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. 2022 Dec 8;7(50):46756–46768. doi: 10.1021/acsomega.2c05833

Proliferation and Metabolic Profiling of Cynanchum wilfordii Adventitious Roots Using Explants from Different Cultivation Methods

Hyejin Hyeon †, Eun Bi Jang †, Weon-Jong Yoon †, Jong-Du Lee †, Ho Bong Hyun †, Yong-Hwan Jung †, Jung Min ‡, Young-Min Ham †,*
PMCID: PMC9773952  PMID: 36570270

Abstract

graphic file with name ao2c05833_0009.jpg

Cynanchum wilfordii root is used in traditional herbal medicine owing to its various pharmacological activities. However, C. wilfordii roots are misused owing to their morphological similarities with C. auriculatum. Adventitious root (AR) culture can prevent such misuse, and the selection of plant materials is an important procedure for producing high-quality ARs. This study aimed to compare the proliferation and metabolic profiles of C. wilfordii ARs in two types of explants from different cultivation methods (either cultivated in open field (ECF) or cultivated on a heap of C. wilfordii (ECH)). After 4 weeks of culture, the proliferation rate and number and length of secondary ARs were determined, and 3/4 Murashige and Skoog (MS) salt medium, 4.92 μM indole-3-butyric acid (IBA), and 5% sucrose were suggested as the best proliferation conditions for ARs originating from both ECF and ECH. Through metabolic profiling, ARs from ECH were found to show higher accumulation patterns for flavonoids, polysaccharides, hydroxyacetophenones, aromatic amino acids, and mono-unsaturated fatty acids, which were ascribed to the activation of flavonoid biosynthesis, the phenylpropanoid pathway, and fatty acid desaturase, stimulated by abiotic stresses. In contrast, ARs from ECF had higher levels of TCA cycle intermediates, amino acids in the aspartate–glutamate pathway, and saturated and polyunsaturated fatty acids, indicating energy metabolism and plant development. Overall, the current study provided information on the optimal conditions for inducing C. wilfordii ARs with higher amounts of bioactive compounds.

Introduction

Cynanchum wilfordii (Maxim.) Hemsl. (family Asclepiadaceae) is a perennial vine, and its tubers, also called “Baeksuo,” are grown for 2–3 years for subsequent use as herbs in oriental medicine.1 A previous human clinical study has been demonstrated that C. wilfordii intake helps in maintaining healthy blood cholesterol levels.2 Moreover, several in vitro and rodent in vivo studies have reported that C. wilfordii assists in bone disease prevention, protects hepatocytes, and prevents progression of aging.3−5C. auriculatum is another plant belonging to the Asclepiadaceae family and has morphological characteristics similar to those of C. wilfordii; however, it is not recognized as a food ingredient in Korea.6 Based on a study that revealed an increased risk of miscarriage associated with C. auriculatum extract ingestion, the Korea Consumer Agency and the US Food and Drug Administration (FDA) have added it to the list of toxic plants, thereby prohibiting its use.7 However, both the plants were named ‘Baeksuo’, which led to the availability of their mixture in the Korean herbal medicine market.8 Mislabeling and misuse of C. wilfordii as C. auriculatum, to obtain economic benefits, has become a critical issue for consumers because it is directly linked to the quality and authenticity of herbal plants.

In vitro culture is a technology for culturing plant cells and tissues aseptically under artificial culture conditions, and it can stably produce specialized metabolites regardless of geographic or seasonal changes.9 During in vitro culture of C. wilfordii, there is no risk of interference from C. auriculatum, since continuous culturing proceeds only after confirming that the explants belong to the original plant C. wilfordii.10 Among the various in vitro culture techniques, adventitious root (AR) culture reportedly provides higher genetic and biochemical stability, along with higher contents of specialized metabolites, compared to callus culture.11 Ahn et al. (2018) performed a metabolite analysis of C. wilfordii roots in plants, cultivated in vitro and ex vitro, using FT-IR spectroscopy and found the overall metabolite pattern to be identical, indicating the possibility that in vitro cultured roots may be supplied as an herbal medicine instead of ex vitro plants.10

Various strategies have been implemented to increase the physiologically active metabolites in in vitro culture, including explant selection, finding a suitable medium composition, and elicitor treatment.9,12,13 Among them, explant selection helps to establish better cell lines since the physiological activities of the cell lines become greater when they are derived from mother plants with high-yielding bioactive substances.14 However, during plant growth of the same species, the production of bioactive compounds can be altered by exposure to different environmental conditions. For instance, a previous study had shown that antioxidant activity is significantly different between plant materials from artificial hydroponic cultivation and those from natural soil cultivation.15 Using plant sources as cultivation substrates has been reported to cause a rapid change in flavonoid biosynthesis in cultured Dendrobium officinale.16 For the above reasons, selection of proper cultivation methods for explants, for the efficient use of ARs as functional resources containing various specialized metabolites, is absolutely necessary.

Metabolomics provides a comprehensive identification of systematic chemical processes involving various metabolites in biological samples, such as plants, cells, and tissues.17 All chemical constituents can be broadly divided into two groups, namely, primary and specialized metabolites.18 Primary metabolites are essential for plant growth and development and are used as building blocks of specialized metabolites.18 Specialized metabolites, such as pharmaceuticals, flavors, and fragrances, play important roles in environmental responses and are commercially used as biologically active compounds with great economic value.19 Considering the increase in industrial demand for metabolites with health-promoting properties, metabolic profiling should be conducted to understand the interaction between primary and specialized metabolism and to find a way to regulate the levels of desired compounds in C. wilfordii ARs.

