Li X, Blanca Ramirez M, Purtell K, Choi I, Lu JH, et al. Selective autophagy of AKAP11 activates cAMP/PKA to fuel mitochondrial metabolism and tumor cell growth. Proc Natl Acad Sci USA. 2021;118:e2020215118.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Rangaraju V, Lauterbach M, Schuman EM. Spatially stable mitochondrial compartments fuel local translation during plasticity. Cell. 2019;176:73–84 e15.

    Article  PubMed  CAS  Google Scholar 

  69. Osimo EF, Beck K, Reis Marques T, Howes OD. Synaptic loss in schizophrenia: a meta-analysis and systematic review of synaptic protein and mRNA measures. Mol Psychiatry. 2019;24:549–61.

    Article  PubMed  CAS  Google Scholar 

  70. Niemsiri V, Rosenthal SB, Nievergelt CM, Maihofer AX, Marchetto MC, Santos R, et al. Focal adhesion is associated with lithium response in bipolar disorder: evidence from a network-based multi-omics analysis. Mol Psychiatry. 2023;29:6–19.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Ou AH, Rosenthal SB, Adli M, Akiyama K, Akula N, Alda M, et al. Lithium response in bipolar disorder is associated with focal adhesion and PI3K-Akt networks: a multi-omics replication study. Transl Psychiatry. 2024;14:109.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  72. Noritake J, Watanabe T, Sato K, Wang S, Kaibuchi K. IQGAP1: a key regulator of adhesion and migration. J Cell Sci. 2005;118:2085–92.

    Article  PubMed  CAS  Google Scholar 

  73. Johnson M, Sharma M, Henderson BR. IQGAP1 regulation and roles in cancer. Cell Signal. 2009;21:1471–8.

    Article  PubMed  CAS  Google Scholar 

  74. Logue JS, Whiting JL, Tunquist B, Sacks DB, Langeberg LK, Wordeman L, et al. AKAP220 protein organizes signaling elements that impact cell migration. J Biol Chem. 2011;286:39269–81.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  75. Pantazopoulos H, Katsel P, Haroutunian V, Chelini G, Klengel T, Berretta S. Molecular signature of extracellular matrix pathology in schizophrenia. Eur J Neurosci. 2021;53:3960–87.

    Article  PubMed  Google Scholar 

  76. Matthews PR, Eastwood SL, Harrison PJ. Reduced myelin basic protein and actin-related gene expression in visual cortex in schizophrenia. PLoS One. 2012;7:e38211.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  77. Tee JY, Sutharsan R, Fan Y, Mackay-Sim A. Cell migration in schizophrenia: Patient-derived cells do not regulate motility in response to extracellular matrix. Mol Cell Neurosci. 2017;80:111–22.

    Article  PubMed  CAS  Google Scholar 

  78. Kahler AK, Djurovic S, Kulle B, Jonsson EG, Agartz I, Hall H, et al. Association analysis of schizophrenia on 18 genes involved in neuronal migration: MDGA1 as a new susceptibility gene. Am J Med Genet B Neuropsychiatr Genet. 2008;147B:1089–1100.

    Article  PubMed  Google Scholar 

  79. Gilman SR, Chang J, Xu B, Bawa TS, Gogos JA, Karayiorgou M, et al. Diverse types of genetic variation converge on functional gene networks involved in schizophrenia. Nat Neurosci. 2012;15:1723–8.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. Fromer M, Pocklington AJ, Kavanagh DH, Williams HJ, Dwyer S, Gormley P, et al. De novo mutations in schizophrenia implicate synaptic networks. Nature. 2014;506:179–84.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  81. Zhao Z, Xu J, Chen J, Kim S, Reimers M, Bacanu SA, et al. Transcriptome sequencing and genome-wide association analyses reveal lysosomal function and actin cytoskeleton remodeling in schizophrenia and bipolar disorder. Mol Psychiatry. 2015;20:563–72.

    Article  PubMed  CAS  Google Scholar 

  82. Schmitt A, Leonardi-Essmann F, Durrenberger PF, Wichert SP, Spanagel R, Arzberger T, et al. Structural synaptic elements are differentially regulated in superior temporal cortex of schizophrenia patients. Eur Arch Psychiatry Clin Neurosci. 2012;262:565–77.

    Article  PubMed  PubMed Central  Google Scholar 

  83. Cristino AS, Williams SM, Hawi Z, An JY, Bellgrove MA, Schwartz CE, et al. Neurodevelopmental and neuropsychiatric disorders represent an interconnected molecular system. Mol Psychiatry. 2014;19:294–301.

    Article  PubMed  CAS  Google Scholar 

  84. Prieto GA, Cotman CW. Cytokines and cytokine networks target neurons to modulate long-term potentiation. Cytokine Growth Factor Rev. 2017;34:27–33.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  85. Momtazmanesh S, Zare-Shahabadi A, Rezaei N. Cytokine alterations in schizophrenia: an updated review. Front Psychiatry. 2019;10:892.

