Panton-Valentine Leucocidin of Staphylococcus aureus Induces Oxidative Stress and Neurotransmitter Imbalance in a Retinal Explant Model.

Purpose: Endophthalmitis models have reported the virulent role of Panton-Valentine leucocidin (PVL) secreted by Staphylococcus aureus on the retina. PVL targets retinal ganglion cells (RGCs), expressing PVL membrane receptor C5aR. Interactions between PVL and retinal cells lead to glial activation, retinal inflammation, and apoptosis. In this study, we explored oxidative stress and retinal neurotransmitters in a rabbit retinal explant model incubated with PVL. Methods: Reactive oxygen species (ROS) production in RGCs has been assessed with fluorescent probes and immunohistochemistry. Nuclear magnetic resonance (NMR) spectroscopy quantified retinal concentrations of antioxidant molecules and neurotransmitters, and concentrations of neurotransmitters released in the culture medium. Quantifying the expression of some pro-inflammatory and anti-inflammatory factors was performed using RT-qPCR. Results: PVL induced a mitochondrial ROS production in RGCs after four hours' incubation with the toxin. Enzymatic sources of ROS, involving nicotinamide adenine dinucleotide phosphate-oxidase and xanthine oxidase, were also activated after four hours in PVL-treated retinal explants. Retinal antioxidants defenses, that is, glutathione, ascorbate and taurine, decreased after two hours' incubation with PVL. Glutamate retinal concentrations and glutamate release in the culture medium remained unaltered in PVL-treated retinas. GABA, glycine, and acetylcholine (Ach) retinal concentrations decreased after PVL treatment. Glycine release in the culture medium decreased, whereas Ach release increased after PVL treatment. Expression of proinflammatory and anti-inflammatory cytokines remained unchanged in PVL-treated explants. Conclusions: PVL activates oxidative pathways and alters neurotransmitter retinal concentrations and release, supporting the hypothesis that PVL could induce a neurogenic inflammation in the retina.

Machine learning assisted intraoperative assessment of brain tumor margins using HRMAS NMR spectroscopy.

Complete resection of the tumor is important for survival in glioma patients. Even if the gross total resection was achieved, left-over micro-scale tissue in the excision cavity risks recurrence. High Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HRMAS NMR) technique can distinguish healthy and malign tissue efficiently using peak intensities of biomarker metabolites. The method is fast, sensitive and can work with small and unprocessed samples, which makes it a good fit for real-time analysis during surgery. However, only a targeted analysis for the existence of known tumor biomarkers can be made and this requires a technician with chemistry background, and a pathologist with knowledge on tumor metabolism to be present during surgery. Here, we show that we can accurately perform this analysis in real-time and can analyze the full spectrum in an untargeted fashion using machine learning. We work on a new and large HRMAS NMR dataset of glioma and control samples (n = 565), which are also labeled with a quantitative pathology analysis. Our results show that a random forest based approach can distinguish samples with tumor cells and controls accurately and effectively with a median AUC of 85.6% and AUPR of 93.4%. We also show that we can further distinguish benign and malignant samples with a median AUC of 87.1% and AUPR of 96.1%. We analyze the feature (peak) importance for classification to interpret the results of the classifier. We validate that known malignancy biomarkers such as creatine and 2-hydroxyglutarate play an important role in distinguishing tumor and normal cells and suggest new biomarker regions. The code is released at http://github.com/ciceklab/HRMAS_NC.

Metabolomic Profile of Aggressive Meningiomas by Using High-Resolution Magic Angle Spinning Nuclear Magnetic Resonance.

