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Identification of Damaging SNPs and Their Effects on Alzheimer’s Disease-Associated PSEN1 Protein: Computational Analysis

Year 2021, Volume 11, Issue 2, 321 - 349, 31.12.2021
https://doi.org/10.37094/adyujsci.884889

Abstract

Alzheimer’s Disease (AD) is a progressive neurodegenerative disease and pathologically characterized by the presence of neurofibrillary tangles (tau aggregation) and amyloid plaques (amyloid-beta (A𝛽) aggregation). PSEN1 protein with 9 transmembrane helices acts as aspartyl protease and is one of the catalytic components of γ secretase complex, that cleaves amyloid precursor protein (APP). Furthermore, PSEN1 protein plays a significant role in the process of APP and in the generation of amyloid beta (Aβ). In the present study, it was aimed to estimate the probable deleterious effects of missense SNPs in PSEN1 gene that is associated with AD on protein stability and structure by using bioinformatics tools. SIFT, PolyPhen-2, PROVEAN, PhD-SNP, and PANTHER PSEP software were used to estimate the deleterious SNPs, whereas I-Mutant 3.0 and MUpro web tools were used to determine the effects of amino acid substitution on protein stability. Additionally, the effects of wild type and mutant amino acids on protein three-dimensional structure via modeling were predicted by Project HOPE webserver. The phylogenetic conservation of amino acid residues of PSEN1 protein was analyzed by ConSurf. In total, 386 missense SNPs were found in the human PSEN1 gene from the National Center for Biotechnology Information Single Nucleotide Polymorphism (NCBI dbSNP) database and 65 SNPs of which were determined to be deleterious or damaging. In the present study, 8 significant missense SNPs- rs63749891 (R278T), rs63750301 (P264L), rs63750353 (N135D), rs63750524 (R278S), rs63750772 (E273A), rs63751229 (P267S), rs121917807 (G266S), and rs201617677 (R157S)- were determined as high-risk pathogenic. Some differences between wild-type amino acids and mutant amino acids such as hydrophobicity, charge, size, and folding properties were determined according to the modeling findings. Our study demonstrates that high-risk pathogenic missense SNPs have the potential to alter the catalytic activity of the γ secretase complex and subsequently the amount of Aβ40 and Aβ42. Therefore, these missense SNPs may contribute to AD pathogenesis studies.

