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Article

Physiology, Transcriptome and Root Exudates Analysis of Response to Aluminum Stress in Pinus massoniana

by
Jinyan Ling
1,2,3,
Jianhui Tan
1,2,4,5,
Hu Chen
1,2,4,5,
Zhangqi Yang
1,2,3,4,5,*,
Qunfeng Luo
1,2,4,5 and
Jie Jia
1,2,4,5
1
Key Laboratory of Central South Fast-Growing Timber Cultivation of Forestry Ministry of China, Guangxi Forestry Research Institute, Nanning 530002, China
2
Guangxi Key Laboratory of Superior Timber Trees Resource Cultivation, Nanning 530002, China
3
College of Life Science, Guangxi Normal University, Guilin 541006, China
4
Masson Pine Engineering Research Center of the State Forestry Administration, Nanning 530002, China
5
Masson Pine Engineering Research Center of Guangxi, Nanning 530002, China
*
Author to whom correspondence should be addressed.
Forests 2023, 14(7), 1410; https://doi.org/10.3390/f14071410
Submission received: 17 May 2023 / Revised: 21 June 2023 / Accepted: 3 July 2023 / Published: 11 July 2023
(This article belongs to the Section Genetics and Molecular Biology)
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Abstract

:
Pinus massoniana is an important timber tree species in southern China, and acid aluminum stress seriously endangers its growth. This study focuses on physiology, gene regulation and root exudates. Aluminum stress increased the activity of malondialdehyde (MDA), proline (PRO), peroxidase (POD), soluble proteins (SP), soluble sugars (SS) and superoxide dismutase (SOD) in P. massoniana seedlings, and led to changes in growth. We identified hub genes (UCHL3, TCP1, SEC27, GluRS and ACTF) responding to aluminum stress of low concentration and hub genes (RGP, MPT, RPL24, RPL7A and EC3.2.1.58) responding to aluminum stress of high concentration. Aluminum stress mainly affected phenylpropanoid biosynthesis and flavonoid biosynthesis, and it may alleviate aluminum toxicity by inducing the upregulation of genes such as CHS, COMT, DFR and LAR to enhance root exudation of catechin. These results lay the foundation for in-depth studying the molecular mechanism of P. massoniana aluminum stress.

1. Introduction

Aluminum is the most abundant metal element in the earth’s crust, and the solid and complex aluminum in the soil is non-toxic or low-toxic to plants [1]. However, natural processes or human activities can lead to soil acidification (pH < 5.5), and trivalent aluminum (Al3+) released from acidic soils has a strong toxic effect on plants [2]. By interfering with the structure and function of cell wall [3], plasma membrane [4], chloroplast [5] and cytoskeleton [6], a series of cellular processes are affected, including excessive reactive oxygen species production [7], signal transduction disorder [8] and low cell wall elongation [3], thus limiting plant growth and development. More than 30% of the land and more than 40% of the potential arable land are acidic soils, most of which are distributed in tropical and subtropical regions [9]. Therefore, analyzing the impact of Al3+ in acidic soil on plant growth and the Al tolerance mechanism of plants is one of the research focuses of scientists [10].
In recent years, the effects of acid-aluminum stress on plants are based on changes in growth and physiological indicators involving plants such as Fagopyrum species [11], Neolamarckia cadamba [12] and Oryza sativa [13]. When plants are under aluminum stress, with the extension of time and the deepening of the degree, the imbalance between the production and degradation of reactive oxygen species and free radicals will lead to oxidative stress, enzyme inactivation and cell membrane peroxidation [14]. Antioxidant defense and osmotic regulation systems are considered to play an important role in the oxidative stress of excessive aluminum. Superoxide dismutase and peroxidase produced in plants can alleviate the increase in reactive oxygen species and free radicals. Non-enzymatic low-molecular-weight compounds such as proline, soluble sugar and soluble protein can neutralize aluminum toxicity [11]. Therefore, the determination of antioxidant enzyme activity and osmoregulatory substances is essential for predicting cellular metabolic activity and health status.
The rapid development of sequencing technology provides a possibility for understanding the deep molecular mechanism of plants in response to aluminum stress. Gene regulation mechanisms of membrane transporters, signal transduction, transcription factors, oxidative stress, cytoskeletal dynamics, energy, and metabolism of plants under aluminum stress were explained [2]. Aluminum-tolerant transcription factors such as ARTs, STOPs and WRKYs have been identified in a variety of plants [10,15,16]. For example, at least thirty-one aluminum-responsive genes in rice are regulated by OsART1 to participate in the detoxification of aluminum damage [9]. NtSTOP1 in tobacco is involved in aluminum tolerance [17]. OsWRKY22 promotes Al-induced OsFRDL4 expression by binding to W-box cis elements within the promoter of OsFRDL4, resulting in enhancing aluminum-induced citrate secretion and aluminum tolerance [18]. The genes encoding the citrate cycle enzyme and several specifically expressed genes positively regulate the tolerance of maize to aluminum stress [19]. In addition, the phenylpropanoid pathway also plays a key role in enhancing plant tolerance to Al. Under aluminum stress, the transcripts of key enzymes affecting the synthesis of lignin monomers in Neolamarckia cadamba were upregulated [12]. Studies relating to P. massoniana showed that the upregulated expression of lignin synthesis genes Pm4CL, PmCAD and PmCOMT participated in cell reconstruction in the late stage of aluminum stress [20].
Root exudates are a variety of biochemicals actively or passively secreted by plant roots and play an important role in plant responses to abiotic stress [21]. Heavy metal stress can change the composition and quantity of root exudates [22], increase the content of defensive compounds in plant root exudates, and prevent the direct effect of heavy metals on sensitive sites [23]. Studies have found that two transporter families, ALMT and MATE, circumvent the toxicity of aluminum by secreting malic acid and citric acid, respectively [2,9]. Apart from organic acids, several other organic compounds like phenolics [24] and benzoxazinoids [25] can also tolerate aluminum toxicity. However, the genes network involved with the secretion of organic compounds under aluminum stress in any of the plant species still remains to be identified. In addition, root marginal cells can regulate the rhizosphere environment and combines with Al3+ by secreting mucus substances such as polysaccharides, glucose and polysaccharides aldehydes to reduce the toxic effect of aluminum on root tips [26,27]. Therefore, the study of root exudates has great theoretical and practical significance for elucidating the mechanism of plants in response to aluminum stress.
Pinus massoniana is the main timber tree species in southern China. It has the characteristics of strong adaptability, fast growth and good stress resistance, providing energy, economic and ecological support [28]. However, the soil in southern China is generally acidic [29]. Acid aluminum stress seriously poisons the roots of P. massoniana, causing large-scale growth decline and even death [30]. Our research group has studied the changes in root transcriptome at different treatment stages and different times, and we identified the early and late response genes to aluminum stress [20]. However, the regulatory mechanism between physiological, molecular and root exudates of P. massoniana from different concentrations of aluminum toxicity is unclear. Therefore, this study analyzed the changes in root transcriptome and root exudates of P. massoniana under different concentrations of aluminum treatment, in order to identify aluminum stress response genes, differential root exudates and their key pathways. This study provides a theoretical basis for elucidating the molecular mechanism of detoxification and tolerance of P. massoniana seedlings to different concentrations of aluminum.

2. Materials and Methods

2.1. Materials and Seedling Culture

The experimental material was the second-generation excellent family of P. massoniana, and the seeds were from Guangxi Forestry Research Institute (Nanning, China). Seeds were sterilized with sodium hypochlorite for 30 min, washed with sterile water 3–5 times, and then subjected to pregermination in an artificial climate box at 28 °C for 24 h. After that, every 60 seeds, seeds were sown evenly in 15 plastic pots containing yellow soil and cultured under constant temperature conditions (25 ± 2 °C) and photoperiod (14/10 h light/dark cycle) for 10 days.
Then, healthy seedlings of similar size were transferred to a non-woven fiber cloth container (diameter 8 cm, height 12 cm) containing quartz sand (0.2–1.0 mm) and cultured for 60 days (1 seedling per container, 750 seedlings in total). Before being used, quartz sand shall be soaked with 3% dilute hydrochloric acid for one week and then washed with deionized water until there is no obvious chloride ion reaction in the water. When the seedlings were cultured for 30 days and 45 days, each seedling was irrigated with 10 mL of 1/4 Hoagland complete nutrient solution twice. After 60 days of culturing, we washed away the quartz sand from the seedling roots with water, then healthy seedlings of similar size were selected for treatment.

2.2. Experimental Treatments

We selected seedlings with consistent growth status for Al treatment. First, seedlings were cultured in 1/4 Hoagland complete nutrient solution 7 days, and then the seedlings were transferred into aluminum-containing nutrient solutions, with 8 L nutrient solution for every box and 30 seedlings per box. Five AlCl3 concentration gradients were set, which were 0 mmol/L, 0.1 mmol/L, 0.3 mmol/L, 0.6 mmol/L and 1.2 mmol/L, which are referred to as the Al0, Al1, Al3, Al6 and Al12 treatments. Then, we used NaOH and HCl to adjust the pH of solution to 4.00 ± 0.05. The nutrient solution was changed every 7 days. Oxygen pump was added to each culture box, and oxygen was applied twice a day for 30 min each time. When replacing the nutrient solution, wipe the incubator with 0.05% thymol solution to inhibit microbial activity.

2.3. Measurement of Plant Growth and Biochemical Assays

After treatment with different concentrations of Al for 60 days, the height, root length and ground diameter of each P. massoniana seedling were measured by vernier caliper and steel ruler. Twelve seedlings were pooled as one treatment.
Leaves of P. massoniana seedlings were used to determine biochemical assays. Each sample to be tested was placed into a mortar and ground into powder with liquid nitrogen. Then, 0.1 g was weighed. The contents and activities of malondialdehyde (MDA), superoxide dismutase (SOD, EC 1.15.1.1), peroxidase (POD, EC 1.11.1.7), proline (PRO), soluble sugars (SS), and soluble proteins (SP) were determined using the thiobarbituric acid colorimetry method, nitroblue tetrazolium (NBT) method, guaiacol method, ninhydrin colorimetric method, anthrone colorimetric method and bicinchoninic acid (BCA) method, respectively. In these tests, reagent kits were from Suzhou Keming Biotechnology Co., Ltd. (Suzhou, China). The optical densities (ODs) of the samples were measured using a SuPerMax 3100 (Shanghai Shanpu Biotechnology Co., Ltd., Shanghai, China) microplate reader, with three repeats per sample.

2.4. RNA Extraction and Transcriptome Sequencing

After 60 days, the roots of P. massoniana seedling treated with 0 mmol/L, 0.1 mmol/L, 0.3 mmol/L, 0.6 mmol/L, and 1.2 mmol/L AlCl3 were harvested, respectively, for RNA extraction. Each sample was frozen in liquid nitrogen with three biological replicates. Total RNA was extracted, and an RNA-seq library was constructed. The RNA extraction kit was from Tiangen Biotech Co., Ltd. (Beijing, China), and the reverse transcription kit was from Bao Biological Engineering Co., Ltd. (Dalian, China); the specific methods are in the instructions provided with the kits. Sequencing libraries were generated using NEBNext® UltraTM RNALibrary Prep Kit for Illumina® (NEB, Ipswich, MA, USA), and transcriptome sequencing was performed using TruSeq PE Cluster Kit v3-cBot-HS (Illumina, San Diego, CA, USA) according to the manufacturer’s instructions. The cDNA libraries were sequenced on the Illumina NovaSeq 6000 platform, and 150 bp paired-end reads were generated. To obtain high-quality read data for sequence analysis, the raw reads containing adapter sequences and low-quality sequences were removed. After that, the clean reads were assembled into unigenes as the reference sequences using the Trinity (v2.4.0) software package and were mapped back to reference sequences using the Bowtie2 (v2.2.5) package.
Gene expression levels were estimated by RSEM (v1.2.8) [31] and then the value of fragments per kilobase of transcript per million fragments mapped (FPKM) of each gene was calculated based on the gene length. Differentially expressed genes (DEGs) analysis between two samples was performed using the DESeq2 (v1.22.2) software [32]. The |log2(fold change)| ≥ 1 and FDR < 0.05 in each pairwise comparison were used as the threshold for significant difference expression. To infer the putative functions of DEGs, the Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed using the Diamond (v2.0.9) software package [33], and a corrected p-value below 0.05 was considered as significantly enriched by DEGs.
Weighted correlation network analysis (WGCNA) was performed using the R package WGCNA (v1.70.3) [34]. We used Visio software for drawing metabolic pathway maps and we used OmicShare Tools, an online platform for data analysis (https://www.omicshare.com/tools (accessed on 10 May 2023)), for mapping gene cluster heat maps.

2.5. Root Exudate Sample and Identification

After aluminum stress for 60 days, the seedlings with good growth status were taken out, and the roots were repeatedly washed with deionized water 2–3 times to fully remove the residual components of the nutrient solution. Then, the cleaned seedlings (three seedlings in a group) were cultured with 45 mL deionized water under light, and the roots were shaded to simulate the root growth environment. After 24 h of cultivation, the root exudates of P. massoniana seedling treated with 0 mmol/L, 0.1 mmol/L, 0.3 mmol/L, 0.6 mmol/L and 1.2 mmol/L AlCl3 were harvested.
The liquid sample was vortexed and mixed, 10 mL of concentrated dry was drawn, and 1 mL of ultrapure water was added to reconstitute. Add 0.02 mL of internal standard (10 μg/mL), blow dry with nitrogen blower, and place it in a lyophilizer for lyophilization. Add 0.1 mL of methoxyamine salt pyridine (0.015 g/mL), and after oximation at 37 °C for 2 h in an oven, add 0.1 mL of BSTFA (with 1% TMCS), and then react in an oven at 37 °C for 30 min to obtain a derivatized solution. Add n-hexane to dilute to 1 mL, filter the organic phase with a 0.22 μm syringe filter, and store at −20 °C until GC-MS detection. Agilent 8890 gas chromatograph coupled to a 5977B mass spectrometer with a DB-5MS column (30 m length × 0.25 mm i.d. × 0.25 μm film thickness, J&W Scientific, Folsom, CA, USA) was employed for gas chromatograph-mass spectrometer (GC-MS) analysis of the extracting solution.
For two-group analysis, differential accumulated root exudates were determined by VIP ≥ 1 and |log2 (fold change)| ≥ 1. Identified root exudates were annotated using KEGG Compound database (http://www.kegg.jp/kegg/compound/ (accessed on 10 May 2023)), and annotated metabolites were then mapped to KEGG Pathway database (http://www.kegg.jp/kegg/pathway.html (accessed on 10 May 2023)). Pathways with significantly regulated metabolites mapped to were then fed into metabolite sets enrichment analysis (MSEA), and their significance was determined using the hypergeometric test p-values.

3. Results

3.1. Changes in Growth and Physiological under Aluminum Stress

In this study, P. massoniana seedlings were used as materials, and five concentrations (0, 0.1, 0.3, 0.6 and 1.2 mmol/L) of AlCl3 were used for liquid culture to determine the effects of different concentrations of Al on the growth of P. massoniana seedlings (Figure 1). We found that aluminum stress had no significant effect on the ground diameter growth of P. massoniana. The seedling height and root length increased first and then decreased with the increase in aluminum concentration. Compared with Al0 group, the seedling height and root length of the Al1 group increased the most (24.05%, 18.08%). Compared with the Al0 group, the seedling height and root length of the Al12 group decreased by 6.74% and 43.70%. It can be seen that a certain concentration of aluminum stress can promote the growth of P. massoniana and produce a toxic excitation effect, while a high concentration of aluminum may inhibit growth.
We measured the physiological changes in P. massoniana leaves under different concentrations of aluminum stress (Figure 2). MDA content reflects the degree of membrane lipid peroxidation. Aluminum treatment significantly increased MDA content, reaching a peak in the Al12 group. SS, SP and PRO are three important osmotic regulators. SP content reached the peak in the Al6 group, which increased by 54.47% compared with the Al0 group. The trend of PRO and SS was consistent with that of MDA, which increased with the increase in aluminum concentration. SOD and POD can alleviate the damage caused by reactive oxygen species and free radicals. Compared with the Al0 group, there was no significant difference in SOD content in the Al1 group (p < 0.05), and then it increased. The change in trend of POD was consistent with MDA, PRO and SS, but the Al3, Al6 and Al12 groups were significantly higher than Al0 and Al1 groups (p < 0.05).

3.2. Transcriptomic Analysis under Aluminum Stress

RNA sequencing generated 43,893,636 to 51,565,404 reads from 15 different root samples at different aluminum stress concentrations (Al0, Al1, Al3, Al6 and Al12). After trimming the adapter and low-quality reads, the counts of clean reads range from 41,847,540 to 50,203,796. The percentages of Q30 were greater than 90%, and the GC content in high quality reads was more than 45% (Table S1). This result indicates that the sequencing data were of high quality.

3.3. Identification of DEGs under Aluminum Stress

These individual genes were successfully annotated to a total of 35,074 DEGs in seven public databases, as shown in a diagram (Figure 3). We compared the upregulated and downregulated DEGs in the Al0 treatment group with other aluminum treatments group (Figure 3A). The results revealed 19,222 (6567 upregulated and 12,655 downregulated), 24,163 (5841 upregulated and 18,322 downregulated), 23,268 (5091 upregulated and 18,177 downregulated) and 26,028 (7491 upregulated and 18,537 downregulated) DEGs in the Al0_vs_Al1, Al0_vs_Al3, Al0_vs_Al6 and Al0_vs_Al12 comparison groups. The largest number of DEGs was detected in the Al0_vs_Al12 comparison. Among all these comparisons above, the total number of downregulated DEGs was higher than upregulated DEGs. Subsequently, 13,722 DEGs were identified in P. massoniana treated with all the different aluminum concentrations tested. Meanwhile, 2507, 1056, 1636 and 3295 DEGs were specifically induced in the four comparisons groups (Figure 3B). The same results are also shown in a heatmap (Figure 3C).

