Skip to content
2000
Volume 21, Issue 3
  • ISSN: 1875-6921
  • E-ISSN: 1875-6913

Abstract

Introduction

The escalating challenge of multidrug resistance among ESKAPE pathogens has become a prominent concern for global healthcare providers, leading to amplified morbidity and mortality rates.

Methods

We conducted this study to elucidate the genetic architecture of ESKAPE constituents with the intent of ameliorating pathogenicity and facilitating drug development efforts. A comprehensive array of computational tools and statistical methodologies were employed to scrutinize the genomes of ESKAPE pathogens.

Results

Translational selection profoundly influences the codon usage bias within this pathogenic cohort. Notably, leucine emerged as the predominant amino acid, except in the case of Acinetobacter baumannii, where arginine exhibited preeminence. There was a universal preference for at least one histidine codon across all ESKAPE pathogens. GpC emerged as the most prominently overrepresented dinucleotide at the codon pair junction in all ESKAPE pathogens. Furthermore, a comparison of gene sequences and phylogenic tree construction showed a distinct evolutionary relationship between AT-rich and GC-rich ESKAPE pathogens. Additionally, identification, characterization, and phylogenetic analysis of multiple antibiotic resistance genes revealed distinct evolutionary relationships.

Conclusion

It was discerned that despite substantial variability amongst antibiotic resistance genes of pathogens, leucine emerged as the predominant amino acid.

Loading

Article metrics loading...

/content/journals/cppm/10.2174/0118756921344687241015063919
2024-10-25
2025-02-20
Loading full text...

Full text loading...

