Rapid quantitative analysis of binary mixtures of Escherichia coli strains using pyrolysis mass spectrometry with multivariate calibration and artificial neural networks

Pyrolysis mass spectrometry (PyMS) and multivariate calibration were used to show the high degree of relatedness between Escherichia coli HB101 and E. coli UB5201. Next, binary mixtures of these two phenotypically closely related E. coli strains were prepared and subjected to PyMS. Fully interconnected feedforward artificial neural networks (ANNs) were used to analyse the pyrolysis mass spectra to obtain quantitative information representative of the level of E. coli UB5201 in E. coli HB101. The ANNs exploited were trained using the standard back propagation algorithm, and the nodes used sigmoidal squashing functions. Accurate quantitative information was obtained for mixtures with >3% E. coli UB5201 in E. coli HB101. To remove noise from the pyrolysis mass spectra and so lower the limit of detection, the spectra were reduced using principal components analysis (PCA) and the first 13 principal components used to train ANNs. These PCA-ANNs allowed accurate estimates at levels as low as 1% E. coli UB5201 in E. coli HB101 to be predicted. In terms of bacterial numbers, it was shown that the limit of detection for PyMS in conjunction with ANNs was 3 × 10⁴ E. coli UB5201 cells in 1·6 × 10⁷ E. coli HB101 cells. It may be concluded that PyMS with ANNs provides a powerful and rapid method for the quantification of mixtures of closely related bacterial strains.     

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

Curie-point pyrolysis mass spectra were obtained from reference Propionibacterium strains and canine isolates. Artificial neural networks (ANNs) were trained by supervised learning (with the back-propagation algorithm) to recognize these strains from their pyrolysis mass spectra; all the strains isolated from dogs were identified as human wild type P. acnes. This is an important nosological discovery, and demonstrates that the combination of pyrolysis mass spectrometry and ANNs provides an objective, rapid and accurate identification technique. Bacteria isolated from different biopsy specimens from the same dog were found to be separate strains of P. acnes , demonstrating a within-animal variation in microflora. The classification of the canine isolates by Kohonen artificial neural networks (KANNs) was compared with the classical multivariate techniques of canonical variates analysis and hierarchical cluster analysis, and found to give similar results. This is the first demonstration, within microbiology, of KANNs as an unsupervised clustering technique which has the potential to group pyrolysis mass spectra both automatically and relatively objectively.     

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.     

Pyrolysis mass spectrometry characterisation and numerical taxonomy ofAeromonas spp.

Reference strains (2) and 29 isolates ofAeromonas spp. from clinical material and environmental specimens were characterised in traditional biochemical tests, and in pyrolysis mass spectrometry, which gives data reflecting whole-cell composition. Numerical taxonomic analyses of the data sets were compared with conventional identification at species level, and pathogenic potential, as inferred from the origin of the isolates. Clustering with conventional test reaction patterns showed, for each of the species represented, a clearly defined core group of typical isolates, surrounded by a halo of aberrant strains. One further cluster comprised strains intermediate betweenA. caviae andA. hydrophila, and one strain was grossly atypical in both analyses. Clustering from pyrolysis data corresponded less well with species identification. Broadly, the biochemical division between core and halo strains was supported in pyrolysis forA. caviae andA. sobria, but the main group ofA. hydrophila in pyrolysis comprised strains clustering in the core and halo groups of this species, and three strains intermediate betweenA. hydrophila andA. caviae in biochemical tests. Two further pyrolysis clusters comprised core and halo strains ofA. hydrophila. However, pyrolysis clustering correlated well with inferred pathogenicity, showing four clusters of probable pathogens, six clusters of probable nonpathogens, and one two member cluster of doubtful status. Most strains that clustered in the species haloes, or in species-intermediate groups in biochemical tests, were non-human isolates, or were isolated in the absence of symptomatic infection. The correlation of inferred pathogenicity with biochemical clustering was poorer than that with pyrolysis clustering.Abbreviations CTRP conventional test reaction pattern - PyMS pyrolysis mass spectrometry     

The value of pyrolysis mass spectrometry to investigate nosocomial outbreaks caused by Serratia marcescens

Simultaneous outbreaks of S. marcescens infection going on in the Neonatal Intensive Care Unit and the Surgical Department of the same hospital were investigated by pyrolysis mass spectrometry (PyMS). The PyMS analysis of the strains clearly demonstrated that the two outbreaks were caused by different strains. The 14 S. marcescens isolates from the first outbreak were closely related, with the exception of one environmental isolate, which did not harbour the ESBL plasmid, which was present in all other isolates. However, the phage type of all 14 isolates was the same. Among the 9 S. marcescens isolates from the second outbreak, PyMS clearly distinguished 3 that exhibited gentamicin resistance from the remaining 6 gentamicin-susceptible isolates. Phage typing was unhelpful in this case, as none of the isolates were typable. The PyMS typing of nosocomial outbreak strains can reach the level of discrimination approaching that achieved by molecular genetic analysis.     

Rapid phenotypic characterization of Salmonella enterica strains by pyrolysis metastable atom bombardment mass spectrometry with multivariate statistical and artificial neural network pattern recognition

Pyrolysis mass spectrometry was investigated for rapid characterization of bacteria. Spectra of Salmonella were compared to their serovars, pulsed-field gel electrophoresis (PFGE) patterns, antibiotic resistance profiles, and MIC values. Pyrolysis mass spectra generated via metastable atom bombardment were analyzed by multivariate principal component-discriminant analysis and artificial neural networks (ANNs). Spectral patterns developed by discriminant analysis and tested with Leave-One-Out (LOO) cross-validation distinguished Salmonella strains by serovar (97% correct) and by PFGE groups (49%). An ANN model of the same PFGE groups was cross-validated, using the LOO rule, with 92% agreement. Using an ANN, thirty previously unseen spectra were correctly classified by serotype (97%) and at the PFGE level (67%). Attempts by ANN to model spectra grouped by resistance profile-but ignoring PFGE or serotype-failed (10% correct), but ANNs differentiating ten samples of the same serotype/PFGE class were more successful. To assess the information content of PyMS data serendipitously associated with or directly related to resistance character, the ten isolates were grouped into four, three, or two categories. The four categories corresponded to four resistance profiles. The four class and three class ANNs showed much improved but insufficient modeling power. The two-class ANN and a corresponding multivariate model maximized inferential power for a coarse antibiotic-resistance-related distinction. They each cross-validated by LOO at 90%. This is the first direct correlation of pyrolysis metastable atom bombardment mass spectrometry with immunological (e.g. serology) or molecular biology (e.g. PFGE) based techniques.     

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

Summary 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.     

Discrimination of higher plant calluses based on embryogenic capacity and taxonomic classification by pyrolysis mass spectrometry

Pyrolysis mass spectrometry (PyMS) is a rapid, simple, high-resolution analytical method based on thermal degradation of complex material in a vacuum, and has been widely applied to the discrimination of closely related microbial strains. Minimally prepared samples of embryogenic and non-embryogenic calluses derived from various higher plants (sweet potato, morning glory, Korean ginseng, Siberian ginseng, and balloon flower) were subjected to PyMS for spectral fingerprinting. A dendrogram based on the unweighted pair group method, with arithmetic mean of pyrolysis mass spectra, divided the calluses into Siberian ginseng embryogenic callus and the others, which were subsequently divided into embryogenic and non-embryogenic callus groups, regardless of plant species from which the calluses were derived. In the non-embryogenic callus group, the dendrogram was in agreement with the known taxonomy of the plants. These results indicate that PyMS analysis could be applied for discriminating plant calluses based on embryogenic capacity and taxonomic classification.     

FT-IR Spectroscopy and Artificial Neural Network Identification of Fusarium Species

A rapid spectroscopic approach for whole‐organism fingerprinting of Fourier transform infrared (FT‐IR) spectroscopy was used to analyse 16 isolates from five closely related species of Fusarium: F. graminearum, F. moniliforme, F. nivale, F. semitectum and F. oxysporum. Principal components analysis and hierarchical cluster analysis were used to study the clusters in the data. On visual inspection of the clusters from both methods, the spectra were not differentiated into five separate clusters corresponding to species and these unsupervised methods failed to identify these fungal strains. When the data were trained by back propagation algorithm of artificial neural networks (ANNs) with principal components scores of spectra used as input modes, the strains were accurately predicted and recognized. The results in this study show that FT‐IR spectroscopy in combination with principal component artificial neural networks (PC‐ANNs) is well suited for identifying Fusarium spp. It would be advantageous to establish a comprehensive database of taxonomically well‐defined Fusarium species to aid the identification of unknown strains.     

Taxonomic discrimination of higher plants by pyrolysis mass spectrometry

Pyrolysis mass spectrometry (PyMS) is a rapid, simple, high-resolution analytical method based on thermal degradation of complex material in a vacuum and has been widely applied to the discrimination of closely related microbial strains. Leaf samples of six species and one variety of higher plants (Rosa multiflora, R. multiflora var. platyphylla, Sedum kamtschaticum, S. takesimense, S. sarmentosum, Hepatica insularis, and H. asiatica) were subjected to PyMS for spectral fingerprinting. Principal component analysis of PyMS data was not able to discriminate these plants in discrete clusters. However, canonical variate analysis of PyMS data separated these plants from one another. A hierarchical dendrogram based on canonical variate analysis was in agreement with the known taxonomy of the plants at the variety level. These results indicate that PyMS is able to discriminate higher plants based on taxonomic classification at the family, genus, species, and variety level.Abbreviations ANNs Artificial neural networks - CVA Canonical variate analysis - GC/MS Gas chromatography/mass spectrometry - PCA Principal component analysis - PyMS Pyrolysis mass spectrometry - UPGMA Unweighted pair group method with arithmetic mean Communicated by I.S. Chung     

Isolation and characterization of xylose- and xylan-utilizing anaerobic bacteria

By enrichment with xylose, nine mesophilic strains of anaerobic bacteria were obtained from various sources. Two isolates appear to belong to the genus Eubacterium. Six other strains belong to the genus Clostridium. Three of the isolated strains utilized larch wood xylan. The percentage of utilization of xylose and xylan and the yield of fermentation end products — viz. acetic acid and butyric acid-are equivalent to that of Clostridium acetobutylicum (ATCC 824) and reported thermophilic strains.     

Rapid analysis of the expression of heterologous proteins in Escherichia coli using pyrolysis mass spectrometry and Fourier transform infrared spectroscopy with chemometrics: application to alpha 2-interferon production.

Cell pastes and supernatant Escherichia coli samples, taken from an industrial bioprocess overproducing recombinant alpha 2 IFN were analysed using pyrolysis mass spectrometry (PyMS) and diffuse reflectance-absorbance Fourier transform infrared spectroscopy (FT-IR). PyMS and FT-IR are physico-chemical methods which measure predominantly the bond strengths of molecules and the vibrations of bonds within functional groups, respectively. They therefore give quantitative information about the total biochemical composition of the bioprocess sample. The interpretation of these hyperspectral data, in terms of the quantity of alpha 2 IFN in the cell pastes and supernatant samples was possible only after the application of the 'supervised learning' methods of artificial neural networks (ANNs) and partial least squares (PLS) regression. Both PyMS and FT-IR are novel, rapid and economical methods for the screening and the quantitative analysis of complex biological bioprocess over producing recombinant proteins. Models established using either spectral data set had a similarly satisfactory predictive ability. This shows that whole-reaction mixture spectral methods, which measure all molecules simultaneously, do contain enough information to allow their quantification when the entire spectra are used as the inputs to methods based on supervised learning. Moreover, this is the first study where FT-IR in the mid-IR range has been used to quantify the expression of a heterologous protein directly from fermentation broths and the first study to compare the abilities of PyMS and FT-IR for the quantitative analyses of an industrial bioprocess.     

Molecular evidence for shifts in polysaccharide composition associated with adaptation of soybean Bradyrhizobium strains to the Brazilian Cerrado soils

Pyrolysis mass spectrometry (PyMS) and DNA fingerprinting (RAPD and RSα hybridization) were used to characterize soybean inoculant strains and root nodule isolates of bradyrhizobia from the Brazilian Cerrado soils. Most isolates were shown to be derived from the inoculant strains on the basis of genotype comparisons by DNA fingerprinting. Phenotypic analysis (using PyMS) of the strains and separately of the polysaccharides derived from them showed that the nodule isolates differed from the parental strains, suggesting adaptation to the Cerrado soil environment. The extent of the differences between the derivatives and inoculant strains was similar for comparisons made on the basis of whole-cell preparations or from the isolated polysaccharides, indicating that the adaptation was caused by changes in the composition of the polysaccharides produced.     

Characterization of Carnobacterium species by pyrolysis mass spectrometry

L.N. MANCHESTER, A. TOOLE AND R. GOODACRE. 1995. Forty-eight strains of Carnobacterium were examined by pyrolysis mass spectrometry (PyMS). The effects of culture age and reproducibility over a 4 week period were also examined. The results were analysed by multivariate statistical techniques and compared with those from a previous numerical taxonomic study based on morphological, physiological and biochemical characteristics and with studies which used DNA-DNA and 16S rRNA sequence homologies. Taxonomic correlations were observed between the PyMS data and the previous studies. Culture age was observed to have little effect on the mass spectra obtained and the reproducibility study indicated that there was very little variation over the 4 week period. It was concluded that PyMS provides a reliable method for studying carnobacterial classification and provides a rapid way for clarifying and refining subgeneric relationships within the genus Carnobacterium. Further work may also show that it offers a potentially very rapid and accurate method for the identification of Carnobacterium.     