3.4. Weighted Gene Co-Expression Network Analysis (WGCNA) under Aluminum Stress

We used WGCNA to further explore the relationship between key genes and aluminum stress of P. massoniana (genes filtered by the varFilter function). In WGCNA, highly correlated gene clusters are defined as modules, and genes in the same cluster are highly correlated. WGCNA revealed 23 different modules, which are marked with different colors in Figure 4A. Figure 4B shows the correlation coefficients and significance between characteristic genes of the 23 different modules and different aluminum concentrations. Notably, 2 out of 23 co-expression modules were highly expressed under aluminum stress (Figure 4C,D). The red module identified 1597 genes and was highly correlated with the Al1 sample (r = 0.98, p = 0.003), and the expression levels of the genes belonging to the module were considered low-concentration aluminum-responsive genes. The brown module identified 3745 genes and was highly correlated with the Al12 sample (r = 0.89, p = 0.04). The expression levels of the genes belonging to the module were considered high-concentration aluminum-responsive genes. According to the connectivity of genes in the module, the top 5 genes in the module were selected as hub genes (Table 1). The ubiquitin carboxyl-terminal hydrolase L3 (UCHL3), T-complex protein 1 subunit alpha (TCP1), coatomer subunit beta (SEC27), glutamyl-tRNA synthetase (GluRS) and actin (ACTF) were identified as hub genes in the red module (Table 1). Reversibly glycosylated polypeptide (RGP), mitochondrial phosphate transporter (MPT), 60S ribosomal protein L24 (RPL24), 60S ribosomal protein L7a-2 (RPL7A) and glucan 1,3-beta-glucosidase (EC3.2.1.58) were identified as hub genes in the brown module.

3.5. Functional Annotations of Aluminum-Responsive Genes

To further assess the biological functions of aluminum-responsive genes, we performed the GO and KEGG enrichment analyses. The GO term enrichment analysis of the low-concentration aluminum-responsive genes was mainly enriched in biological processes, such as neutral amino acid transport, monoterpene metabolic process and monoterpene biosynthetic process; cellular components, such as lytic vacuole, lysosome and photosystem; molecular function, such as catalase activity, terpene synthase activity and carbon-oxygen lyase activity acting on phosphates (Table S2A). However, the GO term enrichment analysis of the high-concentration aluminum-responsive genes were mainly enriched in biological processes, such as salicylic acid metabolic process, phenol-containing compound metabolic process and phenol-containing compound biosynthetic process; cellular components, such as Golgi medial cisterna, Golgi cisterna and Golgi stack; and molecular function, such as cadmium ion transmembrane transporter activity, calcium ion transmembrane transporter activity and lipase activity (Table S2B).
The KEGG pathway enrichment analysis of the low-concentration aluminum-responsive genes was mainly enriched in ubiquitin-mediated proteolysis, tryptophan metabolism, phagosome and protein processing in endoplasmic reticulum (Figure 5A). However, the KEGG pathway enrichment analysis of the high-concentration aluminum-responsive genes was mainly enriched in phenylpropanoid biosynthesis, brassinosteroid biosynthesis, zeatin biosynthesis, flavonoid biosynthesis, proteasome and plant hormone signal transduction, and we found that the phenylpropanoid biosynthesis pathway significantly enriched DEGs (Figure 5B).

3.6. Root Exudates Changes after Aluminum Stress

The root exudates were identified by GC-MS, and 15 differentially accumulated root exudates were identified. Compared with the Al0 group, 15, 10, 9 and 6 differentially accumulated root exudates were identified in the Al0_vs_Al1, Al0_vs_Al3, Al0_vs_Al6 and Al0_vs_Al12 comparison groups (Table 2). The same exudates among them were trans-O-Dithiane-4,5-diol 2, oxalic acid isohexyl neopentyl ester, catechin and 3-hydroxy-Butanoic Acid. We noticed that aluminum stress significantly induced the exudation of catechin (|log2 (fold change)| > 8.5). We identified five identical KEGG pathways in four comparison groups, namely, ABC transporters, flavonoid biosynthesis, galactose metabolism, metabolic pathways and biosynthesis of secondary metabolites (Figure 6). These pathways are involved in the synthesis and release of root exudates of P. massoniana seedlings under aluminum stress.

