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Sambles CM, Salmon DL, Florance H, et al. Ash leaf metabolomes reveal differences between trees tolerant and susceptible to ash dieback disease. Sci Data. 2017;4:170190doi: 10.1038/sdata.2017.190.
Sambles, C. M., Salmon, D. L., Florance, H., Howard, T. P., Smirnoff, N., Nielsen, L. R., McKinney, L. V., Kjær, E. D., Buggs, R. J. A., Studholme, D. J., & Grant, M. (2017). Ash leaf metabolomes reveal differences between trees tolerant and susceptible to ash dieback disease. Scientific data, 4170190. https://doi.org/10.1038/sdata.2017.190
Sambles, Christine M, et al. "Ash leaf metabolomes reveal differences between trees tolerant and susceptible to ash dieback disease." Scientific data vol. 4 (2017): 170190. doi: https://doi.org/10.1038/sdata.2017.190
Sambles CM, Salmon DL, Florance H, Howard TP, Smirnoff N, Nielsen LR, McKinney LV, Kjær ED, Buggs RJA, Studholme DJ, Grant M. Ash leaf metabolomes reveal differences between trees tolerant and susceptible to ash dieback disease. Sci Data. 2017 Dec 19;4:170190. doi: 10.1038/sdata.2017.190. PMID: 29257137; PMCID: PMC5735976.
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Sci Data. 2017 Dec 19;4:170190. doi: 10.1038/sdata.2017.190.

Ash leaf metabolomes reveal differences between trees tolerant and susceptible to ash dieback disease.

Scientific data

Christine M Sambles, Deborah L Salmon, Hannah Florance, Thomas P Howard, Nicholas Smirnoff, Lene R Nielsen, Lea V McKinney, Erik D Kjær, Richard J A Buggs, David J Studholme, Murray Grant

Affiliations

  1. Biosciences, Geoffrey Pope Building, University of Exeter, Stocker Road, Exeter EX4 4QD, UK.
  2. School of Life Sciences, Gibbet Hill Campus, University of Warwick, Coventry CV4 7AL, UK.
  3. SynthSys, Roger Land Building, Alexander Crum Brown Road, The King's Buildings, Edinburgh EH9 3FF, UK.
  4. School of Biology, Devonshire Building, Newcastle University, Newcastle upon, Tyne NE1 7RU, UK.
  5. Department of Geosciences and Natural Resource Management, University of Copenhagen, Rolighedsvej 23, Frederiksberg C 1958, Denmark.
  6. Royal Botanic Gardens Kew, Richmond, Surrey TW9 3AB, UK.
  7. School of Biological and Chemical Sciences, Queen Mary University of London, Mile End Road, London E1 4NS, UK.

PMID: 29257137 PMCID: PMC5735976 DOI: 10.1038/sdata.2017.190

Abstract

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.

References

  1. Nucleic Acids Res. 2015 Jul 1;43(W1):W251-7 - PubMed
  2. Planta Med. 2013 Nov;79(16):1576-87 - PubMed
  3. Rapid Commun Mass Spectrom. 2008 Jun;22(12):1941-54 - PubMed
  4. Mol Plant Pathol. 2013 Dec;14 (9):856-60 - PubMed
  5. Mol Plant Pathol. 2014 Jan;15(1):5-21 - PubMed
  6. Sci Rep. 2016 Feb 22;6:21895 - PubMed
  7. Genome Res. 2003 Nov;13(11):2498-504 - PubMed
  8. J Nat Prod. 2010 Jan;73(1):2-6 - PubMed
  9. Phytochemistry. 2009 Dec;70(17-18):2072-7 - PubMed
  10. Anal Chem. 2006 Feb 1;78(3):779-87 - PubMed
  11. Anal Chem. 2012 Jan 3;84(1):283-9 - PubMed
  12. BMC Bioinformatics. 2008 Nov 28;9:504 - PubMed
  13. Nature. 2012 Apr 11;484(7393):186-94 - PubMed
  14. Nucleic Acids Res. 2013 Jan;41(Database issue):D781-6 - PubMed
  15. Molecules. 2015 May 05;20(5):8107-24 - PubMed
  16. Sci Rep. 2016 Jan 13;6:19335 - PubMed
  17. Phytopathology. 2016 Dec;106(12 ):1535-1543 - PubMed
  18. Heredity (Edinb). 2011 May;106(5):788-97 - PubMed
  19. Nature. 2017 Jan 12;541(7636):212-216 - PubMed
  20. Evol Appl. 2012 Apr;5(3):219-28 - PubMed
  21. Anal Chem. 2006 Sep 1;78(17):6140-52 - PubMed
  22. Curr Protoc Bioinformatics. 2016 Mar 24;53:14.13.1-18 - PubMed

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