Biomedical subjects

Thomas Hankemeier

Publications and source records attributed to Thomas Hankemeier.

9 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

Hyphenation of infrared spectroscopy to liquid chromatography for qualitative and quantitative polymer analysis: degradation of poly(bisphenol A)carbonate.

Hyphenation of infrared spectroscopy (IR) to liquid chromatography (LC) has been applied to study chemical changes in poly(bisphenol A)carbonate (PC) as a result of degradation. Especially coupling of LC to FTIR through solvent elimination is a sensitive approach to identify changes in functionality observed in the LC chromatograms as has been demonstrated by coupling of liquid chromatography under critical conditions (LCCC) to IR. Furthermore, an example is shown in which two-dimensional liquid chromatography, i.e. LCCC x SEC, was coupled to IR by means of a flow cell. This resulted in data sets containing most probably valuable data, but extracting relevant information from these large data sets is not straightforward at all. Therefore, multivariate data analysis (MVDA) of SEC-FTIR data was used to extract relevant data from large data sets. This approach revealed chemical differences due to degradation that could not be detected by other means. Spectral features could be identified that allowed to quantitatively predict the degradation of poly(bisphenol A)carbonate as a function of degradation conditions.

Benzhydryl Compounds↗

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

Metabolite profiling of human urine by CE-ESI-MS using separation electrolytes at low pH.

We investigated the potential of CE coupled to electrospray MS (CE-ESI-MS) in metabolite profiling of human urine without any sample prefractionation step. A heterogeneous mixture of biologically relevant compounds covering a broad range of physicochemical properties was used to optimize separation conditions in fused-silica capillaries. A running electrolyte containing 50 mM of acetic acid and 50 mM of formic acid at pH 2.5 was used for the CE separations. A sheath-flow electrospray interface was employed for CE-ESI-MS analysis. Sheath liquids containing 80:20 v/v methanol/water with 0.1% v/v of acetic acid or 60:40 v/v isopropanol/water with 0.5% v/v of ammonia were selected for optimum detection in positive and negative ESI modes, respectively. Reproducibility and sensitivity were studied, and strategies for identification of the separated urinary compounds are suggested. We report major advantages and disadvantages of CE-ESI-MS for metabolite profiling of human body fluids. This work may be regarded as a first step in the use of CE-ESI-MS for reliable differential analysis of body fluids from healthy and diseased individuals.

Electrophoresis, Capillary↗

PubMed2006

Effects of growth conditions and processing on Rehmannia glutinosa using fingerprint strategy.

Metabolite profiling in combination with multivariate statistics is a sophisticated method for quality assessment of natural products. For the development of a quality control strategy in Traditional Chinese Medicine (TCM), we have measured the metabolite fingerprints of Rehmannia glutinosa by GC-MS. Plants were grown under different climate and soil conditions in a phytotron and were processed by a variable number of repetitive steps to investigate the effects on both growth conditions and processing for material medica of R. glutinosa. The GC-MS data have been analyzed by principal component analysis (PCA) and the new approach of the ANOVA-simultaneous component analysis (ASCA) which can combine the information from a structured data design with multivariate analysis. The results clearly show the effect of the different factors and indicate directions for process improvement. When plants were grown under various temperatures, humidity and light intensities for a short period (3 weeks), no significant changes on studied metabolites were observed. However, significant changes were found between different processing cycles. The present data clearly indicate the importance of strictly controlling processing in R. glutinosa and illustrate the impact of growth conditions. This is the first report on the metabolite profile of R. glutinosa that provides a base for the establishment of a quality control strategy.

Climate↗

PubMed2006

Typical diffusion behaviour in packaging polymers - application to functional barriers.

When plastics are collected for recycling, possibly contaminated articles might be recycled into food packaging, and thus the contaminants might subsequently migrate into the food. Multilayer functional barriers may be used to delay and to reduce such migration. The contribution of the work reported here is to establish reference values (at 40 degrees C) of diffusion coefficients and of activation energies to predict the functional barrier efficiency of a broad range of polymers (polyolefins, polystyrene, polyamide, PVC, PET, PVDC, [ethylene vinyl alcohol copolymer], polyacrylonitrile and [ethylene vinyl acetate copolymer]). Diffusion coefficients (D) and activation energies (Ea) were measured and were compiled together with literature data. This allowed identification of new trends for the log D=f(molecular weight) relationships. The slopes were a function of the barrier efficiency of the polymer and temperature. The apparent activation energy of diffusion displayed two domains of variation with molecular weight (M). For low M (gases), there was little variation of Ea. Focusing on larger molecules, high barrier polymers displayed a larger dependence of Ea with M. The apparent activation energy decreased with T. These results suggest a discontinuity between rubbery and glassy polymers.

Acrylic Resins↗

PubMed2006

Metabolomics-based systems biology and personalized medicine: moving towards n = 1 clinical trials?

Personalized medicine - defined as customized medical care for each patient's unique condition - in the broader context of personalized health, will make significant strides forward when a systems approach is implemented to achieve the ultimate in disease phenotyping and to create novel therapeutics that address system-wide molecular perturbations caused by disease processes. Combination drug therapies with individualized optimization are likely to become a major focus. Metabolomics incorporates the most advanced approaches to molecular phenotype system readout and provides the ideal theranostic technology platform for the discovery of biomarker patterns associated with healthy and diseased states, for use in personalized health monitoring programs and for the design of individualized interventions.

Animals↗

PubMed2005

Sign constraints improve the detection of differences between complex spectral data sets: LC-IR as an example.

Spectroscopy is a fast and rich analytical tool. On many occasions, spectra are acquired of two or more sets of samples that differ only slightly. These data sets then need to be compared and analyzed, but sometimes it is difficult to find the differences. We present a simple and effective method that detects and extracts new spectral features in a spectrum coming from one set with respect to spectra of another set on the basis of the fact that these new spectral features are essentially positive quantities. The proposed procedure (i) characterizes the spectra of the reference set by a component model and (ii) uses asymmetric least squares (ASLS) to find differences with respect to this component model. It should be stressed that the method only focuses on new features and does not trace relative changes of spectral features that occur in both sets of spectra. A comparison is made with the conventional ordinary least squares (OLS) approach. Both methods (OLS and ASLS) are illustrated with simulations and are tested for size-exclusion chromatography with infrared detection (SEC-IR) of mixtures of polymer standards. Both methods are able to provide information about new spectral features. It is shown that the ASLS-based procedure yields the best recovery of new features in the simulations and in the SEC-IR experiments. Band positions and band shapes of new spectral features are better retrieved with the ASLS than with the OLS method, even those which could hardly be detected visually. Depending on the spectroscopic technique used, the ASLS-based method facilitates identification of the new chemical compounds.

Chromatography, Liquid↗

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↗