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Biomedical subjects

Douglas B Kell

Publications and source records attributed to Douglas B Kell.

17 recordsLinked to original sources
PubMed2003

High-throughput classification of yeast mutants for functional genomics using metabolic footprinting.

Many technologies have been developed to help explain the function of genes discovered by systematic genome sequencing. At present, transcriptome and proteome studies dominate large-scale functional analysis strategies. Yet the metabolome, because it is 'downstream', should show greater effects of genetic or physiological changes and thus should be much closer to the phenotype of the organism. We earlier presented a functional analysis strategy that used metabolic fingerprinting to reveal the phenotype of silent mutations of yeast genes. However, this is difficult to scale up for high-throughput screening. Here we present an alternative that has the required throughput (2 min per sample). This 'metabolic footprinting' approach recognizes the significance of 'overflow metabolism' in appropriate media. Measuring intracellular metabolites is time-consuming and subject to technical difficulties caused by the rapid turnover of intracellular metabolites and the need to quench metabolism and separate metabolites from the extracellular space. We therefore focused instead on direct, noninvasive, mass spectrometric monitoring of extracellular metabolites in spent culture medium. Metabolic footprinting can distinguish between different physiological states of wild-type yeast and between yeast single-gene deletion mutants even from related areas of metabolism. By using appropriate clustering and machine learning techniques, the latter based on genetic programming, we show that metabolic footprinting is an effective method to classify 'unknown' mutants by genetic defect.

Cells, Cultured↗
PubMed2003

A systematic approach to modeling, capturing, and disseminating proteomics experimental data.

Both the generation and the analysis of proteome data are becoming increasingly widespread, and the field of proteomics is moving incrementally toward high-throughput approaches. Techniques are also increasing in complexity as the relevant technologies evolve. A standard representation of both the methods used and the data generated in proteomics experiments, analogous to that of the MIAME (minimum information about a microarray experiment) guidelines for transcriptomics, and the associated MAGE (microarray gene expression) object model and XML (extensible markup language) implementation, has yet to emerge. This hinders the handling, exchange, and dissemination of proteomics data. Here, we present a UML (unified modeling language) approach to proteomics experimental data, describe XML and SQL (structured query language) implementations of that model, and discuss capture, storage, and dissemination strategies. These make explicit what data might be most usefully captured about proteomics experiments and provide complementary routes toward the implementation of a proteome repository.

Database Management Systems↗
PubMed2002

Monitoring of complex industrial bioprocesses for metabolite concentrations using modern spectroscopies and machine learning: application to gibberellic acid production.

Two rapid vibrational spectroscopic approaches (diffuse reflectance-absorbance Fourier transform infrared [FT-IR] and dispersive Raman spectroscopy), and one mass spectrometric method based on in vacuo Curie-point pyrolysis (PyMS), were investigated in this study. A diverse range of unprocessed, industrial fed-batch fermentation broths containing the fungus Gibberella fujikuroi producing the natural product gibberellic acid, were analyzed directly without a priori chromatographic separation. Partial least squares regression (PLSR) and artificial neural networks (ANNs) were applied to all of the information-rich spectra obtained by each of the methods to obtain quantitative information on the gibberellic acid titer. These estimates were of good precision, and the typical root-mean-square error for predictions of concentrations in an independent test set was <10% over a very wide titer range from 0 to 4925 ppm. However, although PLSR and ANNs are very powerful techniques they are often described as "black box" methods because the information they use to construct the calibration model is largely inaccessible. Therefore, a variety of novel evolutionary computation-based methods, including genetic algorithms and genetic programming, were used to produce models that allowed the determination of those input variables that contributed most to the models formed, and to observe that these models were predominantly based on the concentration of gibberellic acid itself. This is the first time that these three modern analytical spectroscopies, in combination with advanced chemometric data analysis, have been compared for their ability to analyze a real commercial bioprocess. The results demonstrate unequivocally that all methods provide very rapid and accurate estimates of the progress of industrial fermentations, and indicate that, of the three methods studied, Raman spectroscopy is the ideal bioprocess monitoring method because it can be adapted for on-line analysis.

