126
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Structural effect of the H992D/H418D mutation of angiotensin-converting enzyme in the Indian population: implications for health and disease

&
Received 11 Aug 2023, Accepted 14 Feb 2024, Published online: 27 Feb 2024

References

  • Adzhubei, I., Jordan, D. M., & Sunyaev, S. R. (2013). Predicting functional effect of human missense mutations using PolyPhen-2. Curr Protoc Hum Genet. Chapter 7: P. Unit7 20.
  • Amadei, A., Linssen, A. B., & Berendsen, H. J. (1993). Essential dynamics of proteins. Proteins, 17(4), 412–425. https://doi.org/10.1002/prot.340170408
  • Ames, M. K., Atkins, C. E., & Pitt, B. (2019). The renin-angiotensin-aldosterone system and its suppression. Journal of Veterinary Internal Medicine, 33(2), 363–382. https://doi.org/10.1111/jvim.15454
  • Ashkenazy, H., Abadi, S., Martz, E., Chay, O., Mayrose, I., Pupko, T., & Ben-Tal, N. (2016). ConSurf 2016: An improved methodology to estimate and visualize evolutionary conservation in macromolecules. Nucleic Acids Research, 44(W1), W344–50. https://doi.org/10.1093/nar/gkw408
  • Ban, H.-J., Heo, J. Y., Oh, K.-S., & Park, K.-J. (2010). Identification of type 2 diabetes-associated combination of SNPs using support vector machine. BMC Genetics, 11(1), 26. https://doi.org/10.1186/1471-2156-11-26
  • Bao, L., Zhou, M., & Cui, Y. (2005). nsSNPAnalyzer: Identifying disease-associated nonsynonymous single nucleotide polymorphisms. Nucleic Acids Research, 33(Web Server issue), W480–2. https://doi.org/10.1093/nar/gki372
  • Bartel, D. P. (2009). MicroRNAs: Target recognition and regulatory functions. Cell, 136(2), 215–233. https://doi.org/10.1016/j.cell.2009.01.002
  • Becari, C., Oliveira, E. B., & Salgado, M. C. (2011). Alternative pathways for angiotensin II generation in the cardiovascular system. Brazilian Journal of Medical and Biological Research = Revista Brasileira de Pesquisas Medicas e Biologicas, 44(9), 914–919. https://doi.org/10.1590/s0100-879x2011007500093
  • Becari, C., Silva, M. A. B., Durand, M. T., Prado, C. M., Oliveira, E. B., Ribeiro, M. S., Salgado, H. C., Salgado, M. C. O., & Tostes, R. C. (2017). Elastase-2, an angiotensin II-generating enzyme, contributes to increased angiotensin II in resistance arteries of mice with myocardial infarction. British Journal of Pharmacology, 174(10), 1104–1115. https://doi.org/10.1111/bph.13755
  • Bellomo, R., Wunderink, R. G., Szerlip, H., English, S. W., Busse, L. W., Deane, A. M., Khanna, A. K., McCurdy, M. T., Ostermann, M., Young, P. J., Handisides, D. R., Chawla, L. S., Tidmarsh, G. F., & Albertson, T. E. (2020). Angiotensin I and angiotensin II concentrations and their ratio in catecholamine-resistant vasodilatory shock. Critical Care (London, England), 24(1), 43. https://doi.org/10.1186/s13054-020-2733-x
  • Bendl, J., Stourac, J., Salanda, O., Pavelka, A., Wieben, E. D., Zendulka, J., Brezovsky, J., & Damborsky, J. (2014). PredictSNP: Robust and accurate consensus classifier for prediction of disease-related mutations. PLoS Computational Biology, 10(1), e1003440. https://doi.org/10.1371/journal.pcbi.1003440
  • Benigni, A., Cassis, P., & Remuzzi, G. (2010). Angiotensin II revisited: New roles in inflammation, immunology and aging. EMBO Molecular Medicine, 2(7), 247–257. https://doi.org/10.1002/emmm.201000080
  • Braliou, G. G., Grigoriadou, A.-M G., Kontou, P. I., & Bagos, P. G. (2014). The role of genetic polymorphisms of the Renin-Angiotensin System in renal diseases: A meta-analysis. Computational and Structural Biotechnology Journal, 10(16), 1–7. https://doi.org/10.1016/j.csbj.2014.05.006
