Transcriptomics and metabolomics analysis reveal the molecular responses of rice to Cd stress and/or elevated CO2 concentration

doi:10.21203/rs.3.rs-2003547/v1

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Research Article

Transcriptomics and metabolomics analysis reveal the molecular responses of rice to Cd stress and/or elevated CO₂ concentration

https://doi.org/10.21203/rs.3.rs-2003547/v1

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Purpose

To explore the regulatory mechanism of rice to Cd stress and/or elevated CO₂ concentration.

Methods

The rice seedlings (Oryza sativa L.) were exposed to two CO₂ concentrations (400 ± 20 µmol mol^− 1, AC; 800 ± 20 µmol mol^− 1, EC) and CdCl₂ concentrations (0 µmol/L, 150 µmol/L) for 10 days. Antioxidant enzymes activities, respiration rate, transcriptomics and metabolomics changes of leaves were studied.

Results

GR activity, respiration rate, many sugars, polyols, amino acids and organic acids contents increased under Cd stress. DEGs (differentially expressed genes) annotated in photosynthesis-antenna proteins were down-regulated; When CO₂ increases, some antioxidant enzymes activities and respiration rate decreased. Genes and metabolites related to photosynthesis were enhanced; Under the composite treatment, ASA-GSH cycle was regulated, some amino acids contents increased, respiration rate decreased. The DEGs mainly enriched in substances transmembrane movement and enzymes activities, etc.

Conclusion

Under Cd stress, GR played an important antioxidant role. Sugar, polyol and amino acid metabolisms were enhanced to provide energy, improve osmotic adjustments, maintain cell membrane stability, etc. Organic acids contents increased for regulating plant nutrition, TCA cycle and as the secondary metabolites. Photosynthesis was adversely affected; Under high CO₂, photosynthesis increased, the decrease of partial O₂ pressure resulted in the decrease of some antioxidant enzymes activities and respiration rate; Under the composite treatment, Cd stress played a dominant role, elevated CO₂ alleviated the Cd stress damage by regulating ASA-GSH cycle and amino acids metabolism.

Elevated CO2

Cd stress

Transcriptome

Metabolome

Antioxidant enzymes activities

Respiration rate

The elevated atmospheric CO₂ concentration and the heavy metals pollution are two environmental problems to plants at present. Since 2011 (measurements reported in AR5), CO₂ concentration has continued to increase in the atmosphere, reaching annual averages of 410 µmol/mol, in 2019, atmospheric CO₂ concentrations were higher than at any time in at least 2 million years (high confidence), and CO₂ emissions that roughly double from current levels by 2050 (IPCC 2021). Most of the heavy metal pollutions in soil are from the industrial wastewater discharge, agricultural phosphate fertilizer and sewage sludge, and have seriously affected much farmland and crops (Zou et al. 2020). Cadmium and mercury pollutions are especially severer than other elements (Hang et al. 2009; Xiao et al. 2019). The plants under Cd stress can be affected in cell damages, production of toxic metabolites, inhibition of photosynthesis and respiration (Meng et al. 2009; Liu et al. 2012; Hassan et al. 2014; Singh et al. 2016); Our study has shown that high CO₂ could improve photosynthesis, reduce stomatal conductance, regulate endogenous hormones and organic acids contents of rice seedling leaves (Qi et al. 2021), and also could alleviate the damage of Pb stress by changes in hormone ratios and enhanced photosynthesis (Li et al. 2021; Wang et al. 2021). Other researchers have also studied the alleviating effects of elevated CO₂ when plants under heavy metals stresses (Tukaj et al. 2007;Li et al. 2010).

Plant antioxidant system is a defense mechanism dealing with various abiotic and biotic stresses. Pritchard et al. (2000) showed that under high CO₂ concentration, the activities of SOD (superoxide dismutase), POD (peroxidase), CAT (catalase), GR (glutathione reductase), APX (ascorbate peroxidase) and GPX (glutathione peroxidase) of the two genotypes of soybean decreased, which may be because the partial pressure of CO₂ in chloroplast increased and the partial pressure of O₂ decreased, enhancing the assimilation capacity of CO₂ and reducing the excess excitation energy. At the same time, oxygen as electron acceptor has less chance to form the reactive oxygen species, which leads to the decrease of reactive oxygen species and antioxidant enzyme activities; Heavy metals such as cadmium can destroy the homeostasis environment of plant cells, increase the ROS (reactive oxygen species) contents in cells, and then cause the defense and antioxidant response mechanism in plant cells, including the enzymatic scavenging system of reactive oxygen species. The ascorbate-glutathione cycle (ASA-GSH) and glutathione oxide cycle are the main metabolic pathways of ROS removal in plants (Mittler, 2002), in which antioxidant enzymes such as SOD, CAT, POD, APX, DHAR (dehydroascorbate reductase), MDAR (monodehydroascorbate reductase) and GR play a major role.

The accumulation of biomass and the ability to adapt to the environment during plant growth can be seen as the ultimate expression of metabolic pathways (Meyer et al. 2007). At present, more than 200,000 kinds of metabolites have been discovered in plants. Metabolomics research can provide insight into the deep and valuable molecular stress response, which is of great significance in understanding the metabolic pathways of plants, improving the response of plants to adverse environmental stresses, and improving crop yield and quality (Saito and Matsuda 2010, Adamski and Suhre 2013). Elevated CO₂ can regulate the generation of secondary metabolites in plants, increasing the contents of phenolic compounds, which is related to strong antioxidant capacity of plants (Akula et al. 2011; Li et al. 2018); When plants are stressed by heavy metals, the metabolites and metabolic pathways in plants will change accordingly, so as to achieve the purpose of self-protection. Zeng (2021) found that most of carbohydrates and amino acids of rice grain decreased under Cd stresses, energy metabolism was affected, content of inositol increased (has inhibitory effect on cadmium toxicity), 13 (S)-HPOT (associated with α-linolenic acid metabolism and jasmonic acid metabolism), 9, 10-DHOME (associated with linolenic acid metabolism), citric acid, hexadecanoic acid and organic acids contents increased.

RNA-seq is a high-throughput sequencing technique that does not require genomic background and can be used to analyze gene transcription and expression in plants (Huang et al. 2018). The transcriptome analysis on rice seedlings under Cd stress showed that the related genes including five family of transcription factors (zinc finger, AP2-EREBP, WRKY, NAC and MYB), mainly related to primary metabolism, redox, irritant response, not folded protein binding, sulfur assimilation, ROS clearance, stress reaction, cell wall formation, and the biological processes, such as ion transport and signal transduction (Lin et al. 2013; He et al. 2015). Oono's study showed that several genes related to defense system in rice were strongly up-regulated under cadmium stress, and the expression of metal ion transporter genes may be related to Cd concentration (2016).

The plants response to the changed environment is the process interacting with multi-gene, multi-signal molecule and multi-metabolite. Combined transcriptomics and metabolomics analysis have been used to reveal gene function, which provides a powerful tool for understanding plant biological processes and molecular tolerance mechanisms (Schaefer et al. 2017, Huang et al. 2018b, Wang et al. 2019). There were many studies on single environmental factor effects on plants, the combined effect of high CO₂ and Cd stress on plant antioxidant system, transcriptome and metabolome responses needs further study. Rice is one of the most important crops, and it is also a type plant of C3 in monocotyledonous plants, so rice was used to measure antioxidant enzymes activities and screen differentially expressed genes, different metabolites and identify different metabolic pathways under Cd stress and elevated CO₂ concentration basing on combination of transcriptomics and metabolomics techniques, so as to have a deeper understanding of the molecular responses of rice to the future environmental changes.

Plant materials and treatments

The uniform and healthy rice seeds (Oryza sativa L.) were sterilized, rinsed, and germinated in the dark. The germinating seeds were transferred to beakers containing Hoagland solution. Seedlings were maintained in a carbon dioxide artificial climate box (16/8 h light/dark period, 26/22°C Day/night, 80% relative air humidity and 800 µmol m^− 2 s^− 1 photosynthetic photon flux density) until to the stage of two leaves. Total of 6 treatments in this experiment (Table 1).

