Rapid identification using pyrolysis mass spectrometry and artificial neural networks of Propionibacterium acnes isolated from dogs

Variable selection in discriminant partial least-squares analysis

Variable selection enhances the understanding and interpretability of multivariate classification models. A new chemometric method based on the selection of the most important variables in discriminant partial least-squares (VS-DPLS) analysis is described. The suggested method is a simple extension of DPLS where a small number of elements in the weight vector w is retained for each factor. The optimal number of DPLS factors is determined by cross-validation. The new algorithm is applied to four different high-dimensional spectral data sets with excellent results. Spectral profiles from Fourier transform infrared spectroscopy and pyrolysis mass spectrometry are used. To investigate the uniqueness of the selected variables an iterative VS-DPLS procedure is performed. At each iteration, the previously found selected variables are removed to see if a new VS-DPLS classification model can be constructed using a different set of variables. In this manner, it is possible to determine regions rather than individual variables that are important for a successful classification.

Rapid authentication of animal cell lines using pyrolysis mass spectrometry and auto-associative artificial neural networks

Pyrolysis mass spectrometry (PyMS) was used to produce biochemical fingerprints from replicate frozen cell cultures of mouse macrophage hybridoma 2C11-12, human leukaemia K562, baby hamster kidney BHK 21/C13, and mouse tumour BW-O, and a fresh culture of Chinese hamster ovary CHO cells. The dimensionality of these data was reduced by the unsupervised feature extraction pattern recognition technique of auto-associative neural networks. The clusters observed were compared with the groups obtained from the more conventional statistical approaches of hierarchical cluster analysis. It was observed that frozen and fresh cell line cultures gave very different pyrolysis mass spectra. When only the frozen animal cells were analysed by PyMS, auto-associative artificial neural networks (ANNs) were employed to discriminate between them successfully. Furthermore, very similar classifications were observed when the same spectral data were analysed using hierarchical cluster analysis. We demonstrate that this approach can detect the contamination of cell lines with low numbers of bacteria and fungi; this approach could plausibly be extended for the rapid detection of mycoplasma infection in animal cell lines. The major advantages that PyMS offers over more conventional methods used to type cell lines and to screen for microbial infection, such as DNA fingerprinting, are its speed, sensitivity and the ability to analyse hundreds of samples per day. We conclude that the combination of PyMS and ANNs can provide a rapid and accurate discriminatory technique for the authentication of animal cell line cultures.

Rapid screening for metabolite overproduction in fermentor broths, using pyrolysis mass spectrometry with multivariate calibration and artificial neural networks

Binary mixtures of model systems consisting of the antibiotic ampicillin with either Escherichia coli or Staphylococcus auresu were subjected to pyrolysis mass spectrometry (PyMS). To deconvolute the pyrolysis mass spectra, so as to obtain quantitative information on the concentration of ampicilin in the mixtures, partial least squares regression (PLS), principal components regression (PCR), and fully interconnected feedforward artificial neural networks (ANNs) were studied. In the latter case, the weights were modified using the standard backpropagation algorithm, and the nodes used a sigmoidal squsahing funciton. It was found that each of the methods could be used to provide calibration models which gave excellent predictions for the concentrations of ampicillin in samples on which they had not been trained. Furthermore, ANNs trained to predict the amount of ampicilin in E. coli were able to generalise so as to predict the concentration of ampicillin in a S. aureus background, illustrating the robustness of ANNs to rather substantial variations in the biological background. The PyMS of the complex mixture of ampicilin in bacteria could not be expressed simply in terms of additive combinations of the spectra describing the pure components of the mixtures and their relative concentrations. Intermolecular reactions took place in the pyrolysate, leading to a lack of superposition of the spectral components and to a dependence of the normalized mass spectrum on sample size. Samples from fermentations of a single organism in a complex production medium were also analyzed quantitatively for a drug of commercial interest. The drug could also be quantified in a variety of mutant-producing strains cultivated in the same medium. The combination of PyMS and ANNs constitutes a novel, rapid, and convenient method for exploitation in strain improvement screening programs. (c) 1994 John Wiley & Sons, Inc.

Conventional and Proteomic Technologies for the Detection of Early Stage Malignancies: Markers for Ovarian Cancer

Our understanding of the tumor microenvironment continues to evolve and allows for the identification of biomarkers that should detect the presence of early stage malignancies. Recent advances in computational analysis and biomedical technologies have come together to elucidate signatures associated with cancer and that are capable of identifying unique tumor-specific proteins. Within the tumor microenvironment, we continue to characterize the proteophysiology of the different steps associated with tumor progression. The urgent need for biomarkers accurately detecting early-stage epithelial ovarian cancer has prompted us, and others, to engage in a search for specific peptide signatures that may discriminate transformed cells from those of the normal ovarian microenvironment. This endeavor also provides new insights into the biology of the disease, which may not only be applicable to detection but may also help to initiate new therapies and optimize patient care.

Artificial neural networks in biology and chemistry: the evolution of a new analytical tool

Once regarded as an eccentric and unpromising algorithm for the analysis of scientific data, the neural network has been developed in the last decade into a powerful computational tool. Its use now spans all areas of science, from the physical sciences and engineering to the life sciences and allied subjects. Applications range from the assessment of epidemiological data or the deconvolution of spectra to highly practical applications, such as the electronic nose. This introductory chapter considers briefly the growth in the use of neural networks and provides some general background in preparation for the more detailed chapters that follow.

Use of unsupervised and supervised artificial neural networks for the identification of lactic acid bacteria on the basis of SDS-PAGE patterns of whole cell proteins

Conventional multivariate statistical techniques (hierarchical cluster analysis, linear discriminant analysis) and unsupervised (Kohonen Self Organizing Map) and supervised (Bayesian network) artificial neural networks were compared for as tools for the classification and identification of 352 SDS-PAGE patterns of whole cell proteins of lactic acid bacteria belonging to 22 species of the genera Lactobacillus, Leuconostoc, Enterococcus, Lactococcus and Streptococcus including 47 reference strains. Electrophoretic data were pre-treated using the logistic weighting function described by Piraino et al. [Piraino, P., Ricciardi, A., Lanorte, M. T., Malkhazova, I., Parente, E., 2002. A new procedure for data reduction in electrophoretic fingerprints of whole-cell proteins. Biotechnol. Lett. 24, 1477-1482]. Hierarchical cluster analysis provided a satisfactory classification of the patterns but was unable to discriminate some species (Leuconostoc, Lb. sakei/Lb. curvatus, Lb. acidophilus/Lb. helveticus, Lb. plantarum/Lb. paraplantarum, Lc. lactis/Lc. raffinolactis). A 7x7 Kohonen self-organizing map (KSOM), trained with the patterns of the reference strains, provided a satisfactory classification of the patterns and was able to discriminate more species than hierarchical cluster analysis. The map was used in predictive mode to identify unknown strains and provided results which in 85.5% of cases matched the classification obtained by hierarchical cluster analysis. Two supervised tools, linear discriminant analysis and a 23:5:2 Bayesian network were proven to be highly effective in the discrimination of SDS-PAGE patterns of Lc. lactis from those of other species. We conclude that data reduction by logistic weighting coupled to traditional multivariate statistical analysis or artificial neural networks provide an effective tool for the classification and identification of lactic acid bacteria on the basis of SDS-PAGE patterns of whole cell proteins.

