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125 results for “metabolite profile”
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>
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>
Analyses of metabolite profiling of Drosophila Parkinson's Disease model for identifying novel glial-based therapeutic targets
<p>Analysis for genetic screening and metabolomics identify glial adenosine metabolism as a therapeutic target in Parkinson’s disease</p> <p>This project contains the analysis of metabolite abundance measurements obtained with four different liquid chromatography mass spectrometry methods of synuclein expressing or control or fly brains in a wilde type or Adk1 knockout background.</p> <p> </p> <div> <h2>Table of contents</h2> <a href="https://github.com/jravilap/Olsen_Analyses#table-of-contents"></a></div> <div> <h3>Prerequisites</h3> <a href="https://github.com/jravilap/Olsen_Analyses#prerequisites"></a></div> <ul> <li>R (version 4.3.1 or higher)</li> <li>RStudio (optional, but recommended)</li> </ul> <div> <h3>R Packages</h3> <a href="https://github.com/jravilap/Olsen_Analyses#r-packages"></a></div> <p>The following R packages are required. You can install them using the commands below:</p> <div> <pre>install.packages(c(<span><span>"</span>readxl<span>"</span></span>, <span><span>"</span>calibrate<span>"</span></span>, <span><span>"</span>dplyr<span>"</span></span>, <span><span>"</span>ggplot2<span>"</span></span>))</pre> <div> </div> </div> <div> <h3>Package versions</h3> <a href="https://github.com/jravilap/Olsen_Analyses#package-versions"></a></div> <ul> <li>ggplot2_3.5.1</li> <li>dplyr_1.1.4</li> <li>yaml_2.3.8</li> <li>calibrate_1.7.7</li> <li>readxl_1.4.3</li> </ul> <div> <h2>Project Structure</h2> <a href="https://github.com/jravilap/Olsen_Analyses#project-structure"></a></div> <ul> <li><code>code/</code>: Contains the R scripts for the analysis.</li> <li><code>data/</code>: Processed data files. <ul> <li><code>22_0322_alphaSyn_fly_pilot_Classes.xlsx</code>: metabolite profiling data</li> <li><code>dup_metabs_decision.csv</code>: Table defining which metabolites profiled in more than one method should be used.</li> </ul> </li> <li><code>results/</code>: Output files, including plots and tables.</li> <li><code>common_functions/</code>: Custom R functions used in the analysis.</li> <li><code>config.yml</code>: Configuration file for setting paths.</li> </ul>
Traditional medicinal use is linked with apparency, not specialized metabolite profiles in the order Caryophyllales
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Datasets and code for manuscript: Age, breed, sex, and diet influence serum metabolite profiles of 2000 pet dogs
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GC-MS metabolite profile of Pseudocercospora fijiensis exposed to thiabendazole
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Fig. 3 in Profiling of volatile and non-volatile metabolites in Polianthes tuberosa L. flowers reveals intraspecific variation among cultivars
Fig. 3. Soluble and wall-bound phenolics profile of tuberose cultivars. Histogram A shows the amount of rutin determined by UHPLC analysis in ng g −1 fr. wt of flower tissue, and B shows the amount of caffeic acid, p-coumaric acid and ferulic acid in ng mg−1 pellet dry wt of. Note: All values are mean ± s.d. of three replicates.
Fig. 2 in Profiling of volatile and non-volatile metabolites in Polianthes tuberosa L. flowers reveals intraspecific variation among cultivars
Fig. 2. Heat map representing the diversity of nonvolatile compounds detected in P. tuberosa flowers. Floral extracts after derivatization were analysed by GC-MS. Classes 0, 1, 2, 3, 4, 5, and 6 indicates group average abundances of Calcutta double, Calcutta single, Jyothi, Ujwal, Shnigdha, Shringhar, and Phule rajani cultivars respectively. Red and green color denotes highest and lowest relative abundances of non-volatile compounds, respectively. Abbreviations: Met a-D-gal - Methyl α-D-galactofuranoside, Met b-D-gal - Methyl β-D-galactopyranoside, Met b-D-glu - Methyl β-D-glucofuranoside, 4- HBALD - 4-hydroxybenzaldehyde, 4-HBA - 4-hydroxybenzoic acid, 2, 5-DHBA - 2, 5-Dihydroxybenzoic acid. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 5 in Profiling of volatile and non-volatile metabolites in Polianthes tuberosa L. flowers reveals intraspecific variation among cultivars
Fig. 5. Metabolite map representing the normalized relative abundances of few important primary and specialized metabolites during the blooming stage of P. tuberosa flowers. The amount of metabolites from four different P. tuberosa cultivar clusters were colour coded for low to high contents as per the scale shown. Cluster A includes CD, cluster B includes CS, cluster C includes JY and PR and cluster D includes SN, UJ, and SR. Solid arrows represent single step reactions, dotted arrows represent multi step reactions and block arrows represent volatile emission or exchange within the cell. Emitted levels of volatiles are shown outside the cell, while endogenous levels are represented inside the cytosol. Glycosyl-bound volatiles are shown inside in a representative vacuolar structure. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1 in Profiling of volatile and non-volatile metabolites in Polianthes tuberosa L. flowers reveals intraspecific variation among cultivars
Fig. 1. Profile of floral scent volatiles in studied cultivars of P. tuberosa. Stack plots represent (A) volatiles emitted by flowers in μg g−1 fr. wt h−1, (B) free endogenous volatiles of whole flower in μg μg g−1 fr. wt and (C) glycosyl-bound volatiles of whole flower in μg μg g−1 fr. wt. Abbreviations: CD - Calcutta double, CS - Calcutta single, JY - Jyothi, UJ - Ujwal, SN - Shnigdha, SR - Shringhar, and PR - Phule rajani.
