Molecular and Physiological Responses of Toona ciliata to Simulated Drought Stress.

Author(s): Linxiang Yang [1]; Peixian Zhao [2]; Xiaobo Song [3]; Yongpeng Ma [4]; Linyuan Fan (corresponding author) [5,*]; Meng Xie [1]; Zhilin Song [6]; Xuexing Zhang [7]; Hong Ma (corresponding author) [1,8,*]

1. Introduction

As the global population continues to grow, the demand for water resources has increased dramatically, exacerbating the already severe issue of global water scarcity [1]. Furthermore, climate change has intensified drought conditions, further constraining the availability of water resources [2]. Currently, more than 60% of the Earth’s landmass is affected by drought [1,2]. Drought stress, as one of the most common environmental factors, severely impacts seedling establishment, as well as plant growth and productivity. Drought stress induces osmotic stress [3,4]. A deep understanding of the drought response mechanism is crucial for promoting plant adaptation to drought.

Drought stress is exacerbated when the transpiration rate of a plant exceeds the rate of water uptake, leading to physiological changes [5]. This causes visible symptoms such as leaf wilting, along with more subtle impacts on the photosynthetic apparatus [6]. Furthermore, cellular integrity and function are compromised, leading to disruptions in metabolic pathways and a decrease in nutrient availability due to impaired uptake and transport mechanisms [5,6,7,8]. Plants have evolved complex morphological and physiological mechanisms in response to drought stress [9]. In arid environments, plant leaves develop unique structures that facilitate water storage and minimize transpiration, enabling them to survive in water-scarce conditions. Stomata, the tiny pores on plant leaves, are crucial for plant drought resistance, and their regulation significantly affects water use efficiency [9]. The number of stomata varies not only among different plant species but also in response to the intensity of drought stress [10].

Under normal conditions, the production of reactive oxygen species (ROS) in plants is maintained in a dynamic balance. However, under drought stress, ROS accumulates significantly, causing harm to the plant itself [11,12]. The content of malondialdehyde (MDA) is often used as an indicator to reflect the degree of oxidation of the cell membrane, thereby providing insights into the plant’s resistance to drought stress [13]. To combat this accumulation of ROS, plants can utilize their own antioxidant enzymes, such as peroxidase (POD), catalase (CAT), and superoxide dismutase (SOD) [14]. Under drought conditions, proline, soluble sugars, and soluble proteins all undergo corresponding changes in response to the stress imposed by drought [1]. These enzymes and other substances undergo corresponding changes in response to drought stress, with varying trends observed among different plant species as well as at different developmental stages within the same species. The changes in these enzymatic and non-enzymatic substances are intricately regulated by transcription factors, particularly MYB, WRKY, NAC, bHLH, and bZIP, which have been extensively studied through transcriptome analyses under drought stress conditions, revealing their pivotal roles in modulating plant drought tolerance [15,16,17,18].

Transcriptome analyses under drought stress conditions have extensively explored the molecular mechanisms of drought response in many plants [15]. These studies have identified numerous drought-inducible genes, signaling pathways, and transcription factors (TFs), with MYB, WRKY, NAC, bHLH, and bZIP families emerging as key regulators [16]. Plant gene families, generally, play pivotal roles throughout plant growth and development, intricately involved in regulating secondary metabolism and responses to hormones and adversity stresses [17]. Among these, WRKY TFs occupy a central position in modulating various stress responses in plants [18]. The NAC (NAM, ATAF1/2, and CUC2) family, specific to plants, governs several biological processes including secondary wall formation, stem apical meristem development, and responses to environmental stimuli [19,20,21]. NAC TFs directly bind to the NAC recognition site (NACRS) [CGT(G/A)] in promoter regions of target genes, thereby regulating transcription [15]. For instance, in Arabidopsis , ANAC019 expression is induced by dehydration and salinity, and its encoded protein mediates abiotic stress defenses through jasmonic acid and ABA signaling pathways [22,23]. Similarly, bHLH TFs actively contribute to plant drought stress responses. In grapes, VvbHLH1 exhibits a dominant effect on salinity and drought response in transgenic Arabidopsis thaliana by enhancing flavonoid accumulation and ABA signaling under stress conditions [24]. The bZIP family of TFs is also integral to multiple key pathways in plant drought stress responses [25]. They aid plants in adapting to drought environments and amplifying their drought resilience by regulating gene expression, collaborating with other TFs, and modulating phytohormone signaling. These TFs regulate the expression of stress-responsive genes implicated in physiological processes such as stomatal closure, osmotic regulation, and antioxidant defenses, ultimately enhancing water use efficiency and mitigating drought-induced plant damage [25].

