Computerized procedures for the classification and discrimination of yeasts are presented which base on feature data files and classification techniques involved in a data bank project (BD1). Microbiological (physiological and morphological) and analytical feature data classes (fatty acid patterns from gas chromatography, Fourier coefficients from FTIR spectra, fluorescence patterns) were used to show the possibilities of yeast classification, carried out by means of these features and statistical or non-statistical methods. Results obtained by a suitable combination of these methods are presented. 1ntroduct.ionThe characterization and identification of microorganisms gains an increasing interest in microbiology and biotechnology, e.g., for the selection of production strains, for the control of strain stability, for the identification of known strains and for the classification of unknown strains. Usually, diagnostic methods include the investigation of morphological, physiological and biochemical features. However, for positive identification, large test series are required. This fact makes the application of conventional methods more difficult, time consuming and inefficient. The application of analytical methods such as chromatography, optical spectroscopy and mass spectrometry has been proved useful for the characterization of microorganisms because of the fast availability of data, good reproducibility of measurements and the possibility of computer-aided data acquisition and processing. The aim of the paper is to present the possibilities of a combined application of several microbiological and analytical methods and different mathematical procedures to the classification of microbiological and biotechnological objects, mainly related to microorganisms as the most important analytical objects in biotechnology [ 13. Particularly, aspects of yeast classification are considered. To improve data handling by computational methods, the data bank project BD1 was developed which includes software for data processing by means of statistical and non-statistical procedures [Z].
An analytical method in which we used the selective adsorption of several fluorophores by yeast cells is described. The suitability of using binary mixtures of 1-pyrene butyric acid, 3,6-dimethylamino acridine, 4-acetamido-4'-isothiocyanatostilbene-2,2'-disulfonic acid, and rhodamine B isothiocyanate for the characterization and identification of microorganisms was tested with 98 yeast strains belonging to the genera Candida, Hansenulu, Kluyveromyces, Pichia, Rhodotorula, and Saccharomyces. The application of multivariate statistical methods and pattern recognition methods to the allocation of the yeast strains into genus-species-strain structures and to a comparison of fluorescence data sets for differentiation and identification purposes showed _-
A simple photometric method for the determination of the agglutination rate of cells by lectins in a continuously stirred suspension is presented. Besides the agglutination rate the method allows the estimation of an average degree of agglutination, i.e. the number of cells per aggregate. The influence of the Con A-concentration, cell number, temperature, and pH on the agglutination rate of Saccharomyces cerevisiae H155 have been studied. The results are of good reproducibility and, therefore, the methods is suitable to describe interactions between cell-bound receptors and receptor-specific proteins, e.g. lectins and antibodies.
Institute of Biotechnology, Permoserstrae 15, 0-7050 Leipzig Robert Koch-Insitute of the Federal Health Office of Germany, Nordufer 20, W-l000 Berlin 65 1. ABSTRACTThe Fourier-Transform infrared spectra of intact procaryotic cells (bacteria) have already been used in the past to characterize (differentite, classify and identify) a variety of bacterial strains and taxa In this paper the essential features of a methodology are described which extend the FT-IR pattern recognition approach to intact eucaryotic cells (yeasts/fungi) . Basically, the characteristic information pertaining to microbial FT-IR patterns is explored by applying multivariate statistics and cluster analysis to both, the time and frequency domain of the mid-ir spectral data. MATERIALS, METHODS AND RESULTSFT-IR spectra have been recorded on an IFS-48 spectrometer (Bruker, Analytische Metechnik, Karlsruhe) . The digital spectra were transferred to a MicroVAX workstation 2000 (Digital Equipment) for further evaluation. Prior to all IR measurements a standard experimental procedure concerning culture media, cultivation time, harvesting, sample preparation (here as KBr pellets) and spectral parameters was applied.The discrimination of microorganisms was attained via comparison of FT-IR data either in the time or the frequency domain. The "reproducibility level" ot repetitive and independent measurements of seven different strains over a period of six months gave similarity ratios of approximatly 98% (Fig. 1) . Different yeast species of the genus Rhodotorula, Kluyveromyces, Hansenula and Candida were classified on the basis of IR-data in the time domain according to classical taxonomic schemes: Rhodotorula, Kluyveromyces and Hansenula are grouped into dense clusters. The genus Candida proved to be a heter%genous group as already described by conventional taxonomic techniques Grouping of different strains of the genus Rhodotorula and Saccharomyces in the frequency domain using only the first 64 Fourier coeff icients is shown in Fig. 2. Classification of 108 yeast strains by a linear discriminance analysis with non-elementary d'iscriminance coefficients on the basis of the first 46 elements of the FT-IR patterns yielded to a resubstitution rate of 92%, i.e. 92% of the strains wee correctly grouped in agreement with conventional taxonomic schemes O-8194-0706-2/92/$4.oo Si°/E Vol. 1575 8th International Conference on Fourier Transform Spectroscopy(1991) / 441 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 08/01/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx
The kinetic of lectin or antibody induced agglutination between single cells in a complex but highly specific reproducible marker. It is useful for the identification of strains and mutants. Using the described method we are able to differentiate the mutants of yeast strains.
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