Although a previous study applied an in vitro culture technique to prevent misuse of C. wilfordii and to obtain stable and sufficient biomass ingredients, to the best of our knowledge, none of the studies have evaluated the optimal growth condition of ARs using different types of C. wilfordii explants, nor did they have conducted a comprehensive profiling of both primary and specialized metabolites.10 Thus, the current study aimed to establish a medium composition suitable for the growth of C. wilfordii ARs, using explants derived from different cultivation methods, and to uncover the dynamic primary and specialized metabolism regulated by the abiotic factors of explants. Two types of plant materials were used in this experiment, namely, explants cultivated in an open field (ECF) and those cultivated on a heap of C. wilfordii (ECH). To identify the cultivation method of explants that would be better for the growth of ARs, primary and specialized metabolites (including amino acids, organic acids, sugars, fatty acids, hydroxyacetophenones, and phenolic compounds) were compared using high-performance liquid chromatography (HPLC) and gas chromatography–mass spectrometry (GC–MS). Species authentication of C. wilfordii ARs was performed by analyzing the chemical marker, conduritol F.

Results and Discussion

Growth Characteristics of ARs Originating from ECF and ECH

To identify the optimum concentration of minerals in the medium for the growth of C. wilfordii ARs induced by ECF and ECH, C. wilfordii ARs were cultured in three different concentrations of Murashige and Skoog (MS) salt (1/2, 3/4, and 1 strength).20 Our study showed that both ECF and ECH produced higher proliferation rates in the 3/4 and 1 MS treatment groups than in the 1/2 MS treatment group (p < 0.05), and there was no significant difference between 3/4 and 1 MS concentrations in both ECF and ECH (Figure 1A). The number of secondary ARs was the highest in the 3/4 MS treatment group in both ECF and ECH types, and those were 3.3 and 1.9 times higher than in the 1/2 MS concentration, respectively. Considering the explant types, the overall number of secondary ARs from ECF was higher than that from ECH. In the 3/4 MS medium composition, the number of secondary ARs from ECF was 1.3 times higher than that from ECH (Figure 1B). In particular, browning started in the 1/2 MS medium after 3 weeks of culture, indicating that browning occurred faster than in the other media, when cultured for the same period (Figure S1). Inorganic salt concentration in the medium affects organogenesis and plant growth, and the MS medium contains a higher nitrogen content than other media.13 In case of AR proliferation, the concentration of suitable inorganic salts varies depending on the plant. According to previous studies, Echinacea angustifolia ARs grow best at 1/2 MS concentration, whereas 1 MS was the optimal growth condition for Pseudostellaria heterophylla ARs.21,22 In high-strength MS medium, low water potential affects the absorption of water and nutrients in the roots, thereby reducing growth. In contrast, ARs grow rapidly in low-strength MS medium, although a lack of nutrients reduces the biomass levels.23−25

Figure 1.

Figure 1

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Characteristics of adventitious roots (ARs) derived from explants cultivated in an open field (ECF) and those cultivated on a heap of C. wilfordii roots (ECH) by MS salt concentration. Proliferation rate (A), number of secondary ARs (B). Basal medium: 4.92 μM IBA + 30 g/L sucrose + 2.4 g/L gelrite. zDifferent letters indicate significant differences at p < 0.05 according to Duncan’s multiple range test (n = 3).

Indole-3-butyric acid (IBA) is another important factor for AR induction, since it initiates cell division and primordium formation, and induces cell dedifferentiation to form apical meristems.26 The proliferation rate based on IBA concentration showed no significant difference, with a proliferation rate of 80% or more for both ECF and ECH (data not shown). The number of induced secondary ARs was the highest when both types were treated with 4.92 μM IBA, and the length of secondary ARs in ECF and ECH was the longest at 0.49 μM IBA (Figure 2). In Hypericum perforatum, secondary ARs were thin and long when treated with 2.46 μM IBA, but at a high concentration of 14.8 μM, the secondary ARs were shorter and thicker, similar to the results of this study.27 Therefore, based on the number of secondary ARs and morphological observations of ARs of C. wilfordii, 4.92 μM IBA was considered optimal. Ahn et al. (2018) had reported the IBA concentration suitable for the proliferation of C. wilfordii ARs to be 24.60 μM, which differed from the results of this study.10 A high concentration of exogenous IBA was not essential in this study, since the change in hormonal sensitivity was presumed to differ depending on the tissue, age, and developmental stage of the plant as well as the physiological conditions.28

Figure 2.

Figure 2

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Effect of IBA concentration on the proliferation of C. wilfordii adventitious roots (ARs) derived from explants cultivated in an open field (ECF) and those cultivated on a heap of C. wilfordii roots (ECH). The number of secondary ARs (A), length of secondary ARs (B). Basal medium: 3/4 MS + 30 g/L sucrose + 2.4 g/L gelrite. zDifferent letters indicate significant differences at p < 0.05 according to Duncan’s multiple range test (n = 3).

