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Article

Comparative Transcriptome and Metabolome Analysis of Rubber Trees (Hevea brasiliensis Muell. Arg.) Response to Aluminum Stress

by
Xiaowei Ma
1,2,†,
Linlin Cheng
1,†,
Wentao Peng
1,
Guishui Xie
1,
Zifan Liu
2,*,
Zongming Yang
1,2,
Ying Wang
3 and
Feng An
1,*
1
Hainan Danzhou Agro-Ecosystem National Observation and Research Station, Rubber Research Institute of Chinese Academy of Tropical Agricultural Sciences, Danzhou 571737, China
2
College of Tropical Crops, Hainan University, Haikou 570228, China
3
Institute of Tropical Bioscience and Biotechnology, Chinese Academy of Tropical Agricultural Sciences, Haikou 571101, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Forests 2023, 14(3), 568; https://doi.org/10.3390/f14030568
Submission received: 2 February 2023 / Revised: 6 March 2023 / Accepted: 9 March 2023 / Published: 13 March 2023
(This article belongs to the Special Issue Stress Resistance of Rubber Trees: From Genetics to Ecosystem)
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Abstract

:
Aluminum (Al) toxicity severely restricts crop growth and productivity in acidic soils. The rubber tree is one of the most economically important crops in tropical regions, which is tolerant to high concentrations of Al in sand or hydroponic culture conditions compared with other plants that have been reported. However, the mechanisms of Al tolerance in rubber trees remain unknown. In this study, we conducted a transcriptome and metabolome analysis for rubber tree sapling roots treated with 200 mM Al for 0 (CK), 2 or 5 days, respectively. Compared with the CK, a total of 9534 differentially expressed genes (DEGs) and 3821 differentially expressed metabolites (DEMs) were identified in 2 d of Al treatment. There were 10,373 DEGs and 4636 DEMs after 5 d of Al treatment, and 1626 DEGs and 1674 DEMs between 2 and 5 d of Al treatment. The DEGs mainly concentrated in transporters, transcription factors (TFs), cell wall biosynthesis and antioxidant systems, and the DEMs were mainly focused on lipids and lipid-like molecules, organic acids and derivatives, organic oxygen compounds, phenylpropanoids and polyketides. The combined transcriptome and metabolome analysis indicated DEGs and DEMs involved in ABC transporters, glutathione metabolism, flavonoid biosynthesis and phenylalanine metabolic pathways were identified to be closely associated with the Al tolerance of rubber trees. Our study elucidated the mechanism of rubber trees’ tolerance to Al at the transcriptional and metabolic levels, which provides a theoretical basis for the study of Al tolerance both for rubber trees and other woody plants.

