Differences in the metabolite composition and mechanical properties of extracellular vesicles secreted by hepatic cellular models.

Liver constitutes the major metabolic factory in the organism and is involved in the synthesis, secretion and clearance of many blood-circulating molecules. Previously, we have characterised the protein and RNA cargo of extracellular vesicles (EVs) secreted by two hepatic cellular models, a mouse hepatocyte progenitor cell line (MLP29) and primary rat hepatocytes (RHs). Here, we report the metabolome profile of these vesicles by performing a targeted UHPLC-MS metabolomics analysis of these two cellular models and their respective secreted EVs. Visual inspection of the data through principal component analysis allows clear separation of the metabolic profile of cells and EVs, and also of both cellular models. Correlation matrix supported that lipid composition of EVs is mainly determined by parent cell composition. EVs derived from MLP29 and RHs showed a negative correlation in their percentage composition of ceramides, glycerophospholipids, sphingomyelins and triglycerides. Metabolites enriched in EVs were also different depending on the cellular model. EVs secreted by MLP29 were enriched in different species of sphingomyelins and ceramides underrepresented in EVs secreted by RHs. Remarkably, triglycerides constitute an important percentage of the composition of EVs derived from RHs. We further investigate if the differences in lipid composition were also accompanied by differences in mechanical behaviour, by using atomic force microscopy complemented with nanoindentation-based methodology. To compare the stiffness and brittleness of EVs derived from MLP29 cell line and RH primary cells, FZ curves were performed in the centre of single vesicles and the differences found in their force-vs.-indentation curves suggest that RHs EVs are softer (less stiff) and less resistance to mechanical failure than MLP29 EVs. Therefore, we can conclude that EVs from different origin carry a characteristic lipid composition related to their parental cell composition, and exhibit different mechanical properties. Abbreviations: For the identification of the different species of lipids, the following abbreviations has been employed: Cer, ceramide; ChoE, Cholesteryl Ester; CMH, monohexosylceramide; DAG, diglycerid; LPC, lysophosphatidylcholin; LPI, lysophosphatidyinositol; PC, phosphocoline; PE, phoethanolamine; PI, phosphoinositol; SM, sphingomyelin; TAG, triglycerid.

Targeted UPLC-MS Metabolic Analysis of Human Faeces Reveals Novel Low-Invasive Candidate Markers for Colorectal Cancer.

Low invasive tests with high sensitivity for colorectal cancer and advanced precancerous lesions will increase adherence rates, and improve clinical outcomes. We have performed an ultra-performance liquid chromatography/time-of-flight mass spectrometry (UPLC-(TOF) MS)-based metabolomics study to identify faecal biomarkers for the detection of patients with advanced neoplasia. A cohort of 80 patients with advanced neoplasia (40 advanced adenomas and 40 colorectal cancers) and 49 healthy subjects were analysed in the study. We evaluated the faecal levels of 105 metabolites including glycerolipids, glycerophospholipids, sterol lipids and sphingolipids. We found 18 metabolites that were significantly altered in patients with advanced neoplasia compared to controls. The combinations of seven metabolites including ChoE(18:1), ChoE(18:2), ChoE(20:4), PE(16:0/18:1), SM(d18:1/23:0), SM(42:3) and TG(54:1), discriminated advanced neoplasia patients from healthy controls. These seven metabolites were employed to construct a predictive model that provides an area under the curve (AUC) median value of 0.821. The inclusion of faecal haemoglobin concentration in the metabolomics signature improved the predictive model to an AUC of 0.885. In silico gene expression analysis of tumour tissue supports our results and puts the differentially expressed metabolites into biological context, showing that glycerolipids and sphingolipids metabolism and GPI-anchor biosynthesis pathways may play a role in tumour progression.

Metabolic alterations in urine extracellular vesicles are associated to prostate cancer pathogenesis and progression.

Urine contains extracellular vesicles (EVs) that concentrate molecules and protect them from degradation. Thus, isolation and characterisation of urinary EVs could increase the efficiency of biomarker discovery. We have previously identified proteins and RNAs with differential abundance in urinary EVs from prostate cancer (PCa) patients compared to benign prostate hyperplasia (BPH). Here, we focused on the analysis of the metabolites contained in urinary EVs collected from patients with PCa and BPH. Targeted metabolomics analysis of EVs was performed by ultra-high-performance liquid chromatography-mass spectrometry. The correlation between metabolites and clinical parameters was studied, and metabolites with differential abundance in PCa urinary EVs were detected and mapped into cellular pathways. We detected 248 metabolites belonging to different chemical families including amino acids and various lipid species. Among these metabolites, 76 exhibited significant differential abundance between PCa and BPH. Interestingly, urine EVs recapitulated many of the metabolic alterations reported in PCa, including phosphathidylcholines, acyl carnitines, citrate and kynurenine. Importantly, we found elevated levels of the steroid hormone, 3beta-hydroxyandros-5-en-17-one-3-sulphate (dehydroepiandrosterone sulphate) in PCa urinary EVs, in line with the potential elevation of androgen synthesis in this type of cancer. This work supports urinary EVs as a non-invasive source to infer metabolic changes in PCa.

