Metabolomics As a Tool for the Characterization of Drug-Resistant Epilepsy.

PURPOSE: Drug resistance is a critical issue in the treatment of epilepsy, contributing to clinical emergencies and increasing both serious social and economic burdens on the health system. The wide variety of potential drug combinations followed by often failed consecutive attempts to match drugs to an individual patient may mean that this treatment stage may last for years with suboptimal benefit to the patient. Given these challenges, it is valuable to explore the availability of new methodologies able to shorten the period of determining a rationale pharmacologic treatment. Metabolomics could provide such a tool to investigate possible markers of drug resistance in subjects with epilepsy. METHODS: Blood samples were collected from (1) controls (C) (n = 35), (2) patients with epilepsy "responder" (R) (n = 18), and (3) patients with epilepsy "non-responder" (NR) (n = 17) to the drug therapy. The samples were analyzed using nuclear magnetic resonance spectroscopy, followed by multivariate statistical analysis. KEY FINDINGS: A different metabolic profile based on metabolomics analysis of the serum was observed between C and patients with epilepsy and also between R and NR patients. It was possible to identify the discriminant metabolites for the three classes under investigation. Serum from patients with epilepsy were characterized by increased levels of 3-OH-butyrate, 2-OH-valerate, 2-OH-butyrate, acetoacetate, acetone, acetate, choline, alanine, glutamate, scyllo-inositol (C  R > NR). SIGNIFICANCE: In conclusion, metabolomics may represent an important tool for discovery of differences between subjects affected by epilepsy responding or resistant to therapies and for the study of its pathophysiology, optimizing the therapeutic resources and the quality of life of patients

Human myotonic dystrophy protein kinase effect in S. cerevisiae

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ipaPy2: Integrated Probabilistic Annotation (IPA) 2.0-an improved Bayesian-based method for the annotation of LC-MS/MS untargeted metabolomics data.

SummaryThe Integrated Probabilistic Annotation (IPA) is an automated annotation method for LC-MS-based untargeted metabolomics experiments that provides statistically rigorous estimates of the probabilities associated with each annotation. Here, we introduce ipaPy2, a substantially improved and completely refactored Python implementation of the IPA method. The revised method is now able to integrate tandem MS fragmentation data, which increases the accuracy of the identifications. Moreover, ipaPy2 provides a much more user-friendly interface, and isotope peaks are no longer treated as individual features but integrated into isotope fingerprints, greatly speeding up the calculations. The method has also been fully integrated with the mzMatch pipeline, so that the results of the annotation can be explored through the newly developed PeakMLViewerPy tool available at https://github.com/UoMMIB/PeakMLViewerPy.Availability and implementationThe source code, extensive documentation, and tutorials are freely available on GitHub at https://github.com/francescodc87/ipaPy2

Expression and characterization of two new alkane-inducible cytochrome P450s from Trichoderma harzianum

Abstract The inducibility CYPs by various carbon sources, including some n-alkanes and fatty acids, has been studied in Trichoderma harzianum. It was observed that n-dodecane and a mixture of fatty acids were good inducers of total CYP content and ω-hydroxylase of lauric acid, a marker for ω-hydroxylation of n-alkanes. By RACE it was isolated a cDNA containing an open reading frame of 1520 bp which encoded a CYP52 protein of 520 amino acids. Further, another n-alkane inducible CYP was identified in a library of T. harzianum by LC-MS/Ms analysis of a microsomal protein band induced by n-dodecane exposure. Thus, the filamentous fungus T. harzianum is expected to have a CYP dependent conversion of alkanes to fatty acids and their incorporation into cellular lipids

1H NMR spectroscopy-based metabolomics analysis for the diagnosis of symptomatic E. coli-associated urinary tract infection (UTI).

