Literature DB >> 28052586

Modelling the effect of pH and water activity in the growth of Aspergillus fumigatus isolated from corn silage.

V Alonso1,2, L Cavaglieri1,2, A J Ramos3, A Torres1,2, S Marin3.   

Abstract

AIMS: The aim of this work was to use mathematical kinetic modelling to assess the combined effects of aW, pH, O2 availability and temperature on the growth rate and time to growth of Aspergillus fumigatus strains isolated from corn silage. METHODS AND
RESULTS: A full factorial design was used in which two factors were assayed: pH and aW . The aW levels assayed were 0·80, 0·85, 0·90, 0·92, 0·94, 0·96, 0·98 and 0·99. The levels of pH assayed were 3·5, 4, 4·5, 5, 6, 7, 7·5 and 8. The assay was performed at normal oxygen tension at 25 and 37°C, and at reduced oxygen tension at 25°C. Two strains of A. fumigatus isolated from corn silage were used. Kinetic models were built to predict growth of the strain under the assayed conditions. The cardinal models gave a good quality fit for radial growth rate data. The results indicate that the environmental conditions which take place during silage production, while limiting the growth of most micro-organisms, would not be able to control A. fumigatus. Moreover, pH levels in silage, far from limiting its growth, are also close to its optimum. Carbon dioxide at 5% in the environment did not significantly affect its growth.
CONCLUSIONS: A need for a further and controlled acidification of the silage exists, as no growth of A. fumigatus was observed at pH 3·5, as long as the organoleptic characteristics of the silage are not much compromised. SIGNIFICANCE AND IMPACT OF THE STUDY: Aspergillus fumigatus is one of the major opportunistic pathogens able to cause illness such as allergic bronchopulmonary aspergillosis, aspergilloma and invasive aspergillosis to rural workers. Exposure of animals to A. fumigatus spores can result in infections, particularly in those organs exposed to external invasion, such as the airways, mammary gland and uterus at birth.
© 2017 The Society for Applied Microbiology.

Entities:  

Keywords:  cardinal parameters model; environmental mycology; fungi; predictive modelling; predictive mycology

Mesh:

Substances:

Year:  2017        PMID: 28052586     DOI: 10.1111/jam.13395

Source DB:  PubMed          Journal:  J Appl Microbiol        ISSN: 1364-5072            Impact factor:   3.772


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