Meningiomas are in most cases benign brain tumors. The WHO 2016 classification defines three grades of meningiomas. This classification had a prognosis value because grade III meningiomas have a worse prognosis value compared to grades I and II meningiomas. However, some benign or atypical meningiomas can have a clinical aggressive behavior. There are currently no reliable markers which allow distinguishing between the meningiomas with a good prognosis and those which may recur. High-resolution magic angle spinning (HRMAS) spectrometry is a noninvasive method able to determine the metabolite profile of a tissue sample. We retrospectively analyzed 62 meningioma samples by using HRMAS spectrometry (43 metabolites). We described a metabolic profile defined by a high concentration for acetate, threonine, N-acetyl-lysine, hydroxybutyrate, myoinositol, ascorbate, scylloinositol, and total choline and a low concentration for aspartate, glucose, isoleucine, valine, adenosine, arginine, and alanine. This metabolomic signature was associated with poor prognosis histological markers [Ki-67 ≥ 40%, high histological grade and negative progesterone receptor (PR) expression]. We also described a similar metabolomic spectrum between grade III and grade I meningiomas. Moreover, all grade I meningiomas with a low Ki-67 expression and a positive PR expression did not have the same metabolomic profile. Metabolomic analysis could be used to determine an aggressive meningioma in order to discuss a personalized treatment. Further studies are needed to confirm these results and to correlate this metabolic profile with survival data.

Metabolomics of Small Intestine Neuroendocrine Tumors and Related Hepatic Metastases.

To assess the metabolomic fingerprint of small intestine neuroendocrine tumors (SI-NETs) and related hepatic metastases, and to investigate the influence of the hepatic environment on SI-NETs metabolome. Ninety-four tissue samples, including 46 SI-NETs, 18 hepatic NET metastases and 30 normal SI and liver samples, were analyzed using 1H-magic angle spinning (HRMAS) NMR nuclear magnetic resonance (NMR) spectroscopy. Twenty-seven metabolites were identified and quantified. Differences between primary NETs vs. normal SI and primary NETs vs. hepatic metastases, were assessed. Network analysis was performed according to several clinical and pathological features. Succinate, glutathion, taurine, myoinositol and glycerophosphocholine characterized NETs. Normal SI specimens showed higher levels of alanine, creatine, ethanolamine and aspartate. PLS-DA revealed a continuum-like distribution among normal SI, G1-SI-NETs and G2-SI-NETs. The G2-SI-NET distribution was closer and clearly separated from normal SI tissue. Lower concentration of glucose, serine and glycine, and increased levels of choline-containing compounds, taurine, lactate and alanine, were found in SI-NETs with more aggressive tumors. Higher abundance of acetate, succinate, choline, phosphocholine, taurine, lactate and aspartate discriminated liver metastases from normal hepatic parenchyma. Higher levels of alanine, ethanolamine, glycerophosphocholine and glucose was found in hepatic metastases than in primary SI-NETs. The present work gives for the first time a snapshot of the metabolomic characteristics of SI-NETs, suggesting the existence of complex metabolic reality, maybe characteristic of different tumor evolution.

Hypoxic Environment and Paired Hierarchical 3D and 2D Models of Pediatric H3.3-Mutated Gliomas Recreate the Patient Tumor Complexity.

BACKGROUND: Pediatric high-grade gliomas (pHGGs) are facing a very dismal prognosis and representative pre-clinical models are needed for new treatment strategies. Here, we examined the relevance of collecting functional, genomic, and metabolomics data to validate patient-derived models in a hypoxic microenvironment. METHODS: From our biobank of pediatric brain tumor-derived models, we selected 11 pHGGs driven by the histone H3.3K28M mutation. We compared the features of four patient tumors to their paired cell lines and mouse xenografts using NGS (next generation sequencing), aCGH (array comparative genomic hybridization), RNA sequencing, WES (whole exome sequencing), immunocytochemistry, and HRMAS (high resolution magic angle spinning) spectroscopy. We developed a multicellular in vitro model of cell migration to mimic the brain hypoxic microenvironment. The live cell technology Incucyte© was used to assess drug responsiveness in variable oxygen conditions. RESULTS: The concurrent 2D and 3D cultures generated from the same tumor sample exhibited divergent but complementary features, recreating the patient intra-tumor complexity. Genomic and metabolomic data described the metabolic changes during pHGG progression and supported hypoxia as an important key to preserve the tumor metabolism in vitro and cell dissemination present in patients. The neurosphere features preserved tumor development and sensitivity to treatment. CONCLUSION: We proposed a novel multistep work for the development and validation of patient-derived models, considering the immature and differentiated content and the tumor microenvironment of pHGGs.

