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A causal association of ANKRD37 with human hippocampal volume

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Abstract

Human hippocampal volume has been separately associated with single nucleotide polymorphisms (SNPs), DNA methylation and gene expression, but their causal relationships remain largely unknown. Here, we aimed at identifying the causal relationships of SNPs, DNA methylation, and gene expression that are associated with hippocampal volume by integrating cross-omics analyses with genome editing, overexpression and causality inference. Based on structural neuroimaging data and blood-derived genome, transcriptome and methylome data, we prioritized a possibly causal association across multiple molecular phenotypes: rs1053218 mutation leads to cg26741686 hypermethylation, thus leads to overactivation of the associated ANKRD37 gene expression in blood, a gene involving hypoxia, which may result in the reduction of human hippocampal volume. The possibly causal relationships from rs1053218 to cg26741686 methylation to ANKRD37 expression obtained from peripheral blood were replicated in human hippocampal tissue. To confirm causality, we performed CRISPR-based genome and epigenome-editing of rs1053218 homologous alleles and cg26741686 methylation in mouse neural stem cell differentiation models, and overexpressed ANKRD37 in mouse hippocampus. These in-vitro and in-vivo experiments confirmed that rs1053218 mutation caused cg26741686 hypermethylation and ANKRD37 overexpression, and cg26741686 hypermethylation favored ANKRD37 overexpression, and ANKRD37 overexpression reduced hippocampal volume. The pairwise relationships of rs1053218 with hippocampal volume, rs1053218 with cg26741686 methylation, cg26741686 methylation with ANKRD37 expression, and ANKRD37 expression with hippocampal volume could be replicated in an independent healthy young (n = 443) dataset and observed in elderly people (n = 194), and were more significant in patients with late-onset Alzheimer’s disease (n = 76). This study revealed a novel causal molecular association mechanism of ANKRD37 with human hippocampal volume, which may facilitate the design of prevention and treatment strategies for hippocampal impairment.

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Fig. 1: A schematic summary of the study design.
Fig. 2: Identifying possibly casual S → M → E → H associations in blood and hippocampal tissues.
Fig. 3: Validation of S → M, S → E and M → E causal effects in mouse neural stem cell (NSC).
Fig. 4: Validation of E → H causal association in mice hippocampus.
Fig. 5: Pairwise replication of the S → M → E → H associations of ANKRD37 in HYC and pairwise comparisons in different populations.

Code availability

Custom code that supports the findings of this study is available from the corresponding author upon request.

References

  1. Lisman J, Buzsáki G, Eichenbaum H, Nadel L, Ranganath C, Redish AD. Viewpoints: how the hippocampus contributes to memory, navigation and cognition. Nat Neurosci. 2017;20:1434–47.

    CAS  Google Scholar 

  2. Kim EJ, Pellman B, Kim JJ. Stress effects on the hippocampus: a critical review. Learn Mem. 2015;22:411–6.

    Google Scholar 

  3. Moodley K, Chan D. The hippocampus in neurodegenerative disease. Front Neurol Neurosci. 2014;34:95–108.

    CAS  Google Scholar 

  4. Chatzikonstantinou A. Epilepsy and the hippocampus. Front Neurol Neurosci. 2014;34:121–42.

    Google Scholar 

  5. MacQueen G, Frodl T. The hippocampus in major depression: evidence for the convergence of the bench and bedside in psychiatric research? Mol Psychiatry. 2011;16:252–64.

    CAS  Google Scholar 

  6. Roeske MJ, Konradi C, Heckers S, Lewis AS. Hippocampal volume and hippocampal neuron density, number and size in schizophrenia: a systematic review and meta-analysis of postmortem studies. Mol Psychiatry. 2021;26:3524–35.

    Google Scholar 

  7. Halliday G. Pathology and hippocampal atrophy in Alzheimer’s disease. Lancet Neurol. 2017;16:862–4.

    Google Scholar 

  8. Bayram E, Caldwell JZ, Banks SJ. Current understanding of magnetic resonance imaging biomarkers and memory in Alzheimer’s disease. Alzheimers Dement (N Y). 2018;4:395–413.

    Google Scholar 

  9. Henneman WJ, Sluimer JD, Barnes J, van der Flier WM, Sluimer IC, Fox NC, et al. Hippocampal atrophy rates in Alzheimer disease: added value over whole brain volume measures. Neurology. 2009;72:999–1007.

    CAS  Google Scholar 

  10. Jack CR Jr, Petersen RC, Xu Y, O’Brien PC, Smith GE, Ivnik RJ, et al. Rates of hippocampal atrophy correlate with change in clinical status in aging and AD. Neurology. 2000;55:484–90.

    Google Scholar 

  11. Stein JL, Medland SE, Vasquez AA, Hibar DP, Senstad RE, Winkler AM, et al. Identification of common variants associated with human hippocampal and intracranial volumes. Nat Genet. 2012;44:552–61.

