Metabolic Fingerprinting (metabolic + fingerprinting)

Distribution by Scientific Domains


Selected Abstracts


Cover Picture: Electrophoresis 15/2008

ELECTROPHORESIS, Issue 15 2008
Article first published online: 24 JUL 200
Regular issues provide a wide range of research and review articles covering all aspects of electrophoresis. Here you will find cutting-edge articles on methods and theory, instrumentation, nucleic acids, CE and CEC, miniaturization and microfluidics, proteomics and two-dimensional electrophoresis. Selected topics of issue 15 are: The application of perfluorooctanoate to investigate trimerization of the human immunodeficiency virus-1 gp41 ectodomain by electrophoresis Metabolic fingerprinting of schistosoma mansoni infection in mice urine with capillary electrophoresis Supercritical fluid extraction as an on-line clean-up technique for determination of riboflavin vitamins in food samples by capillary electrophoresis with fluorimetric detection A two-step electro-dialysis method for DNA purification from polluted metallic environmental samples. [source]


Data processing in metabolic fingerprinting by CE-UV: Application to urine samples from autistic children

ELECTROPHORESIS, Issue 6 2007
Ana C. Soria
Abstract Metabolic fingerprinting of biofluids such as urine can be used to detect and analyse differences between individuals. However, before pattern recognition methods can be utilised for classification, preprocessing techniques for the denoising, baseline removal, normalisation and alignment of electropherograms must be applied. Here a MEKC method using diode array detection has been used for high-resolution separation of both charged and neutral metabolites. Novel and generic algorithms have been developed for use prior to multivariate data analysis. Alignment is achieved by combining the use of reference peaks with a method that uses information from multiple wavelengths to align electropherograms to a reference signal. This metabolic fingerprinting approach by MEKC has been applied for the first time to urine samples from autistic and control children in a nontargeted and unbiased search for markers for autism. Although no biomarkers for autism could be determined using MEKC data here, the general approach presented could also be applied to the processing of other data collected by CE with UV,Vis detection. [source]


Metabolic fingerprinting allows discrimination between Ulmus pumila and U. minor, and between U. minor clones of different susceptibility to Dutch elm disease

FOREST PATHOLOGY, Issue 4 2008
J. A. Martin
Summary Experiments were conducted to test whether Fourier transform-infrared (FT-IR) spectroscopy, coupled with chemometric methods, can distinguish healthy xylem tissues collected from elms known to differ significantly in their susceptibility to Ophiostoma novo-ulmi Brasier. Twig samples from resistant Ulmus pumila L., susceptible U. minor Mill. and resistant U. minor clones were harvested on 1 May, 15 May, 1 June, 1 July and 1 September 2004, and subjected to FT-IR analysis. The application of principal component analysis to the spectral data, followed by discriminant function analysis, discriminated between the three groups of samples in all harvesting dates. The examination of the DF-loading plots allowed the identification of key regions within the spectra for the separation between clusters. The chemical assignments of these key regions allowed the following interpretations to be made: (i) U. pumila tissues contained enhanced levels of starch, cellulose and lignin with respect to U. minor tissues and (ii) resistant U. minor tissues contained enhanced levels of starch, cellulose and pectic polysaccharides with respect to susceptible U. minor tissues. The possible roles of the compositional differences in disease resistance, as well as the potential use of FT-IR spectroscopy and chemometrics as a tool for screening resistant elms are discussed. [source]


The autocorrelation matrix probing biochemical relationships after metabolic fingerprinting with CE

