[Comparison of Staphylococcus spp. cellular and extracellular proteins by SDS-PAGE]. / Sravnenie Staphylococcus spp. po belkovym profiliam posle denaturiruiushchego élektroforeza kletochnykh i vnekletochnykh belkov v polyakrilamidnom gele.

In this study, a total of fifteen staphylococcal strains belonging to different species were characterized by whole-cell and extracellular protein profiles using sodium dodecylsulfate-polyacrylamide gel electrophoresis (SDS-PAGE). The results are presented as dendrograms after quantitative analysis of the band patterns with a computer program. Visual inspection of protein bands and cluster analysis of protein patterns of 15 strains representing 10 Staphylococcus species showed that whole-cell and extracellular protein profiles differed in several protein bands in Staphylococcus aureus, S. epidermidis, S. simulans and other species of Staphylococcus; however, the differences were insufficient for reliable differentiation of Staphylococcus species by the SDS-PAGE method.

Biosystematic studies on novel streptomycetes from soil.

Members of three putatively novel Streptomyces species, designated Streptomyces groups A, B and C, were repeatedly isolated from environmental samples taken from four hay meadow plots at Cockle Park Experimental Farm, Northumberland (UK). Representative isolates were found to have properties consistent with their classification in the genus Streptomyces and were recovered in three taxa using different phenotypic criteria, namely morphological and pigmentation properties, rapid enzyme tests, and whole-organism fatty acid, protein electrophoretic and pyrolysis mass-spectrometric data. The isolates were rapidly characterised as three taxonomic groups using pyrolysis mass spectrometry. The three taxa were also distinguished from one another and from validly described species of Streptomyces using rapid enzyme tests based on the fluorophores 7-amino-methylcoumarin and 4-methylumbelliferone, and computer-assisted identification procedures. The results indicate that selective isolation and rapid characterisation of streptomycetes using pyrolysis mass spectrometry provide a practical way of determining the phenotypic species diversity of streptomycetes in natural habitats. The experimental data also indicate that representative sampling of cultivable streptomycetes from soil can best be achieved using a multi-step extraction procedure coupled with the use of selective isolation procedures.

Rapid identification of streptomycetes by artificial neural network analysis of pyrolysis mass spectra.

An artificial neural network was trained to distinguish between three putatively novel species of Streptomyces using normalised, scaled prolysis mass spectra from three representative strains of each of the taxa, each sampled in triplicate. Once trained, the artificial neural network was challenged with spectral data from the original organisms, the 'training set', from additional members of the putative novel taxa and from over a hundred strains representing six other actinomycete genera. All of the streptomycetes were correctly identified but many of the other actinomycetes were mis-identified. A modified network topology was developed to recognise the mass spectral patterns of the non-streptomycete strains. The resultant neural network correctly identified the streptomycetes, whereas all of the remaining actinomycetes were recognised as unknown organisms. The improved artificial neural network provides a rapid, reliable and cost-effective method of identifying members of the three target streptomycete taxa.

Artificial neural network analysis of pyrolysis mass spectrometric data in the identification of Streptomyces strains.

Sixteen representatives of three morphologically distinct groups of streptomycetes were recovered from soil using selective isolation procedures. Duplicated batches of the test strains were examined by Curie-point pyrolysis mass spectrometry and the first data set used for conventional multivariate statistical analyses and as a training set for an artificial neural network. The second set of data was used for 'operational fingerprinting' and for testing the artificial neural network. All of the test strains were correctly identified using the artificial neural network whereas only fifteen of the sixteen strains were assigned to the correct group using the conventional operational fingerprinting procedure. Artificial neural network analysis of pyrolysis mass spectrometric data provides a rapid, cost-effective and reproducible way of identifying and typing large numbers of microorganisms.