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L-3,4-Dihydroxyphenylalanin (l-DOPA or levodopa) is currently the most used drug to treat symptoms of Parkinson’s disease (PD). After crossing the blood–brain barrier, it is enzymatically converted to dopamine by neuronal cells and restores depleted endogenous neurotransmitter levels. l-DOPA is prone to auto-oxidation and reactive intermediates of its degradation including reactive oxygen species (ROS) have been implicated in cellular damage. In this study, we investigated how oxygen tension effects l-DOPA stability. We applied oxygen tensions comparable to those in the mammalian brain and demonstrated that 2% oxygen almost completely stopped its auto-oxidation. l-DOPA even exerted a ROS scavenging function. Further mechanistic analysis indicated that l-DOPA reprogrammed mitochondrial metabolism and reduced oxidative phosphorylation, depolarized the mitochondrial membrane, induced reductive glutamine metabolism, and depleted the NADH pool. These results shed new light on the cellular effects of l-DOPA and its neuro-toxicity under physiological oxygen levels that are very distinct to normoxic in vitro conditions.

// Pit Ullmann 1 , Komal Qureshi-Baig 1 , Fabien Rodriguez 1 , Aurelien Ginolhac 1 , Yannic Nonnenmacher 2 , Dominik Ternes 1 , Jil Weiler 1 , Karoline Gabler 1 , Christelle Bahlawane 1 , Karsten Hiller 2 , Serge Haan 1 , Elisabeth Letellier 1 1 Life Sciences Research Unit, University of Luxembourg, L-4367 Belvaux, Luxembourg 2 Luxembourg Centre for Systems Biomedicine, L-4367 Belvaux, Luxembourg Correspondence to: Elisabeth Letellier, email: elisabeth.letellier@uni.lu Keywords: colorectal cancer, tumor-initiating cell, hypoxia, miR-210, self-renewal capacity Received: May 20, 2016 Accepted: August 25, 2016 Published: August 31, 2016 ABSTRACT Low oxygen concentrations (hypoxia) are known to affect the cellular metabolism and have been suggested to regulate a subpopulation of cancer cells with tumorigenic properties, the so-called tumor-initiating cells (TICs). To better understand the mechanism of hypoxia-induced TIC activation, we set out to study the role of hypoxia-responsive miRNAs in recently established colon cancer patient-derived TICs. We were able to show that low oxygen concentrations consistently lead to the upregulation of miR-210 in different primary TIC-enriched cultures. Both stable overexpression of miR-210 and knockdown of its target gene ISCU resulted in enhanced TIC self-renewal. We could validate the tumorigenic properties of miR- 210 in in vivo experiments by showing that ectopic expression of miR-210 results in increased tumor incidence. Furthermore, enhanced miR-210 expression correlated with reduced TCA cycle activity and increased lactate levels. Importantly, by blocking lactate production via inhibition of LDHA, we could reverse the promoting effect of miR-210 on self-renewal capacity, thereby emphasizing the regulatory impact of the glycolytic phenotype on colon TIC properties. Finally, by assessing expression levels in patient tissue, we could demonstrate the clinical relevance of the miR-210/ISCU signaling axis for colorectal carcinoma. Taken together, our study highlights the importance of hypoxia-induced miR-210 in the regulation of colon cancer initiation.

Itaconic acid is produced by mammalian leukocytes upon pro-inflammatory activation. It appears to inhibit bacterial growth and to rewire the metabolism of the host cell by inhibiting succinate dehydrogenase. Yet, it is unknown whether itaconic acid acts only intracellularly, locally in a paracrine fashion, or whether it is even secreted from the inflammatory cells at meaningful levels in peripheral blood of patients with severe inflammation or sepsis. The aim of this study was to determine the release rate of itaconic acid from pro-inflammatory activated macrophages in vitro and to test for the abundance of itaconic acid in bodyfluids of patients suffering from acute inflammation. We demonstrate that excretion of itaconic acid happens at a low rate and that it cannot be detected in significant amounts in plasma or urine of septic patients or in liquid from bronchial lavage of patients with pulmonary inflammation. We conclude that itaconic acid may serve as a pro-inflammatory marker in immune cells but that it does not qualify as a biomarker in the tested body fluids.