ELECTROPHORESIS, Issue 7 2009
Santiago Angulo
Abstract Fingerprinting together with statistical analysis is often employed to compare samples in metabonomic studies of a disease. Correlation algorithms can aid by extracting information based on the variation patterns of key metabolites. This information can be linked to metabolite identification or to specific up/down-regulated biochemical pathways. Matlab-based software employing the Pearson's correlation algorithm was applied to urine electropherograms from 20 mice infected with the schistosoma parasite. The fingerprints were the sum of electropherograms analysed with normal and reverse polarity, in two different modes MEKC and CZE and with two different capillaries (uncoated and polyacrylamide coated) to provide a broad picture of the samples. Hippurate, a metabolite that was depleted in the infected group and is present in both polarities, was chosen as a test variable; it correlated with itself to a p value of <0.000. Phenylacetylglycine, a metabolite shown as over expressed in the disease, was positively correlated to three metabolites in its same pathway with a correlation coefficient of 0.7 and p<0.000 to phenylalanine, 0.7 and p<0.000 to 2-hydroxyphenylacetic and 0.55 and p<0.003 to phenylacetate. The study shows that the autocorrelation matrix is able to provide extra information from data files acquired by CE analyses. It underlined an up-regulated metabolic path by association in the schistosoma infection model. [source]


Data processing in metabolic fingerprinting by CE-UV: Application to urine samples from autistic children

ELECTROPHORESIS, Issue 6 2007
Ana C. Soria
Abstract Metabolic fingerprinting of biofluids such as urine can be used to detect and analyse differences between individuals. However, before pattern recognition methods can be utilised for classification, preprocessing techniques for the denoising, baseline removal, normalisation and alignment of electropherograms must be applied. Here a MEKC method using diode array detection has been used for high-resolution separation of both charged and neutral metabolites. Novel and generic algorithms have been developed for use prior to multivariate data analysis. Alignment is achieved by combining the use of reference peaks with a method that uses information from multiple wavelengths to align electropherograms to a reference signal. This metabolic fingerprinting approach by MEKC has been applied for the first time to urine samples from autistic and control children in a nontargeted and unbiased search for markers for autism. Although no biomarkers for autism could be determined using MEKC data here, the general approach presented could also be applied to the processing of other data collected by CE with UV,Vis detection. [source]


PARAFASCA: ASCA combined with PARAFAC for the analysis of metabolic fingerprinting data

JOURNAL OF CHEMOMETRICS, Issue 2 2008
Jeroen J. Jansen
Abstract Novel post-genomics experiments such as metabolomics provide datasets that are highly multivariate and often reflect an underlying experimental design, developed with a specific experimental question in mind. ANOVA-simultaneous component analysis (ASCA) can be used for the analysis of multivariate data obtained from an experimental design instead of the widely used principal component analysis (PCA). This increases the interpretability of the model in terms of the experimental question. Aside from the levels of individual factors, variation that can be described by the experimental design may also depend on levels of multiple (crossed) factors simultaneously, e.g. the interactions. ASCA describes each contribution with a PCA model, but a contribution depending on crossed factors may be described more parsimoniously by multiway models like parallel factor analysis (PARAFAC). The combination of PARAFAC and ASCA, named PARAFASCA, provides a view on the data that is both parsimonious and focused on the experimental question. The novel method is used to analyze a dataset in which the effect of two doses of hydrazine on the urinary chemical composition of rats is investigated by time-resolved metabolic fingerprinting with nuclear magnetic resonance (NMR) spectroscopy. This experiment has been conducted to monitor the dose-specific urine composition changes in time upon hydrazine administration. Comparison of the PCA, the ASCA and the PARAFASCA models shows that ASCA and PARAFASCA describe the data more dedicated to the experimental question than PCA, but that PARAFASCA is more parsimonious than ASCA, and separates the variation underlying different effects better. Copyright 2008 John Wiley & Sons, Ltd. [source]


