Gareth Catchpole

Metabolomic and transcriptomic stress response of Escherichia coli.

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.

High-Resolution Direct Infusion-Based Mass Spectrometry in Combination with Whole 13C Metabolome Isotope Labeling Allows Unambiguous Assignment of Chemical Sum Formulas

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.

Metabolic profiling reveals key metabolic features of renal cell carcinoma

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.

Assessment of sampling strategies for gas chromatography–mass spectrometry (GC–MS) based metabolomics of cyanobacteria

Metabolomics is the comprehensive analysis of the small molecules that compose an organisms 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.

Rapid metabolic evolution in human prefrontal cortex

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.

Stability of metabolic correlations under changing environmental conditions in Escherichia coli--a systems approach.

BACKGROUND Biological systems adapt to changing environments by reorganizing their cellular and physiological program with metabolites representing one important response level. Different stresses lead to both conserved and specific responses on the metabolite level which should be reflected in the underlying metabolic network. METHODOLOGY/PRINCIPAL FINDINGS Starting from experimental data obtained by a GC-MS based high-throughput metabolic profiling technology we here develop an approach that: (1) extracts network representations from metabolic condition-dependent data by using pairwise correlations, (2) determines the sets of stable and condition-dependent correlations based on a combination of statistical significance and homogeneity tests, and (3) can identify metabolites related to the stress response, which goes beyond simple observations about the changes of metabolic concentrations. The approach was tested with Escherichia coli as a model organism observed under four different environmental stress conditions (cold stress, heat stress, oxidative stress, lactose diauxie) and control unperturbed conditions. By constructing the stable network component, which displays a scale free topology and small-world characteristics, we demonstrated that: (1) metabolite hubs in this reconstructed correlation networks are significantly enriched for those contained in biochemical networks such as EcoCyc, (2) particular components of the stable network are enriched for functionally related biochemical pathways, and (3) independently of the response scale, based on their importance in the reorganization of the correlation network a set of metabolites can be identified which represent hypothetical candidates for adjusting to a stress-specific response. CONCLUSIONS/SIGNIFICANCE Network-based tools allowed the identification of stress-dependent and general metabolic correlation networks. This correlation-network-based approach does not rely on major changes in concentration to identify metabolites important for stress adaptation, but rather on the changes in network properties with respect to metabolites. This should represent a useful complementary technique in addition to more classical approaches.

Identification of an intracellular metabolic signature impairing beta cell function in the rat beta cell line INS-1E and human islets

Aims/hypothesisChronic hyperglycaemia promotes the progressive failure of pancreatic beta cells in patients with type 2 diabetes mellitus, a clinically highly relevant phenomenon known as glucotoxicity. The intracellular metabolic consequences of a chronically high availability of glucose in beta cells are, as yet, poorly understood in its full complexity.MethodsAn unbiased metabolite profiling analysis (GC-time-of-flight-MS) was used to identify the time course of core metabolite patterns in rat beta cell line INS-1E during exposure to high glucose concentrations and its relation to insulin expression.ResultsWe report here that pentose phosphate pathway (PPP) metabolites accumulate remarkably during chronic but not acute glucose treatment, indicating altered processing of glucose through the pentose phosphate pathway. Subsequent functional studies in INS-1E cells and human islets revealed that a disturbance in this pathway contributes to decreases in insulin gene expression and a lack of glucose-stimulated insulin secretion. These effects were found to depend on the activation of extracellular-regulated-kinase (ERK1/2). Long-term inhibition of 6-phosphogluconic acid dehydrogenase resulted in accumulation of PPP metabolites, induced ERK1/2 activation independently of high glucose and impaired beta cell function. In turn, inhibition of ERK1/2 overstimulation during chronic glucose exposure partly inhibited metabolite accumulation and restored beta cell function.Conclusions/interpretationBased on unbiased metabolite analyses, the data presented here provide novel targets, namely the inhibition of PPP metabolite accumulation towards the therapeutic goal to preserve and potentially improve beta cell function in diabetes.

Comparison of metabolite profiles in U87 glioma cells and mesenchymal stem cells

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.