3.7. Combined Transcriptome and Root Exudates Analysis

KEGG pathway and root exudates analysis showed that aluminum stress significantly affected phenylpropanoid biosynthesis and significantly induced the exudation of catechin. We speculate that they are important potential pathways and substances to alleviate the aluminum toxicity of P. massoniana. Therefore, a closer pathway analysis of the genes of phenylpropanoid biosynthesis and flavonoid biosynthesis was conducted (Figure 7). A total of 366 DEGs were identified in the phenylpropanoid biosynthesis pathway (Figure 7A, Table S3A). Compared with the Al0 group, we found that two identical PAL genes, two identical 4CL genes, one identical C4H gene, two identical HCT genes, four identical CCoAOMT genes, one identical F5H gene, six identical COMT genes, one identical CCR gene and thirty-nine identical POD genes were significantly upregulated in the four comparison groups (Al0_vs_Al1, Al0_vs_Al3, Al0_vs_Al6, Al0_vs_Al12). A total of 154 DEGs were identified in the flavonoid biosynthesis pathway (Figure 7B, Table S3B). Compared with the Al0 group, we found that sixteen identical CHS genes, two identical F3’5’H genes, seven identical F3’M genes, one identical F3H gene, two identical DFR genes, five identical ANS genes, one identical ANR gene and two identical LAR genes were significantly upregulated in the four comparison groups (Al0_vs_Al1, Al0_vs_Al3, Al0_vs_Al6, Al0_vs_Al12). These DEGs, continuously upregulated under different concentrations of aluminum stress, may be important regulatory genes for P. massoniana to resist aluminum stress.
In order to further explore the regulatory network of catechin under aluminum stress, we performed a Pearson correlation analysis on catechin and DEGs involved in phenylpropanoid biosynthesis and flavonoid biosynthesis. Seventeen DEGs had a high correlation with catechin (Figure 8). We found that catechin were significantly negatively correlated with six DEGs, including one 4CL gene (Cluster-134160.5_4CL), two POD genes (Cluster-139180.0_POD and Cluster-153114.2_POD), two CAD genes (Cluster-151823.12_CAD and Cluster-151823.11_CAD) and one ANR gene (Cluster-132280.15_ANR). However, there was a significant positive correlation with eleven DEGs. This included two COMT genes (Cluster-32187.21_COMT and Cluster-32187.19_COMT), five POD genes (Cluster-37316.8_POD, Cluster-56106.3_POD, Cluster-127497.0_POD, Cluster-65799.0_POD and Cluster-129453.3_POD), one CAT gene (Cluster-33484.0_CAT), one DFR gene (Cluster-20843.19_DFR) and two ANS genes (Cluster-117574.9_ANS and Cluster-117574.6_ANS). These DEGs may be important regulatory genes involved in catechin alleviating aluminum toxicity.

