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1,108 results for “Metabolomics”
Non-targeted metabolomics-based molecular networking enables the chemical characterization of Rumex sanguineus, a wild edible plant
<p>This dataset delves into the chemical composition of Rumex sanguineus employing non-targeted metabolomics and Feature-Based Molecular Networking (FBMN), with compound annotation facilitated by SIRIUS. Utilizing UHPLC-HRMS, we conducted comprehensive analyses on samples extracted from Rumex roots, stems, and leaves, further enhancing our insights through molecular networking.</p>
Exo-Metabolomics Data for "Phocaeicola vulgatus shapes the long-term growth dynamics and evolutionary adaptations of Clostridioides difficile"
<p>Exo-Metabolomics Data for "<em>Phocaeicola vulgatus</em> shapes the long-term growth dynamics and evolutionary adaptations of <em>Clostridioides difficile</em>"</p>
Raw data and supporting files for "Modular comparison of untargeted metabolomics processing steps"
<p>Raw data and supporting files for the Paper titled "Modular comparison of untargeted metabolomics processing steps". The dataset encompasses 42 samples, with 3 solvent blanks, 7 QC samples, and 32 biological samples (4 biological replicates: Banane, Bergrose, Narbe, Ricky) spiked with 42 compounds in different concentrations (0 ngmL, 30 ngmL, 100 ngmL, 300 ngmL). The files were uploaded in the vendor format (.raw) and in the open format (.mzML). Also, further supporting data for the processing results was uploaded as well.</p>
Title of Dataset: NMR metabolomic analysis of Drosophila head extracts: 2 genotypes (control, paraKO)
<p>Characterization of <em>para<sup>ko</sup></em> model in <em>Drosophila melanogaster</em> showed homeostasis disturbances as heat-induced phenotype, neuromuscular and cognitive alterations. Moreover, preliminary results during starvation assay revealed possibly differences in metabolism. To assess that NMR spectroscopy was made, comparing heads from young control and mutant flies.</p>
Local thermal environment and warming influence supercooling and drive widespread shifts in the metabolome of diapausing Pieris rapae butterflies
<p>Global climate change has the potential to negatively impact biological systems as organisms are exposed to novel temperature regimes. Increases in annual mean temperature have been accompanied by disproportionate rates of change in temperature across seasons, and winter is the season warming most rapidly. Yet, we know relatively little about how warming will alter the physiology of overwintering organisms. Here, we simulated future warming conditions by comparing diapausing <i>Pieris rapae</i> butterfly pupae collected from disparate thermal environments and by exposing <i>P. rapae </i>pupae to acute and chronic increases in temperature. First, we compared internal freezing temperatures (supercooling points) of diapausing pupae that were developed in common-garden conditions but whose parents were collected from northern Vermont, USA, or North Carolina, USA. Matching the warmer winter climate of North Carolina, North Carolina pupae had significantly higher supercooling points than Vermont pupae. Next, we measured the effects of acute and chronic warming exposure in Vermont pupae and found that warming induced higher supercooling points. We further characterized the effects of chronic warming by profiling the metabolomes of Vermont pupae via untargeted LC-MS metabolomics. Warming caused significant changes in abundance of hundreds of metabolites across the metabolome. Notably, there were warming-induced shifts in key biochemical pathways, such as pyruvate metabolism, fructose and mannose metabolism, and β-alanine metabolism, suggesting shifts in energy metabolism and cryoprotection. These results suggest that warming affects various aspects of overwintering physiology in <i>P. rapae</i> and may be detrimental depending on the frequency and variation of winter warming events. Further research is needed to ascertain the extent to which the effects of warming are felt among a broader set of populations of <i>P. rapae</i>,<i> </i>and among other species, in order to better predict how insects may respond to changes in winter thermal environments.</p>
A novel UPLC-MS metabolomic analysis-based strategy to monitor the course and extent of iPSC differentiation to hepatocytes
<p>ms2 raw data, peak tables generated in Quantitative Analysis Software from Agilent and Matlab functions for QC-SVRC, data clean-up and analysis for the publication with title "Monitoring the differentiation of iPSC to hepatocytes by means of UPLC-MS metabolomics".</p>
Mechanistic study on direct and indirect thyroid toxicity in male Wistar rats - Serum Metabolomics
