GNAT toxin may have a potential role in Pseudomonas aeruginosa persistence: an in vitro and in silico study

Papers of special note have been highlighted as: • of interest; •• of considerable interest

References

• 1. Tuon FF, Dantas LR, Suss PH, Tasca Ribeiro VS. Pathogenesis of the Pseudomonas aeruginosa biofilm: a review. Pathogens 11(3), 300 (2022). • This article thoroughly discusses the role of biofilm in the pathogenicity of Pseudomonas aeruginosa.
  Medline CASGoogle Scholar
• 2. Rad ZR, Rad ZR, Goudarzi H et al. Detection of New Delhi metallo-β-lactamase-1 among Pseudomonas aeruginosa isolated from adult and pediatric patients in Iranian hospitals. Gene Rep. 23, 101152 (2021).
  CASGoogle Scholar
• 3. Golmoradi Zadeh R, Mirshekar M, Sadeghi Kalani B, Pourghader J, Barati M, Masjedian Jazi F. The expression of type II TA system genes following persister cell formation in Pseudomonas aeruginosa isolates in the exponential and stationary phases. Arch. Microbiol. 204(8), 451 (2022). • This article provides persister cell assay in Pseudomonas aeruginosa.
  MedlineGoogle Scholar
• 4. Fisher RA, Gollan B, Helaine S. Persistent bacterial infections and persister cells. Nat. Rev. Microbiol. 15(8), 453–464 (2017).
  Medline CASGoogle Scholar
• 5. Zadeh RG, Kalani BS, Ari MM, Talebi M, Razavi S, Jazi FM. Isolation of persister cells within the biofilm and relative gene expression analysis of type II toxin/antitoxin system in Pseudomonas aeruginosa isolates in exponential and stationary phases. J. Glob. Antimicrob. Resist 28, 30–37 (2022). • This article provides persister cell assay in Pseudomonas aeruginosa.
  Medline CASGoogle Scholar
• 6. Cao Z, Chen X, Chen J et al. Gold nanocluster adjuvant enables the eradication of persister cells by antibiotics and abolishes the emergence of resistance. Nanoscale 14(28), 10016–10032 (2022).
  Medline CASGoogle Scholar
• 7. Amraei F, Narimisa N, Lohrasbi V, Jazi FM. Persister cells formation and expression of type II toxin–antitoxin system genes in Brucella melitensis (16M) and Brucella abortus (B19). Iran J. Pathol. 15(2), 127 (2020).
  MedlineGoogle Scholar
• 8. Shivaee A, Mohammadzadeh R, Shahbazi S, Pardakhtchi E, Ohadi E, Kalani BS. Time-variable expression levels of mazF, atlE, sdrH, and bap genes during biofilm formation in Staphylococcus epidermidis. Acta Microbiol. Immunol. Hung. 66(4), 499–508 (2019).
  Medline CASGoogle Scholar
• 9. Talwar S, Pandey M, Sharma C et al. Role of VapBC12 toxin–antitoxin locus in cholesterol-induced mycobacterial persistence. MSystems 5(6), e00855-20 (2020).
  MedlineGoogle Scholar
• 10. Singh G, Yadav M, Ghosh C, Rathore JS. Bacterial toxin–antitoxin modules: classification, functions, and association with persistence. Curr. Res. Microb. Sci. 2, 100047 (2021).
  Medline CASGoogle Scholar
• 11. Jurėnas D, Fraikin N, Goormaghtigh F, Van Melderen L. Biology and evolution of bacterial toxin–antitoxin systems. Nat. Rev. Microbiol. 20(6), 335–350 (2022).
  Medline CASGoogle Scholar
• 12. Narimisa N, Amraei F, Kalani BS, Azarnezhad A, Jazi FM. Biofilm establishment, biofilm persister cell formation, and relative gene expression analysis of type II toxin–antitoxin system in Klebsiella pneumoniae. Gene Rep. 21, 100846 (2020). • This article provides persister cell assay.
  CASGoogle Scholar
• 13. Baek MS, Chung ES, Jung DS, Ko KS. Effect of colistin-based antibiotic combinations on the eradication of persister cells in Pseudomonas aeruginosa. J. Antimicrob. Chemother. 75(4), 917–924 (2020).
  Medline CASGoogle Scholar
• 14. Zelenitsky SA, Harding GK, Sun S, Ubhi K, Ariano RE. Treatment and outcome of Pseudomonas aeruginosa bacteraemia: an antibiotic pharmacodynamic analysis. J. Antimicrob. Chemother. 52(4), 668–674 (2003).
  Medline CASGoogle Scholar
• 15. Lenoir G, Antypkin YG, Miano A et al. Efficacy, safety, and local pharmacokinetics of highly concentrated nebulized tobramycin in patients with cystic fibrosis colonized with Pseudomonas aeruginosa. Paediatr. Drugs 9, 11–20 (2007).
  MedlineGoogle Scholar
• 16. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2^-ΔΔCt method. Methods 25(4), 402–408 (2001). • This article provides real-time PCR analysis method.
  Medline CASGoogle Scholar
• 17. Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJ. The Phyre2 web portal for protein modeling, prediction and analysis. Nat. Protoc. 10(6), 845–858 (2015).
