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Article

Transcriptome and Proteome Association Analysis to Screen Candidate Genes Related to Salt Tolerance in Reaumuria soongorica Leaves under Salt Stress

by
Hanghang Liu
1,
Peifang Chong
1,*,
Shipeng Yan
2,
Zehua Liu
1,
Xinguang Bao
1 and
Bingbing Tan
1
1
College of Forestry, Gansu Agricultural University, Lanzhou 730070, China
2
School of Forestry Engineering, Shandong Agriculture and Engineering University, Jinan 250100, China
*
Author to whom correspondence should be addressed.
Plants 2023, 12(20), 3542; https://doi.org/10.3390/plants12203542
Submission received: 14 September 2023 / Revised: 6 October 2023 / Accepted: 6 October 2023 / Published: 12 October 2023
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Abstract

:
This work aims at studying the molecular mechanisms underlying the response of Reaumuria soongorica to salt stress. We used RNA sequencing (RNA-Seq) and Tandem Mass Tag (TMT) techniques to identify differentially expressed genes (DEGs) and differentially expressed proteins (DEPs) in R. soongorica leaves treated with 0, 200, and 500 mM NaCl for 72 h. The results indicated that compared with the 0 mM NaCl treatment group, 2391 and 6400 DEGs were identified in the 200 and 500 mM NaCl treatment groups, respectively, while 47 and 177 DEPs were also identified. Transcriptome and proteome association analysis was further performed on R. soongorica leaves in the 0/500 mM NaCl treatment group, and 32 genes with consistent mRNA and protein expression trends were identified. SYP71, CS, PCC13-62, PASN, ZIFL1, CHS2, and other differential genes are involved in photosynthesis, vesicle transport, auxin transport, and other functions of plants, and might play a key role in the salt tolerance of R. soongorica. In this study, transcriptome and proteome association techniques were used to screen candidate genes associated with salt tolerance in R. soongorica, which provides an important theoretical basis for understanding the molecular mechanism of salt tolerance in R. soongorica and breeding high-quality germplasm resources.

1. Introduction

Due to climate change and human activities, soil salinization in arid and semiarid regions has become increasingly severe, which has become a major obstacle to the high-quality development of the ecological environment and economy in these regions [1]. Excessive accumulation of soil salts inhibits plant growth, reduces species diversity, changes the composition of plant communities, and reduces the biological productivity and biomass of the ecosystem [2,3]. Plants have a series of tolerance mechanisms to cope with salt stress, including modulation of various morpho-physiological attributes, antioxidant machinery, osmotic balance, and phytohormones [4]. Farhangi et al. [5] found that the body of Phaseolus vulgaris L. accumulates a large amount of soluble sugars to alleviate salt stress damage to cells. Glucose and sucrose in soluble sugars play important roles in plant growth and development and may be involved in regulating plant response to salt as signaling substances [6]. In addition, recent findings have shown that STI is a suitable tolerance index that can evaluate the salt tolerance of plants at different salt concentrations and determine the salt threshold concentration of plants [7,8]. Therefore, it is of great significance to understand the perception and response of plants to salt stress in order to improve the salt tolerance of plants.
The response of plants to abiotic stress involves a complete set of fine expression regulation mechanisms, such as transcriptional regulation, post-transcriptional regulation, translational regulation, and post-translational regulation [9]. The genomic resources of plants growing under salt stress provide important benchmarks for improving plant salt tolerance, alleviating salt damage, and improving soils [10,11]. Aliakbari et al. [12] analyzed the gene expression pattern of Salicornia persica using RNA-Seq technology and identified 1595 differentially expressed genes under salinity. Functional annotation analysis showed that energy homeostasis and primary metabolite synthesis play key roles in salinity adaptation. Chen et al. [13] investigated the leaf proteome of Apocynum venetum L. under salt stress and found that differential proteins were mainly involved in carbohydrate and energy metabolism, metabolite biosynthesis, and signal transduction. Dehydrin 1, annexin, pathogenesis-related protein, and peroxidase were also identified. Zhang et al. [14] used transcriptional sequencing and isobaric tag for relative and absolute quantitation (iTRAQ) proteomics analysis to identify 5432 genes and 43 proteins differentially expressed in Helianthus tuberosus L. leaves under salt stress, and these genes were mainly enriched in carbohydrate metabolism, ribosome activation and translation, redox, and ion binding. Transcriptome and proteome association found that the induced activity of ribosome and sugar signaling may confer Helianthus tuberosus L. with salt tolerance. Therefore, transcriptome and proteome techniques can be used as an effective method to screen potential salt-tolerance genes in plants. Since transcriptome and proteome reflect gene expression at different levels, it is necessary to detect mRNA and protein expression levels and perform omics data integration analysis to comprehensively explore the complex life activities of organisms and lay the foundation for revealing the regulatory rules of complex life activities of organisms at the molecular level.
Reaumuria soongorica is a super xerophytic salt-tolerant small shrub. It is a typical construction species and dominant species in the ecosystem of arid and semiarid regions, with strong stress resistance, grazing tolerance, sand-collecting ability, etc. [15]. Its leaves are rich in proteins, fats, and trace elements. It is the main vegetation for the construction of fodder bushes and the cultivation of degraded grasslands [16]. The unique salt gland structure of R. soongorica is an essential guarantee for its survival in saline environments. Its primary function is to transport salt secreted by the plant from the surrounding tissues to large vesicles that collect the salt. Subsequently, the substances accumulated by the secretory cells are transported into their small vesicles. These are constantly moving towards the cell membrane side and are finally excreted through the outer plasma membrane of the secretory cells, thus improving their osmotic adjustment ability and maintaining normal plant growth under salt stress [15]. At present, a number of research teams have conducted physiological and biochemical studies on R. soongorica seedlings under salt stress, mainly focusing on ion absorption, seed germination, and the antioxidant capacity of callus [17,18,19,20,21]. However, there are few studies on the molecular mechanism of salt tolerance of R. soongorica seedlings [9,22]. The study of integrated transcriptome and proteome analysis of the response to salt stress has not been reported. In this study, modern biological techniques were used to investigate the differentially expressed genes and protein expression profiles of R. soongorica responding to salt stress, the key genes and proteins involved in the salt tolerance defense system were explored, and the regulatory mechanism of R. soongorica responding to salt stress was elucidated. This study not only provided a new understanding of the molecular response mechanism of R. soongorica to salt stress but also provided potential genetic resources for breeding R. soongorica.

