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Epigenetic analysis suggests aberrant cerebellum brain aging in old-aged adults with autism spectrum disorder and schizophrenia

Molecular Psychiatry (2023)Cite this article

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Abstract

The aberrant aging hypothesis of schizophrenia (SCZ) and autism spectrum disorder (ASD) has been proposed, and the DNA methylation (DNAm) clock, which is a cumulative evaluation of DNAm levels at age-related CpGs, could serve as a biological aging indicator. This study evaluated epigenetic brain aging of ASD and SCZ using Horvath’s epigenetic clock, based on two public genome-wide DNA methylation datasets of post-mortem brain samples (NASD = 222; NSCZ = 142). Total subjects were further divided into subgroups by gender and age. The epigenetic age acceleration (AgeAccel) for each sample was calculated as the residual value resulting from the regression model and compared between groups. Results showed DNAm age has a strong correlation with chronological age in both datasets across multiple brain regions (P < 0.05). When divided into equally sized age groups, the AgeAccel of the cerebellum (CB) region from people over 45 years of age was greater compared to the control sample (AgeAccel of ASD vs control: 5.069 vs −6.249; P < 0.001). And a decelerated epigenetic aging process was observed in the CB region of individuals with SCZ aged 50–70 years (AgeAccel of SCZ vs control: −3.171 vs 2.418; P < 0.05). However, our results showed no significant difference in AgeAccel between ASD and control groups, and between SCZ and control groups in the total and gender-specific groups (P > 0.05). This study’s results revealed some evidence for aberrant epigenetic CB brain aging in old-aged patients with ASD and SCZ, indicating a different pattern of CB aging in older adults with these two diseases. However, further studies of larger ASD and SCZ cohorts are necessary to make definitive conclusions on this observation.

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Fig. 1: Assessment of epigenetic age in ASD patients and non-psychiatry controls.
Fig. 2: Assessment of epigenetic age in SCZ patients and non-psychiatry controls.
Fig. 3: Assessment of epigenetic age acceleration in ASD patients and non-psychiatry controls.
Fig. 4: Assessment of epigenetic age acceleration in SCZ patients and non-psychiatry controls.

Data availability

Brain sample metadata are provided in Table S1–S7. Raw DNA methylation data for ASD and controls can be found in the PsychENCODE Knowledge Portal (https://www.synapse.org/#!Synapse:syn8263588). Raw DNA methylation data for SCZ and controls are available in the Gene Expression Omnibus (GEO) datasets database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE89707).

References

  1. van ’t Hof M, Tisseur C, van Berckelear-Onnes I, van Nieuwenhuyzen A, Daniels AM, Deen M, et al. Age at autism spectrum disorder diagnosis: a systematic review and meta-analysis from 2012 to 2019. Autism. 2020;25:862–73.

    Article  PubMed  Google Scholar 

  2. Jutla A, Foss-Feig J, Veenstra-VanderWeele J. Autism spectrum disorder and schizophrenia: an updated conceptual review. Autism research : official journal of the International Society for Autism. Research .2022;15:384–412.

    Google Scholar 

  3. Gao R, Penzes P. Common mechanisms of excitatory and inhibitory imbalance in schizophrenia and autism spectrum disorders. Curr Mol Med. 2015;15:146–67.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Gandal MJ, Haney JR, Parikshak NN, Leppa V, Ramaswami G, Hartl C, et al. Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Sci (N. Y, NY). 2018;359:693–7.

    Article  CAS  Google Scholar 

  5. Du Y, Fu Z, Xing Y, Lin D, Pearlson G, Kochunov P, et al. Evidence of shared and distinct functional and structural brain signatures in schizophrenia and autism spectrum disorder. Commun Biol. 2021;4:1073.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Meyer U, Feldon J, Dammann O. Schizophrenia and autism: both shared and disorder-specific pathogenesis via perinatal inflammation? Pediatr Res. 2011;69:26–33.

    Article  Google Scholar 

  7. Grimm A, Eckert A. Brain aging and neurodegeneration: from a mitochondrial point of view. J Neurochem. 2017;143:418–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Lu AT, Hannon E, Levine ME, Crimmins EM, Lunnon K, Mill J, et al. Genetic architecture of epigenetic and neuronal ageing rates in human brain regions. Nat Commun. 2017;8:15353.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Kaufmann T, van der Meer D, Doan NT, Schwarz E, Lund MJ, Agartz I, et al. Common brain disorders are associated with heritable patterns of apparent aging of the brain. Nat Neurosci. 2019;22:1617–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Wertz J, Caspi A, Ambler A, Broadbent J, Hancox RJ, Harrington H, et al. Association of history of psychopathology with accelerated aging at midlife. JAMA Psychiatry. 2021;78:530–9.

