DOI QR코드

DOI QR Code

Codon Usage Patterns of Tyrosinase Genes in Clonorchis sinensis

  • Bae, Young-An (Department of Microbiology, Gachon University College of Medicine)
  • Received : 2017.01.13
  • Accepted : 2017.04.06
  • Published : 2017.04.30

Abstract

Codon usage bias (CUB) is a unique property of genomes and has contributed to the better understanding of the molecular features and the evolution processes of particular gene. In this study, genetic indices associated with CUB, including relative synonymous codon usage and effective numbers of codons, as well as the nucleotide composition, were investigated in the Clonorchis sinensis tyrosinase genes and their platyhelminth orthologs, which play an important role in the eggshell formation. The relative synonymous codon usage patterns substantially differed among tyrosinase genes examined. In a neutrality analysis, the correlation between $GC_{12}$ and $GC_3$ was statistically significant, and the regression line had a relatively gradual slope (0.218). NC-plot, i.e., $GC_3$ vs effective number of codons (ENC), showed that most of the tyrosinase genes were below the expected curve. The codon adaptation index (CAI) values of the platyhelminth tyrosinases had a narrow distribution between 0.685/0.714 and 0.797/0.837, and were negatively correlated with their ENC. Taken together, these results suggested that CUB in the tyrosinase genes seemed to be basically governed by selection pressures rather than mutational bias, although the latter factor provided an additional force in shaping CUB of the C. sinensis and Opisthorchis viverrini genes. It was also apparent that the equilibrium point between selection pressure and mutational bias is much more inclined to selection pressure in highly expressed C. sinensis genes, than in poorly expressed genes.