2. Results

2.1. Effects of NaCl Concentrations on Growth and Development of R. soongorica Seedling

Under the same stress treatment time, the plant height of R. soongorica first increased and then decreased with increasing salt concentration. Compared with the control at 24 h of stress treatments, the plant height of all NaCl treatments showed no significant difference. At 72, 144, and 216 h of stress treatments, plant height was highest under the 200 mM NaCl concentration, and plant height was the lowest under the 500 mM NaCl concentration. In the same salt concentration treatment, plant height showed an increasing trend with increasing days of stress. Compared to 24 h of stress, plant height increased by 36.62%, 37.67%, 44.43%, 28.45%, 17.59%, and 13.26% under different concentration treatments at 216 h of stress (Table 1). Furthermore, it can be seen from Table 1 that the root length of R. soongorica seedlings first increased and then decreased with increasing NaCl treatment concentration. At 24 h and 72 h of stress, the root length under the 200 mM NaCl treatment increased by 6.98% and 4.09% compared with the control, respectively. At 144 h and 216 h of stress, the root length gradually decreased with increasing NaCl concentration.
Figure 1A shows that different NaCl treatment concentrations significantly changed the soluble sugar content in R. soongorica leaves. The soluble sugar content decreased with the increase in NaCl treatment concentration at 24 h of treatment, and the soluble sugar content showed a trend of first increasing and then decreasing with the increase in NaCl treatment concentration at 72 h of treatment. The soluble sugar content gradually increased with the increase in NaCl treatment concentration at 144 and 216 h of treatment. The soluble sugar content seemed to increase at different rates during the whole treatment cycle, especially when the soluble sugar content in leaves increased to the maximum after 144 h of the 500 mM NaCl treatment and then remained at a high level. Figure 1B shows that the tolerance index of R. soongorica seedlings to NaCl was significantly lower than that of other treatments at 400 and 500 mM NaCl for 24 h. Except for the 24 h NaCl treatment, the tolerance index of seedlings to NaCl first increased and then decreased with increasing NaCl concentration at the other treatment time. The tolerance index of seedlings was the highest under the 200 mM NaCl treatment, and then it started to decrease gradually. With the prolongation of salt stress time, the tolerance index of seedlings was gradually decreased under the 400 and 500 mM NaCl treatments. When the treatment concentration reached 500 mM NaCl after 216 h of NaCl treatment, the tolerance index of the seedlings was only 58.42%.

2.2. De Novo Assembly and Annotation of the R. soongorica Transcriptome

To comprehensively understand the transcriptomic profiles of R. soongorica and to identify differentially expressed genes (DEGs) in response to salt stress, we sequenced the transcriptomes of nine different R. soongorica libraries, and (A1, A2, A3), (B1, B2, B3), and (C1, C2, C3) were three biological replicates of 0 mM NaCl (control), 200 mM NaCl, and 500 mM NaCl, respectively (Figure 2). First, total RNA was extracted from the 0 mM NaCl (control), 200 mM NaCl, and 500 mM NaCl-treated samples, and RNA sequencing was performed by Illumina Hiseq. A total of 388,789,330 clean reads were obtained from nine samples using low-quality (Q-value < 20) and multiple N-base filtered reads (Table S3). The filtered clean reads were assembled using Trinity 2.5.1 software and the longest transcripts were selected as unigenes. After isoform detection, a total of 79,307 unigenes longer than 300 bp were obtained from these nine libraries. All single gene sequences were identified using Blastx and compared with NR (NCBI non-redundant protein sequences), GO, KEGG, egg NOG (Evolutionary genealogy of Genes: Non-supervised Orthologous Groups), Swiss-Prot and Pfam databases, and the annotation rates were 37.65%, 16.88%, 15.26%, 21.90%, 35.79%, and 28.58%, respectively (Figure 3).

2.3. Transcriptome Data Profiling of R. soongorica Leaves

The expression levels of the same gene in different samples and the expression patterns of DEGs in the same sample are based on RNA-Seq data. A heat map was constructed based on the Euclidean method to calculate distances and the hierarchical clustering longest distance method (complete linkage) to analyze quantitative differences in the expression levels of DEGs in all comparison groups (Figure 4B). The cluster analysis results showed significant differences between the control and the salt-treated groups. In addition, principal component analysis (PCA) was performed on each sample according to the expression level (Figure 4A). Significant differences were found between the transcriptomes of R. soongorica leaves under different salt treatments, and the data were well reproducible within groups. Statistical results of DEGs between the 0, 200, and 500 mM NaCl treatment groups are shown in Figure 4C: compared with the 0 mM NaCl treatment, under the 200 mM NaCl treatment, i.e., 2391 (1057 up-regulated/1334 down-regulated) DEGs, compared with the 500 mM NaCl treatment, i.e., 6400 (3093 up-regulated/3307 down-regulated) DEGs, and the 500 mM NaCl treatment compared to the 200 mM NaCl treatment, i.e., 3642 (2169 up-regulated/1473 down-regulated) DEGs (Table S4). The comprehensive results showed that there were more down-regulated genes than up-regulated genes under the salinity treatment compared to the 0 mM NaCl treatment, while the number of DEGs of R. soongorica leaves treated with high NaCl concentrations was significantly higher than that of leaves treated with low NaCl concentrations.
GO enrichment analysis was performed on the DEGs treated with different concentrations of NaCl, and GO classification was performed according to molecular function, biological process, and cellular component. The top 10 GO terms with the most significant enrichment in each GO classification were selected for display, and the results are shown in Figure 5A,B. Compared with the 0 mM NaCl treatment, DEGs in the 200 mM and 500 mM NaCl treatment groups were significantly enriched in the extracellular region, cell wall, photosystem, oxidoreductase and peroxidase activity, xyloglucan metabolic process, flavonoid metabolic process, etc. Compared with the 200 mM NaCl treatment group, DEGs in the 500 mM NaCl treatment group were significantly enriched in the apoplast, 3-dehydroquinate dehydratase activity, dioxide-reduction process, flavonoid biosynthesis process, and so on (Figure 5C). In addition, KEGG enrichment analysis showed that DEGs in mannose-type O-glycan biosynthesis, anthocyanin biosynthesis, flavone and flavonol biosynthesis, photosynthesis-antenna proteins, brassinosteroid biosynthesis, etc., were significantly enriched after the 200 mM and 500 mM NaCl treatments compared with the 0 mM NaCl treatment (Figure 5D,E). Comparing the 500 mM and 200 mM NaCl treatment groups, DEGs in flavone and flavonol biosynthesis, linoleic acid metabolism, festoon antenna proteins, and other pathways were significantly enriched (Figure 5F). These results suggest that R. soongorica leaves respond to salt stress primarily by regulating cellular metabolism and photosynthesis.