Currently, changes in metabolic fluxes following consumption of stable isotope-enriched foods are usually limited to the analysis of postprandial kinetics of glucose. Kinetic information on a larger diversity of metabolites is often lacking, mainly due to the marginal percentage of fully isotopically enriched plant material in the administered food product, and hence, an even weaker 13C enrichment in downstream plasma metabolites. Therefore, we developed an analytical workflow to determine weak 13C enrichments of diverse plasma metabolites with conventional gas chromatography-mass spectrometry (GC-MS). The limit of quantification was increased by optimizing (1) the metabolite extraction from plasma, (2) the GC-MS measurement, and (3) most importantly, the computational data processing. We applied our workflow to study the catabolic dynamics of 13C-enriched wheat bread in three human subjects. For that purpose, we collected time-resolved human plasma samples at 16 timepoints after the consumption of 13C-labeled bread and quantified 13C enrichment of 12 metabolites (glucose, lactate, alanine, glycine, serine, citrate, glutamate, glutamine, valine, isoleucine, tyrosine, and threonine). Based on isotopomer specific analysis, we were able to distinguish catabolic profiles of starch and protein hydrolysis. More generally, our study highlights that conventional GC-MS equipment is sufficient to detect isotope traces below 1% if an appropriate data processing is integrated.

Itaconic acid is well known as a precursor for polymer synthesis and has been involved in industrial processes for decades. In a recent surprising discovery, itaconic acid was found to play a role as an immune-supportive metabolite in mammalian immune cells, where it is synthesized as an antimicrobial compound from the citric acid cycle intermediate cis-aconitic acid. Although the immune-responsive gene 1 protein (IRG1) has been associated to immune response without a mechanistic function, the critical link to itaconic acid production through an enzymatic function of this protein was only recently revealed. In this review, we highlight the history of itaconic acid as an industrial and antimicrobial compound, starting with its biotechnological synthesis and ending with its antimicrobial function in mammalian immune cells.

a b s t r a c t Robust quantification of analytes is a prerequisite for meaningful metabolomics experiments. In nontargeted metabolomics it is still hard to compare measurements across multiple batches or instruments. For targeted analyses isotope dilution mass spectrometry is used to provide a robust normalization reference. Here, we present an approach that allows for the automated semi-quantification of metabolites relative to a fully stable isotope-labeled metabolite extract. Unlike many previous approaches, we include both identified and unidentified compounds in the data analysis. The internal standards are detected in an automated manner using the non-targeted tracer fate detection algorithm. The ratios of the light and heavy form of these compounds serve as a robust measure to compare metabolite levels across different mass spectrometric platforms. As opposed to other methods which require high resolution mass spectrometers, our methodology works with low resolution mass spectrometers as commonly used in gas chromatography electron impact mass spectrometry (GC–EI-MS)-based metabolomics. We demonstrate the validity of our method by analyzing compound levels in different samples and show that it outperforms conventional normalization approaches in terms of intra- and inter-instrument reproducibility. We show that a labeled yeast metabolite extract can also serve as a reference for mammalian metabolite extracts where complete stable isotope labeling is hard to achieve. © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Graphical abstract Three-phase methanol–water–chloroform extraction for biological samples. Examples of components available from each phase are shown. These different phases can be then used for a variety of different analysis methods on different levels of cellular regulation., Proper sample preparation is an integral part of all omics approaches, and can drastically impact the results of a wide number of analyses. As metabolomics and proteomics research approaches often yield complementary information, it is desirable to have a sample preparation procedure which can yield information for both types of analyses from the same cell population. This protocol explains a method for the separation and isolation of metabolites and proteins from the same biological sample, in order for downstream use in metabolomics and proteomics analyses simultaneously. In this way, two different levels of biological regulation can be studied in a single sample, minimizing the variance that would result from multiple experiments. This protocol can be used with both adherent and suspension cell cultures, and the extraction of metabolites from cellular medium is also detailed, so that cellular uptake and secretion of metabolites can be quantified. Advantages of this technique includes:1.Inexpensive and quick to perform; this method does not require any kits.2.Can be used on any cells in culture, including cell lines and primary cells extracted from living organisms.3.A wide variety of different analysis techniques can be used, adding additional value to metabolomics data analyzed from a sample; this is of high value in experimental systems biology.