  • Bromberg, Y., & Rost, B. (2007). SNAP: Predict effect of non-synonymous polymorphisms on function. Nucleic Acids Research, 35(11), 3823–3835. https://doi.org/10.1093/nar/gkm238
  • Bussard, R. L., & Busse, L. W. (2018). Angiotensin II: A new therapeutic option for vasodilatory shock. Therapeutics and Clinical Risk Management, 14, 1287–1298. https://doi.org/10.2147/TCRM.S150434
  • Cai, L., Wheeler, E., Kerrison, N. D., Luan, J., Deloukas, P., Franks, P. W., Amiano, P., Ardanaz, E., Bonet, C., Fagherazzi, G., Groop, L. C., Kaaks, R., Huerta, J. M., Masala, G., Nilsson, P. M., Overvad, K., Pala, V., Panico, S., Rodriguez-Barranco, M., … Wareham, N. J. (2020). Genome-wide association analysis of type 2 diabetes in the EPIC-InterAct study. Scientific Data, 7(1), 393. https://doi.org/10.1038/s41597-020-00716-7
  • Calabrese, R., Capriotti, E., Fariselli, P., Martelli, P. L., & Casadio, R. (2009). Functional annotations improve the predictive score of human disease-related mutations in proteins. Human Mutation, 30(8), 1237–1244. https://doi.org/10.1002/humu.21047
  • 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 (Oxford, England), 22(22), 2729–2734. https://doi.org/10.1093/bioinformatics/btl423
  • Capriotti, E., Fariselli, P., & Casadio, R. (2005). I-Mutant2.0: Predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Research, 33(Web Server issue), W306–10. (Web Server issue): https://doi.org/10.1093/nar/gki375
  • Carpenter, C., Honkanen, A. A., Mashimo, H., Goss, K. A., Huang, P., Fishman, M. C., Asaad, M., Dorso, C. R., & Cheung, H. (1996). Renal abnormalities in mutant mice. Nature, 380(6572), 292–292. https://doi.org/10.1038/380292a0
  • Carracedo, J., Alique, M., Vida, C., Bodega, G., Ceprián, N., Morales, E., Praga, M., de Sequera, P., & Ramírez, R. (2020). Mechanisms of cardiovascular disorders in patients with chronic kidney disease: A process related to accelerated senescence. Frontiers in Cell and Developmental Biology, 8, 185. https://doi.org/10.3389/fcell.2020.00185
  • Castellanos-Rubio, A., & Ghosh, S. (2019). Disease-associated SNPs in inflammation-related lncRNAs. Frontiers in Immunology, 10, 420. https://doi.org/10.3389/fimmu.2019.00420
  • Chakrabarty, B., & Parekh, N. (2016). NAPS: Network analysis of protein structures. Nucleic Acids Research, 44(W1), W375–82. https://doi.org/10.1093/nar/gkw383
  • Chen, Y., Lu, H., Zhang, N., Zhu, Z., Wang, S., & Li, M. (2020). PremPS: Predicting the impact of missense mutations on protein stability. PLoS Computational Biology, 16(12), e1008543. https://doi.org/10.1371/journal.pcbi.1008543
  • Choi, Y., & Chan, A. P. (2015). PROVEAN web server: A tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics (Oxford, England), 31(16), 2745–2747. https://doi.org/10.1093/bioinformatics/btv195
  • Chovancova, E., Pavelka, A., Benes, P., Strnad, O., Brezovsky, J., Kozlikova, B., Gora, A., Sustr, V., Klvana, M., Medek, P., Biedermannova, L., Sochor, J., & Damborsky, J. (2012). CAVER 3.0: A tool for the analysis of transport pathways in dynamic protein structures. PLoS Computational Biology, 8(10), e1002708. https://doi.org/10.1371/journal.pcbi.1002708
  • D'Abrosca, G., Russo, L., Palmieri, M., Baglivo, I., Netti, F., de Paola, I., Zaccaro, L., Farina, B., Iacovino, R., Pedone, P. V., Isernia, C., Fattorusso, R., & Malgieri, G. (2016). The (unusual) aspartic acid in the metal coordination sphere of the prokaryotic zinc finger domain. Journal of Inorganic Biochemistry, 161, 91–98. https://doi.org/10.1016/j.jinorgbio.2016.05.006