    Article  PubMed  Google Scholar 

  11. Koutsouleris N, Davatzikos C, Borgwardt S, Gaser C, Bottlender R, Frodl T, et al. Accelerated brain aging in schizophrenia and beyond: a neuroanatomical marker of psychiatric disorders. Schizophrenia Bull. 2014;40:1140–53.

    Article  Google Scholar 

  12. Pearson BL, Simon JM, McCoy ES, Salazar G, Fragola G, Zylka MJ. Identification of chemicals that mimic transcriptional changes associated with autism, brain aging and neurodegeneration. Nat Commun. 2016;7:11173.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013;49:359–67.

    Article  CAS  PubMed  Google Scholar 

  14. Jaenisch R, Bird A. Epigenetic regulation of gene expression: how the genome integrates intrinsic and environmental signals. Nat Genet. 2003;33:245–54.

    Article  CAS  PubMed  Google Scholar 

  15. Guarasci F, D’Aquila P, Montesanto A, Corsonello A, Bellizzi D, Passarino G. Individual DNA methylation profile is correlated with age and can be targeted to modulate healthy aging and longevity. Curr Pharm Des. 2019;25:4139–49.

    Article  CAS  PubMed  Google Scholar 

  16. Zbieć-Piekarska R, Spólnicka M, Kupiec T, Parys-Proszek A, Makowska Ż, Pałeczka A, et al. Development of a forensically useful age prediction method based on DNA methylation analysis. Forensic Sci Int: Genet. 2015;17:173–9.

    Article  PubMed  Google Scholar 

  17. Okazaki S, Kimura R, Otsuka I, Funabiki Y, Murai T, Hishimoto A. Epigenetic clock analysis and increased plasminogen activator inhibitor-1 in high-functioning autism spectrum disorder. PloS One. 2022;17:e0263478.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. McKinney BC, Lin H, Ding Y, Lewis DA, Sweet RA. DNA methylation evidence against the accelerated aging hypothesis of schizophrenia. NPJ Schizophr. 2017;3:13.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Dada O, Adanty C, Dai N, Jeremian R, Alli S, Gerretsen P, et al. Biological aging in schizophrenia and psychosis severity: DNA methylation analysis. Psychiatry Res. 2021;296:113646.

    Article  CAS  PubMed  Google Scholar 

  20. Viana J, Hannon E, Dempster E, Pidsley R, Macdonald R, Knox O, et al. Schizophrenia-associated methylomic variation: molecular signatures of disease and polygenic risk burden across multiple brain regions. Hum Mol Genet. 2017;26:210–25.

    CAS  PubMed  Google Scholar 

  21. Davis S, Du P, Bilke S, Triche J, Bootwalla M. Methylumi: Handle Illumina Methylation Data. In R package, Vol. version 2.28.0. 2018.

  22. Pidsley R, Wong CCY, Volta M, Lunnon K, Mill J, Schalkwyk LC. A data-driven approach to preprocessing Illumina 450K methylation array data. BMC Genom. 2013;14:293.

    Article  CAS  Google Scholar 

  23. Chen YA, Lemire M, Choufani S, Butcher DT, Grafodatskaya D, Zanke BW, et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics .2013;8:203–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Price ME, Cotton AM, Lam LL, Farré P, Emberly E, Brown CJ, et al. Additional annotation enhances potential for biologically-relevant analysis of the Illumina Infinium HumanMethylation450 BeadChip array. Epigenet Chromatin. 2013;6:4.

    Article  CAS  Google Scholar 

  25. Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14:3156.

    Article  Google Scholar 

  26. Horvath S. Erratum to: DNA methylation age of human tissues and cell types. Genome Biol. 2015;16:96.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Werling DM, Geschwind DH. Sex differences in autism spectrum disorders. Curr Opin Neurol. 2013;26:146–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Halladay AK, Bishop S, Constantino JN, Daniels AM, Koenig K, Palmer K, et al. Sex and gender differences in autism spectrum disorder: summarizing evidence gaps and identifying emerging areas of priority. Mol Autism. 2015;6:36.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Voisey J, Lawford BR, Morris CP, Wockner LF, Noble EP, Young RM, et al. Epigenetic analysis confirms no accelerated brain aging in schizophrenia. NPJ Schizophrenia. 2017;3:26.

