Next Article in Journal
The Impact of Pre-Slaughter Fasting on the Ruminal Microbial Population of Commercial Angus Steers
Next Article in Special Issue
Comparative Whole-Genome Analysis of Russian Foodborne Multidrug-Resistant Salmonella Infantis Isolates
Previous Article in Journal
Mycobacterium avium subsp. paratuberculosis Virulence: A Review
Previous Article in Special Issue
Hybrid Assembly Provides Improved Resolution of Plasmids, Antimicrobial Resistance Genes, and Virulence Factors in Escherichia coli and Klebsiella pneumoniae Clinical Isolates
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Editorial

Whole-Genome Sequencing of Pathogenic Bacteria—New Insights into Antibiotic Resistance Spreading

Central Research Institute of Epidemiology, Rospotrebnadzor, 111123 Moscow, Russia
Microorganisms 2021, 9(12), 2624; https://doi.org/10.3390/microorganisms9122624
Submission received: 15 December 2021 / Accepted: 16 December 2021 / Published: 19 December 2021
In recent years, the acquisition of antimicrobial resistance (AMR) by both pathogenic and opportunistic bacteria has become a major problem worldwide, which was already noticed as a global healthcare threat by the World Health Organization [1]. The spread of multidrug-resistant bacteria, especially those producing extended-spectrum β-lactamases (ESBLs), represents a major challenge in clinical settings [2]. According to some estimations, the number of deaths caused by antibiotic resistant bacteria will exceed those caused by all cancer types by 2050 if the existing trend continues [3].
However, the mechanisms of antibiotic resistance acquisition and spreading within and across bacterial species cannot be revealed by traditional phenotypic or PCR-based genotypic tests, although such analyses provide valuable information and are still extensively used in molecular epidemiology studies [4]. Whole-genome sequencing (WGS) is currently attracting an increasing amount of attention since it allows accurate, rapid and cost-effective acquisition of the data regarding the presence of specific antibiotic resistance genes, as well as the determination of various isolate and plasmid replicon classification and typing markers. In addition to traditional molecular tools such as multilocus sequence typing (MLST) or serotyping, which are available for most clinically important bacterial species [5], WGS provides an easy access to additional sequence-based typing techniques which have significantly higher specificity and resolution. These include capsular polysaccharide (KL) and lipooligosaccharide outer core (OCL)-based schemes for Acinetobacter baumannii [6,7] and Klebsiella pneumoniae [8], which can still be implemented using traditional molecular methods, but become dramatically easier to perform using WGS, as well as even more complex and precise typing schemes such as the ones based on a hybrid KL/OCL/MLST approach [9], CRISPR sequences [10], frequency-domain sequence characteristics [11], or core genome MLST (cgMLST) [12], which become cost-ineffective or even impossible to perform without WGS data.
Thus, WGS provides additional tools for bacterial isolate typing with better specificity and higher resolution, which can be easily implemented in epidemiological surveillance protocols to provide additional valuable data regarding the spread and transfer of the given species or genes on the level of a particular hospital, region, country or even worldwide.
Recent advances in this field have already given rise to the development of ‘genomic epidemiology’ approaches which combine various bioinformatics methods for tracing the spread of bacterial and viral pathogens. Such approaches have been successfully used for outbreak investigations [13,14,15] and for studying the variety and predominance of pathogenic bacteria isolates belonging to different species on the scale of a particular hospital [16,17,18], region [19] or country [20,21,22].
However, typing the isolates and monitoring their spread and/or predominance in a particular setting will not per se provide the insights into the ways of resistance spreading and acquisition, although the isolates belonging to some MLST-based sequence types were associated with higher AMR in various bacterial species [23,24,25]. Since AMR is often conferred by either resistance gene acquisition through external mobile elements or mutations in chromosomal genes [26], then the information concerning accurate chromosome and, especially, plasmid structures could provide substantial benefits for AMR investigations. For this purpose, WGS becomes indispensable, in particular, when different sequencing methods (short- and long-read) are applied simultaneously to the same isolate. Plasmid reconstruction using long-read sequencing data can provide essential information regarding the mechanisms of antibiotic resistance acquisition and the way it spreads across different species and world regions.
In addition, portable long-read sequencers such as MinION (Oxford Nanopore Technologies, Oxford, UK) could provide rapid identification of resistance determinants with a potential application in clinical analyses and antimicrobial therapy corrections [27,28,29].
Thus, WGS provides an efficient way of studying the presence and transfer of antibiotic resistance determinants with a potential application in clinical settings.
In view of the aforesaid, the aim of this special issue was to additionally highlight the role of WGS in studying the ways and mechanisms of AMR acquisition, which was already noticed by respected organizations, such as the EUCAST [30] and NIHR [31]. Kuleshov et al. [32] applied the power of WGS to investigate the plasmid-mediated colistin resistance of Salmonella enterica isolates from Russia. Using long-read sequencing, they determined the precise locations of mcr-1 and mcr-9 resistance genes in S. enterica plasmids. Khezri et al. [33] performed a comprehensive analysis of different mono- and hybrid (short- and long-read) assembly tools, which allowed them to conclude that the utilization of two sequencing technologies provides better sequence resolution and allows the refined determination of AMR and virulence genes’ presence and location in Escherichia coli and K. pneumoniae clinical isolates. The other planned papers are supposed to provide additional examples of WGS application to AMR investigations in both clinical and foodborne pathogens.
We believe that the information provided in this special issue will facilitate the investigations of AMR spreading mechanisms and will contribute to the development of novel approaches to epidemiological surveillance and infection prevention for various bacterial pathogens.

Funding

This research received no external funding.

Acknowledgments

The guest editor wishes to thank all the contributors to this Special Issue, and would like to recognize the valuable contribution of all those involved in the peer-review and manuscript publication processes.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. World Health Organisation. Guidelines for the Prevention and Control of Carbapenem-Resistant Enterobacteriaceae, Acinetobacter baumannii and Pseudomonas aeruginosa in Health Care Facilities; World Health Organization: Geneva, Switzerland, 2017. [Google Scholar]
  2. Castanheira, M.; Simner, P.J.; Bradford, P.A. Extended-spectrum beta-lactamases: An update on their characteristics, epidemiology and detection. JAC Antimicrob. Resist. 2021, 3, dlab092. [Google Scholar] [CrossRef] [PubMed]
  3. Avershina, E.; Shapovalova, V.; Shipulin, G. Fighting antibiotic resistance in hospital-acquired infections: Current state and emerging technologies in disease prevention diagnostics and therapy. Front. Microbiol. 2021, 2044, 707330. [Google Scholar] [CrossRef]
  4. Oniciuc, E.A.; Likotrafiti, E.; Alvarez-Molina, A.; Prieto, M.; Santos, J.A.; Alvarez-Ordonez, A. The present and future of whole genome sequencing (wgs) and whole metagenome sequencing (wms) for surveillance of antimicrobial resistant microorganisms and antimicrobial resistance genes across the food chain. Genes 2018, 9, 258. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Jolley, K.A.; Bray, J.E.; Maiden, M.C.J. Open-access bacterial population genomics: BIGSdb software, the PubMLST.org website and their applications. Wellcome Open Res. 2018, 3, 124. [Google Scholar] [CrossRef]
  6. Shashkov, A.S.; Cahill, S.M.; Arbatsky, N.P.; Westacott, A.C.; Kasimova, A.A.; Shneider, M.M.; Popova, A.V.; Shagin, D.A.; Shelenkov, A.A.; Mikhailova, Y.V.; et al. Acinetobacter baumannii K116 capsular polysaccharide structure is a hybrid of the K14 and revised K37 structures. Carbohydr. Res. 2019, 484, 107774. [Google Scholar] [CrossRef] [PubMed]
  7. Wyres, K.L.; Cahill, S.M.; Holt, K.E.; Hall, R.M.; Kenyon, J.J. Identification of acinetobacter baumannii loci for capsular polysaccharide (kl) and lipooligosaccharide outer core (ocl) synthesis in genome assemblies using curated reference databases compatible with kaptive. Microb. Genom. 2020, 6, e000339. [Google Scholar] [CrossRef]
  8. Lam, M.M.C.; Wick, R.R.; Watts, S.C.; Cerdeira, L.T.; Wyres, K.L.; Holt, K.E. A genomic surveillance framework and genotyping tool for Klebsiella pneumoniae and its related species complex. Nat. Commun. 2021, 12, 4188. [Google Scholar] [CrossRef]
  9. Shelenkov, A.; Mikhaylova, Y.; Yanushevich, Y.; Samoilov, A.; Petrova, L.; Fomina, V.; Gusarov, V.; Zamyatin, M.; Shagin, D.; Akimkin, V. Molecular typing, characterization of antimicrobial resistance, virulence profiling and analysis of whole-genome sequence of clinical klebsiella pneumoniae isolates. Antibiotics 2020, 9, 261. [Google Scholar] [CrossRef]
  10. Yeh, H.Y.; Awad, A. genotyping of campylobacter jejuni isolates from poultry by clustered regularly interspaced short palindromic repeats (CRISPR). Curr. Microbiol. 2020, 77, 1647–1652. [Google Scholar] [CrossRef]
  11. Shelenkov, A.; Korotkov, E. Search of regular sequences in promoters from eukaryotic genomes. Comput. Biol. Chem. 2009, 33, 196–204. [Google Scholar] [CrossRef] [PubMed]
  12. Higgins, P.G.; Prior, K.; Harmsen, D.; Seifert, H. Development and evaluation of a core genome multilocus typing scheme for whole-genome sequence-based typing of Acinetobacter baumannii. PLoS ONE 2017, 12, e0179228. [Google Scholar] [CrossRef] [Green Version]