  • Das, S. S., & Chakravorty, N. (2020). Identification of deleterious SNPs and their effects on BCL11A, the master regulator of fetal hemoglobin expression. Genomics, 112(1), 397–403. https://doi.org/10.1016/j.ygeno.2019.03.002
  • DuBay, K. H., Bowman, G. R., & Geissler, P. L. (2015). Fluctuations within folded proteins: Implications for thermodynamic and allosteric regulation. Accounts of Chemical Research, 48(4), 1098–1105. https://doi.org/10.1021/ar500351b
  • Elshamaa, M. F., Sabry, S. M., Bazaraa, H. M., Koura, H. M., Elghoroury, E. A., Kantoush, N. A., Thabet, E. H., & Abd-El Haleem, D. A. (2011). Genetic polymorphism of ACE and the angiotensin II type1 receptor genes in children with chronic kidney disease. Journal of Inflammation (London, England), 8(1), 20. https://doi.org/10.1186/1476-9255-8-20
  • Ferrari, R., Guardigli, G., & Ceconi, C. (2010). Secondary prevention of CAD with ACE inhibitors: A struggle between life and death of the endothelium. Cardiovascular Drugs and Therapy, 24(4), 331–339. https://doi.org/10.1007/s10557-010-6244-x
  • Fourati Ben Mustapha, S., Coulet, F., Eyries, M., De Larouziere, V., Ravel, C., Berthaut, I., Antoine, J.-M., Soubrier, F., & Mandelbaum, J. (2013). In Vitro fertilization failure of normozoospermic men: Search for a lack of testicular isozyme of angiotensin-converting enzyme. Basic and Clinical Andrology, 23(1), 4. https://doi.org/10.1186/2051-4190-23-4
  • Fuchs, S., Frenzel, K., Hubert, C., Lyng, R., Muller, L., Michaud, A., Xiao, H. D., Adams, J. W., Capecchi, M. R., Corvol, P., Shur, B. D., & Bernstein, K. E. (2005). Male fertility is dependent on dipeptidase activity of testis ACE. Nature Medicine, 11(11), 1140–1142. author reply 1142-3. https://doi.org/10.1038/nm1105-1140
  • Fuchs, S., Xiao, H. D., Cole, J. M., Adams, J. W., Frenzel, K., Michaud, A., Zhao, H., Keshelava, G., Capecchi, M. R., Corvol, P., & Bernstein, K. E. (2004). Role of the N-terminal catalytic domain of angiotensin-converting enzyme investigated by targeted inactivation in mice. The Journal of Biological Chemistry, 279(16), 15946–15953. https://doi.org/10.1074/jbc.M400149200
  • Gangadharappa, B. S., Sharath, R., Revanasiddappa, P. D., Chandramohan, V., Balasubramaniam, M., & Vardhineni, T. P. (2020). Structural insights of metallo-beta-lactamase revealed an effective way of inhibition of enzyme by natural inhibitors. Journal of Biomolecular Structure & Dynamics, 38(13), 3757–3771. https://doi.org/10.1080/07391102.2019.1667265
  • Gribouval, O., Gonzales, M., Neuhaus, T., Aziza, J., Bieth, E., Laurent, N., Bouton, J. M., Feuillet, F., Makni, S., Ben Amar, H., Laube, G., Delezoide, A.-L., Bouvier, R., Dijoud, F., Ollagnon-Roman, E., Roume, J., Joubert, M., Antignac, C., & Gubler, M. C. (2005). Mutations in genes in the renin-angiotensin system are associated with autosomal recessive renal tubular dysgenesis. Nature Genetics, 37(9), 964–968. https://doi.org/10.1038/ng1623
  • Gribouval, O., Morinière, V., Pawtowski, A., Arrondel, C., Sallinen, S.-L., Saloranta, C., Clericuzio, C., Viot, G., Tantau, J., Blesson, S., Cloarec, S., Machet, M. C., Chitayat, D., Thauvin, C., Laurent, N., Sampson, J. R., Bernstein, J. A., Clemenson, A., Prieur, F., … Gubler, M. C. (2012). Spectrum of mutations in the renin-angiotensin system genes in autosomal recessive renal tubular dysgenesis. Human Mutation, 33(2), 316–326. https://doi.org/10.1002/humu.21661
  • Halushka, M. K., Fan, J. B., Bentley, K., Hsie, L., Shen, N., Weder, A., Cooper, R., Lipshutz, R., & Chakravarti, A. (1999). Patterns of single-nucleotide polymorphisms in candidate genes for blood-pressure homeostasis. Nature Genetics, 22(3), 239–247. https://doi.org/10.1038/10297
