#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Comparative in silico analysis of ftsZ gene from different bacteria reveals the preference for core set of codons in coding sequence structuring and secondary structural elements determination


Autoři: Ayon Pal aff001;  Barnan Kumar Saha aff001;  Jayanti Saha aff001
Působiště autorů: Microbiology & Computational Biology Laboratory, Department of Botany, Raiganj University, Raiganj, West Bengal, India aff001
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0219231

Souhrn

The deluge of sequence information in the recent times provide us with an excellent opportunity to compare organisms on a large genomic scale. In this study we have tried to decipher the variation in the gene organization and structuring of a vital bacterial gene called ftsZ which codes for an integral component of the bacterial cell division, the FtsZ protein. FtsZ is homologous to tubulin protein and has been found to be ubiquitous in eubacteria. FtsZ is showing increasing promise as a target for antibacterial drug discovery. Our study of ftsZ protein from 143 different bacterial species spanning a wider range of morphological and physiological type demonstrates that the ftsZ gene of about ninety three percent of the organisms show relatively biased codon usage profile and significant GC deviation from their genomic GC content. Comparative codon usage analysis of ftsZ and a core housekeeping gene rpoB demonstrated that codon usage pattern of ftsZ CDS is shaped by natural selection to a large extent and mimics that of a housekeeping gene. We have also detected a tendency among the different organisms to utilize a core set of codons in structuring the ftsZ coding sequence. We observed that the compositional frequency of the amino acid serine in the FtsZ protein appears to be a indicator of the bacterial lifestyle. Our meticulous analysis of the ftsZ gene linked with the corresponding FtsZ protein show that there is a bias towards the use of specific synonymous codons particularly in the helix and strand regions of the multi-domain FtsZ protein. Overall our findings suggest that in an indispensable and vital protein such as FtsZ, there is an inherent tendency to maintain form for optimized performance in spite of the extrinsic variability in coding features.

Klíčová slova:

Amino acid analysis – Bacterial genomics – Bacterial pathogens – Burkholderia – Comparative genomics – Gram negative bacteria – Sequence alignment – Streptococcus


Zdroje

1. Powell JR, Dion K (2015) Effects of codon usage on gene expression: empirical studies on Drosophila. Journal of molecular evolution 80: 219–226. doi: 10.1007/s00239-015-9675-y 25838108

2. Wang L, Xing H, Yuan Y, Wang X, Saeed M, et al. (2018) Genome-wide analysis of codon usage bias in four sequenced cotton species. PLOS ONE 13: e0194372. doi: 10.1371/journal.pone.0194372 29584741

3. Zhou Z, Dang Y, Zhou M, Li L, Yu C-h, et al. (2016) Codon usage is an important determinant of gene expression levels largely through its effects on transcription. Proceedings of the National Academy of Sciences 113: E6117.

4. Belalov IS, Lukashev AN (2013) Causes and Implications of Codon Usage Bias in RNA Viruses. PLOS ONE 8: e56642. doi: 10.1371/journal.pone.0056642 23451064

5. Prat Y, Fromer M, Linial N, Linial M (2009) Codon usage is associated with the evolutionary age of genes in metazoan genomes. BMC Evolutionary Biology 9: 285. doi: 10.1186/1471-2148-9-285 19995431

6. LaBella AL, Opulente DA, Steenwyk JL, Hittinger CT, Rokas A (2019) Variation and selection on codon usage bias across an entire subphylum. PLOS Genetics 15: e1008304. doi: 10.1371/journal.pgen.1008304 31365533

7. Shields DC, Sharp PM (1987) Synonymous codon usage in Bacillus subtilis reflects both translational selection and mutational biases. Nucleic Acids Res 15: 8023–8040. doi: 10.1093/nar/15.19.8023 3118331

8. Sharp PM, Stenico M, Peden JF, Lloyd AT (1993) Codon usage: mutational bias, translational selection, or both? Biochem Soc Trans 21: 835–841. doi: 10.1042/bst0210835 8132077

9. Ikemura T (1985) Codon usage and tRNA content in unicellular and multicellular organisms. Mol Biol Evol 2: 13–34. doi: 10.1093/oxfordjournals.molbev.a040335 3916708

10. Bulmer M (1991) The selection-mutation-drift theory of synonymous codon usage. Genetics 129: 897–907. 1752426

11. Tuller T, Waldman YY, Kupiec M, Ruppin E (2010) Translation efficiency is determined by both codon bias and folding energy. Proceedings of the National Academy of Sciences 107: 3645.

