Dynamic changes in the levels of metabolites and endogenous hormones during the germination of Zanthoxylum nitidum (Roxb.) DC. Seeds

ABSTRACT

Accumulating experimental data have shown that endogenous hormones play important roles in regulating seed dormancy and germination. Zanthoxylum nitidum is a medicinal plant that propagates via seeds, which require a long dormancy period for normal germination, and complex changes in metabolites occur during the germination process. However, the regulatory network of endogenous hormones and metabolites during the germination of Z. nitidum seeds remains unclear. This study investigated the dynamic changes in the levels of metabolites and endogenous hormones during the germination of Z. nitidum seeds. The results revealed an increase in the levels of gibberellin 3 (GA₃), 12-oxophytodienoic acid (OPDA), 1-aminocyclopropane-1-carboxylic acid (ACC) and trans-zeatin (TZ) and decrease in the levels of abscisic acid (ABA), jasmonic acid (JA), N-[(-)-jasmonoyl]-(S)-isoleucine (JA-Ile) and trans-zeatin riboside (TZR). Overall, 112 differential metabolites (DAMs) were screened from 3 seed samples (Sa, Sb and Sc), most of which are related to primary metabolism. A total of 16 DAMs (including 3 monosaccharides, 3 phosphate lipids, 3 carboxylic acids, 1 amino acid, 2 pyrimidines, and 4 nucleotides) were identified in the three sample comparison pairs (Sa vs Sb, Sa vs Sc, and Sb vs Sc); these DAMs were significantly enriched in purine metabolism; glycerophospholipid metabolism, citrate cycle (TCA cycle), alanine, aspartate and glutamate metabolism and pyruvate metabolism. OPDA, ACC and GAs were significantly positively correlated with upregulated metabolites, whereas ABA and JA were significantly positively correlated with downregulated metabolites. Finally, a hypothetical metabolic network of endogenous hormones that regulate seed germination was constructed. This study deepens our understanding of the importance of endogenous hormonal profiles that mediate seed germination.

KEYWORDS:

• Zanthoxylum nitidum
• seed germination
• seed dormancy
• nontargeted metabolome
• endogenous hormone

1. Introduction

Zanthoxylum nitidum (Roxb.) DC is a plant of the genus Zanthoxylum belonging to the family Rutaceae, which is mainly found in Guangxi and Guangdong in China.¹ Its root, also known as Liang-mian-zhen, is used as an important raw material in traditional Chinese medicines, with the active ingredients of alkaloids, flavonoids and lignin.² It possesses various pharmacological activities, such as anti-inflammatory,^3,4 antibacterial,^5,6 anticancer.^7,8 and antinociceptive.⁹ Liang-mian-zhen is the main raw material of > 60 well-known traditional Chinese patent medicines, and its extract is also widely used in daily chemical products, including toothpaste, soap and shampoo.¹⁰

In recent years, owing to uncontrolled deforestation, the resources of wild Z. nitidum have gradually decreased.^11,12 Artificial cultivation is the primary approach to alleviate this shortage of resources. Z. nitidum mainly propagates via seeds; however, the germination rate of fresh seeds is only 4%, and the process from sowing to emergence usually requires 180 days.^13,14 The low germination rate and long germination time have become the main factors limiting the growth and yield of Z. nitidum. To overcome the challenge of germination Z. nitidum seeds, previous studies have mainly focused on germination techniques. For example, studies have confirmed that gibberellin (GA) can break the dormancy of Z. nitidum seeds and promote their germination. According to these studies, immersing the seeds in 300 mg/L GA solution for 2 days significantly increased the germination rate from 3.7% to 22%,¹⁵ and layering the seeds onto 100 mg/L GA solution for 1 month significantly increased the germination rate from 0.7% to 47.9%.¹⁶ However, to date there has been still limited research on the physiological and molecular regulatory mechanisms of seed germination.

When seeds break their dormancy and enter the germination stage, they usually need to undergo swelling, hydration, activation of related enzymes, and cell division and growth; the embryos then break through the seed coat, and finally grow into seedlings.^17,18 The process of seed germination changes from a static metabolic state to a highly active metabolic state, where the starch, fat, protein, and other reserves are mobilized to sustain the metabolic and energetic demands during the early vegetative growth.^19,20 In this process, various metabolites that promote seed germination are produced and accumulated, such as the plant endogenous hormone GA;²¹ in contrast some metabolites that inhibit seed germination are gradually decomposed, such as abscisic acid (ABA).²² Recent studies have revealed the role of endogenous hormones in regulating seed dormancy and germination.^23,24 However, to the best of our knowledge, no studies have been reported on the interaction between endogenous hormones and metabolites during the germination of Z. nitidum seeds.