Toona ciliata belongs to the monophyletic genus Toona of the Meliaceae family [26], listed as a key wild plant under national secondary protection in the China Red Data Book of Plants [27]. It is housed in the Chinese Virtual Herbarium, with the collection number of PE 01924525. Characterized by high tree strength, rapid growth, a straight trunk, reddish-brown wood, fine structure, beautiful wood grain, good corrosion resistance, easy processing, and a special flavor, this deciduous tree offers timber suitable for construction, high-grade furniture, plywood veneer panels, among others [26]. Additionally, it can serve as an ornamental garden tree. The bark of this tree is rich in tannins, making it a valuable raw material for various products and earning it the reputation of “Chinese mahogany” in the international market, with high economic value and promising development prospects [26,28]. However, its growth is constrained by soil moisture, as drought stress can reduce its yield and limit its suitable growing environment [29]. Currently, research on its drought response primarily focuses on morphology and physiology, with relatively limited exploration into the underlying molecular mechanisms of its drought resistance.

2. Materials and Methods

2.1. Plant Materials and Experimental Design

The experimental materials were obtained from Toona ciliata seedlings grown in Haizidi Forestry, Shuangbai, China. (101°34'8.28?, 24°29'49.55?). They were brought back to the laboratory, transplanted in new pots, and watered every two days for good daily water management (The experiment was conducted by a soil cultivation method. A round pot 18 cm in diameter and 19 cm in height was used, filled with 15 cm of soil and watered with 500 mL of water every two days). We used different concentrations of PEG-6000 to simulate drought stress (simulating drought stress via PEG-6000 forming osmotic stress). The study was conducted utilizing a replicated experimental design, with a total of three independent replicates to ensure the reliability and reproducibility of the results. Within each replicate, three pairs of leaves were carefully selected from each of the four distinct treatment groups: Control (CK), Treatment 1 (T1), Treatment 2 (T2), and Treatment 3 (T3). The CK group was the control; the T1, T2, and T3 groups were the experimental groups. The concentration of PEG-6000 in each treatment group and the duration of the experiment were determined by reviewing the relevant literature and pre-experimentation [1,7]. The T1 group had a 10% PEG-6000 solution, the T2 group a 20% PEG-6000 solution, and the T3 group a 30% PEG-6000 solution to simulate the drought stress. The experimental site was located at the Institute of Highland Forest Science, the Chinese Academy of Forestry, Kunming China (25°45'0944? N, 102°45'23.7? E). The leaves of the experimental and control groups were sampled at 0 days, 1 day, 3 days, 5 days, 7 days, and 9 days after the application of PEG-6000 solution to determine the physiological indices. The T3 group collected two sets of samples: one set for the determination of physiological indices, and the other for transcriptome sequencing, using liquid nitrogen, and stored in a refrigerator at -80 °C for transcriptome sequencing with three replicates. Six leaves were sampled each time.

2.2. Measurement of Physiological Indexes

Chlorophyll measurements were conducted with reference to the method of measuring chlorophyll content in oats by Hongying Xie et al. [7]. To measure the chlorophyll content, freshly cut and mixed leaves were quickly weighed to approximately 0.2 g and placed in a 25 mL graduated test tube. About 10 mL of 95% ethanol was added, and the tube was kept in darkness at room temperature for 24 h until all the leaves turned green. Add 80% ethanol to the 25 mL scale. This solution served as the chlorophyll extract. A colorimetric cup with a 1 cm aperture was used, and the chlorophyll extract was poured into the cup to a depth of 1 cm. Using 95% ethanol as a control, the absorbance (A) values were measured at 663 nm and 645 nm [7]. The relative water content of leaves was measured by the weighing method [7]. Malondialdehyde (MDA), soluble protein (SP), soluble sugar (SS), POD, and SOD and were determined using the corresponding microtiter plate assay kits produced by Suzhou Keming Biotechnology Co., Suzhou, China (Serial numbers: MDA-2-Y, BCAP-2-W, KT-2-Y, POD-2-Y, and SOD-2-W). The analysis was conducted using a one-way ANOVA with SPSS 26 software.

