Next Article in Journal
Effect of Melatonin on Germination and Seedling Growth in Aging Seeds or under Drought Conditions
Previous Article in Journal
Study of Grapevine (Vitis vinifera L.) Seed Morphometry and Comparison with Archaeological Remains in Central Apennines
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Seeds
Volume 3
Issue 3
10.3390/seeds3030024
seeds-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Tissue-Specific Accumulation Profiles of Phorbol Esters in Response to Abiotic and Biotic Stresses in Jatropha curcas

by
Wei Zhang
1,2,*,
Lei Wei
1,3,
Shijuan Li
1,
Fang Chen
1,4 and
Ying Xu
1,*
1
Key Laboratory of Bio-Resources and Eco-Environment, Ministry of Education, College of Life Sciences, University of Sichuan, 29 Wangjiang Road, Chengdu 610064, China
2
Institute for Integrative Genome Biology, Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
3
Institute Biotechnology Developing Center, Henan Academy of Sciences, Hongzhuan Road, Zhengzhou 450002, China
4
Key Laboratory of Resource Biology and Biopharmaceutical Engineering, College of Life Sciences, University of Sichuan, 29 Wangjiang Road, Chengdu 610064, China
*
Authors to whom correspondence should be addressed.
Seeds 2024, 3(3), 324-340; https://doi.org/10.3390/seeds3030024
Submission received: 19 March 2024 / Revised: 13 June 2024 / Accepted: 28 June 2024 / Published: 2 July 2024
Browse Figures
Versions Notes

Abstract

:
Jatropha curcas L. (J. curcas), a shrub plant of the Euphorbiaceae family, has received enormous attention as a promising biofuel plant for the production of biodiesel and medical potential in ethnopharmacology. However, the tumor-promoter toxin phorbol esters present in J. curcas raise concerns for health and environmental risk as its large-scale cultivation limits the use of meal obtained after oil extraction for animal feed. Here, we determined the variation of phorbol ester profiles and contents in eight J. curcas tissues by high-performance liquid chromatography (HPLC) and found phorbol esters present in all parts of the plant except the seed shell. We showed tissue-specific patterns of accumulation of phorbol esters and associated terpenoids at the transcriptional level with high transcript levels in reproductive and young tissues. Genes involved in the same module of terpenoids biosynthesis were positively correlated. We further present diverse abiotic and biotic stresses that had different effects on the accumulation of transcripts in terpenoids shared and branched terpenoid pathways in plant seedlings. The fine-tuning of terpenoids biosynthesis may link with ecological functions in plants under extreme environments and defense against pathogens.

1. Introduction

Jatropha curcas (J. curcas, Euphorbiaceae) is a multipurpose shrub distributed widely in tropical and subtropical regions of Latin America, Asia, and Africa [1]. Its ability to survive under extreme conditions, such as heat and drought, makes it popular as domesticated hedge in barren lands [2]. Recently, J. curcas has gained enormous attention as a promising feedstock crop in the biodiesel industry [3,4]. J. curcas seeds contain oil that can be used as a diesel substitute in the biodiesel industry. Additionally, seed meals, the co-product after oil extraction, contain high-level proteins and can serve as good sources for livestock [5,6]. However, the presence of toxic phorbol esters in seeds and other parts of J. curcas dramatically restricts the widespread use of its oil and seed meal because the tumor-promoting activity of phorbol esters may pose risks on human health and the environment [7,8,9].
Presently, six types of phorbol esters have been isolated from J. curcas seed oil [10]. These esters share the same phorbol core, a tetracyclic carbon skeleton, with diverse intramolecular di-esters. Although accumulated as a phytoalexin in plants to facilitate their fitness, phorbol esters are known for their extensive bioactivities, especially as a carcinogen, in a wide range of organisms. For example, they can induce acute toxic effects in animals and insets and cause skin irritant and promote tumors in human beings [11,12,13,14,15]. In contrast, recent studies revealed that phorbol esters can exhibit antimicrobial and antitumor activities when applied at low concentrations [16,17]. The diverse bioactivities of phorbol esters are not only dependent on dosage effects but also associated with varied structures among phorbol esters and their derivates. These varied structures are the basis for phorbol esters to activate protein kinase C involved signaling transduction, which is involved in diverse cellular processes and promotes tumors. Indeed, the ethnobotanical use of J. curcas is not only attributed to some pharmaceutical compounds but also associated with toxins, such as phorbol esters and protein curcin [18,19,20,21,22,23,24,25,26,27,28]. Elucidating the types and abundance of phorbol esters in different parts of J. curcas is necessary for harnessing this toxic plant as a value to the biodiesel industry and with high pharmaceutical potential.
Phorbol esters are diterpenoids that are synthesized via the terpenoid pathway mainly in the Euphorbiaceae and Thymelaeaceae family. Their biosynthesis can be divided into three separate phases: (a) formation of the phorbol core, the C20 tetracyclic skeleton, (b) esterification of polyhydroxy groups at C-12, C-13, and C-20 positions of the phorbol core, and (c) further modification of the carbon skeleton and side chains [29]. In plants, formation of C20 diterpene skeleton precursor gernanylgeranyl diphosphate (GGPP) is catalyzed by gernanylgeranyl diphosphate synthase (GGPPs) and requires the two C5 isoprenoid precursor, isopentenyl diphosphate (IPP), and its isomer dimethylallyl diphosphate (DMAPP) [30]. These precursors derived from mevalonate (MVA) pathway or non-MVA pathway (MEP or DXP pathway) are required by all terpenoid productions, including geranyl diphosphate (GPP, C10)-derived monoterpenoids, farnesyl diphosphate (FPP, C15)-derived sesquiterpenes and triterpenoids, and geranylgeranyl diphosphate (GGPP, C20)-derived diterpenoids [31,32]. The GGPP backbones can be further cyclized to form the phorbol core by a terpene synthase named casbene synthase (CAS) [33,34]. Then, some modification enzymes, such as acyltransferase, terpene hydroxylase, and cytochrome P450monooxygenases and reductases, are responsible for adding ester groups to side chains and further modification [35]. Terpenoid accumulation displays tissue- and developmental stage-specific patterns and can be induced upon abiotic and biotic stresses [36,37]. Although Euphorbiaceae plants are tolerant of heat and drought, little is known about how they manipulate terpenoid production, especially the major phytoalexin phorbol esters, to adapt to abiotic and biotic stresses [38,39,40].
In this study, we investigated the accumulation of phorbol esters and associated gene expression across different J. curcas tissues. Using HPLC, we show that phorbol esters are found in all tested J. curcas tissues except seed shells at varying types and abundances. The quantitative RT-PCR revealed that the expression profiles of genes involved in phorbol esters and terpenoids biosynthesis exhibited tissue-specific patterns with higher accumulations in reproductive organs and young tissues. Additionally, we found that genes involved in terpenoids production in J. curcas are differentially regulated in response to abiotic and biotic stressors. In summary, phorbol ester content and gene expression data provide support for the reduction of phorbol ester content through chemical detoxification or breeding and genome editing. Such detoxification is required for large-scale application of J. curcas oil in the biofuel industry, seed meal in agriculture, and bioactive components in pharmaceuticals. The regulation of terpenoid gene expression in response to abiotic and biotic stresses such as drought and fungal pathogens indicates that terpenoids, especially phorbol esters, play an important role in Euphorbiaceae and Thymelaeaceae plants’ tolerance against the extreme environmental condition.

