1887

Microbial Genomics logo

Volume 8, Issue 11

Novel application of metagenomics for the strain-level detection of bacterial contaminants within non-sterile industrial products – a retrospective, real-time analysis Open Access

This article is part of the Diversity in Microbiology collection

Abstract

The home and personal care (HPC) industry generally relies on initial cultivation and subsequent biochemical testing for the identification of microorganisms in contaminated products. This process is slow (several days for growth), labour intensive, and misses organisms which fail to revive from the harsh environment of preserved consumer products. Since manufacturing within the HPC industry is high-throughput, the process of identification of microbial contamination could benefit from the multiple cultivation-independent methodologies that have developed for the detection and analysis of microbes. We describe a novel workflow starting with automated DNA extraction directly from a HPC product, and subsequently applying metagenomic methodologies for species and strain-level identification of bacteria. The workflow was validated by application to a historic microbial contamination of a general-purpose cleaner (GPC). A single strain of was detected metagenomically within the product. The metagenome mirrored that of a contaminant isolated in parallel by a traditional cultivation-based approach. Using a dilution series of the incident sample, we also provide evidence to show that the workflow enables detection of contaminant organisms down to 100 CFU/ml of product. To our knowledge, this is the first validated example of metagenomics analysis providing confirmatory evidence of a traditionally isolated contaminant organism, in a HPC product.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): bioinformatics , metagenome binning , metagenomics , microbial contamination and strain identification
Funding
This study was supported by the:
  • Medical Research Council (Award MR/L015080/1)
    • Principle Award Recipient: ThomasR Connor
  • Biotechnology and Biological Sciences Research Council (Award BB/M009122/1)
    • Principle Award Recipient: EdwardCunningham-Oakes
© 2022 The Authors
  • 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.
Loading

Article metrics loading...

/content/journal/mgen/10.1099/mgen.0.000884
2022-11-24
2024-04-19
Loading full text...

Full text loading...

/deliver/fulltext/mgen/8/11/mgen000884.html?itemId=/content/journal/mgen/10.1099/mgen.0.000884&mimeType=html&fmt=ahah

