4.8 Article

Relationships of Leaf Net Photosynthesis, Stomatal Conductance, and Mesophyll Conductance to Primary Metabolism: A Multispecies Meta-Analysis Approach

期刊

PLANT PHYSIOLOGY
卷 171, 期 1, 页码 265-279

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OXFORD UNIV PRESS INC
DOI: 10.1104/pp.15.01660

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资金

  1. Max Planck Society
  2. CNPq (National Council for Technological and Scientific Development, Brazil) scholarship
  3. Plan Nacional, Spain [BFU2011-23294, CTM2014-53902-C2-1-P]

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Plant metabolism drives plant development and plant-environment responses, and data readouts from this cellular level could provide insights in the underlying molecular processes. Existing studies have already related key in vivo leaf gas-exchange parameters with structural traits and nutrient components across multiple species. However, insights in the relationships of leaf gas-exchange with leaf primary metabolism are still limited. We investigated these relationships through a multispecies meta-analysis approach based on data sets from 17 published studies describing net photosynthesis (A) and stomatal (g(s)) and mesophyll (g(m)) conductances, alongside the 53 data profiles from primary metabolism of 14 species grown in different experiments. Modeling results highlighted the conserved patterns between the different species. Consideration of species-specific effects increased the explanatory power of the models for some metabolites, including Glc-6-P, Fru-6-P, malate, fumarate, Xyl, and ribose. Significant relationships of A with sugars and phosphorylated intermediates were observed. While gs was related to sugars, organic acids, myo-inositol, and shikimate, g(m) showed a more complex pattern in comparison to the two other traits. Some metabolites, such as malate and Man, appeared in the models for both conductances, suggesting a metabolic coregulation between g(s) and g(m). The resulting statistical models provide the first hints for coregulation patterns involving primary metabolism plus leaf water and carbon balances that are conserved across plant species, as well as species-specific trends that can be used to determine new biotechnological targets for crop improvement.

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