2. Materials and Methods

2.1. Plant Materials and Growth Condition

J. curcas seeds used for phorbol ester content analysis were collected in September from trees growing at the field in Panzhihua City of Sichuan Province, China. All seeds were mixed to minimize the impact of variation generated by sample differences. The kernel, shell (episperm, the black woody part at outside of the seed), and endotesta (the white paper-like part at the outside of the kernel) were carefully separated from the above seeds. Other J. curcas tissues used for phorbol ester content analysis, including root barks, stem barks, leaves, and latex, were collected in July from six-year-old J. curcas seedlings growing in a nursery at Yanyuan County, Sichuan Province, China. The above tissues were shade-dried, ground into fine powders, and stored at −20 °C until use. The latex was gathered into 1.5 mL centrifuge tubes stored at −20 °C immediately.
The J. curcas tissues used for gene expression profile analysis, including root, young stem, leaf, bud, young seed, yellow seed, and green fruit, were collected from the same nursery and preserved in RNAlater (Ambion, Chengdu, China) at 3x the volume of RNAlater/tissue. Preserved tissue was placed at −80 °C for long-term storage.
The J. curcas seedlings used for abiotic and biotic stresses analysis were grown in a greenhouse at Sichuan University. All materials for stress treatment expression analysis were collected from seedlings with five true leaves. A total of 21 potted plants were used for seven different stress treatments, including cold, drought, salt, exogenous hormone JA and SA, biotrophic fungus powdery mildew, and necrotrophic fungus Botrytis cinerea. Three biological replicates were performed for each treatment. Briefly, for cold treatment, the potted plants were transferred to a refrigeration house (4 °C ± 0.5 °C) for six hours. For drought and salt treatments, the potted plants were watered with 30% polyethylene glycol 4000 (PEG) or 100 mM/L of NaCl to mimic the drought or salt stresses, respectively. For exogenous hormone treatments, the potted plants were sprayed with an aqueous solution containing 1 mM/L of SA and JA, respectively. For biotic stress treatments, the potted plants were sprayed with powdery mildew or Botrytis cinerea spore suspensions (10 spores/µL). The seedling leaves were collected at six hours post each treatment and flash-frozen in liquid nitrogen and stored at −80 °C until RNA extraction.

2.2. Extraction of Phorbol Esters from Diverse J. curcas Tissues

The ultrasound-assisted extraction (UAE) method was used in this study for the extraction of phorbol esters from all types of plant material samples as described previously [41,42]. J. curcas kernels were used in the UAE method for optimization of the five parameters, including four extraction solvents, soaking temperature, solvent/materials ratio, ultrasound irritation time, and extraction temperature. In brief, an aliquot of powdered samples was weighed into 5 mL of Eppendorf safe lock tubes. The solvent was added into samples, mixed well, and incubated for 40 min in a water bath with a fixed soaking temperature. The mixture was then sonicated in an ultrasound processor (Transsonic KH-300DB, Kunshan Hechuang, Ultrasonic Instrument Co., Ltd., Kunshan, China) for a while at a fixed temperature. The supernatant was collected. The residue remaining at the bottom of the tube was re-extracted three times using the same method. The supernatants from four extractions were pooled and evaporated for 15 min at 35 °C by a nitrogen blowing instrument to obtain an oily sample.
To minimize the impacts of coloring impurities on the phorbol ester determination from the extracts of root barks, stem barks, and leaves, we conducted a removal of pigments experiment using powdered activated carbon (PAC). The adsorption effect on phorbol esters was investigated by comparing phorbol esters’ contents between kernel extracts with and without PAC treatment. In brief, the kernel extracts were divided into two samples. One is used as a treatment by adding PAC and the other one without PAC was used as control. Samples were first dissolved in a 50 mL conical flask with methanol, only the treated sample was added with 0.2 g PAC. The samples were incubated in a water bath at 35 ℃ for 40 min to achieve a dynamic equilibrium between adsorbate and adsorbent. The mixture was filtered, and filtrates were evaporated and re-dissolved in 10 mL of methanol. The Extracts A group was subjected to the same procedure as the Extracts B group to obtain a blank reading. The total phorbol ester content from two extracts was determined by HPLC.
The samples from root barks, stem barks, and leaves were subjected to the same PAC treatment for removal of coloring impurities. In brief, eight aliquots of 0.300 g of powder were simultaneously extracted using the optimized UAE procedure. All extracts were pooled together, dissolved in methanol, and subjected to PAC treatment. The PAC treatment was conducted twice. The final extracts were re-dissolved in 2 mL of methanol and stored at 4 °C before HPLC analysis.
The pigments present in PE extracts from root barks, stem barks, and leaves always interfere in PE detection because of the same UV absorbance at the wavelength of 280 nm as phorbol esters. We developed a purification procedure by using activated carbons as a decolorization agent. The chromatographic profile of phorbol esters from kernel extract did not change between purification-treated samples and control samples, and the yields were 5.613 mg/g ± 0.044 and 5.595 mg/g ± 0.086, respectively. The results indicated that the purification treatment showed no significant impacts on phorbol esters’ extraction and can be used for the removal of pigments present in colorful extracts.

2.3. High-Performance Liquid Chromatography

The extraction yield of phorbol esters was determined by a Waters HPLC system coupled with a 2489 UV–visible dual-wavelength absorbance detector. Phorbol esters were detected at 280 nm based on the maximum absorption wavelength measured using a TU-1900 UV–vis spectrophotometer (Beijing Purkinje general instrument Co., Ltd., Chengdu, China). A Spherisorb reversed-phase ODS C18 column (250 mm × 4.6 mm, 5 μm, Welch Materials, Inc., West Haven, CT, USA) was used for separating phorbol esters with the column temperature at 25 °C. The isocratic binary mobile phase consisted of ACN and water (80:20, v/v) containing 0.2% formic acid, and the flow speed was set at 1.0 mL/min. An N2000 workstation (Zhejiang University) was employed for instrument control and data acquisition. The extract was dissolved in a 10 mL amber volumetric flask in methanol and filtered before HPLC injection. The extraction and HPLC analysis were performed in triplicates. A completely randomized experimental design was used for parameter optimization. Five parameters were optimized systematically, including organic solvents, soaking temperatures, solvent–sample ratio, sonication time, and temperature.
Phorbol 12-myristate-13-acetate (PMA, CAS number 16561-29-8) was supplied by Sigma Chemical Co., Ltd. (Shanghai, China). The stock solution was prepared by dissolving the standard PMA in methanol and stored at 4 °C until use. The standard working solutions were prepared freshly by appropriate dilution of a stock solution in methanol. Seven different concentrations of working standard solutions were injected into the HPLC in triplicates. External standard PMA was present at 39 min under 280 nm detection wavelength. The peak area values were plotted versus the corresponding PMA concentrations to obtain the calibration curve. The identity of HPLC peaks in kernel extracts was based on the comparison of retention time and UV absorption spectrum as determined on a diode-array detector with those described previously [41,42,43,44]. Phorbol esters’ yields were calculated as PMA standard equivalents and given as mg/g dry weight tissue. Total phorbol ester contents were calculated by summarizing six phorbol esters’ contents. The results were expressed as Means ± Standard error. The external calibration curve was plotted using the linear least-squares regression method. The linear equation was obtained: YPeakArea = 3025.3Xconcentation − 33.38 (R2 > 0.9998) over a linearity concentration range from 20 to 500 μg/mL. The limit of detection (LOD) and limit of quantification (LOQ) were less than 2.07 μg/mL and 6.87 μg/mL, respectively. The precision was tested by five replicated injections, and the repeatability of the peak areas was expressed as a relative standard deviation (RSD). The RSDs for PMA and PEs were 1.50% and 2.96%, respectively. Additionally, peak areas and retention times of PMA and phorbol esters were found to be quite stable over 48 h.

2.4. Total RNA Extraction and Quantitative RT-PCR Analysis

Total RNA was extracted from the three independent samples from J. curcas seedlings. A total of 4.0 μg of RNA from each sample was reverse transcribed to cDNA. Then, the three independent transcribed cDNA samples were used as templates for qRT-PCR. Genes involved in the phorbol ester pathway in J. curcas were tested, and the sequences of the primers are shown in Table 1. The J. curcas ACTIN gene was used as a reference for relative expression calculation, and ddH2O was used as the negative control sample. The qRT-PCR was conducted to quantify the expression levels of phorbol ester-associated genes [45,46]. The qRT-PCR was performed in a CFX96 Real-Time PCR system (BioRad, Hercules, CA, USA), three biological replicates with two technical replicates for each primer pair were performed with SYBR Select Master Mix (BioRad) for CFX. The procedure for qRT-PCR was as below: 1 µL of 1/10 diluted cDNA was added into 5 µL of 2x SYBR Green buffer, followed by 0.1 µM of primer pairs, and ddH2O to bring the volume to 10 µL. The reaction procedure was: 95 °C for 3 min, 95 °C for 5 s, 60 °C for 30 s, a total of 40 cycles, 95 °C for 5 s, 60 °C for 1 min, 95 °C for 5 s. The relative expressions were calculated by 2−ΔΔCT method.