Keywords

References

  1. Decker H, Tuczek F. Tyrosinase/catecholoxidase activity of hemocyanins: structural basis and molecular mechanism. Trends Biochem Sci 2000; 25: 392-397. https://doi.org/10.1016/S0968-0004(00)01602-9
  2. Aguilera F, McDougall C, Degnan BM. Origin, evolution and classification of type-3 copper proteins: lineage-specific gene expansions and loss across the Metazoa. BMC Evol Biol 2013; 13: 96. https://doi.org/10.1186/1471-2148-13-96
  3. Sanchez-Ferrer A, Rodriguez-Lopez JN, Garcia-Canovas F, Garcia-Carmona F. Tyrosinase: a comprehensive review of its mechanism. Biochim Biophys Acta 1995; 1247: 1-11. https://doi.org/10.1016/0167-4838(94)00204-T
  4. Decker H, Dillinger R, Tuczek F. How does tyrosinase work? Recent insights from model chemistry and structural biology. Angew Chem Int Ed Engl 2000; 39: 1591-1595. https://doi.org/10.1002/(SICI)1521-3773(20000502)39:9<1591::AID-ANIE1591>3.0.CO;2-H
  5. Smyth JD, Halton DW. The Physiology of Trematodes. 2nd ed. Cambridge, UK. Cambridge University. 1983.
  6. Cordingley JS. Trematode eggshells: novel protein biopolymers. Parasitol Today 1987; 3: 341-344. https://doi.org/10.1016/0169-4758(87)90118-9
  7. Lun ZR, Gasser RB, Lai DH, Li AX, Zhu XQ, Yu XB, Fang YY. Clonorchiasis: a key foodborne zoonosis in China. Lancet Infect Dis 2005; 5: 31-41. https://doi.org/10.1016/S1473-3099(04)01252-6
  8. Bouvard V, Baan R, Straif K, Grosse Y, Secretan B, El Ghissassi F, Benbrahim-Tallaa L, Guha N, Freeman C, Galichet L, Cogliano V, WHO International Agency for Research on Cancer Monograph Working Group. A review of human carcinogens. Part B: Biological agents. Lancet Oncol 2009; 10: 321-322. https://doi.org/10.1016/S1470-2045(09)70096-8
  9. Shin HR, Oh JK, Masuyer E, Curado MP, Bouvard V, Fang YY, Wianqnon S, Sripa B, Hong ST. Epidemiology of cholangiocarcinoma: an update focusing on risk factors. Cancer Sci 2010; 101: 579-585. https://doi.org/10.1111/j.1349-7006.2009.01458.x
  10. Bae YA, Cai GB, Kim SH, Sohn WM, Kong Y. Expression pattern and substrate specificity of Clonorchis sinensis tyrosinases. Int J Parasitol 2013; 43: 891-900. https://doi.org/10.1016/j.ijpara.2013.05.006
  11. Papadakis ED, Nicklin SA, Baker AH, White SJ. Promoters and control elements: designing expression cassettes for gene therapy. Curr Gene Ther 2004; 4: 89-113. https://doi.org/10.2174/1566523044578077
  12. Raghava GP, Han JH. Correlation and prediction of gene expression level from amino acid and dipeptide composition of its protein. BMC Bioinformatics 2005; 6: 59. https://doi.org/10.1186/1471-2105-6-59
  13. Castillo-Davis CI, Mekhedov SL, Hartl DL, Koonin EV, Kondrashov FA. Selection for short introns in highly expressed genes. Nat Genet 2002; 31: 415-418. https://doi.org/10.1038/ng940
  14. Hiraoka Y, Kawamata K, Haraguchi T, Chikashige Y. Codon usage bias is correlated with gene expression levels in the fission yeast Schizosaccharomyces pombe. Genes Cells 2009; 14: 499-509. https://doi.org/10.1111/j.1365-2443.2009.01284.x
  15. Duret L, Mouchiroud D. Expression pattern and, surprisingly, gene length shape codon usage in Caenorhabditis, Drosophila, and Arabidopsis. Proc Natl Acad Sci USA 1999; 96: 4482-4487. https://doi.org/10.1073/pnas.96.8.4482
  16. Supek F, Vlahovicek K. Comparison of codon usage measures and their applicability in prediction of microbial gene expressivity. BMC Bioinformatics 2005; 6: 182. https://doi.org/10.1186/1471-2105-6-182
  17. Xu C, Cai X, Chen Q, Zhou H, Cai Y, Ben A. Factors affecting synonymous codon usage bias in chloroplast genome of oncidium gower ramsey. Evol Bioinform Online 2011; 7: 271-278.
  18. Duret L. tRNA gene number and codon usage in the C. elegans genome are co-adapted for optimal translation of highly expressed genes. Trends Genet 2000; 16: 287-289. https://doi.org/10.1016/S0168-9525(00)02041-2
  19. Goetz RM, Fuglsang A. Correlation of codon bias measures with mRNA levels: analysis of transcriptome data from Escherichia coli. Biochem Biophys Res Commun 2005; 327: 4-7. https://doi.org/10.1016/j.bbrc.2004.11.134