After culturing for 4 weeks with different sucrose concentrations, the number of secondary ARs in ECF was the highest in 3% and 5% treatment groups (Figure 3B). In ECH, the number of secondary ARs was the highest at 5% sucrose, and proliferation was inhibited at 7% sucrose (Figure 3A,B). The optimal sucrose concentration differed between ECF and ECH, even though they originated from the same species. In plant culture, exogenous carbohydrates regulate various physiological functions, including plant growth and metabolite synthesis, respiration, and osmotic pressure regulation, and maximal uptake of sucrose and other nutrients increases biomass and specialized metabolite content.24,29 The type and concentration of sugar in apple root MM106 reportedly affects the rooting rate, number of roots, and root length.30 Sucrose, the most commonly used carbon source, is used at a concentration range of 2–5% in most AR cultures. An increase in sucrose concentration within the appropriate range increases the AR biomass due to a decrease in the cell water content.31 However, since high sucrose concentration reduces osmotic pressure, it can act as a stressor and directly stimulate the defense response, which can inhibit the growth of ARs, thereby affecting metabolite synthesis.13,32

Figure 3.

Figure 3

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Characteristics of C. wilfordii adventitious roots (ARs) derived from explants cultivated in an open field (ECF) and those cultivated on a heap of C. wilfordii roots (ECH) by varying sucrose concentration. Proliferation rate (A), number of secondary ARs (B), and length of secondary ARs (C). Basal medium included 3/4 MS + 4.92 μM IBA + 2.4 g/L gelrite. zDifferent letters indicate significant difference at p < 0.05 according to Duncan’s multiple range test (n = 3).

Analysis of Conduritol F as a Chemical Biomarker of C. wilfordii

Development and enhancement of the culture system are crucial for the production of high-quality ARs. However, species authentication should primarily be conducted before the production of AR biomass to reduce indeterminacy in authentic C. wilfordii ARs. Previous studies had shown that conduritol F is a single biomarker that can be used to discriminate between C. wilfordii and C. auriculatum.6,8 Thus, we analyzed conduritol F, which exists only in C. wilfordii, in ARs from ECF and ECH by GC–MS. Our results represented that conduritol F showed the formation of two derivatization products with characteristic fragment ions at m/z 117 [M – 29]+, m/z 147 (base peak), and m/z 160 [M + 14]+ (Table 1). Among these ions, [M – 29]+ was the most dominant and corresponded to the loss of two methyl radicals in the molecule.33 The precursor and product ions in the mass spectra of ribitol, used as an IS, agreed with those reported in the literature.34 For accurate quantification, m/z 117 was selected for the analysis of conduritol F, and m/z 147 was selected for the IS. In the quantification of conduritol F in C. wilfordii ARs, a significantly higher level of conduritol F was detected in ECF types (0.112 ± 0.008 μg/mg) than in ECH types (0.048 ± 0.006 μg/mg) (Table 1). Our results confirmed that the two AR samples were cultured from genuine C. wilfordii root. Additionally, the results suggested that metabolites could be produced differently depending on the properties of the initial explants.

Table 1. Identification of Conduritol F in C. Wilfordii Adventitious Roots (ARs) from Explants Cultivated in an Open Field (ECF) and Those Cultivated on a Heap of C. Wilfordii Roots (ECH).

          quantity (μg/mg)d
compound RTa (min) RRTb (min) mass fragment quantification ionc ECF ECH
conduritol F-1 11.382 0.997 117, 147, 160 117 0.112 ± 0.008 0.048 ± 0.006
conduritol F-2 11.463 1.004 117, 147, 160 117
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a

Retention time.

b

Relative retention time (retention time of analyte/retention time of internal standard).

c

Specific mass ion used for quantification.

d

Each value is the mean of three replicates ± standard deviation.

Multivariate Analysis

Generally, specialized metabolites, such as hydroxyacetophenones and phenolic compounds, are important for C. wilfordii in view of their extensive biological activities, including antioxidant, anti-inflammatory, neuroprotective, and anti-tumor activities.35,36 Primary metabolites, such as amino acids, organic acids, sugars, and fatty acids, affect the biosynthesis of various specialized metabolites as precursors.37 For these reasons, both primary and specialized metabolites were analyzed in the present study. We identified 54 metabolites, including conduritol F (Figure S2), hydroxyacetophenones (Figure S3, Table S1), phenolic compounds (Figure S4, Table S2), low-molecular-weight hydrophilic compounds (Figure S5, Table S3), and fatty acids (Figure S6, Table S4).

Multivariate analysis is an approach for measuring several biologically relevant features in a large-scale metabolome dataset, with two of the most popular methods being principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA). PCA provides an informative first look at group structures with unbiased dimensionality reduction.38 Thus, PCA was initially applied to conceptualize the overall patterns in metabolite composition between ARs from ECF and ECH. In the score plot, the two highest-ranking principal components accounted for 85.1% of the total variance in the dataset. Principal component 1 (PC1), accounting for 68.8% of the total variance, resolved a clear separation between ARs from ECF and those from ECH (Figure 4A). The corresponding loading plot enabled the identification of several metabolites that contributed to the separation between the two groups (Figure 4B). In the loading plot, positive PC1 values were mainly attributable to major phenolic compounds, amino acids, and hydroxyacetophenones, which implied that these metabolites were more abundant in ARs from ECH than in those from ECF. The notable metabolites with positive values for PC1 were rutin, phenylalanine, and 4′-hydroxyacetophenone with eigenvector values of 0.1606, 0.1611, and 0.1374, respectively. On the other hand, negative PC1 values were ascribable to most organic acids and polyunsaturated fatty acids, which indicated that the levels of these compounds were higher in ARs from ECF than in those from ECH. The representative metabolites with negative values for PC1 were citric acid and linolenic acid, with eigenvector values of −0.1621 and −0.1609, respectively. The results agreed with previous results in the fact that the types of explants used for AR induction influenced the accumulation of various phytochemicals differently due to their endogenous and exogenous factors.39,40

Figure 4.