    Article  PubMed  PubMed Central  Google Scholar 

  30. McEwen LM, O’Donnell KJ, McGill MG, Edgar RD, Jones MJ, MacIsaac JL, et al. The PedBE clock accurately estimates DNA methylation age in pediatric buccal cells. Proc Natl Acad Sci USA. 2020;117:23329–35.

    Article  CAS  PubMed  Google Scholar 

  31. Yoon SH, Choi J, Lee WJ, Do JT. Genetic and epigenetic etiology underlying autism spectrum disorder. J Clin Med. 2020;9:966.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Magwai T, Shangase KB, Oginga FO, Chiliza B, Mpofana T, Xulu KR. DNA methylation and schizophrenia: current literature and future perspective. Cells .2021;10:2890.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Keil KP, Lein PJJEE. DNA methylation: a mechanism linking environmental chemical exposures to risk of autism spectrum disorders? Environ Epigenet. 2016;2:dvv012.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Siu MT, Butcher DT, Turinsky AL, Cytrynbaum C, Stavropoulos DJ, Walker S, et al. Functional DNA methylation signatures for autism spectrum disorder genomic risk loci: 16p11.2 deletions and CHD8 variants. Clin Epigenet. 2019;11:103.

    Article  CAS  Google Scholar 

  35. Shadyab AH, McEvoy LK, Horvath S, Whitsel EA, Rapp SR, Espeland MA, et al. Association of blood-based epigenetic age acceleration with cognitive impairment and brain outcomes by cardiovascular disease among women. Alzheimer’s Dement. 2021;17:e051774.

    Article  Google Scholar 

  36. Vaccarino V, Huang M, Wang Z, Hui Q, Shah AJ, Goldberg J, et al. Epigenetic age acceleration and cognitive decline: a twin study. J Gerontology: Ser A. 2021;76:1854–63.

    CAS  Google Scholar 

  37. McKinney BC, Lin H, Ding Y, Lewis DA, Sweet RA. DNA methylation age is not accelerated in brain or blood of subjects with schizophrenia. Schizophr Res. 2018;196:39–44.

    Article  PubMed  Google Scholar 

  38. Fransquet PD, Lacaze P, Saffery R, Shah RC, Vryer R, Murray A, et al. Accelerated epigenetic aging in peripheral blood does not predict dementia risk. Curr Alzheimer Res. 2021;18:443–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Li Z, He Y, Ma X, Chen X. Epigenetic age analysis of brain in major depressive disorder. Psychiatry Res. 2018;269:621–4.

    Article  PubMed  Google Scholar 

  40. Bernard JA, Seidler RD. Moving forward: age effects on the cerebellum underlie cognitive and motor declines. Neurosci Biobehav Rev. 2014;42:193–207.

    Article  PubMed  Google Scholar 

  41. Lu AT, Hannon E, Levine ME, Hao K, Crimmins EM, Lunnon K, et al. Genetic variants near MLST8 and DHX57 affect the epigenetic age of the cerebellum. Nat Commun. 2016;7:10561.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. D’Mello AM, Stoodley CJJFIN. Cerebro-cerebellar circuits in autism spectrum disorder. Front Neurosci. 2015;9:408.

    PubMed  PubMed Central  Google Scholar 

  43. Zhu J-D, Wu Y-F, Tsai S-J, Lin C-P, Yang AC. Investigating brain aging trajectory deviations in different brain regions of individuals with schizophrenia using multimodal magnetic resonance imaging and brain-age prediction: a multicenter study. Transl Psychiatry. 2023;13:82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Andreasen NC, Pierson R. The role of the cerebellum in schizophrenia. Biol Psychiatry. 2008;64:81–8.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Horvath S, Mah V, Lu AT, Woo JS, Choi OW, Jasinska AJ, et al. The cerebellum ages slowly according to the epigenetic clock. Aging (Albany NY). 2015;7:294–306.

    Article  CAS  PubMed  Google Scholar 

  46. Crespi B, Badcock C. Psychosis and autism as diametrical disorders of the social brain. Behav Brain Sci. 2008;31:241–61. discussion 61-320

    Article  PubMed  Google Scholar 

  47. Cropley VL, Klauser P, Lenroot RK, Bruggemann J, Sundram S, Bousman C, et al. Accelerated gray and white matter deterioration with age in schizophrenia. Am J Psychiatry. 2016;174:286–95.

    Article  PubMed  Google Scholar 

  48. Finger CE, Moreno-Gonzalez I, Gutierrez A, Moruno-Manchon JF, McCullough LD. Age-related immune alterations and cerebrovascular inflammation. Mol psychiatry. 2022;27:803–18.

