Investigating the Epigenetic Landscape of Major Depressive Disorder: A Genome-Wide Meta-Analysis of DNA Methylation Data, Including New Insights into Stochastic Epigenetic Mutations and Epivariations
Abstract
:1. Introduction
2. Materials and Methods
2.1. Selection of Datasets
2.2. Methylation Quality Control Data Preprocessing and Differential Methylation Analysis
2.3. Age Acceleration Analysis
2.4. Epigenetic Drift and Stochastic Epigenetic Mutations Analysis (SEMs)
2.5. Epigenetic Variation Analysis
2.6. Meta-Analysis
2.7. Gene Set Enrichment Analysis and Gene Prioritisation Analysis
3. Results
3.1. Datasets and Differential Methylation Meta-Analyses
3.2. Age Acceleration
3.3. SEM Analysis and Epimutation Load
3.4. Epigenetic Variation Analysis Results
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Regier, D.A.; Kuhl, E.A.; Kupfer, D.J. The DSM-5: Classification and criteria changes. World Psychiatry 2013, 12, 92–98. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
- Rybak, Y.E.; Lai, K.S.P.; Ramasubbu, R.; Vila-Rodriguez, F.; Blumberger, D.M.; Chan, P.; Delva, N.; Giacobbe, P.; Gosselin, C.; Kennedy, S.H.; et al. Treatment-Resistant Major Depressive Disorder: Canadian Expert Consensus on Definition and Assessment. Depress. Anxiety 2021, 38, 456–467. [Google Scholar] [CrossRef]
- Crawford, B.; Craig, Z.; Mansell, G.; White, I.; Smith, A.; Spaull, S.; Imm, J.; Hannon, E.; Wood, A.; Yaghootkar, H.; et al. DNA Methylation and Inflammation Marker Profiles Associated with a History of Depression. Hum. Mol. Genet. 2018, 27, 2840–2850. [Google Scholar] [CrossRef] [PubMed]
- Li, Q.S.; Morrison, R.L.; Turecki, G.; Drevets, W.C. Meta-Analysis of Epigenome-Wide Association Studies of Major Depressive Disorder. Sci. Rep. 2022, 12, 18361. [Google Scholar] [CrossRef]
- Xiu, J.; Li, J.; Liu, Z.; Wei, H.; Zhu, C.; Han, R.; Liu, Z.; Zhu, W.; Shen, Y.; Xu, Q.; et al. Elevated BICD2 DNA Methylation in Blood of Major Depressive Disorder Patients and Reduction of Depressive-like Behaviors in Hippocampal Bicd2-Knockdown Mice. Proc. Natl. Acad. Sci. USA 2022, 119, e2201967119. [Google Scholar] [CrossRef] [PubMed]
- Moore, S.R.; Halldorsdottir, T.; Martins, J.; Lucae, S.; Müller-Myhsok, B.; Müller, N.S.; Piechaczek, C.; Feldmann, L.; Freisleder, F.J.; Greimel, E.; et al. Sex Differences in the Genetic Regulation of the Blood Transcriptome Response to Glucocorticoid Receptor Activation. Transl. Psychiatry 2021, 11, 632. [Google Scholar] [CrossRef]
- Murphy, T.M.; Crawford, B.; Dempster, E.L.; Hannon, E.; Burrage, J.; Turecki, G.; Kaminsky, Z.; Mill, J. Methylomic Profiling of Cortex Samples from Completed Suicide Cases Implicates a Role for PSORS1C3 in Major Depression and Suicide. Transl. Psychiatry 2017, 7, e989. [Google Scholar] [CrossRef]
- Guintivano, J.; Aryee, M.J.; Kaminsky, Z.A. A Cell Epigenotype Specific Model for the Correction of Brain Cellular Heterogeneity Bias and Its Application to Age, Brain Region and Major Depression. Epigenetics 2013, 8, 290–302. [Google Scholar] [CrossRef]
- Penner-Goeke, S.; Binder, E.B. Epigenetics and depression. Dialogues Clin Neurosci. 2019, 21, 397–405. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