Table 1

Names of treatments
Treatments	Cd²⁺(CdCl₂·2.5H₂O μmol/L)	CO₂(μmol/mol)
AC	0	400±20
Cd	150	400±20
EC	0	800±20
EC+Cd	150	800±20

After 10 days treatments, leaves of rice seedlings were measured. 5 repeats were selected randomly in each treatment for antioxidant enzymes activities measurement, 9 repeats for respiratory rates measurement, 3 repeats for transcriptomic sequencing and analysis, and 6 repeats for metabolic analysis.

Antioxidant enzymes activities and respiratory rates measurements and data analysis

The activity of SOD was determined by photoreduction of azoblue tetrazole method as described by Beyer and Fridovich (1987); Activity of POD were measured according to Chance and Maehly (1955); CAT activity was determined by UV absorption method according to Knorzer et al. (1996); Krivosheeva et al. (1996) determined the activities of APX, DHAR, MDAR and GR.

The respiration rate of rice leaves was measured by oxygen electrode (Chlorolab2 + liquid oxygen electrode, Hanshatech, UK). After the equipment was connected, the temperature of the reaction chamber was set at 20 ℃ and the atmospheric pressure was 101 kPa. The electrode was calibrated, and air-saturated water was added into the reaction cup. After the signal line was stabilized, an appropriate amount of insurance powder (Na₂S₂O₄) was added to establish the zero-oxygen line, and the correction result was saved after the zero-oxygen line stabilized. The reaction cup was rinsed several times to eliminate interference from the safety powder. Rice leaves(about 1 cm²)after 10 days treatments were cut into uniform fragments, and were put into a large needle filled with measuring medium (air-saturated water), the air was extracted until the leaves sank. Then the leaf fragments were taken out and put into the reaction cup containing the determination medium, the reaction cup plug was covered for measurement. The measurement was stopped after the respiration curve was stable. A slope of the same size interval was taken and the record was saved. Respiration rate\(=\)slope×reaction volume/leaf area (µmol/cm²/min).

The significant differences of parameters were using the method of two-way ANOVA followed by LSD’s multiple-range test for multiple comparisons. The data analysis was done with the SPSS statistical software (v20.0, SPSS, USA) and the figures were drawn by Origin (v9.0, Origin, USA) software.

Metabolic analysis

Metabolite extraction and profiling analysis

Rice leaves (50 ± 1 mg) were transferred to 2 ml Eppendorf tubes (EP), 0.45 ml methanol-water (3:1, v/v) and 10 µl L-2-chlorophenylalanine were added, then swirled for 30 s. Magnetic beads were then added for 4 min grinding and 5 min ultrasonic in ice water bath. The leaf extracting solution was centrifuged at 4 ℃ for 15 min at 12000 rpm. 300 µl supernatant was transferred to 1.5 ml EP tubes, 60 µl was taken from each sample and mixed to QC sample, then the extract was dried in vacuum concentrator, 60 µl methoxamine salt reagent (dissolved in 20 mg/ml pyridine) was added to the dried metabolite, mixed well and put into an oven at 80 ℃ for 30 min. 80 µl derivatization reagent BSTFA (containing 1% TMCS, v/v) was added to each sample and incubated for 1.5 h at 70 ℃. After cooled to indoor temperature, 5 µl FAMEs (soluble in chloroform) was added to the mixed samples for detection by GC-TOF/MS, the sequence was random.

1 µl grinding sample was injected into the injector (without shunt mode) with helium as carrier gas. The inlet purge flow rate was 3 ml/min, the flow rate was 1 ml/min, and the initial temperature was maintained at 50 ℃ for 1 min. The temperature was then increased to 310 ℃ at a rate of 10 ℃/min and kept for 8 min. The temperature of the forward sample outlet, transmission line and ion source were 280 ℃, 280 ℃ and 250 ℃ respectively, and the ionization voltage was − 70 eV. The mass spectrum data were collected after a solvent delay of 6.25 min, with a mass range of 50–500 m/z and a scanning rate of 20 spectra/s.

Data analysis

Data were acquired and pre-processed using the manufacturer’ Chroma TOF software (versions 4.3x, LECO). Data analysis was performed by SIMCA-P 14.1 software package (Umetrics). Additionally, differential metabolites were identified using Student’s t-test (p < 0.05) and VIP (VIP > 1), combined with similarity values > 700. Subsequently, KEGG (http://www.genome.jp/kegg/) was used to construct metabolic pathway, which were then analyzed by MetaboAnalyst 5.0 (https://www.metaboanalyst.ca/) according to the screened differential metabolites. The metabolic pathways with impact value > 0.1 were screened out.

Transcriptomic sequencing and analysis

RNA isolation and library construction

Samples were collected and stored at -80 ℃ after liquid nitrogen quick-freezing. Total RNA was extracted from polysaccharide polyphenol plant total RNA extraction kit, and the integrity, purity and concentration of RNA were detected by electrophoresis and then the sequenced library was constructed. Qubit2.0 was used to measure the RNA concentration and Agilent Bioanalyzer 2100 system was used to assess the RNA integrity. After the library inspection was qualified, the pooling was conducted according to the target disembarkation data volume, and the Illumina HiSeq platform was used for sequencing.

Quality control

TopHat2 was used to filter the outgoing data to get the Clean Data, and then alignment of clean reads of each sample were sequenced with the specified reference genome to get the Mapped Data, and the quality of the library Get Mapped Data was evaluated, and library quality was evaluated.

Quantification of gene expression levels

After sequencing, bioinformatics analysis was performed, and sequences were compared between HISAT2 (version 2.0.4) and the reference genome (Oryza sativa Japonica Group 4.0). Using StringTie (version 1.3.4D), gene expression levels were measured using FPKM. DESeq2 (version 1.6.3) was used to analyze gene expression differences between samples. FDR values < 0.01 and FC ≥ 3.0 were used as DEGs screening indexes. The differentially expressed genes were functionally annotated in GO and KEGG databases, and p ≤ 0.05 was considered statistically significant.

Quantitative real-time PCR analysis

qRT-PCR (quantitative real-time PCR) was used to validate the expression level of genes from RNA sequencing (RNA seq). 12 DEGs (differentially expressed genes) were randomly selected for qRT-PCR to verify the validity of the data. The qRT-PCR experiment was carried out by real-time fluorescence quantitative PCR instrument (analytikjena-qTOWER2.2, Germany). Each gene was repeated 3 times in each sample. Sequences of all primers used in the qRT-PCR were listed in Table S1.

Gene function annotation and differentially expressed genes (DEGs) analysis

Differential expression analysis, functional annotation and functional enrichment of differentially expressed genes (based on GO and KEGG) were conducted according to the expression levels of genes in different samples.

Antioxidant enzymes activities change under Cd stress and/or elevated CO₂

Under Cd stress, compared with AC, SOD, POD and GR activities increased, CAT and MDAR activities decreased significantly, APX and DHAR activities showed no significant change; CAT, APX and DHAR activities increased, SOD, POD, MDAR and GR activities decreased significantly under EC, compared with AC; Under EC + Cd, compared with AC, POD, DHAR and GR activities increased, SOD, CAT and MDAR activities decreased and APX activity show no significant change; Under EC + Cd, compared with Cd, DHAR and GR activities increased, SOD, POD, CAT and MDAR activities decreased significantly, APX activity showed no significant change. (Fig. 1)

Respiratory rates change under Cd stress and/or elevated CO₂

Compared with AC, respiratory rate increased significantly under Cd, but decreased significantly under EC and EC + Cd. Compared with Cd stress, it also decreased significantly under EC + Cd. (Fig. 2)

Metabolic changes under Cd stress and/or elevated CO₂

Principal component analysis (PCA) was used to determine the factors influencing metabolite differences. The first principal component (PC1) explained 76.20% of the variance, indicating metabolic changes between leaves of under Cd and no Cd stress (Fig. 3a). The main metabolites contributing to PC1 were alanine, glucose, L-allothreonine, serine, proline, citric acid, valine, aspartic acid, isoleucine, asparagine, succinic acid and oxalic acid (Fig. 3b; Table S2). The samples under atmospheric CO₂ and elevated CO₂ treatments were almost separated by the second principal component (PC2), which represented 8.05% of the total variation (Fig. 3a). Alanine, asparagine, oxalic acid, proline, isoleucine and succinic acid were the main factors contributing to PC2 (Fig. 3b; Table S2). Obvious differences in responses to the Cd stress and/or elevated CO₂ concentration of metabolite levels in metabolic pathways in leaves were shown by the PCA and loading plots (Fig. 3). Total of 41 contents of metabolites changed significantly under different treatments. These differential metabolites consisted of 10 sugars and polyols, 15 amino acids, 12 organic acids, 1 fatty acid, and 3 others (Table 2).

