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Combinatorial Chemistry & High Throughput Screening

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ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

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General Research Article

Exploring Single Nucleotide Polymorphisms in ITGAV for Gastric, Pancreatic and Liver Malignancies: An Approach Towards the Discovery of Biomarker

Author(s): Shreya Bhattacharya, Pragati Prasad Sah, Arundhati Banerjee and Sujay Ray*

Volume 24, Issue 6, 2021

Published on: 18 August, 2020

Page: [860 - 873] Pages: 14

DOI: 10.2174/1386207323999200818164104

Price: $65

Abstract

Background: Integrin αV, encoded by ITGAV gene, is one of the most studied protein subunits, closely associated with liver, pancreatic and stomach cancer progression and metastasis via regulation of angiogenesis. The occurrence of Single Nucleotide Polymorphisms (SNPs) in cancer- associated proteins is a key determinant for varied susceptibility of an individual towards cancer.

Methodology: The study investigated the deleterious effects of these cancer-associated SNPs on the protein’s structure, stability and cancer causing potential using an in silico approach. Numerous computational tools were employed that identified the most deleterious cancer-associated SNPs and those to get actively involved in post-translational modifications. The impact of these SNPs on the protein structure, function and stability was also examined.

Conclusion and Future Scope: A total 63 non-synonymous SNPs in ITGAV gene were observed to be associated in these three gastrointestinal cancers and among this, 63, 19 were the most deleterious ones. The structural and functional importance of residues altered by most damaging SNPs was analyzed through evolutionary conservation and solvent accessibility. The study also elucidated three-dimensional structures of the 19 most damaging mutants. The analysis of conformational variation identified 5 SNPs (D379Y, G188E, G513V, L950P, and R540L) in integrin αV, which influence the protein’s structure. Three calcium binding sites were predicted at residues: D379, G384 and G408 and a peptide binding site at residue: R369 in integrin αV. Therefore, SNPs D379Y, G384C, G408R and R369W have the potential to alter the binding properties of the protein. Screening and characterization of deleterious SNPs could advance novel biomarker discovery and therapeutic development in the future.

Keywords: Integrin αV, deleterious single nucleotide polymorphisms, post translational modifications, evolutionary conservation, three-dimensional modelling, ligand binding site prediction.