2.4. Proteomic Data Profiling of R. soongorica Leaves

A total of 236,522 chromatograms were obtained from the mass spectrometry experiment. After analysis by Proteome Discoverer 2.2 software, 32,743 chromatograms were matched: 4432 proteins and 22,447 peptides were identified, including 21,011 TMT-labeled peptides. The peptide labeling efficiency was 93.6% (Figure 6A). PCA shows that PC1, which explains 42.3% of the total variation, cleanly separates plants from the 200 mM NaCl treatment from those from the 500 mM and 0 mM NaCl treatments, and the data within the group have good repeatability. PC2, which explained 23.9% of the total variation, showed differences between the 0 mM and 500 mM NaCl treatments (Figure 6B). Statistical results of differentially expressed proteins (DEPs) between the 0, 200, and 500 mM NaCl treatments are shown in Figure 6C. Comparison of salt-treated plants with the 0 mM NaCl treatment revealed differential expression of 47 (36 up-regulated/11 down-regulated) and 177 (126 up-regulated/51 down-regulated) DEPs at 200 and 500 mM NaCl treatment, respectively. Compared to the 200 mM NaCl treatment, 69 up-regulated and 67 down-regulated proteins were recorded in the 500 mM NaCl treatment (Table S5). By analyzing different DEPs, it was found that the number of DEPs induced by 500 mM NaCl was significantly higher than that induced by 200 mM NaCl compared with the 0 mM NaCl treatment.
GO functional enrichment analysis of the DEPs in the three comparison groups showed that there were 10, 12, and 11 significant enrichment items (p < 0.05) in the biological processes of the 0/200, 0/500, and 200/500 comparison groups, respectively (Figure 7A–C). They mainly included metabolic processes, response to osmotic stress, biological regulation, etc. In the 0/200 comparison group (Figure 7A), 11 items were enriched in molecular function, mainly involving transferase activity, ion binding, protein binding, etc. There were 16 items enriched in cellular component, mainly involving membrane, endomembrane system, cytosol, etc. In the 0/500 comparison group (Figure 7B), 11 items were enriched in molecular function, mainly involving ion binding, hydrolase activity, protein binding, etc. There were 16 items enriched in cellular component, mainly involving membrane, cytosol, nucleus, etc. In the 200/500 comparison group (Figure 7C), 14 items were enriched in molecular function, mainly involving ion binding, protein binding, hydrolase activity, etc. There were 17 items enriched in cellular component, including membrane, cytosol, and protein-containing complex. According to the above data, the DEPs of R. soongorica leaves respond to a variety of biological functions under salt treatment.
To further understand the biological functions of the proteins, KEGG enrichment analysis was performed on the annotated DEPs. The results showed that compared with 0 mM NaCl treatment, the DEPs in the 200 mM NaCl treatment group were significantly enriched in sesquiterpenoid and triterpenoid biosynthesis, glucosinolate biosynthesis, and other pathways (Figure 7D). In the 500 mM NaCl treatment group, DEPs were significantly enriched in linoleic acid metabolism, porphyrin, chlorophyll metabolism, and other pathways (Figure 7E). Compared with the 200 mM treatment group, DEPs in the 500 mM NaCl treatment group were significantly enriched in glucosinolate biosynthesis, nitrogen metabolism, SNARE interactions in vesicular transport, and other pathways (Figure 7F).

2.5. Transcriptomic and Proteomic Association Analysis

To correlate transcript and protein expression profiles, accession numbers were extracted from the proteome and compared to annotated RNA-Seq libraries (Table S6). According to the association analysis, there were 5, 32, and 10 genes with the same protein and mRNA changes in the 0/200, 0/500, and 200/500 mM NaCl comparison groups, respectively. There were zero, eight, and two genes with opposite trends in protein and mRNA expression, respectively. The genes with differentially expressed proteins but no differentially expressed mRNA were 40, 118, and 122, respectively, indicating that only a few proteins were directly regulated at the transcriptional level. Meanwhile, correlation analysis was performed for the DEPs and DEGs with consistent expression levels in the three control groups, and the Pearson correlation coefficients (r) were 0.977 (Figure 8A), 0.833 (Figure 8B), and 0.881 (Figure 8C), respectively. Further analysis of the differentially expressed genes screened from the 0/500 mM NaCl treatment group (Figure 8D) showed that 18 of the 32 genes with consistent mRNA and protein expression trends were up-regulated and 14 were down-regulated (Table 2).
Using the KEGG pathway database, pathway enrichment analysis was performed on the DEPs with the same gene expression trend in the 0/500 comparison group to identify the major metabolic and signaling pathways involved in the proteins. The results showed that 13 of the 32 DEPs were distributed over 19 pathways (Table 3). The DEPs were involved in cysteine and methionine metabolism, citrate cycle (TCA cycle), SNARE interactions in vesicular transport and photosynthesis, etc. Through the above association analysis, some target genes that might be related to the salt stress response of R. soongorica were screened out, such as SYP71, CS, PCC13-62, PASN, ZIFL1, and CHS2 (Table 4). These differential genes may play a key role in the molecular mechanism of salt stress response in R. soongorica.

3. Materials and Methods

3.1. Experimental Materials and Treatment

The research subjects were R. soongorica seeds collected from natural growing sites in Laohukou, Wuwei, Gansu, China (102°58′ E, 38°44′ N) at the end of October 2019. The seeds were collected according to the Technical Regulations for Seed Collection of Rare and Endangered Wild Plants (LYT 2590-2016) and stored in a storage cabinet (CZ-250FC, Top Yunong, Zhejiang, China) for later use. The present study was conducted by pot culture in 2020 at the Experimental Station of Longqu Seed Orchard, Gansu Province Academy of Qilian Water Resource Conservation Forests Research, in Zhangye, Gansu, China. In April 2020, seeds of the same full size were selected, disinfected with 0.3% KMnO4 solution for 15 min, rinsed 5 times with deionized water, and planted in a plug tray with a diameter of 4.5 cm and a height of 8.5 cm. The culture medium was vegetative soil, quartz sand, and vermiculite (3:1:1), and three seeds were planted in each plug tray. The seedlings were then grown in a greenhouse at 25 ± 1 °C, 50% humidity, natural ventilation, and good lighting. They were regularly irrigated with groundwater. On 15 June 2020, the uniformly sized R. soongorica seedlings were carefully removed from the plug trays, transferred to plastic pots (20 × 23 × 25 cm) with 2.5 kg of soil, and then grown in the greenhouse. The available phosphorus, salinity, and pH of the tested soil were 26.6 mg/kg, 0.2%, and 8.3, respectively. The smart irrigation control systems were used to maintain the soil water content close to the field capacity (60%). The R. soongorica seedlings were planted in a total of 1000 pots (4 seedlings per pot). After growing the seedlings for 40 d, 720 pots of seedlings with relatively uniform growth were selected as experimental material. The R. soongorica plants were subjected to the following six salt treatments: NaCl concentrations of 0, 100, 200, 300, 400, and 500 (mM). Four blocks of the same NaCl treatment were performed (morphological, physiological, transcriptomic, proteomic) and each NaCl treatment concentration was replicated on 10 pots per and three biological replicates were performed, using a total of 180 pots per block.
According to the experimental design, the corresponding NaCl solution was prepared with deionized water, and the NaCl solution was poured evenly around the root of R. soongorica with a syringe (to make sure that there was no permeability phenomenon when the prepared NaCl solutions were completely poured into the flowerpot). In order to avoid osmotic shock caused by salt shock reaction, the target concentration was reached within 24 h by gradual application of salt. Corresponding indices were determined after NaCl treatment for 24, 72, 144, and 216 h, respectively. A total of 5 g of leaves from each treatment was rapidly frozen in liquid nitrogen and then stored in an ultra-low temperature refrigerator (−80 °C) for proteome and transcriptome determination.

