medigraphic.com
Instituto Nacional de Neurología y Neurocirugía

2022, Número 2

<< Anterior Siguiente >>

Arch Neurocien 2022; 27 (2)


Bioinformatic Analysis of Epigenomic Studies for Major Depressive Disorder

Alam FB, González-Giraldo Y, Forero DA

Idioma: Ingles.
Referencias bibliográficas: 44
Paginas: 11-18
Archivo PDF: 420.15 Kb.


RESUMEN

Sin resumen.


REFERENCIAS (EN ESTE ARTÍCULO)

  1. Otte C, Gold SM, Penninx BW, Pariante CM, Etkin A, Fava M,et al. Major depressive disorder. Nat Rev Dis Primers. 2016;

  2. 2:16065.2. Gonda X, Petschner P, Eszlari N, Baksa D, Edes A, Antal P,et al. Genetic variants in major depressive disorder: Frompathophysiology to therapy. Pharmacol Ther. 2019;194:22-43.

  3. Liu Q, He H, Yang J, Feng X, Zhao F, Lyu J. Changes in theglobal burden of depression from 1990 to 2017: Findingsfrom the Global Burden of Disease study. J Psychiatr Res.2020;126:134-140.

  4. Major Depressive Disorder Working Group of the PsychiatricGC, Ripke S, Wray NR, Lewis CM, Hamilton SP, Weissman MM,et al. A mega-analysis of genome-wide association studies formajor depressive disorder. Mol Psychiatry. 2013;18(4):497-511.

  5. Forero DA, Guio-Vega GP, Gonzalez-Giraldo Y. A comprehensiveregional analysis of genome-wide expression profiles for majordepressive disorder. J Affect Disord. 2017; 218:86-92.

  6. Nagy C, Vaillancourt K, Turecki G. A role for activity-dependentepigenetics in the development and treatment of major depressivedisorder. Genes Brain Behav. 2018; 17(3):e12446.

  7. Januar V, Saffery R, Ryan J. Epigenetics and depressive disorders:a review of current progress and future directions. Int J Epidemiol.2015; 44(4):1364-87.

  8. Story-Jovanova O, Nedeljkovic I, Spieler D, Walker RM, Liu C,Luciano M, et al. DNA Methylation Signatures of DepressiveSymptoms in Middle-aged and Elderly Persons: Meta-analysisof Multiethnic Epigenome-wide Studies. JAMA psychiatry. 2018;75(9):949-59.

  9. Guintivano J, Aryee MJ, Kaminsky ZA. A cell epigenotype specificmodel for the correction of brain cellular heterogeneity biasand its application to age, brain region and major depression.Epigenetics. 2013; 8(3):290-302.

  10. Niculescu AB, Le-Niculescu H. Convergent Functional Genomics:what we have learned and can learn about genes, pathways, andmechanisms. Neuropsychopharmacology. 2010; 35(1):355-6.

  11. Ramasamy A, Mondry A, Holmes CC, Altman DG. Key issuesin conducting a meta-analysis of gene expression microarraydatasets. PLoS medicine. 2008; 5(9):e184.

  12. Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, TomashevskyM, et al. NCBI GEO: archive for functional genomics data setsupdate.Nucleic Acids Res. 2013;41(Database issue):D991-5.

  13. Chen C, Zhang C, Cheng L, Reilly JL, Bishop JR, Sweeney JA, etal. Correlation between DNA methylation and gene expressionin the brains of patients with bipolar disorder and schizophrenia.Bipolar Disord. 2014; 16(8):790-9.

  14. Murphy TM, Crawford B, Dempster EL, Hannon E, Burrage J,Turecki G, et al. Methylomic profiling of cortex samples fromcompleted suicide cases implicates a role for PSORS1C3 in majordepression and suicide. Transl Psychiatry. 2017; 7(1):e989.

  15. Crawford B, Craig Z, Mansell G, White I, Smith A, Spaull S,et al. DNA methylation and inflammation marker profilesassociated with a history of depression. Hum Mol Genet. 2018;27(16):2840-50.

  16. Kolde R, Laur S, Adler P, Vilo J. Robust rank aggregation forgene list integration and meta-analysis. Bioinformatics. 2012;28(4):573-80.

  17. Vősa U, Kolde R, Vilo J, Metspalu A, Annilo T. ComprehensiveMeta-analysis of MicroRNA Expression Using a Robust RankAggregation Approach. In: Alvarez ML, Nourbakhsh M, editors.RNA Mapping: Methods and Protocols. New York: Springer NewYork; 2014. 361-73.

  18. Forero DA, Guio-Vega GP, González-Giraldo Y. A comprehensiveregional analysis of genome-wide expression profiles for majordepressive disorder. J Affect Disord. 2017; 218:86-92.

  19. Hek K, Demirkan A, Lahti J, Terracciano A, Teumer A, CornelisMC, et al. A Genome-Wide Association Study of DepressiveSymptoms. Biol Psychiatry. 2013; 73(7):667-78.

  20. Genetics of Personality C. Meta-analysis of Genome-wideAssociation Studies for Neuroticism, and the PolygenicAssociation With Major Depressive Disorder. JAMA psychiatry.2015; 72(7):642-50.

  21. Ripke S, Wray NR, Lewis CM, Hamilton SP, Weissman MM, BreenG, et al. A mega-analysis of genome-wide association studies formajor depressive disorder. Mol Psychiatry. 2013; 18(4):497-511.

