Multi-platform metabolomics data from Ndufs4-/- skeletal muscles (part-V)
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus Musculus
TISSUE(S): Muscle
DISEASE(S): Leigh Syndrome
PROVIDER: ST001078 | MetabolomicsWorkbench |
REPOSITORIES: MetabolomicsWorkbench
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| mwtab | Other |
Items per page: 5 1 - 1 of 1 |
Similar Datasets
Project description: Not available
| ST001077 | MetabolomicsWorkbench
Project description: Not available
| ST001085 | MetabolomicsWorkbench
Project description: Not available
| ST001076 | MetabolomicsWorkbench
Project description: Not available
| ST001084 | MetabolomicsWorkbench
Project description:BackgroundAnalysis of data from high-throughput experiments depends on the availability of well-structured data that describe the assayed biomolecules. Procedures for obtaining and organizing such meta-data on genes, transcripts and proteins have been streamlined in many data analysis packages, but are still lacking for metabolites. Chemical identifiers are notoriously incoherent, encompassing a wide range of different referencing schemes with varying scope and coverage. Online chemical databases use multiple types of identifiers in parallel but lack a common primary key for reliable database consolidation. Connecting identifiers of analytes found in experimental data with the identifiers of their parent metabolites in public databases can therefore be very laborious.ResultsHere we present a strategy and a software tool for integrating metabolite identifiers from local reference libraries and public databases that do not depend on a single common primary identifier. The program constructs groups of interconnected identifiers of analytes and metabolites to obtain a local metabolite-centric SQLite database. The created database can be used to map in-house identifiers and synonyms to external resources such as the KEGG database. New identifiers can be imported and directly integrated with existing data. Queries can be performed in a flexible way, both from the command line and from the statistical programming environment R, to obtain data set tailored identifier mappings.ConclusionsEfficient cross-referencing of metabolite identifiers is a key technology for metabolomics data analysis. We provide a practical and flexible solution to this task and an open-source program, the metabolite masking tool (MetMask), available at http://metmask.sourceforge.net, that implements our ideas.
| S-EPMC2879285 | biostudies-literature
Project description:Twenty metabolites across 6 studies, with 6 groups of experimental subjects
2008-06-21 | E-TABM-289 | biostudies-arrayexpress
Project description:Muscular dystrophy-dystroglycanopathies comprise a heterogeneous and complex group of disorders caused by loss-of-function mutations in a multitude of genes that disrupt the glycobiology of α-dystroglycan, thereby affecting its ability to function as a receptor for extracellular matrix proteins. Of the various genes involved, FKRP codes for a protein that plays a critical role in the maturation of a novel glycan found only on α-dystroglycan. Yet despite knowing the genetic cause of FKRP-related dystroglycanopathies, the molecular pathogenesis of disease and metabolic response to therapeutic intervention has not been fully elucidated. To address these challenges, we utilized mass spectrometry-based metabolomics to generate comprehensive metabolite profiles of skeletal muscle across diseased, treated, and normal states. Notably, FKRP-deficient mice elicit diverse metabolic abnormalities in biomarkers of extracellular matrix remodeling and/or aging, pentoses/pentitols, glycolytic intermediates, and lipid metabolism. More importantly, the restoration of FKRP protein activity following AAV-mediated gene therapy induced a substantial correction of these metabolic impairments. While interconnections of the affected molecular mechanisms remain unclear, our datasets support the notion that global metabolic profiling can be valuable for determining the involvement of previously unsuspected regulatory or pathological pathways as well as identifying potential targets for drug discovery and diagnostics.
