Peaks were tentatively classified and annotated using the SIRIUS software version 5.5.7, which contained SIRIUS[1], ZODIAC[2], CSI:FingerID[3][4] and CANOPUS[5][6]. Default settings were used for SIRIUS, ZODIAC and CSI:FingerID, however only formulas from natural-product-based databases were considered (Bio Database, Biocyc, CHEBI, COCONUT, EcoCyc Mine, CNPS, HMDB, KEGG, KEGG Mine, KNApSAcK, Natural Products and Plantcyc) for CANOPUS. Manual analysis of the SIRIUS, ZODIAC, CSI:FingerID and CANPOUS scores was conducted to assign the selected features.
Refs:
[1] Dührkop K, Fleischauer M, Ludwig M, Aksenov AA, Melnik AV, Meusel M, Dorrestein PC, Rousu J, Böcker S. SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information. Nat Methods. 2019 Apr;16(4):299-302. doi:10.1038/s41592-019-0344-8. Epub 2019 Mar 18. PMID:30886413.
[2] Ludwig M, Nothias LF, Dührkop K, Koester I, Fleischauer M, Hoffmann MA, Petras D, Vargas F, Morsy M, Aluwihare L, Dorrestein PC. Database-independent molecular formula annotation using Gibbs sampling through ZODIAC. Nature Machine Intelligence. 2020 Oct;2(10):629-41. doi:10.1038/s42256-020-00234-6.
[3] Dührkop K, Shen H, Meusel M, Rousu J, Böcker S. Searching molecular structure databases with tandem mass spectra using CSI:FingerID. Proc Natl Acad Sci U S A. 2015 Oct 13;112(41):12580-5. doi:10.1073/pnas.1509788112. Epub 2015 Sep 21. PMID:26392543.
[4] Hoffmann MA, Nothias LF, Ludwig M, Fleischauer M, Gentry EC, Witting M, Dorrestein PC, Dührkop K, Böcker S. Assigning confidence to structural annotations from mass spectra with COSMIC. BioRxiv. 2021 Mar 19:2021-03. doi:10.1101/2021.03.18.435634.
[5] Djoumbou Feunang Y, Eisner R, Knox C, Chepelev L, Hastings J, Owen G, Fahy E, Steinbeck C, Subramanian S, Bolton E, Greiner R, Wishart DS. ClassyFire: automated chemical classification with a comprehensive, computable taxonomy. J Cheminform. 2016 Nov 4;8:61. doi:10.1186/s13321-016-0174-y. PMID:27867422.
[6] Dührkop K, Nothias LF, Fleischauer M, Reher R, Ludwig M, Hoffmann MA, Petras D, Gerwick WH, Rousu J, Dorrestein PC, Böcker S. Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra. Nat Biotechnol. 2021 Apr;39(4):462-471. doi:10.1038/s41587-020-0740-8. Epub 2020 Nov 23. PMID:33230292.
"],"disease":[""],"extraction_protocol":["The procedure from Blatt-Janmaat et al. was followed with no modifications[1]. Frozen plants were extracted with 80:20, MeOH:H2O and samples were reconstituted to 10 mg fresh weight per 100 µL for LC-MS analysis.
Briefly, frozen plants were homogenized for 5 min at 30 Hz in Teflon chambers equipped with 2 stainless steel balls. The chambers were rinsed with 4 x 250 μL of cold 80:20 methanol:H2O supplemented with 5 μM Kinetin (Sigma), 5 μM Biochanin A (Sigma), and 5 μM N-(3-Indolylacetyl)-L-alanine (Sigma) and the slurry was shook at 1000 rpm for 15 min at room temperature. Shaken samples were sonicated for 15 min and shook for an additional 15 min at 1000 rpm. The mixture was centrifuged at 13,000 rpm for 15 min and 750 μL of the supernatant was concentrated in vacuo. Samples were reconstituted to 10 mg fresh weight per 100 μL with 80:20 methanol:H2O before LC-MS/MS analysis.
Ref:
[1] Blatt-Janmaat K, Neumann S, Schmidt F, Ziegler J, Peters K, Qu Y. Impact of in vitro hormone treatments on the bibenzyl production of Radula complanata. Botany. 2022 Jun 27. doi: 10.1139/cjb-2022-0048.
"],"organism":["blank","reference compound","Radula complanata"],"data_transformation_protocol":["Raw LC-MS files (Bruker Daltonics .d format) were converted to .mzML with MSConvertGUI version 3 from the ProteoWizard software suite[1]. The procedure from Blatt-Janmaat et al. was used for peak detection[2]. Any modified parameters will be listed, however no major modifications were made.
