BioISI - Biosystems & Integrative Sciences Institute, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal.
Department of Plant and Environmental Sciences, Section of Crop Science, Copenhagen University, Højbakkegård Allé 13, 2630 Tåstrup, Denmark.
J Exp Bot. 2022 Sep 3;73(15):5235-5251. doi: 10.1093/jxb/erac160.
Interannual and local fluctuations in wheat crop yield are mostly explained by abiotic constraints. Heatwaves and drought, which are among the top stressors, commonly co-occur, and their frequency is increasing with global climate change. High-throughput methods were optimized to phenotype wheat plants under controlled water deficit and high temperature, with the aim to identify phenotypic traits conferring adaptative stress responses. Wheat plants of 10 genotypes were grown in a fully automated plant facility under 25/18 °C day/night for 30 d, and then the temperature was increased for 7 d (38/31 °C day/night) while maintaining half of the plants well irrigated and half at 30% field capacity. Thermal and multispectral images and pot weights were registered twice daily. At the end of the experiment, key metabolites and enzyme activities from carbohydrate and antioxidant metabolism were quantified. Regression machine learning models were successfully established to predict plant biomass using image-extracted parameters. Evapotranspiration traits expressed significant genotype-environment interactions (G×E) when acclimatization to stress was continuously monitored. Consequently, transpiration efficiency was essential to maintain the balance between water-saving strategies and biomass production in wheat under water deficit and high temperature. Stress tolerance included changes in carbohydrate metabolism, particularly in the sucrolytic and glycolytic pathways, and in antioxidant metabolism. The observed genetic differences in sensitivity to high temperature and water deficit can be exploited in breeding programmes to improve wheat resilience to climate change.
小麦作物产量的年际和局地波动主要归因于非生物胁迫。热胁迫和干旱是主要胁迫因子，它们通常共同发生，且随着全球气候变化，其发生频率也在增加。本研究优化了高通量方法，以在受控水分亏缺和高温下对小麦植株进行表型分析，旨在鉴定赋予适应性胁迫反应的表型特征。将 10 个基因型的小麦植株在完全自动化的植物设施中培养 30 天，在 25/18℃昼/夜温度下，然后将温度升高 7 天（38/31℃昼/夜），同时保持一半的植株充分灌溉，另一半的植株保持田间持水量的 30%。每天两次记录热成像和多光谱图像以及盆重。实验结束时，从碳水化合物和抗氧化代谢中量化了关键代谢物和酶活性。使用从图像中提取的参数成功建立了回归机器学习模型，以预测植物生物量。当连续监测到对胁迫的适应时，蒸腾特性表现出显著的基因型-环境互作（G×E）。因此，在水分亏缺和高温下，小麦的水分利用效率对于维持节水策略和生物量生产之间的平衡至关重要。胁迫耐受性包括碳水化合物代谢的变化，特别是蔗糖和糖酵解途径，以及抗氧化代谢的变化。观察到的对高温和水分亏缺的敏感性的遗传差异可在育种计划中加以利用，以提高小麦对气候变化的适应能力。