- Zhu, Y.; Strachan, E.; Fowler, E.; Bacus, T.; Roy-Byrne, P.; Zhao, J. Genome-Wide Profiling of DNA Methylome and Transcriptome in Peripheral Blood Monocytes for Major Depression: A Monozygotic Discordant Twin Study. Transl. Psychiatry 2019, 9, 215. [Google Scholar] [CrossRef]
- Jovanova, O.S.; Nedeljkovic, I.; Spieler, D.; Walker, R.M.; Liu, C.; Luciano, M.; Bressler, J.; Brody, J.; Drake, A.J.; Evans, K.L.; et al. DNA Methylation Signatures of Depressive Symptoms in Middle-Aged and Elderly Persons: Meta-Analysis of Multiethnic Epigenome-Wide Studies. JAMA Psychiatry 2018, 75, 949–959. [Google Scholar] [CrossRef]
- Beydoun, M.A.; Hossain, S.; Chitrala, K.N.; Tajuddin, S.M.; Beydoun, H.A.; Evans, M.K.; Zonderman, A.B. Association between Epigenetic Age Acceleration and Depressive Symptoms in a Prospective Cohort Study of Urban-Dwelling Adults. J. Affect. Disord 2019, 257, 64–73. [Google Scholar] [CrossRef] [PubMed]
- Shindo, R.; Tanifuji, T.; Okazaki, S.; Otsuka, I.; Shirai, T.; Mouri, K.; Horai, T.; Hishimoto, A. Accelerated Epigenetic Aging and Decreased Natural Killer Cells Based on DNA Methylation in Patients with Untreated Major Depressive Disorder. npj Aging 2023, 9, 19. [Google Scholar] [CrossRef] [PubMed]
- Huynh, J.L.; Garg, P.; Thin, T.H.; Yoo, S.; Dutta, R.; Trapp, B.D.; Haroutunian, V.; Zhu, J.; Donovan, M.J.; Sharp, A.J.; et al. Epigenome-Wide Differences in Pathology-Free Regions of Multiple Sclerosis-Affected Brains. Nat. Neurosci. 2014, 17, 121–130. [Google Scholar] [CrossRef] [PubMed]
- Han, L.K.M.; Dinga, R.; Hahn, T.; Ching, C.R.K.; Eyler, L.T.; Aftanas, L.; Aghajani, M.; Aleman, A.; Baune, B.T.; Berger, K.; et al. Brain Aging in Major Depressive Disorder: Results from the ENIGMA Major Depressive Disorder Working Group. Mol. Psychiatry 2021, 26, 5124–5139. [Google Scholar] [CrossRef] [PubMed]
- Ptak, C.; Petronis, A. Epigenetic Approaches to Psychiatric Disorders. Dialogues Clin. Neurosci. 2010, 12, 25–35. [Google Scholar] [CrossRef]
- Davide, G.; Rebecca, C.; Irene, P.; Luciano, C.; Francesco, R.; Marta, N.; Miriam, O.; Natascia, B.; Pierluigi, P. Epigenetics of Autism Spectrum Disorders: A Multi-Level Analysis Combining Epi-Signature, Age Acceleration, Epigenetic Drift and Rare Epivariations Using Public Datasets. Curr. Neuropharmacol. 2023, 21, 2362–2373. [Google Scholar] [CrossRef]
- Tian, Y.; Morris, T.J.; Webster, A.P.; Yang, Z.; Beck, S.; Feber, A.; Teschendorff, A.E. ChAMP: Updated Methylation Analysis Pipeline for Illumina BeadChips. Bioinformatics 2017, 33, 3982–3984. [Google Scholar] [CrossRef]
- Ritchie, M.E.; Phipson, B.; Wu, D.; Hu, Y.; Law, C.W.; Shi, W.; Smyth, G.K. Limma Powers Differential Expression Analyses for RNA-Sequencing and Microarray Studies. Nucleic Acids Res. 2015, 43, e47. [Google Scholar] [CrossRef]
- Johnson, W.E.; Li, C.; Rabinovic, A. Adjusting Batch Effects in Microarray Expression Data Using Empirical Bayes Methods. Biostatistics 2007, 8, 118–127. [Google Scholar] [CrossRef]
- Zheng, S.C.; Breeze, C.E.; Beck, S.; Dong, D.; Zhu, T.; Ma, L.; Ye, W.; Zhang, G.; Teschendorff, A.E. EpiDISH Web Server: Epigenetic Dissection of Intra-Sample-Heterogeneity with Online GUI. Bioinformatics 2020, 36, 1950–1951. [Google Scholar] [CrossRef] [PubMed]
- McCrory, C.; Fiorito, G.; Hernandez, B.; Polidoro, S.; O’Halloran, A.M.; Hever, A.; Ni Cheallaigh, C.; Lu, A.T.; Horvath, S.; Vineis, P.; et al. GrimAge Outperforms Other Epigenetic Clocks in the Prediction of Age-Related Clinical Phenotypes and All-Cause Mortality. J. Gerontol. Ser. A Biol. Sci. Med. Sci. 2021, 76, 741–749. [Google Scholar] [CrossRef]
