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opencc-byApr 2023View details →

Metabolome analysis and immunity of Phlomis purpurea against Phytophthora cinnamomi

Abstract   Phlomis purpurea is spontaneous in the southern Iberian Peninsula namely in cork oak (Quercus suber) forests. In a previous transcriptome analysis, we reported on its immunity against Phytophthora cinnamomi. However, little is known about the involvement of secondary metabolites on P. purpurea defence response. It is known though that root exudates are toxic to this pathogen. To understand the involvement of secondary metabolites in the defence of P. purpurea, a metabolome analysis was performed using leaves and roots of plants challenged with the pathogen along 72 h. The compounds putatively identified in challenged plants were constitutively produced. Alkaloids, fatty acids, flavonoids, glucosinolates, polyketides, prenol lipids, phenylpropanoids, sterols and terpenoids, were differentially produced in leaves or roots along the experiment timescale. It must be emphasized the constitutive production of taurine in leaves and its increase soon after challenging which suggests its role in P. purpurea immunity to the stress imposed by the oomycete. The rapid increase of secondary metabolites production by this plant species accounts for a concerted action of multiple compounds and genes on innate protection of P. purpurea against P. cinnamomi. The combination of metabolome with the transcriptome data previously disclosed, confirms the mentioned auto-immunity of this plant to a devastating pathogen and suggests its potential as antagonist for phytopathogens biological control opening a way for its application in green forestry / agriculture.

opencc-by-4.0Apr 2023View details →

Bacillus subtilis metabolomics

Bacillus subtilis metabolomics: Analyses were performed using an UHPLC (1290 Infinity LC, Agilent Technologies) coupled to a QTRAP MS (AB 6500+, ABSciex) in Shanghai Applied Protein Technology Co., Ltd. The analytes were separated on HILIC (Waters UPLC BEH Amidecolumn, 2.1 mm × 100 mm, 1.7μm) and C18 columns (Waters UPLC BEH C18-2.1x100 mm, 1.7 μm). For HILIC separation, the column temperature was set at 35 ℃; and the injection volume was 2 μL. Mobile phase A: 90%H2O + 2 mM ammonium formate + 10% acetonitrile , mobile phase B: 0.4% formic acid in acetonitrile. A gradient (85% B at 0-1 min, 80% B at 3-4 min, 70% B at 6 min, 50% B at 10-15.5 min, 85% B at 15.6 -23 min ) was then initiated at a flow rate of 300μL/min. For RPLC separation, the column temperature was set at 40℃, and the injection volume was 2 μL. Mobile phase A: 5 mMammonium acetate in water, mobile phase B: 99.5% acetonitrile.A gradient (5% B at 0 min, 60% B at 5 min, 100% B at 11-13min, 5% B at 13.1-16 min ) was then initiated at a flow rate of 400 μL/min. The sample was placed at 4 ℃ during the wholeanalysis process. 6500+ QTRAP (AB SCIEX) was performed in positive and negative switch mode. The ESI positive source conditions were asfollows: Source temperature: 580℃; Ion Source Gas1 (GS1): 45; Ion Source Gas2 (GS2): 60; Curtain Gas (CUR): 35; IonSprayVoltage(IS): +4500 V; The ESI negative source conditions were as follows: Source temperature: 580℃; Ion Source Gas1(GS1): 45; Ion Source Gas2 (GS2): 60; Curtain gas (CUR): 35; IonSpray Voltage(IS): -4500 V. MRM method was used for massspectrometry quantitative data acquisition. The MRM ion pairs are showed in the attached file. A polled quality control (QC)samples were set in the sample queue to evaluate the stability and repeatability of the system.

opencc-by-4.0Sep 2019View details →

Metabolomics analysis of Sleep and Aging

UPLC-MS/MS based semi-targeted analysis of overlapping effects of sleep and aging using a mouse model of sleep deprivation.