  20. Wright F. The 'effective number of codons' used in a gene. Gene 1990; 87: 23-29. https://doi.org/10.1016/0378-1119(90)90491-9
  21. Sharp PM, Li WH. The codon adaptation index--a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res 1987; 15: 1281-1295. https://doi.org/10.1093/nar/15.3.1281
  22. Sharp PM, Li WH. Codon usage in regulatory genes in Escherichia coli does not reflect selection for 'rare' codons. Nucleic Acids Res 1986; 14: 7737-7749. https://doi.org/10.1093/nar/14.19.7737
  23. Abril JF, Cebria F, Rodriguez-Esteban G, Horn T, Fraguas S, Calvo B, Bartscherer K, Salo E. Smed454 dataset: unravelling the transcriptome of Schmidtea mediterranea. BMC Genomics 2010; 11: 731. https://doi.org/10.1186/1471-2164-11-731
  24. Young ND, Nagarajan N, Lin SJ, Korhonen PK, Jex AR, Hall RS, Safavi-Hemami H, Kaewkong W, Bertrand D, Gao S, Seet Q, Wongkham S, Teh BT, Wongkham C, Intapan PM, Maleewong W, Yang X, Hu M, Wang Z, Hofmann A, Sternberg PW, Tan P, Wang J, Gasser RB. The Opisthorchis viverrini genome provides insights into life in the bile duct. Nat Commun 2014; 5: 4378. https://doi.org/10.1038/ncomms5378
  25. Ikemura T. Correlation between the abundance of Escherichia coli transfer RNAs and the occurrence of the respective codons in its protein genes: a proposal for a synonymous codon choice that is optimal for the E. coli translational system. J Mol Biol 1981; 151: 389-409. https://doi.org/10.1016/0022-2836(81)90003-6
  26. Nair RR, Nandhini MB, Sethuraman T, Doss G. Mutational pressure dictates synonymous codon usage in freshwater unicellular ${\alpha}$-cyanobacterial descendant Paulinella chromatophora and ${\beta}$-cyanobacterium Synechococcus elongatus PCC6301. Springerplus 2013; 2: 492. https://doi.org/10.1186/2193-1801-2-492
  27. Hershberg R, Petrov DA. General rules for optimal codon choice. PLoS Genet 2009; 5: e1000556. https://doi.org/10.1371/journal.pgen.1000556
  28. Sueoka N. Directional mutation pressure and neutral molecular evolution. Proc Natl Acad Sci USA 1988; 85: 2653-2657. https://doi.org/10.1073/pnas.85.8.2653
  29. Nie X, Deng P, Feng K, Liu P, Du X, You FM, Weining S. Comparative analysis of codon usage patterns in chloroplast genomes of the Asteraceae family. Plant Mol Biol Rep 2014; 32: 828-840. https://doi.org/10.1007/s11105-013-0691-z
  30. Yang X, Luo X, Cai X. Analysis of codon usage pattern in Taenia saginata based on a transcriptome dataset. Parasit Vectors 2014; 7: 527. https://doi.org/10.1186/s13071-014-0527-1
  31. Subramanian A, Sarkar RR. Comparison of codon usage bias across Leishmania and Trypanosomatids to understand mRNA secondary structure, relative protein abundance and pathway functions. Genomics 2015; 106: 232-241. https://doi.org/10.1016/j.ygeno.2015.05.009
  32. Cai GB, Bae YA, Zhang Y, He Y, Jiang MS, He L. Expression and characterization of two tyrosinase from the trematode Schistosoma japonicum. Parasitol Res 2009; 104: 601-609. https://doi.org/10.1007/s00436-008-1236-5
  33. Fitzpatrick JM, Hirai Y, Hirai H, Hoffmann KF. Schistosome egg production is dependent upon the activities of two developmentally regulated tyrosinases. FASEB J 2007; 21: 823-835. https://doi.org/10.1096/fj.06-7314com
  34. Bae YA, Kim SH, Ahn CS, Kim JG, Kong Y. Molecular and biochemical characterization of Paragonimus westermani tyrosinase. Parasitology 2015; 142: 807-815. https://doi.org/10.1017/S0031182014001942
  35. Naya H, Romero H, Carels N, Zavala A, Musto H. Translational selection shapes codon usage in the GC-rich genome of Chlamydomonas reinhardtii. FEBS Lett 2001; 501: 127-130. https://doi.org/10.1016/S0014-5793(01)02644-8
  36. Gupta SK, Bhattacharyya TK, Ghosh TC. Synonymous codon usage in Lactococcus lactis: mutational bias versus translational selection. J Biomol Struct Dyn 2004; 21: 527-535. https://doi.org/10.1080/07391102.2004.10506946
  37. Yang X, Ma X, Luo X, Ling H, Zhang X, Cai X. Codon usage bias and determining forces in Taenia solium genome. Korean J Parasitol 2015; 53: 689-697. https://doi.org/10.3347/kjp.2015.53.6.689