Table 2

Difference of metabolite profiles in rice seedlings under Cd stress and /or elevated CO₂.
Metabolite’s names	AC	Cd	EC	EC + Cd	Fold changes log₂ /AC			Fold changes log₂ /Cd
Metabolite’s names	AC	Cd	EC	EC + Cd	Cd	EC	EC + Cd	EC + Cd
Sugars and polyols
Glucose	79.49 ± 6.71	181.02 ± 28.84	84.84 ± 7.72	169.56 ± 14.71	1.19**	0.09	1.09**	-0.09
Glucose-6-P	3.28 ± 0.52	4.96 ± 0.77	3.35 ± 0.53	4.25 ± 0.83	0.68**	0.03	-0.37*	-0.22
Glucose-1-P	8.00 ± 0.86	19.92 ± 2.67	11.36 ± 2.42	14.90 ± 3.10	1.32**	0.51**	0.90**	-0.42*
Raffinose	8.58 ± 1.10	29.44 ± 6.81	11.12 ± 2.46	32.14 ± 4.05	1.78**	0.37*	1.90**	0.13
Myo-inositol	98.27 ± 9.21	150.03 ± 27.37	73.92 ± 7.51	128.12 ± 18.28	0.61**	-0.41**	0.38**	-0.23
Trehalose	3.03 ± 0.62	15.47 ± 1.37	2.24 ± 0.26	10.03 ± 1.24	2.35**	-0.44**	1.73**	-0.63**
Melibiose	1.65 ± 0.31	4.57 ± 0.82	2.42 ± 0.69	3.91 ± 0.43	1.47**	0.55**	1.24**	-0.22
Maltose	0.39 ± 0.08	1.26 ± 0.21	0.41 ± 0.07	0.96 ± 0.14	1.68**	0.07	1.30**	-0.38*
Galactinol	6.23 ± 0.85	12.53 ± 2.63	9.03 ± 0.86	17.64 ± 3.45	1.01**	0.54**	1.50**	0.49*
Cellobiose	0.25 ± 0.06	0.93 ± 0.07	0.26 ± 0.07	0.54 ± 0.06	1.89**	0.05	1.09**	-0.79**
Amino acids
Alanine	213.47 ± 47.72	434.45 ± 50.86	329.40 ± 37.70	520.21 ± 62.93	1.03**	0.63**	1.29**	0.26*
Phenylalanine	10.79 ± 2.11	21.98 ± 3.86	4.08 ± 0.76	34.06 ± 5.71	1.03**	-1.40**	1.66**	0.63**
Glutamic acid	29.48 ± 4.26	50.18 ± 7.86	22.83 ± 2.51	57.59 ± 0.09	0.77**	-0.37**	0.97**	0.20
Oxoproline	87.60.00 ± 4.34	117.90 ± 15.97	70.68 ± 8.08	114.12 ± 13.23	0.43**	-0.31**	0.38**	-0.05
Proline	47.02 ± 4.87	151.77 ± 49.35	21.13 ± 3.24	315.29 ± 73.29	1.69**	-1.15**	2.75**	1.05**
L-allothreonine	40.74 ± 5.24	191.33 ± 17.07	20.83 ± 4.38	178.89 ± 30.97	2.23**	-0.97**	2.13**	-0.10
Aspartic acid	222.71 ± 26.59	313.21 ± 26.63	184.21 ± 29.05	306.99 ± 30.53	0.49**	-0.27*	0.46**	-0.03
Asparagine	384.81 ± 84.83	338.87 ± 55.04	94.59 ± 2.99	661.37 ± 56.15	-0.18	-2.02**	0.78**	0.96**
Valine	56.97 ± 11.77	142.40 ± 7.49	34.90 ± 4.69	179.74 ± 26.18	1.32**	-0.71**	1.66**	0.34**
Serine	102.03 ± 24.51	328.21 ± 58.25	75.07 ± 4.16	338.53 ± 34.52	1.69**	-0.44*	1.73**	0.04
Glycine	30.73 ± 5.37	48.28 ± 4.82	16.98 ± 1.57	44.41 ± 4.53	0.65**	-0.86**	0.53**	-0.12
Isoleucine	19.03 ± 6.07	39.01 ± 2.92	5.56 ± 0.86	87.71 ± 5.83	1.04**	-1.77**	2.20**	1.17**
L-cysteine	0.43 ± 0.04	1.51 ± 0.23	0.44 ± 0.08	2.15 ± 0.44	1.82**	0.05	2.34**	0.52**
Lysine	4.27 ± 0.80	16.33 ± 2.80	3.48 ± 0.81	31.52 ± 8.81	1.93**	-0.29	2.88**	0.95**
Tyrosine	5.73 ± 0.78	12.94 ± 2.17	5.61 ± 0.88	21.44 ± 6.15	1.18**	-0.03	1.90**	0.73**
Organic acids
Oxalic acid	810.46 ± 59.66	220.17 ± 56.14	326.80 ± 33.78	104.54 ± 14.58	-1.88**	-1.31**	-2.95**	-1.07**
Citric acid	114.61 ± 26.47	211.99 ± 24.66	91.63 ± 6.96	212.76 ± 18.01	0.89**	-0.32	0.89**	0.01
Succinic acid	119.94 ± 21.81	61.83 ± 6.73	110.37 ± 32.62	87.28 ± 8.41	-0.96**	-0.12	-0.46**	0.50**
L-malic acid	164.40 ± 18.22	223.11 ± 20.24	166.43 ± 8.65	160.78 ± 41.04	0.44**	0.02	-0.03*	-0.47**
Aconitic acid	0.32 ± 0.06	0.78 ± 0.09	0.20 ± 0.03	0.60 ± 0.04	1.28**	-0.67**	0.89**	-0.38**
Fumaric acid	5.04 ± 0.79	3.79 ± 0.59	3.53 ± 0.40	3.02 ± 0.61	-0.41*	-0.51**	-0.74**	-0.33*
Quinic acid	20.49 ± 2.87	47.46 ± 6.93	27.24 ± 4.98	25.79 ± 6.53	1.21**	0.41*	0.33	-0.88**
Salicylic acid	13.80 ± 2.47	18.43 ± 2.35	10.29 ± 1.46	13.48 ± 3.15	0.42**	-0.42*	-0.03	-0.45*
D-glyceric acid	15.42 ± 3.71	27.68 ± 6.12	11.14 ± 2.23	18.44 ± 3.51	0.84**	-0.47*	0.26	-0.59**
Shikimic acid	56.08 ± 7.56	77.82 ± 9.07	41.18 ± 5.50	44.40 ± 7.02	0.47**	-0.45**	-0.34*	-0.81**
Ferulic acid	0.75 ± 0.10	1.28 ± 0.18	0.62 ± 0.12	1.22 ± 0.25	0.77**	-0.28	0.70**	-0.07
Glycolic acid	8.15 ± 0.28	9.58 ± 1.18	7.48 ± 0.51	6.68 ± 0.74	0.23*	-0.12*	-0.29**	-0.52**
Fatty acids
Linolenic acid	2.74 ± 0.64	1.84 ± 0.19	2.18 ± 0.22	1.91 ± 0.27	0.58**	-0.33	-0.52*	0.05
Others
O-phosphorylethanolamine	1.59 ± 0.35	3.32 ± 0.49	1.62 ± 0.51	3.03 ± 0.55	1.06**	0.03	0.93**	-0.13
Putrescine	3.67 ± 0.46	8.13 ± 0.66	7.21 ± 0.52	11.91 ± 3.31	1.15**	0.97**	1.70**	0.55*
5-Methoxytryptamine	1.43 ± 0.34	15.63 ± 2.61	0.46 ± 0.08	18.68 ± 3.05	3.45**	-1.65**	3.70**	0.26
As the low content of individual metabolites, the relative content value and standard deviation increased by 100 times, and retaining two decimal places. Significant differences between treatments were determined by the ANOVA test and marked as * p < 0.05 and ** p < 0.01.