What Does Reduced FDG Uptake Mean in High-Grade Gliomas?

PURPOSE: As well as in many others cancers, FDG uptake is correlated with the degree of malignancy in gliomas, that is, commonly high FDG uptake in high-grade gliomas. However, in clinical practice, it is not uncommon to observe high-grade gliomas with low FDG uptake. Our aim was to explore the tumor metabolism in 2 populations of high-grade gliomas presenting high or low FDG uptake. METHODS: High-resolution magic-angle spinning nuclear magnetic resonance spectroscopy was realized on tissue samples from 7 high-grade glioma patients with high FDG uptake and 5 high-grade glioma patients with low FDG uptake. Tumor metabolomics was evaluated from 42 quantified metabolites and compared by network analysis. RESULTS: Whether originating from astrocytes or oligodendrocytes, the high-grade gliomas with low FDG avidity represent a subgroup of high-grade gliomas presenting common characteristics: low aspartate, glutamate, and creatine levels, which are probably related to the impaired electron transport chain in mitochondria; high serine/glycine metabolism and so one-carbon metabolism; low glycerophosphocholine-phosphocholine ratio in membrane metabolism, which is associated with tumor aggressiveness; and finally negative MGMT methylation status. CONCLUSIONS: It seems imperative to identify this subgroup of high-grade gliomas with low FDG avidity, which is especially aggressive. Their identification could be important for early detection for a possible personalized treatment, such as antifolate treatment.

Metabolomic profiling highlights the metabolic bases of acute-on-chronic and post-hepatectomy liver failure.

BACKGROUND: Posthepatectomy liver failure (PHLF) is the main limitation to extending liver resection but its pathophysiology is not yet fully understood. The aim of the study was to describe the metabolic adaptations that occur with PHLF. METHODS: A retrospective study of 82 patients using nuclear magnetic resonance metabolomics to identify and quantify intra-hepatic metabolites was performed. The metabolite levels were compared using metabolic network analysis ADEMA between fatal PHLF (FLF) and non fatal PHLF and according to PHLF/ACLF grading. RESULTS: Metabolomic profiles were significantly different between patients presenting FLF and non FLF or grade 3 ACLF versus < grade 3 ACLF. In the patients undergoing hepatectomy, valine, alanine and glycerophosphocholine were identified as powerful biomarkers to predict FLF (AUROC 0.806, 0.802 and 0.856 respectively). Network analysis showed an activation of aerobic glycolysis with glutaminolysis as observed in highly proliferating systems. Inversely, ACLF3 showed deprivation of glucose and lactate compared to lower ACLF grade. CONCLUSION: Clinical andbiological severity of ACLF and PHLF correlate with specific metabolic adaptations. Metabolomics can predict fatal liver failure after hepatectomy and underline significant differences in the metabolic patterns of ACLF and PHLF.

A metabolic database for biomedical studies of biopsy specimens by high-resolution magic angle spinning nuclear MR: a qualitative and quantitative tool.

PURPOSE: The aim of this study is to generate a metabolic database for biomedical studies of biopsy specimens by high-resolution magic angle spinning (HRMAS) nuclear MR (NMR). METHODS: Seventy-six metabolites, classically found in human biopsy samples, were prepared in aqueous solution at a known concentration and analyzed by HRMAS NMR. The spectra were recorded under the same conditions as the ones used for the analysis of biopsy specimens routinely performed in our hospital. RESULTS: For each metabolite, a complete set of NMR spectra (1D 1 H, 1D 1 H-CPMG, 2D J-Resolved, 2D TOCSY, and 2D 1 H-13 C HSQC) was recorded at 500 MHz and 277 K. All spectra were manually assigned using the information contained in the different spectra and existing databases. Experiments to measure the T1 and the T2 of the different protons present in the 76 metabolites were also recorded. CONCLUSION: This new HRMAS metabolic database is a useful tool for all scientists working on human biopsy specimens, particularly in the field of oncology. It will make the identification of metabolites in biopsy specimens faster and more reliable. Additionally, the knowledge of the T1 and T2 values will allow to obtain a more accurate quantification of the metabolites present in biopsy specimens.