2.3. RNA Extraction and Library Construction

Eighteen Toona ciliata leaf samples taken at 0 d, 1 d, 3 d, 5 d, 7 d, and 9 d from the T3 group were transported to Huada Gene Science and Technology Service Co. Ltd., Shenzhen, China. Using specially formulated reagents in the laboratory for RNA extraction and library preparation, followed by sequencing on the Novaseq 6000 platform, sequencing was performed using paired-end sequencing with a read length of 150 bp. The high-throughput sequencing platform generated a large amount of raw data in the FASTQ format from cDNA library sequencing.

2.4. Data Filtering and Reference Genome Comparison

Raw data obtained by sequencing were filtered using the filtering software SOAPnuke (v1.5.2) to remove reads containing linkages (linkage contamination), reads with greater than 10% content of unknown bases, and low-quality reads, and clean data were obtained. The clean data were compared with the reference gene sequences using Bowtie2 (v2.2.5) software; the whole genome of Toona ciliata was published in 2022, and the data are available under accession number No. CNP0001985 in the CNGB Nucleotide Sequence Archive [26]. Gene expression levels were calculated using the RESM (v1.2.8) software package, which illustrates gene expression using the FPKM value and is considered to be expressed when the FPKM value of three replicates is greater than or equal to 0.1.

2.5. Differentially Expressed Genes

The analysis of differential gene expression within groups was conducted using DESeq with |log[sub.2] fold change| = 1. A gene was defined as differential when the |log[sub.2] fold change| value of the expression of the genes in the two comparison groups (any of the experimental groups and the control group) was =1. GO and KEGG enrichment analyses were performed, with a Qvalue of =0.05 as the threshold. Those meeting this condition were defined as significantly enriched among candidate genes.

2.6. Co-Expression Network Analysis

A co-expression network analysis was performed using the weighted co-expression network analysis (WGCNA) package in R. The WGCNA package was used to analyze the co-expression network. The scale-free topology fit index was 0.8 with a soft threshold of 14. The degree of connectivity (degree) was calculated as the number of edges of all nodes. Co-expression networks were visualized using Cytoscape 3.10.2 software.

2.7. Quantification and Validation of Gene Expression Levels

To verify the accuracy of the RNA-Seq data from Toona ciliata leaves under PEG-simulated drought stress, 12 DEGs were randomly selected for quantitative real-time PCR (RT-qPCR) analysis. The samples were sent to Shanghai Shenggong Biotechnology Co., Ltd., Shanghai, China, for testing, and the relative expression levels of the genes were calculated using the 2[sup.-??Ct] method.

3. Results

3.1. Physiological Response of Toona ciliata under Drought Stress

As the drought period progressed, significant variations in chlorophyll content were observed in the leaves of Toona ciliata seedlings among different treatment groups. Compared to the control group (CK), the T1, T2, and T3 groups generally had higher chlorophyll content. Specifically, on the fifth day, the chlorophyll content in the T1, T2, and T3 groups was 1.05 times, 1.06 times, and 1.07 times higher, respectively, than that in the CK group. However, on the seventh and ninth days, the chlorophyll content in the T2 group was lower than that in the CK group (Figure 1A). Significant differences were also noted in MDA content between the control and experimental groups. The MDA content in the T1 and T3 groups was higher than that in the CK group, especially on the seventh day, when the MDA content in the T1 and T3 groups was 1.49 times and 1.71 times higher, respectively, than that in the CK group. The MDA content in the T2 group was lower than that in the CK group on days 1, 3, and 9 but higher at other times, peaking on the fifth day at 1.9 times the MDA content of the CK group (Figure 1B). In terms of POD activity, the T1, T2, and T3 groups generally had higher POD levels than the CK group during the first seven days. On the seventh day, the POD content in the T1 and T3 groups was 2.27 times and 1.09 times higher, respectively, than that in the CK group. However, on the ninth day, the POD content in the T2 and T3 groups was lower than that in the CK group. Overall, the T3 group had a higher POD content than the T1 and T2 groups (Figure 1C). Regarding SP content, the T1, T2, and T3 groups consistently had higher SP levels than the CK group. On the fifth day, the SP content in the T1, T2, and T3 groups was 1.8 times, 1.21 times, and 1.62 times higher, respectively, than that in the CK group. In general, the T3 group had a higher SP content than the T1 and T2 groups (Figure 1D). Significant differences were also observed in SS content between the control and experimental groups. During the first five days, the SS content in the T1, T2, and T3 groups was generally higher than that in the CK group (see Figure 1E). Finally, concerning the relative water content of leaves, all treatment groups had significantly lower relative water content than the CK group (Figure 1F).