1. Introduction

Al is the most abundant metal element and the third most abundant element in the Earth’s crust [1]. Aluminum is usually present in the form of silicates, phosphates, sulfides and oxides under normal soil conditions that are non-toxic to plants [1,2]. However, when the soil pH is below 5.5 or lower, toxic Al3+ is solubilized into the soil solution and absorbed by the plant roots, which can inhibit root growth and limit its capacity to uptake water and nutrients, thereby causing a significant reduction in crop yields [3,4]. It is reported that 50% of the potentially arable lands across the world are acidic and up to 60% of acidic soils are distributed in tropical and subtropical regions where food security is most tenuous [5]. Therefore, Al toxicity may be the primary factor limiting crop production in acidic soils [6]. To cope with Al-toxic environments, plants have evolved a range of adaptive strategies, including external exclusion and internal tolerance mechanisms. The external exclusion mechanism is primarily mediated by the release of organic acids, phenolic compounds, phosphate and other secretions from plant roots to form non-toxic Al complexes, thereby preventing the binding of Al to cellular components [6]. The internal tolerance mechanism mainly relies on chelation of Al by organic acids and phosphate anions, which are then sequestrated into vacuoles, thereby protecting Al-sensitive cytoplasmic structures and organelles from damage [7,8]. Among them, the secretion of organic acids from plant roots plays a critical role in Al detoxification [9]. Malic acid, citric acid and oxalic acid are the most common organic acids, which effectively chelate and detoxify Al in the rhizosphere [5,9,10]. A large number of plant species may secret different organic acids for Al detoxification. For instance, Arabidopsis thaliana, wheat (Triticum aestivum), rape (Brassica napus) and rye (Secale cereale) secret malate to detoxify Al [11,12,13,14], whereas maize (Zea mays), sorghum (Sorghum bicolor) and rice bean (Vigna umbellata) secrete citrate for Al detoxification [15,16,17,18]. In addition, the roots of spinach (Spinacia oleracea), buckwheat (Fagopyrum esculentum) and tomato (Solanum lycopersicum) secrete oxalate to alleviate Al toxicity [19,20,21,22]. Recently, genes responsible for Al-activated secretion of malate (ALMT, aluminum-activated malate transporter) and citrate (MATE, multidrug and toxin efflux protein) have been identified and proven to confer Al resistance in several plants [1]. Additional genes related to Al detoxification have also been identified from model plants, such as A. thaliana and rice. Using mutant screening and map-based gene cloning approaches, transcription factors STOP1 (Sensitive to Proton Rhizotoxicity1) and ART1 (Al Resistance Transcription Factor1) involved in Al resistance have been identified in Arabidopsis and rice (Oryza sativa), respectively [23,24]. Both STOP1 and ART1 are C2H2-type zinc finger transcription factors. In A. thaliana, AtALS1 (Aluminum Sensitive1), AtALS3 (Aluminum Sensitive3) and AtSTAR1 (Sensitive to Aluminum Rhizotoxicity1) genes have proven to be related to Al resistance. The expression of Al-tolerance genes, including AtALMT1, AtMATE1 and AtALS3 is regulated by STOP1 [23,25,26]. In rice, ART1 regulates the expression of 31 genes and some of them have been documented to be associated with Al resistance [24]. OsSTAR1 and OsSTAR2 together form a bacterial-type ABC (ATP-binding cassette) transporter that alleviates Al toxicity by transporting UDP-glucose to the root cell wall, thereby preventing cell wall sites from binding to Al [27]. OsNrat1 (Nramp Aluminum Transporter1) and OsALS1 operate coordinately to remove Al from the cell wall and sequester it in the root cell vacuole [28,29]. These Al-stress-responsive genes are involved in a series of physiological and metabolic processes including organic acid metabolism and exudation, reactive oxygen species (ROS) scavenging, protection against cell wall toxicity and oxidative stress, Al transportation and hormone signal transduction. In general, Al detoxification in plants is a very complicated process, which may require the participation of multiple genes. Therefore, it is necessary to further explore new Al resistance genes.
Recently, a high-throughput mRNA sequencing (RNA-seq) method has provided an opportunity for identifying new genes and estimating transcript abundance at the genome-wide scale [30,31]. RNA-seq has been applied to discover Al-stress-responsive genes in several plant species. ASR5 was determined as a key transcription factor for Al-responsive gene expression in rice by transcriptome analysis [32]. Xu et al. [33] identified 57 candidate Al tolerance genes, including 30 transporter genes and 27 transcription factors, by transcriptomic analysis of buckwheat root apex. A total of 668 differentially expressed genes were found in the leaves of maize under Al treatment, and also revealed that auxin and brassinosteroids may show positive effects of on the Al resistance of maize [34]. In alfalfa (Medicago sativa), most up-regulated genes are concentrated in the early and late stages after Al exposure [31]. In addition, a series of candidate Al-tolerant genes have also been identified in Anthoxanthum odoratum [35], Hydrangea macrophylla [36] and Medicago truncatula [37] applied to RNA-seq technology. In summary, Al detoxification in plants is a relatively complicated process that requires the participation of multiple genes; therefore, it is difficult to elucidate the mechanism of Al detoxification in plants from the transcription level alone. With the rapid development of multi-omics, the metabolome has been used to analyze the mechanism of Al resistance in plants. Under Al treatment, 45 and 83 differential metabolites were detected in the Al-tolerant Oryza sativa variety Nipponbare and the Al-sensitive variety H570, respectively. Among their up-regulated differential metabolites, phenolic acids and alkaloids were shown in Nipponbare, while amino acids and their derivatives were found in H570 [38]. The accumulation of secondary metabolites (polyphenol, proanthocyanidins and phenolamides) may enhance pH-mediated mitigation of root Al-toxicity in Citrus sinensis [39]. Metabolome analysis reveals that Al stress could induce the expression of metabolites (cinnamate and quercetin) in the phenylpropane metabolism pathway in Vitis quinquangularis [40]. However, the changes of metabolites cannot fully explain the mechanism of Al resistance in plants. Therefore, an integrated analysis of the transcriptome and metabolome may provide a clearer understanding of plant response mechanisms under Al stress.
The rubber tree (Hevea brasiliensis Muell. Arg.) is an important tropical tree species for natural rubber production and is widely cultivated in tropical and subtropical regions in the world, and its planting exceeds 95,000 hectares [41]. Nevertheless, Al toxicity has been a prominent challenge for rubber tree cultivation in these regions [42]. Our previous studies showed that rubber tree saplings could tolerate 100–200 mmol/L of Al at pH 4.2 for 2 or 5 d, which is far higher than other crops grown in sand or hydroponic culture conditions [43,44]. However, previous studies on the response of rubber trees to Al stress were mostly limited to the physiological level. Up to date, the processes of Al absorption, translocation and accumulation in rubber trees and the related molecular mechanisms are still unclear. Based on our previous studies and related reports, we hypothesized that the Al-tolerant mechanism of rubber trees might be related to the secretion of organic acids. Here, we further investigated the transcriptome and metabolome changes of rubber trees under Al stress. The objective was to identify potential Al-stress-responsive genes and their associated metabolites so as to provide some basis for understanding the mechanism of the high Al resistance of rubber trees.

2. Materials and Methods

2.1. Plant Materials and Al Treatments

The high-yielding rubber tree cultivar, “Reyan7-33-97”, which was provided by the Rubber Research Institute of the Chinese Academy of Tropical Agriculture Sciences (Danzhou, Hainan, China), was used as the experimental plant material. The two-year-old whorled-leaf rubber tree sapling tissues were cultured in Hoagland’s solution (2.8 mg/L H3BO3, 3.4 mg/L MnSO4·H2O, 0.1 mg/L CuSO4·5H2O, 0.22 mg/L ZnSO4·7H2O, 0.1 mg/L (NH4)6·7H2O, 20 mg/L Na2Fe-EDTA, 0.94 g/L Ca(NO3)2·4H2O, 0.52 g/L MgSO4·7H2O, 0.66 g/L KNO3, 0.12 g/L NH4H2PO4) in a growth chamber with a day-long photoperiod (28 °C with 16 h light and 25 °C with 8 h dark) and 65% humidity and 200 μmol m−2 s−1 of light intensity. The nutrient solution was supplemented with 0, 50, 100, and 200 mM AlCl3, respectively, and 1 mol·L−1 HCl and 25% ammonia solution (Guangzhou Chemical Reagent Factory, Guangzhou, China) were used to maintain the pH at 4.2. The nutrient solution was continuously aerated with a pump and renewed every 2 d. For transcriptome and metabolome analysis, those treatments with or without 200 mM AlCl3 on 0 (CK), 2 and 5 d were taken out separately. At least three biological replicates were performed and every root sample was obtained from four rubber tree saplings as a pool for RNA extraction and metabolic profiling.

2.2. RNA Extraction, cDNA Library Construction and Sequencing

The total RNA of all samples was extracted using the Plant RNA extraction kit (Tiangen Biochemical Technology Co., Ltd., Beijing, China) following the manufacturer’s instructions. The extracted RNA was digested with RNase-free DNase I (Tiangen Biochemical Technology Co., Ltd., Beijing, China) to remove residual genomic DNA. Subsequently, the quality of RNA was assessed by using the spectrophotometer (NanoDrop 2000C, Thermo Fisher Scientific lnc., Waltham, MA, USA). The cDNA library was conducted based on the Illumina manufacturer’s instructions [31,45] and was sequenced using the Illumina HiSeqTM 2000 high-throughput sequencing platform as operated by Guangdong Longsee Biomedical Corporation Co., Ltd. (Guangzhou, China).