Figure 4

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Score (A) and loading (B) plots of principal components 1 and 2 from the principal component analysis results obtained from conduritol F, hydroxyacetophenones, phenolic compounds, low-molecular-weight hydrophilic compounds, and fatty acids data for C. wilfordii adventitious roots (ARs) derived from explants cultivated in an open field (ECF) and those cultivated on a heap of C. wilfordii roots (ECH). Plot annotation: 1, lactic acid; 2, glycolic acid; 3, alanine; 4, valine; 5, urea; 6, ethanolamine; 7, glycerol; 8, phosphoric acid; 9, leucine; 10, isoleucine; 11, proline; 12, glycine; 13, succinic acid; 14, fumaric acid; 15, serine; 16, threonine; 17, β-alanine; 18, malic acid; 19, aspartic acid; 20, methionine; 21, pyroglutamic acid; 22, GABA; 23, glutamic acid; 24, phenylalanine; 25, xylose; 26, asparagine; 27, glutamine; 28, citric acid; 29, fructose; 30, mannose; 31, galactose; 32, glucose; 33, inositol; 34, tryptophan; 35, sucrose; 36, raffinose; 37, caffeic acid; 38, epicatechin; 39, rutin; 40, benzoic acid; 41, kaempferol; 42, C16:0; 43, C16:1; 44, C18:0; 45, C18:1; 46, C18:2; 47, C18:3; 48, C20:0; 49, C22:0; 50, C22:1; 51, C24:0; 52, conduritol F; 53, 4’-hydroxyacetophenone; 54, 2′,4’-dihydroxyacetophenone.

Compared to PCA, which preserves information regarding class groups, PLS-DA is a chemometric technique used to maximize the separation between different groups of samples and is more accurate in measuring significant metabolites.41 To further explore the novel metabolic differences between the two AR types, PLS-DA was performed (Figure 5A). In this experiment, PLS-DA score plots showed clear separation by explant origin. To estimate the quality of the PLS-DA model, we calculated the two quality parameters, goodness of fit (R2) and goodness of prediction (Q2). Generally, R2 values closer to 1 are considered to represent a good description of the data by the PLS-DA model, and Q2 values higher than 0.9 are considered to represent excellent predictability.42 We obtained an R2Y of 0.997 and Q2 of 0.985 by validation analysis using PLS-DA. This implied that PLS-DA significantly contributed to the separation of the two types of ARs, with high goodness of fit and prediction. Variable importance in projection (VIP) plots were used to examine the significant features responsible for C. wilfordii AR differentiation (Figure 5B). VIP scores higher than 1 strongly contributed to sample differentiation.43 In this study, a total of 33 metabolites, including amino acids, organic acids, fatty acids, and phenolic compounds, were included in the group with VIP values greater than 1, underlining their importance in discriminating C. wilfordii ARs between ECF and ECH. The outcomes were consistent with the PCA results. In particular, the significant importance of most of the phenolic compounds (except benzoic acid) and 4′-hydroxyacetophenone was notable, since the phenolic compounds and 4′-hydroxyacetophenone exhibit medicinal activity and efficacy of C. wilfordii ARs as bioactive compounds.35,36

Figure 5.

Figure 5

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Partial least squares-discriminant analysis (PLS-DA) score plots (A) and variable importance in projection (VIP) score above 1 (B) for conduritol F, hydroxyacetophenones, phenolic compounds, low-molecular-weight hydrophilic compounds, and fatty acids data for C. wilfordii adventitious roots (ARs) derived from explants cultivated in an open field (ECF) and those cultivated on a heap of C. wilfordii roots (ECH).

Metabolites–Metabolites Correlations

To investigate the relationships across metabolites of C. wilfordii ARs involved in closely related metabolic pathways, we performed Pearson’s correlation and hierarchical cluster analyses (HCA). Two major composition clusters have been described by HCA (Figure 6, boxed within dotted lines). In the first group, positive correlations were observed between organic acids and five amino acids (glutamic acid, aspartic acid, asparagine, glycine, and pyroglutamic acid), which belong to the TCA cycle and nitrogen metabolism into amino acids. For example, aspartic acid was positively correlated with citric acid (r = 0.9957, p < 0.001), malic acid (r = 0.9800, p < 0.001), and succinic acid (r = 0.9928, p < 0.001). Furthermore, all saturated and polyunsaturated fatty acids, except C16:0, were strongly correlated with organic acids and amino acids. Significant correlations between the compounds participating in the TCA cycle, nitrogen metabolism, and fatty acid metabolism were associated with the modulation of energy metabolism.44 Our findings were in agreement with the previous reports that revealed energy generation to be activated by the release of TCA cycle intermediates, aspartate–glutamate pathway, and fatty acids under regular growth conditions.45,46

Figure 6.

Figure 6

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Correlation matrix for conduritol F, hydroxyacetophenones, phenolic compounds, low-molecular-weight hydrophilic compounds, and fatty acids data for C. wilfordii adventitious roots (ARs) derived from explants cultivated in open field (ECF) and those cultivated on a heap of C. wilfordii roots (ECH). Each square indicates the Pearson’s correlation coefficient of a pair of compounds, with the value of this coefficient represented by the intensity of blue or red color, indicated on the color scale.