2.5. Statistical Analysis

The statistical calculations and analyses in this study were performed using an R programming language (R Development Core Team, 2015). Tukey’s honestly (HSD) test was conducted to identify all significant differences between means.
To test the significance of the effect of each parameter, or a specific interaction between parameters on the extraction yields, we conducted an ANOVA model containing each parameter as individual effects. The ANOVA model was phorbol esters’ content = solvent + soaking temperature + ratio + extraction time + extraction temperature + solvent × soaking temperature + … + soaking temperature × ratio + … + ratio × extraction time + … + extraction time × extraction temperature. This model was used to obtain the F-value, significance, means, and standard error for each parameter (Supplemental Table S1).

3. Results

3.1. Phorbol Ester Metabolic Profiling Shows a Tissue-Specific Pattern

To investigate the types and abundance of phorbol esters in J. curcas, we determined the metabolic profile of phorbol esters in different plant parts. We first established the standard by determination of phorbol ester profile in seeds by HPLC based on previous studies [41,42,43,44]. We identified five phorbol ester peaks containing phorbol ester factors (Jc1, Jc2, Jc3, Jc4, and Jc5, and Jc6) based on their retention times (Figure 1A). To effectively and consistently extract phorbol esters from different parts of J. curcas, we developed a sonication-assisted extraction method and optimized five extraction parameters, including solvent, soaking temperature, solvent-to-material ratio, extraction time, and sonication temperature. Analysis of variance (ANOVA) revealed that the extraction solvent had the most significant impacts on phorbol esters yield (Supplemental Table S1). The highest yield was obtained at the optimized parameters (Supplemental Figure S1). Thereafter, we used the optimized extraction method coupled with HPLC determination for all phorbol ester content analysis in this study.
We identified varied types and abundance of phorbol esters in root bark, stem bark, leaf, latex, shell, and endotesta by comparing the retention time with kernel extraction (Figure 1). We identified all six phorbol esters factors, Jc1 to Jc6, from five plant tissues and seed meals except root bark. The extraction and determination methods used in this study failed to identify phorbol ester in root barks due to the presence of impurities at the background at the same retention time (Figure 1B). Comparison of the chromatogram profiles obtained from different tissues revealed that both types and abundance of phorbol esters varied among plant tissues. For example, endotesta and leaves shared a similar phorbol ester composition as kernels but varied at total content. Whereas, latex and stem bark showed only one or two Jc factors compared with the complete phorbol ester profile in kernels (Figure 1C,E). The endotesta showed the highest level of total phorbol esters among all examined tissues (Figure 1G,F). Leaves, latex, and stem bark, which are usually used as traditional medicines, showed very low amounts of phorbol esters. The seed meal showed dramatically reduced but still considerable content of phorbol esters (1.517 ± 0.120 mg/g, Figure 2I), suggesting that detoxification is required before use as a feed resource. No detectable phorbol esters were identified in seed shells.

3.2. Phorbol Esters Prefer to Accumulate in Plant Reproductive and Young Tissues

To understand the molecular mechanisms controlling phorbol ester tissue-specific accumulation, we investigated phorbol ester-associated gene expression profiles across eight plant tissues using quantitative reverse-transcription polymerase chain reaction (qRT-PCR, Table 1 and Figure 2). All gene expression profiles from J. curcas tissues are standardized to their expression in field-grown wild leaf tissue. We targeted phorbol esters’ pathway-specific genes, including GGPPs encoding enzyme for diterpene precursor GGPP, CAS responsible for cyclization of phorbol core. Both are highly expressed in buds (13.1- and 45.9-fold change) and green fruit (2.7- and 12.14-fold change, Figure 2H,I). A cytochrome P450 reductase (CPR) responsible for secondary modification of phorbol core and side chains also showed a relatively high expression in flower buds (8.37-fold change) and green fruits (6.72-fold change) (Figure 2J). The upregulation of phorbol ester pathway-specific genes in buds and green fruits suggests that the biosynthesis of phorbol esters occurs at a higher rate in reproductive tissues.
We next analyzed the genes involved in both shared and branched isoprenoid pathways. Five genes involved in the shared pathway are responsible for C5 isoprenoid production, including DXS and DXR in the MEP pathway, HMGR and HDR in the MVA pathway, and IPI responsible for cytosolic IPP and DMAPP formation. We selected two genes, GPPs and FPPs, from branched pathways that are essential for monoterpene, sesquiterpenoid, and triterpenoid production. The seven genes involved in isoprenoid shared and branched pathways showed the highest expression level in seedling leaves (Figure 2A–E). For example, DXS, DXR, and HMGR showed remarkably high expression levels in seedling leaves with 19.65-, 20.69-, and 45.33-fold change, respectively. Four genes involved in both shared and branched pathways (DXR, HMGR, GPPs, and FPPs) also showed relatively higher expression levels in reproduction tissues of flower buds and young seeds (Figure 2B,C,F,G). Collectively, phorbol ester genes were highly expressed in young and reproductive tissues compared with genes involved in common and other branch pathways, suggesting the protective roles of phorbol esters during plant development.

3.3. Terpenoids Pathways Showed a Tissue-Specific Pattern

To gain insight into the relationship among diverse terpenoid pathways and their disproportionate accumulation among plant tissues, we tested correlations among genes condensed in different pathways based on their expression profile across eight tissues (Figure 3A). We identified four gene co-expression modules with genes showing strong positive correlations (Figure 3A). We also found that the four modules showed tissue-specific patterns, which were consistent with their biological functions. For instance, phorbol ester pathway-specific genes of GGPPs and CAS clustered in the same module with a strong positive correlation (R2 = 0.905, p = 0.005). Furthermore, they both are closely linked with genes from the MEP pathway but negatively correlated with the module containing genes from the cytosolic MVA pathway, indicating that phorbol esters may derive from the MEP pathway in the plastid of J. curcas (Figure 3A). Whereas, the GPPs essential for monoterpene production is clustered together with HMGR and IPI in the MVA module, suggesting that the C10 geranyl diphosphate (GDP)-based monoterpenoids may form through the cytosolic MVA pathway. The negative correlation between the diterpene module and the MVA module indicates a relationship of competition between phorbol ester production and monoterpenoid production in J. curcas. Surprisingly, the FPPs, which is responsible for C15 sesquiterpene and C30 triterpene and steroid (two FDP units) production, also showed a closer relationship with genes in the MEP pathway.
To obtain a systematic view of the relationships in terpenoid biosynthesis of different tissues, we conducted principal component analysis (PCA) on the terpenoid gene expression profile. The first and second principal components captured 53.68% and 42.45% of the total variance, respectively (Figure 3B). The scatter plot on the scores of PC1 and PC2 showed that the accumulation patter of terpenoids’ transcripts varied across diverse J. curcas tissues (Figure 3B). Gene expression profiles in seedling leaf tissue and the flowering buds showed dramatic differences from other tissues. The dendrogram based on the first two principal components divided J. curcas tissues into four groups: Group I (seedling leaf), Group II (Bud), Group III (young seeds and green fruits), Group IV (yellow seeds, young stem, wild leaf, and root) (Figure 3C). Collectively, the gene expression profiles of terpenoids showed tissue-specific patterns, which were associated with tissue age (i.e., young or old tissues) and tissue function (i.e., vegetative or reproductive tissues). This tissue-specific pattern of terpenoids accumulation in planta may link with their ecological function to protect young and reproductive tissues against abiotic and biotic stresses.

