Comparative Transcriptomic Analysis of Root and Leaf Transcript Profiles Reveals the Coordinated Mechanisms in Response to Salinity Stress in Common Vetch

Abstract

Owing to its strong environmental suitability to adverse abiotic stress conditions, common vetch (Vicia sativa) is grown worldwide for both forage and green manure purposes and is an important protein source for human consumption and livestock feed. The germination of common vetch seeds and growth of seedlings are severely affected by salinity stress, and the response of common vetch to salinity stress at the molecular level is still poorly understood. In this study, we report the first comparative transcriptomic analysis of the leaves and roots of common vetch under salinity stress. A total of 6361 differentially expressed genes were identified in leaves and roots. In the roots, the stress response was dominated by genes involved in peroxidase activity. However, the genes in leaves focused mainly on Ca²⁺ transport. Overexpression of six salinity-inducible transcription factors in yeast further confirmed their biological functions in the salinity stress response. Our study provides the most comprehensive transcriptomic analysis of common vetch leaf and root responses to salinity stress. Our findings broaden the knowledge of the common and distinct intrinsic molecular mechanisms within the leaves and roots of common vetch and could help to develop common vetch cultivars with high salinity tolerance.

Keywords: common vetch; full-length transcripts; leaf; root; salinity stress; yeast.

Figure 1

Analysis of dynamic physiological effects under NaCl stress. (A) Chlorophyll content. (B) Electrolyte leakage. (C) MDA (Malonydialdehyde) content. The results are the means and SDs of three replicates. The different letters above the bars indicate significant differences at the 0.05 level according to Duncan’s multiple range test.

Figure 2

Pearson’s correlations between twelve samples. R² represents the coefficient of determination. An orange background represents a greater coefficient of determination (L0: leaf 0 h, R0: root 0 h, L2: leaf 2 h, R2: root 2 h, L24: leaf 24 h, R24: root 24 h).

Figure 3

Summary of differentially expressed genes. (A) Summary of the numbers of root and leaf differentially expressed genes (DEGS) at different duration of salinity treatment. (B) Number of genes whose expression is differentially regulated between the different conditions. Orange bar: down-regulated genes; blue bar: up-regulated genes.

Figure 4

Differentially expressed gene expression patterns. The light grey lines represent the expression pattern of each gene, while the thick, dark lines represent the expression tendency of all the genes. (A) Patterns of gene expression in the leaves across three time points. (B) Patterns of gene expression in the roots across three time points.

Figure 5

Gene Ontology (GO) enrichment analysis of DEGs. Genes were assigned to three main categories: Biological process, Molecular function, and Cellular component. The names of the GO categories are listed along the x-axis. The degree of GO enrichment is represented by the false discovery rate (FDR) value and the number of genes enriched in each category. The FDR value indicates the corrected p-value, ranging from 0 to 1, and an FDR value closer to 0 indicates greater enrichment.

Figure 6

Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment scatter diagram of DEGs. Only the top 20 most strongly represented pathways are displayed in the diagram. The degree of KEGG pathway enrichment is represented by an enrichment factor, the FDR value, and the number of genes enriched in a KEGG pathway. The enrichment factor indicates the ratio of differentially expressed genes enriched in this pathway to the total number of annotated unigenes in this pathway. The red underline represents the same enriched pathways among whole plants, leaves, roots, and co-induced DEGs. (A) KEGG enrichment analysis of the leaves. (B) KEGG enrichment analysis of the roots.

Figure 7

Clustering dendrograms of genes. Dissimilarity was based on topological overlap, together with assigned module colours. The 23 co-expression modules are shown in different colours.

Figure 8

GO pathways associated with modules associated with stress-responsive traits. The number ahead of the items indicates the gene number. (A) Dark grey module GO pathways. (B) Purple module GO pathways.

Figure 9

Network relationship among modules. (A) The thirty-six genes with the greatest MCODE score are in the dark grey module. (B) The twenty-eight genes with the greatest MCODE score are in purple module.

Figure 10

Distribution of the top 12 TFs responsive to salinity stress in common vetch.

Figure 11

The expression patterns of thirteen selected genes identified by RNA-seq were verified by qRT-PCR. (A) Bar chart showing the expression changes in response to the L0 to L24 treatments for each candidate gene, as measured by RNA-seq and qRT-PCR. (B) Expression changes in response to the R0 to R24 treatments for each candidate gene, as measured by RNA-seq and qRT-PCR. STP7—sugar transporter protein 7; NO—NAD(P)-dependent oxidoreductase; RBCS1A—ribulose bisphosphate carboxylase small chain 1A; JAZ1—jasmonate-zim-domain protein 1; CAX3—cation exchanger 3; FUP1—function unknown protein 1; CAX1—cation exchanger 1; FUP2—function unknown protein 2; LEA4-5—late embryogenesis abundant 4–5; AWPM-19—ABA-induced wheat plasma membrane polypeptide-19; PEBP—phosphatidylethanolamine-binding protein; TZF4—CCCH-type zinc finger protein; SUS3—sucrose synthase 3.

Figure 12

Phenotypic growth assays of Saccharomyces cerevisiae INVSc1 cells transformed with a pYES2 empty vector, NAC1;2, ERF1;2, or MYB1;2 were spotted on SC-Ura media in 2 mL aliquots of 10-fold serially diluted (1, 10⁻¹, 10⁻², 10⁻³, 10⁻⁴, and 10⁻⁵) cultures under salinity stress.