The second group mainly comprised phenolic compounds, polysaccharides, aromatic amino acids, hydroxyacetophenones, and mono-unsaturated fatty acids, some of which are involved in phenylpropanoid and flavonoid biosynthesis. For instance, raffinose was significantly correlated with phenolic compounds, including epicatechin (r = 0.9717, p < 0.01), rutin (r = 0.9370, p < 0.01), and kaempferol (r = 0.9605, p < 0.01), as well as with aromatic amino acids, including phenylalanine (r = 0.9851, p < 0.001) and tryptophan (r = 0.9672, p < 0.01). The strong correlations across raffinose, flavonoids, and aromatic amino acids were in agreement with a previous report by Kim et al. (2021).47 However, this study also revealed that phenolic compounds were highly correlated with hydroxyacetophenones and mono-unsaturated fatty acids. For example, epicatechin was positively correlated with 4′-hydroxyacetophenone (r = 0.8636, p < 0.05), C16:1 (r = 0.9794, p < 0.001), and C18:1 (r = 0.9706, p < 0.01). The result indicated positive interactions among phenolic compounds, hydroxyacetophenones, and mono-unsaturated fatty acids, as supported by previous studies. Zhu et al. (2018) had reported that the concentration of aromatic compounds, such as ferulic acids and vanillin, were accelerated together with those of 4′-hydroxyacetophenone by the degradation of lignin in plant cell walls.48 O’Brien et al. (2012) also noted that hydroxyacetophenones as well as other phenolic compounds are utilized to reduce reactive oxygen species (ROS) during defense mechanisms.49 Moreover, the concentrations of mono-unsaturated fatty acids, including C16:1 and C18:1, were increased under stress conditions, and the compounds could be the key to reducing the burst of ROS.50,51 Overall, our results largely supported the positive relationship across hydroxyacetophenones, mono-unsaturated fatty acids, and phenolic compounds to improve stress tolerance.

Metabolic Pathway Analysis

Based on multivariate analysis results using PCA, PLS-DA, and HCA, metabolic differences between the two ARs from ECH and ECF were notable, and the differences were closely related to metabolic pathways. To systematically compare the metabolites involved in adaptation to different growth conditions, metabolic pathway maps were drawn using PathVisio, and significant metabolites with VIP values greater than 1.0 and p values less than 0.05 were indicated by asterisks (*) (Figure 7). Abundance of metabolites in the ARs from ECH and ECF was visualized using red and green color gradients, respectively.

Figure 7.

Figure 7

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Metabolic pathway diagrams of C. wilfordii adventitious roots (ARs) derived from explants cultivated in an open field (ECF) and those cultivated on a heap of C. wilfordii roots (ECH). The expression data consist of log2-transformed fold change (FC) values (log2FC). A log2FC value range is −1.5 < log2FC < 1.5. If log2FC value is higher than zero (indicated in red), metabolite content would be higher in ARs from ECH than in those from ECF. If log2FC value is less than zero (indicated in green), metabolite content would be lower in ARs from ECH. If log2FC value is zero (indicated in white), metabolite content would be identical in both types of AR. Significant differences between ARs induced from ECF and ECH were identified by the t test (*p < 0.05).

Regarding flavonoid biosynthesis and the phenylpropanoid pathway, our results showed the levels of flavonoids (rutin, epicatechin, and kaempferol) and aromatic amino acids (phenylalanine and tryptophan) to be higher in the ARs from ECH. Plants have various tolerance mechanisms for survival under environmental conditions. In addition, major metabolites induced by environmental stress usually exhibit antioxidant activity.52 Zhang et al. (2017) had reported that adverse environmental conditions lead to the expression of genes encoding enzymes involved in the accumulation of rutin, a major flavonoid in tartary buckwheat, thereby improving abiotic stress resistance as an antioxidant.53 Similarly, kaempferol and epicatechin have been reported to protect plant cells as in vitro ROS scavengers under environmental stress conditions in Arabidopsis thaliana.54,55 Further, the synthetic pathway of aromatic amino acids has been reported to be consecutively upregulated owing to the higher levels of flavonoids required for antioxidant activities.44,56 Thus, we suggested that the increase in rutin, epicatechin, and kaempferol contents could be associated with the stress response metabolism of mother plants against the heap-cultivation conditions. Moreover, our metabolic pathway results indicated the accumulation of these flavonoids to be manipulated by the enhanced production of phenylalanine and tryptophan, which are components of the phenylpropanoid pathway.

With regard to sugar metabolism, most sugar compounds did not show prominent changes. However, we confirmed that ARs from ECH, exposed to harsh environmental conditions, had higher amounts of raffinose. Raffinose is an abiotic stress-responsive metabolite with osmoprotective properties.57 A myriad of abiotic stresses stimulate genes and enzymes related to the activation of raffinose synthase.58 On the other hand, the content of xylose showed a prominent decrease in ARs from ECH. Xylose is a known precursor of hemicellulose in the plant cell walls.59 Napoleão et al. (2017) observed hemicellulose biosynthesis and a reduction in xylose content in Brachypodium distachyon stems after treatment with methyl jasmonate, which induces plant defense responses.60 Therefore, we proposed that the heap cultivation method increases the accumulation of raffinose and reduces the content of xylose to protect C. wilfordii mother plants that are exposed to abiotic stresses.

