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1,168 results for “metabolites”
S81 | THSTPS | Thirdhand Smoke Specific Metabolites
<p>This is the collection associated with list S81 THSTPS Thirdhand Smoke Specific Metabolites on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>Data compiled by Carla Merino and colleagues, URV, Spain.</p> <p> </p> <p>v0.1.1: fixed formatting issues in CSV files (spacing around CIDs, name errors, char errors in refs)</p>
Differential response of α-synuclein expression to bacterial ligands and metabolites in mouse enteroendocrine cells
<p>Dataset for manuscript <em>"<strong> </strong></em><strong>α</strong><strong>-synuclein expression in response to bacterial ligands and metabolites in gut enteroendocrine cells</strong><em>". </em>Tabs in excel file are title with the figure number. </p>
Raw data: Specialized metabolites accumulation pattern in buckwheat is strongly influenced by accession choice and co-existing weeds
<p>Screening suitable allelopathic crops and crop genotypes that are competitive with weeds can be a sustainable weed control strategy to reduce the massive use of herbicides. In this study, three accessions of common buckwheat <em>Fagopyrum esculentum</em> Moench. (Gema, Kora, and Eva) and one of Tartary buckwheat <em>Fagopyrum tataricum</em> Gaertn. (PI481671) were screened against the germination and growth of the herbicide-resistant weeds <em>Lolium rigidum </em>Gaud. and <em>Portulaca oleracea</em> L. The chemical profile of the four buckwheat accessions was characterised in their shoots, roots, and root exudates in order to know more about their ability to sustainably manage weeds and the relation of this ability with the polyphenol accumulation and exudation from buckwheat plants. Our results show that different buckwheat genotypes may have different capacities to produce and exude several types of specialized metabolites, which lead to a wide range of allelopathic and defence functions in the agroecosystem to sustainably manage the growing weeds in their vicinity. The ability of the different buckwheat accessions to suppress weeds was accession-dependent without differences between species, as the common (Eva, Gema, and Kora) and Tartary (PI481671) accessions did not show any species-dependent pattern in their ability to control the germination and growth of the target weeds. Finally, Gema appeared to be the most promising accession to be evaluated in organic farming due to its capacity to sustainably control target weeds while stimulating the root growth of buckwheat plants.</p>
Fecal and Blood Metabolites of Pigs
<p>The experimental design of the animal study and the sanitary challenge model used have been described by Van der Meer et al. (2020). Pigs were divided into high sanitary condition (HSC) or to low sanitary condition (LSC), for details please see the original publication by Van der Meer et al. (2020). At the dissection day, three pigs per room were euthanized to collect blood and digesta samples for further analysis. We used colon digesta and blood samples from pigs in this study that received a diet with a basal amino acid (AA) ratios (indicated as “diet AA-B” in the paper of van der Meer et al. 2020) and a protein content of CP 168 g/kg; LSC (n=18) and HSC (n=18). </p> <p>These samples were analyzed by Nuclear Magnetic Resonance (NMR) and by Triple Quad Mass Spectrometry (TQMS). The details of these laboratory analysis are described in the journal article entitled "Sanitary conditions affect the colonic microbiome and the colonic and systemic metabolome of female pigs" (doi: will update accordingly). The data uploaded here are by the format of the Joint Committee on Atomic and Molecular Physical Data (JCAMP). Moreover the metadata file shows the link between the samples and their corresponding group, i.e. HSC or LSC, and other characteristics.</p>
Data used in "Fast metabolite identification with Input Output Kernel Regression"
