Research Article
BibTex RIS Cite

Alzheimer Hastalığıyla İlişkili BID, MAPK10 ve AGER Genlerindeki SNP ve miRNA'ların In Silico Araçlar Kullanılarak Değerlendirilmesi

Year 2023, Volume: 10 Issue: 1, 181 - 208, 31.05.2023
https://doi.org/10.35193/bseufbd.1205700

Abstract

Alzheimer hastalığı (AH), beyinde hücre içi hiperfosforile tau proteini, nörofibril yumakları ve hücre dışı amiloid β proteininin birikimi ile patolojik olarak tanımlanan hem genetik hem de çevresel faktörlerden kaynaklanan multifaktöriyel bir hastalıktır. Bu çalışmanın amacı, çeşitli in silico araçları kullanarak AH ile ilişkili BID, MAPK10 ve AGER genlerindeki yanlış anlamlı tek nükleotid polimorfizmlerinin (SNP'ler) potansiyel olarak zarar verici etkilerini tahmin etmek ve SNP'lerin miRNA'lar üzerindeki etkilerini belirlemektir. Ayrıca çeşitli yazılım araçları ile gen-gen ve protein-protein etkileşimlerinin belirlenmesi amaçlanmaktadır.
Sonuç olarak, BID geninde yedi, MAPK10 geninde yirmi yedi ve AGER geninde üç polimorfizmin zararlı etkilerinin olabileceği tahmin edilmiştir. BID ve MAPK10 genlerinde bazı SNP'lerin miRNA-mRNA bağlanmasının etkinliğini azalttığı, arttırdığı, kırdığı, yeni bir bağlanma bölgesi oluşturduğu ve/veya miRNA-mRNA bağlama bölgesini yok ettiği elde edilmiştir. miRNA-SNP analizlerinde AGER genine ait bilgi edinilememiştir.
Bu çalışmada BID, MAPK10 ve AGER genlerindeki yüksek riskli olduğu tahmin edilen SNP'ler gelecekteki genotipleme çalışmaları için veri sağlayabilecektir. Yüksek riskli olduğu tahmin edilen SNP'ler ve miRNA-mRNA aktivitesinde rolü olabilecek SNP'ler AH ile ilgili deneysel çalışmalarda öncelikli olarak değerlendirilebilecektir. Gelecekte, zararlı/hastalıkla ilgili yanlış anlamlı SNP'lerin ve mRNA-miRNA etkileşimini etkileyen SNP'lerin klinik etkilerini araştırmak için deneysel çalışmalar önerilmektedir.