Under Cd stress, compared with AC, sugar and polyol metabolism was greatly enhanced, contents of glucose, glucose-6-P and glucose-1-P (related to photosynthesis and glycolysis), raffinose, myo-inositol, trehalose, melibiose, maltose, galactinol and cellobiose were all increased; Amino acid metabolism was also enhanced, alanine, phenylalanine, glutamic acid, oxoproline, proline, aspartic acid, valine, serine, glycine, isoleucine, L-cysteine, lysine and tyrosine contents were all increased; Among organic acids, citric acid, L-malic acid, aconitic acid, quinic acid, salicylic acid, D-glyceric acid, shikimic acid, ferulic acid and glycolic acid contents increased, but oxalic acid, succinic acid and fumaric acid contents decreased; Linolenic acid and O-phosphorylethanolamine, putrescine, 5-Methoxytryptamine contents increased (Table 2; Fig. 4). The main differential metabolic pathways were glycine, serine and threonine metabolism, citrate cycle (TCA cycle), glyoxylate and dicarboxylate metabolism, pyruvate metabolism, alanine, aspartate and glutamate metabolism, galactose metabolism and alpha-Linolenic acid metabolism (Fig. 5a).

Under EC, compared with AC, some of sugars and polyols contents increased, include glucose-1-P, raffinose, melibiose and galactinol, but myo-inositol and trehalose contents decreased; Related to amino acid metabolism, phenylalanine, glutamic acid, oxoproline, proline, aspartic acid, asparagine, valine, serine, glycine and isoleucine contents all decreased, only alanine content increased; Many organic acids contents decreased, include oxalic acid, aconitic acid, fumaric acid, salicylic acid, D-glyceric acid, shikimic acid and glycolic acid, only quinic acid content increased; Putrescine content increased, and 5-methoxytryptamine content decreased (Table 2; Fig. 4). The main differential metabolic pathways were glycine, serine and threonine metabolism, glyoxylate and dicarboxylate metabolism, alanine, aspartate and glutamate metabolism and galactose metabolism (Fig. 5b).

Under EC + Cd, compared with AC, the overall metabolic trend was similar to that under Cd treatment except that glucose-6-P, L-malic acid, shikimic acid, glycolic acid and linolenic acid contents decreased, but asparagine content increased. Quinic acid and D-glyceric acid showed no significant change (Table 2; Fig. 4); Under EC + Cd, compared with Cd, among sugars and polyols, only galactinol content increased, glucose-1-P, trehalose, maltose, cellobiose contents decreased; Many amino acids (include alanine, phenylalanine, proline, asparagine, valine, isoleucine, L-cysteine, lysine and tyrosine) contents increased; Among organic acids, oxalic acid, L-malic acid, aconitic acid, fumaric acid, quinic acid, salicylic acid, D-glyceric acid, shikimic acid and glycolic acid contents decreased, only succinic acid content increased. Also, content of putrescine increased (Table 2; Fig. 4). The main differential metabolic pathways were pyruvate metabolism, glyoxylate and dicarboxylate metabolism and citrate cycle (TCA cycle) (Fig. 5c).

Transcriptional changes under Cd²⁺ stress and/or elevated CO₂

The results of gene expression levels obtained by qRT-PCR and RNA-Seq were consistent (Fig. S1). Transcriptome analysis of seedling leaves under both treatments showed that 77.87 Gb of clean data (Q30 > 94.33%) was obtained, with clean data for each sample up to 5.88 Gb. The sequence alignment rate between clean data of each sample and reference genome ranged within 94.83–95.86%, and the GC content was greater than 50.54% (Table 3).

Table 3

Mapping of RNA-Seq data of 12 samples of four treatments to reference genome.
Samples	Total Reads	Mapped Reads	Clean reads	Clean bases	GC Content	%≥Q30
AC1	42020668	40235115 (95.75%)	21010334	6291229800	51.93%	94.64%
AC2	39326336	37697399 (95.86%)	19663168	5883700370	52.49%	94.60%
AC3	49787168	47619572 (95.65%)	24893584	7448299222	52.45%	94.36%
Cd1	41040066	39162761 (95.43%)	20520033	6141067438	51.63%	94.41%
Cd2	40893088	38780577 (94.83%)	20446544	6114591458	51.55%	94.49%
Cd3	47141952	44872686 (95.19%)	23570976	7039313378	51.59%	94.54%
EC1	44343092	42184117 (95.13%)	22171546	6631142724	50.54%	94.33%
EC2	47024130	44759126 (95.18%)	23512065	7023296558	51.19%	94.66%
EC3	39558262	37699158 (95.30%)	19779131	5910440722	50.93%	94.68%
EC + Cd1	43333694	41324421 (95.36%)	21666847	6475544680	51.37%	94.53%
EC + Cd2	42203892	40112315 (95.04%)	21101946	6314904616	50.99%	94.52%
EC + Cd3	44156776	42039013 (95.20%)	22078388	6592293700	51.86%	94.64%

In this study, 2745, 2314, 3184 and 1142 genes with significant expression differences were screened from AC vs Cd, AC vs EC, AC vs EC + Cd and Cd vs EC + Cd sample groups respectively. Among them, 1491 DEGs in AC vs Cd were up-regulated and 1254 DEGs were down-regulated; 1250 DEGs in AC vs EC were up-regulated and 1064 DEGs were down-regulated; 1849 DEGs in AC vs EC + Cd were up-regulated and 1335 DEGs were down-regulated; In Cd vs EC + Cd, 753 DEGs were up-regulated and 389 DEGs were down-regulated. The unique differential genes of AC vs Cd, AC vs EC, AC vs EC + Cd and Cd vs EC + Cd were 815, 691, 555 and 235 respectively (Table S3; Fig. S2).

The GO functional annotation analysis showed that these co-expressed genes were mainly distributed in 3 terms: biological process, cellular component and molecular function. When AC vs Cd, DEGs mainly enriched in photosynthesis, flavonoid biosynthesis, second metabolite, toxin catabolic, glutathione metabolic processes and oxalate oxidase activity, etc; When AC vs EC, DEGs mainly enriched in photosynthesis, such as light harvesting in photosystem Ⅰ, chloroplast thylakoid membrane, chloroplast stroma and chlorophyⅡ binding, etc; When AC vs EC + Cd, DEGs mainly enriched in photosynthesis, flavonoid biosynthesis, nitrate assimilation, etc; when Cd vs EC + Cd, except DEGs enriched in photosynthesis and flavonoid biosynthetic process, they also enriched in glycosy Ⅰ compound metabolic process, and water, glycerol, iron transmembrane and plasmodesmata-mediated intercellular transport, and glucosidase, glucosyltransferase, alcohol dehydrogenase and oxalate decarboxylase activities, etc (Fig. 6).

