Integrated transcriptomic and metabolomic analysis reveals the salt tolerance mechanisms in Lagerstroemia indica

Abstract

Background

Lagerstroemia indica, a key summer flower in China, has weak salt tolerance. Understanding its molecular mechanisms is crucial for breeding.

Results

In this study, we investigated the physiological responses and the changes of gene expression and metabolites in L. indica seedlings subjected to varying concentrations of NaCl using integrated transcriptomic and metabolomic approaches. The results showed that increasing NaCl concentrations significantly inhibited root elongation and stem growth. Significant differences were observed in SOD activities, POD activities, and MDA content among treatment groups. Salt stress markedly influenced gene expression and metabolite accumulation, with the 4‰ NaCl treatment group (Li4) exhibiting the highest number of DEGs and DAMs compared to the control (Li0).

Transcriptome analysis revealed that pathways including plant hormone signal transduction, MAPK signaling, ribosome function, valine, leucine and isoleucine degradation, photosynthesis, and autophagy were significantly enriched. Weighted Gene Co-Expression Network Analysis (WGCNA) highlighted four key pathways: autophagy-other, peroxisome, valine, leucine and isoleucine degradation, and alanine, aspartate and glutamate metabolism.

Metabolomic profiling indicated that salt stress induced the accumulation of specific amino acids (L-tyrosine, L-phenylalanine, L-arginine, e.g.) and secondary metabolites (4-indolecarboxaldehyde, indole-3-acrylic acid, N-benzylcarboxamide, e.g.), while reducing levels of others such as L-threonine, D-sucralose, and MEHP. These compounds may serve as potential biomarkers of salt stress response in L. indica.

Association analysis further indicated that biosynthesis of amino acids and ascorbate and aldarate metabolism play key roles in the response of L. indica seedlings to salt stress. Further integrative analysis revealed strong correlations between metabolites and genes. Specifically, L-threonic acid was highly correlated with Lin_chr4_0495 (VTC2), Lin_chr14_0562 (GAE), and XLOC_006237 (VTC2), while D-saccharic acid showed a strong correlation with XLOC_023487 (ARATH).

Conclusions

In this study, we investigated the physiological changes in L. indica seedlings subjected to varying concentrations of NaCl treatment. The relationship between DEGs and DAMs in L. indica was analyzed, and changes in ascorbate and aldarate metabolism were further investigated. Notably, we observed for the first time in L. indica that the accumulation of L-threonic acid exhibits a significant positive correlation with key ascorbate and aldarate metabolism genes, namely VTC2 (Lin_chr4_0495), GAE (Lin_chr14_0562), and VTC2 (XLOC_006237). Additionally, the content of D-saccharic acid was found to be closely associated with the glucose metabolism-related gene ARATH (XLOC_023487). These specific molecular associations advance our understanding of the salt tolerance mechanism from the pathway level to precise metabolic markers and functional genes, thereby providing a direct operational foundation and candidate genes for marker-assisted selection or genetic engineering to improve salt tolerance in L. indica.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12870-026-08926-8.

Background

Soil salinization is a major abiotic stress factor limiting plant growth and agricultural productivity. According to the 2024 Global Report on the Status of Saline Soils by the Food and Agriculture Organization (FAO), salt-affected soils globally span approximately 1.381 billion hectares, accounting for 10.7% of the global land area. In China, saline soils cover about 36 million hectares and are widely distributed across diverse regions such as the coastal zone, northeastern and northwestern inland area [1]. In addition to natural causes, agricultural irrigation is a major contributor to secondary salinization, with global estimates indicating that nearly 50% of irrigated soils are affected by salt accumulation [2–5]. Furthermore, seawater intrusion into rivers and groundwater aquifers has become an increasingly significant driver of salinization in coastal areas [2]. High salt concentrations adversely affect plant growth and development by inducing osmotic stress and sodium ion toxicity. These physiological disruptions manifest as reduced seed germination, inhibited seedling growth, and impaired photosynthetic efficiency [6, 7]. Osmotic stress limits water uptake, hinders cell elongation, and suppresses leaf development. Simultaneously, sodium toxicity leads to the accumulation of excess sodium ions in plant aboveground tissues, which disrupts protein synthesis, enzyme activity, and photosynthetic processes [6, 8, 9].

Lagerstroemia indica, as an important ornamental woody plant in China, is widely appreciated by landscape designers and the public for its prolonged flowering period, vibrant flower colors, and graceful plant architecture. However, L. indica exhibits low salt tolerance, which limits its cultivation in saline soils and marginal ecological zones to some extent. Traditional breeding efforts have largely focused on improving aesthetic and agronomic traits such as flower color, plant morphology, and resistance to pest and disease. In contrast, research aimed at enhancing salt stress adaptation has lagging significantly behind. Salt stress negatively impacts L. indica at multiple developmental stages. It suppresses seed germination and induces a range of physiological disorders including ionic imbalance, osmotic stress, and oxidative damage. Experimental evidence has shown that increasing salt concentrations significantly reduce germination rate, germination index, and vigor index in L. indica seeds [10]. Moreover, salt stress markedly inhibits plant height and stem diameter growth, enhances membrane lipid peroxidation, and causes varying degrees of cellular damage. Physiological indicators such as relative electrical conductivity and malondialdehyde (MDA) concentration in L. indica leaves increase with higher NaCl concentrations, whereas net photosynthetic rate shows a corresponding decline [11–13]. While substantial progress has been made in understanding the molecular mechanisms of salt tolerance in various plant species, research on L. indica remains limited—particularly regarding the regulation of gene expression and metabolite accumulation under salt stress.

With advances in high-throughput sequencing and mass spectrometry, integrated transcriptomic and metabolomic analyses have become key tools for dissecting complex biological responses to abiotic stress in plants. Transcriptomics identifies salt-responsive genes and signaling pathways, while metabolomics reveals corresponding physiological changes. Compared to single-omics, combined analysis offers a systems-level understanding of salt tolerance, enabling construction of gene–metabolite networks. Previous studies in various plant species have demonstrated the power of combined analysis of the transcriptomic and metabolomic. Studies in cotton, Brassica rapa, and rice have linked salt tolerance to pathways such as starch and sucrose metabolism, hormone signaling, and antioxidant activity [14–16]. In Cerasus humilis, sorghum, and Bambusa distegia, combined analyses revealed that salt stress responses involve primary metabolism, phenylpropanoid biosynthesis, and epigenetic regulation [17–19]. Overall, multi-omics approaches have greatly advanced our understanding of plant adaptation to abiotic stressors such as salinity [20–22]. Applying this strategy to L. indica will help clarify its salt stress regulatory mechanisms and support the molecular breeding of salt-tolerant cultivars.