<p>The present oral toxicity study in male Wistar rats is focusing on direct and indirect thyroid<br> toxicity. Biomaterials from this study were used to generate transcriptomics, proteomics,<br> and metabolomics data sets. Featuring a time- and concentration-resolved design<br> including recovery after treatment, this study allows investigating adaptive versus adverse<br> effects in several dimensions.<br> Two well-described test chemicals, i.e., Phenytoin and Propylthiouracil, were administered<br> at each two dose levels to male Wistar rats for 2 (Subset A) and 4 weeks via the diet<br> (Subset B). In addition, a recovery period of 2 weeks without test substance application was<br> included, in order to monitor reversibility of the induced effects (Subset C).<br> During the course of the study, blood samples were taken for metabolome analyses.</p>
Comparative proteomic and metabolomic analyses of plasma reveal the novel biomarker panels for thyroid dysfunction
<p><strong>Abstract</strong><strong>:</strong></p> <p><em>Objectives: </em>Thyroid dysfunction such as hypothyroidism (THO) and hyperthyroidism (THE) are the disease caused by pathological processes in the thyroid. The current diagnosis of thyroid dysfunction is variable because of ages and genders. The aim of this study was to explore the novel candidate biomarker panels for hypothyroidism and hyperthyroidism screening with mass spectrometry and bioinformatics.</p> <p><em>Methods:</em> Plasma samples were collected from 15 THE patients, 9 THO patients, and 15 healthy controls. DIA-based proteomic and untargeted metabolomic analyses were performed to identify the novel biomarker panels for THO and THE. Finally, three candidate biomarkers were verified by ELISA in 34 samples.</p> <p><em>Results:</em> A total of 2738 proteins and 6103 metabolites were identified, and 173 proteins and 2487 metabolites were found to be differentially expressed among THE, THO and control groups. The results of the ensemble feature selection, K-means clustering and the least absolute shrinkage and selection operator (LASSO) regression model showed that four proteins (C4A, C3/C5 convertase, APOL1, and ITIH4) and four metabolites (L-arginine, L-proline, cortisol, and cortisone) identified by plasma proteomics and metabolomics could help distinguish THO and THE patients from healthy controls.</p> <p><em>Conclusions:</em> This study identified and verified two pairs of biomarker panels that can distinguish the THE and THO patients regardless of ages and genders. Consequently, our findings represent a comprehensive analyses of thyroid dysfunction plasma, which is significant for the clinical diagnosis.</p> <p> </p>
SUPEREGO urinary metabolomics
<p>Urinary metabolomics liquid-chromatography coupled to mass spectrometry data of military personnel with normal weight, overweight, or obese. The dataset contains supplemental material for the data analysis (KNIME workflow, R script) and results from files from MetaboAnalyst/ Mummichog analyses.</p>
Metabolomics-based phenotypic screens for evaluation of drug synergy via DIMS
<p>Drugs used in combination can synergize to increase efficacy, decrease toxicity, and prevent drug resistance. While conventional high-throughput screens relied on univariate data are incredibly valuable to identify promising drug candidates, phenotypic screening methodologies could be beneficial to provide deep insight into the molecular response of drug combination with a likelihood of improved clinical outcomes. We developed a high-content metabolomics drug screening platform using stable isotope tracer direct infusion mass spectrometry that informs a novel algorithm to determine synergy from multivariate phenomics data. Using a cancer drug library, we validated the drug screening integrating isotope enriched metabolomics data and computational data mining on a panel of prostate cell lines and verified the synergy between CB-839 and docetaxel both in vitro (three-dimensional model) and in vivo. The proposed unbiased metabolomics screening platform can be used to rapidly generate phenotype-informed datasets and quantify synergy for combinatorial drug discovery. Drugs used in combination can synergize to increase efficacy, decrease toxicity, and prevent drug resistance. While conventional high-throughput screens relied on univariate data are incredibly valuable to identify promising drug candidates, phenotypic screening methodologies could be beneficial to provide deep insight into the molecular response of drug combination with a likelihood of improved clinical outcomes. We developed a high-content metabolomics drug screening platform using stable isotope tracer direct infusion mass spectrometry that informs a novel algorithm to determine synergy from multivariate phenomics data. Using a cancer drug library, we validated the drug screening integrating isotope enriched metabolomics data and computational data mining on a panel of prostate cell lines and verified the synergy between CB-839 and docetaxel both in vitro (three-dimensional model) and in vivo. The proposed unbiased metabolomics screening platform can be used to rapidly generate phenotype-informed datasets and quantify synergy for combinatorial drug discovery.</p>