The most significant 30 DEGs (15 up-regulated, 15 down-regulated) were analyzed based on KEGG, When AC vs Cd, up-regulated DEGs mainly annotated in phenylpropanoid biosynthesis, amino acid (alanine, aspartate, glutamate) metabolism, glutathione metabolism, amino sugar and nucleotide sugar metabolism and protein processing in endoplasmic reticulum, etc. Down-regulated DEGs mainly annotated in phenylpropanoid biosynthesis, cysteine and methionine metabolism, photosynthesis-antenna proteins, brassinosteroid biosynthesis, plant hormone signal transduction, oxidative phosphorylation and carotenoid biosynthesis, etc; When AC vs EC, up-regulated DEGs mainly annotated in photosynthesis, plant hormone signal transduction, starch and sucrose metabolism, alanine, aspartate and glutamate metabolism, glutathione metabolism, etc. Down-regulated DEGs mainly annotated in carotenoid biosynthesis, photosynthesis-antenna proteins, TCA cycle and plant hormone signal transduction, etc; When AC vs EC + Cd, up-regulated DEGs annotations were just like AC vs Cd, of course, other annotations just like photosynthesis, phosphatidylinositol signaling system, and glycolysis/gluconeogenesis are worthy of attention. Down-regulated DEGs mainly annotated in carotenoid biosynthesis, photosynthesis-antenna proteins, nitrogen metabolism and citrate cycle (TCA cycle), etc; When Cd vs EC + Cd, up-regulated DEGs mainly annotated in phenylpropanoid biosynthesis, peroxisome, brassinosteroid biosynthesis, photosynthesis, and cysteine and methionine metabolism, etc. Down-regulated DEGs mainly annotated in phenylpropanoid biosynthesis, photosynthesis-antenna proteins, TCA cycle and cysteine and methionine metabolism, etc (Table 4, 5, 6, 7).

Table 4

KEGG pathway annotation information of AC vs Cd DEGs.
Gene names	log₂FC	KEGG pathway annotation
Os09g0483200	6.8235	Ribosome (ko03010)
Os02g0811800 (OsCCR10)	6.5545	Phenylpropanoid biosynthesis (ko00940)
Os02g0466400 (OsITPK4)	6.5088	Phosphatidylinositol signaling system (ko04070)
Os04g0614500	5.9878	Alanine, aspartate and glutamate metabolism (ko00250)
Os09g0367700	5.7029	Glutathione metabolism (ko00480)
Os05g0399300	5.6779	Amino sugar and nucleotide sugar metabolism (ko00520)
Os07g0638400	5.6308	Phenylpropanoid biosynthesis (ko00940)
Os03g0828300	5.4449	Fructose and mannose metabolism (ko00051)
Os07g0529000	5.3364	Carbon metabolism (ko01200)
Os04g0107900 (OsHSP1)	5.2436	Protein processing in endoplasmic reticulum (ko04141)
Os01g0136100	5.1679	Protein processing in endoplasmic reticulum (ko04141)
Os05g0373900	5.1023	mRNA surveillance pathway (ko03015)
Os01g0136200 (OsHSP17.0; OsSHSP2)	4.9078	Protein processing in endoplasmic reticulum (ko04141)
Os03g0277300	4.8173	Protein processing in endoplasmic reticulum (ko04141)
Os03g0712800 (OsGS1; 3)	4.5326	Alanine, aspartate and glutamate metabolism (ko00250)
Os05g0135500	-6.7583	Phenylpropanoid biosynthesis (ko00940)
Os01g0703000	-6.3364	mRNA surveillance pathway (ko03015)
Os10g0320100	-5.9872	Flavonoid biosynthesis (ko00941)
Os11g0285000	-5.7607	Sesquiterpenoid and triterpenoid biosynthesis (ko00909)
Os01g0192900 (OsACS5)	-5.6227	Cysteine and methionine metabolism (ko00270)
Os10g0109300	-5.5699	Phenylpropanoid biosynthesis (ko00940)
Os11g0530600	-5.4913	Circadian rhythm - plant (ko04712)
Os04g0457100	-5.2240	Photosynthesis - antenna proteins (ko00196)
Os01g0388000 (CYP734A6)	-4.9472	Brassinosteroid biosynthesis (ko00905)
Os02g0697400 (Os4CL2)	-4.9340	Phenylpropanoid biosynthesis (ko00940)
Os12g0601400 (OsIAA3)	-4.6904	Plant hormone signal transduction (ko04075)
Os05g0114000	-4.6874	Oxidative phosphorylation (ko00190)
Os10g0533500	-4.6352	Carotenoid biosynthesis (ko00906)
Os01g0805900	-4.5539	Phagosome (ko04145)
Os08g0489300	-4.4988	Base excision repair (ko03410)
log₂FC positive value (+) represents DEGs up-regulated and negative value (-) represents DEGs down-regulated.

Table 5

KEGG pathway annotation information of AC vs EC DEGs.
Gene names	log₂FC	KEGG pathway annotation
Os02g0578400	8.7500	Photosynthesis (ko00195)
Os03g0679700	7.9409	Thiamine metabolism (ko00730)
Os07g0529600 (OsDR8; OsXNP)	5.1310	Thiamine metabolism (ko00730)
Os09g0482680	5.0662	RNA transport (ko03013)
Os05g0535800	4.8333	Ribosome (ko03010)
Os12g0586100 (OsSAPK9)	4.8022	Plant hormone signal transduction (ko04075)
Os08g0248800	4.79262	Pyrimidine metabolism (ko00240)
Os01g0814800	4.6900	Carbon metabolism (ko01200)
Os08g0473900 (OsAmy3D; RAmy3D)	4.1071	Starch and sucrose metabolism (ko00500)
Os05g0555600 (OsNADH-GOGAT2)	4.0333	Alanine, aspartate and glutamate metabolism (ko00250)
Os08g0175300	3.9768	Purine metabolism (ko00230)
Os01g0182600 (OsGI)	3.9220	Circadian rhythm - plant (ko04712)
Os11g0547000 (OsFKF1)	3.7968	Circadian rhythm - plant (ko04712)
Os07g0406800	3.7166	Purine metabolism (ko00230)
Os08g0522400	3.6590	Glutathione metabolism (ko00480)
Os10g0533500	-10.3987	Carotenoid biosynthesis (ko00906)
Os01g0600900	-8.2712	Photosynthesis - antenna proteins (ko00196)
Os09g0346500 (OsCAB1R)	-8.1948	Photosynthesis - antenna proteins (ko00196)
Os04g0457100	-6.8262	Photosynthesis - antenna proteins (ko00196)
Os03g0592500	-6.4207	Photosynthesis - antenna proteins (ko00196)
Os07g0562700	-6.1958	Photosynthesis - antenna proteins (ko00196)
Os05g0171000 (OsPLDα2)	-6.0909	Glycerophospholipid metabolism (ko00564)
Os08g0157600 (OsCCA1; OsLHY)	-6.0504	Circadian rhythm - plant (ko04712)
Os07g0630800	-5.8256	Carbon metabolism (ko01200); Citrate cycle (TCA cycle) (ko00020)
Os11g0242800 (LHCB5)	-5.7385	Photosynthesis - antenna proteins (ko00196)
Os01g0720500	-5.6834	Photosynthesis - antenna proteins (ko00196)
Os02g0197600	-5.3953	Photosynthesis - antenna proteins (ko00196)
Os08g0435900	-5.3653	Photosynthesis - antenna proteins (ko00196)
Os02g0194700 (OsLOX1)	-4.8997	alpha-Linolenic acid metabolism (ko00592)
Os12g0601400 (OsIAA3)	-4.8045	Plant hormone signal transduction (ko04075)
log₂FC positive value (+) represents DEGs up-regulated and negative value (-) represents DEGs down-regulated.