« Previous Next »
[1]
Cancer deaths (2019) World Health Organization 2019.http://gco.iarc.fr/today/data/pdf/fact-sheets/cancers/cancer-fact-sheets-5.pdf
[2]
Lin, L.; Zhang, J. Role of intestinal microbiota and metabolites on gut homeostasis and human diseases. BMC Immunol., 2017, 18(1), 2.
[http://dx.doi.org/10.1186/s12865-016-0187-3] [PMID: 28061847]
[3]
Mowat, A.M.; Agace, W.W. Regional specialization within the intestinal immune system. Nat. Rev. Immunol., 2014, 14(10), 667-685.
[http://dx.doi.org/10.1038/nri3738] [PMID: 25234148]
[4]
Hanahan, D.; Weinberg, R.A. Hallmarks of cancer: the next generation. Cell, 2011, 144(5), 646-674.
[http://dx.doi.org/10.1016/j.cell.2011.02.013] [PMID: 21376230]
[5]
Wang, H.; Naghavi, M.; Allen, C. GBD 2015 Mortality and Causes of Death Collaborators. Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet, 2016, 388(10053), 1459-1544.
[http://dx.doi.org/10.1016/S0140-6736(16)31012-1] [PMID: 27733281]
[6]
Hynes, R.O. Integrins: bidirectional, allosteric signaling machines. Cell, 2002, 110(6), 673-687.
[http://dx.doi.org/10.1016/S0092-8674(02)00971-6] [PMID: 12297042]
[7]
Harburger, D.S.; Calderwood, D.A. Integrin signalling at a glance. J. Cell Sci., 2009, 122(Pt 2), 159-163.
[http://dx.doi.org/10.1242/jcs.018093] [PMID: 19118207]
[8]
Ruoslahti, E. Integrins. J. Clin. Invest., 1991, 87(1), 1-5.
[http://dx.doi.org/10.1172/JCI114957] [PMID: 1985087]
[9]
Brooks, P.C.; Clark, R.A.; Cheresh, D.A. Requirement of vascular integrin alpha v beta 3 for angiogenesis. Science, 1994, 264(5158), 569-571.
[http://dx.doi.org/10.1126/science.7512751] [PMID: 7512751]
[10]
Filardo, E.J.; Brooks, P.C.; Deming, S.L.; Damsky, C.; Cheresh, D.A. Requirement of the NPXY motif in the integrin beta 3 subunit cytoplasmic tail for melanoma cell migration in vitro and in vivo. J. Cell Biol., 1995, 130(2), 441-450.
[http://dx.doi.org/10.1083/jcb.130.2.441] [PMID: 7542248]
[11]
Felding-Habermann, B.; O’Toole, T.E.; Smith, J.W.; Fransvea, E.; Ruggeri, Z.M.; Ginsberg, M.H.; Hughes, P.E.; Pampori, N.; Shattil, S.J.; Saven, A.; Mueller, B.M. Integrin activation controls metastasis in human breast cancer. Proc. Natl. Acad. Sci. USA, 2001, 98(4), 1853-1858.
[http://dx.doi.org/10.1073/pnas.98.4.1853] [PMID: 11172040]
[12]
Weis, S.M.; Cheresh, D.A. αV integrins in angiogenesis and cancer. Cold Spring Harb. Perspect. Med., 2011, 1(1)a006478
[http://dx.doi.org/10.1101/cshperspect.a006478] [PMID: 22229119]
[13]
Seguin, L.; Desgrosellier, J.S.; Weis, S.M.; Cheresh, D.A. Integrins and cancer: regulators of cancer stemness, metastasis, and drug resistance. Trends Cell Biol., 2015, 25(4), 234-240.
[http://dx.doi.org/10.1016/j.tcb.2014.12.006] [PMID: 25572304]
[14]
Walker, C.; Mojares, E.; Del Río Hernández, A. Role of extracellular matrix in development and cancer progression. Int. J. Mol. Sci., 2018, 19(10), 3028.
[http://dx.doi.org/10.3390/ijms19103028] [PMID: 30287763]
[15]
Jacquemet, G.; Humphries, M.J.; Caswell, P.T. Role of adhesion receptor trafficking in 3D cell migration. Curr. Opin. Cell Biol., 2013, 25(5), 627-632.
[http://dx.doi.org/10.1016/j.ceb.2013.05.008] [PMID: 23797030]
[16]
Marelli, U.K.; Rechenmacher, F.; Sobahi, T.R.A.; Mas-Moruno, C.; Kessler, H. Tumor Targeting via Integrin Ligands. Front. Oncol., 2013, 3, 222.