Algorithms↗
PubMed2002

The cytochrome P450 complement (CYPome) of Streptomyces coelicolor A3(2).

In the present study we describe the complete cytochrome P450 complement, the "CYPome," of Streptomyces coelicolor A3(2). Eighteen cytochromes P450 (CYP) are described, in contrast to the absence of CYPs in Escherichia coli, and the twenty observed in Mycobacterium tuberculosis. Here we confirm protein identity as cytochromes P450 by heterologous expression in E. coli and measurement of reduced carbon monoxide difference spectra. We also report on their arrangement in the linear chromosome and relatedness to other CYPs in the superfamily. The future development of manipulation of antibiotic pathways and the use of streptomycetes in bioremediation and biotransformations will involve many of the new CYP forms identified here.

Base Sequence↗
PubMed2002

Sample preparation in matrix-assisted laser desorption/ionization mass spectrometry of whole bacterial cells and the detection of high mass (>20 kDa) proteins.

Three sample preparation strategies commonly employed in matrix-assisted laser desorption/ionization mass spectrometry (MALDI-TOFMS) of whole bacterial cells were investigated for the detection of high mass signals; these included the dried droplet, the seed-layer/two-layer, and the bottom-layer methods. Different sample preparation approaches favoured the detection of high- or low-mass proteins. The low-mass peaks were best detected using the bottom-layer method. By contrast, the dried droplet method using a solvent with higher water content, and hence effecting a slower crystallization process, gave the best results for the detection of high-mass signals. Signals up to m/z 158 000 could be detected with this methodology for Bacillus sphaericus. Sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE) analysis of the same extracts used for MALDI-TOFMS showed bands in the molecular weight range in which high-mass peaks were observed in MALDI-MS, suggesting that the high-mass signals are not polymeric adducts of low-mass protein monomers. In addition, one of the high molecular weight proteins (approximately 126 kDa) was putatively identified as an S-layer protein by an in-gel tryptic digest. The bacterial samples spotted on the target wells for MALDI-TOFMS, using the different sample preparation strategies, were examined under a scanning electron microscope and differences were observed between the different strategies, suggesting that the nature of the crystals and the distribution of the analytes amidst the crystals could influence the spectral pattern observed in MALDI-TOFMS of whole bacterial cells. Finally, evidence is presented to indicate that, although the determinants are intact cells, cell lysis occurs both before and during the MALDI process.

Bacterial Proteins↗
PubMed2002

Genotype-phenotype mapping: genes as computer programs.

The effects of genes on phenotype are mediated by processes that are typically unknown but whose determination is desirable. The conversion from gene to phenotype is not a simple function of individual genes, but involves the complex interactions of many genes; it is what is known as a nonlinear mapping problem. A computational method called genetic programming allows the representation of candidate nonlinear mappings in several possible trees. To find the best model, the trees are 'evolved' by processes akin to mutation and recombination, and the trees that more closely represent the actual data are preferentially selected. The result is an improved tree of rules that represent the nonlinear mapping directly. In this way, the encoding of cellular and higher-order activities by genes is seen as directly analogous to computer programs. This analogy is of utility in biological genetics and in problems of genotype-phenotype mapping.

Biological Evolution↗
PubMed2002

Flow-injection electrospray ionization mass spectrometry of crude cell extracts for high-throughput bacterial identification.

Flow-injection electrospray ionization mass spectrometry (FI-ESI-MS) of unfractionated cell-free extracts obtained from bacterial cells suspended in a solvent mixture was investigated as a rapid analytical method for reproducible, high-throughput bacterial identification. Five bacterial strains (two Escherichia coli, two Bacillus spp. and one Brevibacillus laterosporus) were studied in this investigation. Axenically grown bacterial cells were suspended in an acidic organic solvent and the cell-free extract was sequentially injected into a solvent flow stream that was sprayed into the ionization chamber of the ESI-MS. The spectra produced contained reproducible information, which was useful for discriminating between the bacteria. Tandem mass spectrometry was used to characterize further the peaks, and at least three classes of macromolecules, namely phospholipids, glycolipids, and proteins, were found to contribute most to the spectral information. Bacterial extracts stored under different conditions gave very similar mass spectra for each of the five bacterial strains, indicating that the extracts were stable even at room temperature for up to 24 h, with no loss of information content, which has obvious implications for automated high-throughput analysis. An analysis of the components of the extracting solvent mixture and their effects on the spectral information showed that acetonitrile contributes most significantly to the extraction process and hence to the information content of the spectra.