Table 6

KEGG pathway annotation information of AC vs EC + Cd DEGs.
Gene names	log₂FC		KEGG pathway annotation
Os02g0578400	7.8161		Photosynthesis (ko00195)
Os03g0679700	7.1425		Thiamine metabolism (ko00730)
Os07g0529600 (OsDR8; OsXNP)	6.3715		Thiamine metabolism (ko00730)
Os02g0466400 (OsITPK4)	6.3108		Phosphatidylinositol signaling system (ko04070)
Os09g0483200	6.1760		Ribosome (ko03010)
Os04g0614500	6.0388		Alanine, aspartate and glutamate metabolism (ko00250)
Os07g0638400	5.8296	Phenylpropanoid biosynthesis (ko00940)
Os11g0210600	5.6962	Glycolysis / Gluconeogenesis (ko00010)
Os07g0638300	5.6674	Phenylpropanoid biosynthesis (ko00940)
Os10g0530800	5.5998	Glutathione metabolism (ko00480)
Os07g0529000	5.4800	Carbon metabolism (ko01200)
Os02g0113200	5.4356	Steroid biosynthesis (ko00100)
Os05g0399300	5.3394	Amino sugar and nucleotide sugar metabolism (ko00520)
Os04g0493400	5.3364	Amino sugar and nucleotide sugar metabolism (ko00520)
Os03g0828300	5.2944	Fructose and mannose metabolism (ko00051)
Os10g0533500	-8.9800	Carotenoid biosynthesis (ko00906)
Os01g0600900	-8.8464	Photosynthesis - antenna proteins (ko00196)
Os09g0346500 (OsCAB1R)	-7.0415	Photosynthesis - antenna proteins (ko00196)
Os05g0171000 (OsPLDα2)	-6.4822	Glycerophospholipid metabolism (ko00564)
Os08g0157600 (OsCCA1; OsLHY)	-6.4627	Circadian rhythm - plant (ko04712)
Os02g0770800 (OsNR2; qCR2)	-6.1891	Nitrogen metabolism (ko00910)
Os03g0592500	-6.0977	Photosynthesis - antenna proteins (ko00196)
Os07g0562700	-6.0288	Photosynthesis - antenna proteins (ko00196)
Os08g0435900	-5.9305	Photosynthesis - antenna proteins (ko00196)
Os12g0189400	-5.8617	Photosynthesis (ko00195)
Os04g0457100	-5.8131	Photosynthesis - antenna proteins (ko00196)
Os10g0109300	-5.5237	Phenylpropanoid biosynthesis (ko00940)
Os07g0630800	-5.4543	Carbon metabolism (ko01200); Citrate cycle (TCA cycle) (ko00020)
Os10g0320100	-5.4408	Flavonoid biosynthesis (ko00941)
Os11g0242800 (LHCB5)	-5.2621	Photosynthesis - antenna proteins (ko00196)
log₂FC positive value (+) represents DEGs up-regulated and negative value (-) represents DEGs down-regulated.

Table 7

KEGG pathway annotation information of Cd vs EC + Cd DEGs.
Gene names	log₂FC	KEGG pathway annotation
Os04g0354600	5.5910	Peroxisome (ko04146)
Os06g0604200 (OsPLDα4)	5.3605	Glycerophospholipid metabolism (ko00564)
Os05g0135500	4.6313	Phenylpropanoid biosynthesis (ko00940)
Os05g0521600	4.5598	Starch and sucrose metabolism (ko00500)
Os03g0227700 (OsDWARF4; CYP90B2)	4.4900	Brassinosteroid biosynthesis (ko00905)
Os02g0578400	4.3595	Photosynthesis (ko00195)
Os11g0210600	4.2353	Fatty acid degradation (ko00071)
Os09g0400200	4.0084	Phenylpropanoid biosynthesis (ko00940)
Os03g0708000	3.8167	alpha-Linolenic acid metabolism (ko00592)
Os01g0192900 (OsACS5)	3.7646	Cysteine and methionine metabolism (ko00270)
Os06g0179000	3.5371	Glycosaminoglycan degradation (ko00531)
Os03g0679700	3.4990	Thiamine metabolism (ko00730)
Os09g0491100	3.4719	Phenylpropanoid biosynthesis (ko00940)
Os04g0137100	3.4227	Pentose and glucuronate interconversions (ko00040)
Os08g0248800	3.3218	Alanine, aspartate and glutamate metabolism (ko00250)
Os11g0707100	-6.6489	/
Os05g0171000 (OsPLDα2)	-5.7501	Glycerophospholipid metabolism (ko00564)
Os03g0843800	-5.4442	/
Os08g0157600 (OsCCA1; OsLHY)	-4.9177	Circadian rhythm - plant (ko04712)
Os09g0346500 (OsCAB1R)	-4.5720	Photosynthesis - antenna proteins (ko00196)
Os04g0513700	-4.5492	/
Os07g0630800	-4.3508	Citrate cycle (TCA cycle) (ko00020); Carbon metabolism (ko01200)
Os03g0375300	-4.3452	/
Os06g0253100	-4.2108	Protein processing in endoplasmic reticulum (ko04141)
Os12g0189300	-3.6663	/
Os02g0758000 (OsHSP24.1)	-3.3598	Protein processing in endoplasmic reticulum (ko04141)
Os05g0171050	-3.3373	Glycerophospholipid metabolism (ko00564)
Os01g0820000	-3.1124	Phenylpropanoid biosynthesis (ko00940)
Os12g0189400	-3.1073	Photosynthesis (ko00195)
Os09g0424300	-3.1002	Cysteine and methionine metabolism (ko00270)
log₂FC positive value (+) represents DEGs up-regulated and negative value (-) represents DEGs down-regulated.

Responses of antioxidant enzymes activities, respiratory rate, metabolomics and transcriptomics to Cd stress

SOD, POD and CAT are the core of antioxidant enzyme system. In this study, SOD and POD activities increased, promoted the conversion of more reactive oxygen species to H₂O₂. The ASA-GSH cycle is one of the key material cycles in plants, and the key enzymes involved in it include APX, DHAR, MDAR and GR. Glutathione (GSH) and ascorbic acid (ASA) are major intracellular antioxidants with strong reducibility and are substrates of various antioxidant enzymes, especially GSH, which has been proved to play an important role in plant tolerance to cadmium toxicity by many studies (Howden et al. 1995; Cobbett et al. 1998; Uraguchi et al. 2009). In oxidative stress response, GR not only removes H₂O₂, but also regenerates NADP to ensure normal electron transfer (Gamble and Burke 1984). GR has the function of making GSSG (reduced glutathione) to GSH and plays an important role in scavenging ROS (Alscher et al. 1997). Cysteine residues in GSH molecules have strong nucleophilic properties and can react directly with ROXs to remove them (May et al. 1998; Graham et al. 1998). In this study, GR activity was significantly increased and played an important antioxidant role under Cd stress in rice. Studies have found the genes participate in ASA-GSH cycle were activated, and glutathione, ascorbic acid and plants chelating protein content increased, in order to ease the growth inhibition induced by oxidative stress and Cd, reduce cadmium toxicity in plants (Liang et al. 2016; Lou et al. 2017). Our study showed DEGs also enriched in glutathione metabolic process, Os09g0367700 (up-regulated DEG) mainly annotated in glutathione metabolism. The role of DHAR is to reduce the oxidation product of ascorbate to ASA and maintain the dynamic balance of ASA in cells (Chen et al. 2003; Deutsch 2000). APX uses ASA as an electron donor to remove H₂O₂ and protect chloroplasts (Foyer and Hailiwell 1976). But in this study, DHAR and APX did not play a leading role in antioxidant activity under Cd stress.

