Integrative bioinformatics analysis of transcriptomic data sheds light on the molecular complexity of parkinson's disease and potential therapeutic targets

Qudsia Yousafi, Sawaira Asghar, Misbah Shaukat


Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects millions of individuals worldwide, with an increasing prevalence in aging populations. In this study, we retrieved the mRNA expression dataset, GSE165082, for PD through GEOmnibus. Total of 220 downregulated and 354 upregulated genes were identified after data normalization. Functional annotation carried out by DAVID tools, revealed that these DEGs were mainly enriched in biological processes i.e., cell division and protein phosphorylation, and they were localized mostly cytoplasm and nucleus. Two molecular function protein binding and ATP binding were predominant.  Additionally KEGG pathway analysis highlighted their involvement in neurodegenerative, cancer, alzheimer's and coronavirus diseases. Armadillo-type fold and Armadillo-like helical domains were found by INTERPRO while TKc domain by SMART. Transcription factors IRF1 was predicted by FunRich tool. Upregulated genes were found expressed in 6 sites i.e., Palate, Ventral striatum, Pluripotent stem cells, Ganglia, Curtilage and Ciliary muscle. A protein-protein interaction network was constructed by using Cytoscape v 6.0. Ten hub genes EFI3A, RPL28, SMG8. UPF2, XAF1, IFITM1, IFIT3, LY63, IFI3 and LY6B were identified by Cytohubba. The expression patterns of hub genes across different organs and immune response cells using a heatmap and expression of EIF3 was found in almost all organs except liver. MicroRNA for were predicted by FunRich tool. Finally, we predicted microRNAs for RPL28, SMG8, UPF2 and EIF3a that could potentially regulate these hub genes, providing insights into post-transcriptional gene regulation. This comprehensive analysis contributes to our understanding of the molecular mechanisms underlying Parkinson's disease and provides a foundation for future research and therapeutic development in this complex and challenging condition.


miRNA, gene ontology, hub genes, heat map, IRF1

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Angelucci, F., Cechova, K., Valis, M., Kuca, K., Zhang, B., Hort, J., 2019. MicroRNAs in Alzheimer’s disease: diagnostic markers or therapeutic agents? Frontiers in pharmacology 10, 665.

Ardekani, A.M., Naeini, M.M., 2010. The role of microRNAs in human diseases. Avicenna journal of medical biotechnology 2, 161.

Armstrong, M.J., Okun, M.S., 2020. Diagnosis and treatment of Parkinson disease: a review. Jama 323, 548–560.

Calabrese, G., Molzahn, C., Mayor, T., 2022. Protein interaction networks in neurodegenerative diseases: From physiological function to aggregation. Journal of Biological Chemistry 298.

Can, T., 2014. Introduction to Bioinformatics, in: Yousef, M., Allmer, J. (Eds.), miRNomics: MicroRNA Biology and Computational Analysis, Methods in Molecular Biology. Humana Press, Totowa, NJ, pp. 51–71.

Chin, C.-H., Chen, S.-H., Wu, H.-H., Ho, C.-W., Ko, M.-T., Lin, C.-Y., 2014a. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC systems biology 8, 1–7.

Chin, C.-H., Chen, S.-H., Wu, H.-H., Ho, C.-W., Ko, M.-T., Lin, C.-Y., 2014b. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC systems biology 8, 1–7.

Chung, S.-H., 2009. Aberrant phosphorylation in the pathogenesis of Alzheimer’s disease. BMB reports 42, 467–474.

Cline, M.S., Smoot, M., Cerami, E., Kuchinsky, A., Landys, N., Workman, C., Christmas, R., Avila-Campilo, I., Creech, M., Gross, B., 2007. Integration of biological networks and gene expression data using Cytoscape. Nature protocols 2, 2366–2382.

Crow, Y.J., Stetson, D.B., 2022. The type I interferonopathies: 10 years on. Nature Reviews Immunology 22, 471–483.

De Lau, L.M., Breteler, M.M., 2006. Epidemiology of Parkinson’s disease. The Lancet Neurology 5, 525–535.

Deng, L., Ding, L., Duan, X., Peng, Y., 2023. Shared molecular signatures between coronavirus infection and neurodegenerative diseases provide targets for broad-spectrum drug development. Scientific Reports 13, 5457.

Dong, Z., Liu, Z., Cui, P., Pincheira, R., Yang, Y., Liu, J., Zhang, J.-T., 2009. Role of eIF3a in regulating cell cycle progression. Experimental cell research 315, 1889–1894.