opencc-by-4.0Jul 2023View details →

Effects of maternal calcium propionate supplementation on offspring productivity and meat metabolomic profile in sheep

data set from meat metabolome from lambs

opencc-by-4.0Jul 2023View details →

Metabolomic Dataset - EAR study

Rheumatoid arthritis (RA) is a chronic autoimmune disease that affects millions of people worldwide, and early detection is crucial for effective treatment and management. Metabolomics, the study of small molecules in biological systems, has the potential to provide important insights into the early diagnosis and treatment of RA. One promising area of metabolomics research in RA is the identification of early biomarkers. By analyzing metabolic changes that occur in the serum of RA patients naïve to treatment we have identified a panel of 3 metabolites: glyceric acid, lactic acid, and 3-hydroxyisovaleric acid as the best metabolite combination for early RA diagnosis (ERA) identifying patients with 96.7% of certainty outperforming the classical anti-cyclic citrullinated peptide marker by 2.9% and the C-reactive protein inflammatory marker by 15.4%. This early detection could allow for early intervention and treatment, potentially preventing the progression of the disease. Additionally, the deregulated metabolites have been associated with alterations in pathways related to amino acid metabolism such as aminoacyl-tRNA biosynthesis and the metabolism of serine, glycine, and phenylalanine that can be used to identify potential targets for therapeutic intervention.

Fig. 7 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe

Fig. 7. Multivariate data analysis (MVDA) of fresh and dried Peruvian ginger samples. A. Principal component analysis (PCA) showed clear separation of fresh and dried ginger samples. B. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) showed clear separation of fresh and dried ginger samples. C. Splot of OPLS-DA. D. VIP scores based on the metabolite data from fresh and dry ginger extracts.

Fig. 9 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe

Fig. 9. [6]-Gingerol (A) and [6]-shogaol (B) contents in μg/g fresh weight (μg/ g FW) among the tested fresh ginger samples using different extraction solvents. Error bars indicate mean ± SE for five replicates. Black asterisks indicate significance difference from the Peruvian samples (*P <0.05, Student's t-test).

Fig. 5 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe

Fig. 5. The influence of geographical distribution of ginger on the gingerols and gingerol-related metabolites. The figure also describes the biosynthetic pathway of [6]-gingerol and hexahydrocurcumin and subsequent transformations. The opposed abundances of the precursors (i.e., phenylalanine and cinnamic acid) and end products (i.e., [6]-gingerol, hexahydrocurcumin, and gingerenone A and B) hypothesize that they have the same biosynthetic pathway.

Fig. 3 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe

Fig. 3. The use of retention time, accurate mass, and co-elution pattern for compound validation. A. Total ion chromatogram (TIC) and extracted ion chromatograms (EIC) of [6]-gingerol measured by UPLC/MS in positive and negative ionization modes. B. Different gingerols within the same class show a retention time pattern according to their chain length. Intra-class variability is shown by XIC in positive ionization mode for the 6, 8 and 10-gingerol. C. Scatter plot representation of different gingerols annotated from the tested samples. The m/z of the loss of water from the protonated adducts is given on the x-axis and the observed RT (min) is given in the y-axis. The plot illustrates how the correlation between elution (RT) and chain length in the annotated gingerols can be used for the prediction of other compounds within the same class.

Fig. 4 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe

Fig. 4. Multivariate data analysis (MVDA) of fresh Ginger samples collected from different localities. A. Principal component analysis (PCA) showed clear separation of geographically different fresh ginger samples. B. VIP scores showing the top 25 metabolites discriminating ginger samples C. Dendrogram of the investigated fresh ginger samples based on the metabolites obtained after MS data analysis.

Fig. 2 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe

Fig. 2. Schematic diagram showing the steps for confident compound identification. The feature with the retention time of 12.94 min and 295.191 m/z, representing 6-gingerol was selected. Total ion chromatogram (TIC) and extracted ion chromatograms (EIC) were measured by UPLC/MS in the positive ionization mode.