2.3. Transcriptomic Data Processing and Bioinformatic Analysis

The clean reads were obtained by filtering the adapter, poly-N, and low-quality reads from the raw data using the FASTX toolkit (version 0.0.13), and then mapped to the reference genome via HISAT2 (version 2.0.4) software with default parameters [46]. The gene expression abundances were calculated based on the number of clean reads mapped to the reference sequence using the FPKM method. The differentially expressed genes (DEGs) were analyzed using the R package DESeq (version 2.0) with the thresholds of |log2 (fold change)| ≥ 1 and FDR (false discovery rate) < 0.05 [47]. Gene ontology (GO) classification was performed by the Blast2GO (version 2.5.0) program. KOBAS (version 3.0) software was used to test the statistical enrichment of differentially expressed genes in KEGG pathways [48,49].

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

The RevertAidTM First Strand cDNA Synthesis Kit (Fermentas, Vilnius, Lithuania) was used to synthesize the first-stand cDNA according to the manufacturer’s protocol. qRT-PCR was performed utilizing the CFX 96 real-time PCR system (Bio-Rad, Hercules, CA, USA) with a 20 μL reaction system containing 10 μL 2×SYBR Green qRT-PCR Mix (Takara Biomedical Technology (Beijing) Co., Ltd., Beijing, China), 1 μL cDNA template and 0.4 μL each of forward and reverse gene-specific primers (10 μM), to which ddH2O was added until a volume of 20 μL was reached. The qRT-PCR program was set as follows: 95 °C for 3 min, followed by 44 cycles of 95 °C for 10 s and 60 °C for 20 s and 30 s of extension at 72 °C. The rubber tree UBC4 (GenBank: HQ323249) gene, the most stably expressed genes in response to Al stress, was selected as the internal reference gene [50]. The amplification results were analyzed using the 2−ΔΔCT method [51]. Each qRT-PCR experiment was performed with three biological replicates and three technical replicates, and all primers used in this study are shown in Table S2.

2.5. Metabolite Extraction and Detection

The roots of each sample were weighed to 50 mg and placed in an EP tube, and then 1000 μL extract solution was added (acetonitrile:methanol:water = 2:2:1, with isotopically-labelled internal standard mixture). After 30 s of vortexing, the samples were homogenized at 35 Hz for 4 min and sonicated for 5 min in ice-water bath. The homogenization and sonication cycle was repeated 3 times. Then the samples were incubated for 1h at −40 °C and centrifuged at 15,000× g for 15 min at 4 °C. The resulting supernatant was transferred to a fresh glass vial for analysis. The quality control (QC) sample was prepared by mixing an equal aliquot of the supernatants from all of the samples [52].
LC-MS/MS analyses were performed using a Vanquish UHPLC system (Thermo Fisher Scientific, Milan, Italy) with a UPLC HSS T3 column (2.1 mm × 100 mm, 1.8 μm) coupled to a Q Exactive HFX mass spectrometer (Thermo Fisher Scientific, Milan, Italy). The mobile phase consisted of 5 mmol/L ammonium acetate and acetic acid in water (A) and acetonitrile (B). The elution gradient was set as follows: 0~0.7 min, 1% B, 0.35 mL/min; 0.7–9.5 min, 1%~99% B, 0.35 mL/min; 9.5–11.8 min, 99% B, 0.35–0.5 mL/min; 11.8–12.0 min, 99%–1% B, 0.5 mL/min; 12.0–14.6 min, 1% B, 0.5 mL/min; 14.6–14.8 min, 1% B, 0.5–0.35 mL/min. The column temperature was 35 °C, and that of the auto-sampler was 4 °C. The injected volume was 2 μL [53].
The QE HFX mass spectrometer was used for its ability to acquire MS/MS spectra on information-dependent acquisition (IDA) mode in the control of the acquisition software (Xcalibur version 4.0.27, Thermo Fisher Scientific lnc., Waltham, MA, USA). In this mode, the acquisition software continuously evaluates the full-scan MS spectrum. The ESI source conditions were set as follows: sheath gas flow rate as 30 Arb, Aux gas flow rate as 10 Arb, capillary temperature 350 °C, full MS resolution as 60,000, MS/MS resolution as 7500, collision energy as 10/30/60 in NCE mode and spray Voltage as 4.0 kV (positive) or −3.8 kV (negative), respectively.

2.6. Statistical Analysis

Three biological replicates were performed for each experiment and the values are presented as the mean ± standard deviation (SD). Statistical analyses were performed with Duncan one-way analysis of variance (ANOVA) using the SAS version 9.1 software (SAS Institute Inc., Cary, NC, USA). Principal component analysis (PCA) was carried out by SAS 9.1 software (SAS Institute Inc., Cary, NC, USA), and orthogonal partial least-squares discriminant analysis (OPLS-DA) was performed by SIMCA version 16.0.2 software (Sartorius Stedim Data Analytics AB, Umea, Sweden). Transcriptome and metabolome data were converted to log2 values to detect the associations between DEGs and DEMs by Pearson correlation tests with the screening criteria set as follows: PCC (Pearson correlation coefficient) > 0.80 and PCCP (Corresponding p-values) < 0.05 [54].

3. Results

3.1. The Effects of Al Stress on Rubber Tree Saplings

As shown in Figure 1, after two days of Al treatment, the leaves of rubber tree saplings treated with high concentrations (200 mM) turned slightly yellow (Figure 1G), but there was no significant difference compared to other Al treatments (Figure 1C,E). However, most of the rubber tree sapling leaves lost greenness, dried up, fell off, the main stem bent, and they tended to die under high Al concentrations (200 mM) after 5 days (Figure 1H). These results indicated that rubber trees are a relatively Al-tolerant plant.

3.2. Transcriptome Sequencing Data Analysis

Nine cDNA libraries, including biological replicates for the CK and Al-treated samples (2 d and 5 d), were constructed by sequencing RNA extracted from the roots of rubber trees. The number of raw reads for each library ranged from 38,268,442 to 72,889,692. Raw data were qualified to generate 37,538,264 to 71,270,614 clean reads. The percentages of Q20 and Q30 were more than 96.43% and 91.27%, respectively (Table S1). These results indicated that the sequencing data could be used for further analysis.