References

  • Guerreiro, R., Hardy, J. (2014). Genetics of Alzheimer’s Disease. Neurotherapeutics, 11, 732-737.
  • Liu, X., Han, Z., Yang, C. (2017). Associations of microRNA single nucleotide polymorphisms and disease risk and pathophysiology. Clin. Genet. 92(3), 235–242
  • Brookes, A. J. (1999). The essence of SNPs. Gene, 234(2), 177–186.
  • Lonetti, A., Fontana, M. C., Martinelli, G., Iacobucci, I. (2016). Single Nucleotide Polymorphisms as Genomic Markers for High-Throughput Pharmacogenomic Studies. Microarray Technology: Methods and Applications, 143-159.
  • Single-nucleotide polymorphism - ISOGG Wiki. https://isogg.org/wiki/Single-nucleotide_polymorphism.
  • Battaloğlu, E., Başak, A. N. (2010). Kompleks Hastalık Genetiği Güncel Kavramlar ve Nörolojik Hastalıkların Tanısında Kullanılan Genomik Yöntemler. Klinik Gelişim dergisi Cilt 23 / NO: 1- NÖROLOJİ 128–133.
  • Kim, V. N. & Nam, J. W. (2006). Genomics of microRNA. Trends Genet. 22(3), 165–173.
  • Lee, Y., Kim, M., Han, J., Yeom, K.H., Lee, S., Baek, S.H., Kim, V.N., (2004). MicroRNA genes are transcribed by RNA polymerase II. EMBO J. 23(20), 4051-60.
  • Cogswell, J. P., Ward, J., Taylor, I.A., Waters, M., Shi, Y., Cannon, B., Kelnar, K., Kemppainen, C., Brown, D., Chen, C., Prinjha, R.K., Richardson, R.C., Saunders, A.M., Roses, A.D., Richards C.A., (2008). Identification of miRNA Changes in Alzheimer’s Disease Brain and CSF Yields Putative Biomarkers and Insights into Disease Pathways. J. Alzheimer’s Dis. 14(1), 27–41.
  • Martino, S., Di Girolamo, I., Orlacchio, A., Datti, A., Orlacchio, A. (2009). MicroRNA Implications across Neurodevelopment and Neuropathology. J. Biomed. Biotechnol. 2009, 13.
  • GeneMANIA. http://genemania.org/.
  • NCBI dbSNP database. https://www.ncbi.nlm.nih.gov/snp/.
  • UniProt database. https://www.uniprot.org/.
  • Warde-Farley, D., Donaldson, S.L., Comes, O., Zuberi, K., Badrawi, R., Chao, P., Franz, M., Grouios, C., Kazi, F., Lopes, C.T., Maitland, A., Mostafavi, S., Montojo, J., Shao, Q., Wright, G., Bader, G.D., Morris, Q. (2010). The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic acids research, 38(suppl_2), W214-W220.
  • STRING: functional protein association networks. https://string-db.org/.
  • Szklarczyk, D., Gable, A.L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J.,Simonovic, M., Doncheva, N.T., Morris, J.H., Bork, P., Jensen, L.J., Mering, C.V.(2019). STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47(D1):D607–13.
  • SIFT - Predict effects of nonsynonymous missense variants. https://sift.bii.a-star.edu.sg/.
  • Ng PC., Henikoff S. Predicting Deleterious Amino Acid Substitutions (2001). Genome Res.11(5):863-874.
  • Veitia, R. (2001). SIFTing the effects of SNPs. Genome Biol. 2(7), reports0019.
  • PolyPhen-2: prediction of functional effects of human nsSNPs. http://genetics.bwh.harvard.edu/pph2/.
  • Adzhubei, I. A. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P., Kondrashov A.S., Sunyaev, S.E., (2010). A method and server for predicting damaging missense mutations. Nat. Methods 7(4), 248–249.
  • PROVEAN. http://provean.jcvi.org/index.php.
  • Choi, Y., Sims, G. E., Murphy, S., Miller, J. R., Chan, A. P. (2012). Predicting the functional effect of amino acid substitutions and indels. PLoS One 7.
  • Choi, Y., Chan, A. P. (2015). PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics 31(16), 2745–2747.
  • SNAP2 - Predicting functional effects of sequence variants. https://rostlab.org/services/snap2web/.
  • Hecht, M., Bromberg, Y., Rost, B. (2015). Better prediction of functional effects for sequence variants. BMC genomics, 16(8), 1-12.
  • SNPs&GO - Predicting disease associated SNPs using GO terms. https://snps.biofold.org/snps-and-go/pages/method.html.
  • Capriotti, E., Calabrese, R., Fariselli, P., Martelli, P.L., Altman, R.B., Casadio, R.(2013). WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation. BMC Genomics. BMC genomics, 14, 1-7.
  • PhD-SNP: Predictor of human Deleterious Single Nucleotide Polymorphisms. https://snps.biofold.org/phd-snp/phd-snp.html.
  • Capriotti, E., Calabrese, R.,Casadio, R. (2006). Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information. Bioinformatics 22, 2729–2734.
  • MutationAssessor.org / functional impact of protein mutations. http://mutationassessor.org/r3/.
  • Reva, B., Antipin, Y., Sander, C.(2011). Predicting the functional impact of protein mutations: Application to cancer genomics. Nucleic Acids Res.39(17):e118.
  • PANTHER - Evolutionary analysis of coding SNPs. http://www.pantherdb.org/tools/csnpScoreForm.jsp.
  • Thomas, P. D., Ebert, D., Muruganujan, A., Mushayahama, T., Albou, L. P., Mi, H. (2022). PANTHER: Making genome-scale phylogenetics accessible to all. Protein Science, 31(1), 8–22.
  • Meta-SNP - Meta-predictor of disease causing variants. https://snps.biofold.org/meta-snp/.
  • Capriotti, E., Altman, R. B., Bromberg, Y. (2013). Collective judgment predicts disease-associated single nucleotide variants. BMC Genomics 14, S2.