  13. Venditti, C.; Vulcano, A.; D’Arezzo, S.; Gruber, C.E.M.; Selleri, M.; Antonini, M.; Lanini, S.; Marani, A.; Puro, V.; Nisii, C.; et al. Epidemiological investigation of an Acinetobacter baumannii outbreak using core genome multilocus sequence typing. J. Glob. Antimicrob. Resist. 2019, 17, 245–249. [Google Scholar] [CrossRef] [PubMed]
  14. Esper, F.P.; Cheng, Y.W.; Adhikari, T.M.; Tu, Z.J.; Li, D.; Li, E.A.; Farkas, D.H.; Procop, G.W.; Ko, J.S.; Chan, T.A.; et al. Genomic epidemiology of SARS-CoV-2 infection during the initial pandemic wave and association with disease severity. JAMA Netw. Open 2021, 4, e217746. [Google Scholar] [CrossRef]
  15. Gilchrist, C.A.; Turner, S.D.; Riley, M.F.; Petri, W.A., Jr.; Hewlett, E.L. Whole-genome sequencing in outbreak analysis. Clin. Microbiol. Rev. 2015, 28, 541–563. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Shelenkov, A.; Petrova, L.; Zamyatin, M.; Mikhaylova, Y.; Akimkin, V. Diversity of international high-risk clones of Acinetobacter baumannii revealed in a Russian multidisciplinary medical center during 2017–2019. Antibiotics 2021, 10, 1009. [Google Scholar] [CrossRef]
  17. Lim, S.C.; Collins, D.A.; Imwattana, K.; Knight, D.R.; Perumalsamy, S.; Hain Saunders, N.M.R.; Putsathit, P.; Speers, D.; Riley, T.V. Whole genome sequencing links Clostridium (Clostridioides) difficile in a single hospital to diverse environmental sources in the community. J. Appl. Microbiol. 2021. [Google Scholar] [CrossRef]
  18. Hirabayashi, A.; Yanagisawa, H.; Takahashi, H.; Yahara, K.; Boeing, P.; Wolfenden, B.; Nov, V.; Lorn, V.; Veng, M.; Ann, V.; et al. On-site genomic epidemiological analysis of antimicrobial-resistant bacteria in Cambodia with portable laboratory equipment. Front Microbiol. 2021, 12, 675463. [Google Scholar] [CrossRef]
  19. Pascoe, B.; Schiaffino, F.; Murray, S.; Meric, G.; Bayliss, S.C.; Hitchings, M.D.; Mourkas, E.; Calland, J.K.; Burga, R.; Yori, P.P.; et al. Genomic epidemiology of Campylobacter jejuni associated with asymptomatic pediatric infection in the Peruvian Amazon. PLoS Negl. Trop. Dis. 2020, 14, e0008533. [Google Scholar] [CrossRef]
  20. Palmieri, M.; D’Andrea, M.M.; Pelegrin, A.C.; Mirande, C.; Brkic, S.; Cirkovic, I.; Goossens, H.; Rossolini, G.M.; van Belkum, A. Genomic epidemiology of carbapenem- and colistin-resistant klebsiella pneumoniae isolates from Serbia: Predominance of ST101 strains carrying a novel OXA-48 plasmid. Front. Microbiol. 2020, 11, 294. [Google Scholar] [CrossRef] [PubMed]
  21. Gaiarsa, S.; Comandatore, F.; Gaibani, P.; Corbella, M.; Dalla Valle, C.; Epis, S.; Scaltriti, E.; Carretto, E.; Farina, C.; Labonia, M.; et al. Genomic epidemiology of Klebsiella pneumoniae in Italy and novel insights into the origin and global evolution of its resistance to carbapenem antibiotics. Antimicrob. Agents Chemother. 2015, 59, 389–396. [Google Scholar] [CrossRef] [Green Version]
  22. Baker, K.S.; Dallman, T.J.; Field, N.; Childs, T.; Mitchell, H.; Day, M.; Weill, F.X.; Lefevre, S.; Tourdjman, M.; Hughes, G.; et al. Genomic epidemiology of Shigella in the United Kingdom shows transmission of pathogen sublineages and determinants of antimicrobial resistance. Sci. Rep. 2018, 8, 1–8. [Google Scholar] [CrossRef]
  23. Al Suwayyid, B.A.; Coombs, G.W.; Speers, D.J.; Pearson, J.; Wise, M.J.; Kahler, C.M. Genomic epidemiology and population structure of Neisseria gonorrhoeae from remote highly endemic Western Australian populations. BMC Genomics 2018, 19, 1–12. [Google Scholar] [CrossRef] [Green Version]
