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.

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.

Obesity-dependent metabolic signatures associated with nonalcoholic fatty liver disease progression

Our understanding of the mechanisms by which nonalcoholic fatty liver disease (NAFLD) progresses from simple steatosis to steatohepatitis (NASH) is still very limited. Despite the growing number of studies linking the disease with altered serum metabolite levels, an obstacle to the development of metabolome-based NAFLD predictors has been the lack of large cohort data from biopsy-proven patients matched for key metabolic features such as obesity. We studied 467 biopsied individuals with normal liver histology (n=90) or diagnosed with NAFLD (steatosis, n=246; NASH, n=131), randomly divided into estimation (80% of all patients) and validation (20% of all patients) groups. Qualitative determinations of 540 serum metabolite variables were performed using ultraperformance liquid chromatography coupled to mass spectrometry (UPLC-MS). The metabolic profile was dependent on patient body-mass index (BMI), suggesting that the NAFLD pathogenesis mechanism may be quite different depending on an individual's level of obesity. A BMI-stratified multivariate model based on the NAFLD serum metabolic profile was used to separate patients with and without NASH. The area under the receiver operating characteristic curve was 0.87 in the estimation and 0.85 in the validation group. The cutoff (0.54) corresponding to maximum average diagnostic accuracy (0.82) predicted NASH with a sensitivity of 0.71 and a specificity of 0.92 (negative/positive predictive values=0.82/0.84). The present data, indicating that a BMI-dependent serum metabolic profile may be able to reliably distinguish NASH from steatosis patients, have significant implications for the development of NASH biomarkers and potential novel targets for therapeutic intervention.

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.