References

  1. Rushton L, Sass A, Baldwin A, Dowson CG, Donoghue D et al. Key role for efflux in the preservative susceptibility and adaptive resistance of Burkholderia cepacia complex bacteria. Antimicrob Agents Chemother 2013; 57:2972–2980 [View Article] [PubMed]
     [Google Scholar]
  2. Smart R, Spooner DF. Microbiological spoilage in pharmaceuticals and cosmetics. J Soc Cosmet Chem Soc Cosmet Chem Gt Britain 1972; 23:721–737
     [Google Scholar]
  3. Halla N, Fernandes IP, Heleno SA, Costa P, Boucherit-Otmani Z et al. Cosmetics preservation: a review on present strategies. Molecules 2018; 23:E1571 [View Article]
     [Google Scholar]
  4. Sutton S. Cosmetic Microbiology: A Practical Approach 2006 Epub ahead of print 2006 [View Article]
     [Google Scholar]
  5. Orth DS, Kabara JJ, Denyer SP, Tan SK. Cosmetic and drug microbiology New York, USA: Informa Healthcare; 2006
     [Google Scholar]
  6. Silva V, Silva C, Soares P, Garrido EM, Borges F et al. Isothiazolinone Biocides: Chemistry, Biological, and Toxicity Profiles. Molecules 2020; 25:991 [View Article] [PubMed]
     [Google Scholar]
  7. Cunningham-Oakes E, Weiser R, Pointon T, Mahenthiralingam E. Understanding the challenges of non-food industrial product contamination. FEMS Microbiol Lett 2019; 366:fnaa010 [View Article]
     [Google Scholar]
  8. Abdel Malek SMA, Badran YR. Pseudomonas aeruginosa PAO1 adapted to 2-phenoxyethanol shows cross-resistance to dissimilar biocides and increased susceptibility to antibiotics. Folia Microbiol (Praha) 2010; 55:588–592 [View Article] [PubMed]
     [Google Scholar]
  9. Zlosnik JEA, Zhou G, Brant R, Henry DA, Hird TJ et al. Burkholderia species infections in patients with cystic fibrosis in British Columbia, Canada. 30 years’ experience. Ann Am Thorac Soc 2015; 12:70–78 [View Article]
     [Google Scholar]
  10. Ashish A, Shaw M, Winstanley C, Ledson MJ, Walshaw MJ. Increasing resistance of the Liverpool Epidemic Strain (LES) of Pseudomonas aeruginosa (Psa) to antibiotics in cystic fibrosis (CF)--A cause for concern?. J Cyst Fibros 2012; 11:173–179 [View Article] [PubMed]
     [Google Scholar]
  11. Laupland KB, Valiquette L. The changing culture of the microbiology laboratory. Can J Infect Dis Med Microbiol 2013; 24:125–128 [View Article] [PubMed]
     [Google Scholar]
  12. Woo PCY, Lau SKP, Teng JLL, Tse H, Yuen KY. Then and now: use of 16S rDNA gene sequencing for bacterial identification and discovery of novel bacteria in clinical microbiology laboratories. Clin Microbiol Infect 2008; 14:908–934 [View Article] [PubMed]
     [Google Scholar]
  13. Henry DA, Mahenthiralingam E, Vandamme P, Coenye T, Speert DP. Phenotypic methods for determining genomovar status of the Burkholderia cepacia complex. J Clin Microbiol 2001; 39:1073–1078 [View Article] [PubMed]
     [Google Scholar]
  14. Postollec F, Falentin H, Pavan S, Combrisson J, Sohier D. Recent advances in quantitative PCR (qPCR) applications in food microbiology. Food Microbiol 2011; 28:848–861 [View Article] [PubMed]
     [Google Scholar]
  15. van Belkum A, Durand G, Peyret M, Chatellier S, Zambardi G et al. Rapid clinical bacteriology and its future impact. Ann Lab Med 2013; 33:14–27 [View Article]
     [Google Scholar]
  16. Maukonen J, Mättö J, Wirtanen G, Raaska L, Mattila-Sandholm T et al. Methodologies for the characterization of microbes in industrial environments: A review. J Ind Microbiol Biotechnol 2003; 30:327–356 [View Article] [PubMed]
     [Google Scholar]
  17. Rubio E, Zboromyrska Y, Bosch J, Fernandez-Pittol MJ, Fidalgo BI et al. Evaluation of flow cytometry for the detection of bacteria in biological fluids. PLoS One 2019; 14:e0220307 [View Article]
     [Google Scholar]
  18. Linklater N, Örmeci B. Evaluation of the adenosine triphosphate (ATP) bioluminescence assay for monitoring effluent quality and disinfection performance. Water Quality Research Journal 2014; 49:114–123 [View Article]
     [Google Scholar]
  19. Cunningham-Oakes E, Pointon T, Murphy B, Campbell-Lee S, Webster G. Genomics reveals the novel species placement of industrial contaminant isolates incorrectly identified as Burkholderia lata. Microb Genom 2021; 7:000564 [View Article]
     [Google Scholar]
  20. Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol 2019; 20:257 [View Article] [PubMed]
     [Google Scholar]
  21. Zampieri A, Babbucci M, Carraro L, Milan M, Fasolato L et al. Combining Culture-Dependent and Culture-Independent Methods: New Methodology Insight on the Vibrio Community of Ruditapes philippinarum. Foods 2021; 10:1271 [View Article]
     [Google Scholar]