- Horvath, H.; Horvath, S. DNA Methylation Age of Human Tissues and Cell Types; Springer: Berlin/Heidelberg, Germany, 2013; Volume 14. [Google Scholar]
- Yan, Q.; Paul, K.C.; Lu, A.T.; Kusters, C.; Binder, A.M.; Horvath, S.; Ritz, B. Epigenetic mutation load is weakly correlated with epigenetic age acceleration. Aging 2020, 12, 17863–17894. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
- Chen, P.; Shi, W.; Liu, Y.; Cao, X. Slip Rate Deficit Partitioned by Fault-Fold System on the Active Haiyuan Fault Zone, Northeastern Tibetan Plateau. J. Struct. Geol. 2022, 155, 104516. [Google Scholar] [CrossRef]
- Grolaux, R.; Hardy, A.; Olsen, C.; Van Dooren, S.; Smits, G.; Defrance, M. Identification of Differentially Methylated Regions in Rare Diseases from a Single-Patient Perspective. Clin. Epigenetics 2022, 14, 174. [Google Scholar] [CrossRef] [PubMed]
- Gentilini, D.; Somigliana, E.; Pagliardini, L.; Rabellotti, E.; Garagnani, P.; Bernardinelli, L.; Papaleo, E.; Candiani, M.; Di Blasio, A.M.; Viganò, P. Multifactorial Analysis of the Stochastic Epigenetic Variability in Cord Blood Confirmed an Impact of Common Behavioral and Environmental Factors but Not of In Vitro Conception. Clin. Epigenetics 2018, 10, 77. [Google Scholar] [CrossRef]
- Spada, E.; Calzari, L.; Corsaro, L.; Fazia, T.; Mencarelli, M.; Di Blasio, A.M.; Bernardinelli, L.; Zangheri, G.; Vignali, M.; Gentilini, D. Epigenome Wide Association and Stochastic Epigenetic Mutation Analysis on Cord Blood of Preterm Birth. Int. J. Mol. Sci. 2020, 21, 5044. [Google Scholar] [CrossRef]
- Garg, P.; Jadhav, B.; Rodriguez, O.L.; Patel, N.; Martin-Trujillo, A.; Jain, M.; Metsu, S.; Olsen, H.; Paten, B.; Ritz, B.; et al. A Survey of Rare Epigenetic Variation in 23,116 Human Genomes Identifies Disease-Relevant Epivariations and CGG Expansions. Am. J. Hum. Genet. 2020, 107, 654–669. [Google Scholar] [CrossRef]
- Willer, C.J.; Li, Y.; Abecasis, G.R. METAL: Fast and Efficient Meta-Analysis of Genomewide Association Scans. Bioinformatics 2010, 26, 2190–2191. [Google Scholar] [CrossRef]
- Viechtbauer, W. Conducting Meta-Analyses in R with the Metafor Package. J. Stat. Softw. 2010, 36, 1–48. [Google Scholar] [CrossRef]
- Aguet, F.; Brown, A.A.; Castel, S.E.; Davis, J.R.; He, Y.; Jo, B.; Mohammadi, P.; Park, Y.S.; Parsana, P.; Segrè, A.V.; et al. Genetic Effects on Gene Expression across Human Tissues. Nature 2017, 550, 204–213. [Google Scholar] [CrossRef]
- Stelzer, G.; Plaschkes, I.; Oz-Levi, D.; Alkelai, A.; Olender, T.; Zimmerman, S.; Twik, M.; Belinky, F.; Fishilevich, S.; Nudel, R.; et al. VarElect: The Phenotype-Based Variation Prioritizer of the GeneCards Suite. BMC Genom. 2016, 17, 195–206. [Google Scholar] [CrossRef] [PubMed]
- Fries, G.R.; Saldana, V.A.; Finnstein, J.; Rein, T. Molecular Pathways of Major Depressive Disorder Converge on the Synapse. Mol. Psychiatry 2023, 28, 284–297. [Google Scholar] [CrossRef] [PubMed]
- Hammond, R.K.; Pahl, M.C.; Su, C.; Cousminer, D.L.; Leonard, M.E.; Lu, S.; Doege, C.A.; Wagley, Y.; Hodge, K.M.; Lasconi, C.; et al. Biological constraints on GWAS SNPs at suggestive significance thresholds reveal additional BMI loci. eLife 2021, 10, e62206. [Google Scholar] [CrossRef] [PubMed]
