References
1. Mendoza JA, Weinberger KK, Swan MJ. The Hsp60 protein of helicobacter
pylori displays chaperone activity under acidic conditions.Biochem Biophys Rep . 2016;9:95–99.
2. Yer EN, Baloglu MC, Ayan S. Identification and expression profiling
of all Hsp family member genes under salinity stress in different poplar
clones. Gene . 2018;678:324–336.
3. Sangiorgi C, Vallese D, Gnemmi I, Bucchieri F, et al. Hsp60 activity
on human bronchial epithelial cells. Int J Immunopathol
Pharmacol . 2017;30(4):333–340.
4. Swaroop S, Sengupta N, Suryawanshi AR, Adlakha YK, et al. Hsp60 plays
a regulatory role in IL-1β-induced microglial inflammation via TLR4-p38
MAPK axis. J Neuroinflammation . 2016;13.
5. Sell H, Poitou C, Habich C, Bouillot J-L, et al. Heat shock protein
60 in obesity: effect of bariatric surgery and its relation to
inflammation and cardiovascular risk. Obesity (Silver Spring) .
2017;25(12):2108–2114.
6. Wick C. Tolerization against atherosclerosis using heat shock protein
60. Cell Stress Chaperones . 2016;21(2):201–211.
7. Hong Y, Long J, Li H, Chen S, et al. An analysis of immunoreactive
signatures in early stage hepatocellular carcinoma. EBioMedicine .
2015;2(5):438–446.
8. Cappello F, Angileri F, de Macario EC, Macario AJL. Chaperonopathies
and chaperonotherapy. Hsp60 as therapeutic target in cancer: potential
benefits and risks. Curr. Pharm. Des. 2013;19(3):452–457.
9. Abdeen S, Salim N, Mammadova N, Summers CM, et al. Targeting the
Hsp60/10 chaperonin systems of Trypanosoma brucei as a strategy
for treating African sleeping sickness. Bioorg. Med. Chem. Lett.2016;26(21):5247–5253.
10. Stevens M, Abdeen S, Salim N, Ray A-M, et al. Hsp60/10 chaperonin
systems are inhibited by a variety of approved drugs, natural products,
and known bioactive molecules. Bioorg. Med. Chem. Lett.2019;29(9):1106–1112.
11. Washburn A, Abdeen S, Ovechkina Y, Ray A-M, et al. Dual-targeting
GroEL/ES chaperonin and protein tyrosine phosphatase B (PtpB)
inhibitors: A polypharmacology strategy for treating Mycobacterium
tuberculosis infections. Bioorg. Med. Chem. Lett.2019;29(13):1665–1672.
12. Brocchieri L, Karlin S. Conservation among Hsp60 sequences in
relation to structure, function, and evolution. Protein Science .
2000;9(3):476–486.
13. Karlin S, Brocchieri L. Heat shock protein 60 sequence comparisons:
Duplications, lateral transfer, and mitochondrial evolution. Proc
Natl Acad Sci U S A . 2000;97(21):11348–11353.
14. Tikhomirova TS, Galzitskaya OV. Functionally significant amino acid
motifs of heat shock proteins: structural and bioinformatics analyses of
Hsp60/Hsp10 in five classes of Chordata. Mol Biol .
2018;52(5):761–778.
15. Seddigh S. Proteomics analysis of two heat shock proteins in
insects. J. Biomol. Struct. Dyn. 2019;37(10):2652–2668.
16. Guo L, Yang H, Tang F, Yin R, et al. Oral immunization with a
multivalent epitope-based vaccine, based on NAP, urease, Hsp60, and
HpaA, provides therapeutic effect on H. pylori infection in
Mongolian gerbils. Front Cell Infect Microbiol . 2017;7.
17. Marchan J. In silico identification of epitopes present in
human heat shock proteins (HSPs) overexpressed by tumour cells. J.
Immunol. Methods . 2019.
18. Huang C-H, Chang M-T, Huang L, Chua W-S. Molecular discrimination
and identification of Acetobacter genus based on the partial heat
shock protein 60 gene (Hsp60) sequences. J. Sci. Food Agric.2014;94(2):213–218.
19. Puri A, Rai A, Dhanaraj PS, Lal R, et al. An in silico approach for
identification of the pathogenic species, Helicobacter pylori and
its relatives. Indian J. Microbiol. 2016;56(3):277–286.
20. Kwok AYC, Su S-C, Reynolds RP, Bay SJ, et al. Species identification
and phylogenetic relationships based on partial Hsp60 gene sequences
within the genus Staphylococcus. Int J Syst Evol Microbiol .
1999;49(3):1181–1192.
21. Stenico V, Michelini S, Modesto M, Baffoni L, et al. Identification
of Bifidobacterium spp . using Hsp60 PCR-RFLP analysis: an update.Anaerobe . 2014;26:36–40.