<p>This repository contains the data used in [1] to evaluate the performance for metabolite identification from tandem mass spectra. These data have been extracted and processed in [2]. We used a subset of 4138 MS/MS spectra extracted from the GNPS public spectral library (https://gnps.ucsd.edu/ProteoSAFe/libraries.jsp) for training and evaluation. For searching, we used molecular structures from PubChem as candidate sets.</p> <p>Please mention and cite GNPS when using these data.</p> <p>The implementation of the method proposed in [1] is available on: https://version.aalto.fi/gitlab/kepaco/Fast-metabolite-identification-with-IOKR</p> <p><strong>Files description:</strong></p> <ul> <li><em>spectra.txt</em>: informations about the MS/MS spectra (GNPS identifier, compound name and INCHI identifier)</li> <li><em>data_GNPS.mat</em>: contains the molecular fingerprints, molecular formula and InCHI corresponding to the MS/MS spectra</li> <li><em>cv_ind.txt</em>: indices of the cross-validation folds</li> <li><em>ind_eval.txt</em>: indices of the examples used for evaluation</li> <li><em>candidates</em>: fingerprints and INCHI for the different candidate sets</li> <li><em>input_kernels</em>: contains 24 input kernel matrices</li> </ul> <p><strong>References:</strong></p> <p>[1] Brouard, C., Shen, H., Dührkop, K., d'Alché-Buc, F., Böcker, S. and Rousu, J.: Fast metabolite identification with Input Output Kernel Regression. In the proceedings of ISMB 2016, Bioinformatics 32(12): i28-i36, 2016. DOI: https://doi.org/10.1093/bioinformatics/btw246</p> <p>[2] Dührkop, K., Shen, H., Meusel, M., Rousu, J. and Böcker, S.: Searching molecular structure databases with tandem mass spectra using CSI:FingerID. PNAS, 112(41), 12580-12585, 2015. doi:10.1073/pnas.1509788112</p> <p> </p> <p> </p>
Dataset related to the manuscript entitled "Inhalation with Vitamin D3 Metabolites - A Novel Strategy to Restore Vitamin D3 Deficiencies in Lung Tissue"
<p><strong>The dataset contains the results presented in the manuscript entitled "Inhalation with Vitamin D3 Metabolites - A Novel Strategy to Restore Vitamin D3 Deficiencies in Lung Tissue" Appl. Sci. 2023, 13, 10672. </strong></p><p> </p><p>The presented dataset contains the results of the implementation of the following research project: </p><p><i>Project title</i><strong>:</strong> "Assessment of the possibility of using vitamin D3 in the prevention and treatment of pulmonary fibrosis in the course of hypersensitivity pneumonitis - <i>in vivo</i> studies"</p><p><i>Founder</i><strong>: </strong>National Scientific Center, Poland</p><p><i>Project number</i><strong>:</strong> UMO-2020/38/E/NZ7/00366</p><p><i>Principal Investigator</i>: Marta Kinga Lemieszek</p><p><i>Main investigators:</i><strong> </strong>Michał Chojnacki, Jakub Anisiewicz, Ilona Leśniowska</p><p><i>Place of project implementation</i><strong>:</strong> Department of Medical Biology, Institute of Rural Health, Jaczewskiego 2, 20-090 Lublin, Poland</p>
Insights into intraspecific diversity of central carbon metabolites in Saccharomyces cerevisiae during wine fermentation
<p>Supplementary data including the data set used for the " Insights into intraspecific diversity of central carbon metabolites in <em>Saccharomyces cerevisiae</em> during wine fermentation" publication.</p> <p>Abstract:</p> <p><em>Saccharomyces cerevisiae</em>, as the workhorse of alcoholic fermentation, is a major actor in winemaking. In this context, this yeast species performs alcoholic fermentation to convert sugars from the grape must into ethanol and CO2 with outstanding efficiency as it reaches on average 92% of the maximum theoretical yield of conversion. Primary metabolites produced during fermentation have a great importance in wine where they significantly impact wine characteristics. While ethanol content contributes to the overall profile, others metabolites also have significant impacts, even when present in lower concentrations: glycerol, succinate, acetate, ⍺-ketoglutarate, lactate… <em>S. cerevisiae</em> is known for its great genetic diversity and plasticity that is directly related to its living environment, natural or technological and therefore to domestication. This leads to a wide phenotypic diversity of metabolites production. However, the range of metabolic diversity is variable and depends on the pathway considered. With the aim to improve wine quality, the selection, development and use of strains with dedicated metabolites production without genetic modifications can rely on the already existing natural diversity. Here we detail a screening experiment that aims to assess the diversity of primary metabolites production in a set of 51 <em>S. cerevisiae</em> strains from various genetic backgrounds (wine, flor, rum, West African, sake…). To approximate winemaking conditions, we used a synthetic grape must as fermentation medium and measured seven metabolites by HPLC. Results pointed out great