Biomedical subjects

Mariët J van der Werf

Publications and source records attributed to Mariët J van der Werf.

14 recordsLinked to original sources

PubMed2006

Simultaneous quantitative analysis of metabolites using ion-pair liquid chromatography-electrospray ionization mass spectrometry.

We have developed an analytical method, consisting of ion-pair liquid chromatography coupled to electrospray ionization mass spectrometry (IP-LC-ESI-MS), for the simultaneous quantitative analysis of several key classes of polar metabolites, like nucleotides, coenzyme A esters, sugar nucleotides, and sugar bisphosphates. The use of the ion-pair agent hexylamine and optimization of the pH of the mobile phases were critical parameters in obtaining good retention and peak shapes of many of the above-mentioned polar and acidic metabolites that are impossible to analyze using standard reversed-phase LC/MS. Optimum conditions were found when using a gradient from 5 mM hexylamine in water (pH 6.3) to 90% methanol/10% 10 mM ammonium acetate (pH 8.5). The IP-LC-ESI-MS method was extensively validated by determining the linearity (R2 > 0.995), sensitivity (limit of detection 0.1-1 ng), repeatability, and reproducibility (relative standard deviation <10%). The IP-LC-ESI-MS method was shown to be a useful tool for microbial metabolomics, i.e., the comprehensive quantitative analysis of metabolites in extracts of microorganisms, and for the determination of the energy charge, i.e., the cellular energy status, as an overall quality measure for the sample workup and analytical protocols.

Amines↗

PubMed2006

Centering, scaling, and transformations: improving the biological information content of metabolomics data.

BACKGROUND: Extracting relevant biological information from large data sets is a major challenge in functional genomics research. Different aspects of the data hamper their biological interpretation. For instance, 5000-fold differences in concentration for different metabolites are present in a metabolomics data set, while these differences are not proportional to the biological relevance of these metabolites. However, data analysis methods are not able to make this distinction. Data pretreatment methods can correct for aspects that hinder the biological interpretation of metabolomics data sets by emphasizing the biological information in the data set and thus improving their biological interpretability. RESULTS: Different data pretreatment methods, i.e. centering, autoscaling, pareto scaling, range scaling, vast scaling, log transformation, and power transformation, were tested on a real-life metabolomics data set. They were found to greatly affect the outcome of the data analysis and thus the rank of the, from a biological point of view, most important metabolites. Furthermore, the stability of the rank, the influence of technical errors on data analysis, and the preference of data analysis methods for selecting highly abundant metabolites were affected by the data pretreatment method used prior to data analysis. CONCLUSION: Different pretreatment methods emphasize different aspects of the data and each pretreatment method has its own merits and drawbacks. The choice for a pretreatment method depends on the biological question to be answered, the properties of the data set and the data analysis method selected. For the explorative analysis of the validation data set used in this study, autoscaling and range scaling performed better than the other pretreatment methods. That is, range scaling and autoscaling were able to remove the dependence of the rank of the metabolites on the average concentration and the magnitude of the fold changes and showed biologically sensible results after PCA (principal component analysis).In conclusion, selecting a proper data pretreatment method is an essential step in the analysis of metabolomics data and greatly affects the metabolites that are identified to be the most important.

Cluster Analysis↗

PubMed2006

Microbial metabolomics with gas chromatography/mass spectrometry.

An analytical method was set up suitable for the analysis of microbial metabolomes, consisting of an oximation and silylation derivatization reaction and subsequent analysis by gas chromatography coupled to mass spectrometry. Microbial matrixes contain many compounds that potentially interfere with either the derivatization procedure or analysis, such as high concentrations of salts, complex media or buffer components, or extremely high substrate and product concentrations. The developed method was extensively validated using different microorganisms, i.e., Bacillus subtilis, Propionibacterium freudenreichii, and Escherichia coli. Many metabolite classes could be analyzed with the method: alcohols, aldehydes, amino acids, amines, fatty acids, (phospho-) organic acids, sugars, sugar acids, (acyl-) sugar amines, sugar phosphate, purines, pyrimidines, and aromatic compounds. The derivatization reaction proved to be efficient (>50% transferred to derivatized form) and repeatable (relative standard deviations <10%). Linearity for most metabolites was satisfactory with regression coefficients better than 0.996. Quantification limits were 40-500 pg on-column or 0.1-0.7 mmol/g of microbial cells (dry weight). Generally, intrabatch precision (repeatability) and interbatch precision (reproducibility) for the analysis of metabolites in cell extracts was better than 10 and 15%, respectively. Notwithstanding the nontargeted character of the method and complex microbial matrix, analytical performance for most metabolites fit the requirements for target analysis in bioanalysis. The suitability of the method was demonstrated by analysis of E. coli samples harvested at different growth phases.