  24. Zhang, P.; Zhang, X.; Liu, Y.; Jiang, J.; Shen, Z.; Chen, Q.; Ma, X. Multilocus sequence types and antimicrobial resistance of campylobacter jejuni and c. coli isolates of human patients from Beijing, China, 2017–2018. Front Microbiol. 2020, 11, 2542. [Google Scholar] [CrossRef]
  25. Pelegrin, A.C.; Saharman, Y.R.; Griffon, A.; Palmieri, M.; Mirande, C.; Karuniawati, A.; Sedono, R.; Aditianingsih, D.; Goessens, W.H.F.; van Belkum, A.; et al. High-risk international clones of carbapenem-nonsusceptible pseudomonas aeruginosa endemic to Indonesian intensive care units: Impact of a multifaceted infection control intervention analyzed at the genomic level. mBio 2019, 10, e02384-19. [Google Scholar] [CrossRef] [Green Version]
  26. Munita, J.M.; Arias, C.A. Mechanisms of antibiotic resistance. Microbiol. Spectr. 2016, 4, 2–4. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  27. Chan, W.S.; Au, C.H.; Chung, Y.; Leung, H.C.M.; Ho, D.N.; Wong, E.Y.L.; Lam, T.W.; Chan, T.L.; Ma, E.S.K.; Tang, B.S.F. Rapid and economical drug resistance profiling with Nanopore MinION for clinical specimens with low bacillary burden of mycobacterium tuberculosis. BMC Res. Notes 2020, 13, 1–7. [Google Scholar] [CrossRef]
  28. Taxt, A.M.; Avershina, E.; Frye, S.A.; Naseer, U.; Ahmad, R. Rapid identification of pathogens, antibiotic resistance genes and plasmids in blood cultures by nanopore sequencing. Sci. Rep. 2020, 10, 1–11. [Google Scholar] [CrossRef] [PubMed]
  29. Cao, M.D.; Ganesamoorthy, D.; Elliott, A.G.; Zhang, H.; Cooper, M.A.; Coin, L.J. Streaming algorithms for identification of pathogens and antibiotic resistance potential from real-time MinION(TM) sequencing. Gigascience 2016, 5, s13742-016. [Google Scholar] [CrossRef] [Green Version]
  30. Ellington, M.J.; Ekelund, O.; Aarestrup, F.M.; Canton, R.; Doumith, M.; Giske, C.; Grundman, H.; Hasman, H.; Holden, M.T.G.; Hopkins, K.L.; et al. The role of whole genome sequencing in antimicrobial susceptibility testing of bacteria: Report from the EUCAST Subcommittee. Clin. Microbiol. Infect. 2017, 23, 2–22. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  31. NIHR Global Health Research Unit on Genomic Surveillance of AMR. Whole-genome sequencing as part of national and international surveillance programmes for antimicrobial resistance: A roadmap. BMJ Glob. Health 2020, 5, e002244. [Google Scholar] [CrossRef]
  32. Kuleshov, K.V.; Pavlova, A.S.; Shedko, E.D.; Mikhaylova, Y.V.; Margos, G.; Hepner, S.; Chebotar, I.V.; Korneenko, E.V.; Podkolzin, A.T.; Akimkin, V.G. Mobile colistin resistance genetic determinants of non-typhoid salmonella enterica isolates from Russia. Microorganisms 2021, 9, 2515. [Google Scholar] [CrossRef]
  33. Khezri, A.; Avershina, E.; Ahmad, R. Hybrid assembly provides improved resolution of plasmids, antimicrobial resistance genes, and virulence factors in escherichia coli and klebsiella pneumoniae clinical isolates. Microorganisms 2021, 9, 2560. [Google Scholar] [CrossRef]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Shelenkov, A. Whole-Genome Sequencing of Pathogenic Bacteria—New Insights into Antibiotic Resistance Spreading. Microorganisms 2021, 9, 2624. https://doi.org/10.3390/microorganisms9122624

AMA Style

Shelenkov A. Whole-Genome Sequencing of Pathogenic Bacteria—New Insights into Antibiotic Resistance Spreading. Microorganisms. 2021; 9(12):2624. https://doi.org/10.3390/microorganisms9122624

Chicago/Turabian Style

Shelenkov, Andrey. 2021. "Whole-Genome Sequencing of Pathogenic Bacteria—New Insights into Antibiotic Resistance Spreading" Microorganisms 9, no. 12: 2624. https://doi.org/10.3390/microorganisms9122624

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop