Temperature induces changes in Drosophila energy stores.

Temperature has a profound impact on animal physiology. In this study, we examined the effect of ambient temperature on the energy stores of the model organism Drosophila melanogaster. By exposing adult males to 11 temperatures between 13 °C and 33 °C, we found that temperature significantly affects the amount of energy reserves. Whereas flies increase their fat stores at intermediate temperatures, exposure to temperatures below 15 °C or above 27 °C causes a reduction of fat reserves. Moreover, we found that glycogen stores followed a similar trend, although not so pronounced. To elucidate the underlying mechanism of these changes, we compared the temperature dependence of food consumption and metabolic rate. This analysis revealed that food intake and metabolic rate scale with temperature equally, suggesting that the temperature-induced changes in energy reserves are probably not caused by a mismatch between these two traits. Finally, we assessed the effect of temperature on starvation resistance. We found that starvation survival is a negative exponential function of temperature; however we did not find any clear evidence that implies the relative starvation resistance is compromised at non-optimal temperatures. Our results indicate that whilst optimal temperatures can promote accumulation of energy reserves, exposure to non-optimal temperatures reduces Drosophila energy stores.

Deletion of transketolase triggers a stringent metabolic response in promastigotes and loss of virulence in amastigotes of Leishmania mexicana.

Transketolase (TKT) is part of the non-oxidative branch of the pentose phosphate pathway (PPP). Here we describe the impact of removing this enzyme from the pathogenic protozoan Leishmania mexicana. Whereas the deletion had no obvious effect on cultured promastigote forms of the parasite, the Δtkt cells were not virulent in mice. Δtkt promastigotes were more susceptible to oxidative stress and various leishmanicidal drugs than wild-type, and metabolomics analysis revealed profound changes to metabolism in these cells. In addition to changes consistent with those directly related to the role of TKT in the PPP, central carbon metabolism was substantially decreased, the cells consumed significantly less glucose, flux through glycolysis diminished, and production of the main end products of metabolism was decreased. Only minor changes in RNA abundance from genes encoding enzymes in central carbon metabolism, however, were detected although fructose-1,6-bisphosphate aldolase activity was decreased two-fold in the knock-out cell line. We also showed that the dual localisation of TKT between cytosol and glycosomes is determined by the C-terminus of the enzyme and by engineering different variants of the enzyme we could alter its sub-cellular localisation. However, no effect on the overall flux of glucose was noted irrespective of whether the enzyme was found uniquely in either compartment, or in both.

Malleable mitochondrion of Trypanosoma brucei.

The importance of mitochondria for a typical aerobic eukaryotic cell is undeniable, as the list of necessary mitochondrial processes is steadily growing. Here, we summarize the current knowledge of mitochondrial biology of an early-branching parasitic protist, Trypanosoma brucei, a causative agent of serious human and cattle diseases. We present a comprehensive survey of its mitochondrial pathways including kinetoplast DNA replication and maintenance, gene expression, protein and metabolite import, major metabolic pathways, Fe-S cluster synthesis, ion homeostasis, organellar dynamics, and other processes. As we describe in this chapter, the single mitochondrion of T. brucei is everything but simple and as such rivals mitochondria of multicellular organisms.

Use of reconstituted metabolic networks to assist in metabolomic data visualization and mining.

Metabolomics experiments seldom achieve their aim of comprehensively covering the entire metabolome. However, important information can be gleaned even from sparse datasets, which can be facilitated by placing the results within the context of known metabolic networks. Here we present a method that allows the automatic assignment of identified metabolites to positions within known metabolic networks, and, furthermore, allows automated extraction of sub-networks of biological significance. This latter feature is possible by use of a gap-filling algorithm. The utility of the algorithm in reconstructing and mining of metabolomics data is shown on two independent datasets generated with LC-MS LTQ-Orbitrap mass spectrometry. Biologically relevant metabolic sub-networks were extracted from both datasets. Moreover, a number of metabolites, whose presence eluded automatic selection within mass spectra, could be identified retrospectively by virtue of their inferred presence through gap filling. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-009-0196-9) contains supplementary material, which is available to authorized users.