    Article  PubMed  PubMed Central  Google Scholar 

  86. Lesh TA, Careaga M, Rose DR, McAllister AK, Van de Water J, Carter CS, et al. Cytokine alterations in first-episode schizophrenia and bipolar disorder: relationships to brain structure and symptoms. J Neuroinflammation. 2018;15:165.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  87. Rizzardi LF, Hickey PF, Rodriguez DiBlasi V, Tryggvadottir R, Callahan CM, Idrizi A, et al. Neuronal brain-region-specific DNA methylation and chromatin accessibility are associated with neuropsychiatric trait heritability. Nat Neurosci. 2019;22:307–16.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  88. Hannon E, Marzi SJ, Schalkwyk LS, Mill J. Genetic risk variants for brain disorders are enriched in cortical H3K27ac domains. Mol Brain. 2019;12:7.

    Article  PubMed  PubMed Central  Google Scholar 

  89. Girdhar K, Hoffman GE, Bendl J, Rahman S, Dong P, Liao W, et al. Chromatin domain alterations linked to 3D genome organization in a large cohort of schizophrenia and bipolar disorder brains. Nat Neurosci. 2022;25:474–83.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  90. Selleri L, DiMartino J, van Deursen J, Brendolan A, Sanyal M, Boon E, et al. The TALE homeodomain protein Pbx2 is not essential for development and long-term survival. Mol Cell Biol. 2004;24:5324–31.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  91. Wang W, Stock RE, Gronostajski RM, Wong YW, Schachner M, Kilpatrick DL. A role for nuclear factor I in the intrinsic control of cerebellar granule neuron gene expression. J Biol Chem. 2004;279:53491–7.

    Article  PubMed  CAS  Google Scholar 

  92. Reifel-Miller AE, Calnek DS, Grinnell BW. Tyrosine phosphorylation regulates the DNA binding activity of a nuclear factor 1-like repressor protein. Journal of Biological Chemistry. 1994;269:23861–4.

    Article  PubMed  CAS  Google Scholar 

  93. Kawamura H, Nagata K, Masamune Y, Nakanishi Y. Phosphorylation of NF-I in vitro by cdc2 kinase. Biochem Biophys Res Commun. 1993;192:1424–31.

    Article  PubMed  CAS  Google Scholar 

  94. Nebl G, Mermod N, Cato AC. Post-transcriptional down-regulation of expression of transcription factor NF1 by Ha-ras oncogene. Journal of Biological Chemistry. 1994;269:7371–8.

    Article  PubMed  CAS  Google Scholar 

  95. Chen KS, Lim JWC, Richards LJ, Bunt J. The convergent roles of the nuclear factor I transcription factors in development and cancer. Cancer Lett. 2017;410:124–38.

    Article  PubMed  CAS  Google Scholar 

  96. Catapano LA, Manji HK. G protein-coupled receptors in major psychiatric disorders. Biochim Biophys Acta. 2007;1768:976–93.

    Article  PubMed  CAS  Google Scholar 

  97. Cruceanu C, Schmouth JF, Torres-Platas SG, Lopez JP, Ambalavanan A, Darcq E, et al. Rare susceptibility variants for bipolar disorder suggest a role for G protein-coupled receptors. Mol Psychiatry. 2018;23:2050–6.

    Article  PubMed  CAS  Google Scholar 

  98. Boczek T, Mackiewicz J, Sobolczyk M, Wawrzyniak J, Lisek M, Ferenc B, et al. The role of G Protein-Coupled Receptors (GPCRs) and calcium signaling in schizophrenia. Focus on GPCRs activated by neurotransmitters and chemokines. Cells. 2021;10:1228.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  99. Bosse A, Zulch A, Becker MB, Torres M, Gomez-Skarmeta JL, Modolell J, et al. Identification of the vertebrate Iroquois homeobox gene family with overlapping expression during early development of the nervous system. Mech Dev. 1997;69:169–81.

    Article  PubMed  CAS  Google Scholar 

  100. Werner S, Stamm H, Pandjaitan M, Kemming D, Brors B, Pantel K, et al. Iroquois homeobox 2 suppresses cellular motility and chemokine expression in breast cancer cells. BMC Cancer. 2015;15:896.

    Article  PubMed  PubMed Central  Google Scholar 

  101. Kakinuma N, Roy BC, Zhu Y, Wang Y, Kiyama R. Kank regulates RhoA-dependent formation of actin stress fibers and cell migration via 14-3-3 in PI3K-Akt signaling. J Cell Biol. 2008;181:537–49.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  102. Gee HY, Zhang F, Ashraf S, Kohl S, Sadowski CE, Vega-Warner V, et al. KANK deficiency leads to podocyte dysfunction and nephrotic syndrome. J Clin Invest. 2015;125:2375–84.