Bacillus subtilis↗

PubMed2006

Multivariate analysis of microarray data by principal component discriminant analysis: prioritizing relevant transcripts linked to the degradation of different carbohydrates in Pseudomonas putida S12.

The value of the multivariate data analysis tools principal component analysis (PCA) and principal component discriminant analysis (PCDA) for prioritizing leads generated by microarrays was evaluated. To this end, Pseudomonas putida S12 was grown in independent triplicate fermentations on four different carbon sources, i.e. fructose, glucose, gluconate and succinate. RNA isolated from these samples was analysed in duplicate on an anonymous clone-based array to avoid bias during data analysis. The relevant transcripts were identified by analysing the loadings of the principal components (PC) and discriminants (D) in PCA and PCDA, respectively. Even more specifically, the relevant transcripts for a specific phenotype could also be ranked from the loadings under an angle (biplot) obtained after PCDA analysis. The leads identified in this way were compared with those identified using the commonly applied fold-difference and hierarchical clustering approaches. The different data analysis methods gave different results. The methods used were complementary and together resulted in a comprehensive picture of the processes important for the different carbon sources studied. For the more subtle, regulatory processes in a cell, the PCDA approach seemed to be the most effective. Except for glucose and gluconate dehydrogenase, all genes involved in the degradation of glucose, gluconate and fructose were identified. Moreover, the transcriptomics approach resulted in potential new insights into the physiology of the degradation of these carbon sources. Indications of iron limitation were observed with cells grown on glucose, gluconate or succinate but not with fructose-grown cells. Moreover, several cytochrome- or quinone-associated genes seemed to be specifically up- or downregulated, indicating that the composition of the electron-transport chain in P. putida S12 might change significantly in fructose-grown cells compared to glucose-, gluconate- or succinate-grown cells.

Carbohydrate Metabolism↗

PubMed2005

Fusion of mass spectrometry-based metabolomics data.

A general method is presented for combining mass spectrometry-based metabolomics data. Such data are becoming more and more abundant, and proper tools for fusing these types of data sets are needed. Fusion of metabolomics data leads to a comprehensive view on the metabolome of an organism or biological system. The ideas presented draw upon established techniques in data analysis. Hence, they are also widely applicable to other types of X-omics data provided there is a proper pretreatment of the data. These issues are discussed using a real-life metabolomics data set from a microbial fermentation process.

Databases as Topic↗

PubMed2005

Mathematical design of prokaryotic clone-based microarrays.

BACKGROUND: Clone-based microarrays, on which each spot represents a random genomic fragment, are a good alternative to open reading frame-based microarrays, especially for microorganisms for which the complete genome sequence is not available. Since the generation of a genomic DNA library is a random process, it is beforehand uncertain which genes are represented. Nevertheless, the genome coverage of such an array, which depends on different variables like the insert size and the number of clones in the library, can be predicted by mathematical approaches. When applying the classical formulas that determine the probability that a certain sequence is represented in a DNA library at the nucleotide level, massive amounts of clones would be necessary to obtain a proper coverage of the genome. RESULTS: This paper describes the development of two complementary equations for determining the genome coverage at the gene level. The first equation predicts the fraction of genes that is represented on the array in a detectable way and cover at least a set part (the minimal insert coverage) of the genomic fragment by which these genes are represented. The higher this minimal insert coverage, the larger the chance that changes in expression of a specific gene can be detected and attributed to that gene. The second equation predicts the fraction of genes that is represented in spots on the array that only represent genes from a single transcription unit, which information can be interpreted in a quantitative way. CONCLUSION: Validation of these equations shows that they form reliable tools supporting optimal design of prokaryotic clone-based microarrays.

Bacteria↗

PubMed2005

Quenching of microbial samples for increased reliability of microarray data.

Messenger RNA levels change on a minutes scale due to both degradation and de novo transcription. Consequently, alterations in the transcript profiles that are not representative for the condition of interest are easily introduced during sample harvesting and work-up. In order to avoid these unwanted changes we have validated a -45 degrees C methanol-based quenching method for obtaining reliable and reproducible 'snapshot' samples of Lactobacillus plantarum cells for transcriptome analyses. Transcript profiles of cells harvested with the quenching method were compared with transcript profiles of cells that were harvested according to two different commonly applied protocols. Significant differences between the transcript profiles of cells harvested by the different methods from the same steady-state culture were observed. In total, 42 genes or operons were identified from which the transcript levels were altered when the cells were not immediately quenched upon harvesting. Among these, several have previously been associated with cold-shock response. Furthermore, the reproducibility of transcript profiles improved, as indicated by the fact that the variation in the data sets obtained from the quenched cells was smaller than in the data sets obtained from the cells that were harvested under non-quenched conditions.