Fig. 1 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe

Fig. 1. Experimental design for metabolic profiling of fresh ginger rhizomes collected from different geographical sources and the effect of drying.

opencc-by-4.0Jul 2023View details →

Spatialized metabolomic annotation combining MALDI imaging and molecular network

These data are linked to a publication "Spatialized metabolomic annotation combining MALDI imaging and molecular network" where we studied the in situ chemical diversity of fruits of the species Sextonia rubra (Mez.) Van der Werff (Lauraceae) using mass spectrometry imaging techniques and the annotation of molecular species detected by molecular networks using MetGem software. This repository contains: MS1 and MS2 raw data, ion mapping of the fruit according to different tissues, total molecular networks and a script for processing the acquired data to reproduce this approach.

opennotspecifiedJan 2023View details →

Fig. 8. Untargeted metabolomic analysis A in Multivariate analysis of chemical and genetic diversity of wild Humulus lupulus L. (hop) collected in situ in northern France

Fig. 8. Untargeted metabolomic analysis A. Principle component analysis of the 63 chemotypes of hop studied. Each symbol represents a single plant from the different accessions. Commercial varieties (10 accessions), heirloom varieties (3 accessions), wild hops collected on different locations (50 accessions, Fig. 3). B. Principle component analysis of the chemical markers.

Fig. 2 in Global metabolome analysis of Dunaliella tertiolecta, Phaeobacter italicus R11 Co-cultures using thermal desorption - Comprehensive two-dimensional gas chromatography - Time-of-flight mass spectrometry (TD-GC×GC-TOFMS)

Fig. 2. Workflow for sample preparation and injection. Culture samples were filtered and dried (A–B). Dried filter papers were placed in clean vials (C) and then resuspended in methanol (D) before being extracted with Chloroform (E). Water was added (F) and subsequently, the chloroform layer was aliquotted into GC vials (G) for further sample preparation. Extracts were dried (H) and then derivatized using a two-step methoximation/silylation process to yield derivatized extracts (I). 9-μL aliquots of derivatized extracts were automatically transferred to microvial inserts in thermal desorption tubes for injection (J) using an initial solvent vent step to remove excess solvent and derivatisation reagents (K), followed by thermal desorption to a cooled PTV inlet and subsequent splitless injection to the GC × GC-TOFMS system. Non-volatile residues from the extracts remained in the microvial insert for subsequent disposal (L). See text for details.

Fig. 4 in Global metabolome analysis of Dunaliella tertiolecta, Phaeobacter italicus R11 Co-cultures using thermal desorption - Comprehensive two-dimensional gas chromatography - Time-of-flight mass spectrometry (TD-GC×GC-TOFMS)

Fig. 4. From left to right: results of principal component analysis of the raw data (autoscaled), similarly scaled data normalised to class-specific TUPA, and the normalised, scaled data using the selected features from the FS-CR routine. Quality control samples were not included in the feature selection routine, and are displayed as filled icons connected to their corresponding replicate with a straight line, following projection into the optimised principal component space. Confidence ellipses were drawn about each sample class for a confidence interval of 0.95. Note the convention: DUN refers to D. tertiolecta samples, CO refers to co-culture samples, and BAC refers to P. italicus R11 samples.

Fig. 6 in Profiling alkaloids in Aconitum pendulum N. Busch collected from different elevations of Qinghai province using widely targeted metabolomics

Fig. 6. Comparison of the peak areas of various classes of significantly different abundant metabolites among HZX, MYG, ZKW, GLM, YSZ, and GNG. Bars represent the peak areas of the significantly differentially abundant metabolites.

Fig. 3 in Profiling alkaloids in Aconitum pendulum N. Busch collected from different elevations of Qinghai province using widely targeted metabolomics

Fig. 3. Differential metabolite analysis OPLS-DA plots of MYG, ZKW, GLM, YSZ, and GNG compared to HZX.

Fig. 7 in Profiling alkaloids in Aconitum pendulum N. Busch collected from different elevations of Qinghai province using widely targeted metabolomics

Fig. 7. Correlation analysis between the environmental parameters and differentially abundant alkaloids (a), and. between differentially abundant alkaloids and anti-inflammatory activity (b). The red block indicates a positive correlation; the green block indicates a negative correlation; * indicates significant correlation; ** indicates extremely significant correlation.. (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 alkaloids in Aconitum pendulum N. Busch collected from different elevations of Qinghai province using widely targeted metabolomics

Fig. 5. Venn diagram illustrating shared or unique metabolite contents that differed significantly among the different comparison groups.