This study aimed to elucidate the molecular mechanisms underlying salt tolerance in L. indica through integrated transcriptomic and metabolomic analyses under three NaCl concentrations (0‰, 2‰, and 4‰), using hybrids of L. best red × L. pure white. The grouping of each sample and the corresponding information is shown in Table S1. We profiled global gene expression and metabolite accumulation in seedlings and identified differentially expressed genes and metabolites using KEGG enrichment and multivariate statistical analyses. Regulatory networks were constructed to reveal key components involved in salt stress responses. Taking a systems biology perspective, this study explored how salt stress affects multiple physiological processes. The integration of transcriptomic and metabolomic data enabled the identification of core regulators and metabolic markers associated with salt tolerance. These findings provide a theoretical basis for functional validation of candidate genes and offer potential targets for molecular breeding of salt-tolerant L. indica.

Materials and methods

Plant material and sampling

The hybrid seeds of Black Diamond series (L. best red×L. pure white) were used as the experimental materials. Mature seeds were collected in mid-November 2023 from L. indica plants cultivated at the Jiangsu Yanjiang Institute of Agricultural Sciences. The seeds were air-dried in a ventilated, cool environment, and subsequently stored at 4 °C in a dry condition. Salt solutions at concentrations of 0‰, 2‰, and 4‰ were prepared using sodium chloride (NaCl) and half-strength Hoagland’s nutrient solution. Square germination boxes (12 cm × 12 cm) were lined with two layers of germination paper, and 50 seeds were sown per box. Each box received 15 mL of the respective salt solution to fully saturate the paper. The solution and paper were renewed every three days to avoid contamination. At 9:00 a.m. on the 28th day, seedlings were uniformly harvested from each treatment and pooled into biological replicates, with approximately 1 g per replicate. Six biological replicates were prepared for physiological, transcriptomic, and metabolomic analyses. These six biological replicates were individually subjected to library construction, sequencing, and analysis, and were not pooled before sequencing. Transcriptomic and untargeted metabolomic analyses were performed by UW Biotechnology Co. in July 2024.

Determination of morphological and physiological indicators

Plant height and root length of 10 seedlings were measured with a straightedge, and the mean values were calculated. To measure root length, each seedling was carefully held at the stem base with forceps, and the root system was gently straightened. The total length from the root base to the root tip was then measured. Plant height was recorded as the distance from the root–shoot junction to the highest point of the shoot apex. Malondialdehyde (MDA) content, peroxidase (POD), superoxide dismutase (SOD), and catalase (CAT) activities were measured using the corresponding kits (Solarbio, Beijing, China).

Transcriptome analysis

RNA extraction

Total RNA from L. indica was extracted using a modified CTAB method (Bio Basic, Toronto, Canada). The quality and quantity of the extracted RNA were then assessed using a Fragment Analyzer and an Agilent 2100 Bioanalyzer (Agilent Technologies, CA, USA).

Library construction and sequencing

mRNA library preparation was performed using the Optimal Dual-mode mRNA Library Prep Kit (BGI, Shenzhen, China) according to the manufacturer’s instructions. The resulting single-stranded circularized libraries were subsequently amplified via phi29 polymerase-mediated rolling circle amplification (RCA) to produce DNA nanoballs (DNBs), each comprising more than 300 copies of the original circularized molecule. The DNBs were then loaded onto patterned nanoarrays, and paired-end (PE) reads of 100 or 150 bases were generated using the G400, T7, or T10 sequencing platforms (BGI, Shenzhen, China).

Data analysis

Data filtering, variant detection

The sequencing data were filtered using SOAPnuke (v2.2.1) [23]. The subsequent analyses and data mining were conducted using the Dr. Tom Multi-omics Data Mining System (https://biosys.bgi.com). The clean reads were then mapped to the reference genome with HISAT2 [24]. Fusion genes and differentially spliced genes (DSGs) were identified using Ericscript (v0.5.5) [25]and rMATS (v4.1.2) [26], respectively.

Differential gene analysis, KEGG and GO analysis

Gene expression levels were quantified using RSEM (v1.3.1) [27]. Heatmaps were generated with pheatmap (v1.0.12) based on difference in gene expression across samples. Differential expression analysis was performed using DESeq2 (v1.34.0) [28], with a significance threshold of p-value < 0.05 (or FDR ≤ 0.001 (False discovery rate)). To gain further insights into phenotypic changes, Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes༈KEGG༉enrichment analyses of the annotated differentially expressed genes were performed by Phyper function in R software. The significance of enriched terms and pathways was corrected using the p-value, with a rigorous threshold of p-value < 0.05.

qPCR

Total RNA was extracted using the Tengen RNAprep Pure Polysaccharide and Polyphenol Plant Total RNA Kit (DP441). Reverse transcription was performed with the Hifair^® III 1st Strand cDNA Synthesis SuperMix for qPCR (gDNA digester plus, YEASEN). qPCR was carried out using the 2×HS Taq Universal SYBR Green qPCR Master Mix (SAIPUBIO). Each 20 µL qPCR reaction mixture contained 1 µL of cDNA template, 1 µL each of forward and reverse primers, 10 µL of SYBR Premix Ex Taq (2×), and 7 µL of ddH₂O. The thermal cycling conditions were as follows: initial denaturation at 95 °C for 5 min (1 cycle), followed by 40 cycles of 95 °C for 10 s and 60 °C for 30 s. The relative expression levels were calculated using the 2^−△△Ct method, with EF_1α as the internal reference gene. Detailed primer information is shown in Supplementary Table 2.

Metabolomic analysis

Sample extraction, testing

A 50 µg aliquot of each sample was weighed into a 1.5 mL Eppendorf tube and extracted with 800 µL of pre-cooled extraction solution (CH₃OH: H₂O = 7:3, v/v) and 20 µL of internal standard 1 (IS1). Samples were homogenized using a braided grinder at 50 Hz for 10 min, followed by sonication in a water bath at 4 °C for 30 min. After standing at − 20 °C for 1 h, the extracts were centrifuged at 14,000 rpm for 15 min at 4 °C. A 600µL of the supernatant was then filtered through a 0.22 μm membrane. Subsequently, 20 µL of the filtered solution from each sample was pooled to prepare quality control (QC) samples, which were used to assess the reproducibility and stability of the LC/MS analysis. Both the filtered samples and the mixed QC samples were transferred to 1.5mL sample vials for instrumental analysis.