4. Discussion

The phenomenon characterized by a low-dose stimulation and a high-dose inhibition occurs widely in nature and is called hormesis [35]. For instance, Cd at low concentrations induced a significant increase in plant growth in Lonicera japonica [36], Dianthus carthusianorum [37] and broccoli [35]. In this study, we found that a certain concentration range of aluminum stress can promote the growth of P. massoniana and produce hormesis. However, with the deepening of stress (0.6 mmol/L–1.2 mmol/L AlCl3), growth began to be inhibited. Therefore, the tolerance of P. massoniana seedlings to aluminum stress and the mechanism of hormetic growth promotion should be given great attention.
The exposure of Al to plants stimulates excessive generation of ROS and disturbs the homeostasis between cellular redox metabolisms, which leads to an oxidative stress condition [38,39]. Plants activate their protective enzyme systems to increase the activity of antioxidant enzymes such as SOD and POD, which remove excess ROS [11,12]. The increase in ROS is also accompanied by membrane lipid peroxidation, leading to the accumulation of MDA [2,40,41]. In this study, the contents of MDA, SOD and POD increased overall when the plant suffered from aluminum stress. Although the activity of SOD decreased in the Al12 group, it was still higher than that in the control group, indicating that the cell membrane system of P. massoniana was damaged by aluminum toxicity, but POD and SOD could effectively prevent cell membrane damage. Osmotic regulating substances such as SS, SP and PRO in plant cells play an important role in maintaining cell osmotic balance. They can improve the water-holding capacity of cells, maintain the stability of cell structure, and alleviate the imbalance of physiological metabolism caused by stress [42,43]. We found that the contents of PRO, SS and SP were higher than those of the control, indicating that P. massoniana seedlings resisted aluminum stress by increasing osmotic adjustment substances.
Aluminum stress caused a transcriptional defense response in P. massoniana, and it involved multigene responses. Genes involved in stress regulation are often regulated through coordinated expression. Therefore, correlation-based models are used to identify gene networks. In this study, we found that UCHL3, TCP1, SEC27, GluRS and ACTF plays an important role in the response to low-concentration aluminum stress. The hub genes identified in low-concentration aluminum stress indicated that aluminum stress may triggers root DNA damage, brassinosteroid synthesis and protein sorting. For example, UCHL3 is a conserved deubiquitinating enzyme that is reported to be involved in the key process of DNA damage repair by deubiquitinating the substrate RAD51 [44]. Similarly, TCP1 modulates brassinosteroid biosynthesis by regulating the expression of the key biosynthetic gene DWARF4 in Arabidopsis thaliana [45,46]. Meanwhile, the vesicle-related protein SEC27 relates to protein sorting [47]. GluRS is involved in a variety of life activities, including porphyrin metabolism, aminoacyl-tRNA biosynthesis and biosynthesis of cofactors (Table 1). We also found that RGP, MPT, RPL24, RPL7A and EC3.2.1.58 plays an important role in the response to high-concentration aluminum stress. The hub genes identified in high-concentration aluminum stress indicated that aluminum stress may triggers cell wall synthesis, phosphorus transport, ribosomal protein synthesis and starch and sucrose metabolism. For example, RGP is involved in sugar metabolism, such as amino sugar and nucleotide sugar metabolism and the biosynthesis of nucleotide sugars (Table 1), and also participated in the biosynthesis of plant cell wall [48], while MPT modulates plant responses to salt stress via affecting ATP and gibberellin metabolism in Arabidopsis thaliana [49]. RPL24 and RPL7A genes encode ribosomal proteins (Table 1). EC3.2.1.58 was involved in starch and sucrose metabolism (Table 1). Our results showed that a variety of complex defense responses promoted the resistance of P. massoniana seedlings under aluminum stress.
Cell wall is the key part of plant for storing heavy metal ions [50], and it is also the first barrier for plant to absorb and transport heavy metal ions [51]. It is generally believed that when Al3+ enters the root tip, due to the large number of negative charges on the pectin residues on the cell wall, the positively charged Al3+ can bind to the cell wall by ion exchange [52], thus affecting the structure and function of the cell wall. We found that aluminum stress induced the gene of phenylpropanoid biosynthesis expression significantly. As one of the main components of plant cell wall, lignin plays an important role in the immobilization of heavy metal ions [51,53]. In this study, the phenylpropanoid biosynthesis-related genes PAL, 4CL, C4H, HCT, CCoAOMT, COMT, CCR and POD were upregulated in the four comparison groups under aluminum stress, which was similar to the results of Wang et al. [20]. Previous studies have shown that overexpression of 4CL gene in arabidopsis can improve the tolerance to drought stress by improving antioxidant enzyme activity, reducing the accumulation of MDA and H2O2 and upregulating stress-related genes [54]. CCoAOMT-mediated lignin synthesis is necessary for rice to resist excessive Cu stress [53]. The silencing of HCT gene resulted in the decrease in lignin content and the change in lignin composition in the callus of Pinus radiata [55]. Antisense overexpression of the MsCOMT gene induces changes in lignin and total phenol contents in transgenic tobacco plants [56]. The transcription of lignin synthesis genes 4CL and CAD are upregulated, leading to lignin deposition, thickening of secondary cell walls, and enhancing of the salt-resistance and permeability of birch and apple [57,58]. We also found a large number of POD genes were upregulated under aluminum stress. Peroxidases are the key enzymes in lignin biosynthesis, taking part in the formation of radicals of lignin units before polymerization [50] and also participating in H2O2 scavenging and phenolic metabolism [59]. In summary, the upregulated expression of lignin synthesis genes may help P. massoniana survive under aluminum stress and increase tolerance.
Root exudates play an important role in the tolerance mechanism of plants [21], which can reduce toxicity mainly through the chelation and precipitation of low-molecular-weight compounds (amino acids, organic acids, sugars, phenolics and an array of secondary metabolites), and high-molecular-weight compounds (like mucilage and proteins) [60]. Under aluminum stress, we identified multiple types of root exudates (Table 2), such as carbohydrate (Fructose 2; sucrose; alpha. -D-Galactopyranoside, methyl; d-Mannose 1; d-Galactose 3; and D-Mannitol 2), alcoholic substances (Myo-Inositol 1; Inositol; Muco-Inositol; and D-Pinitol), acid (Cyanuric acid and 3-hydroxy-Butanoic Acid), lipid (oxalic acid isohexyl neopentyl ester) and heterocyclic compound (catechin and trans-O-Dithiane-4,5-diol 2). These substances are involved in the detoxification mechanism of P. massoniana seedlings under different concentrations of aluminum stress. The way in which plant root exudates regulate plant adaptation to abiotic stress is a complex process, involving the synthesis and accumulation of a series of osmotic regulators [61]. Sucrose involved in plant responses to abiotic stresses has been well documented [62,63]. Increased sucrose content increases maize tolerance [64]. Fructose plays a role in osmotic regulation of leaves and roots, especially under salt stress [65], enhancing plant tolerance. Al-induced secretion of organic acids from plant roots is considered to be an important means of plant tolerance to aluminum toxicity, it can chelate with Al to form a non-toxic complex to reduce the toxicity of aluminum to plants [66,67]. Studies have shown that aluminum can also form stable complexes with some phenolic compounds, although phenolic compounds are less efficient chelating agents than organic acids [68]. However, aluminum-induced phenolics (catechol, catechin and quercetin) exudation enhancement plays an important role in the detoxification of aluminum in maize root tip apoplast [24]. Interestingly, catechins are abundant components of tea [69], and tea plants are known to accumulate and tolerate high levels of aluminum [70]. Our study found that the accumulation of catechin was much higher than that of other secretory substances, and the enhancement of catechin exudation induced by aluminum stress may be an important mechanism for P. massoniana to alleviate aluminum toxicity.
The synthesis of catechin involves phenylpropanoid and flavonoid biosynthesis pathways. We found that genes relating to phenylpropanoid biosynthesis were significantly enriched under high-concentration aluminum stress, and genes relating to flavonoid biosynthesis, such as CHS, F3’5’H, F3’M, F3H, DFR, ANS, ANR and LAR, were also continuously expressed under different concentrations of aluminum stress. We found that sixteen CHS genes were continuously expressed under different concentrations of aluminum stress (Figure 7B). The CHS gene is the first key enzyme in the synthesis of flavonoids, providing a basic carbon skeleton for the synthesis of flavonoids [71]. CHI, F3’5’H, F3’M and F3H genes catalyzed Naringenin Chalcone to generate Dihydroflavonols (Dihydrokaempferol, Dihydroquercetin and Dihydromyricetin), and then the DFR gene catalyzed Dihydroflavonols to generate Leucocyanidin. Leucocyanidin can be directly reduced to catechin via the LAR gene, and we also found that two LAR genes were continuously expressed under different concentrations of aluminum stress. Leucocyanidin can also be continuously catalyzed by ANS and ANR genes to form Epiafzelechin, Epicatechin and Epigallocatechin. In this study, we found that the COMT, ANS, DFR, POD and CAT genes were significantly positively correlated with catechin. Studies have confirmed that downregulated MsCOMT transgenic lines had decreased the flavonol content [56]. DFR is an upstream gene that catalyzes the formation of catechin and participates in the regulation of catechin formation together with LAR. It can be seen that the synthesis of catechin involves multiple genes, in which genes such as CHS, COMT, DFR and LAR may play a key regulatory role.

5. Conclusions

In this study, we found that phenylpropanoid and flavonoid biosynthesis pathways may play a key role in the detoxification of aluminum; in particular, the enhancement of catechin exudation in roots helps P. massoniana adapt to aluminum stress. In the future, we will further verify whether adding exogenous catechin will be more conducive to discovering the key regulatory genes in P. massoniana responding to aluminum stress.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f14071410/s1, Table S1: Sequencing statistics for P. massoniana in 15 samples under aluminum stress; Table S2A,B: The top ten GO pathways of DEGs enrichment degree; Table S3A: Expression of genes associated with phenylpropanoid biosynthesis; Table S3B: Expression of genes associated with flavonoid biosynthesis.

