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

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.

Leaf transcriptome profiling of contrasting sugarcane genotypes for drought tolerance under field conditions

“…Under mild stress, despite the lower number of DEGs in the ‘IACSP94-2094’ genotype, some were uniquely associated with redox enriched pathways. Those included polyphenol oxidase (SCSGLR1045A10.g), cinnamyl alcohol dehydrogenase (SCSBAD1054A07.g), chalcone-flavanone isomerase 1 (SCCCLR1C03G09.g), and cytochrome P450-related genes (SCCCCL3001H12.g), which have been reported to participate in acclimation to water and oxidative stresses in sugarcane 33 – 35 , sorghum 29 and model species 36 . In special, induction of CYP450 gene expression may represent an early mechanism for the tolerant genotype to avoid oxidative injury induced by high levels of hydrogen peroxide, as reported in drought-stressed CYP450-RNAi knockdown cotton plants 37 .…”

Proteomics-Based Data Integration of Wheat Cultivars Facing Fusarium graminearum Strains Revealed a Core-Responsive Pattern Controlling Fusarium Head Blight

“…The rCCA was performed following the methodology described in Gonzalez et al (2008) and using the mixOMICS r-package v5.2 (Rohart et al, 2017). Since the number of subjects was lower than the number of variables in both datasets, the regularization parameters λ 1 and λ 2 estimated following the methodology described in Budzinski et al (2019) were used for the covariance matrices X and Y. The pairwise association matrix was computed for the first eight dimensions, and all canonical correlations have been plotted using the network function with a threshold set to 0.95.…”

Network Analysis Combining Proteomics and Metabolomics Reveals New Insights Into Early Responses of Eucalyptus grandis During Rust Infection

“…Envisioning new strategies for enhancing plant disease resistance, one of the principal purposes of plant-pathogen studies is to understand how plants modulate genes, transcripts, proteins and metabolites to physiologically adapt and respond to pathogen invasion. Hence, researchers have used network analyses to group co-expressed genes, integrate omics datasets and reveal new insights into plant physiology (Walley et al, 2013;Ployet et al, 2019), as well as plant response to stress (El-Sharkawy et al, 2015;Yuan et al, 2018;Budzinski et al, 2019;Song et al, 2019).…”