  22. McDonald D, Clemente JC, Kuczynski J, Rideout JR, Stombaugh J et al. The Biological Observation Matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome. GigaSci 2012; 1:7 [View Article] [PubMed]
     [Google Scholar]
  23. McMurdie PJ, Holmes S, Watson M. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 2013; 8:e61217 [View Article]
     [Google Scholar]
  24. Winsor GL, Griffiths EJ, Lo R, Dhillon BK, Shay JA et al. Enhanced annotations and features for comparing thousands of Pseudomonas genomes in the Pseudomonas genome database. Nucleic Acids Res 2016; 44:D646–53 [View Article]
     [Google Scholar]
  25. Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J 2017; 11:2864–2868 [View Article] [PubMed]
     [Google Scholar]
  26. 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]
     [Google Scholar]
  27. 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]
  28. 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]
  29. Uritskiy GV, DiRuggiero J, Taylor J. MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 2018; 6:158 [View Article] [PubMed]
     [Google Scholar]
  30. Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res 2017; 27:824–834 [View Article]
     [Google Scholar]
  31. Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J et al. Binning metagenomic contigs by coverage and composition. Nat Methods 2014; 11:1144–1146 [View Article] [PubMed]
     [Google Scholar]
  32. Wu Y-W, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 2016; 32:605–607 [View Article] [PubMed]
     [Google Scholar]
  33. Kang DD, Li F, Kirton E, Thomas A, Egan R et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 2019; 7:e7359 [View Article] [PubMed]
     [Google Scholar]
  34. Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res 2015; 25:1043–1055 [View Article] [PubMed]
     [Google Scholar]
  35. Bowers RM, Kyrpides NC, Stepanauskas R, Harmon-Smith M, Doud D et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat Biotechnol 2017; 35:725–731 [View Article] [PubMed]
     [Google Scholar]
  36. 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]
     [Google Scholar]
  37. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods 2012; 9:357–359 [View Article] [PubMed]
     [Google Scholar]
  38. Olm MR, Crits-Christoph A, Bouma-Gregson K, Firek BA, Morowitz MJ et al. inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains. Nat Biotechnol 2021; 39:727–736 [View Article]
     [Google Scholar]
  39. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009; 25:1754–1760 [View Article] [PubMed]
     [Google Scholar]
  40. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 2009; 25:2078–2079 [View Article] [PubMed]
     [Google Scholar]
  41. Moser CL, Meyer BK. Comparison of compendial antimicrobial effectiveness tests: a review. AAPS PharmSciTech 2011; 12:222–226 [View Article] [PubMed]
     [Google Scholar]
  42. Chiu CY, Miller SA. Clinical metagenomics. Nat Rev Genet 2019; 20:341–355 [View Article] [PubMed]
     [Google Scholar]
  43. Chen L, Liu W, Zhang Q, Xu K, Ye G et al. RNA based mNGS approach identifies a novel human coronavirus from two individual pneumonia cases in 2019 Wuhan outbreak. Emerg Microbes Infect 2020; 9:313–319 [View Article] [PubMed]
     [Google Scholar]
  44. Mojarro A, Hachey J, Ruvkun G, Zuber MT, Carr CE. CarrierSeq: a sequence analysis workflow for low-input nanopore sequencing. BMC Bioinformatics 2018; 19:108 [View Article] [PubMed]
     [Google Scholar]
  45. Rodriguez-R LM, Konstantinidis KT. Estimating coverage in metagenomic data sets and why it matters. ISME J 2014; 8:2349–2351 [View Article] [PubMed]
     [Google Scholar]
  46. Corby-Edwards AK. FDA regulation of cosmetics and personal care products. In Cosmetics and FDA Regulation 2013
     [Google Scholar]
  47. Zeng D, Chen Z, Jiang Y, Xue F, Li B. Advances and Challenges in Viability Detection of Foodborne Pathogens. Front Microbiol 2016; 7:1833 [View Article] [PubMed]
     [Google Scholar]
  48. Gaio D, Anantanawat K, To J, Liu M, Monahan L et al. Hackflex: low-cost, high-throughput, Illumina Nextera Flex library construction. Microb Genom 2022; 8: Epub ahead of print January 2022 [View Article]
     [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/mgen/10.1099/mgen.0.000884
Loading
Novel application of metagenomics for the strain-level detection of bacterial contaminants within non-sterile industrial products – a retrospective, real-time analysis
M Gen 8, 000884 (2022); https://doi.org/10.1099/mgen.0.000884
/content/journal/mgen/10.1099/mgen.0.000884
/content/journal/mgen/10.1099/mgen.0.000884
Loading

Data & Media loading...

Supplements

Supplementary material 1

PDF

Supplementary material 2

PDF

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