Author Contributions

J.L. and Z.Y. designed and conducted the experiments and wrote the manuscript; J.T. and H.C. contributed to manuscript writing and editing; J.L. executed the bioinformatics tools; Q.L. and J.J. performed the physiology and biochemistry experiments and analyzed the data; and Z.Y. contributed to the experimental design and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Guangxi Key Laboratory of Superior Timber Trees Resources Cultivation [2020-A-02-03], the Guangxi Natural Science Foundation (2019GXNSFDA245033), the Special Fund for Bagui Scholars (2019A26), and the Guangxi Science and Technology and Talents Special Project (AD19254004).

Data Availability Statement

Data are contained within the article or Supplementary Materials. Data are also available from the corresponding author ([email protected]).

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Changes in growth characteristics of P. massoniana seedlings under aluminum stress. (A) Morphological. (B) Seedling height, root length and ground diameter. Different lowercase letters between treatment groups indicate significance at p < 0.05. The same is true below.
Figure 1. Changes in growth characteristics of P. massoniana seedlings under aluminum stress. (A) Morphological. (B) Seedling height, root length and ground diameter. Different lowercase letters between treatment groups indicate significance at p < 0.05. The same is true below.
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Figure 2. Changes in leaves physiological characteristics of P. massoniana seedlings under aluminum stress.
Figure 2. Changes in leaves physiological characteristics of P. massoniana seedlings under aluminum stress.
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Figure 3. Change in DEGs expression. (A) Upregulation and downregulation of DEGs. The yellow box represents upregulation, and the blue box represents downregulation. (B) Coregulation of DEGs in all comparison groups. (C) Heatmaps of DEGs compared between different groups.
Figure 3. Change in DEGs expression. (A) Upregulation and downregulation of DEGs. The yellow box represents upregulation, and the blue box represents downregulation. (B) Coregulation of DEGs in all comparison groups. (C) Heatmaps of DEGs compared between different groups.
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Figure 4. Weighted gene co−expression network analysis (WGCNA) of DEGs identified. (A) Gene cluster dendrogram tree showing 23 modules of co−expressed genes. Each leaflet in the tree corresponds to an individual gene. The color row underneath the dendrogram shows the module assignment determined by the Dynamic Tree Cut. (B) Module−trait relationships. Each row corresponds to a module, and each column corresponds to an aluminum concentration. The colors from blue to red represent r values from −1 to 1. (C) Gene expression of the red module. (D) Gene expression of the brown module.
Figure 4. Weighted gene co−expression network analysis (WGCNA) of DEGs identified. (A) Gene cluster dendrogram tree showing 23 modules of co−expressed genes. Each leaflet in the tree corresponds to an individual gene. The color row underneath the dendrogram shows the module assignment determined by the Dynamic Tree Cut. (B) Module−trait relationships. Each row corresponds to a module, and each column corresponds to an aluminum concentration. The colors from blue to red represent r values from −1 to 1. (C) Gene expression of the red module. (D) Gene expression of the brown module.
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Figure 5. Genes enriched on different KEGG pathway. (A) KEGG pathway enrichment analysis of the low-concentration aluminum-responsive genes. (B) KEGG pathway enrichment analysis of the high-concentration aluminum-responsive genes.
Figure 5. Genes enriched on different KEGG pathway. (A) KEGG pathway enrichment analysis of the low-concentration aluminum-responsive genes. (B) KEGG pathway enrichment analysis of the high-concentration aluminum-responsive genes.
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Figure 6. Root exudates enrich on different KEGG pathway. (A) Al0_vs_Al1 group. (B) Al0_vs_Al3 group. (C) Al0_vs_Al6 group. (D) Al0_vs_Al12 group.
Figure 6. Root exudates enrich on different KEGG pathway. (A) Al0_vs_Al1 group. (B) Al0_vs_Al3 group. (C) Al0_vs_Al6 group. (D) Al0_vs_Al12 group.
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Figure 7. Expression of genes associated with phenylpropanoid biosynthesis (A) and flavonoid biosynthesis (B). The colors ranging from blue to red indicates the expression of genes, and the gene expression is normalized using FPKM and represents the means of three biological replicates. While the horizontal direction shows the sample names, the vertical direction shows the names of genes. PAL, phenylalanine ammonia-lyase; 4CL, 4−coumarate−CoA ligase; C4H, trans−cinnamate 4− monooxygenase; HCT, shikimate O−hydroxycinnamoyltransferase; CCoAOMT, caffeoyl−CoA O-methyltransferase; CCR, cinnamoyl−CoA reductase; F5H, ferulate-5-hydroxylase; COMT, caffeic acid 3−O−methyltransferase; CAD, cinnamyl−alcohol dehydrogenase; CAT, catalase−peroxidase; POD, peroxidase; CHS, chalcone synthase; CHI, chalcone isomerase; F3′5′H, flavonoid 3’,5’ −hydroxylase; F3′M, flavonoid 3’−monooxygenase; F3H, naringenin 3−dioxygenase; DFR, bifunctional dihydroflavonol 4−reductase; LAR, leucoanthocyanidin reductase; ANS, anthocyanidin synthase; ANR, anthocyanidin reductase.