In the TCA cycle and nitrogen metabolism, the amounts of organic acids (citric acid, succinic acid, fumaric acid, malic acid, and urea) and amino acids in the aspartate–glutamate pathway (aspartic acid, asparagine, glutamic acid, and pyroglutamic acid) were significantly higher in the ARs from ECF than in those from ECH. The TCA cycle is usually activated to generate energy and promote plant development in favored modes of cultivation.44 As a consecutive enhancement of organic acid content, the synthetic pathway of amino acids could be upregulated by supplying carbon skeletons.61 Thus, ARs from ECF, which spent time in non-stress conditions during the growth of mother plants, were expected to contain higher concentrations of aspartic acid, asparagine, glutamic acid, and pyroglutamic acid through activation of the TCA cycle. However, other amino acids, including valine and β-alanine, were largely accumulated in ARs from ECH. This was because amino acids in the aspartate–glutamate pathway are commonly used as nitrogen carriers and are converted into other amino acids, such as valine, threonine, and isoleucine.62 We speculated that the large amount of amino acids in the aspartate–glutamate pathway was consumed for adaptation to the heap-cultivation conditions, resulting in the utilization of these amino acids for the synthesis of other amino acids, such as valine and β-alanine.

With regard to fatty acid metabolism, the present study showed that the levels of mono-unsaturated fatty acids were higher in ARs from ECH, whereas those of saturated and polyunsaturated fatty acids were higher in ARs from ECF. The change in the levels of saturated and unsaturated fatty acids was found to protect plant cells from multiple environmental stresses. Analysis of tobacco plants exposed to chilling stress showed a significant decrease in 16- and 18-carbon saturated fatty acid levels and expression of fatty acid desaturase genes.63 In soybean leaves, salt resistance showed a decreased ratio of C18:2 and C18:3 to C18:1, representing the association of fatty acid composition with stress responses.64 In addition, exposure of pepper seedlings to metal stress resulted in the reduction of polyunsaturated fatty acids, activating efficient defense mechanisms against ROS.65 Likewise, in the present study, the comparatively low content of saturated fatty acids in ARs from ECH might be initiated by fatty acid desaturase. Subsequently, synthesis of mono-unsaturated fatty acids was enhanced and levels of polyunsaturated fatty acids were reduced in C. wilfordii explants under heap cultivation to support plant defense against abiotic stresses.

Conclusions

The objective of this study was to examine the optimal medium composition for the growth of C. wilfordii ARs and the content of central primary and bioactive specialized metabolites for the efficient selection of mother plants. The study found that 3/4 MS salt medium, 4.92 μM IBA, and 5% sucrose were the best for the proliferation of both ARs originating from ECH and ECF. Before metabolic profiling, authenticity of the AR species was confirmed by analyzing conduritol F, a chemical marker of C. wilfordii. Multivariate analysis showed the metabolite differences and correlations to be derived from common or closely related pathways related to abiotic stress. ARs from ECH featured higher levels of phenolic compounds, polysaccharides, aromatic amino acids, hydroxyacetophenones, and mono-unsaturated fatty acids, which were associated with various mechanisms to protect the mother plants from abiotic stresses. In contrast, ARs from ECF had higher levels of TCA intermediates, amino acids in the aspartate–glutamate pathway, and saturated and polyunsaturated fatty acids, most of which were related to energy metabolism and plant development. Our results suggested that ECH is a better line for the production of valuable specialized metabolites that participate in the biosynthesis of flavonoids and hydroxyacetophenones, including rutin, epicatechin, kaempferol, and 4′-hydroxyacetophenone. The results suggested that using plants of the same species as substrates can be a new strategy to increase the content of functional specialized metabolites. Our study might help in selecting optimal explants for the induction of ARs with high contents of bioactive metabolites. Future study will focus on additional scale-up and in vitro screening studies to expand the possibility of C. wilfordii ARs in industrial use, along with various functional characteristics.

Methods

Plant Materials and AR Induction

C. wilfordii root, provided by Jeju Chyeonnyeonyakcho Farming Co. (Aewol-eup, Jeju Island, Korea), was used as the raw material, and two methods of cultivation of mother plants were used for the induction of ARs. One method was initiated from explants cultivated in an open field for 3 years. The other method was initiated from explants cultivated on a heap of C. wilfordii roots for 6 months, after cultivation in an open field for 2 years and 6 months. The workflow designed in this experiment is shown in Figure S7. For surface sterilization, explants were soaked in 70% ethanol for 1 min, rinsed with sterile deionized water, and immersed in a 2% (v/v) sodium hypochlorite solution supplemented with 1–2 drops of Tween-20 for 20 min. After rinsing thrice with sterile deionized water, explants were cultured in MS medium supplemented with 4.92 μM IBA, 30 g/L sucrose, and 2.4 g/L gelrite at 24 ± 1 °C under dark conditions. Explants (0.5 cm) were cut and placed in the medium.

Proliferation of C. wilfordii ARs

To obtain a medium composition suitable for the growth of ARs, MS salt (1/2, 3/4, and 1 strength), IBA (0.49, 4.92, 14.76, and 24.60 μM), and sucrose (1, 3, 5, and 7% (v/v)) concentrations were varied and culture was performed for 4 weeks. pH of all media was adjusted to 5.8 ± 1 before autoclaving, and then the media were sterilized at 121 °C and 1.2 atm for 15 min. The experiments were performed with one factor at a time, and the ARs were randomly placed. After 4 weeks of culture, the proliferation rate and number and length of the secondary ARs were recorded. Proliferation rate was measured as the ratio of the explants induced from the secondary ARs induced of 10 explants. Number and length of secondary ARs are the average of those derived from each explant. For metabolic comparison of ARs originating from ECF and ECH, 0.1 g (fresh weight) of ARs was inoculated in 100 mL medium containing 3/4 MS, 4.92 μM IBA, and 3% sucrose, cultured with shaking at 100 rpm for 4 weeks, washed with deionized water, and eventually freeze-dried at −85 °C.

