1887

Abstract

Application of whole-genome sequencing (WGS) to characterize foodborne pathogens has advanced our understanding of circulating genotypes and evolutionary relationships. Herein, we used WGS to investigate the genomic epidemiology of , a leading cause of foodborne disease. Among the 214 strains recovered from patients with gastroenteritis in Michigan, USA, 85 multilocus sequence types (STs) were represented and 135 (63.1 %) were phenotypically resistant to at least one antibiotic. Horizontally acquired antibiotic resistance genes were detected in 128 (59.8 %) strains and the genotypic resistance profiles were mostly consistent with the phenotypes. Core-gene phylogenetic reconstruction identified three sequence clusters that varied in frequency, while a neighbour-net tree detected significant recombination among the genotypes (pairwise homoplasy index <0.01). Epidemiological analyses revealed that travel was a significant contributor to pangenomic and ST diversity of , while some lineages were unique to rural counties and more commonly possessed clinically important resistance determinants. Variation was also observed in the frequency of lineages over the 4 year period with chicken and cattle specialists predominating. Altogether, these findings highlight the importance of geographically specific factors, recombination and horizontal gene transfer in shaping the population structure of . They also illustrate the usefulness of WGS data for predicting antibiotic susceptibilities and surveillance, which are important for guiding treatment and prevention strategies.

Funding
This study was supported by the:
  • Michigan State University (Award AgBioResearch)
    • Principle Award Recipient: ShannonD. Manning
  • Michigan State University (Award College of Osteopathic Medicine)
    • Principle Award Recipient: JoseA. Rodrigues
  • Michigan State University (Award MSU Foundation)
    • Principle Award Recipient: ShannonD. Manning
  • Michigan Department of Health and Human Services (Award Michigan Sequencing and Academic Partnerships for Public Health Innovation and Response (MI-SAPPHIRE) initiative at the MDHHS, which is supported with funds from the CDC through the Epidemiology and Laboratory Capacity for Prevention and Control of Emerging Infectious Diseases Enhancing Detection Expansion program (6NU50CK000510-02-07))
    • Principle Award Recipient: HeatherM. Blankenship
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License. This article was made open access via a Publish and Read agreement between the Microbiology Society and the corresponding author’s institution.
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2023-08-01
2024-07-23
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References

  1. Kaakoush NO, Castaño-Rodríguez N, Mitchell HM, Man SM. Global epidemiology of Campylobacter infection. Clin Microbiol Rev 2015; 28:687–720 [View Article] [PubMed]
    [Google Scholar]
  2. Centers for Disease Control and Prevention Antibiotic resistance threats in the United States. DOI: CS239559-B; 2019 https://www.cdc.gov/drugresistance/biggest_threats.html accessed 13 March 2021
  3. Domingues AR, Pires SM, Halasa T, Hald T. Source attribution of human campylobacteriosis using a meta-analysis of case-control studies of sporadic infections. Epidemiol Infect 2012; 140:970–981 [View Article] [PubMed]
    [Google Scholar]
  4. National Antimicrobial Resistance Monitoring System for Enteric Bacteria (NARMS) Centers for disease control and prevention. n.d https://www.cdc.gov/narms/index.html accessed 13 March 2021
  5. Hardnett FP, Hoekstra RM, Kennedy M, Charles L, Angulo FJ et al. Epidemiologic issues in study design and data analysis related to FoodNet activities. Clin Infect Dis 2004; 38:S121–S127 [View Article] [PubMed]
    [Google Scholar]
  6. Rodrigues JA, Cha W, Mosci RE, Mukherjee S, Newton DW et al. Epidemiologic associations vary between tetracycline and fluoroquinolone resistant Campylobacter jejuni infections. Front Public Health 2021; 9:672473 [View Article] [PubMed]
    [Google Scholar]
  7. Cha W, Mosci R, Wengert SL, Singh P, Newton DW et al. Antimicrobial susceptibility profiles of human Campylobacter jejuni isolates and association with phylogenetic lineages. Front Microbiol 2016; 7:1–12 [View Article] [PubMed]
    [Google Scholar]
  8. Mukherjee S, Mosci RE, Anderson CM, Snyder BA, Collins J et al. Antimicrobial drug-resistant Shiga toxin-producing Escherichia coli infections, Michigan, USA. Emerg Infect Dis 2017; 23:1609–1611 [View Article] [PubMed]
    [Google Scholar]
  9. Colles FM, Maiden MCJ. Campylobacter sequence typing databases: applications and future prospects. Microbiology 2012; 158:2695–2709 [View Article] [PubMed]
