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1,108 results for “Metabolomics”
NMR metabolomic analysis of Drosophila extracts: 3 genotypes (control, RNAi Opa1 in muscle, RNAi Marf in muscle), 3 body regions (head, thorax, abdomen), 2 ages (30 days, 65 days)
<p>RNAi of the Drosophila mitochondrial fusion genes Opa1 and Marf (Mitofusin 2) in the muscle results in an extended lifespan. In order to unravel the metabolic changes in the long-living flies we analysed the metabolome by NMR spectroscopy, comparing the two knock-down genotypes to a wild type control at two ages: 30 days (young flies) and 65 days (old flies). In order to detect autonomous and non-autonomous changes, we also divided the samples in three anatomical regions: head (mostly neural tissue) thorax (mostly muscle), and abdomen (visceral, reproductive, metabolic control)</p>
Toy datasets for GTN materials about Metabolomics data processing
<p>These are the datasets needed for the GTN tutorial about Data processing of Metabolomics MS-based data.</p> <p>The dataMatrix and corresponding sampleMetadata files are simulated data, specially created from scratch for the GTN tutorial.</p> <p>The variableMetadata file's columns of m/z and rt characteristics have been filled with values picked from a table obtained by an extraction process of some untargetted MS raw files. MS raw files used are from https://zenodo.org/record/3757956; extraction was performed using XCMS with parameters that might not be optimal.</p>
GTTAtlas - Metabolomics atlas of oral 13C-glucose tolerance test in mice
<p>GTTAtlas - Metabolomics atlas of oral <sup>13</sup>C<sub>6</sub>-glucose tolerance test in mice</p> <p>GTTAtlas web application: <a href="https://gttatlas.metabolomics.fgu.cas.cz">https://gttatlas.metabolomics.fgu.cas.cz</a></p> <p>Repository of LC–MS data in *.abf format: <a href="https://repository.metabolomics.fgu.cas.cz/">https://repository.metabolomics.fgu.cas.cz/</a></p> <p>LC–MS platforms: <a href="https://www.metabolomics.fgu.cas.cz">https://www.metabolomics.fgu.cas.cz</a></p> <p>LIMeX platforms: BEH Amide positive, BEH amide negative, HSS T3 negative, BEH C18 positive, BEH C18 positive-TG, BEH C18 negative</p> <p>LC−MS system: Vanquish UHPLC System (Thermo Fisher Scientific, Bremen, Germany) coupled to a Q Exactive Plus mass spectrometer (Thermo Fisher Scientific)</p> <p>Samples: plasma, liver, pancreas, kidney, heart, gastrocnemius muscle, soleus muscle, duodenum, jejunum, ileum, subcutaneous adipose tissue, epididymal adipose tissue, brown adipose tissue.</p> <p>Time points: 0, 15, 30, 60, 120, 180, 240 minutes</p>
Normalized NMR integration values from the metabolomic analysis of Drosophila larvae extracts from 2 genotypes at 3 time points.