3.2. Determination of Morphological and Physiological Indicators

After 24, 72, 144, and 216 h of salt treatment, 3 seedlings were randomly selected from each replicate of each treatment, and a total of 9 seedlings were gently straightened. Plant height and root length were measured with a ruler (measuring range: 20 cm, accuracy: 0.1 cm) and averaged.
The soluble sugar content of R. soongorica leaves was determined using the methods described by Tan et al. [23]. Briefly, 0.1 g of R. soongorica leaves were weighed and placed in a 20 mL glass tube with a stopper, 10 mL of distilled water was added, the extract was extracted in boiling water for 30 min, the extract was filtered into a 25 mL volumetric flask, and the volume was kept constant. A total of 0.5 mL of the sample extract was absorbed into a 20 mL scale tube; 1.5 mL of distilled water was added; 0.5 mL of anthrone ethyl acetate reagent and 5 mL of concentrated sulfuric acid were added to the tube, shaken well, and cooled to room temperature, and the absorbance value was measured at a wavelength of 630 nm.
Soluble   sugar   content   ( μ g ⋅ g − 1 ) = C ⋅ V ⋅ N V t ⋅ W × 100 %
where C represents the glucose content determined from the standard curve ( µ g ); V is the total volume of the extract (mL); N is the dilution ratio; V t represents the amount of sample added during the determination (mL); W represents the fresh weight of the sample (g).
The salt tolerance index (STI) was calculated according to the method of Roshdy et al. [24]. The formula is as follows:
STI = DW Nacl / DW contrast × 100 %
where DW NaCl represents the dry weight of plants under salt treatment (g); DW contrast represents the dry weight of control plants (g).

3.3. RNA Sample Preparation and Transcriptome Analyses

Differential gene analysis and identification of R. soongorica leaves was performed according to the method described by Anders et al. [25] with some modifications. Briefly, total RNA was extracted from the collected 50 mg of R. soongorica leaves using the plant RNA purification kit (Norgen, Thorold, ON, Canada) according to the manufacturer’s instructions. The quality of total RNA was then measured using an Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA, USA). Oligo (dT) magnetic beads (Biomag, Wuxi, China) were used to enrich mRNA with PolyA structure in total RNA, and the mRNA was cleaved into 200−300 bp fragments by ion disruption. Using the RNA as a template, the first strand cDNA was synthesized using 6-base random primers (Gdsbio, Guangzhou, China) and reverse transcriptase (Aidlab, Beijing, China). The first strand cDNA was used as a template to synthesize the second strand cDNA, and the library size was 300–400 bp. Quality control was performed by Agilent 2100 Bioanalyzer, and double-terminal sequencing was performed by Illumina HiSeqTM2000 (NGS Solexa Hiseq2000, Illumina, CA, USA). All the above tests were performed by Suzhou Panomico Biotechnology Co., LTD.
RNA-Seq raw sequencing data were converted by invoking Base, followed by quality control of the raw reads. Cutadapt 1.16 software was used to remove the original data to obtain clean reads, and Trinity 2.5.1 software (Broad Institute, Hebrew University of Jerusalem, Jerusalem, IL) was used to splice clean reads to obtain transcripts. The longest transcript under each gene is extracted as the representative sequence of the gene after splicing, called the unigene. The unigene was used as the reference sequence for subsequent analysis. Clean reads for each gene were calculated and normalized to reads per kilobase per million reads (RPKM) for gene expression analysis. Differential analysis of gene expression was performed by DESeq screening for differentially expressed genes with the following conditions: multiple expression differences |log2FoldChange| > 1, significance p-value < 0.05.
Meanwhile, to verify the reliability of the transcriptome data, we randomly selected 10 differential genes with significant changes in expression for quantitative real-time PCR (qRT-PCR) analysis in the A vs. B and A vs. C comparison groups, respectively. Specific primers were designed using Primer 3.0 software (Table S1) and synthesized by Sangon Biotech (Shanghai) Co., Ltd. The internal reference gene used was “DN11735_c0_g2”. Sample RNA was extracted using the Plant Total RNA Extraction Kit (DP-437) (Tiangen, Beijing, China). cDNA synthesis was performed using the PrimeScript™ RT Master Mix (Perfect Real Time) Kit (TaKaRa, Dalian, China), and the instructions for experimental procedures were followed. The qRT-PCR program was 95 °C for 30 s, 95 °C for 5 s, 60 °C for 30 s, 95 °C for 5 s, 60 °C for 60 s, and 50 °C for 30 s, for a total of 40 cycles. The experiment was subjected to three biological replications. The relative expression of each gene was calculated using the 2−ΔΔCt method. The results showed that the expression trends of the 20 differential genes selected from R. soongorica leaves were highly correlated with the RNA-Seq results (Table S2). This indicates that the transcriptome data are reliable.

3.4. Protein Sample Preparation and Proteomic Analysis

Proteins were extracted from 2 g of R. soongorica leaves according to the method of Chen et al. [26], protein quality was detected by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), and protein samples were stored in a refrigerator at −80 °C until use. The tandem mass tag (TMT) assay was performed by Suzhou Panomic Biotechnology Co., LTD. according to the manufacturer’s recommendations (Applied Biosystems, Foster City, CA, USA). Briefly, 200 mL of protein lysates were taken from each sample for typing digestion, and then the peptides were labeled by TMT. The labeled groups were mixed and the mixed peptides were pre-separated by strong cation exchange chromatography. The liquid phase was separated on a SCX column after vacuum drying. Then, mass spectrometry (LC-MS/MS) (Applied Protein Technology, Shanghai, China) based on Orbitrap Fusion Lumos (Thermo Fisher Scientific, Waltham, MA, USA) was performed. Peak identification was performed on the original documents of mass spectrometry to obtain the peak list, and then the reference database was established, and the peptides and proteins were identified. In this study, the screening conditions of differentially expressed proteins were as follows: when the protein difference multiple was >1.5 or <0.66, the significance p-value < 0.05 was used as the screening condition for differential proteins.

3.5. Statistical and Bioinformatic Analysis

Statistical analyses were performed using SPSS 19 software. All data are expressed as mean ± standard error (SE) of three independent replicates. Significant mean differences between treatments were performed by one-way analysis of variance based on Duncan’s multiple range test at the level of p ≤ 0.05. Blast2go 2.5.0 software was used for Gene Ontology (GO) annotation analysis of the identified differential genes/proteins. During the analysis, the gene list and gene number of each term were calculated using the differential genes annotated by the GO term. Then, the p-value was calculated by the hypergeometric distribution method. The threshold for significant enrichment was set at p-value < 0.05. Kobas 3.0 software was used for pathway enrichment analysis of the identified genes/proteins in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database, with a p-value < 0.05 as the screening criterion for significant enrichment. Blast comparison was performed between the identified protein sequences and the GO and KEGG background databases, respectively. Arabidopsis was confirmed as the species with the best comparison results, and the protein information of the mutual comparison was determined. Therefore, the comparative protein information of Arabidopsis was used for the subsequent functional enrichment analysis. The expression patterns of DEPs and DEGs were hierarchically clustered using the MultiExperiment Viewer (MeV) software version 4.9.0 [27]. The relative ratios of DEPs and DEGs were subjected to log2 transformation, the Euclidean distance similarity metric was used to define similarity, and hierarchical clusters were assembled using the complete linkage clustering method. The clustering results were visualized by MeV 4.9.0 software.

