Protein responses in kenaf plants exposed to drought conditions determined using iTRAQ technology

Abstract

The molecular mechanisms that underlie drought stress responses in kenaf, an important crop for the production of natural fibers, are poorly understood. To address this issue, we describe here the first iTRAQ-based comparative proteomic analysis of kenaf seedlings. Plants were divided into the following three treatment groups: Group A, watered normally (control); Group B, not watered for 6 days (drought treatment); and Group C, not watered for 5 days and then rewatered for 1 day (recovery treatment). A total of 5014 proteins were detected, including 4932 (i.e., 98.36%) that were matched to known proteins in a BLAST search. We detected 218, 107, and 348 proteins that were upregulated in Group B compared with Group A, Group C compared with Group A, and Group B compared with Group C, respectively. Additionally, 306, 145, and 231 downregulated proteins were detected during the same comparisons. Seventy differentially expressed proteins were analyzed and classified into 10 categories: photosynthesis, sulfur metabolism, amino sugar and nucleotide sugar metabolism, oxidative phosphorylation, ribosome, fatty acid elongation, thiamine metabolism, tryptophan metabolism, plant-pathogen interaction, and propanoate. Kenaf adapted to stress mainly by improving the metabolism of ATP, regulating photosynthesis according to light intensity, promoting the synthesis of osmoregulators, strengthening ion transport signal transmission, and promoting metabolism and cell stability. This is the first study to examine changes in protein expression in kenaf plants exposed to drought stress. Our results identified key drought-responsive genes and proteins and may provide useful genetic information for improving kenaf stress resistance.

Keywords: comparative proteomic analysis; drought stress; iTRAQ; kenaf; stress resistance.

Figure 1

The phenotypes of kenaf seedlings were observed under normal irrigation (A), drought stress (B), and rewatering conditions (C). Plants that were approximately 30 cm tall were divided into three groups and treated as follows: Plant C represents a control plant, which was watered normally for the entire treatment period, as described in the Methods. Plant D represents a plant that was not watered for the treatment period (drought stress). Plant R represents a plant that was subjected to drought stress for 5 days and then watered on Day 6. Panel a shows the plants before treatment. Panel b shows the plants 5 days after the beginning of treatment (C was watered normally during this time, while R and D did not receive water). Panel c shows the plants 6 days after the beginning of treatment (C was watered normally during this time, D did not receive water, and R was subjected to drought stress and then watered normally on Day 6).

Figure 2

Percentage numbers of the six most abundant annotated species.

Figure 3

(A) The number of differentially expressed proteins in any two different groups. (B) Venn diagrams representing the overlap of identified differentially expressed proteins, which were upregulated and downregulated in any two groups.

Figure 4

Bioinformatics analysis of 1736 identified differential abundance proteins was shown in Group B vs. Group A. Biological process, cell component, molecule function, and KEGG pathway are four categories of functional analysis (A). The ten most significantly enriched terms in level 4 GO hierarchy are shown (B). Enriched KEGG pathways are clustered into the metabolism subcategories, and the number of involved proteins in a specific pathway and corresponding P‐value are shown on the right side of column (C).

Figure 5

Bioinformatics analysis of 1848 identified differential abundance proteins was shown in Group C vs. Group A. Biological process, cell component, molecule function, and KEGG pathway are four categories of functional analysis (A). The ten most significantly enriched terms in level 4 GO hierarchy are shown (B). Enriched KEGG pathways are clustered into the metabolism subcategories, and the number of involved proteins in a specific pathway and corresponding P‐value are shown on the right side of column (C).

Figure 6

Bioinformatics analysis of 1364 identified differential abundance proteins were shown in Group C vs. Group B. Biological process, cell component, molecule function, and KEGG pathway are four categories of functional analysis (A). The ten most significantly enriched terms in level 4 GO hierarchy are shown (B). Enriched KEGG pathways are clustered into the metabolism subcategories, and the number of involved proteins in a specific pathway and corresponding P‐value are shown on the right side of column (C).

Figure 7

The categorization of proteins is based on GO annotation 70 identified significant differential proteins.

Figure 8

The above network model is generated with a Cytoscape web application, based on information gained from up to four levels of functional analysis: fold change of gene/protein, protein–protein interaction, KEGG pathway enrichment, and BP enrichment. Circle nodes: genes/proteins; rectangle nodes: KEGG pathway or BP. Pathways are colored in a gradient color from yellow to blue; yellow indicates a lower P‐value, and blue indicates a higher P‐value. In the case of fold‐change analysis, genes/proteins are colored in red (upregulation) and green (downregulation). A default confidence cutoff of 400 was used: Interactions with a higher confident score are shown as solid lines between genes/proteins; dashed lines indicate otherwise .