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. 2026 Jun 9;26:1254. doi: 10.1186/s12870-026-09216-z

Integrated transcriptomic and metabolomic analyses reveal drought-response mechanisms in Suriana maritima from tropical coral islands

Chengzhi Yuan 1,2, Wanmin Zhou 1,2,3, Ruoyi Lin 4, Shike Zhang 5, Shuguang Jian 1,2,✉
PMCID: PMC13397635  PMID: 42265640

Abstract

Drought is a major abiotic stress limiting plant survival on tropical coral islands. Suriana maritima, an ecologically important shrub species in the Xisha Islands, shows strong adaptation to harsh coastal environments, but its molecular responses to drought remain poorly understood. Here, we integrated transcriptomic and metabolomic analyses to characterize leaf responses of S. maritima to progressive drought stress. Severe drought induced extensive transcriptional and metabolic reprogramming, accompanied by leaf yellowing, wilting and partial shedding. Stress-related genes, including those encoding chitinases, late embryogenesis abundant proteins, glutathione S-transferase and glutathione peroxidase, were up-regulated, suggesting enhanced oxidative stress protection and defense-related responses. Genes involved in plant hormone signaling pathways, particularly abscisic acid, jasmonic acid, gibberellin and ethylene, also showed marked transcriptional changes. In contrast, photosynthesis-associated genes, especially those related to photosystems and light-harvesting complexes, were strongly repressed under severe drought but were rapidly reactivated after rehydration, indicating indicating reversible suppression of photosynthetic activity. Metabolomic profiling showed accumulation of amino acids and lipids, including tryptophan, valine and lysophosphatidylethanolamine, whereas many flavonoids and phenylpropanoid-related metabolites declined. Integrated analysis identified coordinated repression of flavonoid and phenylpropanoid biosynthesis under severe drought. These results suggest that S. maritima responds to severe drought by reallocating resources from photosynthesis and secondary metabolite biosynthesis toward stress protection, antioxidant defense, and recovery capacity. This study provides molecular insight into drought adaptation in a non-model coastal plant and offers useful baseline information for future conservation and vegetation restoration strategies on tropical coral islands.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12870-026-09216-z.

Keywords: Suriana maritima, Drought stress, Transcriptomics, Metabolomics, Tropical coral islands

Introduction

Drought is a major abiotic stress that severely constrains plant growth, development, and productivity [1, 2]. Water deficit disrupts cellular homeostasis by promoting reactive oxygen species accumulation, damaging membrane integrity, disturbing ion balance, and inhibiting key physiological processes such as enzyme activity, photosynthesis, and respiration [3]. As climate warming intensifies evaporative demand and increases the frequency and severity of drought events, understanding the mechanisms underlying plant drought responses has become increasingly important. To cope with drought stress, plants have evolved a wide range of adaptive strategies, including activation of reactive oxygen species scavenging systems, accumulation of compatible solutes, and induction of protective molecules [4]. In addition, phytohormones play central roles in coordinating drought adaptation [5]. For instance, abscisic acid (ABA) regulates stomatal closure, improves water-use efficiency, and activates drought-responsive gene expression, thereby enhancing drought tolerance [6], whereas jasmonic acid (JA) contributes to antioxidant defense and stress resistance [7]. Therefore, elucidating the response mechanisms of plants to drought is essential for understanding plant resilience under ongoing climate change.

With the development of high-throughput sequencing technology, transcriptomic analysis has been widely used to identify the key genes and molecular mechanisms underlying plant responses to drought stress [8]. In Casuarina equisetifolia, the differentially expressed genes (DEGs) were primarily involved in plant hormone signal transduction, jasmonic acid biosynthesis, flavonoid biosynthesis, and phenylpropanoid biosynthesis after different drought stress treatments [9]. In addition, metabolomic analysis can reveal the dynamic changes in metabolic pathways and metabolites during drought stress, providing insights into how plants maintain energy homeostasis and reduce water loss [10]. Some metabolomic studies have reported that the levels of glucose, fructose, raffinose, and branched-chain amino acids were significantly higher in the leaves of drought-stressed plants compared to well-watered plants [11–13]. However, the response of plants to drought stress is a highly complex process, and a series of physiological, genetic, and metabolic changes may be involved in response to an adverse environment [14]. Integrative transcriptomic and metabolomic analyses have established a robust connection between gene regulation and metabolite generation, facilitating the comprehension of plant responses to dynamic environmental changes.

Suriana maritima L., also known as bay cedar, is widely distributed on the tropical islands and coasts of America, Asia, Australia, East Africa, and the Pacific Ocean. S. maritima is included in the International Union for Conservation of Nature’s ‌Red List of Threatened Species. In China, it is only naturally found in the Xisha Islands, with relatively few fragmented populations and individuals owing to the impacts of climate change and human activities [15, 16]. Due to the harsh environmental conditions, the vegetation and ecosystems in the Xisha Islands are highly vulnerable, with ongoing degradation emphasizing the urgent need for protection and restoration efforts [17]. The research on the response mechanisms of suitable species to abiotic stresses in the Xisha Islands is conducive to the vegetation restoration and protection of tropical coral islands. Drought is one of the most important abiotic stresses facing vegetation restoration and conservation in the Xisha Islands [18]. As an important component of the tropical coral island ecosystem, S. maritima has high tolerance to salinity, alkalinity, temperature and drought, and is an ideal material for studying the response mechanisms of plant responses to drought stress in tropical coral islands [19]. Currently, studies on the adaptive response mechanisms of S. maritima to extreme environments on tropical coral islands have focused on physiological traits, genomic compression, and phyllosphere fungal communities [15, 20, 21], yet have not addressed transcriptional and metabolic regulation. In this study, we employed Illumina sequencing and ultra-high-performance liquid chromatography/tandem mass spectrometry (UHPLC-MS/MS) to identify transcripts and metabolites in the leaves of S. maritima under different drought stress conditions to investigate the drought response mechanism. This research will be beneficial for identifying the key genes and metabolites responsible for drought resistance in S. maritima, providing a theoretical basis for the conservation and utilization of S. maritima on tropical coral islands, particularly for vegetation restoration and protection in the Xisha Islands. Additionally, it will further inform the study of the molecular mechanisms of plant responses to drought.

