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MS-BioGraphs MS

<p><a href="https://blogs.qub.ac.uk/DIPSA/MS-BioGraphs/">https://blogs.qub.ac.uk/DIPSA/MS-BioGraphs/</a></p> <table> <tbody> <tr> <td>Name</td> <td>MS-BioGraphs – MS</td> </tr> <tr> <td>URL</td> <td><a href="https://blogs.qub.ac.uk/DIPSA/MS-BioGraphs-MS">https://blogs.qub.ac.uk/DIPSA/MS-BioGraphs-MS</a></td> </tr> <tr> <td>Graph Explanation</td> <td>Vertices represent proteins and each edge represents the sequence similarity between its two endpoints</td> </tr> <tr> <td>Edge Weighted</td> <td>Yes</td> </tr> <tr> <td>Directed</td> <td>No</td> </tr> <tr> <td>Number of Vertices</td> <td>1,757,323,526</td> </tr> <tr> <td>Number of Edges</td> <td>2,488,069,027,875</td> </tr> <tr> <td>Maximum Degree</td> <td>814,957</td> </tr> <tr> <td>Minimum Weight</td> <td>98</td> </tr> <tr> <td>Maximum Weight</td> <td>634,925</td> </tr> <tr> <td>Number of Zero-Degree Vertices</td> <td>6,437,984</td> </tr> <tr> <td>Average Degree</td> <td>1,415.8</td> </tr> <tr> <td>Size of The Largest WCC</td> <td>2,486,890,448,664</td> </tr> <tr> <td>Number of WCC</td> <td>148,861,367</td> </tr> <tr> <td>Creation Details</td> <td><a href="https://blogs.qub.ac.uk/DIPSA/MS-BioGraphs-Sequence-Similarity-Graph-Datasets/">MS-BioGraphs: Sequency Similarity Graph Datasets</a></td> </tr> <tr> <td>Format</td> <td><a href="https://webgraph.di.unimi.it/">WebGraph</a></td> </tr> <tr> <td>License</td> <td><a href="https://creativecommons.org/licenses/by-nc-sa/2.0/">CC BY-NC-SA</a></td> </tr> <tr> <td>QUB IDF</td> <td>2223-052</td> </tr> <tr> <td>DOI</td> <td><a href="https://doi.org/10.5281/zenodo.7820808">10.5281/zenodo.7820808</a></td> </tr> <tr> <td>Citation</td> <td> <pre> Koohi Esfahani, Mohsen, Boldi, Paolo, Vandierendonck, Hans, Kilpatrick, Peter, Vigna,Sebastiano. (2023). MS-BioGraphs - MS. https://doi.org/10.5281/zenodo.7820808. http://blogs.qub.ac.uk/DIPSA/MS-BioGraphs-MS.</pre> </td> </tr> <tr> <td>Bibtex</td> <td> <pre> @misc{MS-BioGraphs-MS, year = {2023}, author = {Mohsen Koohi Esfahani and Paolo Boldi and Hans Vandierendonck and Peter Kilpatrick and Sebastiano Vigna}, title = {{MS-BioGraphs - MS}}, doi = {10.5281/zenodo.7820808}, url = {http://blogs.qub.ac.uk/DIPSA/MS-BioGraphs-MS}, howpublished= {\url{http://blogs.qub.ac.uk/DIPSA/MS-BioGraphs-MS}}}</pre> </td> </tr> </tbody> </table>

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.

Fig. 4 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. 4. HPLC-MS chromatogram of desulfoglucosinolates (dGSLs) prepared from glucosinolates (GSLs) from leaves (A) and petioles (B) of horseradish (Armoracia rusticana), focusing on trace level GSLs. The chromatograms were made by combining extracted ion chromatograms corresponding to [M+Na]+ of the indicated dGSLs, from analyses that were much overloaded with respect to the dominating dGSL d107 from Pren. In panel A, an insert shows magnification of the chromatogram from 5.2 to 5.8 min. Neither suggested BAR nor EBAR were detectable. In panel B, only extracted ion chromatograms of m/z 382 (BAR/EBAR), 352 (BZ), 366 (PE), 380 (3PP), 394 (4PB), 408 (5PP at high tR and 6mSOh at 5.4 min), 422 (7mSOh) and 436 (8mSOo) are included. Unlabeled trace peaks did not exhibit a combination of tR and m/z suitable for any of the mentioned candidates. HPLC conditions as in Olsen et al. (2016). Panel A depicts analysis of the Copenhagen garden accession; panel B from the naturalized population at Lake Fures¨o. An asterisk after a peak number indicates tentative identification.