Exposure to air pollution resulting from fossil fuel combustion has been linked to multiple short-term and long term health effects. In a previous study, exposure of lung epithelial cells to engine exhaust from heavy fuel oil (HFO) and diesel fuel (DF), two of the main fuels used in marine engines, led to an increased regulation of several pathways associated with adverse cellular effects, including pro-inflammatory pathways. In addition, DF exhaust exposure was shown to have a wider response on multiple cellular regulatory levels compared to HFO emissions, suggesting a potentially higher toxicity of DF emissions over HFO. In order to further understand these effects, as well as to validate these findings in another cell line, we investigated macrophages under the same conditions as a more inflammation-relevant model. An air-liquid interface aerosol exposure system was used to provide a more biologically relevant exposure system compared to submerged experiments, with cells exposed to either the complete aerosol (particle and gas phase), or the gas phase only (with particles filtered out). Data from cytotoxicity assays were integrated with metabolomics and proteomics analyses, including stable isotope-assisted metabolomics, in order to uncover pathways affected by combustion aerosol exposure in macrophages. Through this approach, we determined differing phenotypic effects associated with the different components of aerosol. The particle phase of diluted combustion aerosols was found to induce increased cell death in macrophages, while the gas phase was found more to affect the metabolic profile. In particular, a higher cytotoxicity of DF aerosol emission was observed in relation to the HFO aerosol. Furthermore, macrophage exposure to the gas phase of HFO leads to an induction of a pro-inflammatory metabolic and proteomic phenotype. These results validate the effects found in lung epithelial cells, confirming the role of inflammation and cellular stress in the response to combustion aerosols.

Graphical abstract Quantitative multi-component analysis of body fluids with GC–MS. After sample collection, the samples are pre-processed and used for metabolite extraction. After this, GC–MS analysis and subsequent data processing and analysis are performed., Metabolome analyses of body fluids are challenging due pre-analytical variations, such as pre-processing delay and temperature, and constant dynamical changes of biochemical processes within the samples. Therefore, proper sample handling starting from the time of collection up to the analysis is crucial to obtain high quality samples and reproducible results. A metabolomics analysis is divided into 4 main steps: 1) Sample collection, 2) Metabolite extraction, 3) Data acquisition and 4) Data analysis. Here, we describe a protocol for gas chromatography coupled to mass spectrometry (GC–MS) based metabolic analysis for biological matrices, especially body fluids. This protocol can be applied on blood serum/plasma, saliva and cerebrospinal fluid (CSF) samples of humans and other vertebrates. It covers sample collection, sample pre-processing, metabolite extraction, GC–MS measurement and guidelines for the subsequent data analysis. Advantages of this protocol include: • Robust and reproducible metabolomics results, taking into account pre-analytical variations that may occur during the sampling process • Small sample volume required • Rapid and cost-effective processing of biological samples • Logistic regression based determination of biomarker signatures for in-depth data analysis

Introduction Metabolome analysis is complicated by the continuous dynamic changes of metabolites in vivo and ex vivo. One of the main challenges in metabolomics is the robustness and reproducibility of results, partially driven by pre-analytical variations. Objectives The objective of this study was to analyse the impact of pre-centrifugation time and temperature, and to determine a quality control marker in plasma samples. Methods Plasma metabolites were measured by gas chromatography-mass spectrometry (GC–MS) and analysed with the MetaboliteDetector software. The metabolites, which were the most labile to pre-analytical variations, were further measured by enzymatic assays. A score was calculated for their use as quality control markers. Results The pre-centrifugation temperature was shown to be critical in the stability of plasma samples and had a significant impact on metabolite concentration profiles. In contrast, pre-centrifugation delay had only a minor impact. Based on the results of this study, whole blood should be kept on wet ice and centrifuged within maximum 3 h as a prerequisite for preparing EDTA plasma samples fit for the purpose of metabolome analysis. Conclusions We have established a novel blood sample quality control marker, the LacaScore, based on the ascorbic acid to lactic acid ratio in plasma, which can be used as an indicator of the blood pre-centrifugation conditions, and hence the suitability of the sample for metabolome analyses. This method can be applied in research institutes and biobanks, enabling assessment of the quality of their plasma sample collections. Electronic supplementary material The online version of this article (doi:10.1007/s11306-016-1038-1) contains supplementary material, which is available to authorized users.