Fig. 4 in Profiling of volatile and non-volatile metabolites in Polianthes tuberosa L. flowers reveals intraspecific variation among cultivars
Fig. 4. Multivariate analysis of P. tuberosa cultivars (classes 0–6). The scores plot obtained after PLS-DA analysis has been shown in A, where components 1 and 2 together can explain the maximum variance between the cultivars. Metabolites having higher VIP scores are shown in B where their relative abundances are color coded from high to low as per the scale shown. The dendrogram as shown in C was obtained after hierarchical clustering analysis of studied P. tuberosa cultivars. Classes 0, 1, 2, 3, 4, 5, and 6 represent Calcutta double, Calcutta single, Jyothi, Ujwal, Shnigdha, Shringhar and Phule rajani cultivars. Metabolites enlisted as 1–25 in B are sequentially represented as follows: quinic acid, V_D-limonene (V: detected as emitted volatile), myo-inositol, D-mannose, V_3-carene, EV_cis-methyl eugenol (EV: detected as endogenous volatile), V_Z- β-caryophyllene, V_benzyl salicylate, EV_benzyl benzoate, EV_eugenol, methyl α-D-galactofuranoside, EV_(Z, E)-farnesol, GV_2-nitrocumene (GV: detected as glycosyl-bound volatile), EV_1,8-cineole, V_1,R-α-pinene, Methyl β-D-galactopyranoside, GV_4-propyl benzaldehyde, Methyl α-Dglucofuranoside, V_β-terpineol, D-fructofuranose, L-threonine, EV_2-hydroxy cineole, EV_3-exo-hydroxy-1,8-cineole, EV_5-hydroxy-7(Z)-decenoic acid-δ-lactone and GV_5-hydroxy-7Z-decenoic acid- δ –lactone.
Metabolite profiling of the social spider Stegodyphus dumicola along a climate gradient
<p>Animals experience climatic variation in their natural habitats, which may lead to variation in phenotypic responses among populations through local adaptation or phenotypic plasticity. In ectotherm arthropods, the expression of thermoprotective metabolites such as free amino acids, sugars, and polyols, in response to temperature stress, may facilitate temperature tolerance by regulating cellular homeostasis. If populations experience differences in temperatures, individuals may exhibit population-specific metabolite profiles through differential accumulation of metabolites that facilitate thermal tolerance. Such thermoprotective metabolites may originate from the animals themselves or from their associated microbiome, and hence microbial symbionts may contribute to shape the thermal niche of their host.</p> <p>The social spider Stegodyphus dumicola has extremely low genetic diversity, yet it occupies a relatively broad temperature range occurring across multiple climate zones in Southern Africa. We investigated whether the metabolome, including thermoprotective metabolites, differs between populations, and whether population genetic structure or the spider microbiome may explain potential differences. To address these questions, we assessed metabolite profiles, phylogenetic relationships, and microbiomes in three natural populations along a temperature gradient.</p> <p>The spider microbiomes in three genetically distinct populations of S. dumicola showed no significant population-specific pattern, and none of its dominating genera (Borrelia, Diplorickettsia, and Mycoplasma) are known to facilitate thermal tolerance in hosts. These results do not support a role of the microbiome in shaping the thermal niche of S. dumicola. Metabolite profiles of the three spider populations were significantly different. The variation was driven by multiple metabolites that can be linked to temperature stress (e.g. lactate, succinate or xanthine) and thermal tolerance (e.g. polyols, trehalose, or glycerol): these metabolites had higher relative abundance in spiders from the hottest geographic region. These distinct metabolite profiles are consistent with a potential role of the metabolome in temperature response.</p>
Metabolite profiles from Foxfonna, Svalbard
<p>Raw metabolite data for "Icescape-scale metabolomics reveals cyanobacterial and topographic control of the core metabolism of the cryoconite ecosystem of an Arctic ice cap"</p>
Fig. 2 in Guaiane-rich phytochemical profile of Centaurea kotschyi subsp. persica (Boiss.) Wagenitz and identification of hypoglycaemic metabolites
Fig. 2. LC-MS profile of the CH2Cl2 extract obtained from Centaurea kotschyi subsp. persica aerial parts.
