In Silico Analysis of Alzheimer's Disease Mechanism Through DNA Methylation and Gene Expression Data

Yıl 2024, , 1019 - 1026, 20.08.2024

Fatih Özen , Tuba Sevimoğlu

https://doi.org/10.35414/akufemubid.1332018

Öz

Alzheimer's Disease (AD) is a debilitating disease impairing memory and thought process with currently no cure. In the current study, GEO2R, an integrated tool in the GEO database, was used to analyze DNA methylation and gene expression datasets associated with AD. Data from the BioGRID Database were used to create a PPI network of differentially methylation and expressed AD genes (DEMEGs). Cytoscape was used to image and analyze the PPI network topologically. The DAVID bioinformatics program was utilized to do enrichment analysis in order to uncover disease associations and signaling pathways. Furthermore, small molecules were predicted using Connectivity Map 2 (Cmap 2) as potential therapeutic agents that might be exploited as pharmacological targets for the study's DEMEGs. As a result, 502 mutual DEMEGs and several hubs that may be researched further as new biomarker candidates for AD such as SMURF1 and UBE2D2 were identified. The link between AD and the MAPK signaling pathway, as well as addiction and brain diseases such as ADHD and epilepsy has been established. Additionally, candidate small molecules that can be used as therapeutics such as flucloxacillin, butamben, and acetohexamide were proposed. This study integrated DNA methylation and gene expression data to further our knowledge of the AD disease mechanism.

Anahtar Kelimeler

Alzheimer, Gene Expression, DNA Methylation, Biomarker, Small Molecules

Alzheimer Hastalığı Mekanizmasının DNA Metilasyonu ve Gen Ekspresyon Verileri Üzerinden İn Silico Analizi

Öz

Alzheimer Hastalığı (AD), şu anda tedavisi olmayan, hafızayı ve düşünce sürecini bozan, zayıflatıcı bir hastalıktır. Mevcut çalışmada, GEO veritabanında entegre bir araç olan GEO2R, AD ile ilişkili DNA metilasyonunu ve gen ekspresyonu veri kümelerini analiz etmek için kullanıldı. Diferansiyel olarak metillenmiş ve eksprese edilmiş AD genlerinden (DEMEG'ler) oluşan PPI ağı oluşturmak için BioGRID Veri Tabanından elde edilen veriler kullanıldı. PPI ağını topolojik olarak görüntülemek ve analiz etmek için Cytoscape kullanıldı. Hastalık ilişkilerini ve sinyal yolaklarını ortaya çıkarmak amacıyla zenginleştirme analizi yapmak için DAVID biyoinformatik programından yararlanıldı. Ayrıca, Connectivity Map 2 (Cmap 2) kullanılarak çalışmanın DEMEG'leri için farmakolojik hedefler olarak kullanılabilecek potansiyel terapötik ajanlar olarak küçük moleküller ortaya konuldu. Sonuç olarak, SMURF1 ve UBE2D2 gibi AD için yeni biyobelirteç adayları olarak daha fazla araştırılabilecek 502 ortak DEMEG ve çeşitli merkezi proteinler belirlendi. Alzheimer ile MAPK sinyal yolağının yanı sıra bağımlılık ve DEHB ve epilepsi gibi beyin hastalıkları arasındaki bağlantı da belirlendi. Ayrıca flukloksasilin, butamben, asetoheksamid gibi tedavi edici olarak kullanılabilecek aday küçük moleküller de önerilmiştir. Bu çalışma, AD hastalığı mekanizması hakkındaki bilgimizi ilerletmek için DNA metilasyonu ve gen ekspresyonu verilerini birleştirmiştir.

Anahtar Kelimeler

Alzheimer Hastalığı, Gen İfadesi, DNA Metilasyonu, Biyobelirteç, Küçük Moleküller