3.2. Transcriptomic Analysis of Leaves of Toona ciliata Seedlings under Simulated Drought Stress

A total of 18 samples were sequenced using the Illumina system. After filtering out the low-quality data and articulators, 115.33 GB of clean bases was obtained in the transcriptome libraries of the 18 samples. For each sample, 41.92–43.69 million reads were obtained. For each sample, 41.05–43.13 million reads of clean bases were obtained, the Q20 of clean bases was >98%, the ratio of clean bases was greater than 96%, and among these clean reads, 78.14–80.55 could be localized to the reference genome of Toona ciliata (Supplementary Table S1). This result indicated that the quality of the transcriptomics data met the requirements for the subsequent analysis.

The analysis of differential genes across all groups revealed that the number of up-regulated genes was consistently higher than that of down-regulated genes at all time points. Specifically, compared to 0 d, there were 612 up-regulated genes and 264 down-regulated genes at 1 d; 711 up-regulated genes and 422 down-regulated genes at 3 d; 454 up-regulated genes and 419 down-regulated genes at 5 d; 619 up-regulated genes and 541 down-regulated genes at 7 d; and 478 up-regulated genes and 310 down-regulated genes at 9 d. Both up-regulated and down-regulated genes showed a trend of an initial increase followed by a decrease. In total, there were 4830 differential genes, with 260 differential genes shared among the five groups, comprising 183 up-regulated genes and 77 down-regulated genes (Figure 2A), as illustrated in the Venn diagrams. Furthermore, the five groups collectively had 260 differential genes, consisting of 183 up-regulated genes and 77 down-regulated genes (Figure 2B–D).

The common genes at five sampling time points were analyzed via time sequence analysis, and all the sequences were divided into 10 clusters. A total of 2473 genes in cluster 1 showed a gradual decrease in expression with the prolongation of drought stress, and 3026 genes in cluster 2 showed a decreasing and then increasing trend with the prolongation of drought. A total of 11,684 genes in cluster 3 showed a gradual increase.

A total of 2201 genes in cluster 7 showed a decreasing and then increasing trend with the prolongation of drought; the expression was the lowest on day 3, and then the expression reached the highest on day 5. The expression of 2201 genes in cluster 7 showed a decreasing-rising-decreasing trend with the prolongation of drought time, reaching the lowest on day 3 and the highest on day 5; 1806 genes in cluster 10 showed a rising–decreasing trend with the prolongation of drought time, reaching the highest value on day 1 (Figure 3).

3.3. Analysis of Differential Gene Enrichment

GO and KEGG enrichment analyses were performed on each group of differential genes to evaluate the biological functions of Toona ciliata leaves. (GO) enrichment analysis of the differentially expressed genes across various time points (1 d, 3 d, 5 d, 7 d, 9 d) versus the initial day (0 d) in Toona ciliata leaves revealed significant changes in cellular components, biological processes, and molecular functions. Chloroplasts emerged as the primary cellular component, indicating substantial functional variations during leaf development, with plastids closely associated, contributing to photosynthesis. Thylakoids and plastid envelopes were also significantly enriched, crucial for light capture and conversion. Rhythmic processes, including the regulation of circadian rhythm, were prevalent across all comparisons, demonstrating the leaf’s diurnal regulation. Small molecule metabolism and circadian rhythms were essential for energy supply and growth, while the response to abiotic stimuli intensified with leaf maturity. At the molecular function level, fructose-bisphosphate aldolase activity was prominent, linked to glycolysis and carbon fixation in photosynthesis. Chlorophyllide and oxygenase activity related to chlorophyll synthesis and degradation impacted photosynthesis. Oxidoreductase activity facilitated energy transfer and redox reactions, while sigma factor and DNA-binding transcription factor activities indicated evolving gene expression patterns. Glycogen phosphorylase activity was enriched, implicating sugar storage and utilization dynamics during leaf development (Figure 4A–F).

