Journal of Clinical Monitoring and Computing

, Volume 23, Issue 1, pp 51–57 | Cite as

Determination of Mutation Patterns in Human Ornithine Transcarbamylase Precursor

  • Shaomin Yan
  • Guang Wu


Objective. The ornithine transcarbamylase is a mitochondrial matrix homotrimeric enzyme, whose deficiency is the most common genetic defect of the urea cycle and an X-linked semidominant disorder. To understand its mutation pattern is very helpful for managing its clinical manifest and outcome. Methods. The amino-acid pair predictability is used to transfer the symbolized human ornithine transcarbamylase and its 117 missense point mutants to scalar data and classify the amino-acid pairs as predictable and unpredictable in order that we can analyse the mutation pattern in scalar data domain rather than symbol domain. Results. The results show that the mutation is highly likely to occur at the unpredictable amino-acid pairs, and the mutation has the trend to make an amino-acid pair approach predictable. Conclusion. The results provide insight on mutation from the viewpoint based on random mechanism.


amino-acid pair mutation ornithine transcarbamylase OTC deficiency 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chou KC. Structure bioinformatics and its impact to biomedical science. Curr Med Chem 2004; 11: 2105–2134.PubMedGoogle Scholar
  2. 2.
    Wu G, Yan S. Lecture notes on computational mutation. Nova Science Publisher: New York, 2008.Google Scholar
  3. 3.
    Lindgren V, de Martinville B, Horwich AL, Rosenberg LE, Francke U. Human ornithine transcarbamylase locus mapped to band Xp211 near the Duchenne muscular dystrophy locus. Science 1984; 226: 698–700.CrossRefPubMedGoogle Scholar
  4. 4.
    Horwich AL, Kalousek F, Fenton WA, Pollock RA, Rosenberg LE. Targeting of pre-ornithine transcarbamylase to mitochondria: definition of critical regions and residues in the leader peptide. Cell 1986; 44: 451–459.CrossRefPubMedGoogle Scholar
  5. 5.
    Tuchman M, Lee B, Lichter-Konecki U, Summar ML, Yudkoff M, Cederbaum SD, Kerr DS, Diaz GA, Seashore MR, Lee HS, McCarter RJ, Krischer JP, Batshaw ML; Urea cycle disorders consortium of the rare diseases clinical research network. Cross-sectional multicenter study of patients with urea cycle disorders in the United States. Mol Genet Metab 2008; 94: 397–402.CrossRefPubMedGoogle Scholar
  6. 6.
    Smith W, Kishnani PS, Lee B, Singh RH, Rhead WJ, Sniderman King L, Smith M, Summar M. Urea cycle disorders: clinical presentation outside the newborn period. Crit Care Clin 2005; 21(4 Suppl): S9-S17.CrossRefPubMedGoogle Scholar
  7. 7.
    Wraith JE. Ornithine carbamoyltransferase deficiency. Arch Dis Child 2001; 84: 84–88.CrossRefPubMedGoogle Scholar
  8. 8.
    Gordon N. Ornithine transcarbamylase deficiency: a urea cycle defect. Eur J Paediatr Neurol 2003; 7: 115–121.CrossRefPubMedGoogle Scholar
  9. 9.
    Butterworth RF. Evidence for forebrain cholinergic neuronal loss in congenital ornithine transcarbamylase deficiency. Metab Brain Dis 2000; 15: 83–91.PubMedGoogle Scholar
  10. 10.
    Summar ML, Barr F, Dawling S, Smith W, Lee B, Singh RH, Rhead WJ, Sniderman King L, Christman BW. Unmasked adult-onset urea cycle disorders in the critical care setting. Crit Care Clin. 2005; 21(4 Suppl): S1-S8.CrossRefPubMedGoogle Scholar
  11. 11. Access number P00480, the last annotations on January 15, 2008, Entry version 114.
  12. 12.
    Amino-acid pair predictability. 2008.
  13. 13.
    Suriano G, Azevedo L, Novais M, Boscolo B, Seruca R, Amorim A, Ghibaudi EM. In vitro demonstration of intra-locus compensation using the ornithine transcarbamylase protein as model. Hum Mol Genet 2007; 16: 2209–2214.CrossRefPubMedGoogle Scholar
  14. 14.
    Arranz JA, Riudor E, Marco-Marín C, Rubio V. Estimation of the total number of disease-causing mutations in ornithine transcarbamylase (OTC) deficiency. Value of the OTC structure in predicting a mutation pathogenic potential. J Inherit Metab Dis 2007; 30: 217–226.CrossRefPubMedGoogle Scholar
  15. 15.
    Dobrowolski SF, Ellingson CE, Caldovic L, Tuchman M. Streamlined assessment of gene variants by high resolution melt profiling utilizing the ornithine transcarbamylase gene as a model system. Hum Mutat 2007; 28: 1133–1140.CrossRefPubMedGoogle Scholar
  16. 16.
    Ogino W, Takeshima Y, Nishiyama A, Okizuka Y, Yagi M, Tsuneishi S, Saiki K, Kugo M, Matsuo M. Mutation analysis of the ornithine transcarbamylase (OTC) gene in five Japanese OTC deficiency patients revealed two known and three novel mutations including a deep intronic mutation. Kobe J Med Sci 2007; 53: 229–240.PubMedGoogle Scholar