3.3. Identification of Differentially Expressed Genes (DEGs) in Response to Al Stress

The differences in DEGs among the three treatments are shown in Figure 2. A total of 9534 (2998 up-regulated genes and 6536 down-regulated genes) and 10,373 (4379 up-regulated genes and 5994 down-regulated genes) DEGs responded to Al stress within the Al-treated group when treated for 2 d and 5 d, respectively. In addition, 1626 DEGs with 1006 up-regulated and 620 down-regulated genes were detected in the groups treated with Al for 2 d and 5 d, respectively. Among these DEGs, 2272 (44.5%) genes were common Al-responsive up-regulated genes, whereas 4391 (54%) down-regulated genes were shared by the two treatments. These results indicated that Al stress caused significant changes in gene expression in rubber tree roots.

3.4. GO and KEGG Analysis of DEGs Response to Al Stress

According to the functional differences of DEGs, GO (Gene ontology) could be divided into three categories: molecular function, cellular composition and biological process. GO enrichment analysis showed that all DEGs were enriched in 53 terms, including 21 sub-terms in molecular functions (MF), 18 sub-terms in cell composition (CC) and 14 sub-terms in biological process (BP). Within the BP category, the top enriched GO terms were cellular process, single-organism process and metabolic process, showing that rubber trees responded to Al stress through the action of related cells and the change of metabolites. In the group of CC, DEGs accounted for the highest proportion in cells, cell parts and organelles, revealing that the reaction of rubber trees to Al stress was mainly reflected at the cell level. In terms of MF, DEGs mainly focused on catalytic activity, binding, transport activity and nucleic acid binding factor activity, indicating that transport genes and the related transcription factors played an important role in regulating the rubber trees’ responses to Al stress (Figure 3).
In order to further understand the effects of Al stress on the metabolic process of rubber trees, KEGG pathway annotation and enrichment analysis were performed on the detected DEGs. After Al treatment, most of the DEGs were significantly enriched in KEGG metabolic pathways, including metabolic pathways (ko01100) and biosynthesis of secondary metabolites (ko01110), plant signal transduction (KO04075) and biosynthesis of antibiotic (ko01130) pathways (Figure 4).

3.5. Functional Analysis of DEGs Response to Al Stress

The DEGs in rubber tree saplings after Al treatment were mainly associated with membrane transporters, transcription factors, oxidative stress, polysaccharide and cell wall metabolism, organic anion and enzyme metabolism and plant hormone signal transduction. These results indicated rubber trees triggered a series of physiological processes in response to Al stress.

3.5.1. Transporter Genes

Transporters played an important role in metal stress response, which could transport toxic metal ions away from vulnerable and sensitive parts of plants. Here, DEGs were observed for different types of transporters after Al treatment. As shown in Table S3, the transporters under different periods of Al treatment mainly include the ABC transporter family, ALMT (aluminum-activated malate transporter family), amino acid transporter family, aquaporin family, auxin transporter family, sugar transporter family, magnesium ion transporter family, ammonium transporter family, and sulfate transporter family. Among the 29 ABC transporter family genes, 4 genes (LOC110661722, LOC110633616, LOC110641050, LOC110669863) were up-regulated, while the expression of 6 ALMT genes and 13 aquaporins (NIPs) were significantly down-regulated at 2 and 5 d of Al stress. These results showed that Al stress could cause changes in the transport activity of channel proteins to cope with Al toxicity.

3.5.2. Transcription Factors

Transcription factors were protein molecules that directly or indirectly recognized and bound cis-acting elements such as promoters and their upstream regulatory sequences, and were induced to be expressed in response to biotic and abiotic stresses in plants to cope with environmental stresses. According to Table S4, transcription factors under Al stress were mainly concentrated in MYB, WRKY, NAC, bHLH, ARF and ERF families. MYB was a kind of transcription factor protein widely existing in plants. Under Al stress, most of the MYB transcription factors were up-regulated, suggesting that MYB transcription factors played an important role in coping with Al stress. WRKY and NAC transcription factors were also significantly up-regulated under Al stress, while ARF, ERF and bHLH transcription factors were mostly down-regulated.

3.5.3. Genes Related to Cell Wall and Plasma Membrane Biosynthesis

The cell wall was the first barrier for Al3+ to enter the cell and was also the main site of Al3+ toxicity. Under Al treatment, 28 DEGs related to cell wall structure were obtained from rubber tree roots. Among them, genes related to pectinesterase and xyloglucase/hydrolase were significantly enriched, and most of the genes of pectinesterase and xyloglucase/hydrolase were down-regulated. Only three pectin esterase genes (LOC110647915, LOC110667041, LOC110662175) and four xyloglucase/hydrolase genes (LOC110641159, LOC110659254, LOC110644230, LOC110646360) were up-regulated, showing that these seven genes played key roles in tolerating Al (Table S5).
As shown in Table S6, we isolated 119 (51 up-regulated and 68 down-regulated), 131 (52 up-regulated and 79 down-regulated) and 28 (11 up-regulated and 17 down-regulated) DEGs involved in lipid metabolism in the CK vs. 2 d, CK vs. 5 d and 2 d vs. 5 d groups, respectively. These DEGs were derived from alpha-linolenic acid metabolism (ko00592), linoleic acid metabolism (ko00591), steroid biosynthesis (ko00100), cutin, suberine and wax biosynthesis (ko00073), biosynthesis of unsaturated fatty acids (ko01040), arachidonic acid metabolism (ko00590), ether lipid metabolism (ko00565), fatty acid degradation (ko00071), glycerophospholipid metabolism (ko00564), fatty acid elongation (ko00062), glycerolipid metabolism (ko00561), synthesis and degradation of ketone bodies (ko00072), fatty acid biosynthesis (ko00061) and sphingolipid metabolism (ko00600) pathways. These results showed that Al stress had a significant effect on lipid metabolism.