Currently, with the development of metabolomics technology, a deeper understanding of the material basis underlying complex germination processes has been obtained. For example, an in-depth study on the germination of wheat seeds revealed 82 differential metabolites (DAMs) during the five germination stages, which are mainly related to protein, sugar, and lipid metabolism.²⁵ A large number of DAMs have also been detected during the five germination stages of quinoa (Chenopodium quinoa) seeds, which are mainly related to plant hormone signal transduction, nutrition, and energy metabolism.²⁶

This study analyzed the dynamic changes in the levels of hormones and metabolites during the germination of Z. nitidum seeds using plant hormone targeted and nontargeted metabolomic technology, to identify the biological pathways related to metabolite level changes and reveal the potential molecular mechanism of seed germination.

2. Materials and methods

2.1 Seed preparation

Ripe Z. nitidum seeds were collected from Nanning City, Guangxi Province (108°11′ E, 22°31′ N) in September 2021 (hundred-grain weight of 2.11 g, 13.5% water content). For stratification treatment, the seeds (20 g) were immersed in GA₃ for 48 h and then mixed with moist sand (particle size, 0.1–0.2 mm; 200 g of river sand +30 g of purified water); the mixture was stored at 15°C for 30 days.

2.2 Seed germination

After stratification, the seeds were arranged on a wet sand bed in an incubator for germination (25°C; light/dark photoperiod of 12 h/12 h). A layer of moist fine sand (60 g of 1–2 mm fine sand +12 g of tap water) was placed onto a culture dish (diameter, 9 cm), and 60 stratified seeds were evenly inoculated onto the dish.¹⁶ The number of germinated seeds was recorded every 5 days until the germination was complete, and each treatment was repeated five times. Fifty seed embryos at each stage were sampled and stored at −80°C for further analysis.

2.3 Plant endogenous hormone analysis

Sixteen plant hormone standards were accurately weighed and dissolved in methanol to obtain a mixed standard solution. Overall 50 mg of the sample was weighed and dissolved in methanol, followed by mixing of the internal standard stock solution, sonication for 10 min, transfer to a constant-temperature shaking metal bath for 4 h (1,500 rpm, 20°C), and centrifugation for 10 min (12,000 rpm, 4°C). Then, 0.5 mL of 80% dimethyl sulfoxide/methanol (V: V) was added to the remaining residue, followed by shaking for 2 h (1,500 rpm, 20°C). The supernatants from the two extractions were then combined.²⁷

The ExionLC UPLC system (AB Sciex, USA) was employed to analyze the hormone contents. The chromatographic column conditions were as follows: Acquity UPLC® CSH C18 column (1.7 μm, 2.1 × 150 mm, Waters); column temperature, 40°C; injection volume, 2 μL; eluent composition, 0.05% formic acid with 2 mM ammonium formate water (eluent A) and 0.05% formic acid methanol (eluent B); and flow rate, 0.25 mL/min. The elution gradient was set as follows: 0–2 min, 10% B; 2–4 min, 10%–30% B; 4–19 min, 30%–95% B; 19–19.10 min, 95%–10% B; and 19.10–22 min, 10% B. For mass spectrometric detection, the electrospray ionization (ESI) parameters were as follows: ion source voltage, 4,500 V; ion source temperature, 400°C; curtain gas pressure, 40 psi; atomization gas pressure, 40 psi; and auxiliary gas pressure, 25 psi.^28,29

2.4 Metabolome analysis

Approximately 50 mg of the sample was accurately weighed and grinded with 400 µL of extract buffer (methanol: water = 4:1 (v: v)) at −10°C for 6 min (50 Hz), followed by ultrasound treatment (40 KHz) at 5°C for 30 min. The extract was further stored at −20°C for 30 min and centrifuged at 13,000 rpm at 4°C for 15 min. Then the supernatants were filtrated before conducting instrumental analysis.

