Biomedical subjects

David Broadhurst

Publications and source records attributed to David Broadhurst.

10 recordsLinked to original sources

PubMed2007

Closed-loop, multiobjective optimization of two-dimensional gas chromatography/mass spectrometry for serum metabolomics.

Metabolomics seeks to measure potentially all the metabolites in a biological sample, and consequently, we need to develop and optimize methods to increase significantly the number of metabolites we can detect. We extended the closed-loop (iterative, automated) optimization system that we had previously developed for one-dimensional GC-TOF-MS (O'Hagan, S.; Dunn, W. B.; Brown, M.; Knowles, J. D.; Kell, D. B. Anal. Chem. 2005, 77, 290-303) to comprehensive two-dimensional (GCxGC) chromatography. The heuristic approach used was a multiobjective version of the efficient global optimization algorithm. In just 300 automated runs, we improved the number of metabolites observable relative to those in 1D GC by some 3-fold. The optimized conditions allowed for the detection of over 4000 raw peaks, of which some 1800 were considered to be real metabolite peaks and not impurities or peaks with a signal/noise ratio of less than 5. A variety of computational methods served to explain the basis for the improvement. This closed-loop optimization strategy is a generic and powerful approach for the optimization of any analytical instrumentation.

Biomarkers↗

PubMed2006

PYCHEM: a multivariate analysis package for python.

UNLABELLED: We have implemented a multivariate statistical analysis toolbox, with an optional standalone graphical user interface (GUI), using the Python scripting language. This is a free and open source project that addresses the need for a multivariate analysis toolbox in Python. Although the functionality provided does not cover the full range of multivariate tools that are available, it has a broad complement of methods that are widely used in the biological sciences. In contrast to tools like MATLAB, PyChem 2.0.0 is easily accessible and free, allows for rapid extension using a range of Python modules and is part of the growing amount of complementary and interoperable scientific software in Python based upon SciPy. One of the attractions of PyChem is that it is an open source project and so there is an opportunity, through collaboration, to increase the scope of the software and to continually evolve a user-friendly platform that has applicability across a wide range of analytical and post-genomic disciplines. AVAILABILITY: http://sourceforge.net/projects/pychem

Algorithms↗

PubMed2006

Huntington disease patients and transgenic mice have similar pro-catabolic serum metabolite profiles.

There has been considerable progress recently towards developing therapeutic strategies for Huntington's disease (HD), with several compounds showing beneficial effects in transgenic mouse models. However, human trials in HD are difficult, costly and time-consuming due to the slow disease course, insidious onset and patient-to-patient variability. Identification of molecular biomarkers associated with disease progression will aid the development of effective therapies by allowing further validation of animal models and by providing hopefully more sensitive measures of disease progression. Here, we apply metabolic profiling by gas chromatography-time-of-flight-mass spectrometry to serum samples from human HD patients and a transgenic mouse model in a hypothesis-generating search for disease biomarkers. We observed clear differences in metabolic profiles between transgenic mice and wild-type littermates, with a trend for similar differences in human patients and control subjects. Thus, the metabolites responsible for distinguishing transgenic mice also comprised a metabolic signature tentatively associated with the human disease. The candidate biomarkers composing this HD-associated metabolic signature in mouse and humans are indicative of a change to a pro-catabolic phenotype in early HD preceding symptom onset, with changes in various markers of fatty acid breakdown (including glycerol and malonate) and also in certain aliphatic amino acids. Our data raise the prospect of a robust molecular definition of progression of HD prior to symptom onset, and if validated in a genuinely prospective fashion these biomarker trajectories could facilitate the development of useful therapies for this disease.

Adult↗

PubMed2005

Rapid identification of closely related muscle foods by vibrational spectroscopy and machine learning.

Muscle foods are an integral part of the human diet and during the last few decades consumption of poultry products in particular has increased significantly. It is important for consumers, retailers and food regulatory bodies that these products are of a consistently high quality, authentic, and have not been subjected to adulteration by any lower-grade material either by accident or for economic gain. A variety of methods have been developed for the identification and authentication of muscle foods. However, none of these are rapid or non-invasive, all are time-consuming and difficulties have been encountered in discriminating between the commercially important avian species. Whilst previous attempts have been made to discriminate between muscle foods using infrared spectroscopy, these have had limited success, in particular regarding the closely related poultry species, chicken and turkey. Moreover, this study includes novel data since no attempts have been made to discriminate between both the species and the distinct muscle groups within these species, and this is the first application of Raman spectroscopy to the study of muscle foods. Samples of pre-packed meat and poultry were acquired and FT-IR and Raman measurements taken directly from the meat surface. Qualitative interpretation of FT-IR and Raman spectra at the species and muscle group levels were possible using discriminant function analysis. Genetic algorithms were used to elucidate meaningful interpretation of FT-IR results in (bio)chemical terms and we show that specific wavenumbers, and therefore chemical species, were discriminatory for each type (species and muscle) of poultry sample. We believe that this approach would aid food regulatory bodies in the rapid identification of meat and poultry products and shows particular potential for rapid assessment of food adulteration.