A Metabolomics Signature Linked To Liver Fibrosis In The Serum Of Transplanted Hepatitis C Patients.

Liver fibrosis must be evaluated in patients with hepatitis C virus (HCV) after liver transplantation because its severity affects their prognosis and the recurrence of HCV. Since invasive biopsy is still the gold standard to identify patients at risk of graft loss from rapid fibrosis progression, it becomes crucial the development of new accurate, non-invasive methods that allow repetitive examination of the patients. Therefore, we have developed a non-invasive, accurate model to distinguish those patients with different liver fibrosis stages. Two hundred and three patients with HCV were histologically classified (METAVIR) into five categories of fibrosis one year after liver transplantation. In this cross-sectional study, patients at fibrosis stages F0-F1 (n = 134) were categorised as "slow fibrosers" and F2-F4 (n = 69) as "rapid fibrosers". Chloroform/methanol serum extracts were analysed by reverse ultra-high performance liquid chromatography coupled to mass spectrometry. A diagnostic model was built through linear discriminant analyses. An algorithm consisting of two sphingomyelins and two phosphatidylcholines accurately classifies rapid and slow fibrosers after transplantation. The proposed model yielded an AUROC of 0.92, 71% sensitivity, 85% specificity, and 84% accuracy. Moreover, specific bile acids and sphingomyelins increased notably along with liver fibrosis severity, differentiating between rapid and slow fibrosers.

Lipidomic profile of GM95 cell death induced by Clostridium perfringens alpha-toxin.

Clostridium perfringens alpha-toxin (ATX) is considered as a prototype of cytotoxic bacterial phospholipases C, and is the major virulence factor in C. perfringens-induced gas gangrene. It is known that, depending on the dose, ATX causes membrane disruption and cytolysis or only limited hydrolysis of its substrates. In the latter case, toxin activity leads to the unregulated generation of bioactive lipids that can ultimately induce cell death. We have characterized apoptosis and necrosis in highly ATX-sensitive, ganglioside-deficient cells exposed to different concentrations of ATX and we have studied the lipidomic profile of cells treated with ATX as compared to native cells to detect the main changes in the lipidomic profile and the possible involvement of lipid signals in cell death. ATX causes both apoptosis and necrosis, depending on dose and time. ATX activates cell death, stimulating the release of cytochrome C from mitochondria and the consequent activation of caspases-3. Moreover GM95 cells treated with ATX showed important lipidomic alterations, among them we detected a general decrease in several phospholipid species and important changes in lipids involved in programmed cell death e.g. ceramide. The data suggest two different mechanisms of cell death caused by ATX, one leading to (mainly saturated) glycerophospholipid hydrolysis related to an increase in diacylglycerols and associated to membrane damage and necrosis, and a second mechanism involving chiefly sphingomyelin hydrolysis and generation of proapoptotic lipidic mediators such as ceramide, N-acylethanolamine and saturated non-esterified fatty acids.

Metabolically active extracellular vesicles released from hepatocytes under drug-induced liver-damaging conditions modify serum metabolome and might affect different pathophysiological processes.

Hepatocytes are involved in the endogenous and drug metabolism; many of the enzymes involved in those processes are incorporated into extracellular vesicles and secreted into the bloodstream. Liver-damaging conditions modify the molecular cargo of those vesicles significantly. However, no information about the effect of these hepatic vesicles on the extracellular environment is available. Drug-induced liver damage increases the number of circulating extracellular vesicles and affects the release and content of hepatocyte-derived vesicles. In this work, we evaluated the metabolic effect of these vesicles on the composition of the serum. We performed a targeted ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) metabolomics analysis of serum samples. The samples had been first incubated with hepatic extracellular vesicles from hepatocytes challenged with acetaminophen or diclofenac. The incubation affected the serum levels of 67 metabolites, such as amino acids and different species of lipids. The metabolites included various species of phosphatidylcholines and phosphatidylethanolamines. These compounds are the components of biological membranes; our observations suggest that the vesicles might take part in remodelling and maintenance of the membranes. Alterations in the levels of some other serum metabolites might have deleterious consequences, for example, the tetracosanoic acid with its cardiovascular effects. However, some of the metabolites whose levels were increased, including alpha-linoleic and tauroursodeoxycholic acids, have been reported to have a protective effect. Our targeted metabolomics analysis indicated that the hepatic extracellular vesicles act as nano-metabolic machines supplying the extracellular environment with the means to integrate diverse tissue responses. In conclusion, we show that the hepatic extracellular vesicles are metabolically active and might play a role in the physiopathological response to hepatic insults, including drug-induced liver injury.