References

  • [1] Reitz, C., Genetic diagnosis and prognosis of Alzheimer’s disease: challenges and opportunities, Expert Review of Molecular Diagnostics, 15 (3), 339-348, 2015.
  • [2] Liu, Z., Li, T., Li, P., et al., The ambiguous relationship of oxidative stress, tau hyperphosphorylation, and autophagy dysfunction in Alzheimer’s disease, Oxidative Medicine and Cellular Longevity, 2015, 352723, 2015.
  • [3] Goldman, J.S., Deerlin, V.M., Alzheimer’s disease and frontotemporal dementia: the current state of genetics and genetic testing since the advent of next generation sequencing, Molecular Diagnosis & Therapy, 22 (5), 505-513, 2018.
  • [4] Bagyinszky, E., Lee, H.M., Giau, V.V., et al., PSEN1 p.Thr116Ile variant in two korean families with young onset Alzheimer’s disease, International Journal of Molecular Sciences, 19 (9), 2604, 2018.
  • [5] Sproul, A.A., Jacob, S., Pre, D., et al., Characterization and molecular profiling of PSEN1 familial Alzheimer’s disease iPSC-derived neural progenitors, PLoS One, 9 (1), e84547, 2014.
  • [6] Ramirez-Bello, J., Jimenez-Morales, M., Functional implications of single nucleotide polymorphisms (SNPs) in protein-coding and non-coding RNA genes in multifactorial diseases, Gaceta medica de Mexico, 153 (2), 238-250, 2017.
  • [7] Jamal, S., Goyal, S., Shanker, A., Grover, A., Computational screening and exploration of disease-associated genes in Alzheimer’s disease, Journal of Cellular Biochemistry, 118(6), 1471-1479, 2016.
  • [8] Montojo, J., Zuberi, K., Rodriguez, H., Bader, G.D., Morris, Q., GeneMANIA: Fast gene network construction and function prediction for Cytoscape [v1]; ref status: indexed, http://f1000r.es/3rv], F1000Research, 3 (153), 2014.
  • [9] Bhagwat, M., Searching NCBI’s dbSNP database, Current Protocols in Bioinformatics, Chapter1, Unit 1-19, 2010.
  • [10] The Uniprot Consortium, UniProt: the universal protein knowledgebase, Nucleic Acids Research, 45, D158-D169, 2017.
  • [11] Arshad, M., Bhatti, A., John, P., Identification and in silico analysis of functional SNPs of human TAGAP protein: A comprehensive study, PLoS ONE, 13 (1): e0188143.
  • [12] Sim, N.L., Kumar, P., Hu, J., Henikoff, S., Schneider, G., Ng, P.C., SIFT web server: predicting effects of amino acid substitutions on proteins, Nucleic Acids Research, 40 (W1), W452-W457, 2012.
  • [13] Osman, M.M., Khalifa, A.S., Mutasim, A.E.Y., Massaad, S.O., Gasemelseed, M.M., Abdagader, M.A., Ahmed, S.A., Ahmed, A.M., Altayb, H.N., Salih, M.A., In silico Analysis of Single Nucleotide Polymorphisms (Snps) in Human FTO Gene, JSM Bioinformatics, Genomics and Proteomics, 1 (1), 1003, 2016.
  • [14] Kaur, T., Khakur, T., Singh, J., Kamboj, S.S., Kaur, M., Identification of functional SNPs in human LGALS3 gene by in silico analyses, Egyptian Journal of Medical Human Genetics, 18 (4), 321-328, 2017.
  • [15] Adzhubei, I., Jordan, D.M., Sunyaev, S.R., Predicting functional effect of human UNIT 7.20 missense mutations using PolyPhen-2, Current Protocols in Human Genetics, 76 (1), 7.20.1-7.20.41, 2013.
  • [16] Choi, Y., Chan, A.P., PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels, Bioinformatics, 31(16), 2745–2747, 2015.
  • [17] Capriotti, E., Fariselli, P., PhD-SNPg: a webserver and lightweight tool for scoring single nucleotide variants, Nucleic Acids Research, 45, W247–W252, 2017.
  • [18] Tang, H., Thomas, P.D., PANTHER-PSEP: predicting disease-causing genetic variants using position-specific evolutionary preservation, Bioinformatics, 32 (14), 2230-2232, 2016.
  • [19] Capriotti, E., Fariselli, P., Casadio, R., I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure, Nucleic Acids Research, 33, 306-310, 2005.
  • [20] Cheng, J., Randall, A., Baldi, P., Prediction of protein stability changes for single-site mutations using support vector machines, Proteins, 62 (4), 1125-32, 2006.
  • [21] Venselaar, H., Beek, T.A., Kuipers, R.K., Hekkelman, M.L., Vriend, G., Protein structure analysis of mutations causing inheritable diseases. An e-Science approach with life scientist friendly interfaces, BMC Bioinformatics, 11, 548, 2010.
  • [22] Ashkenazy, H., Abadi, S., Martz, E., Chay, O., Mayrose, I., Pupko, T., Ben-Ta, N., ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules, Nucleic Acids Research, 44, W344-W350, 2016.
  • [23] Hossain, S., Roy, A.S., Islam, S., In silico analysis predicting effects of deleterious SNPs of human RASSF5 gene on its structure and functions. Scientific Reports, 10, 14542, 2020.
  • [24] Giau, V.V., Pyun, J.M., Suh, J., Bagyinszky, E., An, S.S.A., Kim, S.Y., A pathogenic PSEN1 Trp165Cys mutation associated with early-onset Alzheimer’s disease, BMC Neurology, 19, 188, 2018.
  • [25] Kelleher, R.J., Shen, J., Presenilin-1mutations and Alzheimer’s disease, PNAS, 144 (4), 629–631, 2017.
  • [26] Cargill, M., Altshuler, D., Ireland, J., et al., Characterization of single-nucleotide polymorphisms in coding regions of human genes, Nature Genetics, 22 (3), 231-8, 1999.
  • [27] Teng, S., Wang, L., Srivastava, A.K., Schwartz, C.E., Alexov, E., Structural assessment of the effects of amino acid substitutions on protein stability and protein-protein interaction, International Journal of Computational Biology and Drug Design, 3 (4), 334-349, 2010.
  • [28] Ozkan Oktay, E., Kaman, T., Karasakal, O.F., Ulucan, K., Konuk, M., Tarhan, N., Alzheimer hastalığı ile ilişkilendirilen APH1A genindeki zararlı SNP’lerin in silico yöntemler ile belirlenmesi, Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 23 (2), 472-480, 2019.
  • [29] Van Giau, V.V., Bagyinszky, E., Yang, Y., Youn, Y.C., Soo, S., Kim, S.Y., Genetic analyses of early-onset Alzheimer’s disease using next generation sequencing, Scientific Reports, 9, 8368, 2019.
  • [30] Van Giau, V., Pyun, J.M., Suh, J., Bagyinszky, E., Soo, S., Kim, S.Y., A pathogenic PSEN1 Trp165Cys mutation associated with early-onset Alzheimer’s disease, BMC Neurology, 19, 188, 2019.
  • [31] Lohmann, E., Guerreiro, R.J., Erginel-Unaltuna, N., Gurunlian, N., Bilgic, B., Gurvit, H., Hanagasi, H.A., Luu, N., Emre, M., Singleton, A., Identification of PSEN1 and PSEN2 gene mutations and variants in Turkish dementia patients, Neurobiology of Aging, 33 (8), 1850.e17-1850.e27, 2012.
  • [32] Veugelen, S., Saito, T., Saido, T.C., Chavez-Gutierrez, L., Stroope, B., Familial Alzheimer’s disease mutations in presenilin generate amyloidogenic Ab peptide seeds, Neuron, 90 (2), 410-416, 2016.