3.4. Cold and Drought Stressors Increased but Salinity Decreased Many Transcript Levels of Terpenoid Biosynthetic Genes in J. curcas

Terpenoid metabolism facilitates plant adaptation to fluctuated environment and defense against biotic stresses [32,37]. To examine how Euphorbiaceae and Thymelaeaceae plants coordinate terpenoid production to adapt to environmental challenges, we measured the variation of terpenoid transcripts’ accumulation in J. curcas seedlings upon abiotic stresses, such as cold, drought, and salinity. Besides the above eight genes involved in terpenoid common and branch pathways, we also included two genes, SQS and SE, involved in the triterpenoid pathway (Table 1 and Figure 4).
Terpenoid gene expressions were differentially regulated by three abiotic stressors of cold, drought, and salinity (Figure 4). Cold treatment significantly affected the accumulation of terpenoid transcripts in plant tissue. When treating with cold at 4 °C for eight hours, all tested terpenoid genes showed as highly expressed, ranging from 5.8- to 34.8-fold change except GGPPs (Figure 4). In particular, DXS and GPPs showed the highest upregulation of 34.8- and 26.1-fold change after treatment, respectively (Figure 4A,E). Three genes (CAS, SQS, and SE) that are involved in phorbol ester and squalene productions also induced more than 10-fold change in response to low temperature (Figure 4H–J). The upregulation of terpenoids’ gene expression in J. curcas suggests that the accumulation of terpenoids may protect plant seedlings against freezing damage. Drought stress also regulated transcripts’ accumulation of terpenoid genes but with a distinct upregulation pattern (Figure 4). For example, the gene encoding IPI in the shared isoprenoid pathway and the genes in diterpenoid and triterpenoid pathways (SQS and SE) showed dramatic upregulation in response to water-deficient conditions (Figure 4G,H,J).
Strongly positive correlations were identified among the four genes based on their transcriptional accumulation levels in response to all abiotic and biotic stresses (Figure 5A). In contrast with cold and drought stresses, the high-salt treatment downregulated all terpenoids genes (Figure 4). This result was different from previous studies in which exposure to the drought and salinity shared many common physiological reactions in plants [47,48,49]. The scatter plot of the scores of PC1 and PC2 highlighted that cold, drought, and salinity are distributed sparsely as PCA on terpenoid gene expression, indicating that three abiotic stressors have differing effects on terpenoid transcript accumulation (Figure 5B). Collectively, the upregulation of terpenoid transcripts’ accumulation in plants may help plant seedlings adapt to low temperature and water-deficient conditions by increasing terpenoid content in plant cells.

3.5. Attacking by Biotrophic and Necrotrophic Fungal Pathogens Had Different Effects on Transcript Levels of Terpenoid Biosynthetic Genes in J. curcas

The biotic stress effects on regulation of terpenoid gene expression were evaluated by either exogenous application of biotic hormones of salicylic acid (SA) and methyl jasmonate (MeJA) or treated with biotroph (powdery mildew, Podosphaera xanthii) or necrotroph (grey mode, Botrytis cinerea). Previous studies showed that SA is essential for plant resistance upon biotrophic pathogen attack, whereas JA primes plant defense against necrotrophic pathogens and insects [50,51,52]. Exogenous SA treatment caused the increase of all targeted terpenoid genes except SE (Figure 4). Specifically, the GPPs and FPPs showed the highest upregulation with 20.2- and 7.3-fold change, respectively (Figure 4E,F). MeJA treatment showed similar but subtle induction of all targeted terpenoid genes with less than 3-fold change except DXR, which showed a 20-fold change (Figure 4). The varied induction patterns suggest that SA and JA signaling may finely tune different terpenoids’ production.
We then investigated whether biotrophic and necrotrophic pathogen invasion trigger terpenoid gene expression by inoculation of powdery mildew (Podosphaera xanthii) and grey mode (Botrytis cinerea) on J. curcas seedlings [53,54,55]. Four genes in ten were upregulated in response to powdery mildew infection. For example, DXS and in shared pathway showed 3.6- and 2.4-fold change, respectively. Two genes in the monoterpenoid (GPPs) and sesquiterpenoid (FPPs) pathways showed 1.3- and 42.7-fold change, respectively (Figure 4A,C,E,F). In contrast, Botrytis cinerea infection caused upregulation of all tested terpenoid genes, and six in ten showed higher induction with more than 5-fold change upregulation compared to the control. Thus, an attack by biotroph and necrotroph had different effects on terpenoid pathways at the transcriptional level in J. curcas. However, SA treatment and powdery mildew infection, or MeJA treatment and Botrytis cinerea infection, were not clustered together by PCA plot (Figure 5B). Furthermore, under abiotic and biotic stresses, all tested terpenoid transcripts were positively correlated except FPPs. Although three clusters are identified, the genes within individual clusters are not consistent with their biosynthesis pathways, suggesting that there might be cross talk between terpenoid shared and branched pathways. Thus, the transcriptional accumulation of terpenoids is regulated in planta in response to diverse abiotic and biotic stresses.

4. Discussion

Phorbol esters are diterpenoids with broad biological activities in J. curcas. In this study, we determined phorbol esters distributing in whole J. curcas plant except for seed shells. We further showed tissue-specific patterns of the accumulation of terpenoids and phorbol esters at the transcriptional level with high transcripts levels in reproductive and young tissues. Diverse abiotic and biotic stress treatments on J. curcas seedlings revealed that plants fine-tune gene expression in terpenoid shared and branched pathways to facilitate their fitness.
Understanding the phorbol ester distribution in organs or tissues is essential for the estimation of risks of exposure to crushed seeds or oil in agriculture and the application of J. curcas in medicine. This study showed that phorbol esters are accumulated in all vegetative and reproductive parts in J. curcas but absent in seed shell (Figure 1I). Although the extraction of oil can take away a majority of phorbol esters from seeds, the remaining meals still contain a relatively high level of phorbol esters (Figure 1H,I). Thus, protection measurements are necessary to those who have occupational exposure to J. curcas oils and co-product of seed meals since phorbol esters are not just tumor promoters but also skin irritants. It is also necessary for the evaluation of risks and side effects in terms of phorbol ester content when different parts of the J. curcas plant are used in traditional medicine and the pharmaceutical industry for both human and veterinary ailments.
The accumulation of phorbol esters showed a tissue-specific pattern with varied content and composition across eight tested tissues. Phorbol esters are accumulated highest in seeds, especially in endotesta, compared with leaf, stem, and latex. Further investigation of transcript accumulation revealed that genes directly responsible for phorbol ester biosynthesis are highly expressed in reproductive or young organs, including buds, green fruits, young seeds. Considering terpenoids’ function in defense against herbivores and pathogen attackers, phorbol esters’ allocation and gene expression patterns are thought to reflect the optimal allocation of defensive compounds in organs that contribute most to plant fitness. The different phorbol ester patterns in leaf, stem, flower, and seeds may be associated with specific differences of selection pressures from herbivores and pathogens acting on different plant organs. Indeed, the J. curcas tree is also attacked and consumed by some fungal pathogens and several insects. Some of the specialists have evolved the ability to modify and store phorbol esters as their chemical defense against predators. Although the tissue-specific allocation pattern is a hallmark of most plant-specialized metabolites, the driving evolutionary power remains largely unknown.
Terpenoids are the dominant secondary metabolites in Euphorbia plants, including J. curcas. We have demonstrated that the accumulation of terpenoid transcripts in J. curcas seedlings was upregulated in response to cold and drought stresses with a distinguished expression profile (Figure 4 and Figure 5A). Terpenoid pathways may be the cross talks between cold and drought stressors, which were previously identified in Arabidopsis [47,48]. Further work is required to identify the molecular mechanism of the regulatory network for terpenoid production in plants in response to diverse single or combination of environmental stresses. This oilseed crop has been described as a drought-resistant perennial plant that can grow in marginal and poor soils in the tropical and subtropical regions [56,57]. Low temperature is one of the limiting factors for J. curcas widespread. Like many other secondary metabolites, terpenoids and the transcripts of corresponding biosynthetic genes are accumulated in response to various environmental stresses. The terpenoids’ production in J. curcas may associate with its growing environment and serve as protective metabolites that increase plants’ tolerance to adverse growth conditions.
As both constitutive and inducible defense compounds, terpenoids’ accumulation in Euphorbia plants may link with their ecological functions of defense against fungal pathogen and insect attack. Previous reports suggest that J. curcas plants have suffered from several severe diseases caused by bacterial and fungal pathogens [58]. We showed that the expression levels of terpenoid genes are upregulated with the treatment of SA and JA or inoculation of fungi with different infection patterns (Figure 4 and Figure 5). SA and JA have been associated with the plant biotic stress hormone signals in the regulation of secondary metabolite production. Generally, SA-mediated signaling pathways are implicated in the regulation of biotrophic pathogens, while the JA pathway is associated with defense responses against necrotrophic pathogens and insects [50,51]. Terpenoid genes are regulated by SA and JA signaling pathways in distinguished patterns, suggesting that different types of terpenoids’ production may be controlled by different signaling pathways. The fine-tune regulation of the specific types of terpenoid production may be associated with their ecological functions. For example, volatile terpenoids released from plants play crucial roles in attracting pollinators and repelling herbivore predators [59]. These volatile terpenoids represented by mainly isoprene, monoterpenes, and sesquiterpenes may be under the regulation of JA signaling pathways. Elucidating the ecological importance of terpenoids is helpful to understand and identify the molecular regulation mechanism in response to biotic stresses.
We observed that genes involved in the same type of terpenoids tend to co-expression in response to environmental signals (Figure 5A). This is not a unique feature for terpenoids’ production but is shared by all specialized metabolites [60,61]. The regulation of inducible production of specialized metabolites and the transcripts involved in biosynthetic pathways in plants are coordinated at the individual gene module unit. This is the theoretical basis of the gene co-expression network approach that is broadly used in a whole-genome transcriptomic analysis to identify gene modules with associated biological functions [52,60,62,63]. Transcriptional regulation of specialized metabolites production is under tight regulation at different levels by many cis- and trans-regulators. Transcription factors are one of such regulators that integrate signals and interact with similar patterns of regulatory (often promoter) regions of target genes to control the specific accumulation of transcripts and specialized metabolites. Future works are required to analyze the patterns shared by regulatory regions of terpenoid genes and their associations with different types of transcription factors, which will help identify the underlying molecular regulatory mechanism on terpenoids’ induction and interpret their ecological functions.