3.5.4. Genes Associated with Oxidative Stress

Under Al treatment, a large number of ROS (reactive oxygen species) accumulated in plants and caused damage to plant cells. Therefore, a series of ROS-scavenging mechanisms have been formed in plants. As shown in Table S7, the expression of most antioxidant enzyme genes, including peroxidase and glutathione sulfur transferase genes, was significantly down-regulated at 2 and 5 d of Al treatment, indicating that Al toxicity had a significant effect on ROS production and accumulation in rubber tree roots.

3.6. Validation of DEGs by qRT-PCR

To confirm the reliability of RNA-seq data, we randomly selected 13 DGEs for qRT-PCR. The results showed that a positive correlation coefficient (R2 = 0.6814) between the RNA-seq and qRT-PCR data by linear regression analysis (Figure 5). Therefore, our transcriptome data were reliable.

3.7. Metabolite Profiles of Rubber Tree Roots in Response to Al Stress

To detect the changes of metabolites in rubber tree sapling roots under Al stress, the UHPLC-QE-MS approach was used in this study. In the positive ion detection mode, there were 86 named differentially expressed metabolites (DEMs) between CK and 2 d treatment, including 32 up-regulated metabolites and 54 down-regulated metabolites. There were 110 DEMs between CK and 5 d treatment, of which 34 were up-regulated and 76 were down-regulated. In addition, a total of 53 DEMs were in the 2 d vs. 5 d group, with 14 up-regulated metabolites and 39 down-regulated metabolites (Figure 6A). Among these DEMs, 18 (37.5%) common up-regulated metabolites were shown in CK vs. 2 d and CK vs. 5 d groups, whereas 42 (47.7%) common down-regulated metabolites were shared by the two groups (Figure 6C,E). Most of the DEMs were mainly concentrated in lipids and lipid-like molecules, organic acids and derivatives, organic oxygen compounds, phenylpropanoids and polyketides, organic oxygen compounds and organoheterocyclic compounds (Figure 7A, Table S8).
In the negative ion detection mode, there were 62 named DEMs in the CK vs. 2 d group, of which 31 were up-regulated and 31 were down-regulated. A total of 62 DEMs were in the CK vs. 5 d group, including 29 up-regulated and 33 down-regulated. Additionally, 21 DEMs were detected in the 2 d vs. 5 d group, of which 8 were up-regulated and 13 were down-regulated (Figure 6B). For all DEMs, 22 (57.9%) common up-regulated DEMs were found in the CK vs. 2 d and CK vs. 5 d groups, while 26 (68.4%) down-regulated DEMs were identified in the two comparative groups (Figure 6D,F). As shown in Figure 7B and Table S9, the main DEMs included lipids and lipid-like molecules, organic acids and derivatives, phenylpropanoids and polyketides, organic oxygen compounds, and nucleosides, nucleotides and analogues.

3.8. Combined Analysis of Transcriptome and Metabolome Data

As shown in Figure 8, the DEMs screened by metabolome and the DEGs screened by transcriptome of the same pathway were mapped to the KEGG pathway to better demonstrate the relationship between DEMs and DEGs in rubber tree roots under Al stress. There were 32, 24 and 7 common enriched KEGG pathways for DEGs and DAMs in CK vs. 2 d, CK vs. 5 d and 2 d vs. 5 d, respectively. Among them, metabolic pathways (ko01100) and biosynthesis of secondary metabolites (ko01110) enriched the most DEGs and DEMs (Table S10).
To reveal the association of DEGs and DEMs in rubber trees under Al stress, we selected five KEGG pathways for DEGs and DEMs associated with ABC transporters (ko02010), flavonoid biosynthesis (ko00941), biosynthesis of unsaturated fatty acids (ko01040), nitrogen metabolism (ko00910) and glutathione metabolism (ko00480) for further analysis (Figure 9 and Table S12). In the ABC transporter pathway, 3 DEMs (l-glutamic acid, l-glutamine and guanosine) were significantly correlated with 11, 7 and 9 DEGs, respectively. Among them, most of the DEGs were positively correlated with l-glutamic acid and l-glutamine, while most of the DEGs were negatively associated with guanosine (Figure 9A). There were significant correlations between 2 DEMs (naringenin and desmethylxanthohumol) and 10 DEGs in the flavonoid biosynthesis pathway, and only 4 DEGs (LOC110649288, LOC110666021, LOC110665143 and LOC110660132) were positively correlated with 2 DEMs, while the other DEGs and DEMs were negatively correlated (Figure 9B). In the biosynthesis of unsaturated fatty acids pathway, linoleic acid, gamma linolenic acid, docosahexaenoic acid and docosapentaenoic acid (22n−3) were strongly associated with 7, 8, 4 and 8 DEGs, respectively. Additionally, most of the DEGs were negatively correlated with four DEMs (Figure 9C). As shown in Figure 9D, 2 DEMs (l-glutamic acid, d-glutamine) and 13 and 11 DEGs, respectively in the nitrogen metabolism pathway were significantly correlated, with most of the DEGs positively related to 2 DEMs. In the glutathione metabolism pathway, 26 DEGs were significantly related to 3 DEMs, and mainly showed negatively correlations (Figure 9E).

4. Discussion

Al toxicity is one of the most limiting factors for plant growth in acidic soils [5]. In order to adapt to acidic soils, plants have evolved a series of regulatory mechanisms in response to Al toxicity, including changes in gene expression and metabolites [10,30,31,40,55]. In this study, transcriptome and metabolome sequencing technology were used to clarify the molecular mechanisms of rubber trees’ responses to Al toxicity. Based on the results of this study, we found that rubber trees’ tolerance to Al is a complex process, which is not only related to the secretion of organic acid ions, but also involved with the synthesis of secondary metabolites, transporters, cell wall modification and transcription factors.

