Identifying metabolites by integrating metabolome databases with mass spectrometry cheminformatics.

Novel metabolites distinct from canonical pathways can be identified through the integration of three cheminformatics tools: BinVestigate, which queries the BinBase gas chromatography-mass spectrometry (GC-MS) metabolome database to match unknowns with biological metadata across over 110,000 samples; MS-DIAL 2.0, a software tool for chromatographic deconvolution of high-resolution GC-MS or liquid chromatography-mass spectrometry (LC-MS); and MS-FINDER 2.0, a structure-elucidation program that uses a combination of 14 metabolome databases in addition to an enzyme promiscuity library. We showcase our workflow by annotating N-methyl-uridine monophosphate (UMP), lysomonogalactosyl-monopalmitin, N-methylalanine, and two propofol derivatives

Pharmacometabolomic mapping of early biochemical changes induced by sertraline and placebo.

In this study, we characterized early biochemical changes associated with sertraline and placebo administration and changes associated with a reduction in depressive symptoms in patients with major depressive disorder (MDD). MDD patients received sertraline or placebo in a double-blind 4-week trial; baseline, 1 week, and 4 weeks serum samples were profiled using a gas chromatography time of flight mass spectrometry metabolomics platform. Intermediates of TCA and urea cycles, fatty acids and intermediates of lipid biosynthesis, amino acids, sugars and gut-derived metabolites were changed after 1 and 4 weeks of treatment. Some of the changes were common to the sertraline- and placebo-treated groups. Changes after 4 weeks of treatment in both groups were more extensive. Pathway analysis in the sertraline group suggested an effect of drug on ABC and solute transporters, fatty acid receptors and transporters, G signaling molecules and regulation of lipid metabolism. Correlation between biochemical changes and treatment outcomes in the sertraline group suggested a strong association with changes in levels of branched chain amino acids (BCAAs), lower BCAAs levels correlated with better treatment outcomes; pathway analysis in this group revealed that methionine and tyrosine correlated with BCAAs. Lower levels of lactic acid, higher levels of TCA/urea cycle intermediates, and 3-hydroxybutanoic acid correlated with better treatment outcomes in placebo group. Results of this study indicate that biochemical changes induced by drug continue to evolve over 4 weeks of treatment and that might explain partially delayed response. Response to drug and response to placebo share common pathways but some pathways are more affected by drug treatment. BCAAs seem to be implicated in mechanisms of recovery from a depressed state following sertraline treatment

Identifying individual differences of fluoxetine response in juvenile rhesus monkeys by metabolite profiling

Fluoxetine is the only psychopharmacological agent approved for depression by the US Food and Drug Administration for children and is commonly used therapeutically in a variety of neurodevelopmental disorders. Therapeutic response shows high individual variability, and severe side effects have been observed. In the current study we set out to identify biomarkers of response to fluoxetine as well as biomarkers that correlate with impulsivity, a measure of reward delay behavior and potential side effect of the drug, in juvenile male rhesus monkeys. The study group was also genotyped for polymorphisms of monoamine oxidase A (MAOA), a gene that has been associated with psychiatric disorders. We used peripheral metabolite profiling of blood and cerebrospinal fluid (CSF) from animals treated daily with fluoxetine or vehicle for one year. Fluoxetine response metabolite profiles and metabolite/reward delay behavior associations were evaluated using multivariate analysis. Our analyses identified a set of plasma and CSF metabolites that distinguish fluoxetine-from vehicle-treated animals and metabolites that correlate with impulsivity. Some metabolites displayed an interaction between fluoxetine and MAOA genotype. The identified metabolite biomarkers belong to pathways that have important functions in central nervous system physiology. Biomarkers of response to fluoxetine in the normally functioning brain of juvenile nonhuman primates may aid in finding predictors of response to treatment in young psychiatric populations and in progress toward the realization of a precision medicine approach in the area of neurodevelopmental disorders

The Chemical Translation Service—a web-based tool to improve standardization of metabolomic reports

