PubMed Health⌕ Search

Biomedical subjects

Royston Goodacre

Publications and source records attributed to Royston Goodacre.

At least 19 recordsLinked to original sources
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

Rapid characterization of microbial biodegradation pathways by FT-IR spectroscopy.

Fourier transform-infrared (FT-IR) spectroscopy has become an important tool for rapid analysis of complex biological samples. The infrared absorbance spectrum could be regarded as a "fingerprint" which is characteristic of biochemical substances. In this study, Pseudomonas putida NCIMB 9869 was grown with either 3,5-xylenol or m-cresol as the sole carbon source, each inducing different metabolic pathways for m-cresol biotransformation. FT-IR spectroscopy was capable of differentiating both induced cultures of P. putida NCIMB 9869 as well as the resulting biotransformation product mixtures. FT-IR spectral analysis indicated that carboxylic acids were key chemicals responsible for distinguishing the products of the two catabolic pathways. Gas chromatography-mass spectrometry (GC-MS) was performed to validate the FT-IR analysis, indicating that two carboxylic acids, 3-hydroxybenzoic acid and 2,5-dihydroxybenzoic acid, were present as m-cresol biotransformation products from 3,5-xylenol-grown cells, but were absent in m-cresol-grown cells. The ability to use FT-IR to rapidly distinguish between biotransformation product mixtures as well as differentially induced bacterial strains suggests this approach might be a valuable tool for screening large biotransformation assays for novel products and metabolic mutants.

Cresols↗
PubMed2006

Metabolic fingerprinting in disease diagnosis: biomedical applications of infrared and Raman spectroscopy.

The ability to diagnose the early onset of disease, rapidly, non-invasively and unequivocally has multiple benefits. These include the early intervention of therapeutic strategies leading to a reduction in morbidity and mortality, and the releasing of economic resources within overburdened health care systems. Some of the routine clinical tests currently in use are known to be unsuitable or unreliable. In addition, these often rely on single disease markers which are inappropriate when multiple factors are involved. Many diseases are a result of metabolic disorders, therefore it is logical to measure metabolism directly. One of the strategies employed by the emergent science of metabolomics is metabolic fingerprinting; which involves rapid, high-throughput global analysis to discriminate between samples of different biological status or origin. This review focuses on a selective number of recent studies where metabolic fingerprinting has been forwarded as a potential tool for disease diagnosis using infrared and Raman spectroscopies.

Animals↗
PubMed2006

Metabolomics: current technologies and future trends.

The ability to sequence whole genomes has taught us that our knowledge with respect to gene function is rather limited with typically 30-40% of open reading frames having no known function. Thus, within the life sciences there is a need for determination of the biological function of these so-called orphan genes, some of which may be molecular targets for therapeutic intervention. The search for specific mRNA, proteins, or metabolites that can serve as diagnostic markers has also increased, as has the fact that these biomarkers may be useful in following and predicting disease progression or response to therapy. Functional analyses have become increasingly popular. They include investigations at the level of gene expression (transcriptomics), protein translation (proteomics) and more recently the metabolite network (metabolomics). This article provides an overview of metabolomics and discusses its complementary role with transcriptomics and proteomics, and within system biology. It highlights how metabolome analyses are conducted and how the highly complex data that are generated are analysed. Non-invasive footprinting analysis is also discussed as this has many applications to in vitro cell systems. Finally, for studying biotic or abiotic stresses on animals, plants or microbes, we believe that metabolomics could very easily be applied to large populations, because this approach tends to be of higher throughput and generally lower cost than transcriptomics and proteomics, whilst also providing indications of which area of metabolism may be affected by external perturbation.

Biomarkers↗
PubMed2006

Direct infusion electrospray ionization mass spectra of crude cell extracts for microbial characterizations: influence of solvent conditions on the detection of proteins.

Direct infusion electrospray ionization mass spectrometry (DIES-MS) of crude bacterial extracts is a rapid method that can be used to characterize microbial cells. Phospholipids, metabolites, and proteins can be detected rapidly with minimal sample preparation. However, several factors influence the detection of signals in such high-throughput analyses. We studied the influence of solvent conditions, including the organic content and pH of the solvent, on the extraction and subsequent detection of signals in DIES-MS, with a view to improving the detection of protein signals. Unfractionated cell extracts from three strains of the Gram-negative Escherichia coli (including one encoding a recombinant green fluorescence protein), and the Gram-positive Bacillus sphaericus and B. subtilis were investigated. Both pH and the organic content of the solvent were found to influence the spectral information as observed from principal component analysis of the spectral data. A polar solvent with higher organic content resulted in the extraction of phospholipids that overtly dominate the spectral information. Decreasing the organic content of the extraction solvent resulted in the improved detection of protein peaks. Altering the pH of the extraction solvent resulted in different protein profiles from the same bacterium, as observed after spectral deconvolution. In addition, the protein profiles were also different when using different organic solvents. Spectral deconvolution showed several protein peaks that had mass-based homology with those in protein databases for the (sequenced) organisms studied. These results suggest that a combination of solvent conditions can be used to generate protein profiles rapidly that when combined can provide additional valuable proteomic information.

