Use of predictive modeling for Propionibacterium strain classification

doi:10.1078/0723-2020-00129

Systematic and Applied Microbiology

Volume 25, Issue 3, 2002, Pages 386-395

https://doi.org/10.1078/0723-2020-00129 Get rights and content

Summary

Computed data analysis of biochemical or molecular profiles is currently used in studies of microbial taxonomy, epidemiology, and microbial diversity. We assessed the use of Partial Least Squares (PLS) regression for multivariate data analysis in bacteriology. We identified clear relationships between RAPD profiles of propionibacteria strains and their species classification, autolytic capacities, and their origins. The PLS regression also predicted species identity of some strains with RAPD profiles partially related to those of reference strains. The PLS analysis also allowed us to identify key characteristics to use to classify strains. PLS regression is particularly well adapted to i) describing a collection of bacterial isolates, ii) justifying bacterial groupings using several sets of data, and iii) predicting phenotypic characters of strains that have been classified by routine typing methods.

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Species of dairy propionibacteria are used as cheese-ripening cultures as well as probiotics. However, no rapid identification methods are currently available. With this in mind, the present study compared three methods, (i) carbohydrate fermentation, (ii) ARDRA (amplified ribosomal DNA restriction analysis) and (iii) peptidoglycan hydrolase (PGH) activity profiles to improve the identification of Propionibacterium thoenii, Propionibacterium jensenii, Propionibacterium acidipropionici and Propionibacterium microaerophilum. The species Propionibacterium freudenreichii and Propionibacterium cyclohexanicum have previously been shown to be easily distinguishable from the other species. Principal component analysis of the carbohydrate fermentation profiles of 113 P. thoenii, P. jensenii, P. acidipropionici and P. microaerophilum strains correctly classified 85% of the strains based on the fermentation of seven carbohydrates. Regarding PGH profiles, optimized conditions of PGH-renaturing SDS–PAGE were applied to 34 of the strains. The PGH profiles of P. acidipropionici and P. microaerophilum were indistinguishable from one another, but were easily distinguished from P. jensenii and P. thoenii. However, four strains exhibited atypical profiles. Hence, in general, the PGH profiles were shown to be conserved within a species, with some exceptions. Four endonucleases were tested for ARDRA and the four species differentiated by combining the profiles obtained with MspI and HaeIII. P. freudenreichii and P. cyclohexanicum profiles were also performed but showed wide differences. Consequently, ARDRA was shown to be the most appropriate method for rapidly distinguishing strains of propionibacteria. Carbohydrate fermentation and peptidoglycan hydrolase activity profiles are useful as complementary identification tools, since about 15% of the 34 strains tested showed atypical profiles.
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Use of predictive modeling for Propionibacterium strain classification

Summary

System. Appl. Microbiol.

Syst. Appl. Microbiol.

Res. Microbiol.

System. Appl. Microbiol.

J. Dairy Sci.

Int. J. Food Microbiol.

System. Appl. Microbiol.

System. Appl. Microbiol.

Random amplified polymorphism DNA typing versus pulsed-field gel electrophoresis for epidemiological typing of vancomycin-resistant enterococci

J. Clin. Microbiol.

Molecular diversity of lactic acid bacteria from cassava sour starch (Colombia)

System. Appl. Microbiol.

GenBank

Nucleic Acids Res.

A numerical taxonomic study of Propionibacterium strains from dairy sources

J. Appl. Bacteriol.

Inter- and intrageneric relationships of the genus Propionibacterium as determined by 16S rRNA sequences

J. Gen. Microbiol.

An ecological study of lactococci isolated from raw milk in the Camembert cheese registered designation of origin area

Appl. Environ. Microbiol.

Rapid identification of dairy Propionibacterium species by restriction analysis of the insertion within the 23S rRNA gene

Lait

Propionibacteria flora in Swiss raw milk from lowlands and Alps

Lait