    CAS  Google Scholar 

  12. Hibar DP, Stein JL, Renteria ME, Arias-Vasquez A, Desrivières S, Jahanshad N, et al. Common genetic variants influence human subcortical brain structures. Nature. 2015;520:224–9.

    CAS  Google Scholar 

  13. Elliott LT, Sharp K, Alfaro-Almagro F, Shi S, Miller KL, Douaud G, et al. Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature. 2018;562:210–6.

    CAS  Google Scholar 

  14. Alexander RP, Fang G, Rozowsky J, Snyder M, Gerstein MB. Annotating non-coding regions of the genome. Nat Rev Genet. 2010;11:559–71.

    CAS  Google Scholar 

  15. Jasinska AJ, Zelaya I, Service SK, Peterson CB, Cantor RM, Choi OW, et al. Genetic variation and gene expression across multiple tissues and developmental stages in a nonhuman primate. Nat Genet. 2017;49:1714–21.

    CAS  Google Scholar 

  16. Kepa A, Martinez Medina L, Erk S, Srivastava DP, Fernandes A, Toro R, et al. Associations of the intellectual disability gene MYT1L with helix–loop–helix gene expression, hippocampus volume and hippocampus activation during memory retrieval. Neuropsychopharmacology. 2017;42:2516–26.

    CAS  Google Scholar 

  17. Jia T, Chu C, Liu Y, van Dongen J, Papastergios E, Armstrong NJ, et al. Epigenome-wide meta-analysis of blood DNA methylation and its association with subcortical volumes: findings from the ENIGMA Epigenetics Working Group. Mol Psychiatry. 2021;26:3884–95.

    Google Scholar 

  18. McRae AF, Powell JE, Henders AK, Bowdler L, Hemani G, Shah S, et al. Contribution of genetic variation to transgenerational inheritance of DNA methylation. Genome Biol. 2014;15:1–10.

    Google Scholar 

  19. Gutierrez-Arcelus M, Lappalainen T, Montgomery SB, Buil A, Ongen H, Yurovsky A, et al. Passive and active DNA methylation and the interplay with genetic variation in gene regulation. Elife. 2013;2:e00523.

    Google Scholar 

  20. Wu H, Zhang Y. Reversing DNA methylation: mechanisms, genomics, and biological functions. Cell. 2014;156:45–68.

    CAS  Google Scholar 

  21. Blake LE, Roux J, Hernando-Herraez I, Banovich NE, Perez RG, Hsiao CJ, et al. A comparison of gene expression and DNA methylation patterns across tissues and species. Genome Res. 2020;30:250–62.

    CAS  Google Scholar 

  22. 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.

    CAS  Google Scholar 

  23. 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.

    Google Scholar 

  24. Millstein J, Zhang B, Zhu J, Schadt EE. Disentangling molecular relationships with a causal inference test. BMC Genet. 2009;10:1–15.

    Google Scholar 

  25. Delaneau O, Ongen H, Brown AA, Fort A, Panousis NI, Dermitzakis ET. A complete tool set for molecular QTL discovery and analysis. Nat Commun. 2017;8:15452.

    CAS  Google Scholar 

  26. Davies MN, Volta M, Pidsley R, Lunnon K, Dixit A, Lovestone S, et al. Functional annotation of the human brain methylome identifies tissue-specific epigenetic variation across brain and blood. Genome Biol. 2012;13:R43.

    CAS  Google Scholar 

  27. Montano C, Taub MA, Jaffe A, Briem E, Feinberg JI, Trygvadottir R, et al. Association of DNA methylation differences with schizophrenia in an epigenome-wide association study. JAMA Psychiatry. 2016;73:506–14.

    Google Scholar 

  28. Schulz H, Ruppert AK, Herms S, Wolf C, Mirza-Schreiber N, Stegle O, et al. Genome-wide mapping of genetic determinants influencing DNA methylation and gene expression in human hippocampus. Nat Commun. 2017;8:1511.

    Google Scholar 

  29. Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, et al. Guidelines for performing Mendelian randomization investigations. Wellcome Open Res. 2019;4:186.

    Google Scholar 

  30. Schumann G, Loth E, Banaschewski T, Barbot A, Barker G, Büchel C, et al. The IMAGEN study: reinforcement-related behaviour in normal brain function and psychopathology. Mol Psychiatry. 2010;15:1128–39.

    CAS  Google Scholar 

  31. Diedenhofen B, Musch J. cocor: a comprehensive solution for the statistical comparison of correlations. PLoS One. 2015;10:e0121945.

    Google Scholar 

  32. Smith SM, Douaud G, Chen W, Hanayik T, Alfaro-Almagro F, Sharp K, et al. An expanded set of genome-wide association studies of brain imaging phenotypes in UK Biobank. Nat Neurosci. 2021;24:737–45.

    CAS  Google Scholar 

  33. Bork P. Hundreds of ankyrin‐like repeats in functionally diverse proteins: mobile modules that cross phyla horizontally? Proteins. 1993;17:363–74.

