Use of Sanger Sequencing in Etiological Diagnostics of Bacterial Complications in Hospital Environment

Background. The precise identification of microorganism species that cause infectious complications in hospitalized patients is beyond doubt relevant in modern healthcare. The aim of this study was to determine the possibility of using Sanger sequencing in routine microbiological examination of patients in an Internal Medicine Clinic to improve the quality of etiological diagnosis of bacterial complications. Material and methods. Clinical isolates of microorganisms isolated from patients of a multidisciplinary medical center were studied. The study used classical microbiological methods of seeding and identification of cultures, as well as Sanger sequencing. Results. Sanger identification using the MicroSeq system (Applied Biosystems, USA) ensured identification of all 231 studied bacterial isolates – causative agents of nosocomial infections. For differential diagnostics of streptococci and coagulase-negative staphylococci, in some cases, when the known sequences of the first 500 nucleotides of the 16S rRNA gene of two species differed by 1–2 nucleotides, increasing the discrimination level of species identification to 100% allowed valid determination of the species affiliation of the studied microorganism. Phenotypic methods failed to identify a significant proportion of species (25.9%) of nosocomial infection pathogens, and only 8 (13.8%) of them were reliably identified in all cases. The use of Sanger sequencing to identify bacteria led to a long-term effect associated with improved qualifications of laboratory doctors, and enhanced discriminatory capabilities of visual assessment of the macromorphology of bacterial cultures, which is important for identifying all types of microorganisms present in biosubstrates. Conclusion. The Sanger sequencing method is highly efficient and quite cost effective, compared to the biochemical test panels widely used in clinical practice — the «gold standard» method in the etiological diagnosis of bacterial complications in the clinic of internal diseases.

1. Cocquyt T., Zhou Z., Plomp J., van Eijck L. Neutron tomography of Van Leeuwenhoek's microscopes. Sci Adv. 2021; 7 (20): eabf2402. doi: 10.1126/sciadv.abf2402.

2. Wollman A. J., Nudd R., Hedlund E. G., Leake M.C. From Animaculum to single molecules: 300 years of the light microscope. Open Biol. 2015; 5 (4): 150019. doi: 10.1098/rsob.150019.

3. Escobar-Zepeda A., Vera-Ponce de León A., Sanchez-Flores A. The road to metagenomics: from microbiology to dna sequencing technologies and bioinformatics. Front Genet. 2015; 6: 348. doi: 10.3389/fgene.2015.00348.

4. Watson J., Crick F. Genetical implications of the structure of deoxyribonucleic acid. Nature. 1953; 171: 964–967/ doi: 10.1038/171964b0.

5. Tan S. Y., McCoy A. N. James Dewey Watson (1928): Co-discoverer of the structure of DNA. Singapore Med J. 2020; 61 (10): 507–508. doi: 10.11622/smedj.2020145.

6. Maguin P., Marraffini L. A. From the discovery of DNA to current tools for DNA editing. J Exp Med. 2021; 218 (4): e20201791. doi: 10.1084/jem.20201791.

7. Heather J. M., Chain B. The sequence of sequencers: The history of sequencing DNA. Genomics. 2016; 107 (1): 1–8. doi: 10.1016/j.ygeno.2015.11.003.

8. Shendure J., Balasubramanian S., Church G. M., Gilbert W., Rogers J., Schloss J. A. et al. DNA sequencing at 40: past, present and future. Nature. 2017; 9; 550 (7676): 345–353. doi: 10.1038/nature24286.

9. Crossley B. M., Bai J., Glaser A., Maes R., Porter E., Killian M. L. et al. Guidelines for Sanger sequencing and molecular assay monitoring. J Vet Diagn Invest. 2020; 32 (6): 767–775. doi: 10.1177/1040638720905833.

10. Brooks H. J. Modern microbiology — a quiet revolution with many benefits. Australas Med J. 2013; 6 (7): 378–381. doi: 10.4066/AMJ.2013.1830.

11. Fuchs V. R. New priorities for future biomedical innovations. N Engl J Med. 2010; 363: 704–706. doi: 10.1056/NEJMp0906597.

12. Mishra S. Does modern medicine increase life-expectancy: Quest for the Moon Rabbit? Indian Heart J. 2016; 68 (1): 19–27. doi: 10.1016/j.ihj.2016.01.003.

