Plants Release Precursors of Histone Deacetylase Inhibitors to Suppress Growth of Competitors.

To secure their access to water, light, and nutrients, many plant species have developed allelopathic strategies to suppress competitors. To this end, they release into the rhizosphere phytotoxic substances that inhibit the germination and growth of neighbors. Despite the importance of allelopathy in shaping natural plant communities and for agricultural production, the underlying molecular mechanisms are largely unknown. Here, we report that allelochemicals derived from the common class of cyclic hydroxamic acid root exudates directly affect the chromatin-modifying machinery in Arabidopsis thaliana. These allelochemicals inhibit histone deacetylases both in vitro and in vivo and exert their activity through locus-specific alterations of histone acetylation and associated gene expression. Our multilevel analysis collectively shows how plant-plant interactions interfere with a fundamental cellular process, histone acetylation, by targeting an evolutionarily highly conserved class of enzymes.

MIA: non-targeted mass isotopolome analysis.

UNLABELLED: MIA detects and visualizes isotopic enrichment in gas chromatography electron ionization mass spectrometry (GC-EI-MS) datasets in a non-targeted manner. It provides an easy-to-use graphical user interface that allows for visual mass isotopomer distribution analysis across multiple datasets. MIA helps to reveal changes in metabolic fluxes, visualizes metabolic proximity of isotopically enriched compounds and shows the fate of the applied stable isotope labeled tracer. AVAILABILITY AND IMPLEMENTATION: Linux and Windows binaries, documentation, and sample data are freely available for download at http://massisotopolomeanalyzer.lu MIA is a stand-alone application implemented in C ++ and based on Qt5, NTFD and the MetaboliteDetector framework. CONTACT: karsten.hiller@uni.lu.

Analysis of mitochondrial metabolism in situ: Combining stable isotope labeling with selective permeabilization.

To date, it is well-established that mitochondrial dysfunction does not only play a vital role in cancer but also in other pathological conditions such as neurodegenerative diseases and inflammation. An important tool for the analysis of cellular metabolism is the application of stable isotope labeled substrates, which allow for the tracing of atoms throughout metabolic networks. While such analyses yield very detailed information about intracellular fluxes, the determination of compartment specific fluxes is far more challenging. Most approaches for the deconvolution of compartmented metabolism use computational models whereas experimental methods are rare. Here, we developed an experimental setup based on selective permeabilization of the cytosolic membrane that allows for the administration of stable isotope labeled substrates directly to mitochondria. We demonstrate how this approach can be used to infer metabolic changes in mitochondria induced by either chemical or genetic perturbations and give an outlook on its potential applications.

Immune-responsive gene 1 protein links metabolism to immunity by catalyzing itaconic acid production.

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.

Fragment formula calculator (FFC): determination of chemical formulas for fragment ions in mass spectrometric data.

The accurate determination of mass isotopomer distributions (MID) is of great significance for stable isotope-labeling experiments. Most commonly, MIDs are derived from gas chromatography/electron ionization mass spectrometry (GC/EI-MS) measurements. The analysis of fragment ions formed during EI, which contain only specific parts of the original molecule can provide valuable information on the positional distribution of the label. The chemical formula of a fragment ion is usually applied to derive the correction matrix for accurate MID calculation. Hence, the correct assignment of chemical formulas to fragment ions is of crucial importance for correct MIDs. Moreover, the positional distribution of stable isotopes within a fragment ion is of high interest for stable isotope-assisted metabolomics techniques. For example, (13)C-metabolic flux analyses ((13)C-MFA) are dependent on the exact knowledge of the number and position of retained carbon atoms of the unfragmented molecule. Fragment ions containing different carbon atoms are of special interest, since they can carry different flux information. However, the process of mass spectral fragmentation is complex, and identifying the substructures and chemical formulas for these fragment ions is nontrivial. For that reason, we developed an algorithm, based on a systematic bond cleavage, to determine chemical formulas and retained atoms for EI derived fragment ions. Here, we present the fragment formula calculator (FFC) algorithm that can calculate chemical formulas for fragment ions where the chemical bonding (e.g., Lewis structures) of the intact molecule is known. The proposed algorithm is able to cope with general molecular rearrangement reactions occurring during EI in GC/MS measurements. The FFC algorithm is able to integrate stable isotope labeling experiments into the analysis and can automatically exclude candidate formulas that do not fit the observed labeling patterns.1 We applied the FFC algorithm to create a fragment ion repository that contains the chemical formulas and retained carbon atoms of a wide range of trimethylsilyl and tert-butyldimethylsilyl derivatized compounds. In total, we report the chemical formulas and backbone carbon compositions for 160 fragment ions of 43 alkylsilyl-derivatives of primary metabolites. Finally, we implemented the FFC algorithm in an easy-to-use graphical user interface and made it publicly available at http://www.ffc.lu .