<p>We measured the metabolites related to energy production using 1H nuclear magnetic resonance spectroscopy (NMR). No alterations in the levels of carbohydrate stores or free amino acids were found between control and Sema1ai animals, corroborating the notion that the main metabolic changes are in the lipid metabolism. The exception is the glycolytic amino acid alanine (elevated in Sema1ai animals), confirming alterations in glycolysis. The levels of the ß-alanine amino acid are markedly reduced in 256 h AEL or 10.5-day-old Sema1ai animals, probably indicating muscle degeneration in the severely obese larvae that is consistent with the deteriorated state and reduced movement of the 10-day-old (256 hours) mutant larvae. Gluconeogenesis is stimulated by high lactate, and the concentration of lactate is higher in Sema1ai larvae than controls, though the difference is not statistically significant. Glycolysis is stimulated by glucose and inhibited by citrate, an early intermediate of the citric acid cycle. The increased citrate levels in the 10.5-day-old Sema1ai larvae suggest that glycolysis is lower at this age, consistent with the increased level of glucose in the severely obese larvae. The fact that both gluconeogenesis and glycolysis pathways are simultaneously enhanced in Sema1ai larvae support the hypothesis that the animals defecting in adiposity signaling are in a state of perceived energy insufficiency despite having sufficient energy stored.</p>
Kanchanara Guggulu metabolomics
<p>In order to identify the metabolites present in Kanchanara Guggulu, an important Ayurvedic formulation, we have performed a LC-MS/MS-based metabolomics.</p>
Ecological and metabolomic responses of plants to deer exclosure in a suburban forest
<p>Trees and shrubs in suburban forests can be subject to chronic herbivory from abundant white-tailed deer, influencing survival, growth, secondary metabolites and ecological success in the community. We investigated how deer affect the size, cover, and metabolomes of four species in the understory of a suburban forest in central New Jersey, USA: the woody shrubs E<em>uonymus alatus</em> and <em>Lindera benzoin</em>, the tree <em>Nyssa sylvatica</em>, and the semi-woody shrub <em>Rosa multiflora</em>. For each species, we compared plants in 38 16 m<sup>2</sup> plots with or without deer exclosure, measuring proportion cover and mean height after 6.5 years of fencing. We scored each species in all plots for deer browsing over eight years and assessed selection by deer among the species. We did untargeted metabolomics by sampling leaves from three plants of each species in an equal number of fenced and unfenced plots, conducting chloroform-methanol extractions followed by LC-MS/MS, and conducting statistical analysis on MetaboAnalyst. The proportion of a species browsed ranged from 0.24 to 0.35. <em>Nyssa sylvatica</em> appeared most selected by and susceptible to deer; in unfenced plots both its cover and mean height were significantly lower. Only cover or height was lower for <em>E. alatus</em> and <em>L. benzoin</em> in unfenced plots, while <em>R. multiflora</em> height was greater. The metabolomic analysis identified 2,333 metabolites, which clustered by species but not fencing treatment. However, targeted analysis of the top metabolites grouped by fencing for all samples and for each species alone, and was especially clear in<em> N. sylvatica,</em> which also grouped by fencing using all metabolites. The most significant metabolites that were upregulated in fenced plants include some involved in defense-related metabolic pathways, e.g. monoterpenoid biosynthesis. In overbrowsed suburban forests, variation of deer impact on species' ecological success, potentially mediated by metabolome-wide chemical responses to deer, may contribute to changes in community structure.</p>
Lung proteome and metabolome endotype in HIV-associated obstructive lung disease