Untargeted metabolomics data for the publication Weiss et al. 2022 "In vitro interaction network of a synthetic gut bacterial community"
<p>This dataset contains the untargeted metabolomics data for the publication Weiss et al. 2022 "In vitro interaction network of a synthetic gut bacterial community". The dataset has also been submitted to MetaboLights repository with ID "MTBLS3535". Please refer to the MetaboLights repository for the most up-to-date datasets. </p> <p>Publication abstract:</p> <p>A key challenge in microbiome research is to predict the functionality of microbial communities based on community membership and (meta)-genomic data. As central microbiota functions are determined by bacterial community networks, it is important to gain insight into the principles that govern bacteria-bacteria interactions. Here, we focused on the growth and metabolic interactions of the Oligo-Mouse-Microbiota (OMM<sup>12</sup>) synthetic bacterial community, which is increasingly used as a model system in gut microbiome research. Using a bottom-up approach, we uncovered the directionality of strain-strain interactions in mono- and pairwise co-culture experiments as well as in community batch culture. Metabolic network reconstruction in combination with metabolomics analysis of bacterial culture supernatants provided insights into the metabolic potential and activity of the individual community members. Thereby, we could show that the OMM<sup>12</sup> interaction network is shaped by both exploitative and interference competition in vitro in nutrient-rich culture media and demonstrate how community structure can be shifted by changing the nutritional environment. In particular, <em>Enterococcus faecalis</em> KB1 was identified as an important driver of community composition by affecting the abundance of several other consortium members in vitro. As a result, this study gives fundamental insight into key drivers and mechanistic basis of the OMM<sup>12</sup> interaction network in vitro, which serves as a knowledge base for future mechanistic in vivo studies.</p>
Metabolomics data for HS fed flies containing a w1118 background and CG4625 knockdown via RNAi
<p>Full metabolomics of normalized peak height for fat body tissue from w1118 background and <em>CG4625</em> knockdown (via RNAi) flies fed a high-sugar diet for three weeks.</p>
Data from: Single, but not dual, attack by a biotrophic pathogen and sap-sucking insect affects the oak leaf metabolome
<p>Plants interact with a multitude of microorganisms and insects, both belowand above ground, which might influence plant metabolism. Despite this, we lack knowledge of the impact of natural soil communities and multiple aboveground attackers on the metabolic responses of plants, and whether plant metabolic responses to single attack can predict responses to dual attack. We used untargeted metabolic fingerprinting (gas chromatographymass spectrometry, GC-MS) on leaves of the pedunculate oak, <em>Quercus robur</em>, to assess the metabolic response to different soil microbiomes and aboveground single and dual attack by oak powdery mildew (<em>Erysiphe alphitoides</em>) and the common oak aphid (<em>Tuberculatus annulatus</em>). Distinct soil microbiomes were not associated with differences in the metabolic profile of oak seedling leaves. Single attacks by aphids or mildew had pronounced but different effects on the oak leaf metabolome, but we detected no difference between the metabolomes of healthy seedlings and seedlings attacked by both aphids and powdery mildew. Our findings show that aboveground attackers can have species-specific and non-additive effects on the leaf metabolome of oak. The lack of a metabolic signature detected by GC-MS upon dual attack might suggest the existence of a potential negative feedback, and highlights the importance of considering the impacts of multiple attackers to gain mechanistic insights into the ecology and evolution of species interactions and the structure of plant-associated communities, as well as for the development of sustainable strategies to control agricultural pests and diseases and plant breeding.</p>
NMR_raw_data for "Native Metabolomics Identifies the Rivulariapeptolide Family of Protease Inhibitors"