Bacillus↗
PubMed2006

Matrix-suppressed laser desorption/ionisation mass spectrometry and its suitability for metabolome analyses.

Matrix-assisted laser desorption/ionisation (MALDI) mass spectrometry was investigated for the simultaneous detection of several metabolites, as applicable to global metabolite analysis (metabolomics). The commonly employed organic matrices alpha-cyano-4-hydroxycinnamic acid and 3,5-dihydroxybenzoic acid, in both the crystalline and ionic liquid forms, were investigated. The employment of a low matrix-to-analyte molar ratio suppressed matrix peaks and was effective in detecting all the metabolites with a unique mass in a 30-metabolite synthetic cocktail, albeit to varying degrees. These matrix-suppressed laser desorption/ionisation (MSLDI) analyses were performed in the positive ion mode, and metabolites were detected as the protonated [M+H]+, sodiated [M+Na]+ or potassiated [M+K]+ species. The spectral signals were dominated by basic metabolites. It was possible to detect components of a synthetic cocktail when it was spiked quantitatively into a microbial extract, demonstrating the feasibility of using the technique for detecting metabolite signals in a complex biological matrix. However, analyte suppression effects were noted when the relative proportion of one analyte was allowed to increasingly dominate the others in a mixture. The implications of the findings with respect to applications in metabolomic investigations are discussed.

Amino Acids↗
PubMed2006

Surface-enhanced Raman scattering for the rapid discrimination of bacteria.

Raman spectroscopy is attracting interest for the rapid identification of bacteria and fungi and is now becoming accepted as a potentially powerful whole-organism fingerprinting technique. However, the Raman effect is so weak that collection times are lengthy, and this insensitivity means that bacteria must be cultured to gain enough biomass, which therefore limits its usefulness in clinical laboratories where high-throughput analyses are needed. The Raman effect can fortunately be greatly enhanced (by some 10(3)-10(6)-fold) if the molecules are attached to, or microscopically close to, a suitably roughened surface; a technique known as surface-enhanced Raman scattering (SERS). In this study we investigated SERS, employing an aggregated silver colloid substrate, for the analysis of a closely related group of bacteria belonging to the genus Bacillus. Each spectrum took only 20 s to collect and highly reproducible data were generated. The multivariate statistical technique of principal components-discriminant function analysis (PC-DFA) was used to group these bacteria based on their SERS fingerprints. The resultant ordination plots showed that the SERS spectra were highly discriminatory and gave accurate identification at the strain level. In addition, Bacillus species also undergo sporulation, and we demonstrate that SERS peaks that could be attributed to the dipicolinic acid biomarker, could be readily generated from Bacillus spores.

Bacillus↗
PubMed2006

Metabolic fingerprints of Mycobacterium bovis cluster with molecular type: implications for genotype-phenotype links.

Mycobacterium bovis is the causative agent of bovine tuberculosis. Various genetic typing techniques have been used to trace the reservoirs of infection; however, they have limited success in population genetics and outbreak studies. Fourier-transform infrared spectroscopy (FT-IR) is a rapid phenotypic typing technique, which may be used to generate a metabolic fingerprinting and is increasingly used to characterize bacteria. When coupled with multivariate cluster analysis, this powerful combination has sufficient resolving power to discriminate bacteria down to subspecies level; however, to date this method has not been used in the differentiation of mycobacteria. Multiple isolates of the ten major spoligotypes in the UK, recovered from different geographical locations, were analysed using FT-IR. Hierarchical cluster analysis of the spectra showed that the isolates could be differentiated according to their spoligotypes. Six of the spoligotype FT-IR clusters were very homogeneous and all isolates were recovered together. However, the remaining four groups displayed a more heterogeneous phenotype, which may reflect greater variation than previously suspected within these groups. Included in the ten spoligotypes are the two most dominant isolates in the UK, designated types 9 and 17. Whilst type 17 showed a highly conserved phenotype as judged by FT-IR, type 9 showed a very heterogeneous metabolic profile and isolates were recovered throughout the dendrogram. This variation in type 9 is reflected in the high degree of diversity observed by variable number tandem repeats (VNTR) analysis, underlining the exquisite resolving power of FT-IR.