5. Conclusions

In conclusion, the accumulation of phorbol esters at metabolic and transcriptional levels in plants showed tissue-specific patterns and prefer to accumulate in reproductive and young tissues and organs. The variation in the distribution of phorbol esters across diverse tissues provides further support for the implementation of the J. curcas plant in pharmaceutical, agricultural, and industrial applications. Furthermore, abiotic and biotic stresses had different effects on terpenoids, especially phorbol esters, accumulation in J. curcas seedlings at the transcriptional level. Plants can coordinate shared and branch biosynthetic pathways of terpenoids to adapt to abiotic stresses and defense against biotic stresses. This study, therefore, provides insights for understanding the ecological roles of terpenoids in plants and environment interaction.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/seeds3030024/s1, Supplemental Figure S1. Effects of extraction parameters on the yields of phorbol esters from Jatropha curcas kernel extracts. (A) Effect of extraction solvents; (B) Effect of soaking temperature; (C) Effect of the solvent/material ratio; (D) Effect of ultrasound irradiation time; and (E) Effect of extraction temperature. Error bars represent the standard error of the mean. Letters represent significant differences determined by Tukey’s HSD test. Supplemental Table S1. ANOVA table for the effects of extraction parameters on yields of phorbol esters from Jatropha curcas kernel extracts. A linear model was used for phorbol esters’ yields extracted and determined by ultrasound-assisted extraction coupled with high-performance liquid chromosome. Five extraction parameters were determined and compared. Df means degrees of freedom, Sum of squares means Type II Sums of Squares variation, F values means the F statistic test, p-value means the probability value from an F test. Significance was determined as follows: *** < 0.001; ** < 0.01, * < 0.05.