Bacteria↗
PubMed2002

Metabolic profiling using direct infusion electrospray ionisation mass spectrometry for the characterisation of olive oils.

There is a continuing need for improved methods for assessing the adulteration of foodstuffs. We report some highly encouraging data, where we have developed direct infusion electrospray ionisation mass spectrometry (ESI-MS) together with chemometrics as a novel, rapid (1 min per sample) and powerful technique to elucidate key metabolite differences in vegetable and nut oils. Principal components analysis of these ESI-MS spectra show that the reproducibility of this approach is high and that olive oil can be discriminated from oils which are commonly used as adulterants. These adulterants include refined hazelnut oil, which is particularly challenging given its chemical similarity to olive oils.

Food Contamination↗
PubMed2002

The rpf gene of Micrococcus luteus encodes an essential secreted growth factor.

Micrococcus luteus secretes a small protein called Rpf, which has autocrine and paracrine signalling functions and is required for the resuscitation of dormant cells. Originally isolated from the supernatant of actively growing cultures, Rpf was also detected on the surface of actively growing bacteria. Most molecules may be sequestered non-productively at the cell surface, as a truncated form of the protein, encompassing only the 'Rpf domain' is fully active. The C-terminal LysM module, which probably mediates binding to the cell envelope, is not required for biological activity. Rpf was essential for growth of M. luteus. Washed cells, inoculated at low density into a minimal medium, could not grow in its absence. Moreover, the incorporation of anti-Rpf antibodies into the culture medium at the time of inoculation also prevented bacterial growth. We were unable to inactivate rpf using a disrupted form of the gene, in which most of the coding sequence was replaced with a selectable thiostrepton resistance marker. Gene disruption was possible in the presence of a second, functional, plasmid-located copy of rpf, but not in the presence of a rpf derivative whose protein product lacked the secretory signal sequence. As far as we are aware, Rpf is the first example of a truly secreted protein that is essential for bacterial growth. If the Rpf-like proteins elaborated by Mycobacterium tuberculosis and other mycobacteria prove similarly essential, interference with their proper functioning may offer novel opportunities for protecting against, and treating, tuberculosis and other mycobacterial disease.

Bacterial Proteins↗
PubMed2002

A family of autocrine growth factors in Mycobacterium tuberculosis.

Mycobacterium tuberculosis and its close relative, Mycobacterium bovis (BCG) contain five genes whose predicted products resemble Rpf from Micrococcus luteus. Rpf is a secreted growth factor, active at picomolar concentrations, which is required for the growth of vegetative cells in minimal media at very low inoculum densities, as well as the resuscitation of dormant cells. We show here that the five cognate proteins from M. tuberculosis have very similar characteristics and properties to those of Rpf. They too stimulate bacterial growth at picomolar (and in some cases, subpicomolar) concentrations. Several lines of evidence indicate that they exert their activity from an extra-cytoplasmic location, suggesting that they are also involved in intercellular signalling. The five M. tuberculosis proteins show cross-species activity against M. luteus, Mycobacterium smegmatis and M. bovis (BCG). Actively growing cells of M. bovis (BCG) do not respond to these proteins, whereas bacteria exposed to a prolonged stationary phase do. Affinity-purified antibodies inhibit bacterial growth in vitro, suggesting that sequestration of these proteins at the cell surface might provide a means to limit or even prevent bacterial multiplication in vivo. The Rpf family of bacterial growth factors may therefore provide novel opportunities for preventing and controlling mycobacterial infections.