  Medline CASGoogle Scholar
• 18. Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35(6), 1547 (2018).
  Medline CASGoogle Scholar
• 19. Ko J, Park H, Heo L, Seok C. GalaxyWEB server for protein structure prediction and refinement. Nucleic Acids Res. 40(W1), W294–W297 (2012).
  Medline CASGoogle Scholar
• 20. Kozakov D, Hall DR, Xia B et al. The ClusPro web server for protein–protein docking. Nat. Protoc. 12(2), 255–278 (2017).
  Medline CASGoogle Scholar
• 21. Laskowski RA, Swindells MB. LigPlot+: multiple ligand–protein interaction diagrams for drug discovery. J. Chem. Inf. Model 51(10), 2778–2786 (2011).
  Medline CASGoogle Scholar
• 22. Sedan Y, Marcu O, Lyskov S, Schueler-Furman O. Peptiderive server: derive peptide inhibitors from protein–protein interactions. Nucleic Acids Res. 44(W1), W536–W541 (2016). • This article provides peptide design for inhibition of protein–protein interaction.
  Medline CASGoogle Scholar
• 23. Lamiable A, Thévenet P, Rey J, Vavrusa M, Derreumaux P, Tufféry P. PEP-FOLD3: faster de novo structure prediction for linear peptides in solution and in complex. Nucleic Acids Res. 44(W1), W449–W454 (2016).
  Medline CASGoogle Scholar
• 24. Delano WL. PyMOL: an open-source molecular graphics tool. CCP4 Newsl. Protein Crystallogr. 40(1), 82–92 (2002).
  Google Scholar
• 25. Wallace AC, Laskowski RA, Thornton JM. LIGPLOT: a program to generate schematic diagrams of protein–ligand interactions. Protein Eng. Des. Sel. 8(2), 127–134 (1995).
  CASGoogle Scholar
• 26. Harrison JJ, Turner RJ, Ceri H. Persister cells, the biofilm matrix and tolerance to metal cations in biofilm and planktonic Pseudomonas aeruginosa. Environ. Microbiol. 7(7), 981–994 (2005).
  Medline CASGoogle Scholar
• 27. Kamruzzaman M, Wu AY, Iredell JR. Biological functions of type II toxin–antitoxin systems in bacteria. Microorganisms 9(6), 1276 (2021).
  Medline CASGoogle Scholar
• 28. Page R, Peti W. Toxin–antitoxin systems in bacterial growth arrest and persistence. Nat. Chem. Biol. 12(4), 208–214 (2016).
  Medline CASGoogle Scholar
• 29. Wen Y, Behiels E, Devreese B. Toxin–antitoxin systems: their role in persistence, biofilm formation, and pathogenicity. Pathog. Dis. 70(3), 240–249 (2014). •• This article comprehensively discusses the role of toxin–antitoxin systems in bacterial persistence.
  Medline CASGoogle Scholar
• 30. Yeo CC. GNAT toxins of bacterial toxin–antitoxin systems: acetylation of charged tRNAs to inhibit translation. Mol. Microbiol. 108(4), 331–335 (2018). • This article discusses the role of GNAT toxin in bacterial persistence.
  Medline CASGoogle Scholar
• 31. Wójcik P, Berlicki Ł. Peptide-based inhibitors of protein–protein interactions. Bioorganic Med. Chem. Lett. 26(3), 707–713 (2016).
  Medline CASGoogle Scholar
• 32. Wang X, Ni D, Liu Y, Lu S. Rational design of peptide-based inhibitors disrupting protein–protein interactions. Front. Chem. 9, 682675 (2021).
  Medline CASGoogle Scholar
• 33. Bojadzic D, Alcazar O, Chen J et al. Small-molecule inhibitors of the coronavirus spike: ACE2 protein–protein interaction as blockers of viral attachment and entry for SARS-CoV-2. ACS Infect. Dis. 7(6), 1519–1534 (2021).
  Medline CASGoogle Scholar
• 34. Li B, Rong D, Wang Y. Targeting protein–protein interaction with covalent small-molecule inhibitors. Curr. Top Med. Chem. 19(21), 1872–1876 (2019).
  Medline CASGoogle Scholar
• 35. Sundar S, Rajan MP, Piramanayagam S. In silico derived peptides for inhibiting the toxin–antitoxin systems of Mycobacterium tuberculosis: basis for developing peptide-based therapeutics. Int. J. Pept. Res. 25, 1467–1475 (2019). •• This article illustrates peptide design for inhibition of the toxin–antitoxin system.
  CASGoogle Scholar
• 36. Mohammadzadeh R, Shivaee A, Ohadi E, Kalani BS. In silico insight into the dominant type II toxin–antitoxin systems and Clp proteases in Listeria monocytogenes and designation of derived peptides as a novel approach to interfere with this system. Int. J. Pept. Res. 26, 613–623 (2020). •• This article illustrates peptide design for inhibition of the toxin–antitoxin system.
  CASGoogle Scholar
• 37. Kang S-M, Moon H, Han S-W, Kim D-H, Kim BM, Lee B-J. Structure-based de novo design of Mycobacterium tuberculosis VapC-activating stapled peptides. ACS Chem. Biol. 15(9), 2493–2498 (2020).
  Medline CASGoogle Scholar