Abstract
Vitiligo is an idiopathic disorder characterized by depigmented patches on the skin due to progressive loss of melanocytes. Several genetic, immunological, and pathophysiological investigations have established vitiligo as a polygenetic disorder with multifactorial etiology. However, no definite model explaining the interplay between these causative factors has been established hitherto. Therefore, we studied the disorder at the system level to identify the key proteins involved by exploring their molecular connectivity in terms of topological parameters. The existing research data helped us in collating 215 proteins involved in vitiligo onset or progression. Interaction study of these proteins leads to a comprehensive vitiligo map with 4845 protein nodes linked with 107,416 edges. Based on centrality measures, a backbone network with 500 nodes has been derived. This has presented a clear overview of the proteins and processes involved and the crosstalk between them. Clustering backbone proteins revealed densely connected regions inferring major molecular interaction modules essential for vitiligo. Finally, a list of top order proteins that play a key role in the disease pathomechanism has been formulated. This includes SUMO2, ESR1, COPS5, MYC, SMAD3, and Cullin proteins. While this list is in fair agreement with the available literature, it also introduces new candidate proteins that can be further explored. A subnetwork of 64 vitiligo core proteins was built by analyzing the backbone and seed protein networks. Our finding suggests that the topology, along with functional clustering, provides a deep insight into the behavior of proteins. This in turn aids in the illustration of disease condition and discovery of significant proteins involved in vitiligo.
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Abbreviations
- BC:
-
Betweenness Centrality
- PPI:
-
Protein–Protein Interaction
- PPIN:
-
Protein–Protein Interaction network
- PDB:
-
Protein Data Bank
- CC:
-
Closeness Centrality
References
Guerra L, Dellambra E, Brescia S, Raskovic D (2010) Vitiligo: pathogenetic hypotheses and targets for current therapies. Curr Drug Metab 11(5):451–467
Speeckaert R, Speeckaert MM, van Geel N (2015) Why treatments do(n’t) work in vitiligo: an autoinflammatory perspective. Autoimmun Rev 14(4):332–340. doi:10.1016/j.autrev.2014.12.003
Reimann E, Kingo K, Karelson M, Reemann P, Loite U, Keermann M, Abram K, Vasar E, Silm H, Koks S (2012) Expression profile of genes associated with the dopamine pathway in vitiligo skin biopsies and blood sera. Dermatology (Basel, Switzerland) 224 (2):168–176. doi:10.1159/000338023
Westerhof W, d’Ischia M (2007) Vitiligo puzzle: the pieces fall in place. Pigment cell research/sponsored by the European Society for Pigment Cell Research and the International Pigment Cell. Society 20(5):345–359. doi:10.1111/j.1600-0749.2007.00399.x
Spritz RA (2011) Recent progress in the genetics of generalized vitiligo. J Genet Genom 38(7):271–278. doi:10.1016/j.jgg.2011.05.005
Mutation C, Pathway Analysis working group of the International Cancer Genome C (2015) Pathway and network analysis of cancer genomes. Nature Methods 12(7):615–621. doi:10.1038/nmeth.3440
Zuberi K, Franz M, Rodriguez H, Montojo J, Lopes CT, Bader GD, Morris Q (2013) GeneMANIA prediction server 2013 update. Nucleic Acids Res 41:W115-122. doi:10.1093/nar/gkt533