  • Han, Y., Jia, Q., Jahani, P. S., Hurrell, B. P., Pan, C., Huang, P., Gukasyan, J., Woodward, N. C., Eskin, E., Gilliland, F. D., Akbari, O., Hartiala, J. A., & Allayee, H. (2020). Genome-wide analysis highlights contribution of immune system pathways to the genetic architecture of asthma. Nature Communications, 11(1), 1776. https://doi.org/10.1038/s41467-020-15649-3
  • Hasnain, M. J. U., Shoaib, M., Qadri, S., Afzal, B., Anwar, T., Abbas, S. H., Sarwar, A., Talha Malik, H. M., & Tariq Pervez, M. (2020). Computational analysis of functional single nucleotide polymorphisms associated with SLC26A4 gene. PloS One, 15(1), e0225368. https://doi.org/10.1371/journal.pone.0225368
  • Hecht, M., Bromberg, Y., & Rost, B. (2015). Better prediction of functional effects for sequence variants. BMC Genomics, 16 Suppl 8(Suppl 8), S1. https://doi.org/10.1186/1471-2164-16-S8-S1
  • Hossain, M. S., Roy, A. S., & Islam, M. S. (2020). In silico analysis predicting effects of deleterious SNPs of human RASSF5 gene on its structure and functions. Scientific Reports, 10(1), 14542. https://doi.org/10.1038/s41598-020-71457-1
  • Huang, J., & MacKerell, A. D. Jr., (2013). CHARMM36 all-atom additive protein force field: Validation based on comparison to NMR data. Journal of Computational Chemistry, 34(25), 2135–2145. https://doi.org/10.1002/jcc.23354
  • Iso, H., Harada, S., Shimamoto, T., Sato, S., Kitamura, A., Sankai, T., Tanigawa, T., Iida, M., & Komachi, Y. (2000). Angiotensinogen T174M and M235T variants, sodium intake and hypertension among non-drinking, lean Japanese men and women. Journal of Hypertension, 18(9), 1197–1206. https://doi.org/10.1097/00004872-200018090-00005
  • Jain, A., et al. (2021). IndiGenomes: A comprehensive resource of genetic variants from over 1000 Indian genomes. Nucleic Acids Research, 49(D1), D1225–D1232.
  • James, P. A., Oparil, S., Carter, B. L., Cushman, W. C., Dennison-Himmelfarb, C., Handler, J., Lackland, D. T., LeFevre, M. L., MacKenzie, T. D., Ogedegbe, O., Smith, S. C., Svetkey, L. P., Taler, S. J., Townsend, R. R., Wright, J. T., Narva, A. S., & Ortiz, E. (2014). 2014 evidence-based guideline for the management of high blood pressure in adults: Report from the panel members appointed to the Eighth Joint National Committee (JNC 8). JAMA, 311(5), 507–520. https://doi.org/10.1001/jama.2013.284427
  • Jeunemaitre, X., Soubrier, F., Kotelevtsev, Y. V., Lifton, R. P., Williams, C. S., Charru, A., Hunt, S. C., Hopkins, P. N., Williams, R. R., & Lalouel, J. M. (1992). Molecular basis of human hypertension: Role of angiotensinogen. Cell, 71(1), 169–180. https://doi.org/10.1016/0092-8674(92)90275-h
  • Ji, L.-D., Li, J.-Y., Yao, B.-B., Cai, X.-B., Shen, Q.-J., & Xu, J. (2017). Are genetic polymorphisms in the renin-angiotensin-aldosterone system associated with essential hypertension? Evidence from genome-wide association studies. Journal of Human Hypertension, 31(11), 695–698. https://doi.org/10.1038/jhh.2017.29
  • Jo, S., Kim, T., Iyer, V. G., & Im, W. (2008). CHARMM-GUI: A web-based graphical user interface for CHARMM. Journal of Computational Chemistry, 29(11), 1859–1865. https://doi.org/10.1002/jcc.20945
  • Jurcik, A., Bednar, D., Byska, J., Marques, S. M., Furmanova, K., Daniel, L., Kokkonen, P., Brezovsky, J., Strnad, O., Stourac, J., Pavelka, A., Manak, M., Damborsky, J., & Kozlikova, B. (2018). CAVER Analyst 2.0: Analysis and visualization of channels and tunnels in protein structures and molecular dynamics trajectories. Bioinformatics (Oxford, England), 34(20), 3586–3588. https://doi.org/10.1093/bioinformatics/bty386
