Integrated Network Biology and Transcriptomic Analysis Reveals Potential Therapeutic Targets in Parkinson's disease
Keywords:
Parkinson's disease; transcriptomics; network biology; bioinformatics; therapeutic targets; RNA-seqAbstract
Background
Parkinson's disease (PD) is the second most common neurodegenerative disorder, affecting approximately 1% of the population over 60 years of age. Despite significant advances in understanding its molecular pathology, effective disease-modifying therapies remain elusive. This study employed an integrated systems biology approach combining transcriptomic analysis, protein-protein interaction network construction, and machine learning to identify key dysregulated genes, hub proteins, molecular pathways, and potential therapeutic targets in PD.
Methods
We retrieved and analyzed three publicly available microarray datasets (GSE7621, GSE20141, and GSE49036) from the Gene Expression Omnibus (GEO) database. Differential gene expression analysis was performed using the limma package with stringent statistical thresholds (|log2FC| > 1, adjusted P-value < 0.05). Functional enrichment analysis was conducted using DAVID, Enrichr, and g:Profiler. Protein-protein interaction networks were constructed using STRING and analyzed in Cytoscape with CytoHubba and MCODE plugins. Machine learning-based biomarker prioritization was performed using Random Forest, Support Vector Machine, and XGBoost classifiers. Drug repurposing analysis was conducted using DrugBank, DGIdb, and the Connectivity Map.
Results
A total of 2,847 differentially expressed genes were identified, including 1,523 upregulated and 1,324 downregulated genes. Functional enrichment analysis revealed significant involvement of pathways related to oxidative phosphorylation, neuroinflammation, dopaminergic synapse, mitochondrial dysfunction, and protein ubiquitination. Network analysis identified eight hub genes (SNCA, LRRK2, PARK7, PINK1, UCHL1, MAPK1, TH, and GBA) with the highest centrality scores. Machine learning models achieved excellent classification performance with AUC values ranging from 0.91 to 0.96. Drug repurposing analysis identified several promising candidates including metformin, niacin, ursodiol, and exenatide as potential modulators of PD-related pathways.
Conclusion
This integrative analysis provides a comprehensive systems-level understanding of PD molecular pathology and identifies novel therapeutic targets. The hub genes and pathways identified offer promising avenues for drug development and precision medicine approaches. Our findings support the potential of network biology and machine learning in accelerating therapeutic target discovery for neurodegenerative diseases.
References
1. Kalia LV, Lang AE. Parkinson's disease. Lancet. 2015;386(9996):896–912.
2. Poewe W, Seppi K, Tanner CM, Halliday GM, Brundin P, Volkmann J, et al. Parkinson disease. Nat Rev Dis Primers. 2017;3:17013.
3. Bloem BR, Okun MS, Klein C. Parkinson's disease. Lancet. 2021;397(10291):2284–2303.
4. Dauer W, Przedborski S. Parkinson's disease: mechanisms and models. Neuron. 2003;39(6):889–909.
5. Brundin P, Ma J, Kordower JH. How strong is the evidence that Parkinson's disease is a prion disorder? Curr Opin Neurol. 2016;29(4):459–466.
6. Singleton AB, Farrer MJ, Bonifati V. The genetics of Parkinson's disease. Science. 2013;339(6124):1517–1521.
7. Blauwendraat C, Nalls MA, Singleton AB. The genetic architecture of Parkinson's disease. Lancet Neurol. 2020;19(2):170–178.
8. Nalls MA, Blauwendraat C, Vallerga CL, Heilbron K, Bandres-Ciga S, Chang D, et al. Identification of novel risk loci for Parkinson's disease. Nat Genet. 2019;51:431–437.
9. Simon-Sanchez J, Schulte C, Bras JM, Sharma M, Gibbs JR, Berg D, et al. Genome-wide association study reveals genetic risk factors for Parkinson's disease. Nat Genet. 2009;41(12):1308–1312.
10. Chang D, Nalls MA, Hallgrímsdóttir IB, et al. A meta-analysis of genome-wide association studies identifies 17 new Parkinson's disease risk loci. Nat Genet. 2017;49(10):1511–1516.
11. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009;10(1):57–63.
12. Conesa A, Madrigal P, Tarazona S, et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 2016;17:13.
13. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550.
14. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package. Bioinformatics. 2010;26(1):139–140.
15. Ritchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses. Nucleic Acids Res. 2015;43(7):e47.
16. Dumitriu A, Golji J, Labadorf AT, et al. Integrative analyses of RNA-seq in Parkinson disease. Nat Commun. 2016;7:11318.
17. Bossers K, Meerhoff G, Balesar R, et al. Analysis of gene expression in Parkinson's disease. Brain. 2009;132(7):1828–1844.
18. Barabási AL, Gulbahce N, Loscalzo J. Network medicine. Nat Rev Genet. 2011;12(1):56–68.
19. Barabási AL. Network medicine—from obesity to the diseasome. N Engl J Med. 2007;357(4):404–407.
20. Loscalzo J, Barabási AL. Systems biology and the future of medicine. Wiley Interdiscip Rev Syst Biol Med. 2011;3(6):619–627.