UPLC-MS

Metabolite separation and detection were performed using a Waters UPLC I-Class Plus system (Waters, USA) coupled with a Q Exactive high-resolution mass spectrometer (Thermo Fisher Scientific, USA). Chromatographic separation was achieved on a Hypersil GOLD aQ Dim column (1.9 μm, 2.1 × 100 mm; Thermo Fisher Scientific, USA). Primary and secondary mass spectrometry data were acquired using the Q Exactive mass spectrometer.

Metabolite annotation and differential metabolite screening

The identified metabolites were subjected to taxonomic and functional annotation to elucidate their taxonomic classification and functional characteristics. The chemical, molecular biological and biochemical information for the metabolites were retrieved from the Human Metabolome Database (HMDB), while the functional pathway annotation was performed by KEGG PATHWAY database to identify the major biochemical and signaling pathways involved in the metabolites.

Differential accumulation of metabolites was determined based on the criteria of VIP > 1.0 (VIP, Variable importance in the projection) and FC ≥ 1.2 (Fold change) or FC ≤ 0.833 and p-value < 0.05.

Combined transcriptomics and metabolomics analysis

All identified differentially expressed genes and differential metabolites were separately mapped to the KEGG PATHWAY database to retrieve their associated pathway information. Common pathways shared by both datasets were then identified, enabling the characterization of key biochemical and signaling pathways jointly regulated by the differential genes and metabolites. The transcriptional pattern heatmap was generated using TBtools software based on FPKM values (Fragments per kilobase per million), using the log2 method.

One-year-old cuttings of L. Zi Guifei were treated with salt concentrations of 0, 2‰, and 4‰. Root, stem, and leaf tissues were sampled at 0, 24, and 48 h (R-CK, R-2-24, R-2-48, R-4-24, R-4-48, S-CK, S-2-24, S-2-48, S-4-24, S-4-48, L-CK, L-2-24, L-2-48, L-4-24, L-4-48 ). Each sample was repeated three times. Total RNA was extracted using the Tengen RNAprep Pure Polysaccharide and Polyphenol Plant Total RNA Kit (DP441). Reverse transcription was performed with the Hifair^® III 1st Strand cDNA Synthesis SuperMix for qPCR (gDNA digester plus, YEASEN). The relative expression levels were calculated using the 2^−△△Ct method, with EF_1α as the internal reference gene. Detailed primer information is shown in Supplementary Table 3.

Statistical analysis

All data were analyzed using Microsoft Excel 2010 and SPSS 26.0 (IBM Corp., Armonk, NY, USA). Differences were considered statistically significant at p<0.05. All results are presented as mean ± standard error (SE) based on at least six biological replicates.

Results

Effect of salt stress on seed germination and seedling growth of L. indica

Salt stress significantly affected the germination rate, root length and plant height of L. indica. Germination rate, seedling root length and plant height all decreased significantly with increasing salinity. The germination rates of groups Li0, Li2, and Li4 were 98.7%, 86.5% and 62.3%, respectively, showing significant differences among the groups. After 28 days of treatment, the growth performance of L. indica seedlings differed significantly, with the Li0 group exhibiting vigorous growth and longer roots. In contrast, both plant height and root length in group Li4 were significantly lower than those in groups Li0 and Li2 (Fig. 1A-C).

Fig. 1.

Phenotypic and physiological changes in L. indica under salt stress. A Growth performance of Indian quinoa seeds on the 28th day of germination when subjected to salt stress conditions with 0, 2‰, and 4‰ sodium chloride. B-C The root length and plant height of L. indica seedlings on the 28th day after germination. D-F Changes in enzyme activities in L. indica seedlings. G Changes in content of MDA in L. indica seedlings

To further explore the physiological responses to salt stress, the activities of antioxidant enzymes and the malondialdehyde (MDA) content were measured across groups (Fi.1D-G). With increasing salt concentrations, the superoxide dismutase (SOD) activity initially increased and then declined, with groups Li2 and Li4 exhibiting significantly higher SOD activity than Li0, although no significant difference was observed between Li2 and Li4. Peroxidase (POD) activity increased steadily with salinity, with significant differences detected among all groups. Catalase (CAT) activity gradually decreased as salt concentration increased, but no significant differences were observed among the groups. Meanwhile, MDA content increased progressively with salinity, and the differences among groups were all statistically significant. The results demonstrated that salt stress significantly affected the MDA content and specific antioxidant enzyme activities in L. indica.

Transcriptome analysis of L. indica seedlings to salt stress

Analysis of differentially expressed genes between groups of L. indica under salt stress

According to Supplementary Tables 4 and Fig. 2, a total of 16,414 differentially expressed genes (DEGs) were identified across the comparisons of Li0 vs. Li2, Li0 vs. Li4, and Li2 vs. Li4, with Li0 vs. Li4 exhibiting the largest number of DEGs (13,974; Fig. 2A). In the Li0 vs. Li2 comparison, 7,515 DEGs were detected, including 4,331 upregulated and 3,184 downregulated genes. In the Li0 vs. Li4 comparison, 13,974 DEGs were identified, with 7,124 genes upregulated and 6,850 downregulated. In the Li2 vs. Li4 comparison, 4,135 DEGs were detected, comprising 1,438 upregulated and 2,697 downregulated genes (Fig. 2B and D). A total of 257 genes were consistently upregulated and 336 genes consistently downregulated across L. indica samples with increasing NaCl concentration (Supplementary Table 5). The box plot (Fig. 2C) displays the distribution of gene expression levels across all samples. The heatmap (Fig. 2E) illustrates the expression levels of all differentially expressed genes, with redder color intensities indicating higher expression levels and bluer intensities representing lower levels.

Fig. 2.