Materials and methods

Plant material and drought treatment

The seedlings of Suriana maritima L. identified by Professor Shuguang Jian were grown in a greenhouse at the South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China (23° 10′ N, 113° 21′ E). The ambient temperature was 31–35 °C during daytime and 24–27 °C during nighttime. The seedlings with consistent growth were selected and used for pot cultivation with one seedling per pot. The potting soil, which was composed of coral sand, coconut coir, and red soil (at a ratio of 1:2:2), was placed in pots (20 cm top diameter, 14 cm bottom diameter and 18 cm height).

According to the soil water content (SWC) of tropical coral islands in dry season [22], we established five treatments for this experiment: well-watered control group (CK, with an SWC of 70–80%), mild drought treatment (D1, with an SWC of 45–50%), moderate drought treatment (D2, with an SWC of 30–35%), severe drought treatment (D3, with an SWC of 15–20%), and rehydration treatment (DR, initiated after D3 by restoring the water supply, with an SWC of 70–80%). SWC was calculated as the ratio of actual to saturated water content. Water was replenished daily between 8:00 and 9:00 using the weighing method to maintain preset soil water content and ensure consistent water loss across all treatments. After 15 days of treatment, leaves were collected from the CK, D1, D2, and D3 groups. DR samples were collected from the D3 plants after 72 h of rehydration. All samples were collected from 17:00 to 18:00. To maintain consistency, all samples were using the fresh and healthy leaves from the top of each plant. Immediately, all leaves were snap-frozen in liquid nitrogen and stored at -80 °C for RNA extraction and metabolite profiling. Five biological replicates were used for each treatment.

RNA extraction, library construction and sequencing

Fresh leaves were rapidly frozen in liquid nitrogen and stored at -80 °C until further analysis. Total RNA was extracted using TRIzol reagent (Invitrogen, USA) following the manufacturer’s procedure. The purity, concentration, and integrity of RNA samples were assessed using agarose gels, NanoPhotometer spectrophotometer (Thermo Scientific, USA), and Agilent 2100 bioanalyzer(Agilent, USA). Poly(A) mRNA was isolated from total RNA using poly-T oligo-attached magnetic beads (Invitrogen, USA). Following purification, the poly(A) or poly(A) + RNA fraction was fragmented into small pieces using the NEB Fragmentation Buffer. Then, the cleaved RNA fragments were reverse-transcribed to create the final cDNA library in accordance with the protocol for the NEBNext Ultra RNA Library Prep kit (Illumina, USA), and the average insert size for the paired-end libraries was 300 bp (± 50 bp). The cDNA library quality was assessed using Qubit 2.0 Fluorometer and Agilent 2100 Bioanalyzer. Sequencing was completed using the Illumina HiSeq 2000 System (Illumina, USA) at Beijing Novogene Bioinformatics Technology Co., Ltd. (https://cn.novogene.com/).

De novo assembly, unigene annotation and differential expression analysis

Reads obtained from the sequencing machines included raw reads containing adapters or low-quality bases which would affect the following assembly and analysis. Thus, to get high quality clean reads, reads were further filtered by fastp (v0.18.0) [23]. The parameters were as follows: (1) removing reads containing adapters; (2) removing reads containing more than 10% of unknown nucleotides (N); (3) removing low quality reads containing more than 50% of low quality (Q-value ≤ 20) bases. The high-quality reads were de novo assembled using Trinity (v2.4.0) [24] and then the redundancy sequences were removed using Corset (v4.6) [25] and further spliced the longest unigenes. BUSCO evaluation showed that the stitching quality had good integrity and high accuracy.

All assembled unigenes were compared with NCBI non-redundant protein sequences(NR), National Center for Biotechnology Information nucleotide sequences (NT), Swiss-Prot, Gene Ontology (GO), Clusters of Orthologous Groups (COG), euKaryotic Ortholog Groups (KOG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases using Diamond (v0.8.22) [26] with a threshold of E value < 0.00001.

Gene quantification was performed using RSEM (v1.2.15) [27], and gene expression levels are reported as fragments per kilobase of exon model per million mapped fragments (FPKM) values. Read count data were analyzed using DESeq2 (v1.6.3) [28] to identify differentially expressed genes (DEGs) within the treatment groups. Subsequently, the hypothesis testing probability (P-value) was adjusted for multiple hypothesis testing using the Benjamini–Hochberg method to obtain the False Discovery Rate (FDR). The final selection of DEGs was based on the criteria of |log2(FC)| ≥ 1 and FDR < 0.05. Goseq (v1.10.0) [29] and KOBAS (v2.0.12) [30] were utilized to analyze the GO enrichment function and KEGG metabolic pathways of DEGs.

Quantitative real-time PCR

Quantitative reverse transcription PCR (qRT-PCR) analysis was performed using the ABI ViiA 7 Real-Time PCR System. Eight DEGs were selected for qRT-PCR using specific primers designed with Primer Software v2.0 (Table S1). The amplification system included 6.6 µl of H2O, 8 µl of 2×Taq PCR Master Mix (QIAGEN), 1 µl of cDNA, 0.2 µl of forward primer, and 0.2 µl of in a final volume of 16 µl. The qRT-PCR reactions were executed using the following program: 95℃ for 2 min, followed by 40 cycles of 94℃ for 10 s and 59℃ for 10 s, and 72℃ for 40 s. The relative expression levels of genes were calculated using the 2−∆∆Ct method.

