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8 results for “LC MS Metabolomics”
Fingerprint Matrix Files for "Machine Learning-based Bioactivity Classification of Natural Products Using LC-MS/MS Metabolomics"
<p>These files are the necessary dataset to reproduce the observed machine learning metrics in the paper "Machine Learning-based Bioactivity Classification of Natural Products Using LC-MS/MS Metabolomics" in review at the Journal of Natural Products. </p> <ul> <li>Multiclassifier_23_Drug_Class_Train-Test_Fingerprint_Matrix.tsv is the accumulated positive training set for the 23 different classes demonstrated in the training and testing sets.</li> <li>Negative_Train-Test_Fingerprint_Matrix.tsv is the negatives training and testing examples derived from the RIKEN NP Depo which represent a diverse set of natural product compounds that serve as the counter points to the positive examples.</li> <li>GNPS_23_Drug_Class_Fingerprints_Matrix.tsv is the dataset of fingerprints generated from the publically available GNPS MSMS dataset. These training examples serve to confirm the ability of the machine learning model to generalize to experimental data. </li> <li> Negative_Train-Test_Fingerprint_Matrix.tsv is the dataset of negative training examples derived from the publically available spectra from the GNPS dataset. It is composed of nearly 2,800 random MSMS spectra to compose a diverse negative evaluation set. </li> <li>Random_GNPS_Fingerprints.tsv is the dataset of fingeprints of 9,443 random spectra from GNPS used to evaluate the false positive rate of each model.</li> </ul>
Untargeted LC–MS metabolomics reveals an adverse effect of high-fat diet on hepatic metabolism of Oreochromis niloticus
<p>Hepatic steatosis commonly occurs in intensively farmed tilapia. This disease is harmful to fish growth and health, but knowledge of the metabolic changes in tilapia with fatty liver is limited. In the present study, we compared genetically improved farmed tilapia (GIFT, <em>Oreochromis niloticus</em>) fed a high-fat diet (HFD) with those fed a normal-fat diet (NFD) for 8 weeks using LC-MS-based hepatic metabolomic assays and traditional nutritional assessments. Juvenile GIFT fed a HFD displayed higher fat disposition in the liver than did those fed a NFD. The metabolomic analyses revealed 61 differentially accumulated metabolites between the groups, and these metabolites were involved in 37 signaling pathways. Our results illustrate the development of metabolic disorders related to various hepatic biological processes, including protein metabolism, lipid metabolism, carbohydrate metabolism, and nucleotide metabolism in HFD-fed GIFT. These physiological changes may be related to the lower growth rate of GIFT. Overall, our study reveals the metabolic disorders in GIFT fed HFD, and enhance our knowledge of the mechanism of fatty liver formation in GIFT.</p>
Lipidomics and metabolomics datasets for "Adverse effects of arsenic uptake in rice metabolome and lipidome revealed by untargeted liquid chromatography coupled to mass spectrometry (LC-MS) and regions of interest multivariate curve resolution"
<p><strong>Files description</strong></p> <p>Raw files for lipidomics and metabolics studies on the impact of arsenic exposure on rice growth.</p> <p>File details on the worksheets lipids_files.xlsx and metabolomics_files.xlsx</p> <p>Files have been organized as follows:</p> <p><strong>Lipidomics</strong></p> <blockquote> <p>1) Control samples: lip_controls.rar<br> 2) Watering low As exposure: lip_water_1.rar<br> 3) Watering high As exposure: lip_water_1000.rar<br> 4) Soil low As exposure: lip_soil_5.rar<br> 5) Soil high As exposure: lip_soil_50.rar<br> 6) QC samples: lip_qcs.rar</p> </blockquote> <p><strong>Metabolomics (positive ionization mode)</strong></p> <blockquote> <p>1) Control samples: met_pos_controls.rar<br> 2) Watering low As exposure: met_pos_water_1.rar<br> 3) Watering high As exposure: met_pos_water_1000.rar<br> 4) Soil low As exposure: met_pos_soil_5.rar<br> 5) Soil high As exposure: met_pos_soil_50.rar<br> 6) QC samples: met_pos_qcs.rar</p> </blockquote> <p><strong>Metabolomics (negative ionization mode)</strong></p> <blockquote> <p>1) Control samples: met_neg_controls.rar<br> 2) Watering low As exposure: met_neg_water_1.rar<br> 3) Watering high As exposure: met_neg_water_1000.rar<br> 4) Soil low As exposure: met_neg_soil_5.rar<br> 5) Soil high As exposure: met_neg_soil_50.rar<br> 6) QC samples: met_neg_qcs.rar<br> </p> </blockquote> <p> </p> <p><strong>Experimental details</strong></p> <blockquote> <p><strong>Arsenic Exposure</strong></p> <p>Arsenic was supplied through two main routes: watering with contaminated water or soil containing arsenic. In addition, this new study includes metabolomic as well as lipidomic analysis, in order to have a more global overview of arsenic exposure.