Gene expression of L. indica seedlings under salt stress. A Venn diagram showing the shared and specific number of different expression genes among different comparison groups. B Bar chart of differential gene number statistics between different comparison groups. C Box line plot of differential gene expression. D Volcano plot showing the differential gene expression levels in Li0 vs. Li2, Li0 vs. Li4, Li2 vs. Li4, respectively. E Heat map of differential gene expression in Li0 vs. Li2, Li0 vs. Li4, Li2 vs. Li4, respectively. The color scale from red to blue indicates the gene expression levels using log2-transferred FPKM

GO functional analysis of differentially expressed genes

DEGs were functionally classified based on GO annotation and official categorization. The analysis revealed that the DEGs were primarily associated with biological processes (BP), cellular components (CC), and molecular functions (MF) (Fig. 3A). In the Li0 vs. Li2 comparison, a large number of DEGs were enriched in processes such as protein ubiquitination, response to abscisic acid, vesicular membrane function, and response to water deficit, suggesting that hormones like abscisic acid were involved in the regulation of L. indica growth and development under salt stress, and that salt stress also affected tonoplast membrane function. In the Li0 vs. Li4 comparison, DEGs were mainly enriched in categories related to chloroplasts, cytoplasm, chloroplast stroma, and protein ubiquitination. In the Li2 vs. Li4 comparison, DEGs were predominantly enriched in chloroplasts, chloroplast membranes, and photosystems (Fig. 3B). It is implied that chloroplasts of L. indica seedlings may be damaged with the increase of salt stress concentration, resulting in impaired function, which in turn affects photosynthesis and normal growth.

Fig. 3.

GO analysis of genes in L. indica seedlings under salt stress. A Categorized bar graph in Li0 vs. Li2, Li0 vs. Li4, Li2 vs. Li4, respectively. B Top 10 pathways of GO enrichment of DEGs in Li0 vs. Li2, Li0 vs. Li4, Li2 vs. Li4, respectively

KEGG pathway enrichment analysis of DEGs under salt stress

To further elucidate the functional distribution of DEGs in L. indica seedlings under salt stress, KEGG pathway annotation was performed. The results showed that DEGs were primarily associated with cellular processes, environmental information processing, genetic information processing, metabolism, and organismal systems (Fig. 4A), with metabolic processes containing the largest number of DEGs. Across the three comparison groups, a high number of DEGs were enriched in pathways related to carbohydrate metabolism, signal transduction, translation, lipid metabolism, amino acid metabolism, and terpenoid and polyketide metabolism.

Fig. 4.

KEGG classification and enrichment analysis plots of DEGs. A Classification chart of DEGs in Li0 vs. Li2, Li0 vs. Li4, Li2 vs. Li4, respectively. B KEGG enrichment of upregulated DEGs in Li0 vs. Li2, Li0 vs. Li4, Li2 vs. Li4, respectively. C KEGG enrichment of downregulated DEGs in Li0 vs. Li2, Li0 vs. Li4, Li2 vs. Li4, respectively

Subsequent enrichment analysis of DEGs (Fig. 4B-C) revealed that DEGs in three pathways were significantly upregulated in both the Li0 vs. Li2 and Li0 vs. Li4 comparisons: protein processing in endoplasmic reticulum (ko04141), MAPK signaling pathway–plant (ko04016), and galactose metabolism (ko00052). Notably, the MAPK signaling pathway–plant (ko04016) was also significantly upregulated in the Li2 vs. Li4 comparison. Regarding downregulated pathways, DEGs in the Li0 vs. Li2 group were significantly enriched in ribosome (ko03010) and ribosome biogenesis in eukaryotes (ko03008). In the Li0 vs. Li4 group, significantly downregulated DEGs were enriched in ribosome (ko03010), photosynthesis (ko00195), and carbon fixation in photosynthetic organisms (ko00710). Similarly, in the Li2 vs. Li4 group, downregulated DEGs were mainly enriched in photosynthesis (ko00195), carbon fixation in photosynthetic organisms (ko00710), and photosynthesis-antenna proteins (ko00196).

Identification of key gene modules related to salt tolerance by WGCNA

Sample gene matrices were analyzed by grouping highly correlated genes into the same module by correlation and clustering analyses (Fig. 5A). The sample clustering results showed that samples from different treatments were largely clustered together (Fig. 5B). WGCNA analysis classified the DGEs of L. indica seedlings under salt stress into 20 modules, with the MEturquoise module containing the largest number of genes (2,295) and the MElightyellow module containing the fewest (22) (Supplementary Table 6). Modules with a correlation coefficient greater than 0.8 were selected for further analysis, with darker colors in the figure representing stronger correlations (Fig. 5C). Specifically, the MEturquoise module was significantly correlated with MDA content, root length, and salt concentration, while the MEblue module had the strongest correlations with MDA content, POD activity, plant height, root length, and salt concentration. The genes within the MEblue module are inversely associated with both the severity of salt stress and the growth performance of L. indica seedlings. In contrast, genes in the METurquoise module exhibited reduced expression under increasing salt stress, correlating with impaired growth. Notably, METurquoise module genes represent promising candidates with potential to enhance salt stress tolerance.

Fig. 5.

Differential gene co-expression network analysis. A WGCNA of all DEGs. Each branch and color correspond to a gene and a coexpression module, respectively. B Hierarchical clustering of all samples. C Module-trait correlation heatmap linking gene modules to physiological traits. Red indicates positive correlation, blue negative correlation; numbers in cells show correlation values and p-values in parentheses, highlighting key modules associated with stress-related traits. D-E GO enrichment bar plot and gene-term interaction network revealing key biological processes and hub gene functions. F KEGG pathway enrichment bubble plot identifying critical metabolic pathways

Based on these results, the MEblue module and MEturquoise module were selected as the main reference module for identifying salt tolerance-related genes in L. indica. We conducted GO and KEGG analyses separately for the MEblue module and the METurquoise module. In the MEblue module, GO analysis of genes within in this module revealed that the most significantly enriched biological processes were organic acid catabolic process, carboxylic acid catabolic process, and small molecule catabolic process, each associated with the highest number of genes (Fig. 5D, E). KEGG pathway analysis further showed significant enrichment in four pathways: autophagy-other (ko04136), peroxisome (ko04146), valine, leucine and isoleucine degradation (ko00280), alanine, aspartate and glutamate metabolism (ko00250) (Fig. 5F). GO enrichment analysis revealed in MEturquoise module that genes are enriched in chloroplast organization, photosynthesis, and protein targeting to chloroplasts (Supplementary Fig. 1). KEGG analysis revealed that, in addition to metabolic pathways such as plant-pathogen interaction(ko04626), purine metabolism(ko00230) and aminoacyl-tRNA biosynthesis(ko00970) that were significantly enriched, the ascorbate and aldarate metabolism(ko00053) related to plant salt tolerance was also identified, although the difference was not significant (Supplementary Fig. 2).

qPCR validation of gene expression

To confirm the accuracy of the genes obtained by RNA-Seq, we randomly selected 15 DEGs (Fig. 6). They were detected by qPCR. The similar expression trends of the selected DEGs were generally consistent with illumina sequencing results, indicating the reliability of the RNA-Seq data.

