In Silico Analysis of Extremophilic Bacteria Isolated from Food-Service Environments in Lahore
Main Article Content
Abstract
Background: Food-service surfaces in dense urban settings act as reservoirs for Enterobacteriaceae, and routine alkaline sanitation may select for pH-tolerant organisms. Enterobacter bugandensis has emerged as a multidrug-resistant pathogen, yet its recovery from food-contact surfaces in South Asian settings remains poorly characterised. Objective: To isolate alkali-tolerant bacteria from high-contact dining surfaces in Lahore, Pakistan, and computationally evaluate the haemolysin-coregulated protein (Hcp) of the Type VI secretion system and the heme transporter ChuA as candidate inhibitor targets. Methods: In this cross-sectional laboratory-based study, sterile swabs were collected from high-contact surfaces in thirty restaurants. Isolates were recovered on nutrient agar, screened across pH 7–10, and characterised by staining, biochemical profiling, and bidirectional 16S rRNA Sanger sequencing. Phylogeny was inferred in MEGA X. Hcp and ChuA were modelled by SWISS-MODEL and validated by Ramachandran and MolProbity analysis. Binding pockets were predicted by PrankWeb. Three matrix metalloprotease inhibitors were docked into Hcp and five heme analogues into ChuA using CB-Dock2 (AutoDock Vina), ranked by predicted binding energy. Results: A dominant alkali-tolerant Gram-negative bacillus grew sustainably at pH 10. Biochemical profile matched Enterobacter; 16S rRNA BLASTn returned closest identity to Enterobacter bugandensis (96.12%). The Hcp model achieved GMQE 0.92 with 98.39% favoured Ramachandran residues. Ilomastat returned the most favourable Hcp binding energy (–6.3 kcal/mol), engaging R26, D25, and Y99. Mn(III) and Ru(III) protoporphyrin IX both yielded –7.4 kcal/mol against ChuA, though template and ligand-identity limitations restrict interpretation. Conclusion: An alkali-tolerant Enterobacter closely related to E. bugandensis persisted on routinely cleaned dining surfaces, indicating that conventional sanitation may inadequately eliminate clinically relevant Enterobacteriaceae. Ilomastat emerged as a candidate Hcp inhibitor; ChuA findings require re-validation. Routine molecular surveillance of food-contact surfaces is warranted.
Article Details
Section

This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
References
1. Doijad SP, Imirzalioglu C, Yao Y, Pati NB, Falgenhauer L, Hain T, et al. Enterobacter bugandensis sp. nov., isolated from neonatal blood. Int J Syst Evol Microbiol. 2016;66(2):968–74.
2. Kämpfer P, Sing A, Kulkarni GR, Scholz HC, Busse HJ, Spröer C. Enterobacter bugandensis sp. nov., isolated from blood cultures of patients from Tanzania. Int J Syst Evol Microbiol. 2016;66(3):968–74.
3. Falgenhauer J, Imirzalioglu C, Falgenhauer L, Yao Y, Hauri AM, Erath B, et al. Whole-genome sequences of clinical Enterobacter bugandensis isolates from Germany. Microbiol Resour Announc. 2019;8(29):e00465-19.
4. Singh NK, Bezdan D, Checinska Sielaff A, Wheeler K, Mason CE, Venkateswaran K. Multi-drug resistant Enterobacter bugandensis species isolated from the International Space Station and comparative genomic analyses with human pathogenic strains. BMC Microbiol. 2018;18(1):175.
5. Igbinosa IH, M'Zali FH, Igbinosa EO. Antibiotic resistance, virulence genes, and biofilm formation in Enterobacter species from hospital environments in Nigeria. BMC Microbiol. 2017;17:146.*
6. Kargar S, Parekh S. A study of siderophore production from Enterobacter bugandensis R1 and effect of abiotic stress on it. Int J Adv Life Sci. 2018;11(1):1–10.*
7. Coelho AÍM, Milagres RCRM, Martins JDF, Azeredo RMC, Santana ÂMC. Microbiological contamination of environments and surfaces at commercial restaurants. Cienc Saude Coletiva. 2010;15(Suppl 1):1597–606.
