1887

Microbial Genomics logo

Volume 8, Issue 2

Identification of novel, cryptic species isolates from environmental samples collected from diverse geographical locations Open Access

Abstract

is a pathogen often associated with hospital-acquired infection or antimicrobial-induced disease; however, increasing evidence indicates infections can result from community or environmental sources. Most genomic sequencing of has focused on clinical strains, although evidence is growing that spores are widespread in soil and water in the environment. In this study, we sequenced 38 genomes collected from soil and water isolates in Flagstaff (AZ, USA) and Slovenia in an effort targeted towards environmental surveillance of . At the average nucleotide identity (ANI) level, the genomes were divergent to at a threshold consistent with different species. A phylogenetic analysis of these divergent genomes together with genomes available in public repositories confirmed the presence of three previously described, cryptic s species and added two additional clades. One of the cryptic species (C-III) was almost entirely composed of Arizona and Slovenia genomes, and contained distinct sub-groups from each region (evidenced by SNP and gene-content differences). A comparative genomics analysis identified multiple unique coding sequences per clade, which can serve as markers for subsequent environmental surveys of these cryptic species. Homologues to the toxin genes, and , were found in cryptic species genomes, although they were not part of the typical pathogenicity locus observed in , and PCR suggested that some would not amplify with widely used PCR diagnostic tests. We also identified gene homologues in the binary toxin cluster, including some present on phage and, for what is believed to be the first time, on a plasmid. All isolates were obtained from environmental samples, so the function and disease potential of these toxin homologues is currently unknown. Enzymatic profiles of a subset of cryptic isolates (=5) demonstrated differences, suggesting that these isolates contain substantial metabolic diversity. Antimicrobial resistance (AMR) was observed across a subset of isolates (=4), suggesting that AMR mechanisms are intrinsic to the genus, perhaps originating from a shared environmental origin. This study greatly expands our understanding of the genomic diversity of . These results have implications for One Health research, for more sensitive diagnostics, as well as for understanding the evolutionary history of and the development of pathogenesis.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): Clostridioides difficile , cryptic species , genomics and toxin
Funding
This study was supported by the:
  • Flinn Foundation (Award 2062)
    • Principle Award Recipient: PaulKeim
  • Flinn Foundation (Award 2234)
    • Principle Award Recipient: JasonWilliam Sahl
© 2022 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License.
Loading

Article metrics loading...

/content/journal/mgen/10.1099/mgen.0.000742
2022-02-15
2024-04-23
Loading full text...

Full text loading...

/deliver/fulltext/mgen/8/2/mgen000742.html?itemId=/content/journal/mgen/10.1099/mgen.0.000742&mimeType=html&fmt=ahah