    [Google Scholar]
  10. Gwinn M, MacCannell D, Armstrong GL. Next-generation sequencing of infectious pathogens. JAMA 2019; 321:893–894 [View Article] [PubMed]
    [Google Scholar]
  11. Sheppard SK, Colles F, Richardson J, Cody AJ, Elson R et al. Host association of Campylobacter genotypes transcends geographic variation. Appl Environ Microbiol 2010; 76:5269–5277 [View Article] [PubMed]
    [Google Scholar]
  12. Sheppard SK, Cheng L, Méric G, de Haan CPA, Llarena A-K et al. Cryptic ecology among host generalist Campylobacter jejuni in domestic animals. Mol Ecol 2014; 23:2442–2451 [View Article] [PubMed]
    [Google Scholar]
  13. Mourkas E, Taylor AJ, Méric G, Bayliss SC, Pascoe B et al. Agricultural intensification and the evolution of host specialism in the enteric pathogen Campylobacter jejuni. Proc Natl Acad Sci 2020; 117:11018–11028 [View Article] [PubMed]
    [Google Scholar]
  14. Thépault A, Méric G, Rivoal K, Pascoe B, Mageiros L et al. Genome-wide identification of host-segregating epidemiological markers for source attribution in Campylobacter jejuni. Appl Environ Microbiol 2017; 83:e03085-16 [View Article] [PubMed]
    [Google Scholar]
  15. Gripp E, Hlahla D, Didelot X, Kops F, Maurischat S et al. Closely related Campylobacter jejuni strains from different sources reveal a generalist rather than a specialist lifestyle. BMC Genomics 2011; 12:584 [View Article] [PubMed]
    [Google Scholar]
  16. Nielsen LN, Sheppard SK, McCarthy ND, Maiden MCJ, Ingmer H et al. MLST clustering of Campylobacter jejuni isolates from patients with gastroenteritis, reactive arthritis and Guillain-Barré syndrome. J Appl Microbiol 2010; 108:591–599 [View Article] [PubMed]
    [Google Scholar]
  17. Epping L, Antão E-M, Semmler T. Population biology and comparative genomics of Campylobacter species. Curr Top Microbiol Immunol 2021; 431:59–78 [View Article] [PubMed]
    [Google Scholar]
  18. Mukherjee S, Blankenship HM, Rodrigues JA, Mosci RE, Rudrik JT et al. Antibiotic susceptibility profiles and frequency of resistance genes in clinical Shiga toxin-producing Escherichia coli isolates from Michigan over a 14-year period. Antimicrob Agents Chemother 2021; 65:e0118921 [View Article] [PubMed]
    [Google Scholar]
  19. Blankenship HM, Mosci RE, Dietrich S, Burgess E, Wholehan J et al. Population structure and genetic diversity of non-O157 Shiga toxin-producing Escherichia coli (STEC) clinical isolates from Michigan. Sci Rep 2021; 11:4461 [View Article] [PubMed]
    [Google Scholar]
  20. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014; 30:2114–2120 [View Article] [PubMed]
    [Google Scholar]
  21. Prjibelski A, Antipov D, Meleshko D, Lapidus A, Korobeynikov A. Using SPAdes De Novo assembler. Curr Protoc Bioinformatics 2020; 70:1–29 [View Article] [PubMed]
    [Google Scholar]
  22. Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies. Bioinformatics 2013; 29:1072–1075 [View Article] [PubMed]
    [Google Scholar]
  23. Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 2016; 32:3047–3048 [View Article] [PubMed]
    [Google Scholar]
  24. Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 2014; 30:2068–2069 [View Article] [PubMed]
    [Google Scholar]
  25. Page AJ, Cummins CA, Hunt M, Wong VK, Reuter S et al. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics 2015; 31:3691–3693 [View Article] [PubMed]
    [Google Scholar]
  26. Löytynoja A. Phylogeny-aware alignment with PRANK. Met Mol Biol 2014; 1079: [View Article]
    [Google Scholar]
  27. Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 2014; 30:1312–1313 [View Article] [PubMed]
    [Google Scholar]
  28. Huson DH, Bryant D. Application of phylogenetic networks in evolutionary studies. Mol Biol Evol 2006; 23:254–267 [View Article] [PubMed]
    [Google Scholar]
  29. Bruen TC, Philippe H, Bryant D. A simple and robust statistical test for detecting the presence of recombination. Genetics 2006; 172:2665–2681 [View Article] [PubMed]
    [Google Scholar]
  30. Rodriguez-R LM, Gunturu S, Harvey WT, Rosselló-Mora R, Tiedje JM et al. The Microbial Genomes Atlas (MiGA) webserver: taxonomic and gene diversity analysis of Archaea and Bacteria at the whole genome level. Nucleic Acids Res 2018; 46:W282–W288 [View Article] [PubMed]
    [Google Scholar]
  31. Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun 2018; 9:1–8 [View Article] [PubMed]
    [Google Scholar]
  32. Bharat A, Petkau A, Avery BP, Chen JC, Folster JP et al. Correlation between phenotypic and In Silico detection of antimicrobial resistance in Salmonella enterica in Canada Using staramr. Microorganisms 2022; 10:292 [View Article] [PubMed]