Metabolomic profiling and statistical analysis

The fresh leaves were collected from different treatments for metabolite analysis, the samples were frozen immediately in liquid nitrogen and stored at -80 °C. The freeze-dried samples were crushed with a mixer mill for 2 min at 60 Hz. A 100 mg sample was transferred to 2 mL EP tube and extracted with 1500 µL methanol/water mixture (v: v = 3: 1). The samples were vortexed for 30 s and ultrasonicated for 15 min on ice. And samples were incubated overnight at 4 °C on a shaker. Then the samples were centrifuged at 12,000 rpm for 15 min at 4 ℃. A 500 µL supernatants of the sample was dried under a gentle nitrogen flow. The residue was reconstituted with 350 µL of methanol/ water mixture (v: v = 1: 1). The samples were vortexed for 30 s and ultrasonicated for 15 min on ice. Then the samples were centrifuged at 12,000 rpm for 15 min at 4 ℃. The resulting supernatants were transferred to 2 mL sample vials and stored at -80 ℃. The quality control sample was prepared by mixing an equal volume aliquot from all samples.

The metabolites were extracted for the UHPLC-QTOF-MS analysis. LC-MS/MS analyses were performed with an UHPLC system (1290, Agilent Technologies) coupled with a UPLC HSS T3 column (2.1 mm × 100 mm, 1.8 μm, Waters) coupled to TripleTOF 5600+ (Q-TOF, AB Sciex). The mobile phase A was 0.1% formic acid in water, and the mobile phase B was acetonitrile. The elution gradient was programmed as follows: 2% B (v/v) from 0 to 0.5 min, 2%-95% B (v/v) from 0.5 to 13 min, 95%-2% B (v/v) from 13.1 to 15 min The flow rate was set at 0.4 mL/min while the column temperature was 40 °C. The injection volume was 4 µL and all samples were kept at 4 °C throughout the analysis. The TripleTOF 5600 + mass spectrometer was used to gain MS/MS spectra based on a simultaneous scan POS and NEG and information dependent acquisition (IDA) triggered the enhanced ion scan (EPI) mode. The ESI source conditions were set as following: sheath gas flow rate as 50 Arb, aux gas flow rate as 15 Arb, capillary temperature 320 ℃, full MS resolution as 60000, MS/MS resolution as 15000, collision energy: 20/30/40 eV, spray voltage as 3.8 kV (positive) and − 3.4 kV (negative), respectively. The raw data were transformed to the mzXML format by ProteoWizard, and processed by R package XCMS (version 3.2) [31]. The preprocessing results created a data matrix containing the retention time (RT), mass-to-charge ratio (m/z) values, and peak intensity. R package CAMERA was applied for peak annotation after XCMS data processing. The high resolution data were processed by MAPS software and identified by BiotreeDB database (Shanghai Biotree Biotech Co., Ltd. (https://www.biotree.cn/). The standards for identifying metabolites were the accurate masses (m/z ± 15 ppm), retention time (± 20 s), and MS/MS similarity scores larger than 0.8 (dot product score).

Principal Components Analysis (PCA) was employed to observe sample distribution, while orthogonal partial least squares discriminant analysis (OPLS-DA) was used to analyze drought effects on metabolites via SIMCA 14.1 software. The selection of differentially accumulated metabolites (DAMs) was determined by variable importance in projection (VIP) values derived from the OPLS-DA model and student’s t-test P-values. The Benjamini-Hochberg method was used to correct the P-values. Metabolites with VIP > 1 and p < 0.05 were considered DAMs. DAMs were annotated for metabolic pathways through KEGG database (http://www.kegg.jp/kegg/pathway.html), identifying the pathways in which DAMs participate.

Correlation analysis of transcriptomic and metabolomic data

To identify the common pathways, DEGs and DAMs were simultaneously mapped to the KEGG database. We determined the correlation between genes and metabolites, and utilized the hmisc package in R to calculate the Pearson correlation coefficient and p values. The correlation coefficients between genes and metabolites greater than 0.8 and p value less than 0.05 were obtained. The correlation network diagram between genes and metabolites from common KEGG pathways were visualized using Cytoscape software (v3.8.1) [32]. To further elucidate the relationship between genes and corresponding metabolites, the KEGG pathway map was constructed.

Results

Morphological observation

Under mild drought stress, the leaves of S. maritima remained as fresh as the control. A few leaves turned slightly yellow under moderate drought stress. However, most of the leaves started to yellow, wilt, and shed when exposed to severe drought stress. The wilted but still alive leaves gradually regained their freshness after rehydration following the drought (Fig. 1).

Fig. 1.

Fig. 1

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Phenotypic comparison of S. maritima under drought stress. CK: well-watered control group, D1: mild drought treatment, D2: moderate drought treatment, D3: severe drought treatment, DR: rehydration treatment

Transcriptome of S. maritima in response to drought stress

To determine the molecular response of S. maritima to drought stress, we performed transcriptome sequencing of leaves collected from five treatment groups: CK, D1, D2, D3, and DR. A total of 143.08 million clean reads were generated by RNA-seq, with GC contents ranging from 43.37% to 44.50%. The Q30 base percentage of each library was > 92%, indicating that the data could be used for further investigation (Table S2). De novo assembly was further checked through Trinity and as a result, 173,559 transcripts with 705,824,599 bp were detected, while N50 value was 5677 bp with an average length of 4067 bp. Meanwhile, the assembly produced 30,098 unigenes having an N50 length of 5213 bp (average length = 2753 bp) were assembled and retained for analyses (Table S3).