Zarar Verici SNP’lerin ve Alzheimer Hastalığıyla İlişkili PSEN1 Proteinine Etkilerinin Tanımlanması: Hesaplamalı Analiz

Year 2021, Volume 11, Issue 2, 321 - 349, 31.12.2021
https://doi.org/10.37094/adyujsci.884889

Abstract

Alzheimer Hastalığı (AH), progresif nörodejeneratif hastalıktır ve patolojik olarak nörofibriler yumaklar (tau agregasyonu) ve amiloid plakların (amiloid beta (A𝛽) agregasyonu) varlığı ile karakterize edilir. 9 transmembran heliks içeren PSEN1 proteini, aspartil proteaz olarak işlev görmektedir ve amiloid öncü proteini (APP) parçalayan γ sekretaz kompleksinin katalitik bileşenlerinden biridir. Ayrıca, PSEN1 proteini APP sürecinde ve amiloid beta (A𝛽) oluşumunda önemli rol oynamaktadır. Bu çalışmada, AH ile ilişkili PSEN1 genindeki missense (yanlış anlamlı) SNP’lerin protein stabilitesi ve yapısı üzerindeki olası zararlı etkilerinin biyoinformatik araçlar kullanılarak tahmin edilmesi amaçlanmıştır. Zararlı SNP’lerin tahmin edilmesinde SIFT, PolyPhen-2, PROVEAN, PhD-SNP ve PANTHER PSEP yazılımları kullanılırken, amino asit değişiminin protein stabilitesi üzerindeki etkilerini belirlemek için I-Mutant 3.0 ve MUpro web araçları kullanıldı. Ek olarak, yabanıl tip ve mutant amino asitlerin proteinin üç boyutlu yapısı üzerindeki etkileri ise modelleme yoluyla Project HOPE programı ile tahmin edilmiştir. PSEN1 proteininin amino asit kalıntılarının filogenetik korunumu ConSurf ile analiz edildi. NCBI dbSNP veritabanında insan PSEN1 geninde toplam 386 missense SNP bulunduğu ve 65 SNP’nin ise zararlı veya zarar verici olduğu belirlendi. Bu çalışmada, 8 önemli missense SNP- rs63749891 (R278T), rs63750301 (P264L), rs63750353 (N135D), rs63750524 (R278S), rs63750772 (E273A), rs63751229 (P267S), rs121917807 (G266S), ve rs201617677 (R157S)- yüksekli riskli patojenik olarak belirlendi. Yabanıl tip ve mutant amino asitler arasındaki hidrofobiklik, yük, boyut ve katlanma özellikleri gibi bazı farklılıklar modelleme bulgularına göre belirlenmiştir. Çalışmamız, yüksek riskli patojenik missense SNP’lerin γ sekretaz kompleksinin katalitik aktivitesini ve akabinde Aβ40 ve Aβ42 miktarını değiştirme potansiyelinin olduğunu göstermektedir. Bu nedenle, bu missense SNP'ler, AH patogenez çalışmalarına katkı sağlayabilir.