Lactobacillus↗

PubMed2005

Microbial metabolomics: replacing trial-and-error by the unbiased selection and ranking of targets.

Microbial production strains are currently improved using a combination of random and targeted approaches. In the case of a targeted approach, potential bottlenecks, feed-back inhibition, and side-routes are removed, and other processes of interest are targeted by overexpressing or knocking-out the gene(s) of interest. To date, the selection of these targets has been based at its best on expert knowledge, but to a large extent also on 'educated guesses' and 'gut feeling'. Therefore, time and thus money is wasted on targets that later prove to be irrelevant or only result in a very minor improvement. Moreover, in current approaches, biological processes that are not known to be involved in the formation of a specific product are overlooked and it is impossible to rank the relative importance of the different targets postulated. Metabolomics, a technology that involves the non-targeted, holistic analysis of the changes in the complete set of metabolites in the cell in response to environmental or cellular changes, in combination with multivariate data analysis (MVDA) tools like principal component discriminant analysis and partial least squares, allow the replacement of current empirical approaches by a scientific approach towards the selection and ranking of targets. In this review, we describe the technological challenges in setting up the novel metabolomics technology and the principle of MVDA algorithms in analyzing biomolecular data sets. In addition to strain improvement, the combined metabolomics and MVDA approach can also be applied to growth medium optimization, predicting the effect of quality differences of different batches of complex media on productivity, the identification of bioactives in complex mixtures, the characterization of mutant strains, the exploration of the production potential of strains, the assignment of functions to orphan genes, the identification of metabolite-dependent regulatory interactions, and many more microbiological issues.

Bacteria↗

PubMed2005

Towards replacing closed with open target selection strategies.

Increasingly, microbial production processes are being improved by targeted approaches. In directed strain improvement, the selection of the relevant targets is the limiting step in metabolic engineering. Currently, the identification of leads is still a random process relying largely on expert knowledge. Recently, this approach has been complemented by metabolic flux and control analysis approaches. However, both are closed approaches, and biological processes or interactions that are not currently known to exist, or to be important for bioproduct formation, are not taken into account. By contrast, the recently introduced functional genomics technologies enable an open approach towards target selection. In the near future, we might see that metabolomics, and its integration with transcriptomics and/or proteomics into a systems biology approach, in combination with multivariate data analysis tools, will become of increasing importance for the unbiased selection and ranking of targets, not only for strain improvement but also for bioprocess improvement.

Bacteria↗

PubMed2005

Unravelling the multiple effects of lactic acid stress on Lactobacillus plantarum by transcription profiling.

The organic acid lactate is the predominant fermentation product of Lactobacillus plantarum. The undissociated form of this organic acid is a strong growth inhibitor for the organism. Different theories have been postulated to explain the inhibitory effects of lactic acid: (i) toxicity arising from the dissipation of the membrane potential, (ii) acidification of the cytosol, or (iii) intracellular anion accumulation. In general, organic acid stresses are complex to study, since their toxicity is highly dependent on their degree of dissociation and thus on the pH. In this study, transcription profiles of L. plantarum grown in steady-state cultures that varied in lactate/lactic acid concentration, pH, osmolarity and absolute and relative growth rate, were compared by microarray analysis. By doing so, the differential expression of multiple groups of genes could specifically be attributed to the different aspects of lactic acid stress. A highly coherent group of lactic acid-responsive, cell surface protein-encoding genes was identified, to which no function has previously been assigned. Moreover, a group of genes that showed increased expression in response to the combination of lactic acid and a lower growth rate is expected to be involved in the formation of the alternative fermentation end-products malate, acetate and ethanol. One of these pathways is the phosphoketolase by-pass that is typical for bifidobacteria.

Bioreactors↗

PubMed2003

Structure of Rhodococcus erythropolis limonene-1,2-epoxide hydrolase reveals a novel active site.

Epoxide hydrolases are essential for the processing of epoxide-containing compounds in detoxification or metabolism. The classic epoxide hydrolases have an alpha/beta hydrolase fold and act via a two-step reaction mechanism including an enzyme-substrate intermediate. We report here the structure of the limonene-1,2-epoxide hydrolase from Rhodococcus erythropolis, solved using single-wavelength anomalous dispersion from a selenomethionine-substituted protein and refined at 1.2 A resolution. This enzyme represents a completely different structure and a novel one-step mechanism. The fold features a highly curved six-stranded mixed beta-sheet, with four alpha-helices packed onto it to create a deep pocket. Although most residues lining this pocket are hydrophobic, a cluster of polar groups, including an Asp-Arg-Asp triad, interact at its deepest point. Site-directed mutagenesis supports the conclusion that this is the active site. Further, a 1.7 A resolution structure shows the inhibitor valpromide bound at this position, with its polar atoms interacting directly with the residues of the triad. We suggest that several bacterial proteins of currently unknown function will share this structure and, in some cases, catalytic properties.