4.1. The Role of Organic Acids in the Al Tolerance of Rubber Trees

The secretion of chelating molecules and mucilage secretion is an important mechanism of Al tolerance in plants. Organic acids, especially malic acid, citric acid and oxalic acid, play an important role in Al detoxification in plants [44,56,57]. Our study found that the expression levels of a series of organic acids transporter genes were significantly changed after Al treatment. For example, most of the malate transporter genes (ALMT) were significantly expressed after Al treatment, indicating that Al could induce transporter genes expression in response to Al toxicity. ALMT has been proven to improve the capacity of Al tolerance by regulating malate transport in many plants [58], indicating that ALMT also plays a role in Al detoxification of rubber trees. Therefore, it is necessary to identify the ALMT gene family in rubber trees. In our previous study, 17 HbALMTs were identified from the rubber tree genome, and 4 of them were possibly candidate Al-tolerant genes [3]. These results were further verified by metabolome analysis. As shown in Figure 7A,B, the proportion of organic acids and their derivatives in DEMs was second only to lipids and lipid-like molecules, which were 11.81% (positive ion mode) and 18.75% (negative ion mode), respectively. Compared with the CK, malic acid decreased by 4.57 and 5.40 times, respectively, and citric acid decreased by 4.23 and 5.84 times, respectively at 2 and 5 d after Al treatment (Table S9). These results showed that rubber tree roots respond to Al toxicity by regulating the content of malic acid and citric acid. In addition, our previous study indicated that the regulation of oxalic acid efflux by rubber tree roots is also an important mechanism of Al resistance in rubber trees [44]. However, oxalic acid was not included in the metabolite database due to its complicated determination process, so, temporarily, it was not quantitatively analyzed. Next, it is necessary to further excavate and verify the organic acid transporters in rubber trees.

4.2. Accumulation of Secondary Metabolites Enhanced the Capacity of Al Tolerance in Rubber Trees

Secondary metabolites (SM) play a crucial role in response to abiotic and biotic stress in plants. Chen et al. [59] indicated that Al stress caused changes in the biosynthesis of secondary metabolites. In this study, genes involved in secondary metabolism, including phenylpropanoid biosynthesis (ko00940), flavonoid biosynthesis (ko00941), glucosinolate biosynthesis (ko00966), monobactam biosynthesis (ko00261), tropane, piperidine and pyridine alkaloid biosynthesis (ko00960), isoquinoline alkaloid biosynthesis (ko00950), betalain biosynthesis (ko00965), stilbenoid, diarylheptanoid and gingerol biosynthesis (ko00945) and caffeine metabolism (ko00232) were found in rubber tree roots after Al treatment (Table S11). The biosynthetic pathway of phenylpropanoids is the key pathway of SM biosynthesis in plants response to abiotic stress, and the synthesis of polyphenolic compounds and lignin mainly occurs through the phenylpropanoid pathway [60]. Most of the 125 DEGs in the phenylpropanoid pathway were down-regulated, whereas some of the DEGs (LOC110637334, LOC110648919, LOC110651960, LOC110655708, LOC110670891, LOC110671352, LOC110673346, LOC110673948 and LOC110673949) involved in ROS scavenging were up-regulated. Chen et al. [59] found that lettuce protects roots from oxidative damage by up-regulating the metabolism of phenolic compounds. Thus, our result suggested that rubber trees may tolerate Al by scavenging ROS through secondary metabolic biosynthesis. In addition, phenolic compounds and Al3+ chelate to form non-toxic compounds to reduce the toxicity of Al to plant cells [39]. The role of flavonoid metabolic pathways in plant response to biotic and abiotic stresses has been extensively studied [61]. MsMYB741 confers Al-tolerance to alfalfa by regulating flavonoid biosynthesis [62]. In the flavonoid biosynthesis pathway, five DEGs (LOC110649288, LOC110654380, LOC110660132, LOC110665143 and LOC110666021) and two DEMs were up-regulated after Al treatment, indicating that flavonoids play a key role in tolerating Al in rubber trees. These results showed that the accumulation of SMs (phenylpropanoids, flavonoid, phenolamides, polyphenol, total phenolics and lignin) might contribute to the Al tolerance of rubber trees.

4.3. Transporter Genes Increase the Adaptation of Rubber Trees to Al Toxicity

The functional repertoire of ABC transporters ranges from transport of phytohormones and secondary metabolites to detoxification in plants, and this transporter family is also reported to be involved in Al toxicity modulation [63]. In this study, four ABC transporter up-regulated genes (LOC110661722, LOC110633616, LOC110641050, LOC110669863) were detected after Al treatment, but relevant genes involving more detailed mechanisms in rubber trees need to be researched in subsequent studies.
In addition, many genes related to transport were differentially expressed under Al stress, such as the sulfate efflux gene, potassium ion transport gene, nitrate transport gene and aquaporin gene. Takashi et al. [64] identified two aquaporin family genes, i.e., VALT and PALT1, in hydrangea, and proved that they could transport Al3+. Our study found that there was a difference in the expression of aquaporins, PIP and NIP, under Al stress, and the expression of aquaporins was closely related to the water conductivity of roots. Our previous study also showed that the water conductivity of roots decreased significantly under aluminum stress [43], which indicated that Al stress was associated with the function of aquaporins (Table S3).