References

  1. Janezic S, Smrke J, Rupnik M. Isolation of Clostridioides difficile from different outdoor sites in the domestic environment. Anaerobe 2020; 62:102183 [View Article] [PubMed]
     [Google Scholar]
  2. Lim SC, Knight DR, Riley TV. Clostridium difficile and one health. Clin Microbiol Infect 2020; 26:857–863 [View Article] [PubMed]
     [Google Scholar]
  3. Rodriguez Diaz C, Seyboldt C, Rupnik M. Non-human C. difficile reservoirs and sources: animals, food, environment. Adv Exp Med Biol 2018; 1050:227–243 [View Article] [PubMed]
     [Google Scholar]
  4. Janezic S, Potocnik M, Zidaric V, Rupnik M. Highly divergent Clostridium difficile strains isolated from the environment. PLoS One 2016; 11:e0167101 [View Article] [PubMed]
     [Google Scholar]
  5. Dingle KE, Elliott B, Robinson E, Griffiths D, Eyre DW et al. Evolutionary history of the Clostridium difficile pathogenicity locus. Genome Biol Evol 2014; 6:36–52 [View Article] [PubMed]
     [Google Scholar]
  6. Bletz S, Janezic S, Harmsen D, Rupnik M, Mellmann A. Defining and evaluating a core genome multilocus sequence typing scheme for genome-wide typing of Clostridium difficile. J Clin Microbiol 2018; 56:e01987-17 [View Article] [PubMed]
     [Google Scholar]
  7. Knight DR, Imwattana K, Kullin B, Guerrero-Araya E, Paredes-Sabja D et al. Major genetic discontinuity and novel toxigenic species in Clostridioides difficile taxonomy. elife 2021; 10:e64325 [View Article] [PubMed]
     [Google Scholar]
  8. Hammond GA, Johnson JL. The toxigenic element of Clostridium difficile strain VPI 10463. Microb Pathog 1995; 19:203–213 [View Article] [PubMed]
     [Google Scholar]
  9. Lyras D, O’Connor JR, Howarth PM, Sambol SP, Carter GP et al. Toxin B is essential for virulence of Clostridium difficile. Nature 2009; 458:1176–1179 [View Article] [PubMed]
     [Google Scholar]
  10. Gerding DN, Johnson S, Rupnik M, Aktories K. Clostridium difficile binary toxin CDT: mechanism, epidemiology, and potential clinical importance. Gut Microbes 2014; 5:15–27 [View Article] [PubMed]
     [Google Scholar]
  11. Smits WK, Lyras D, Lacy DB, Wilcox MH, Kuijper EJ. Clostridium difficile infection. Nat Rev Dis Primers 2016; 2:16020 [View Article] [PubMed]
     [Google Scholar]
  12. Janezic S, Marín M, Martín A, Rupnik M. A new type of toxin A-negative, toxin B-positive Clostridium difficile strain lacking a complete tcdA gene. J Clin Microbiol 2015; 53:692–695 [View Article] [PubMed]
     [Google Scholar]
  13. Kuehne SA, Collery MM, Kelly ML, Cartman ST, Cockayne A et al. Importance of toxin A, toxin B, and CDT in virulence of an epidemic Clostridium difficile strain. J Infect Dis 2014; 209:83–86 [View Article] [PubMed]
     [Google Scholar]
  14. Carter GP, Chakravorty A, Pham Nguyen TA, Mileto S, Schreiber F et al. Defining the roles of TcdA and TcdB in localized gastrointestinal disease, systemic organ damage, and the host response during Clostridium difficile infections. mBio 2015; 6:e00551 [View Article] [PubMed]
     [Google Scholar]
  15. Janezic S, Dingle K, Alvin J, Accetto T, Didelot X et al. Comparative genomics of Clostridioides difficile toxinotypes identifies module-based toxin gene evolution. Microb Genom 2020; 6:000449 [View Article] [PubMed]
     [Google Scholar]
  16. Monot M, Eckert C, Lemire A, Hamiot A, Dubois T et al. Clostridium difficile: new insights into the evolution of the pathogenicity locus. Sci Rep 2015; 5:15023 [View Article] [PubMed]
     [Google Scholar]
  17. Eastwood K, Else P, Charlett A, Wilcox M. Comparison of nine commercially available Clostridium difficile toxin detection assays, a real-time PCR assay for C. difficile tcdB, and a glutamate dehydrogenase detection assay to cytotoxin testing and cytotoxigenic culture methods. J Clin Microbiol 2009; 47:3211–3217 [View Article] [PubMed]
     [Google Scholar]
  18. Gateau C, Couturier J, Coia J, Barbut F. How to: diagnose infection caused by Clostridium difficile. Clin Microbiol Infect 2018; 24:463–468 [View Article] [PubMed]
     [Google Scholar]
  19. Mutters R, Nonnenmacher C, Susin C, Albrecht U, Kropatsch R et al. Quantitative detection of Clostridium difficile in hospital environmental samples by real-time polymerase chain reaction. J Hosp Infect 2009; 71:43–48 [View Article] [PubMed]
     [Google Scholar]
  20. Lemee L, Dhalluin A, Testelin S, Mattrat M-A, Maillard K et al. Multiplex PCR targeting tpi (triose phosphate isomerase), tcdA (toxin A), and tcdB (toxin B) genes for toxigenic culture of Clostridium difficile. J Clin Microbiol 2004; 42:5710–5714 [View Article] [PubMed]
     [Google Scholar]
  21. Bagdasarian N, Rao K, Malani PN. Diagnosis and treatment of Clostridium difficile in adults: a systematic review. JAMA 2015; 313:398–408 [View Article] [PubMed]
     [Google Scholar]
  22. Nguyen VK, Rihn B, Heckel C, Bisseret F, Girardot R et al. Enzyme immunoassay (ELISA) for detection of Clostridium difficile toxin B in specimens of faeces. J Med Microbiol 1990; 31:251–257 [View Article] [PubMed]
     [Google Scholar]
  23. Kim KH, Fekety R, Batts DH, Brown D, Cudmore M et al. Isolation of Clostridium difficile from the environment and contacts of patients with antibiotic-associated colitis. J Infect Dis 1981; 143:42–50 [View Article] [PubMed]
     [Google Scholar]
  24. Weese JS, Avery BP, Rousseau J, Reid-Smith RJ. Detection and enumeration of Clostridium difficile spores in retail beef and pork. Appl Environ Microbiol 2009; 75:5009–5011 [View Article] [PubMed]
     [Google Scholar]
  25. Dumford DM 3rd, Nerandzic MM, Eckstein BC, Donskey CJ. What is on that keyboard? Detecting hidden environmental reservoirs of Clostridium difficile during an outbreak associated with North American pulsed-field gel electrophoresis type 1 strains. Am J Infect Control 2009; 37:15–19 [View Article] [PubMed]
     [Google Scholar]
  26. Rodriguez-Palacios A, Staempfli HR, Duffield T, Weese JS. Clostridium difficile in retail ground meat, Canada. Emerg Infect Dis 2007; 13:485–487 [View Article] [PubMed]
     [Google Scholar]
  27. Dove CH, Wang SZ, Price SB, Phelps CJ, Lyerly DM et al. Molecular characterization of the Clostridium difficile toxin A gene. Infect Immun 1990; 58:480–488 [View Article] [PubMed]
     [Google Scholar]
  28. Barroso LA, Wang SZ, Phelps CJ, Johnson JL, Wilkins TD. Nucleotide sequence of Clostridium difficile toxin B gene. Nucleic Acids Res 1990; 18:4004 [View Article] [PubMed]
     [Google Scholar]
  29. Tkalec V, Janezic S, Skok B, Simonic T, Mesaric S et al. High Clostridium difficile contamination rates of domestic and imported potatoes compared to some other vegetables in Slovenia. Food Microbiol 2019; 78:194–200 [View Article] [PubMed]
     [Google Scholar]
  30. Stone NE, Sidak-Loftis LC, Sahl JW, Vazquez AJ, Wiggins KB et al. More than 50% of Clostridium difficile isolates from pet dogs in Flagstaff, USA, carry toxigenic genotypes. PLoS One 2016; 11:e0164504 [View Article] [PubMed]
     [Google Scholar]
  31. Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol 2012; 19:455–477 [View Article] [PubMed]
     [Google Scholar]
  32. Walker BJ, Abeel T, Shea T, Priest M, Abouelliel A et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS One 2014; 9:e112963 [View Article] [PubMed]
     [Google Scholar]
  33. Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 2014; 30:2068–2069 [View Article] [PubMed]
     [Google Scholar]
  34. Wick RR, Judd LM, Gorrie CL, Holt KE. Unicycler: resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput Biol 2017; 13:e1005595 [View Article] [PubMed]
     [Google Scholar]
  35. Gupta A, Jordan IK, Rishishwar L. stringMLST: a fast k-mer based tool for multilocus sequence typing. Bioinformatics 2017; 33:119–121 [View Article] [PubMed]
     [Google Scholar]
  36. Ondov BD, Treangen TJ, Melsted P, Mallonee AB, Bergman NH et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol 2016; 17:132 [View Article] [PubMed]
     [Google Scholar]
  37. Pritchard L, Glover RH, Humphris S, Elphinstone JG, Toth IK. Genomics and taxonomy in diagnostics for food security: soft-rotting enterobacterial plant pathogens. Anal Methods 2016; 8:12–24 [View Article]
     [Google Scholar]
  38. Kurtz S, Phillippy A, Delcher AL, Smoot M, Shumway M et al. Versatile and open software for comparing large genomes. Genome Biol 2004; 5:R12 [View Article] [PubMed]
     [Google Scholar]
  39. 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; 9:5114 [View Article] [PubMed]
     [Google Scholar]
  40. Delcher AL, Salzberg SL, Phillippy AM. Using MUMmer to identify similar regions in large sequence sets. Curr Protoc Bioinformatics 2003; 10:10.3.1 [View Article] [PubMed]
     [Google Scholar]