  17. 17.
    Engel K, Nuoffer JM, Mühlhausen C, Klaus V, Largiadèr CR, Tsiakas K, Santer R, Wermuth B, Häberle J. Analysis of mRNA transcripts improves the success rate of molecular genetic testing in OTC deficiency. Mol Genet Metab 2008; 94: 292–297.CrossRefPubMedGoogle Scholar
  18. 18.
    McCullough BA, Yudkoff M, Batshaw ML, Wilson JM, Raper SE, Tuchman M. Genotype spectrum of ornithine transcarbamylase deficiency: correlation with the clinical and biochemical phenotype. Am J Med Genet 2000; 93: 313–319.CrossRefPubMedGoogle Scholar
  19. 19.
    Jones PA, Rideout WM 3rd, Shen JC, Spruck CH, Tsai YC. Methylation, mutation and cancer. Bioessays 1992; 14: 33–36.CrossRefPubMedGoogle Scholar
  20. 20.
    Bichara M, Schumacher S, Fuchs RP. Genetic instability within monotonous runs of CpG sequences in Escherichia coli. Genetics 1995; 140: 897–907.PubMedGoogle Scholar
  21. 21.
    Majewski J, Ott J. Distribution and characterization of regulatory elements in the human genome. Genome Res 2002; 12: 1827–1836.CrossRefPubMedGoogle Scholar
  22. 22.
    Rogozin I, Kondrashov F, Glazko G. Use of mutation spectra analysis software. Hum Mutat 2001; 17: 83–102.CrossRefPubMedGoogle Scholar
  23. 23.
    Rogozin IB, Pavlov YI. Theoretical analysis of mutation hotspots and their DNA sequence context specificity. Mutat Res 2003; 544: 65–85.CrossRefPubMedGoogle Scholar
  24. 24.
    Acharya N, Abu-Nasr NF, Kawaguchi G, Imai M, Yamamoto K. Frameshift mutations produced by 9-aminoacridine in wild-type, uvrA and recA strains of Escherichia coli; specificity within a hotspot. J Radiat Res (Tokyo) 2007; 48: 361–368.CrossRefGoogle Scholar
  25. 25.
    Dzikiewicz-Krawczyk A. The importance of making ends meet: mutations in genes and altered expression of proteins of the MRN complex and cancer. Mutat Res 2008; 659: 262–273.CrossRefPubMedGoogle Scholar
  26. 26.
    Levy LS. Advances in understanding molecular determinants in FeLV pathology. Vet Immunol Immunopathol 2008; 123: 14–22.CrossRefPubMedGoogle Scholar
  27. 27.
    Martinez-Picado J, Martínez MA. HIV-1 reverse transcriptase inhibitor resistance mutations and fitness: a view from the clinic and ex vivo. Virus Res 2008; 134: 104–123.CrossRefPubMedGoogle Scholar
  28. 28.
    Wu G, Yan S. Prediction of mutations engineered by randomness in H5N1 neuraminidases from influenza A virus. Amino Acids 2008; 34: 81–90.CrossRefPubMedGoogle Scholar
  29. 29.
    Wu G, Yan S. Prediction of mutation in H3N2 hemagglutinins of influenza A virus from North America based on different datasets. Protein Pept Lett 2008; 15: 144–152.PubMedCrossRefGoogle Scholar
  30. 30.
    Wu G, Yan S. Prediction of mutations initiated by internal power in H3N2 hemagglutinins of influenza A virus from North America. Int J Pept Res Ther 2008; 14: 41–51.CrossRefGoogle Scholar
  31. 31.
    Wu G, Yan S. Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influenza A virus. Amino Acid 2008; 35: 365–373.CrossRefGoogle Scholar
  32. 32.
    Wu G, Yan S. Three sampling strategies to predict mutations in H5N1 hemagglutinins from influenza A virus. Protein Pept Lett 2008; 15: 731–738.CrossRefPubMedGoogle Scholar
  33. 33.
    Yan S, Wu G. Describing evolution of hemagglutinins from influenza A viruses using a differential equation. Protein Pept Lett (in press).Google Scholar
  34. 34.
    Jäckel C, Kast P, Hilvert D. Protein design by directed evolution. Annu Rev Biophys 2008; 37: 153–173.CrossRefPubMedGoogle Scholar
  35. 35.
    King LS, Singh RH, Rhead WJ, Smith W, Lee B, Summar ML. Genetic counseling issues in urea cycle disorders. Crit Care Clin 2005; 21(4 Suppl): S37–S44.CrossRefGoogle Scholar
  36. 36.
    Camps M, Herman A, Loh E, Loeb LA. Genetic constraints on protein evolution. Crit Rev Biochem Mol Biol 2007; 42(5): 313–316.CrossRefPubMedGoogle Scholar
  37. 37.
    Wilcken B Problems in the management of urea cycle disorders. Mol Genet Metab 2004; 81 Suppl 1: S86–S91.Google Scholar
  38. 38.
    Kuhara T. Noninvasive human metabolome analysis for differential diagnosis of inborn errors of metabolism. J Chromatogr B Analyt Technol Biomed Life Sci 2007; 855: 42–50.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Guangxi Academy of SciencesNanningChina
  2. 2.Computational Mutation Project, DreamSciTech ConsultingShenzhenChina

Personalised recommendations