    [Google Scholar]
  33. Bortolaia V, Kaas RS, Ruppe E, Roberts MC, Schwarz S et al. ResFinder 4.0 for predictions of phenotypes from genotypes. J Antimicrob Chemother 2020; 75:3491–3500 [View Article] [PubMed]
    [Google Scholar]
  34. Zankari E, Allesøe R, Joensen KG, Cavaco LM, Lund O et al. PointFinder: a novel web tool for WGS-based detection of antimicrobial resistance associated with chromosomal point mutations in bacterial pathogens. J Antimicrob Chemother 2017; 72:2764–2768 [View Article] [PubMed]
    [Google Scholar]
  35. Jolley KA, Maiden MCJ. BIGSdb: scalable analysis of bacterial genome variation at the population level. BMC Bioinformatics 2010; 11:595 [View Article] [PubMed]
    [Google Scholar]
  36. Carattoli A, Zankari E, García-Fernández A, Voldby Larsen M, Lund O et al. In silico detection and typing of plasmids using PlasmidFinder and plasmid multilocus sequence typing. Antimicrob Agents Chemother 2014; 58:3895–3903 [View Article] [PubMed]
    [Google Scholar]
  37. Cobo-Díaz JF, González del Río P, Álvarez-Ordóñez A. Whole resistome analysis in Campylobacter jejuni and C. coli genomes available in public repositories. Front Microbiol 2021; 12: [View Article]
    [Google Scholar]
  38. Argimón S, Abudahab K, Goater RJE, Fedosejev A, Bhai J et al. Microreact: visualizing and sharing data for genomic epidemiology and phylogeography. Microb Genom 2016; 2:e000093 [View Article] [PubMed]
    [Google Scholar]
  39. Paradis E, Schliep K, Schwartz R. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 2019; 35:526–528 [View Article] [PubMed]
    [Google Scholar]
  40. Yu G, Smith DK, Zhu H, Guan Y, Lam TTY. ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol Evol 2017; 8: [View Article]
    [Google Scholar]
  41. Warburton PJ, Amodeo N, Roberts AP. Mosaic tetracycline resistance genes encoding ribosomal protection proteins. J Antimicrob Chemother 2016; 71:3333–3339 [View Article] [PubMed]
    [Google Scholar]
  42. Gootz TD, Martin BA. Characterization of high-level quinolone resistance in Campylobacter jejuni. In Antimicrob Agents Chemother vol 35 1991 pp 840–845 [View Article] [PubMed]
    [Google Scholar]
  43. Iovine NM. Resistance mechanisms in Campylobacter jejuni. Virulence 2013; 4:230–240 [View Article] [PubMed]
    [Google Scholar]
  44. Huson DH, Bryant D. Application of phylogenetic networks in evolutionary studies. Mol Biol Evol 2006; 23:254–267 [View Article] [PubMed]
    [Google Scholar]
  45. Park CJ, Li J, Zhang X, Gao F, Benton CS et al. Genomic epidemiology and evolution of diverse lineages of clinical Campylobacter jejuni cocirculating in New Hampshire, USA, 2017. J Clin Microbiol 2020; 58:e02070-19 [View Article] [PubMed]
    [Google Scholar]
  46. Mourkas E, Yahara K, Bayliss SC, Calland JK, Johansson H et al. Host ecology regulates interspecies recombination in bacteria of the genus Campylobacter. Elife 2022; 11:e73552 [View Article] [PubMed]
    [Google Scholar]
  47. Dingle KE, Colles FM, Ure R, Wagenaar JA, Duim B et al. Molecular characterization of Campylobacter jejuni clones: a basis for epidemiologic investigation. Emerg Infect Dis 2002; 8:949–955 [View Article] [PubMed]
    [Google Scholar]
  48. Kelley BR, Ellis JC, Large A, Schneider LG, Jacobson D et al. Whole-genome sequencing and bioinformatic analysis of environmental, agricultural, and human Campylobacter jejuni isolates from East Tennessee. Front Microbiol 2020; 11:571064 [View Article] [PubMed]
    [Google Scholar]
  49. Cha W, Mosci RE, Wengert SL, Venegas Vargas C, Rust SR et al. Comparing the genetic diversity and antimicrobial resistance profiles of Campylobacter jejuni recovered from cattle and humans. Front Microbiol 2017; 8:818 [View Article] [PubMed]
    [Google Scholar]
  50. Mourkas E, Bayliss S, Yahara K, Calland J, Pascoe B et al. Comparative pangenomics of Campylobacter species. Access Microbiology 2019; 1:810 [View Article]
    [Google Scholar]
  51. Young KT, Davis LM, Dirita VJ. Campylobacter jejuni: molecular biology and pathogenesis. Nat Rev Microbiol 2007; 5:665–679 [View Article] [PubMed]
    [Google Scholar]
  52. Mukherjee S, Anderson CM, Mosci RE, Newton DW, Lephart P et al. Increasing frequencies of antibiotic resistant non-typhoidal Salmonella infections in Michigan and risk factors for disease. Front Med 2019; 6:250 [View Article] [PubMed]
    [Google Scholar]
  53. Cha W, Henderson T, Collins J, Manning SD. Factors associated with increasing campylobacteriosis incidence in Michigan, 2004-2013. Epidemiol Infect 2016; 144:3316–3325 [View Article] [PubMed]
    [Google Scholar]
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