The hierarchical clustering heatmap of all DEGs showed distinct gene expression patterns among the different drought treatments in S. maritima (Fig. 2a). Compared with CK, 1080 DEGs were identified in D1, including 259 up-regulated and 821 down-regulated genes; 520 DEGs were identified in D2, including 144 up-regulated and 376 down-regulated genes; 6098 DEGs were identified in D3, including 2610 up-regulated and 3488 down-regulated genes; 3807 DEGs were identified in DR, including 2399 up-regulated and 1408 down-regulated genes (Fig. 2b). Overall, drought stress predominantly repressed gene expression, as more genes were down-regulated than up-regulated under drought conditions. In contrast, the DR treatment contained more up-regulated than down-regulated genes, suggesting transcriptional recovery after rewatering. A total of 75 genes were present in D1 vs. CK, D2 vs. CK, and D3 vs. CK comparisons, of which 22 genes were consistently up-regulated and were considered potential candidate genes associated with drought tolerance (Fig. 2c, Table S4). Because the D3 vs. CK comparison contained the largest number of DEGs and more treatment-specific genes, subsequent analyses focused primarily on this comparison.

Fig. 2.

Fig. 2

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Transcriptomic changes in S. maritima under drought stress. a The number of up- and down-regulated DEGs in four comparisons (D1 vs. CK, D2 vs. CK, D3 vs. CK, DR vs. CK). b The Venn diagram of DEGs in four comparisons to display the number of common and unique DEG. c DEGs clustering heatmaps for the D1, D2, D3, and DR groups in comparison to the control. The color bar represents normalized expression level of DEGs. Red colors represent the higher expression level of DEGs, while purple colors represent the lower expression level of DEGs

Among the 22 DEGs, 6 genes were annotated as encoding hypothetical proteins, uncharacterized proteins, and unannotated proteins, whereas the remaining 16 genes were annotated as encoding transcription factors, protein kinases, functional proteins, and enzymes related to oxidative metabolism (Table S4). Notably, the CHI gene showed the strongest up-regulation under severe drought stress (Table S4). In addition, several chitinase-encoding genes were up-regulated under severe drought (Table S5), suggesting the activation of defense-related mechanisms in S. maritima under severe drought. Genes encoding glutathione S-transferase (GST) and glutathione peroxidase (GPX) were also up-regulated in D3 (Table S5), indicating activation of the antioxidant enzyme system in response to drought-induced oxidative stress. To validate the transcriptomic sequencing results, eight DEGs were randomly selected under drought stress to ensure the accuracy of the RNA-seq (Fig. S1). The qRT-PCR expression patterns of these genes were consistent with the RNA-seq data, confirming the accuracy and reproducibility of the transcriptomic analysis.

GO and KEGG enrichment analysis of DEGs

To explore the function of the DEGs, we performed GO and KEGG enrichment analyses. GO analysis revealed distinct enrichment patterns among the drought treatments (Fig. 3). In the D1 vs. CK comparison, up-regulated genes were significantly enriched in the biological process terms “cell recognition” and “multi-multicellular organism process,” whereas down-regulated genes were mainly enriched in the cellular component terms “cell wall” and “external encapsulating structure.” In the D3 vs. CK comparison, down-regulated genes were enriched in biological process terms related to energy metabolism and photosynthesis, including “ATP generation from ADP,” “photosynthesis,” and “ADP metabolic process.” These genes were also enriched in photosynthesis-related cellular component terms, such as “thylakoid,” “thylakoid part,” “photosystem II (PSII) oxygen-evolving complex,” “thylakoid membrane,” “photosynthetic membrane,” “photosystem,” and “photosystem I (PSI).” In the DR vs. CK comparison, up-regulated genes were enriched in the biological process terms “oxidation-reduction process” and “cellular carbohydrate metabolic process,” the cellular component terms “cell wall” and “external encapsulating structure,” and the molecular function terms “oxidoreductase activity” and “xyloglucan transferase activity.” These results suggest that drought stress, particularly severe drought, suppresses photosynthesis-related processes, whereas rehydration promotes metabolic recovery and cell wall-related activities.

Fig. 3.

Fig. 3

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GO enrichment analysis of the DEGs between different comparison groups. The number represents counts of DEGs enriched in GO terms. The color bars indicate the significance level of GO enrichment, expressed as -log₁₀(FDR). The red color bar represents enrichment significance for up-regulated genes, while the blue color bar represents enrichment significance for down-regulated genes. * denotes FDR < 0.05. D1U and D1D: the up-regulated genes and the down-regulated genes in D1 vs. CK; D2U and D2D: the up-regulated genes and the down-regulated genes in D2 vs. CK; D3U and D3D: the up-regulated genes and the down-regulated genes in D3 vs. CK; DRU and DRD: the up-regulated genes and the down-regulated genes in DR vs. CK

KEGG enrichment analysis further supported the functional shifts observed in the GO analysis (Fig. 4). In the D1 vs. CK comparison, up-regulated DEGs were enriched in the plant-pathogen interaction pathway, whereas down-regulated DEGs were enriched in the starch and sucrose metabolism pathway (Fig. 4a). In the D2 vs. CK comparison, down-regulated DEGs were enriched in protein processing in the endoplasmic reticulum and alpha-linolenic acid metabolism pathways (Fig. 4b). In the D3 vs. CK comparison, up-regulated DEGs were enriched in the plant hormone signal transduction pathway, whereas down-regulated DEGs were enriched in the photosynthesis and flavonoid biosynthesis pathways (Fig. 4c). In the DR vs. CK comparison, up-regulated DEGs were enriched in photosynthesis-antenna proteins, glyoxylate and dicarboxylate metabolism, carbon fixation in photosynthetic organisms, and photosynthesis pathways (Fig. 4d). Together, these findings indicate that severe drought strongly inhibits photosynthesis-related pathways, while rehydration activates pathways associated with photosynthetic recovery and carbon metabolism.

Fig. 4.

Fig. 4

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KEGG analysis of DEGs in S. maritima under drought stress. KEGG analysis of DEGs in D1 vs. CK (a), D2 vs. CK (b), D3 vs. CK (c), and DR vs. CK (d)

Plant hormone signal transduction pathways involved in the response to drought stress

To analyze the relationship between plant hormones and the drought stress response in S. maritima, we examined genes involved in plant hormone signal transduction using RNA-seq data. Six hormone signaling pathways, including cytokinin, gibberellin (GA), abscisic acid (ABA), ethylene, jasmonic acid (JA), and salicylic acid (SA), were associated with the drought stress response (Fig. 5; Table S6).

Fig. 5.

Fig. 5

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DEGs involved in plant hormone signal transduction in S. maritima under drought stress. Different colors in the heatmap represent log2FoldChange values, red indicates up-regulated genes (log2FC > 1), purple indicates down-regulated genes (log2FC < -1)

Notably, up-regulated DEGs in S. maritima were significantly enriched in the plant hormone signal transduction pathway under severe drought stress (Fig. 5). In the cytokinin pathway, DEGs encoding cerberus 1 (CER1) and histidine phosphor transfer protein (AHP) were up-regulated, whereas one DEG encoding type-A Arabidopsis response regulator (A-ARR) was down-regulated. In the GA pathway, DEGs encoding phytochrome-interacting factor 3 (PIF3), phytochrome-interacting factor 4 (PIF4), DELLA protein (DELLA), gibberellin-insensitive dwarf1 (GID1), and gibberellin-insensitive dwarf2 (GID2) were up-regulated. In the ABA pathway, most DEGs of S. maritima were also up-regulated, including one DEG encoding ABA receptors pyrabactin resistance/pyrabactin resistance-like (PYR/PYL) proteins, seven DEGs encoding type 2 C protein phosphatase (PP2C), one DEG encoding ABA-responsive element binding factor (ABF), and one DEG encoding ABA-activated SNF1-related protein kinases 2 (SnRK2). In the ethylene pathway, DEGs encoding ET-insensitive proteins 2 and 3 (EIN2 and EIN3), EIN3 binding F-box protein (EBF1/2), and ethylene response transcription factor 1 (ERF1) were up-regulated, while DEGs encoding ET receptor (ETR) were down-regulated. In the JA pathway, DEGs encoding jasmonic acid-amino synthetase (JAR1) and coronatine insensitive 1 (COI1) were up-regulated. In the SA pathway, one DEG encoding pathogenesis-related protein 1 (PR-1) was down-regulated, while three DEGs encoding TGA transcription factors (TGA) showed differential expression, with two up-regulated and one down-regulated. Together, these results indicate that multiple hormone signaling pathways, particularly ABA, GA, ethylene, and JA signaling, may coordinate the response of S. maritima to severe drought stress.

Photosynthesis pathways involved in the response to drought stress

The down-regulated DEGs in D3 vs. CK comparison were enriched in the photosynthesis pathway (Fig. 6; Table S7). These DEGs were mainly associated with photosystem I (PSI), photosystem II (PSII), electron transport, ATP synthesis, and the cytochrome b6/f complex, indicating that severe drought stress suppressed multiple components of the photosynthetic machinery. In particular, DEGs encoding light-harvesting chlorophyll a/b-binding proteins (LHCB) in PSII were down-regulated in D3 vs. CK. Among these genes, LHCB1 showed the strongest down-regulation, including cluster-4379.15725 and cluster-4379.12597, which encode major light-harvesting antenna proteins. After rehydration, the expression levels of these photosynthesis-related DEGs increased and even exceeded those in the control group, suggesting a strong recovery response of the photosynthetic system.

Fig. 6.

Fig. 6

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DEGs involved in the photosynthesis pathway in S. maritima under drought stress. Different colors in the heatmap represent log2FoldChange values, red indicates up-regulated genes (log2FC > 1), purple indicates down-regulated genes (log2FC < -1)

Metabolome analysis of S. maritima under drought stress

To characterize metabolite changes in S. maritima under drought stress, we conducted metabolomics analysis and identified DAMs. PCA analysis revealed that the distribution of the samples was reasonable, with significant differences (Fig. 7a). To further clarify the metabolic profile of S. maritima seedlings, the identified metabolites were classified and statistically analyzed according to their chemical categories. A total of 229 metabolites were detected across all samples. As shown in Table S8, lipids and lipid-like molecules accounted for the largest proportion of the total metabolites, representing 34.35%, followed by phenylpropanoids and polyketides at 25.22%, benzenoids at 11.30%, organic oxygen compounds at 9.13%, and organoheterocyclic compounds at 8.70%.

Fig. 7.

Fig. 7

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Metabolomics analysis in S. maritima under drought stress. a Principal component analysis of metabolome under different treatments. b DEMs of up- (red) and down-regulated (blue) metabolites in different comparison. c Heatmap analysis of flavonoids, amino acids and lipids induced by drought stress in S. maritima. Red and green are employed to symbolize high and low Fold Changes, respectively

A total of 9 DAMs were detected in D1 vs. CK, 36 DAMs in D2 vs. CK, 100 DAMs in D3 vs. CK, and 90 DAMs in DR vs. CK (Fig. 7b, Table S9). The number of DAMs increased with drought severity, indicating that severe drought caused more extensive metabolic reprogramming in S. maritima. Among these metabolites, amino acids, lipids, and flavonoids were closely associated with plant responses to drought stress. L-tryptophan accumulated markedly in S. maritima leaves under drought stress, suggesting activation of the tryptophan metabolism pathway (Fig. 7c). Its accumulation increased with drought severity, indicating that L-tryptophan may serve as a potential metabolic indicator of drought stress. In contrast, glutamic acid, N-acetyl-D-phenylalanine, and p-chlorophenylalanine decreased in D3 vs. CK, whereas glutamic acid accumulated in DR vs. CK, suggesting partial metabolic recovery after rehydration. In the D3 vs. CK comparison, 14 metabolites related to fatty acids, glycerophospholipids, and prenol lipids changed significantly, among which 3-pyridinebutanoic acid, 13-L-hydroperoxylinoleic acid, stearidonic acid, and lysophosphatidylethanolamine (LysoPE) accumulated under severe drought stress.