<p><span><strong>Purpose</strong>: Obstructive lung disease is increasingly common among persons with HIV in both smokers and non-smokers. We used aptamer proteomics to identify proteins and associated pathways in HIV-associated obstructive lung disease.</span></p> <p><span><strong>Methods</strong>: Bronchoalveolar lavage fluid (BALF) samples from 26 persons living with HIV with obstructive lung disease were matched to persons living with HIV without obstructive lung disease based on age, smoking status, and antiretroviral treatment. 6,414 proteins were measured using SomaScan aptamer-based assay. We used sparse distance-weighted discrimination (sDWD) to test for a difference in protein expression and permutation tests to identify univariate associations between proteins and forced expiratory volume in 1s precent predicted (FEV1pp). Significant proteins were entered into a pathway overrepresentation analysis (ORA). We also constructed protein-driven endotypes using K-means clustering and performed ORA on the proteins that were significantly different between clusters. We compared protein-associated clusters to those obtained from BALF and plasma metabolomics data on the same patient cohort.</span></p> <p><span><strong>Results</strong>: After filtering, we retained 3872 proteins for further analysis. Based on sDWD, protein expression was able to separate cases and controls. We found </span><span>575 </span><span>proteins that were significantly correlated with FEV1pp </span><span>after multiple comparisons adjustment</span><span>. We identified </span><span>two protein-driven endotypes, one of which was</span><span> associated with poor lung function, and found that insulin and apoptosis pathways were differentially represented. We found similar clusters driven by metabolomics in BALF but not plasma.</span></p> <p><span><strong>Conclusion</strong>: Protein expression differs in persons living with HIV with and without obstructive lung disease. We were not able to identify specific pathways differentially expressed among patients based on FEV1pp; however, we identified a unique protein endotype associated with insulin and apoptotic pathways. </span></p>
Improving spectral library search untargeted metabolomics identification by dynamic tolerance peak matching and false discovery estimation
<p><strong>The unprocessed and processed benchmarking data used in the paper of "Improving spectral library search untargeted metabolomics identification by dynamic tolerance peak matching and false discovery estimation"</strong></p>
KidDO project update - quantitative proteomic and metabolomic analysis of five mouse models with chronic kidney disease
<p>Chronic kidney disease (CKD) is one of the most deadly diseases faced by patients and is a major global health and socioeconomic burden.CKD increases cardiovascular morbidity and premature mortality and decreases quality of life. Hypertension (HTN) and type 2 diabetes mellitus (T2DM), which are reaching epidemic levels, are major risk factors for CKD. CKD diagnosis and progression is based on estimated GFR (eGFR) and urinary albumin excretion. However, eGFR only has a predictive value in advanced disease and there is risk of progressive CKD in non-albuminuric individuals. Thus, there is an urgent need for new approaches for early detection of the most “at risk” individuals and identification of CKD signatures to aid in designing novel drugs and preventive measures that could ameliorate progression of CKD.</p> <p>Our overarching goal is to identify metabolites that predict kidney cell phenotypes during CKD and how crosstalk of these metabolites with the proteome drive CKD progression. We will integrate metabolomics and proteomic information from animal models of CKD with human CKD patient biopsies to identify common signatures in the tubulointerstitium that correlate with human pathophysiology.</p> <p>Here we provide quantitative proteomic and metabolomic datasets, as well as plasma and urine electrolyte measurements on five CKD mouse models.</p>
Glass Eel metabolomics Dataset
<p>Glass eel metabolite dataset by HRMS. Polar metabolome and lipidome are included. Supplementary data of "Metabolomics to study the sublethal effects of diazepam and irbesartan on glass eels (<em>Anguilla anguilla</em>)"</p>
Non-targeted metabolome profiling in splenic monocyte-derived dendritic cells from Plasmodium chabaudi-infecetd mice
<p>Spleens from mice infected with Plasmodium chabaudi were processed and stained for localization of monocyte-derived dendritic cells (MODCs) by flow cytometry. The markers utilized were: Live/Dead, F4/80, CD11b, DCSign, MHCII, CD11c and CD3. After sorted out, MODCs were frozen in liquid nitrogen, followed by metabolite extraction, as recommended by The Metabolomics Facility at MD Anderson. Metabolites were extracted using ice-cold 0.1% Ammonium hydroxide in 80/20 (v/v) methanol/water. Extracts were centrifuged at 17,000 g for 5 min at 4°C, and supernatants were transferred to clean tubes, followed by evaporation to dryness under nitrogen. Dried extracts were reconstituted in deionized water, and 5 μL was injected for analysis by ion chromatography (IC)-MS. IC mobile phase A (MPA; weak) was water, and mobile phase B (MPB; strong) was water containing 100 mM KOH. A Thermo Scientific Dionex ICS-5000+ system included a Thermo IonPac AS11 column (4 µm particle size, 250 x 2 mm) with column compartment kept at 30°C. The autosampler tray was chilled to 4°C. The mobile phase flow rate was 350 µL/min and gradient from 1mM to 100mM KOH was used. The total run time was 60 min. To assist the desolvation for better sensitivity, methanol was delivered by an external pump and combined with the eluent via a low dead volume mixing tee. Data were acquired using a Thermo Orbitrap Fusion Tribrid Mass Spectrometer under ESI negative ionization mode at a resolution of 240,000.</p>
