Targeting Mitochondrial Complex I Overcomes Chemoresistance in High OXPHOS Pancreatic Cancer.

Mitochondrial respiration (oxidative phosphorylation, OXPHOS) is an emerging target in currently refractory cancers such as pancreatic ductal adenocarcinoma (PDAC). However, the variability of energetic metabolic adaptations between PDAC patients has not been assessed in functional investigations. In this work, we demonstrate that OXPHOS rates are highly heterogeneous between patient tumors, and that high OXPHOS tumors are enriched in mitochondrial respiratory complex I at protein and mRNA levels. Therefore, we treated PDAC cells with phenformin (complex I inhibitor) in combination with standard chemotherapy (gemcitabine), showing that this treatment is synergistic specifically in high OXPHOS cells. Furthermore, phenformin cooperates with gemcitabine in high OXPHOS tumors in two orthotopic mouse models (xenografts and syngeneic allografts). In conclusion, this work proposes a strategy to identify PDAC patients likely to respond to the targeting of mitochondrial energetic metabolism in combination with chemotherapy, and that phenformin should be clinically tested in appropriate PDAC patient subpopulations.

Metabolic modulations of Pseudomonas graminis in response to H2O2 in cloud water.

In cloud water, microorganisms are exposed to very strong stresses especially related to the presence of reactive oxygen species including H2O2 and radicals, which are the driving force of cloud chemistry. In order to understand how the bacterium Pseudomonas graminis isolated from cloud water respond to this oxidative stress, it was incubated in microcosms containing a synthetic solution of cloud water in the presence or in the absence of H2O2. P. graminis metabolome was examined by LC-MS and NMR after 50 min and after 24 hours of incubation. After 50 min, the cells were metabolizing H2O2 while this compound was still present in the medium, and it was completely biodegraded after 24 hours. Cells exposed to H2O2 had a distinct metabolome as compared to unexposed cells, revealing modulations of certain metabolic pathways in response to oxidative stress. These data indicated that the regulations observed mainly involved carbohydrate, glutathione, energy, lipid, peptides and amino-acids metabolisms. When cells had detoxified H2O2 from the medium, their metabolome was not distinguishable anymore from unexposed cells, highlighting the capacity of resilience of this bacterium. This work illustrates the interactions existing between the cloud microbial metabolome and cloud chemistry.

Vnn1 pantetheinase limits the Warburg effect and sarcoma growth by rescuing mitochondrial activity.

Like other tumors, aggressive soft tissue sarcomas (STS) use glycolysis rather than mitochondrial oxidative phosphorylation (OXPHOS) for growth. Given the importance of the cofactor coenzyme A (CoA) in energy metabolism, we investigated the impact of Vnn1 pantetheinase-an enzyme that degrades pantetheine into pantothenate (vitamin B5, the CoA biosynthetic precursor) and cysyteamine-on tumor growth. Using two models, we show that Vnn1+ STS remain differentiated and grow slowly, and that in patients a detectable level of VNN1 expression in STS is associated with an improved prognosis. Increasing pantetheinase activity in aggressive tumors limits their growth. Using combined approaches, we demonstrate that Vnn1 permits restoration of CoA pools, thereby maintaining OXPHOS. The simultaneous production of cysteamine limits glycolysis and release of lactate, resulting in a partial inhibition of STS growth in vitro and in vivo. We propose that the Warburg effect observed in aggressive STS is reversed by induction of Vnn1 pantetheinase and the rewiring of cellular energy metabolism by its products.

Workflow methodology for rat brain metabolome exploration using NMR, LC-MS and GC-MS analytical platforms.

We developed a multi-platform approach for the metabolome exploration of rat brain tissue, using liquid chromatography coupled with mass spectrometry (LC-MS), nuclear magnetic resonance spectroscopy (NMR) and gas-chromatography coupled with mass spectrometry (GC-MS). The critical steps for metabolite exploration of cerebral tissues are tissue lysis and metabolites extraction. We first evaluated the impact of freeze-drying compared to wet tissue metabolites extraction using NMR and LC-MS with a reversed phase liquid chromatography. Then, we compared four metabolite extraction methods Based on the number of metabolites extracted, their intensity and their coefficient of variation (%CV), the most reproducible protocol (one-step extraction with acetonitrile on lyophilized material) was chosen to further evaluate the impact of sample mass on method performance (3, 6, and 9mg were essayed). GC-MS analysis was also investigated by analyzing four different methoximation/silylation derivatization combinations. The optimal analytical protocols were proposed to establish the reliability required to realize untargeted brain tissue metabolomics exploration. The most reliable workflow was then exemplified by analyzing three rat brain regions (cerebellum, frontal and parietal cortices, n=12) by 1H NMR, LC-MS and GC-MS, allowing their clustering based on their metabolic profiles. We present here an example of development of methodology that should be done before running analysis campaigns.