Kaynakça

• Ahn, K., Song, J.H., Kim, D.K. and Park, M.H., 2009. Ubc9 gene polymorphisms and late-onset Alzheimer's disease in the Korean population: a genetic association study. Neurosci Lett, 465, 272-275. https://doi.org/10.1016/j.neulet.2009.09.017
• Becker, K., Barnes, K., Bright, T. et al., 2004. The Genetic Association Database. Nat Genet, 36, 431–432. https://doi.org/10.1038/ng0504-431
• Brooks AC.., Henderson B.J., 2021. Systematic Review of Nicotine Exposure's Effects on Neural Stem and Progenitor Cells. Brain Sci, 11(2):172. https://doi.org/10.3390/brainsci11020172
• Critchley WR, Smith GA, Zachary IC, Harrison MA, Ponnambalam S., 2023. The E2 ubiquitin-conjugating enzymes UBE2D1 and UBE2D2 regulate VEGFR2 dynamics and endothelial function. J Cell Sci, 136(10):jcs260657. https://doi.org/10.1242/jcs.260657
• Chin, C.H., Chen, S.H., Wu, H.H. and Ho, C.W., 2004. CytoHubba: Identification of hub objects and subnets from the complex interactome. BMC Systems Biology, 8:11. https://doi.org/10.1186/1752-0509-8-S4-S11
• Diniz, B.S., Teixeira, A.L., Cao, F., Gildengers, A., Soares, J.C., Butters, M.A., Reynolds, C.F. 3rd., 2017. History of Bipolar Disorder and the Risk of Dementia: A Systematic Review and Meta-Analysis. Am J Geriatr Psychiatry. 25(4):357-362. https://doi.org/10.1016/j.jagp.2016.11.014
• Ehrlich, M., 2019. DNA hypermethylation in disease: mechanisms and clinical relevance. Epigenetics, 14, 1141–1163. https://doi.org/10.1080/15592294.2019.1638701
• Haertle, L., Müller, T., Lardenoije, R. and Maierhofer, A., 2019. DNA methylome comparison of low IQ versus high IQ trisomy 21. Clin Epigenetics, 11, 195. https://doi.org/10.1186/s13148-019-0787-x
• Han, L., Witmer, P.D., Casey, E. and Valle, D., 2007. DNA methylation regulates microRNA expression. Cancer Biol Ther, 6, 1284–1288. https://doi.org/10.4161/cbt.6.8.4486
• Hanger, D.P. and Wray, S., 2010. Tau cleavage and tau aggregation in neurodegenerative disease. Biochem. Soc. Trans, 38, 1016-1020. https://doi.org/10.1042/BST0381016
• Hoffman, J.L., Faccidomo, S., Kim, M. and Taylor, S.M., 2019. Alcohol drinking exacerbates neural and behavioral pathology in the 3xTg-AD mouse model of Alzheimer's disease. Int Rev Neurobiol, 148, 169-230. https://doi.org/10.1016/bs.irn.2019.10.017
• Hong, Y., Chan, C.B., Kwon, I.S. and Li, X., 2012. SRPK2 phosphorylates tau and mediates the cognitive defects in Alzheimer's disease. J Neurosci, 32, 17262-72. https://doi.org/10.1523/JNEUROSCI.3300-12.2012
• Huang, D.W., Sherman, B.T. and Lempicki, R.A., 2019. Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res, 37, 1–13. https://doi.org/10.1093/nar/gkn923
• Irizarry, R.A., Hobbs, B., Collin, F. and Beazer-Barclay, Y.D., 2003. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 4, 249-264. https://doi.org/10.1093/biostatistics/4.2.249
• Kamondi, A., Grigg-Damberger, M., Löscher, W. et al., 2024. Epilepsy and epileptiform activity in late-onset Alzheimer disease: clinical and pathophysiological advances, gaps and conundrums. Nat Rev Neurol, 20, 162–182. https://doi.org/10.1038/s41582-024-00932-4
• Kim, E.K. and Choi, E.J., 2015. Compromised MAPK signaling in human diseases: an update. Archives of Toxicology, 89, 867–882. https://doi.org/10.1007/s00204-015-1472-2
• Lamb, J., Crawford, E.D., Peck, D. and Modell, J.W., 2006. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science, 29, 1929-35. https://doi.org/10.1126/science.1132939
• Nourian, Y.H., Pajooh, A.B., Aliomrani, M. and Amini M., 2021. Changes in DNA methylation in APOE and ACKR3 genes in multiple sclerosis patients and the relationship with their heavy metal blood levels. Neurotoxicology, 87, 182-187. https://doi.org/10.1016/j.neuro.2021.09.008
• Oh, T.K., Song, I.A., 2024. Impact of prescribed opioid use on development of dementia among patients with chronic non-cancer pain. Sci Rep 14, 3313. https://doi.org/10.1038/s41598-024-53728-3
• Oughtred, R., Stark, C., Breitkreutz, B.J. and Rust, J., 2018. BioGRID integration: 2019 update. Nucleic Acids Research, 47(D1):D529-D541. https://doi.org/10.1093/nar/gky1079
• Parenti, R, Paratore, S, Torrisi, A, Cavallaro, S.A, 2007. A natural antisense transcript against Rad18, specifically expressed in neurons and upregulated during beta-amyloid-induced apoptosis. Eur J Neurosci, 26(9):2444-57. https://doi.org/10.1111/j.1460-9568.2007.05864.x