  • Khan, A. U., Ali, A., Srivastava, G., Sharma, A. (2017). Potential inhibitors designed against NDM-1 type metallo-beta-lactamases: An attempt to enhance efficacies of antibiotics against multi-drug-resistant bacteria. Scientific Reports, 7(1): p. 9207. https://doi.org/10.1038/s41598-017-09588-1
  • Khanna, T., Hanna, G., Sternberg, M. J. E., & David, A. (2021). Missense3D-DB web catalogue: An atom-based analysis and repository of 4M human protein-coding genetic variants. Human Genetics, 140(5), 805–812. https://doi.org/10.1007/s00439-020-02246-z
  • Kim, H.-K., Lee, H., Kwon, J.-T., & Kim, H.-J. (2015). A polymorphism in AGT and AGTR1 gene is associated with lead-related high blood pressure. Journal of the Renin-Angiotensin-Aldosterone System: JRAAS, 16(4), 712–719. https://doi.org/10.1177/1470320313516174
  • Klausen, M. S., Jespersen, M. C., Nielsen, H., Jensen, K. K., Jurtz, V. I., Sønderby, C. K., Sommer, M. O. A., Winther, O., Nielsen, M., Petersen, B., & Marcatili, P. (2019). NetSurfP-2.0: Improved prediction of protein structural features by integrated deep learning. Proteins, 87(6), 520–527. https://doi.org/10.1002/prot.25674
  • Krawczak, M., Reiss, J., & Cooper, D. N. (1992). The mutational spectrum of single base-pair substitutions in mRNA splice junctions of human genes: Causes and consequences. Human Genetics, 90(1-2), 41–54. https://doi.org/10.1007/BF00210743
  • Kumar, S. U., Sankar, S., Kumar, D. T., Younes, S., Younes, N., Siva, R., Doss, C. G. P., & Zayed, H. (2021). Molecular dynamics, residue network analysis, and cross-correlation matrix to characterize the deleterious missense mutations in GALE causing galactosemia III. Cell Biochemistry and Biophysics, 79(2), 201–219. https://doi.org/10.1007/s12013-020-00960-z
  • Kumari, R., Kumar, R., & Lynn, A. (2014). g_mmpbsa–a GROMACS tool for high-throughput MM-PBSA calculations. Journal of Chemical Information and Modeling, 54(7), 1951–1962. https://doi.org/10.1021/ci500020m
  • Laitaoja, M., Valjakka, J., & Jänis, J. (2013). Zinc coordination spheres in protein structures. Inorganic Chemistry, 52(19), 10983–10991. https://doi.org/10.1021/ic401072d
  • Land, H., & Humble, M. S. (2018). YASARA: A Tool to Obtain Structural Guidance in Biocatalytic Investigations. Methods in Molecular Biology (Clifton, N.J.), 1685, 43–67.
  • Landrum, M. J., Chitipiralla, S., Brown, G. R., Chen, C., Gu, B., Hart, J., Hoffman, D., Jang, W., Kaur, K., Liu, C., Lyoshin, V., Maddipatla, Z., Maiti, R., Mitchell, J., O'Leary, N., Riley, G. R., Shi, W., Zhou, G., Schneider, V., … Kattman, B. L. (2020). ClinVar: Improvements to accessing data. Nucleic Acids Research, 48(D1), D835–D844. https://doi.org/10.1093/nar/gkz972
  • Landrum, M. J., Lee, J. M., Riley, G. R., Jang, W., Rubinstein, W. S., Church, D. M., & Maglott, D. R. (2014). ClinVar: Public archive of relationships among sequence variation and human phenotype. Nucleic Acids Research, 42(Database issue), D980–5. https://doi.org/10.1093/nar/gkt1113
  • Li, M. J., Liu, Z., Wang, P., Wong, M. P., Nelson, M. R., Kocher, J.-P A., Yeager, M., Sham, P. C., Chanock, S. J., Xia, Z., & Wang, J. (2016). GWASdb v2: An update database for human genetic variants identified by genome-wide association studies. Nucleic Acids Research, 44(D1), D869–76. https://doi.org/10.1093/nar/gkv1317
  • Lu, H., Cassis, L. A., Kooi, C. W. V., & Daugherty, A. (2016). Structure and functions of angiotensinogen. Hypertension Research, 39(7), 492–500. https://doi.org/10.1038/hr.2016.17
  • Macours, N., Poels, J., Hens, K., Francis, C., & Huybrechts, R. (2004). Structure, evolutionary conservation, and functions of angiotensin- and endothelin-converting enzymes. International Review of Cytology, 239, 47–97.