References

  • [1] Reitz, C., Genetic diagnosis and prognosis of Alzheimer’s disease: challenges and opportunities, Expert Review of Molecular Diagnostics, 15 (3), 339-348, 2015.
  • [2] Liu, Z., Li, T., Li, P., et al., The ambiguous relationship of oxidative stress, tau hyperphosphorylation, and autophagy dysfunction in Alzheimer’s disease, Oxidative Medicine and Cellular Longevity, 2015, 352723, 2015.
  • [3] Goldman, J.S., Deerlin, V.M., Alzheimer’s disease and frontotemporal dementia: the current state of genetics and genetic testing since the advent of next generation sequencing, Molecular Diagnosis & Therapy, 22 (5), 505-513, 2018.
  • [4] Bagyinszky, E., Lee, H.M., Giau, V.V., et al., PSEN1 p.Thr116Ile variant in two korean families with young onset Alzheimer’s disease, International Journal of Molecular Sciences, 19 (9), 2604, 2018.
  • [5] Sproul, A.A., Jacob, S., Pre, D., et al., Characterization and molecular profiling of PSEN1 familial Alzheimer’s disease iPSC-derived neural progenitors, PLoS One, 9 (1), e84547, 2014.
  • [6] Ramirez-Bello, J., Jimenez-Morales, M., Functional implications of single nucleotide polymorphisms (SNPs) in protein-coding and non-coding RNA genes in multifactorial diseases, Gaceta medica de Mexico, 153 (2), 238-250, 2017.
  • [7] Jamal, S., Goyal, S., Shanker, A., Grover, A., Computational screening and exploration of disease-associated genes in Alzheimer’s disease, Journal of Cellular Biochemistry, 118(6), 1471-1479, 2016.
  • [8] Montojo, J., Zuberi, K., Rodriguez, H., Bader, G.D., Morris, Q., GeneMANIA: Fast gene network construction and function prediction for Cytoscape [v1]; ref status: indexed, http://f1000r.es/3rv], F1000Research, 3 (153), 2014.
  • [9] Bhagwat, M., Searching NCBI’s dbSNP database, Current Protocols in Bioinformatics, Chapter1, Unit 1-19, 2010.
  • [10] The Uniprot Consortium, UniProt: the universal protein knowledgebase, Nucleic Acids Research, 45, D158-D169, 2017.
  • [11] Arshad, M., Bhatti, A., John, P., Identification and in silico analysis of functional SNPs of human TAGAP protein: A comprehensive study, PLoS ONE, 13 (1): e0188143.
  • [12] Sim, N.L., Kumar, P., Hu, J., Henikoff, S., Schneider, G., Ng, P.C., SIFT web server: predicting effects of amino acid substitutions on proteins, Nucleic Acids Research, 40 (W1), W452-W457, 2012.
  • [13] Osman, M.M., Khalifa, A.S., Mutasim, A.E.Y., Massaad, S.O., Gasemelseed, M.M., Abdagader, M.A., Ahmed, S.A., Ahmed, A.M., Altayb, H.N., Salih, M.A., In silico Analysis of Single Nucleotide Polymorphisms (Snps) in Human FTO Gene, JSM Bioinformatics, Genomics and Proteomics, 1 (1), 1003, 2016.
  • [14] Kaur, T., Khakur, T., Singh, J., Kamboj, S.S., Kaur, M., Identification of functional SNPs in human LGALS3 gene by in silico analyses, Egyptian Journal of Medical Human Genetics, 18 (4), 321-328, 2017.
  • [15] Adzhubei, I., Jordan, D.M., Sunyaev, S.R., Predicting functional effect of human UNIT 7.20 missense mutations using PolyPhen-2, Current Protocols in Human Genetics, 76 (1), 7.20.1-7.20.41, 2013.
  • [16] Choi, Y., Chan, A.P., PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels, Bioinformatics, 31(16), 2745–2747, 2015.