KEGG analysis showed that the major pathways in the 1 d vs. 0 d differential genes were the circadian rhythm–plant, carbon fixation in photosynthetic organisms, carbon metabolism, and porphyrin metabolism (Figure 5A). The major pathways in the 3 d vs. 0 d differential genes were the circadian rhythm–plant, carbon fixation in photosynthetic organisms, porphyrin metabolism, and one carbon pool by folate (Figure 5B). The major pathways in the 5 d vs. 0 d differential genes were the circadian rhythm-plant, starch and sucrose metabolism, fructose and mannose metabolism, and carbon fixation in photosynthetic organisms (Figure 5C). The major pathways in the 7 d vs. 0 d differential genes were the circadian rhythm–plant; porphyrin metabolism; the biosynthesis of amino acids; and alanine, aspartate, and glutamate metabolism (Figure 5D). The circadian rhythm–plant, porphyrin metabolism, carbon fixation in photosynthetic organisms, and starch and sucrose metabolism were the major pathways in the 9 d vs. 0 d differential genes (Figure 5E). The circadian rhythm–plant, carbon fixation in photosynthetic organisms, carbon metabolism, porphyrin metabolism, and glycolysis/gluconeogenesis were common pathways in all five groups.

3.4. Cluster Analysis of Shared Pathways

The cluster analysis of the differential genes of the five pathways in the five groups showed that there were 48 differential genes in the circadian rhythm–plant pathway; a total of 14 genes were down-regulated, and 34 genes were up-regulated with the prolongation of the drought (Figure 6A). Carbon fixation in the photosynthetic organism pathway had a total of 25 differential genes, with 3 genes down-regulated and 22 genes up-regulated with a prolonged drought (Figure 6B). The carbon metabolism pathway had a total of 60 differential genes, with 20 genes down-regulated and 40 genes up-regulated with a prolonged drought (Figure 6C). The porphyrin metabolism pathway had 22 differential genes; 7 genes were down-regulated, and 15 genes were up-regulated with the extension of the drought (Figure 6D). The glycolysis/gluconeogenesis pathway had 35 differential genes; 13 genes were down-regulated, and 22 genes were up-regulated with the extension of the drought (Figure 6E).

3.5. Transcription Factors

All differential genes in the five comparison groups of 1 d vs. 0 d, 3 d vs. 0 d, 5 d vs. 0 d, 7 d vs. 0 d, and 9 d vs. 0 d belonged to 57 different transcription factor families. After removing the transcription factor families with a number of differential genes less than or equal to 10 genes, among the transcription factor families commonly related to drought resistance, the MYB transcription factor family contained the highest number of differential genes (195 differential genes). The NAC transcription factor family had 92 differential genes, the WRKY transcription factor family had 65 differential genes, the bZIP transcription factor family had 16 transcription factor families, the bHLH transcription factor family had 113 differential genes, and the AP2-EREBP transcription factor family had 95 differential genes (Figure 4A). After KEGG enrichment analysis, we performed an in-depth transcription factor family attribution analysis for the differential genes in the five key pathways screened. The results showed that among these five pathways, only the differential genes in the circadian rhythm–plant pathway could be clearly attributed to specific transcription factor families. In the circadian rhythm–plant pathway, the 1 d vs. 0 d group had seven genes in the MYB transcription factor family, all up-regulated. There were four differential genes in the bHLH transcription factor family, all up-regulated; five differential genes in the C2C2-Dof transcription factor family; and five genes in the C2C2-CO-like transcription factor family, two of which were up-regulated and three of which were down-regulated. In the 3 d vs. 0 d group, there were seven genes in the MYB transcription factor family, all up-regulated; five differentially differentiated genes in the bHLH transcription factor family, all up-regulated; five differentially differentiated genes in the C2C2-Dof transcription factor family, all up-regulated; and seven genes in the C2C2-CO-like transcription factor family, two up-regulated and five down-regulated. In the 5 d vs. 0 d group, there were six genes in the MYB transcription factor family, all up-regulated; four differential genes in the bHLH transcription factor family, three up-regulated and one down-regulated; four differential genes in the C2C2-Dof transcription factor family, all up-regulated; and four genes in the C2C2-CO-like transcription factor family, two up-regulated and two down-regulated. There were seven genes in the 7 d vs. 0 d group in the MYB transcription factor family, all up-regulated; four differential genes in the bHLH transcription factor family, all up-regulated; six differential genes in the C2C2-Dof transcription factor family, all up-regulated; and seven genes in the C2C2-CO-like transcription factor family, three up-regulated and four down-regulated. The 9 d vs. 0 d group had seven genes in the MYB transcription factor family, all up-regulated; three differential genes in the bHLH transcription factor family, all up-regulated; six differential genes in the C2C2-Dof transcription factor family, all up-regulated; and eight genes in the C2C2-CO-like transcription factor family, two up-regulated and six down-regulated; the remaining four pathways shared had no transcription factor families.