NTFD--a stand-alone application for the non-targeted detection of stable isotope-labeled compounds in GC/MS data.

SUMMARY: Most current stable isotope-based methodologies are targeted and focus only on the well-described aspects of metabolic networks. Here, we present NTFD (non-targeted tracer fate detection), a software for the non-targeted analysis of all detectable compounds derived from a stable isotope-labeled tracer present in a GC/MS dataset. In contrast to traditional metabolic flux analysis approaches, NTFD does not depend on any a priori knowledge or library information. To obtain dynamic information on metabolic pathway activity, NTFD determines mass isotopomer distributions for all detected and labeled compounds. These data provide information on relative fluxes in a metabolic network. The graphical user interface allows users to import GC/MS data in netCDF format and export all information into a tab-separated format. AVAILABILITY: NTFD is C++- and Qt4-based, and it is freely available under an open-source license. Pre-compiled packages for the installation on Debian- and Redhat-based Linux distributions, as well as Windows operating systems, along with example data, are provided for download at http://ntfd.mit.edu/.

N-acetyl-aspartate metabolism at the interface of cancer, immunity, and neurodegeneration.

N-acetyl-L-aspartic acid (NAA) is a prominent amino acid derivative primarily associated with vertebrate brain metabolism. This review delineates the critical role of NAA across various cell types and its significance in pathophysiological contexts, including Canavan disease and cancer metabolism. Although traditionally linked with myelination and aspartoacylase-driven carbon donation, its significance as a carbon source for myelination remains debated. Evidence suggests that intact NAA might substantially impact cellular signaling, particularly processes such as histone acetylation. Beyond the brain, NAA metabolism's relevance is evident in diverse tissues, such as adipocytes, immune cells, and notably, cancer cells. In several cancer types, there is an observed upregulation of NAA synthesis accompanied by a simultaneous downregulation of its degradation. This pattern highlights the potential signaling role of intact NAA in disease.

Isotope cluster-based compound matching in gas chromatography/mass spectrometry for non-targeted metabolomics.

Gas chromatography coupled to mass spectrometry (GC/MS) has emerged as a powerful tool in metabolomics studies. A major bottleneck in current data analysis of GC/MS-based metabolomics studies is compound matching and identification, as current methods generate high rates of false positive and false-negative identifications. This is especially true for data sets containing a high amount of noise. In this work, a novel spectral similarity measure based on the specific fragmentation patterns of electron impact mass spectra is proposed. An important aspect of these algorithmic methods is the handling of noisy data. The performance of the proposed method compared to the dot product, the current gold standard, was evaluated on a complex biological data set. The analysis results showed significant improvements of the proposed method in compound matching and chromatogram alignment compared to the dot product.

Instrumental Drift in Untargeted Metabolomics: Optimizing Data Quality with Intrastudy QC Samples.