3.6. Integrative Transcriptome-Proteome Analysis

The integrated transcriptome-proteome analysis calculates Pearson correlation coefficients (r) from the fold change of expressed transcripts and proteins to assess the correlation between the expression levels of specific transcripts and proteins in the transcriptome and proteome profiles of the samples [28].

4. Discussion

The occurrence of soil salinization has been further exacerbated by changes in climatic conditions, such as global warming and increased drought [29]. As one of the most detrimental factors among abiotic stresses, salt stress has disrupted normal physiological metabolic processes in plants, causing severe growth dysfunction in photosynthesis, protein synthesis, and energy metabolism [30]. Meanwhile, the maintenance of plant growth is directly related to the salt tolerance of plants [31]. In saline environments, halophytes can employ a variety of morphological and physiological adaptation strategies to reduce the excessive production of salt ions [32]. For example, a study of Halogeton glomeratus found that its optimal growth occurred under conditions of approximately 100 mM NaCl; however, it began to decline at higher salinities [11]. Rahman et al. [33] reported that salinity significantly reduced the growth and development of Achras sapota, which was accompanied by a significant decrease in plant height, root length, and plant STI. In the present study, after 72 h of NaCl stress, the plant height and root length of R. soongorica seedlings first increased and then decreased with increasing NaCl concentration, and the highest values were found at a 200 mM NaCl concentration. However, when the NaCl concentration exceeded 200 mM, plant height and root length decreased with increasing NaCl concentration. This result is consistent with the findings of Wang et al. [11], but different from those of Rahman et al. [33]. This difference in results is most likely due to the different salt-adaptation strategies of the species. First of all, R. soongorica and Halogeton glomeratus have been identified as salt-tolerant plants and the growing areas are mainly distributed in northwestern China [11,19], while Achras sapota is mainly a fruit tree native to Central America and Mexico [33]. At the same time, the main salt-adaptation mechanism in H. glomeratus is the translocation of sodium and some toxic ions into specific salt storage cells in the leaves, which is the same role of the specially organized salt glands in the leaves of R. soongorica [19]. In contrast, the salt-adaptation strategy of A. sapota is to retain sodium ions in the roots and accumulate compatible solutes to mitigate salt-toxic effects. Finally, it was found that both R. soongorica and H. glomeratus possessed good salt-adapted growth at less than 200 mM NaCl stress based on the data, while A. sapota showed a positive correlation of growth indicators with concentration when based on salt stress. At the same time, this study found that when treated with NaCl for 24 h, the soluble sugar content gradually decreased with increasing treatment concentration. When treated with NaCl for 72 h, the soluble sugar content gradually increased with increasing treatment concentration, had the maximum increase after 144 h, and then remained at a high level. This is consistent with the changes in soluble sugar content in R. soongorica leaves after different times and concentrations of NaCl treatment by Yang et al. [34], which may be due to the fact that soluble sugar was mainly used as an energy source to ensure the normal growth of R. soongorica in the early stages of NaCl treatment. With the prolongation of NaCl treatment time, the intracellular ion content increased, and soluble sugar was mainly used as an osmotic adjustment substance to maintain the osmotic balance of cells to ensure the growth of cells under salt stress.
In this study, the trends of gene expression and protein levels in R. soongorica leaves under different salt treatments (0, 200, and 500 mM NaCl) were analyzed. More differential genes and proteins were found in the 0/500 compared group. Meanwhile, the results of DEG and DEP analysis showed that the number of down-regulated genes was greater than that of up-regulated genes, while slightly more up-regulated proteins were identified.
Through association analysis of transcriptomic and proteomic data, the internal relationship between genes and proteins can be deeply understood, which is of great significance in mining reliable genes for plant breeding and improvement [35]. Jiang et al. [27] performed correlation analysis on the transcriptome and proteome of cucumber seedlings treated with H2S under salt stress. The results showed that the correlation coefficient of differentially expressed proteins and genes with the same trend of change under H2S treatment was 0.839, and most of the associated differential proteins were enriched in photosynthesis, cysteine metabolism, and energy metabolism. Ding et al. [36] analyzed tomato (Solanum lycopersicum) leaves under stress conditions by combining transcriptome and proteome and found 79 differential proteins with the same expression trend of the transcriptome, most of which were related to stress response and protein folding. In this study, transcriptome and proteome association analysis was performed on three comparison groups (0/200, 200/500, and 0/500) of R. soongorica leaves after 72 h of NaCl stress. After screening, only a small number of R. soongorica DEPs are consistent with the mRNA expression level. Correlation analysis revealed that DEPs and DEGs with consistent expression levels in the three control groups had a Pearson correlation > 8. This is because the transcription of DNA into mRNA and the translation of mRNA into proteins are affected by a variety of transcriptional, translational, and post-translational factors, including changes in the amount of transcribed mRNA and in the amount and function of proteins. This indicates that only a few proteins are directly regulated at the transcriptional level. Thus, the screened R. soongorica DEPs and DEGs showed a strong positive correlation with each other based on the number of their expression levels and their similar expression. Further analysis revealed that the number of genes with consistent transcriptome and proteome expression was significantly higher in the 0/500 comparison group (32) than in the 0/200 comparison group (5) and the 200/500 comparison group (10). Therefore, the 0/500 comparison group was focused as a study and further study revealed 32 differential proteins distributed in 19 metabolic pathways. It mainly involves cysteine and methionine metabolism, TCA cycle, SNARE interactions in vesicular transport, metabolism, etc. This further showed that SYP71, CS, ZIFL1, PCC13-62, PASN, and CHS2 genes may play an important role in the molecular mechanism of R. soongorica response to salt stress.
SNARE factors are divided into Q-SNARE and R-SNARE [37]. When plants are exposed to adverse conditions, a large number of SNARE proteins are required to mediate the membrane fusion mechanism during vesicle trafficking to ensure the smooth closure of ion channels and transporters on the plasma membrane and the separation of harmful ions [38]. For example, the expression levels of R-SNARE VAMP7-type, QC-SNARE, and Qb/c-SNARE were significantly increased in tomatoes under salt stress [39], suggesting that the SNARE-mediated vesicle trafficking pathway plays an important role in the salt stress response. The SYP7 family is a plant-specific family of QC-SNARE proteins consisting mainly of SYP71, SYP72, and SYP73 homologous proteins. [40]. Rice showed stronger antioxidant and disease resistance by overexpressing the SYP71 protein [41]. In this study, it was found that the expression levels of the SYP71 gene and protein were significantly up-regulated in R. soongorica leaves after the 500 mM NaCl treatment, suggesting that SYP71 may be involved in the vesicle transport pathway in leaf cells and thus play an important role in the response to salt stress in R. soongorica leaves. Citrate synthase (CS) is the core enzyme of the mitochondrial TCA cycle and an organic acid that regulates the TCA cycle, which directly controls cellular function [42]. The adaptation of plant cells to salt stress is closely related to various metabolic processes [43]. CS can improve plant tolerance to saline-alkali soil [44]. Similar to NaCl-treated Zea mays [45] and Haloxylon salicornicum [46], high NaCl stress significantly increased the abundance of citrate synthase in R. soongorica. The abundance of the CS gene was also significantly increased in this study, indicating that CS is a key protein of R. soongorica in response to salt stress.
Salt stress in plants is regulated by many signaling molecules, and auxin is a key medium for plants to respond to salt stress [11], which plays an important role in plant development and salt stress. Studies have shown that salt stress significantly disrupts auxin homeostasis and distribution in primary roots and inhibits auxin signaling [47]. Auxin treatment can significantly restore the growth of Arabidopsis’s primary roots under salt stress [48]. The above results indicate that the distribution and signaling of auxin mediate the response of plants to salt stress. ABC and MFS are two families of transporters in the plant kingdom. Zinc-induced facilitator-like 1 (ZIFL1), a member of the MFS family, is critical for auxin transport [49]. Overexpression of ZIF1 can enhance auxin transport and improve stress tolerance in Arabidopsis [50]. The dehydration-related protein PCC13-62 can improve plant tolerance to extreme drought [51]. Li et al. [52] found that the expression of PCC13-62 in upland cotton was up-regulated under salt stress based on iTRAQ proteomics techniques. In addition, Giarola et al. [53] also found that salt stress could activate the PCC13-62 promoter and increase the tolerance of Arabidopsis to salt stress. The results of this study showed that after 500 mM NaCl treatment, the abundance of ZIFL1 and PCC13-62 significantly increased, suggesting that the leaves of R. soongorica could enhance the tolerance to salt stress by up-regulating the expression of ZIFL1 and PCC13-62. Meanwhile, our study also found that the expression of PSAN in R. soongorica leaves decreased under salt stress. PSAN is a PSI-related gene, and salt stress can significantly reduce the photochemical activity of PSI, ultimately leading to lower photosynthetic efficiency [54]. This is consistent with the findings on photoinhibition of Cinnamomum camphora L. by NaCl stress [55], where salinity resulted in the down-regulation of protein expression located on PSI (PSAN). The expression of the PASN gene and protein in this study also showed the same trend of down-regulation. The results showed that PSAN may play a key role in the molecular mechanism of R. soongorica’s response to salt stress.
Polyketide synthases (PKSs) are a family of multifunctional proteins that exhibit remarkable versatility in structural fusion and functional organization to produce different classes of compounds. Structurally, chalcone synthase (CHS) is considered to be the simplest type III PKS. This enzyme is also known to catalyze the first step of the flavonoid/isoflavone pathway [56]. Flavonoids, an important secondary metabolite, are closely related to the antioxidant capacity of plants [13]. Since chalcone synthase is the first enzyme in the flavonoid biosynthetic pathway, its expression and regulation are important [57]. The significant enrichment of CHS protein in Pongamia roots under salt treatment contributes to the protection of Pongamia with high antioxidant activity from ROS damage and promotes root growth under high salt stress [58]. The results of this study showed that the abundance of CHS2 was significantly increased after the 500 mM NaCl treatment, indicating that R. soongorica could be used to increase secondary metabolites to mitigate salt damage by up-regulating the expression of CHS2.

5. Conclusions

The effects of different salt concentrations and treatment times on the morphology and physiological indices of R. soongorica were studied. It was found that low NaCl treatment (100, 200 mM) promoted the growth of R. soongorica, while high NaCl treatment (400, 500 mM) inhibited its growth. At the early stage of treatment (24 h), the soluble sugar content in the leaves of R. soongorica decreased with the increase in NaCl concentration, which provided an energy source for the smooth progress of various metabolisms. After 72 h of treatment, it increased with the increase in NaCl treatment concentration and then remained at a higher level as an osmotic regulator to maintain osmotic balance in vivo. When treated with 200 mM NaCl for 72 h, R. soongorica seedlings showed the strongest salt tolerance. Furthermore, the molecular mechanism of R. soongorica leaves was investigated under normal culture (0 mM NaCl) and salt stress treatment (200, 500 mM NaCl) for 72 h. Through transcriptome and proteome association analysis, 40 differential proteins with the same expression trend of differential genes were identified, among which 25 were up-regulated and 15 were down-regulated, and finally, SYP71, CS, PCC13-62, PASN, ZIFL1, CHS2, and other genes were found to be potential target genes for salt tolerance of R. soongorica. This study laid a theoretical foundation for further understanding the molecular mechanism of R. soongorica in response to salt stress.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants12203542/s1, Table S1: Selected genes and their primers in the A vs. B and A vs. C comparison groups; Figure S1: Expression correlation analysis of differential genes in RNA-Seq and qRT-PCR results; Table S2: Expression correlation analysis of differential genes in RNA-Seq and qRT-PCR results; Table S3: Result of RNA-Seq expression profile sequencing; Table S4: Differentially expressed genes in the transcriptome of Reaumuria soongorica leaves; Table S5: Differentially expressed proteins in the proteome of Reaumuria soongorica leaves; Table S6: Types of protein and mRNA expression changes.

Author Contributions

All authors contributed to the conception and design of the study. Material preparation, data collection, and analysis were performed by H.L., S.Y., P.C., Z.L., X.B. and B.T. The first draft of the manuscript was written by H.L. and all authors commented on earlier versions of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the National Natural Foundation of China (32160407); the Gansu Provincial Key Research and Development Program (23YFFA0065); the Grass Industry Open Project (KLGE202215); the Outstanding Doctoral Projects Funded by Gansu Provincial Science and Technology Program (23JRRA1451); the Postgraduate Innovation Star Project (2023CXZX-643).