  • Welcome to I-Mutant Suite Home Page: http://gpcr2.biocomp.unibo.it/cgi/predictors/I-Mutant3.0/I-Mutant3.0.cgi
  • Capriotti, E., Fariselli, P., Casadio, R.(2005).I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res. 33(Web Server issue).
  • Prediction of Protein Stability Changes upon Mutations: http://mupro.proteomics.ics.uci.edu/
  • Cheng, J., Randall, A., Baldi, P. (2006). Prediction of protein stability changes for single-site mutations using support vector machines. Proteins: Structure, Function, and Bioinformatics, 62(4), 1125-1132.
  • ProjectHOPE. https://www3.cmbi.umcn.nl/hope/.
  • Verselaar H., Beek, T.A., Kuipers, R.K., Hekkelman, M.L., Vriend, G., (2010). Protein structure analysis of mutations causing inheritable diseases. An e-Science approach with life scientist friendly interfaces. BMC Bioinformatics, 11, 548.
  • MirSNP: collection of human SNPs in predicted miRNA target sites. http://cmbi.bjmu.edu.cn/mirsnp.
  • Liu, C., Zhang F, Li T, Lu M, Wang L, Yue W, Zhang, D. (2012). MirSNP, a database of polymorphisms altering miRNA target sites, identifies miRNA-related SNPs in GWAS SNPs and eQTLs. BMC Genomics, 13, 661.
  • PolymiRTS: https://compbio.uthsc.edu/miRSNP/.
  • Bhattacharya, A., Ziebarth, J. D., Cui, Y. (2014). PolymiRTS Database 3.0: linking polymorphisms in microRNAs and their target sites with human diseases and biological pathways. Nucleic Acids Res. 42(D1), D86-D91.
  • Liu, Z-P.,Wang, Y.,Zhang, X-S.,Chen, L.,(2010). Identifying dysfunctional crosstalk of pathways in various regions of Alzheimer’s disease brains - BMC Syst Biol., 4 (Suppl 2), S11.
  • Krauthammer, M., Kaufmann, C. A., Gilliam, T. C. & Rzhetsky, A. (2004). Molecular triangulation: bridging linkage and molecular-network information for identifying candidate genes in Alzheimer’s disease. Proc. Natl. Acad. Sci. U. S. A. 101 (42), 15148–15153.
  • Mooney, S. D., Krishnan, V. G., Evani, U. S. (2010). Bioinformatic tools for identifying disease gene and SNP candidates. Methods Mol. Biol. 628, 307–319.
  • Thusberg, J.,Vihinen, M. (2009). Pathogenic or not? and if so, then how? Studying the effects of missense mutations using bioinformatics methods. Hum. Mutat. 30(5), 703–714.
  • Hicks, S., Wheeler, D. A., Plon, S. E., Kimmel, M. (2011). Prediction of missense mutation functionality depends on both the algorithm and sequence alignment employed. Hum. Mutat. 32(6), 661–668.
  • Li, K., Dai D, Zhao B, Yao L, Yao S, Wang B, Yang, Z. (2009). Association between the RAGE G82S polymorphism and Alzheimer’s disease. J. Neural Transm. 117(1), 97–104.
  • Wang, Z., Moult, J. (2001). SNPs, protein structure, and disease. Hum. Mutat. 17(4), 263–270.
  • Xu, J., Zhang, J. (2014). Why Human Disease-Associated Residues Appear as the Wild-Type in Other Species: Genome-Scale Structural Evidence for the Compensation Hypothesis. Mol. Biol. Evol. 31(7), 1787–1792.
  • Cargill, M., Altshuler D., Ireland J., Sklar P., Ardlie K., Patil N., Shaw, N., Lane, C.R., Lim, E.P., Kalyanaraman, N., Nemesh, J., Ziaugra L., Friedland L., Rolfe A., Warrington, J., Lipshuttz, R., Daley, G.Q., Lander, E.S. (1999). Characterization of single-nucleotide polymorphisms in coding regions of human genes. Nat. Genet. 22(3), 231–238.
  • Teng, S., Srivastava, A. K., Schwartz, C. E., Alexov, E. & Wang, L. (2010). Structural assessment of the effects of amino acid substitutions on protein stability and protein protein interaction. Int. J. Comput. Biol. Drug Des. 3(4), 334–349.
  • Dill, K. A., Fiebig, K. M. & Chan, H. S. (1993). Cooperativity in protein-folding kinetics. Proc. Natl. Acad. Sci. 90(5), 1942–1946.
  • Biro, J. C. (2006). Amino acid size, charge, hydropathy indices and matrices for protein structure analysis. Theor. Biol. Med. Model. 3(1),1-12.
  • Doss, C. G. P., NagaSundaram, N. (2012). Investigating the structural impacts of I64T and P311S mutations in APE1-DNA complex: a molecular dynamics approach. PLoS One 7(2), e31677.
  • Rose, G. D., Wolfenden, R. (1993). Hydrogen bonding, hydrophobicity, packing, and protein folding. Annu. Rev. Biophys. Biomol. Struct. 22(1), 381–415.
  • Gromiha, M. M., Oobatake, M., Kono, H., Uedaira, H., Sarai, A. (1999). Role of structural and sequence information in the prediction of protein stability changes: comparison between buried and partially buried mutations. Protein Eng. 12(7), 549–555.
  • Gong, S., Blundell, T. L. (2010). Structural and Functional Restraints on the Occurrence of Single Amino Acid Variations in Human Proteins. PLoS One 5(2), e9186.
  • Shirley, B. A., Nick Pace, C., Stanssens, P., Hahn, U. (1992). Contribution of hydrogen bonding to the conformational stability of ribonuclease T1. Biochemistry 31(3), 725–732.
  • Cai, T. T., Li J, An X, Yan N, Li D, Jiang Y, Wang, W., Shi, L., Qin, Q., Song, R., Wang., G., Jiang, W., Zhang J.A.. (2017). Polymorphisms in MIR499A and MIR125A gene are associated with autoimmune thyroid diseases. Mol. Cell. Endocrinol. 440, 106–115.