Briefly, R 4.1.1 (available from https://cran.r-project.org/) with the XCMS 3.14.1 package[3] was used for peak detection. Parameters were optimized with IPO[4] and manual adjustments were made based on instrument knowledge. The centWave algorithm was applied for peak detection[5] with the following adjusted parameters: ppm = 9.5, mzCenterFun = 'wMean', peakwidth = c(4, 33), prefilter = c(2, 140), mzdiff = 0.0012, noise = 0, integrate = 1, firstBaselineCheck = TRUE, verboseColumns = FALSE, fitgauss = FALSE, roiList = list(), roiScales = numeric(). Additionally, snthresh = 10 for negative mode and snthresh = 5 for positive mode. Detected peaks were grouped (parameters: minFraction = 0.7, bw = 2), and retention time was corrected using the adjustRtime (parameters: minFraction = 0.7, smooth = 'loess', span = 0.2, family = 'gaussian') function in XCMS, and finally, peaks were filled with the fillChromPeaks function in XCMS. Data were quality checked manually by extracting internal standard peaks and analyzing the shape and retention time of the peaks. Positive and negative data sets were independently processed and merged into a combined feature table which was used to construct several other constrained tables. A presence/absence table was constructed to determine if a peak was present in the MS1 data (cut-off 0.1% of maximum peak), and a compound table containing features that had MS2 spectra available was also constructed. In preparation for statistical analysis, missing MS1 data were replaced with the value 0.1. MS2 spectra were extracted from MS1 spectra and imported to SIRIUS 5.5.7 for further analysis.
All statistical testing was conducted in R. Variation partitioning was conducted with the varpart function from the vegan package to identify key factors influencing the data. Sample separation was visualized by conducting Principal Component Analyses (PCA) using the prcomp function. PLS analysis was conducted to select those variables that are most different between the samples[6].
Source code to recreate the results has been made available on GitHub - https://github.com/ipb-halle/iESTIMATE/tree/main/use-cases/radula-environmental.
Refs:
[1] Chambers MC, Maclean B, Burke R, Amodei D, Ruderman DL, Neumann S, Gatto L, Fischer B, Pratt B, Egertson J, Hoff K, Kessner D, Tasman N, Shulman N, Frewen B, Baker TA, Brusniak MY, Paulse C, Creasy D, Flashner L, Kani K, Moulding C, Seymour SL, Nuwaysir LM, Lefebvre B, Kuhlmann F, Roark J, Rainer P, Detlev S, Hemenway T, Huhmer A, Langridge J, Connolly B, Chadick T, Holly K, Eckels J, Deutsch EW, Moritz RL, Katz JE, Agus DB, MacCoss M, Tabb DL, Mallick P. A cross-platform toolkit for mass spectrometry and proteomics. Nat Biotechnol. 2012 Oct;30(10):918-20. doi:10.1038/nbt.2377. PMID:23051804.
[2] Blatt-Janmaat K, Neumann S, Schmidt F, Ziegler J, Peters K, Qu Y. Impact of in vitro hormone treatments on the bibenzyl production of Radula complanata. Botany. 2022 Jun 27. doi: 10.1139/cjb-2022-0048.
[3] Smith CA, Want EJ, O'Maille G, Abagyan R, Siuzdak G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem. 2006 Feb 1;78(3):779-87. doi:10.1021/ac051437y. PMID:16448051.
[4] Libiseller G, Dvorzak M, Kleb U, Gander E, Eisenberg T, Madeo F, Neumann S, Trausinger G, Sinner F, Pieber T, Magnes C. IPO: a tool for automated optimization of XCMS parameters. BMC Bioinformatics. 2015 Apr 16;16:118. doi:10.1186/s12859-015-0562-8. PMID:25888443.
[5] Tautenhahn R, Böttcher C, Neumann S. Highly sensitive feature detection for high resolution LC/MS. BMC Bioinformatics. 2008 Nov 28;9:504. doi:10.1186/1471-2105-9-504. PMID:19040729.
[6] Peters K, Balcke G, Kleinenkuhnen N, Treutler H, Neumann S. Untargeted In Silico Compound Classification-A Novel Metabolomics Method to Assess the Chemodiversity in Bryophytes. Int J Mol Sci. 2021 Mar 23;22(6):3251. doi:10.3390/ijms22063251. PMID:33806786.