Zeng et al. (2020) found, under Cd stress, levels of most carbohydrates were down-regulated in the metabolomics study of two different varieties of indica and hybrid rice grains. But in this study, in rice leaves, sugar and polyol metabolism was greatly enhanced. The increase of soluble sugar contents is a typical stress response in plants under heavy metal stress which requires a large amount of energy provided by carbohydrate compounds and osmotic adjustment substances. Respiration of plant can release energy to resist stress, and our study showed that rice leaf respiration rate was enhanced under Cd stress, glucose, glucose-6-P and glucose-1-P (related to glycolysis) contents increased, citric acid, L-malic acid, aconitic acid (related to TCA cycle) contents also increased. Dehydrogenation process in such reaction is accompanied by NADH formation, which is essential to preserve the antioxidant capacity of cells (Petrat et al. 2003); Raffinose is an oligosaccharide with the function of storing energy and can be a protective agent under stress. The synthesis of raffinose involves in the formation of myo-inositol (Fig. 4), which is not only an important osmotic protective substance, but also is involved in a variety of metabolic regulation, such as the regulation of plant cell stress resistance, the promotion of seed dehydration and the regulation of plant auxin, and also participates in the synthesis of cell wall (Loewus and Murthy 1964; Madden et al. 1985; Conde et al. 2015). In this study, myo-inositol and raffinose were all increased under Cd as important metabolic regulators. The related up-regulated DEG Os02g0466400 (1,3, 4-phosphoinositol 5/6-kinase gene) need further focus.

The enhanced amino acid metabolism and the accumulated small molecule amino acids can contribute to improving osmotic adjustments and maintaining cell membrane stability (Widodo et al. 2009). It can also be used as a mediator for heavy metals and form metal complexes (Bottini and Festa 1996). Heavy metal stress inhibits normal nitrogen metabolic pathways in plants. Free amino acids, as an important form of N assimilates in plants and the main transport form, can reflect the supply capacity of N assimilates (Zhao et al. 2019). Our study showed that the large increase of carbohydrate promoted pyruvate metabolism and thus promoted the formation of many amino acids (Fig. 4). Up-regulated DEGs also annotated in amino acid (alanine, aspartate, glutamate) metabolism. Os04g0614500 and Os03g0712800 annotated in alanine, aspartic acid and glutamic acid metabolism are significantly up-regulated. Alanine and valine are glucogenic amino acids, they also might promote glyconeogenesis as an effective transformation pathway to resist Cd stress. The stress response of alanine is mainly reflected in the regulation of intracellular environment pH, and the increase of alanine content may be caused by the reduction of protein synthesis rate and the slow reaction of alanine transaminase after heavy metal stress (Xu et al. 2012). Oxoproline is related with proline and arginine metabolism, the accumulation of proline can decrease intracellular osmotic potential, balance protoplast osmotic pressure, protect enzyme activity, and reduce soluble protein precipitation (Yang et al. 2017).

Some abiotic stresses can induce plants to secrete and accumulate organic acids, which can promote the activation and absorption of mineral elements in soil, and thus have the function of regulating plant nutrition (Yang et al. 2017; Wang et al. 2018), also they are vital osmotic adjustment solutes (Yang et al. 2017). Oxalic acid has also been identified as a differential metabolite in several metabolomic studies under heavy metal stress (Zeng et al. 2008; Lyubenova et al. 2013), but only in root of plants. In our experiment, oxalic content of rice leaves decreased under Cd stress. Related to secondary metabolism, salicylic acid, ferulic acid, quinic acid and shikimic acid contents increased, DEGs also enriched in the second metabolite. The increase of this important secondary metabolites’ contents related to strong stress resistant capacity of plants.

In our study, linolenic acid content increased, alpha-Linolenic acid metabolism was enhanced and O-phosphorylethanolamine, putrescine, 5-Methoxytryptamine contents increased under Cd stress. Ethanolamine is important for synthesis of choline, phosphatidylethanolamine (PE) and phosphatidylcholine (PC) in plants. They are important in plants growth and developmental processes as the major phospholipids in eukaryotic membranes (Kwon et al. 2012). The increase of O-phosphorylethanolamine under Cd maybe related to its protective effect on cell membranes under adverse conditions. Polyamines are a kind of low molecular weight secondary metabolites in nitrogen metabolism. Putrescine is the central product of polyamine biosynthesis pathway. Studies have shown that polyamines are closely related to stress tolerance in plants (Walters, 2003). Under stress, the polyamine content and polyamine synthase activity of plants increased significantly in a certain range (Cvikrová et al, 2012; Xu et al. 2012). The accumulation of putrescine is a sign of plant tolerance to stress or alleviating the damage of stress to plants, but whether it has special physiological significance remains to be further studied.

When AC vs Cd, DEGs also enriched in photosynthesis, flavonoid biosynthesis, toxin catabolic and oxalate oxidase activity, etc; Up-regulated DEGs mainly annotated in phenylpropanoid biosynthesis and protein processing in endoplasmic reticulum etc. Down-regulated DEGs mainly annotated in photosynthesis-antenna proteins, brassinosteroid biosynthesis, plant hormone signal transduction, oxidative phosphorylation and carotenoid biosynthesis, etc. Os04g0457100 and Os10g0533500 were significantly down-regulated in photosynthesis-antenna protein, and carotenoid biosynthesis. That indicates that the photosynthetic process of rice has been adversely affected, which has been verified by our previous study (Li et al. 2021).

Responses of antioxidant enzymes activities, respiratory rate, metabolomics and transcriptomics to elevated CO₂ concentration

In this study, under EC, compared with AC, CAT, APX and DHAR activities increased, SOD, POD, MDAR and GR activities decreased significantly. High concentration of CO₂ promotes the synthesis of photosynthates in plants, and sufficient photosynthates are conducive to the synthesis and metabolism of ascorbic acid (ASA). ASA is the substrate of APX, so high ASA synthesis capacity is consistent with high APX activity. Study showed DHAR activity of S-type soybean increased under high CO₂ concentration (Asada and Takahashi 1987), we got the same result. Plants growing under high concentrations of CO₂ can regulate the photosynthetic electron conduction system and synthesize more NADPH (Lichtenthaler 1987), and the synthesized NADPH can be used for the ascorbate-glutathione cycle (Rao et al. 1995), which keeps ascorbic acid and glutathione in a high reduction state in plants, thus reducing the activities of antioxidant enzymes, such as SOD (Foyer et al. 1994a, b). Studies have shown that the activities of GR, SOD and POD in soybean decreased under high CO₂ concentration (Pritchard et al. 2000), which is consistent with the results of this study. However, the decrease of antioxidant enzyme activities under high CO₂ may be caused by the increase of partial pressure of CO₂ in chloroplast and the decrease of partial pressure of O₂, and the decrease of oxygen as electron acceptor to form reactive oxygen species, and the decrease of reactive oxygen species and antioxidant enzymes leading to the decrease of antioxidant enzyme activities (Pritchard et al. 2000).

Our study has shown that high CO₂ reduced stomatal opening of leaves (Wang et al. 2019), then O₂ intake dropped, so the respiration rate further decreased. The decrease of aconitic acid and fumaric acid contents (the downstream products of TCA cycle) and down-regulated DEG (Os07g0630800, annotated in TCA cycle) also verify this result. Many sugars and amino acids contents decreased, related to galactose metabolism, glycine, serine and threonine metabolism, alanine and aspartate and glutamate metabolism. Glutamic acid is the precursor of chlorophyll (Woodward et al. 1990; Ito et al. 1994). The change of the glutamic acid content in plants under high CO₂ may be related to photosynthesis (Gong et al. 2013). It was possible to synthesize more photosynthetic pigments by consuming glutamic acid to increase the photosynthetic intensity.

The increase of CO₂ concentration can regulate the production of secondary metabolites in plants (Idso et al. 2001). In our study, shikimic acid, salicylic acid contents decreased under EC, but quinic acid content increased. The decrease of D-glyceric acid and glycolic acid contents may be related to the decrease of photorespiration under EC, the contents of amino acids (glycine and serine) and oxalic acid associated with this process were also decreased (Fig. 4). Putrescine content also increased under EC.