The UHPLC-Q Exactive HF-X system (Thermo Scientific, USA) was employed to analyze the metabolites of Z. nitidum seed embryos. The chromatographic column conditions were as follows: ACQUITY UPLC HSS T3 column (100 mm × 2.1 mm, 1.8 µm, Waters, Milford, USA); column temperature, 40°C; mobile phase A, 95% water + 5% acetonitrile (containing 0.1% formic acid); mobile phase B, 47.5% acetonitrile + 47.5% isopropanol + 5% water (containing 0.1% formic acid); and injection volume, 3 μL. For mass spectrometric detection, samples were ionized via ESI. The irons were scanned using both positive and negative modes at a resolution of 60,000 and scan range of 70–1,050 m/z. The flow rates of sheath gas and aux gas were 50 and 13 arb, respectively, and the temperature was 425°C.

2.5 Statistical analysis

The hormone data were presented as mean ± standard deviation. Means were compared using one-way analysis of variance via SPSS 23.0. The DAMs of the metabolome were screened via principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA). The metabolites with a p-value of ≤ 0.05 and VIP ≥ 1 were considered to indicate a significant difference. The publicly available metabolite databases (http://www.hmdb.ca/and https://metlin.scripps.edu/) were used to annotate DAMs, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to analyze the identified DAMs.

3. Results

3.1 Seed germination process

The Z. nitidum seeds were first stratified for 1 month. The stratified seeds had already begun to germinate on the 10th day, with a rapid germination period of 10–20 days and a germination rate of 41.7% at the end of 30 days (Figure 1). The results revealed that treatment with 50–400 mg/L GA₃ significantly increased the germination rate compared with treatment with water, and GA₃ exerted the optimal effect on germination at a concentration of 100 mg/L. Therefore, in this study, we sampled seeds at three important time points during the germination process to analyze the changes in metabolites: Sa, dry seeds (i.e., seeds in the initial state); Sb, stratified seeds (i.e., Sa immersed in 100 mg/L GA₃ for 48 h and then stratified at 15°C for 30 days); Sc, germinated seeds (i.e., Sb placed on a sand bed at 25°C for 15 days, with the radicle growing to a length of approximately 2 mm).

Figure 1. Germination process of Z. nitidum seeds. (a) germination rate, (b) Appearance of the three seed samples analyzed in this study (Sa: dry seeds; Sb: stratified seeds; Sc: Germinated seeds.).

3.2 Dynamic changes in the levels of endogenous hormones during germination

During transformation from Sa to Sb, the total content of auxin (Aux), including indole-3-acetic acid (IAA), 3-indoleformic acid (ICA), indole-3-carboxaldehyde (ICAId), significantly increased. In contrast, during transformation from Sb to Sc, the Aux content decreased to 89% of that in Sa. GA₁, GA₃, GA₇, and SA exhibited the same trend, forming a bell shape. The levels of Jasmonic acid (JA) and N-[(-)-jasmonoyl]-(S)-isoleucine (JA-Ile) significantly decreased during transformation from Sa to Sb (from 313.78 ± 10.78 ng/g to 149.67 ± 4.46 ng/g) and then significantly increased during transformation from Sb to Sc (from 149.67 ± 4.46 ng/g to 194.94 ± 5.90 ng/g), forming an inverted bell shape. The level of 1-aminocyclopropane-1-carboxylic acid (ACC) significantly increased during the germination process, from 18,951.49 ± 1282.14 ng/g to 40,767.31 ± 2562.39 ng/g. OPDA exhibited the same trend, showing a linear increase. The level of of ABA in Sa, Sb, and Sc was 93.78 ± 2.49, 42.99 ± 2.52, and 16.71 ± 0.88 ng/g, respectively, showing a linear decrease. Among the four types of cytokinins (CTKs), namely, (N6-isopentenyladenosine-D6 (IPA), trans-zeatin riboside (TZR), N6-(delta 2-Isopentenyl)-adenine (IP), and trans-zeatin (TZ)), IPA showed the highest level, which significantly decreased during the germination process, from 42.87 ± 0.42 ng/g to 4.50 ± 0.12 ng/g (Figure 2).

Figure 2. Dynamic changes in the levels of endogenous hormones during germination.

Different letters in the histograms indicate significant differences between the groups (p < 0.05).