  • Ghanbari, M. Ikram, M.A., De Looper, H.W.J., Hofman, A., Erkeland, S.J., Franco, O.H., Dehghan, A. (2016). Genome-wide identification of microRNA-related variants associated with risk of Alzheimer’s disease. Sci. Rep. 6(1), 28387.
  • Kim, J., Choi GH, Ko KH, Kim JO, Oh SH, Park YS, Kim, O.J., Kim, N.K. (2016). Association of the Single Nucleotide Polymorphisms in microRNAs 130b, 200b, and 495 with Ischemic Stroke Susceptibility and Post-Stroke Mortality. PLoS One 11(9):e0162519.
  • Morales, S., Gulppi F, Gonzalez-Hormazabal P, Fernandez-Ramires R, Bravo T, Reyes JM, Gomez, F., Waugh, E., Jara, L. (2016). Association of single nucleotide polymorphisms in Pre-miR-27a, Pre-miR-196a2, Pre-miR-423, miR-608 and Pre-miR-618 with breast cancer susceptibility in a South American population. BMC Genet. 17.
  • Moszyńska, A., Gebert, M., Collawn, J. F., Bartoszewski, R. (2017). SNPs in microRNA target sites and their potential role in human disease. Open Biol. 7(4):170019.
  • Mullany, L. E., Herrick, J. S., Wolff, R. K., Slattery, M. L. (2017). Single nucleotide polymorphisms within MicroRNAs, MicroRNA targets, and MicroRNA biogenesis genes and their impact on colorectal cancer survival. Genes. Chromosomes Cancer 56(4), 285–295.
  • Sethupathy, P., Collins, F. S. (2008). MicroRNA target site polymorphisms and human disease. Trends Genet. 24(10), 489–497.
  • Dzikiewicz-Krawczyk, A. (2015). MicroRNA polymorphisms as markers of risk, prognosis and treatment response in hematological malignancies. Crit. Rev. Oncol. Hematol. 93(1), 1–17.
  • Gottwein, E., Cai, X., Cullen, B. R. (2006). A novel assay for viral microRNA function identifies a single nucleotide polymorphism that affects Drosha processing. J. Virol. 80(11), 5321–5326.
  • Duan, R., Pak, C. H., Jin, P. (2007). Single nucleotide polymorphism associated with mature miR-125a alters the processing of pri-miRNA. Hum. Mol. Genet. 16(9), 1124–1131.
  • Kawahara, Y. Kawahara, Y., Zinshteyn, B., Sethupathy, P., Iizasa, H., Hatzigeorgiou, A.G., Nishikura, K.. (2007). Redirection of silencing targets by adenosine-to-inosine editing of miRNAs. Science 315(5815), 1137–1140.
  • Saunders, M. A., Liang, H., Li, W. H. (2007). Human polymorphism at microRNAs and microRNA target sites. Proc. Natl. Acad. Sci. U. S. A. 104(9), 3300–3305.
  • Sun, G., Yan J, Noltner K, Feng J, Li H, Sarkis DA, Sommer, S.S., Rossi, J.J.(2009). SNPs in human miRNA genes affect biogenesis and function. RNA 15(9), 1640–1651.
  • Abelson, J. F., Abelson, J.F., Kwan, K.Y., O’Roak, B.J., Baek, D.Y., Stillman, A.A., Morgan, T.M., Mathews C.A., Pauls D.L., Rasin M.R., Gunel, M., Davis N.R., Sencicek A.G.E, Guez D.H., Spertus J.A., Leckman J.F., Dure L.S., Kurlan R., Singer H.S., Gilbert D.L., Farhi A., Louvi A., Lifton R.P., Sestan N, State M.W. (2005). Sequence variants in SLITRK1 are associated with Tourette’s syndrome. Science, 310(5746), 317–320.
  • Arisawa, T., Tahara T, Shibata T, Nagasaka M, Nakamura M, Kamiya Y, Fujita H., Hasegawa, S., Takagi, T., Wang, F.Y. Hirata, I., Nakano, H.. (2007). A polymorphism of microRNA 27a genome region is associated with the development of gastric mucosal atrophy in Japanese male subjects. Dig. Dis. Sci. 52, 1691–1697.
  • Martin, M. M. Buckenberger, J.A., Jiang, J., Malana, G.E., Nuovo, G.J., Chotani, M., Feldman, D.S., Schmittgen, T.D., Elton, T.S.. (2007). The human angiotensin II type 1 receptor +1166 A/C polymorphism attenuates microRNA-155 binding. J. Biol. Chem. 282(33), 24262–24269.
  • Mishra PJ, Humeniuk R, Mishra PJ, Longo-Sorbello GSA, Banerjee D, Bertino JR. (2007). A miR-24 microRNA binding-site polymorphism in dihydrofolate reductase gene leads to methotrexate resistance. Proc. Natl. Acad. Sci. U. S. A. 104(33), 13513–13518.
  • Sethupathy, P., Borel C, Gagnebin, M., Grant, G.R., Deutsch, S., Elton, T.S., Hatzigeorgiou, A.G., Stylianos E Antonarakis, S.E.. (2007). Human microRNA-155 on chromosome 21 differentially interacts with its polymorphic target in the AGTR1 3’ untranslated region: a mechanism for functional single-nucleotide polymorphisms related to phenotypes. Am. J. Hum. Genet. 81(2), 405–413.
  • Yu, Z., Li Z., Jolicoeur, N., Zhang, L., Fortin Y., Wang, E., Wu, M., Shen, S.H. (2007). Aberrant allele frequencies of the SNPs located in microRNA target sites are potentially associated with human cancers. Nucleic Acids Res. 35(13), 4535–4541.
  • Ramakrishna, S., Muddashetty, R. S. (2019). Emerging Role of microRNAs in Dementia. J. Mol. Biol. 431(9), 1743–1762.
  • Zhang, N., Zhao, L., Su, Y., Liu, X., Zhang, F., Gao, Y. (2021). Syringin Prevents Aβ 25–35 -Induced Neurotoxicity in SK-N-SH and SK-N-BE Cells by Modulating miR-124-3p/BID Pathway. Neurochem. Res. 46(3):675–685.
  • Li, L., Luo, Z. (2017). Dysregulated miR-27a-3p promotes nasopharyngeal carcinoma cell proliferation and migration by targeting Mapk10. Oncol. Rep. 37(5), 2679–2687.
  • Wang, D., Fei, Z., Luo, S., Wang, H. (2020). MiR-335-5p Inhibits β-Amyloid (Aβ) Accumulation to Attenuate Cognitive Deficits Through Targeting c-jun-N-terminal Kinase 3 in Alzheimer’s Disease. Curr. Neurovasc. Res. 17(1), 93–101.

Evaluation of SNPs and miRNAs in the BID, MAPK10, and AGER Genes Related to Alzheimer's Disease by Using In Silico Tools

Year 2023, Volume: 10 Issue: 1, 181 - 208, 31.05.2023
https://doi.org/10.35193/bseufbd.1205700

Abstract

Alzheimer's disease (AD) is a multifactorial disease resulting from both genetic and environmental factors, which are pathologically defined by the accumulation of intracellular hyperphosphorylated tau protein, neurofibrils tangles, and extracellular amyloid β protein in the brain. The purpose of this study is to estimate the potentially damaging effects of missense single nucleotide polymorphisms (SNPs) in the BID, MAPK10 and AGER genes associated with AD using various in silico tools and to determine the effects of SNPs on miRNAs. In addition, it is aimed to determine the gene-gene and protein-protein interactions through various software tools.
Consequently, it was estimated that there may be harmful effects of seven polymorphisms in the BID gene, twenty-seven in the MAPK10 gene and three in the AGER gene. It was obtained that some SNPs decrease the effectiveness of miRNA-mRNA binding, enhance, break, create a new binding zone and/or destroy the miRNA-mRNA binding zone in the BID and MAPK10 genes. miRNA-SNP analyses could not provide information on the AGER gene.
In this study, SNPs in the BID, MAPK10, and AGER genes, which are estimated to be high-risk SNPs, will be able to provide data for future genotyping studies. SNPs that are estimated to be high-risk and SNPs that may have a role in miRNA- mRNA activity can be assessed as a priority in experimental studies related to AD.
In the future, experimental studies are proposed to investigate the clinical effects of harmful/disease-related missense SNPs and SNPs affecting mRNA-miRNA interaction.