13. Ranabhat C. L., Atkinson J., Park M. B., Kim C. B., Jakovljevic M. The Influence of universal health coverage on Life Expectancy at Birth (LEAB) and Healthy Life Expectancy (HALE): a multi-country cross-sectional study. Front Pharmacol. 2018; 9: 960. doi: 10.3389/fphar.2018.00960.

14. Miller J. M., Binnicker M. J., Campbell S., Carroll K. C., Chapin K. C., Gilligan P. H. et al. A guide to utilization of the microbiology laboratory for diagnosis of infectious diseases: 2018 update by the infectious diseases society of America and the American society for microbiology. Clin Infect Dis. 2018; 67 (6): e1–e94. doi: 10.1093/cid/ciy381.

15. Peker N., Garcia-Croes S., Dijkhuizen B., Wiersma H. H., van Zanten E., Wisselink G. et al. A comparison of three different bioinformatics analyses of the 16S-23S rRNA encoding region for bacterial identification. Front Microbiol. 2019; 10: 620. Published 2019 Apr 16. doi: 10.3389/fmicb.2019.00620.

16. Barantsevich N. E., Vetokhina A. V., Ayushinova N. I., Orlova O. E., Barantsevich E. P. Candida auris bloodstream infections in Russia. Antibiotics (Basel). 2020; 9 (9): 557. doi: 10.3390/antibiotics9090557.

17. Wayne L. G., Brenner D. J., Colwell R., Grimont P., Krichevsky M., Moore L. H., et al. Report of the ad hoc committee on reconciliation of approaches to bacterial systematics. International Journal of Systematic Bacteriology. 1987; 37 (4): 463–464. doi: 10.1099/00207713-37-4-463.

18. Bergey D. H., Holt J. G. Bergey's manual of determinative bacteriology. 9th ed. Philadelphia: Lippincott Williams & Wilkins. 2000.

19. CLSI Performance Standards For Antimicrobial Susceptibility Testing. 29th Ed. CLSI guideline M100. Wayne, PA: Clinical and Laboratory Standards Institute. 2019.

20. Sicheritz-Pontén T., Andersson S. G. A phylogenomic approach to microbial evolution. Nucleic Acids Res. 2001; 29 (2): 545–552. doi: 10.1093/nar/29.2.545.

21. Romalde J. L., Balboa S., Ventosa A. Editorial: microbial taxonomy, phylogeny and biodiversity. Front Microbiol. 2019; 10: 1324. doi: 10.3389/fmicb.2019.01324.

22. Palmer M., Steenkamp E. T., Coetzee M. P. A., Blom J., Venter S. N. Genome-based characterization of biological processes that differentiate closely related bacteria. Front Microbiol. 2018; 9: 113. doi: 10.3389/fmicb.2018.00113.

23. Bishop C. J., Aanensen D. M., Jordan G. E., Kilian M., Hanage W. P., Spratt B.G. Assigning strains to bacterial species via the internet. BMC Biol. 2009; 7: 3. doi: 10.1186/1741-7007-7-3.

24. Hanage W. P., Kaijalainen T., Herva E., Saukkoriipi A., Syrjänen R., Spratt B. G. Using multilocus sequence data to define the pneumococcus. J Bacteriol. 2005; 187 (17): 6223–6230. doi: 10.1128/JB.187.17.6223-6230.2005.

25. Hoshino T., Fujiwara T., Kilian M. Use of phylogenetic and phenotypic analyses to identify nonhemolytic streptococci isolated from bacteremic patients. J Clin Microbiol. 2005; 43 (12): 6073–6085. doi: 10.1128/JCM.43.12.6073-6085.2005.

26. Jensen A., Kilian M. Delineation of Streptococcus dysgalactiae, its subspecies, and its clinical and phylogenetic relationship to Streptococcus pyogenes. J Clin Microbiol. 2012; 50 (1): 113–126. doi: 10.1128/JCM.05900-11.

27. Ageevets V. A., Sulian O. S., Avdeeva A. A., Chulkova P. S., Gostev V. V., Ageevets I.V. et al. Comparative activity of carbapenem antibiotics against gram-negative carbapenemase producers of different groups. Antibiot Khimioter = Antibiotics and Chemotherapy. 2022; 67 (1–2): 9–15. doi: https://doi.org/10.37489/0235-2990-2022-67-1-2-9-15. (in Russian)

28. Hou T. Y., Chiang-Ni C., Teng S. H. Current status of MALDI-TOF mass spectrometry in clinical microbiology. J Food Drug Anal. 2019; 27 (2): 404–414. doi: 10.1016/j.jfda.2019.01.001.

