Proteomics data analysis using multiple statistical approaches identified proteins and metabolic networks associated with sucrose accumulation in sugarcane

Abstract

Background Sugarcane is the most important sugar crop, contributing > 80% of global sugar production. High sucrose content is a key target of sugarcane breeding, yet sucrose improvement in sugarcane remains extremely slow for decades. Molecular breeding has the potential to break through the genetic bottleneck of sucrose improvement. Dissecting the molecular mechanism(s) and identifying the key genetic elements controlling sucrose accumulation will accelerate sucrose improvement by molecular breeding. In our previous work, a proteomics dataset based on 12 independent samples from high- and low-sugar genotypes treated with ethephon or water was established. However, in that study, employing conventional analysis, only 25 proteins involved in sugar metabolism were identified . Results In this work, the proteomics dataset used in our previous study was reanalyzed by three different statistical approaches, which include a logistic marginal regression, a penalized multiple logistic regression named Elastic net, as well as a Bayesian multiple logistic regression method named Stochastic search variable selection (SSVS) to identify more sugar metabolism-associated proteins. A total of 507 differentially abundant proteins (DAPs) were identified from this dataset, with 5 of them were validated by western blot. Among the DAPs, 49 proteins were found to participate in sugar metabolism-related processes including photosynthesis, carbon fixation as well as carbon, amino sugar, nucleotide sugar, starch and sucrose metabolism. Based on our studies, a putative network of key proteins regulating sucrose accumulation in sugarcane is proposed, with glucose-6-phosphate isomerase, 2-phospho-D-glycerate hydrolyase, malate dehydrogenase and phospho-glycerate kinase, as hub proteins. Conclusions The sugar metabolism-related proteins identified in this work are potential candidates for sucrose improvement by molecular breeding. Further, this work provides an alternative solution for omics data processing.

Genome-Wide Association Study Unravels Quantitative Trait Loci and Genes Associated with Yield-Related Traits in Sugarcane

“…β-Glucosidase (EC3.2.1.21) hydrolyzes the terminal nonreducing β- d -glucose bond and releases β- d -glucose and the corresponding ligands, which is vital in cellulose metabolism . Rice β-glucosidase Os12BGlu38 is required for the synthesis of intine cell wall, while two beta-glucosidases, m.27280 (beta-glucosidase 22-like), and m.97819 (beta-glucosidase 30-like) showed higher abundance in a low-sugar genotype than in a high-sugar genotype, hinting that beta-glucosidase may be negatively associated with sucrose accumulation in sugar cane. , In this work, Sspon.01G0012470-1A , encoding β-1,3-glucosidase, was also identified as being associated with yield-related traits, suggesting its crucial role in sugar cane yield.…”

“…85 PFK activity is negatively correlated with sucrose content, and the abundance of an FRK, m.98290 (frk1, fructokinase-1), was found to be higher in a low-sugar genotype than in a high-sugar genotype. 86,87 We, therefore, speculated that PFK was controlled by Sspon.02G0014680-4D, and a decrease in PFK enzyme activity may lead to an increase in the sucrose content in sugar cane. Furthermore, the differential expression of Sspon.02G0014680-4D in the leaves and stalks of the highand low-sugar sugar cane confirmed the above speculations (Figure 5B).…”