Integrated Phenotypic and Molecular Profiling Reveals Strain-Specific Susceptibility to Novel Antimicrobial Agents in Clinical Isolates

The rise of multiple drug resistant (MDR) bacteria requires a shift from conventional diagnostics to integrated methods that can help explain unpredictable strain-specific antimicrobial responses. This study employed a multi-method characterization strategy of clinical isolates to investigate divergent susceptibility to novel agents. Clinical isolates of Enterococcus faecalis, Bacillus cereus, and two Proteus mirabilis strains were obtained from Sacred Heart Hospital, Abeokuta. Comprehensive profiling included biochemical identification, 16S rRNA gene sequencing, phylogenetic reconstruction, and restriction enzyme analysis. Susceptibility to Nigella sativa seed oil (NSO), biosynthesized silver nanoparticles (AgNPs), and silver nitrate was evaluated using broth micro-dilution and agar well diffusion assays. Phylogenetic analysis confirmed that the two P. mirabilis strains were closely related though restriction enzyme mapping revealed distinct, strain-specific molecular fingerprints. Antimicrobial susceptibility testing showed significant variation. E. faecalis was most susceptible to AgNPs, while P. mirabilis FELIX004 was completely resistant to AgNPs even though susceptible to both NSO and silver nitrate, a paradoxical profile not seen in the closely related susceptible strain FELIX003. Comparative analysis is suggestive of an association between unique restriction patterns in FELIX004 and its AgNP-resistant phenotype. Integrative phenotypic and molecular profiling uncovered substantial intraspecies variation in novel antimicrobial susceptibility testing. The specific paradoxical resistance of P. mirabilis FELIX004 to AgNPs underscores the emergence of agent-specific resistance mechanisms. The study demonstrates that standard phylogenetic relatedness is insufficient to predict strain-specific behavior and highlights the value of multiple approaches for identifying phenotypic outliers that warrant deeper genomic investigation.

Clinical isolates; Integrative profiling; 16S rRNA sequencing; Restriction enzyme mapping; Phylogenetic analysis

1. Arya, S. S., & Mishra, S. K. (2023). Phylogenetic markers fail to predict bacterial susceptibility to green-synthesized metal nanoparticles: Evidence from Enterobacteriaceae clinical isolates. Microbial Pathogenesis, 174, 105912. [Google Scholar] [Crossref]

2. Balouiri, M., Sadiki, M., & Ibnsouda, S. K. (2021). Methods for in vitro evaluating antimicrobial activity: A review. *Journal of Pharmaceutical Analysis, 11(2), 133-140. [Google Scholar] [Crossref]

3. Bankier, C., Cheong, Y., Mahalingam, S., Edirisinghe, M., Ren, G., Cloutman-Green, E., & Ciric, L. (2021). A comparison of methods to determine the antimicrobial activity of nanoparticle formulations against clinical bacterial isolates. Journal of Applied Microbiology, 130(6), 1839–1851. [Google Scholar] [Crossref]

4. Břinda, K., Callendrello, A., Ma, K. C., et al. (2020). Rapid inference of antibiotic resistance and susceptibility by genomic neighbour typing. Nature Microbiology, 5(3), 455–464. [Google Scholar] [Crossref]

5. Cantor, C. R., & Schimmel, P. R. (1980). Biophysical Chemistry: Part II: Techniques for the Study of Biological Structure and Function. W. H. Freeman. [Google Scholar] [Crossref]

6. Clinical and Laboratory Standards Institute. (2018). Performance Standards for Antimicrobial Susceptibility Testing (28th ed.). CLSI supplement M100. [Google Scholar] [Crossref]

7. Clinical and Laboratory Standards Institute (CLSI). (2023). *M100-Ed33: Performance Standards for Antimicrobial Susceptibility Testing.* 33rd ed. Wayne, PA: Clinical and Laboratory Standards Institute. [Google Scholar] [Crossref]

8. Cruz-Córdova, A., Flores-Oropeza, M. A., Arellano-Galindo, J., Hernández-Castro, R., Ochoa, S. A., González-Pedrajo, B., Xicohtencatl-Cortes, J., & Hernández-Chiñas, U. (2022). Genomic rearrangements and chromosomal inversions in Proteus mirabilis clinical isolates: Impact on restriction patterns and virulence. Frontiers in Microbiology, 13, 872345. https://doi.org/10.3389/fmicb.2022.872345 [Google Scholar] [Crossref]

9. Drzewiecka, D. (2016). Significance and roles of Proteus spp. bacteria in natural environments. Microbial Ecology, 72(4), 741–758. [Google Scholar] [Crossref]

10. Du, D., Wang-Kan, X., Neuberger, A., van Veen, H. W., Pos, K. M., Piddock, L. J., & Luisi, B. F. (2018). Multidrug efflux pumps: structure, function and regulation. Nature Reviews Microbiology, 16(9), 523–539. [Google Scholar] [Crossref]