yield differences between strains depending on the metabolite considered. Ethanol appeared as the one with the smallest variation among our set of strains, although it was by far the most produced. A clear negative correlation between ethanol and glycerol was observed, confirming glycerol synthesis as a suitable lever to reduce ethanol yield. Genetic groups were linked to specific metabolic yields such as high α-ketoglutarate and low acetate yields for wine strains. This study thus helps to characterise the phenotypic diversity of <em>S. cerevisiae</em> in a wine-like context and comforts the use of natural diversity in the development of new strains. Finally, it provides a detailed data set usable to study diversity of well known (ethanol, glycerol, acetate) or little-known (lactate) primary metabolites production, including in common commercial wine strains.</p> <p> </p> <p> </p>
Traditional medicinal use is linked with apparency, not specialized metabolite profiles in the order Caryophyllales
<p>A better understanding of the relationship between plant specialized metabolism and traditional medicine has the potential to aid in bioprospecting and untangling of cross-cultural use patterns. However, given the limited information available for metabolites in most plant species, understanding medicinal use-metabolite relationships can be difficult. The order Caryophyllales has a unique pattern of lineages of tyrosine- or phenylalanine-dominated specialized metabolism, represented by mutually exclusive anthocyanin and betalain pigments, making Caryophyllales a compelling system to explore the relationship between medicine and metabolites by using pigment as a proxy for tyrosine- vs phenylalanine-dominant metabolism. We compiled a list of medicinal species in select tyrosine- or phenylalanine-dominant families of Caryophyllales (Nepenthaceae, Polygonaceae, Simmondsiaceae, Microteaceae, Caryophyllaceae, Amaranthaceae, Limeaceae, Molluginaceae, Portulacaceae, Cactaceae, and Nyctaginaceae) by searching scientific literature until no new medicinal uses were recovered. We then tested for phylogenetic clustering of uses using a "hot nodes" approach. To test potential non-metabolite drivers of medicinal use, like how often humans encounter a species (apparency), we repeated the analysis using only North American species across the entire order and performed phylogenetic generalized least squares regression (PGLS) with occurrence data from the Global Biodiversity Information Facility (GBIF). We hypothesized families with tyrosine-enriched metabolism would show clustering of different types of medicinal use compared to phenylalanine-enriched metabolism. Instead, wide-ranging, apparent clades in Polygonaceae and Amaranthaceae are overrepresented across nearly all types of medicinal use. Our results suggest that apparency is a better predictor of medicinal use than metabolite profile, although metabolism type may still be a contributing factor.</p>
Supplementary data of article "Domestication has altered gene expression and secondary metabolites in pea seed coat".
<p><strong>Table S1.</strong> Excel- GO_terms_MF_selected_WGCNA_modules.</p> <p><strong>Table S2.</strong> Excel- GO_terms_MF_DEGs_UP_and_DOWN.</p> <p><strong>Table S3.</strong> Excel- GO_terms_MF_DEGs_summary.</p> <p><strong>Table S4.</strong> Excel- List of DEGs involved in flavonoid pathway found in WILD gene set.</p> <p><strong>Table S5.</strong> Protein recoveries calculated for individual pea protein samples. Numbers 1, 2, 3 denote treatment groups corresponding to seed developmental stages (D1, D2 and mature seeds, respectively). Letters a–d denote biological replicates within the treatment groups.</p> <p><strong>Table S6.</strong> Excel- Annotation of proteins differentially expressed in wild and domesticated pea seed coat samples.</p> <p><strong>Table S7.</strong> Primary metabolites identified by spectral similarity library search and/or co-elution with authentic standards in pea seed coats aqua methanolic extracts. Metabolite analysis relied on GC-EI-Q-MS analysis after derivatization of the lyophilized extracts with methoxamine hydrochloride (MOA) and <em>N</em>-methyl-<em>N</em>-(trimethylsilyl)trifluoroacetamide (MSTFA).</p> <p><strong>Table S8.</strong> Primary metabolites detected in the aq. methanolic extracts of mature Cameor seed coats demonstrating statistically significant up- and down-regulation in comparison to those of wild JI261.