Untargeted metabolomics is an important tool in studying health and disease and is employed in fields such as biomarker discovery and drug development, as well as precision medicine. Although significant technical advances were made in the field of mass-spectrometry driven metabolomics, instrumental drifts, such as fluctuations in retention time and signal intensity, remain a challenge, particularly in large untargeted metabolomics studies. Therefore, it is crucial to consider these variations during data processing to ensure high-quality data. Here, we will provide recommendations for an optimal data processing workflow using intrastudy quality control (QC) samples that identifies errors resulting from instrumental drifts, such as shifts in retention time and metabolite intensities. Furthermore, we provide an in-depth comparison of the performance of three popular batch-effect correction methods of different complexity. By using different evaluation metrics based on QC samples and a machine learning approach based on biological samples, the performance of the batch-effect correction methods were evaluated. Here, the method TIGER demonstrated the overall best performance by reducing the relative standard deviation of the QCs and dispersion-ratio the most, as well as demonstrating the highest area under the receiver operating characteristic with three different probabilistic classifiers (Logistic regression, Random Forest, and Support Vector Machine). In summary, our recommendations will help to generate high-quality data that are suitable for further downstream processing, leading to more accurate and meaningful insights into the underlying biological processes.

PGM3 inhibition shows cooperative effects with erastin inducing pancreatic cancer cell death via activation of the unfolded protein response.

Background: Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with a poor patient prognosis. Remarkably, PDAC is one of the most aggressive and deadly tumor types and is notorious for its resistance to all types of treatment. PDAC resistance is frequently associated with a wide metabolic rewiring and in particular of the glycolytic branch named Hexosamine Biosynthetic Pathway (HBP). Methods: Transcriptional and bioinformatics analysis were performed to obtain information about the effect of the HBP inhibition in two cell models of PDAC. Cell count, western blot, HPLC and metabolomics analyses were used to determine the impact of the combined treatment between an HBP's Phosphoglucomutase 3 (PGM3) enzyme inhibitor, named FR054, and erastin (ERA), a recognized ferroptosis inducer, on PDAC cell growth and survival. Results: Here we show that the combined treatment applied to different PDAC cell lines induces a significant decrease in cell proliferation and a concurrent enhancement of cell death. Furthermore, we show that this combined treatment induces Unfolded Protein Response (UPR), NFE2 Like BZIP Transcription Factor 2 (NRF2) activation, a change in cellular redox state, a greater sensitivity to oxidative stress, a major dependence on glutamine metabolism, and finally ferroptosis cell death. Conclusion: Our study discloses that HBP inhibition enhances, via UPR activation, the ERA effect and therefore might be a novel anticancer mechanism to be exploited as PDAC therapy.

A Chemical Chaperone Restores Connexin 26 Mutant Activity.

Mutations in connexin 26 (Cx26) cause hearing disorders of a varying degree. Herein, to identify compounds capable of restoring the function of mutated Cx26, a novel miniaturized microarray-based screening system was developed to perform an optical assay of Cx26 functionality. These molecules were identified through a viability assay using HeLa cells expressing wild-type (WT) Cx26, which exhibited sensitivity toward the HSP90 inhibitor radicicol in the submicromolar concentration range. Open Cx26 hemichannels are assumed to mediate the passage of molecules up to 1000 Da in size. Thus, by releasing radicicol, WT Cx26 active hemichannels in HeLa cells contribute to a higher survival rate and lower cell viability when Cx26 is mutated. HeLa cells expressing Cx26 mutations exhibited reduced viability in the presence of radicicol, such as the mutants F161S or R184P. Next, molecules exhibiting chemical chaperoning activity, suspected of restoring channel function, were assessed regarding whether they induced superior sensitivity toward radicicol and increased HeLa cell viability. Through a viability assay and microarray-based flux assay that uses Lucifer yellow in HeLa cells, compounds 3 and 8 were identified to restore mutant functionality. Furthermore, thermophoresis experiments revealed that only 3 (VRT-534) exhibited dose-responsive binding to recombinant WT Cx26 and mutant Cx26K188N with half maximal effective concentration values of 19 and ∼5 µM, respectively. The findings of this study reveal that repurposing compounds already being used to treat other diseases, such as cystic fibrosis, in combination with functional bioassays and binding tests can help identify novel potential candidates that can be used to treat hearing disorders.

