Analysis using top‐k skyline query of protein‐protein interaction reveals alpha‐synuclein as the most important protein in Parkinson’s disease

https://doi.org/10.22146/ijbiotech.63023

Mohammad Romano Diansyah(1), Annisa Annisa(2), Wisnu Ananta Kusuma(3*)

(1) Department of Computer Science, IPB University, Jln. Meranti, Kampus IPB Darmaga, Bogor 16680, Indonesia
(2) Trophical Biopharmaca Research Center, IPB University, Jl. Raya Dramaga, Kampus IPB Dramaga, Bogor 16680, Indonesia
(3) Department of Computer Science, IPB University, Jln. Meranti, Kampus IPB Darmaga, Bogor 16680, Indonesia
(*) Corresponding Author

Abstract


Parkinson’s disease is the second‐most‐common neurodegenerative disorder and can reduce patients’ quality of life. The disease is caused by abnormalities in dopaminergic neurons, such as reactive oxygen species (ROS) imbalance leading to programmed cell death, protein misfolding, and vesicle trafficking. Protein‐protein interaction (PPI) analysis has been demonstrated to understand better candidate proteins that might contribute to multifactorial neurodegenerative diseases, particularly in Parkinson’s disease. PPI analysis can be obtained from experiments and computational predictions. However, experiment data is often limited in interactome coverage. Therefore, additional computational prediction methods are required to provide more comprehensive PPI information. PPI can be represented as protein‐protein networks and analyzed based on centrality measures. The previous study has shown that top‐k skyline query, a method using dominance rule‐based centrality measures, reveals important protein candidates in Parkinson’s diseases. This study applied the top‐k skyline query to PPIs containing experiment and prediction data to find important proteins in Parkinson’s disease. The result shows that alpha‐synuclein (SNCA) is the most important protein and is expected to be a potential biomarker candidate for Parkinson’s disease.


Keywords


centrality measures; Parkinson’s disease; significant protein; top‐k skyline query

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References

Berger BS, Acebron SP, Herbst J, Koch S, Niehrs C. 2017. Parkinson’s disease associated receptor GPR 37 is an ER chaperone for LRP 6 . EMBO Rep. 18(5):712– 725. doi:10.15252/embr.201643585.

Borzsonyi S, Kossmann D, Stocker K. 2001. The Skyline Operator. In: Proceedings 17th International Conference on Data Engineering. p. 1–20. doi:10.1109/ICDE.2001.914855.

Bottero V, Santiago JA, Potashkin JA. 2018. PTPRC expression in blood is downregulated in Parkinson’s and progressive supranuclear palsy disorders. J Parkinsons Dis. 8(4):529–537. doi:10.3233/JPD-181391.

Chang JW, Zhou YQ, Ul Qamar MT, Chen LL, Ding YD. 2016. Prediction of protein–protein interactions by evidence combining methods. Int J Mol Sci. 17(11). doi:10.3390/ijms17111946.

Chen Y, Lian Y, Ma Y, Wu C, Zheng Y, Xie N. 2017. The expression and significance of tyrosine hydroxylase in the brain tissue of Parkinson’s disease rats. Exp Ther Med. 14(5):4813–4816. doi:10.3892/etm.2017.5124.

Chi J, Xie Q, Jia J, Liu X, Sun J, Deng Y, Yi L. 2018. Integrated analysis and identification of novel biomarkers in Parkinson’s disease. Front Aging Neurosci. 10(JUN). doi:10.3389/fnagi.2018.00178.

Chung JY, Park HR, Lee SJ, Lee SH, Kim JS, Jung YS, Hwang SH, Ha NC, Seol WG, Lee J, Park BJ. 2013. Elevated TRAF2/6 expression in Parkinson’s disease is caused by the loss of Parkin E3 ligase activity. Lab Investig. 93(6):663–676. doi:10.1038/labinvest.2013.60.

DeMaagd G, Philip A. 2015. Parkinson’s disease and its management part 1: Disease entity, risk factors, pathophysiology, clinical presentation, and diagnosis. P T 40(8):504–532.

Diansyah MR, Kusuma WA, Annisa. 2019. Analysis of protein-protein interaction using skyline query on Parkinson disease. In: 2019 Int Conf Adv Comput Sci Inf Syst. ICACSIS 2019. p. 175–180. doi:10.1109/ICACSIS47736.2019.8979892.

