Assessing Drought and Heat Stress-Induced Changes in the Cotton Leaf Metabolome and Their Relationship With Hyperspectral Reflectance.

Abstract

The study of phenotypes that reveal mechanisms of adaptation to drought and heat stress is crucial for the development of climate resilient crops in the face of climate uncertainty. The leaf metabolome effectively summarizes stress-driven perturbations of the plant physiological status and represents an intermediate phenotype that bridges the plant genome and phenome. The objective of this study was to analyze the effect of water deficit and heat stress on the leaf metabolome of 22 genetically diverse accessions of upland cotton grown in the Arizona low desert over two consecutive years. Results revealed that membrane lipid remodeling was the main leaf mechanism of adaptation to drought. The magnitude of metabolic adaptations to drought, which had an impact on fiber traits, was found to be quantitatively and qualitatively associated with different stress severity levels during the two years of the field trial. Leaf-level hyperspectral reflectance data were also used to predict the leaf metabolite profiles of the cotton accessions. Multivariate statistical models using hyperspectral data accurately estimated ( R 2 > 0.7 in ∼34% of the metabolites) and predicted ( Q 2 > 0.5 in 15-25% of the metabolites) many leaf metabolites. Predicted values of metabolites could efficiently discriminate stressed and non-stressed samples and reveal which regions of the reflectance spectrum were the most informative for predictions. Combined together, these findings suggest that hyperspectral sensors can be used for the rapid, non-destructive estimation of leaf metabolites, which can summarize the plant physiological status.

Plant phenotyping relevance

葉のハイパースペクトル反射から代謝物プロファイルを非破壊推定する手法を統計モデルで評価しており、植物の生理状態の推定が中心的な方法的貢献として記述されている。

Code and data availability

The article's Supplementary Data 1 publicly provides best linear unbiased estimators for all fiber, metabolite, hyperspectral, and vegetation index measurements of this study, accessible via the Frontiers supplementary-material page. No author analysis code or trained model deposit is mentioned.