FiatFlux – a software for metabolic flux analysis from (13)C-glucose experiments

BACKGROUND: Quantitative knowledge of intracellular fluxes is important for a comprehensive characterization of metabolic networks and their functional operation. In contrast to direct assessment of metabolite concentrations, in vivo metabolite fluxes must be inferred indirectly from measurable quantities in (13)C experiments. The required experience, the complicated network models, large and heterogeneous data sets, and the time-consuming set-up of highly controlled experimental conditions largely restricted metabolic flux analysis to few expert groups. A conceptual simplification of flux analysis is the analytical determination of metabolic flux ratios exclusively from MS data, which can then be used in a second step to estimate absolute in vivo fluxes. RESULTS: Here we describe the user-friendly software package FiatFlux that supports flux analysis for non-expert users. In the first module, ratios of converging fluxes are automatically calculated from GC-MS-detected (13)C-pattern in protein-bound amino acids. Predefined fragmentation patterns are automatically identified and appropriate statistical data treatment is based on the comparison of redundant information in the MS spectra. In the second module, absolute intracellular fluxes may be calculated by a (13)C-constrained flux balancing procedure that combines experimentally determined fluxes in and out of the cell and the above flux ratios. The software is preconfigured to derive flux ratios and absolute in vivo fluxes from [1-(13)C] and [U-(13)C]glucose experiments and GC-MS analysis of amino acids for a variety of microorganisms. CONCLUSION: FiatFlux is an intuitive tool for quantitative investigations of intracellular metabolism by users that are not familiar with numerical methods or isotopic tracer experiments. The aim of this open source software is to enable non-specialists to adapt the software to their specific scientific interests, including other (13)C-substrates, labeling mixtures, and organisms

Knockout of the high-coupling cytochrome aa3 oxidase reduces TCA cycle fluxes in Bacillus subtilis

The metabolic impact of electron rerouting in the respiratory chain of Bacillus subtilis was quantitatively assessed during batch growth of quinol oxidase mutants by 13C-tracer experiments. While disruption of the low-coupling cytochrome bd oxidase was without any apparent phenotype, deletion of the high-coupling cytochrome aa3 oxidase caused a severe reduction of tricarboxylic acid cycle fluxes and increased overflow metabolism. Since the product-corrected biomass yields were identical in mutants and parent, the results show that efficient ATP generation is not overly important for exponential growth of B. subtilis in batch cultur

Non-targeted LC-MS based metabolomics analysis of the urinary steroidal profile

The urinary steroidal fraction has been extensively explored as non-invasive alternative to monitor pathological conditions as well as to unveil the illicit intake of pseudo-endogenous anabolic steroids in sport. However, the majority of previous approaches involved the a priori selection of potentially relevant target analytes. Here we describe the non-targeted analysis of the urinary steroidal profiles. The workflow includes minimal sample pretreatment and normalization according to the specific gravity of urine, a 20 min reverse phase ultra-performance liquid chromatographic separation hyphenated to electrospray time-of-flight mass spectrometry. As initial validation, we analyzed a set of quality control urines spiked with glucurono- and sulfo-conjugated steroids at physiological ranges. We then applied the method for the analysis of samples collected after single transdermal administration of testosterone in hypogonadal men. The method allowed profiling of approximately three thousand metabolic features, including steroids of clinical and forensic relevance. It successfully identified metabolic pathways mostly responsible for groups clustering even in the context of high inter-individual variability and allowed the detection of currently unknown metabolic features correlating with testosterone administration. These outcomes set the stage for future studies aimed at implementing currently monitored urinary steroidal markers both in clinical and forensic analysis

Genetics - Getting closer to the whole picture

A quantitative data set of RNA, proteins, and metabolites provides an unprecedented starting point to understand, at a systems level, the effects of perturbations on a cell

Metabolite identification and molecular fingerprint prediction through machine learning

Motivation: Metabolite identification from tandem mass spectra is an important problem in metabolomics, underpinning subsequent metabolic modelling and network analysis. Yet, currently this task requires matching the observed spectrum against a database of reference spectra originating from similar equipment and closely matching operating parameters, a condition that is rarely satisfied in public repositories. Furthermore, the computational support for identification of molecules not present in reference databases is lacking. Recent efforts in assembling large public mass spectral databases such as MassBank have opened the door for the development of a new genre of metabolite identification methods. Results: We introduce a novel framework for prediction of molecular characteristics and identification of metabolites from tandem mass spectra using machine learning with the support vector machine. Our approach is to first predict a large set of molecular properties of the unknown metabolite from salient tandem mass spectral signals, and in the second step to use the predicted properties for matching against large molecule databases, such as PubChem. We demonstrate that several molecular properties can be predicted to high accuracy and that they are useful in de novo metabolite identification, where the reference database does not contain any spectra of the same molecule. Availability: An Matlab/Python package of the FingerID tool is freely available on the web at http://www.sourceforge.net/p/fingerid. Contact: [email protected]