12. Xu C, Cai X, Chen Q, Zhou H, Cai Y, et al. (2011) Factors Affecting Synonymous Codon Usage Bias in Chloroplast Genome of Oncidium Gower Ramsey. Evolutionary bioinformatics online 7: 271–278. doi: 10.4137/EBO.S8092 22253533

13. Xu C, Dong J, Tong C, Gong X, Wen Q, et al. (2013) Analysis of Synonymous Codon Usage Patterns in Seven Different Citrus Species. Evolutionary Bioinformatics 9: EBO.S11930.

14. Chithambaram S, Prabhakaran R, Xia X (2014) The Effect of Mutation and Selection on Codon Adaptation in <em>Escherichia coli</em> Bacteriophage. Genetics 197: 301. doi: 10.1534/genetics.114.162842 24583580

15. Plotkin JB, Kudla G (2011) Synonymous but not the same: the causes and consequences of codon bias. Nat Rev Genet 12: 32–42. doi: 10.1038/nrg2899 21102527

16. Sharp PM, Matassi G (1994) Codon usage and genome evolution. Curr Opin Genet Dev 4: 851–860. doi: 10.1016/0959-437x(94)90070-1 7888755

17. Quax TE, Claassens NJ, Soll D, van der Oost J (2015) Codon Bias as a Means to Fine-Tune Gene Expression. Mol Cell 59: 149–161. doi: 10.1016/j.molcel.2015.05.035 26186290

18. Song H, Gao H, Liu J, Tian P, Nan Z (2017) Comprehensive analysis of correlations among codon usage bias, gene expression, and substitution rate in Arachis duranensis and Arachis ipaënsis orthologs. Scientific Reports 7: 14853. doi: 10.1038/s41598-017-13981-1 29093502

19. Zhang R, Zhang L, Wang W, Zhang Z, Du H, et al. (2018) Differences in Codon Usage Bias between Photosynthesis-Related Genes and Genetic System-Related Genes of Chloroplast Genomes in Cultivated and Wild Solanum Species. Int J Mol Sci 19.

20. Sahoo S, Das SS, Rakshit R (2019) Codon usage pattern and predicted gene expression in Arabidopsis thaliana. Gene: X 2: 100012.

21. Hershberg R, Petrov DA (2008) Selection on codon bias. Annu Rev Genet 42: 287–299. doi: 10.1146/annurev.genet.42.110807.091442 18983258

22. Bennetzen JL, Hall BD (1982) Codon selection in yeast. J Biol Chem 257: 3026–3031. 7037777

23. Gouy M, Gautier C (1982) Codon usage in bacteria: correlation with gene expressivity. Nucleic Acids Res 10: 7055–7074. doi: 10.1093/nar/10.22.7055 6760125

24. Frumkin I, Lajoie MJ, Gregg CJ, Hornung G, Church GM, et al. (2018) Codon usage of highly expressed genes affects proteome-wide translation efficiency. Proceedings of the National Academy of Sciences of the United States of America 115: E4940–E4949. doi: 10.1073/pnas.1719375115 29735666

25. Salim HMW, Cavalcanti ARO (2008) Factors influencing codon usage bias in genomes. Journal of the Brazilian Chemical Society 19: 257–262.