11. Dwyer, D. J., Belenky, P. A., Yang, J. H., et al. (2014). Antibiotics induce redox-related physiological alterations as part of their lethality. Proceedings of the National Academy of Sciences, 111(20), E2100–E2109. [Google Scholar] [Crossref]

12. El-Batal, A. I., El-Sayyad, G. S., El-Ghamery, A. A., & Gobara, M. (2020). Response of Proteus mirabilis to biosynthesized silver nanoparticles using Nigella sativa seed extract: Susceptibility and resistance mechanisms. Journal of Environmental Chemical Engineering, 8(5), 104377. [Google Scholar] [Crossref]

13. DOI: 10.1016/j.jece.2020.104377 [Google Scholar] [Crossref]

14. Ellington, M. J., Ekelund, O., Aarestrup, F. M., Canton, R., Doumith, M., et al.(2017). The role of whole genome sequencing in antimicrobial susceptibility testing of bacteria: Report from the EUCAST Subcommittee. Clinical Microbiology and Infection, 23(1), 2–22. [Google Scholar] [Crossref]

15. El-Maati, M. F., Labib, S. M., Al-Gaby, A. M., & Ramadan, M. F. (2012). Functional properties and antimicrobial activity of Nigella sativa L. oil extracted by different methods. Industrial Crops and Products, 187, 115404. [Google Scholar] [Crossref]

16. Forbes, B. A., Sahm, D. F., & Weissfeld, A. S. (2022). Bailey & Scott’s Diagnostic Microbiology (15th ed.). Elsevier. [Google Scholar] [Crossref]

17. Gao, Y., Chen, Y., Cao, Y., Mo, A., & Peng, Q. (2023). Precision targeting of multidrug-resistant bacteria via antimicrobial nanoparticle surface functionalization. Advanced Materials, 35(18), 2207902. [Google Scholar] [Crossref]

18. Green, M. R., & Sambrook, J. (2020). Analysis of DNA by agarose gel electrophoresis. In Molecular Cloning: A Laboratory Manual (5th ed., Vol. 2, pp. 5.2-5.14). Cold Spring Harbor Laboratory Press. [Google Scholar] [Crossref]

19. Hirose, Y., Sato, K., Kanda, H., Endo, S., & Matsuoka, M. (2022). Comparative genomic and restriction fragment length polymorphism analysis of Proteus mirabilis isolates reveals cryptic diversity within the same multilocus sequence typing clade. Journal of Medical Microbiology, 71(4), 001512. https://doi.org/10.1099/jmm.0.001512 [Google Scholar] [Crossref]

20. Humphries, R., Bobenchik, A. M., Hindler, J. A., & Schuetz, A. N. (2021). Overview of changes to the Clinical and Laboratory Standards Institute Performance Standards for Antimicrobial Susceptibility Testing, M100, 31st Edition. Journal of Clinical Microbiology, 59(12), e0021321. [Google Scholar] [Crossref]

21. Janda, J. M., & Abbott, S. L. (2002). Bacterial identification for publication: when is enough enough? Journal of Clinical Microbiology, 40(6), 1887–1891. [Google Scholar] [Crossref]

22. Janda, J. M., & Abbott, S. L. (2022). Bacterial identification for publication: When is enough enough? An update. *Journal of Clinical Microbiology, 60(8), e00273-22. [Google Scholar] [Crossref]

23. Johnson, J. S., Spakowicz, D. J., Hong, B. Y., et al. (2019). Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nature Communications, 10, 5029. [Google Scholar] [Crossref]

24. Jones, B. V., Young, R., Mahenthiralingam, E., & Stickler, D. J. (2004). Ultrastructure of Proteus mirabilis swarmer cell rafts and role of swarming in catheter-associated urinary tract infection. Infection and Immunity, 72(7), 3941–3950. [Google Scholar] [Crossref]

25. Khan, M., Al-Marri, A. H., & Khan, M. (2021). Nigella sativa seed extract mediated synthesis of silver nanoparticles and their antimicrobial activity. Arabian Journal of Chemistry, 14(5),103122. [Google Scholar] [Crossref]

26. Kumar, S., Stecher, G., Li, M., Knyaz, C., & Tamura, K. (2018). MEGA X: Molecular Evolutionary Genetics Analysis across computing platforms. Molecular Biology and Evolution, 35(6), 1547–1549. [Google Scholar] [Crossref]

27. Kumar, S., Stecher, G., Li, M., Knyaz, C., & Tamura, K. (2021). MEGA X: Molecular Evolutionary Genetics Analysis across computing platforms. Molecular Biology and Evolution, 38(8), 3022-3027. [Google Scholar] [Crossref]

28. Kvitek, L., Panáček, A., Prucek, R., Soukupova, J., Vanickova, L., Kolar, M., & Zboril, R. (2020). Antibacterial activity of silver nanoparticles against multidrug-resistant Proteus mirabilis: Role of bacterial surface structures and restriction-modification systems. International Journal of Molecular Sciences, 21(15), 5361. https://doi.org/10.3390/ijms21155361 [Google Scholar] [Crossref]