References

  1. SantajitS. IndrawattanaN. Mechanisms of antimicrobial resistance in ESKAPE pathogens.BioMed Res. Int.201620161810.1155/2016/2475067 27274985
    [Google Scholar]
  2. MulaniM.S. KambleE.E. KumkarS.N. TawreM.S. PardesiK.R. Emerging strategies to combat ESKAPE pathogens in the era of antimicrobial resistance: A review.Front. Microbiol.20191053910.3389/fmicb.2019.00539 30988669
    [Google Scholar]
  3. MancusoG. MidiriA. GeraceE. BiondoC. Bacterial antibiotic resistance: The most critical pathogens.Pathogens20211010131010.3390/pathogens10101310 34684258
    [Google Scholar]
  4. MillerW.R. MurrayB.E. RiceL.B. AriasC.A. Resistance in vancomycin-resistant enterococci.Infect. Dis. Clin. North Am.202034475177110.1016/j.idc.2020.08.004 33131572
    [Google Scholar]
  5. OkwuM.U. OlleyM. AkpokaA.O. IzevbuwaO.E. Methicillin-resistant Staphylococcus aureus (MRSA) and anti-MRSA activities of extracts of some medicinal plants: A brief review.AIMS Microbiol.20195211713710.3934/microbiol.2019.2.117 31384707
    [Google Scholar]
  6. RubicZ. JelicM. SoprekS. Molecular characterization of colistin resistance genes in a high-risk ST101/KPC-2 clone of Klebsiella pneumoniae in a University Hospital of Split, Croatia.Int. Microbiol.202326363163710.1007/s10123‑023‑00327‑3 36683114
    [Google Scholar]
  7. DahalU. PaulK. GuptaS. The multifaceted genus Acinetobacter: From infection to bioremediation.J. Appl. Microbiol.20231348lxad14510.1093/jambio/lxad145 37442632
    [Google Scholar]
  8. MillerW.R. AriasC.A. ESKAPE pathogens: Antimicrobial resistance, epidemiology, clinical impact and therapeutics.Nat. Rev. Microbiol.2024221059861610.1038/s41579‑024‑01054‑w 38831030
    [Google Scholar]
  9. HsuA.J. TammaP.D. Treatment of multidrug-resistant gram-negative infections in children.Clin. Infect. Dis.201458101439144810.1093/cid/ciu069 24501388
    [Google Scholar]
  10. PatersonD.L. BonomoR.A. Extended-spectrum β-lactamases: A clinical update.Clin. Microbiol. Rev.200518465768610.1128/CMR.18.4.657‑686.2005 16223952
    [Google Scholar]
  11. GouJ. LiuN. GuoL. Carbapenem-resistant enterobacter hormaechei ST1103 with IMP-26 carbapenemase and ESBL gene blaSHV-178.Infect. Drug Resist.20201359760510.2147/IDR.S232514 32110070
    [Google Scholar]
  12. BotelhoJ. CazaresA. SchulenburgH. The ESKAPE mobilome contributes to the spread of antimicrobial resistance and CRISPR-mediated conflict between mobile genetic elements.Nucleic Acids Res.202351123625210.1093/nar/gkac1220 36610752
    [Google Scholar]
  13. PandeyD. SinghalN. KumarM. Investigating the OXA variants of ESKAPE pathogens.Antibiotics (Basel)20211012153910.3390/antibiotics10121539 34943751
    [Google Scholar]
  14. BotzmanM. MargalitH. Variation in global codon usage bias among prokaryotic organisms is associated with their lifestyles.Genome Biol.20111210R10910.1186/gb‑2011‑12‑10‑r109 22032172
    [Google Scholar]
  15. HartA. CortésM.P. LatorreM. MartinezS. Codon usage bias reveals genomic adaptations to environmental conditions in an acidophilic consortium.PLoS One2018135e019586910.1371/journal.pone.0195869 29742107
    [Google Scholar]
  16. BrandisG. HughesD. The selective advantage of synonymous codon usage bias in salmonella.PLoS Genet.2016123e100592610.1371/journal.pgen.1005926 26963725
    [Google Scholar]
  17. DehlingerB. JurssJ. LychukK. PutontiC. The dynamic codon Biaser: Calculating prokaryotic codon usage biases.Microb. Genom.202171000066310.1099/mgen.0.000663 34699346
    [Google Scholar]
  18. DasS. BombaywalaS. SrivastavaS. KapleyA. DhodapkarR. DafaleN.A. Genome plasticity as a paradigm of antibiotic resistance spread in ESKAPE pathogens.Environ. Sci. Pollut. Res. Int.20222927405074051910.1007/s11356‑022‑19840‑5 35349073
    [Google Scholar]
  19. PriyamvadaP. DebroyR. AnbarasuA. RamaiahS. A comprehensive review on genomics, systems biology and structural biology approaches for combating antimicrobial resistance in ESKAPE pathogens: Computational tools and recent advancements.World J. Microbiol. Biotechnol.202238915310.1007/s11274‑022‑03343‑z 35788443
