Reveal the kernel dehydration mechanisms in maize based on proteomic and metabolomic analysis

Abstract

Background: Kernel dehydration is an important factor for the mechanized harvest in maize. Kernel moisture content (KMC) and kernel dehydration rate (KDR) are important indicators for kernel dehydration. Although quantitative trait loci and genes related to KMC have been identified, where most of them only focus on the KMC at harvest, these are still far from sufficient to explain all genetic variations, and the relevant regulatory mechanisms are still unclear. In this study, we tried to reveal the key proteins and metabolites related to kernel dehydration in proteome and metabolome levels. Moreover, we preliminarily explored the relevant metabolic pathways that affect kernel dehydration combined proteome and metabolome. These results could accelerate the development of further mechanized maize technologies.

Results: In this study, three maize inbred lines (KB182, KB207, and KB020) with different KMC and KDR were subjected to proteomic analysis 35, 42, and 49 days after pollination (DAP). In total, 8,358 proteins were quantified, and 2,779 of them were differentially expressed proteins in different inbred lines or at different stages. By comparative analysis, K-means cluster, and weighted gene co-expression network analysis based on the proteome data, some important proteins were identified, which are involved in carbohydrate metabolism, stress and defense response, lipid metabolism, and seed development. Through metabolomics analysis of KB182 and KB020 kernels at 42 DAP, 18 significantly different metabolites, including glucose, fructose, proline, and glycerol, were identified.

Conclusions: In sum, we inferred that kernel dehydration could be regulated through carbohydrate metabolism, antioxidant systems, and late embryogenesis abundant protein and heat shock protein expression, all of which were considered as important regulatory factors during kernel dehydration process. These results shed light on kernel dehydration and provide new insights into developing cultivars with low moisture content.

Keywords: Kernel dehydration rate (KDR); Kernel moisture content (KMC); Maize; Metabolomics; Proteomics.

Fig. 1

Variant of moisture content of different inbred lines in different periods. A Changes in moisture content of three inbred lines at 7–70 days after pollination. B Changes in AUDDC of three inbred lines at AUDDC1-AUDDC9

Fig. 2

Basic information of all peptides and the number of identified proteins in all samples. A Distribution of peptide length in samples. B Distribution of proteins in samples

Fig. 3

Comparison of differentially expressed proteins (DEPs) in each inbred line at different periods. A Number of DEPs of KB182 in three periods. B Number of DEPs of KB020 in three periods. C Number of DEPs of KB207 in three periods. D Distribution of DEPs in three inbred lines

Fig. 4

Comparison of differentially expressed proteins (DEPs) in different inbred lines in the same period. A Number of DEPs of three inbred lines at 35 days after pollination (DAP). B Number of DEPs of three inbred lines at 42 DAP. C Number of DEPs of three inbred lines at 49 DAP. D Distribution of DEPs in the three periods. E Gene Ontology enrichment results of key proteins after comparison. F Kyoto Encyclopedia of Genes and Genomes enrichment results of key proteins after comparison

Fig. 5

Expression patterns of three inbred lines at three time points. A Protein expression pattern of KB182. B Protein expression pattern of KB020. C Protein expression pattern of KB207. D Venn diagram of high-expression proteins in KB182 and KB020. E Gene Ontology enrichment results of 1,787 high-expression proteins. F Kyoto Encyclopedia of Genes and Genomes enrichment results of 1,787 high-expression proteins

Fig. 6

Weighted gene co-expression network analysis identified a dehydration-related module. A Protein expression module. B Relationships between the modules and dehydration. C Gene Ontology enrichment results of important modules. D Kyoto Encyclopedia of Genes and Genomes enrichment results of important modules

Fig. 7

Protein–protein interaction network of hub proteins (Top 5%)

Fig. 8

Metabolome analysis between KB182 and KB020. A Partial least-square discriminant analysis (PLS-DA) score plot of samples in different lines. B Top 15 most closely associated metabolites and proteins

Fig. 9

Role of different types of protein during maize kernel dehydration. AA (ascorbic acid); ROS (reactive oxygen species); APX (ascorbate peroxidase); CAT (catalase); DHA (dehydroascorbate); DHAR (dehydroascorbate reductase); GR (glutathione reductase); GSH (glutathione); GSSG (glutathione); H₂O₂ (hydrogen peroxide); MDA (monodehydroascorbate); MDAR (monodehydroascorbate reductase); NADPH (nicotinamide dinucleotide phosphate); SOD (superoxide dismutase); AMY (α-amylase); α-GLU (glucosidase); HK (hexokinase); SPS (sucrose-phosphate synthase); SP (sucrose phosphorylase); SPP (sucrose-6-phosphatase); PYG (glucan phosphorylase); PGM (phosphoglucomutase); UGP2 (UTP–glucose-1-phosphate uridylyltransferase); sHSP (small HSP); LEA (late embryogenesis abundant) protein