The F. velutina materials were obtained from the Experimental Base of Afforestation on Saline—Alkali Soil of Shandong Provincial Academy of Forestry, Shouguang City, Shandong Province, China (118°42′9.18″ E, 37°9′38.94″ N), China. The salt-tolerant F. velutina R7 accession and salt-sensitive F. velutina S4 accession were identified from 189 F. velutina accessions (unpublished data) by measuring Fv/Fm ratio on the excised leaves exposed to 250 mM NaCl for 48 h according to the previously described method (Smethurst et al., 2009). Due to easy availability and appropriate size of 1-year-old cuttings of F. velutina (height: 26.40 ± 0.50 cm) for the experiment. In addition, 1-year-old cuttings are frequently used as plant materials for salt resistance research (Gucci et al., 1997; Tsabarducas et al., 2015; Ran et al., 2021). So, 1-year-old cuttings of F. velutina R7 and S4 were used in this study. After gently removing the soil around the root, the cuttings were firstly precultivated in distilled water for 2 weeks and then transferred to a plastic container containing 6 L of half-strength Hoagland’s solution for 4 weeks. The solutions were refreshed every 7 days. The cuttings were grown in a growth incubator (LICHEN, Shanghai, China) under 25/20°C (day/night temperature), 65% relative humidity, 16 h light (1,200 μmol m^–2 s^–1)/8 h dark.

As previously described (Gabilly et al., 2019), chlorophyll fluorescence parameters (Fv/Fm ratio) were determined using a pulse-amplitude modulated chlorophyll fluorometer (FMS2, Hansatech Instruments, Pentney King’s Lynn, United Kingdom). The images reflecting chlorophyll fluorescence parameters were acquired with FluorCam imaging fluorimeters (Photon Systems Instruments, Brno, Czech Republic) according to the instructions of the manufacturer.

After 6 weeks of acclimatization, the healthy cuttings with appropriate size (height: 26.40 ± 0.50 cm) for this experiment were selected and exposed to the solution containing 250 mM NaCl for 12 h, and the clone without NaCl treatment were considered as control. The NaCl concentration and exposure time were selected based on our preliminary tests and previous studies (Li et al., 2012; Yan et al., 2019; Catalá et al., 2021). The leaves and roots of each clone from three independent biological replicates were harvested and stored in -80°C until being used.

For easy understanding, the letters “S” and “C” were used to represent the salt-treated samples and control, respectively, while “L” and “R” represented the leaves and roots samples, respectively. For instance, the sample R7SL represented the leaves of R7 clone treated with NaCl solution.

The contents of proline, soluble sugar, and H₂O₂ were determined using the commercial kits purchased from Nanjing Jiangcheng Bioengineering Institute (Nanjing, China) following the instructions of the manufacturer. Histochemical location of H₂O₂ was conducted by staining with 3,3′-diaminobenzidine (DAB) according to the previously described (Daudi and O’Brien, 2012). Cell death was determined by trypan blue staining as previously described (Pogány et al., 2009). Each sample group contained five biological replicates.

Total RNA was extracted using the GeneJET Plant RNA Purification Mini Kit (Thermo Fisher Scientific, Waltham, Massachusetts, United States). The concentration and integrity of RNA were evaluated using a NanoDrop ND-2000 (Thermo Fisher Scientific, Waltham, Massachusetts, United States) and Agilent Bioanalyzer 2100 (Agilent Technologies, Santa Clara, California, United States), respectively. About 1 μg of qualified total RNA was used to construct cDNA libraries with an insert size of 350 bp using the TruSeq RNA Sample Preparation Kit v2 (Illumina, San Diego, California, United States). The qualified libraries were sequenced on an Illumina NovaSeq 6000 platform (KeGene Science and Technology Corporation Ltd., Shandong, China) with a paired-end 150 mode.

