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Computational Study of ADD1 Gene Polymorphism Associated with Hypertension

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Abstract

We have determined the non-synonymous single-nucleotide polymorphisms (nsSNPs) of α adducin 1 (ADD1) gene and its variations in different populations to understand its role in hypertension. Out of 1,113 SNPs, 9 are found to be non-synonymous, of which 7 showed significant damaging effect and one of them showed SNP variability with large differences among the minor allele frequency observed in various populations. The amino acid change found for rs4961 is from glycine to tryptophan, i.e., from an alkyl amino acid to an aromatic amino acid. This residual change is observed in the coiled region of the protein and is also predicted to be disordered by computational algorithm. Protein disorder plays an important role in structural and functional genomics. Hence, because of the complete change in side chains of the amino acid residues occurring in the coiled and disordered region of the protein, the structure of the protein might be altered and the function might be affected, leading to the risk for hypertension.

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References

  1. Hong, K. W., Jin, H. S., Lim, J. E., et al. (2010). Non-synonymous single-nucleotide polymorphisms associated with blood pressure and hypertension. Journal of Human Hypertension, 24, 763–774.

    Article  PubMed  CAS  Google Scholar 

  2. Oparil, S., Zaman, A. M., & Calhoun, D. A. (2003). Pathogenesis of hypertension. Annals of Internal Medicine, 139, 761–776.

    PubMed  CAS  Google Scholar 

  3. Parmentier, J. H., Pavicevic, Z., & Malik, K. U. (2006). ANG II stimulates phospholipase D through PKCζ activation in VSMC: Implications in adhesion, spreading, and hypertrophy. American Journal of Physiology Heart and Circulatory Physiology, 290, 46–54.

    Article  Google Scholar 

  4. Liu, Y., Zhuoma, C., Shan, G., et al. (2002). A1166C polymorphism of the angiotensin ii type 1 receptor gene and essential hypertension in Han, Tibetan and Yi populations. Hypertension Research, 25, 515–521.

    Article  PubMed  CAS  Google Scholar 

  5. Andresen, B. T., Jackson, E. K., & Romero, G. G. (2001). Angiotensin II signaling to phospholipase D in renal microvascular smooth muscle cells in SHR. Hypertension, 37, 635–639.

    Article  PubMed  CAS  Google Scholar 

  6. Kundu, A., Ramakrishnan, P., Rajendran, A. et al. (2011). Analysis of non-synonymous single-nucleotide polymorphisms and population variability of PLD2 gene associated with hypertension. International Journal of Bioinformatics Research and Applications (in press).

  7. Manunta, P., Burnier, M., Amico, M. D., et al. (1999). Adducin polymorphism affects renal proximal tubule reabsorption in hypertension. Hypertension, 33, 694–697.

    Article  PubMed  CAS  Google Scholar 

  8. Clifford, R. J., Edmonson, M. N., Titia, C. N., et al. (2004). Bioinformatics tools for single nucleotide polymorphism discovery and analysis. Annals of the New York Academy of Sciences, 20, 101–109.

    Article  Google Scholar 

  9. Staessena, J. A., Wanga, J. G., Brand, E., et al. (2001). Effects of three candidate genes on prevalence and incidence of hypertension in a Caucasian population. Journal of Hypertension, 19, 1349–1358.

    Article  Google Scholar 

  10. Cusi, D., Barlassina, C., Azzani, T., et al. (1997). Polymorphisms of alpha-adducin and salt sensitivity in patients with essential hypertentions. Lancet, 349, 1353–1357.

    Article  PubMed  CAS  Google Scholar 

  11. Hughes, C. A., & Bennett, V. (1995). Adducin: A physical model with implications for function in assembly of spectrin–actin complexes. Journal of Biological Chemistry, 270, 18990–18996.

    Article  PubMed  CAS  Google Scholar 

  12. Matsuoka, Y., Hughes, C. A., & Bennett, V. (1996). Definition of the calmodulin-binding domain and sites of phosphorylation by protein kinases A and C. Journal of Biological Chemistry, 271, 25157–25166.

    Article  PubMed  CAS  Google Scholar 

  13. Kuhlman, P. A., Hughes, C. A., Bennet, V., et al. (1996). A new function for adducin: calcium/calmodulin-regulated capping of the barbed ends of actin filaments. Journal of Biological Chemistry, 271, 7986–7991.

    Article  PubMed  CAS  Google Scholar 

  14. Cantiello, H. F. (1995). Role of the actin cytoskeleton on epithelial sodium channel regulation. Kidney International, 48, 970–984.