Artificial neural network based identification of Campylobacter species by Fourier transform infrared spectroscopy

Two prototypes of artificial neural network (ANN), multilayer perceptron (MLP), and probabilistic neural network (PNN), were used to analyze infrared (IR) spectral data obtained from intact cells belonging to the species Campylobacter coli and Campylobacter jejuni. In order to establish a consistent identification and typing procedure, mid infrared spectra of these species were obtained by means of a Fourier transform infrared (FT-IR) spectroscope. FT-IR patterns belonging to 26 isolates subclassified into 4 genotypes were pre-processed (normalized, smoothed and derivatized) and grouped into training, verification and test sets. The two architectures tested (PNN, MLP) were developed and trained to identify or leave unassigned a number of IR patterns. Two window ranges (w(4), 1200 to 900 cm(-1); and w(5), 900 to 700 cm(-1)) in the mid IR spectrum were presented as input to the ANN models functioning as pattern recognition systems. No matter the ANN used all the training sets were correctly identified at subspecies level. For the test set, the four-layer MLP network was found to be specially suitable to recognize FT-IR data since it correctly identified 99.16% of unknowns using the w(4) range, and was fully successful in detecting atypical patterns from closely related Campylobacter strains and other bacterial species. The PNN network obtained lower percentages in assignation and rejection. Overall, ANNs constitute an excellent mathematical tool in microbial identification, since they are able to recognize with a high degree of confidence typical as well as atypical FT-IR fingerprints from Campylobacter spp.

Gut microorganisms, mammalian metabolism and personalized health care

The mammalian gut microbiota interact extensively with the host through metabolic exchange and co-metabolism of substrates. Such metabolome-metabolome interactions are poorly understood, but might be implicated in the aetiology of many human diseases. In this paper, we assess the importance of the gut microbiota in influencing the disposition, fate and toxicity of drugs in the host, and conclude that appropriate consideration of individual human gut microbial activities will be a necessary part of future personalized health-care paradigms.

Genetic algorithm optimization for pre-processing and variable selection of spectroscopic data

MOTIVATION

The major difficulties relating to mathematical modelling of spectroscopic data are inconsistencies in spectral reproducibility and the black box nature of the modelling techniques. For the analysis of biological samples the first problem is due to biological, experimental and machine variability which can lead to sample size differences and unavoidable baseline shifts. Consequently, there is often a requirement for mathematical correction(s) to be made to the raw data if the best possible model is to be formed. The second problem prevents interpretation of the results since the variables that most contribute to the analysis are not easily revealed; as a result, the opportunity to obtain new knowledge from such data is lost.

METHODS

We used genetic algorithms (GAs) to select spectral pre-processing steps for Fourier transform infrared (FT-IR) spectroscopic data. We demonstrate a novel approach for the selection of important discriminatory variables by GA from FT-IR spectra for multi-class identification by discriminant function analysis (DFA).

RESULTS

The GA selects sensible pre-processing steps from a total of approximately 10(10) possible mathematical transformations. Application of these algorithms results in a 16% reduction in the model error when compared against the raw data model. GA-DFA recovers six variables from the full set of 882 spectral variables against which a satisfactory DFA model can be formed; thus inferences can be made as to the biochemical differences that are reflected by these spectral bands.

Recognition of anaerobic bacterial isolates in vitro using electronic nose technology

AIMS

Use of an electronic nose (e.nose) system to differentiation between anaerobic bacteria grown in vitro on agar media.

METHODS AND RESULTS

Cultures of Clostridium spp. (14 strains) and Bacteroides fragilis (12 strains) were grown on blood agar plates and incubated in sampling bags for 30 min before head space analysis of the volatiles. Qualitative analyses of the volatile production patterns was carried out using an e.nose system with 14 conducting polymer sensors. Using data analysis techniques such as principal components analysis (PCA), genetic algorithms and neural networks it was possible to differentiate between agar blanks and individual species which accounted for all the data. A total of eight unknowns were correctly discriminated into the bacterial groups.

CONCLUSIONS

This is the first report of in vitro complex volatile pattern recognition and differentiation of anaerobic pathogens.

SIGNIFICANCE AND IMPACT OF THE STUDY

These results suggest the potential for application of e.nose technology in early diagnosis of microbial pathogens of medical importance.