MALDI-MS dataset for use with open-source untargeted metabolomic workflow for complex biological samples
<p class="MsoNormal">Untargeted metabolomics is a powerful tool for measuring and understanding complex biological chemistries. However, employment, bioinformatics and downstream analysis of mass spectrometry (MS) data can be daunting for inexperienced users. Numerous open-source and free to-use data processing and analysis tools exist for various untargeted MS approaches, but choosing the 'correct' pipeline isn't straight-forward. This data set can be used in conjunction with a user-friendly online guide which presents a workflow for connecting these tools to process, analyse and annotate various untargeted MS datasets. The workflow is intended to guide exploratory analysis in order to inform decision-making regarding costly and time-consuming downstream targeted MS approaches. The workflow provides practical advice concerning experimental design, organisation of data and downstream analysis, and offers details on sharing and storing valuable MS data for posterity. The workflow is editable and modular, allowing flexibility for updated/ changing methodologies and increased clarity and detail as user participation becomes more common allowing contributions and improvements to the workflow via the online repository. </p>
Metabolomics data: glutaminase inhibition in combination with azacytidine in myelodysplastic syndromes
<p>Malignancies can become reliant on glutamine as an alternative energy source and as a facilitator of aberrant DNA methylation, thus implicating glutaminase (GLS) as a potential therapeutic target. We demonstrate preclinical synergy of Telaglenastat (CB-839), a selective GLS inhibitor, when combined with AZA, <em>in vitro</em> and <em>in vivo</em>, followed by a phase Ib/II study of the combination in patients with advanced MDS. Treatment with Telaglenastat/AZA led to an ORR of 70% with CRs in 66% patients and a median overall survival of 11.6 months. scRNAseq and flow cytometry demonstrated a myeloid differentiation program at the stem cell level in clinical responders. Expression of non-canonical glutamine transporter, SLC38A1, was found to be overexpressed in MDS stem cells and was associated with clinical responses to Telaglenastat/AZA; was predictive of worse prognosis in a large MDS cohort. These data demonstrate the safety and efficacy of a combined metabolic and epigenetic approach in MDS.</p>
Pan-metabolome of the genus Nicotiana", Mendeley Data, V1, doi: 10.17632/rhnxrfzm6n.1
<p>Raw data from metabolite analysis of 20 Nicotiana species. LC-ESI-QTof analysis of polar extracts of leaf, GC-MS analysis of polar and non-polar extracts of leaf, UPLC analysis of carotenoids and chlorophylls in leaf and SPME-GC-MS analysis of freeze dried leaf powder.</p>
Datasets for the manuscript "Metabolomic and Sphingolipidomic Profiling of Human Hepatoma Cells Exposed to Widely Used Pharmaceuticals"
<p>See experimental details on the main text of the manuscript "Metabolomic and Sphingolipidomic Profiling of Human Hepatoma Cells Exposed to Widely Used Pharmaceuticals" <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jpba.2024.116378" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.jpba.2024.116378</span></span></a></p> <h2><strong>Data description</strong></h2> <p>This study investigates the impact of three commonly used pharmaceuticals (amoxicillin, carbamazepine, and trazodone) on human liver cells. To mimic real-world conditions, liver cells were encapsulated in spheroids and exposed to various concentrations of these drugs for 24 hours. The study employs metabolomic and sphingolipid analyses to identify metabolic changes induced by drug exposure.</p> <div></div> <p></p> <div> <div> <div> <h3>LC-MS/MS Method for Sphingolipid Analysis</h3> <p>Targeted sphingolipid analysis was conducted using a Waters ACQUITY UPLC System coupled to a Waters Xevo TQ-S system equipped with an Electrospray Ion Source (ESI) and ScanWave™ collision cell technology, operating in positive mode [16]. Sphingolipids were quantified using a Zorbax Rapid Resolution RRHD C18 Column (80 Å, 1.8 µm, 2.1 mm × 100 mm).