To investigate the biological roles of the identified DAMs in S. maritima, KEGG pathway enrichment analysis was performed (Fig. S2). In the D1 vs. CK comparison, the top five enriched pathways were indole alkaloid biosynthesis; phenylalanine, tyrosine, and tryptophan biosynthesis; galactose metabolism; tryptophan metabolism; and glycine, serine, and threonine metabolism. In the D2 vs. CK comparison, the top five enriched pathways were indole alkaloid biosynthesis, anthocyanin biosynthesis, flavonoid biosynthesis, flavone and flavonol biosynthesis, and stilbenoid, diarylheptanoid, and gingerol biosynthesis. In the D3 vs. CK comparison, the top five enriched pathways were flavone and flavonol biosynthesis, flavonoid biosynthesis, stilbenoid, diarylheptanoid, and gingerol biosynthesis, glucosinolate biosynthesis, and indole alkaloid biosynthesis. In the DR vs. CK comparison, the top five enriched pathways were flavone and flavonol biosynthesis, pantothenate and CoA biosynthesis, flavonoid biosynthesis, valine, leucine, and isoleucine biosynthesis, and glutathione metabolism. These results suggest that drought stress mainly affects amino acid metabolism, lipid metabolism, and secondary metabolite biosynthesis, while rehydration may promote metabolic adjustment through glutathione metabolism and branched-chain amino acid biosynthesis.

Integrative analysis of transcriptomics and metabolomics

An integrated transcriptomic and metabolomic analysis was conducted to elucidate the regulatory mechanisms underlying metabolic pathway changes during drought stress. DEGs and DAMs were mapped to KEGG pathways, revealing 4, 4, 18, and 18 commonly enriched pathways in the D1 vs. CK, D2 vs. CK, D3 vs. CK, and DR vs. CK comparisons, respectively (Table S10). In the D3 vs. CK comparison, both DEGs and DAMs were significantly enriched in the flavonoid biosynthesis pathway. Therefore, to further investigate the molecular mechanism regulating flavonoid biosynthesis in S. maritima seedlings under drought stress, we reconstructed the flavonoid biosynthesis pathway.

Flavonoid biosynthesis originates from the phenylpropanoid biosynthesis pathway, which begins with phenylalanine (Fig. 8a). Phenylalanine ammonia-lyase (PAL) and 4-coumarate-CoA ligase (4CL) are key enzymes involved in the formation of cinnamoyl-CoA and related CoA-activated intermediates. Across the four comparison groups, two PAL genes and four 4CL genes were identified. In the D3 vs. CK comparison, all identified PAL and 4CL genes were down-regulated, which may partly explain the significant decreases in kaempferol, quercetin, dihydroquercetin, (+)-catechin, chlorogenic acid, and methylchavicol under severe drought stress (Fig. 8b). In addition, genes encoding trans-cinnamate 4-monooxygenase (C4H), chalcone isomerase (CHI), chalcone synthase (CHS), naringenin 3-dioxygenase (F3H), flavonol synthase (FLS), and dihydroflavonol 4-reductase (DFR) were also down-regulated in D3 vs. CK. These results suggest that severe drought stress suppresses multiple key steps in the phenylpropanoid and flavonoid biosynthesis pathways, thereby reducing the accumulation of several flavonoid-related metabolites.

Fig. 8.

Fig. 8

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DEGs and DAMs in the flavonoid and phenylpropanoid biosynthesis pathways in S. maritima under drought stress. a Transcript profiling of genes in flavonoid and phenylpropanoid biosynthesis pathways. Blue connecting lines mean phenylpropanoid biosynthesis pathway; yellow connecting lines mean flavonoid biosynthesis pathway. The color bar represents log2FoldChange values of DEGs. Red colors denote up-regulated DEGs, while blue colors represent down-regulated DEGs. b Relative content of metabolites in flavonoid and phenylpropanoid biosynthesis pathways

Correlation analysis further revealed strong associations between drought-responsive genes and metabolites in these pathways. Seven DEGs were strongly correlated with four DAMs in the flavonoid biosynthesis pathway (Fig. S3; Table S11). Quercetin was positively correlated with three genes encoding F3H, C4H, and shikimate O-hydroxycinnamoyltransferase (HCT). Catechin was positively correlated with four genes encoding caffeoyl-CoA O-methyltransferase (CCoAOMT), CHS, FLS, and HCT. Dihydroquercetin and kaempferol were positively correlated with two genes encoding CCoAOMT and HCT. In contrast, genes encoding leucoanthocyanidin reductase (LAR) were negatively correlated with kaempferol, dihydroquercetin, and chlorogenic acid. In the phenylpropanoid biosynthesis pathway, genes encoding peroxidase (POD) and LAR showed strong correlations with chlorogenic acid, while genes encoding CHS and beta-glucosidase (BGL) were strongly correlated with methylchavicol. Together, these results indicate that coordinated changes in key structural genes may regulate flavonoid and phenylpropanoid metabolite accumulation in S. maritima under severe drought stress.

Discussion

Defense mechanism under drought stress

Chitinase breaks down chitin during biotic stress and plays a pivotal role in alleviating oxidative stress, safeguarding cellular components and membranes, and contributing to improved drought tolerance in plants [33]. Therefore, chitinase may represent an important link between drought responses signaling pathways in plants. In our study, more up-regulated chitinase-encoding genes detected under severe drought stress (D3). Chitinase genes are commonly activated by wounding, stress stimuli, or phytohormone regulation in plants. Drought stress induced leaf curling in S. maritima, and the up-regulation of chitinase-encoding genes may help alleviate this effect by promoting cell wall remodeling and reducing mechanical damage associated with rigid cell walls. Chitinase genes are frequently induced by including ABA, JA, and SA, in response to wounding or stress [34, 35]. Previous studies have shown that ABA and JA markedly induce ACtChi expression in Arabidopsis [36], whereas SA significantly upregulated the strawberry chitinase gene FnCHIT2 [37]. In this study, most differentially expressed genes involved in ABA, JA, and SA signaling were up-regulated in S. maritima leaves under D3. These findings suggest that severe drought stress may activate defense responses in S. maritima, through the regulation of chitinase-encoding genes. Additionally, the DEGs encoding GST and GPX were up-regulated under drought stress, suggesting activation of protective mechanisms in S. maritima. The upregulation of GST and GPX indicates the activation of reactive oxygen species (ROS) scavenging pathways, which are crucial for mitigating oxidative damage under drought conditions [37]. These findings collectively suggest that S. maritima activates a complex defense and protection network in response to severe drought stress, encompassing both oxidative stress management and structural adaptations.

Plant hormone signal transduction under drought stress

Plants respond to drought stress through various mechanisms, among which hormone signal transduction is an important mechanism for physiological adaptation [38]. In this study, we found that the up-regulated DEGs under severe drought stress were significantly enriched in the hormone signal transduction pathway. Therefore, plant hormones are important factors of physiological adaptation to drought in S. maritima. The ABA signal transduction pathway is strictly regulated under drought stress. Our results revealed seven DEGs encoding PP2C and one DEG encoding SnRK2 were significantly up-regulated under severe drought stress. PP2C mediates the regulation of ABA signaling by inhibiting stress-activated SnRK2 kinase [39]. During drought stress, up-regulated DEGs encoding PYL was observed in S. maritima, enhancing the ability of receptor proteins to bind ABA. This binding alleviates the inhibition of PP2C on SnRK2 kinase, activating the ABA signaling pathway and enabling plants to respond to drought stress [40]. Cytokinin is a central regulator of plant growth and stress responses and regulates plant drought tolerance [41]. In this study, an obvious expression of AHP was noted under severe drought stress, but the level of expression of A-ARR decreased. This indicated that it did not inhibit the expression of SnRK2 but promoted the downward transmission of the ABA signal. JA pathway is widely implicated in plant drought stress responses. Previous studies have shown that JAR1-mediated JA signaling improves drought tolerance in Arabidopsis, and the transcription factor MYC2 functions as a key activator of JA signaling and enhances drought resistance in tomato [42, 43]. Consistent with these reports, DEGs encoding JAR1 and MYC2 in S. maritima were significantly up-regulated under severe drought stress. Drought stress also affects proteins involved in SA signal transduction, and GA is likewise crucial in mediating the stress response. For instance, DELLA proteins help tolerate drought stress in tomato, and overexpression of PIF3 and PIF4 enhanced water-saving and drought tolerance in transgenic plants [44, 45]. Correspondingly, the DEGs encoding GID1, PIF3, and PIF4 were up-regulated in S. maritima. In summary, these findings demonstrate that multiple interconnected hormone signaling pathways coordinately regulate the drought stress response in S. maritima.

Photosynthesis under drought stress

Photosynthesis is an essential chemical process for plant growth, and photosynthesis in plants is sensitive particularly to drought stress [46]. Severe drought stress can lead to increased leaf water loss, resulting in reduced stomatal length and inhibition of photosynthesis [47]. In this study, the growth of S. maritima was restrained and its leaves wilted, yellowed, and abscised under severe drought stress. Consistent with morphology, the down-regulated DEGs were enriched in the photosynthetic pathway under severe drought stress, which affected almost all processes of photosynthesis, especially PSⅡ. The LHCB gene family is a crucial component of PSII in plant photosynthesis and is mainly responsible for capturing and converting light energy [48]. All DEGs encoding LHCB in S. maritima showed a downward trend under severe drought stress. S. maritima responded to drought by decreasing photosynthesis, reducing oxygen production, and preventing oxidative damage. Our results are consistent with those of previous research, suggesting that when plants suffer from abiotic stress, they can adapt to stress by reducing photosynthesis [49]. Studies have shown that elevated tryptophan accumulation enhances antioxidant defense systems, thereby mitigating damage to the photosynthetic apparatus and promoting plant growth [50]. In this study, the accumulation of valine and L-tryptophan significantly increased under severe drought stress. When plants are subjected to environmental stress, amino acids act as signals, resist oxidative damage, adjust osmotic stress, and regulate ion transport and energy metabolism [51]. Therefore, the accumulation of tryptophan and valine may contribute to ROS scavenging and serve as a supplementary pathway for energy metabolism because of the decrease in photosynthesis.

Phosphatidylglycerol, a major structural lipid of chloroplast membranes, is essential for photosynthetic function, and its biosynthesis is initiated by glycerol-3-phosphate acyltransferase (GPAT5) [52]. Although most DEGs related to photosynthesis were down-regulated under severe drought, 1 DEG encoding GPAT5 was up-regulated. In addition, LysoPE, a type of phosphatidylglycerol, was highly accumulated under severe drought, which might be associated with protection of thylakoid membrane stability [53]. Similar to the resurrection plant with remarkable tolerance to desiccation, the genes related to photosynthesis of S. maritima were up-regulated after rehydration [54]. This indicated that photosynthetic systems recovered quickly because they were reversibly inactivated rather than destroyed. Reducing photosynthesis might be an important strategy for S. maritima to adapt to drought stress, and short-term rehydration can rapidly restore photosynthesis.