References

  • Guerreiro, R., Hardy, J. (2014). Genetics of Alzheimer’s Disease. Neurotherapeutics, 11, 732-737.
  • Liu, X., Han, Z., Yang, C. (2017). Associations of microRNA single nucleotide polymorphisms and disease risk and pathophysiology. Clin. Genet. 92(3), 235–242
  • Brookes, A. J. (1999). The essence of SNPs. Gene, 234(2), 177–186.
  • Lonetti, A., Fontana, M. C., Martinelli, G., Iacobucci, I. (2016). Single Nucleotide Polymorphisms as Genomic Markers for High-Throughput Pharmacogenomic Studies. Microarray Technology: Methods and Applications, 143-159.
  • Single-nucleotide polymorphism - ISOGG Wiki. https://isogg.org/wiki/Single-nucleotide_polymorphism.
  • Battaloğlu, E., Başak, A. N. (2010). Kompleks Hastalık Genetiği Güncel Kavramlar ve Nörolojik Hastalıkların Tanısında Kullanılan Genomik Yöntemler. Klinik Gelişim dergisi Cilt 23 / NO: 1- NÖROLOJİ 128–133.
  • Kim, V. N. & Nam, J. W. (2006). Genomics of microRNA. Trends Genet. 22(3), 165–173.
  • Lee, Y., Kim, M., Han, J., Yeom, K.H., Lee, S., Baek, S.H., Kim, V.N., (2004). MicroRNA genes are transcribed by RNA polymerase II. EMBO J. 23(20), 4051-60.
  • Cogswell, J. P., Ward, J., Taylor, I.A., Waters, M., Shi, Y., Cannon, B., Kelnar, K., Kemppainen, C., Brown, D., Chen, C., Prinjha, R.K., Richardson, R.C., Saunders, A.M., Roses, A.D., Richards C.A., (2008). Identification of miRNA Changes in Alzheimer’s Disease Brain and CSF Yields Putative Biomarkers and Insights into Disease Pathways. J. Alzheimer’s Dis. 14(1), 27–41.
  • Martino, S., Di Girolamo, I., Orlacchio, A., Datti, A., Orlacchio, A. (2009). MicroRNA Implications across Neurodevelopment and Neuropathology. J. Biomed. Biotechnol. 2009, 13.
  • GeneMANIA. http://genemania.org/.
  • NCBI dbSNP database. https://www.ncbi.nlm.nih.gov/snp/.
  • UniProt database. https://www.uniprot.org/.
  • Warde-Farley, D., Donaldson, S.L., Comes, O., Zuberi, K., Badrawi, R., Chao, P., Franz, M., Grouios, C., Kazi, F., Lopes, C.T., Maitland, A., Mostafavi, S., Montojo, J., Shao, Q., Wright, G., Bader, G.D., Morris, Q. (2010). The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic acids research, 38(suppl_2), W214-W220.
  • STRING: functional protein association networks. https://string-db.org/.
  • Szklarczyk, D., Gable, A.L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J.,Simonovic, M., Doncheva, N.T., Morris, J.H., Bork, P., Jensen, L.J., Mering, C.V.(2019). STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47(D1):D607–13.
  • SIFT - Predict effects of nonsynonymous missense variants. https://sift.bii.a-star.edu.sg/.
  • Ng PC., Henikoff S. Predicting Deleterious Amino Acid Substitutions (2001). Genome Res.11(5):863-874.
  • Veitia, R. (2001). SIFTing the effects of SNPs. Genome Biol. 2(7), reports0019.
  • PolyPhen-2: prediction of functional effects of human nsSNPs. http://genetics.bwh.harvard.edu/pph2/.
  • Adzhubei, I. A. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P., Kondrashov A.S., Sunyaev, S.E., (2010). A method and server for predicting damaging missense mutations. Nat. Methods 7(4), 248–249.
  • PROVEAN. http://provean.jcvi.org/index.php.
  • Choi, Y., Sims, G. E., Murphy, S., Miller, J. R., Chan, A. P. (2012). Predicting the functional effect of amino acid substitutions and indels. PLoS One 7.
  • Choi, Y., Chan, A. P. (2015). PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics 31(16), 2745–2747.
  • SNAP2 - Predicting functional effects of sequence variants. https://rostlab.org/services/snap2web/.
  • Hecht, M., Bromberg, Y., Rost, B. (2015). Better prediction of functional effects for sequence variants. BMC genomics, 16(8), 1-12.
  • SNPs&GO - Predicting disease associated SNPs using GO terms. https://snps.biofold.org/snps-and-go/pages/method.html.
  • Capriotti, E., Calabrese, R., Fariselli, P., Martelli, P.L., Altman, R.B., Casadio, R.(2013). WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation. BMC Genomics. BMC genomics, 14, 1-7.
  • PhD-SNP: Predictor of human Deleterious Single Nucleotide Polymorphisms. https://snps.biofold.org/phd-snp/phd-snp.html.
  • Capriotti, E., Calabrese, R.,Casadio, R. (2006). Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information. Bioinformatics 22, 2729–2734.
  • MutationAssessor.org / functional impact of protein mutations. http://mutationassessor.org/r3/.
  • Reva, B., Antipin, Y., Sander, C.(2011). Predicting the functional impact of protein mutations: Application to cancer genomics. Nucleic Acids Res.39(17):e118.
  • PANTHER - Evolutionary analysis of coding SNPs. http://www.pantherdb.org/tools/csnpScoreForm.jsp.
  • Thomas, P. D., Ebert, D., Muruganujan, A., Mushayahama, T., Albou, L. P., Mi, H. (2022). PANTHER: Making genome-scale phylogenetics accessible to all. Protein Science, 31(1), 8–22.
  • Meta-SNP - Meta-predictor of disease causing variants. https://snps.biofold.org/meta-snp/.
  • Capriotti, E., Altman, R. B., Bromberg, Y. (2013). Collective judgment predicts disease-associated single nucleotide variants. BMC Genomics 14, S2.
  • Welcome to I-Mutant Suite Home Page: http://gpcr2.biocomp.unibo.it/cgi/predictors/I-Mutant3.0/I-Mutant3.0.cgi
  • Capriotti, E., Fariselli, P., Casadio, R.(2005).I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res. 33(Web Server issue).