Analysis of Conduritol F with GC–MS

To identify conduritol F in C. wilfordii ARs, samples were extracted and derivatized using previously reported methods with some modifications.66,67 Each 50 mg of lyophilized sample was dissolved in 1 mL of methanol:water:chloroform 2.5:1:1 (v/v/v) solution, and ribitol (60 μL, 200 μg/mL in methanol) was introduced as an internal standard (IS). After incubation at 37 °C for 30 min, the samples were centrifuged at 13,000 rpm for 3 min. The polar phases (800 μL) were transferred to fresh tubes, mixed with water (400 μL), and centrifuged under identical conditions as above. The upper layers (900 μL) were pipetted into new tubes and concentrated for 3 h. The remaining residues were freeze-dried at −85 °C for 18 h. For derivatization, 80 μL of 2% methoxyamine hydrochloride (MOX) in pyridine (v/v) was added and incubated at 30 °C for 90 min. Next, 80 μL of N-methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA) was added and incubated again at 37 °C for 30 min. The derivatized samples were analyzed on an Agilent 7890A GC (Agilent, Palo Alto, CA, USA) equipped with an Agilent 5975C MS and separated on a CP-Sil 8CB low-bleed/MS fused-silica capillary column (30 m × 0.25 mm i.d. × 0.25 μm film thickness; Agilent). For each run, 1 μL of sample was injected using a split ratio of 25:1. Helium was used as the carrier gas, flowing at a constant rate of 1.00 mL/min. The injection, MS quadrupole, and ion source temperatures were 230, 150, and 230 °C, respectively. The oven temperature program was initially set at 80 °C for 2 min, ramped to 320 °C at 15 °C/min, and was finally held at 320 °C for 10 min. The ionization voltage was set at 70 eV. The mass spectrum of each compound was acquired for m/z 85–600, and m/z 117 and m/z 147 were detected in the selected ion monitoring (SIM) acquisition mode. Qualification of conduritol F was conducted by direct comparison of retention time and mass spectra with those of commercially available standards acquired by methoxime derivatization and trimethylsilyl etherification procedures. For absolute quantification, an accurate calibration curve was prepared by loading conduritol F in the range of 2.5–30 μg with fixation of the IS at 12 μg. Chromatographic data were processed using ChemStation (Agilent) software.

Analysis of Hydroxyacetophenones by HPLC

Hydroxyacetophenones were extracted and analyzed according to previously described procedures with slight modifications.8 For extraction of hydroxyacetophenones, 2 g of each AR sample was mixed with 70% ethanol (40 mL) for 24 h. Solvent residues were filtered through a filter paper (No. 2; Advantec, Tokyo, Japan). After evaporation, the extracted solvents were lyophilized under vacuum in a freeze-dryer (FD8512; IlShinBioBase, Dongducheon, Korea). Hydroxyacetophenones were analyzed from 10 mg of 70% ethanol extracts, which were mixed with 1 mL of 70% methanol, sonicated for 1 h, and centrifuged at 13,000 rpm for 10 min. The supernatants were filtered using a 0.50 μm PTFE filter (Advantec) into amber glass vials with a screw cap. The sample extracts were analyzed using a Thermo U3000 HPLC system (Thermo Fisher Scientific, Waltham, MA, USA) equipped with a DAD-3000 UV–vis detector (Thermo Fisher Scientific). The dissolved sample extract (10 μL) was loaded into a Cadenza CD-C18 column (150 × 4.6 mm, 3 μm particle size; Imtakt Corp., Kyoto, Japan). The flow rate was set to 1 mL/min, and the column temperature was maintained at 40 °C. Mobile phases A and B comprised 0.5% acetic acid in water and acetonitrile, respectively. The gradient program used was as follows: 0 min–20% B; 2 min–20% B; 20 min–30% B; 21 min–70% B; 26 min–100% B; 27 min–20% B; 42 min–20% B. The chromatogram was recorded at 200–400 nm, and peaks were obtained at 270 nm. The retention times and UV absorption spectra of each hydroxyacetophenone were compared to those of the standards. The amount of each compound was calculated from the calibration curve constructed by plotting the concentrations of standards ranging from 1.25 to 50 μg/mL. The resultant data were processed using Chromeleon 7 software (Thermo Fisher Scientific).

Analysis of Phenolic Compounds with HPLC

Extraction and analysis of phenolic compounds were performed according to a previously published method, with some modifications.68 For each sample, 100 mg of lyophilized powder was extracted with 1 mL of methanol and 200 μL of 0.1 M hydrochloric acid (HCl). After sonication for 1 h, samples were centrifuged at 13,000 rpm for 10 min. The upper layer (900 μL) was transferred to a fresh tube, and the remaining pellet was re-extracted with methanol (1 mL). Following sonication and centrifugation under conditions similar to those described above, the upper layer was collected and concentrated using a centrifugal evaporator (CVE-3000, EYELA, Tokyo, Japan). The resulting extract was dissolved in methanol (1 mL) and filtered through a 0.50 μm syringe filter (Advantec). The obtained compounds were analyzed using a Waters Alliance e2695 HPLC system (Waters Corp., Milford, MA, USA) equipped with a photodiode array detector set at 280 nm. The dissolved sample extract (20 μL) was separated using a Zorbax CB-C18 column (250 × 4.6 mm, 5 μm particle size; Agilent). The flow rate and column temperature were set at 1 mL/min and 40 °C, respectively. The mobile phase solvent A was 0.1% acetic acid in water and solvent B was 0.1% acetic acid in acetonitrile. Composition of the mobile phase was programmed as follows: 0 min–8% B; 2 min–10% B; 35 min–30% B; 50 min–90% B; 51 min–100% B; 60 min–100% B; 63 min–8% B; wavelengths were monitored in the range of 200–400 nm, and the peak was obtained at 280 nm. Peak identification was performed by comparing the retention times and UV absorption spectra with those of the standards. Quantitative analysis of phenolic compounds was done using external calibration curves with concentrations ranging from 2.5 to 80 μg/mL. Data acquisition was performed using the Empower 3 software (Waters Corporation).