Correction: Elucidating time-dependent changes in the urinary metabolome of renal transplant patients by a combined 1H NMR and GC-MS approach.

Correction for 'Elucidating time-dependent changes in the urinary metabolome of renal transplant patients by a combined 1H NMR and GC-MS approach' by Kienana Muhrez et al., Mol. BioSyst., 2015, 11, 2493-2510.

Metabolomics Study of Urine in Autism Spectrum Disorders Using a Multiplatform Analytical Methodology.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with no clinical biomarker. The aims of this study were to characterize a metabolic signature of ASD and to evaluate multiplatform analytical methodologies in order to develop predictive tools for diagnosis and disease follow-up. Urine samples were analyzed using (1)H and (1)H-(13)C NMR-based approaches and LC-HRMS-based approaches (ESI+ and ESI- on HILIC and C18 chromatography columns). Data tables obtained from the six analytical modalities on a training set of 46 urine samples (22 autistic children and 24 controls) were processed by multivariate analysis (orthogonal partial least-squares discriminant analysis, OPLS-DA). The predictions from each of these OPLS-DA models were then evaluated using a prediction set of 16 samples (8 autistic children and 8 controls) and receiver operating characteristic curves. Thereafter, a data fusion block-scaling OPLS-DA model was generated from the 6 best models obtained for each modality. This fused OPLS-DA model showed an enhanced performance (R(2)Y(cum) = 0.88, Q(2)(cum) = 0.75) compared to each analytical modality model, as well as a better predictive capacity (AUC = 0.91, p-value = 0.006). Metabolites that are most significantly different between autistic and control children (p < 0.05) are indoxyl sulfate, N-α-acetyl-l-arginine, methyl guanidine, and phenylacetylglutamine. This multimodality approach has the potential to contribute to find robust biomarkers and characterize a metabolic phenotype of the ASD population.

Analytical methodology for metabolomics study of adherent mammalian cells using NMR, GC-MS and LC-HRMS.

We developed a methodology for the analysis of intracellular metabolites using nuclear magnetic resonance spectrometry (NMR), gas-chromatography coupled with mass spectrometry (GC-MS), and liquid chromatography coupled with high resolution mass spectrometry (LC-HRMS). The main steps for analysis of adherent cells in order to recover the widest possible range of intracellular compounds are blocking metabolic activity by quenching and extraction of intracellular metabolites. We explored three protocols to quench NSC-34 cell metabolism and four different extraction methods, analyzed by NMR. On the basis of the number of metabolites extracted and their relative standard deviation (RSD) analyzed by NMR, the most reproducible protocol [quenching by MeOH at -40 °C and extraction with CH2Cl2/MeOH/H2O (3:3:2)] was used to obtain intracellular media to be analyzed by GC-MS and LC-HRMS. GC-MS analysis was optimized by three oximation procedures followed by silylation derivatization and these were compared to silylation alone. Using reversed-phase liquid chromatography (C18), four different gradients for LC-MS were compared. The analytical protocols were determined to establish the reliability and suitability of sample treatments required to achieve the correct biological analysis of untargeted mammalian cell metabolomics.

Assessing the metabolic effects of calcineurin inhibitors in renal transplant recipients by urine metabolic profiling.

BACKGROUND: Biomarkers that can predict graft function and/or renal side effects of calcineurin inhibitors (CNI) at each stage of treatment in kidney transplantation are still lacking. We report the first untargeted GC-MS-based metabolomic study on urines of renal transplant patients. This approach would bring insight in biomarkers useable for graft function monitoring. METHODS: All consecutive patients receiving a kidney allograft in our transplantation department over a 6-month period were prospectively included and followed up for 12 months. We collected urine samples on the seventh day (D7) after transplantation, then at month 3 (M3) and month 12 (M12), and obtained mass-spectrometry-based urinary metabolic profiles. Multivariate analyses were conducted to compare metabolic profiles at the 3 different periods and to assess potential differences between cyclosporine and tacrolimus. Differences in metabolic signatures were also assessed according to graft function at D7 and renal function at M3 and M12. RESULTS: The urinary metabolic patterns varied over time in cyclosporine- and tacrolimus-treated patients and were somewhat different at D7, M3, and M12 between the 2 treatment groups. Principal metabolites that differed, regardless of the treatment used, were mainly sugars, inositol, and hippuric acid. Interestingly, among tacrolimus-treated patients, different metabolic signatures were found between patients with immediate or delayed graft function at D7. CONCLUSION: Urinary metabolomics represents a noninvasive way of monitoring immunosuppressive therapy in renal transplant patients. Although it is too early to consider it as a biomarker of CNI-induced injury or graft function, metabolomics appears a promising evaluation tool in this area.