The urease inhibitor NBPT negatively affects DUR3-mediated uptake and assimilation of urea in maize roots

Despite the widespread use of urease inhibitors in agriculture, little information is available on their effect on nitrogen (N) uptake and assimilation. Aim of this work was to study, at physiological and transcriptional level, the effects of N-(n-butyl) thiophosphoric triamide (NBPT) on urea nutrition in hydroponically grown maize plants. Presence of NBPT in the nutrient solution limited the capacity of plants to utilize urea as a N-source; this was shown by a decrease in urea uptake rate and 15N accumulation. Noteworthy, these negative effects were evident only when plants were fed with urea, as NBPT did not alter 15N accumulation in nitrate-fed plants. NBPT also impaired the growth of Arabidopsis plants when urea was used as N-source, while having no effect on plants grown with nitrate or ammonium. This response was related, at least in part, to a direct effect of NBPT on the high affinity urea transport system. Impact of NBPT on urea uptake was further evaluated using lines of Arabidopsis overexpressing ZmDUR3 and dur3-knockout; results suggest that not only transport but also urea assimilation could be compromised by the inhibitor. This hypothesis was reinforced by an over-accumulation of urea and a decrease in ammonium concentration in NBPT-treated plants. Furthermore, transcriptional analyses showed that in maize roots NBPT treatment severely impaired the expression of genes involved in the cytosolic pathway of ureic-N assimilation and ammonium transport. NBPT also limited the expression of a gene coding for a transcription factor highly induced by urea and possibly playing a crucial role in the regulation of its acquisition. This work provides evidence that NBPT can heavily interfere with urea nutrition in maize plants, limiting influx as well as the following assimilation pathway. \ua9 2015 Zanin, Tomasi, Zamboni, Varanini and Pinton

A high-throughput metabolomics method to predict high concentration cytotoxicity of drugs from low concentration profiles

A major source of drug attrition in pharmacological development is drug toxicity, which eventually manifests itself in detrimental physiological effects. These effects can be assessed in large sample cohorts, but generating rich sets of output variables that are necessary to predict toxicity from lower drug dosages is problematic. Currently the throughput of methods that enable multi-parametric cellular readouts over many drugs and large ranges of concentrations is limited. Since metabolism is at the core of drug toxicity, we develop here a high-throughput intracellular metabolomics platform for relative measurement of 50-100 targeted metabolites by flow injection-tandem mass spectrometry. Specifically we focused on central metabolism of the yeast Saccharomyces cerevisiae because potential cytotoxic effects of drugs can be expected to affect this ubiquitous core network. By machine learning based on intracellular metabolite responses to 41 drugs that were administered at seven concentrations over three orders of magnitude, we demonstrate prediction of cytotoxicity in yeast from intracellular metabolome patterns obtained at much lower drug concentrations that exert no physiological toxicity. Furthermore, the 13C-determined intracellular response of metabolic fluxes to drug treatment demonstrates the functional performance of the network to be rather robust, until growth was compromised. Thus we provide evidence that phenotypic robustness to drug challenges is achieved by a flexible make-up of the metabolom

Deficiency in glutamine but not glucose induces MYC-dependent apoptosis in human cells

The idea that conversion of glucose to ATP is an attractive target for cancer therapy has been supported in part by the observation that glucose deprivation induces apoptosis in rodent cells transduced with the proto-oncogene MYC, but not in the parental line. Here, we found that depletion of glucose killed normal human cells irrespective of induced MYC activity and by a mechanism different from apoptosis. However, depletion of glutamine, another major nutrient consumed by cancer cells, induced apoptosis depending on MYC activity. This apoptosis was preceded by depletion of the Krebs cycle intermediates, was prevented by two Krebs cycle substrates, but was unrelated to ATP synthesis or several other reported consequences of glutamine starvation. Our results suggest that the fate of normal human cells should be considered in evaluating nutrient deprivation as a strategy for cancer therapy, and that understanding how glutamine metabolism is linked to cell viability might provide new approaches for treatment of cancer