[http://dx.doi.org/10.3389/fonc.2013.00222] [PMID: 24010121]
[17]
Bolley, J.; Lalatonne, Y.; Haddad, O.; Letourneur, D.; Soussan, M.; Pérard-Viret, J.; Motte, L. Optimized multimodal nanoplatforms for targeting α(v)β3 integrins. Nanoscale, 2013, 5(23), 11478-11489.
[http://dx.doi.org/10.1039/c3nr03763k] [PMID: 24154564]
[18]
Berghoff, A.S.; Rajky, O.; Winkler, F.; Bartsch, R.; Furtner, J.; Hainfellner, J.A.; Goodman, S.L.; Weller, M.; Schittenhelm, J.; Preusser, M. Invasion patterns in brain metastases of solid cancers. Neuro-oncol., 2013, 15(12), 1664-1672.
[http://dx.doi.org/10.1093/neuonc/not112] [PMID: 24084410]
[19]
Wu, S.; Powers, S.; Zhu, W.; Hannun, Y.A. Substantial contribution of extrinsic risk factors to cancer development. Nature, 2016, 529(7584), 43-47.
[http://dx.doi.org/10.1038/nature16166] [PMID: 26675728]
[20]
Earl, J; Greenhalf, W Single-Nucleotide Polymorphism (SNP) Analysis to Associate Cancer Risk Methods Molecul. Biol. Cancer Gene Profil., 2009, 171-196.
[21]
Das, S.S.; Mitra, A.; Chakravorty, N. Diseases and their clinical heterogeneity - Are we ignoring the SNiPers and micRomaNAgers? An illustration using Beta-thalassemia clinical spectrum and fetal hemoglobin levels. Genomics, 2019, 111(1), 67-75.
[http://dx.doi.org/10.1016/j.ygeno.2018.01.002] [PMID: 29309842]
[22]
Lander, E.S. The new genomics: global views of biology. Science, 1996, 274(5287), 536-539.
[http://dx.doi.org/10.1126/science.274.5287.536] [PMID: 8928008]
[23]
Langsenlehner, U.; Renner, W.; Yazdani-Biuki, B.; Eder, T.; Wascher, T.C.; Paulweber, B.; Clar, H.; Hofmann, G.; Samonigg, H.; Krippl, P. Integrin alpha-2 and beta-3 gene polymorphisms and breast cancer risk. Breast Cancer Res. Treat., 2006, 97(1), 67-72.
[http://dx.doi.org/10.1007/s10549-005-9089-4] [PMID: 16317580]
[24]
Gerger, A.; Hofmann, G.; Langsenlehner, U.; Renner, W.; Weitzer, W.; Wehrschütz, M.; Wascher, T.; Samonigg, H.; Krippl, P. Integrin alpha-2 and beta-3 gene polymorphisms and colorectal cancer risk. Int. J. Colorectal Dis., 2009, 24(2), 159-163.
[http://dx.doi.org/10.1007/s00384-008-0587-9] [PMID: 18836731]
[25]
Chandramohan, V.; Nagaraju, N.; Rathod, S.; Kaphle, A.; Muddapur, U. Identification of deleterious SNPs and their effects on structural level in CHRNA3 gene. Biochem. Genet., 2015, 53(7-8), 159-168.
[http://dx.doi.org/10.1007/s10528-015-9676-y] [PMID: 26002565]
[26]
Desai, M.; Chauhan, J. In silico analysis of nsSNPs in human methyl CpG binding protein 2. Meta Gene, 2016, 10, 1-7.
[http://dx.doi.org/10.1016/j.mgene.2016.09.004]
[27]
Arshad, M.; Bhatti, A.; John, P. Identification and in silico analysis of functional SNPs of human TAGAP protein: A comprehensive study. PLoS One, 2018, 13(1)e0188143
[http://dx.doi.org/10.1371/journal.pone.0188143] [PMID: 29329296]
[28]
Das, S.S.; Chakravorty, N. Identification of deleterious SNPs and their effects on BCL11A, the master regulator of fetal hemoglobin expression. Genomics, 2019.
[http://dx.doi.org/10.1016/j.ygeno.2019.03.002] [PMID: 30853596]
[29]
Wu, T-J.; Shamsaddini, A.; Pan, Y.; Smith, K.; Crichton, D.J.; Simonyan, V.; Mazumder, R. A framework for organizing cancer-related variations from existing databases, publications and NGS data using a High-performance Integrated Virtual Environment (HIVE). Database (Oxford), 2014, 2014bau022