Heath JN (2010) Epigenetic analysis of promiscuous gene expression in central tolerance. University of Birmingham
Kim KK, Kim HB (2009) Protein interaction network related to Helicobacter pylori infection response. World J Gastroenterol 15(36):4518–4528
Ran J, Li H, Fu J, Liu L, Xing Y, Li X, Shen H, Chen Y, Jiang X, Li Y, Li H (2013) Construction and analysis of the protein-protein interaction network related to essential hypertension. BMC Syst Biol 7:32. doi:10.1186/1752-0509-7-32
Gursoy A, Keskin O, Nussinov R (2008) Topological properties of protein interaction networks from a structural perspective. Biochem Soc Trans 36:1398–1403. doi:10.1042/bst0361398
Winterbach W, Van Mieghem P, Reinders M, Wang H, de Ridder D (2013) Topology of molecular interaction networks. BMC Syst Biol 7:90. doi:10.1186/1752-0509-7-90
Goni J, Esteban FJ, de Mendizabal NV, Sepulcre J, Ardanza-Trevijano S, Agirrezabal I, Villoslada P (2008) A computational analysis of protein-protein interaction networks in neurodegenerative diseases. BMC Syst Biol 2:52. doi:10.1186/1752-0509-2-52
Laddha NC, Dwivedi M, Mansuri MS, Gani AR, Ansarullah M, Ramachandran AV, Dalai S, Begum R (2013) Vitiligo: interplay between oxidative stress and immune system. Exp Dermatol 22(4):245–250. doi:10.1111/exd.12103
Rebhan M, Chalifa-Caspi V, Prilusky J, Lancet D (1998) GeneCards: a novel functional genomics compendium with automated data mining and query reformulation support. Bioinformatics 14(8):656–664
Du P, Feng G, Flatow J, Song J, Holko M, Kibbe WA, Lin SM (2009) From disease ontology to disease-ontology lite: statistical methods to adapt a general-purpose ontology for the test of gene-ontology associations. Bioinformatics 25(12):i63–i68. doi:10.1093/bioinformatics/btp193
Geer LY, Marchler-Bauer A, Geer RC, Han L, He J, He S, Liu C, Shi W, Bryant SH (2010) The NCBI BioSystems database. Nucleic Acids Res 38(Database issue):D492–D496. doi:10.1093/nar/gkp858
UniProt consortium (2015) UniProt: a hub for protein information. Nucleic acids research 43 (Database issue):D204–212. doi:10.1093/nar/gku989
Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA (2005) Online mendelian inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res 33(Database issue):D514–D517. doi:10.1093/nar/gki033
Kohler S, Doelken SC, Mungall CJ, Bauer S, Firth HV, Bailleul-Forestier I, Black GC, Brown DL, Brudno M, Campbell J, FitzPatrick DR, Eppig JT, Jackson AP, Freson K, Girdea M, Helbig I, Hurst JA, Jahn J, Jackson LG, Kelly AM, Ledbetter DH, Mansour S, Martin CL, Moss C, Mumford A, Ouwehand WH, Park SM, Riggs ER, Scott RH, Sisodiya S, Van Vooren S, Wapner RJ, Wilkie AO, Wright CF, Vulto-van Silfhout AT, de Leeuw N, de Vries BB, Washingthon NL, Smith CL, Westerfield M, Schofield P, Ruef BJ, Gkoutos GV, Haendel M, Smedley D, Lewis SE, Robinson PN (2014) The human phenotype ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Res 42(Database issue):D966–D974. doi:10.1093/nar/gkt1026
Schriml LM, Arze C, Nadendla S, Chang YW, Mazaitis M, Felix V, Feng G, Kibbe WA (2012) Disease ontology: a backbone for disease semantic integration. Nucleic Acids Res 40(Database issue):D940–D946. doi:10.1093/nar/gkr972
Pinero J, Queralt-Rosinach N, Bravo A, Deu-Pons J, Bauer-Mehren A, Baron M, Sanz F, Furlong LI (2015) DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database 2015:bav028. doi:10.1093/database/bav028
Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, Lin J, Minguez P, Bork P, von Mering C, Jensen LJ (2013) STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res 41(Database issue):D808–D815. doi:10.1093/nar/gks1094
Kohl M, Wiese S, Warscheid B (2011) Cytoscape: software for visualization and analysis of biological networks. Methods Mol Biol (Clifton, NJ) 696:291–303. doi:10.1007/978-1-60761-987-1_18
Xenarios I, Salwinski L, Duan XJ, Higney P, Kim SM, Eisenberg D (2002) DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Res 30(1):303–305
Zanzoni A, Montecchi-Palazzi L, Quondam M, Ausiello G, Helmer-Citterich M, Cesareni G (2002) MINT: a Molecular INTeraction database. FEBS Lett 513(1):135–140
Chatr-Aryamontri A, Breitkreutz BJ, Oughtred R, Boucher L, Heinicke S, Chen D, Stark C, Breitkreutz A, Kolas N, O’Donnell L, Reguly T, Nixon J, Ramage L, Winter A, Sellam A, Chang C, Hirschman J, Theesfeld C, Rust J, Livstone MS, Dolinski K, Tyers M (2015) The BioGRID interaction database: 2015 update. Nucleic Acids Res 43(Database issue):D470–D478. doi:10.1093/nar/gku1204
Montojo J, Zuberi K, Rodriguez H, Kazi F, Wright G, Donaldson SL, Morris Q, Bader GD (2010) GeneMANIA Cytoscape plugin: fast gene function predictions on the desktop. Bioinformatics 26(22):2927–2928. doi:10.1093/bioinformatics/btq562
Hermjakob H, Montecchi-Palazzi L, Lewington C, Mudali S, Kerrien S, Orchard S, Vingron M, Roechert B, Roepstorff P, Valencia A, Margalit H, Armstrong J, Bairoch A, Cesareni G, Sherman D, Apweiler R (2004) IntAct: an open source molecular interaction database. Nucleic Acids Res 32(Database issue):D452–D455. doi:10.1093/nar/gkh052
Croft D, Mundo AF, Haw R, Milacic M, Weiser J, Wu G, Caudy M, Garapati P, Gillespie M, Kamdar MR, Jassal B, Jupe S, Matthews L, May B, Palatnik S, Rothfels K, Shamovsky V, Song H, Williams M, Birney E, Hermjakob H, Stein L, D’Eustachio P (2014) The Reactome pathway Knowledgebase. Nucleic Acids Res 42(Database issue):D472–D477. doi:10.1093/nar/gkt1102
Paz A, Brownstein Z, Ber Y, Bialik S, David E, Sagir D, Ulitsky I, Elkon R, Kimchi A, Avraham KB, Shiloh Y, Shamir R (2011) SPIKE: a database of highly curated human signaling pathways. Nucleic Acids Res 39(Database issue):D793–D799. doi:10.1093/nar/gkq1167
Jeanquartier F, Jean-Quartier C, Holzinger A (2015) Integrated web visualizations for protein-protein interaction databases. BMC Bioinform 16:195. doi:10.1186/s12859-015-0615-z
Raman K (2010) Construction and analysis of protein-protein interaction networks. Autom Exp 2(1):2. doi:10.1186/1759-4499-2-2
Agapito G, Guzzi PH, Cannataro M (2013) Visualization of protein interaction networks: problems and solutions. BMC Bioinform 14(Suppl 1):S1. doi:10.1186/1471-2105-14-s1-s1
Xie W, Sun J, Wu J (2015) Construction and analysis of a protein-protein interaction network related to self-renewal of mouse spermatogonial stem cells. Mol Biosyst 11(3):835–843. doi:10.1039/c4mb00579a
Assenov Y, Ramirez F, Schelhorn SE, Lengauer T, Albrecht M (2008) Computing topological parameters of biological networks. Bioinformatics 24(2):282–284. doi:10.1093/bioinformatics/btm554
Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393(6684):440–442. doi:10.1038/30918
Doncheva NT, Assenov Y, Domingues FS, Albrecht M (2012) Topological analysis and interactive visualization of biological networks and protein structures. Nature Protoc 7(4):670–685. doi:10.1038/nprot.2012.004