The raw reads obtained were trimmed using Trimmomatic v0.39 (Bolger et al., 2014), and the high-quality reads obtained were mapped to velvet ash reference genome¹ (Kelly et al., 2020) using HISAT2 version 2.2.1 software (Kim et al., 2015). The transcripts were assembled and quantified using StringTie v2.1.5 (Pertea et al., 2015), and the gene expression levels were measured using fragments per kilobase of transcript per million fragments mapped (FPKM). The genes with FPKM ≥ 5 in at least one sample were selected to perform differential gene expression analysis using DESeq2 v4.1 (Love et al., 2014) based on | log2 (fold change)| ≥ 2 and false discovery rate < 0.01.

The Gene Ontology (GO) enrichment analysis of the DEGs was performed using the Cytoscape v3.9.0 plug-in ClueGO v2.5.8 (Bindea et al., 2009). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the DEGs was carried out using clusterProfiler v4.2.0 (Yu et al., 2012). The transcription factors were identified by aligning all the transcripts obtained against the plant TF database PlantTFDB v5.0 (Tian et al., 2020).

To validate the reliability of transcriptome sequencing data, 16 DEGs were randomly selected to perform quantitative real-time (qRT)-PCR analysis. The CFX Connect Real-Time System (Bio-Rad, Hercules, CA, United States) was used for qRT-PCR. The PCR assays were conducted as previously described (Fang et al., 2021). The actin genes were chosen as internal reference (Li et al., 2013, 2019). The genes were quantified using the 2^–ΔΔCT method (Arocho et al., 2006). Each sample group contained three biological replicates. The primers are listed in Supplementary Table 1.

Statistical data were represented as mean ± SD. The Student’s t-test was used to determine the differences between the two groups. The statistical analyses were performed with GraphPad Prism v9.0 (GraphPad Software Inc., La Jolla, United States). *P < 0.05, ^**P < 0.01, and ^***P < 0.001 represented statistical significance.

To evaluate the salt tolerance between R7 and S4, the Fv/Fm parameters in the salt-treated excised leaves were determined. The results showed that when the excised leaves of S4 and R7 were, respectively, exposed to 250 mM NaCl for 48 h, Fv/Fm ratio decreased significantly in S4 than in R7 (Figures 1A,B), suggesting that R7 is more salt tolerant.

Several selected biochemical parameters, such as soluble sugars and proline content in roots, and H₂O₂ content (both leaves and roots) of R7 and S4 clone exposed to salt stress were determined. The results showed that the levels of soluble sugars and proline in R7 roots were significantly higher than in S4 (Figures 1C,D). Moreover, R7 leaves and roots presented lower levels of H₂O₂ than S4 under salt stress (Figures 1E,F), which was further confirmed by the histochemical staining (Figure 1G). The above results further demonstrated that R7 is more tolerant to salt stress.

To explore the differences between R7 and S4 accessions regarding gene expressions, a comparative transcriptome analysis was performed on the leaf and root of R7 and S4 clone exposed to salt stress. The results showed that 1.01 billion clean reads were obtained, with an average Q30 value was 98%. Then the clean reads were mapped to the F. velutina genome assembly and the results showed that 73.96–94.85% of total reads were mapped to the genome. After transcript assembly, 32,887 genes were finally produced by RNA sequencing (Supplementary Table 3).

To evaluate the consistency among the biological replicates, hierarchical clustering of all samples based on the correlation coefficient γ² between each sample was performed. The results showed that leaf and root samples were clustered individually whereas the three biological replicates of each group were clustered together (Supplementary Figure 1), indicating that the biological replicates in each group are highly consistent.

To identify salt-responsive genes, the differential gene expression analysis of pairwise comparisons R7SL vs. R7CL, S4SL vs. S4CL, R7SL vs. S4SL, and R7CL vs. S4CL in leaves and R7SR vs. R7CR, S4SR vs. S4CR, R7SR vs. S4SR, and R7CR vs. S4CR in roots were performed, respectively (Supplementary Table 4). The results showed that 3,218, 2,238, 3,592, and 2,119 DEGs were identified in comparisons R7SL vs. R7CL, S4SL vs. S4CL, R7SL vs. S4SL, and R7CL vs. S4CL in leaves, respectively (Figure 2A and Supplementary Figure 2). There were 8,455, 9,548, 1,797, and 1,898 DEGs in comparisons R7SR vs. R7CR, S4SR vs. S4CR, R7SR vs. S4SR, and R7CR vs. S4CR in roots, respectively (Figure 2B and Supplementary Figure 3).

To validate the reliability of RNA sequencing data, 16 DEGs were randomly selected to perform qRT-PCR analysis. The results showed a high correlation coefficient (R² = 0.7266) between RNA sequencing data and qRT-PCR results, indicating that the RNA sequencing data are reliable (Figure 2C). We, therefore, concluded that the global transcriptome in R7 is altered than in S4.

To understand the potential mechanisms underlying the distinguishing R7 and S4 in response to salt stress, a GO enrichment analysis of the DEGs in the comparisons R7SL vs. S4SL and R7SR vs. S4SR was performed, respectively. The results showed that multiple stress-associated GO terms were significantly enriched in both leaves and roots, such as response to an inorganic substance, response to salt stress, response to an organic substance, and response to the hormone (Figures 3A,C). Several GO terms related to oxidative stress, signal transduction, and metabolic process were also enriched in both leaves and roots. In addition, several GO terms, namely, photosynthesis, transmembrane receptor protein tyrosine kinase signaling pathway, and cell surface receptor signaling pathway were specifically enriched in leaves, whereas the terms including response to cadmium ion and response to osmotic stress were peculiarly enriched in roots (Figures 3B,D and Supplementary Table 5). These results suggested that salt stress induces the higher enrichment of specific stress-responsive genes in R7 than in S4.

To identify the signaling pathways involved in enhancing the salt tolerance of R7, the KEGG pathway enrichment analysis was performed using upregulated and downregulated DEGs from the comparison R7SL vs. S4SL. The results showed that the upregulated DEGs were mainly enriched in “plant hormone signal transduction” and “MAPK signaling pathway-plant” (Figure 4A and Supplementary Table 6), whereas the downregulated DEGs were enriched in “photosynthesis” and “photosynthesis-antenna proteins” (Supplementary Figure 4).

There were 23 DEGs involved in “plant hormone signal transduction,” namely, the signaling network of auxin (IAA and SAUR), cytokinin (AHP and A-ARR), gibberellin (GID1), abscisic acid (PYR/PYL and PP2C), ethylene (EIN3, ETR, ERF1, and EBF1/2), and salicylic acid (PR1and TGA). In addition, there were 21 DEGs involved in the “MAPK signaling pathway plant.” All these genes exhibited upregulation in the leaves of F. velutina R7 in comparison to the one in S4 under salt treatment (Figure 4B). The findings indicated that hormonal and MAPK signaling pathways are altered in the leaves of R7 after salt exposure.

To explore the differences between R7 and S4 roots in response to salt stress, KEGG pathway enrichment analysis was performed based on the DEGs from comparisons R7SR vs. R7CR and S4SR vs. S4CR, respectively (Supplementary Table 7). The results showed that three pathways were overlapped between two comparisons included “carbon metabolism,” “biosynthesis of amino acids,” and “starch and sucrose metabolism” (Figures 5A,B).

Starch and sucrose metabolism can regulate osmotic adjustment via determining the contents of soluble sugars, playing critical roles in salt tolerance (Dahro et al., 2016; Wei et al., 2019). Thus, the expression patterns of DEGs in “starch and sucrose metabolism” in R7 and S4 roots under salt stress were further analyzed. The results showed that the expression patterns of several genes were different between R7 and S4 roots under salt stress (Figure 5C), which was consistent with the significantly elevated levels of soluble sugars in R7 roots than in S4 (Figure 1C), suggesting that starch and sucrose metabolism induced by salt stress in R7 and S4 roots is different.

To explore the common mechanisms in response to salt stress between R7 and S4 roots, an intersection analysis between the comparisons R7SR vs. R7CR and S4SR vs. S4CR was performed. The results identified 5,296 shared DEGs in F. velutina R7 and S4 roots under salt stress (Figure 6A). KEGG enrichment analysis of these genes revealed 16 significantly enriched pathways, such as “biosynthesis of amino acids” and “Arginine and proline metabolism” (Figure 6B and Supplementary Table 8), suggesting that accumulation of amino acids might improve the salt tolerance in both F. velutina R7 and S4 roots. Proline acting as an osmoprotectant plays an important role in improving salt tolerance in the plant (Liang et al., 2018; Rady et al., 2019). As a rate-limiting enzyme involved in proline biosynthesis (Turchetto-Zolet et al., 2009), a gene encoding delta-1-pyrroline-5-carboxylate synthase 1 (P5CS1) was differentially upregulated to higher levels in R7 roots than in S4 (Figure 6C), which was consistent with the significantly elevated levels of proline in R7 roots than in S4 (Figure 1D).

The increase in ROS levels leads to oxidative stress, posing detrimental effects on plant cells and tissues (Choudhury et al., 2017). Therefore, the DEGs involved in response to oxidative stress were analyzed further via dissecting the oxidative stress-related GO terms. Among the genes, 12 encoding peroxidases (PODs) were upregulated higher in R7 leaves (10 genes) and roots (2 genes) than in S4 (Figure 6D and Supplementary Table 4) under salt stress. Consistently, R7 leaves and roots presented lower levels of H₂O₂ than S4 under salt stress (Figures 1E–G). Collectively, these results suggested that R7 exhibits higher ROS scavenging capacity than S4 under salt stress.

Transcription factors (TFs) act as the master switches in regulating multiple downstream target genes, thus playing crucial roles in various biological processes including salt stress response. By dissecting the RNA sequencing data, multiple TF genes were significantly altered between R7 and S4 under salt stress (Figure 7 and Supplementary Tables 9, 10). Among the significantly expressed TFs, 172 and 85 TFs were specifically expressed in R7SL vs. R7CL and S4SL vs. S4CL, respectively, and 115 were overlapped in both R7 and S4 (Figures 7A,B). Of the shared TFs, ERF, WRKY, LBD, and MYB were the most represented TF families induced by salt stress (Figure 7C).

In leaves under salt stress, 30 genes were encoding ERF, 17 encoding MYB, 16 encoding bHLH, and 11 encoding NAC prominently expressed in R7 (Figure 7D), whereas there were 8 encoding ERF, 6 encoding NAC, 6 encoding SBP, and 6 encoding bHLH prominently expressed in S4 (Figure 7E). In root tissues, bHLH, ERF, WRKY, and MYB TF families were prominently induced by salt stress in both R7 and S4 (Figures 7H,I). There were 29 genes encoding bHLH, 25 encoding ERF, and 19 encoding WRKY preferentially induced in R7 (Figure 7J), whereas 27 genes were encoding ERF, 19 encoding NAC, and 18 encoding MYB preferentially induced in S4 (Figure 7K).

To further confirm the roles of these TFs in regulating the stress response, 8 TF genes, namely, ERF113, WRKY71, LBD1, and MYB62 in leaves and MYB108, ERF025, bHLH130, and NAC29 in roots were chosen to perform qRT-PCR analysis. The results showed that both WRKY71 and MYB62 were significantly upregulated higher in R7 leaf than in S4, while LBD1 was downregulated in R7 compared with S4 (Figure 7F). In addition, MYB108 and NAC29 were found downregulated in R7 root than in S4, whereas ERF025 was significantly upregulated in R7 in comparison to S4 (Figure 7L). These results suggested that the stress-responsive TFs, such as WRKY71, MYB62, and ERF025 are the important contributors to enhancing salt tolerance in R7.