3.6. Co-Expression Network Analysis

A co-expression network analysis (WGCNA) was performed on the genes screened from the leaf transcriptome of Toona ciliata . WGCNA was performed, which generated 12 different modules (Figure 5F), which were differentiated according to the colors in the dendrogram, and the weight values indicated the relevance of the relationship pairs of genes in the modules, where every two genes corresponded to one relationship pair. From these, the five genes with the highest connectivity were then selected as hub genes. The 12 modules generated by WGCNA were analyzed with physiological indicators, and the blue module and the red module were selected for network visualization. The hub genes were selected by correlation network analysis for the blue module and the red module, respectively, and the hub genes in the blue module were Tci15G008630, Tci24G023270, Tci05G005320, Tci13G007950, and Tci04G009210 (Figure 7A). The hub genes in the red module were Tci06G010260, Tci01G007790, Tci07G006850, Tci08G008580, and Tci0G012830 (Figure 7B). A KEGG analysis was performed on the hub genes of the two modules separately, and the shared pathway was plant hormone signal transduction.

3.7. Quantitative Real-Time PCR Verification

To confirm the accuracy of the RNA-Seq data from Toona ciliata leaves under PEG-modeled drought stress, 12 DEGs were selected for RT-qPCR analysis. The data were analyzed for correlation, and the correlation was high (Supplementary Figure S1). The results showed that the RT-qPCR expression patterns were basically consistent with the RNA-seq data, indicating that the transcriptome data in this article are reliable and valid (Figure 8).

4. Discussion

4.1. The Effects of Drought Stress on the Leaves Physiology of Toona ciliata

Plants typically exhibit a series of physiological and biochemical changes in response to abiotic stresses [30]. Among them, SS, SP, MDA, and antioxidant enzymes serve as key indicators for regulating osmotic and oxidative stress under drought conditions [31,32]. Changes in MDA content are often used as markers for membrane lipid damage (reactive oxygen species accumulation) [33]. Throughout the experimental process, SS and SP continuously accumulated, with the content in the experimental groups (T1, T2, and T3) consistently higher than that in the control group. This indicates that in the face of drought stress, the leaves of the Toona ciliata tree enhance the accumulation of SS and SP to maintain cellular osmotic potential and osmotic balance. The MDA content peaked on the fifth day. Prior to this, genes related to ROS metabolism were all up-regulated, while on the fifth day, genes related to ROS were not expressed, suggesting that ROS accumulation occurred immediately after drought stress and reached its maximum on the fifth day. Notably, the POD content significantly increased on the third and fifth days, indicating that protective enzymes began to function. Genes related to these periods were mainly associated with biological processes such as chloroplast, plastid, and thylakoid ones, suggesting that MDA accumulation and clearance primarily impact photosynthesis.

4.2. Effects of Drought Stress on Photosynthesis of Toona ciliata Leaves

Photosynthesis is a vital metabolic process in plant growth and development, and it is highly sensitive to changes in water content within the plant [34,35]. Stress can exert a range of effects on photosynthesis, including damage to chloroplast structures, reduced photosynthetic rates, limited electron transport, and impaired pigment complexes [30]. Chlorophyll, a key pigment reflecting plant sensitivity to water stress, directly affects photosynthetic yield [36]. A higher chlorophyll content under drought stress indicates a stronger ability of plants to maintain photosynthesis under adverse conditions [37]. Consequently, in this experiment, the chlorophyll content in the T1 group gradually increased and remained higher than that in the control group. Meanwhile, the chlorophyll content in the T2 and T3 groups surpassed that of the control group after the fifth day, while the T3 group’s chlorophyll content declined below the control after the fifth day, suggesting that mild drought stress can induce chlorophyll accumulation. As drought intensifies, chlorophyll content begins to decrease, indicating that mild drought stress enhances the photosynthetic intensity of Toona ciliata . However, severe drought stress and prolonged drought exposure reduce photosynthetic intensity. The transcriptome data revealed photosynthesis-related differentially expressed genes (DEGs). The enrichment analysis of these DEGs showed that carbon fixation in photosynthetic organisms, related to photosynthesis in the KEGG pathway, encompassed 25 DEGs. Among them, 3 genes were down-regulated and 22 genes were up-regulated as the drought stress duration extended. GO enrichment highlighted chloroplast, plastid, chloroplast stroma, and plastid stroma, indicating that drought stress impacts the photosynthesis of Toona ciliata , corresponding to changes in chlorophyll content.