Azevedo H, Moreira-Filho CA (2015) Topological robustness analysis of protein interaction networks reveals key targets for overcoming chemotherapy resistance in glioma. Sci Rep 5:16830. doi:10.1038/srep16830
Huang da W, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protoc 4(1):44–57. doi:10.1038/nprot.2008.211
Kitano H (2004) Biological robustness. Nature Rev Genet 5(11):826–837. doi:10.1038/nrg1471
Barabasi AL, Oltvai ZN (2004) Network biology: understanding the cell’s functional organization. Nature Rev Genet 5(2):101–113. doi:10.1038/nrg1272
Stelzl U, Worm U, Lalowski M, Haenig C, Brembeck FH, Goehler H, Stroedicke M, Zenkner M, Schoenherr A, Koeppen S, Timm J, Mintzlaff S, Abraham C, Bock N, Kietzmann S, Goedde A, Toksoz E, Droege A, Krobitsch S, Korn B, Birchmeier W, Lehrach H, Wanker EE (2005) A human protein-protein interaction network: a resource for annotating the proteome. Cell 122(6):957–968. doi:10.1016/j.cell.2005.08.029
Jin SY, Park HH, Li GZ, Lee HJ, Hong MS, Park HJ, Park HK, Seo JC, Yim SV, Chung JH, Lee MH (2004) Association of estrogen receptor 1 intron 1 C/T polymorphism in Korean vitiligo patients. J Dermatol Sci 35(3):181–186. doi:10.1016/j.jdermsci.2004.06.008
Murakami H, Arnheiter H (2005) Sumoylation modulates transcriptional activity of MITF in a promoter-specific manner. Pigment cell research/sponsored by the European Society for Pigment Cell Research and the International Pigment Cell. Society 18(4):265–277. doi:10.1111/j.1600-0749.2005.00234.x
Bellei B, Pitisci A, Ottaviani M, Ludovici M, Cota C, Luzi F, Dell’Anna ML, Picardo M (2013) Vitiligo: a possible model of degenerative diseases. PloS One 8(3):e59782. doi:10.1371/journal.pone.0059782
Schallreuter KU, Salem MM (2010) Vitiligo. What is new? Der Hautarzt Zeitschrift fur Dermatologie Venerologie und verwandte Gebiete 61 (7):578–585. doi:10.1007/s00105-009-1916-9
Burn GL, Svensson L, Sanchez-Blanco C, Saini M, Cope AP (2011) Why is PTPN22 a good candidate susceptibility gene for autoimmune disease? FEBS Lett 585(23):3689–3698. doi:10.1016/j.febslet.2011.04.032
Hill RJ, Zozulya S, Lu YL, Ward K, Gishizky M, Jallal B (2002) The lymphoid protein tyrosine phosphatase Lyp interacts with the adaptor molecule Grb2 and functions as a negative regulator of T-cell activation. Exp Hematol 30(3):237–244
Jeong TJ, Shin MK, Uhm YK, Kim HJ, Chung JH, Lee MH (2010) Association of UVRAG polymorphisms with susceptibility to non-segmental vitiligo in a Korean sample. Exp Dermatol 19(8):e323–e325. doi:10.1111/j.1600-0625.2009.01039.x
Eisenberg E, Levanon EY (2013) Human housekeeping genes, revisited. Trends Genet 29 (10):569–574. doi:10.1016/j.tig.2013.05.010
Eapen BR (2004) VIT1 gene and vitiligo. Indian J Dermatol Venereol Leprol 70(3):184–185
Kingo K, Aunin E, Karelson M, Ratsep R, Silm H, Vasar E, Koks S (2008) Expressional changes in the intracellular melanogenesis pathways and their possible role in the pathogenesis of vitiligo. J Dermatol Sci 52(1):39–46. doi:10.1016/j.jdermsci.2008.03.013
Dey-Rao R, Sinha AA (2016) Interactome analysis of gene expression profile reveals potential novel key transcriptional regulators of skin pathology in vitiligo. Genes Immun 17(1):30–45. doi:10.1038/gene.2015.48
Shi F, Kong BW, Song JJ, Lee JY, Dienglewicz RL, Erf GF (2012) Understanding mechanisms of vitiligo development in Smyth line of chickens by transcriptomic microarray analysis of evolving autoimmune lesions. BMC Immunol 13:18. doi:10.1186/1471-2172-13-18