26. Sayers EW, Barrett T, Benson DA, Bolton E, Bryant SH, et al. (2011) Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 39: D38–51. doi: 10.1093/nar/gkq1172 21097890

27. Hasegawa M, Yasunaga T, Miyata T (1979) Secondary structure of MS2 phage RNA and bias in code word usage. Nucleic Acids Res 7: 2073–2079. doi: 10.1093/nar/7.7.2073 537920

28. Arjes HA, Lai B, Emelue E, Steinbach A, Levin PA (2015) Mutations in the bacterial cell division protein FtsZ highlight the role of GTP binding and longitudinal subunit interactions in assembly and function. BMC Microbiology 15: 209. doi: 10.1186/s12866-015-0544-z 26463348

29. Bi E, Lutkenhaus J (1991) FtsZ ring structure associated with division in Escherichia coli. Nature 354: 161–164. doi: 10.1038/354161a0 1944597

30. Sun Q, Margolin W (1998) FtsZ dynamics during the division cycle of live Escherichia coli cells. Journal of bacteriology 180: 2050–2056. 9555885

31. Stricker J, Maddox P, Salmon ED, Erickson HP (2002) Rapid assembly dynamics of the <em>Escherichia coli</em> FtsZ-ring demonstrated by fluorescence recovery after photobleaching. Proceedings of the National Academy of Sciences 99: 3171.

32. Anderson DE, Gueiros-Filho FJ, Erickson HP (2004) Assembly Dynamics of FtsZ Rings in <em>Bacillus subtilis</em> and <em>Escherichia coli</em> and Effects of FtsZ-Regulating Proteins. Journal of Bacteriology 186: 5775. doi: 10.1128/JB.186.17.5775-5781.2004 15317782

33. Chen Y, Erickson HP (2005) Rapid in vitro assembly dynamics and subunit turnover of FtsZ demonstrated by fluorescence resonance energy transfer. The Journal of biological chemistry 280: 22549–22554. doi: 10.1074/jbc.M500895200 15826938

34. Pogliano J, Pogliano K, Weiss DS, Losick R, Beckwith J (1997) Inactivation of FtsI inhibits constriction of the FtsZ cytokinetic ring and delays the assembly of FtsZ rings at potential division sites. Proceedings of the National Academy of Sciences of the United States of America 94: 559–564. doi: 10.1073/pnas.94.2.559 9012823

35. Michie KA, Löwe J (2006) Dynamic Filaments of the Bacterial Cytoskeleton. Annual Review of Biochemistry 75: 467–492. doi: 10.1146/annurev.biochem.75.103004.142452 16756499

36. Romberg L, Levin PA (2003) Assembly Dynamics of the Bacterial Cell Division Protein FtsZ: Poised at the Edge of Stability. Annual Review of Microbiology 57: 125–154. doi: 10.1146/annurev.micro.57.012903.074300 14527275

37. Margolin W (2005) FTSZ AND THE DIVISION OF PROKARYOTIC CELLS AND ORGANELLES. Nature reviews Molecular cell biology 6: 862–871. doi: 10.1038/nrm1745 16227976

38. OLIVEIRA JR AF, FOLADOR EL, GOMIDE ACP, GOES-NETO A, AZEVEDO VAC, et al. (2018) Cell Division in genus Corynebacterium: protein-protein interaction and molecular docking of SepF and FtsZ in the understanding of cytokinesis in pathogenic species. Anais da Academia Brasileira de Ciências 90: 2179–2188. doi: 10.1590/0001-3765201820170385 29451601

39. Erickson HP (1997) FtsZ, a tubulin homologue in prokaryote cell division. Trends Cell Biol 7: 362–367. doi: 10.1016/S0962-8924(97)01108-2 17708981

40. Stokes KD, Osteryoung KW (2003) Early divergence of the FtsZ1 and FtsZ2 plastid division gene families in photosynthetic eukaryotes. Gene 320: 97–108. doi: 10.1016/s0378-1119(03)00814-x 14597393

41. Ma S, Ma S (2012) The development of FtsZ inhibitors as potential antibacterial agents. ChemMedChem 7: 1161–1172. doi: 10.1002/cmdc.201200156 22639193