4.3. The Effects of Drought Stress on Transcription Factors in Toona ciliata Leaves

Plants have evolved complex physiological and molecular networks, and under drought stress, plant cell membranes, the accumulation of osmotic regulators, and photosynthetic properties are altered [38]. Transcription factors respond to drought stress by regulating the expression of target genes [39], and many studies have demonstrated that bZIP, bHLH, WRKY, NAC, and MYB play important roles in drought transcriptional regulation [16]. For example, the expression of the Oryza sativa LOsWRKY 11 gene is induced by pathogens, drought, and high temperatures, and the overexpression of this gene enhances drought resistance in Oryza sativa [40,41,42,43]. In this study, the enriched differential genes in more than 50 transcription families were NAC, WRKY, bZIP, bHLH, AP2-EREBP, C3H, GRAS, and FRAI transcription factor families, and most of the differential genes in 8 eight transcription factor families were up-regulated in expression. This mainly includes the genes Tci26G005500, Tci02G004030, Tci03G004950, Tci02G004030, Tci04G007020, Tci01G000980, etc. This suggests that drought stress promotes the expression of several families of transcription factors and that the expression of several common transcription factor families plays an important role in the complex regulatory network of Toona ciliata in response to drought.

4.4. Effects of Drought Stress on Plant Hormone Signal Transduction in Toona ciliata Leaves

The WGCNA selected hub genes, which are now widely acknowledged as crucial components that form the backbone of the network and play a pivotal role in specific physiological events [44]. One of the KEGG pathways shared by hub genes in both modules is plant hormone signal transduction, which is crucial for the perception and transmission of drought stress signals in drought-stressed plants. Some studies have shown that the expression of genes related to the IAA signaling pathway was altered, and the expression of the growth hormone early-response genes AUX/IAA and GH3 were significantly down-regulated. In contrast, the expression of SAUR was significantly up-regulated. The transcriptional regulation of growth hormones is influenced by AUX/IAA (auxin/indole-3-acetic acid) proteins and the ARF (auxin response factor) family of transcription factors. AUX/IAA proteins are transcriptional repressors of major growth hormone-responsive genes [15,45,46], and the ABA receptor PYR/PYL family negatively regulated PP 2C (protein phosphatase 2C), and the positively regulated ABA response element binding factor has important roles in the ABA signaling pathway [16]. In this study, one of the KEGG pathways shared by hub genes in the two modules was plant hormone signal transduction, indicating that droughts affect the changes of various hormones in the leaves of Toona ciliata .

5. Conclusions

In this study, PEG-6000 was used to simulate drought stress on two-year-old leaves of Toona ciliata . The physiological analysis revealed that Toona ciliata responds to drought stress by increasing the contents of MDA, POD, SS, and SP. The transcriptome analysis indicated that transcription factor families such as NAC, WRKY, bZIP, bHLH, AP2-EREBP, C3H, GRAS, and FRAI may regulate the accumulation of related metabolites by modulating the expression levels of genes in metabolic pathways to cope with drought stress. This mainly includes the genes Tci26G005500, Tci02G004030, Tci03G004950, Tci02G004030, Tci04G007020, Tci01G000980, etc. The primary effects of drought stress on the leaves of Toona ciliata are related to photosynthesis and responses to plant hormone signal transduction. This research contributes to a deeper understanding of the molecular basis of drought resistance in Toona ciliata .

Author Contributions

L.Y.: experiments, data processing, thesis writing, and thesis revision; P.Z., X.S., Y.M. and M.X.: experiments and guidance; Z.S. and X.Z.: guidance; L.F.: supervision and guidance; H.M.: supervision, guidance, project management, and access to funds. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Data are available in the Supplementary Materials.

Conflicts of Interest

The authors declare no conflicts of interest.

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Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/horticulturae10101029/s1, Table S1: Quality of transcriptome sequencing data; Table S2: Differential gene annotations; Figure S1: Heatmap of quantitative real-time PCR validation conformance analysis; Figure S2: Heatmap of correlations between transcriptome sequencing samples; File S1: Real-time fluorescence quantitative PCR correlation interest.

