Abstract
Uncovering the molecular mechanisms involved in the responses of crops to drought is crucial to understand and enhance drought tolerance mechanisms. Sugarcane (Saccharum spp.) is an important commercial crop cultivated mainly in tropical and subtropical areas for sucrose and ethanol production. Usually, drought tolerance has been investigated by single omics analysis (e.g. global transcripts identification). Here we combine label-free quantitative proteomics and metabolomics data (GC-TOF-MS), using a network-based approach, to understand how two contrasting commercial varieties of sugarcane, CTC15 (tolerant) and SP90-3414 (susceptible), adjust their leaf metabolism in response to drought. To this aim, we propose the utilization of regularized canonical correlation analysis (rCCA), which is a modification of classical CCA, and explores the linear relationships between two datasets of quantitative variables from the same experimental units, with a threshold set to 0.99. Light curves revealed that after 4 days of drought, the susceptible variety had its photosynthetic capacity already significantly reduced, while the tolerant variety did not show major reduction. Upon 12 days of drought, photosynthesis in the susceptible plants was completely reduced, while the tolerant variety was at a third of its rate under control conditions. Network analysis of proteins and metabolites revealed that different biological process had a stronger impact in each variety (e.g. translation in CTC15, generation of precursor metabolites, response to stress and energy in SP90-3414). Our results provide a reference data set and demonstrate that rCCA can be a powerful tool to infer experimentally metabolite-protein or protein-metabolite associations to understand plant biology.
Highlights
Understanding how plants adapt to limiting stress conditions, such as drought, is key to improve global food security
In comparison to the tolerant variety, SP903414 was already suffering a limitation in the photosynthetic capacity at 4DI and was mainly limited at 12DI, probably due to a prolonged stomatal closure, leading to a reduced carbon fixation
We propose the application of regularized canonical correlation analysis (rCCA) to explore correlation structures between metabolites and proteins, as an exploratory strategy to improve our knowledge about leaf metabolism in sugarcane under drought, which is one of the main factors negatively impacting sugarcane productivity worldwide
Summary
Understanding how plants adapt to limiting stress conditions, such as drought, is key to improve global food security. In the last few years some publications reported advances in the study of sugarcane responses to drought, using different strategies such as differential gene expression (Vargas et al, 2014; Vantini et al, 2015; Li et al, 2016), sRNA regulation (Thiebaut et al, 2014), morphological and physiological analysis (Cia et al, 2012; Zhao et al, 2013; Jain et al, 2015) and proteomics by twodimensional gel electrophoresis (Jangpromma et al, 2010; Zhou et al, 2012; Almeida et al, 2013; Khueychai et al, 2015) To our knowledge this is the first exploratory analysis applying rCCA to associate metabolite and protein data in sugarcane response to drought. These omics approaches provide the opportunity to evaluate cellular behaviors from a multi-level perspective and enhance our understanding of sugarcane biology
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