  • Malleshappa Gowder, S., Chatterjee, J., Chaudhuri, T., & Paul, K. (2014). Prediction and analysis of surface hydrophobic residues in tertiary structure of proteins. TheScientificWorldJournal, 2014, 971258–971257. https://doi.org/10.1155/2014/971258
  • Martin, A. J. M., Vidotto, M., Boscariol, F., Di Domenico, T., Walsh, I., & Tosatto, S. C. E. (2011). RING: Networking interacting residues, evolutionary information and energetics in protein structures. Bioinformatics (Oxford, England), 27(14), 2003–2005. https://doi.org/10.1093/bioinformatics/btr191
  • Mei, M., Cheng, G., Sun, B., Yang, L., Wang, H., Sun, J., & Zhou, W. (2016). EDN1 Gene Variant is Associated with Neonatal Persistent Pulmonary Hypertension. Scientific Reports, 6(1), 29877. https://doi.org/10.1038/srep29877
  • Methot, D., & Reudelhuber, T. L. (2001). Knockout of renin-angiotensin system genes: Effects on vascular development. Current Hypertension Reports, 3(1), 68–73. https://doi.org/10.1007/s11906-001-0083-x
  • Miller, B. R., McGee, T. D., Swails, J. M., Homeyer, N., Gohlke, H., & Roitberg, A. E. 3rd (2012). MMPBSA.py: An efficient program for end-state free energy calculations. Journal of Chemical Theory and Computation, 8(9), 3314–3321. https://doi.org/10.1021/ct300418h
  • Molster, C. M., Bowman, F. L., Bilkey, G. A., Cho, A. S., Burns, B. L., Nowak, K. J., & Dawkins, H. J. S. (2018). The evolution of public health genomics: Exploring its past, present, and future. Frontiers in Public Health, 6, 247. https://doi.org/10.3389/fpubh.2018.00247
  • Montanucci, L., Capriotti, E., Frank, Y., Ben-Tal, N., & Fariselli, P. (2019). DDGun: An untrained method for the prediction of protein stability changes upon single and multiple point variations. BMC Bioinformatics, 20(Suppl 14), 335. https://doi.org/10.1186/s12859-019-2923-1
  • Nuckel, H., et al. (2007). Association of a novel regulatory polymorphism (-938C > A) in the BCL2 gene promoter with disease progression and survival in chronic lymphocytic leukemia. Blood, 109(1), 290–297.
  • Oscanoa, J., Sivapalan, L., Gadaleta, E., Dayem Ullah, A. Z., Lemoine, N. R., & Chelala, C. (2020). SNPnexus: A web server for functional annotation of human genome sequence variation (2020 update). Nucleic Acids Research, 48(W1), W185–W192. https://doi.org/10.1093/nar/gkaa420
  • Pall, S., et al. (2020). Heterogeneous parallelization and acceleration of molecular dynamics simulations in GROMACS. Journal of Chemical Physics. 153(13), 134110.
  • Patron, J., Serra-Cayuela, A., Han, B., Li, C., & Wishart, D. S. (2019). Assessing the performance of genome-wide association studies for predicting disease risk. PloS One, 14(12), e0220215. https://doi.org/10.1371/journal.pone.0220215
  • Pencheva, M., Keskinova, D., Rashev, P., Koeva, Y., & Atanassova, N. (2021). Localization and Distribution of Testicular Angiotensin I Converting Enzyme (ACE) in Neck and Mid-Piece of Spermatozoa from Infertile Men in Relation to Sperm Motility. Cells, 10(12), 3572. https://doi.org/10.3390/cells10123572
  • Piñero, J., Bravo, À., Queralt-Rosinach, N., Gutiérrez-Sacristán, A., Deu-Pons, J., Centeno, E., García-García, J., Sanz, F., & Furlong, L. I. (2017). DisGeNET: A comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Research, 45(D1), D833–D839. https://doi.org/10.1093/nar/gkw943
  • Piton, A., Redin, C., & Mandel, J. L. (2013). XLID-causing mutations and associated genes challenged in light of data from large-scale human exome sequencing. American Journal of Human Genetics, 93(2), 368–383. https://doi.org/10.1016/j.ajhg.2013.06.013
  • Prabantu, V. M., Naveenkumar, N., & Srinivasan, N. (2020). Influence of Disease-Causing Mutations on Protein Structural Networks. Frontiers in Molecular Biosciences, 7, 620554. https://doi.org/10.3389/fmolb.2020.620554
  • Prates-Costa, T. C., Oliveira, M. D., Fazan, R., Salgado, H. C., & Becari, C. (2022). Impact of angiotensin-converting enzyme inhibition on hemodynamic and autonomic profile of elastase-2 knockout mice. Brazilian Journal of Medical and Biological Research = Revista Brasileira de Pesquisas Medicas e Biologicas, 55, e11774. https://doi.org/10.1590/1414-431X2022e11774