Dias V, Junn E, Mouradian MM. 2013. The Role of Oxidative Stress in Parkinson’s Disease. J Parkinson’s Dis. 3(4):461–491. doi:10.3233/JPD-130230.

Dorsey ER, Constantinescu R, Thompson JP, Biglan KM, Holloway RG, Kieburtz K, Marshall FJ, Ravina BM, Schifitto G, Siderowf A, Tanner CM. 2007. Projected number of people with Parkinson disease in the most populous nations, 2005 through 2030. Neurology 68(5):384–386. doi:10.1212/01.wnl.0000247740.47667.03.

Esposito G, Ana Clara F, Verstreken P. 2012. Synaptic vesicle trafficking and Parkinson’s disease. Dev Neurobiol. 72(1):134–144. doi:10.1002/dneu.20916. Hao T, Peng W, Wang Q, Wang B, Sun J. 2016. Reconstruction and application of protein–protein interaction network. Int J Mol Sci. 17(6). doi:10.3390/ijms17060907.

Jansen R, Lan N, Qian J, Gerstein M. 2002. Integration of genomic datasets to predict protein complexes in yeast. In: J Struct Funct Genomics., volume 2. Kluwer Academic Publishers. p. 71–81. doi:10.1023/A:1020495201615.

Klein C, Westenberger A. 2012. Genetics of Parkinson’s disease. Cold Spring Harb Perspect Med. 2(1). doi:10.1101/cshperspect.a008888.

Konovalova EV, Lopacheva OM, Grivennikov IA, Lebedeva OS, Dashinimaev EB, Khaspekov LG, Fedotova EY, Illarioshkin SN. 2015. Mutations in Parkinson’s disease-associated PARK2 gene are accompanied by imbalance in programmed cell death systems. Acta Naturae. 7(4):146–151. doi:10.32607/20758251- 2015-7-4-146-149.

Kontaki M, Papadopoulos AN, Manolopoulos Y. 2008. Continuous k-dominant skyline computation on multidimensional data streams. In: Proc ACM Symp Appl Comput. p. 956–960. doi:10.1145/1363686.1363908.

Lebouvier T, Chaumette T, Paillusson S, Duyckaerts C, Bruley Des Varannes S, Neunlist M, Derkinderen P. 2009. The second brain and Parkinson’s disease. Eur J Neurosci. 30(5):735–741. doi:10.1111/j.1460- 9568.2009.06873.x.

Lin X, Yuan Y, Zhang Q, Zhang Y. 2007. Selecting stars: The k most representative skyline operator. In: Proc. - Int Conf Data Eng. p. 86–95. doi:10.1109/ICDE.2007.367854.

Liu W, Wu A, Pellegrini M, Wang X. 2015. Integrative analysis of human protein, function and disease networks. Sci Rep. 5. doi:10.1038/srep14344.

Loeffler DA, Klaver AC, Coffey MP, Aasly JO, LeWitt PA. 2016. Age-related decrease in heat shock 70- kDa protein 8 in cerebrospinal fluid is associated with increased oxidative stress. Front Aging Neurosci. 8(JUN). doi:10.3389/fnagi.2016.00178.

Markaki I, Bergström S, Tsitsi P, Remnestål J, Månberg A, Hertz E, Paslawski W, Sorjonen K, Uhlén M, Mangone G, Carvalho S, Rascol O, Meissner WG, Magnin E, Wüllner U, Corvol JC, Nilsson P, Svenningsson P. 2020. Cerebrospinal Fluid Levels of Kininogen-1 Indicate Early Cognitive Impairment in Parkinson’s Disease. Mov Disord. 35(11):2101–2106. doi:10.1002/mds.28192.

Mata IF, Shi M, Agarwal P, Chung KA, Edwards KL, Factor SA, Galasko DR, Ginghina C, Griffith A, Higgins DS, Kay DM, Kim H, Leverenz JB, Quinn JF, Roberts JW, Samii A, Snapinn KW, Tsuang DW, Yearout D, Zhang J, Payami H, Zabetian CP. 2010. SNCA variant associated with Parkinson disease and plasma α-synuclein level. Arch Neurol. 67(11):1350–1356. doi:10.1001/archneurol.2010.279.

Odagaki Y, Toyoshima R. 2006. Dopamine D2 receptor mediated G protein activation assessed by agoniststimulated [35S]guanosine 5′-O-(γ-thiotriphosphate) binding in rat striatal membranes. Prog NeuroPsychopharmacol Biol Psychiatry. 30(7):1304–1312. doi:10.1016/j.pnpbp.2006.05.007.

