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opennotspecifiedFeb 2022View details →

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.

opennotspecifiedFeb 2022View details →

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.

opennotspecifiedFeb 2022View details →

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. 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.

Fig. 1 in Profiling alkaloids in Aconitum pendulum N. Busch collected from different elevations of Qinghai province using widely targeted metabolomics

Fig. 1. Comparison of the total peak areas of various classes of metabolites among HZX, MYG, ZKW, GLM, YSZ, and GNG samples from different regions. Bars represent the sum of the peak areas for all metabolites belonging to each class.

Fig. 4 in Profiling alkaloids in Aconitum pendulum N. Busch collected from different elevations of Qinghai province using widely targeted metabolomics

Fig. 4. Volcano plots for (a) MYG vs. HZX, (b) ZKW vs. HZX, (c) GLM vs. HZX, (d) YSZ vs. HZX, and (e) GNG vs. HZX. The green dots indicate differential metabolites that were significantly downregulated, red dots indicate differential metabolites that were significantly upregulated, and black dots indicate metabolites that were detected in the samples but were not significant. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

opencc-by-4.0Aug 2023View details →

Metabolic profiles of obese soldiers before and after passing an intense 60-days weight-loss course (SEDENA)

Untargeted metabolomics data (HPLC-MS, high resolution) of urine of obese soldiers that participated successfully in a 60-day course for losing weight, in a training center of the Mexican army (SEDENA). The sample names with the ending -2 represent data after the treatment; without this ending, before the treatment. The raw data were converted to .mzML using the ProteoWizard, and compressed to .zip files which can be directly uploaded to the MetaboAnalyst.ca website.

dryad32/100

Multimodal immune profiling of SARS-CoV-2 in Uganda – soluble immune mediators

Little is known about the pathobiology of SARS-CoV-2 infection in sub-Saharan Africa, where severe COVID-19 fatality rates are among the highest in the world and the immunological landscape is unique. In a prospective cohort study of 306 adults encompassing the entire clinical spectrum of SARS-CoV-2 infection in Uganda, we integrated profiling of the peripheral blood proteome and transcriptome to dissect the immunopathology of COVID-19 across multiple phases of the pandemic. Beyond the prognostic importance of myeloid cell-driven immune activation and lymphopenia, we show that multifaceted impairment of host protein synthesis and extensive redox imbalance define core biological signatures of severe COVID-19, with central roles for IL-7, IL-15, and lymphotoxin-α in COVID-19 respiratory failure. While prognostic signatures were generally consistent in SARS-CoV-2/HIV-coinfection, type I interferon responses uniquely scaled with COVID-19 severity in persons living with HIV. Throughout the pandemic, COVID-19 severity peaked during phases dominated by A.23/A.23.1 and Delta B.1.617.2/AY variants. Independent of clinical severity, Delta phase COVID-19 was distinguished by exaggerated pro-inflammatory myeloid cell and inflammasome activation, NK and CD8+ T-cell depletion, and impaired host protein synthesis. Combining these analyses with a contemporary Ugandan cohort of adults hospitalized with influenza and other severe respiratory infections, we found activation of epidermal and platelet-derived growth factor pathways to be distinct features of COVID-19, deepening translational understanding of mechanisms potentially underlying SARS-CoV-2-associated pulmonary fibrosis. Collectively, our findings provide biological rationale for use of broad and targeted immunotherapies for severe COVID-19 in sub-Saharan Africa, illustrate the relevance of local viral and host factors to SARS-CoV-2 immunopathology, and highlight underemphasized yet therapeutically exploitable immune pathways driving COVID-19 severity.

opencc-zeroAug 2023View details →

Fig. 4 in Volatile metabolic profiling and functional characterization of four terpene synthases reveal terpenoid diversity in different tissues of Chrysanthemum indicum L

Fig. 4. Characterization of CiTPS enzymatic activity. (A) TIC of α-pinene produced in the root of C. indicum, the enzymatic products of CiTPS1 (without an MBP tag), the enzymatic products of CiTPS2 (pET-(-tp)-CiTPS2), and the negative control (GPP + boiled protein). (B) TIC of the corresponding metabolites produced in the root, the enzymatic products of CiTPS3 (without an MBP tag), and the negative control (FPP + boiled protein). (C) TIC of the enzymatic products of CiTPS4 (with GPP and FPP as substrate and without an MBP tag) and the negative control (GPP/FPP + boiled protein). TIC, total ion chromatogram.

Fig. 5 in Volatile metabolic profiling and functional characterization of four terpene synthases reveal terpenoid diversity in different tissues of Chrysanthemum indicum L

Fig. 5. Expression pattern analysis of CiTPSs in different tissues of C. indicum. (A–D) Expression pattern analysis of CiTPS1, CiTPS2, CiTPS3, and CiTPS4 in the root, stem, leaf, flower bud, and flower. Error bars represent the standard deviations between the three biological replicates. Different letters represent significant differences at P <0.05. (E) The corresponding products (volatile terpenoids) produced by CiTPS1, CiTPS2, CiTPS3 or CiTPS4 in C. indicum. n. d.: not detected; trace: 0–1 × 10 4 ng/mg, FW; +, 0.1–10 ng/mg, FW; + +, 10–100 ng/mg, FW; +++,>100 ng/mg, FW.