42. Hurley KA, Santos TMA, Nepomuceno GM, Huynh V, Shaw JT, et al. (2016) Targeting the Bacterial Division Protein FtsZ. Journal of Medicinal Chemistry 59: 6975–6998. doi: 10.1021/acs.jmedchem.5b01098 26756351

43. Ojima I, Kumar K, Awasthi D, Vineberg JG (2014) Drug discovery targeting cell division proteins, microtubules and FtsZ. Bioorg Med Chem 22: 5060–5077. doi: 10.1016/j.bmc.2014.02.036 24680057

44. Dai K, Lutkenhaus J (1991) ftsZ is an essential cell division gene in Escherichia coli. Journal of bacteriology 173: 3500–3506. doi: 10.1128/jb.173.11.3500-3506.1991 2045370

45. Erickson HP (1995) FtsZ, a prokaryotic homolog of tubulin? Cell 80: 367–370. doi: 10.1016/0092-8674(95)90486-7 7859278

46. Erickson HP, Taylor DW, Taylor KA, Bramhill D (1996) Bacterial cell division protein FtsZ assembles into protofilament sheets and minirings, structural homologs of tubulin polymers. Proceedings of the National Academy of Sciences 93: 519.

47. Popp D, Iwasa M, Narita A, Erickson HP, Maéda Y (2009) FtsZ condensates: an in vitro electron microscopy study. Biopolymers 91: 340–350. doi: 10.1002/bip.21136 19137575

48. Romberg L, Simon M, Erickson H (2001) Polymerization of FtsZ, a Bacterial Homolog of Tubulin. The Journal of biological chemistry 276: 11743–11753. doi: 10.1074/jbc.M009033200 11152458

49. Eisenberg E, Levanon EY (2013) Human housekeeping genes, revisited. Trends Genet 29: 569–574. doi: 10.1016/j.tig.2013.05.010 23810203

50. Lai Q, Liu Y, Yuan J, Du J, Wang L, et al. (2014) Multilocus Sequence Analysis for Assessment of Phylogenetic Diversity and Biogeography in Thalassospira Bacteria from Diverse Marine Environments. PLOS ONE 9: e106353. doi: 10.1371/journal.pone.0106353 25198177

51. Supek F, Skunca N, Repar J, Vlahovicek K, Smuc T (2010) Translational selection is ubiquitous in prokaryotes. PLoS Genet 6: e1001004. doi: 10.1371/journal.pgen.1001004 20585573

52. Roller M, Lucić V, Nagy I, Perica T, Vlahovicek K (2013) Environmental shaping of codon usage and functional adaptation across microbial communities. Nucleic acids research 41: 8842–8852. doi: 10.1093/nar/gkt673 23921637

53. Botzman M, Margalit H (2011) Variation in global codon usage bias among prokaryotic organisms is associated with their lifestyles. Genome biology 12: R109–R109. doi: 10.1186/gb-2011-12-10-r109 22032172

54. Hart A, Cortés MP, Latorre M, Martinez S (2018) Codon usage bias reveals genomic adaptations to environmental conditions in an acidophilic consortium. PLOS ONE 13: e0195869. doi: 10.1371/journal.pone.0195869 29742107

55. Carbone A, Képès F, Zinovyev A (2004) Codon Bias Signatures, Organization of Microorganisms in Codon Space, and Lifestyle. Molecular Biology and Evolution 22: 547–561. doi: 10.1093/molbev/msi040 15537809

56. Yang DC, Blair KM, Salama NR (2016) Staying in Shape: the Impact of Cell Shape on Bacterial Survival in Diverse Environments. Microbiology and Molecular Biology Reviews 80: 187. doi: 10.1128/MMBR.00031-15 26864431

57. Miller SI, Salama NR (2018) The gram-negative bacterial periplasm: Size matters. PLOS Biology 16: e2004935. doi: 10.1371/journal.pbio.2004935 29342145

58. Navarro Llorens JM, Tormo A, Martínez-García E (2010) Stationary phase in gram-negative bacteria. FEMS Microbiology Reviews 34: 476–495. doi: 10.1111/j.1574-6976.2010.00213.x 20236330

