Antonie van Leeuwenhoek

, Volume 77, Issue 4, pp 359–367 | Cite as

Rapid characterisation of deep-sea actinomycetes for biotechnology screening programmes

  • Joy A. Colquhoun
  • Joseph Zulu
  • Michael Goodfellow
  • Koki Horikoshi
  • Alan C. Ward
  • Alan T. Bull


A continual need in natural product discovery is dereplication, that is the ability to exclude previously tested microorganisms from screening programmes. Whole-cell fingerprinting techniques offer an ideal solution to this problem because of their rapidity and reproducibility, dependence on small samples, and automation. One such technique, Curie-point pyrolysis mass spectrometry (PyMS), has been deployed for the characterisation of a unique collection of actinomycetes recovered from Pacific Ocean sediments approximately 2000 to 6500 m below sea level. This paper addresses the question: to what extent are pyrogroups, defined on the basis of PyMS fingerprinting, related to classifications derived from more conventional microbial systematics? A collection of 44 randomly chosen deep-sea rhodococci were coded and subjected to a double-blind PyMS and numerical taxonomic (NT) analysis; the latter sorted the strains into clusters (taxospecies) using large sets of equally weighted phenotypic data. At the end of the experiment the codes were disclosed and the NT classification shown to generate 6 homogeneous clusters corresponding to different deep-sea sites. The matching of these clusters with the resulting pyrogroups was very high with an overall congruence of nearly 98%. Thus, PyMS characterisation is directly ascribable to the phenotypic variation being sought for biotechnology screens. Moreover, the exquisite discriminatory power of PyMS readily revealed infraspecific diversity in these industrially important bacteria.