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Figures

Figure 1: Physiological indices of Toona ciliata leaves. (A ) Chlorophyll (Chl) content, (B ) malondialdehyde (MDA) content, (C ) peroxidase (POD) activity, (D ) soluble protein (SP) content, (E ) soluble sugar (SS) content, (F ) leaf relative water (RWC) content. The different lowercase letters indicate significant differences between treatments (p < 0.05). [Please download the PDF to view the image]

Figure 2: Transcriptional changes in Toona ciliata leaves: (A ) number of differential genes, (B ) Venn diagram with overlapping differential genes in five groups, (C ) Venn diagram with overlapping up-regulated differential genes in five groups, (D ) Venn diagram with overlapping down-regulated differential genes in five groups. [Please download the PDF to view the image]

Figure 3: Trend analysis of expressed genes from 0 d to 9 d. Some genes will have similar expression patterns at different time stages, and based on the expression amount information of the genes, they can be clustered into time-related gene clusters, and the genes with consistent expression patterns will be clustered in the same cluster, and the center line represents the trend of the expression amount of the genes with consistent expression patterns over time. The color represents the distance from the centerline; purple is close to the centerline, and floral indicates distance from the centerline. [Please download the PDF to view the image]

Figure 4: Transcription factor families and GO enrichment analysis. BP is a biological process, CC is a cellular component, and MF is a molecular function. The horizontal axis is Rich Ratio, with larger values indicating a higher height of enriched cars at that entry. (A ) Transcription factor families, (B ) 1 d vs. 0 d GO enrichment analysis, (C ) 3 d vs. 0 d GO enrichment analysis, (D ) 5 d vs. 0 d GO enrichment analysis, (E ) 7 d vs. 0 d GO enrichment analysis, (F ) 9 d vs. 0 d GO enrichment analysis. [Please download the PDF to view the image]

Figure 5: Differential gene KEGG enrichment analysis: (A ) 1 d vs. 0 d KEGG enrichment analysis; (B ) 3 d vs. 0 d KEGG enrichment analysis; (C ) 5 d vs. 0 d KEGG enrichment analysis; (D ) 7 d vs. 0 d KEGG enrichment analysis: (E ) 9 d vs. 0 d KEGG enrichment analysis; (F ) module correlation graphs with physiological indicators. [Please download the PDF to view the image]

Figure 6: Cluster analysis of shared pathways: (A ) circadian rhythm–plant pathway cluster analysis, (B ) carbon fixation in photosynthetic organism pathway cluster analysis, (C ) porphyrin metabolism pathway cluster analysis, (D ) carbon metabolism pathway cluster analysis, (E ) glycolysis/gluconeogenesis pathway cluster analysis. [Please download the PDF to view the image]

Figure 7: Blue modules and red co-expression networks. Nodes represent genes in the module, while lines represent correlations between two genes. Connectivity is defined as the number of edges of all nodes. Node size and color shades reflect connectivity between genes. Darker red color indicates higher connectivity. The yellow part in the center indicates the screened hub genes. (A ) Analysis of gene interaction network of the blue module; (B ) analysis of gene interaction network of the red module. [Please download the PDF to view the image]

Figure 8: A total of 12 DEGs’ relative expression levels as determined by RNA-Seq and qRT-PCR. [Please download the PDF to view the image]

Author Affiliation(s):

[1] Institute of Highland Forest Science, Chinese Academy of Forestry, Kunming 650233, China; [email protected] (L.Y.); [email protected] (M.X.)

[2] Forestry and Grassland Bureau of Yuanmou, Yuanmou 651300, China; [email protected]

[3] Rushan Inspection and Testing Center, Rushan 264500, China; [email protected]

[4] Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, China; [email protected]

[5] Yunnan General Administration of Forestry Seeds and Seedlings, Kunming 650215, China

[6] Rushan Forestry Development Center, Rushan 264500, China; [email protected]

[7] Yunnan Academy of Forestry and Grassland, Kunming 650204, China; [email protected]

[8] Key Laboratory of Resource Insect Cultivation and Utilization, State Forestry and Grassland Administration, Kunming 650233, China

Author Note(s):

[*] Correspondence: [email protected] (L.F.); [email protected] (H.M.)

DOI: 10.3390/horticulturae10101029

Article Details

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Author:	Linxiang Yang; Peixian Zhao; Xiaobo Song; Yongpeng Ma; Linyuan Fan; Meng Xie; Zhilin Song; Xuexing Z
Publication:	Horticulturae
Article Type:	Report
Geographic Code:	9CHIN
Date:	Oct 1, 2024
Words:	7590
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Topics:	Cellular signal transduction Analysis Droughts Genes Analysis Genetic transcription Analysis Meliaceae Plant hormones Analysis Soil moisture Analysis Stress management Analysis Transcription (Genetics) Analysis Transcription factors