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.

Keywords: Differentially abundant protein; Proteomics; Statistical approach; Sucrose accumulation; Sugarcane.

Fig. 1

The scatter plot of first and second principal components (PCs) ofsugarcane proteomics data. PCs were calculated from the data before (a) and after (b) batch effect correction

Fig. 2

Summary of DAPs detected by three different statistical analysis approaches. Venn diagram of DAPs detected by three methods based on comparisons between genotypes (A) and between water and ethephon treatments (C). Up-regulated and down-regulated proteins detected in genotype (B) and treatments (D)comparisons by the three methodsThe protein number of S1-S3 in (A) is identical to that in Supplementary Tables 1, 2 and 3, and the number of S5-S7 in (C) is identical to that in Supplementary Tables 5, 6 and 7

Fig. 3

KEGG analysis of annotated DAPs detected by three different statistical approaches. The size of the dots corresponds to the number of DAPs in each pathway. The color displays the significance of enrichment

Fig. 4

Western blot validation of differentially abundant proteins. RCK: high-sugar genotype with water control; MCK: low-sugar genotype with water control; R400: high-sugar genotype with ethephon treatment; M400: low-sugar genotype with ethephon treatment. Number after sample code (-1, -2, -3) represents the replicate number

Fig. 5

The protein-protein interaction network based on DAPs related to sugar metabolism. Each node represents a protein. The helical symbol in the node indicates the known 3D structure of the protein, and empty nodes indicate unknown proteins. The line between two nodes represents interaction and multiple lines represent various interactions between two proteins

Fig. 6

The putative network of key proteins associated with sucrose accumulation in sugarcane