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As part of an institutional investigation by University of Bremen, the work carried out by Kathrin Maedler's laboratory has been reviewed.

Gas chromatography-mass spectrometry (GC-MS) profiles were generated from U87 glioma cells and human mesenchymal stem cells (hMSC). 37 metabolites representing glycolysis intermediates, TCA cycle metabolites, amino acids and lipids were selected for a detailed analysis. The concentrations of these. metabolites were compared and Pearson correlation coefficients were used to calculate the relationship between pairs of metabolites. Metabolite profiles and correlation patterns differ significantly between the two cell lines. These profiles can be considered as a signature of the underlying biochemical system and provide snap-shots of the metabolism in mesenchymal stem cells and tumor cells.

Recent evidence suggests that metabolic changes play a pivotal role in the biology of cancer and in particular renal cell carcinoma (RCC). Here, a global metabolite profiling approach was applied to characterize the metabolite pool of RCC and normal renal tissue. Advanced decision tree models were applied to characterize the metabolic signature of RCC and to explore features of metastasized tumours. The findings were validated in a second independent dataset. Vitamin E derivates and metabolites of glucose, fatty acid, and inositol phosphate metabolism determined the metabolic profile of RCC. α-tocopherol, hippuric acid, myoinositol, fructose-1-phosphate and glucose-1-phosphate contributed most to the tumour/normal discrimination and all showed pronounced concentration changes in RCC. The identified metabolic profile was characterized by a low recognition error of only 5% for tumour versus normal samples. Data on metastasized tumours suggested a key role for metabolic pathways involving arachidonic acid, free fatty acids, proline, uracil and the tricarboxylic acid cycle. These results illustrate the potential of mass spectroscopy based metabolomics in conjunction with sophisticated data analysis methods to uncover the metabolic phenotype of cancer. Differentially regulated metabolites, such as vitamin E compounds, hippuric acid and myoinositol, provide leads for the characterization of novel pathways in RCC.

Metabolomics is the comprehensive analysis of the small molecules that compose an organism's metabolism. The main limiting step in microbial metabolomics is the requirement for fast and efficient separation of microbes from the culture medium under conditions in which metabolism is rapidly halted. In this article we compare three different sampling strategies, quenching, filtering, and centrifugation, for arresting the metabolic activities of two morphologically diverse cyanobacteria, the unicellular Synechocystis sp. PCC 6803 and the filamentous Nostoc sp. PCC 7120 for GC-MS analysis. We demonstrate that each sampling technique produces internally consistent and reproducible data, however, cold methanol-water quenching caused leakage and substantial loss of metabolites from various compound classes, while fast filtering and centrifugation produced quite similar metabolite pool sizes, even for metabolites with predicted high turnover. This indicates that cyanobacterial metabolic pools, as measured by GC-MS, do not show high turnover under standard growing conditions. As well, using stable (13)C labeling we show the biological origin of some of the consistently observed unknown analytes. With the development of these techniques, we establish the basis for broad scale comparative metabolite profiling of cyanobacteria.

A new strategy for direct infusion-based metabolite analysis employing a combination of high-resolution mass spectrometry and (13)C-isotope labeling of entire metabolomes is described. Differentially isotope labeled metabolite extracts from otherwise identically grown reference plants were prepared and infused into a Fourier transform ion cyclotron resonance mass spectrometer. The derived accurate mass lists from each extract were searched, using an in-house-developed database search tool, against a number of comprehensive metabolite databases. Comparison of the retrieved chemical formulas from both, the (12)C and (13)C samples, leads to two major advantages compared to nonisotope-based metabolite fingerprinting: first, removal of background contaminations from the result list, due to the (12)C/(13)C peak pairing principle and therefore positive identification of compounds of true biological origin; second, elimination of ambiguity in chemical formula assignment due to the same principle, leading to the clear association of one measured mass to only one chemical formula. Applying this combination of strategies to metabolite extracts of the model plant Arabidopsis thaliana therefore resulted in the reproducible identification of more than 1000 unambiguous chemical sum formulas of biological origin of which more than 80% have not been associated to Arabidopsis before.