Author Contributions

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

Funding

National Natural Science Foundation of China Grant No. 31,870,315.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article and Supplementary Materials.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Montes, J.M.; Melchinger, A.E. Domestication and Breeding of Jatropha curcas L. Trends Plant Sci. 2016, 21, 1045–1057. [Google Scholar] [CrossRef] [PubMed]
  2. Krishnamurthy, L.; Zaman-Allah, M.; Marimuthu, S.; Wani, S.; Rao, A. Root growth in Jatropha and its implications for drought adaptation. Biomass Bioenergy 2012, 39, 247–252. [Google Scholar] [CrossRef]
  3. Devappa, R.; Maes, J.; Makkar, H.; De Greyt, W.; Becker, K. Quality of Biodiesel Prepared from Phorbol Ester Extracted Jatropha curcas Oil. J. Am. Oil Chem. Soc. 2010, 87, 697–704. [Google Scholar] [CrossRef]
  4. Berchmans, H.J.; Hirata, S. Biodiesel production from crude Jatropha curcas L. seed oil with a high content of free fatty acids. Bioresour. Technol. 2008, 99, 1716–1721. [Google Scholar] [CrossRef] [PubMed]
  5. King, A.J.; He, W.; Cuevas, J.A.; Freudenberger, M.; Ramiaramanana, D.; Graham, I.A. Potential of Jatropha curcas as a source of renewable oil and animal feed. J. Exp. Bot. 2009, 60, 2897–2905. [Google Scholar] [CrossRef] [PubMed]
  6. Makkar, H.; Francis, G.; Becker, K. Protein concentrate from Jatropha curcas screw-pressed seed cake and toxic and antinutritional factors in protein concentrate. J. Sci. Food Agric. 2008, 88, 1542–1548. [Google Scholar] [CrossRef]
  7. Saetae, D.; Suntornsuk, W. Toxic Compound, Anti-Nutritional Factors and Functional Properties of Protein Isolated from Detoxified Jatropha curcas Seed Cake. Int. J. Mol. Sci. 2011, 12, 66–77. [Google Scholar] [CrossRef]
  8. Hirota, M.; Suttajit, M.; Suguri, H.; Endo, Y.; Shudo, K.; Wongchai, V.; Hecker, E.; Fujiki, H. A new tumor promoter from the seed oil of Jatropha curcas L., an intramolecular diester of 12-deoxy-16-hydroxyphorbol. Cancer Res. 1988, 48, 5800–5804. [Google Scholar] [PubMed]
  9. Makkar, H.; Becker, K.; Sporer, F.; Wink, M. Studies on nutritive potential and toxic constituents of different provenances of Jatropha curcas. J. Agric. Food Chem. 1997, 45, 3152–3157. [Google Scholar] [CrossRef]
  10. Haas, W.; Sterk, H.; Mittelbach, M. Novel 12-deoxy-16-hydroxyphorbol diesters isolated from the seed oil of Jatropha curcas. J. Nat. Prod. 2002, 65, 1434–1440. [Google Scholar] [CrossRef]
  11. Rug, M.; Ruppel, A. Toxic activities of the plant Jatropha curcas against intermediate snail hosts and larvae of schistosomes. Trop. Med. Int. Health 2000, 5, 423–430. [Google Scholar] [CrossRef] [PubMed]
  12. Devappa, R.; Rajesh, S.; Kumar, V.; Makkar, H.; Becker, K. Activities of Jatropha curcas phorbol esters in various bioassays. Ecotoxicol. Environ. Saf. 2012, 78, 57–62. [Google Scholar] [CrossRef] [PubMed]
  13. Li, C.; Devappa, R.; Liu, J.; Lv, J.; Makkar, H.; Becker, K. Toxicity of Jatropha curcas phorbol esters in mice. Food Chem. Toxicol. 2010, 48, 620–625. [Google Scholar] [CrossRef] [PubMed]
  14. Becker, K.; Makkar, H. Effects of phorbol esters in carp (Cyprinus carpio L). Vet. Hum. Toxicol. 1998, 40, 82–86. [Google Scholar] [PubMed]
  15. Katole, S.; Saha, S.; Sastry, V.; Lade, M.; Prakash, B. Intake, blood metabolites and hormonal profile in sheep fed processed Jatropha (Jatropha curcas) meal. Anim. Feed Sci. Technol. 2011, 170, 21–26. [Google Scholar] [CrossRef]
  16. Devappa, R.K.; Malakar, C.C.; Makkar, H.P.; Becker, K. Pharmaceutical potential of phorbol esters from Jatropha curcas oil. Nat. Prod. Res. 2013, 27, 1459–1462. [Google Scholar] [CrossRef] [PubMed]
  17. Fujiki, H.; Suttajit, M.; Rawangkan, A.; Iida, K.; Limtrakul, P.; Umsumarng, S.; Suganuma, M. Phorbol esters in seed oil of Jatropha curcas L. (saboodam in Thai) and their association with cancer prevention: From the initial investigation to the present topics. J. Cancer Res. Clin. Oncol. 2017, 143, 1359–1369. [Google Scholar] [CrossRef]
  18. Wei, L.; Zhang, W.; Yin, L.; Yan, F.; Xu, Y.; Chen, F. Extraction optimization of total triterpenoids from Jatropha curcas leaves using response surface methodology and evaluations of their antimicrobial and antioxidant capacities. Electron. J. Biotechnol. 2015, 18, 88–95. [Google Scholar] [CrossRef]
  19. Kosasi, S.; Vandersluis, W.; Boelens, R.; Thart, L.; Labadie, R. Labaditin, a Novel Cyclic Decapeptide from the Latex of Jatropha-multifida L. (Euphorbiaceae). FEBS Lett. 1989, 256, 91–96. [Google Scholar] [CrossRef]
  20. Ravindranath, N.; Ravinder Reddy, M.; Ramesh, C.; Ramu, R.; Prabhakar, A.; Jagadeesh, B.; Das, B. New lathyrane and podocarpane diterpenoids from Jatropha curcas. Chem. Pharm. Bull. 2004, 52, 608–611. [Google Scholar] [CrossRef]
  21. Devappa, R.; Makkar, H.; Becker, K. Nutritional, Biochemical, and Pharmaceutical Potential of Proteins and Peptides from Jatropha: Review. J. Agric. Food Chem. 2010, 58, 6543–6555. [Google Scholar] [CrossRef]
  22. Igbinosa, O.; Igbinosa, I.; Chigor, V.; Uzunuigbe, O.; Oyedemi, S.; Odjadjare, E.; Okoh, A.; Igbinosa, E. Polyphenolic Contents and Antioxidant Potential of Stem Bark Extracts from Jatropha curcas (Linn). Int. J. Mol. Sci. 2011, 12, 2958–2971. [Google Scholar] [CrossRef] [PubMed]
  23. Lin, J.; Li, Y.X.; Zhou, X.W.; Tang, K.X.; Chen, F. Cloning and characterization of a curcin gene encoding a ribosome inactivating protein from Jatropha curcas. DNA Seq. 2003, 14, 311–317. [Google Scholar] [CrossRef]
  24. Qin, W.; Ming-Xing, H.; Ying, X.; Xin-Shen, Z.; Fang, C. Expression of a ribosome inactivating protein (curcin 2) in Jatropha curcas is induced by stress. J. Biosci. 2005, 30, 351–357. [Google Scholar] [CrossRef]
  25. Insanu, M.; Dimaki, C.; Wilkins, R.; Brooker, J.; van der Linde, P.; Kayser, O. Rational use of Jatropha curcas L. in food and medicine: From toxicity problems to safe applications. Phytochem. Rev. 2013, 12, 107–119. [Google Scholar] [CrossRef]
  26. Sabandar, C.; Ahmat, N.; Jaafar, F.; Sahidin, I. Medicinal property, phytochemistry and pharmacology of several Jatropha species (Euphorbiaceae): A review. Phytochemistry 2013, 85, 7–29. [Google Scholar] [CrossRef] [PubMed]
  27. Joseph, G.V. Pharmacognostic studies on the fruits of Jatropha curcas Linn. Anc. Sci. Life 2001, 21, 128–134. [Google Scholar]
  28. Debnath, M.; Bisen, P.S. Jatropha curcas L., a multipurpose stress resistant plant with a potential for ethnomedicine and renewable energy. Curr. Pharm. Biotechnol. 2008, 9, 288–306. [Google Scholar] [CrossRef]
  29. King, A.; Brown, G.; Gilday, A.; Larson, T.; Grahama, I. Production of Bioactive Diterpenoids in the Euphorbiaceae Depends on Evolutionarily Conserved Gene Clusters. Plant Cell 2014, 26, 3286–3298. [Google Scholar] [CrossRef]
  30. Lange, B.M.; Rujan, T.; Martin, W.; Croteau, R. Isoprenoid biosynthesis: The evolution of two ancient and distinct pathways across genomes. Proc. Natl. Acad. Sci. USA 2000, 97, 13172–13177. [Google Scholar] [CrossRef]
  31. Vranová, E.; Coman, D.; Gruissem, W. Network analysis of the MVA and MEP pathways for isoprenoid synthesis. Annu. Rev. Plant Biol. 2013, 64, 665–700. [Google Scholar] [CrossRef] [PubMed]
  32. Tholl, D. Biosynthesis and biological functions of terpenoids in plants. Adv. Biochem. Eng. Biotechnol. 2015, 148, 63–106. [Google Scholar] [CrossRef] [PubMed]
  33. Nakano, Y.; Ohtani, M.; Polsri, W.; Usami, T.; Sambongi, K.; Demura, T. Characterization of the casbene synthase homolog from Jatropha (Jatropha curcas L.). Plant Biotechnol. 2012, 29, 185–189. [Google Scholar] [CrossRef]
  34. Li, C.; Ng, A.; Xie, L.; Mao, H.; Qiu, C.; Srinivasan, R.; Yin, Z.; Hong, Y. Engineering low phorbol ester Jatropha curcas seed by intercepting casbene biosynthesis. Plant Cell Rep. 2016, 35, 103–114. [Google Scholar] [CrossRef]
  35. Zerbe, P.; Bohlmann, J. Plant diterpene synthases: Exploring modularity and metabolic diversity for bioengineering. Trends Biotechnol. 2015, 33, 419–428. [Google Scholar] [CrossRef] [PubMed]
  36. Yazaki, K.; Arimura, G.I.; Ohnishi, T. ‘Hidden’ Terpenoids in Plants: Their Biosynthesis, Localization and Ecological Roles. Plant Cell Physiol. 2017, 58, 1615–1621. [Google Scholar] [CrossRef]
  37. Pichersky, E.; Raguso, R.A. Why do plants produce so many terpenoid compounds? New Phytol. 2018, 220, 692–702. [Google Scholar] [CrossRef]
  38. Neto, D.F.M.; Garrett, R.; Domont, G.B.; Campos, F.A.P.; Nogueira, F.C.S. Untargeted Metabolomic Analysis of Leaves and Roots of Jatropha curcas Genotypes with Contrasting Levels of Phorbol Esters. Physiol. Plant. 2024, 176, e14274. [Google Scholar] [CrossRef]