- Yamaguchi-Kabata, Y.; Morihara, T.; Ohara, T.; Ninomiya, T.; Takahashi, A.; Akatsu, H.; Hashizume, Y.; Hayashi, N.; Shigemizu, D.; Boroevich, K.A.; et al. Integrated Analysis of Human Genetic Association Study and Mouse Transcriptome Suggests LBH and SHF Genes as Novel Susceptible Genes for Amyloid-β Accumulation in Alzheimer’s Disease. Hum. Genet. 2018, 137, 521–533. [Google Scholar] [CrossRef]
- Liu, Y.; Gu, H.Y.; Zhu, J.; Niu, Y.M.; Zhang, C.; Guo, G.L. Identification of Hub Genes and Key Pathways Associated with Bipolar Disorder Based on Weighted Gene Co-Expression Network Analysis. Front. Physiol. 2019, 10, 1081. [Google Scholar] [CrossRef]
- Wei, X.; Huang, G.; Liu, J.; Ge, J.; Zhang, W.; Mei, Z. An Update on the Role of Hippo Signaling Pathway in Ischemia-Associated Central Nervous System Diseases. Biomed. Pharmacother. 2023, 162, 114619. [Google Scholar] [CrossRef]
- Anacker, C.; Cattaneo, A.; Luoni, A.; Musaelyan, K.; Zunszain, P.A.; Milanesi, E.; Rybka, J.; Berry, A.; Cirulli, F.; Thuret, S.; et al. Glucocorticoid-Related Molecular Signaling Pathways Regulating Hippocampal Neurogenesis. Neuropsychopharmacology 2013, 38, 872–883. [Google Scholar] [CrossRef]
- Lipton, J.O.; Sahin, M. The Neurology of MTOR. Neuron 2014, 84, 275–291. [Google Scholar] [CrossRef] [PubMed]
- Iranpanah, A.; Kooshki, L.; Moradi, S.Z.; Saso, L.; Fakhri, S.; Khan, H. The Exosome-Mediated PI3K/Akt/MTOR Signaling Pathway in Neurological Diseases. Pharmaceutics 2023, 15, 1006. [Google Scholar] [CrossRef]
- Duman, R.S.; Voleti, B. Signaling Pathways Underlying the Pathophysiology and Treatment of Depression: Novel Mechanisms for Rapid-Acting Agents. Trends Neurosci. 2012, 35, 47–56. [Google Scholar] [CrossRef] [PubMed]
- Varea, V.; Martín de Carpi, J.; Puig, C.; Angel Alda, J.; Camacho, E.; Ormazabal, A.; Artuch, R.; Gómez, L. Malabsorption of Carbohydrates and Depression in Children and Adolescents. J. Pediatr. Gastroenterol. Nutrition 2005, 40, 561–565. [Google Scholar] [CrossRef] [PubMed]
- Zhang, N.; Li, J.; Dong, Z.; Hu, Y.; Zhong, Z.; Gong, Q.; Kuang, W. The Digestion and Dietary Carbohydrate Pathway Contains 100% Gene Mutations Enrichment among 117 Patients with Major Depressive Disorder. Front. Psychiatry 2024, 15, 1362612. [Google Scholar] [CrossRef] [PubMed]
- Brusati, A.; Peverelli, S.; Calzari, L.; Tiloca, C.; Casiraghi, V.; Sorce, M.N.; Invernizzi, S.; Carbone, E.; Cavagnola, R.; Verde, F.; et al. Exploring Epigenetic Drift and Rare Epivariations in Amyotrophic Lateral Sclerosis by Epigenome-Wide Association Study. Front. Aging Neurosci. 2023, 15, 1272135. [Google Scholar] [CrossRef]
- Zhou, H.; Cheng, Z.; Bass, N.; Krystal, J.H.; Farrer, L.A.; Kranzler, H.R.; Gelernter, J. Genome-Wide Association Study Identifies Glutamate Ionotropic Receptor GRIA4 as a Risk Gene for Comorbid Nicotine Dependence and Major Depression. Transl. Psychiatry 2018, 8, 208. [Google Scholar] [CrossRef] [PubMed]
- Chiesa, A.; Crisafulli, C.; Porcelli, S.; Han, C.; Patkar, A.A.; Lee, S.J.; Park, M.H.; Jun, T.Y.; Serretti, A.; Pae, C.U. Influence of GRIA1, GRIA2 and GRIA4 Polymorphisms on Diagnosis and Response to Treatment in Patients with Major Depressive Disorder. Eur. Arch. Psychiatry Clin. Neurosci. 2012, 262, 305–311. [Google Scholar] [CrossRef]
