medigraphic.com
SPANISH

Archivos de Neurociencias

Instituto Nacional de Neurología y Neurocirugía
  • Contents
  • View Archive
  • Information
    • General Information        
    • Directory
  • Publish
    • Instructions for authors        
  • medigraphic.com
    • Home
    • Journals index            
    • Register / Login
  • Mi perfil

2021, Number 4

<< Back Next >>

Arch Neurocien 2021; 26 (4)

Bioinformatics characterization of mutations in the protein presenilin-1, presenilin-2 and Amyloid protein precursor in relation with familiar Alzheimer disease

Soto-Ospina A, Cataño-Sánchez E, de Jesús-Bedoya G, Araque-Marín P, Villegas-Lanau A
Full text How to cite this article

Language: Spanish
References: 42
Page: 17-31
PDF size: 1978.92 Kb.


Key words:

Familial Alzheimer’s disease, mutations, bioinformatics, presenilin-1, presenilin-2, amyloid precursor protein.

ABSTRACT

Introduction: Alzheimer’s disease is manifested as neuronal death due to damage to the nervous tissue and whose neuropathological hallmarks are protein deposits such as amyloid plaques and neurofibrillary tangles. Objective: To relate the clinical reported for Familial Alzheimer’s disease and the changes of 10 selected mutations in the susceptible regions, to understand the effect on the protein structure from the bioinformatics characterization. Methodology: The information was compiled from the Alzforum, Pubmed, Uniprot and Embl databases. The mutation susceptibility analysis is done with the Rostlab SNAP2 software and the post-translational modifications are made with the Swiss-ExPASY tool. Results: A frequency table oriented to clinical cases associated with missense mutations that cause Alzheimer’s disease was established and the frequency of amino acid changes was found and compared with the composition of amino acids present for PS1, with a percentage of 12% for Leu, in PS2 with Leu at 12.9% and for the carboxy terminal fragment of 99 amino acids of APP with a value of 13.1% for Val. Discussion and conclusion: The amino acids produces changes based on their chemical characteristics, the nonpolar amino acids were more frequent and it is due to the high proportion of protein structure located in the neuronal membrane. The susceptibility analysis complements the effect of the change for the 20 amino acids in the protein structure, guided by the polarity changes.


REFERENCES

  1. B. Dubois et al. Advancing research diagnostic criteria for Alzheimer’s disease: the IWG-2 criteria. Lancet Neurol. 2014;13(6): 614-629 doi: 10.1016/S1474-4422(14)70090-0.

  2. A. Delacourte. Fisiopatología de la enfermedad de Alzheimer. EMC Tratado Med. 2002; 6(4): 1–11. doi: 10.1016/ S1636-5410(02)70234-1.

  3. K. Blennow, M. J. de Leon, and H. Zetterberg. Alzheimer’s disease. Lancet (London, England). 2006; 368(9533): 387- 403. doi: 10.1016/S0140-6736(06)69113-7.

  4. M. Kidd. Alzheimer’s Disease--An Electron Microscopical Study. Brain. 1964; 87( 2): 307- 320. doi: 10.1093/brain/87.2.307.

  5. Lu W., Xu Y., Shao X., et al. Uric Acid Produces an Inflammatory Response through Activation of NF-κB in the Hypothalamus: Implications for the Pathogenesis of Metabolic Disorders. Sci Rep. 2015; 5(1):12144. doi: 10.1038/srep12144.

  6. Ballard C., Gauthier S., Corbett A., Brayne C., Aarsland D., Jones E. Alzheimer’s disease. Lancet. 2011; 377(9770):1019- 31. doi: 10.1016/S0140-6736(10)61349-9.

  7. Arbor S.C., Lafontaine M., Cumbay M. Amyloid-beta Alzheimer targets-protein processing, lipid rafts, and amyloid-beta pores. Yale J Biol Med. 2016; 89(1): 5–21.

  8. Yu X., Zheng J. Cholesterol promotes the interaction of alzheimer β-amyloid monomer with lipid bilayer. J Mol Biol. 2012; 421(4–5): 561–71. doi: 10.1016/j.jmb.2011.11.006.

