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Abstract

Purpose. Differential ion mobility spectrometry (DMS) is an analytical technique used to detect volatile organic compounds (VOCs) in gaseous samples at very low concentration ranges from ppb to ppt. The aim of this study was to investigate whether VOC analysis by DMS is capable of detecting Mycobacterium avium subsp. paratuberculosis (MAP).

Methodology. Headspaces of in vitro cultures of two different MAP strains at 1, 2, 3, 4 and 6 weeks after inoculation (each at two dilutions) were analysed with DMS in comparison to control samples without viable bacteria [(i) blank medium, (ii) medium inoculated with heat-inactivated MAP and (iii) sterile-filtered MAP culture broth]. Furthermore, VOC patterns in the headspace over cultures of six non-tuberculous mycobacterial species were compared to MAP-derived VOC patterns. Data analysis included peak detection, cluster analysis, identification of discriminating VOC features (Mann–Whitney U test) and different cross-validated discriminant analyses.

Results. VOC analysis resulted in up to 127 clusters and revealed highly significant differences between MAP strains and controls at all time points. In addition, few clusters allowed differentiation between MAP and other non-tuberculous mycobacteria and even between different MAP strains. Compounds have not been characterized. VOC analysis by DMS was able to identify MAP-positive samples after 1 week of in vitro growth.

Conclusions. This study provides strong evidence that VOC analysis of headspace over mycobacterial cultures in combination with appropriate data analysis has the potential to become a valuable method to identify positive samples much earlier than with current standard procedures.

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/content/journal/jmm/10.1099/jmm.0.000410
2017-03-23
2019-10-23
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References