  • [17] Capriotti, E., Fariselli, P., PhD-SNPg: a webserver and lightweight tool for scoring single nucleotide variants, Nucleic Acids Research, 45, W247–W252, 2017.
  • [18] Tang, H., Thomas, P.D., PANTHER-PSEP: predicting disease-causing genetic variants using position-specific evolutionary preservation, Bioinformatics, 32 (14), 2230-2232, 2016.
  • [19] Capriotti, E., Fariselli, P., Casadio, R., I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure, Nucleic Acids Research, 33, 306-310, 2005.
  • [20] Cheng, J., Randall, A., Baldi, P., Prediction of protein stability changes for single-site mutations using support vector machines, Proteins, 62 (4), 1125-32, 2006.
  • [21] Venselaar, H., Beek, T.A., Kuipers, R.K., Hekkelman, M.L., Vriend, G., Protein structure analysis of mutations causing inheritable diseases. An e-Science approach with life scientist friendly interfaces, BMC Bioinformatics, 11, 548, 2010.
  • [22] Ashkenazy, H., Abadi, S., Martz, E., Chay, O., Mayrose, I., Pupko, T., Ben-Ta, N., ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules, Nucleic Acids Research, 44, W344-W350, 2016.
  • [23] Hossain, S., Roy, A.S., Islam, S., In silico analysis predicting effects of deleterious SNPs of human RASSF5 gene on its structure and functions. Scientific Reports, 10, 14542, 2020.
  • [24] Giau, V.V., Pyun, J.M., Suh, J., Bagyinszky, E., An, S.S.A., Kim, S.Y., A pathogenic PSEN1 Trp165Cys mutation associated with early-onset Alzheimer’s disease, BMC Neurology, 19, 188, 2018.
  • [25] Kelleher, R.J., Shen, J., Presenilin-1mutations and Alzheimer’s disease, PNAS, 144 (4), 629–631, 2017.
  • [26] Cargill, M., Altshuler, D., Ireland, J., et al., Characterization of single-nucleotide polymorphisms in coding regions of human genes, Nature Genetics, 22 (3), 231-8, 1999.
  • [27] Teng, S., Wang, L., Srivastava, A.K., Schwartz, C.E., Alexov, E., Structural assessment of the effects of amino acid substitutions on protein stability and protein-protein interaction, International Journal of Computational Biology and Drug Design, 3 (4), 334-349, 2010.
  • [28] Ozkan Oktay, E., Kaman, T., Karasakal, O.F., Ulucan, K., Konuk, M., Tarhan, N., Alzheimer hastalığı ile ilişkilendirilen APH1A genindeki zararlı SNP’lerin in silico yöntemler ile belirlenmesi, Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 23 (2), 472-480, 2019.
  • [29] Van Giau, V.V., Bagyinszky, E., Yang, Y., Youn, Y.C., Soo, S., Kim, S.Y., Genetic analyses of early-onset Alzheimer’s disease using next generation sequencing, Scientific Reports, 9, 8368, 2019.
  • [30] Van Giau, V., Pyun, J.M., Suh, J., Bagyinszky, E., Soo, S., Kim, S.Y., A pathogenic PSEN1 Trp165Cys mutation associated with early-onset Alzheimer’s disease, BMC Neurology, 19, 188, 2019.
  • [31] Lohmann, E., Guerreiro, R.J., Erginel-Unaltuna, N., Gurunlian, N., Bilgic, B., Gurvit, H., Hanagasi, H.A., Luu, N., Emre, M., Singleton, A., Identification of PSEN1 and PSEN2 gene mutations and variants in Turkish dementia patients, Neurobiology of Aging, 33 (8), 1850.e17-1850.e27, 2012.
  • [32] Veugelen, S., Saito, T., Saido, T.C., Chavez-Gutierrez, L., Stroope, B., Familial Alzheimer’s disease mutations in presenilin generate amyloidogenic Ab peptide seeds, Neuron, 90 (2), 410-416, 2016.