Dong, Z., Zhang, J.-T., 2006. Initiation factor eIF3 and regulation of mRNA translation, cell growth, and cancer. Critical reviews in oncology/hematology 59, 169–180.

Dorsey, E.R., Elbaz, A., Nichols, E., Abbasi, N., Abd-Allah, F., Abdelalim, A., Adsuar, J.C., Ansha, M.G., Brayne, C., Choi, J.-Y.J., 2018. Global, regional, and national burden of Parkinson’s disease, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. The Lancet Neurology 17, 939–953.

Gao, H.-M., Jiang, J., Wilson, B., Zhang, W., Hong, J.-S., Liu, B., 2002. Microglial activation-mediated delayed and progressive degeneration of rat nigral dopaminergic neurons: relevance to Parkinson’s disease: Microglia-mediated dopaminergic neurodegeneration. Journal of Neurochemistry 81, 1285–1297.

Garcia-Moreno, A., López-Domínguez, R., Villatoro-García, J.A., Ramirez-Mena, A., Aparicio-Puerta, E., Hackenberg, M., Pascual-Montano, A., Carmona-Saez, P., 2022. Functional enrichment analysis of regulatory elements. Biomedicines 10, 590.

Haarman, B.C.B., Riemersma-Van der Lek, R.F., Nolen, W.A., Mendes, R., Drexhage, H.A., Burger, H., 2015. Feature-expression heat maps–A new visual method to explore complex associations between two variable sets. Journal of biomedical informatics 53, 156–161.

Hanks, S.K., Quinn, A.M., Hunter, T., 1988. The Protein Kinase Family: Conserved Features and Deduced Phylogeny of the Catalytic Domains. Science 241, 42–52.

Henderson, A.R., Wang, Q., Meechoovet, B., Siniard, A.L., Naymik, M., De Both, M., Huentelman, M.J., Caselli, R.J., Driver-Dunckley, E., Dunckley, T., 2021. DNA methylation and expression profiles of whole blood in Parkinson’s disease. Frontiers in Genetics 12, 640266.

Huang, D.W., Sherman, B.T., Lempicki, R.A., 2009. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols 4, 44–57.

Itoh, S., Harada, H., Nakamura, Y., White, R., Taniguchi, T., 1991. Assignment of the human interferon regulatory factor-1 (IRF1) gene to chromosome 5q23–q31. Genomics 10, 1097–1099.

Jankovic, J., 2008. Parkinson’s disease: clinical features and diagnosis. Journal of neurology, neurosurgery & psychiatry 79, 368–376.

Katzeff, J.S., Kim, W.S., 2021. ATP-binding cassette transporters and neurodegenerative diseases. Essays in Biochemistry 65, 1013–1024.

Katzeff, J.S., Lok, H.C., Bhatia, S., Fu, Y., Halliday, G.M., Kim, W.S., 2022. ATP-binding cassette transporter expression is widely dysregulated in frontotemporal dementia with TDP-43 inclusions. Frontiers in Molecular Neuroscience 15, 1043127.

Langfelder, P., Horvath, S., 2008. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559.

Latchman, D.S., 1997. Transcription factors: an overview. The international journal of biochemistry & cell biology 29, 1305–1312.

Li, C., Liu, J., Lin, J., Shang, H., 2022. COVID-19 and risk of neurodegenerative disorders: A Mendelian randomization study. Translational psychiatry 12, 283.

Li, J., Zhou, D., Qiu, W., Shi, Y., Yang, J.-J., Chen, S., Wang, Q., Pan, H., 2018. Application of weighted gene co-expression network analysis for data from paired design. Scientific reports 8, 622.

Lukiw, W.J., Alexandrov, P.N., 2012. Regulation of Complement Factor H (CFH) by Multiple miRNAs in Alzheimer’s Disease (AD) Brain. Mol Neurobiol 46, 11–19.

Mathews, M.B., Sonenberg, N., Hershey, J.W., 2000. Origins and principles of translational control. COLD SPRING HARBOR MONOGRAPH SERIES 39, 1–32.

Mills, R.D., Mulhern, T.D., Cheng, H.-C., Culvenor, J.G., 2012. Analysis of LRRK2 accessory repeat domains: prediction of repeat length, number and sites of Parkinson’s disease mutations. Biochemical Society Transactions 40, 1086–1089.