Mass spectrometry-based metabolomics

MASS SPECTROMETRY REVIEWS, Issue 1 2007
Katja Dettmer
Abstract This review presents an overview of the dynamically developing field of mass spectrometry-based metabolomics. Metabolomics aims at the comprehensive and quantitative analysis of wide arrays of metabolites in biological samples. These numerous analytes have very diverse physico-chemical properties and occur at different abundance levels. Consequently, comprehensive metabolomics investigations are primarily a challenge for analytical chemistry and specifically mass spectrometry has vast potential as a tool for this type of investigation. Metabolomics require special approaches for sample preparation, separation, and mass spectrometric analysis. Current examples of those approaches are described in this review. It primarily focuses on metabolic fingerprinting, a technique that analyzes all detectable analytes in a given sample with subsequent classification of samples and identification of differentially expressed metabolites, which define the sample classes. To perform this complex task, data analysis tools, metabolite libraries, and databases are required. Therefore, recent advances in metabolomics bioinformatics are also discussed. 2006 Wiley Periodicals, Inc., Mass Spec Rev 26:51,78, 2007 [source]


Direct metabolic fingerprinting of commercial herbal tinctures by nuclear magnetic resonance spectroscopy and mass spectrometry,

PHYTOCHEMICAL ANALYSIS, Issue 4 2009
Matteo Politi
Abstract Introduction Tinctures are widely used liquid pharmaceutical preparations traditionally obtained by maceration of one or more medicinal plants in ethanol,water solutions. Such a process results in the extraction of virtually hundreds of structurally diverse compounds with different polarities. Owing to the large chemical diversity of the constituents present in the herbal tinctures, the analytical tools used for the quality control of tinctures are usually optimised only for the detection of single chemical entities or specific class of compounds. Objective In order to overcome the major limitations of the current methods used for analysis of tinctures, a new methodological approach based on NMR spectroscopy and MS spectrometry has been tested with different commercial tinctures. Methodology Diffusion-edited 1H-NMR (1D DOSY) and 1H-NMR with suppression of the ethanol and water signals have been applied here for the first time to the direct analysis of commercial herbal tinctures derived from Echinacea purpurea, Hypericum perforatum, Ginkgo biloba and Valeriana officinalis. The direct injection of the tinctures in the MS detector in order to obtain the corresponding metabolic profiles was also performed. Results Using both NMR and MS methods it was possible, without evaporation or separation steps, to obtain a metabolic fingerprint able to distinguish between tinctures prepared with different plants. Batch-to-batch homogeneity, as well as degradation after the expiry date of a batch, was also investigated. Conclusion The techniques proposed here represent fast and convenient direct analyses of medicinal herbal tinctures. Copyright 2009 John Wiley & Sons, Ltd. [source]


Proteome approaches combined with Fourier transform infrared spectroscopy revealed a distinctive biofilm physiology in Bordetella pertussis

PROTEINS: STRUCTURE, FUNCTION AND BIOINFORMATICS, Issue 23-24 2008
Diego Omar Serra
Abstract Proteome analysis was combined with whole-cell metabolic fingerprinting to gain insight into the physiology of mature biofilm in Bordetella pertussis, the agent responsible for whooping cough. Recent reports indicate that B. pertussis adopts a sessile biofilm as a strategy to persistently colonize the human host. However, since research in the past mainly focused on the planktonic lifestyle of B. pertussis, knowledge on biofilm formation of this important human pathogen is still limited. Comparative studies were carried out by combining 2-DE and Fourier transform infrared (FT-IR) spectroscopy with multivariate statistical methods. These complementary approaches demonstrated that biofilm development has a distinctive impact on B. pertussis physiology. Results from MALDI-TOF/MS identification of proteins together with results from FT-IR spectroscopy revealed the biosynthesis of a putative acidic-type polysaccharide polymer as the most distinctive trait of B. pertussis life in a biofilm. Additionally, expression of proteins known to be involved in cellular regulatory circuits, cell attachment and virulence was altered in sessile cells, which strongly suggests a significant impact of biofilm development on B. pertussis pathogenesis. In summary, our work showed that the combination of proteomics and FT-IR spectroscopy with multivariate statistical analysis provides a powerful tool to gain further insight into bacterial lifestyles. [source]