Mixed microbial communities are complex, dynamic and heterogeneous. It is therefore essential that biomolecular fractions obtained for high-throughput omic analyses are representative of single samples to facilitate meaningful data integration, analysis and modeling. We have developed a new methodological framework for the reproducible isolation of high-quality genomic DNA, large and small RNA, proteins, and polar and non-polar metabolites from single unique mixed microbial community samples. The methodology is based around reproducible cryogenic sample preservation and cell lysis. Metabolites are extracted first using organic solvents, followed by the sequential isolation of nucleic acids and proteins using chromatographic spin columns. The methodology was validated by comparison to traditional dedicated and simultaneous biomolecular isolation methods. To prove the broad applicability of the methodology, we applied it to microbial consortia of biotechnological, environmental and biomedical research interest. The developed methodological framework lays the foundation for standardized molecular eco-systematic studies on a range of different microbial communities in the future.

Cancer cells often rely on glycolysis to obtain energy and support anabolic growth. Several studies showed that glycolytic cells are susceptible to cell death when subjected to low glucose availability or to lack of glucose. However, some cancer cells, including glycolytic ones, can efficiently acquire higher tolerance to glucose depletion, leading to their survival and aggressiveness. Although increased resistance to glucose starvation has been shown to be a consequence of signaling pathways and compensatory metabolic routes activation, the full repertoire of the underlying molecular alterations remain elusive. Using omics and computational analyses, we found that cyclic adenosine monophosphate-Protein Kinase A (cAMP-PKA) axis activation is fundamental for cancer cell resistance to glucose starvation and anoikis. Notably, here we show that such a PKA-dependent survival is mediated by parallel activation of autophagy and glutamine utilization that in concert concur to attenuate the endoplasmic reticulum (ER) stress and to sustain cell anabolism. Indeed, the inhibition of PKA-mediated autophagy or glutamine metabolism increased the level of cell death, suggesting that the induction of autophagy and metabolic rewiring by PKA is important for cancer cellular survival under glucose starvation. Importantly, both processes actively participate to cancer cell survival mediated by suspension-activated PKA as well. In addition we identify also a PKA/Src mechanism capable to protect cancer cells from anoikis. Our results reveal for the first time the role of the versatile PKA in cancer cells survival under chronic glucose starvation and anoikis and may be a novel potential target for cancer treatment., Author Summary Tumor heterogeneity exists in many human cancers, and it has been shown that it can play a role in tumor progression. Indeed, cell diversity may be critically important when tumors experience selective pressures, like nutrient deprivation, hypoxia, chemotherapy. PKA, through incompletely understood mechanisms, controls several cellular processes like cell growth, cell differentiation, cell metabolism, cell migration and, as more recently observed, also cancer progression. In this work, we show that activation of PKA induces the ability of a cancer cell sub-population to survive under strong stress conditions namely nutrient deprivation and cell detachment. Indeed, PKA activation in these cells results in autophagy induction, and at the same time, in activation of glutamine metabolism and Src kinase. Importantly, blocking directly the PKA pathway, as well as the autophagy, the glutamine metabolism or the Src pathway by inhibitory drugs, almost completely prevents cell growth of this sub-population of resistant cancer cells. These results suggest that drugs, targeting especially PKA pathway as well as downstream processes like autophagy, glutamine metabolism and Src signaling, may specifically inhibit cancer cells ability to survive under selective pressure favoring cancer resistance.