Novel rhodococci and other mycolate actinomycetes from the deep sea

A large number of mycolate actinomycetes have been recovered from deep-sea sediments in the NW Pacific Ocean using selective isolation methods. The isolates were putatively assigned to the genus Rhodococcus on the basis of colony characteristics and mycolic acid profiles. The diversity among these isolates and their relationship to type strains of Rhodococcus and other mycolate taxa were assessed by Curie point pyrolysis mass spectrometry (PyMS). Three major (A, C, D) and two minor (B, E) groups were defined by PyMS. Cluster A was a large group of isolates recovered from sediment in the Izu Bonin Trench (2679 m); Cluster C comprised isolates from both the Izu Bonin Trench (6390 and 6499 m) and from the Japan Trench (4418, 6048 and 6455 m). These Cluster C isolates showed close similarity to Dietzia maris and this was subsequently confirmed using molecular methods. Cluster D contained isolates recovered from a sediment taken from a depth of 1168m in Sagami Bay and were identified as members of the terrestrial species Rhodococcus luteus. Clusters B and E had close affinities with members of the genera Gordonia and Mycobacterium. The presence of Thermoactinomyces in certain of the deep-sea sediments studied was indicative of the movement of terrestrial material into the ocean depths.16S ribosomal RNA gene sequence analyses produced excellent definition of most genera of the mycolata, and indicated that the among the deep sea isolates (1) were novel species of Corynebacterium, Gordonia and Mycobacterium, and (2) a Sea of Japan isolate the phylogenetic depth of which suggests the possibility of a new genus. Polyphasic taxonomic analysis revealed considerable diversity among the deep sea rhodococci and evidence for recently diverged species or DNA groups.     

Isolation and Characterization of Bile Acid 7-Dehydroxylating Bacteria from Human Feces

Methods for isolation of fecal 7α-dehydroxylating bacteria are presented. A total of 219 strains were isolated from feces of healthy humans, and their ability to 7-dehydroxylate cholic, chenodeoxycholic, and ursodeoxycholic acids were examined. Of all the isolates, 14 strains were found to be capable of eliminating the hydroxy group at C-7α and/or C-7β. All the isolates were strictly anaerobic, Gram-positive rods. Thirteen isolates were non-sporeforming bacteria showing certain saccharolytic properties with the production of acid and gas from dextrose, and were catalase-positive but indole-, lecithinase-, urease- and oxidase-negative. Based on the data available at present, it was concluded that they could be regarded as members of the genus Eubacterium. One strain, however was identified as Clostridium sordellii. The isolated strains capable of 7α-dehydroxylating cholic acid and chenodeoxycholic acid were also able to oxidize the hydroxy group at C-7α. Nine strains (10, 12, 36S, M-2, M-17, M-18, Y-98, Y-1112, and Y-1113) of the 7α-dehydroxylating bacteria were confirmed to have 7β-dehydroxylation ability, but five strains (O-51, O-52, O-71, O-72, and Y-67) could not transform ursodeoxycholic acid to lithocholic acid.     

Pyrolysis mass spectrometry as a method for inter-strain discrimination of Candida albicans

A claim that Candida albicans strains NCPF 3153 and B311 were identical was investigated. Authentic strains were shown to be distinct (P less than 0.1%) by pyrolysis mass spectrometry (PyMS). Of twelve strains, provided as clones of NCPF 3153, seven were authenticated, one yielded an equivocal result and four were distinct from both NCPF 3153 and B311. Of eight B311 clones, six were authenticated and two yielded equivocal results. Although five non-C. albicans yeast strains were identified as distinct from B311 and NCPF 3153, Torulopsis glabrata NCPF 3240 was identified as B311, and one clinical isolate of C. albicans as NCPF 3153. This could be explained by the specificity of the mathematical analysis for discrimination between the authentic strains.     

Discrimination and taxonomy of geographically diverse strains of nitrile-metabolizing actinomycetes using chemometric and molecular sequencing techniques

Mycolic acid-containing actinomycetes capable of metabolizing nitriles were recovered from deep-sea sediments and terrestrial soils by enrichment culture on acetonitrile, benzonitrile, succinonitrile or bromoxynil. A total of 43 nitrile-degrading strains were isolated and, together with previously recovered nitrile-degrading rhodococci, were identified by a polyphasic taxonomic approach, which included mycolic acid profiles, pyrolysis mass spectrometry (PyMS), genomic fingerprinting based on sequence variability of the 16S ribosomal RNA gene using polymerase chain reaction-restriction fragment length polymorphism-single-strand conformational polymorphism, and 16S rRNA gene sequence comparison. Isolates phylogenetically related to Rhodococcus erythropolis dominated the culturable microorganisms from most marine and terrestrial samples. These isolates clustered together in a major pyrogroup that showed high congruence with PRS profiles of the 16S rRNA gene. Such high congruence also was obtained for other recovered isolates that were assigned to species of Rhodococcus and Gordonia. Sequencing data validated the results obtained by PRS analysis and enabled phylogenetic relationships to be established. Some of the recovered bacteria probably represent novel microbial species. The fact that nitrile-metabolizing microorganisms were recovered from a wide range of habitat types suggests that nitrile transforming enzymatic activity is geographically widely distributed in nature.