Data Availability Statement

The dataset generated in this study is available under NCBI SRA accession number PRJNA977833, while the mass spectrometry proteomics data have been deposited on ProteomeXchange under accession number PXD042784. Other data are in the supplementary document.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Physiological changes in R. soongorica leaves under salt stress. (A) Effect of NaCl stress on the soluble sugar content of R. soongorica leaves. (B) Changes in salt tolerance coefficient of R. soongorica under NaCl stress. N0: 0 mM NaCl; N100: 100 mM NaCl; N200: 200 mM NaCl; N300: 300 mM NaCl; N400: 400 mM NaCl; N500: 500 mM NaCl. Different lowercase letters indicate significant differences from different salt levels (p < 0.05).
Figure 1. Physiological changes in R. soongorica leaves under salt stress. (A) Effect of NaCl stress on the soluble sugar content of R. soongorica leaves. (B) Changes in salt tolerance coefficient of R. soongorica under NaCl stress. N0: 0 mM NaCl; N100: 100 mM NaCl; N200: 200 mM NaCl; N300: 300 mM NaCl; N400: 400 mM NaCl; N500: 500 mM NaCl. Different lowercase letters indicate significant differences from different salt levels (p < 0.05).
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Figure 2. A representative figure of the phenotypic differences of R. soongorica seedlings under different treatments.
Figure 2. A representative figure of the phenotypic differences of R. soongorica seedlings under different treatments.
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Figure 3. Annotation statistics of Unigene in R. soongorica leaves under NaCl stress.
Figure 3. Annotation statistics of Unigene in R. soongorica leaves under NaCl stress.
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Figure 4. Identification and analysis of DEGs in R. soongorica leaves under 0, 200, and 500 mM NaCl. (A) The similarity of gene expression was compared in three-sample groups using PCA. (B) Cluster analysis of DEGs in R. soongorica leaves under different NaCl treatments. (C) Common or unique DEGs are compared using the Venn diagram.
Figure 4. Identification and analysis of DEGs in R. soongorica leaves under 0, 200, and 500 mM NaCl. (A) The similarity of gene expression was compared in three-sample groups using PCA. (B) Cluster analysis of DEGs in R. soongorica leaves under different NaCl treatments. (C) Common or unique DEGs are compared using the Venn diagram.
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Figure 5. GO classification and KEGG pathway of DEGs under different salt concentration stresses. (A–C) GO classification of DEGs in R. soongorica leaves under different NaCl treatments. (D–F) KEGG pathway enrichment of DEGs in R. soongorica leaves under different NaCl treatments.
Figure 5. GO classification and KEGG pathway of DEGs under different salt concentration stresses. (A–C) GO classification of DEGs in R. soongorica leaves under different NaCl treatments. (D–F) KEGG pathway enrichment of DEGs in R. soongorica leaves under different NaCl treatments.
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Figure 6. Identification and analysis of DEPs in R. soongorica leaves under 0, 200, and 500 mM NaCl. (A) Protein information identified by TMT. (B) The similarity of protein expression was compared in three sample groups using PCA. (C) Common or unique DEPs are compared using the Venn diagram.
Figure 6. Identification and analysis of DEPs in R. soongorica leaves under 0, 200, and 500 mM NaCl. (A) Protein information identified by TMT. (B) The similarity of protein expression was compared in three sample groups using PCA. (C) Common or unique DEPs are compared using the Venn diagram.
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Figure 7. GO classification and KEGG pathway of DEPs under different salt concentration stresses. (A–C) GO classification of DEPs in R. soongorica leaves under different NaCl treatments. (D–F) KEGG pathway enrichment of DEPsin R. soongorica leaves under different NaCl treatments.
Figure 7. GO classification and KEGG pathway of DEPs under different salt concentration stresses. (A–C) GO classification of DEPs in R. soongorica leaves under different NaCl treatments. (D–F) KEGG pathway enrichment of DEPsin R. soongorica leaves under different NaCl treatments.
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Figure 8. Correlation analysis based on proteomics and transcriptome. (A–C) Correlation analysis plots for different concentrations of NaCl comparison groups, with DEP expression levels labeled on the horizontal axis and DEG expression levels labeled on the vertical axis. (D) Expression pattern clustering analysis of 40 differentially expressed mRNAs and proteins. fc: fold of change. Red represents up-regulated and green represents down-regulated.
Figure 8. Correlation analysis based on proteomics and transcriptome. (A–C) Correlation analysis plots for different concentrations of NaCl comparison groups, with DEP expression levels labeled on the horizontal axis and DEG expression levels labeled on the vertical axis. (D) Expression pattern clustering analysis of 40 differentially expressed mRNAs and proteins. fc: fold of change. Red represents up-regulated and green represents down-regulated.
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Table 1. Effects of NaCl stress on the plant height and root length of R. soongorica seedlings.
Table 1. Effects of NaCl stress on the plant height and root length of R. soongorica seedlings.
Growth IndexNaCl Concentration (mM·L−1)Treatment Time/h
2472144216
Plant height (cm)09.42 ± 0.231 a10.33 ± 0.207 bc10.86 ± 0.210 c12.87 ± 0.378 c
1009.77 ± 0.061 a10.79 ± 0.096 b11.12 ± 0.253 bc13.45 ± 0.258 b
2009.97 ± 0.558 a11.37 ± 0.252 a11.78 ± 0.491 a14.40 ± 0.150 a
3009.70 ± 0.387 a10.07 ± 0.113 c11.50 ± 0.452 ab12.46 ± 0.314 c
4009.55 ± 0.444 a10.12 ± 0.150 c10.73 ± 0.111 c11.23 ± 0.469 d
5009.35 ± 0.229 a9.54 ± 0.611 d10.10 ± 0.155 d10.59 ± 0.213 e
Root length (cm)08.60 ± 0.200 bc9.53 ± 0.368 ab10.42 ± 0.187 a11.27 ± 0.436 a
1008.85 ± 0.141 ab9.82 ± 0.104 a10.17 ± 0.262 ab10.98 ± 0.366 ab
2009.20 ± 0.586 a9.92 ± 0.295 a10.06 ± 0.213 abc10.53 ± 0.530 bc
3009.40 ± 0.305 a9.65 ± 0.202 ab9.82 ± 0.369 bc9.97 ± 0.337 cd
4008.42 ± 0.092 bc9.2 ± 0.162 bc9.51 ± 0.413 cd9.74 ± 0.114 d
5008.15 ± 0.218 c8.78 ± 0.144 c9.04 ± 0.291 d9.36 ± 0.240 d
Notes: Data are presented as average ± SE (n = 3). Different lowercase letters denote significant differences at the 0.05 probability level according to the Duncan test.
Table 2. DEPs and DEGs with identical expression patterns identified at the mRNA and protein levels in the 0/500 comparison group.
Table 2. DEPs and DEGs with identical expression patterns identified at the mRNA and protein levels in the 0/500 comparison group.
Accession
Number		Gene NameGene
Log2 Value		Up/
Down		Protein Fold ChangeUp/
Down		Protein Description
D3THI6UGT71A15−1.75down0.51downUDP-glycosyltransferase 71A15
Q9ZSA7DLO2−1.93down0.53downProtein DMR6-LIKE OXYGENASE 2
O24370LOX2.1−2.15down0.53downLinoleate 13S-lipoxygenase 2-1, chloroplastic
Q7X999RCA2−1.58down0.57downRibulose bisphosphate carboxylase/oxygenase activase 2, chloroplastic