There is current debate whether genetically modified (GM) plants might contain unexpected, potentially undesirable changes in overall metabolite composition. However, appropriate analytical technology and acceptable metrics of compositional similarity require development. We describe a comprehensive comparison of total metabolites in field-grown GM and conventional potato tubers using a hierarchical approach initiating with rapid metabolome “fingerprinting” to guide more detailed profiling of metabolites where significant differences are suspected. Central to this strategy are data analysis procedures able to generate validated, reproducible metrics of comparison from complex metabolome data. We show that, apart from targeted changes, these GM potatoes in this study appear substantially equivalent to traditional cultivars.

The primary walls of grasses are composed of cellulose microfibrils, glucuronoarabinoxylans (GAXs), and mixed-linkage β-glucans, together with smaller amounts of xyloglucans, glucomannans, pectins, and a network of polyphenolic substances. Chemical imaging by Fourier transform infrared microspectroscopy revealed large differences in the distributions of many chemical species between different tissues of the maize (Zea mays) coleoptile. This was confirmed by chemical analyses of isolated outer epidermal tissues compared with mesophyll-enriched preparations. Glucomannans and esterified uronic acids were more abundant in the epidermis, whereas β-glucans were more abundant in the mesophyll cells. The localization of β-glucan was confirmed by immunocytochemistry in the electron microscope and quantitative biochemical assays. We used field emission scanning electron microscopy, infrared microspectroscopy, and biochemical characterization of sequentially extracted polymers to further characterize the cell wall architecture of the epidermis. Oxidation of the phenolic network followed by dilute NaOH extraction widened the pores of the wall substantially and permitted observation by scanning electron microscopy of up to six distinct microfibrillar lamellae. Sequential chemical extraction of specific polysaccharides together with enzymic digestion of β-glucans allowed us to distinguish two distinct domains in the grass primary wall. First, a β-glucan-enriched domain, coextensive with GAXs of low degrees of arabinosyl substitution and glucomannans, is tightly associated around microfibrils. Second, a GAX that is more highly substituted with arabinosyl residues and additional glucomannan provides an interstitial domain that interconnects the β-glucan-coated microfibrils. Implications for current models that attempt to explain the biochemical and biophysical mechanism of wall loosening during cell growth are discussed.

BACKGROUND: High blood glucose and diabetes are amongst the conditions causing the greatest losses in years of healthy life worldwide. Therefore, numerous studies aim to identify reliable risk markers for development of impaired glucose metabolism and type 2 diabetes. However, the molecular basis of impaired glucose metabolism is so far insufficiently understood. The development of so called `omics' approaches in the recent years promises to identify molecular markers and to further understand the molecular basis of impaired glucose metabolism and type 2 diabetes. Although univariate statistical approaches are often applied, we demonstrate here that the application of multivariate statistical approaches is highly recommended to fully capture the complexity of data gained using high-throughput methods. METHODS: We took blood plasma samples from 172 subjects who participated in the prospective Metabolic Syndrome Berlin Potsdam follow-up study (MESY-BEPO Follow-up). We analysed these samples using Gas Chromatography coupled with Mass Spectrometry (GC-MS), and measured 286 metabolites. Furthermore, fasting glucose levels were measured using standard methods at baseline, and after an average of six years. We did correlation analysis and built linear regression models as well as Random Forest regression models to identify metabolites that predict the development of fasting glucose in our cohort. RESULTS: We found a metabolic pattern consisting of nine metabolites that predicted fasting glucose development with an accuracy of 0.47 in tenfold cross-validation using Random Forest regression. We also showed that adding established risk markers did not improve the model accuracy. However, external validation is eventually desirable. Although not all metabolites belonging to the final pattern are identified yet, the pattern directs attention to amino acid metabolism, energy metabolism and redox homeostasis. CONCLUSIONS: We demonstrate that metabolites identified using a high-throughput method (GC-MS) perform well in predicting the development of fasting plasma glucose over several years. Notably, not single, but a complex pattern of metabolites propels the prediction and therefore reflects the complexity of the underlying molecular mechanisms. This result could only be captured by application of multivariate statistical approaches. Therefore, we highly recommend the usage of statistical methods that seize the complexity of the information given by high-throughput methods.