Stable isotope assisted metabolomics techniques have emerged as a valuable tool in systems biology for metabolic flux analysis and pathway discovery (Duckwall et al., 2013; Chokkathukalam et al., 2014; Niedenfuhr et al., 2015). Traditionally, stable isotope labeling analyses have been highly targeted, meaning that isotopic enrichment of only a small set of metabolites has been analyzed to deduce metabolic fluxes. However, in recent years, tools for the global non-targeted detection, quantification, and computational analysis of isotopic enrichment have become available (Hiller et al., 2010, 2013; Bueschl et al., 2012; Creek et al., 2012; Chokkathukalam et al., 2013; Cho et al., 2014; Huang et al., 2014). In this article, we will discuss whether and how such novel non-targeted stable isotope labeling analyses can be applied for systems-biomedical research. Pathological alterations of cellular processes usually manifest in altered metabolism. Therefore, analysis of metabolism is an ideal entry-point to diagnose or analyze diseases. Metabolic fluxes are the endpoint of cellular regulation and most likely to reflect changes on the genome, transcriptome or proteome level, and hence, are a valuable read-out for biomedical research (Wegner et al., 2015). Since intracellular metabolic fluxes cannot be measured directly, they are probed using stable isotope labeling: An isotopically enriched substrate is applied and the metabolization of this tracer leads to isotopic enrichment in downstream metabolites, depending on the underlying metabolic fluxes (Buescher et al., 2015). These enrichment patterns are analyzed by mass spectrometry (MS) or nuclear magnetic resonance (NMR) and are used to deduce metabolic fluxes (Truong et al., 2014; Young et al., 2014). Because isotopic labeling patterns are a direct consequence of metabolic fluxes, changes in these patterns indicate metabolic flux changes (Sauer, 2006). Consequently, global analysis of labeling patterns would allow for the global detection of metabolic flux changes. Because isotopic enrichment can be deduced solely from mass spectra (Jennings and Matthews, 2005), differential flux analysis based on differences in these isotopic labeling patterns does not require prior compound identification or a model of the metabolic network. This way, a non-targeted stable isotope labeling analysis can also consider unexpected or unknown compounds and reactions, circumventing current limitations of compound identification. Such a data-driven metabolic flux analysis is perfectly suited to pinpoint disease-specific alterations of cellular metabolism as little information on the experimental outcome is required. However, even in cases where there are specific hypotheses in place, a data-driven analysis may identify previously overlooked features. As such, non-targeted stable isotope labeling analysis can function as a hypothesis generator, aiding the design of subsequent experiments. Other than e.g., 13C metabolic flux analysis (13C-MFA) or flux balance analysis (FBA) which completely rely on the integrity of the underlying metabolic network and its constraints, such a non-targeted analysis does not require much biochemical a priori information and is, therefore, not biased by the limited knowledge of the metabolic network. The experimental effort for a non-targeted stable isotope labeling study is not much higher than for a conventional targeted analysis; the main difference is that a non-enriched sample is required as reference to determine isotopic enrichment in the sample of interest (Figure ​(Figure1A;1A; Weindl et al., 2015). In terms of analytical requirements, the non-targeted analysis is similar to label-free non-targeted metabolomics: The wide range of concentrations and chemical heterogeneity of metabolites pose problems to the metabolome-wide analysis of isotopic enrichment. Therefore, sensitive and robust analytics and powerful ion-chromatographic deconvolution and spectrum matching algorithms are required to accurately quantify also low-intensity isotopic peaks (Wegner et al., 2013). Yet, isotopic labeling patterns are much more robust towards technical variation during sample workup than metabolite levels, because the labeled and unlabeled species are subject to the same biases, thus increasing comparability of different measurements. In comparison to targeted stable isotope labeling analyses, one faces the problem of lower sensitivity because single ion (SIM) or selected reaction monitoring (SRM) cannot be used, as the analytes of interest are not known beforehand. Apart from that, a non-targeted approach would yield information on the same analytes as a targeted analysis, but also include additional compounds. Figure 1 Different approaches for metabolic analyses. (A) Targeted and non-targeted analysis of stable isotope labeling (SIL). Non-targeted detection of isotopic enrichment requires the measurement of an enriched and a non-enriched sample. Targeted approaches ... If non-targeted stable isotope labeling analysis is that powerful, why is not more widely applied? There is a great potential and the experimental effort is manageable, however, the subsequent data interpretation is much more complex. Changes in isotopic enrichment can be detected easily and do not require any biochemical knowledge (Huang et al., 2014). However, interpreting these changes in terms of underlying metabolic flux changes requires a deep understanding of cellular metabolism and is further complicated by the usually high number of compounds, many of which remain unidentified. Furthermore, although algorithms for the non-targeted and quantitative detection of isotopic enrichment in MS data have been around for a couple of years, non-targeted analysis of stable isotope labeling is still hampered by the lack of dedicated and intuitive software tools. For these reasons, only a few studies have successfully applied non-targeted stable isotope labeling analysis to provide novel biological insights, underlining the higher complexity of data interpretation and the need for more advanced tools and algorithms. In vivo application of non-targeted stable isotope labeling analysis is technically possible, but data analysis is complicated by the metabolic complexity of a whole organism. For that reason, the main field of application would be in vitro setups with e.g., cell lines or patient-derived primary cells which are often applied in biomedical research. In such a context, non-targeted stable isotope labeling has great potential to reveal hitherto overlooked metabolic pathways or metabolic flux changes. Although such non-targeted approaches are not meant to replace targeted methods and cannot provide absolute flux information (Figure ​(Figure1B),1B), they provide means to get closer to the full picture. In summary, if the aforementioned limitations can be dealt with, non-targeted stable isotope labeling analysis is worth the effort and a valuable addition to the systems biology toolbox. This rather unbiased approach can help to detect flux changes in regions of the metabolic network which were not expected to be affected or are not known yet, thereby deepening the understanding of pathobiochemical mechanisms. This understanding is the basis for the development of novel diagnostic and therapeutic methods that will impact human health. Further improvements in sensitivity and specificity of current algorithms and the development of easy-to-use software tools will help to realize the great potential of non-targeted stable isotope labeling analysis.