Summary: Metabolomic publications and databases use different database identifiers or even trivial names which disable queries across databases or between studies. The best way to annotate metabolites is by chemical structures, encoded by the International Chemical Identifier code (InChI) or InChIKey. We have implemented a web-based Chemical Translation Service that performs batch conversions of the most common compound identifiers, including CAS, CHEBI, compound formulas, Human Metabolome Database HMDB, InChI, InChIKey, IUPAC name, KEGG, LipidMaps, PubChem CID+SID, SMILES and chemical synonym names. Batch conversion downloads of 1410 CIDs are performed in 2.5 min. Structures are automatically displayed

Inter-laboratory reproducibility of fast gas chromatography–electron impact–time of flight mass spectrometry (GC–EI–TOF/MS) based plant metabolomics

The application of gas chromatography–mass spectrometry (GC–MS) to the ‘global’ analysis of metabolites in complex samples (i.e. metabolomics) has now become routine. The generation of these data-rich profiles demands new strategies in data mining and standardisation of experimental and reporting aspects across laboratories. As part of the META-PHOR project’s (METAbolomics for Plants Health and OutReach: http://www.meta-phor.eu/) priorities towards robust technology development, a GC–MS ring experiment based upon three complex matrices (melon, broccoli and rice) was launched. All sample preparation, data processing, multivariate analyses and comparisons of major metabolite features followed standardised protocols, identical models of GC (Agilent 6890N) and TOF/MS (Leco Pegasus III) were also employed. In addition comprehensive GC×GC–TOF/MS was compared with 1 dimensional GC–TOF/MS. Comparisons of the paired data from the various laboratories were made with a single data processing and analysis method providing an unbiased assessment of analytical method variants and inter-laboratory reproducibility. A range of processing and statistical methods were also assessed with a single exemplary dataset revealing near equal performance between them. Further investigations of long-term reproducibility are required, though the future generation of global and valid metabolomics databases offers much promise

Mesenchymal stem cell-based therapy for ischemic stroke

Ischemic stroke represents a major, worldwide health burden with increasing incidence. Patients affected by ischemic strokes currently have few clinically approved treatment options available. Most currently approved treatments for ischemic stroke have narrow therapeutic windows, severely limiting the number of patients able to be treated. Mesenchymal stem cells represent a promising novel treatment for ischemic stroke. Numerous studies have demonstrated that mesenchymal stem cells functionally improve outcomes in rodent models of ischemic stroke. Recent studies have also shown that exosomes secreted by mesenchymal stem cells mediate much of this effect. In the present review, we summarize the current literature on the use of mesenchymal stem cells to treat ischemic stroke. Further studies investigating the mechanisms underlying mesenchymal stem cells tissue healing effects are warranted and would be of benefit to the field

Mesothelial Cell HIF1 alpha Expression Is Metabolically Downregulated by Metformin to Prevent Oncogenic Tumor-Stromal Crosstalk

The tumor microenvironment (TME) plays a pivotal role in cancer progression, and, in ovarian cancer (OvCa), the primary TME is the omentum. Here, we show that the diabetes drug metformin alters mesothelial cells in the omental microenvironment. Metformin interrupts bidirectional signaling between tumor and mesothelial cells by blocking OvCa cell TGF-beta signaling and mesothelial cell production of CCL2 and IL-8. Inhibition of tumor-stromal crosstalk by metformin is caused by the reduced expression of the tricarboxylic acid (TCA) enzyme succinyl CoA ligase (SUCLG2). Through repressing this TCA enzyme and its metabolite, succinate, metformin activated prolyl hydroxylases (PHDs), resulting in the degradation of hypoxia-inducible factor 1 alpha (HIF1 alpha) in mesothelial cells. Disruption of HIF1 alpha-driven IL-8 signaling in mesothelial cells by metformin results in reduced OvCa invasion in an organotypic 3D model. These findings indicate that tumor-promoting signaling between mesothelial and OvCa cells in the TME can be targeted using metformin