    [Google Scholar]
  20. SahaU. GondiR. PatilA. SarojS.D. CRISPR in modulating antibiotic resistance of ESKAPE pathogens.Mol. Biotechnol.202365111610.1007/s12033‑022‑00543‑8 35939207
    [Google Scholar]
  21. CallensM. ScornavaccaC. BedhommeS. Evolutionary responses to codon usage of horizontally transferred genes in Pseudomonas aeruginosa: Gene retention, amelioration and compensatory evolution.Microb. Genom.20217600058710.1099/mgen.0.000587 34165421
    [Google Scholar]
  22. WestS.E.H. IglewskiB.H. Codon usage in Pseudomonas aeruginosa.Nucleic Acids Res.198816199323933510.1093/nar/16.19.9323 2845370
    [Google Scholar]
  23. QuaxT.E.F. ClaassensN.J. SöllD. van der OostJ. Codon bias as a means to fine-tune gene expression.Mol. Cell201559214916110.1016/j.molcel.2015.05.035 26186290
    [Google Scholar]
  24. SharpP.M. LiW.H. An evolutionary perspective on synonymous codon usage in unicellular organisms.J. Mol. Evol.1986241-2283810.1007/BF02099948 3104616
    [Google Scholar]
  25. BenteleK. SaffertP. RauscherR. IgnatovaZ. BlüthgenN. Efficient translation initiation dictates codon usage at gene start.Mol. Syst. Biol.20139167510.1038/msb.2013.32 23774758
    [Google Scholar]
  26. LiuH. LuY. LanB. XuJ. Codon usage by chloroplast gene is bias in Hemiptelea davidii.J. Genet.2020991810.1007/s12041‑019‑1167‑1 32089527
    [Google Scholar]
  27. SharpP.M. LiW.H. The codon adaptation index-a measure of directional synonymous codon usage bias, and its potential applications.Nucleic Acids Res.19871531281129510.1093/nar/15.3.1281 3547335
    [Google Scholar]
  28. dos ReisM. WernischL. SavvaR. Unexpected correlations between gene expression and codon usage bias from microarray data for the whole Escherichia coli K-12 genome.Nucleic Acids Res.200331236976698510.1093/nar/gkg897 14627830
    [Google Scholar]
  29. WrightF. The ‘effective number of codons’ used in a gene.Gene1990871232910.1016/0378‑1119(90)90491‑9 2110097
    [Google Scholar]
  30. ChakrabortyS. MazumderT.H. UddinA. Compositional dynamics and codon usage pattern of BRCA1 gene across nine mammalian species.Genomics2019111216717610.1016/j.ygeno.2018.01.013 29395657
    [Google Scholar]
  31. JiaX. LiuS. ZhengH. Non-uniqueness of factors constraint on the codon usage in Bombyx mori.BMC Genomics201516135610.1186/s12864‑015‑1596‑z 25943559
    [Google Scholar]
  32. ShenZ. GanZ. ZhangF. YiX. ZhangJ. WanX. Analysis of codon usage patterns in citrus based on coding sequence data.BMC Genomics202021S523410.1186/s12864‑020‑6641‑x 33327935
    [Google Scholar]
  33. SophiaraniY. ChakrabortyS. Comparison of compositional constraints: Nuclear genome vs plasmid genome of Pseudomonas syringae pv. tomato DC3000.J. Biosci.20224745710.1007/s12038‑022‑00296‑1 36222129
    [Google Scholar]
  34. GouyM. GautierC. Codon usage in bacteria: Correlation with gene expressivity.Nucleic Acids Res.198210227055707410.1093/nar/10.22.7055 6760125
    [Google Scholar]
  35. GathererD. McEwanN.R. Small regions of preferential codon usage and their effect on overall codon bias ‐ The case of the plp gene.IUBMB Life199743110711410.1080/15216549700203871 9315288
    [Google Scholar]
  36. SharmaA. GuptaS. PaulK. Codon usage behavior distinguishes pathogenic Clostridium species from the non-pathogenic species.Gene202387314739410.1016/j.gene.2023.147394 37137382
    [Google Scholar]
  37. XiaX. DAMBE7: New and improved tools for data analysis in molecular biology and evolution.Mol. Biol. Evol.20183561550155210.1093/molbev/msy073 29669107
    [Google Scholar]
  38. KariinS. BurgeC. Dinucleotide relative abundance extremes: A genomic signature.Trends Genet.199511728329010.1016/S0168‑9525(00)89076‑9 7482779
    [Google Scholar]
  39. KunecD. OsterriederN. Codon pair bias is a direct consequence of dinucleotide bias.Cell Rep.2016141556710.1016/j.celrep.2015.12.011 26725119
    [Google Scholar]
  40. BehuraS.K. SeversonD.W. Comparative analysis of codon usage bias and codon context patterns between dipteran and hymenopteran sequenced genomes.PLoS One201278e4311110.1371/journal.pone.0043111 22912801
    [Google Scholar]
  41. AroraP. MukhopadhyayC.S. KaurS. Comparative genome wise analysis of codon usage of Staphylococcus genus.Curr. Genet.20247011010.1007/s00294‑024‑01297‑3 39083100
    [Google Scholar]
  42. PatilA.B. DalviV.S. MishraA.A. KrishnaB. AzeezA. Analysis of synonymous codon usage bias and phylogeny of coat protein gene in banana bract mosaic virus isolates.Virusdisease201728215616310.1007/s13337‑017‑0380‑x 28770241
    [Google Scholar]
  43. KyteJ. DoolittleR.F. A simple method for displaying the hydropathic character of a protein.J. Mol. Biol.1982157110513210.1016/0022‑2836(82)90515‑0 7108955
    [Google Scholar]
  44. ChenY. ChenY.F. Analysis of synonymous codon usage patterns in duck hepatitis A virus: A comparison on the roles of mutual pressure and natural selection.Virusdisease201425328529310.1007/s13337‑014‑0191‑2 25674595
    [Google Scholar]
  45. SupekF. ŠkuncaN. ReparJ. VlahovičekK. ŠmucT. Translational selection is ubiquitous in prokaryotes.PLoS Genet.201066e100100410.1371/journal.pgen.1001004 20585573
    [Google Scholar]
  46. ChenS.L. LeeW. HottesA.K. ShapiroL. McAdamsH.H. Codon usage between genomes is constrained by genome-wide mutational processes.Proc. Natl. Acad. Sci. USA2004101103480348510.1073/pnas.0307827100 14990797
    [Google Scholar]
  47. HershbergR. PetrovD.A. General rules for optimal codon choice.PLoS Genet.200957e100055610.1371/journal.pgen.1000556 19593368
    [Google Scholar]
  48. DaugaC. Evolution of the gyrB gene and the molecular phylogeny of Enterobacteriaceae: A model molecule for molecular systematic studies.Int. J. Syst. Evol. Microbiol.200252253154710.1099/00207713‑52‑2‑531 11931166
    [Google Scholar]
  49. BortolaiaV. KaasR.S. RuppeE. ResFinder 4.0 for predictions of phenotypes from genotypes.J. Antimicrob. Chemother.202075123491350010.1093/jac/dkaa345 32780112
    [Google Scholar]
  50. KumarS. NeiM. DudleyJ. TamuraK. MEGA: A biologist-centric software for evolutionary analysis of DNA and protein sequences.Brief. Bioinform.20089429930610.1093/bib/bbn017 18417537
    [Google Scholar]
  51. GuptaS. PaulK. RoyA. Codon usage signatures in the genus Cryptococcus: A complex interplay of gene expression, translational selection and compositional bias.Genomics2021113182183010.1016/j.ygeno.2020.10.013 33096254
    [Google Scholar]
  52. TatsA. TensonT. RemmM. Preferred and avoided codon pairs in three domains of life.BMC Genomics20089146310.1186/1471‑2164‑9‑463 18842120
    [Google Scholar]
  53. TamuraK. StecherG. KumarS. MEGA11: Molecular evolutionary genetics analysis version 11.Mol. Biol. Evol.20213873022302710.1093/molbev/msab120 33892491
    [Google Scholar]
  54. ZhangY. ShenZ. MengX. Codon usage patterns across seven Rosales species.BMC Plant Biol.20222216510.1186/s12870‑022‑03450‑x 35123393
    [Google Scholar]
  55. Díaz-PérezA.L. Díaz-PérezC. Campos-GarcíaJ. Bacterial l-leucine catabolism as a source of secondary metabolites.Rev. Environ. Sci. Biotechnol.201615112910.1007/s11157‑015‑9385‑3
    [Google Scholar]
  56. YáñezM.A. CatalánV. ApráizD. FiguerasM.J. Martínez-MurciaA.J. Phylogenetic analysis of members of the genus Aeromonas based on gyrB gene sequences.Int. J. Syst. Evol. Microbiol.200353387588310.1099/ijs.0.02443‑0 12807216
    [Google Scholar]
  57. DebB. UddinA. ChakrabortyS. Analysis of codon usage of Horseshoe Bat Hepatitis B virus and its host.Virology2021561697910.1016/j.virol.2021.05.008 34171764
    [Google Scholar]
  58. RenX. PalmerL.D. Acinetobacter metabolism in infection and antimicrobial resistance.Infect. Immun.2023916e00433e2210.1128/iai.00433‑22 37191522
    [Google Scholar]
  59. MillerJ.B. McKinnonL.M. WhitingM.F. KauweJ.S.K. RidgeP.G. Codon pairs are phylogenetically conserved: A comprehensive analysis of codon pairing conservation across the tree of life.PLoS One2020155e023226010.1371/journal.pone.0232260 32401752
    [Google Scholar]