| S-EPMC6624266 | biostudies-literature
Project description:ObjectiveTo interrogate the pathogenesis of intrauterine growth restriction (IUGR) and apply Artificial Intelligence (AI) techniques to multi-platform i.e. nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) based metabolomic analysis for the prediction of IUGR.Materials and methodsMS and NMR based metabolomic analysis were performed on cord blood serum from 40 IUGR (birth weight < 10th percentile) cases and 40 controls. Three variable selection algorithms namely: Correlation-based feature selection (CFS), Partial least squares regression (PLS) and Learning Vector Quantization (LVQ) were tested for their diagnostic performance. For each selected set of metabolites and the panel consists of metabolites common in three selection algorithms so-called overlapping set (OL), support vector machine (SVM) models were developed for which parameter selection was performed busing 10-fold cross validations. Area under the receiver operating characteristics curve (AUC), sensitivity and specificity values were calculated for IUGR diagnosis. Metabolite set enrichment analysis (MSEA) was performed to identify which metabolic pathways were perturbed as a direct result of IUGR in cord blood serum.ResultsAll selected metabolites and their overlapping set achieved statistically significant accuracies in the range of 0.78-0.82 for their optimized SVM models. The model utilizing all metabolites in the dataset had an AUC = 0.91 with a sensitivity of 0.83 and specificity equal to 0.80. CFS and OL (Creatinine, C2, C4, lysoPC.a.C16.1, lysoPC.a.C20.3, lysoPC.a.C28.1, PC.aa.C24.0) showed the highest performance with sensitivity (0.87) and specificity (0.87), respectively. MSEA revealed significantly altered metabolic pathways in IUGR cases. Dysregulated pathways include: beta oxidation of very long fatty acids, oxidation of branched chain fatty acids, phospholipid biosynthesis, lysine degradation, urea cycle and fatty acid metabolism.ConclusionA systematically selected panel of metabolites was shown to accurately detect IUGR in newborn cord blood serum. Significant disturbance of hepatic function and energy generating pathways were found in IUGR cases.
| S-EPMC6472728 | biostudies-literature
Project description:Despite recent progress, chemotherapy remains the preferred treatment for cancer. We have shown a link between anticancer drugs and the development of cachexia, i.e., body wasting accompanied by muscle loss. The multi-kinase inhibitors (MKIs) regorafenib and sorafenib, used as second-line treatment for solid tumors, are frequently accompanied by several side effects, including loss of muscle mass and strength. In the present study we aimed to investigate the molecular mechanisms associated with the occurrence of muscle toxicities in in vivo conditions. Hence, we treated 8-week old healthy CD2F1 male mice with MKIs for up to six weeks and observed decreased skeletal and cardiac muscle mass, consistent with muscle weakness. Modulation of ERK1/2 and GSK3β, as well as increased expression of markers of autophagy, previously associated with muscle atrophy conditions, were shown in skeletal muscle upon treatment with either drug. MKIs also promoted cardiac abnormalities consistent with reduced left ventricular mass, internal diameter, posterior wall thickness and stroke volume, despite unchanged overall function. Notably, different signaling pathways were affected in the heart, including reduced expression of mitochondrial proteins, and elevated AKT, GSK3β, mTOR, MEK1/2 and ERK1/2 phosphorylation. Combined, our data demonstrate detrimental effects on skeletal and cardiac muscle in association with chronic administration of MKIs, although different mechanisms would seem to contribute to the cachectic phenotype in the two tissues.
| S-EPMC6520777 | biostudies-literature
Project description:In mass spectrometry (MS)-based metabolomics, there is a great need to combine different analytical separation techniques to cover metabolites of different polarities and apply appropriate multi-platform data processing. Here, we introduce AriumMS (augmented region of interest for untargeted metabolomics mass spectrometry) as a reliable toolbox for multi-platform metabolomics. AriumMS offers augmented data analysis of several separation techniques utilizing a region-of-interest algorithm. To demonstrate the capabilities of AriumMS, five datasets were combined. This includes three newly developed capillary electrophoresis (CE)-Orbitrap MS methods using the recently introduced nanoCEasy CE-MS interface and two hydrophilic interaction liquid chromatography (HILIC)-Orbitrap MS methods. AriumMS provides a novel mid-level data fusion approach for multi-platform data analysis to simplify and speed up multi-platform data processing and evaluation. The key feature of AriumMS lies in the optimized data processing strategy, including parallel processing of datasets and flexible parameterization for processing of individual separation methods with different peak characteristics. As a case study, Saccharomyces cerevisiae (yeast) was treated with a growth inhibitor, and AriumMS successfully differentiated the metabolome based on the augmented multi-platform CE-MS and HILIC-MS investigation. As a result, AriumMS is proposed as a powerful tool to improve the accuracy and selectivity of metabolome analysis through the integration of several HILIC-MS/CE-MS techniques.
| S-EPMC10287804 | biostudies-literature