Fig. 6.

qPCR analysis of 15 genes. Columns indicate the relative expressions by qPCR. Broken lines indicate the expression patterns by RNA-seq. Columns linked with different letters indicate significant difference (p < 0.05) among samples

Metabolome analysis of L. indica under salt stress

PCA and metabolite analysis

To investigate the changes of metabolites in L. indica seedlings under salt stress, a broad-spectrum, non-targeted metabolomics approach was used to assess metabolite variations under different salt concentrations. Principal component analysis (PCA) was conducted to evaluate the overall differences among the samples. The results showed a clear separation between the Li0 group and the Li2 and Li4 group, with each group clustering within a 95% confidence interval, indicating good sample reproducibility (Fig. 7A).

Fig. 7.

Differential metabolite analysis plots. A PCA plot. QC stands for quality control samples. B Categorical loop plots. C Clustered heatmap based on 711 DAMs. The color scale from red to blue indicates the metabolite levels using log2 method. D-F Volcano plots showing the differential metabolite accumulation levels in Li0 vs. Li2, Li0 vs. Li4, Li2 vs. Li4, respectively

A total of 711 metabolites were identified across the three comparison groups, which were classified into 26 categories. According to the classification results (Fig. 7B), the major classes included Lipids (21.32%), Benzene and derivatives (9.14%), Terpenoids (7.11%), Amino acids, peptides, and analogues (6.60%), Carbohydrates (6.09%), Amino acids (5.58%), Flavonoids (5.58%), Alkaloids (4.57%), and Nucleic acids (4.06%). Minor classes included Phenylpropanoids (3.55%), Organic acids (3.05%), Tannins (2.03%), Nucleic acids and analogues (1.52%), Vitamins (1.52%), Cofactors (1.02%), and several other compound types at lower proportions. The heatmap illustrates the expression levels of each metabolite, where red indicates higher abundance and blue indicates lower abundance (Fig. 7C).

Based on volcano plot analysis (Fig. 7D-F) and Supplementary Tables 7, 137 differential metabolites (72 upregulated and 65 downregulated) were identified in the Li0 vs. Li2 group, 164 differential metabolites (83 upregulated and 81 downregulated) in the Li0 vs. Li4 group, and 125 differential metabolites (74 upregulated and 51 downregulated) in the Li2 vs. Li4 group. Among these, 19 metabolites were consistently upregulated and 7 metabolites were consistently downregulated across all three comparisons. These 26 metabolites are hypothesized to play key roles in the salt stress response of L. indica seedlings (Supplementary Table 8).

KEGG enrichment analysis of DAMs

To figure out the metabolic pathways involved in the salt stress response of L. indica seedlings, we performed KEGG enrichment analysis of the differential metabolites. The top 10 pathways with the lowest p-values were selected for bar plot visualization. The results revealed significant enrichment of 16 pathways across the 3 comparative groups (Supplementary Fig. 3, Supplementary Table 9).

Aminoacyl-tRNA biosynthesis, biosynthesis of various plant secondary metabolites, alanine, aspartate and glutamate metabolism, biosynthesis of amino acids, cyanoamino acid metabolism were the common pathways identified across all comparison groups. The result suggests that these metabolic pathways are closely associated with the ability of L. indica seedlings to adapt to increasing salt concentrations.

Further analysis revealed that in the aminoacyl-tRNA biosynthesis pathway, metabolites such as L-tyrosine, L-phenylalanine, asparagine, and L-arginine were commonly detected in both the Li0 vs. Li2 and Li2 vs. Li4 groups. In the biosynthesis of various plant secondary metabolites pathway, L-tyrosine, L-phenylalanine, and 2-hydroxycinnamic acid were consistently detected across Li0 vs. Li2 and Li2 vs. Li4 groups. In the alanine, aspartate and glutamate metabolism pathway, asparagine being the only metabolite shared between the Li0 vs. Li2 and Li2 vs. Li4 groups. In the biosynthesis of amino acids pathway, L-tyrosine, L-phenylalanine, asparagine, L-arginine, and argininosuccinic acid were also commonly detected in both the Li0 vs. Li2 and Li2 vs. Li4 groups. Finally, in the cyanoamino acid metabolism pathway, L-tyrosine, L-phenylalanine, and asparagine being common in both the Li0 vs. Li2 and Li2 vs. Li4 groups. It is hypothesized that this type of continuously changing metabolites is importantly related to the salt stress tolerance of L. indica (Supplementary Table 10).

Trend analysis of differential accumulated metabolites (DAMs)

Some metabolites had similar expression patterns across different groups. Based on their abundance profiles, metabolites with consistent trends were clustered together. We employed the Mfuzz software package to perform clustering analysis of the metabolite expression profiles across all samples. The results showed that the dynamic trends could be categorized into 12 distinct clusters (Fig. 8), containing 16, 11, 29, 22, 11, 14, 29, 17, 16, 7, 17, and 8 metabolites, respectively (Supplementary Table 11).

Fig. 8.

Cluster analysis of differential metabolites by Mfuzz software package and their metabolite categories. Twelve clusters show distinct metabolite expression patterns across different NaCl concentrations, with corresponding bar charts revealing the distribution of metabolite classes in each cluster

Among these, metabolites in cluster 3 and cluster 4 showed a decreasing trend as salt stress intensified, whereas those in cluster 6 and cluster 11 exhibited an increasing trend. In cluster 7, the metabolite levels initially decreased significantly and then stabilized with increasing salt concentrations. Conversely, in cluster 10, metabolite levels first increased and then plateaued with the increase of salt concentration. Analyzing these trends suggests that salt stress exerts a significant influence—either enhancing or suppressing—on the abundance of specific metabolites in L. indica seedlings, although at higher salt concentrations the metabolite levels tend to stabilize. Regarding metabolite classification, cluster 3 showed the most variation among fatty acyls (FA), amino acids, peptides and analogues, terpenoids, and sterol lipids (ST). In cluster 4, flavonoids exhibited the greatest changes. Meanwhile, in cluster 7, the most variable metabolites belonged to polyketides (PK), organic acids, carbohydrates, and fatty acyls (FA).