</p> <p><strong>Table S9.</strong> Primary metabolites of mature JI92 seed coats demonstrating statistically significant up- and down-regulation in comparison with those of wild JI261.</p> <p><strong>Table S10.</strong> Primary metabolites of mature JI1794 seed coats demonstrating statistically significant up- and down-regulation in comparison with those of JI261.</p> <p><strong>Table S11.</strong> Primary metabolites of mature JI64 seed coats demonstrating statistically significant up- and down-regulation in comparison with those of JI261.</p> <p><strong>Table S12.</strong> Mass analyzer settings applied for QqTOF-MS experiments in analysis of seed coat (cell wall) hydrolyzates and reference authentic standards.</p> <p><strong>Table S13.</strong> Cell wall-bound metabolites extracted from the seed coats of wild (JI64, JI1794, JI261) and domesticated (Cameor, JI92) peas upon alkali hydrolysis of corresponding isolated and purified cell wall material.</p> <p><strong>Table S14.</strong> Excel- Coordinates of markers in S-plot obtained from OPLS-DA analysis (FIA-ESI-HRTMS, negative ionization, lock mass uncorrected).</p> <p><strong>Table S15.</strong> List of identified significantly differential metabolites rising during seed coat development.</p> <p><strong>Table S16.</strong> List of identified significantly differential metabolites decreasing during seed coat development (positive ionization mode).</p> <p><strong>Table S17.</strong> List of identified metabolites with significantly higher content in wild compared cultivated genotypes in older developmental stages (D5-6).</p> <p><strong>Table S18.</strong> Excel- Expression of genes encoding enzymes of monolignol pathway in seed coats (SC) and embryos (E) of domesticated (Cameor, JI92 and <em>Pisum abyssinicum</em> PI358617) and wild (JI64, JI1794, JI261) peas over five seed developmental stages (13, 17, 20, 23, 28 DAP, labelled as 1-5). PAL: phenylalanine ammonia-lyase, C4H: cinnamate-4-hydroxylase, 4CL: 4-coumaroyl: CoA ligase, HCT: hydroxycinnamoyl CoA:shikimate hydroxycinnamoyltransferase, COMT: caffeic acid O-methyltransferase, CSE: caffeoyl shikimate esterase, CAD: cinnamyl alcohol dehydrogenase, CCR: cinnamoyl CoA reductase, CCoAMT: caffeoyl CoA-3-methyltransferase, F5H: ferulate-5-hydroxylase</p> <p><strong>Table S19.</strong> Studied metabolites of phenylpropanoid pathway.</p> <p><strong>Table S20.</strong> Instrument settings used in the proteomics LIT-Orbitrap-MS and -MS/MS experiments.</p> <p><strong>Table S21.</strong> Procedures and specific settings for data processing and post-processing of the proteomics data.</p> <p><strong>Table S22.</strong> Gas chromatographic (GC) separation conditions and electron ionization-quadrupole-mass spectrometry (EI-Q-MS) settings for GC-EI-Q-MS analysis of the primary metabolites in pea seed coats.</p> <p><strong>Table S23.</strong> Chromatographic conditions used for UHPLC separation of seed coat (cell wall) hydrolyzates and reference authentic standards.</p> <p><strong>Table S24.</strong> Variable parameters of MS/cIMS/MS measurements.</p> <p><strong>Figure S1.</strong> The dynamics of gene expression between studied developmental stages within all genotypes (a) or among genotypes in particular developmental stages (b).</p> <p><strong>Figure S2.</strong> Twelve representative groups of transcription factors described within 20 gene modules of pea SC. Visualized by Cytoscape 3.9.0.</p> <p><strong>Figure S3.</strong> SDS-PAGE electropherograms of the total protein fractions isolated from the seed coats of JI92 (a, c, e) and JI64 (b, d, f) seeds before and after tryptic hydrolysis. Numbers 1, 2, 3 denote seed developmental stages: DS1, DS2 and mature seeds, respectively. Letters a-d denote biological replicates. The aliquots (10 μg) of samples before hydrolysis (a, b), the incompletely digested aliquots left on filter unit after peptide elution (c, d) and aliquots of tryptic hydrolysates (corresponding to 5 μg of protein), (e, f) were loaded on gels. Inter-gel normalization relied on the total density of the Protein Ladder (PageRuler™ Prestained Protein Ladder #26616, 10–180 kDa) lane (St); the ND (non-digested) sample represents a reference protein not subjected to hydrolysis.</p> <p><strong>Figure S4.