Details

Primary Language English
Subjects Biology
Journal Section Biology
Authors

Orçun AVŞAR (Primary Author)
HİTİT ÜNİVERSİTESİ
0000-0003-3556-6218
Türkiye

Supporting Institution None
Publication Date December 31, 2021
Application Date February 22, 2021
Acceptance Date October 12, 2021
Published in Issue Year 2021, Volume 11, Issue 2

Cite

Bibtex @research article { adyujsci884889, journal = {Adıyaman University Journal of Science}, issn = {2147-1630}, eissn = {2146-586X}, address = {}, publisher = {Adıyaman University}, year = {2021}, volume = {11}, number = {2}, pages = {321 - 349}, doi = {10.37094/adyujsci.884889}, title = {Identification of Damaging SNPs and Their Effects on Alzheimer’s Disease-Associated PSEN1 Protein: Computational Analysis}, key = {cite}, author = {Avşar, Orçun} }
APA Avşar, O. (2021). Identification of Damaging SNPs and Their Effects on Alzheimer’s Disease-Associated PSEN1 Protein: Computational Analysis . Adıyaman University Journal of Science , 11 (2) , 321-349 . DOI: 10.37094/adyujsci.884889
MLA Avşar, O. "Identification of Damaging SNPs and Their Effects on Alzheimer’s Disease-Associated PSEN1 Protein: Computational Analysis" . Adıyaman University Journal of Science 11 (2021 ): 321-349 <https://dergipark.org.tr/en/pub/adyujsci/issue/67254/884889>
Chicago Avşar, O. "Identification of Damaging SNPs and Their Effects on Alzheimer’s Disease-Associated PSEN1 Protein: Computational Analysis". Adıyaman University Journal of Science 11 (2021 ): 321-349
RIS TY - JOUR T1 - Identification of Damaging SNPs and Their Effects on Alzheimer’s Disease-Associated PSEN1 Protein: Computational Analysis AU - Orçun Avşar Y1 - 2021 PY - 2021 N1 - doi: 10.37094/adyujsci.884889 DO - 10.37094/adyujsci.884889 T2 - Adıyaman University Journal of Science JF - Journal JO - JOR SP - 321 EP - 349 VL - 11 IS - 2 SN - 2147-1630-2146-586X M3 - doi: 10.37094/adyujsci.884889 UR - https://doi.org/10.37094/adyujsci.884889 Y2 - 2021 ER -
EndNote %0 Adıyaman University Journal of Science Identification of Damaging SNPs and Their Effects on Alzheimer’s Disease-Associated PSEN1 Protein: Computational Analysis %A Orçun Avşar %T Identification of Damaging SNPs and Their Effects on Alzheimer’s Disease-Associated PSEN1 Protein: Computational Analysis %D 2021 %J Adıyaman University Journal of Science %P 2147-1630-2146-586X %V 11 %N 2 %R doi: 10.37094/adyujsci.884889 %U 10.37094/adyujsci.884889
ISNAD Avşar, Orçun . "Identification of Damaging SNPs and Their Effects on Alzheimer’s Disease-Associated PSEN1 Protein: Computational Analysis". Adıyaman University Journal of Science 11 / 2 (December 2021): 321-349 . https://doi.org/10.37094/adyujsci.884889
AMA Avşar O. Identification of Damaging SNPs and Their Effects on Alzheimer’s Disease-Associated PSEN1 Protein: Computational Analysis. ADYU J SCI. 2021; 11(2): 321-349.
Vancouver Avşar O. Identification of Damaging SNPs and Their Effects on Alzheimer’s Disease-Associated PSEN1 Protein: Computational Analysis. Adıyaman University Journal of Science. 2021; 11(2): 321-349.
IEEE O. Avşar , "Identification of Damaging SNPs and Their Effects on Alzheimer’s Disease-Associated PSEN1 Protein: Computational Analysis", Adıyaman University Journal of Science, vol. 11, no. 2, pp. 321-349, Dec. 2021, doi:10.37094/adyujsci.884889

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