"],"study_factor":["Elevation","Nearest road distance","Host tree family","Collection month","North","East","Location","Host tree","Biological replicate"],"metabolights_link":["https://www.ebi.ac.uk/metabolights/MTBLS6740"],"submitter_email":["sneumann@ipb-halle.de","kpeters@ipb-halle.de"],"sample_collection_protocol":["Plant material was harvested in the summer of 2021 from Sweden, Germany and Canada. Mature plants were collected and transported back to the lab where the gametophytes were separated and frozen in liquid nitrogen. Plants were stored at -20 °C until HPLC analysis. Voucher specimens were deposited under the following accession numbers: from JE04010995 to JE04011000 and from UNB-UH-69080 to UNB-UH-69083.
"],"repository":["MetaboLights"],"study_status":["Public"],"ptm_modification":[""],"omics_type":["Metabolomics"],"instrument_platform":["Liquid Chromatography MS - negative - reverse phase","Liquid Chromatography MS - positive - reverse phase"],"study_design":["Environmental","bryophyte","liquid chromatography mass spectrometry assay","Plant Metabolomics","Ecological data","Mosses, lichens, liverworts (organism)","untargeted metabolite profiling","Radula complanata"],"chromatography_protocol":["The procedure from Blatt-Janmaat et al. was followed with no modifications[1]. Samples were separated using a Bruker Elite HPLC coupled to a Bruker TIMS-TOF equipped with a C18 column. A gradient of 0.1% aqueous formic acid and 0.1% acetonitrile were used as the mobile phase.
Briefly, Samples were separated and analyzed with a Bruker Elite HPLC equipped with a Nucleodur X18 Gravity-SB column (1.8 µm 100 x 2 Macherey Nagel, Dueren, Germany) coupled to a Bruker TIMS-TOF[rew1] (timsTOF Pro, Bruker, Bremen, Germany). Chromatographic separations were performed at 35 °C with the following binary gradient of 0.1% aqueous formic acid (solvent A) and acetonitrile (solvent B): 0.0-1.0 min was isocratic 98% A; 1.0-14.0 min was a linear gradient from 98% to 5% A; 14.0-17.5 min was isocratic 5% A; 18.0-20.0 min was isocratic 98% A. The flow rate was maintained at 0.5 mL/min for the duration of the run, 2 µL was injected per sample and the injection chamber was maintained at 4 °C. For samples with volumes too low to be injected, a 2x dilution was performed and 4 µL was injected instead.
Ref:
[1] Blatt-Janmaat K, Neumann S, Schmidt F, Ziegler J, Peters K, Qu Y. Impact of in vitro hormone treatments on the bibenzyl production of Radula complanata. Botany. 2022 Jun 27. doi: 10.1139/cjb-2022-0048.
"],"publication":["Host Tree and Geography Induce Metabolic Shifts in the Epiphytic Liverwort Radula complanata. 10.3390/plants12030571. PMID:36771656"],"curator_keywords":["Environmental","bryophyte","liquid chromatography mass spectrometry assay","Plant Metabolomics","Ecological data","Mosses, lichens, liverworts (organism)","untargeted metabolite profiling","Radula complanata"],"submitter_name":["Kristian Peters","Steffen Neumann"],"submitter_affiliation":["IPB Halle"],"mass_spectrometry_protocol":["The procedure from Blatt-Janmaat et al. was followed with no modifications[1]. Samples were separated using a Bruker Elite HPLC coupled to a Bruker TIMS-TOF equipped with a C18 column. Data was collected in data-dependent acquisition (DDA) mode.
Briefly, separate injections were performed for analysis in positive and negative mode and ionized with electrospray ionization (ESI). Data-dependent acquisition (DDA) mode (also called auto-MS/MS) was utilized to acquire MS2 spectra for the most abundant peaks with the following instrument settings. For negative mode, the auto-MS/MS conditions were as follows: ion source: end plate; offset: 400 V; capillary voltage: 4000 V; nebulizer: 3 bar; dry gas: 10 L/min; dry temperature: 250 °C; scan mass range: 20-1300 m/z; spectra rate: 3 Hz; cycle time: 0.5 s; threshold: 1150 cts; collision energy: 20. For positive mode, the following conditions were utilized: ion source: end plate; offset: 500 V; capillary voltage: 4500 V; nebulizer: 3 bar; dry gas: 10 L/min; dry temperature: 250 °C; scan mass range: 20-1300 m/z; spectra rate: 3 Hz; cycle time: 0.5 s; threshold: 2285 cts; collision energy: 30. Calibration for both modes was completed with Na-Formate.