Comparison of statistical methods for identification of Streptococcus thermophilus, Enterococcus faecalis, and Enterococcus faecium from randomly amplified polymorphic DNA patterns

Thermophilic streptococci play an important role in the manufacture of many European cheeses, and a rapid and reliable method for their identification is needed. Randomly amplified polymorphic DNA (RAPD) PCR (RAPD-PCR) with two different primers coupled to hierarchical cluster analysis has proven to be a powerful tool for the classification and typing of Streptococcus thermophilus, Enterococcus faecium, and Enterococcus faecalis (G. Moschetti, G. Blaiotta, M. Aponte, P. Catzeddu, F. Villani, P. Deiana, and S. Coppola, J. Appl. Microbiol. 85:25-36, 1998). In order to develop a fast and inexpensive method for the identification of thermophilic streptococci, RAPD-PCR patterns were generated with a single primer (XD9), and the results were analyzed using artificial neural networks (Multilayer Perceptron, Radial Basis Function network, and Bayesian network) and multivariate statistical techniques (cluster analysis, linear discriminant analysis, and classification trees). Cluster analysis allowed the identification of S. thermophilus but not of enterococci. A Bayesian network proved to be more effective than a Multilayer Perceptron or a Radial Basis Function network for the identification of S. thermophilus, E. faecium, and E. faecalis using simplified RAPD-PCR patterns (obtained by summing the bands in selected areas of the patterns). The Bayesian network also significantly outperformed two multivariate statistical techniques (linear discriminant analysis and classification trees) and proved to be less sensitive to the size of the training set and more robust in the response to patterns belonging to unknown species.

Differentiation of brewing yeast strains by pyrolysis mass spectrometry and Fourier transform infrared spectroscopy

Two rapid spectroscopic approaches for whole-organism fingerprinting--pyrolysis mass spectrometry (PyMS) and Fourier transform infrared spectroscopy (FT-IR)--were used to analyse 22 production brewery Saccharomyces cerevisiae strains. Multivariate discriminant analysis of the spectral data was then performed to observe relationships between the 22 isolates. Upon visual inspection of the cluster analyses, similar differentiation of the strains was observed for both approaches. Moreover, these phenetic classifications were found to be very similar to those previously obtained using genotypic studies of the same brewing yeasts. Both spectroscopic techniques are rapid (typically 2 min for PyMS and 10 s for FT-IR) and were shown to be capable of the successful discrimination of both ale and lager yeasts. We believe that these whole-organism fingerprinting methods could find application in brewery quality control laboratories.

Rapid identification of urinary tract infection bacteria using hyperspectral whole-organism fingerprinting and artificial neural networks

Three rapid spectroscopic approaches for whole-organism fingerprinting-pyrolysis mass spectrometry (PyMS), Fourier transform infra-red spectroscopy (FT-IR) and dispersive Raman microscopy--were used to analyse a group of 59 clinical bacterial isolates associated with urinary tract infection. Direct visual analysis of these spectra was not possible, highlighting the need to use methods to reduce the dimensionality of these hyperspectral data. The unsupervised methods of discriminant function and hierarchical cluster analyses were employed to group these organisms based on their spectral fingerprints, but none produced wholly satisfactory groupings which were characteristic for each of the five bacterial types. In contrast, for PyMS and FT-IR, the artificial neural network (ANN) approaches exploiting multi-layer perceptrons or radial basis functions could be trained with representative spectra of the five bacterial groups so that isolates from clinical bacteriuria in an independent unseen test set could be correctly identified. Comparable ANNs trained with Raman spectra correctly identified some 80% of the same test set. PyMS and FT-IR have often been exploited within microbial systematics, but these are believed to be the first published data showing the ability of dispersive Raman microscopy to discriminate clinically significant intact bacterial species. These results demonstrate that modern analytical spectroscopies of high intrinsic dimensionality can provide rapid accurate microbial characterization techniques, but only when combined with appropriate chemometrics.