Fig. 3 in Volatile metabolic profiling and functional characterization of four terpene synthases reveal terpenoid diversity in different tissues of Chrysanthemum indicum L

Fig. 3. (A) Amino acid sequence alignments of CiTPS1, CiTPS2, CiTPS3, and CiTPS4 (unigene 0021,699, 0037,767, 0060,549, and 0062,052, respectively) and six TPSs from other plants. (B) Phylogenetic analysis of four TPSs from C. indicum and some TPSs from other plants. Detailed information, including plant names and GenBank identification numbers, are shown in Supplementary Table S3. Phylogenetic analysis was performed using the maximum likelihood method and the MEGA and ITOL tools (http://itol.embl.de/).

Fig. 1 in Volatile metabolic profiling and functional characterization of four terpene synthases reveal terpenoid diversity in different tissues of Chrysanthemum indicum L

Fig. 1. Volatile terpenoids in different tissues of C. indicum. (A) The representative total ion chromatogram of the root, stem, leaf, flower bud, and flower. (B) Heatmap of monoterpenoids and sesquiterpenoids in different tissues of C. indicum.

Fig. 7 in Glucosinolate profiles and phylogeny in Barbarea compared to other tribe Cardamineae (Brassicaceae) and Reseda (Resedaceae), based on a library of ion trap HPLC-MS/MS data of reference desulfoglucosinolates

Fig. 7. Levels of major glucosinolates in leaves of first year rosette plants of the Gtype (A) and P-type (B) of Barbarea vulgaris in plants subjected to various challenges or no challenge as control. The contrasting general profile of the types is evident from dominance of BAR in the G-type and EBAR in the P-type. Treatment codes are: Control, un-challenged plants harvested after 7 days; Pieris 3d and Pieris 7d, herbivory by Pieris brassicae larvae until harvest at either day 3 or day 7; Plutella, herbivory by Plutella xylostella for 4 days; CuCl2, spraying of leaves with 10 mM CuCl2 (aq.) followed by recovery for 4 days. Bars represent means, whiskers indicate standard deviation (N = 3 for each group).

Fig. 6 in Glucosinolate profiles and phylogeny in Barbarea compared to other tribe Cardamineae (Brassicaceae) and Reseda (Resedaceae), based on a library of ion trap HPLC-MS/MS data of reference desulfoglucosinolates

Fig. 6. Lack of the aliphatic glucosinolate (GSL) Pren (107) in B. vulgaris and spiking of pure (intact) Pren for establishing the limit of detection. A. Total ion chromatogram for the three dominating peaks in G-type B. vulgaris (dGSL preparation). B. Extracted ion trace for desulfo Pren in the same extract as A, showing lack of detection. C, D, E. Results of serial spiking of the crude extract with serial 10-fold dilutions of Pren before the desulfation procedure, showing linearity also at low levels and ability to detect trace levels.

Fig. 9 in Glucosinolate profiles and phylogeny in Barbarea compared to other tribe Cardamineae (Brassicaceae) and Reseda (Resedaceae), based on a library of ion trap HPLC-MS/MS data of reference desulfoglucosinolates

Fig. 9. Extracted ion HPLC-MS chromatograms of desulfoglucosinolates prepared from glucosinolates (GSLs) in seeds (A–F) or leaves (G) of Reseda luteola and seeds of Reseda odorata (H). The three major peaks (A, B, C) represent PE, IM and BAR, much like many Barbarea spp. Focus on minor peaks (D) allowed conclusive identification of EBAR, confirmed by tR and the characteristic MS2 spectrum. A range of putative derivatives were not detected (E, F), but an unidentified hydroxybutylGSL was present (G), as was a known glycoside in R. odorata.

Fig. 5 in Glucosinolate profiles and phylogeny in Barbarea compared to other tribe Cardamineae (Brassicaceae) and Reseda (Resedaceae), based on a library of ion trap HPLC-MS/MS data of reference desulfoglucosinolates

Fig. 5. MS2 spectra of desulfated glucosinolates (GSLs) confirming the identity of two 2-3homoMet-derived GSLs in C. hirsuta. While the spectrum of desulfo Buen contains only the usual fragments for this type of dGSL (a, [anhydroGlc+Na]+; b, [thioGlc+Na]+), the spectrum of desulfo Peen contains an additional usual fragment (c, [M-anhydroGlc Na]+) and two unusual + fragment ions suggesting a structure-specific cyclization and exchange of O during fragmentation: 201, [gluconolactone Na]+ and 152, [C H NS Na]+. + 6 11 + The unusual fragments can be rationalized as fragment a plus O and fragment c minus O, respectively.

Fig. 3 in Glucosinolate profiles and phylogeny in Barbarea compared to other tribe Cardamineae (Brassicaceae) and Reseda (Resedaceae), based on a library of ion trap HPLC-MS/MS data of reference desulfoglucosinolates

Fig. 3. HPLC-MS chromatograms of desulfoglucosinolates (dGSLs) prepared from glucosinolates (GSLs) in Planodes virginica (A) and Nasturtium officinale (B–C) seeds, showing qualitative similarities and quantitative contrasts. Major peaks (B) from N. officinale revealed many of the same GSLs as in A, but levels of EBAR (40R) were much lower while levels were much higher for the biosynthetic precursor PE (105). A focus on trace peaks from N. officinale (C) revealed sharp peaks representing a range of minor constituents. Due to the closely eluting peaks, the latter chromatograms (B–C) were made by combining extracted ion chromatograms corresponding to [M+Na]+ of the indicated dGSLs. In C, the m/z 366 signal of d105 was omitted to allow visualization of minor coeluting peaks. An asterisk after a peak number indicates tentative identification. HPLC-MS conditions as in Olsen et al. (2016). TIC, total ion chromatogram, EIC, extracted ion chromatogram.