Data in support of enhancing metabolomics research through data mining.

Metabolomics research has evolved considerably, particularly during the last decade. Over the course of this evolution, the interest in this 'omic' discipline is now more evident than ever. However, the future of metabolomics will depend on its capability to find biomarkers. For that reason, data mining constitutes a challenging task in metabolomics workflow. This work has been designed in support of the research article entitled "Enhancing metabolomics research through data mining", which proposed a methodological data handling guideline. An aging research in healthy population was used as a guiding thread to illustrate this process. Here we provide a further interpretation of the obtained statistical results. We also focused on the importance of graphical visualization tools as a clue to understand the most common univariate and multivariate data analyses applied in metabolomics.

Enhancing metabolomics research through data mining.

Metabolomics research, like other disciplines utilizing high-throughput technologies, generates a large amount of data for every sample. Although handling this data is a challenge and one of the biggest bottlenecks of the metabolomics workflow, it is also the clue to accomplish valuable results. This work has been designed to supply methodological data mining guidelines, describing systematically the steps to be followed in metabolomics data exploration. Instrumental raw data refinement in the pre-processing step and assessment of the statistical assumptions in pre-treatment directly affect the results of subsequent univariate and multivariate analyses. A study of aging in a healthy population was selected to represent this data mining process. Multivariate analysis of variance and linear regression methods were used to analyze the metabolic changes underlying aging. Selection of both multivariate methods aims to illustrate the treatment of age from two rather different perspectives, as a categorical variable and a continuous variable. BIOLOGICAL SIGNIFICANCE: Metabolomics is a discipline involving the analysis of a large amount of data to gather relevant information. Researchers in this field have to overcome the challenges of complex data processing and statistical analysis issues. A wide range of tasks has to be executed, from the minimization of batch-to-batch/systematic variations in pre-processing, to the application of common data analysis techniques relying on statistical assumptions. In this work, a real-data metabolic profiling research on aging was used to illustrate the proposed workflow and suggest a set of guidelines for analyzing metabolomics data. This article is part of a Special Issue entitled: HUPO 2014.

Serum UPLC-MS/MS metabolic profiling in an experimental model for acute-liver injury reveals potential biomarkers for hepatotoxicity.

A key interest in clinical diagnosis and pharmaceutical industry is to have a repertoire of noninvasive biomarkers to-individually or in combination-be able to infer or predict the degree of liver injury caused by pathological conditions or drugs. Metabolomics-a comprehensive study of global metabolites-has become a highly sensitive and powerful tool for biomarker discovery thanks to recent technological advances. An ultra-performance liquid chromatography/time-of-flight tandem mass spectrometry (UPLC/TOF MS/MS)-based metabolomics approach was employed to investigate sera from galactosamine-treated rats to find potential biomarkers for acute liver injury. Hepatic damage was quantified by determining serum transaminase activity and in situ liver histological lesions. Principal component analysis in combination with coefficient of correlation analysis was used for biomarker selection and identification. According to the data, serum levels of several metabolites including glucose, amino acids, and membrane lipids were significantly modified, some of them showing a high correlation with the degree of liver damage determined by histological examination of the livers. In conclusion, this study supports that UPLC-MS/MS based serum metabolomics in experimental animal models could be a powerful approach to search for biomarkers for drug- or disease-induced liver injury.

Liquid chromatography-mass spectrometry-based parallel metabolic profiling of human and mouse model serum reveals putative biomarkers associated with the progression of nonalcoholic fatty liver disease.

Nonalcoholic fatty liver disease (NAFLD) is the most common form of chronic liver disease in most western countries. Current NAFLD diagnosis methods (e.g., liver biopsy analysis or imaging techniques) are poorly suited as tests for such a prevalent condition, from both a clinical and financial point of view. The present work aims to demonstrate the potential utility of serum metabolic profiling in defining phenotypic biomarkers that could be useful in NAFLD management. A parallel animal model/human NAFLD exploratory metabolomics approach was employed, using ultra performance liquid chromatography-mass spectrometry (UPLC-MS) to analyze 42 serum samples collected from nondiabetic, morbidly obese, biopsy-proven NAFLD patients, and 17 animals belonging to the glycine N-methyltransferase knockout (GNMT-KO) NAFLD mouse model. Multivariate statistical analysis of the data revealed a series of common biomarkers that were significantly altered in the NAFLD (GNMT-KO) subjects in comparison to their normal liver counterparts (WT). Many of the compounds observed could be associated with biochemical perturbations associated with liver dysfunction (e.g., reduced Creatine) and inflammation (e.g., eicosanoid signaling). This differential metabolic phenotyping approach may have a future role as a supplement for clinical decision making in NAFLD and in the adaption to more individualized treatment protocols.