Q8L5A7SOT15−2.36down0.58downCytosolic sulfotransferase 15
O49675CCD4−3.34down0.58downProbable carotenoid cleavage dioxygenase 4, chloroplastic
P49107PSAN−1.86down0.59downPhotosystem I reaction center subunit N, chloroplastic
P27522CAB8−1.17down0.59downChlorophyll a-b binding protein 8, chloroplastic
P20152Vim−2.17down0.60downVimentin
K4BW79EO−3.53down0.61down2-methylene-furan-3-one reductase
Q9LR64PSB27-1−1.11down0.61downPhotosystem II repair protein PSB27-H1, chloroplastic
Q9SQT8EMB3004−2.39down0.64downBifunctional 3-dehydroquinate dehydratase/shikimate dehydrogenase, chloroplastic
Q9SSK9MLP28−4.00down0.65downMLP-like protein 28
Q9ZQI8LTPG12−2.27down0.66downNon-specific lipid-transfer protein-like protein At2g13820
P11432ELIP2.88up1.51upPEA early light-induced protein, chloroplastic
O49432QRT31.51up1.52upPolygalacturonase QRT3
Q9XJ57CHS21.20up1.53upChalcone synthase 2
Q9SF29SYP711.03up1.56upSyntaxin-71
Q9SR86At3g088602.16up1.60upAlanine--glyoxylate aminotransferase 2 homolog 3, mitochondrial
Q8L856CYB561A1.01up1.63upTransmembrane ascorbate ferrireductase 1
Q08507ACO31.09up1.64up1-aminocyclopropane-1-carboxylate oxidase 3
Q6Z1G7Os08g424101.20up1.65upPyruvate dehydrogenase E1 component subunit beta-1, mitochondrial
Q94JX5WLIM11.10up1.73upLIM domain-containing protein WLIM1
Q9SXA6ENDO11.79up1.78upEndonuclease 1
P53800FDFT1.02up1.84upSqualene synthase
A6QP05DHRS121.28up1.90upDehydrogenase/reductase SDR family member 12
Q04980LTI651.61up2.12upLow-temperature-induced 65 kDa protein
P22242PCC13-623.90up2.26upDesiccation-related protein PCC13-62
Q8LPS2ACD66.09up2.51upProtein ACCELERATED CELL DEATH 6
Q94BZ1ZIFL12.80up2.79upProtein ZINC-INDUCED FACILITATOR-LIKE 1
Q55874sll010310.12up3.03upUncharacterized protein sll0103
O80433CS1.01up3.89upCitrate synthase, mitochondrial
Table 3. KEGG classification for differential protein with the same trend of mRNA expression change in the 0/500 comparison group as in the 0/500 comparison group.
Table 3. KEGG classification for differential protein with the same trend of mRNA expression change in the 0/500 comparison group as in the 0/500 comparison group.
NoGene NameProtein DescriptionKEGG PathwayPathway ID
1LOX2.1Linoleate 13S-lipoxygenase 2-1, chloroplasticLinoleic acid metabolismko00591
2LOX2.1Linoleate 13S-lipoxygenase 2-1, chloroplasticalpha-Linolenic acid metabolismko00592
3CCD4Probable carotenoid cleavage dioxygenase 4, chloroplasticCarotenoid biosynthesisko00906
4PSANPhotosystem I reaction center subunit N, chloroplasticPhotosynthesisko00195
5CAB8Chlorophyll a-b binding protein 8, chloroplasticPhotosynthesis—antenna proteinsko00196
6PSB27-1Photosystem II repair protein PSB27-H1, chloroplasticPhotosynthesisko00195
7EMB3004Bifunctional 3-dehydroquinate dehydratase/shikimate dehydrogenase, chloroplasticPhenylalanine, tyrosine, and tryptophan biosynthesisko00400
8CHS2Chalcone synthase 2Flavonoid biosynthesisko00941
9CHS2Chalcone synthase 2Tropane, piperidine, and pyridine alkaloid biosynthesisko00960
10SYP71Syntaxin-71SNARE interactions in vesicular transportko04130
11At3g08860Alanine--glyoxylate aminotransferase 2 homolog 3, mitochondrialAlanine, aspartate and glutamate metabolismko00250
12At3g08860Alanine--glyoxylate aminotransferase 2 homolog 3, mitochondrialGlycine, serine and threonine metabolismko00260
13At3g08860Alanine--glyoxylate aminotransferase 2 homolog 3, mitochondrialCysteine and methionine metabolismko00270
14At3g08860Alanine--glyoxylate aminotransferase 2 homolog 3, mitochondrialValine, leucine and isoleucine degradationko00280
15ACO31-aminocyclopropane-1-carboxylate oxidase 3Cysteine and methionine metabolismko00270
16Os08g42410Pyruvate dehydrogenase E1 component subunit beta-1, mitochondrialGlycolysis / Gluconeogenesisko00010
17Os08g42410Pyruvate dehydrogenase E1 component subunit beta-1, mitochondrialCitrate cycle (TCA cycle)ko00020
18Os08g42410Pyruvate dehydrogenase E1 component subunit beta-1, mitochondrialPyruvate metabolismko00620
19FDFTSqualene synthaseSteroid biosynthesisko00100
20CSCitrate synthase, mitochondrialCitrate cycle (TCA cycle)ko00020
21CSCitrate synthase, mitochondrialGlyoxylate and dicarboxylate metabolismko00630
22LOX2.1Linoleate 13S-lipoxygenase 2-1, chloroplasticLinoleic acid metabolismko00591
Table 4. Candidate genes and their role in R. soongorica tolerance in response to salt stress.
Table 4. Candidate genes and their role in R. soongorica tolerance in response to salt stress.
Gene NameGene
Up/
Down		Protein
Up/
Down		Protein DescriptionRole
PSANdowndownPhotosystem I reaction center subunit N, chloroplasticPSAN is a PSI-related gene, and salt stress significantly reduces the photochemical activity of PSI and decreases photosynthetic efficiency. This results in the down-regulation of the PSAN gene and protein expression.
CHS2upupChalcone synthase 2Up-regulates CHS2 gene and protein expression and increases secondary metabolites to reduce salt damage.
SYP71upupSyntaxin-71Under salt stress, to ensure the smooth closure of ion channels and transporters on the plasma membrane and the separation of harmful ions during vesicular transport. By up-regulating the expression of the SYP71 gene and protein, which is involved in the vesicular transport pathway in leaf cells, the effect of Na+ and related ions on the growth of R. soongorica was mitigated.
PCC13-62upupDesiccation-related protein PCC13-62The high osmotic effect of salt stress induces physiological drought in the plant root system, and the effect of salt stress on the growth of R. soongorica is mitigated by the up-regulation of the PCC13-62 gene and protein expression.
ZIFL1upupProtein ZINC-INDUCED FACILITATOR-LIKE 1Enhanced growth hormone distribution and signaling to regulate R. soongorica’s response to salt stress by up-regulating the ZIFL1 gene and protein expression.
CSupupCitrate synthase, mitochondrialCS is a core enzyme of the mitochondrial tricarboxylic acid cycle, which is an organic acid that regulates the tricarboxylic acid cycle and directly controls cellular functions. By up-regulating the expression of the CS gene and protein, the adaptation of plant cells to salt stress was improved with various metabolic processes.
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Liu, H.; Chong, P.; Yan, S.; Liu, Z.; Bao, X.; Tan, B. Transcriptome and Proteome Association Analysis to Screen Candidate Genes Related to Salt Tolerance in Reaumuria soongorica Leaves under Salt Stress. Plants 2023, 12, 3542. https://doi.org/10.3390/plants12203542

AMA Style

Liu H, Chong P, Yan S, Liu Z, Bao X, Tan B. Transcriptome and Proteome Association Analysis to Screen Candidate Genes Related to Salt Tolerance in Reaumuria soongorica Leaves under Salt Stress. Plants. 2023; 12(20):3542. https://doi.org/10.3390/plants12203542

Chicago/Turabian Style

Liu, Hanghang, Peifang Chong, Shipeng Yan, Zehua Liu, Xinguang Bao, and Bingbing Tan. 2023. "Transcriptome and Proteome Association Analysis to Screen Candidate Genes Related to Salt Tolerance in Reaumuria soongorica Leaves under Salt Stress" Plants 12, no. 20: 3542. https://doi.org/10.3390/plants12203542

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