Animals↗
PubMed2006

Metabolomic approaches reveal that phosphatidic and phosphatidyl glycerol phospholipids are major discriminatory non-polar metabolites in responses by Brachypodium distachyon to challenge by Magnaporthe grisea.

Metabolomic approaches were used to elucidate some key metabolite changes occurring during interactions of Magnaporthe grisea--the cause of rice blast disease--with an alternate host, Brachypodium distachyon. Fourier-transform infrared (FT-IR) spectroscopy provided a high-throughput metabolic fingerprint of M. grisea interacting with the B. distachyon accessions ABR1 (susceptible) and ABR5 (resistant). Principal component-discriminant function analysis (PC-DFA) allowed the differentiation between developing disease symptoms and host resistance. Alignment of projected 'test-set' on to 'training-set' data indicated that our experimental approach produced highly reproducible data. Examination of PC-DFA loading plots indicated that fatty acids were one chemical group that discriminated between responses by ABR1 and ABR5 to M. grisea. To identify these, non-polar extracts of M. grisea-challenged B. distachyon were directly infused into an electrospray ionization mass spectrometer (ESI-MS). PC-DFA indicated that M. grisea-challenged ABR1 and ABR5 were differentially clustered away from healthy material. Subtraction spectra and PC-DFA loadings plots revealed discriminatory analytes (m/z) between each interaction and seven metabolites were subsequently identified as phospholipids (PLs) by ESI-MS-MS. Phosphatidyl glycerol (PG) PLs were suppressed during both resistant and susceptible responses. By contrast, different phosphatidic acid PLs either increased or were reduced during resistance or during disease development. This suggests considerable and differential PL processing of membrane lipids during each interaction which may be associated with the elaboration/suppression of defence mechanisms or developing disease symptoms.

Gas Chromatography-Mass Spectrometry↗
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↗
PubMed2005

Metabonomic evaluation of idiosyncrasy-like liver injury in rats cotreated with ranitidine and lipopolysaccharide.

Idiosyncratic liver injury occurs in a small fraction of people on certain drug regimens. The cause of idiosyncratic hepatotoxicity is not known; however, it has been proposed that environmental factors such as concurrent inflammation initiated by bacterial lipopolysaccharide (LPS) increase an individual's susceptibility to drug toxicity. Ranitidine (RAN), a histamine-2 receptor antagonist, causes idiosyncratic liver injury in humans. In a previous report, idiosyncrasy-like liver toxicity was created in rats by cotreating them with LPS and RAN. In the present study, the ability of metabonomic techniques to distinguish animals cotreated with LPS and RAN from those treated with each agent individually was investigated. Rats were treated with LPS or its vehicle and with RAN or its vehicle, and urine was collected for nuclear magnetic resonance (NMR)- and mass spectroscopy-based metabonomic analyses. Blood and liver samples were also collected to compare metabonomic results with clinical chemistry and histopathology. NMR metabonomic analysis indicated changes in the pattern of metabolites consistent with liver damage that occurred only in the LPS/RAN cotreated group. Principal component analysis of urine spectra by either NMR or mass spectroscopy produced a clear separation of the rats treated with LPS/RAN from the other three groups. Clinical chemistry (serum alanine aminotransferase and aspartate aminotransferase activities) and histopathology corroborated these results. These findings support the potential use of a noninvasive metabonomic approach to identify drug candidates with potential to cause idiosyncratic liver toxicity with inflammagen coexposure.

Alanine Transaminase↗
PubMed2005

Identification of novel genes in Arabidopsis involved in secondary cell wall formation using expression profiling and reverse genetics.

Forward genetic screens have led to the isolation of several genes involved in secondary cell wall formation. A variety of evidence, however, suggests that the list of genes identified is not exhaustive. To address this problem, microarray data have been generated from tissue undergoing secondary cell wall formation and used to identify genes that exhibit a similar expression pattern to the secondary cell wall-specific cellulose synthase genes IRREGULAR XYLEM1 (IRX1) and IRX3. Cross-referencing this analysis with publicly available microarray data resulted in the selection of 16 genes for reverse genetic analysis. Lines containing an insertion in seven of these genes exhibited a clear irx phenotype characteristic of a secondary cell wall defect. Only one line, containing an insertion in a member of the COBRA gene family, exhibited a large decrease in cellulose content. Five of the genes identified as being essential for secondary cell wall biosynthesis have not been previously characterized. These genes are likely to define entirely novel processes in secondary cell wall formation and illustrate the success of combining expression data with reverse genetics to address gene function.