Impact of real-time metabolomics in liver transplantation: Graft evaluation and donor-recipient matching.

BACKGROUND & AIMS: There is an emerging need to assess the metabolic state of liver allografts especially in the novel setting of machine perfusion preservation and donor in cardiac death (DCD) grafts. High-resolution magic-angle-spinning nuclear magnetic resonance (HR-MAS-NMR) could be a useful tool in this setting as it can extemporaneously provide untargeted metabolic profiling. The purpose of this study was to evaluate the potential value of HR-MAS-NMR metabolomic analysis of back-table biopsies for the prediction of early allograft dysfunction (EAD) and donor-recipient matching. METHOD: The metabolic profiles of back-table biopsies obtained by HR-MAS-NMR, were compared according to the presence of EAD using partial least squares discriminant analysis. Network analysis was used to identify metabolites which changed significantly. The profiles were compared to native livers to identify metabolites for donor-recipient matching. RESULTS: The metabolic profiles were significantly different in grafts that caused EAD compared to those that did not. The constructed model can be used to predict the graft outcome with excellent accuracy. The metabolites showing the most significant differences were lactate level >8.3 mmol/g and phosphocholine content >0.646 mmol/g, which were significantly associated with graft dysfunction with an excellent accuracy (AUROClactates = 0.906; AUROCphosphocholine = 0.816). Native livers from patients with sarcopenia had low lactate and glycerophosphocholine content. In patients with sarcopenia, the risk of EAD was significantly higher when transplanting a graft with a high-risk graft metabolic score. CONCLUSION: This study underlines the cost of metabolic adaptation, identifying lactate and choline-derived metabolites as predictors of poor graft function in both native livers and liver grafts. HR-MAS-NMR seems a valid technique to evaluate graft quality and the consequences of cold ischemia on the graft. It could be used to assess the efficiency of graft resuscitation on machine perfusion in future studies. LAY SUMMARY: Real-time metabolomic profiles of human grafts during back-table can accurately predict graft dysfunction. High lactate and phosphocholine content are highly predictive of graft dysfunction whereas low lactate and phosphocholine content characterize patients with sarcopenia. In these patients, the cost of metabolic adaptation may explain the poor outcomes.

A Metabolomic Approach (1H HRMAS NMR Spectroscopy) Supported by Histology to Study Early Post-transplantation Responses in Islet-transplanted Livers.

Intrahepatic transplantation of islets requires a lot of islets because more than 50% of the graft is lost during the 24 hours following transplantation. We analyzed, in a rat model, early post-transplantation inflammation using systemic inflammatory markers, or directly in islet-transplanted livers by immunohistochemistry. 1H HRMAS NMR was employed to investigate metabolic responses associated with the transplantation. Inflammatory markers (Interleukin-6, α2-macroglobulin) are not suitable to follow islet reactions as they are not islet specific. To study islet specific inflammatory events, immunohistochemistry was performed on sections of islet transplanted livers for thrombin (indicator of the instant blood-mediated inflammatory reaction (IBMIR)) and granulocytes and macrophages. We observed a specific correlation between IBMIR and granulocyte and macrophage infiltration after 12 h. In parallel, we identified a metabolic response associated with transplantation: after 12 h, glucose, alanine, aspartate, glutamate and glutathione were significantly increased. An increase of glucose is a marker of tissue degradation, and could be explained by immune cell infiltration. Alanine, aspartate and glutamate are inter-connected in a common metabolic pathway known to be activated during hypoxia. An increase of glutathione revealed the presence of antioxidant protection. In this study, IBMIR visualization combined with 1H HRMAS NMR facilitated the characterization of cellular and molecular pathways recruited following islet transplantation.