3.3 Metabolic data analysis

Overall, 4,092 and 6,106 precursor molecules were obtained via positive and negative ion mode, respectively. In total, 170 metabolites could be annotated in the KEGG compound database (Table S1), mainly including phospholipids (50, 29.4%), amino acids (26, 15.3%), monosaccharides (18, 10.6%), and carboxylic acids (13, 7.6%) (Figure 3a). They were mapped to 81 KEGG biological pathways, such as lipid metabolism (59, 34.7%), amino acid metabolism (34, 20.0%), membrane transport (30, 17.6%), and carbohydrate metabolism (28, 16.4%) (Figure 3b). The PCA score plot showed that principal component 1 and 2 explained 49.30% and 32.20% of the variability, respectively (Fig S1), and the three samples were well separated. In addition, the first and second components of the PLS-DA score plot explained 58.1% and 28.9% of the variability, respectively; large differences were also detected (Fig S2).

Figure 3. Classification of the metabolites. (a) KEGG compound database annotation result, (b)KEGG pathway database annotation result.

3.4 Screening of DAMs

As shown in Figure 4a and Table S2, 76, 59 and 77 DAMs were identified in the three comparison pairs (Sa vs Sb, Sb vs Sc, and Sa vs Sc), respectively. Among them, 33 and 43, 35 and 24, 39 and 38 metabolites were up- and downregulated in Sa vs Sb, Sb vs Sc, and Sa vs Sc, respectively. In total, 112 DAMs were identified, 16 of which were common among the three groups. The results of KEGG pathway analysis revealed that 112 DAMs were highly significantly enriched in ABC transporters; aminoacyl-tRNA biosynthesis; purine metabolism; alanine, aspartate and glutamate metabolism; cyanoamino acid metabolism; pyrimidine metabolism; arginine biosynthesis; glyoxylate and dicarboxylate metabolism; beta-alanine metabolism; citrate cycle (TCA cycle); monobactam biosynthesis; carbon fixation in photosynthetic organisms; galactose metabolism; biosynthesis of cofactors; glucosinolate biosynthesis; pantothenate and CoA biosynthesis; and glycerophospholipid metabolism (p < 0.001). Most of these pathways were related to primary metabolites (Figure 4b).

Figure 4. Dams analysis among the three groups. (a) Venn diagram indicates the common and unique metabolites in the three comparison pairs, (b) pathway enrichment of DAMs. The horizontal axis represents the pathway name, and the vertical axis represents the enrichment rate (representing the ratio of the number of metabolites enriched in the pathway to the total number of metabolites annotated in the pathway). The larger the ratio, the greater the extent of enrichment. The color gradient of the column indicates the significance of enrichment. The darker the color, the more significant the enrichment. ***p < 0.001, **p < 0.01, *p < 0.05.

3.5 Dynamics of the DAMs during germination

Based on K-means cluster analysis, 16 common DAMs were classified into three main categories (Figure 5a), containing 5, 9 and 2 metabolites (Figure 5b). The metabolites in subcluster 1 showed a trend of Sa>Sb>Sc, including two phospholipids and three monosaccharides. The metabolites in subcluster 2, which contained the highest number of compounds (accounting for 56.25% of total metabolites) showed a trend of Sa<Sb<Sc; this subcluster contained one phospholipid, two carboxylic acids, one amino acid, one cyclic nucleotide, three nucleosides and one base. The metabolites in subcluster 3 exhibited bell-shaped changes during germination and included succinic acid and guanine.

Figure 5. Analysis of the varying trends of common DAMs. (a) Heatmap analysis of 16 common DAMs. The colors in the figure represent the relative content of metabolites in the sample, with darker blue indicating lower content and darker red indicating higher content. (b) Dynamics of the compounds in subclusters, the horizontal axis represents the sample name, whereas the vertical axis represents the abundance of the compound.

3.6 Correlation analysis between endogenous hormones and common DAMs

The 16 common DAMs and 16 endogenous hormones exhibited certain correlations (Figure 6). The five compounds in the cluster 1 were positively correlated with the levels of ABA, TZR, IPA, JA, JA-Ile and negatively correlated with the levels of ACC, OPDA, GA₃, and GA₇. The mine compounds in cluster 2, which exhibited completely opposite trends to cluster 1, were positively correlated with the levels of OPDA, ACC, GA₃, GA₇, and TZ and negatively correlated with the levels of ABA, IPA, TZR, JA, and JA-Ile. Overall, OPDA, ACC, and GA levels were significantly positively correlated with upregulated metabolites and negatively correlated with downregulated metabolites; however, ABA and JA showed opposite trends.