    Article  CAS  PubMed  Google Scholar 

  49. Constantinides C, Han LKM, Alloza C, Antonucci LA, Arango C, Ayesa-Arriola R, et al. Brain ageing in schizophrenia: evidence from 26 international cohorts via the ENIGMA Schizophrenia consortium. Mol Psychiatry. 2023;28:1201–9.

    Article  PubMed  Google Scholar 

  50. Harris E. Inflammation genes show age-dependent link with autism. JAMA 2023;329:1054.

    PubMed  Google Scholar 

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Acknowledgements

We thank Shanghai NewCore Biotechnology Co., Ltd. (https://www.bioinformatics.com.cn, last accessed on 10 July 2023) for providing visualization support.

Funding

This work was supported by the Natural Science Basic Research Plan in Shaanxi Province of China [2021JCW-08], the Fundamental-clinical Research Program of the First Affiliated Hospital of Xi’an Jiaotong University [YXJLRH2022027]. This work was supported by US National Institutes of Health grant R01MH094714 to D.H.G. and is part of the PsychEncode Consortium. Data were generated as part of the PsychENCODE Consortium, supported by: U01DA048279, U01MH103339, U01MH103340, U01MH103346, U01MH103365, U01MH103392, U01MH116438, U01MH116441, U01MH116442, U01MH116488, U01MH116489, U01MH116492, U01MH122590, U01MH122591, U01MH122592, U01MH122849, U01MH122678, U01MH122681, U01MH116487, U01MH122509, R01MH094714, R01MH105472, R01MH105898, R01MH109677, R01MH109715, R01MH110905, R01MH110920, R01MH110921, R01MH110926, R01MH110927, R01MH110928, R01MH111721, R01MH117291, R01MH117292, R01MH117293, R21MH102791, R21MH103877, R21MH105853, R21MH105881, R21MH109956, R56MH114899, R56MH114901, R56MH114911, R01MH125516, and P50MH106934 awarded to: Alexej Abyzov, Nadav Ahituv, Schahram Akbarian, Alexander Arguello, Lora Bingaman, Kristin Brennand, Andrew Chess, Gregory Cooper, Gregory Crawford, Stella Dracheva, Peggy Farnham, Mark Gerstein, Daniel Geschwind, Fernando Goes, Vahram Haroutunian, Thomas M. Hyde, Andrew Jaffe, Peng Jin, Manolis Kellis, Joel Kleinman, James A. Knowles, Arnold Kriegstein, Chunyu Liu, Keri Martinowich, Eran Mukamel, Richard Myers, Charles Nemeroff, Mette Peters, Dalila Pinto, Katherine Pollard, Kerry Ressler, Panos Roussos, Stephan Sanders, Nenad Sestan, Pamela Sklar, Nick Sokol, Matthew State, Jason Stein, Patrick Sullivan, Flora Vaccarino, Stephen Warren, Daniel Weinberger, Sherman Weissman, Zhiping Weng, Kevin White, A. Jeremy Willsey, Hyejung Won, and Peter Zandi.

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  1. These authors contributed equally: Li Liu, Xin Qi.

Authors and Affiliations

  1. Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an, 710061, P. R. China

    Li Liu, Shiqiang Cheng, Peilin Meng, Xuena Yang, Chuyu Pan, Na Zhang, Yujing Chen, Chune Li, Huijie Zhang, Zhen Zhang, Jingxi Zhang, Bolun Cheng, Yan Wen, Yumeng Jia, Huan Liu & Feng Zhang

  2. Precision Medicine Center, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, P. R. China

    Xin Qi

  3. Department of Psychiatry, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, P. R. China

    Feng Zhang

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  1. Li Liu
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Contributions

LL and XQ drafted the manuscript. FZ, YJ, and YW designed the study. FZ provided the key datasets regarding our manuscript. SC, BC, and HL performed the statistical analyses. NZ, PM, XY, CP, YC, HZ, ZZ, JZ, and CL provided feasible advice on data analysis and drafting the manuscript. All authors read and approved the final manuscript. All authors discussed the results and commented on the manuscript.

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Correspondence to Feng Zhang.

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Liu, L., Qi, X., Cheng, S. et al. Epigenetic analysis suggests aberrant cerebellum brain aging in old-aged adults with autism spectrum disorder and schizophrenia. Mol Psychiatry (2023). https://doi.org/10.1038/s41380-023-02233-6

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