Flavonoid and phenylpropanoid biosynthesis pathways in response to drought stress

Plants often regulate drought stress through specific metabolic responses [55]. Flavonoids are secondary metabolites widely existing in plants, which have excellent antioxidant properties and participate in growth and development, color reaction, and stress resistance of plants [56]. Many studies found that drought stress induced accumulation of flavonoids and up-regulation of related genes [57, 58]. However, some studies have reported that prolonged drought stress leads to a decrease in flavonoid content and the downregulation of flavonoid-related genes in plants [59, 60]. We speculated that the changes in flavonoid content and transcription patterns of related genes might be related to different plant species, degree and duration of drought stress. In our study, the flavonoid contents and the expression of related genes in S. maritima decreased under severe drought stress. Under drought stress, drought-tolerant plants may trigger a species-specific metabolic allocation strategy, in which resources are preferentially redirected toward survival-related defense and structural adjustment rather than flavonoid accumulation [61]. In our study, S. maritima may rely less on flavonoid accumulation and more on other antioxidant systems under severe drought. We found that both GPX and GST were up-regulated under drought stress, indicating that S. maritima has a robust capacity to cope with drought stress and mitigate oxidative damage [37]. In addition, flavonoids synthesis begins within phenylpropanoid biosynthesis pathway, PAL and 4CL are the main branch point enzymes of the phenylpropanoid pathway, which are involved in the biosynthesis of flavonoids [62]. In this study, the genes encoding PAL and 4CL were down-regulated under severe drought stress, which could be a major factor in the decrease of flavonoids and phenylpropanoids. Different plant species may adopt distinct metabolic strategies under drought stress; therefore, changes in flavonoids, phenylpropanoids, and their related genes warrant further investigation to clarify their protective roles.

Conclusions

This study provides an integrated transcriptomic and metabolomic characterization of drought responses in Suriana maritima. Severe drought induced extensive transcriptional and metabolic reprogramming, as reflected by the up-regulation of stress- and defense-related genes associated with chitinase activity, glutathione metabolism, oxidative stress protection, and hormone signaling pathways, particularly those involving ABA, JA, GA, and ethylene. By contrast, genes involved in photosynthesis, photosystems, and light-harvesting complexes were strongly repressed under severe drought but were reactivated after rehydration. This response suggests that S. maritima may temporarily reduce photosynthetic activity under water deficit while maintaining the capacity for post-drought recovery. Metabolomic analysis further revealed the accumulation of amino acids and lipid-related metabolites, such as tryptophan, valine, and LysoPE, whereas many flavonoid- and phenylpropanoid-related metabolites decreased under severe drought. Integrated pathway analysis indicated coordinated repression of flavonoid and phenylpropanoid biosynthesis, implying a possible shift in metabolic allocation from secondary metabolite production toward stress protection, antioxidant defense, and recovery after drought stress. Overall, these findings improve our understanding of the molecular and metabolic mechanisms underlying drought adaptation in S. maritima and provide useful baseline information for future studies on coastal plant stress tolerance. However, further physiological, proteomic, targeted hormone, and field-based validation is required to confirm the functional roles of the identified genes and metabolites and to evaluate their potential relevance for conservation and vegetation restoration on tropical coral islands.

Supplementary Information

Supplementary Material 1. (10.6KB, xlsx)
Supplementary Material 2. (17KB, xlsx)
Supplementary Material 3. (11.9KB, xlsx)
Supplementary Material 4. (10.8KB, xlsx)
Supplementary Material 5. (10.9KB, xlsx)
Supplementary Material 6. (12KB, xlsx)
Supplementary Material 7. (9.5KB, xlsx)
Supplementary Material 8. (19.1KB, xlsx)
Supplementary Material 9. (14.4KB, xlsx)
Supplementary Material 10. (36.4KB, xlsx)
Supplementary Material 11. (230.5KB, xlsx)
Supplementary Material 12. (782.8KB, docx)

Acknowledgements

We would like to thank the colleagues of South China Botanical Garden for assistance with plant measurements and data collection. Thanks to the anonymous reviewers for very constructive comments for this manuscript.

Authors’ contributions

Chengzhi Yuan: Writing-original draft, Data curation, Formal analysis, Methodology. Wanmin Zhou: Data curation, Formal analysis. Ruoyi Lin: Methodology, Formal analysis. Shike Zhang: Methodology, Formal analysis. Shuguang Jian: Project administration, Supervision, Writing-review & editing, Conceptualization.

Funding

This study was supported by the National Key R&D Program of China (Key Special Project for Marine Environmental Security and Sustainable Development of Coral Reefs 2022 − 700) and the Joint Fund of Henan Province Science and Technology R&D Program (235200810106).

Data availability

The sequenced raw reads generated in this study have been submitted to the National Center for Biotechnology Information (NCBI) with BioProject ID: PRJNA1370594.

Declarations

Ethics approval and consent to participate

Not applicable. All experimental studies on plants in this study were carried out in compliance with local and national regulations.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Wanmin Zhou is co-first author.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1. (10.6KB, xlsx)
Supplementary Material 2. (17KB, xlsx)
Supplementary Material 3. (11.9KB, xlsx)
Supplementary Material 4. (10.8KB, xlsx)
Supplementary Material 5. (10.9KB, xlsx)
Supplementary Material 6. (12KB, xlsx)
Supplementary Material 7. (9.5KB, xlsx)
Supplementary Material 8. (19.1KB, xlsx)
Supplementary Material 9. (14.4KB, xlsx)
Supplementary Material 10. (36.4KB, xlsx)
Supplementary Material 11. (230.5KB, xlsx)
Supplementary Material 12. (782.8KB, docx)

Data Availability Statement

The sequenced raw reads generated in this study have been submitted to the National Center for Biotechnology Information (NCBI) with BioProject ID: PRJNA1370594.

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