</strong> The numbers of tryptic peptides (a), possible proteins (b), and non-redundant proteins (protein groups) (c) identified in domesticated JI92 seed coats at developmental stages D1, D2 and D6. The tryptic digests (<em>n</em> =&thinsp;3) obtained from seed coats were analyzed by nano-high performance liquid chromatography-electrospray ionization linear ion trap-orbital trap mass spectrometry (nanoHPLC-ESI-LIT-Orbitrap-MS) operated in positive DDA mode.</p> <p><strong>Figure S5.</strong> The numbers of tryptic peptides (a), possible proteins (b), and non-redundant proteins (protein groups, c) identified in wild pea JI64 seed coats at D1, D2 and D6 stages. The tryptic digests (<em>n</em> =&thinsp;3), obtained from pea seedlings, were analyzed by nano-high performance liquid chromatography-electrospray ionization linear ion trap-orbital trap mass spectrometry (nanoHPLC-ESI-LIT-Orbitrap-MS) operated in positive DDA mode.</p> <p><strong>Figure S6.</strong> Principal component analysis (PCA) with score plot representation (a) accomplished for seed coat proteins differentially expressed at developmental stages D1 and D2 and in the mature state (D6) and hierarchical clustering with a heatmap representation (b).</p> <p><strong>Figure S7.</strong> Functional annotation (accomplished with the Mercator MapMan v3.6 tool) of the pea seed coat proteins isolated in stage D1. White and black columns denote the functional groups of the proteins, which were more expressed in the developing seeds of domesticated JI92 and wild JI64, respectively.</p> <p><strong>Figure S8.</strong> Functional annotation (accomplished with the Mercator MapMan v3.6 tool) of the pea seed coat proteins isolated in stage D2. White and black columns denote the functional groups of the proteins, which were more expressed in the developing seeds of the domesticated JI92 and wild JI64, respectively.</p> <p><strong>Figure S9.</strong> Prediction of sub-cellular localization of the proteins more expressed in the developing seeds of JI92 and JI64 with the BUSCA prediction tool.</p> <p><strong>Figure S10.</strong> Evaluation of the differences in the metabolic profiles of the mature seeds obtained from the wild JI261 and domesticated Cameor by principal component analysis (PCA).</p> <p><strong>Figure S11.</strong> Representation of the differences in the metabolic profiles of the mature seed coats obtained from the wild JI261 and domesticated Cameor by the t-test with Volcano plot representation (a) and the top 30 differentially abundant metabolites demonstrating the most pronounced differences of corresponding GC-MS signals associated with seed dormancy (b).</p> <p><strong>Figure S12.</strong> Evaluation of the differences in the metabolic profiles of the mature seeds obtained from the wild JI261 and domesticated JI92 by principal component analysis (PCA) with score plot representation (a) and hierarchical clustering with heatmap representation (b).</p> <p><strong>Figure S13.</strong> Principal component analysis (PCA) illustrating distribution of metabolic profiles of mature seed coats of two wild pea genotypes, JI1794 and JI261.</p> <p><strong>Figure S14.</strong> Principal component analysis (PCA) demonstrates the distribution of mature seed coat metabolic profiles of two wild pea genotypes, JI64 and JI261(control).</p> <p><strong>Figure S15.</strong> Evaluation of the differences in the patterns of the cell wall-bound metabolites obtained from mature seed coats of wild JI261 and domesticated Cameor: principal component analysis (PCA) with score plot representation (a), hierarchical clustering with heatmap representation (b) and <em>t</em>-test analysis with the Volcano-plot representation (c).</p> <p><strong>Figure S16.</strong> Statistical analysis (<em>t</em>-test with Volcano plot representation) characterizing the differences between the levels of mature seed coat cell wall-bound metabolites of Cameor compared with those of wild JI261.</p> <p><strong>Figure S17.</strong> Principal component analysis (PCA) illustrates the distribution of mature seed coat metabolic profiles of domesticated JI92 and wild JI261.</p> <p><strong>Figure S18.</strong> Principal component analysis (PCA) shows the distribution of metabolic profiles of mature seed coats of two wild pea genotypes, JI1794 and JI261, control.</p> <p><strong>Figure S19.</strong> Principal component analysis (PCA) demonstrates the distribution of mature seed coat metabolic profiles of two wild genotypes, JI64 and control JI261.</p> <p><strong>Figure S20.