[http://dx.doi.org/10.1093/database/bau022] [PMID: 24667251]
[30]
Dingerdissen, H.M.; Torcivia-Rodriguez, J.; Hu, Y.; Chang, T.C.; Mazumder, R.; Kahsay, R. BioMuta and BioXpress: mutation and expression knowledgebases for cancer biomarker discovery. Nucleic Acids Res., 2018, 46(D1), D1128-D1136.
[http://dx.doi.org/10.1093/nar/gkx907] [PMID: 30053270]
[31]
Heberle, H.; Meirelles, G.V.; da Silva, F.R.; Telles, G.P.; Minghim, R. InteractiVenn: a web-based tool for the analysis of sets through Venn diagrams. BMC Bioinformatics, 2015, 16, 169.
[http://dx.doi.org/10.1186/s12859-015-0611-3] [PMID: 25994840]
[32]
Adzhubei, I.; Jordan, D.M.; Sunyaev, S.R. Predicting functional effect of human missense mutations using PolyPhen-2. Curr. Protoc. Hum. Genet., 2013, Chapter 7, 20.
[http://dx.doi.org/10.1002/0471142905.hg0720s76] [PMID: 23315928]
[33]
Mohamoud, H.S.A.; Hussain, M.R.M.; El-Harouni, A.A.; Shaik, N.A.; Qasmi, Z.U.; Merican, A.F.; Baig, M.; Anwar, Y.; Asfour, H.; Bondagji, N.; Al-Aama, J.Y. First comprehensive in silico analysis of the functional and structural consequences of SNPs in human GalNAc-T1 gene. Comput. Math. Methods Med., 2014, 2014904052
[http://dx.doi.org/10.1155/2014/904052] [PMID: 24723968]
[34]
Choi, Y.; Chan, A.P. PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics, 2015, 31(16), 2745-2747.
[http://dx.doi.org/10.1093/bioinformatics/btv195] [PMID: 25851949]
[35]
Tang, H.; Thomas, P.D. PANTHER-PSEP: predicting disease-causing genetic variants using position-specific evolutionary preservation. Bioinformatics, 2016, 32(14), 2230-2232.
[http://dx.doi.org/10.1093/bioinformatics/btw222] [PMID: 27193693]
[36]
Capriotti, E.; Calabrese, R.; Casadio, R. Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information. Bioinformatics, 2006, 22(22), 2729-2734.
[http://dx.doi.org/10.1093/bioinformatics/btl423] [PMID: 16895930]
[37]
Thomas, P.D.; Campbell, M.J.; Kejariwal, A.; Mi, H.; Karlak, B.; Daverman, R.; Diemer, K.; Muruganujan, A.; Narechania, A. PANTHER: a library of protein families and subfamilies indexed by function. Genome Res., 2003, 13(9), 2129-2141.
[http://dx.doi.org/10.1101/gr.772403] [PMID: 12952881]
[38]
Calabrese, R.; Capriotti, E.; Fariselli, P.; Martelli, P.L.; Casadio, R. Functional annotations improve the predictive score of human disease-related mutations in proteins. Hum. Mutat., 2009, 30(8), 1237-1244.
[http://dx.doi.org/10.1002/humu.21047] [PMID: 19514061]
[39]
Cheng, J.; Randall, A.; Baldi, P. Prediction of protein stability changes for single-site mutations using support vector machines. Proteins, 2006, 62(4), 1125-1132.
[http://dx.doi.org/10.1002/prot.20810] [PMID: 16372356]
[40]
Li, B.; Krishnan, V.G.; Mort, M.E.; Xin, F.; Kamati, K.K.; Cooper, D.N.; Mooney, S.D.; Radivojac, P. Automated inference of molecular mechanisms of disease from amino acid substitutions. Bioinformatics, 2009, 25(21), 2744-2750.
[http://dx.doi.org/10.1093/bioinformatics/btp528] [PMID: 19734154]
[41]
Pejaver, V.; Hsu, W-L.; Xin, F.; Dunker, A.K.; Uversky, V.N.; Radivojac, P. The structural and functional signatures of proteins that undergo multiple events of post-translational modification. Protein Sci., 2014, 23(8), 1077-1093.
[http://dx.doi.org/10.1002/pro.2494] [PMID: 24888500]