- Czarny, P.; Białek, K.; Ziółkowska, S.; Strycharz, J.; Barszczewska, G.; Sliwinski, T. The Importance of Epigenetics in Diagnostics and Treatment of Major Depressive Disorder. J. Pers. Med. 2021, 11, 167. [Google Scholar] [CrossRef]
- Gao, H.; Ma, H.; Gao, M.; Chen, A.; Zha, S.; Yan, J. Long Non-Coding RNA GAS5 Aggravates Myocardial Depression in Mice with Sepsis via the MicroRNA-449b/HMGB1 Axis and the NF-ΚB Signaling Pathway. Biosci. Rep. 2021, 41, BSR20201738. [Google Scholar] [CrossRef]
- Zilmer, M.; Edmondson, A.C.; Khetarpal, S.A.; Alesi, V.; Zaki, M.S.; Rostasy, K.; Madsen, C.G.; Lepri, F.R.; Sinibaldi, L.; Cusmai, R.; et al. Novel Congenital Disorder of O-Linked Glycosylation Caused by GALNT2 Loss of Function. Brain 2020, 143, 1114–1126. [Google Scholar] [CrossRef]
GSE Accession | Tissue | Number of Cases | Number of Controls | Array Type | Country | Publication Year | Study DOI |
---|---|---|---|---|---|---|---|
GSE113725 [3] | Blood | 48 | 48 | Illumina 450K | UK | 2018 | 10.1093/hmg/ddy199 |
GSE198904 [4] | Blood | 548 | 100 | Illumina 850K | USA | 2022 | 10.1038/s41598-022-22744-6 |
GSE201287 [5] | Blood | 40 | 40 | Illumina 450K | China | 2022 | 10.1073/pnas.2201967119 |
GSE125105 [6] | Blood | 489 | 210 | Illumina 450K | Germany | 2019 | 10.1038/s41398-021-01756-2 |
GSE88890(BA2) [7] | Brain | 17 | 18 | Illumina 450K | UK | 2017 | 10.1038/tp.2016.249 |
GSE88890(BA11) [7] | Brain | 20 | 20 | Illumina 450K | UK | 2017 | 10.1038/tp.2016.249 |
GSE41826(Glia) [8] | Brain | 29 | 29 | Illumina 450K | USA | 2013 | 10.4161/epi.23924 |
GSE41826(Neuron) [8] | Brain | 29 | 29 | Illumina 450K | USA | 2013 | 10.4161/epi.23924 |
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Baldrighi, G.N.; Cavagnola, R.; Calzari, L.; Sacco, D.; Costantino, L.; Ferrara, F.; Gentilini, D. Investigating the Epigenetic Landscape of Major Depressive Disorder: A Genome-Wide Meta-Analysis of DNA Methylation Data, Including New Insights into Stochastic Epigenetic Mutations and Epivariations. Biomedicines 2024, 12, 2181. https://doi.org/10.3390/biomedicines12102181
Baldrighi GN, Cavagnola R, Calzari L, Sacco D, Costantino L, Ferrara F, Gentilini D. Investigating the Epigenetic Landscape of Major Depressive Disorder: A Genome-Wide Meta-Analysis of DNA Methylation Data, Including New Insights into Stochastic Epigenetic Mutations and Epivariations. Biomedicines. 2024; 12(10):2181. https://doi.org/10.3390/biomedicines12102181
Chicago/Turabian StyleBaldrighi, Giulia Nicole, Rebecca Cavagnola, Luciano Calzari, Davide Sacco, Lucy Costantino, Fulvio Ferrara, and Davide Gentilini. 2024. "Investigating the Epigenetic Landscape of Major Depressive Disorder: A Genome-Wide Meta-Analysis of DNA Methylation Data, Including New Insights into Stochastic Epigenetic Mutations and Epivariations" Biomedicines 12, no. 10: 2181. https://doi.org/10.3390/biomedicines12102181
APA StyleBaldrighi, G. N., Cavagnola, R., Calzari, L., Sacco, D., Costantino, L., Ferrara, F., & Gentilini, D. (2024). Investigating the Epigenetic Landscape of Major Depressive Disorder: A Genome-Wide Meta-Analysis of DNA Methylation Data, Including New Insights into Stochastic Epigenetic Mutations and Epivariations. Biomedicines, 12(10), 2181. https://doi.org/10.3390/biomedicines12102181