  41. Sahl JW, Lemmer D, Travis J, Schupp JM, Gillece JD et al. NASP: an accurate, rapid method for the identification of SNPs in WGS datasets that supports flexible input and output formats. Microb Genom 2016; 2:e000074 [View Article] [PubMed]
     [Google Scholar]
  42. Minh BQ, Schmidt HA, Chernomor O, Schrempf D, Woodhams MD et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol Biol Evol 2020; 37:1530–1534 [View Article] [PubMed]
     [Google Scholar]
  43. Kalyaanamoorthy S, Minh BQ, Wong TKF, von Haeseler A, Jermiin LS. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods 2017; 14:587–589 [View Article] [PubMed]
     [Google Scholar]
  44. Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv 20131303.3997
     [Google Scholar]
  45. DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet 2011; 43:491–498 [View Article] [PubMed]
     [Google Scholar]
  46. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 2010; 20:1297–1303 [View Article] [PubMed]
     [Google Scholar]
  47. Bush SJ. Generalizable characteristics of false-positive bacterial variant calls. Microb Genom 2021; 7:000615 [View Article] [PubMed]
     [Google Scholar]
  48. Tonkin-Hill G, MacAlasdair N, Ruis C, Weimann A, Horesh G et al. Producing polished prokaryotic pangenomes with the Panaroo pipeline. Genome Biol 2020; 21:180 [View Article] [PubMed]
     [Google Scholar]
  49. Sahl JW, Caporaso JG, Rasko DA, Keim P. The large-scale blast score ratio (LS-BSR) pipeline: a method to rapidly compare genetic content between bacterial genomes. PeerJ 2014; 2:e332 [View Article] [PubMed]
     [Google Scholar]
  50. Rasko DA, Myers GSA, Ravel J. Visualization of comparative genomic analyses by BLAST score ratio. BMC Bioinformatics 2005; 6:2 [View Article] [PubMed]
     [Google Scholar]
  51. Huerta-Cepas J, Forslund K, Coelho LP, Szklarczyk D, Jensen LJ et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-Mapper. Mol Biol Evol 2017; 34:2115–2122 [View Article] [PubMed]
     [Google Scholar]
  52. Gertz EM, Yu Y-K, Agarwala R, Schäffer AA, Altschul SF. Composition-based statistics and translated nucleotide searches: improving the TBLASTN module of BLAST. BMC Biol 2006; 4:41 [View Article] [PubMed]
     [Google Scholar]
  53. Letunic I, Bork P. Interactive Tree Of Life (iTOL): an online tool for phylogenetic tree display and annotation. Bioinformatics 2007; 23:127–128 [View Article] [PubMed]
     [Google Scholar]
  54. Guy L, Kultima JR, Andersson SGE. genoPlotR: comparative gene and genome visualization in R. Bioinformatics 2010; 26:2334–2335 [View Article] [PubMed]
     [Google Scholar]
  55. Amy J, Bulach D, Knight D, Riley T, Johanesen P et al. Identification of large cryptic plasmids in Clostridioides (Clostridium) difficile. Plasmid 2018; 96–97:25–38 [View Article] [PubMed]
     [Google Scholar]
  56. Fortier LC. Bacteriophages contribute to shaping Clostridioides (Clostridium) difficile species. Front Microbiol 2018; 9:2033 [View Article] [PubMed]
     [Google Scholar]
  57. Hornung BVH, Kuijper EJ, Smits WK. An in silico survey of Clostridioides difficile extrachromosomal elements. Microb Genom 2019; 5:000296 [View Article] [PubMed]
     [Google Scholar]
  58. Büchler AC, Rampini SK, Stelling S, Ledergerber B, Peter S et al. Antibiotic susceptibility of Clostridium difficile is similar worldwide over two decades despite widespread use of broad-spectrum antibiotics: an analysis done at the University Hospital of Zurich. BMC Infect Dis 2014; 14:607 [View Article] [PubMed]
     [Google Scholar]
  59. McArthur AG, Waglechner N, Nizam F, Yan A, Azad MA et al. The comprehensive antibiotic resistance database. Antimicrob Agents Chemother 2013; 57:3348–3357 [View Article] [PubMed]
     [Google Scholar]
  60. Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 2010; 26:2460–2461 [View Article] [PubMed]
     [Google Scholar]
  61. Nguyen L-T, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol 2015; 32:268–274 [View Article] [PubMed]
     [Google Scholar]
  62. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci USA 2011; 108 (Suppl. 1):4516–4522 [View Article] [PubMed]
     [Google Scholar]
  63. Sahl JW, Matalka MN, Rasko DA. Phylomark, a tool to identify conserved phylogenetic markers from whole-genome alignments. Appl Environ Microbiol 2012; 78:4884–4892 [View Article] [PubMed]
     [Google Scholar]
  64. Price MN, Dehal PS, Arkin AP. FastTree 2 – approximately maximum-likelihood trees for large alignments. PLoS One 2010; 5:e9490 [View Article] [PubMed]