59. Gontang EA, Fenical W, Jensen PR (2007) Phylogenetic Diversity of Gram-Positive Bacteria Cultured from Marine Sediments. Applied and Environmental Microbiology 73: 3272. doi: 10.1128/AEM.02811-06 17400789

60. Muto A, Osawa S (1987) The guanine and cytosine content of genomic DNA and bacterial evolution. Proc Natl Acad Sci U S A 84: 166–169. doi: 10.1073/pnas.84.1.166 3467347

61. Lightfield J, Fram NR, Ely B (2011) Across Bacterial Phyla, Distantly-Related Genomes with Similar Genomic GC Content Have Similar Patterns of Amino Acid Usage. PLOS ONE 6: e17677. doi: 10.1371/journal.pone.0017677 21423704

62. Marin M (2008) Folding at the rhythm of the rare codon beat. Biotechnol J 3: 1047–1057. doi: 10.1002/biot.200800089 18624343

63. Saunders R, Deane CM (2010) Synonymous codon usage influences the local protein structure observed. Nucleic Acids Research 38: 6719–6728. doi: 10.1093/nar/gkq495 20530529

64. Benson DA, Cavanaugh M, Clark K, Karsch-Mizrachi I, Lipman DJ, et al. (2013) GenBank. Nucleic acids research 41: D36–D42. doi: 10.1093/nar/gks1195 23193287

65. Garrity GM, Boone DR, Castenholz RW (2001) Bergey's Manual of Systematic Bacteriology. New York: Springer-Verlag.

66. Brenner DJ, Krieg NR, Staley JT, Garrity GM (2005) Bergey's Manual of Systematic Bacteriology. New York: Springer-Verlag.

67. Vos P, Garrity G, Jones D, Krieg NR, Ludwig W, et al. (2009) Bergey's Manual of Systematic Bacteriology. New York: Springer-Verlag.

68. Krieg NR, Ludwig W, Whitman WB, Hedlund BP, Paster BJ, et al. (2010) Bergey's Manual of Systematic Bacteriology. New York: Springer-Verlag.

69. Whitman WB, Goodfellow M, Kämpfer P, Busse H-J, Trujillo ME, et al. (2012) Bergey's Manual of Systematic Bacteriology. New York: Springer-Verlag.

70. Wright F (1990) The 'effective number of codons' used in a gene. Gene 87: 23–29. doi: 10.1016/0378-1119(90)90491-9 2110097

71. Peden JF (1999) Analysis of Codon Usage [Doctoral Thesis]: University of Nottingham.

72. Supek F, Vlahovicek K (2004) INCA: synonymous codon usage analysis and clustering by means of self-organizing map. Bioinformatics 20: 2329–2330. doi: 10.1093/bioinformatics/bth238 15059815

73. Fuglsang A (2006) Estimating the "effective number of codons": the Wright way of determining codon homozygosity leads to superior estimates. Genetics 172: 1301–1307. doi: 10.1534/genetics.105.049643 16299393

74. Liu X (2013) A more accurate relationship between 'effective number of codons' and GC3s under assumptions of no selection. Comput Biol Chem 42: 35–39. doi: 10.1016/j.compbiolchem.2012.11.003 23257412

75. Šmarda P, Bureš P, Horová L, Leitch IJ, Mucina L, et al. (2014) Ecological and evolutionary significance of genomic GC content diversity in monocots. Proceedings of the National Academy of Sciences 111: E4096.