    Article  PubMed  PubMed Central  Google Scholar 

  103. Glessner JT, Li J, Wang D, March M, Lima L, Desai A, et al. Copy number variation meta-analysis reveals a novel duplication at 9p24 associated with multiple neurodevelopmental disorders. Genome Med. 2017;9:106.

    Article  PubMed  PubMed Central  Google Scholar 

  104. Consortium GT. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013;45:580–5.

    Article  Google Scholar 

  105. Romanos M, Freitag C, Jacob C, Craig DW, Dempfle A, Nguyen TT, et al. Genome-wide linkage analysis of ADHD using high-density SNP arrays: novel loci at 5q13.1 and 14q12. Mol Psychiatry. 2008;13:522–30.

    Article  PubMed  CAS  Google Scholar 

  106. Larsson H, Ryden E, Boman M, Langstrom N, Lichtenstein P, Landen M. Risk of bipolar disorder and schizophrenia in relatives of people with attention-deficit hyperactivity disorder. Br J Psychiatry. 2013;203:103–6.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The work in G.A.R.’s lab is supported by the ERA-PerMed in partnership with FRQS (JTC2018, 280240). B.C.’s team is supported by ERAPerMed PLOT-BD, a grant from the Fondation Bettencourt Schueller, and a government grant managed by the Agence Nationale de la Recherche under the France 2030 program (ANR-22-EXPR-0013). We also acknowledge the financial support from the Brain & Behavior Research Foundation Young Investigator Award to A.K; 30822. N.F. is supported by the studentship award Fonds de Recherche du Québec – Santé (FRQS). C.L. is supported by the CIHR Banting Fellowship. Computational analysis, data processing, and execution of bioinformatics pipelines for the analyses were performed by the Canadian Centre for Computational Genomics (C3G)–Montréal Node (bioinformatics specialist: Alain Pacis), using infrastructure provided by Compute Canada and Calcul Quebec. ChIP-seq and WGBS library preparation and sequencing were performed by the McGill Genome Center platform.

Author information

Authors and Affiliations

  1. Department of Human Genetics, McGill University, Montreal, QC, Canada

    Nargess Farhangdoost, Farah Aboasali & Guy A. Rouleau

  2. Montreal Neurological Institute-Hospital, Montreal, QC, Canada

    Nargess Farhangdoost, Yumin Liu, Daniel Rochefort, Farah Aboasali, Alessia Pietrantonio, Patrick A. Dion, Anouar Khayachi & Guy A. Rouleau

  3. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA

    Calwing Liao

  4. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Calwing Liao

  5. Department of Medicine, Harvard Medical School, Boston, MA, USA

    Calwing Liao

  6. Department of Neurology & Neurosurgery, McGill University, Montreal, QC, Canada

    Yumin Liu, Daniel Rochefort, Alessia Pietrantonio, Patrick A. Dion, Anouar Khayachi & Guy A. Rouleau

  7. Department of Psychiatry, Dalhousie University, Halifax, NS, Canada

    Martin Alda

  8. Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (INSERM U1266), Institut Pasteur (CNRS UMR3571), GHU Paris Psychiatrie et Neurosciences, Paris, France

    Boris Chaumette

  9. Department of Psychiatry, McGill University, Montreal, QC, Canada

    Boris Chaumette

Authors
  1. Nargess Farhangdoost
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Calwing Liao
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Yumin Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Daniel Rochefort
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Farah Aboasali
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Alessia Pietrantonio
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Martin Alda
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Patrick A. Dion
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Boris Chaumette
    View author publications

    You can also search for this author inPubMed Google Scholar

  10. Anouar Khayachi
    View author publications

    You can also search for this author inPubMed Google Scholar

  11. Guy A. Rouleau
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

NF and CL conceived the study. NF defined the scope and direction of the project and designed, developed, and conducted the laboratory experiments. YL and AK provided guidance on the maintenance and differentiation of iPSCs, NPCs, and neuronal cultures. AK also assisted with confocal microscopy. NF, with input from AK, defined the objectives and provided direction for the computational analyses and figure generation, which were carried out by the C3G–Montréal Node. DR, FA, and AP contributed to laboratory work during the revision process (immunoblotting and additional RT-qPCR validations). MA, PAD, GAR, BC, CL, and AK offered valuable scientific input throughout the study and contributed to revising the manuscript. NF wrote the manuscript.

Corresponding authors

Correspondence to Boris Chaumette, Anouar Khayachi or Guy A. Rouleau.

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

Farhangdoost, N., Liao, C., Liu, Y. et al. Transcriptomic and epigenomic consequences of heterozygous loss-of-function mutations in AKAP11, a shared risk gene for bipolar disorder and schizophrenia. Mol Psychiatry (2025). https://doi.org/10.1038/s41380-025-03040-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41380-025-03040-x

Search

Advanced search

Quick links

Morty Proxy This is a proxified and sanitized view of the page, visit original site.