<p>Raw 1D/2D NMR data and chemical structures for compounds <strong>1 </strong>- <strong>6</strong> of the study "Native Metabolomics Identifies the Rivulariapeptolide Family of Protease Inhibitors".</p>
Metabolomics Library Toxicology Centre (BE)
<p>NIST format metabolite library (.msp) with multidimensional information (retention time values, MS/MS spectra and collision cross section (CCS) values) for endogenous metabolites.</p> <p>NP (Non-polar metabolites)/NEG (Electrospray Negative Mode)/POS (Electrospray Positive Mode)</p>
Proccessed Data for the Pipelines of the Project "Multiomics and quantitative modelling disentangle diet, host, and microbiota contributions to the host metabolome"
<p><strong>Proccessed and Input Data for the Pipelines of the Project "Multiomics and quantitative modelling disentangle diet, host, and microbiota contributions to the host metabolome"</strong></p> <p>-----------------------------------------------------------------------------------------------------</p> <p>Contents:</p> <p>-----------------------------------------------------------------------------------------------------</p> <p>Folder /ProcessedData/metabolomics/ contains processed metabolomics data from the project:</p> <p>/metabolomics/metabolites_allions_combined_norm_intensity.csv - file containing normalized intensities of ions detected across tissues with six measurement methods.<br> /metabolomics/metabolites_allions_combined_formulas_with_metabolite_filters_spatial100clusters_with_mean.csv - file containing metabolite attribution to spatial clusters and mean intensity values across tissues and conditions.</p> <p>Other files are described in README_ProcessedData.md.</p> <p>-----------------------------------------------------------------------------------------------------</p> <p>Folder /ProcessedData/sequencing/ contains raw and normalized counts of metagenomics and metatransriptomics data mapped to bacterial genomes.</p> <p>Folder /ProccessedData/util/ contains files used for data preprocessing and attribution to chemical classes and pathways.</p> <p>Folder /ProcessedData/example_output/ contains example output of the pipelines:</p> <p>/output/model_results_SMOOTH_raw_2LIcoefHost1LIcoefbact_allions.csv - file containing estimated model parameters (intestinal flux and metabolic flux values) for the forward problem for metabolomics measurements in the GIT.<br> /output/model_results_SMOOTH_normbyabsmax_reciprocal_problem_allions.csv - file containing estimated model parameters for the reverse problem (metabolite intensities) for the parameters estimated with the forward problem.<br> /output/model_results_SMOOTH_normbyabsmax_2LIcoefHost1LIcoefbact_allions.csv - file containing estimated model parameters (intestinal flux and metabolic flux values) for the forward problem for metabolomics measurements in the GIT, normalized by absolute maximum value.<br> /output/model_results_SMOOTH_normbyabsmax_ONLYMETCOEF_2LIcoefHost1LIcoefbact_allions.csv - file containing estimated model parameters (only metabolic flux values) for the forward problem for metabolomics measurements in the GIT, normalized by absolute maximum value.<br> /output/table_hierarchical_clustering_groups.csv - file containing attribution of the annotated metabolites to groups according to hierarchical clustering of the normalized model parameters.<br> /output/cgo_clustergrams_of_model_coefficients.mat - matlab object containing clustergram of the normalized model parameters and manually derived sub-clustergrams corresponding to different largest parameter values.</p> <p>Description of other files is provided in the file README_ProcessedData.md.</p> <p>-----------------------------------------------------------------------------------------------------</p> <p>Folder /InputData/ contains HMDB and KEGG tables used for metabolite annotations and chemical group analysis.</p> <p>Folder InputData_KEGGreaction_path contains matlab files with metabolite-metabolite paths calculated from KEGG reaction-pair information (Each matrix contains a subset of paths). These files are used by the script workflow_extract_keggECpathes_for_SPpairs_final.m.</p> <p>Folder InputData_metabolomics_data contains raw metabolomics data from six methods (three LC columns: C08, C18 and HILIC, and positive and negative acquisition modes) and file tissue_weights.txt with tissue weight information used for normalization.</p> <p>Folder InputData_sequencing_data contains folders ballgown_DNA and ballgown_RNA with results of metagenomic and metatranscriptomic data analysis (raw counts, GetMM normalized counts, EdgeR and DeSeq2 analysis). </p> <p>Description of folders is provided in the file readme_InputData.md.</p> <p>-----------------------------------------------------------------------------------------------------</p>