  9. Venugopal C., Demos C.M., Jagannatha Rao K.S., et al. Beta-secretase: structure, function and evolution. CNS Neurol Disord Drug Targets. 2008; 7(3): 278-294. doi: 10.2174/187152708784936626.

  10. Bush A. I. Drug Development Based on the Metals Hypothesis of Alzheimer’s disease. J. Alzheimer’s Dis. 2008; 15: 223-240, 2008. doi: 10.3233/jad-2008-15208

  11. Kanekiyo T. and Bu G. Apolipoprotein E and Amyloid-betaIndependent Mechanisms in Alzheimer’s Disease. Elsevier Inc., 2016. doi: 10.1016/B978-0-12-802851-3.00006-1

  12. Annaert W., De Strooper B. A cell biological perspective on Alzheimer’s disease. Annu. Rev. Cell Dev. Biol. 2002;18:25-51. doi: 10.1146/annurev.cellbio.18.020402.142302.

  13. Rostagno A., Holton JL, Lashley T, Revesz T, et al. Cerebral amyloidosis: Amyloid subunits, mutants and phenotypes. Cell. Mol. Life Sci. 2010; 67(4):581–600. doi: 10.1007/ s00018-009-0182-4.

  14. Rosenberg RN. The molecular and genetic basis of AD: the end of the beginning: the 2000 Wartenberg lecture. Neurology. 2000; 54 (11): 2045–2054. doi: 10.1212/WNL.54.11.2045.

  15. Hardy J. Amyloid, the presenilins and Alzheimer’s disease. Trends Neurosci. 1997; 20(4):154-9. doi:10.1016/ S0166-2236(96)01030-2.

  16. Guven G., Erginel-Unaltuna N., Samanci B., et al. A patient with early-onset Alzheimer’s disease with a novel psen1 p.Leu424Pro mutation. Neurobiol Aging. 2019; 1, 2–5. doi:10.1016/j. neurobiolaging.2019.05.014.

  17. Cai Y., An A., Kim S. Mutations in presenilin 2 and its implications in Alzheimer’s disease and other dementia-associated disorders. Clin Interv Aging. 2015; 14(10):1163-72. doi: 10.2147/CIA. S85808. eCollection 2015.

  18. Sun L., Zhou R.,Yang G., Shi Y. Analysis of 138 pathogenic mutations in presenilin-1 on the in vitro production of Aβ42 and Aβ40 peptides by γ-secretase. Proc Natl Acad Sci USA.2017; 114(4):E476–E485. doi: 10.1073/pnas.1618657114.

  19. Szaruga M. Munteanu B., Lismont S. Alzheimer’s-Causing Mutations Shift Aβ Length by Destabilizing γ-Secretase-Aβn Interactions. Cell. 2017; 170(3):443-456.e14. doi: 10.1016/j. cell.2017.07.004.

  20. Paschkowsky S., Hamzé M., Oestereich F., Munter LM. Alternative processing of the amyloid precursor protein family by rhomboid protease RHBDL4. J Biol Chem. 2016; 291(42): 21903-12. doi: 0.1074/jbc.M116.753582.

  21. Szaruga M., Veugelen S, Benurwar M., et al. Qualitative changes in human γ-secretase underlie familial Alzheimer’s disease. J Exp Med. 2015; 212(12):2003–13. doi: 10.1084/jem.20150892.

  22. Lichtenthaler S. Alpha-secretase cleavage of the amyloid precursor protein: proteolysis regulated by signaling pathways and protein trafficking. Curr Alzheimer Res. 2012; 9(2):165–177. doi: 10.2174/156720512799361655.

  23. Eggert S., Paliga K., Soba P., et al. The proteolytic processing of the amyloid precursor protein gene family members APLP-1 and APLP-2 involves alpha-, beta-, gamma-, and epsilon-like cleavages: modulation of APLP-1 processing by n-glycosylation. J Biol Chem. 2004; 279(18):18146-56. doi:10.1074/jbc. M311601200.

  24. Chávez-Gutiérrez L., Bammens L., Benilova I., et al. The mechanism of γ-Secretase dysfunction in familial Alzheimer disease. EMBO J. 2012; 31(10):2261-74. doi:10.1038/emboj.2012.79.