[42]
Miller, M.P.; Kumar, S. Understanding human disease mutations through the use of interspecific genetic variation. Hum. Mol. Genet., 2001, 10(21), 2319-2328.
[http://dx.doi.org/10.1093/hmg/10.21.2319] [PMID: 11689479]
[43]
Ashkenazy, H.; Erez, E.; Martz, E.; Pupko, T.; Ben-Tal, N. ConSurf 2010: calculating evolutionary conservation in sequence and structure of proteins and nucleic acids. Nucleic Acids Res., 2010, 38(Web Server issue)W529-33
[http://dx.doi.org/10.1093/nar/gkq399] [PMID: 20478830]
[44]
Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The Protein Data Bank. Nucleic Acids Res., 2000, 28(1), 235-242.
[http://dx.doi.org/10.1093/nar/28.1.235] [PMID: 10592235]
[45]
BIOVIA DS. Discovery Studio 4.1; San Diego, CA, USA, 2017.
[46]
Källberg, M.; Wang, H.; Wang, S.; Peng, J.; Wang, Z.; Lu, H.; Xu, J. Template-based protein structure modeling using the RaptorX web server. Nat. Protoc., 2012, 7(8), 1511-1522.
[http://dx.doi.org/10.1038/nprot.2012.085] [PMID: 22814390]
[47]
Fiser, A.; Sali, A. ModLoop: automated modeling of loops in protein structures. Bioinformatics, 2003, 19(18), 2500-2501.
[http://dx.doi.org/10.1093/bioinformatics/btg362] [PMID: 14668246]
[48]
Laskowski, R.A.; Macarthur, M.W.; Moss, D.S.; Thornton, J.M. PROCHECK: a program to check the stereochemical quality of protein structures. J. Appl. Cryst., 1993, 26, 283-291.
[http://dx.doi.org/10.1107/S0021889892009944]
[49]
Wallner, B.; Elofsson, A. Can correct protein models be identified? Protein Sci., 2003, 12(5), 1073-1086.
[http://dx.doi.org/10.1110/ps.0236803] [PMID: 12717029]
[50]
Wiederstein, M.; Sippl, M.J. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res., 2007, 35(Web Server issue)W407-10
[http://dx.doi.org/10.1093/nar/gkm290] [PMID: 17517781]
[51]
Zhang, Y.; Skolnick, J. TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic Acids Res., 2005, 33(7), 2302-2309.
[http://dx.doi.org/10.1093/nar/gki524] [PMID: 15849316]
[52]
Frishman, D.; Argos, P. Knowledge-based protein secondary structure assignment. Proteins, 1995, 23(4), 566-579.
[http://dx.doi.org/10.1002/prot.340230412] [PMID: 8749853]
[53]
Yang, J.; Roy, A.; Zhang, Y. Protein-ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment. Bioinformatics, 2013, 29(20), 2588-2595.
[http://dx.doi.org/10.1093/bioinformatics/btt447] [PMID: 23975762]
[54]
Krishna, RG; Wold, F Post-translational modifications of proteins. Methods Protein Seq. Anal., 1993, 167-172.
[55]
Kragelund, B.B.; Poulsen, K.; Andersen, K.V.; Baldursson, T.; Krøll, J.B.; Neergård, T.B.; Jepsen, J.; Roepstorff, P.; Kristiansen, K.; Poulsen, F.M.; Knudsen, J. Conserved residues and their role in the structure, function, and stability of acyl-coenzyme A binding protein. Biochemistry, 1999, 38(8), 2386-2394.
[http://dx.doi.org/10.1021/bi982427c] [PMID: 10029532]
[56]
Carugo, O.; Pongor, S. A normalized root-mean-square distance for comparing protein three-dimensional structures. Protein Sci., 2001, 10(7), 1470-1473.
[http://dx.doi.org/10.1110/ps.690101] [PMID: 11420449]
[57]
Toniolo, C.; Benedetti, E. The polypeptide 310-helix. Trends Biochem. Sci., 1991, 16(9), 350-353.
[http://dx.doi.org/10.1016/0968-0004(91)90142-I] [PMID: 1949158]

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