Mostafavi, S., Ray, D., Warde-Farley, D., Grouios, C., Morris, Q., 2008. GeneMANIA: A real-time multiple association network integration algorithm for predicting gene function. Genome Biology 9.

Navarro, C.L., Cau, P., Lévy, N., 2006. Molecular bases of progeroid syndromes. Human molecular genetics 15, R151–R161.

Ogata, H., Goto, S., Fujibuchi, W., Kanehisa, M., 1998. Computation with the KEGG pathway database. Biosystems 47, 119–128.

Ogawa, O., Lee, H., Zhu, X., Raina, A., Harris, P.L.R., Castellani, R.J., Perry, G., Smith, M.A., 2003. Increased p27, an essential component of cell cycle control, in Alzheimer’s disease. Aging Cell 2, 105–110.

Park, T., Yi, S.-G., Kang, S.-H., Lee, S., Lee, Y.-S., Simon, R., 2003. Evaluation of normalization methods for microarray data. BMC Bioinformatics 4, 33.

Parkinson, J., 1817. An essay on the shaking palsy: London: Whittingham and Rowland for Sherwood. Neely and jones.

Perluigi, M., Barone, E., Di Domenico, F., Butterfield, D.A., 2016. Aberrant protein phosphorylation in Alzheimer disease brain disturbs pro-survival and cell death pathways. Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease 1862, 1871–1882.

Roy, E., Cao, W., 2022. Glial interference: impact of type I interferon in neurodegenerative diseases. Mol Neurodegeneration 17, 78.

San Segundo-Val, I., Sanz-Lozano, C.S., 2016. Introduction to the Gene Expression Analysis, in: Isidoro García, M. (Ed.), Molecular Genetics of Asthma, Methods in Molecular Biology. Springer New York, New York, NY, pp. 29–43.

Song, L., Chen, J., Lo, C.-Y.Z., Guo, Q., Feng, J., Zhao, X.-M., 2022. Impaired type I interferon signaling activity implicated in the peripheral blood transcriptome of preclinical Alzheimer’s disease. EBioMedicine 82.

Staszewski, J., Lazarewicz, N., Konczak, J., Migdal, I., Maciaszczyk-Dziubinska, E., 2023. UPF1—From mRNA Degradation to Human Disorders. Cells 12, 419.

Sun, Z., Shi, K., Yang, S., Liu, J., Zhou, Q., Wang, G., Song, J., Li, Z., Zhang, Z., Yuan, W., 2018. Effect of exosomal miRNA on cancer biology and clinical applications. Mol Cancer 17, 147.

Thomas, S.N., Cripps, D., Yang, A.J., 2009. Proteomic Analysis of Protein Phosphorylation and Ubiquitination in Alzheimer’s Disease, in: Ottens, A.K., Wang, K.K.W. (Eds.), Neuroproteomics, Methods in Molecular Biology. Humana Press, Totowa, NJ, pp. 109–121.

Thompson, T.B., Chaggar, P., Kuhl, E., Goriely, A., Initiative, A.D.N., 2020. Protein-protein interactions in neurodegenerative diseases: A conspiracy theory. PLoS computational biology 16, e1008267.

Wang, H., Chen, Y., Chen, J., Zhang, Z., Lao, W., Li, X., Huang, J., Wang, T., 2014. Cell cycle regulation of DNA polymerase beta in rotenone-based Parkinson’s disease models. PLoS One 9, e109697.

Yang, W., Hamilton, J.L., Kopil, C., Beck, J.C., Tanner, C.M., Albin, R.L., Ray Dorsey, E., Dahodwala, N., Cintina, I., Hogan, P., 2020. Current and projected future economic burden of Parkinson’s disease in the US. npj Parkinson’s Disease 6, 15.

Yang, Y., Han, L., Yuan, Y., Li, J., Hei, N., Liang, H., 2014. Gene co-expression network analysis reveals common system-level properties of prognostic genes across cancer types. Nature communications 5, 3231.

Ying, S.-Y., Chang, D.C., Lin, S.-L., 2008. The MicroRNA (miRNA): Overview of the RNA Genes that Modulate Gene Function. Mol Biotechnol 38, 257–268.

Zhang, B., Horvath, S., 2005. A General Framework for Weighted Gene Co-Expression Network Analysis. Statistical Applications in Genetics and Molecular Biology 4.

Zuberi, K., Franz, M., Rodriguez, H., Montojo, J., Lopes, C.T., Bader, G.D., Morris, Q., 2013. GeneMANIA prediction server 2013 update. Nucleic acids research 41, W115–W122.



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