Measuring intracellular metabolism has increasingly led to important insights in biomedical research. (13)C tracer analysis, although less information-rich than quantitative (13)C flux analysis that requires computational data integration, has been established as a time-efficient method to unravel relative pathway activities, qualitative changes in pathway contributions, and nutrient contributions. Here, we review selected key issues in interpreting (13)C metabolite labeling patterns, with the goal of drawing accurate conclusions from steady state and dynamic stable isotopic tracer experiments. ispartof: Current Opinion in Biotechnology vol:34 pages:189-201 ispartof: location:England status: published

Upon stimulation with Th1 cytokines or bacterial lipopolysaccharides, resting macrophages shift their phenotype toward a pro-inflammatory state as part of the innate immune response. LPS-activated macrophages undergo profound metabolic changes to adapt to these new physiological requirements. One key step to mediate this metabolic adaptation is the stabilization of HIF1α, which leads to increased glycolysis and lactate release, as well as decreased oxygen consumption. HIF1 abundance can result in the induction of the gene encoding pyruvate dehydrogenase kinase 1 (PDK1), which inhibits pyruvate dehydrogenase (PDH) via phosphorylation. Therefore, it has been speculated that pyruvate oxidation through PDH is decreased in pro-inflammatory macrophages. However, to answer this open question, an in-depth analysis of this metabolic branching point was so far lacking. In this work, we applied stable isotope-assisted metabolomics techniques and demonstrate that pyruvate oxidation is maintained in mature pro-inflammatory macrophages. Glucose-derived pyruvate is oxidized via PDH to generate citrate in the mitochondria. Citrate is used for the synthesis of the antimicrobial metabolite itaconate and for lipogenesis. An increased demand for these metabolites decreases citrate oxidation through the tricarboxylic acid cycle, whereas increased glutamine uptake serves to replenish the TCA cycle. Furthermore, we found that the PDH flux is maintained by unchanged PDK1 abundance, despite the presence of HIF1. By pharmacological intervention, we demonstrate that the PDH flux is an important node for M(LPS) macrophage activation. Therefore, PDH represents a metabolic intervention point that might become a research target for translational medicine to treat chronic inflammatory diseases.

Adaptation to metabolic needs and changing environments is a basic requirement of every living system. These adaptations can be very quick and mild or slower but more drastic. In any case, cells have to constantly monitor their metabolic state and requirements. In this article we review general concepts as well as recent advances on how metabolites can regulate metabolic fluxes. We discuss how cells sense metabolite levels and how changing metabolite levels regulate metabolic enzymes on different levels, from specific allosteric regulation to global transcriptional regulation. We thereby focus on local metabolite sensing in mammalian cells and show that several major discoveries have only very recently been made.

