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Appl Environ Microbiol. 2005 Sep;71(9):4998-5003.

Heterogeneity of times required for germination and outgrowth from single spores of nonproteolytic Clostridium botulinum.

Author information

1
Institute of Food Research, Norwich Research Park, Colney, Norwich NR4 7UA, United Kingdom. Sandra.stringer@bbsrc.ac.uk

Abstract

Knowledge of the distribution of growth times from individual spores and quantification of this biovariability are important if predictions of growth in food are to be improved, particularly when, as for Clostridium botulinum, growth is likely to initiate from low numbers of spores. In this study we made a novel attempt to determine the distributions of times associated with the various stages of germination and subsequent growth from spores and the relationships between these stages. The time to germination (t(germ)), time to emergence (t(emerg)), and times to reach the lengths of one (t(C1)) and two (t(C2)) mature cells were quantified for individual spores of nonproteolytic C. botulinum Eklund 17B using phase-contrast microscopy and image analysis. The times to detection for wells inoculated with individual spores were recorded using a Bioscreen C automated turbidity reader and were compatible with the data obtained microscopically. The distributions of times to events during germination and subsequent growth showed considerable variability, and all stages contributed to the overall variability in the lag time. The times for germination (t(germ)), emergence (t(emerg) - t(germ)), cell maturation (t(C1) - t(emerg)), and doubling (t(C2) - t(C1)) were not found to be correlated. Consequently, it was not possible to predict the total duration of the lag phase from information for just one of the stages, such as germination. As the variability in postgermination stages is relatively large, the first spore to germinate will not necessarily be the first spore to produce actively dividing cells and start neurotoxin production. This information can make a substantial contribution to improved predictive modeling and better quantitative microbiological risk assessment.

PMID:
16151079
PMCID:
PMC1214666
DOI:
10.1128/AEM.71.9.4998-5003.2005
[Indexed for MEDLINE]
Free PMC Article

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