22. Zhu L, Li W, Dong X. Species identification of genus Bifidobacterium
based on partial Hsp60 gene sequences and proposal ofBifidobacterium thermacidophilum subsp. porcinum subsp. nov.Int J Syst Evol Microbiol . 2003;53(5):1619–1623.
23. Sakamoto M, Suzuki N, Benno Y. Hsp60 and 16S rRNA gene sequence
relationships among species of the genus Bacteroides with the
finding that Bacteroides suis and Bacteroides tectus are
heterotypic synonyms of Bacteroides pyogenes . Int J Syst
Evol Microbiol . 2010;60(12):2984–2990.
24. Padmadas N, Panda PK, Durairaj S. Binding patterns associated
Aß-Hsp60 P458 conjugate to HLA-DR-DRB allele of human in Alzheimer’s
disease: an in silico approach. Interdiscip Sci Comput Life
Sci . 2016:1–12.
25. Marino C, Krishnan B, Cappello F, Taglialatela G. Hsp60 protects
against amyloid β oligomer synaptic toxicity via modification of toxic
oligomer conformation. ACS Chem Neurosci . 2019.
26. Sievers F, Higgins DG. Clustal Omega, accurate alignment of very
large numbers of sequences. Methods Mol. Biol.2014;1079:105–116.
27. Cock PJA, Antao T, Chang JT, Chapman BA, et al. Biopython: freely
available Python tools for computational molecular biology and
bioinformatics. Bioinformatics . 2009;25(11):1422–1423.
28. Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and
high throughput. Nucleic Acids Res . 2004;32(5):1792–1797.
29. Kumar S, Stecher G, Peterson D, Tamura K. MEGA-CC: computing core of
molecular evolutionary genetics analysis program for automated and
iterative data analysis. Bioinformatics . 2012;28(20):2685–2686.
30. Lobanov MYu, Galzitskaya OV. Disordered patterns in clustered
protein data bank and in eukaryotic and bacterial proteomes. PLoS
One . 2011;6(11).
31. Virtanen P, Gommers R, Oliphant TE, Haberland M, et al. SciPy 1.0:
fundamental algorithms for scientific computing in Python. Nature
Methods . 2020;17(3):261–272.
32. Jones E, Oliphant T, Peterson P. SciPy: Open source scientific tools
for Python. 2001.
33. Sokal RR, Rohlf FJ. The comparison of dendrograms by objective
methods. Taxon . 1962;11(2):33–40.
34. Sueoka N. Directional mutation pressure, selective constraints, and
genetic equilibria. J. Mol. Evol. 1992;34(2):95–114.
35. Sueoka N. Directional mutation pressure and neutral molecular
evolution. Proc. Natl. Acad. Sci. U.S.A. 1988;85(8):2653–2657.
36. Fuglsang A. The “effective number of codons” revisited.Biochem. Biophys. Res. Commun. 2004;317(3):957–964.
37. Liu X. A more accurate relationship between ‘effective number of
codons’ and GC3s under assumptions of no selection. Computational
Biology and Chemistry . 2013;42:35–39.
38. Li X, Song H, Kuang Y, Chen S, et al. Genome-wide analysis of codon
usage bias in Epichloë festucae . Int J Mol Sci .
2016;17(7).
39. Boto L. Horizontal gene transfer in the acquisition of novel traits
by metazoans. Proc Biol Sci . 2014;281(1777).
40. Bazzocchi C, Jamnongluk W, O’Neill SL, Anderson TJ, et al. Wsp gene
sequences from the Wolbachia of filarial nematodes. Curr.
Microbiol. 2000;41(2):96–100.
41. Bartolucci C, Lamba D, Grazulis S, Manakova E, et al. Crystal
structure of wild-type chaperonin GroEL. J. Mol. Biol.2005;354(4):940–951.
42. Douglas NR, Reissmann S, Zhang J, Chen B, et al. Dual action of ATP
hydrolysis couples lid closure to substrate release into the group II
chaperonin chamber. Cell . 2011;144(2):240–252.
43. Clare DK, Vasishtan D, Stagg S, Quispe J, et al. ATP-triggered
conformational changes delineate substrate-binding and -folding
mechanics of the GroEL chaperonin. Cell . 2012;149(1):113–123.
44. Nisemblat S, Yaniv O, Parnas A, Frolow F, et al. Crystal structure
of the human mitochondrial chaperonin symmetrical football complex.PNAS . 2015;112(19):6044–6049.
45. Shimamura T, Koike-Takeshita A, Yokoyama K, Masui R, et al. Crystal
structure of the native chaperonin complex from Thermus
thermophilus revealed unexpected asymmetry at the cis-cavity.Structure . 2004;12(8):1471–1480.
46. Lassalle F, Périan S, Bataillon T, Nesme X, et al. GC-content
evolution in bacterial genomes: the biased gene conversion hypothesis
expands. PLOS Genetics . 2015;11(2):e1004941.
47. Niu Z, Xue Q, Wang H, Xie X, et al. Mutational biases and GC-biased
gene conversion affect GC content in the plastomes of Dendrobiumgenus. Int J Mol Sci . 2017;18(11).
