| Autor: |
Sambles CM; Biosciences, Geoffrey Pope Building, University of Exeter, Stocker Road, Exeter EX4 4QD, UK.; School of Life Sciences, Gibbet Hill Campus, University of Warwick, Coventry CV4 7AL, UK., Salmon DL; Biosciences, Geoffrey Pope Building, University of Exeter, Stocker Road, Exeter EX4 4QD, UK., Florance H; SynthSys, Roger Land Building, Alexander Crum Brown Road, The King's Buildings, Edinburgh EH9 3FF, UK., Howard TP; School of Biology, Devonshire Building, Newcastle University, Newcastle upon, Tyne NE1 7RU, UK., Smirnoff N; Biosciences, Geoffrey Pope Building, University of Exeter, Stocker Road, Exeter EX4 4QD, UK., Nielsen LR; Department of Geosciences and Natural Resource Management, University of Copenhagen, Rolighedsvej 23, Frederiksberg C 1958, Denmark., McKinney LV; Department of Geosciences and Natural Resource Management, University of Copenhagen, Rolighedsvej 23, Frederiksberg C 1958, Denmark., Kjær ED; Department of Geosciences and Natural Resource Management, University of Copenhagen, Rolighedsvej 23, Frederiksberg C 1958, Denmark., Buggs RJA; Royal Botanic Gardens Kew, Richmond, Surrey TW9 3AB, UK.; School of Biological and Chemical Sciences, Queen Mary University of London, Mile End Road, London E1 4NS, UK., Studholme DJ; Biosciences, Geoffrey Pope Building, University of Exeter, Stocker Road, Exeter EX4 4QD, UK., Grant M; School of Life Sciences, Gibbet Hill Campus, University of Warwick, Coventry CV4 7AL, UK. |
| Abstrakt: |
European common ash, Fraxinus excelsior, is currently threatened by Ash dieback (ADB) caused by the fungus, Hymenoscyphus fraxineus. To detect and identify metabolites that may be products of pathways important in contributing to resistance against H. fraxineus, we performed untargeted metabolomic profiling on leaves from five high-susceptibility and five low-susceptibility F. excelsior individuals identified during Danish field trials. We describe in this study, two datasets. The first is untargeted LC-MS metabolomics raw data from ash leaves with high-susceptibility and low-susceptibility to ADB in positive and negative mode. These data allow the application of peak picking, alignment, gap-filling and retention-time correlation analyses to be performed in alternative ways. The second, a processed dataset containing abundances of aligned features across all samples enables further mining of the data. Here we illustrate the utility of this dataset which has previously been used to identify putative iridoid glycosides, well known anti-herbivory terpenoid derivatives, and show differential abundance in tolerant and susceptible ash samples. |
