Literature DB >> 26449712

The pattern of growth observed for Clostridium botulinum type A1 strain ATCC 19397 is influenced by nutritional status and quorum sensing: a modelling perspective.

Adaoha E C Ihekwaba1, Ivan Mura2, Michael W Peck3, G C Barker3.   

Abstract

Botulinum neurotoxins (BoNTs) produced by the anaerobic bacterium Clostridium botulinum are the most poisonous substances known to mankind. However, toxin regulation and signals triggering synthesis as well as the regulatory network and actors controlling toxin production are unknown. Experiments show that the neurotoxin gene is growth phase dependent for C. botulinum type A1 strain ATCC 19397, and toxin production is influenced both by culture conditions and nutritional status of the medium. Building mathematical models to describe the genetic and molecular machinery that drives the synthesis and release of BoNT requires a simultaneous description of the growth of the bacterium in culture. Here, we show four plausible modelling options which could be considered when constructing models describing the pattern of growth observed in a botulinum growth medium. Commonly used bacterial growth models are unsuitable to fit the pattern of growth observed, since they only include monotonic growth behaviour. We find that a model that includes both the nutritional status and the ability of the cells to sense their surroundings in a quorum-sensing manner is most successful at explaining the pattern of growth obtained for C. botulinum type A1 strain ATCC 19397. © FEMS 2015.

Entities:  

Keywords:  C. botulinum; bacterial growth; mathematical modelling; nutrient sensitivity; population dynamics; quorum sensing; toxin regulation

Mesh:

Substances:

Year:  2015        PMID: 26449712      PMCID: PMC4622173          DOI: 10.1093/femspd/ftv084

Source DB:  PubMed          Journal:  Pathog Dis        ISSN: 2049-632X            Impact factor:   3.166


  42 in total

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Journal:  Int J Food Microbiol       Date:  1995-07       Impact factor: 5.277

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Journal:  J Biol Chem       Date:  1993-11-15       Impact factor: 5.157

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Authors:  Karen K Hill; Theresa J Smith
Journal:  Curr Top Microbiol Immunol       Date:  2013       Impact factor: 4.291

6.  Quantitative risk assessment for hazards that arise from non-proteolytic Clostridium botulinum in minimally processed chilled dairy-based foods.

Authors:  P K Malakar; G C Barker; M W Peck
Journal:  Food Microbiol       Date:  2010-04-14       Impact factor: 5.516

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Authors:  Michael W Peck; Sandra C Stringer; Andrew T Carter
Journal:  Food Microbiol       Date:  2010-03-17       Impact factor: 5.516

8.  Development and application of a new method for specific and sensitive enumeration of spores of nonproteolytic Clostridium botulinum types B, E, and F in foods and food materials.

Authors:  Michael W Peck; June Plowman; Clare F Aldus; Gary M Wyatt; Walter Penaloza Izurieta; Sandra C Stringer; Gary C Barker
Journal:  Appl Environ Microbiol       Date:  2010-08-13       Impact factor: 4.792

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Journal:  Nature       Date:  1994-12-01       Impact factor: 49.962

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Authors:  Andrew T Carter; Catherine J Paul; David R Mason; Susan M Twine; Mark J Alston; Susan M Logan; John W Austin; Michael W Peck
Journal:  BMC Genomics       Date:  2009-03-19       Impact factor: 3.969

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  3 in total

1.  An Integrative Approach to Computational Modelling of the Gene Regulatory Network Controlling Clostridium botulinum Type A1 Toxin Production.

Authors:  Adaoha E C Ihekwaba; Ivan Mura; John Walshaw; Michael W Peck; Gary C Barker
Journal:  PLoS Comput Biol       Date:  2016-11-17       Impact factor: 4.475

Review 2.  Impact of Clostridium botulinum genomic diversity on food safety.

Authors:  Michael W Peck; Arnoud Hm van Vliet
Journal:  Curr Opin Food Sci       Date:  2016-08       Impact factor: 6.031

3.  Specific Isolation of Clostridium botulinum Group I Cells by Phage Lysin Cell Wall Binding Domain with the Aid of S-Layer Disruption.

Authors:  Zhen Zhang; François P Douillard; Hannu Korkeala; Miia Lindström
Journal:  Int J Mol Sci       Date:  2022-07-29       Impact factor: 6.208

  3 in total

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