Isolation and Quantification of Bacterial Membrane Vesicles for Quantitative Metabolic Studies Using Mammalian Cell Cultures.

Bacterial membrane vesicles (BMVs) are produced by most bacteria and participate in various cellular processes, such as intercellular communication, nutrient exchange, and pathogenesis. Notably, these vesicles can contain virulence factors, including toxic proteins, DNA, and RNA. Such factors can contribute to the harmful effects of bacterial pathogens on host cells and tissues. Although the general effects of BMVs on host cellular physiology are well known, the underlying molecular mechanisms are less understood. In this study, we introduce a vesicle quantification method, leveraging the membrane dye FM4-64. We utilize a linear regression model to analyze the fluorescence emitted by stained vesicle membranes to ensure consistent and reproducible vesicle-host interaction studies using cultured cells. This method is particularly valuable for identifying host cellular processes impacted by vesicles and their specific cargo. Moreover, it outcompetes unreliable protein concentration-based methods. We (1) show a linear correlation between the number of vesicles and the fluorescence signal emitted from the FM4-64 dye; (2) introduce the "vesicle load" as a new semi-quantitative unit, facilitating more reproducible vesicle-cell culture interaction experiments; (3) show that a stable vesicle load yields consistent host responses when studying vesicles from Pseudomonas aeruginosa mutants; (4) demonstrate that typical vesicle isolation contaminants, such as flagella, do not significantly skew the metabolic response of lung epithelial cells to P. aeruginosa vesicles; and (5) identify inositol monophosphatase 1 (SuhB) as a pivotal regulator in the vesicle-mediated pathogenesis of P. aeruginosa.

MetAMDB: Metabolic Atom Mapping Database.

MetAMDB is an open-source metabolic atom mapping database, providing atom mappings for around 43,000 metabolic reactions. Each atom mapping can be inspected and downloaded either as an RXN file or as a graphic in SVG format. In addition, MetAMDB offers the possibility of automatically creating atom mapping models based on user-specified metabolic networks. These models can be of any size (small to genome-scale) and can subsequently be used in standard 13C metabolic flux analysis software.

ISG15 deficiency features a complex cellular phenotype that responds to treatment with itaconate and derivatives.

BACKGROUND: Congenital ISG15 deficiency is a rare autoinflammatory disorder that is driven by chronically elevated systemic interferon levels and predominantly affects central nervous system and skin. METHODS AND RESULTS: We have developed induced pluripotent stem cell-derived macrophages and endothelial cells as a model to study the cellular phenotype of ISG15 deficiency and identify novel treatments. ISG15-/- macrophages exhibited the expected hyperinflammatory responses, but normal phagocytic function. In addition, they displayed a multifaceted pathological phenotype featuring increased apoptosis/pyroptosis, oxidative stress, glycolysis, and acylcarnitine levels, but decreased glutamine uptake, BCAT1 expression, branched chain amino acid catabolism, oxidative phosphorylation, ß-oxidation, and NAD(P)H-dependent oxidoreductase activity. Furthermore, expression of genes involved in mitochondrial biogenesis and respiratory chain complexes II-V was diminished in ISG15-/- cells. Defective mitochondrial respiration was restored by transduction with wild-type ISG15, but only partially by a conjugation-deficient variant, suggesting that some ISG15 functions in mitochondrial respiration require ISGylation to cellular targets. Treatment with itaconate, dimethyl-itaconate, 4-octyl-itaconate, and the JAK1/2 inhibitor ruxolitinib ameliorated increased inflammation, propensity for cell death, and oxidative stress. Furthermore, the treatments greatly improved mitochondria-related gene expression, BCAT1 levels, redox balance, and intracellular and extracellular ATP levels. However, efficacy differed among the compounds according to read-out and cell type, suggesting that their effects on cellular targets are not identical. Indeed, only itaconates increased expression of anti-oxidant genes NFE2L2, HMOX1, and GPX7, and dimethyl-itaconate improved redox balance the most. Even though itaconate treatments normalized the elevated expression of interferon-stimulated genes, ISG15-/- macrophages maintained their reduced susceptibility to influenza virus infection. CONCLUSIONS: These findings expand the cellular phenotype of human ISG15 deficiency and reveal the importance of ISG15 for regulating oxidative stress, branched chain amino acid metabolism, and mitochondrial function in humans. The results validate ruxolitinib as treatment for ISG15 deficiency and suggest itaconate-based medications as additional therapeutics for this rare disorder.