  39. Ramadan Hassanien, M.F. (Ed.) Bioactive Phytochemicals from Jatropha (Jatropha curcas L.) Oil Processing By-products. In Bioactive Phytochemicals from Vegetable Oil and Oilseed Processing By-Products; Reference Series in Phytochemistry; Springer: Cham, Switzerland, 2023. [Google Scholar]
  40. Rodrigues, D.A.; Demuner, A.J.; Barbosa, L.C.A.; Pereira, G.A.M.; Fabris, J.D.; Siqueira, F.G.; Pereira, M.T.; Silva, A.J.; Carvalho, O.V. Removing phorbol esters from the biomass to add extra value to the byproduct from deoiling seeds of Jatropha curcas in the biodiesel industry. Biomass Conv. Bioref. 2023, 13, 1779–1791. [Google Scholar] [CrossRef]
  41. Wang, Z.; Tang, L.; Hu, H.; Guo, Y.; Peng, T.; Yan, F.; Chen, F. Metabolic Profiling Assisted Quality Control of Phorbolesters in Jatropha curcas Seed by High-Performance Liquid Chromatography Using a Fused-Core Column. J. Agric. Food Chem. 2012, 60, 9567–9572. [Google Scholar] [CrossRef]
  42. Liu, X.; Li, L.; Li, W.; Lu, D.; Chen, F.; Li, J. Quantitative determination of phorbol ester derivatives in Chinese Jatropha curcas seeds by high-performance liquid chromatography/mass spectrometry. Ind. Crops Prod. 2013, 47, 29–32. [Google Scholar] [CrossRef]
  43. Devappa, R.; Bingham, J.; Khanal, S. High performance liquid chromatography method for rapid quantification of phorbol esters in Jatropha curcas seed. Ind. Crops Prod. 2013, 49, 211–219. [Google Scholar] [CrossRef]
  44. Hua, W.; Hu, H.; Chen, F.; Tangy, L.; Peng, T.; Wang, Z. Rapid Isolation and Purification of Phorbol Esters from Jatropha curcas by High-Speed Countercurrent Chromatography. J. Agric. Food Chem. 2015, 63, 2767–2772. [Google Scholar] [CrossRef] [PubMed]
  45. Karlen, Y.; McNair, A.; Perseguers, S.; Mazza, C.; Mermod, N. Statistical significance of quantitative PCR. BMC Bioinform. 2007, 8, 131. [Google Scholar] [CrossRef] [PubMed]
  46. Heid, C.A.; Stevens, J.; Livak, K.J.; Williams, P.M. Real time quantitative PCR. Genome Res. 1996, 6, 986–994. [Google Scholar] [CrossRef] [PubMed]
  47. Seki, M.; Narusaka, M.; Ishida, J.; Nanjo, T.; Fujita, M.; Oono, Y.; Kamiya, A.; Nakajima, M.; Enju, A.; Sakurai, T.; et al. Monitoring the expression profiles of 7000 Arabidopsis genes under drought, cold and high-salinity stresses using a full-length cDNA microarray. Plant J. 2002, 31, 279–292. [Google Scholar] [CrossRef] [PubMed]
  48. Xiong, L.; Schumaker, K.S.; Zhu, J.K. Cell signaling during cold, drought, and salt stress. Plant Cell 2002, 14 (Suppl. 1), S165–S183. [Google Scholar] [CrossRef] [PubMed]
  49. Krasensky, J.; Jonak, C. Drought, salt, and temperature stress-induced metabolic rearrangements and regulatory networks. J. Exp. Bot. 2012, 63, 1593–1608. [Google Scholar] [CrossRef] [PubMed]
  50. Kunkel, B.N.; Brooks, D.M. Cross talk between signaling pathways in pathogen defense. Curr. Opin. Plant Biol. 2002, 5, 325–331. [Google Scholar] [CrossRef]
  51. Spoel, S.H.; Johnson, J.S.; Dong, X. Regulation of tradeoffs between plant defenses against pathogens with different lifestyles. Proc. Natl. Acad. Sci. USA 2007, 104, 18842–18847. [Google Scholar] [CrossRef]
  52. Zhang, W.; Corwin, J.A.; Copeland, D.; Feusier, J.; Eshbaugh, R.; Chen, F.; Atwell, S.; Kliebenstein, D.J. Plastic Transcriptomes Stabilize Immunity to Pathogen Diversity: The Jasmonic Acid and Salicylic Acid Networks within the Arabidopsis/Botrytis Pathosystem. Plant Cell 2017, 29, 2727–2752. [Google Scholar] [CrossRef] [PubMed]
  53. Wicker, T.; Oberhaensli, S.; Parlange, F.; Buchmann, J.P.; Shatalina, M.; Roffler, S.; Ben-David, R.; Doležel, J.; Šimková, H.; Schulze-Lefert, P.; et al. The wheat powdery mildew genome shows the unique evolution of an obligate biotroph. Nat. Genet. 2013, 45, 1092–1096. [Google Scholar] [CrossRef] [PubMed]
  54. Atwell, S.; Corwin, J.A.; Soltis, N.; Zhang, W.; Copeland, D.; Feusier, J.; Eshbaugh, R.; Kliebenstein, D.J. Resequencing and association mapping of the generalist pathogen Botrytis cinerea. bioRxiv 2018. [Google Scholar] [CrossRef]
  55. Zhang, W.; Kwon, S.T.; Chen, F.; Kliebenstein, D.J. Isolate Dependency of Brassica rapa Resistance QTLs to Botrytis cinerea. Front. Plant Sci. 2016, 7, 161. [Google Scholar] [CrossRef] [PubMed]
  56. Reubens, B.; Achten, W.; Maes, W.; Danjon, F.; Aerts, R.; Poesen, J.; Muys, B. More than biofuel? Jatropha curcas root system symmetry and potential for soil erosion control. J. Arid Environ. 2011, 75, 201–205. [Google Scholar] [CrossRef]
  57. Valdés-Rodríguez, O.A.; Sánchez-Sánchez, O.; Pérez-Vázquez, A.; Caplan, J.S.; Danjon, F. Jatropha curcas L. root structure and growth in diverse soils. Sci. World J. 2013, 2013, 827295. [Google Scholar] [CrossRef] [PubMed]
  58. Chowdhury, J.; Al Basir, F.; Takeuchi, Y.; Ghosh, M.; Roy, P. A mathematical model for pest management in Jatropha curcas with integrated pesticides—An optimal control approach. Ecol. Complex. 2019, 37, 24–31. [Google Scholar] [CrossRef]
  59. Nagegowda, D.A. Plant volatile terpenoid metabolism: Biosynthetic genes, transcriptional regulation and subcellular compartmentation. FEBS Lett. 2010, 584, 2965–2973. [Google Scholar] [CrossRef]
  60. Wisecaver, J.H.; Borowsky, A.T.; Tzin, V.; Jander, G.; Kliebenstein, D.J.; Rokas, A. A Global Coexpression Network Approach for Connecting Genes to Specialized Metabolic Pathways in Plants. Plant Cell 2017, 29, 944–959. [Google Scholar] [CrossRef]
  61. Schläpfer, P.; Zhang, P.; Wang, C.; Kim, T.; Banf, M.; Chae, L.; Dreher, K.; Chavali, A.K.; Nilo-Poyanco, R.; Bernard, T.; et al. Genome-Wide Prediction of Metabolic Enzymes, Pathways, and Gene Clusters in Plants. Plant Physiol. 2017, 173, 2041–2059. [Google Scholar] [CrossRef]
  62. Zhang, W.; Corwin, J.A.; Copeland, D.; Feusier, J.; Eshbaugh, R.; Cook, D.E.; Atwell, S.; Kliebenstein, D.J. Plant-necrotroph co-transcriptome networks illuminate a metabolic battlefield. Elife 2019, 8, e44279. [Google Scholar] [CrossRef] [PubMed]
  63. Soltis, N.E.; Caseys, C.; Zhang, W.; Corwin, J.A.; Atwell, S.; Kliebenstein, D.J. Pathogen Genetic Control of Transcriptome Variation in the Arabidopsis thaliana—Botrytis cinerea Pathosystem. Genetics 2020, 215, 253–266. [Google Scholar] [CrossRef] [PubMed]
Seeds 03 00024 g001
Figure 1. HPLC analysis of phorbol esters in Jatropha curcas. HPLC profiles of six types of phorbol esters extracted from (A) kernel, (B) root, (C) young stem, (D) wild leaf, (E) latex, (F) endotesta, (G) shell, and (H) seed meal. PMA was used as external standards. Jatropha factors 1 to 6 are labeled. (I) Total contents of six phorbol esters in diverse J. curcas tissues were determined by calibrating with a standard curve of PMA. The data represent means ± standard error from triplicate biological replicates. ND = not detected. Letters at the top of (I) indicate statistically homogeneous groups determined by HSD test.
Figure 1. HPLC analysis of phorbol esters in Jatropha curcas. HPLC profiles of six types of phorbol esters extracted from (A) kernel, (B) root, (C) young stem, (D) wild leaf, (E) latex, (F) endotesta, (G) shell, and (H) seed meal. PMA was used as external standards. Jatropha factors 1 to 6 are labeled. (I) Total contents of six phorbol esters in diverse J. curcas tissues were determined by calibrating with a standard curve of PMA. The data represent means ± standard error from triplicate biological replicates. ND = not detected. Letters at the top of (I) indicate statistically homogeneous groups determined by HSD test.
Seeds 03 00024 g001
Seeds 03 00024 g002
Figure 2. Expression of genes involved in terpene metabolism and phorbol esters’ biosynthesis pathway in different tissues of Jatropha curcas. Accumulation levels of transcripts were determined by qRT-PCR using primers in Table 1. Relative expression levels of ten individual transcripts (A–J) in diverse tissues were calculated by comparison of field-growing mature leaf, including root, young stem, bud, young seed, yellow seed, green fruit, and seedling leaf. All graphs represent the values of three replicates with cDNA prepared from different tissues. Each bar represents means ± standard error. ** indicates p < 0.01; *** indicates p < 0.001.
Figure 2. Expression of genes involved in terpene metabolism and phorbol esters’ biosynthesis pathway in different tissues of Jatropha curcas. Accumulation levels of transcripts were determined by qRT-PCR using primers in Table 1. Relative expression levels of ten individual transcripts (A–J) in diverse tissues were calculated by comparison of field-growing mature leaf, including root, young stem, bud, young seed, yellow seed, green fruit, and seedling leaf. All graphs represent the values of three replicates with cDNA prepared from different tissues. Each bar represents means ± standard error. ** indicates p < 0.01; *** indicates p < 0.001.