Raman K, Damaraju N, Joshi GK. 2014. The organisational structure of protein networks: revisiting the centrality–lethality hypothesis. Syst Synth Biol. 8:73–81. doi:10.1007/s11693-013-9123-5.

Scardoni G, Lau C. 2012. Centralities Based Analysis of Complex Networks. In: New Frontiers in Graph Theory, chapter 16. Rijeka: IntechOpen. doi:10.5772/35846.

Scardoni G, Petterlini M, Laudanna C. 2009. Analyzing biological network parameters with CentiScaPe. Bioinformatics 25(21):2857–2859. doi:10.1093/bioinformatics/btp517.

Sharma P, Bhattacharyya DK, Kalita JK. 2016. Centrality analysis in PPI networks. In: 2016 Int Conf Access to Digit World, ICADW 2016 - Proc. p. 135–140. doi:10.1109/ICADW.2016.7942528.

Siddiqui IJ, Pervaiz N, Abbasi AA. 2016. The Parkinson Disease gene SNCA: Evolutionary and structural insights with pathological implication. Sci Rep. 6. doi:10.1038/srep24475.

Stumpf MPH, Thorne T, Silva dE, Stewart R, An HJ, Lappe M. 2005. Estimating thesize of the human interactome. In: Proceedings of the National Academy of Sciences. p. 6959–6964. doi:10.1073/pnas.0708078105.

Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, Von Mering C. 2018. STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47(D1):D607–D613. doi:10.1093/nar/gky1131.

Szybinska A, Lesniak W. 2017. P53 dysfunction in neurodegenerative diseases - The cause or effect of pathological changes? Aging Dis. 8(4). doi:10.14336/AD.2016.1120.

Tan JM, Wong ES, Lim KL. 2009. Protein misfolding and aggregation in Parkinson’s disease. Antioxidants Redox Signal. 11(9):2119–2134. doi:10.1089/ars.2009.2490.

Trist BG, Hare DJ, Double KL. 2018. A Proposed Mechanism for Neurodegeneration in Movement Disorders Characterized by Metal Dyshomeostasis and Oxidative Stress. Cell Chem Biol. 25(7):807–816. doi:10.1016/j.chembiol.2018.05.004.

Tsolakidou A, Czibere L, Pütz B, Trümbach D, Panhuysen M, Deussing JM, Wurst W, Sillaber I, Landgraf R, Holsboer F, Rein T. 2010. Gene expression profiling in the stress control brain region hypothalamic paraventricular nucleus reveals a novel gene network including Amyloid beta Precursor Protein. BMC Genomics 11(1). doi:10.1186/1471-2164-11-546.

Twelves D, Perkins KS, Counsell C. 2003. Systematic review of incidence studies of Parkinson’s disease. Mov Disord. 18(1):19–31. doi:10.1002/mds.10305.

Usman MS, Kusuma WA, Afendi FM, Heryanto R. 2019. Identification of Significant Proteins Associated with Diabetes Mellitus Using Network Analysis of Protein-Protein Interactions. Comput Eng Appl J. 8(1):41–52. doi:10.18495/comengapp.v8i1.283.

Veeriah S, Taylor BS, Meng S, Fang F, Yilmaz E, Vivanco I, Janakiraman M, Schultz N, Hanrahan AJ, Pao W, Ladanyi M, Sander C, Heguy A, Holland EC, Paty PB, Mischel PS, Liau L, Cloughesy TF, Mellinghoff IK, Solit DB, Chan TA. 2010. Somatic mutations of the Parkinson’s disease-associated gene PARK2 in glioblastoma and other human malignancies. Nat Genet. 42(1):77–82. doi:10.1038/ng.491.

WHO. 2004. Atlas : Country Resources for Neurological Disorders. World Health Organization. URL https: //apps.who.int/iris/handle/10665/43075.

Xiromerisiou G, Hadjigeorgiou GM, Papadimitriou A, Katsarogiannis E, Gourbali V, Singleton AB. 2008. Association between AKT1 gene and Parkinson’s disease: A protective haplotype. Neurosci Lett. 436(2):232–234. doi:10.1016/j.neulet.2008.03.026.

Yu J, Fotouhi F. 2006. Computational Approaches for Predicting Protein–Protein Interactions: A Survey. J Med Sys. 30(1):39–44. doi:10.1007/s10916-006-7402-3.



DOI: https://doi.org/10.22146/ijbiotech.63023

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