  • Prediction of Protein Stability Changes upon Mutations: http://mupro.proteomics.ics.uci.edu/
  • Cheng, J., Randall, A., Baldi, P. (2006). Prediction of protein stability changes for single-site mutations using support vector machines. Proteins: Structure, Function, and Bioinformatics, 62(4), 1125-1132.
  • ProjectHOPE. https://www3.cmbi.umcn.nl/hope/.
  • Verselaar H., Beek, T.A., Kuipers, R.K., Hekkelman, M.L., Vriend, G., (2010). Protein structure analysis of mutations causing inheritable diseases. An e-Science approach with life scientist friendly interfaces. BMC Bioinformatics, 11, 548.
  • MirSNP: collection of human SNPs in predicted miRNA target sites. http://cmbi.bjmu.edu.cn/mirsnp.
  • Liu, C., Zhang F, Li T, Lu M, Wang L, Yue W, Zhang, D. (2012). MirSNP, a database of polymorphisms altering miRNA target sites, identifies miRNA-related SNPs in GWAS SNPs and eQTLs. BMC Genomics, 13, 661.
  • PolymiRTS: https://compbio.uthsc.edu/miRSNP/.
  • Bhattacharya, A., Ziebarth, J. D., Cui, Y. (2014). PolymiRTS Database 3.0: linking polymorphisms in microRNAs and their target sites with human diseases and biological pathways. Nucleic Acids Res. 42(D1), D86-D91.
  • Liu, Z-P.,Wang, Y.,Zhang, X-S.,Chen, L.,(2010). Identifying dysfunctional crosstalk of pathways in various regions of Alzheimer’s disease brains - BMC Syst Biol., 4 (Suppl 2), S11.
  • Krauthammer, M., Kaufmann, C. A., Gilliam, T. C. & Rzhetsky, A. (2004). Molecular triangulation: bridging linkage and molecular-network information for identifying candidate genes in Alzheimer’s disease. Proc. Natl. Acad. Sci. U. S. A. 101 (42), 15148–15153.
  • Mooney, S. D., Krishnan, V. G., Evani, U. S. (2010). Bioinformatic tools for identifying disease gene and SNP candidates. Methods Mol. Biol. 628, 307–319.
  • Thusberg, J.,Vihinen, M. (2009). Pathogenic or not? and if so, then how? Studying the effects of missense mutations using bioinformatics methods. Hum. Mutat. 30(5), 703–714.
  • Hicks, S., Wheeler, D. A., Plon, S. E., Kimmel, M. (2011). Prediction of missense mutation functionality depends on both the algorithm and sequence alignment employed. Hum. Mutat. 32(6), 661–668.
  • Li, K., Dai D, Zhao B, Yao L, Yao S, Wang B, Yang, Z. (2009). Association between the RAGE G82S polymorphism and Alzheimer’s disease. J. Neural Transm. 117(1), 97–104.
  • Wang, Z., Moult, J. (2001). SNPs, protein structure, and disease. Hum. Mutat. 17(4), 263–270.
  • Xu, J., Zhang, J. (2014). Why Human Disease-Associated Residues Appear as the Wild-Type in Other Species: Genome-Scale Structural Evidence for the Compensation Hypothesis. Mol. Biol. Evol. 31(7), 1787–1792.
  • Cargill, M., Altshuler D., Ireland J., Sklar P., Ardlie K., Patil N., Shaw, N., Lane, C.R., Lim, E.P., Kalyanaraman, N., Nemesh, J., Ziaugra L., Friedland L., Rolfe A., Warrington, J., Lipshuttz, R., Daley, G.Q., Lander, E.S. (1999). Characterization of single-nucleotide polymorphisms in coding regions of human genes. Nat. Genet. 22(3), 231–238.
  • Teng, S., Srivastava, A. K., Schwartz, C. E., Alexov, E. & Wang, L. (2010). Structural assessment of the effects of amino acid substitutions on protein stability and protein protein interaction. Int. J. Comput. Biol. Drug Des. 3(4), 334–349.
  • Dill, K. A., Fiebig, K. M. & Chan, H. S. (1993). Cooperativity in protein-folding kinetics. Proc. Natl. Acad. Sci. 90(5), 1942–1946.
  • Biro, J. C. (2006). Amino acid size, charge, hydropathy indices and matrices for protein structure analysis. Theor. Biol. Med. Model. 3(1),1-12.
  • Doss, C. G. P., NagaSundaram, N. (2012). Investigating the structural impacts of I64T and P311S mutations in APE1-DNA complex: a molecular dynamics approach. PLoS One 7(2), e31677.
  • Rose, G. D., Wolfenden, R. (1993). Hydrogen bonding, hydrophobicity, packing, and protein folding. Annu. Rev. Biophys. Biomol. Struct. 22(1), 381–415.
  • Gromiha, M. M., Oobatake, M., Kono, H., Uedaira, H., Sarai, A. (1999). Role of structural and sequence information in the prediction of protein stability changes: comparison between buried and partially buried mutations. Protein Eng. 12(7), 549–555.
  • Gong, S., Blundell, T. L. (2010). Structural and Functional Restraints on the Occurrence of Single Amino Acid Variations in Human Proteins. PLoS One 5(2), e9186.
  • Shirley, B. A., Nick Pace, C., Stanssens, P., Hahn, U. (1992). Contribution of hydrogen bonding to the conformational stability of ribonuclease T1. Biochemistry 31(3), 725–732.
  • Cai, T. T., Li J, An X, Yan N, Li D, Jiang Y, Wang, W., Shi, L., Qin, Q., Song, R., Wang., G., Jiang, W., Zhang J.A.. (2017). Polymorphisms in MIR499A and MIR125A gene are associated with autoimmune thyroid diseases. Mol. Cell. Endocrinol. 440, 106–115.
  • Ghanbari, M. Ikram, M.A., De Looper, H.W.J., Hofman, A., Erkeland, S.J., Franco, O.H., Dehghan, A. (2016). Genome-wide identification of microRNA-related variants associated with risk of Alzheimer’s disease. Sci. Rep. 6(1), 28387.
  • Kim, J., Choi GH, Ko KH, Kim JO, Oh SH, Park YS, Kim, O.J., Kim, N.K. (2016). Association of the Single Nucleotide Polymorphisms in microRNAs 130b, 200b, and 495 with Ischemic Stroke Susceptibility and Post-Stroke Mortality. PLoS One 11(9):e0162519.