Fig. 1 in Guaiane-rich phytochemical profile of Centaurea kotschyi subsp. persica (Boiss.) Wagenitz and identification of hypoglycaemic metabolites
Fig. 1. Chemical structures of specialized metabolites isolated from Centaurea kotschyi subsp. persica.
Fig. 5 in Guaiane-rich phytochemical profile of Centaurea kotschyi subsp. persica (Boiss.) Wagenitz and identification of hypoglycaemic metabolites
Fig. 5. COSY (red bold), key HMBC (black arrows) and NOESY (blue arrow) correlations detected for compound 15. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 4 in Metabolite profiles of brown planthopper-susceptible and resistant rice (Oryza sativa) varieties associated with infestation and mechanical stimuli
Fig. 4. Normalized intensity of significant metabolites affected by BPH infestation (n = 3/group) with different response patterns: (A). similar response in BPHsusceptible KD and BPH-resistant RH, (B). late induction in KD variety, (C). early induction in RH but late induction in KD, (D). induction in RH in both BPH and mechanical piercing by a needle common response in KD and RH, (E). higher level in KD than RH, and (F). higher level in RH than KD. Unpaired-t test was used to compare the control (black bar) and the BPH-treated (white bar) groups and to compare the control (black bar) and the mechanical-treated (grey bar) groups (*p- value<0.05, **p-value<0.01 and ***p-value<0.001) per time per genotype. HAT represents hour after treatment. Error bars represent one standard deviation of the mean value. Normalized intensity of each feature was relative to the intensity of a reference sample.
Fig. 3 in Metabolite profiles of brown planthopper-susceptible and resistant rice (Oryza sativa) varieties associated with infestation and mechanical stimuli
Fig. 3. Clustergram analysis of BPH-susceptible KD and BPH-resistant RH varieties between control (no treatment) and BPH treatment at 0, 6, 24 and 96 h using hierarchical clustering. (A). Venn's diagram represents numbers of metabolite features of KD and RH from three different treatments. (B). Clustergrams obtained from 213 metabolite features of ESI+with % relative standard deviation (RSD) less than 30% at least half of total sample numbers and six different alteration patterns (46 metabolite features) with their level changes greater than 1.5 folds comparing between KD and RH, and between treatment and control in each timepoint. B indicates BPH treatment. M indicates mechanical piercing by a needle. C indicates no treatment.
Fig. 2 in Metabolite profiles of brown planthopper-susceptible and resistant rice (Oryza sativa) varieties associated with infestation and mechanical stimuli
Fig. 2. PCA score plot of metabolite profiles (6413 metabolite features) obtained from ESI + mode among control (C, red), mechanical piercing by a needle (M, blue) and BPH (B, green) treatment at (A). all timepoints (0, 6, 24 and 96 h) between BPH-susceptible KD and BPH-resistant RH varieties. QC indicates pooled samples (n = 70) of rice leave extracts (orange star), (B). 0 h, (C). 6 h, (D). 24 h and (E) 96 h between BPH-susceptible KD and BPH-resistant RH varieties. The numbers above the circles indicate time points. Open and filled circles indicate data from KD and RH varieties. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 7 in Metabolite profiling reveals a role for intercellular dihydrocamalexic acid in the response of mature Arabidopsis thaliana to Pseudomonas syringae
Fig. 7. Exogenous infiltration of dihydrocamalexic acid (DHCA) and bacterial quantification of Pseudomonas syringae in rosette leaves of 7-week old Col-0 and cyp71a12/cyp71a13. DHCA (0.07 μg/mL or 0.25 μg/mL) was applied via pressure infiltration to 7-week-old plants at 24 h postinoculation with P. syringae or mock solution (0.06% DMSO in 10 mM MgCl2). Bacterial levels were quantified at 3 days postinoculation with Pst. Values represent the mean ± standard deviation of three sample replicates (n = 3) consisting of 8 plants each. Different letters indicate statistically significant differences (ANOVA, Tukey's honestly significant difference [HSD], P <0.05).
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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.