Heterogeneous root zone salinity mitigates salt injury to Sorghum bicolor (L.) Moench in a split-root system

Abstract

The heterogeneous distribution of soil salinity across the rhizosphere can moderate salt injury and improve sorghum growth. However, the essential molecular mechanisms used by sorghum to adapt to such environmental conditions remain uncharacterized. The present study evaluated physiological parameters such as the photosynthetic rate, antioxidative enzyme activities, leaf Na+ and K+ contents, and osmolyte contents and investigated gene expression patterns via RNA sequencing (RNA-seq) analysis under various conditions of nonuniformly distributed salt. Totals of 5691 and 2047 differentially expressed genes (DEGs) in the leaves and roots, respectively, were identified by RNA-seq under nonuniform (NaCl-free and 200 mmol·L-1 NaCl) and uniform (100 mmol·L-1 and 100 mmol·L-1 NaCl) salinity conditions. The expression of genes related to photosynthesis, Na+ compartmentalization, phytohormone metabolism, antioxidative enzymes, and transcription factors (TFs) was enhanced in leaves under nonuniform salinity stress compared with uniform salinity stress. Similarly, the expression of the majority of aquaporins and essential mineral transporters was upregulated in the NaCl-free root side in the nonuniform salinity treatment, whereas abscisic acid (ABA)-related and salt stress-responsive TF transcripts were more abundant in the high-saline root side in the nonuniform salinity treatment. In contrast, the expression of the DEGs identified in the nonuniform salinity treatment remained virtually unaffected and was even downregulated in the uniform salinity treatment. The transcriptome findings might be supportive of the increased photosynthetic rate, reduced Na+ levels, increased antioxidative capability in the leaves and, consequently, the growth recovery of sorghum under nonuniform salinity stress as well as the inhibited sorghum growth under uniform salinity conditions. The increased expression of salt resistance genes activated in response to the nonuniform salinity distribution implied that the cross-talk between the nonsaline and high-saline sides of the roots exposed to nonuniform salt stress is potentially regulated.

Fig 1

Effects of nonuniform (0 mmol·L^-1 and 200 mmol·L^-1 NaCl) and uniform (100 mmol·L^-1 and 100 mmol·L^-1 NaCl) salinity distribution on the dry weights of leaves and roots (A, measured two weeks after stress), activities of antioxidative enzymes (B), contents of Pro and GSH (C), Na⁺ and K⁺ distribution within the different leaf and root sides (D, 0–0/200 represents the nonsaline root side, 200–0/200 represents the high-saline root side, and 100–100/100 represents the uniform salinity treatment), and photosynthetic parameters (E). The bars represent the mean values of the investigated physiological indexes. The whiskers represent the standard deviations of the investigated physiological indexes. The treatments sharing the same letters do not significantly differ.

Fig 2

Venn diagram of DEGs whose expression was upregulated (A) or downregulated (B) in the leaves and upregulated (C) and downregulated (D) in the roots in the uniform (100 mmol·L^-1 NaCl) and nonuniform (0 mmol·L^-1 and 200 mmol·L^-1 NaCl) salinity treatments, as revealed by RNA-seq.

Fig 3

GO analysis of DEGs in the leaves in the nonuniform salinity treatment (0/200) (A) and uniform salinity treatment (100/100) (B), as determined by RNA-seq. The abscissa of the bar plot represents the gene count within each GO category. All processes listed presented enrichment when P < 0.01.

Fig 4

GO analysis of DEGs in the roots of the high-saline root side (200–0/200) (A), of the nonsaline root side (0–0/200) (B) and in the uniform salinity treatment (100–100/100) (C), as determined by RNA-seq. The abscissa of the bar plot represents the gene count within each GO category. All processes listed presented enrichment when P < 0.01.

Fig 5. Validation of the expression profiles of 20 randomly chosen DEGs obtained from RNA-seq via quantitative PCR (q-PCR).

Shown are the fold changes of the log₂ values resulting from RNA-seq, and the q-PCR results fit a linear regression (y = 1.425x-0.1415, R² = 0.969).