29. Lopansri, B. K., & Bhavsar, S. M. (2022). Specimen collection and processing in clinical microbiology. In Manual of Clinical Microbiology (13th ed., pp. 35-52). ASM Press. [Google Scholar] [Crossref]

30. López, C. A., Miller, B. M., Rivera-Chávez, F., et al. (2021). Virulence factors enhance Citrobacter rodentium expansion through aerobic respiration. Science, 373(6554), eabe6526. [Google Scholar] [Crossref]

31. Mathers, A. J., Stoesser, N., Chai, W., et al. (2023). Molecular dissection of an outbreak of carbapenem-resistant Enterobacteriaceae reveals intergenus transmission of a plasmid encoding blaKPC-2. Journal of Clinical Microbiology, 61(2), e01338-22. [Google Scholar] [Crossref]

32. McDonald, J. H. (2023). Handbook of Biological Statistics (4th ed.). Sparky House Publishing [Google Scholar] [Crossref]

33. Mount, D. W. (2023). Bioinformatics: Sequence and Genome Analysis (3rd ed.). Cold Spring Harbor Laboratory Press. [Google Scholar] [Crossref]

34. Murray, C. J., Ikuta, K. S., Sharara, F., et al. (2022). Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. The Lancet, 399(10325), 629–655. [Google Scholar] [Crossref]

35. Muteeb, G., Rehman, M. T., Shahwan, M., & Aatif, M. (2023). Origin of antibiotics and antibiotic resistance, and their impacts on drug development: A narrative review. Pharmaceuticals, 16(11), 1615. [Google Scholar] [Crossref]

36. O’Hara, C. M., Brenner, F. W., & Miller, J. M. (2000). Classification, identification, and clinical significance of Proteus, Providencia, and Morganella. Clinical Microbiology Reviews, 13(4), 534–546. [Google Scholar] [Crossref]

37. Olive, D. M., & Bean, P. (1999). Principles and applications of methods for DNA-based typing of microbial organisms. Journal of Clinical Microbiology, 37(6), 1661–1669.. [Google Scholar] [Crossref]

38. Panáček, A., Kvítek, L., Smékalová, M., Večeřová, R., Kolář, M., Röderová, M., Dyčka, F., Šebela, M., Prucek, R., Tomanec, O., & Zbořil, R. (2018). Bacterial resistance to silver nanoparticles and how to overcome it. Nature Nanotechnology, 13(1), 65–71. [Google Scholar] [Crossref]

39. Partridge, S. R., Kwong, S. M., Firth, N., & Jensen, S. O. (2018). Mobile genetic elements associated with antimicrobial resistance. Clinical Microbiology Reviews, 31(4), e00088-17. [Google Scholar] [Crossref]

40. Qais, F. A., Ahmad, I., & Husain, F. M. (2021). Strain-specific variability in bacterial susceptibility to biogenic silver nanoparticles: Implications for clinical translation. Journal of Drug Delivery Science and Technology, 61, 102289. [Google Scholar] [Crossref]

41. Sambrook, J., & Russell, D. W. (2022). Precipitation and concentration of nucleic acids. In Molecular Cloning: A Laboratory Manual (5th ed., Vol. 1, pp. 6.1-6.62). Cold Spring Harbor Laboratory Press. [Google Scholar] [Crossref]

42. Sanger, F., Nicklen, S., & Coulson, A. R. (1977). DNA sequencing with chain-terminating inhibitors. *Proceedings of the National Academy of Sciences, 74(12), 5463-5467. [Google Scholar] [Crossref]

43. Schaffer, J. N., & Pearson, M. M. (2017). Proteus mirabilis and urinary tract infections. Microbiology Spectrum, 5(3). [Google Scholar] [Crossref]

44. Slavin, Y. N., Asnis, J., Häfeli, U. O., & Bach, H. (2022). Metal nanoparticles: Understanding the mechanisms behind antibacterial activity. *Journal of Nanobiotechnology, 20(1), 1-32. [Google Scholar] [Crossref]

45. Vincze, T., Posfai, J., & Roberts, R. J. (2003). NEBcutter: A program to cleave DNA with restriction enzymes. Nucleic Acids Research, 31(13), 3688–3691. [Google Scholar] [Crossref]

46. Wang, L., Hu, C., & Shao, L. (2017). The antimicrobial activity of nanoparticles: present situation and prospects for the future. International Journal of Nanomedicine, 12, 1227–1249. [Google Scholar] [Crossref]

47. Weisburg, W. G., Barns, S. M., Pelletier, D. A., & Lane, D. J. (1991). 16S ribosomal DNA amplification for phylogenetic study. *Journal of Bacteriology, 173(2), 697-703. [Google Scholar] [Crossref]

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