Charrier Guillaume, Lacointe André, Améglio Thierry
INRA, PIAF, Université Clermont Auvergne, Clermont-Ferrand, France.
Front Plant Sci. 2018 Dec 5;9:1746. doi: 10.3389/fpls.2018.01746. eCollection 2018.
The leafless period is often considered as inactive, although trees have to actively modulate their metabolism through the cold acclimation/deacclimation processes, to cope with frost exposure during winter and to restore growth ability in spring. Carbon metabolism is a key component of these processes through the osmotic control of extracellular ice formation and the trophic control of bud growth. The influence of temperature on the inter-conversion between starch and soluble carbohydrate has been evidenced for years, but we are currently missing an operational tool to predict starch vs. soluble carbohydrate contents during this period, which should allow to better predict frost hardiness. For this purpose, we exposed 1-year-old branches of to constant temperature for one to 3 weeks and measured the changes in carbohydrate composition at three periods (autumn, winter, and spring). As expected, the temperature significantly affected the changes in carbohydrate composition, but the water content and the sampling period were also relevant. Higher starch hydrolysis was observed at low temperature (<5°C) for all sampling periods. Starch hydrolysis was also observed at warm temperature, but in autumn only. These data were used to compare three modeling approaches simulating the changes in carbohydrate composition through enzymatic analogy. The most empirical and the most mechanistic approach did not succeed to simulate external observations (Root Mean Standard Error of Prediction (RMSEP) > 30 mg.g DM, Efficiency (Eff) <0), whereas the intermediate model was more efficient (RMSEP = 15.19 mg.g DM, Eff = 0.205 and 16.61 mg.g DM, Eff = 0.366, for GFS (Glucose + Fructose + Sucrose) and starch, respectively). The accuracy of the model was further improved when using field data for calibration (RMSEP = 5.86 mg.g DM, Eff = 0.962; RMSEP = 10.56 mg.g DM, Eff = 0.752, for GFS and starch, respectively). This study provided an operative tool to simulate carbohydrate dynamics over leafless period that could predict frost hardiness with approx. 3.4°C accuracy with temperature, water content and initial starch and soluble carbohydrate measurements. It should now be tested under various meteorological conditions and biological systems.
尽管树木必须通过冷驯化/脱驯化过程积极调节其新陈代谢，以应对冬季的霜冻暴露并在春季恢复生长能力，但无叶期通常被认为是不活跃的。碳代谢是这些过程的关键组成部分，通过对细胞外冰形成的渗透控制和对芽生长的营养控制来实现。温度对淀粉和可溶性碳水化合物之间相互转化的影响多年来已有证据，但目前我们缺少一个操作工具来预测这一时期淀粉与可溶性碳水化合物的含量，而这应该有助于更好地预测抗冻性。为此，我们将1年生树枝置于恒温环境中1至3周，并在三个时期（秋季、冬季和春季）测量碳水化合物组成的变化。正如预期的那样，温度显著影响碳水化合物组成的变化，但含水量和采样时期也很重要。在所有采样时期，低温（<5°C）下观察到更高的淀粉水解。在温暖温度下也观察到淀粉水解，但仅在秋季。这些数据用于比较三种通过酶类比模拟碳水化合物组成变化的建模方法。最经验性和最机械性的方法未能成功模拟外部观测结果（预测均方根标准误差（RMSEP）>30 mg·g DM，效率（Eff）<0），而中间模型更有效（对于葡萄糖+果糖+蔗糖（GFS）和淀粉，RMSEP分别为15.19 mg·g DM，Eff = 0.205和16.61 mg·g DM，Eff = 0.366）。当使用田间数据进行校准时，模型的准确性进一步提高（对于GFS和淀粉，RMSEP分别为5.86 mg·g DM，Eff = 0.962；RMSEP = 10.56 mg·g DM，Eff = 0.752）。本研究提供了一个操作工具来模拟无叶期的碳水化合物动态，通过温度、含水量以及初始淀粉和可溶性碳水化合物测量，该工具可以以约3.4°C的精度预测抗冻性。现在应该在各种气象条件和生物系统下对其进行测试。