</strong> Annotated cell wall-bound metabolites extracted from the seed coats of the dormant wild pea genotype JI261 and the seed coats from several pea genotypes varying in their dormancy (Cameor, JI92, JI64, and JI1794) upon alkali hydrolysis of corresponding isolated and purified cell wall material. </p> <p><strong>Figure S21.</strong> Ion mobility separation of <em>m/z</em> 299.0841.</p> <p><strong>Figure S22.</strong> Ion mobility separation of <em>m/z</em> 701.1907. </p> <p><strong>Figure S23.</strong> Ion mobility separation of <em>m/z</em> 619.1041.</p> <p><strong>Figure S24.</strong> Ion mobility separation of <em>m/z</em> 631.1017.</p> <p><strong>Figure S25.</strong> Ion mobility separation of <em>m/z</em> 641.1139.</p> <p><strong>Figure S26.</strong> Ion mobility separation of <em>m/z</em> 771.1346. </p> <p><strong>Figure S27.</strong> Reconstructed chromatograms of p-hydroxybenzoic and salicylic acids in DS5 of dormant JI64 and domesticated landraces JI92 (LC/HRTMS, negative ionization mode).</p> <p><strong>Figure S28.</strong> Module-trait relationship depiction showing the correlation between expression of the gene modules and the abundance of identified metabolites of the monolignol pathway.</p>
Supplementary dataset for "Phenylacetic acid metabolism in land plants: novel pathways and metabolites"
<p>Supplementary dataset with measured data for publication "Phenylacetic acid metabolism in land plants: novel pathways and metabolites"</p>
Data and Scripts for Schweiger et al. (2021) "Chemical properties of key metabolites determine the global distribution of lichens"
<p>Data and Scripts for Schweiger et al. (2021) "Chemical properties of key metabolites determine the global distribution of lichens".</p> <p>A detailed description of the individual files is provided in the "Schweiger-et-al-DataPublication-Index-Submissionfiles.txt" file.</p> <p>Summary of the study:</p> <p>In lichen symbioses, fungal secondary metabolites provide UV protection on which certain lichen algae such as trebouxioid green algae sensitively depend. These metabolites differ in their UV absorbance capability and solvability, and thus vary in their propensity of being leached from the lichen body by high precipitation and temperatures, with still unknown implications for the global distribution of lichens. In this global study, we show that the occurrence and chemical properties of fungal-derived metabolites are of eco-evolutionary significance for the global, latitudinal distribution of lichenized Trebouxiophyceae. This might represent an indirect environmental adaptation in which the mycobiont invests to protect the trebouxioid photobiont from harsh environmental conditions and, by doing this, secures its efficient source of photosynthetic carbon.</p>
Pesticide metabolites database and occurrence data in wine
<p>Pesticide metabolites database is the overview of potential pesticide metabolites originated from selected parent compounds. These metabolites can be found in various source including JMPR (FAO/WHO) documents, EU pesticide database, EFSA etc. Based on their elemental formula respective ions (protonated / deprotonated molecules, their adducts) originated in ESI source can be derived for ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS).</p> <p>Occurrence data were obtained from determination of pesticide residues and their metabolites in samples of grapevine and wine, using UHPLC-HRMS, with the objective of supporting the possibility of the verification of the method of farming. It documents the identification of pesticide metabolites commonly used in conventional farming and provides a characterization of pesticide degradation during grapevine growth, maturation and during the wine-making process.</p> <p>The dataset is useful for anyone working on authentication of organic fruits and vegetables.</p>
Datasets and code for manuscript: Age, breed, sex, and diet influence serum metabolite profiles of 2000 pet dogs
<p>Physiology affects metabolism, but there is a lack of large-scale studies investigating the effects of different physiological factors on canine metabolism. We utilised generalised linear models to study how age, breed, sex, sterilisation status, size, diet type, and fasting time before blood sampling affect serum concentrations of 119 metabolite measurands in over 2000 pet dogs. This dataset contains input files and code for the analyses. </p>
Data for: Nawrocka et al., Variable-temperature NMR spectroscopy for metabolite identification in biological materials, RSC Adv., 2021, 11, 35321, DOI: 10.1039/d1ra05626c