</p> <h3>LC-HRMS Method for Semi-Targeted Metabolomic Analysis</h3> <p>LC-HRMS analysis was performed on an Agilent 1290 Affinity II HPLC system coupled to an Agilent 6550 iFunnel QTOF mass spectrometer equipped with a dual AJS electrospray ionization source operating in both positive and negative modes. Polar metabolite screening was conducted using a SeQuant® ZIC®-pHILIC 5 µm polymer 100 × 2.1 mm column.</p> </div> </div> </div> <p> </p> <h2><strong>Funding</strong></h2> <p>The research leading to these results has received funding from the Spanish Ministry of Science and Innovation MCIN/AEI/ 10.13039/501100011033, Grants CTQ2017-82598-P and CEX2018-000794-S. The authors also want to grant support from the Catalan Agency for Management of University and Research Grants (AGAUR, Grant 2017SGR753). Miriam Pérez-Cova acknowledges a predoctoral FPU 16/02640 scholarship from the Spanish Ministry of Education and Vocational Training (MEFP), and Post-graduate department from CSIC for the funding of the research stay in Karolinska Institute, <em>via</em> the award to best outreach video in the YoInvestigoYosoyCsic contest, 2019 edition. </p>
Metabolome data from bovine preovulatory follicular fluid
<p class="MsoNormal">The intrafollicular milieu influences mammalian fertility by providing the microenvironment for oocyte growth and maturation. A number of studies have linked abundance of follicular fluid metabolites to oocyte developmental competence and pregnancy outcome. Few studies have interrogated the preovulatory follicular fluid metabolome in cattle. This dataset includes preovulatory follicular fluid metabolome profiles from non-lactating Jersey cows. Ultra-High-Performance Liquid Chromatography-High Resolution Mass Spectrometry was performed on preovulatory follicular fluid samples and Xcalibur (RAW) files were converted to an open-source mzML format (msconvert software; ProteoWizard package). The converted files were processed using the Metabolomic Analysis and Visualization Engine (MAVEN; mzroll software, Princeton University) to complete an untargeted analysis of the liquid chromatography mass spectrometry data. The pre-processed peak data tables generated by MAVEN are provided in this dataset. </p>
Disruption of fish gut microbiota composition and holobiont's metabolome during a simulated Microcystis aeruginosa (Cyanobacteria) bloom
<p>This archive contains the R code, QIIME2 script and datasets used to perform the analyses and figures in the manuscript.</p>
Data from: Serum metabolomics predicts treatment response in myasthenia gravis
<p>High-dose prednisone is the primary initial therapy for myasthenia gravis (MG) but upwards of a third of patients do not respond to treatment. No biomarkers can predict clinical responsiveness to corticosteroid treatment. We conducted a discovery-based study to identify treatment responsive biomarkers in MG using sera obtained at study entry to the thymectomy clinical trial (MGTX), an NIH-sponsored randomized, controlled study of thymectomy plus prednisone versus prednisone alone. We applied ultra-performance liquid chromatography coupled with electro-spray quadrupole time of flight mass spectrometry to obtain comparative serum metabolomic and lipidomic profiles at study entry to correlate with treatment response at 6 months. Treatment response was assessed using validated outcome measures of minimal manifestation status (MMS), MG-Activities of Daily Living (MG-ADL), Quantitative MG (QMG) score, or a strictly defined composite measure of response. Increased levels of phospholipids were associated with treatment response as assessed by QMG, MMS, and the Responders classification, but all measures showed limited overlap in metabolomic profiles, in particular the MG-ADL. A panel including histidine, free fatty acid (13:0), <span>γ-</span>cholestenol and guanosine was highly predictive of the strictly defined treatment response measure. The AUC in Responders' prediction for these markers was 0.90. Pathway analysis suggests that xenobiotic metabolism could play a major role in treatment resistance. We have defined a metabolomic and lipidomic profile that can now undergo validation as treatment predictive markers for MG patients undergoing corticosteroid therapy. Distinct metabolomic profiles were appreciated for each outcome measure.</p>
Saliva and plasma metabolome analysis in mares during anestrus, estrus cycle and early gestation for the identification of salivary biomarkers of reproductive stages.