Bacterial Proteins↗
PubMed2002

Schemes of flux control in a model of Saccharomyces cerevisiae glycolysis.

We used parameter scanning to emulate changes to the limiting rate for steps in a fitted model of glucose-derepressed yeast glycolysis. Three flux-control regimes were observed, two of which were under the dominant control of hexose transport, in accordance with various experimental studies and other model predictions. A third control regime in which phosphofructokinase exerted dominant glycolytic flux control was also found, but it appeared to be physiologically unreachable by this model, and all realistically obtainable flux control regimes featured hexose transport as a step involving high flux control.

Adenosine Triphosphate↗
PubMed2002

Differentiation of Phytophthora infestans sporangia from other airborne biological particles by flow cytometry.

The ability of two different flow cytometers, the Microcyte (Optoflow) and the PAS-III (Partec), to differentiate sporangia of the late-blight pathogen Phytophthora infestans from other potential airborne particles was compared. With the PAS-III, light scatter and intrinsic fluorescence parameters could be used to differentiate sporangia from conidia of Alternaria or Botrytis spp., rust urediniospores, and pollen of grasses and plantain. Differentiation between P. infestans sporangia and powdery mildew conidia was not possible by these two methods but, when combined with analytical rules evolved by genetic programming methods, could be achieved after staining with the fluorescent brightener Calcofluor white M2R. The potential application of these techniques to the prediction of late-blight epiphytotics in the field is discussed.

Air Microbiology↗
PubMed2002

Rapid and quantitative detection of the microbial spoilage of meat by fourier transform infrared spectroscopy and machine learning.

Fourier transform infrared (FT-IR) spectroscopy is a rapid, noninvasive technique with considerable potential for application in the food and related industries. We show here that this technique can be used directly on the surface of food to produce biochemically interpretable "fingerprints." Spoilage in meat is the result of decomposition and the formation of metabolites caused by the growth and enzymatic activity of microorganisms. FT-IR was exploited to measure biochemical changes within the meat substrate, enhancing and accelerating the detection of microbial spoilage. Chicken breasts were purchased from a national retailer, comminuted for 10 s, and left to spoil at room temperature for 24 h. Every hour, FT-IR measurements were taken directly from the meat surface using attenuated total reflectance, and the total viable counts were obtained by classical plating methods. Quantitative interpretation of FT-IR spectra was possible using partial least-squares regression and allowed accurate estimates of bacterial loads to be calculated directly from the meat surface in 60 s. Genetic programming was used to derive rules showing that at levels of 10(7) bacteria.g(-1) the main biochemical indicator of spoilage was the onset of proteolysis. Thus, using FT-IR we were able to acquire a metabolic snapshot and quantify, noninvasively, the microbial loads of food samples accurately and rapidly in 60 s, directly from the sample surface. We believe this approach will aid in the Hazard Analysis Critical Control Point process for the assessment of the microbiological safety of food at the production, processing, manufacturing, packaging, and storage levels.

Artificial Intelligence↗
PubMed2001

Characterization of an autostimulatory substance produced by Escherichia coli.

The recovery of dilute populations of stationary phase cells of Escherichia coli was studied using an automatic growth analyser. The addition of 30% supernatant from 2-d-old stationary phase cells of the organism reproducibly shortened the apparent lag times by 22-57.5%, depending on the age of the inoculum. True lag times, as determined by colony counts, of stationary phase cells were reduced by supernatant addition by 41-62%. The growth-stimulating substance was characterized and partly purified from supernatants: the active material was shown to be dialysable, heat-stable, acid- and alkali-stable and protease-resistant. Extraction with ethyl acetate or ion-exchange resins was not successful, but the active material could be quantitatively extracted with ethanol after saturation with salt. It is concluded that the active substance is a small, non-proteinaceous, non-ionic organic molecule. Separation of extracts by HPLC indicated that the stimulatory substance is weakly hydrophobic and has retention times similar to those of uracil. So far, however, the exact chemical identity of the active substance has not been elucidated.

Chromatography, High Pressure Liquid↗
PubMed1998

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.

Bacterial Typing Techniques↗