<p>Raw NMR spectrometer data (Varian/Agilent format) for the paper Nawrocka et al., Variable-temperature NMR spectroscopy for metabolite identification in biological materials, RSC Adv., 2021, 11, 35321, DOI: 10.1039/d1ra05626c</p> <p>Includes variable-temperature series for artificial and natural mixtures of metabolites and spectra of separate metabolites.</p>
Data from: Plant host traits mediated by foliar fungal symbionts and secondary metabolites
<p>Fungal symbionts living inside plant leaves ("endophytes") can vary from beneficial to parasitic, but the mechanisms by which the fungi affect the plant host phenotype remain poorly understood. Chemical interactions are likely the proximal mechanism of interaction between foliar endophytes and the plant, as individual fungal strains are often exploited for their diverse secondary metabolite production. Here, we go beyond single strains to examine commonalities in how 16 fungal endophytes shift plant phenotypic traits such as growth and physiology, and how those relate to plant metabolomics profiles. We inoculated individual fungi on switchgrass, <em>Panicum virgatum</em> L. This created a limited range of plant growth and physiology (2–370% of fungus-free controls on average), but effects of most fungi overlapped, indicating functional similarities in unstressed conditions. Overall plant metabolomics profiles included almost 2000 metabolites, which were broadly correlated with plant traits across all the fungal treatments. Terpenoid-rich samples were associated with larger, more physiologically active plants and phenolic-rich samples were associated with smaller, less active plants. Only 47 metabolites were enriched in plants inoculated with fungi relative to fungus-free controls, and of these, LASSO regression identified 12 metabolites that explained from 14–43% of plant trait variation. Fungal long-chain fatty acids and sterol precursors were positively associated with plant photosynthesis, conductance, and shoot biomass, but negatively associated with survival. The phytohormone gibberellin, in contrast, was negatively associated with plant physiology and biomass. These results can inform ongoing efforts to develop metabolites as crop management tools, either by direct application or via breeding, by identifying how associations with more beneficial components of the microbiome may be affected.</p>
HSDB Metabolism / Metabolites Annotation Content from PubChem
<p>An archived version of annotation content extracted from PubChem for the <a href="https://pubchem.ncbi.nlm.nih.gov/source/11933">HSDB</a> section, as scripted up in <a href="https://gitlab.lcsb.uni.lu/eci/pubchem/-/blob/master/annotations/tps/extractAnnotations.R">extractAnnotations.R</a> (code available on the ECI <a href="https://gitlab.lcsb.uni.lu/eci/pubchem/">pubchem</a> repository)</p>
Metabolite identification dataset: "Magnitude-Preserving Ranking for Structured Outputs" Brouard et al. (2017)
<p>Metabolite identification dataset used to for the IOKR and MP-IOKR experiments in the publication:</p> <p><a href="https://proceedings.mlr.press/v77/brouard17a.html">"Magnitude-Preserving Ranking for Structured Outputs" by Brouard et al. (2017)</a></p> <p>The code to run the experiments can be found on Github: <a href="https://github.com/aalto-ics-kepaco/iokr_for_metabolite_identification">https://github.com/aalto-ics-kepaco/iokr_for_metabolite_identification</a> (mp-iokr/application/ACML17).</p> <p>The tar-files need to be unpacked.</p> <p> </p>
Data from: Genome-wide association mapping within a local Arabidopsis thaliana population more fully reveals the genetic architecture for defensive metabolite diversity
<p>A paradoxical finding from genome-wide association studies (GWAS) in plants is that variation in metabolite profiles typically maps to a small number of loci, despite the complexity of underlying biosynthetic pathways. This discrepancy may partially arise from limitations presented by geographically diverse mapping panels. Properties of metabolic pathways that impede GWAS by diluting the additive effect of a causal variant, such as allelic and genic heterogeneity and epistasis, would be expected to increase in severity with the geographic range of the mapping panel. We hypothesized that a population from a single locality would reveal an expanded set of associated loci. We tested this in a French <em>Arabidopsis