KEGG enrichment of transcriptome and metabolome

To further investigate the response mechanisms of L. indica under different concentrations of salt stress, we performed an integrated analysis of transcriptomic and metabolomic data. KEGG enrichment analysis of DEGs and DAMs showed that eight of the top ten enriched pathways enriched in all three groups (Supplementary Fig. 4). These pathways included: 2-oxocarboxylic acid metabolism, β-alanine metabolism, alanine, aspartate and glutamate metabolism, amino sugar and nucleotide sugar metabolism, aminoacyl-tRNA biosynthesis, arginine and proline metabolism, ascorbate and aldarate metabolism, and biosynthesis of amino acids.

The Li0 vs. Li2 comparison revealed that the biosynthesis of secondary metabolites pathway exhibited the most significant enrichment of both DEGs and DAMs, indicating that early salt stress responses in L. indica seedlings primarily involve secondary metabolite accumulation to maintain ionic homeostasis, osmotic regulation, and antioxidant defense. With increasing salt stress, as shown in the Li2 vs. Li4 comparison, the number of DEGs and DAMs associated with amino acid biosynthesis pathways increased markedly, reflecting enhanced metabolic reprogramming under more severe conditions.

Notably, β-alanine metabolism and ascorbate and aldarate metabolism ranked among the top four enriched pathways across all three comparison groups. In particular, ascorbate and aldarate metabolism plays a crucial role in plant stress tolerance by directly or indirectly scavenging reactive oxygen species (ROS) generated during various physiological processes, thereby mitigating oxidative damage [29]. Ascorbic acid also acts as a cofactor for various enzymes [30], maintaining their activity by preserving the reduced state of metal ions—especially in oxidoreductases [31]. Given its importance, we focused on changes in gene expression and metabolite levels within the ascorbate and aldarate metabolism pathway. Transcriptome analysis revealed that the ascorbate biosynthesis pathway in L. indica under salt stress largely followed the D-mannose/L-galactose pathway proposed by Smirnoff et al. (Fig. 9). A total of 75 DEGs and 3 DAMs were identified as key participants in this pathway. According to annotations in the SwissProt database, these genes included GLDH, GalDH, IMPL2, VTC2, GME, UGDH, GAE, and GALUR, among others. Further integrative analysis revealed strong correlations between two key metabolites and four genes (|r| > 0.8, p-value < 0.05). Specifically, L-threonic acid was highly correlated with Lin_chr4_0495 (VTC2), Lin_chr14_0562 (GAE), and XLOC_006237 (VTC2), while D-saccharic acid showed a strong correlation with XLOC_023487 (ARATH). Interestingly, all these genes are located within the MEturquoise module identified by the WGCNA analysis, and the KME values of these genes are all greater than 0.8, suggesting that these genes are the hub genes of this module.

Fig. 9.

Schematic representation of the ascorbate and aldarate metabolic pathway, illustrating the involvement of relevant differential accumulated metabolites (DAMs) and differential expression patterns of associated genes (DEGs). The heatmap displays the expression levels of DEGs in groups Li0, Li2, and Li4 (from left to right), with values represented as log2-transferred FPKM. Metabolites enclosed in the dashed box are significantly altered across all three comparison groups. Red stars indicate four DEGs exhibiting high correlation with the observed metabolic changes

To verify the expression characteristics of the aforementioned four genes in different tissues of L. indica, Lin_chr14_0562 and XLOC_023487 were selected for further validation. Results indicated distinct expression patterns of Lin_chr14_0562 and XLOC_023487 in roots, stems, and leaves under salt stress (Fig. 10). For example: At 2‰ NaCl, the relative expression of XLOC_023487 in stems and leaves gradually increased with prolonged stress duration. At 4‰ NaCl, the relative expression of XLOC_023487 decreased in stems and roots but increased in leaves over time. This suggests that XLOC_023487 may function as a multifunctional stress-responsive gene, subject to dual regulation by stress intensity and tissue specificity. Its encoded product may contribute to the accumulation of D-saccharic acid, thereby maintaining normal metabolism in stems and leaves under low salt conditions, while prioritizing the protection of photosynthetic organs in leaves under high salt stress. In contrast, its expression was inhibited in roots and stems, possibly due to direct ion toxicity or the implementation of a “source-sink regulation” strategy. Similarly, at 2‰ NaCl, Lin_chr14_0562 expression in stems and leaves gradually increased with stress time. Conversely, at 4‰ NaCl, its expression in stems and leaves gradually decreased over time. L-threonic acid is one of the degradation products of ascorbic acid (vitamin C). Lin_chr14_0562 may play a central role in antioxidant defense, particularly in maintaining leaf function under severe stress. In contrast, XLOC_023487 is potentially involved in the remodeling of carbon metabolism, and its expression is suppressed in both stems and leaves under severe stress, reflecting a survival strategy of resource optimization in plants under energy-limited adverse conditions. These findings demonstrate that both genes play important roles in the salt stress response of L. indica.

Fig. 10.

Expression patterns of Lin_023487 and Lin_0562 in various tissues of L. indica by qPCR

Discussion

Soil salinization-alkalization, one of the primary drivers of global cropland degradation, severely constrains agricultural productivity and ecological security. Statistics indicate that China possesses 36.7 million hectares of saline-alkali land, with approximately 6.67 million hectares showing potential for agricultural development. However, high salinity stress significantly inhibits plant growth and reduces soil productivity. This context underscores the critical importance of screening salt-tolerant plants and elucidating their adaptation mechanisms for ecological restoration and utilization of saline-alkali soils. L. indica, a woody species combining ornamental value with ecological adaptability, warrants particular research attention regarding its salt tolerance. Advancing studies on L. indica’s salt tolerance mechanisms could not only enhance biodiversity of halophytic vegetation in saline soils, but also facilitate the integration of salt-tolerant cultivar breeding with ecological remediation technologies. Such interdisciplinary efforts would ultimately contribute to cropland conservation and sustainable agricultural development.