Human evolution is characterized by the rapid expansion of brain size and drastic increase in cognitive capabilities. It has long been suggested that these changes were accompanied by modifications of brain metabolism. Indeed, human-specific changes on gene expression or amino acid sequence were reported for a number of metabolic genes, but actual metabolite measurements in humans and apes have remained scarce. Here, we investigate concentrations of more than 100 metabolites in the prefrontal and cerebellar cortex in 49 humans, 11 chimpanzees, and 45 rhesus macaques of different ages using gas chromatography–mass spectrometry (GC-MS). We show that the brain metabolome undergoes substantial changes, both ontogenetically and evolutionarily: 88% of detected metabolites show significant concentration changes with age, whereas 77% of these metabolic changes differ significantly among species. Although overall metabolic divergence reflects phylogenetic relationships among species, we found a fourfold acceleration of metabolic changes in prefrontal cortex compared with cerebellum in the human lineage. These human-specific metabolic changes are paralleled by changes in expression patterns of the corresponding enzymes, and affect pathways involved in synaptic transmission, memory, and learning.

GC-MS-based analysis of the metabolic response of Escherichia coli exposed to four different stress conditions reveals reduction of energy expensive pathways. Time-resolved response of E. coli to changing environmental conditions is more specific on the metabolite as compared with the transcript level. Cease of growth during stress response as compared with stationary phase response invokes similar transcript but dissimilar metabolite responses. Condition-dependent associations between metabolites and transcripts are revealed applying co-clustering and canonical correlation analysis., The response of biological systems to environmental perturbations is characterized by a fast and appropriate adjusting of physiology on every level of the cellular and molecular network. Stress response is usually represented by a combination of both specific responses, aimed at minimizing deleterious effects or repairing damage (e.g. protein chaperones under temperature stress) and general responses which, in part, comprise the downregulation of genes related to translation and ribosome biogenesis. This in turn is reflected by growth cessation or reduction observed under essentially all stress conditions and is an important strategy to adjust cellular physiology to the new condition. E. coli has been intensively investigated in relation to stress responses. Thus far, however, the majority of global analyses of E. coli stress responses have been limited to just one level, gene expression. To better understand system response to perturbation, we designed a time-resolved experiment to compare and integrate metabolic and transcript changes of E. coli using four stress conditions including non-lethal temperature shifts, oxidative stress, and carbon starvation relative to cultures grown under optimal conditions covering both states before and directly after stress application, resumption of growth after stress-induced lag phase, and finally the stationary phase. Metabolic changes occurring after stress application were characterized by a reduction in metabolites of central metabolism (TCA cycle and glycolysis) as well as an increase in free amino acids. Whereas the latter is probably due to protein degradation and stalling of translation, the former supports and extends conclusions based on transcriptome data demonstrating a major decrease in energy-consuming processes as a general stress response. Further comparative analysis of the response on the metabolome and transcriptome, however, revealed in addition to these similarities major differences. Thus, the response on the metabolome displayed a significantly higher specificity towards the specific stress as compared with the transcriptome. Further, when comparing the metabolome of cells ceasing growth due to stress application with cells ceasing growth due to reaching stationary phase the metabolome response differed to a significant extent between both growth arrest phases, whereas the transcriptome response showed significant overlap again, suggesting that the response of E. coli on the metabolome level displays a higher level of significance as compared with the transcriptome level. Subsequently, both data sets were jointly analyzed using co-clustering and canonical correlation approaches to identify coordinated changes on the transcriptome and the metabolite level indicative metabolite–transcript associations. A first outcome of this study was that no association was preserved during all conditions analyzed but rather condition-specific associations were observed. One set of associations found was between metabolites from the oxidative pentose phosphate pathway such as glc-6-P, 6-P-gluconic acid, ribose-5-P, and E-4-P and metabolites from the glycolytic pathway (3PGA and PEP in addition to glc-6-P with two genes encoding pathway enzymes, that is rpe encoding ribulose phosphate 3-epimerase and pps encoding PEP synthase. A second example comprises metabolites of the TCA cycle such as pyruvic acid, 2-ketoglutaric acid, fumaric acid, malic acid, and succinic acid and the mqo gene encoding malate-quinone oxidoreductase (MQO). MQO catalyses the irreversible oxidation of malate to oxaloacetate that in turn regulates the activity of citrate synthase, which is a major rate determining enzyme of the TCA cycle. The strong association between mqo gene expression and multiple members of the TCA cycle as well as pyruvate suggest mqo expression to have a major function for the regulation of the TCA cycle, which need to be experimentally validated. Multiple further associations identified show on the one hand the power of integrative systems oriented approaches for developing new hypothesis, on the other hand their condition-dependent behavior shows the extreme flexibility of the biological systems studied thus requesting a much more intense effort toward parallel analysis of biological systems under several environmental conditions., Environmental fluctuations lead to a rapid adjustment of the physiology of Escherichia coli, necessitating changes on every level of the underlying cellular and molecular network. Thus far, the majority of global analyses of E. coli stress responses have been limited to just one level, gene expression. Here, we incorporate the metabolite composition together with gene expression data to provide a more comprehensive insight on system level stress adjustments by describing detailed time-resolved E. coli response to five different perturbations (cold, heat, oxidative stress, lactose diauxie, and stationary phase). The metabolite response is more specific as compared with the general response observed on the transcript level and is reflected by much higher specificity during the early stress adaptation phase and when comparing the stationary phase response to other perturbations. Despite these differences, the response on both levels still follows the same dynamics and general strategy of energy conservation as reflected by rapid decrease of central carbon metabolism intermediates coinciding with downregulation of genes related to cell growth. Application of co-clustering and canonical correlation analysis on combined metabolite and transcript data identified a number of significant condition-dependent associations between metabolites and transcripts. The results confirm and extend existing models about co-regulation between gene expression and metabolites demonstrating the power of integrated systems oriented analysis.