29. Lin J. F., Ge MC, Liu T. P., Chang S. C., Lu J. J. A simple method for rapid microbial identification from positive monomicrobial blood culture bottles through matrix-assisted laser desorption ionization time-offlight mass spectrometry. J Microbiol Immunol Infect. 2018; 51 (5): 659– 665. doi: 10.1016/j.jmii.2017.03.005.

30. Tran A., Alby K., Kerr A., Jones M., Gilligan P. H. Cost savings realized by implementation of routine microbiological identification by matrix-assisted laser desorption ionization-time of flight mass spectrometry. J Clin Microbiol. 2015; 53 (8): 2473–2479. doi: 10.1128/JCM.00833-15.

31. Elbehiry A., Aldubaib M., Abalkhail A., Marzouk E., Albeloushi A., Moussa I. et al. How M.A.LDI-TOF mass spectrometry technology contributes to microbial infection control in healthcare settings. Vaccines (Basel). 2022; 10 (11): 1881. doi: 10.3390/vaccines10111881.

32. Barantsevich E. P., Barantsevich N. E. MALDI-TOF mass-spectrometry in clinical microbiology. Translational Medicine. 2014; (3): 23– 28. doi: https://doi.org/10.18705/2311-4495-2014-0-3-23-28. [in Russian)

33. Calderaro A., Chezzi C. MALDI-TOF MS: A reliable tool in the real life of the clinical microbiology laboratory. Microorganisms. 2024; 12 (2): 322. doi: https://doi.org/10.3390/microorganisms12020322.

34. Morel F., Jacquier H., Desroches M., Fihman V., Kumanski S., Cambau E., et al. Use of Andromas and Bruker M.A.LDI-TOF MS in the identification of Neisseria. Eur J Clin Microbiol Infect Dis. 2018; 37 (12): 2273–2277. doi: 10.1007/s10096-018-3368-6.

35. Nybakken E. J., Oppegaard O., Gilhuus M., Jensen C. S., Mylvaganam H. Identification of Streptococcus dysgalactiae using matrix-assisted laser desorption/ionization-time of flight mass spectrometry; refining the database for improved identification. Diagn Microbiol Infect Dis. 2021; 99 (1): 115207. doi: 10.1016/j.diagmicrobio.2020.115207.

36. Mörtelmaier C., Panda S., Robertson I., Krell M., Christodoulou M., Reichardt N., et al. Identification performance of MALDI-ToF-MS upon monoand bi-microbial cultures is cell number and culture proportion dependent. Anal Bioanal Chem. 2019; 411 (26): 7027–7038. doi: 10.1007/s00216-019-02080-x.

37. Hong E., Bakhalek Y., Taha M.K. Identification of Neisseria meningitidis by MALDI-TOF MS may not be reliable. Clin Microbiol Infect. 2019; 25 (6): 717–722. doi: 10.1016/j.cmi.2018.09.015.

38. Chen L., Gao W., Tan X., Han Y., Jiao F., Feng B., Xie J., Li B., Zhao H., Tu H., Yu S., Wang L. MALDI-TOF MS is an effective technique to classify specific microbiota. Microbiol Spectr. 2023; 11: e00307–23. doi: https://doi.org/10.1128/spectrum.00307-23.

39. Yang Y., Lin Y., Qiao L. Direct M.A.LDI-TOF MS Identification of Bacterial Mixtures. Anal Chem. 2018; 90 (17): 10400–10408. doi: 10.1021/acs.an-alchem.8b02258.

40. Prakash S., Racovita A., Petrucci T., Galizi R., Jaramillo A. qSanger: quantification of genetic variants in bacterial cultures by sanger sequencing. Biodes Res. 2023; 5: 0007. doi: 10.34133/bdr.0007.

41. Tewolde R., Dallman T., Schaefer U., Sheppard C. L., Ashton P., Pichon B., et al. MOST: a modified MLST typing tool based on short read sequencing. Peer J. 2016; 4: e2308. doi: https: //doi.org/10.7717/peerj.2308.

42. Nutman A., Marchaim D. How to: molecular investigation of a hospital outbreak. Clin Microbiol Infect. 2019; 25 (6): 688–695. doi: 10.1016/j.cmi.2018.09.017.

43. Barantsevich E. P., Barantsevich N. E., Shlyakhto E. V. Production of carbapenemases in Klebsiella pneumoniae isolated in Saint-Petersburg. Clinical Microbiology and Antimicrobial Chemotherapy. 2016; 18 (3): 196–199. (in Russian)