48. Weissman JL, Fagan WF, Johnson PLF. Linking high GC content to the
repair of double strand breaks in prokaryotic genomes. PLoS
Genet . 2019;15(11).
49. Villada JC, Duran MF, Lee PKH. Genomic evidence for simultaneous
optimization of transcription and translation through codon variants in
the pmoCAB operon of type Ia methanotrophs. mSystems . 2019;4(4).
50. Seward EA, Kelly S. Dietary nitrogen alters codon bias and genome
composition in parasitic microorganisms. Genome Biol .
2016;17(1):226.
51. Tatarinova T, Kerton O. GC3 biology in Eukaryotes and Prokaryotes.
In: DNA Methylation - From Genomics to Technology . London:
IntechOpen; 2012:55–68.
52. Khrustalev VV, Barkovsky EV. Study of completed archaeal genomes and
proteomes: hypothesis of strong mutational at pressure existed in their
common predecessor. Genomics, Proteomics & Bioinformatics .
2010;8(1):22–32.
53. Brown TA. Mutation, Repair and Recombination. In: Genomes .
2nd ed. Oxford: Wiley-Liss; 2002:1–35.
54. Botzman M, Margalit H. Variation in global codon usage bias among
prokaryotic organisms is associated with their lifestyles. Genome
Biol . 2011;12(10):R109.
55. Arella D, Dilucca M, Giansanti A. Codon usage bias and environmental
adaptation in microbial organisms. Mol Genet Genomics .
2021;296(3):751–762.
56. Carbone A, Képès F, Zinovyev A. Codon bias signatures, organization
of microorganisms in codon space, and lifestyle. Mol Biol Evol .
2005;22(3):547–561.
57. Khandia R, Singhal S, Kumar U, Ansari A, et al. Analysis of Nipah
virus codon usage and adaptation to hosts. Front. Microbiol.2019;10.
58. Martiny AC, Treseder K, Pusch G. Phylogenetic conservatism of
functional traits in microorganisms. ISME J . 2013;7(4):830–838.
59. Wright F. The “effective number of codons” used in a gene.Gene . 1990;87(1):23–29.
60. Butt AM, Nasrullah I, Tong Y. Genome-wide analysis of codon usage
and influencing factors in Chikungunya viruses. PLoS ONE .
2014;9(3):e90905.
61. Guan D-L, Ma L-B, Khan MS, Zhang X-X, et al. Analysis of codon usage
patterns in Hirudinaria manillensis reveals a preference for
GC-ending codons caused by dominant selection constraints. BMC
Genomics . 2018;19.
62. Yi S, Li Y, Wang W. Selection shapes the patterns of codon usage in
three closely related species of genus Misgurnus .Genomics . 2018;110(2):134–142.
63. Chamani Mohasses F, Solouki M, Ghareyazie B, Fahmideh L, et al.
Correlation between gene expression levels under drought stress and
synonymous codon usage in rice plant by in-silico study.PLoS One . 2020;15(8).
64. Xu Q, Chen H, Sun W, Zhu D, et al. Genome-wide analysis of the
synonymous codon usage pattern of Streptococcus suis .Microbial Pathogenesis . 2021;150:104732.
65. Hussain S, Rasool ST, Asif AH. A detailed analysis of synonymous
codon usage in human bocavirus. Arch Virol . 2019;164(2):335–347.
66. Cho M, Kim H, Son HS. Codon usage patterns of LT-Ag genes in
polyomaviruses from different host species. Virol J . 2019;16.
67. Tyagi A, Kumar BTN, Singh NK. Genome dynamics and evolution of codon
usage patterns in shrimp viruses. Arch Virol .
2017;162(10):3137–3142.
68. Behura SK, Severson DW. Codon usage bias: causative factors,
quantification methods and genome-wide patterns: with emphasis on insect
genomes. Biol Rev Camb Philos Soc . 2013;88(1):49–61.
69. Chen Y. A comparison of synonymous codon usage bias patterns in DNA
and RNA virus genomes: quantifying the relative importance of mutational
pressure and natural selection. Biomed Res Int . 2013;2013:406342.
70. Zhou Z, Dang Y, Zhou M, Li L, et al. Codon usage is an important
determinant of gene expression levels largely through its effects on
transcription. PNAS . 2016;113(41):E6117–E6125.
71. Frumkin I, Lajoie MJ, Gregg CJ, Hornung G, et al. Codon usage of
highly expressed genes affects proteome-wide translation efficiency.PNAS . 2018;115(21):E4940–E4949.
72. Ermolaeva MD. Synonymous codon usage in bacteria. Curr Issues
Mol Biol . 2001;3(4):91–97.
73. Korkmaz G, Holm M, Wiens T, Sanyal S. Comprehensive analysis of stop
codon usage in bacteria and its correlation with release factor
abundance. J Biol Chem . 2014;289(44):30334–30342.