Salt stress severely disrupts biochemical and physiological processes in plants, ultimately restricting normal growth and development. For instance, rice exhibits reduced shoot height and root length with increasing salinity levels [32], while poplar (Populus spp.) seedlings demonstrate decreased root length and root-to-shoot ratio under high-salt conditions [33]. In this study, L. indica seedlings exhibited significant reductions in both plant height and root length under escalating salt concentrations, indicating suppressed growth and compromised water balance. This observation aligns with a salt-avoidance mechanism whereby plants mitigate salt accumulation and associated damage through morphological adaptations [34]. Antioxidant enzymes (SOD, POD, CAT et al.) synergistically scavenge ROS to alleviate oxidative damage under salt stress. Concurrently, MDA serves as a reliable biomarker of oxidative stress, reflecting cellular membrane integrity. The dynamic interplay between these parameters elucidates plant adaptation mechanisms to saline environments. We quantified antioxidant enzyme activities and MDA levels in L. indica seedlings across gradient salt concentrations. SOD activity showed significant enhancement at 2‰ salinity compared to the control (CK), but markedly decreased at 4‰ salinity. MDA content progressively increased with salt concentration elevation. The results of the germination experiment showed that as the degree of stress increased, the germination rate continued to decrease. At a salt concentration of 4‰, the germination rate was only 62.3%. These findings suggest that 4‰ salinity likely approached or exceeded the tolerance threshold of L. indica, resulting in diminished SOD activity and elevated MDA accumulation - indicative of cellular membrane damage.

We conducted transcriptome analysis of L. indica seedlings under varying salt stress conditions to elucidate gene expression changes in response to salinity. The Li0 vs. Li2 comparison revealed 7,515 DEGs, while Li0 vs. Li4 exhibited 13,974 DEGs, indicating a progressive increase in differentially expressed genes with elevated salt concentrations. KEGG enrichment analysis demonstrated significant DEG accumulation in pathways including protein processing in endoplasmic reticulum, MAPK signaling pathway-plant, galactose metabolism, and valine, leucine and isoleucine degradation. Defense response pathways extensively documented in salt-stressed plants such as rice, garlic, and poplar [35–37]. Numerous studies have established that salt stress induces ER stress in plants [38–41]. Under salinity conditions, the ER—the central hub for protein synthesis and processing—requires substantial enzymatic activity. Salt stress inhibits multiple enzymes critical for protein folding, leading to delayed folding or misfolding of proteins. In this study, both Li0 vs. Li2 and Li0 vs. Li4 comparisons showed the highest number of upregulated DEGs in ER protein processing pathways, suggesting impaired protein synthesis in L. indica under salt stress. All comparison groups exhibited significant upregulation of DEGs in MAPK signaling pathways, confirming their pivotal role in salt stress response, consistent with findings in rice [42, 43] and alfalfa [44]. Plants synthesize L-ascorbic acid (AsA), a key substrate for ROS detoxification critical to growth and stress tolerance [45, 46], via the L-galactose pathway. Genes including GMP, GME, and GLDH are known regulators of AsA biosynthesis through this pathway [31, 47, 48]. Our detection of these genes in galactose metabolism pathways across all comparison groups implies salinity-induced modulation of AsA synthesis and subsequent ROS scavenging capacity. Additionally, BCAAs (valine, leucine, and isoleucine) function as ROS scavengers to mitigate salt damage [49]. The enrichment of DEGs in valine, leucine, and isoleucine degradation pathways observed in Li2 vs. Li4 suggests that prolonged high salinity may reduce ROS clearance via BCAAs catabolism, potentially leading to oxidative injury through ROS accumulation in L. indica seedlings.

In this study, a total of 711 metabolites were detected in L. indica seedlings under different salt stress treatments. Lipids, benzene and its derivatives, terpenoids, amino acids, peptides, and analogues, collectively accounted for over 50% of the identified metabolites. Based on metabolic pathway classification analysis, these metabolites were predominantly involved in critical biochemical processes including biosynthesis of other secondary metabolites, amino acid metabolism, carbohydrate metabolism, as well as metabolism of terpenoids and polyketides.

Lipid metabolism serves as one of the critical pathways through which plants respond to environmental stresses [50]. Plants adapt to external stresses by modulating intrinsic regulatory mechanisms, such as altering biomembrane fluidity and fatty acid composition. As primary constituents of plant cell membranes, lipids undergo significant alterations in plasma membrane composition, permeability, and ion transport under salt stress, leading to structural and functional impairments. Membrane lipids serve as substrates for generating numerous signaling lipids, potentially involved in salt stress signal transduction processes [51, 52]. In this study, lipid metabolites emerged as predominant differential metabolites in L. indica seedlings under salt stress. Comparative analyses across three experimental groups identified 42 differentially regulated lipid metabolites, spanning six major classes: Fatty Acyls (FA), Glycerophospholipids (GP), Sphingolipids (SP), Sterol Lipids (ST), Prenol Lipids (PR), and Polyketides (PK). Among these, 20 metabolites exhibited upregulated accumulation, while 22 demonstrated downregulated trends, consistent with findings reported by Zhang et al. [53] and Jiang et al. [54]. Notably, five fatty acid derivatives (alpha-linolenic acid, oleamide, avocadyne 1-acetate, malathion, and N-(6-Aminohexanoyl)-6-aminohexanoate) and two phospholipids (sn-glycero-3-phospho-1-inositol and 2-amino-1,3,4-octadecanetriol) displayed upregulated accumulation. As essential membrane components, these metabolites likely contribute to maintaining membrane integrity and facilitating salt stress signaling during adaptation processes.