Figure 7. Expression of genes associated with phenylpropanoid biosynthesis (A) and flavonoid biosynthesis (B). The colors ranging from blue to red indicates the expression of genes, and the gene expression is normalized using FPKM and represents the means of three biological replicates. While the horizontal direction shows the sample names, the vertical direction shows the names of genes. PAL, phenylalanine ammonia-lyase; 4CL, 4−coumarate−CoA ligase; C4H, trans−cinnamate 4− monooxygenase; HCT, shikimate O−hydroxycinnamoyltransferase; CCoAOMT, caffeoyl−CoA O-methyltransferase; CCR, cinnamoyl−CoA reductase; F5H, ferulate-5-hydroxylase; COMT, caffeic acid 3−O−methyltransferase; CAD, cinnamyl−alcohol dehydrogenase; CAT, catalase−peroxidase; POD, peroxidase; CHS, chalcone synthase; CHI, chalcone isomerase; F3′5′H, flavonoid 3’,5’ −hydroxylase; F3′M, flavonoid 3’−monooxygenase; F3H, naringenin 3−dioxygenase; DFR, bifunctional dihydroflavonol 4−reductase; LAR, leucoanthocyanidin reductase; ANS, anthocyanidin synthase; ANR, anthocyanidin reductase.
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Figure 8. Pearson correlation between catechin and DEGs involved in phenylpropanoid biosynthesis and flavonoid biosynthesis. |r|> 0.8, p < 0.05. Orange represents catechin, and blue represents genes. Red line represents a positive relationship, and blue line represents a negative relationship.
Figure 8. Pearson correlation between catechin and DEGs involved in phenylpropanoid biosynthesis and flavonoid biosynthesis. |r|> 0.8, p < 0.05. Orange represents catechin, and blue represents genes. Red line represents a positive relationship, and blue line represents a negative relationship.
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Table
Table 1. Hub genes selected from the co-expression modules.
Table 1. Hub genes selected from the co-expression modules.
Hub Gene IDModuleGene
Symbol		DescriptionKEGG Pathway
Cluster-127161.0ME redUCHL3ubiquitin carboxyl-terminal hydrolase L3-
Cluster-85398.3ME redTCP1T-complex protein 1 subunit alpha-
Cluster-100935.0ME redSEC27coatomer subunit beta’-
Cluster-109492.0ME redGluRSglutamyl-tRNA synthetasePorphyrin metabolism; aminoacyl-tRNA biosynthesis; metabolic pathways; biosynthesis of secondary metabolites; biosynthesis of cofactors
Cluster-124373.2ME redACTFactin-
Cluster-106454.3ME brownRGPreversibly glycosylated polypeptideAmino sugar and nucleotide sugar metabolism; metabolic pathways; biosynthesis of nucleotide sugars
Cluster-119257.1ME brownMPTmitochondrial phosphate transporter-
Cluster-9345.0ME brownRPL2460S ribosomal protein L24Ribosome
Cluster-14596.0ME brownRPL7A60S ribosomal protein L7a-2Ribosome
Cluster-18143.0ME brownEC3.2.1.58glucan 1,3-beta-glucosidaseStarch and sucrose metabolism; metabolic pathways
Table
Table 2. The differentially accumulated root exudates of different groups.
Table 2. The differentially accumulated root exudates of different groups.
GroupsCompoundsLog2 (Fold Change)VIPp-Value
Al0_vs_Al13-hydroxy-Butanoic Acid1.151.517.00 × 10−6
Oxalic acid isohexyl neopentyl ester1.351.502.58 × 10−3
trans-O-Dithiane-4,5-diol 21.431.511.64 × 10−5
Inositol3.041.503.44 × 10−4
Cyanuric acid1.171.501.39 × 10−3
D-Pinitol1.181.501.12 × 10−3
Fructose 24.211.511.01 × 10−3
D-Galactose 31.531.511.55 × 10−4
d-Mannose 11.531.516.37 × 10−4
D-Mannitol 21.171.503.73 × 10−3
Myo-Inositol 13.131.511.08 × 10−3
Muco-Inositol2.271.491.04 × 10−5
.alpha.-D-Galactopyranoside, methyl1.561.512.51 × 10−5
Sucrose2.621.515.29 × 10−8
Catechin10.911.519.99 × 10−5
Al0_vs_Al33-hydroxy-Butanoic Acid1.381.241.78 × 10−5
Oxalic acid isohexyl neopentyl ester1.271.235.02 × 10−3
trans-O-Dithiane-4,5-diol 21.361.241.80 × 10−3
Inositol1.931.243.62 × 10−4
D-Galactose 3−1.201.224.40 × 10−4
d-Mannose 1−1.231.221.18 × 10−3
D-Mannitol 2−1.471.232.61 × 10−3
Myo-Inositol 11.821.241.48 × 10−3
Muco-Inositol1.081.229.04 × 10−4
Catechin11.411.241.35 × 10−4
Al0_vs_Al63-hydroxy-Butanoic Acid3.021.327.70 × 10−4
Oxalic acid isohexyl neopentyl ester1.491.303.66 × 10−3
trans-O-Dithiane-4,5-diol 21.261.312.50 × 10−3
Inositol1.961.301.51 × 10−2
D-Galactose 3−1.411.312.33 × 10−3
d-Mannose 1−1.511.311.23 × 10−3
Myo-Inositol 12.171.321.02 × 10−3
Muco-Inositol1.071.274.10 × 10−3
Catechin10.731.323.04 × 10−5
Al0_vs_Al123-hydroxy-Butanoic Acid2.521.338.14 × 10−5
Oxalic acid isohexyl neopentyl ester1.051.312.90 × 10−4
trans-O-Dithiane-4,5-diol 21.161.322.70 × 10−5
Fructose 23.631.322.74 × 10−3
Sucrose3.511.334.46 × 10−4
Catechin8.711.331.62 × 10−4
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Ling, J.; Tan, J.; Chen, H.; Yang, Z.; Luo, Q.; Jia, J. Physiology, Transcriptome and Root Exudates Analysis of Response to Aluminum Stress in Pinus massoniana. Forests 2023, 14, 1410. https://doi.org/10.3390/f14071410

AMA Style

Ling J, Tan J, Chen H, Yang Z, Luo Q, Jia J. Physiology, Transcriptome and Root Exudates Analysis of Response to Aluminum Stress in Pinus massoniana. Forests. 2023; 14(7):1410. https://doi.org/10.3390/f14071410

Chicago/Turabian Style

Ling, Jinyan, Jianhui Tan, Hu Chen, Zhangqi Yang, Qunfeng Luo, and Jie Jia. 2023. "Physiology, Transcriptome and Root Exudates Analysis of Response to Aluminum Stress in Pinus massoniana" Forests 14, no. 7: 1410. https://doi.org/10.3390/f14071410

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