4.4. The Cell Wall and Plasma Membrane Played a Crucial Role in Al the Tolerance of Rubber Trees

The root cell wall is the main binding site of Al and the target of Al toxicity and exclusion in plants [65,66,67]. In our study, we found some DEGs related to cell wall biosynthesis. Pectin acetylesterase (PAE) and pectinesterase (PE) can catalyze the deesterification and deacetylation of pectin and alleviate cell wall hardening under Al stress [30]. The PAE gene was tolerant to Al, as demonstrated in Medicago truncatula by RNAi-induced silencing technology [68]. In rubber trees, 8 of the 10 genes associated with pectin acetylesterase and pectinesterase were down-regulated under Al stress, while only 2 genes (LOC110662175 and LOC110647915) were up-regulated, indicating that the up-regulated expression of these 2 genes may contribute to the improvement of Al tolerance. The xyloglucosylase/hydrolase (XTHs) protein family can catalyze the primary cell wall and keep the cell wall relaxed [69]. In this study, four XTH protein family genes (LOC110646360, LOC110644230, LOC110659254 and LOC110641159) were found to be up-regulated after Al treatment, suggesting that these four genes may play an important role in maintaining cell wall morphology (Table S5).
The alteration of the lipid composition of the plasma membrane has an important role in Al tolerance in plants [39,70]. Al tolerance increases in response to a decrease in the proportion of phospholipids of the root cell PM in rice and timber species [71]. In this study, we identified 165 DEGs and 110 DEMs involved in lipid metabolism (Tables S6, S8 and S9), and most of the DEGs and DEMs were down-regulated after Al treatment, indicating that Al stress strongly affects the lipid metabolism pathway. In previous studies, researchers have shown that a reduction in the proportion of phospholipids in root cells enhances tolerance to Al in rice [72]. Both Melastoma malabathricum L. and Melaleuca cajuputi Powell are dominant tree species growing in tropical acidic soils, and are more tolerant to Al than rice. It was reported that the proportion of phospholipids in the plasma membrane of the root cells of these two tree species was lower [71]. Here, we found that most of the genes related to synthesizing phospholipids (LOC110666821, LOC110671008, LOC110671009, LOC110634729, LOC110659095, LOC110642225, LOC110666138, LOC110638644, LOC110635770 and LOC110647788) were down-regulated after Al stress, resulting in a decrease in the proportion of phospholipids in the plasma membrane, which may be one of the Al tolerance mechanisms of rubber trees. In the metabolism analysis, LysoPC (P-16:0), LysoPC (18:1(11Z)), LysoPC (18:2(9Z,12Z)) and LysoPC (18:3(6Z,9Z,12Z)) were significantly accumulated after Al treatment. These results suggest that lipid metabolism may have crucial roles in Al detoxification.

4.5. The Roles of Transcription Factor in Response to Al Stress

Transcription factors also play an important role in regulating Al resistance in plants via binding to cis-acting elements of target genes. In this study, most of the transcription factors, including MYB, WRKY, bHLH, AP2/ERF and NAC families, were differentially expressed under Al stress, which was consistent with the transcriptome sequencing results of other plants [30,31,34,36,55,73]. For example, 10 WRKY transcription factors were detected in rubber tree roots under Al treatment, including seven up-regulated and three down-regulated genes. Among them, the up-regulated expression of LOC110671897 was increased by 4.92 and 4.73 times after 2 and 5 days of Al stress, respectively. WRKY46 has been shown to negatively regulate the expression of ALMT1 and enhance Al resistance in plants [74]. Therefore, the role of WRKY in Al tolerance of rubber trees needs to be further explored. The MYB is one of the largest families of transcription factors in plant response to diverse abiotic stresses, such as drought, salt, temperature and metal stresses [75]. MsMYB741 improved resistance to Al stress by positively regulating flavonoid biosynthesis in alfalfa [62]. Transcription factor ART1 and putrescine co-regulate OsMYB30-dependent cell wall modification to enhance Al resistance in rice [76]. Many MYB transcription factors were also identified in this study, and the detailed Al resistance mechanism needs to be further analyzed. The function of STOP1 (SENSITIVE TO PROTON RHIZOTOXICITY 1-like) in tolerating Al was elucidated [23,26], but the differential expression of homologous STOP1 in rubber trees was not found in this study. The homology of STOP1 in rubber trees may be identified in an updated rubber tree genome. In addition, numerous other types of transcription factors, such as bHLH, MYC, NAC, MADS and b-ZIP families (Table S4), were also detected as DEGs after Al stress, suggesting that these TFs are also involved in Al detoxification.

5. Conclusions

In this study, transcriptome and metabolite sequencing technologies were used to analyze the DEGs and DEMs of rubber trees in different periods of Al stress. A total of 9534 DEGs and 3821 DEMs were identified over 2 d of Al treatment compared with the CK. There were 10,373 DEGs and 4636 DEMs after 5 d of Al treatment, and 1626 DEGs and 1674 DEMs between 2 and 5 d of Al treatment. The DEGs mainly concentrated in transporters, transcription factors, cell wall biosynthesis and antioxidant systems, while the DEMs were mainly focused on lipids, organic acids and derivatives. In addition, some specific genes and metabolites were found in rubber trees after Al treatment. The combined data indicate that lipids, organic acids and transcription factors may be involved in the complex regulation of rubber tree responses to Al stress. Our study provides a series of Al-tolerant candidate genes in rubber trees for further investigation into molecular mechanisms and offers a reference for the study of other woody plants.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f14030568/s1, Table S1: Summary of the sequence data analysis; Table S2: Primers used in qRT-PCR analysis; Table S3: DEGs related to transporter in Al-treated rubber tree sapling roots; Table S4: Transcription factors in Al-treated rubber tree sapling roots; Table S5: DEGs related to cell wall biosynthesis in Al-treated rubber tree sapling roots; Table S6: DEGs related to lipid metabolism in Al-treated rubber tree sapling roots; Table S7: DEGs related to antioxidant system in Al-treated rubber tree sapling roots; Table S8: DEMs of rubber tree sapling roots under Al stress (positive ion mode); Table S9: DEMs of rubber tree sapling roots under Al stress (negative ion model). Table S10: KEGG pathway enrichment of DEGs and DEMs; Table S11: DEGs and DAMs related to biosynthesis of secondary metabolites; Table S12: Combined analysis of DEGs and DEMs related to ABC transporters (ko02010), nitrogen metabolism (ko00910), glutathione metabolism (ko00480), flavonoid biosynthesis (ko00941) and Biosynthesis of unsaturated fatty acids(ko01040).