3' Untranslated Regions↗
PubMed2005

Rapid monitoring of antibiotics using Raman and surface enhanced Raman spectroscopy.

Comparatively few studies have explored the ability of Raman spectroscopy for the quantitative analysis of microbial secondary metabolites in fermentation broths. In this study we investigated the ability of Raman spectroscopy to differentiate between different penicillins and to quantify the level of penicillin in fermentation broths. However, the Raman signal is rather weak, therefore the Raman signal was enhanced using surface enhanced Raman spectroscopy (SERS) employing silver colloids. It was difficult by eye to differentiate between the five different penicillin molecules studied using Raman and SERS spectra, therefore the spectra were analysed by multivariate cluster analysis. Principal components analysis (PCA) clearly showed that SERS rather than the Raman spectra produced reproducible enough spectra to allow for the recovery of each of the different penicillins into their respective five groups. To highlight this further the first five principal components were used to construct a dendrogram using agglomerative clustering, and this again clearly showed that SERS can be used to identify which penicillin molecule was being analysed, despite their molecular similarities. With respect to the quantification of penicillin G it was shown that Raman spectroscopy could be used to quantify the amount of penicillin present in solution when relatively high levels of penicillin were analysed (>50 mM). By contrast, the SERS spectra showed reduced fluorescence, and improved signal to noise ratios from considerably lower concentrations of the antibiotic. This could prove to be advantageous in industry for monitoring low levels of penicillin in the early stages of antibiotic production. In addition, SERS may have advantages for quantifying low levels of high value, low yield, secondary metabolites in microbial processes.

Culture Media↗
PubMed2005

Monitoring the mode of action of antibiotics using Raman spectroscopy: investigating subinhibitory effects of amikacin on Pseudomonas aeruginosa.

During the last 20 years the rate at which new antimicrobial agents are produced has decreased dramatically, with concomitant increase in the number of pathogens that are becoming multidrug resistant. Together these have created a patient healthcare risk and this is of great concern. A crucial aspect for the discovery of new antibiotics is the development of new techniques that allow rapid and accurate characterization of the mode of action of the pharmacophore. In this work UV resonance Raman (UVRR) spectroscopy has been developed to monitor the concentration effect of antibiotics on bacterial cells. UVRR was conducted at 244 nm and spectra were collected in typically 60 s. Supervised multivariate analysis and 2D correlation spectroscopy were used to evaluate whether the UVRR spectra contained valuable information that could be used to study the mode of action of antibiotics. The clustering pattern in the discriminant factors space correlated directly to the concentration of amikacin, and partial least squares (PLS) regression analysis of the UVRR spectra was able to predict the concentration of amikacin to which bacterial cells had been exposed. 2D correlation spectroscopy contour maps indicated that spectral changes due to the presence of amikacin in the growth media occur according to the known mode of action of the studied antibiotic. Therefore, we conclude that UVRR spectroscopy, when coupled with chemometrics and 2D correlation spectroscopy, constitutes a powerful approach for the development and screening of new antibiotics.

Amikacin↗
PubMed2004

Making sense of the metabolome using evolutionary computation: seeing the wood with the trees.

One should perhaps start off by asking the question, 'But what wood is it we want to see?' There are so many trees that make up the wood; within a post-genomics context, genes, transcripts, proteins, and metabolites are the more tangible ones. Rather than studying these components in isolation, a more holistic approach is to unravel the interactions between the myriad of subcellular components and this is vital to systems biology. Moreover, this will help define the phenotype of the organism under investigation. Metabolomics is complementary to transcriptomics and proteomics, and despite the immense metabolite diversity observed in plants, metabolomics has been embraced by the plant community and in particular for studying metabolic networks. Whilst post-genomic science is producing vast data torrents, it is well known that data do not equal knowledge and so the extraction of the most meaningful parts of these data is key to the generation of useful new knowledge. A metabolomics experiment is guaranteed to generate thousands of data points (e.g. samples multiplied by the levels of particular metabolites) of which only a handful might be needed to describe the problem adequately. Evolutionary computational-based methods such as genetic algorithms and genetic programming are ideal strategies for mining such high-dimensional data to generate useful relationships, rules, and predictions. This article describes these techniques and highlights their usefulness within metabolomics.

Algorithms↗
PubMed2004

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.

Algorithms↗