When AC vs EC, DEGs mainly enriched in photosynthesis, such as light harvesting in photosystemⅠ, chloroplast thylakoid, chloroplast stroma and chlorophyl Ⅱ binding, etc; 1250 genes in AC vs EC were up-regulated, mainly annotated in photosynthesis, plant hormone signal transduction, starch and sucrose metabolism, alanine, aspartate and glutamate metabolism, glutathione metabolism etc. Os02g0578400 annotation is most significant in photosynthesis, Os08g0473900 is annotated in starch metabolism and sucrose metabolism. Those results showed that EC promoted photosynthesis, growth and energy metabolism of rice to a certain extent, which can be verified by our early studies (Li et al. 2021; Wang et al. 2021); 1064 DEGs were down-regulated, mainly annotated in carotenoid biosynthesis, photosynthesis-antenna proteins, and plant hormone signal transduction, etc. In the photosynthesis-antenna proteins, Os01g0600900, Os09g0346500, Os04g0457100, Os03g0592500, Os07g0562700, Os11g0242800, Os01g0720500, Os02g0197600 and Os080g435900 were all down-regulated indicating that high CO₂ would inhibit the role of photosynthesis-antenna protein. It perhaps inhibited and regulated the light reaction process of rice to cope with the phenomenon of "photosynthetic adaptation" under high CO₂.

Responses of antioxidant enzymes activities, respiratory rate, metabolomics and transcriptomics to Cd stress and elevated CO₂ concentration

Under EC + Cd, compared with AC, POD, DHAR and GR activities increased significantly. The results indicated that the antioxidant enzymes activities of leaves were relatively higher, the peroxidation damage of rice leaves by compound treatment still existed; Compared with Cd stress, EC could enhance the oxidation resistance of rice by the higher DHAR and GR activities, regulating the ASA-GSH cycle, so as to alleviate membrane lipid peroxidation to a certain extent.

Under EC + Cd, compared with AC, the overall metabolic trend was similar to that of Cd treatment. That means when rice seedlings were treated under EC and Cd stress together, Cd stress play a dominant role. Decrease of glucose-6-P, succinic acid, L-malic acid and glycolic acid contents was related to glycolysis, TCA cycle and photorespiration pathway changes under high CO₂; When compared with Cd treatment, EC promoted the increase of many amino acids for osmotic regulation under Cd stress. In addition, DEGs (Os01g0192900, Os08g0248800) regulating amino acid metabolism were up-regulated. The decrease of glucose-1-P, L-malic acid, aconitic acid and fumaric acid contents were related to glyoxylate and dicarboxylate metabolism and TCA cycle under EC. The down-regulated DEGs also annotated in TCA cycle. Under EC + Cd, secondary metabolites (quinic acid, salicylic acid and shikimic acid) did not play an important role in anti-stress. But, content of putrescine also increased.

When EC + Cd vs AC, 1849 genes were up-regulated and annotations were just like Cd vs AC, of course, other annotations just like photosynthesis, phosphatidylinositol signaling system, and glycolysis/gluconeogenesis are worthy of attention. Os02g0466400 was up-regulated in the phosphatidylinositol signal system, and inositol plays an important role in coping with environmental stress, mainly by participating in osmotic stress regulation, conveying information as a signal molecule, and enhancing antioxidant enzyme activity. This is consistent with the above results of increased myo-inositol content. 1335 genes were down-regulated, mainly annotated in carotenoid biosynthesis, photosynthesis-antenna proteins, nitrogen metabolism and TCA cycle, etc. The results showed that the effects of Cd stress on photosynthesis and energy metabolism of rice leaves were greater than that of high CO₂ treatment.

When EC + Cd vs Cd, DEGs enriched in glycosyⅠ compound metabolic process, and water, glycerol, iron transmembrane and plasmodesmata-mediated interlellular transport, and glucosidase, glucosyltransferase, alcohol dehydrogenase and oxalate decarboxylase activities. That indicated, under Cd stress, EC could regulate the metabolic function of rice by regulating the transmembrane movement of substances and the activities of some enzymes. This may be related to CO₂ alleviating the loss of cellular moisture caused by Cd stress, the underlying mechanism needs to be further studied. 753 genes were up-regulated, Os05g0135500, Os09g0400200 and Os09g0491100 are up-regulated in phenylpropanoid biosynthesis. This metabolic pathway involved in plant stress resistance. The first compound of phenylpropanoid pathway is L-phenylalanine which biosynthesis from phenylpropanoid shikimate pathway (Hu et al. 2017). This was consistent with the variation of phenylalanine content in the above metabolic analysis; 389 DEGs were down-regulated mainly annotated in photosynthesis-antenna proteins and TCA cycle, etc. Studies have shown that increased CO₂ concentration can alleviate the negative effects of drought stress by improving photosynthesis, but this mitigation effect decreases or even disappears with the increase of drought severity (Gray et al. 2016). In this study, the concentration and treatment time of Cd were relatively higher and longer, so the photosynthetic relief effect of high CO₂ on Cd stress was weak, mainly manifested in the down-regulated gene (Os09g0346500) related to photosynthesis-antenna proteins.

High concentration of CO₂ and Cd pollution affect plant growth and metabolism, our results showed that the responding mechanism of rice leaves to the above complex environmental changes was as follows:

(1) Under Cd stress, DEGs enriched in glutathione metabolic process, and GR played an important antioxidant role; sugar and polyol metabolism was greatly enhanced to provide energy and the osmotic adjustment substances. Rice leaf respiration rate was enhanced which also can release energy to resist stress. Myo-inositol and raffinose increased under Cd as the important metabolic regulators; The large increase of carbohydrate promoted pyruvate metabolism and thus promoted the formation of many amino acids to improve osmotic adjustment, maintain cell membrane stability and supply N assimilates. Organic acids contents increased for regulating plant nutrition, glycolysis and TCA cycle. They can also strong antioxidant capacity of plants as the secondary metabolites. Increase of putrescine content need further research. Down-regulated DEGs in photosynthesis-antenna protein showed photosynthetic process of rice had been adversely affected.

(2) Under EC, compared with AC, sufficient photosynthates are consistent with high APX and DHAR activities. However, the decrease of some antioxidant enzymes activities may be caused by the decrease of partial pressure of O₂; High CO₂ could reduce stomatal opening of leaves, and the dropped O₂ intake can also decreased respiration rate of rice leaves. The decrease of aconitic acid and fumaric acid contents and down-regulated DEG (Os07g0630800) in our study verified this result. Glutamic acid maybe consumed to synthesize more photosynthetic pigments. Down-regulated DEGs annotated in photosynthesis-antenna protein maybe inhibited and regulated the light reaction process of rice to cope with the phenomenon of "photosynthetic adaptation" under high CO₂.

(3) Under composite treatments, compared with AC, peroxidation damage still existed, the overall metabolic trend was similar to that under Cd treatment; Compared with Cd stress, EC could enhance the oxidation resistance of rice by the higher DHAR and GR activities through regulating the ASA-GSH cycle. EC promoted the increase of many amino acids for osmotic regulation under Cd stress, DEGs (Os01g0192900, Os08g0248800) regulating amino acid metabolism were up-regulated. Affected by EC, respiration rate decreased, lucose-1-P, L-malic acid, aconitic acid and fumaric acid contents (related to glyoxylate and dicarboxylate metabolism and TCA cycle) decreased, down-regulated DEGs were also annotated in TCA cycle. Contents of putrescine and myo-inositol increased. EC could alleviate the loss of cellular moisture by regulating the transmembrane movement of substances and the activities of some enzymes.

Funding: This work was supported by National Natural Science Foundation of China (31600314) the Department of Education of Liaoning Province, China (LJKZ0991).

Conflicts of interest/Competing interests: The authors have no relevant financial or non-financial interests to disclose.

Availability of data and material: The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Code availability: Not applicable

Authors' contributions: All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Lanlan Wang, Xuemei Li, Meng Li and Jiayu Wang. The first draft of the manuscript was written by Lanlan Wang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Ethics approval: Not applicable

Consent to participate: Not applicable

Consent for publication: Not applicable

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Transcriptomics and metabolomics analysis reveal the molecular responses of rice to Cd stress and/or elevated CO₂ concentration

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