The deconvolution of pyrolysis mass spectra using genetic programming: application to the identification of some Eubacterium species

Pyrolysis mass spectrometry was used to produce complex biochemical fingerprints of Eubacterium exiguum, E. infirmum, E. tardum and E. timidum. To examine the relationship between these organisms the spectra were clustered by canonical variates analysis, and four clusters, one for each species, were observed. In an earlier study we trained artificial neural networks to identify these clinical isolates successfully; however, the information used by the neural network was not accessible from this so-called 'black box' technique. To allow the deconvolution of such complex spectra (in terms of which masses were important for discrimination) it was necessary to develop a system that itself produces 'rules' that are readily comprehensible. We here exploit the evolutionary computational technique of genetic programming; this rapidly and automatically produced simple mathematical functions that were also able to classify organisms to each of the four bacterial groups correctly and unambiguously. Since the rules used only a very limited set of masses, from a search space some 50 orders of magnitude greater than the dimensionality actually necessary, visual discrimination of the organisms on the basis of these spectral masses alone was also then possible.

Rapid differentiation of closely related Candida species and strains by pyrolysis-mass spectrometry and Fourier transform-infrared spectroscopy

Two rapid spectroscopic approaches for whole-organism fingerprinting of pyrolysis-mass spectrometry (PyMS) and Fourier transform-infrared spectroscopy (FT-IR) were used to analyze a group of 29 clinical and reference Candida isolates. These strains had been identified by conventional means as belonging to one of the three species Candida albicans, C. dubliniensis (previously reported as atypical C. albicans), and C. stellatoidea (which is also closely related to C. albicans). To observe the relationships of the 29 isolates as judged by PyMS and FT-IR, the spectral data were clustered by discriminant analysis. On visual inspection of the cluster analyses from both methods, three distinct clusters, which were discrete for each of the Candida species, could be seen. Moreover, these phenetic classifications were found to be very similar to those obtained by genotypic studies which examined the HinfI restriction enzyme digestion patterns of genomic DNA and by use of the 27A C. albicans-specific probe. Both spectroscopic techniques are rapid (typically, 2 min for PyMS and 10 s for FT-IR) and were shown to be capable of successfully discriminating between closely related isolates of C. albicans, C. dubliniensis, and C. stellatoidea. We believe that these whole-organism fingerprinting methods could provide opportunities for automation in clinical microbial laboratories, improving turnaround times and the use of resources.

Artificial neural network identification of heterotrophic marine bacteria based on their fatty-acid composition

The traditional approach to biochemical identification of marine fresh isolates requires considerably long culture preparation times and large quantities of expensive materials and reagents, and the results are not reliable. On the other hand, taxonomy tests based on DNA composition, although sensitive and reliable, require long execution time and high costs. A method is presented for the classification of fatty-acid profiles, extracted from marine bacteria strains, at genus level based on supervised artificial neural networks. The proposed method allows the correct identification of all patterns belonging to the test set. Moreover, a quantitative measure of the importance of each fatty acid for bacterial classification is also achieved. This measure allows the determination of a cluster of fatty acids to be controlled with greater care. The results show that the proposed method is reproducible and rapid, so that it can be routinely used in the marine microbiology laboratory to identify fresh isolates.

Epidemiological typing of Klebsiella pneumoniae by pyrolysis mass spectrometry

Thirteen isolates of ceftazidime-resistant Klebsiella pneumoniae from a suspected cross-infection outbreak involving patients on an intensive care unit and a haematology ward were examined in pyrolysis-mass spectrometry (Py-MS), along with eight concurrent non-outbreak-associated clinical isolates of klebsiellae as controls. Py-MS showed tight clustering of the suspected outbreak isolates, suggesting cross-infection with a single strain. Non-outbreak isolates were clearly distinct from one another and from the outbreak strain. The results confirm that Py-MS is a powerful tool for rapid strain comparison in investigations of cross-infection incidents.