BACKGROUND: Urinary tract infection (UTI) is one of the most common diagnoses in girls and women, and to a lesser extent in boys and men younger than 50 years. Escherichia coli, followed by Klebsiella spp. and Proteus spp., cause 75-90% of all infections. Infection of the urinary tract is identified by growth of a significant number of a single species in the urine, in the presence of symptoms. Urinary culture is an accurate diagnostic method but takes several hours or days to be carried out. Metabolomics analysis aims to identify biomarkers that are capable of speeding up diagnosis. METHODS: Urine samples from 51 patients with a prior diagnosis of Escherichia coli-associated UTI, from 21 patients with UTI caused by other pathogens (bacteria and fungi), and from 61 healthy controls were analyzed. The 1H-NMR spectra were acquired and processed. Multivariate statistical models were applied and their performance was validated using permutation test and ROC curve. RESULTS: Orthogonal Partial Least Squares-discriminant Analysis (OPLS-DA) showed good separation (R2Y = 0.76, Q2=0.45, p < 0.001) between UTI caused by Escherichia coli and healthy controls. Acetate and trimethylamine were identified as discriminant metabolites. The concentrations of both metabolites were calculated and used to build the ROC curves. The discriminant metabolites identified were also evaluated in urine samples from patients with other pathogens infections to test their specificity. CONCLUSIONS: Acetate and trimethylamine were identified as optimal candidates for biomarkers for UTI diagnosis. The conclusions support the possibility of a fast diagnostic test for Escherichia coli-associated UTI using acetate and trimethylamine concentrations

(1)H-NMR analysis provides a metabolomic profile of patients with multiple sclerosis

OBJECTIVE: To investigate the metabolomic profiles of patients with multiple sclerosis (MS) and to define the metabolic pathways potentially related to MS pathogenesis. METHODS: Plasma samples from 73 patients with MS (therapy-free for at least 90 days) and 88 healthy controls (HC) were analyzed by (1)H-NMR spectroscopy. Data analysis was conducted with principal components analysis followed by a supervised analysis (orthogonal partial least squares discriminant analysis [OPLS-DA]). The metabolites were identified and quantified using Chenomx software, and the receiver operating characteristic (ROC) curves were calculated. RESULTS: The model obtained with the OPLS-DA identified predictive metabolic differences between the patients with MS and HC (R2X = 0.615, R2Y = 0.619, Q2 = 0.476; p < 0.001). The differential metabolites included glucose, 5-OH-tryptophan, and tryptophan, which were lower in the MS group, and 3-OH-butyrate, acetoacetate, acetone, alanine, and choline, which were higher in the MS group. The suitability of the model was evaluated using an external set of samples. The values returned by the model were used to build the corresponding ROC curve (area under the curve of 0.98). CONCLUSION: NMR metabolomic analysis was able to discriminate different metabolic profiles in patients with MS compared with HC. With the exception of choline, the main metabolic changes could be connected to 2 different metabolic pathways: tryptophan metabolism and energy metabolism. Metabolomics appears to represent a promising noninvasive approach for the study of M

Hyperosmotic stress induces metacaspase - and mitochondria - dependent apoptosis in Saccharomyces cerevisiae

Prova tipográfica (In Press)During the last years, several reports described an apoptosis-like programmed cell death process in yeast in response to different environmental aggressions. Here, evidence is presented that hyperosmotic stress induces in Saccharomyces cerevisiae a cell death process accompanied by morphological and biochemical indicators of apoptotic programmed cell death, namely chromatin condensation along the nuclear envelope, mitochondrial swelling and reduction of cristae number, production of reactive oxygen species and DNA strand breaks, with maintenance of plasma membrane integrity. Disruption of AIF1 had no effect on cell survival, but lack of Yca1p drastically reduced metacaspase activation and decreased cell death indicating that this death process was associated to activation of this protease. Supporting the involvement of mitochondria and cytochrome c in caspase activation, the mutant strains cyc1Δ cyc7Δ and cyc3Δ, both lacking mature cytochrome c, displayed a decrease in caspase activation associated to increased cell survival when exposed to hyperosmotic stress. These findings indicate that hyperosmotic stress triggers S. cerevisiae into an apoptosis-like programmed cell death that is mediated by a caspase-dependent mitochondrial pathway partially dependent on cytochrome c