  • Morales, S., Gulppi F, Gonzalez-Hormazabal P, Fernandez-Ramires R, Bravo T, Reyes JM, Gomez, F., Waugh, E., Jara, L. (2016). Association of single nucleotide polymorphisms in Pre-miR-27a, Pre-miR-196a2, Pre-miR-423, miR-608 and Pre-miR-618 with breast cancer susceptibility in a South American population. BMC Genet. 17.
  • Moszyńska, A., Gebert, M., Collawn, J. F., Bartoszewski, R. (2017). SNPs in microRNA target sites and their potential role in human disease. Open Biol. 7(4):170019.
  • Mullany, L. E., Herrick, J. S., Wolff, R. K., Slattery, M. L. (2017). Single nucleotide polymorphisms within MicroRNAs, MicroRNA targets, and MicroRNA biogenesis genes and their impact on colorectal cancer survival. Genes. Chromosomes Cancer 56(4), 285–295.
  • Sethupathy, P., Collins, F. S. (2008). MicroRNA target site polymorphisms and human disease. Trends Genet. 24(10), 489–497.
  • Dzikiewicz-Krawczyk, A. (2015). MicroRNA polymorphisms as markers of risk, prognosis and treatment response in hematological malignancies. Crit. Rev. Oncol. Hematol. 93(1), 1–17.
  • Gottwein, E., Cai, X., Cullen, B. R. (2006). A novel assay for viral microRNA function identifies a single nucleotide polymorphism that affects Drosha processing. J. Virol. 80(11), 5321–5326.
  • Duan, R., Pak, C. H., Jin, P. (2007). Single nucleotide polymorphism associated with mature miR-125a alters the processing of pri-miRNA. Hum. Mol. Genet. 16(9), 1124–1131.
  • Kawahara, Y. Kawahara, Y., Zinshteyn, B., Sethupathy, P., Iizasa, H., Hatzigeorgiou, A.G., Nishikura, K.. (2007). Redirection of silencing targets by adenosine-to-inosine editing of miRNAs. Science 315(5815), 1137–1140.
  • Saunders, M. A., Liang, H., Li, W. H. (2007). Human polymorphism at microRNAs and microRNA target sites. Proc. Natl. Acad. Sci. U. S. A. 104(9), 3300–3305.
  • Sun, G., Yan J, Noltner K, Feng J, Li H, Sarkis DA, Sommer, S.S., Rossi, J.J.(2009). SNPs in human miRNA genes affect biogenesis and function. RNA 15(9), 1640–1651.
  • Abelson, J. F., Abelson, J.F., Kwan, K.Y., O’Roak, B.J., Baek, D.Y., Stillman, A.A., Morgan, T.M., Mathews C.A., Pauls D.L., Rasin M.R., Gunel, M., Davis N.R., Sencicek A.G.E, Guez D.H., Spertus J.A., Leckman J.F., Dure L.S., Kurlan R., Singer H.S., Gilbert D.L., Farhi A., Louvi A., Lifton R.P., Sestan N, State M.W. (2005). Sequence variants in SLITRK1 are associated with Tourette’s syndrome. Science, 310(5746), 317–320.
  • Arisawa, T., Tahara T, Shibata T, Nagasaka M, Nakamura M, Kamiya Y, Fujita H., Hasegawa, S., Takagi, T., Wang, F.Y. Hirata, I., Nakano, H.. (2007). A polymorphism of microRNA 27a genome region is associated with the development of gastric mucosal atrophy in Japanese male subjects. Dig. Dis. Sci. 52, 1691–1697.
  • Martin, M. M. Buckenberger, J.A., Jiang, J., Malana, G.E., Nuovo, G.J., Chotani, M., Feldman, D.S., Schmittgen, T.D., Elton, T.S.. (2007). The human angiotensin II type 1 receptor +1166 A/C polymorphism attenuates microRNA-155 binding. J. Biol. Chem. 282(33), 24262–24269.
  • Mishra PJ, Humeniuk R, Mishra PJ, Longo-Sorbello GSA, Banerjee D, Bertino JR. (2007). A miR-24 microRNA binding-site polymorphism in dihydrofolate reductase gene leads to methotrexate resistance. Proc. Natl. Acad. Sci. U. S. A. 104(33), 13513–13518.
  • Sethupathy, P., Borel C, Gagnebin, M., Grant, G.R., Deutsch, S., Elton, T.S., Hatzigeorgiou, A.G., Stylianos E Antonarakis, S.E.. (2007). Human microRNA-155 on chromosome 21 differentially interacts with its polymorphic target in the AGTR1 3’ untranslated region: a mechanism for functional single-nucleotide polymorphisms related to phenotypes. Am. J. Hum. Genet. 81(2), 405–413.
  • Yu, Z., Li Z., Jolicoeur, N., Zhang, L., Fortin Y., Wang, E., Wu, M., Shen, S.H. (2007). Aberrant allele frequencies of the SNPs located in microRNA target sites are potentially associated with human cancers. Nucleic Acids Res. 35(13), 4535–4541.
  • Ramakrishna, S., Muddashetty, R. S. (2019). Emerging Role of microRNAs in Dementia. J. Mol. Biol. 431(9), 1743–1762.
  • Zhang, N., Zhao, L., Su, Y., Liu, X., Zhang, F., Gao, Y. (2021). Syringin Prevents Aβ 25–35 -Induced Neurotoxicity in SK-N-SH and SK-N-BE Cells by Modulating miR-124-3p/BID Pathway. Neurochem. Res. 46(3):675–685.
  • Li, L., Luo, Z. (2017). Dysregulated miR-27a-3p promotes nasopharyngeal carcinoma cell proliferation and migration by targeting Mapk10. Oncol. Rep. 37(5), 2679–2687.
  • Wang, D., Fei, Z., Luo, S., Wang, H. (2020). MiR-335-5p Inhibits β-Amyloid (Aβ) Accumulation to Attenuate Cognitive Deficits Through Targeting c-jun-N-terminal Kinase 3 in Alzheimer’s Disease. Curr. Neurovasc. Res. 17(1), 93–101.
There are 86 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Nur Demirci 0000-0002-2925-0703

Ebru Özkan Oktay 0000-0002-0395-9845

Mesut Karahan 0000-0002-8971-678X

Publication Date May 31, 2023
Submission Date November 16, 2022
Acceptance Date March 1, 2023
Published in Issue Year 2023 Volume: 10 Issue: 1

Cite

APA Demirci, N., Özkan Oktay, E., & Karahan, M. (2023). Evaluation of SNPs and miRNAs in the BID, MAPK10, and AGER Genes Related to Alzheimer’s Disease by Using In Silico Tools. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, 10(1), 181-208. https://doi.org/10.35193/bseufbd.1205700