  • Ramensky, V., Bork, P., & Sunyaev, S. (2002). Human non-synonymous SNPs: Server and survey. Nucleic Acids Research, 30(17), 3894–3900. https://doi.org/10.1093/nar/gkf493
  • Ramoni, R. B., Himes, B. E., Sale, M. M., Furie, K. L., & Ramoni, M. F. (2009). Predictive genomics of cardioembolic stroke. Stroke, 40(3 Suppl), S67–S70. https://doi.org/10.1161/STROKEAHA.108.533273
  • Raygan, F., Karimian, M., Rezaeian, A., Bahmani, B., & Behjati, M. (2016). Angiotensinogen-M235T as a risk factor for myocardial infarction in Asian populations: A genetic association study and a bioinformatics approach. Croatian Medical Journal, 57(4), 351–362. https://doi.org/10.3325/cmj.2016.57.351
  • Reza, M. N., Ferdous, N., Emon, M. T. H., Islam, M. S., Mohiuddin, A. K. M., & Hossain, M. U. (2021). Pathogenic genetic variants from highly connected cancer susceptibility genes confer the loss of structural stability. Scientific Reports, 11(1), 19264. https://doi.org/10.1038/s41598-021-98547-y
  • Rozario, L. T., Sharker, T., & Nila, T. A. (2021). In silico analysis of deleterious SNPs of human MTUS1 gene and their impacts on subsequent protein structure and function. PLoS One, 16(6), e0252932. https://doi.org/10.1371/journal.pone.0252932
  • Rozman, V., & Kunej, T. (2018). Harnessing Omics Big Data in Nine Vertebrate Species by Genome-Wide Prioritization of Sequence Variants with the Highest Predicted Deleterious Effect on Protein Function. Omics: A Journal of Integrative Biology, 22(6), 410–421. https://doi.org/10.1089/omi.2018.0046
  • Sabater Molina, M., Nicolás Rocamora, E., Bendicho, A. I., Vázquez, E. G., Zorio, E., Rodriguez, F. D., Gil Ortuño, C., Rodríguez, A. I., Sánchez-López, A. J., Jara Rubio, R., Moreno-Docón, A., Marcos, P. J., García Pavía, P., Villa, R. B., & Gimeno Blanes, J. R. (2022). Polymorphisms in ACE, ACE2, AGTR1 genes and severity of COVID-19 disease. PloS One, 17(2), e0263140. https://doi.org/10.1371/journal.pone.0263140
  • Said, S., & Hernandez, G. T. (2014). The link between chronic kidney disease and cardiovascular disease. Journal of Nephropathology, 3(3), 99–104.
  • Sim, N.-L., Kumar, P., Hu, J., Henikoff, S., Schneider, G., & Ng, P. C. (2012). SIFT web server: Predicting effects of amino acid substitutions on proteins. Nucleic Acids Research, 40(Web Server issue), W452–7. (Web Server issue): https://doi.org/10.1093/nar/gks539
  • Smigielski, E. M., Sirotkin, K., Ward, M., & Sherry, S. T. (2000). dbSNP: A database of single nucleotide polymorphisms. Nucleic Acids Research, 28(1), 352–355. https://doi.org/10.1093/nar/28.1.352
  • Speakman, J. R., Loos, R. J. F., O'Rahilly, S., Hirschhorn, J. N., & Allison, D. B. (2018). GWAS for BMI: A treasure trove of fundamental insights into the genetic basis of obesity. International Journal of Obesity (2005), 42(8), 1524–1531. https://doi.org/10.1038/s41366-018-0147-5
  • Stone, E. A., & Sidow, A. (2005). Physicochemical constraint violation by missense substitutions mediates impairment of protein function and disease severity. Genome Research, 15(7), 978–986. https://doi.org/10.1101/gr.3804205
  • Tang, H., & Thomas, P. D. (2016). Tools for Predicting the Functional Impact of Nonsynonymous Genetic Variation. Genetics, 203(2), 635–647. https://doi.org/10.1534/genetics.116.190033
  • Thomas, P. D., & Kejariwal, A. (2004). Coding single-nucleotide polymorphisms associated with complex vs. Mendelian disease: Evolutionary evidence for differences in molecular effects. Proceedings of the National Academy of Sciences of the United States of America, 101(43), 15398–15403. https://doi.org/10.1073/pnas.0404380101