Amino Acid Sequence↗

PubMed2003

What can spores do for us?

Many organisms have the ability to form spores, a remarkable phase in their life cycles. Compared with vegetative cells, spores have several advantages (e.g. resistance to toxic compounds, temperature, desiccation and radiation) making them well suited to various applications. The applications of spores that first spring to mind are bio-warfare and the related, but more positive, field of biological control. Although they are often considered metabolically inert, spores can also be used as biocatalysts. Other uses for spores are found in the fields of probiotics, tumour detection and treatment, biosensing and in the "war against drugs".

Animals↗

PubMed2002

Toxicity of terpenes to spores and mycelium of Penicillium digitatum.

Spores, although often considered metabolically inert, catalyze a variety of reactions. The use of spores instead of mycelium for bioconversions has several advantages. In this paper, we describe the difference in susceptibility of mycelium and spores against toxic substrates and products. A higher resistance of spores toward the toxic effects of bioconversion substrates and products is an advantage that has not been studied in detail until now. This paper shows that spores of Penicillium digitatum ATCC 201167 are on average over 2.5 times more resistant than mycelium toward the toxicity of substrates, intermediates, and products of the geraniol bioconversion pathway. Furthermore, the higher resistance of spores to citral was shown as an advantage in its biotransformation by P. digitatum. Using three different approaches the toxicity of the compounds were tested. The order of toxicity toward P. digitatum was, starting with the most toxic, citral > nerol/geraniol > geranic acid > methylheptenone >> acetaldehyde.

Acyclic Monoterpenes↗

PubMed2000

Metabolism of carveol and dihydrocarveol in Rhodococcus erythropolis DCL14.

Rhodococcus erythropolis DCL14 assimilates all stereoisomers of carveol and dihydrocarveol as sole source of carbon and energy. Induction experiments with carveol- or dihydrocarveol-grown cells showed high oxygen consumption rates with these two compounds and with carvone and dihydrocarvone. (Dihydro)carveol-grown cells of R. erythropolis DCL14 contained the following enzymic activities involved in the carveol and dihydrocarveol degradation pathways of this micro-organism: (dihydro)carveol dehydrogenase (both NAD+- and dichlorophenolindophenol-dependent activities), an unknown cofactor-dependent carvone reductase, (iso-)dihydrocarvone isomerase activity, NADPH-dependent dihydrocarvone monooxygenase (Baeyer-Villiger monooxygenase), epsilon-lactone hydrolase and an NAD+-dependent 6-hydroxy-3-isopropenylheptanoate dehydrogenase. Product accumulation studies identified (4R)-carvone, (1R,4R)-dihydrocarvone, (4R,7R)-4-isopropenyl-7-methyl-2-oxo-oxepanone, (3R)-6-hydroxy-3-isopropenylheptanoate, (3R)-3-isopropenyl-6-oxoheptanoate, (3S,6R)-6-isopropenyl-3-methyl-2-oxooxepanone and (5R)-6-hydroxy-5-isopropenyl-2-methylhexanoate as intermediates in the (4R)-carveol degradation pathway. The opposite stereoisomers of these compounds were identified in the (4S)-carveol degradation pathway. With dihydrocarveol, the same intermediates are involved except that carvone was absent. These results show that R. erythropolis DCL14 metabolizes all four diastereomers of carveol via oxidation to carvone, which is subsequently stereospecifically reduced to (1R)-(iso-) dihydrocarvone. At this point also dihydrocarveol enters the pathway, and this compound is directly oxidized to (iso-)dihydrocarvone. Cell extracts contained both (1R)-(iso-)dihydrocarvone 1,2-monooxygenase and (1S)-(iso)-dihydrocarvone 2,3-monooxygenase activity, resulting in a branch point of the degradation pathway; (1R)-(iso-)dihydrocarvone was converted to 4-isopropenyl-7-methyl-2-oxo-oxepanone, while (1S)-(iso)-dihydrocarvone, which in vivo is isomerized to (1R)-(iso-)dihydrocarvone, was converted to 6-isopropenyl-3-methyl-2-oxo-oxepanone. 4-Isopropenyl-7-methyl-2-oxooxepanone is hydrolysed to 6-hydroxy-3-isopropenylheptanoate, which is subsequently oxidized to 3-isopropenyl-6-oxoheptanoate, thereby linking the (dihydro)carveol degradation pathways to the limonene degradation pathway of this micro-organism. 6-Isopropenyl-3-methyl-2-oxo-oxepanone is, in vitro, hydrolysed to 6-hydroxy-5-isopropenyl-2-methylhexanoate, which is thought to be a dead-end metabolite.

2,6-Dichloroindophenol↗