76. McCutcheon JP, Moran NA (2010) Functional convergence in reduced genomes of bacterial symbionts spanning 200 My of evolution. Genome Biol Evol 2: 708–718. doi: 10.1093/gbe/evq055 20829280

77. Lassalle F, Périan S, Bataillon T, Nesme X, Duret L, et al. (2015) GC-Content evolution in bacterial genomes: the biased gene conversion hypothesis expands. PLoS genetics 11: e1004941–e1004941. doi: 10.1371/journal.pgen.1004941 25659072

78. Sharp PM, Bailes E, Grocock RJ, Peden JF, Sockett RE (2005) Variation in the strength of selected codon usage bias among bacteria. Nucleic acids research 33: 1141–1153. doi: 10.1093/nar/gki242 15728743

79. Aho KA (2013) Foundational and Applied Statistics for Biologists Using R. New York: Chapman and Hall/CRC.

80. Spiegel MR (1961) Schaum's outline of theory and problems of statistics: Schaum Pub. Co.

81. Shang M LF, Hua J, Wang K, (2011) Analysis on codon usage of chloroplast genome of Gossypium hirsutum. Scientia Agricultura Sinica 44: 245–253.

82. Zhou Y, Skidmore ST (2018) A Reassessment of ANOVA Reporting Practices: A Review of Three APA Journals. Journal of Methods and Measurement in the Social Sciences; Vol 8, No 1 (2017).

83. Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, et al. (2011) Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol 7: 539. doi: 10.1038/msb.2011.75 21988835

84. Li W, Godzik A (2006) Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22: 1658–1659. doi: 10.1093/bioinformatics/btl158 16731699

85. Kumar S, Stecher G, Li M, Knyaz C, Tamura K (2018) MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms. Mol Biol Evol 35: 1547–1549. doi: 10.1093/molbev/msy096 29722887

86. Saitou N, Nei M (1987) The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol 4: 406–425. doi: 10.1093/oxfordjournals.molbev.a040454 3447015

87. Felsenstein J (1985) CONFIDENCE LIMITS ON PHYLOGENIES: AN APPROACH USING THE BOOTSTRAP. Evolution 39: 783–791. doi: 10.1111/j.1558-5646.1985.tb00420.x 28561359

88. Tajima F, Nei M (1984) Estimation of evolutionary distance between nucleotide sequences. Mol Biol Evol 1: 269–285. doi: 10.1093/oxfordjournals.molbev.a040317 6599968

89. Letunic I, Bork P (2019) Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Research 47: W256–W259. doi: 10.1093/nar/gkz239 30931475

90. Letunic I, Bork P (2007) Interactive Tree Of Life (iTOL): an online tool for phylogenetic tree display and annotation. Bioinformatics 23: 127–128. doi: 10.1093/bioinformatics/btl529 17050570

91. Magnan CN, Baldi P (2014) SSpro/ACCpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity. Bioinformatics (Oxford, England) 30: 2592–2597.

92. Wang S-F, Su M-W, Tseng S-P, Li M-C, Tsao C-H, et al. (2016) Analysis of codon usage preference in hemagglutinin genes of the swine-origin influenza A (H1N1) virus. Journal of Microbiology, Immunology and Infection 49: 477–486. doi: 10.1016/j.jmii.2014.08.011 25442859

93. Sharp PM, Tuohy TM, Mosurski KR (1986) Codon usage in yeast: cluster analysis clearly differentiates highly and lowly expressed genes. Nucleic Acids Res 14: 5125–5143. doi: 10.1093/nar/14.13.5125 3526280

94. Pal A, Banerjee R, Mondal UK, Mukhopadhyay S, Bothra AK (2015) Deconstruction of Archaeal Genome Depict Strategic Consensus in Core Pathways Coding Sequence Assembly. PLOS ONE 10: e0118245. doi: 10.1371/journal.pone.0118245 25674789

95. Quiroz-Castañeda RE, Amaro-Estrada I, Rodríguez-Camarillo SD (2016) Anaplasma marginale: Diversity, Virulence, and Vaccine Landscape through a Genomics Approach. BioMed Research International 2016: 9032085. doi: 10.1155/2016/9032085 27610385

96. Wallach JC, Samartino LE, Efron A, Baldi PC (1997) Human infection by Brucella melitensis: an outbreak attributed to contact with infected goats. FEMS Immunology & Medical Microbiology 19: 315–321.