To ensure consistency throughout the campaign, a quality control (QC) sample was injected every 8 samples to check retention time drift and to detect potential carryover in the column. The QC sample was composed of the 3 internal standard compounds (Biochanin A, Kinetin and 5 µM N-(3-Indolylacetyl)-L-alanine) as well as 6 bibenzyl metabolites (Radulanin A, Radulanin H, Radulanin L, 4-prenyldihydropinosylvin, 3,5-dihydroxy-6-carbomethoxy-2-(3-methyl-2-butenyl)bibenzyl and 2-(3,7-Dimethylocta-2,6-dienyl)-5-(2-phenylethyl)benzene-1,3-diol) that were previously isolated from Radula complanata.
Ref:
[1] Blatt-Janmaat K, Neumann S, Schmidt F, Ziegler J, Peters K, Qu Y. Impact of in vitro hormone treatments on the bibenzyl production of Radula complanata. Botany. 2022 Jun 27. doi: 10.1139/cjb-2022-0048.
"],"pubmed_abstract":["Bryophytes are prolific producers of unique, specialized metabolites that are not found in other plants. As many of these unique natural products are potentially interesting, for example, pharmacological use, variations in the production regarding ecological or environmental conditions have not often been investigated. Here, we investigate metabolic shifts in the epiphytic Radula complanata L. (Dumort) with regard to different environmental conditions and the type of phorophyte (host tree). Plant material was harvested from three different locations in Sweden, Germany, and Canada and subjected to untargeted liquid chromatography high-resolution mass-spectrometry (UPLC/ESI-QTOF-MS) and data-dependent acquisition (DDA-MS). Using multivariate statistics, variable selection methods, in silico compound identification, and compound classification, a large amount of variation (39%) in the metabolite profiles was attributed to the type of host tree and 25% to differences in environmental conditions. We identified 55 compounds to vary significantly depending on the host tree (36 on the family level) and 23 compounds to characterize R. complanata in different environments. Taken together, we found metabolic shifts mainly in primary metabolites that were associated with the drought response to different humidity levels. The metabolic shifts were highly specific to the host tree, including mostly specialized metabolites suggesting high levels of ecological interaction. As R. complanata is a widely distributed generalist species, we found it to flexibly adapt its metabolome according to different conditions. We found metabolic composition to also mirror the constitution of the habitat, which makes it interesting for conservation measures."],"pubmed_title":["Host Tree and Geography Induce Metabolic Shifts in the Epiphytic Liverwort Radula complanata."],"pubmed_authors":["Blatt-Janmaat Kaitlyn L KL, Neumann Steffen S, Ziegler Jörg J, Peters Kristian K"],"additional_accession":[]},"is_claimable":false,"name":"Host Tree and Geography Induce Metabolic Shifts in the Epiphytic Liverwort Radula complanata","description":"Bryophytes are prolific producers of unique, specialized metabolites that are not found in other plants. As many of these unique natural products are potentially interesting, for example, pharmacological use, variations in the production regarding ecological or environmental conditions have not often been investigated. Here, we investigate metabolic shifts in the epiphytic Radula complanata L. (Dumort) with regard to different environmental conditions and the type of phorophyte (host tree). Plant material was harvested from three different locations in Sweden, Germany, and Canada and subjected to untargeted liquid chromatography high-resolution mass-spectrometry (UPLC/ESI-QTOF-MS) and data-dependent acquisition (DDA-MS). Using multivariate statistics, variable selection methods, in silico compound identification, and compound classification, a large amount of variation (39%) in the metabolite profiles was attributed to the type of host tree and 25% to differences in environmental conditions. We identified 55 compounds to vary significantly depending on the host tree (36 on the family level) and 23 compounds to characterize R. complanata in different environments. Taken together, we found metabolic shifts mainly in primary metabolites that were associated with the drought response to different humidity levels. The metabolic shifts were highly specific to the host tree, including mostly specialized metabolites suggesting high levels of ecological interaction. As R. complanata is a widely distributed generalist species, we found it to flexibly adapt its metabolome according to different conditions. We found metabolic composition to also mirror the constitution of the habitat, which makes it interesting for conservation measures.","dates":{"publication":"2026-01-29","submission":"2024-04-14"},"accession":"MTBLS6740","cross_references":{"pubmed":["36771656"]}}