Figure 6. Correlation analysis between endogenous hormones and common DAMs.

Right side: names of the metabolites; bottom, names of the endogenous hormones. Each grid in the figure represents the correlation between the metabolite and the endogenous hormone, with red representing a positive correlation, blue representing a negative correlation, and the intensity of the color representing the magnitude of the correlation coefficient.

3.7 Correlation network and metabolic pathway analyses

In total, 16 common DAMs were significantly enriched in five pathways (p < 0.001), with 4, 3, 2, 2, and 2 metabolites in purine metabolism; glycerophospholipid metabolism; TCA cycle; alanine, aspartate, and glutamate metabolism; and pyruvate metabolism, respectively. Therefore, based on data regarding endogenous hormones and the abovementioned five key pathways, a pathway map was integrated and proposed (Figure 7). In this pathway, hormone signals first promote the metabolism of fatty acids, proteins, and purines, forming various small-molecule species, and then promote the TCA cycle to generate more energy, thereby enhancing seed germination.

Figure 7. Hypothesized pathway map during the germination of Z. nitidum (Roxb.) DC. Seed.

In the histogram, the X-axis represents the sample name, the Y-axis stands the relative concentration of metabolites, and different letters represent significant differences between the groups (p < 0.05). The boxes display five metabolic pathways, with dashed lines indicating indirect regulatory relationships.

4. Discussion

Dormant seeds cannot germinate even when they are placed under conditions suitable for germination. Depending on different reasons, dormancy is usually divided into five categories: physiological dormancy (PD), morphological dormancy (MD), morphophysiological dormancy (MPD), physical dormancy (PY) and combinational dormancy (PY + PD).³⁰ PD is the most common type of dormancy, and stratification is the most effective method to release the dormancy and stimulate germination.³¹ Z. nitidum seeds exhibit PD.¹⁶ The results of this study showed that both stratification and exogenous GA treatment are effective methods for breaking the dormancy of Z. nitidum seeds. The germination method is the same as that used for most physiologically dormant seeds.

Plant endogenous hormones play a crucial role in regulating seed germination and dormancy.³² GA is the primary hormone that facilitates the breakdown of seed dormancy by promoting the synthesis of Aux and CTK and activating amylase and protease.^33,34 ABA is a major hormone that induces seed dormancy by regulating the accumulation of storage proteins and lipids in seeds.³⁵ JA cooperates with ABA to inhibit seed germination.³⁶ Exogenous SA can improve seed germination under environmental stress;³⁷ moreover, exogenous ethylene exerts the same effect.³⁸ To the best of our knowledge, this is the first study to analyze the levels of various endogenous hormones during the germination of Z. nitidum seeds. Our results revealed that the levels of GA₃, OPDA, ACC and TZ increased and those of ABA, JA, JA-Ile and TZR decreased during the germination of Z. nitidum seeds. These results are consistent with previous findings in various seeds of plants, such as Phoebe sheareri,³⁹ Scutellaria baicalensis,⁴⁰ Bupleurum chinense.⁴¹, further supporting the positive role of GA in regulating seed germination, as well as the negative role of ABA.

Seed germination is the core event in the plant life cycle. In general, it is considered to begin from water absorption and seed expansion and end at radicle budding,⁴² with the process involving a series of morphological, physiological, and biochemical changes.^43,44 In this study, we identified 170 metabolites and screened 112 DAMs during seed germination, and most of these DAMs were primary metabolites. Furthermore, we identified 16 common DAMs in the three comparative pairs, which were closely related to endogenous hormone levels and highly significantly enriched in purine metabolism; glycerophospholipid metabolism; TCA cycle; alanine, aspartate and glutamate metabolism; and pyruvate metabolism.

Purine metabolism is involved in many plant biochemical processes. Purines are an energy source and precursors for the synthesis of primary products, such as sucrose, polysaccharides, phospholipids, as well as secondary products.⁴⁵ In the process of seed germination, the involvement of purine metabolism in the myriad of bioenergetic processes is required for the mobilization of storage products; moreover, active nucleotide biosynthesis is needed to provide sufficient pyrimidine nucleotides to the embryos to facilitate nucleic acid synthesis.⁴⁶ In this study, we observed that some nucleotides (cytosine, CAMP, cytidine, deoxyadenosine and guanosine) involved in the purine metabolism pathway accumulated significantly during the germination of Z. Nitidum seed, consistent with the varying trend of nucleotides during seed germination of common beans,⁴⁷ castor,⁴⁸ Arabidopsis thaliana⁴⁹and other seeds. This finding indicates that a large number of nucleotides are required for the germination of Z. nitidum seeds in addition to the mobilization of nutrients in cotyledons.