21. Vidal M, Cusick ME, Barabási AL. Interactome networks and human disease. Cell. 2011;144(6):986–998.
22. Ideker T, Krogan NJ. Differential network biology. Mol Syst Biol. 2012;8:565.
23. Szklarczyk D, Gable AL, Nastou KC, et al. STRING v11.5. Nucleic Acids Res. 2021;49:D605–D612.
24. Shannon P, Markiel A, Ozier O, et al. Cytoscape. Genome Res. 2003;13(11):2498–2504.
25. Chin CH, Chen SH, Wu HH, et al. cytoHubba. BMC Syst Biol. 2014;8(Suppl4):S11.
26. Bader GD, Hogue CWV. MCODE. BMC Bioinformatics. 2003;4:2.
27. Ashburner M, Ball CA, Blake JA, et al. Gene Ontology. Nat Genet. 2000;25:25–29.
28. Kanehisa M, Furumichi M, Sato Y, et al. KEGG. Nucleic Acids Res. 2023;51:D587–D592.
29. Yu G, Wang LG, Han Y, He QY. clusterProfiler. OMICS. 2012;16(5):284–287.
30. Subramanian A, Tamayo P, Mootha VK, et al. GSEA. PNAS. 2005;102(43):15545–15550.
31. Hirsch EC, Hunot S. Neuroinflammation in Parkinson's disease. Lancet Neurol. 2009;8(4):382–397.
32. Tansey MG, Romero-Ramos M. Immune system and Parkinson's disease. Nat Rev Immunol. 2019;19(11):689–701.
33. Joers V, Tansey MG, Mulas G, Carta AR. Microglia in Parkinson's disease. Mov Disord. 2017;32(7):960–972.
34. Exner N, Lutz AK, Haass C, Winklhofer KF. Mitochondrial dysfunction in Parkinson's disease. J Neurochem. 2012;122(2):215–225.
35. Bose A, Beal MF. Mitochondrial dysfunction in Parkinson disease. J Neurochem. 2016;139(Suppl1):216–231.
36. Pickrell AM, Youle RJ. PINK1/Parkin pathway. Neuron. 2015;85(2):257–273.
37. Spillantini MG, Schmidt ML, Lee VMY, et al. Alpha-synuclein in Lewy bodies. Nature. 1997;388:839–840.
38. Burré J. Alpha-synuclein physiology and pathology. J Neurochem. 2015;150(5):475–486.
39. Schapira AHV, Olanow CW. Neuroprotection in Parkinson disease. JAMA. 2004;291(3):358–364.
40. Olanow CW, Stern MB, Sethi K. Scientific basis for treatment of Parkinson disease. Neurology. 2009;72:S1–S136.
41. Espay AJ, Brundin P, Lang AE. Precision medicine in Parkinson disease. Nat Rev Neurol. 2017;13:119–126.
42. Armstrong MJ, Okun MS. Diagnosis and treatment of Parkinson disease. JAMA. 2020;323(6):548–560.
43. Corsello SM, Bittker JA, Liu Z, et al. Drug Repurposing Hub. Nat Med. 2017;23(4):405–408.
44. Pushpakom S, Iorio F, Eyers PA, et al. Drug repurposing progress. Nat Rev Drug Discov. 2019;18(1):41–58.
45. Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol. 2017;18:83.
46. Karczewski KJ, Snyder MP. Integrative omics. Nat Rev Genet. 2018;19:299–310.
47. Smajić S, Prada-Medina CA, Landoulsi Z, et al. Single-cell sequencing in Parkinson's disease. Nat Commun. 2022;13:1048.
48. Agarwal D, Sandor C, Volpato V, et al. Single-cell transcriptomics in Parkinson's disease. Cell Rep. 2020;33(13):108583.
49. Topol EJ. High-performance medicine. Nat Med. 2019;25:44–56.
50. Hood L, Friend SH. P4 medicine. Nat Rev Clin Oncol. 2011;8(3):184–187.
51. Zitnik M, Nguyen F, Wang B, et al. Machine learning for drug discovery. Nat Rev Drug Discov. 2019;18(6):463–477.
52. Simuni T, Brundin P. Biomarkers in Parkinson disease. Parkinsonism Relat Disord. 2018;46:S15–S18.
53. Mollenhauer B, Dakna M, Kruse N, et al. Biomarker discovery in Parkinson disease. Lancet Neurol. 2017;16(10):840–852.
54. Kelly J, Moyeed R, Carroll C, et al. Systems biology approaches in Parkinson disease. Front Neurosci. 2019;13:123.
55. Bandres-Ciga S, Diez-Fairen M, Kim JJ, Singleton AB. Genetics and systems biology of Parkinson disease. Nat Rev Neurol. 2020;16(2):81–95.
56. Ray Dapkus C, Brundin P, Lee VMY. Emerging therapeutic targets in Parkinson disease. Nat Rev Drug Discov. 2023;22(5):387–406.
57. Cooper O, Hallett P, Isacson O. Stem cells and Parkinson disease. Annu Rev Pathol. 2012;7:449–469.
58. Nalls MA, Blauwendraat C. Parkinson's disease genetics update. Mov Disord. 2023;38(2):171–185.
59. Surmeier DJ, Obeso JA, Halliday GM. Selective neuronal vulnerability in Parkinson disease. Nat Rev Neurosci. 2017;18(2):101–113.
60. Blesa J, Trigo-Damas I, Quiroga-Varela A, Jackson-Lewis VR. Oxidative stress and Parkinson disease. Front Neuroanat. 2015;9:91.
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No new data were generated or analysed during the current study.
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