Author Contributions

X.M. and F.A. conceived and designed the experiments; X.M., F.A., W.P., Z.Y., L.C., Y.W. and G.X. performed the experiments; X.M., F.A., G.X., L.C. and Z.L. analyzed the data; X.M. and F.A. wrote the paper. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Hainan Provincial Natural Science Foundation of China (2019RC326, 2019RC304), National Natural Science Foundation of China: 31670633, Earmarked Fund for China Agriculture Research System: CARS-33-Natural Rubber.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data are available on reasonable request to the corresponding authors.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Phenotypic profiles of rubber tree saplings treated with Al for 2 days ((A) (CK), (C) (50 mM Al), (E) (100 mM Al), (G) (200 mM Al)) and 5 days ((B) (CK), (D) (50 mM Al), (F) (100 mM Al), (H) (200 mM Al)).
Figure 1. Phenotypic profiles of rubber tree saplings treated with Al for 2 days ((A) (CK), (C) (50 mM Al), (E) (100 mM Al), (G) (200 mM Al)) and 5 days ((B) (CK), (D) (50 mM Al), (F) (100 mM Al), (H) (200 mM Al)).
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Figure 2. Statistical analysis of the identified differentially expressed genes (DEGs) in rubber trees under Al treatment. (A) The number of DEGs that responded to Al stress. Venn diagram of Al-responsive up-regulated genes (B) and down-regulated genes (C).
Figure 2. Statistical analysis of the identified differentially expressed genes (DEGs) in rubber trees under Al treatment. (A) The number of DEGs that responded to Al stress. Venn diagram of Al-responsive up-regulated genes (B) and down-regulated genes (C).
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Figure 3. GO enrichment analysis of the DEGs identified in (A) CK vs. 2 d, (B) CK vs. 5 d, and (C) 2 d vs. 5 d.
Figure 3. GO enrichment analysis of the DEGs identified in (A) CK vs. 2 d, (B) CK vs. 5 d, and (C) 2 d vs. 5 d.
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Figure 4. KEGG pathway analysis of the DEGs identified in (A) CK vs. 2 d, (B) CK vs. 5 d and (C) 2 d vs. 5 d. The vertical axis of the bubble chart represents the KEGG pathway name, and the horizontal axis is the enrichment factor. The bubble color represents q-value; the smaller the q-value is, the more reliable the enrichment significance is. The size of bubble indicates the number of DEGs, and the greater the bubble size, the more the DEGs involved.
Figure 4. KEGG pathway analysis of the DEGs identified in (A) CK vs. 2 d, (B) CK vs. 5 d and (C) 2 d vs. 5 d. The vertical axis of the bubble chart represents the KEGG pathway name, and the horizontal axis is the enrichment factor. The bubble color represents q-value; the smaller the q-value is, the more reliable the enrichment significance is. The size of bubble indicates the number of DEGs, and the greater the bubble size, the more the DEGs involved.
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Figure 5. The confirmation of transcriptome data by qRT-PCR. (A) Comparison of DEGs and qRT-PCR between CK and 2 d after Al treatment, (B) comparison of DEGs and qRT-PCR between CK and 5 d after Al treatment and (C) correlation analysis between RNA-seq (x axis) and qRT-PCR (y axis).
Figure 5. The confirmation of transcriptome data by qRT-PCR. (A) Comparison of DEGs and qRT-PCR between CK and 2 d after Al treatment, (B) comparison of DEGs and qRT-PCR between CK and 5 d after Al treatment and (C) correlation analysis between RNA-seq (x axis) and qRT-PCR (y axis).
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Figure 6. Statistical analysis of differentially expressed metabolites (DEMs) in rubber tree sapling roots treated with 200 mM Al for 0, 2 or 5 days. The number of DEMs under positive (A) and negative (B) ion mode. Venn diagram of DEMs under positive ion mode (C,E) and negative ion mode (D,F).
Figure 6. Statistical analysis of differentially expressed metabolites (DEMs) in rubber tree sapling roots treated with 200 mM Al for 0, 2 or 5 days. The number of DEMs under positive (A) and negative (B) ion mode. Venn diagram of DEMs under positive ion mode (C,E) and negative ion mode (D,F).
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Figure 7. Classification of differentially accumulated metabolites (DEMs) identified in (A) positive ion mode and (B) negative ion mode.
Figure 7. Classification of differentially accumulated metabolites (DEMs) identified in (A) positive ion mode and (B) negative ion mode.
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Figure 8. Statistical analysis of DEGs and DAMs in the same KEGG pathway in (A) CK vs. 2 d, (B) CK vs. 5 d and (C) 2 d vs. 5 d.
Figure 8. Statistical analysis of DEGs and DAMs in the same KEGG pathway in (A) CK vs. 2 d, (B) CK vs. 5 d and (C) 2 d vs. 5 d.
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Figure 9. Gene–metabolite Pearson correlation network between DEGs and DAMs related to (A) ABC transporters (ko02010), (B) flavonoid biosynthesis (ko00941), (C) biosynthesis of unsaturated fatty acids (ko01040), (D) nitrogen metabolism (ko00910) and (E) glutathione metabolism (ko00480).
Figure 9. Gene–metabolite Pearson correlation network between DEGs and DAMs related to (A) ABC transporters (ko02010), (B) flavonoid biosynthesis (ko00941), (C) biosynthesis of unsaturated fatty acids (ko01040), (D) nitrogen metabolism (ko00910) and (E) glutathione metabolism (ko00480).
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MDPI and ACS Style

Ma, X.; Cheng, L.; Peng, W.; Xie, G.; Liu, Z.; Yang, Z.; Wang, Y.; An, F. Comparative Transcriptome and Metabolome Analysis of Rubber Trees (Hevea brasiliensis Muell. Arg.) Response to Aluminum Stress. Forests 2023, 14, 568. https://doi.org/10.3390/f14030568

AMA Style

Ma X, Cheng L, Peng W, Xie G, Liu Z, Yang Z, Wang Y, An F. Comparative Transcriptome and Metabolome Analysis of Rubber Trees (Hevea brasiliensis Muell. Arg.) Response to Aluminum Stress. Forests. 2023; 14(3):568. https://doi.org/10.3390/f14030568

Chicago/Turabian Style

Ma, Xiaowei, Linlin Cheng, Wentao Peng, Guishui Xie, Zifan Liu, Zongming Yang, Ying Wang, and Feng An. 2023. "Comparative Transcriptome and Metabolome Analysis of Rubber Trees (Hevea brasiliensis Muell. Arg.) Response to Aluminum Stress" Forests 14, no. 3: 568. https://doi.org/10.3390/f14030568

APA Style

Ma, X., Cheng, L., Peng, W., Xie, G., Liu, Z., Yang, Z., Wang, Y., & An, F. (2023). Comparative Transcriptome and Metabolome Analysis of Rubber Trees (Hevea brasiliensis Muell. Arg.) Response to Aluminum Stress. Forests, 14(3), 568. https://doi.org/10.3390/f14030568

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