Arginine catabolism mobilizes stored nitrogen and fine-tunes the production of nitric oxide, polyamines and potentially proline. It can provide a nitrogen source for synthesizing the proteins required for seed germination.^50,51 Exogenous arginine can promote the germination of seeds such as beans⁵² and maize,⁵³ whereas the loss of function of the arginine methyltransferase gene AtPRMT3 leads to delayed germination of Arabidopsis seed,⁵⁴ indicating that arginine metabolism plays an important role in the seed germination process. In this study, we observed a significant accumulation of argininosuccinate during the germination of Z. Nitidum seeds, suggesting that arginine is an important nitrogen source for synthesizing proteins required for the growth of Z. nitidum seedlings.

In most plants, oil is stored in the seed tissue in the form of triacylglycerol.^55,56 Phosphatidylcholine (LysoPC) is an important intermediate of triacylglycerol catabolism.⁵⁷ In this study, we found that LysoPC accumulates in large quantities during the germination of Z. Nitidum seeds. it is speculated that the hydrolyzates of LysoPC provide energy for seed germination through the TCA cycle and pyruvic acid metabolic pathway, which is consistent with the physiological process of plant seed germination identified in previous studies.^58,59

Based on the results of this study, we integrated data regarding endogenous hormones and DAMs during seed germination and suggested a metabolic network. This network indicates that the accumulation of endogenous GA and reduction of ABA are signals that stimulate the decomposition of primary metabolite, further promoting the TCA cycle to provide energy for seed germination.

5. Conclusions

This study evaluated the dynamic changes in the levels of 16 endogenous hormones and 170 metabolites during the germination of Z. nitidum seeds. A total of 112 DAMs were screened from different seed samples, most of which were related to primary metabolism. OPDA, ACC, and GAs were significantly positively correlated with upregulated metabolites, whereas ABA and JA were significantly positively correlated with downregulated metabolites. In addition, a hypothetical metabolic network map of endogenous hormones regulating seed germination was constructed. Accumulating experimental data have shown that endogenous hormones play an important role in regulating seed dormancy and germination. This study deepens our understanding of the importance of endogenous hormonal profiles that mediate seed germination.

Author contributions

Conceptualization, Yanxia Zhu and Guiyu Tan; methodology, Liang Wang, Qing Ma and Hongsheng Zhang; software, Jianping Jiang and qing Ma; formal analysis, Yanxia Zhu, Jianping Jiang and Hongsheng Zhang; resources, Liang Wang and Yanxia Zhu; writing-original draft preparation, Liang Wang, Yanxia Zhu and Jianping Jiang; writing-review and editing, Yanxia Zhu and Guiyu Tan; visualization, Qing Ma and Hongsheng Zhang; supervision, Yanxia Zhu and Guiyu Tan. All authors have read and agreed to the published version of the manuscript.

Abbreviation

ABA	=	abscisic acid
ACC	=	1-aminocyclopropane-1-carboxylic acid
Aux	=	auxin), CTK (cytokinins
DAMs	=	differential metabolites
GAs	=	gibberellins
IAA	=	indole-3-acetic acid
ICA	=	3-indoleformic acid
ICAId	=	indole-3-carboxaldehyde
IP	=	N6-(delta 2-Isopentenyl)-adenine
IPA	=	N6-isopentenyladenosine-D6
JA	=	jasmonic acid
JA-Ile	=	N-[(-)-jasmonoyl]-(S)-isoleucine
OPDA	=	12-oxophytodienoic acid
SA	=	salicylic acid
TZ	=	trans-zeatin
TZR	=	trans-zeatin riboside
Z. nitidum	=	Zanthoxylum nitidum (Roxb.) DC

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15592324.2023.2251750

Additional information

Funding

This research was funded by the Scientific Research and Technology Development Program of Guangxi (GUIKE AB22035068), and the Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine. (No: ZYYCXTD-D-202005).