Fructose Metabolism in Cancer.

The interest in fructose metabolism is based on the observation that an increased dietary fructose consumption leads to an increased risk of obesity and metabolic syndrome. In particular, obesity is a known risk factor to develop many types of cancer and there is clinical and experimental evidence that an increased fructose intake promotes cancer growth. The precise mechanism, however, in which fructose induces tumor growth is still not fully understood. In this article, we present an overview of the metabolic pathways that utilize fructose and how fructose metabolism can sustain cancer cell proliferation. Although the degradation of fructose shares many of the enzymes and metabolic intermediates with glucose metabolism through glycolysis, glucose and fructose are metabolized differently. We describe the different metabolic fates of fructose carbons and how they are connected to lipogenesis and nucleotide synthesis. In addition, we discuss how the endogenous production of fructose from glucose via the polyol pathway can be beneficial for cancer cells.

The role of HIF-1 in oncostatin M-dependent metabolic reprogramming of hepatic cells.

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.

Bridging the gap between non-targeted stable isotope labeling and metabolic flux analysis.

BACKGROUND: Metabolism gained increasing interest for the understanding of diseases and to pinpoint therapeutic intervention points. However, classical metabolomics techniques only provide a very static view on metabolism. Metabolic flux analysis methods, on the other hand, are highly targeted and require detailed knowledge on metabolism beforehand. RESULTS: We present a novel workflow to analyze non-targeted metabolome-wide stable isotope labeling data to detect metabolic flux changes in a non-targeted manner. Furthermore, we show how similarity-analysis of isotopic enrichment patterns can be used for pathway contextualization of unidentified compounds. We illustrate our approach with the analysis of changes in cellular metabolism of human adenocarcinoma cells in response to decreased oxygen availability. Starting without a priori knowledge, we detect metabolic flux changes, leading to an increased glutamine contribution to acetyl-CoA production, reveal biosynthesis of N-acetylaspartate by N-acetyltransferase 8-like (NAT8L) in lung cancer cells and show that NAT8L silencing inhibits proliferation of A549, JHH-4, PH5CH8, and BEAS-2B cells. CONCLUSIONS: Differential stable isotope labeling analysis provides qualitative metabolic flux information in a non-targeted manner. Furthermore, similarity analysis of enrichment patterns provides information on metabolically closely related compounds. N-acetylaspartate and NAT8L are important players in cancer cell metabolism, a context in which they have not received much attention yet.

Non-targeted Tracer Fate Detection.

Stable isotopes have been used to trace atoms through metabolism and quantify metabolic fluxes for several decades. Only recently non-targeted stable isotope labeling approaches have emerged as a powerful tool to gain insights into metabolism. However, the manual detection of isotopic enrichment for a non-targeted analysis is tedious and time consuming. To overcome this limitation, the non-targeted tracer fate detection (NTFD) algorithm for the automated metabolome-wide detection of isotopic enrichment has been developed. NTFD detects and quantifies isotopic enrichment in the form of mass isotopomer distributions (MIDs) in an automated manner, providing the means to trace functional groups, determine MIDs for metabolic flux analysis, or detect tracer-derived molecules in general. Here, we describe the algorithmic background of NTFD, discuss practical considerations for the freely available NTFD software package, and present potential applications of non-targeted stable isotope labeling analysis.

How metabolites modulate metabolic flux.

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.

Isotopologue ratio normalization for non-targeted metabolomics.

Robust quantification of analytes is a prerequisite for meaningful metabolomics experiments. In non-targeted 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.