actinomycetes deep-sea dereplication pyrolysis mass spectrometry screening 


  1. Bull AT, Goodfellow M & Slater JH (1992) Biodiversity as a source of innovation in biotechnology. Annu. Rev. Microbiol. 46: 219–252.PubMedGoogle Scholar
  2. Chun J, Atalan E, Ward AC & Goodfellow M(1993) Artificial neural network analysis of pyrolysis mass spectrometric data in the identification of Streptomyces strains. FEMS Microbiol. Lett. 107: 321–325.PubMedGoogle Scholar
  3. Chun J, Kang SO, Hah C & Goodfellow M (1996) Phylogeny of mycolic acid-containing actinomycetes. J. Ind. Microbiol. 17: 205–213.Google Scholar
  4. Colquhoun JA, Mexson J, Goodfellow M, Ward AC, Horikoshi K & Bull AT (1988a) Novel rhodococci and other mycolate actinomycetes from the deep-sea. Antonie van Leeuwenhoek 74: 27–40.Google Scholar
  5. Colquhoun JA, Heald SC, Li L, Tamaoka J, Kato C, Horikoshi K & Bull A (1998b) Taxonomy and biotransformation activities of some deep-sea actinomycetes. Extremophiles 2: 269–277.Google Scholar
  6. Donnison AM, Gutteridge CS, Norris JR, Morgan HW & Daniel RM (1986) A preliminary grouping of New Zealand Thermus strains by pyrolysis mass spectrometry. J. Anal. Appl. Pyrol. 9: 281–295.Google Scholar
  7. Ferguson EV, Ward AC, Sanglier JJ & Goodfellow M (1997) Evaluation of Streptomyces species-groups by pyrolysis mass spectrometry. Zbl. Bakt. 285: 169–181.Google Scholar
  8. Fox G, Wisotskey JD & Jurtshuk P (1992) How close is close? 16S rRNA sequence identity may not be sufficient to guarantee species identity. Int. J. Syst. Bacteriol. 42: 166 170.PubMedGoogle Scholar
  9. Freeman R, Goodacre R, Sisson PR, Magee PR, Ward AC & Lightfoot NF (1994) Rapid identification of species within the Mycobacterium tuberculosis complex by artificial neural network analysis of pyrolysis mass spectra. J. Med. Microbiol. 40: 170–173.PubMedGoogle Scholar
  10. Goodacre R et al. (1998a) Rapid identification of urinary tract infection bacteria using hyperspectral whole-organism fingerprinting and artificial neural networks. Microbiol. 144: 1157–1170.Google Scholar
  11. Goodacre R, Rooney PJ & Kell DB (1998b) Discrimination between methicillin-resistant and methicillin-susceptible Staphylococcus aureus using pyrolysis mass spectrometry and artificial neural networks. J. Antimicrob. Chemother. 41: 23–34.PubMedGoogle Scholar
  12. Goodfellow M & Alderson G (1977) The actinomycete-genus Rhodococcus: a home for the ‘rhodochrous’ complex. J. Gen. Microbiol. 100: 99–122.PubMedGoogle Scholar
  13. Goodfellow M & Haynes JA (1984) In: Ortiz-Ortiz L et al. (Eds) Biological, Biochemical and Biomedical Aspects of Actinomycetes. Academic Press, Orlando.Google Scholar
  14. Goodfellow M, Freeman R & Sisson PR (1997) Curie-point pyrolysis mass spectrometry as a tool in clinical microbiology. Zbl. Bakt. 285: 133–156.Google Scholar
  15. Goodfellow M, Weaver CR & Minnikin DB (1982) Numerical classification of some rhodococci, corynebacteria and related organisms. J. Gen. Microbiol. 128: 731–745.PubMedGoogle Scholar
  16. Goodfellow M, Alderson G & Chun J (1998) Rhodococcal systematics: problems and developments. Antonie van Leeuwenhoek 74: 3–20.Google Scholar
  17. Hughes J, Armitage YC & Symes KC (1998) Application of whole rhodococcal biocatalysts in acrylic polymer manufacture. Antonie van Leeuwenhoek 74: 107–118.Google Scholar
  18. Li L, Kato C & Horikoshi K (1999) Bacterial diversity in deep-sea sediments from different depths. Biodiv. Conserv. 8: 659–677.Google Scholar
  19. Magee PR (1994) In: Goodfellow M & O'Donnell AG (Eds) Chemical Methods in Prokaryotic Systematics (pp 523–553). John Wiley & Sons, Chichester.Google Scholar
  20. Minnikin DE (1988) In: Hancock IC & Poxton IR (Eds) Bacterial Cell Surface Techniques (pp 125–135). John Wiley & Sons, Chichester.Google Scholar
  21. Nisbet LJ & Moore M (1997) Will natural products remain an important source of drug research for the future? Curr. Opin. Microbiol. 8: 708–712.Google Scholar
  22. Sanglier J-J, Whitehead JD, Saddler GS, Ferguson EV & Goodfellow M (1992) Pyrolysis mass spectrometry as a method for the classification, identification and selection of actinomycetes. Gene 115: 235–242.PubMedGoogle Scholar
  23. Sneath PHA (1995) Thirty years of numerical taxonomy. Syst. Biol. 44: 281–298.Google Scholar
  24. Sneath PHA & Johnson R (1972) The influence on numerical taxonomic similarities of errors in microbiological tests. J. Gen. Microbiol. 72: 377–392.PubMedGoogle Scholar
  25. Sneath PHA & Sokal RR (1973) Numerical Taxonomy: The Principles and Practice of Numerical Classification. Freeman, San Francisco.Google Scholar
  26. Takizawa M, Colwell RR & Hill RT (1993) Isolation and diversity of actinomycetes in the Chesapeake Bay. Appl. Env. Microbiol. 59: 997–1002.Google Scholar
  27. Weyland H (1969) Actinomycetes in North Sea and Atlantic Ocean sediments. Nature 223: 858.Google Scholar

Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • Joy A. Colquhoun
    • 1
  • Joseph Zulu
    • 2
  • Michael Goodfellow
    • 2
  • Koki Horikoshi
    • 3
  • Alan C. Ward
    • 2
  • Alan T. Bull
    • 4
  1. 1.Research School of BiosciencesUniversity of Kent, CanterburyKentUK
  2. 2.Department of Agricultural and Environmental ScienceUniversity of NewcastleNewcastle upon TypeUK
  3. 3.The DeepStar GroupJapan Marine Science and Technology Center (JAMSTEC)YokosukaJapan
  4. 4.Research School of BiosciencesUniversity of Kent, CanterburyKentUK

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