  25. Vassar R. BACE1: the beta-secretase enzyme in Alzheimer’s disease. J Mol Neurosci. 2004;23(1-2):105-114. doi: 10.1385/ JMN:23:1-2:105.

  26. Sanabria-Castro A, Alvarado-Echeverría I, Monge-Bonilla C. Molecular pathogenesis of alzheimer’s disease: An update. Ann Neurosci. 2017; 24(1):46–54. doi: 10.1159/000464422.

  27. Apweiler R, Bateman A, Martin M, O’Donovan C, Magrane M, Alam-Faruque Y, et al. Activities at the Universal Protein Resource (UniProt). Nucleic Acids Res. 2014; 42(D1):191–8. doi: 10.1093/nar/gkt1140.

  28. The UniProt Consortium. The Universal Protein Resource (UniProt). Nucleic Acids Res. 2007; 35(S1): D193–D197. doi: 10.1093/nar/gkl929.

  29. Alzforum. ALZFORUM Networking for a cure. Biomedical Research Forum. 2021. https://www.alzforum.org/

  30. Larkin MA, Blackshields G, Brown NP, Chenna R, Mcgettigan PA, McWilliam H, et al. Clustal W and Clustal X version 2.0. Bioinformatics. 2007; 23(21):2947–8. doi: 10.1093/bioinformatics/btm404.

  31. Waterhouse AM, Procter JB, Martin DMA, Clamp M, Barton GJ. Jalview Version 2-a multiple sequence alignment editor and analysis workbench. Bioinformatics. 2009; 25(9):1189–91. doi: 10.1093/bioinformatics/btp033.

  32. Hecht M, Bromberg Y, Rost B. Better prediction of functional effects for sequence variants. BMC Genomics. 2015; 16(S8):S1. doi: 10.1186/1471-2164-16-S8-S1.

  33. Hecht M, Bromberg Y, Rost B. News from the protein mutability landscape. J Mol Biol. 2013; 425(21):3937–48. doi: 10.1016/j. jmb.2013.07.028.

  34. Bromberg Y, Rost B. SNAP: Predict effect of non-synonymous polymorphisms on function. Nucleic Acids Res. 2007; 35(11):3823–35. doi: 10.1093/nar/gkm238.

  35. Artimo P, Jonnalagedda M, Arnold K, Baratin D, Csardi G, De Castro E, et al. ExPASy: SIB bioinformatics resource portal. Nucleic Acids Res. 2012; 40(W1):597–603. doi: 10.1093/nar/gks400.

  36. Blom N, Sicheritz-Pontén T, Gupta R, Gammeltoft S, Brunak S. Prediction of post-translational glycosylation and phosphorylation of proteins from the amino acid sequence. Proteomics. 2004; 4(6):1633–49. doi: 10.1002/pmic.200300771.

  37. Blom N, Gammeltoft S, Brunak S. Sequence and structure-based prediction of eukaryotic protein phosphorylation sites. J Mol Biol. 1999;294(5):1351–62. doi: 10.1006/jmbi.1999.3310.

  38. Gupta R, Jung E, Brunak S. Prediction of N-glycosylation sites in human proteins. 2004; 46:203-206.

  39. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, et al. UCSF Chimera-A visualization system for exploratory research and analysis. J Comput Chem. 2004; 25(13):1605–12. doi: 10.1002/jcc.20084.

  40. Phillips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E, Villa E, et al. Scalable molecular dynamics with NAMD. J Comput Chem. 2005; 26(16):1781–802. doi: 10.1002/jcc.20289.

  41. Humphrey W, Dalke A, Schulten K. VMD- Visual molecular dynamics. J Mol Graph. 1996; 14(1):33–8. doi: 10.1016/0263-7855(96)00018-5.

  42. Schedin-Weiss S, Winblad B, Tjernberg LO. The role of protein glycosylation in Alzheimer disease. FEBS J. 2014; 281(1):46–62. doi: 10.1111/febs.12590




2020     |     www.medigraphic.com

Mi perfil

C?MO CITAR (Vancouver)

Arch Neurocien. 2021;26