97. Contini C, Seraceni S, Cultrera R, Castellazzi M, Granieri E, et al. (2010) Chlamydophila pneumoniae Infection and Its Role in Neurological Disorders. Interdisciplinary Perspectives on Infectious Diseases 2010: 273573. doi: 10.1155/2010/273573 20182626

98. White P, Murphy M, Moss J, Armstrong G, Spencer SP (2007) Chronic fatigue syndrome or myalgic encephalomyelitis. BMJ 335: 411. doi: 10.1136/bmj.39316.472361.80 17762005

99. Nishida H (2012) Evolution of genome base composition and genome size in bacteria. Frontiers in microbiology 3: 420–420. doi: 10.3389/fmicb.2012.00420 23230432

100. Garcia-Vallve S, Romeu A, Palau J (2000) Horizontal gene transfer in bacterial and archaeal complete genomes. Genome Res 10: 1719–1725. doi: 10.1101/gr.130000 11076857

101. Lawrence JG, Ochman H (2002) Reconciling the many faces of lateral gene transfer. Trends Microbiol 10: 1–4. doi: 10.1016/s0966-842x(01)02282-x 11755071

102. Liu Q (2012) Mutational Bias and Translational Selection Shaping the Codon Usage Pattern of Tissue-Specific Genes in Rice. PLOS ONE 7: e48295. doi: 10.1371/journal.pone.0048295 23144748

103. He B, Dong H, Jiang C, Cao F, Tao S, et al. (2016) Analysis of codon usage patterns in Ginkgo biloba reveals codon usage tendency from A/U-ending to G/C-ending. Scientific Reports 6: 35927. doi: 10.1038/srep35927 27808241

104. Song H, Liu J, Song Q, Zhang Q, Tian P, et al. (2017) Comprehensive Analysis of Codon Usage Bias in Seven Epichloë Species and Their Peramine-Coding Genes. Frontiers in microbiology 8: 1419–1419. doi: 10.3389/fmicb.2017.01419 28798739

105. Sueoka N (1988) Directional mutation pressure and neutral molecular evolution. Proc Natl Acad Sci U S A 85: 2653–2657. doi: 10.1073/pnas.85.8.2653 3357886

106. Holm S (1979) A Simple Sequentially Rejective Multiple Test Procedure. Scandinavian Journal of Statistics 6: 65–70.

107. Aickin M, Gensler H (1996) Adjusting for multiple testing when reporting research results: the Bonferroni vs Holm methods. American Journal of Public Health 86: 726–728. doi: 10.2105/ajph.86.5.726 8629727

108. Beveridge TJ (1990) Mechanism of gram variability in select bacteria. Journal of bacteriology 172: 1609–1620. doi: 10.1128/jb.172.3.1609-1620.1990 1689718

109. Cheng J, Randall AZ, Sweredoski MJ, Baldi P (2005) SCRATCH: a protein structure and structural feature prediction server. Nucleic acids research 33: W72–W76. doi: 10.1093/nar/gki396 15980571


Článek vyšel v časopise

PLOS One


2019 Číslo 12
Nejčtenější tento týden
Nejčtenější v tomto čísle
Kurzy

Zvyšte si kvalifikaci online z pohodlí domova

KOST
Koncepce osteologické péče pro gynekology a praktické lékaře
nový kurz
Autoři: MUDr. František Šenk

Sekvenční léčba schizofrenie
Autoři: MUDr. Jana Hořínková

Hypertenze a hypercholesterolémie – synergický efekt léčby
Autoři: prof. MUDr. Hana Rosolová, DrSc.

Svět praktické medicíny 5/2023 (znalostní test z časopisu)

Imunopatologie? … a co my s tím???
Autoři: doc. MUDr. Helena Lahoda Brodská, Ph.D.

Všechny kurzy
Kurzy Podcasty Doporučená témata Časopisy
Přihlášení
Zapomenuté heslo

Zadejte e-mailovou adresu, se kterou jste vytvářel(a) účet, budou Vám na ni zaslány informace k nastavení nového hesla.

Přihlášení

Nemáte účet?  Registrujte se

#ADS_BOTTOM_SCRIPTS#