Fragestellung: Chronisch hohe Glucosekonzentrationen fuhren in pankreatischen Betazellen und Inseln zur Verminderung der Insulinexpression und -sekretion, ein Phanomen, dass unter dem Namen Glucosetoxizitat bekannt ist. Trotz seiner moglichen Relevanz bei der Entstehung des Typ-2-Diabetes sind die zu Grunde liegenden Mechanismen bisher nicht genau geklart. Obwohl intensive Untersuchungen zur Regulation der Insulingenexpression stets neue Erkenntnisse liefern ist die Beteilung des Glucosestoffwechsels weitgehend unklar. Mithilfe von Metabolitenprofilanalysen wird der Frage nachgegangen, welche metabolischen Veranderungen durch hohe Glucosekonzentrationen in Betazellen induziert werden. Methodik: INS-1 Zellen wurden bei 3 mM oder 16 mM Glucose fur 48h kultiviert. Die Messung der Insulin mRNA Spiegel erfolgte mittels semiquantitativer real-time RT-PCR. Intrazellulare Metaboliten wurden mit Methanol:Chloroform:Wasser extrahiert und mittels GC-tofMS analysiert. Ergebnisse: Die Insulingenexpression ist unter chronisch hohen Glucosebedingungen signifikant vermindert (P

Two rapid methods for highly selective detection and quantification of the two major glycoalkaloids in potatoes, alpha-chaconine and alpha-solanine, were compared for robustness in high-throughput operations for over 1000 analytical runs using potato tuber samples from field trials. Glycoalkaloids were analyzed using liquid chromatography coupled to tandem mass spectrometry in multiple reaction monitoring mode. An electrospray interface was used in the detection of glycoalkaloids in positive ion mode. Classical reversed phase (RP) and hydrophilic interaction (HILIC) columns were investigated for chromatographic separation, ruggedness, recovery, precision, and accuracy. During the validation procedure both methods proved to be precise and accurate enough in relation to the high degree of endogenous biological variability found for field-grown potato tubers. However, the RP method was found to be more precise, more accurate, and, more importantly, more rugged than the HILIC method for maintaining the analytes' peak shape symmetry in high-throughput operation. When applied to the comparison of six classically bred potato cultivars to six genetically modified (GM) lines engineered to synthesize health beneficial inulins, the glycoalkaloid content in potato peels of all GM lines was found within the range of the six cultivars. We suggest complementing current unbiased metabolomic strategies by validating quantitative analytical methods for important target analytes such as the toxic glycoalkaloids in potato plants.

Cell wall polysaccharides are some of the most complex biopolymers known, and yet their functions remain largely mysterious. Advances in imaging methods permit direct visualisation of the molecular architecture of cell walls and the modifications that occur to polymers during growth and development. To address the structural and functional relationships of individual cell wall components, we need to better characterise a broad range of structural and architectural alterations in cell walls, appearing as a consequence of developmental regulation, environmental adaptation or genetic modification. We have developed a rapid method to screen large numbers of plants for a broad range of cell wall phenotypes using Fourier transform infrared microspectroscopy and Principal Component Analysis. We are using model systems to uncover the genes that encode some of the cell-wall-related biosynthetic and hydrolytic enzymes, and structural proteins.