</p> <p>For the watering treatment, during the first 11 days, rice was irrigated with Milli-Q water. From that day until harvesting, plants were watered with 1 and 1000 μM of As (V) for the two concentration levels of exposure, and with Milli-Q water for control samples. The lowest concentration was established at 1 μM as it is the limit of the acceptable arsenic concentration in water by European legislation. The upper concentration was set at 1000 μM, a threshold established to ensure that the experiment was performed under sub-lethal arsenic concentration for the plant, based on previous studies.</p> <p>For the soil treatment, two containers were prepared with 1 kg of soil two days before planting. Soil from the container was exposed to two arsenic concentration levels (5 and 50 mg L<sup>-1</sup>). Once sowing, rice was irrigated the whole growth period with a solution containing 0.001 μM of As (V). The lowest arsenic limit in this treatment was set at 5 mg L<sup>-1</sup> as a maximum value of common arsenic leaches without toxic characteristics, although background soil content of arsenic varies between one and 40 ppm according to the US food and drug administration (FDA) report. The highest arsenic limit was established to 50 mg L<sup>-1</sup>, as a considerably high arsenic content in the soil, slightly above the maximum frequently encountered levels.</p> <p><strong>Lipidomic Analysis</strong></p> <p>The lipidomic analysis was performed using a Waters Acquity UPLC system (Waters Corporation, MA, USA), connected to a Waters LCT Premier orthogonal accelerated time of flight mass spectrometer (Waters), operated in both positive and negative electrospray (ESI) ionization modes. Full scan spectra were acquired from 50 to 1500 Da.</p> <p>The chromatographic column employed was a Kinetex C8 (100 x 2.1 mm, 1.7 μm) (Phenomenex) under the following conditions (already used in [47]): temperature at 30˚C, injection volume at 10 μL, and flow rate at 0.3 mL min<sup>-1</sup>. Mobile phases selected were (A) MeOH 1mM ammonium formate, and (B) H<sub>2</sub>O 2mM ammonium formate, both at 0.2% formic acid. The gradient started at 80% A, increased to 90% A in 3 min, from 3 to 6 min remained at 90% A, changed to 99 % A until minute 15, remained constant 1 min, and returned to initial conditions until minute 20.</p> <p><strong>Metabolomic analysis</strong></p> <p>The metabolomic analysis was performed using a Waters Acquity UPLC system connected to a Q-Exactive (Thermo Fisher Scientific, Hemel Hempstead, UK) equipped with a quadrupole-Orbitrap mass analyzer. Electrospray (ESI) was used as an ionization source in both positive and negative ion modes. Full scan mass range was set from <em>m/z</em> 90 to 1000, and all ion fragmentation (AIF) was performed with normalized collision energy (NCE) of 35 eV.</p> <p>The column employed was an HILIC TSK gel amide-80 column (250 x 2.0 mm i.d., 5 μm) provided by Tosoh Bioscience (Tokyo, Japan), under the following experimental conditions (already employed in [45]): flow rate at 0.15 mL min<sup>-1</sup>, at room temperature, and 5 μL injection volume. Mobile phases were (A) AcN, and (B) 5 mM ammonium acetate, adjusted at pH 5.5 with acetic acid. The gradient employed was: starting conditions at 25% B, then increased until 30% B in 8 min; a 60% B was reached at 10 min, held for 2 min more and then back to 25% B until minute 14 min; lastly, a re-equilibration step was added and from 14 to 20 min at 25% B.</p> </blockquote> <p> </p> <p><strong>Funding:</strong> This research was funded by the Spanish Ministry of Science and Innovation (MCI, Grant CTQ2017-82598-P) and Severo Ochoa Project CEX2018-000794-S (funded by MCIN/AEI/ 10.13039/501100011033), and supported from the Catalan Agency for Management of University and Research Grants (AGAUR, Grant 2017SGR753). MPC was funded by a predoctoral FPU 16/02640 scholarship from the Spanish Ministry of Education and Vocational Training (MEFP). </p> <p> </p>
G-Aligner: a graph-based feature alignment method for untargeted LC-MS-based metabolomics
<p>Benchmark datasets, manual annotation results, evaluation methods and results of the paper "G-Aligner: a graph-based feature alignment method for untargeted LC-MS-based metabolomics".</p>
Untargeted LC–MS metabolomics reveals an adverse effect of high-fat diet on hepatic metabolism of Oreochromis niloticus
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2D-LC-MS/MS based non-targeted metabolomics of marine DOM (.raw files)
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2D-LC-MS/MS based non-targeted metabolomics of marine DOM (.mzML files)
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Transcriptomic (DNA microarray) and Metabolome (LC-TOF-MS) analyses of the liver in high-fat diet mice after intranasal administration of GALP (Galanin-Like Peptide)
GEO Series GSE243376. Mus musculus. 4 samples. Type: Expression profiling by array.
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