Algorithms↗

PubMed2004

Discrimination of modes of action of antifungal substances by use of metabolic footprinting.

Diploid cells of Saccharomyces cerevisiae were grown under controlled conditions with a Bioscreen instrument, which permitted the essentially continuous registration of their growth via optical density measurements. Some cultures were exposed to concentrations of a number of antifungal substances with different targets or modes of action (sterol biosynthesis, respiratory chain, amino acid synthesis, and the uncoupler). Culture supernatants were taken and analyzed for their "metabolic footprints" by using direct-injection mass spectrometry. Discriminant function analysis and hierarchical cluster analysis allowed these antifungal compounds to be distinguished and classified according to their modes of action. Genetic programming, a rule-evolving machine learning strategy, allowed respiratory inhibitors to be discriminated from others by using just two masses. Metabolic footprinting thus represents a rapid, convenient, and information-rich method for classifying the modes of action of antifungal substances.

Antifungal Agents↗

PubMed2004

High-throughput metabolic fingerprinting of legume silage fermentations via Fourier transform infrared spectroscopy and chemometrics.

Silage quality is typically assessed by the measurement of several individual parameters, including pH, lactic acid, acetic acid, bacterial numbers, and protein content. The objective of this study was to use a holistic metabolic fingerprinting approach, combining a high-throughput microtiter plate-based fermentation system with Fourier transform infrared (FT-IR) spectroscopy, to obtain a snapshot of the sample metabolome (typically low-molecular-weight compounds) at a given time. The aim was to study the dynamics of red clover or grass silage fermentations in response to various inoculants incorporating lactic acid bacteria (LAB). The hyperspectral multivariate datasets generated by FT-IR spectroscopy are difficult to interpret visually, so chemometrics methods were used to deconvolute the data. Two-phase principal component-discriminant function analysis allowed discrimination between herbage types and different LAB inoculants and modeling of fermentation dynamics over time. Further analysis of FT-IR spectra by the use of genetic algorithms to identify the underlying biochemical differences between treatments revealed that the amide I and amide II regions (wavenumbers of 1,550 to 1,750 cm(-1)) of the spectra were most frequently selected (reflecting changes in proteins and free amino acids) in comparisons between control and inoculant-treated fermentations. This corresponds to the known importance of rapid fermentation for the efficient conservation of forage proteins.

Fabaceae↗

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

Metabolic fingerprinting of salt-stressed tomatoes.

The aim of this study was to adopt the approach of metabolic fingerprinting through the use of Fourier transform infrared (FT-IR) spectroscopy and chemometrics to study the effect of salinity on tomato fruit. Two varieties of tomato were studied, Edkawy and Simge F1. Salinity treatment significantly reduced the relative growth rate of Simge F1 but had no significant effect on that of Edkawy. In both tomato varieties salt-treatment significantly reduced mean fruit fresh weight and size class but had no significant affect on total fruit number. Marketable yield was however reduced in both varieties due to the occurrence of blossom end rot in response to salinity. Whole fruit flesh extracts from control and salt-grown tomatoes were analysed using FT-IR spectroscopy. Each sample spectrum contained 882 variables, absorbance values at different wavenumbers, making visual analysis difficult and therefore machine learning methods were applied. The unsupervised clustering method, principal component analysis (PCA) showed no discrimination between the control and salt-treated fruit for either variety. The supervised method, discriminant function analysis (DFA) was able to classify control and salt-treated fruit in both varieties. Genetic algorithms (GA) were applied to identify discriminatory regions within the FT-IR spectra important for fruit classification. The GA models were able to classify control and salt-treated fruit with a typical error, when classifying the whole data set, of 9% in Edkawy and 5% in Simge F1. Key regions were identified within the spectra corresponding to nitrile containing compounds and amino radicals. The application of GA enabled the identification of functional groups of potential importance in relation to the response of tomato to salinity.

Algorithms↗

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

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↗