  22. Forero DA, Prada CF, Perry G. Functional and Genomic Featuresof Human Genes Mutated in Neuropsychiatric Disorders. TheOpen Neurology Journal. 2016; 10:143-8.

  23. Kelley KW, Nakao-Inoue H, Molofsky AV, Oldham MC. Variationamong intact tissue samples reveals the core transcriptionalfeatures of human CNS cell classes. Nat Neurosci. 2018;21(9):1171-84.

  24. Huang da W, Sherman BT, Lempicki RA. Systematic and integrativeanalysis of large gene lists using DAVID bioinformatics resources.Nat Protoc. 2009; 4(1):44-57.

  25. Hawrylycz M, Miller JA, Menon V, Feng D, Dolbeare T, Guillozet-Bongaarts AL, et al. Canonical genetic signatures of the adulthuman brain. Nat Neurosci. 2015; 18(12):1832-44.

  26. Rolland T, Tasan M, Charloteaux B, Pevzner SJ, Zhong Q, SahniN, et al. A proteome-scale map of the human interactomenetwork. Cell. 2014; 159(5):1212-26.

  27. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D,et al. Cytoscape: a software environment for integrated modelsof biomolecular interaction networks. Genome Res. 2003;13(11):2498-504.

  28. Forero DA, González-Giraldo Y. Convergent functional genomicsof cocaine misuse in humans and animal models. Am J DrugAlcohol Abuse. 2020; 46(1):22-30.

  29. Forero DA, Gonzalez-Giraldo Y. Integrative In Silico Analysis ofGenome-Wide DNA Methylation Profiles in Schizophrenia. J MolNeurosci. 2020; 70(11):1887-1893.

  30. Ayalew M, Le-Niculescu H, Levey DF, Jain N, Changala B, PatelSD, et al. Convergent functional genomics of schizophrenia:from comprehensive understanding to genetic risk prediction.Mol Psychiatry. 2012; 17(9):887-905.

  31. Hoseth EZ, Krull F, Dieset I, Morch RH, Hope S, Gardsjord ES,et al. Attenuated Notch signaling in schizophrenia and bipolardisorder. Sci Rep. 2018; 8(1):5349.

  32. Pasterkamp RJ, Giger RJ. Semaphorin function in neural plasticityand disease. Curr Opin Neurobiol. 2009; 19(3):263-74.

  33. Cattaneo A, Cattane N, Malpighi C, Czamara D, SuarezA, Mariani N, et al. FoxO1, A2M, and TGF-beta1: threenovel genes predicting depression in gene X environmentinteractions are identified using cross-species and cross-tissuestranscriptomic and miRNomic analyses. Mol Psychiatry. 2018;23(11):2192-208.

  34. Blendy JA. The role of CREB in depression and antidepressanttreatment. Biol Psychiatry. 2006; 59(12):1144-50.

  35. Uddin M, Koenen KC, Aiello AE, Wildman DE, de los Santos R,Galea S. Epigenetic and inflammatory marker profiles associatedwith depression in a community-based epidemiologic sample.Psychol Med. 2011;41(5):997-1007.

  36. Sabunciyan S, Aryee MJ, Irizarry RA, Rongione M, Webster MJ,Kaufman WE, et al. Genome-wide DNA methylation scan inmajor depressive disorder. PloS one. 2012; 7(4):e34451.

  37. Na K-S, Won E, Kang J, Chang HS, Yoon H-K, Tae WS, et al.Brain-derived neurotrophic factor promoter methylation andcortical thickness in recurrent major depressive disorder. Sci Rep.2016; 6(1):21089.

  38. Roy B, Shelton RC, Dwivedi Y. DNA methylation and expressionof stress related genes in PBMC of MDD patients with and withoutserious suicidal ideation. J Psychiatr Res. 2017; 89:115-24.

  39. Kizil C, Kuchler B, Yan JJ, Ozhan G, Moro E, Argenton F, etal. Simplet/Fam53b is required for Wnt signal transduction byregulating beta-catenin nuclear localization. Development.2014; 141(18):3529-39.

  40. Gelernter J, Sherva R, Koesterer R, Almasy L, Zhao H,Kranzler HR, et al. Genome-wide association study of cocainedependence and related traits: FAM53B identified as a risk gene.Mol Psychiatry. 2014; 19(6):717-23.

  41. Ni H, Xu M, Zhan GL, Fan Y, Zhou H, Jiang HY, et al. TheGWAS Risk Genes for Depression May Be Actively Involved inAlzheimer's Disease. Journal of Alzheimer's disease : JAD. 2018;64(4):1149-61.

  42. Dogan MV, Beach SRH, Philibert RA. Genetically contextualeffects of smoking on genome wide DNA methylation. Am J MedGenet B Neuropsychiatr Genet. 2017; 174(6):595-607.

  43. Brazma A, Hingamp P, Quackenbush J, Sherlock G, SpellmanP, Stoeckert C, et al. Minimum information about a microarrayexperiment (MIAME)-toward standards for microarray data. NatGenetics. 2001; 29(4):365-71.

  44. Forero DA. Available Software for Meta-analyses of GenomewideExpression Studies. Curr Genomics. 2019; 20(5):325-31.




2020     |     www.medigraphic.com