Long-term identification of streptomycetes using pyrolysis mass spectrometry and artificial neural networks

Sixteen reference strains and thirteen fresh isolates of three putatively novel Streptomyces species were examined six times over twenty months using pyrolysis mass spectrometry to examine the long-term reproducibility of the procedure. The reference strains and new isolates were correctly identified using information in each of the datasets and operational fingerprinting, but direct statistical comparison of the datasets for strain identification was unsuccessful between datasets. Artificial neural networks were also used to identify the strains held in the datasets. Neural networks trained with pyrolysis mass spectra from a single dataset were found to successfully identify the reference strains and fresh isolates in that dataset but were unable to identify many of the strains in the other datasets. However, a neural network trained on representative pyrolysis mass spectra from each of the first three datasets were found to identify the reference strains and fresh isolates in those three datasets and in the three subsequent datasets. Therefore, artificial neural network analysis of pyrolysis mass spectrometric data can provide a rapid, cost-effective, accurate and long-term reproducible way of identifying and typing microorganisms.

Curie-point pyrolysis mass spectrometry as a tool in clinical microbiology

Pyrolysis mass spectrometry is a well established analytical tool that has received a considerable boost from the development of low cost, dedicated instruments and sophisticated statistical analyses on personal computers. Further analytical developments, especially in the area of neural networks, are pushing the technology to the forefront of methods for the discrimination and identification of microorganisms and their products. The speed and reproducibility of pyrolysis mass spectrometry and its applicability to a wide range of microorganisms make it an attractive method for epidemiological studies. For inter-strain comparisons, the method is at least as discriminatory as conventional typing systems and usually gives discrimination similar to that of nucleic acid fingerprinting techniques. There has been some success in using neural networks to make identifications across pyrolysis mass spectrometric batches. Further development of methods used to handle data from multiple PyMS analyses can be expected to extend the value of pyrolysis mass spectrometry in clinical microbiology.

Application of neural networks to the analysis of pyrolysis mass spectra

Pyrolysis mass spectrometry is a data rich analysis technique now becoming widely applied in microbiology. Data analysis is a key step in the exploitation of the technique and the application of neural network analysis to pyrolysis mass spectrometric data offers new opportunities for classification, identification and inter-strain comparison of microorganisms in biotechnology and clinical microbiology. The use of a supervised neural network for the identification of members of a streptomycete species-group is described.

Quantitative analysis of multivariate data using artificial neural networks: a tutorial review and applications to the deconvolution of pyrolysis mass spectra

The implementation of artificial neural networks (ANNs) to the analysis of multivariate data is reviewed, with particular reference to the analysis of pyrolysis mass spectra. The need for and benefits of multivariate data analysis are explained followed by a discussion of ANNs and their optimisation. Finally, an example of the use of ANNs for the quantitative deconvolution of the pyrolysis mass spectra of Staphylococcus aureus mixed with Escherichia coli is demonstrated.

Sub-species discrimination, using pyrolysis mass spectrometry and self-organising neural networks, of Propionibacterium acnes isolated from normal human skin

Curie-point pyrolysis mass spectra were obtained from 30 Propionibacterium acnes strains isolated from the foreheads of six healthy humans. Multivariate analyses and Kohonen artificial neural networks (KANNs), employing unsupervised learning, were used successfully to discriminate between the P.acnes isolates from different individual hosts. The classification of the isolates by KANNs was compared with the more classical multivariate techniques of canonical variates analysis and hierarchical cluster analysis and found to give similar groupings. The combination of pyrolysis mass spectrometry with these numerical methods also showed that more than one strain of P.acnes had been isolated from three of the human hosts.

Identification methods for campylobacters, helicobacters, and related organisms

The organisms which are referred to as campylobacteria are associated with a diverse range of diseases and habitats and are important from both clinical and economic perspectives. Accurate identification of these organisms is desirable for deciding upon appropriate therapeutic measures, and also for furthering our understanding of their pathology and epidemiology. However, the identification process is made difficult because of the complex and rapidly evolving taxonomy, fastidious nature, and biochemical inertness of these bacteria. These problems have resulted in a proliferation of phenotypic and genotypic methods for identifying members of this group. The purpose of this review is to summarize the problems associated with identifying campylobacteria, critically appraise the methods that have been used for this purpose, and discuss prospects for improvements in this field.