Fig. 2 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. 2. Detection of thioglucose-acylated glucosinolates (GSLs) by HPLC-MS of desulfated derivatives prepared from the indicated species. (A–C) Analysis of seeds of Arabidopsis thaliana Col-0 used as reference material for characteristic GSLs. Shown are the total ion chromatogram (A) and extracted ion chromatograms for sodium adducts of desulfo 6′Bz 4BzOb (d125) (B) and desulfo 6′Bz 4mSb (d127) (C). (D–F) Analysis of seeds of Barbarea grayi for dominating GSLs. Shown are total ion chromatograms (D), and extracted ion chromatograms for sodium adducts of desulfo 6'iF BAR (d131S) (E) and desulfo 6'iF PE (d129) (F).

Fig. 1 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. 1. MS2 spectra of pairs of desulfoglucosinolates with and without a side chain double bond. Four short chain desulfoglucosinolates were investigated, including Na+ adducts of all (A–D) and in addition H+ adducts of the methylthio substituted (E–F), as indicated in each spectrum. The desulfo derivative of the putative 9mSn ([89]), poorly characterized in the literature, was also investigated (G).

Fig. 5. Proposed fragmentation scheme for 2-aminobenzoyl O in Chemotaxonomic investigation of Apocynaceae for retronecine-type pyrrolizidine alkaloids using HPLC-MS/MS

Fig. 5. Proposed fragmentation scheme for 2-aminobenzoyl O-β-D-apiofuranosyl-(1 → 6)- β-D-glucopyranoside (m/z 432.15 when protonated) in positive ion mode ESI to m/z 300.11, 138.06, and 120.04 fragments.

Fig. 2 in Chemotaxonomic investigation of Apocynaceae for retronecine-type pyrrolizidine alkaloids using HPLC-MS/MS

Fig. 2. Common esterification patterns between necic acids and necine bases observed in retronecine-type PAs.

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Fig. 4 in Alkaloids from Lepidium meyenii (Maca), structural revision of macaridine and UPLC-MS/MS feature-based molecular networking

Fig. 4. Cluster containing identified imidazole and amidine alkaloids. Nodes were numbered from lowest to highest m/z values. Detailed information on unknown nodes can be found as Supporting Information.

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LC-MS/MS analysis of Buxus

<p>Lc-MS/MS analysis of the polar extracts of <em>Buxus</em></p>

Fig. 5 in Untargeted metabolite profiling of petal blight in field-grown Rhododendron agastum using GC-TOF-MS and UHPLC-QTOF-MS/MS

Fig. 5. The hierarchical clustering of the differentially abundant metabolites based on GC-TOF-MS in the healthy flowers and petal blight flowers of R. agastum. The data were log2 transformed, and similarity assessment for clustering was based on the Euclidean distance coefficient and complete clustering algorithm. Columns and rows represent individual metabolites and different samples, respectively.

Fig. 2 in Untargeted metabolite profiling of petal blight in field-grown Rhododendron agastum using GC-TOF-MS and UHPLC-QTOF-MS/MS

Fig. 2. Total ion current (TIC) chromatogram of healthy flowers (HF) and petal blight flowers (PBF) of R. agastum using GC-TOF-MS.

Fig. 3 in Untargeted metabolite profiling of petal blight in field-grown Rhododendron agastum using GC-TOF-MS and UHPLC-QTOF-MS/MS

Fig. 3. The data analysis of the metabolites based on GC-TOF-MS in the healthy flowers and petal blight flowers of R. agastum. (A) Principal component analysis (PCA). (B) Orthogonal projections to latent structures discriminant analysis (OPLS-DA). The green and red circles display 95% confidence regions of petal blight and healthy flowers. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 1 in Untargeted metabolite profiling of petal blight in field-grown Rhododendron agastum using GC-TOF-MS and UHPLC-QTOF-MS/MS

Fig. 1. The petal blight of R. agastum. (A) R. agastum grown in field habitat. (B) The healthy flower of R. agastum. (C) The petal blight flower of R. agastum (white arrow).

Fig. 4 in Untargeted metabolite profiling of petal blight in field-grown Rhododendron agastum using GC-TOF-MS and UHPLC-QTOF-MS/MS

Fig. 4. The data analysis of the metabolites based on UHPLC-QTOF-MS/MS in the healthy flowers and petal blight flowers of R. agastum. (A) Principal component analysis (PCA) analysis in positive ion modes. (B) Principal component analysis (PCA) analysis in negative ion modes. (C) Orthogonal projections to latent structures discriminant (OPLS-DA) analysis in positive ion modes. (D) Orthogonal projections to latent structures discriminant (OPLS-DA) analysis in negative ion modes. The green and red circles display 95% confidence regions of petal blight and healthy flowers. (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 Untargeted metabolite profiling of petal blight in field-grown Rhododendron agastum using GC-TOF-MS and UHPLC-QTOF-MS/MS

Fig. 6. The hierarchical clustering of the differentially abundant metabolites in the positive ion modes (A) and in the negative ion modes (B) based on UHPLC-QTOFMS/MS in the healthy flowers and petal blight flowers of R. agastum. The data were log2 transformed, and similarity assessment for clustering was based on the Euclidean distance coefficient and complete clustering algorithm. Columns and rows represent individual metabolites and different samples, respectively.