    Article  PubMed  CAS  Google Scholar 

  15. Berdiev, B. K., Prat, A. G., Cantiello, H. F., et al. (1996). Regulation of epithelial sodium channels by short actin filaments. Journal of Biological Chemistry, 271, 17704–17710.

    Article  PubMed  CAS  Google Scholar 

  16. Wu, M. S., Bens, M., Cluzeaud, F., et al. (1994). Role of F-actin in the activation of Na1-N(1)-Cl cotransport by forskolin and vasopressin in mouse kidney cultured thick ascending limb cells. Journal of Membrane Biology, 142, 323–336.

    PubMed  CAS  Google Scholar 

  17. Wheeler, D. L., Barrett, T., Benson, D. A., et al. (2008). Database resources of the National Center for Biotechnology Information. Nucleic Acids Research, 36, 13–21.

    Article  Google Scholar 

  18. Lee, P. H., & Shatkay, H. (2009). An integrative scoring system for ranking SNPs by their potential deleterious effects. Bioinformatics, 25, 1048–1055.

    Article  PubMed  CAS  Google Scholar 

  19. Hubbard, T. J. P., Aken, B. L., Ayling, S., et al. (2009). Ensembl. Nucleic Acids Research, 37, 690–697.

    Article  Google Scholar 

  20. Ng, P., & Henikoff, S. (2001). Predicting deleterious amino acid substitutions. Genome Research, 11, 863–874.

    Article  PubMed  CAS  Google Scholar 

  21. Zhu, Y., Hoffman, A., Wu, X., et al. (2008). Correlating observed odds ratios from lung cancer case–control studies to SNP functional scores predicted by bioinformatic tools. Mutation Research, 639, 80–88.

    Article  PubMed  CAS  Google Scholar 

  22. Reumers, J., Maurer-Stroh, S., Schymkowitz, J., et al. (2006). SNPeffect v2.0: A new step in investigating the molecular phenotypic effects of human non-synonymous SNPs. Bioinformatics, 22, 2183–2185.

    Article  PubMed  CAS  Google Scholar 

  23. Yue, P., Melamud, E., Moult, J., et al. (2006). SNPs3D: Candidate gene and SNP selection for association studies. BMC Bioinformatics, 7, 166–181.

    Article  PubMed  Google Scholar 

  24. Cartegni, L., Wang, J., Zhu, Z., et al. (2003). ESEfinder: A web resource to identify exonic splicing enhancers. Nucleic Acids Research, 31, 3568–3571.

    Article  PubMed  CAS  Google Scholar 

  25. Fairbrother, W. G., Yeo, G. W., Yeh, R., et al. (2004). RESCUE-ESE identifies candidate exonic splicing enhancers in vertebrate exons. Nucleic Acids Research, 32, 187–190.

    Article  Google Scholar 

  26. Fairbrother, W. G., Yeh, R. F., Sharp, P. A., et al. (2002). Predictive identification of exonic splicing enhancers in human genes. Science, 297, 1007–1013.

    Article  PubMed  CAS  Google Scholar 

  27. Zhang, X. H. F., Kangsamaksin, T., Chao, M. S. P., et al. (2005). Exon inclusion is dependent on predictable exonic splicing enhancers. Molecular and Cellular Biology, 25, 7323–7332.

    Article  PubMed  CAS  Google Scholar 

  28. Kuhn, R., Karolchik, D., Zweig, A. S., et al. (2007). The UCSC genome browser database: Update 2007. Nucleic Acids Research, 35, 668–673.

    Article  Google Scholar 

  29. Amigo, J., Salas, A., Phillips, C., et al. (2008). SPSmart: Adapting population based SNP genotype databases for fast and comprehensive web access. BMC Bioinformatics, 9, 428–433.

    Article  PubMed  Google Scholar 

  30. Fisher, A., & Sali, A. (2003). Modeller: Generation and refinement of homology-based protein structure models. Methods in Enzymology, 374, 461–491.

    Article  Google Scholar 

  31. Linding, R., Jensen, L. J., Diella, F., et al. (2003). Protein disorder prediction: Implications for structural proteomics. Structure, 11, 1453–1459.

    Article  PubMed  CAS  Google Scholar 

  32. Shen, J., Deininger, P. L., & Zhao, H. (2006). Applications of computational algorithm tools to identify functional SNPs in cytokine genes. Cytokine, 35, 62–66.