8. Aycicek H, Oguz U, Karci K. Comparison of results of ATP bioluminescence and traditional hygiene swabbing methods for the determination of surface cleanliness at a hospital kitchen. Int J Hyg Environ Health. 2006;209(2):203–6.
9. Bolton DJ, Meally A, McDowell D, Blair IS. A survey for serotyping, antibiotic resistance profiling and PFGE characterization of Salmonella isolates from restaurants. J Appl Microbiol. 2007;103(5):1681–90.
10. Angulo FJ, Jones TF. Eating in restaurants: a risk factor for foodborne disease? Clin Infect Dis. 2006;43(10):1324–8.
11. Horikoshi K. Alkaliphiles: some applications of their products for biotechnology. Microbiol Mol Biol Rev. 1999;63(4):735–50.
12. Bergdoll MS. Staphylococcus aureus. In: Doyle MP, editor. Foodborne bacterial pathogens. New York: Marcel Dekker; 1989. p. 463–523.
13. Ghosh S, Chakraborty A, Das M. Microbial contamination of surfaces in food-service environments: a case study of cafés in Kolkata. Int J Environ Health Res. 2017;27(4):314–24.*
14. Joshi SB, Sharma P, Reddy NR. Bacterial contamination of dining tables in cafés and restaurants: a study from urban India. Int J Environ Sci Technol. 2018;15(7):1543–52.*
15. Khan MZ, Ali S. Bacterial contamination in food-service establishments: implications for public health. Foodborne Pathog Dis. 2021;18(3):214–22.*
16. Mougous JD, Cuff ME, Raunser S, Shen A, Zhou M, Gifford CA, et al. A virulence locus of Pseudomonas aeruginosa encodes a protein secretion apparatus. Science. 2006;312(5779):1526–30.
17. Galardy RE, Cassabonne ME, Giese C, Gilbert JH, Lapierre F, Lopez H, et al. Low molecular weight inhibitors in corneal ulceration. Ann N Y Acad Sci. 1994;732:315–23.
18. Cobessi D, Meksem A, Brillet K. Structure of the heme/hemoglobin outer membrane receptor ShuA from Shigella dysenteriae: heme binding by an induced fit mechanism. Proteins. 2010;78(2):286–94.
19. Wandersman C, Delepelaire P. Bacterial iron sources: from siderophores to hemophores. Annu Rev Microbiol. 2004;58:611–47.
20. Kaper JB, Nataro JP, Mobley HLT. Pathogenic Escherichia coli. Nat Rev Microbiol. 2004;2(2):123–40.
21. Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30(9):1312–3.
22. Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, Gumienny R, et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 2018;46(W1):W296–303.
23. Liu Y, Yang X, Gan J, Chen S, Xiao ZX, Cao Y. CB-Dock2: improved protein-ligand blind docking by integrating cavity detection, docking and homologous template fitting. Nucleic Acids Res. 2022;50(W1):W159–64.
24. Jakubec D, Skoda P, Krivak R, Novotny M, Hoksza D. PrankWeb 3: accelerated ligand-binding site predictions for experimental and modelled protein structures. Nucleic Acids Res. 2022;50(W1):W593–7.
25. Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596(7873):583–9.
26. Yang J, Yan R, Roy A, Xu D, Poisson J, Zhang Y. The I-TASSER suite: protein structure and function prediction. Nat Methods. 2015;12(1):7–8.
27. Tamura K, Stecher G, Kumar S. MEGA11: Molecular Evolutionary Genetics Analysis version 11. Mol Biol Evol. 2021;38(7):3022–7.
28. Yarza P, Yilmaz P, Pruesse E, Glöckner FO, Ludwig W, Schleifer KH, et al. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat Rev Microbiol. 2014;12(9):635–45.