  • Tseng, M.-H., Huang, S.-M., Huang, J.-L., Fan, W.-L., Konrad, M., Shaw, S. W., Lien, R., Chien, H.-P., Ding, J.-J., Wu, T.-W., Tsai, J.-D., Tian, Y.-C., Lee, H.-J., Cheng, P.-J., Hsu, J.-F., & Lin, S.-H. (2020). Autosomal Recessive Renal Tubular Dysgenesis Caused by a Founder Mutation of Angiotensinogen. Kidney International Reports, 5(11), 2042–2051. https://doi.org/10.1016/j.ekir.2020.08.011
  • Turner, A. J., & Hooper, N. M. (2002). The angiotensin-converting enzyme gene family: Genomics and pharmacology. Trends in Pharmacological Sciences, 23(4), 177–183. https://doi.org/10.1016/s0165-6147(00)01994-5
  • Venselaar, H., Te Beek, T. A. H., Kuipers, R. K. P., 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(1), 548. https://doi.org/10.1186/1471-2105-11-548
  • Vervoort, V. S., Beachem, M. A., Edwards, P. S., Ladd, S., Miller, K. E., de Mollerat, X., Clarkson, K., DuPont, B., Schwartz, C. E., Stevenson, R. E., Boyd, E., & Srivastava, A. K. (2002). AGTR2 mutations in X-linked mental retardation. Science, 296(5577), 2401–2403. https://doi.org/10.1126/science.1072191
  • Wang, Y., & Wang, J. G. (2019). Genome-Wide Association Studies of Hypertension and Several Other Cardiovascular Diseases. Pulse (Basel, Switzerland), 6(3-4), 169–186. https://doi.org/10.1159/000496150
  • Welter, D., MacArthur, J., Morales, J., Burdett, T., Hall, P., Junkins, H., Klemm, A., Flicek, P., Manolio, T., Hindorff, L., & Parkinson, H. (2014). The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Research, 42(Database issue), D1001–6. https://doi.org/10.1093/nar/gkt1229
  • Wightman, D. P., Jansen, I. E., Savage, J. E., Shadrin, A. A., Bahrami, S., Holland, D., Rongve, A., Børte, S., Winsvold, B. S., Drange, O. K., Martinsen, A. E., Skogholt, A. H., Willer, C., Bråthen, G., Bosnes, I., Nielsen, J. B., Fritsche, L. G., Thomas, L. F., Pedersen, L. M., … Posthuma, D. (2021). A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer’s disease. Nature Genetics, 53(9), 1276–1282. https://doi.org/10.1038/s41588-021-00921-z
  • Wu, Y., Duan, H., Tian, X., Xu, C., Wang, W., Jiang, W., Pang, Z., Zhang, D., & Tan, Q. (2018). Genetics of Obesity traits: A bivariate genome-wide association analysis. Frontiers in Genetics, 9, 179. https://doi.org/10.3389/fgene.2018.00179
  • Xu, D., & Zhang, Y. (2011). Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization. Biophysical Journal, 101(10), 2525–2534. https://doi.org/10.1016/j.bpj.2011.10.024
  • Yancy, C. W., Jessup, M., Bozkurt, B., Butler, J., Casey, D. E., Colvin, M. M., Drazner, M. H., Filippatos, G. S., Fonarow, G. C., Givertz, M. M., Hollenberg, S. M., Lindenfeld, J., Masoudi, F. A., McBride, P. E., Peterson, P. N., Stevenson, L. W., & Westlake, C. (2017). ACC/AHA/HFSA focused update of the 2013 ACCF/AHA guideline for the management of heart failure: A report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure Society of America. Circulation, 2017. 136(6), e137–e161. https://doi.org/10.1161/CIR.0000000000000509
  • Zhang, C., Wu, S., & Xu, D. (2013). Catalytic mechanism of angiotensin-converting enzyme and effects of the chloride ion. The Journal of Physical Chemistry. B, 117(22), 6635–6645. https://doi.org/10.1021/jp400974n
  • Zhou, T. B., Yin, S. S., & Qin, Y. H. (2014). Association between angiotensin-converting enzyme insertion/deletion gene polymorphism and end-stage renal disease susceptibility. Journal of the Renin-Angiotensin-Aldosterone System: JRAAS, 15(1), 22–31. https://doi.org/10.1177/1470320312460898
  • Zisman, L. S. (2005). ACE and ACE2: A tale of two enzymes. European Heart Journal, 26(4), 322–324. https://doi.org/10.1093/eurheartj/ehi043
  • Živná, M., Hůlková, H., Matignon, M., Hodaňová, K., Vylet’al, P., Kalbáčová, M., Barešová, V., Sikora, J., Blažková, H., Živný, J., Ivánek, R., Stránecký, V., Sovová, J., Claes, K., Lerut, E., Fryns, J.-P., Hart, P. S., Hart, T. C., Adams, J. N., … Kmoch, S. (2009). Dominant renin gene mutations associated with early-onset hyperuricemia, anemia, and chronic kidney failure. The American Journal of Human Genetics, 85(2), 204–213. https://doi.org/10.1016/j.ajhg.2009.07.010

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.