Immunoresponsive gene 1 ( Irg1 ) is highly expressed in mammalian macrophages during inflammation, but its biological function has not yet been elucidated. Here, we identify Irg1 as the gene coding for an enzyme producing itaconic acid (also known as methylenesuccinic acid) through the decarboxylation of cis -aconitate, a tricarboxylic acid cycle intermediate. Using a gain-and-loss-of-function approach in both mouse and human immune cells, we found Irg1 expression levels correlating with the amounts of itaconic acid, a metabolite previously proposed to have an antimicrobial effect. We purified IRG1 protein and identified its cis -aconitate decarboxylating activity in an enzymatic assay. Itaconic acid is an organic compound that inhibits isocitrate lyase, the key enzyme of the glyoxylate shunt, a pathway essential for bacterial growth under specific conditions. Here we show that itaconic acid inhibits the growth of bacteria expressing isocitrate lyase, such as Salmonella enterica and Mycobacterium tuberculosis . Furthermore, Irg1 gene silencing in macrophages resulted in significantly decreased intracellular itaconic acid levels as well as significantly reduced antimicrobial activity during bacterial infections. Taken together, our results demonstrate that IRG1 links cellular metabolism with immune defense by catalyzing itaconic acid production.

The oncogene DJ-1 has been originally identified as a suppressor of PTEN. Further on, loss-of-function mutations have been described as a causative factor in Parkinson's disease (PD). DJ-1 has an important function in cellular antioxidant responses, but its role in central metabolism of neurons is still elusive. We applied stable isotope assisted metabolic profiling to investigate the effect of a functional loss of DJ-1 and show that DJ-1 deficient neuronal cells exhibit decreased glutamine influx and reduced serine biosynthesis. By providing precursors for GSH synthesis, these two metabolic pathways are important contributors to cellular antioxidant response. Down-regulation of these pathways, as a result of loss of DJ-1 leads to an impaired antioxidant response. Furthermore, DJ-1 deficient mouse microglia showed a weak but constitutive pro-inflammatory activation. The combined effects of altered central metabolism and constitutive activation of glia cells raise the susceptibility of dopaminergic neurons towards degeneration in patients harboring mutated DJ-1. Our work reveals metabolic alterations leading to increased cellular instability and identifies potential new intervention points that can further be studied in the light of novel translational medicine approaches. published

Background Hypoxia and inflammation have been identified as hallmarks of cancer. A majority of hepatocellular carcinomas are preceded by hepatitis B- or C-related chronic infections suggesting that liver cancer development is promoted by an inflammatory microenvironment. The inflammatory cytokine oncostatin M (OSM) was shown to induce the expression of hypoxia-inducible factor-1 α (HIF-1 α) under normoxic conditions in hepatocytes and hepatoma cells. HIF-1 α is known to orchestrate the expression of numerous genes, many of which code for metabolic enzymes that play key roles in the adaptation of cellular metabolism to low oxygen tension. Results Here, we show that OSM-induced upregulation of HIF-1 α reprograms cellular metabolism in three clones of the human hepatocyte cell line PH5CH (PH5CH1, PH5CH7, and PH5CH8) towards a hypoxia-like metabolic phenotype but has no significant effect on cellular metabolism of HepG2 and JHH-4 hepatoma cells. Although we observed only minor changes in glucose uptake and lactate secretion in PH5CH8 upon OSM treatment, we identified more pronounced changes in intracellular fluxes based on stable isotope labeling experiments. In particular, glucose oxidation in the tricarboxylic acid (TCA) cycle is reduced through pyruvate dehydrogenase kinase 1 (PDK1)-mediated inhibition of the pyruvate dehydrogenase complex, thereby reducing the oxidative TCA cycle flux. As a result of the impaired mitochondrial glucose and glutamine oxidation, the reductive isocitrate dehydrogenase flux was increased. Conclusions We provide evidence that connects the inflammatory mediator OSM to a hypoxia-like metabolic phenotype. In the human hepatocyte cell line PH5CH, OSM-mediated upregulation of HIF-1 α and PDK1 can induce hypoxia-like metabolic changes, although to a lesser extent than hypoxia itself. Since PDK1 is overexpressed in several cancers, it might provide a causal link between chronic inflammation and malignant cellular transformation. Electronic supplementary material The online version of this article (doi:10.1186/s40170-016-0141-0) contains supplementary material, which is available to authorized users.