Impact of maternal obesity on the gestational metabolome and infant metabolome, brain, and behavioral development in rhesus macaques
<p>Maternal gestational obesity is associated with elevated risks for neurodevelopmental disorder, including autism spectrum disorder. However, the mechanisms by which maternal adiposity influences fetal developmental programming remain to be elucidated. We aimed to understand the impact of maternal obesity on the metabolism of both pregnant mothers and their offspring, as well as on metabolic, brain, and behavioral development of offspring by utilizing metabolomics, protein, and behavioral assays in a non-human primate model. We found that maternal obesity was associated with elevated inflammation and significant alterations in metabolites of energy metabolism and one-carbon metabolism in maternal plasma and urine, as well as in placenta. Infants born to obese mothers were significantly larger at birth compared to those born to lean mothers. Additionally, they exhibited significantly reduced novelty preference and significant alterations in their emotional response to stress situations. These changes coincided with differences in phosphorylation of enzymes in the brain mTOR signaling pathway between infants born to obese and lean mothers and correlated with the concentration of maternal plasma betaine during pregnancy. In summary, gestational obesity significantly impacted the infant systemic and brain metabolome and adaptive behaviors.</p>
Urine NMR metabolomics for precision oncology in colorectal cancer
<p>Tables summarizing the data used for the review. Up to 7 tables, and a list of the included studies is provided.</p>
Gut metabolome and cytokine profiling of MSM prior to and following HIV seroconversion
<p>Alterations in the gut microbiome have been associated with HIV infection, but the relative impact of HIV versus other factors on the gut microbiome has been difficult to determine in cross-sectional studies. To address this, we examined the gut microbiome, serum metabolome, and cytokines longitudinally within individuals before and after HIV seroconversion. We identified few changes in the microbiome following HIV infection, but greater differences including decreased Bacteroides were seen between pre-HIV infection visits compared to matched controls. Those who acquired HIV also had elevated inflammatory cytokines and bioactive lipids prior to HIV acquisition compared to matched controls. Following HIV seroconversion, alterations in serum metabolites involving secondary bile acid metabolism and amino acid metabolism were observed. These data highlight the importance of understanding the role of the microbiome in HIV susceptibility.</p>
Annotation of Metabolites in Stable Isotope Tracing Untargeted Metabolomics via Khipu-web
<p>This is the data and analysis scripts needed to recreate the analyses shown in "Annotation of Metabolites in Stable Isotope Tracing Untargeted Metabolomics via Khipu-web"</p> <p>The abstract of the manuscript summarizes the goal of the paper:</p> <p> </p> <p>Stable isotope tracing is a crucial technique for understanding the metabolic wiring of biological systems, determining metabolic flux through pathways of interest, and detecting novel metabolites and pathways. Despite the potential insights provided by this technique, its application remains limited to a small number of targeted molecules and pathways. Because previous software tools usually require chemical formulas to find relevant features, and the data are highly complex, especially in untargeted metabolomics and when the reactions and metabolites downstream the labeled substrates are poorly characterized. We report here Khipu version 2 and its new user-friendly web application. New functions are added to enhance analyzing stable isotope tracing data including metrics that evaluate peak enrichment in labeled samples, scoring methods to facilitate robust detection of intensity patterns and integrated natural abundance correction. We demonstrate that this approach can be applied to untargeted metabolomics to systematically extract isotope-labeled compounds and annotate the unidentified metabolites.</p> <p> </p> <p>This repository stores the code and data needed to recreate all presented analyses. The code and instructions are in the AnalysisCode.zip. The DDA in the dda_mzML.zip, the MS1 in the dataset_mzml.zip, and the asari results in the AsariResults.zip. The readme is in the AnalysisCode.zip and has more detailed instructions. This directory also has the output data in tabular format for the figures as the figures were mostly made with Excel. </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.