• Razani, E., Pourbagheri-Sigaroodi, A., Safaroghli-Azar, A., Zoghi, A., Shanaki-Bavarsad, M., Bashash, D., 2021. The PI3K/Akt signaling axis in Alzheimer's disease: a valuable target to stimulate or suppress? Cell Stress Chaperones, 26(6):871-887. https://doi.org/10.1007/s12192-021-01231-3
• Santos, D.C., Henriques, R.R., Junior, M.A.A.L. and Farias, A.B., 2020. Acylhydrazones as isoniazid derivatives with multi-target profiles for the treatment of Alzheimer's disease: Radical scavenging, myeloperoxidase/acetylcholinesterase inhibition and biometal chelation. Bioorg Med Chem, 28, 115470. https://doi.org/10.1016/j.bmc.2020.115470
• Savelkoul, P.J., Janickova, H., Kuipers, A.A. and Hageman, R.J., 2012. A specific multi-nutrient formulation enhances M1 muscarinic acetylcholine receptor responses in vitro. J Neurochem, 120, 631-40. https://doi.org/10.1111/j.1471-4159.2011.07616.x
• Semick, S.A., Bharadwaj, R.A., Collado-Torres, L. and Tao R., 2019. Integrated DNA methylation and gene expression profiling across multiple brain regions implicate novel genes in Alzheimer's disease. Acta Neuropathol, 137, 557-569. https://doi.org/10.1007/s00401-019-01966-5
• Sevimoglu, T., 2023. In silico analysis of autism spectrum disorder through the integration of DNA methylation and gene expression data for biomarker search. Minerva Biotechnology and Biomolecular Research, 35, 73-80. https://doi.org/10.23736/S2724-542X.23.02956-5
• Shannon, P., Markiel, A., Ozier, O, and Baliga, N.S., 2003. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res, 13, 2498-504. https://doi.org/10.1101%2Fgr.1239303
• Shao, L., Liu, X., Zhu, S. and Liu, C., 2017. The Role of Smurf1 in Neuronal Necroptosis after Lipopolysaccharide-Induced Neuroinflammation. Cell Mol Neurobiol, 38, 809-816. https://doi.org/10.1007/s10571-017-0553-6
• Sharma B., Pal D., Sharma U. and Kumar A., 2022 Mitophagy: An Emergence of New Player in Alzheimer’s Disease. Front Mol Neurosci, 15:921908. https://doi.org/10.3389/fnmol.2022.921908
• Signor, S.A., and Nuzhdin, S.V., 2018. The Evolution of Gene Expression in cis and trans. Trends Genet, 34, 532-544. https://doi.org/10.1016/j.tig.2018.03.007
• Tsutsui, Y. and Hays, F.A., 2018. A Link Between Alzheimer's and Type II Diabetes Mellitus? Ca (+2) -Mediated Signal Control and Protein Localization. Bioessays, 40(6). https://dx.doi.org/10.1002/bies.201700219
• Ulep, M.G., Saraon, S.K., Mclea, S., 2018. Alzheimer’s Disease. The Journal for Nurse Practitioners, 14(3), 129-135. https://doi.org/10.1016/j.nurpra.2017.10.014
• Venkataraman, A., Kalk, N., Sewell, G. and Ritchie, C.W., 2017. Alcohol and Alzheimer's Disease-Does Alcohol Dependence Contribute to Beta-Amyloid Deposition, Neuroinflammation and Neurodegeneration in Alzheimer's Disease? Alcohol Alcohol, 52, 151-158. https://doi.org/10.1093/alcalc/agw092
• Wallin, C., Sholts, S.B., Österlund, N. et al., 2017 Alzheimer’s disease and cigarette smoke components: effects of nicotine, PAHs, and Cd(II), Cr(III), Pb(II), Pb(IV) ions on amyloid-β peptide aggregation. Sci Rep, 7, 14423. https://doi.org/10.1038/s41598-017-13759-5
• Wettenhall, J. and Smyth, G., 2004. limmaGUI: A graphical user interface for linear modeling of microarray data. Bioinformatic, 20, 3705-3706. https://doi.org/10.1093/bioinformatics/bth449
• Yan, X.S., Yang, Z.J., Jia, J.X. and Song, W., 2019. Protective mechanism of testosterone on cognitive impairment in a rat model of Alzheimer's disease. Neural Regen Res, 14, 649-657. https://doi.org/10.4103/1673-5374.245477
• Yulug, B., Hanoglu, L., Ozansoy, M. and Isık, D., 2018. Therapeutic role of rifampicin in Alzheimer's disease. Psychiatry Clin Neurosci, 72, 152-159. https://doi.org/10.1111/pcn.12637
• Zhang, M.J., Xia, F. and Zou, J., 2019. Fast and covariate-adaptive method amplifies detection power in large-scale multiple hypothesis testing. Nat Commun, 10, 3433. https://doi.org/10.1038/s41467-019-11247-0
• Zhang, L., Rietz, E.D., Halkola, R.K. and Dobrosavjevic M., 2022. Attention-deficit/hyperactivity disorder and Alzheimer's disease and any dementia: A multi-generation cohort study in Sweden. Alzheimers Dement, 18, 1155-1163. https://doi.org/10.1002/alz.12462
• Zhu M., Xiao B., Xue T., Qin S., Ding J., Wu Y., Tang Q., Huang M., Zhao N., Ye Y., Zhang Y., Zhang B., Li J., Guo F., Jiang Y., Zhang L., Zhang L., 2023. Cdc42GAP deficiency contributes to the Alzheimer's disease phenotype. Brain. 146(10):4350-4365 https://doi.org/10.1093/brain/awad184
• Internet References
• URL-1- https://biit.cs.ut.ee/gprofiler/convert (Accessed: 25.02.2021)
• URL-2- https://www.genome.jp/kegg/pathway.html (Accessed: 15.03.2021)