Table_5_Network Analyses and Data Integration of Proteomics and Metabolomics From Leaves of Two Contrasting Varieties of Sugarcane in Response to Drought.xlsx

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.

揭示作物对干旱反应所涉及的分子机制，对于理解和增强干旱耐受机制至关重要。甘蔗（Saccharum spp.）作为一种重要的商业作物，主要在热带和亚热带地区种植，用于生产蔗糖和乙醇。通常，干旱耐受性通过单一组学分析（例如，全球转录本鉴定）进行研究。在本研究中，我们采用基于网络的方法，结合无标记定量蛋白质组学和代谢组学数据（GC-TOF-MS），旨在探究两种具有对比性的商业品种——耐旱的CTC15和易感旱的SP90-3414——如何调整其叶片代谢以应对干旱。为此，我们提出了使用正则化典型相关分析（rCCA），这是经典CCA的一种改进，它探索了同一实验单元的两个定量变量数据集之间的线性关系，阈值为0.99。光曲线显示，在干旱4天后，易感旱品种的光合作用能力已经显著降低，而耐旱品种并未表现出显著减少。在干旱12天后，易感旱植物的光合作用完全减少，而耐旱品种的光合作用速率仅为对照组的1/3。蛋白质和代谢物的网络分析揭示了不同的生物学过程对每个品种的影响更为显著（例如，CTC15中的翻译，SP90-3414中的前体代谢物生成、对压力的响应和能量）。我们的研究结果提供了一套参考数据集，并证明了rCCA可以作为一种强大的工具，推断实验中代谢物-蛋白质或蛋白质-代谢物之间的关联，以理解植物生物学。