GNPS - Monotropa mzml file annotation
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Orbitrap Fusion Lumos
ORGANISM(S): Monotropa Uniflora (ncbitaxon:50148)
SUBMITTER: 
Josh J Kellogg
PROVIDER: MSV000095253 | GNPS | Thu Jul 04 20:39:00 BST 2024
REPOSITORIES: GNPS
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Project description:Punarnava [Boerhaavia diffusa L.] is a medicinal plant and constituent of several Indian traditional medicines. According to Ayurveda, this plant is a rich source of nutrients. Traditionally, it is used to provide relief against various gastrointestinal disorders, treat wounds, reduce joint pains, and as an anti-stress agent. Despite its pharmacological importance, detailed characterization of the metabolite composition of this plant has not been reported to date. Therefore, we have taken up metabolomic profiling of Punarnava choorna, as part of a larger project of metabolomic profiling of ayurvedic drugs. We carried out a global metabolomic analysis using a high-resolution mass spectrometry to investigate metabolite composition of Punarnava choorna. In total 1747 and 4031 features were identified at MS1 using XCMS in the positive and negative modes, respectively. Using MS2Compound, we identified 1229 and 709 features in the positive and negative modes, respectively. We also identified 362 and 191 metabolites at MS2 level in the positive and negative modes, respectively using the MS2Compound tool. The data were searched against the PlantCyc, KEGG, PhenolExplorer and HMDB databases. A large number of nutritionally important metabolites including amino acids, sugars and vitamins were identified in Purnarnava choorna. Further, the identified metabolites were mapped to their potential protein interactors using BindingDB tool. Highest number of interactions was observed for plant serotonins. The data provides molecular evidence to accelerate the discovery of mode of action of Purnarnava choorna.
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Project description:Scope: The Caco2/HT29-MTX co-culture system is widely used as a cell model of the intestinal epithelium. Although the gut epithelium plays an important role in the uptake of free fatty acids and the resynthesis of triglycerides the lipid distribution profile of the co-culture system is not well understood. Desorption electrospray ionization (DESI) is a mass spectrometry (MS) technique which has been widely used to study the main classes of lipid molecules on different tissue surfaces. This has been used to map lipid species and their distribution in Caco2 and HT29-MTX co-culture system. Methods and results: Caco2 and HT29-MTX cells were seeded on coverslips either singly or as cocultures in ratios of 75:25 and 50:50. Cells were cultured for 21 days before MS imaging using a DESI source in both the positive and negative ionization modes. The identity of selected lipids was confirmed in negative and positive ionisation modes using tandem MS. Although many lipids were common to both cell lines, there were distinctive patterns in the lipidomes. Thus, the lipidome of Caco2 cells was more heterogeneous and rich in cholesterol esters and triglycerides whilst HT29-MTX cells has a distinctive lipidome relating to phosphatidylethanolamines, phosphatidylinositols and odd chain lipids, including C17 fatty acids. Conclusion: DESI-MSI has shown that Caco2 and HT29-MTX cells have distinctive lipidomes which are still evident when the cells are cocultured. It has potential to both allow further validation of these widely used cell models and provide insights into how dietary components may modify lipid metabolism in future.
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Project description:<p><strong>OBJECTIVE:</strong> This study aimed to investigate the plasma metabolic profile of patients with extracranial arteriovenous malformations (AVM).</p><p><strong>METHOD:</strong> Plasma samples were collected from 32 AVM patients and 30 healthy controls (HC). Ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) was employed to analyze the metabolic profiles of both groups. Metabolic pathway enrichment analysis was performed through Kyoto Encyclopedia of Genes and Genomes (KEGG) database and MetaboAnalyst. Additionally, machine learning algorithms such as Least Absolute Shrinkage and Selection Operator (LASSO) and random forest (RF) were conducted to screen characteristic metabolites. The effectiveness of the serum biomarkers for AVM was evaluated using a receiver-operating characteristics (ROC) curve.</p><p><strong>RESULT:</strong> In total, 184 differential metabolites were screened in this study, with 110 metabolites in positive ion mode and 74 metabolites in negative mode. Lipids and lipid-like molecules were the predominant metabolites detected in both positive and negative ion modes. Several significant metabolic pathways were enriched in AVMs, including lipid metabolism, amino acid metabolism, carbohydrate metabolism and protein translation. Through machine learning algorithms, 9 metabolites were identify as characteristic metabolites, including hydroxy-proline, L-2-Amino-4-methylenepentanedioic acid, piperettine, 20-hydroxy-PGF2a, 2,2,4,4-tetramethyl-6-(1-oxobutyl)-1,3,5-cyclohexanetrione, DL-tryptophan, 9-oxoODE, alpha-Linolenic acid and dihydrojasmonic acid.</p><p><strong>CONCLUSION:</strong> Patients with extracranial AVMs exhibited significantly altered metabolic patterns compared to healthy controls, which could be identified using plasma metabolomics. These findings suggest that metabolomic profiling can aid in the understanding of AVM pathophysiology and potentially inform clinical diagnosis and treatment.</p>
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