<p>1H-NMR spectra of saliva and blood samples collected at seven physiological stages from six pony mares:</p> <ul> <li>in seasonal anestrus,</li> <li>in the follicular phase 3 days, 2 days and 1 day before ovulation, and the day when ovulation was detected,</li> <li>in the luteal phase 6 days after ovulation,</li> <li>in gestation 18 days after ovulation and artificial insemination.</li> </ul> <p> </p> <p>The 42 saliva samples were prepared for <sup>1</sup>H-NMR by precipitation of proteins using methanol. Briefly, 200 µL of saliva was mixed with 400 µL of cold methanol and vortexed. The mixtures were cooled at -20°C for 20 min before centrifugation at 15,000 × g for 15 min at 4°C. The supernatants were recovered and transferred to glass tubes for further evaporation of the solvent. For the 42 plasma samples, a modified Folch’s method (200 µL of plasma, 300 µL of cold methanol and 500 µL of cold chloroform) was preferred to extract metabolites and eliminate proteins and lipids from the plasmas in order to avoid the overlapping of the metabolites of interest with broad lipid and protein signals. The samples were vortexed during 1 min. The polar fractions, containing the metabolites, were separated after centrifugation at 15,000 × g for 15 min at 4°C and 300 µL of the samples were collected in glass tubes. The solvent of saliva supernatants and plasma polar fractions were evaporated in a SpeedVac (Thermo Fisher Scientific, Illkirch-Graffenstaden, France) for 2 h at 35°C followed by another 2 h at room temperature before storage at -20°C until analysis. Five quality control samples for the saliva and plasma experiments were prepared by pooling a fraction of each samples (either saliva or plasma) and were further processed with all the other samples.</p> <p>Before <sup>1</sup>H-NMR analyses, the dried residues were recovered in 210 μL phosphate buffer (pH 7.4, 200 mM) prepared in D<sub>2</sub>O supplemented with 152 µM (final concentration) of 3-trimethylsilylpropionic acid (TSP) as internal reference and transferred to 3 mm NMR tubes.</p> <p><sup>1</sup>H-NMR spectra of the saliva and plasma samples were acquired at 298K on an AVANCE III HD 600 MHz system (Bruker Biospin, Karlsruhe, Germany) equipped with a Bruker 5 mm TCI CryoProbe with Z-gradient. <sup>1</sup>H-NMR spectra were recorded with the «noesypr1d» pulse sequence with a relaxation delay of 20 s, on a sweep width of 12 ppm, 64 k data points, an acquisition time of 4.56 s, with 64 transients, and 8 dummy scans. Sample shimming was performed automatically on the D<sub>2</sub>O signal.</p>
Untargeted metabolomic approach using UHPLC-HRMS to unravel the impact of fermentation on color and phenolic composition of Rosé wines
<p>Color is a major quality trait of rosé wines due to their packaging in clear glass bottles. This color is due to the presence of phenolic pigments extracted from grapes to wines and products of reactions taking place during the wine-making process. This study focuses on changes occurring during alcoholic fermentation of Syrah, Grenache and Cinsault musts, conducted at laboratory (250 mL) and pilot (100 L) scales. Color and phenolic composition of the musts and wines were analyzed using UV-visible spectrophotometry and metabolomics fingerprints were acquired by Ultra-High Performance Liquid Chromatography−High Resolution Mass Spectrometry. The acquisition dataset protocol is available in the affiliated publication on Molecules</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.