     [Google Scholar]
  65. Terhes G, Urbán E, Sóki J, Hamid KA, Nagy E. Community-acquired Clostridium difficile diarrhea caused by binary toxin, toxin A, and toxin B gene-positive isolates in Hungary. J Clin Microbiol 2004; 42:4316–4318 [View Article] [PubMed]
     [Google Scholar]
  66. Persson S, Torpdahl M, Olsen KEP. New multiplex PCR method for the detection of Clostridium difficile toxin A (tcdA) and toxin B (tcdB) and the binary toxin (cdtA/cdtB) genes applied to a Danish strain collection. Clin Microbiol Infect 2008; 14:1057–1064 [View Article] [PubMed]
     [Google Scholar]
  67. Ramírez-Vargas G, López-Ureña D, Badilla A, Orozco-Aguilar J, Murillo T et al. Novel clade C-I Clostridium difficile strains escape diagnostic tests, differ in pathogenicity potential and carry toxins on extrachromosomal elements. Sci Rep 2018; 8:13951 [View Article] [PubMed]
     [Google Scholar]
  68. Hall CM, Jaramillo S, Jimenez R, Stone NE, Centner H et al. Burkholderia pseudomallei, the causative agent of melioidosis, is rare but ecologically established and widely dispersed in the environment in Puerto Rico. PLoS Negl Trop Dis 2019; 13:e0007727 [View Article] [PubMed]
     [Google Scholar]
  69. Huang H, Weintraub A, Fang H, Nord CE. Antimicrobial resistance in Clostridium difficile. Int J Antimicrob Agents 2009; 34:516–522 [View Article] [PubMed]
     [Google Scholar]
  70. Forster AJ, Taljaard M, Oake N, Wilson K, Roth V et al. The effect of hospital-acquired infection with Clostridium difficile on length of stay in hospital. CMAJ 2012; 184:37–42 [View Article] [PubMed]
     [Google Scholar]
  71. Mullish BH, Williams HR. Clostridium difficile infection and antibiotic-associated diarrhoea. Clin Med 2018; 18:237–241 [View Article] [PubMed]
     [Google Scholar]
  72. Sorg JA, Dineen SS. Laboratory maintenance of Clostridium difficile. Curr Protoc Microbiol 2009; 9:9A.1 [View Article] [PubMed]
     [Google Scholar]
  73. Edwards AN, McBride SM. Isolating and purifying Clostridium difficile spores. Methods Mol Biol 2016; 1476:117–128 [View Article] [PubMed]
     [Google Scholar]
  74. Cowardin CA, Buonomo EL, Saleh MM, Wilson MG, Burgess SL et al. The binary toxin CDT enhances Clostridium difficile virulence by suppressing protective colonic eosinophilia. Nat Microbiol 2016; 1:16108 [View Article] [PubMed]
     [Google Scholar]
  75. Aktories K, Papatheodorou P, Schwan C. Binary Clostridium difficile toxin (CDT) – a virulence factor disturbing the cytoskeleton. Anaerobe 2018; 53:21–29 [View Article] [PubMed]
     [Google Scholar]
  76. Riedel T, Wittmann J, Bunk B, Schober I, Spröer C et al. A Clostridioides difficile bacteriophage genome encodes functional binary toxin-associated genes. J Biotechnol 2017; 250:23–28 [View Article] [PubMed]
     [Google Scholar]
  77. Kim M, Oh HS, Park SC, Chun J. Towards a taxonomic coherence between average nucleotide identity and 16S rRNA gene sequence similarity for species demarcation of prokaryotes. Int J Syst Evol Microbiol 2014; 64:346–351 [View Article] [PubMed]
     [Google Scholar]
  78. Adékambi T, Drancourt M, Raoult D. The rpoB gene as a tool for clinical microbiologists. Trends Microbiol 2009; 17:37–45 [View Article] [PubMed]
     [Google Scholar]
  79. Sahl JW, Vazquez AJ, Hall CM, Busch JD, Tuanyok A et al. The effects of signal erosion and core genome reduction on the identification of diagnostic markers. mBio 2016; 7:e00846-16 [View Article] [PubMed]
     [Google Scholar]
  80. Williamson CHD, Wagner DM, Keim P, Sahl JW. Developing inclusivity and exclusivity panels for testing diagnostic and detection tools targeting Burkholderia pseudomallei, the causative agent of melioidosis. J AOAC Int 2018; 101:1920–1926 [View Article] [PubMed]
     [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/mgen/10.1099/mgen.0.000742
Loading
Identification of novel, cryptic Clostridioides species isolates from environmental samples collected from diverse geographical locations
M Gen 8, 000742 (2022); https://doi.org/10.1099/mgen.0.000742
/content/journal/mgen/10.1099/mgen.0.000742
/content/journal/mgen/10.1099/mgen.0.000742
Loading

Data & Media loading...

Supplements

Supplementary material 1

PDF

Supplementary material 2

EXCEL

Most cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error