The biosynthesis of other secondary metabolites emerged as the most significantly enriched pathway in L. indica’s metabolic response to salt stress. Substantial literature documents the in vitro antioxidant activities of secondary metabolites, with key compounds including flavonoids, anthocyanins, phenolic acids, sesquiterpenes, and coumarins demonstrating critical roles in plant stress resistance mechanisms [55]. In this study, the biosynthetic pathway of other secondary metabolites exhibited the highest number of detected metabolites (31). The secondary metabolite biosynthesis pathway showed significant differential expression across all three comparison groups, with nine metabolites being conclusively identified: L-tyrosine, L-phenylalanine, L-aspartic acid, 2-hydroxycinnamic acid, esculetin, nicotianamine, 2’-deoxymugineic acid, anthranilic acid, and coumarin. Notably, L-tyrosine, L-phenylalanine, and 2-hydroxycinnamic acid consistently exhibited significant differential accumulation across all three comparison groups. Phenylpropanoid metabolism plays a pivotal role in plant adaptation to salt stress. The phenylpropanoid pathway enhances plant structural defense against abiotic stress through biosynthesis of secondary metabolites such as lignin and coumarins. Under salt stress, these compounds improve resistance by reinforcing cellular integrity [56]. The metabolic pathway is initiated by L-tyrosine conversion to coumaric acid via tyrosine ammonia-lyase (TAL), which subsequently enters phenylpropanoid metabolism to synthesize phenolic acids and lignin, thereby strengthening cell walls against ionic infiltration. Concurrently, tyrosine may regulate stress responses through ROS signaling modulation [57]. As the primary substrate of phenylpropanoid metabolism, L-phenylalanine is converted to cinnamic acid by phenylalanine ammonia-lyase (PAL) under salt stress, which serves as a precursor for both lignin and flavonoids. Furthermore, 2-hydroxycinnamic acid, a key intermediate in this pathway, functions as a coumarin precursor that mitigates salt-induced damage through dual mechanisms: promoting cell wall lignification and directly scavenging ROS. Significant enrichment of these metabolites in L. indica seedlings under saline conditions confirms the essential role of phenylpropanoid metabolism in salt stress adaptation.

Salt stress typically induces excessive production of ROS, leading to oxidative stress that challenges plant physiological homeostasis [58]. As an efficient non-enzymatic antioxidant, AsA plays crucial roles not only in regulating plant growth and development but also in mediating salt stress responses through ROS scavenging [59]. Previous studies have demonstrated that Arabidopsis thaliana AsA-deficient mutant vtc1 exhibits significantly elevated H₂O₂ levels under salt stress [60]. Furthermore, exogenous application of AsA has been shown to enhance salt tolerance in crops such as maize and rice [61, 62]. In this study, we integrated transcriptomic and metabolomic data to reveal significant enrichment of ascorbate and aldarate metabolism pathways in L. indica under salt stress, demonstrating their distinct and significant roles in salt stress response. Correlation analysis between DEGs and DAMs within this pathway identified strong associations (|r| > 0.8, p < 0.05) between two metabolites (L-threonic acid and D-saccharic acid) and four genes (Lin_chr4_0495 (VTC2), Lin_chr14_0562 (GAE), XLOC_006237 (VTC2), and XLOC_023487 (ARATH)). Notably, the VTC2 gene encoding AsA biosynthesis demonstrated crucial functions in ROS scavenging and maintenance of cell membrane integrity under salt stress [63]. Previous studies in Suaeda corniculata showed significant upregulation of VTC2 under oxidative stress [58]. The VTC2 protein interacts with ABI4 to regulate ROS metabolism during salt-stressed seed germination in Arabidopsis [63]. Intriguingly, two VTC2 homologs in L. indica seedlings (Lin_chr4_0495 and XLOC_006237) exhibited positive correlations with L-threonic acid but showed decreasing expression trends with increasing salt concentrations. This suggests that salt stress may induce excessive ROS accumulation, suppress VTC2 expression, compromise cell membrane integrity, and ultimately impede normal seedling growth - observations consistent with MDA accumulation patterns and phenotypic manifestations. Furthermore, other strongly correlated metabolites and genes provide novel insights into the complex regulatory networks underlying salt stress response in this species. The precise molecular mechanisms governing these regulatory functions require comprehensive elucidation through targeted functional studies.

Conclusions

This study systematically revealed the physiological, transcriptomic, and metabolomic responses of L. indica to salt stress. Physiological data showed that increasing salt concentrations significantly reduced seed germination rate, root length, and seedling height, while affecting antioxidant enzyme activities and lipid peroxidation levels, indicating oxidative stress under salinity. Transcriptomic and metabolomic analyses identified thousands of differentially expressed genes (DEGs) and metabolites (DAMs) across salt treatments. Enriched pathways included plant hormone signal transduction, MAPK signaling, photosynthesis, autophagy, and amino acid metabolism. The WGCNA analysis highlighted the MEblue module and MEturquoise module as highly associated with salt tolerance, involving genes related to autophagy, branched-chain amino acid degradation, ascorbate and aldarate metabolism, et al. Notably, the biosynthesis of secondary metabolites and amino acid pathways were significantly upregulated under high salinity. The integration of multi-omics data demonstrates that L. indica responds to salt stress through coordinated regulation of gene expression and metabolic reprogramming. Through an in-depth analysis of the ascorbate and aldarate metabolism pathway, combined with tissue-specific validation of related genes, it was clarified that two metabolites (L-threonic acid and D-saccharic acid) and four genes (Lin_chr4_0495 (VTC2), Lin_chr14_0562 (GAE), XLOC_006237 (VTC2), and XLOC_023487 (ARATH)) are highly positively correlated. Furthermore, the expression patterns of Lin_chr14_0562 and XLOC_023487 were found to vary across different tissues (roots, stems, and leaves) of L. indica. In future studies, we will prioritize the functional characterization of four genes (Lin_chr4_0495, Lin_chr14_0562, XLOC_006237, and XLOC_023487) and two metabolites (L-threonic acid and D-saccharic acid). Targeted quantitative analysis and exogenous application experiments will be employed to validate the key metabolites. These findings not only improve our understanding of its salt tolerance mechanisms but also provide a theoretical basis for breeding salt-tolerant varieties and improving stress resistance in ornamental woody plants.

Authors’ contributions

1. C. and A. H. conceived and designed the experiments; Y.C., L.W., C.G., X.H., Y.N. and P.K. performed experiment, Y.C., C.G. and Y.J. analysed data; Y.C. wrote the manuscript; C.G., Q.G. and A. H. revised the manuscript; F.C. and Y.L. supervised. All authors read and approved the manuscript.

Funding

This study was financially supported by Jiangsu Agricultural Science and Technology Innovation Fund (CX(23)3133), Nantong Science and Technology Project (JC2024082) and the Youth Fund of Natural Science Foundation of Nantong City (JC2023027).

Data availability

The datasets can be accessed under the accession numbers PRJCA044754 (https://ngdc.cncb.ac.cn/gsa/browse/CRA029030) and PRJCA044833 (https://ngdc.cncb.ac.cn/omix/release/OMIX011496), respectively.

Declarations

Ethical approval and consent to participate

Not applicable.

Consent for publication

All authors have approved the final version of the manuscript and agreed to the publication.

Competing interests

The authors declare no competing interests.