  1. de Juan L, Alvarez J, Aranaz A, Rodríguez A, Romero B et al. Molecular epidemiology of types I/III strains of Mycobacterium avium subspecies paratuberculosis isolated from goats and cattle. Vet Microbiol 2006;115:102–110 [CrossRef][PubMed]
    [Google Scholar]
  2. Kim SG, Kim EH, Lafferty CJ, Miller LJ, Koo HJ et al. Use of conventional and real-time polymerase chain reaction for confirmation of Mycobacterium avium subsp. paratuberculosis in a broth-based culture system ESP II. J Vet Diagn Invest 2004;16:448–453[PubMed][CrossRef]
    [Google Scholar]
  3. Grant IR, Kirk RB, Hitchings E, Rowe MT. Comparative evaluation of the MGIT and BACTEC culture systems for the recovery of Mycobacterium avium subsp. paratuberculosis from milk. J Appl Microbiol 2003;95:196–201[PubMed][CrossRef]
    [Google Scholar]
  4. Thorn RM, Greenman J. Microbial volatile compounds in health and disease conditions. J Breath Res 2012;6:024001 [CrossRef][PubMed]
    [Google Scholar]
  5. Bos LD, Sterk PJ, Schultz MJ. Volatile metabolites of pathogens: a systematic review. PLoS Pathog 2013;9:e1003311 [CrossRef][PubMed]
    [Google Scholar]
  6. Mcnerney R, Mallard K, Okolo PI, Turner C. Production of volatile organic compounds by mycobacteria. FEMS Microbiol Lett 2012;328:150–156 [CrossRef][PubMed]
    [Google Scholar]
  7. Prasad S, Pierce KM, Schmidt H, Rao JV, Güth R et al. Constituents with independence from growth temperature for bacteria using pyrolysis-gas chromatography/differential mobility spectrometry with analysis of variance and principal component analysis. Analyst 2008;133:760–767 [CrossRef][PubMed]
    [Google Scholar]
  8. Cheung W, Xu Y, Thomas CL, Goodacre R. Discrimination of bacteria using pyrolysis-gas chromatography-differential mobility spectrometry (Py-GC-DMS) and chemometrics. Analyst 2009;134:557–563 [CrossRef][PubMed]
    [Google Scholar]
  9. Pavlou A, Turner AP, Magan N. Recognition of anaerobic bacterial isolates in vitro using electronic nose technology. Lett Appl Microbiol 2002;35:366–369[PubMed][CrossRef]
    [Google Scholar]
  10. Moens M, Smet A, Naudts B, Verhoeven J, Ieven M et al. Fast identification of ten clinically important micro-organisms using an electronic nose. Lett Appl Microbiol 2006;42:121–126 [CrossRef][PubMed]
    [Google Scholar]
  11. Bruins M, Bos A, Petit PL, Eadie K, Rog A et al. Device-independent, real-time identification of bacterial pathogens with a metal oxide-based olfactory sensor. Eur J Clin Microbiol Infect Dis 2009;28:775–780 [CrossRef][PubMed]
    [Google Scholar]
  12. Dolch ME, Hornuss C, Klocke C, Praun S, Villinger J et al. Volatile organic compound analysis by ion molecule reaction mass spectrometry for Gram-positive bacteria differentiation. Eur J Clin Microbiol Infect Dis 2012;31:3007–3013 [CrossRef][PubMed]
    [Google Scholar]
  13. Syhre M, Chambers ST. The scent of Mycobacterium tuberculosis. Tuberculosis 2008;88:317–323 [CrossRef][PubMed]
    [Google Scholar]
  14. Trefz P, Koehler H, Klepik K, Moebius P, Reinhold P et al. Volatile emissions from Mycobacterium avium subsp. paratuberculosis mirror bacterial growth and enable distinction of different strains. PLoS One 2013;8:e76868 [CrossRef][PubMed]
    [Google Scholar]
  15. Cunha MG, Hoenigman S, Kanchagar C, Rearden P, Sassetti CS et al. 2008; Joint analysis of differential mobility spectrometry and mass spectrometry features for tuberculosis biomarkers. In: Proceedings of the 30th Annual International IEEE EMBS Conference Vancouver, British Columbia, Canada: August 20–24, 359–362
    [Google Scholar]
  16. Crespo E, de Ronde H, Kuijper S, Pol A, Kolk AH et al. Potential biomarkers for identification of mycobacterial cultures by proton transfer reaction mass spectrometry analysis. Rapid Commun Mass Spectrom 2012;26:679–685 [CrossRef][PubMed]
    [Google Scholar]
  17. Jünger M, Vautz W, Kuhns M, Hofmann L, Ulbricht S et al. Ion mobility spectrometry for microbial volatile organic compounds: a new identification tool for human pathogenic bacteria. Appl Microbiol Biotechnol 2012;93:2603–2614 [CrossRef][PubMed]
    [Google Scholar]
  18. Filipiak W, Sponring A, Baur MM, Filipiak A, Ager C et al. Molecular analysis of volatile metabolites released specifically by Staphylococcus aureus and Pseudomonas aeruginosa. BMC Microbiol 2012;12:113 [CrossRef][PubMed]
    [Google Scholar]
  19. Filipiak W, Sponring A, Baur MM, Ager C, Filipiak A et al. Characterization of volatile metabolites taken up by or released from Streptococcus pneumoniae and Haemophilus influenzae by using GC-MS. Microbiology 2012;158:3044–3053 [CrossRef][PubMed]
    [Google Scholar]
  20. Stach J, Baumbach JI. Ion mobility spectrometry – basic elements and applications. IJIMS 2002;5:1–21
    [Google Scholar]
  21. Purkhart R, Köhler H, Liebler-Tenorio E, Meyer M, Becher G et al. Chronic intestinal mycobacteria infection: discrimination via VOC analysis in exhaled breath and headspace of feces using differential ion mobility spectrometry. J Breath Res 2011;5:027103 [CrossRef][PubMed]
    [Google Scholar]
  22. Mukherjee R, Chatterji D. Stationary phase induced alterations in mycobacterial RNA polymerase assembly: a cue to its phenotypic resistance towards rifampicin. Biochem Biophys Res Commun 2008;369:899–904 [CrossRef][PubMed]
    [Google Scholar]
  23. Cocito C, Vanlinden F. Metabolism of the TMA group of antigens during the growth cycle of mycobacteria. Med Microbiol Immunol 1988;177:357–367[PubMed][CrossRef]
    [Google Scholar]
  24. Lambrecht RS, Carriere JF, Collins MT. A model for analyzing growth kinetics of a slowly growing Mycobacterium sp. Appl Env Microbiol 1988;54:910–916
    [Google Scholar]
  25. Miller MB, Bassler BL. Quorum sensing in bacteria. Annu Rev Microbiol 2001;55:165–199 [CrossRef][PubMed]
    [Google Scholar]
  26. Borrmann E, Möbius P, Diller R, Köhler H. Divergent cytokine responses of macrophages to Mycobacterium avium subsp. paratuberculosis strains of types II and III in a standardized in vitro model. Vet Microbiol 2011;152:101–111 [CrossRef][PubMed]
    [Google Scholar]
  27. Daffé M. The cell envelope of tubercle bacilli. Tuberculosis 2015;95:S155–S158 [CrossRef][PubMed]
    [Google Scholar]
  28. Lanéelle MA, Nigou J, Daffé M. Lipid and lipoarabinomannan isolation and characterization. Methods Mol Biol 2015;1285:77–103 [CrossRef][PubMed]
    [Google Scholar]
  29. Rivera-Betancourt OE, Karls R, Grosse-Siestrup B, Helms S, Quinn F et al. Identification of mycobacteria based on spectroscopic analyses of mycolic acid profiles. Analyst 2013;138:6774–6785 [CrossRef][PubMed]
    [Google Scholar]
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