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Rapid screening of secondary aromatic metabolites in Populus trichocarpa leaves

BACKGROUND: High-throughput metabolomics analytical methodology is needed for population-scale studies of bioenergy-relevant feedstocks such as poplar (Populus sp.). Here, the authors report the relative abundance of extractable aromatic metabolites in Populus trichocarpa leaves rapidly estimated us...

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Autores principales: Harman-Ware, Anne E., Martin, Madhavi Z., Engle, Nancy L., Doeppke, Crissa, Tschaplinski, Timothy J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
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Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9999501/
https://www.ncbi.nlm.nih.gov/pubmed/36899393
http://dx.doi.org/10.1186/s13068-023-02287-2
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author Harman-Ware, Anne E.
Martin, Madhavi Z.
Engle, Nancy L.
Doeppke, Crissa
Tschaplinski, Timothy J.
author_facet Harman-Ware, Anne E.
Martin, Madhavi Z.
Engle, Nancy L.
Doeppke, Crissa
Tschaplinski, Timothy J.
author_sort Harman-Ware, Anne E.
collection PubMed
description BACKGROUND: High-throughput metabolomics analytical methodology is needed for population-scale studies of bioenergy-relevant feedstocks such as poplar (Populus sp.). Here, the authors report the relative abundance of extractable aromatic metabolites in Populus trichocarpa leaves rapidly estimated using pyrolysis-molecular beam mass spectrometry (py-MBMS). Poplar leaves were analyzed in conjunction with and validated by GC/MS analysis of extracts to determine key spectral features used to build PLS models to predict the relative composition of extractable aromatic metabolites in whole poplar leaves. RESULTS: The Pearson correlation coefficient for the relative abundance of extractable aromatic metabolites based on ranking between GC/MS analysis and py-MBMS analysis of the Boardman leaf set was 0.86 with R(2) = 0.76 using a simplified prediction approach from select ions in MBMS spectra. Metabolites most influential to py-MBMS spectral features in the Clatskanie set included the following compounds: catechol, salicortin, salicyloyl-coumaroyl-glucoside conjugates, α-salicyloylsalicin, tremulacin, as well as other salicylates, trichocarpin, salicylic acid, and various tremuloidin conjugates. Ions in py-MBMS spectra with the highest correlation to the abundance of extractable aromatic metabolites as determined by GC/MS analysis of extracts, included m/z 68, 71, 77, 91, 94, 105, 107, 108, and 122, and were used to develop the simplified prediction approach without PLS models or a priori measurements. CONCLUSIONS: The simplified py-MBMS method is capable of rapidly screening leaf tissue for relative abundance of extractable aromatic secondary metabolites to enable prioritization of samples in large populations requiring comprehensive metabolomics that will ultimately inform plant systems biology models and advance the development of optimized biomass feedstocks for renewable fuels and chemicals. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13068-023-02287-2.
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spelling pubmed-99995012023-03-11 Rapid screening of secondary aromatic metabolites in Populus trichocarpa leaves Harman-Ware, Anne E. Martin, Madhavi Z. Engle, Nancy L. Doeppke, Crissa Tschaplinski, Timothy J. Biotechnol Biofuels Bioprod Methodology BACKGROUND: High-throughput metabolomics analytical methodology is needed for population-scale studies of bioenergy-relevant feedstocks such as poplar (Populus sp.). Here, the authors report the relative abundance of extractable aromatic metabolites in Populus trichocarpa leaves rapidly estimated using pyrolysis-molecular beam mass spectrometry (py-MBMS). Poplar leaves were analyzed in conjunction with and validated by GC/MS analysis of extracts to determine key spectral features used to build PLS models to predict the relative composition of extractable aromatic metabolites in whole poplar leaves. RESULTS: The Pearson correlation coefficient for the relative abundance of extractable aromatic metabolites based on ranking between GC/MS analysis and py-MBMS analysis of the Boardman leaf set was 0.86 with R(2) = 0.76 using a simplified prediction approach from select ions in MBMS spectra. Metabolites most influential to py-MBMS spectral features in the Clatskanie set included the following compounds: catechol, salicortin, salicyloyl-coumaroyl-glucoside conjugates, α-salicyloylsalicin, tremulacin, as well as other salicylates, trichocarpin, salicylic acid, and various tremuloidin conjugates. Ions in py-MBMS spectra with the highest correlation to the abundance of extractable aromatic metabolites as determined by GC/MS analysis of extracts, included m/z 68, 71, 77, 91, 94, 105, 107, 108, and 122, and were used to develop the simplified prediction approach without PLS models or a priori measurements. CONCLUSIONS: The simplified py-MBMS method is capable of rapidly screening leaf tissue for relative abundance of extractable aromatic secondary metabolites to enable prioritization of samples in large populations requiring comprehensive metabolomics that will ultimately inform plant systems biology models and advance the development of optimized biomass feedstocks for renewable fuels and chemicals. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13068-023-02287-2. BioMed Central 2023-03-10 /pmc/articles/PMC9999501/ /pubmed/36899393 http://dx.doi.org/10.1186/s13068-023-02287-2 Text en © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Methodology
Harman-Ware, Anne E.
Martin, Madhavi Z.
Engle, Nancy L.
Doeppke, Crissa
Tschaplinski, Timothy J.
Rapid screening of secondary aromatic metabolites in Populus trichocarpa leaves
title Rapid screening of secondary aromatic metabolites in Populus trichocarpa leaves
title_full Rapid screening of secondary aromatic metabolites in Populus trichocarpa leaves
title_fullStr Rapid screening of secondary aromatic metabolites in Populus trichocarpa leaves
title_full_unstemmed Rapid screening of secondary aromatic metabolites in Populus trichocarpa leaves
title_short Rapid screening of secondary aromatic metabolites in Populus trichocarpa leaves
title_sort rapid screening of secondary aromatic metabolites in populus trichocarpa leaves
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9999501/
https://www.ncbi.nlm.nih.gov/pubmed/36899393
http://dx.doi.org/10.1186/s13068-023-02287-2
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