Pyrolysis mass spectrometry and its applications in biotechnology

Pyrolysis mass spectrometry is a rapid and high-resolution method for the analysis of otherwise non-volatile material and has been widely applied for discriminating between closely related microbial strains. Recent advances in statistical and neural network methods based on supervised learning have now permitted exploitation of pyrolysis mass spectrometry in the quantitative analysis of many diverse samples of biotechnological interest; the technique may thus be regarded as an 'anything-sensor'.

Correction of mass spectral drift using artificial neural networks

For pyrolysis mass spectrometry (PyMS) to be used for the routine identification of microorganisms, for quantifying determinands in biological and biotechnological systems, and in the production of useful mass spectral libraries, it is paramount that newly acquired spectra be compared to those previously collected. Neural network and other multivariate calibration models have been used to relate mass spectra to the biological features of interest. As commonly observed, however, mass spectral fingerprints showed a lack of long-term reproducibility, due to instrumental drift in the mass spectrometer; when identical materials were analyzed by PyMS at dates from 4 to 20 months apart, neural network models produced at earlier times could not be used to give accurate estimates of determinand concentrations or bacterial identities. Neural networks, however, can be used to correct for pyrolysis mass spectrometer instrumental drift itself, so that neural network or other multivariate calibration models created using previously collected data can be used to give accurate estimates of determinand concentration or the nature of bacteria (or, indeed, other materials) from newly acquired pyrolysis mass spectra. This approach is not limited solely to pyrolysis mass spectrometry but is generally applicable to any analytical tool which is prone to instrumental drift, such as IR, ESR, NMR and other spectroscopies, and gas and liquid chromatography, as well as other types of mass spectrometry.

Reclassification of "Propionibacterium rubrum" as P. jensenii

The taxonomic relationship of strains previously designated as "Propionibacterium rubrum" to P. thoenii and P. jensenii was investigated by use of 16S ribosomal RNA sequence comparison, biochemical characteristics and DNA hybridization. A total of 46 strains representing the species P. jensenii and P. thoenii and the former species "P. rubrum" and also including 21 reference strains and 25 strains isolated from dairy sources were studied. The 16S rRNA sequence of strain "P. rubrum" CNRZ 85 (= ATCC 4871) was found to be almost identical to that of the type strain of P. jensenii. DNA hybridization data indicated that "P. rubrum" should belong to the species P. jensenii rather than P. thoenii, as formerly proposed. The "P. rubrum" strains should then be reclassified as a beta-haemolytic biovar of P. jensenii. The genomic species P. jensenii and P. thoenii could be differentiated by biochemical characteristics such as the production of acid from myo-inositol and starch.

A simple method for differentiation of Propionibacterium acnes and Propionibacterium propionicum

TLC glycolipid profiles of several culture collection and clinical strains of Propionibacterium acnes and Propionibacterium propionicum were examined. The former were characterized by weak orcinol-positive minor glycolipids of type g, while the others had mainly strong orcinol-positive major glycolipids of type G. The simple and rapid small scale procedure seemed to be useful for differentiation of these phenotypically similar and genotypically closely related species irrespective of their serotypes.

Rapid and quantitative analysis of recombinant protein expression using pyrolysis mass spectrometry and artificial neural networks: application to mammalian cytochrome b5 in Escherichia coli

Recombinant Escherichia coli clones encoding between 0 and 6 copies of the mammalian cytochrome b5 gene were subjected to pyrolysis mass spectrometry (PyMS). To deconvolute the pyrolysis mass spectra so as to obtain quantitative information on the amount of cytochrome b5 produced fully-interconnected feedforward artificial neural networks (ANNs) were studied. It was found that the combination of PyMS and ANNs could be used to predict the amount of cytochrome b5 expressed in E. coli. PyMS is a novel, convenient and rapid method for the screening and analysis of microbial and other cultures producing recombinant proteins.