thaliana</em> population (< 1 km transect) by profiling and conducting GWAS for glucosinolates, a suite of defensive metabolites that have been studied in depth through functional and genetic mapping approaches. For two distinct classes of glucosinolates, we discovered more associations at biosynthetic loci than previous GWAS with continental-scale mapping panels. Candidate genes underlying novel associations were supported by concordance between their observed effects in the TOU-A population and previous functional genetic and biochemical characterization. Local populations complement geographically diverse mapping panels to reveal a more complete genetic architecture for metabolic traits.</p>
Growth, nutrient uptake, blood metabolites and bone properties in broilers consuming feed with mineral-enriched whole black soldier fly larvae
<p>Recycling critical minerals like phosphorus is essential for future circular agriculture. This study explored adding mineral-enriched black soldier fly larvae (BSFL), grown on substrates with sewage sludge recyclates (SSR), to broiler feed to reintroduce minerals from restricted waste streams into the nutrient cycle. Results showed that including 15% mineral-enriched BSFL in broiler diets for 42 days had no adverse effects on growth, nutrient intake, or bone condition, and maintained acceptable levels of heavy metals.</p>
Data and code for: Previous assessments of faecal glucocorticoid metabolites in Cape Mountain zebra (Equus zebra zebra) were flawed
<p>Steroid hormones, especially glucocorticoids (GCs), are widely used to assess physiological responses to stressors. As steroid hormones are heavily metabolised prior to excretion, it is essential to validate enzyme immunoassays (EIAs) for measuring faecal glucocorticoid metabolites (fGCMs). Although problems with unvalidated assays have been raised repeatedly, their use persists widely. Lea et al. (2017) used an unvalidated corticosterone assay (CJM006) to relate fGCM concentrations to habitat quality, demography, and population performance in the Cape mountain zebra (<em>Equus zebra zebra</em>). Here, we revisit their findings and evaluate the validity of their conclusions using a validated EIA. First, we evaluate the biological sensitivity of six EIAs (three group-specific metabolite assays and three corticosterone assays, including CJM006) through a biological validation experiment (translocation) for two sub-species of mountain zebra, Cape mountain and Hartmann's mountain zebra (<em>E. z. hartmannae</em>). Second, we reanalyse the faecal extracts from Lea et al. (2017) using a validated EIA. fGCM concentrations consistently increased following translocation, when using two 11-oxoaetiocholanolone (lab codes: 72T and 72a) and an 11ß-hydroxyaetiocholanolone (69a) EIA, but did not with three different corticosterone EIAs. All corticosterone EIAs (including CJM006) failed to detect an increase in fGCMs within the critical 48–72-hour period post translocation. Therefore, the CJM006 EIA utilised in Lea et al. (2017) does not sensitively measure hypothalamic-pituitary-adrenal (HPA) axis activity in CMZ faeces.</p> <p>Using a validated assay (72T), fGCM concentrations were no longer associated with adult sex ratio or habitat quality (measured by grassiness) and these variables were dropped from predictive models. fGCM concentrations now varied between seasons and were negatively associated with female fecundity (foal:mare ratio). Consequently, we can conclude that the results of the previous study are unreliable. We introduce the terms "insensitive" and "sub-optimal" to categorise assays that are tested but fail validation, and assays that are comparatively poor at detecting relevant hormone changes, respectively. We discuss how both "insensitive" and "sub-optimal" assays could lead to incorrect inferences about population stressors and counterproductive conservation recommendations.</p> <p>In this dataset, we provide all R code scripts, raw data and data to reproducible all results and figures in Previous assessments of faecal glucocorticoid metabolites in Cape Mountain zebra (<em>Equus zebra zebra</em>) were flawed. We have structured the project as an internally consistent directory with files corresponding to code, data, raw lab_ouputs, and figures. </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.
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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.