    Article  PubMed  CAS  Google Scholar 

  33. Smith, P. J., Zhang, C., Wang, J., et al. (2006). An increased specificity score matrix for the prediction of SF2/ASF-specific exonic splicing enhancers. Human Molecular Genetics, 15, 2490–2508.

    Article  PubMed  CAS  Google Scholar 

  34. Karchin, R., Diekhans, M., Kelly, L., et al. (2005). LS-SNP: Large-scale annotation of coding non-synonymous SNPs based on multiple information sources. Bioinformatics, 21, 2814–2820.

    Article  PubMed  CAS  Google Scholar 

  35. Reynolds, C. A., Hong, M. G., Eriksson, U. K., et al. (2009). A survey of ABCA1 sequence variation confirms association with dementia. Human Mutation, 30, 1348–1354.

    Article  PubMed  CAS  Google Scholar 

  36. Wang, Z., Wang, J., Chong, S. S., et al. (2009). Mining potential functionally significant polymorphisms at the ATP-binding-cassette transporter genes. Current Pharmacogenomics and Personalized Medicine, 7, 40–58.

    Article  Google Scholar 

  37. Tsunoda, T., Lathrop, G. M., Sekine, A., et al. (2004). Variation of gene-based SNPs and linkage disequilibrium patterns in the human genome. Human Molecular Genetics, 13, 1623–1632.

    Article  PubMed  CAS  Google Scholar 

  38. Scheeff, E. D., & Fink, J. L. (2003). Structural bioinformatics. In P. E. Bourne & H. Weissig (Eds.), Fundamentals of protein structure (pp. 15–39). Hoboken, NJ: Wiley.

    Google Scholar 

  39. Linding, R., Letunic, I., Gibson, T. J., & Bork, P. (2005). Computational analysis of modular protein architectures. In G. Cesareni, M. Gimona, & M. Sudol (Eds.), Modular protein domains (pp. 439–476). Weinheim: Wiley–VCH.

    Chapter  Google Scholar 

  40. Wright, P. E., & Dyson, H. J. (2009). Linking folding and binding. Current Opinion in Structural Biology, 19, 31–38.

    Article  PubMed  CAS  Google Scholar 

  41. Cross, D. S., Ivacic, L. C., Stefanski, E. L., et al. (2010). Population based allele frequencies of disease associated polymorphisms in the personalized medicine research project. BMC Genetics, 11, 51–66.

    Article  PubMed  Google Scholar 

  42. Liu, Y., Zhuoma, C., Shan, G., et al. (2002). A1166C polymorphism of the angiotensin II type 1 receptor gene and essential hypertension in Han, Tibetan and Yi populations. Hypertension Research, 25, 515–521.

    Article  PubMed  CAS  Google Scholar 

  43. Katsua, T., Ishikawa, K., Sugimoto, K., et al. (2003). Salt sensitivity of Japanese from the viewpoint of gene polymorphism. Hypertension Research, 26, 521–525.

    Article  Google Scholar 

  44. Yue, P., Li, Z., & Moult, J. (2005). Loss of protein structure stability as a major causative factor in monogenic disease. Journal of Molecular Biology, 353, 459–473.

    Article  PubMed  CAS  Google Scholar 

  45. Bianchi, G., & Cusi, D. (2000). Association and linkage analysis of á-adducin polymorphism: Is the glass half full or half empty? American Journal of Hypertension, 13, 739–743.

    Article  PubMed  CAS  Google Scholar 

  46. Barlassina, C., Schork, N. J., Manuta, P., et al. (2000). Synergistic effect of α-adducin and ACE genes causes blood hypertension and three candidate genes. Kidney International, 57, 1083–1090.

    Article  PubMed  CAS  Google Scholar 

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Acknowledgments

Dr. Anand Anbarasu gratefully acknowledges the Indian Council of Medical Research (ICMR), Government of India Agency for the research grant. The authors would like to thank the management of VIT for providing us the necessary infrastructure for conducting this project.

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  1. Medical & Biological Computing Laboratory, Bioinformatics Division, School of Biosciences and Technology, VIT University, Vellore, 632014, Tamil Nadu, India

    Arpita Kundu & Anbarasu Anand

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  1. Arpita Kundu
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Correspondence to Anbarasu Anand.

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Kundu, A., Anand, A. Computational Study of ADD1 Gene Polymorphism Associated with Hypertension. Cell Biochem Biophys 65, 13–19 (2013). https://doi.org/10.1007/s12013-012-9398-2

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