MetExplore: a web server to link metabolomic experiments and genome-scale metabolic networks.

High-throughput metabolomic experiments aim at identifying and ultimately quantifying all metabolites present in biological systems. The metabolites are interconnected through metabolic reactions, generally grouped into metabolic pathways. Classical metabolic maps provide a relational context to help interpret metabolomics experiments and a wide range of tools have been developed to help place metabolites within metabolic pathways. However, the representation of metabolites within separate disconnected pathways overlooks most of the connectivity of the metabolome. By definition, reference pathways cannot integrate novel pathways nor show relationships between metabolites that may be linked by common neighbours without being considered as joint members of a classical biochemical pathway. MetExplore is a web server that offers the possibility to link metabolites identified in untargeted metabolomics experiments within the context of genome-scale reconstructed metabolic networks. The analysis pipeline comprises mapping metabolomics data onto the specific metabolic network of an organism, then applying graph-based methods and advanced visualization tools to enhance data analysis. The MetExplore web server is freely accessible at http://metexplore.toulouse.inra.fr.

Digital gene expression analysis of two life cycle stages of the human-infective parasite, Trypanosoma brucei gambiense reveals differentially expressed clusters of co-regulated genes.

BACKGROUND: The evolutionarily ancient parasite, Trypanosoma brucei, is unusual in that the majority of its genes are regulated post-transcriptionally, leading to the suggestion that transcript abundance of most genes does not vary significantly between different life cycle stages despite the fact that the parasite undergoes substantial cellular remodelling and metabolic changes throughout its complex life cycle. To investigate this in the clinically relevant sub-species, Trypanosoma brucei gambiense, which is the causative agent of the fatal human disease African sleeping sickness, we have compared the transcriptome of two different life cycle stages, the potentially human-infective bloodstream forms with the non-human-infective procyclic stage using digital gene expression (DGE) analysis. RESULTS: Over eleven million unique tags were generated, producing expression data for 7360 genes, covering 81% of the genes in the genome. Compared to microarray analysis of the related T. b. brucei parasite, approximately 10 times more genes with a 2.5-fold change in expression levels were detected. The transcriptome analysis revealed the existence of several differentially expressed gene clusters within the genome, indicating that contiguous genes, presumably from the same polycistronic unit, are co-regulated either at the level of transcription or transcript stability. CONCLUSIONS: DGE analysis is extremely sensitive for detecting gene expression differences, revealing firstly that a far greater number of genes are stage-regulated than had previously been identified and secondly and more importantly, this analysis has revealed the existence of several differentially expressed clusters of genes present on what appears to be the same polycistronic units, a phenomenon which had not previously been observed in microarray studies. These differentially regulated clusters of genes are in addition to the previously identified RNA polymerase I polycistronic units of variant surface glycoproteins and procyclin expression sites, which encode the major surface proteins of the parasite. This raises a number of questions regarding the function and regulation of the gene clusters that clearly warrant further study.

Increasing the mass accuracy of high-resolution LC-MS data using background ions: a case study on the LTQ-Orbitrap.

With the advent of a new generation of high-resolution mass spectrometers, the fields of proteomics and metabolomics have gained powerful new tools. In this paper, we demonstrate a novel computational method that improves the mass accuracy of the LTQ-Orbitrap mass spectrometer from an initial +/- 1-2 ppm, obtained by the standard software, to an absolute median of 0.21 ppm (SD 0.21 ppm). With the increased mass accuracy it becomes much easier to match mass chromatograms in replicates and different sample types, even if compounds are detected at very low intensities. The proposed method exploits the ubiquitous presence of background ions in LC-MS profiles for accurate alignment and internal mass calibration, making it applicable for all types of MS equipment. The accuracy of this approach will facilitate many downstream systems biology applications, including mass-based molecule identification, ab initio metabolic network reconstruction, and untargeted metabolomics in general.