    CAS  Google Scholar 

  34. Benita Y, Kikuchi H, Smith AD, Zhang MQ, Chung DC, Xavier RJ, et al. An integrative genomics approach identifies Hypoxia Inducible Factor-1 (HIF-1)-target genes that form the core response to hypoxia. Nucleic Acids Res. 2009;37:4587–602.

    CAS  Google Scholar 

  35. Galbraith MD, Allen MA, Bensard CL, Wang X, Schwinn MK, Qin B, et al. HIF1A employs CDK8-mediator to stimulate RNAPII elongation in response to hypoxia. Cell. 2013;153:1327–39.

    CAS  Google Scholar 

  36. Majmundar AJ, Wong WJ, Simon MC. Hypoxia-inducible factors and the response to hypoxic stress. Mol Cell. 2013;40:294–309.

    Google Scholar 

  37. Zhang X, Le W. Pathological role of hypoxia in Alzheimer’s disease. Exp Neurol. 2010;223:299–303.

    CAS  Google Scholar 

  38. Sun X, He G, Qing H, Zhou W, Dobie F, Cai F, et al. Hypoxia facilitates Alzheimer’s disease pathogenesis by up-regulating BACE1 gene expression. Proc Natl Acad Sci U S A. 2006;103:18727–32.

    CAS  Google Scholar 

  39. Lana D, Ugolini F, Giovannini MG. An Overview on the Differential Interplay Among Neurons–Astrocytes–Microglia in CA1 and CA3 Hippocampus in Hypoxia/Ischemia. Front Cell Neurosci. 2020;14:585833.

    CAS  Google Scholar 

  40. Yasukochi Y, Shin S, Wakabayashi H, Maeda T. Transcriptomic changes in young Japanese males after exposure to acute hypobaric hypoxia. Front Genet. 2020;11:559074.

    CAS  Google Scholar 

  41. Jones PA. Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nat Rev Genet. 2012;13:484–92.

    CAS  Google Scholar 

  42. Neri F, Rapelli S, Krepelova A, Incarnato D, Parlato C, Basile G, et al. Intragenic DNA methylation prevents spurious transcription initiation. Nature. 2017;543:72–77.

    CAS  Google Scholar 

  43. Weinberg DN, Papillon-Cavanagh S, Chen H, Yue Y, Chen X, Rajagopalan KN, et al. The histone mark H3K36me2 recruits DNMT3A and shapes the intergenic DNA methylation landscape. Nature. 2019;573:281–6.

    CAS  Google Scholar 

  44. Wu H, Coskun V, Tao J, Xie W, Ge W, Yoshikawa K, et al. Dnmt3a-dependent nonpromoter DNA methylation facilitates transcription of neurogenic genes. Nature. 2010;329:444–8.

    CAS  Google Scholar 

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Acknowledgements

This work was partly supported by the National Key Research and Development Program of China (Grant No. 2018YFC1314301), National Natural Science Foundation of China (Grant No. 82001797), Tianjin Applied Basic Research Diversified Investment Foundation (Grant No. 21JCYBJC01360), Tianjin Health Technology Project (Grant No. TJWJ2021QN002), Science&Technology Development Fund of Tianjin Education Commission for Higher Education (2019KJ195), National Key Research and Development Program of China (Grant No. 2017YFA0604401 and 2017YFA0504102), National Natural Science Foundation of China (Grant No. 82030053, 32070675 and 81801687), Natural Science Foundation of Tianjin City (Grant No. 19JCJQJC63600 for MJL and 18JCJQJC48200 for XW), Tianjin Key Medical Discipline (Specialty) Construction Project (Grant No. TJYXZDXK-001A). Further support was received by GS from the Horizon 2020 funded ERC Advanced Grant “STRATIFY” (Brain network-based stratification of reinforcement-related disorders) (695313), the National Institute of Health (NIH) (R01DA049238, A decentralized macro and micro gene-by-environment interaction analysis of substance use behavior and its brain biomarkers), the Human Brain Project (SGA3; 945539), and the Chinese National High-end Foreign Expert Recruitment Plan. Further support was provided by grants from the ANR (AAPG2019-GeBra), the Eranet Neuron (ANR-18-NEUR00002-01- ADORe). Healthy elderly controls and LOAD cases data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

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JX, MJL, XW, and CY designed the study. JX, XX, YD, MJL, XW, and CY wrote the manuscript. JX, QL, XX and ZS analysed the data. All authors critically reviewed the manuscript. XS, NL, YH, XS, YH, WQ and SZ were the principal investigators. TB, HF, AG, PG, AH, RB, JM, EA, FN, TP, LP, SH, HW, PS and GS acquired the data.

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Correspondence to Xudong Wu, Mulin Jun Li or Chunshui Yu.

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Xu, J., Xia, X., Li, Q. et al. A causal association of ANKRD37 with human hippocampal volume. Mol Psychiatry 27, 4432–4445 (2022). https://doi.org/10.1038/s41380-022-01800-7

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