Seeds 03 00024 g002
Seeds 03 00024 g003
Figure 3. Tissue-specific patterns of transcripts’ accumulation involved in terpene metabolism and phorbol ester pathway in Jatropha curcas. (A) Heat map illustrates the Spearman’s rank correlation co-efficiency on expression levels of target genes involved in terpene metabolism and phorbol ester pathway. Accumulation levels of transcripts were determined by qRT-PCR using primers in Table 1. Relative expression levels of individual transcripts in diverse tissues were calculated by comparison of field-growing mature leaf, including root, young stem, bud, young seed, yellow seed, green fruit, and seedling leaf. (B) Scatter plot of the scores of the first two principal eigengene vectors derived from the principal component analysis (PCA) on the expression of genes. (C) Dendrogram plot of the PCA scores visualized in the cluster of Jatropha curcas tissues based on the accumulation of terpene transcripts.
Figure 3. Tissue-specific patterns of transcripts’ accumulation involved in terpene metabolism and phorbol ester pathway in Jatropha curcas. (A) Heat map illustrates the Spearman’s rank correlation co-efficiency on expression levels of target genes involved in terpene metabolism and phorbol ester pathway. Accumulation levels of transcripts were determined by qRT-PCR using primers in Table 1. Relative expression levels of individual transcripts in diverse tissues were calculated by comparison of field-growing mature leaf, including root, young stem, bud, young seed, yellow seed, green fruit, and seedling leaf. (B) Scatter plot of the scores of the first two principal eigengene vectors derived from the principal component analysis (PCA) on the expression of genes. (C) Dendrogram plot of the PCA scores visualized in the cluster of Jatropha curcas tissues based on the accumulation of terpene transcripts.
Seeds 03 00024 g003
Seeds 03 00024 g004
Figure 4. Accumulation of transcripts involved in terpene metabolism and phorbol ester pathway in response to abiotic and biotic stressors. Abiotic stressors are cold, drought, and salt. Biotic stressors are plant defense hormones of salicylic acid (SA) and methyl jasmonate (MeJA), as well as biotrophic fungi powdery mildew, and necrotrophic fungi Botrytis cinerea. Accumulation levels of transcripts were determined by qRT-PCR using primers in Table 1. Relative expression levels of ten individual transcripts (A–J) were calculated by comparison of control samples. All graphs represent the values of three replicates with cDNA prepared from seedling leaves of control samples or diverse stress treatments, respectively. Each bar represents means ± standard error. The up and down relations are determined based on significance analysis of p value < 0.01 and labeled on the top of each bar.
Figure 4. Accumulation of transcripts involved in terpene metabolism and phorbol ester pathway in response to abiotic and biotic stressors. Abiotic stressors are cold, drought, and salt. Biotic stressors are plant defense hormones of salicylic acid (SA) and methyl jasmonate (MeJA), as well as biotrophic fungi powdery mildew, and necrotrophic fungi Botrytis cinerea. Accumulation levels of transcripts were determined by qRT-PCR using primers in Table 1. Relative expression levels of ten individual transcripts (A–J) were calculated by comparison of control samples. All graphs represent the values of three replicates with cDNA prepared from seedling leaves of control samples or diverse stress treatments, respectively. Each bar represents means ± standard error. The up and down relations are determined based on significance analysis of p value < 0.01 and labeled on the top of each bar.
Seeds 03 00024 g004
Seeds 03 00024 g005
Figure 5. The response of genes involved in phorbol ester biosynthesis pathway to abiotic and biotic stressors. (A) Heat map illustrates the Spearman’s rank correlation co-efficiency on expression levels of target genes involved in terpene metabolism and phorbol ester pathway. The accumulation levels of transcripts were determined by qRT-PCR using primers in Table 1. The cDNA was prepared from seedling leaves of control samples or under diverse abiotic and biotic stress conditions. Abiotic stressors are cold, drought, and salt. Biotic stressors are plant defense hormones of salicylic acid (SA) and methyl jasmonate (MeJA), as well as biotrophic fungi powdery mildew, and necrotrophic fungi B. cinerea. Relative expression levels (log2) of individual transcripts were calculated by comparison with the control samples. (B) Scatter plot of the scores of the first two principal eigengene vectors derived from the principal component analysis (PCA) on the expression of the above genes. Dendrogram plot of the PCA scores visualized the profile of abiotic and biotic stressors based on the terpene gene induction.
Figure 5. The response of genes involved in phorbol ester biosynthesis pathway to abiotic and biotic stressors. (A) Heat map illustrates the Spearman’s rank correlation co-efficiency on expression levels of target genes involved in terpene metabolism and phorbol ester pathway. The accumulation levels of transcripts were determined by qRT-PCR using primers in Table 1. The cDNA was prepared from seedling leaves of control samples or under diverse abiotic and biotic stress conditions. Abiotic stressors are cold, drought, and salt. Biotic stressors are plant defense hormones of salicylic acid (SA) and methyl jasmonate (MeJA), as well as biotrophic fungi powdery mildew, and necrotrophic fungi B. cinerea. Relative expression levels (log2) of individual transcripts were calculated by comparison with the control samples. (B) Scatter plot of the scores of the first two principal eigengene vectors derived from the principal component analysis (PCA) on the expression of the above genes. Dendrogram plot of the PCA scores visualized the profile of abiotic and biotic stressors based on the terpene gene induction.
Seeds 03 00024 g005
Table 1. Nucleotide sequences and efficiency of primers used in qPCR.
Table 1. Nucleotide sequences and efficiency of primers used in qPCR.
GenesForward Primer (5′-3′)Reverse Primer (5′-3′)Efficiency R2
18S RNACAACCATAAACGATGCCGACCCAGCCTTGCGACCATACTCCC0.963
1-deoxy-d-xylulose 5-phosphate synthase (DXS)CAATGGGAGGAGGAACAGGCGCTCTGTCCATCGCAAATCTTAC0.987
1-deoxy-D-xylulose 5-phosphate reductoisomerase (DXR)GCCTCGCCTTGATCTTTGATTCTGCCCGATGCTTATT 0.939
Geranyl pyrophosphate synthase (GPPs)GGGTTCATTATCTGACATTCGTGTATTCCACGGCTCTTTCCAAGGT 0.999
Geranylgeranyl pyrophosphate synthase (GGPPs)CATTCGAGCACATAGCTAGAGCAACAACAAGTCCTTCTGACCCAACA0.964
3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGR)GTGAGGCAATTATCAAGGAAGAGCCACCAAGACCTATTGCTCTG0.997
Farnesyl pyrophosphate synthase (FPPs)TGTGCCTGGAGGGAAGCTGGAGCCATTCAATACACC0.993
4-hydroxy-3-methylbut-2-enyl diphosphate reductase (HDR)TGGAATACCGTTGAGAAGCCAAGTTGACCACCAAGAATGT0.999
Isopentenyl diphosphate isomerase (IDI)GTAAATCCAAACCCTAATGAGTTTGCATGTCAACCACA0.999
Squalene synthase (SQS)GATGAGGCGTGGTCTAACTGGCAGAATTGTATCTTGGCTTACT0.996
Squalene epoxidase (SE)CCCGTGGCATCAACAATCTCAGCCTAGCACCAATCC0.98
Casbene synthase (CAS)CCGTCTGGTGAATGATGTAGTGGACCGCACGATGA0.964
Cytochrome P450 reductase (CPR)CAACCAGAAGGAACGGAGAAAGAGGAGGAGGGAGGGAAC0.996
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zhang, W.; Wei, L.; Li, S.; Chen, F.; Xu, Y. Tissue-Specific Accumulation Profiles of Phorbol Esters in Response to Abiotic and Biotic Stresses in Jatropha curcas. Seeds 2024, 3, 324-340. https://doi.org/10.3390/seeds3030024

AMA Style

Zhang W, Wei L, Li S, Chen F, Xu Y. Tissue-Specific Accumulation Profiles of Phorbol Esters in Response to Abiotic and Biotic Stresses in Jatropha curcas. Seeds. 2024; 3(3):324-340. https://doi.org/10.3390/seeds3030024

Chicago/Turabian Style

Zhang, Wei, Lei Wei, Shijuan Li, Fang Chen, and Ying Xu. 2024. "Tissue-Specific Accumulation Profiles of Phorbol Esters in Response to Abiotic and Biotic Stresses in Jatropha curcas" Seeds 3, no. 3: 324-340. https://doi.org/10.3390/seeds3030024

APA Style

Zhang, W., Wei, L., Li, S., Chen, F., & Xu, Y. (2024). Tissue-Specific Accumulation Profiles of Phorbol Esters in Response to Abiotic and Biotic Stresses in Jatropha curcas. Seeds, 3(3), 324-340. https://doi.org/10.3390/seeds3030024

Article Metrics

Back to TopTop