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Front. Plant Sci. | doi: 10.3389/fpls.2019.01524

Network analyses and data integration of proteomics and metabolomics from leaves of two contrasting varieties of sugarcane in response to drought

 Carlos A. Labate1, 2*,  Ilara F. Budzinski1, 2,  Fabricio E. de Moraes1, 2,  Thais R. Cataldi1, 2 and Livia M. Franceschini1, 2
  • 1Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Brazil
  • 2University of São Paulo, Brazil

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 four days of drought, the susceptible variety had its photosynthetic capacity already significantly reduced, while the tolerant variety did not show major reduction. Upon twelve 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.

Keywords: drought, Label-free quantitative proteomics, Metabolomics, canonical correlation analysis, sugarcane

Received: 04 Jan 2019; Accepted: 01 Nov 2019.

Copyright: © 2019 Labate, Budzinski, de Moraes, Cataldi and Franceschini. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Carlos A. Labate, Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil,