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Article

Transcriptomic Insights into Salt Stress Tolerance Mechanisms in Melia azedarach: 24-Epibrassinolide-Mediated Modulation of Auxin and ABA Signaling Pathways

by
Xiaoxian Li
1,†,
Zin Myo Htet
1,†,
Hong Chen
1,
Jianbing Liu
2 and
Fangyuan Yu
1,*
1
Collaborative Innovation Centre of Sustainable Forestry in Southern China, College of Forest Science, Nanjing Forestry University (NJFU), Nanjing 210037, China
2
Hunan Academy of Forestry, Changsha 410004, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2025, 15(7), 1653; https://doi.org/10.3390/agronomy15071653
Submission received: 10 June 2025 / Revised: 2 July 2025 / Accepted: 5 July 2025 / Published: 8 July 2025
(This article belongs to the Section Plant-Crop Biology and Biochemistry)
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Abstract

The global expansion of soil salinization has intensified the need to understand plants’ salt tolerance mechanisms. This study investigates the molecular basis of salt stress responses in Melia azedarach L. and the modulating role of 24-epibrassinolide (EBR) through transcriptomic analysis. While salt stress significantly inhibited seedling growth, EBR application substantially mitigated these effects. Transcriptomic analysis identified 11,747 differentially expressed genes (DEGs) in the salt-treated versus control seedlings (SA vs. CK) comparison, 3786 DEGs in the Salt + EBR-treated versus control seedlings (E1 vs. CK) comparison, and 8019 DEGs in the Salt + EBR-treated versus salt-treated seedlings (E1 vs. SA) comparison. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis identified the pivotal pathways in salt stress adaptation, such as plant hormone signal transduction, phenylpropanoid biosynthesis, and ribosome pathways. Notably, key regulators such as AUX1, TIR1, IAA, SAUR, PYL, and ABF showed dynamic expression patterns under salt stress and EBR treatment, revealing their critical roles in stress mitigation. Our findings provide novel insights into EBR-mediated salt tolerance, highlighting its potential to modulate phytohormone signaling networks. This study advances both the fundamental knowledge of salt stress adaptation and practical strategies for enhancing plant resilience in saline environments.

1. Introduction

In recent years, there has been a growing interest and focus on understanding the genetic basis and molecular mechanisms underlying plants’ ability to tolerate salinity [1]. This area of research has become increasingly important and has garnered significant attention [2]. This urgency is driven by the fact that soil salinization is spreading in over 100 countries [3], affecting more than one billion hectares of land worldwide [4]. The global annual increase in soil salinization is estimated to be around 1–2% [5]. Consequently, plants suffer from various detrimental effects due to soil salinization, including inhibited growth and development, disrupted cell structure, and reduced synthesis of essential biomolecules [6]. Their regulatory mechanisms are complex, involving signal transduction, regulation of gene expression, and physiological responses through transcription and translation [7,8,9,10,11]. For instance, maintaining hydration and ion homeostasis are the responses of plants under salt stress. These responses involve the regulation of multiple genes that control ion movement, osmotic substance production, signaling, and regulatory element biosynthesis [12]. They are mediated through the production of specific proteins and metabolites, alterations in hormonal signaling, and an increase in antioxidant capacity [13].
Phytohormones play a critical role in integrated signaling pathways, modulating physiological responses under salinity stress [14,15]. Extensive research has elucidated the molecular mechanisms of plant hormone signaling pathways [16,17]. On the other hand, exogenous application of plant growth regulators is a proven strategy to enhance stress resilience, as hormones are vital for plant growth, development, and stress responses [18,19]. Among these, brassinosteroids (BRs) have demonstrated significant benefits in promoting growth and alleviating salt stress effects [20]. Specifically, 24-epibrassinolide (EBR) enhances osmolyte accumulation, reduces oxidative damage [21], and boosts antioxidant enzyme activity [22]. Additionally, exogenous EBR application upregulates differentially expressed genes (DEGs) linked to signal transduction, particularly in hyperosmotic salinity response, auxin signaling, cell wall organization, and transcriptional regulation [3]. Extensive studies have established that BRs, auxin, and abscisic acid (ABA) engage in complex crosstalk to coordinately regulate plant growth and salt stress tolerance. For instance, BRs and auxin act synergistically to mitigate salt stress, with BR catabolism and signaling, spatiotemporally regulating auxin-related genes (e.g., AUX/IAA, PINs, TIR/AFB) via BZR1/BES1–ARF interactions, fine-tuning development and halotropism [23]. Under stress, BR signaling is attenuated while ABA dominates to activate survival responses (e.g., stomatal closure). Upon stress relief, BR signaling pathways are reactivated to resume growth processes, demonstrating how these antagonistic hormones work complementarily to balance stress adaptation and growth recovery [24]. Mechanistically, BR–auxin crosstalk involves direct modulation of auxin transport and signaling by BIN2, BZR1, and BES1/BZR2 [25], while BR–ABA interplay occurs through phosphorylation and transcriptional regulation of core pathway components, ensuring an adaptive balance between stress responses and developmental progression [24]. This coordinated hormonal network maintains a balance between growth and stress adaptation by integrating developmental cues [23] with environmental signals, thereby offering potential targets for enhancing plant resilience.
RNA sequencing analysis is an emerging tool for understanding plant tolerance to abiotic stress. Transcriptomics reveals the molecular regulatory mechanisms of plant response to salt stress through mRNA transcription levels, crucial for studying salt tolerance at the molecular level [26]. In recent years, transcriptome analysis has gained widespread usage to assess the salinity tolerance across various species, including Lycium ruthenicum [27], Aquilegia vulgaris [26], Actinidia deliciosa [6], Sophora alopecuroides [8], Tamarix ramosissima [28], Apocynum venetum [29], Asparagus officinalis [30], alfalfa [31], common bean [32], mulberry [33], cabbage [34], peanut [35], and garlic [36]. Notably, most studies have focused on model plants and specific crops, limiting their broader applicability and understanding of salinity adaptation across diverse plant species [1].
Melia azedarach L. is a valuable species for investigating salt tolerance and perennial tree adaptation to environmental stress. Its wide natural distribution across diverse climates and soils [37,38] reflects an inherent capacity to withstand abiotic challenges, including drought [39], water scarcity [40], and salinity [41]. However, despite its ecological adaptability, molecular mechanisms underlying its stress responses—particularly hormone crosstalk involving BRs, auxin, and ABA—remain poorly characterized. While our prior work demonstrated EBR’s ability to enhance salt tolerance in M. azedarach [42], the transcriptomic basis of this effect and its interaction with auxin/ABA pathways remained unexplored. This study, therefore, examined salt-stressed M. azedarach seedlings from Sheyang through transcriptome sequencing. We identified DEGs in auxin/ABA pathways—key regulators of salt stress responses—revealing how EBR modulates these hormonal networks to improve adaptation. These findings provide molecular insights into M. azedarach’s stress tolerance, offering valuable implications for improving plant resistance in saline environments.

2. Materials and Methods

2.1. Plant Materials and Experiment Design

The experiment was conducted in a greenhouse nursery at Sheyang Tourism Investment Development Co., Ltd. (Sheyang County, Jiangsu Province, China) using one-year-four-month-old potted seedlings of Melia azedarach from the Sheyang seed source, grown under uniform conditions as per previous methods [42]. A randomized block design was applied with 90 seedlings divided into three treatments (10 seedlings × 3 replicates): (i) the control (CK); (ii) 4‰ salt (SA); and (iii) 4‰ salt + EBR 1 mg/L (E1). EBR was foliar-sprayed 15 days after salt treatment, and leaf samples were collected 35 days post-EBR application. Each replicate consisted of leaves pooled from 10 seedlings, immediately frozen in liquid nitrogen, and stored at −80 °C for RNA extraction.

2.2. RNA Extraction, cDNA Library Construction, Sequencing, and Functional Annotation

Total RNA was extracted using the Plant RNA Kit (Ambion, Austin, TX, USA) following the manufacturer’s protocol. Total RNA quantity and quality were tested by the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Samples meeting the RNA integrity number (RIN) threshold of ≥7 were used for library construction with the TruSeq Stranded mRNA LT Sample Prep Kit (Illumina, San Diego, CA, USA). Sequencing was performed on the Illumina HiSeq 4000 platform (OE Biotech Co., Ltd., Shanghai, China) to generate paired-end reads. Raw reads were quality-filtered using Trimmomatic to remove adapters, poly-N sequences, and low-quality reads, followed by de novo assembly using Trinity to generate transcripts [43,44,45]. The functions of the assembled unigenes were annotated using several databases such as the NCBI non-redundant protein (NR), Protein family (Pfam), the Swiss-Prot protein (SwissProt), Gene Ontology (GO), Clusters of Orthologous Groups of proteins (KOG), evolutionary genealogy of genes: Non-supervised Orthologous Groups (eggNOG), and the Kyoto Encyclopedia of Genes and Genomes (KEGG). This annotation process involved performing Basic Local Alignment Search Tool (BLAST) [46] against these databases with an e-value cutoff of less than 1 × 10−5. The transcriptome assembly results and the extent of functional annotation are summarized in Tables S1 and S2, respectively.

2.3. Transcriptome Analysis, Enrichment Analysis, and Quantitative Real-Time PCR

The expression levels of the unigenes were calculated as Fragments Per Kilobase Million Mapped Reads (FPKM), and the analysis by the DESeq2 method was performed to standardize the count number of each sample gene; the base mean value was used to estimate the expression level [47]. Additionally, the fold change (FC) was also calculated, and differential expression significance was assessed via negative binomial testing (p-value), with multiple-testing correction applied (Benjamini–Hochberg FDR, q-value), and genes were classified as differentially expressed genes (DEGs) if they met threshold criteria of |log2FC| > 1 and q-value < 0.05. Then, KEGG enrichment analysis was performed on the DEGs.
The genes involved in the most significant pathways were randomly selected for quantitative real-time PCR (qRT-PCR) analysis. It was carried out to confirm the expression levels of selected genes by using a Step One Real-Time PCR System using SYBR Green Dye from Applied Biosystems (Foster City, CA, USA). SYBR Green Premix Pro Taq HS qPCR Kits (AG11701, Accurate Biotechnology Co., Ltd., Changsha, Hunan, China) were utilized in the experiment. The relative expression levels of selected genes were calculated using the 2−∆∆Ct method [48], with the GADPH gene serving as an internal control. The primers for each of the selected target genes are shown in Table S3.

3. Results

3.1. Growth Changes

To evaluate the phenotypic responses of Melia azedarach seedlings to salinity stress and their potential mitigation by EBR, we systematically monitored height and diameter growth throughout the experimental period. Quantitative assessment of growth parameters demonstrated that 4‰ NaCl treatment significantly inhibited seedling development. While EBR application showed a tendency to alleviate salt stress effects, the observed differences in growth parameters were not statistically significant (Figure 1). Nevertheless, to investigate potential molecular-level responses, we collected the leaves of control seedlings, those subjected to salt treatment alone, and those treated with both salt and EBR for the transcriptome analysis.

3.2. Evaluation of Transcriptome Sequencing and De Novo Assembly

Transcriptome sequencing was conducted on nine samples, comprising three treatments and three replications, to explore alterations in gene expression. In this study, we obtained a total of 59.47 G of clean data from the leaf samples of M. azedarach seedlings. Each sample exhibited an effective data distribution ranging from 5.64 to 7.06 G. The Q30 base distribution, which indicates high-quality sequencing reads, ranged between 91.91% and 93.55%. Furthermore, the average GC content of the samples was determined to be 44.38% (Table S4). The mapping results revealed that the total number of reads in the samples ranged from 42,159,544 to 52,355,796. When compared to the unigenes, the read-to-unigene ratio varied from 89.91% to 91.71% (Table S5). These statistics indicate that the overall quality of all samples met the required criteria for further analysis.

3.3. Principal Component Analysis

Principal component analysis (PCA) was conducted to assess the variation among different treatments and the similarity among replication groups. The PCA plot (Figure S1) demonstrated that the three sample replications were closely located, indicating a high correlation between sequencing samples of M. azedarach from the same treatment. The distance between the samples indicated significant differences in gene expression levels among the different treatment groups. These findings suggest that the high replication of sequencing samples resulted in less systematic error and highly reliable data.

3.4. DEGs Across Different Pairwise Comparisons

Differential gene expression analysis identified 11,747 DEGs (4047 downregulated and 7700 upregulated) in the SA vs. CK comparison (salt-treated vs. control seedlings; Figure S2). In addition, the E1 vs. CK comparison (salt + EBR-treated vs. control seedlings) showed 3786 DEGs (2076 downregulated and 1710 upregulated; Figure S3), while the E1 vs. SA comparison (salt + EBR-treated vs. salt-treated seedlings) revealed 8019 DEGs (5583 downregulated and 2436 upregulated; Figure S4). Notably, the SA vs. CK and E1 vs. SA groups exhibited the highest number of DEGs, indicating a significant impact of salt stress and, correspondingly, the effectiveness of EBR in modulating the transcription of a subset of genes involved in salt stress response (Figure 2a). Furthermore, a Venn diagram was employed to visually represent the distribution of all DEGs and the overlap across the three pairwise comparison groups (Figure 2b). Interestingly, the SA vs. CK and E1 vs. SA comparisons exhibited the majority of non-overlapping DEGs.

3.5. KEGG Enrichment Analysis of DEGs

To thoroughly investigate the metabolic or signal pathways associated with the response of M. azedarach to salt stress and the potential alleviating effect of exogenous EBR against salt-stress-induced damage, we performed KEGG enrichment analysis of DEGs. Our analysis focused on two pairwise comparisons: (i) salt-treated seedlings versus control seedlings (SA vs. CK) and (ii) salt + EBR-treated seedlings versus salt-alone-treated seedlings (E1 vs. SA). KEGG analysis has identified several pathways that play a role in the observed differences. In Figure 3, the top 20 pathways are displayed for each of the two pairwise comparisons.
In the comparison between SA and CK, DEGs that were upregulated were found to be most abundant in the ribosome (p: 1.17 × 10−13 and enrichment score: 1.50) and oxidative phosphorylation (p: 1.03 × 10−30 and enrichment score: 1.9806) related pathways (Figure 3a). Specifically, there were 228 differentially enriched genes in the ribosome pathway and 247 in the oxidative phosphorylation pathway. On the other hand, the downregulated DEGs were notably associated with the plant hormone signal transduction pathway (p: 4.68 × 10−27 and enrichment score: 4.43). This pathway was followed by phenylpropanoid biosynthesis (p: 1.00 × 10−8 and enrichment score: 3.35), with 68 and 28 enriched gene numbers, respectively (Figure 3b). In the comparison between E1 and SA, the plant hormone signal transduction pathway (p: 8.43 × 10−11 and enrichment score: 3.38) was found to have the highest number of upregulated DEGs, with 36 genes observed (Figure 3c). On the other hand, the downregulated DEGs were mostly associated with the ribosome (p: 8.72 × 10−26 and enrichment score: 1.87) and oxidative phosphorylation (p: 1.55 × 10−19 and enrichment score: 1.91) related pathways, with 236 genes in the former and 169 genes in the later (Figure 3d). The findings indicate that multiple pathways are pivotal in the response of M. azedarach to salt stress.
Notably, the plant hormone signal transduction pathway appears to be commonly involved in both the response to salt stress and the alleviation mechanism of M. azedarach. This highlights the significance of hormonal regulation in mediating the plant’s ability to cope with and mitigate the adverse effects of salt stress.

3.6. Expression Patterns of DEGs Enriched in Plant Hormone Signal Transduction

Given that the plant hormone signal transduction pathway was among the most significantly enriched terms in both comparisons (SA vs. CK and E1 vs. SA), we conducted a comparative analysis of their associated DEGs to elucidate their regulatory roles under stress conditions. Notably, these hormone-related genes exhibited opposing expression trends between salt-stressed (SA) and EBR-treated (E1) seedlings, suggesting that EBR may mediate stress adaptation through hormonal reprogramming. In the SA vs. CK comparison, a total of 94 DEGs related to plant hormone signaling pathways were identified. Among these DEGs, 26 were upregulated, while 68 were downregulated (Table S6). Similarly, in the E1 vs. SA comparison, 54 DEGs associated with plant hormone signaling pathways were identified. Out of these, 36 DEGs were upregulated, and 18 were downregulated (Table S7). To visualize the plant hormone signal transduction pathways affected by these two distinct pairwise comparisons, KEGG maps for plant hormone signal transduction (ko04075) were generated (Figure S5). These maps provide a visual representation of the molecular interactions and signaling cascades involved in plant hormone responses under salt stress alone and in the presence of EBR under salt stress. Therein, particular emphasis was placed on the auxin and abscisic acid (ABA) signaling-related genes due to their notable abundance of DEGs. These differently regulated genes represent important candidates responsible for regulating various hormones involved in various metabolic functions of plants for salt stress tolerance. The results are shown in Figure 4.
In the context of auxin signal transduction, several genes encoding key proteins, such as auxin influx carrier (AUX1), transport inhibitor response (TIR1), and auxin-responsive protein (IAA), consistently showed downregulation in the SA vs. CK comparison. In contrast, in the E1 vs. SA comparison, a majority of these genes exhibited upregulation (Figure 4). Furthermore, the auxin response factor (ARF) and auxin-responsive GH3 gene family (GH3) genes demonstrated both upregulation and downregulation under the SA vs. CK comparison. However, in the E1 vs. SA comparison, all GH3 genes displayed downregulation. Notably, the majority of SAUR family proteins (SAURs) were found to be downregulated in the SA vs. CK comparison, while they showed upregulation in the E1 vs. SA comparison. These findings highlight the dynamic regulation of genes involved in auxin signal transduction under different conditions. The downregulation of certain genes in the SA vs. CK comparison suggests a potential suppression of auxin signaling pathways, while the contrasting expression patterns observed in the E1 vs. SA comparison indicate a distinct response to EBR treatment under salt stress.
In the context of ABA signal transduction, the abscisic acid receptor PYR/PYL family (PYL) encoding genes were found to be downregulated in the SA vs. CK comparison, while one of them exhibited upregulation in the E1 vs. SA comparison. In terms of protein phosphatase 2C (PP2C) encoding genes, one gene showed upregulation, and another gene experienced downregulation in the SA vs. CK comparison. However, in the E1 vs. SA comparison, two PP2C genes displayed downregulation. On the other hand, the ABA-responsive element binding factor (ABF) encoding gene demonstrated upregulation under both conditions. These observations mirror inconsistent regulatory trends in ABA signal transduction. The downregulation of PYL genes in the SA vs. CK comparison implies a potential modulation of ABA perception, while the contrasting expression patterns observed in the E1 vs. SA comparison indicate a different response to EBR treatment. Additionally, the differential regulation of PP2C genes and the consistent upregulation of the ABF encoding gene under the two pairwise comparisons further underscore the complexity of ABA signaling pathways.

3.7. Quantitative Real-Time PCR (qRT-PCR) Analysis

In order to validate the transcriptome data obtained, the expression levels of eight randomly selected genes were analyzed using qRT-PCR (Figure 5). The results demonstrated consistent expression patterns across the different treatments, providing evidence for the reliability of the transcriptome data utilized in this study. This validation reinforces confidence in the accuracy of the gene expression profiles obtained through the transcriptome analysis.

4. Discussion

Salt stress triggers complex regulatory mechanisms in plants, modulating gene expression and physiological responses through signal transduction [6,7]. In Melia azedarach seedlings, salt stress significantly suppressed the growth, consistent with its known inhibitory effects. However, exogenous EBR application alleviated these stress-induced impairments. To decipher the molecular basis of salt tolerance in M. azedarach, we conducted RNA-seq analysis, identifying differentially expressed genes (DEGs) and key pathways involved in stress adaptation. Notably, DEGs associated with plant hormone signaling, phenylpropanoid biosynthesis, and ribosome biogenesis were prominently regulated, suggesting their critical role in stress mitigation. These findings align with prior studies in other species, reinforcing the importance of these adaptive mechanisms. For example, Wu et al. [6] reported similar regulation of hormone signaling and phenylpropanoid pathways in salt-stressed Actinidia deliciosa, while the study on Apocynum venetum highlighted alterations in carbohydrate metabolism, MAPK signaling, and phytohormone pathways under salinity [49].
The critical role of plant hormone signal transduction under salt stress is well-documented [8,50,51,52,53,54,55]. Our findings in M. azedarach corroborate this, demonstrating significant downregulation of hormone signaling pathways under salt stress, likely suppressing growth-promoting hormones to prioritize stress adaptation [6,56]. However, EBR application reversed this trend, upregulating these pathways and restoring growth, highlighting EBR’s possible role in salt stress mitigation through hormonal regulation.
Our study revealed significant suppression of auxin signaling components under salt stress, including key auxin-related genes (AUX1, TIR1, IAA, SAUR) (Figure 4). This downregulation correlates with the observed growth inhibition (Figure 1), potentially representing slowed growth as an adaptive strategy [8,57,58,59]. EBR treatment mostly upregulates these components. However, these transcript-level changes, while suggestive, require validation through auxin quantification and transport assays to confirm functional hormonal modulation. On the other hand, upregulation of three GH3 genes and downregulation of one GH3 gene under stress were observed. GH3 upregulation is proposed to balance growth and stress resistance through auxin conjugation, which modulates active auxin levels during stress adaptation [60]. The contrasting responses of different gene families highlight the need for isoform-specific functional studies to fully understand auxin pathway regulation in stress adaptation. The coordinated expression changes in auxin signaling components provide a plausible molecular explanation for EBR-mediated growth maintenance under stress, though future work should incorporate direct auxin measurements and functional validation of key regulators.
ABA also plays a critical role in stress tolerance by inducing stomatal closure to reduce water loss [61]. In M. azedarach, salt stress altered the expression of key ABA pathway components: two PYLs, two PP2Cs, and one ABF gene (Figure 4). While two PYLs and one PP2C were downregulated under salt stress, EBR application differentially modulated their expression (one PYL upregulated, two PP2Cs downregulated). These variable expression patterns of PYL and PP2C genes mirror observations in other plant systems under salinity stress, where studies have reported inconsistent regulatory trends depending on stress severity [62,63]. Notably, one ABF gene was significantly upregulated under both salt and salt + EBR treatments. Although ABF upregulation is well-documented to mediate stomatal closure under saline stress [58,64,65], our study did not include direct anatomical (e.g., stomatal aperture measurements) or hormonal (ABA quantification) validation in M. azedarach. While the observed upregulation of ABF and associated ABA signaling genes (PYL, PP2C) suggests conserved stomatal regulation may occur, future studies should quantify endogenous ABA levels, image stomatal dynamics, and correlate these with the expression of key genes to confirm functional conservation in this species.
In summary, our results suggest that EBR may enhance salt tolerance in M. azedarach through potential modulation of auxin and ABA signaling pathways, as outlined in our hypothetical framework (Figure 6). Future studies integrating transcriptomics with targeted hormone assays, cellular phenotyping, and functional validation of candidate genes will be essential to establish causal relationships in this system.

5. Conclusions

This study proposed that the application of EBR mitigates the detrimental effects of salt stress at the molecular level. Transcriptome analysis of Melia azedarach seedlings subjected to salinity and EBR treatments revealed numerous DEGs associated with key signaling pathways and metabolic processes. These findings highlight the critical role of EBR in modulating salt stress responses. Notably, the plant hormone signaling pathway exhibited pronounced activity, particularly in genes associated with auxin and ABA signaling. The differential expression of key auxin-related genes—AUX1, TIR1, IAA, and SAUR—played a critical role in modulating growth and stress tolerance under both salt stress and EBR-supplemented conditions. Furthermore, the altered expression patterns of PYL and ABF genes suggest their involvement in salinity adaptation. This work establishes a foundation for understanding EBR’s role in perennial plants’ stress adaptation while highlighting remaining knowledge gaps for future studies.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agronomy15071653/s1, Table S1: Summary of transcriptome assembly results, Table S2: Functional annotation of unigenes in different databases, Table S3: Selected Genes and Primers used for qRT-PCR analysis, Table S4: Statistic of transcripts per sample, Table S5: Mapping statistics of each sample, Table S6: DEGs Related to Plant Hormone Signal Transduction under SA vs. CK comparison, Table S7: DEGs Related to Plant Hormone Signal Transduction under E1 vs. SA comparison, Figure S1: PCA between all samples, Figure S2: Volcano plot showing upregulated and downregulated DEGs in the SA vs. CK comparison, Figure S3: Volcano plot showing upregulated and downregulated DEGs in the E1 vs. CK comparison, Figure S4: Volcano plot showing upregulated and downregulated DEGs in the E1 vs. SA comparison, Figure S5: KEGG diagrams of plant hormone signal transduction pathway in M. azedarach.

Author Contributions

X.L. and Z.M.H. contributed equally to this work. Conceptualization, X.L. and F.Y.; methodology, X.L. and Z.M.H.; software, Z.M.H.; validation, H.C., J.L. and F.Y.; formal analysis, X.L. and Z.M.H.; investigation, X.L. and Z.M.H.; resources, F.Y.; data curation, H.C. and J.L.; writing—original draft preparation, X.L. and Z.M.H.; writing—review and editing, H.C. and J.L.; visualization, Z.M.H.; supervision, F.Y.; project administration, F.Y.; funding acquisition, F.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Innovation and Promotion of Forestry Science and Technology Program of Jiangsu Province (LYKJ (2021)30) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

Data Availability Statement

The transcriptome data used in this article have been uploaded to the National Center for Biotechnology Information (NCBI); the accession number is PRJNA1111672.

Acknowledgments

We are sincerely grateful to our friends from the laboratory for their help during the experimental phase of this research.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
SA vs. CKSalt-treated seedlings versus control seedlings
E1 vs. CKSalt + EBR-treated seedlings versus control seedlings
E1 vs. SASalt + EBR-treated seedlings versus salt-treated seedlings
KEGGKyoto Encyclopedia of Genes and Genomes

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Figure 1. Growth changes under different treatments: (a) height growth; (b) diameter growth. The data represent mean ± sd of 3 replications. Different letters indicate significant differences at the 0.05 level. CK, control; SA, salt; E1, salt + EBR.
Figure 1. Growth changes under different treatments: (a) height growth; (b) diameter growth. The data represent mean ± sd of 3 replications. Different letters indicate significant differences at the 0.05 level. CK, control; SA, salt; E1, salt + EBR.
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Figure 2. Number of DEGs and Venn diagram analysis across three pairwise comparison groups. (a) Total, upregulated, and downregulated DEGs; (b) Venn diagram analysis of DEGs. CK, control; SA, salt treatment; E1, salt + EBR treatment.
Figure 2. Number of DEGs and Venn diagram analysis across three pairwise comparison groups. (a) Total, upregulated, and downregulated DEGs; (b) Venn diagram analysis of DEGs. CK, control; SA, salt treatment; E1, salt + EBR treatment.
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Figure 3. KEGG enrichment analysis of DEGs: (a) top 20 pathways with upregulated DEGs under SA vs. CK comparison; (b) top 20 pathways with downregulated DEGs under SA vs. CK comparison; (c) top 20 pathways with upregulated DEGs under E1 vs. SA comparison; and (d) top 20 pathways with downregulated DEGs under E1 vs. SA comparison.
Figure 3. KEGG enrichment analysis of DEGs: (a) top 20 pathways with upregulated DEGs under SA vs. CK comparison; (b) top 20 pathways with downregulated DEGs under SA vs. CK comparison; (c) top 20 pathways with upregulated DEGs under E1 vs. SA comparison; and (d) top 20 pathways with downregulated DEGs under E1 vs. SA comparison.
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Figure 4. Salt-responsive genes related to auxin and abscisic acid (ABA) signal transduction pathways. Salt-responsive genes were annotated to each component of the signal transduction pathway, listed with their log2(FC). The colors present upregulation (red) or downregulation (blue) under different pairwise comparisons (SA vs. CK and E1 vs. SA).
Figure 4. Salt-responsive genes related to auxin and abscisic acid (ABA) signal transduction pathways. Salt-responsive genes were annotated to each component of the signal transduction pathway, listed with their log2(FC). The colors present upregulation (red) or downregulation (blue) under different pairwise comparisons (SA vs. CK and E1 vs. SA).
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Figure 5. Expression patterns of eight selected genes: (a) F5H; (b) E1.11.1.7; (c) AMY; (d) TPS; (e) RPS25; (f) RPL10A; (g) IAA; and (h) TGA. The right y-axis represents gene expression based on RNA-Seq analysis. The left y-axis represents relative gene expression values of qRT-PCR analysis. Different letters indicate significant differences at the 0.05 level. CK, control; SA, salt treatment; E1, salt + EBR treatment.
Figure 5. Expression patterns of eight selected genes: (a) F5H; (b) E1.11.1.7; (c) AMY; (d) TPS; (e) RPS25; (f) RPL10A; (g) IAA; and (h) TGA. The right y-axis represents gene expression based on RNA-Seq analysis. The left y-axis represents relative gene expression values of qRT-PCR analysis. Different letters indicate significant differences at the 0.05 level. CK, control; SA, salt treatment; E1, salt + EBR treatment.
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Figure 6. A schematic hypothetical framework of 24-Epibrassinolide (EBR) involvement in the salt tolerance of Melia azedarach.
Figure 6. A schematic hypothetical framework of 24-Epibrassinolide (EBR) involvement in the salt tolerance of Melia azedarach.
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Li, X.; Htet, Z.M.; Chen, H.; Liu, J.; Yu, F. Transcriptomic Insights into Salt Stress Tolerance Mechanisms in Melia azedarach: 24-Epibrassinolide-Mediated Modulation of Auxin and ABA Signaling Pathways. Agronomy 2025, 15, 1653. https://doi.org/10.3390/agronomy15071653

AMA Style

Li X, Htet ZM, Chen H, Liu J, Yu F. Transcriptomic Insights into Salt Stress Tolerance Mechanisms in Melia azedarach: 24-Epibrassinolide-Mediated Modulation of Auxin and ABA Signaling Pathways. Agronomy. 2025; 15(7):1653. https://doi.org/10.3390/agronomy15071653

Chicago/Turabian Style

Li, Xiaoxian, Zin Myo Htet, Hong Chen, Jianbing Liu, and Fangyuan Yu. 2025. "Transcriptomic Insights into Salt Stress Tolerance Mechanisms in Melia azedarach: 24-Epibrassinolide-Mediated Modulation of Auxin and ABA Signaling Pathways" Agronomy 15, no. 7: 1653. https://doi.org/10.3390/agronomy15071653

APA Style

Li, X., Htet, Z. M., Chen, H., Liu, J., & Yu, F. (2025). Transcriptomic Insights into Salt Stress Tolerance Mechanisms in Melia azedarach: 24-Epibrassinolide-Mediated Modulation of Auxin and ABA Signaling Pathways. Agronomy, 15(7), 1653. https://doi.org/10.3390/agronomy15071653

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