Ippoliti F, Frediani T, Santis WD, Lucarelli S, Canitano N, Frediani S, Frati C (2005) The role of heat shock proteins (HSPs) in vitiligo: deviation of cytotoxic response? J Dermatol Sci 37(2):114–117. doi:10.1016/j.jdermsci.2004.10.004
Stromberg S, Bjorklund MG, Asplund A, Rimini R, Lundeberg J, Nilsson P, Ponten F, Olsson MJ (2008) Transcriptional profiling of melanocytes from patients with vitiligo vulgaris. Pigment Cell Melanoma Res 21(2):162–171. doi:10.1111/j.1755-148X.2007.00429.x
Pshenichnaya I, Schouwey K, Armaro M, Larue L, Knoepfler PS, Eisenman RN, Trumpp A, Delmas V, Beermann F (2012) Constitutive gray hair in mice induced by melanocyte-specific deletion of c-Myc. Pigment Cell Melanoma Res 25(3):312–325. doi:10.1111/j.1755-148X.2012.00998.x
Jang HM, Erf GF, Rowland KC, Kong BW (2014) Genome resequencing and bioinformatic analysis of SNP containing candidate genes in the autoimmune vitiligo Smyth line chicken model. BMC Genom 15:707. doi:10.1186/1471-2164-15-707
Rouillard AD, Gundersen GW, Fernandez NF, Wang Z, Monteiro CD, McDermott MG, Ma’ayan A (2016) The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins. Database. doi:10.1093/database/baw100
Melikoglu MA, Melikoglu M, Karatay S, Ugur M, Senel K (2008) A coincidence of rheumatoid arthritis, autoimmune thyroid disease and vitiligo in a single patient: a possible pathogenetic linkage. Eurasian J Medi 40(1):42–44
Gould IM, Gray RS, Urbaniak SJ, Elton RA, Duncan LJ (1985) Vitiligo in diabetes mellitus. Br J Dermatol 113(2):153–155
Vrijman C, Kroon MW, Limpens J, Leeflang MM, Luiten RM, van der Veen JP, Wolkerstorfer A, Spuls PI (2012) The prevalence of thyroid disease in patients with vitiligo: a systematic review. Br J Dermatol 167(6):1224–1235. doi:10.1111/j.1365-2133.2012.11198.x
Coenen MJ, Gregersen PK (2009) Rheumatoid arthritis: a view of the current genetic landscape. Genes Immun 10(2):101–111. doi:10.1038/gene.2008.77
Agarwal P, Rashighi M, Essien KI, Richmond JM, Randall L, Pazoki-Toroudi H, Hunter CA, Harris JE (2015) Simvastatin prevents and reverses depigmentation in a mouse model of vitiligo. J Invest Dermatol 135(4):1080–1088. doi:10.1038/jid.2014.529
Craiglow BG, King BA (2015) Tofacitinib citrate for the treatment of vitiligo: a pathogenesis-directed therapy. JAMA DermatoL 151(10):1110–1112. doi:10.1001/jamadermatol.2015.1520
Kubic JD, Young KP, Plummer RS, Ludvik AE, Lang D (2008) Pigmentation PAX-ways: the role of Pax3 in melanogenesis, melanocyte stem cell maintenance, and disease. Pigment Cell Melanoma Res 21(6):627–645. doi:10.1111/j.1755-148X.2008.00514.x
Guan C, Lin F, Zhou M, Hong W, Fu L, Xu W, Liu D, Wan Y, Xu A (2010) The role of VIT1/FBXO11 in the regulation of apoptosis and tyrosinase export from endoplasmic reticulum in cultured melanocytes. Int J Mol Med 26(1):57–65
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The authors thankfully acknowledge the help, support, and guidance provided by Dr. Ajay Pandey, Department of Mechanical Engineering, MANIT, Bhopal.
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Malhotra, A.G., Jha, M., Singh, S. et al. Construction of a Comprehensive Protein–Protein Interaction Map for Vitiligo Disease to Identify Key Regulatory Elements: A Systemic Approach. Interdiscip Sci Comput Life Sci 10, 500–514 (2018). https://doi.org/10.1007/s12539-017-0213-z
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DOI: https://doi.org/10.1007/s12539-017-0213-z