Literature DB >> 26423132

Non-structural carbohydrates in woody plants compared among laboratories.

Audrey G Quentin1, Elizabeth A Pinkard2, Michael G Ryan3, David T Tissue4, L Scott Baggett5, Henry D Adams6, Pascale Maillard7, Jacqueline Marchand8, Simon M Landhäusser9, André Lacointe10, Yves Gibon11, William R L Anderegg12, Shinichi Asao13, Owen K Atkin14, Marc Bonhomme10, Caroline Claye15, Pak S Chow9, Anne Clément-Vidal16, Noel W Davies17, L Turin Dickman6, Rita Dumbur18, David S Ellsworth4, Kristen Falk19, Lucía Galiano20, José M Grünzweig18, Henrik Hartmann21, Günter Hoch22, Sharon Hood23, Joanna E Jones15, Takayoshi Koike24, Iris Kuhlmann21, Francisco Lloret25, Melchor Maestro26, Shawn D Mansfield27, Jordi Martínez-Vilalta25, Mickael Maucourt28, Nathan G McDowell6, Annick Moing11, Bertrand Muller29, Sergio G Nebauer30, Ülo Niinemets31, Sara Palacio26, Frida Piper32, Eran Raveh33, Andreas Richter34, Gaëlle Rolland29, Teresa Rosas35, Brigitte Saint Joanis10, Anna Sala23, Renee A Smith4, Frank Sterck36, Joseph R Stinziano37, Mari Tobias31, Faride Unda27, Makoto Watanabe38, Danielle A Way39, Lasantha K Weerasinghe40, Birgit Wild41, Erin Wiley9, David R Woodruff42.   

Abstract

Non-structural carbohydrates (NSC) in plant tissue are frequently quantified to make inferences about plant responses to environmental conditions. Laboratories publishing estimates of NSC of woody plants use many different methods to evaluate NSC. We asked whether NSC estimates in the recent literature could be quantitatively compared among studies. We also asked whether any differences among laboratories were related to the extraction and quantification methods used to determine starch and sugar concentrations. These questions were addressed by sending sub-samples collected from five woody plant tissues, which varied in NSC content and chemical composition, to 29 laboratories. Each laboratory analyzed the samples with their laboratory-specific protocols, based on recent publications, to determine concentrations of soluble sugars, starch and their sum, total NSC. Laboratory estimates differed substantially for all samples. For example, estimates for Eucalyptus globulus leaves (EGL) varied from 23 to 116 (mean = 56) mg g(-1) for soluble sugars, 6-533 (mean = 94) mg g(-1) for starch and 53-649 (mean = 153) mg g(-1) for total NSC. Mixed model analysis of variance showed that much of the variability among laboratories was unrelated to the categories we used for extraction and quantification methods (method category R(2) = 0.05-0.12 for soluble sugars, 0.10-0.33 for starch and 0.01-0.09 for total NSC). For EGL, the difference between the highest and lowest least squares means for categories in the mixed model analysis was 33 mg g(-1) for total NSC, compared with the range of laboratory estimates of 596 mg g(-1). Laboratories were reasonably consistent in their ranks of estimates among tissues for starch (r = 0.41-0.91), but less so for total NSC (r = 0.45-0.84) and soluble sugars (r = 0.11-0.83). Our results show that NSC estimates for woody plant tissues cannot be compared among laboratories. The relative changes in NSC between treatments measured within a laboratory may be comparable within and between laboratories, especially for starch. To obtain comparable NSC estimates, we suggest that users can either adopt the reference method given in this publication, or report estimates for a portion of samples using the reference method, and report estimates for a standard reference material. Researchers interested in NSC estimates should work to identify and adopt standard methods.
© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  extraction and quantification consistency; non-structural carbohydrate chemical analysis; particle size; reference method; soluble sugars; standardization; starch

Mesh:

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Year:  2015        PMID: 26423132     DOI: 10.1093/treephys/tpv073

Source DB:  PubMed          Journal:  Tree Physiol        ISSN: 0829-318X            Impact factor:   4.196


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