  60. MoonD.C. ChoiC.H. LeeS.M. Nuclear translocation of Acinetobacter baumannii transposase induces DNA methylation of CpG regions in the promoters of E-cadherin gene.PLoS One201276e3897410.1371/journal.pone.0038974 22685614
    [Google Scholar]
  61. BobetsisY.A. BarrosS.P. LinD.M. Bacterial infection promotes DNA hypermethylation.J. Dent. Res.200786216917410.1177/154405910708600212 17251518
    [Google Scholar]
  62. RobertsonK.D. DNA methylation and human disease.Nat. Rev. Genet.20056859761010.1038/nrg1655 16136652
    [Google Scholar]
  63. DuM.Z. ZhangC. WangH. LiuS. WeiW. GuoF.B. The GC content as a main factor shaping the amino acid usage during bacterial evolution process.Front. Microbiol.20189294810.3389/fmicb.2018.02948 30581420
    [Google Scholar]
  64. KhrustalevV.V. ArjomandzadeganM. BarkovskyE.V. TitovL.P. Low rates of synonymous mutations in sequences of Mycobacterium tuberculosis GyrA and KatG genes.Tuberculosis (Edinb.)201292433334410.1016/j.tube.2012.03.004 22521568
    [Google Scholar]
  65. TrivediR.R. CrooksJ.A. AuerG.K. Mechanical genomic studies reveal the role of d-alanine metabolism in Pseudomonas aeruginosa cell stiffness.MBio201895e01340e1810.1128/mBio.01340‑18 30206169
    [Google Scholar]
  66. AkashiH. GojoboriT. Metabolic efficiency and amino acid composition in the proteomes of Escherichia coli and Bacillus subtilis.Proc. Natl. Acad. Sci. USA20029963695370010.1073/pnas.062526999 11904428
    [Google Scholar]
  67. TakumiK. NonakaG. Bacterial cysteine-inducible cysteine resistance systems.J. Bacteriol.201619891384139210.1128/JB.01039‑15 26883827
    [Google Scholar]
  68. MisawaK. KikunoR.F. Relationship between amino acid composition and gene expression in the mouse genome.BMC Res. Notes2011412010.1186/1756‑0500‑4‑20 21272306
    [Google Scholar]
  69. NewmanZ.R. YoungJ.M. IngoliaN.T. BartonG.M. Differences in codon bias and GC content contribute to the balanced expression of TLR7 and TLR9.Proc. Natl. Acad. Sci. USA201611310E1362E137110.1073/pnas.1518976113 26903634
    [Google Scholar]
  70. BrooksL.E. Ul-HasanS. ChanB.K. SistromM.J. Quantifying the evolutionary conservation of genes encoding multidrug efflux pumps in the ESKAPE pathogens to identify antimicrobial drug targets.mSystems201833e00024e1810.1128/mSystems.00024‑18 29719870
    [Google Scholar]
  71. BoulocP. VinellaD. D’AriR. Leucine and serine induce mecillinam resistance in Escherichia coli.Mol. Gen. Genet.19922352-324224610.1007/BF00279366 1465098
    [Google Scholar]
  72. IdreesM. MohammadA.R. KarodiaN. RahmanA. Multimodal role of amino acids in microbial control and drug development.Antibiotics (Basel)20209633010.3390/antibiotics9060330 32560458
    [Google Scholar]
  73. MuteebG. RehmanM.T. ShahwanM. AatifM. Origin of antibiotics and antibiotic resistance, and their impacts on drug development: A narrative review.Pharmaceuticals (Basel)20231611161510.3390/ph16111615 38004480
    [Google Scholar]
  74. MauroV.P. ChappellS.A. A critical analysis of codon optimization in human therapeutics.Trends Mol. Med.2014201160461310.1016/j.molmed.2014.09.003 25263172
    [Google Scholar]
  75. HaasJ. ParkE.C. SeedB. Codon usage limitation in the expression of HIV-1 envelope glycoprotein.Curr. Biol.19966331532410.1016/S0960‑9822(02)00482‑7 8805248
    [Google Scholar]
  76. LiuC. ChangY. XuY. Distribution of virulence-associated genes and antimicrobial susceptibility in clinical Acinetobacter baumannii isolates.Oncotarget2018931216632167310.18632/oncotarget.24651 29774093
    [Google Scholar]
  77. ParvathyS.T. UdayasuriyanV. BhadanaV. Codon usage bias.Mol. Biol. Rep.202249153956510.1007/s11033‑021‑06749‑4 34822069
    [Google Scholar]
/content/journals/cppm/10.2174/0118756921344687241015063919
Loading
/content/journals/cppm/10.2174/0118756921344687241015063919
Loading

Data & Media loading...

Supplements

Supplementary material is available on the publisher’s website along with the published article.

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test