Analysis of Low-Molecular-Weight Hydrophilic Compounds with GC–MS

For the analysis of low-molecular-weight hydrophilic compounds, the methods for sample extraction, derivatization, GC–MS operation, and oven temperature programming were adopted from the protocol employed for conduritol F analysis. Mass spectra were acquired in scan mode over a mass range of 85–600 m/z. In-house and NIST libraries were used to identify the metabolites, and the identities were confirmed using commercial standards. Quantitative calculation was conducted based on the ratio of the peak area of the analyte to that of the IS.

Analysis of Fatty Acids with GC–MS

The extraction method used for fatty acids was described previously.44 Briefly, 100 mg of lyophilized samples was weighed, and 2.5 mL of chloroform:methanol (2:1, v/v) solution was added. Pentadecanoic acid (200 μL, 1 mg/mL in chloroform) was added to the mixture as the IS. All the samples were vortexed and placed in a sonicator for 20 min. Next, 2.5 mL of sodium chloride solution (0.58% w/v) was added to the sample mixtures and centrifuged at 4000 rpm at 4 °C for 10 min. After pipetting the bottom layers into new tubes, the contents were evaporated using a centrifugal evaporator (EYELA) for 1 h. Methanol (180 μL), toluene (100 μL), and 5 M sodium hydroxide (20 μL) were introduced to the concentrated samples and incubated at 85 °C for 5 min. Methylation was activated by the addition of 14% (w/v) boron trifluoride followed by incubation under the same conditions described above. Water (400 μL) and pentane (800 μL) were then added to the samples. After centrifugation at 13,000 rpm for 15 min, the upper layers were collected in new tubes and concentrated for 30 min. The resulting extract was dissolved in hexane (200 μL) and filtered through a 0.50 μm syringe filter (Advantec). Samples were analyzed using an Agilent 7890A GC coupled with an Agilent 5975C MS. For each run, 1 μL of sample was injected into an HP-5MS column (30 m × 0.25 mm i.d. × 0.25 μm film thickness; Agilent) with a split ratio of 25:1. Helium gas was passed through the column at a flow rate of 1.00 mL/min. The injection, MS quadrupole, and ion source temperatures were set at 280, 150, and 230 °C, respectively. The oven temperature was initially maintained at 100 °C for 3 min and subsequently ramped to 180 °C at a rate of 15 °C/min. Subsequently, the temperature was increased to 250 °C at 5 °C/min and maintained for 3 min. The temperature was finally increased to 320 °C at a rate of 20 °C/min and maintained for 12 min. The mass range was set from 85 to 600 m/z, and ions were detected in SIM acquisition mode for peak analysis. Ionization voltage was set at 70 eV. Qualitative and quantitative analyses of fatty acids were performed using standards and a fatty acid methyl ester (FAME) mixture (C8–C24). Chromatographic data were obtained using ChemStation (Agilent) software.

Statistical Analysis

For statistical analysis of the growth characteristics of ARs, one-way analysis of variance (ANOVA) was performed using Statistical Analysis System (SAS) software (version 9.4; SAS Institute Inc., Cary, NC, USA). Statistical assessments of the differences across mean values were performed using Duncan’s multiple range test. For the metabolic comparison of ARs originating from ECF and ECH, all data were normalized with unit-variance scaling and then applied to PCA and PLS-DA using soft independent modeling of class analogy (SIMCA) software (version 17.0; Umetrics, Umeå, Sweden). HCA and Pearson’s correlation analyses were performed using MetaboAnalyst 5.0 (www.metaboanalyst.ca) to confirm metabolite–metabolite correlations. For metabolite mapping, fold changes (FCs) were calculated by dividing the average data of ARs from ECH by those of ARs from ECF and then transformed into log2 values. PathVisio software (version 3.3.0, http://www.pathvisio.org, Maastricht University, Maastricht, The Netherlands) was used to link log2-transformed values with the metabolic pathways drawn based on the Arabidopsis thaliana pathway in WikiPathways, reference pathway in the KEGG database, and the previously published literature.48,69 Using GraphPad Prism 8 (San Diego, CA, USA), independent Student’s t tests were conducted to represent significantly different metabolites with p < 0.05 as the threshold for significance.

Acknowledgments

This work did not receive funding from any private, public, or not-for-profit agency organization.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.2c05833.

  • Pictures of C. wilfordii adventitious roots (ARs) affected by MS salt concentration (Figure S1); analysis results of conduritol F (Figure S2), hydroxyacetophenones (Figure S3, Table S1), phenolic compounds (Figure S4, Table S2), low-molecular-weight hydrophilic compounds (Figure S5, Table S3), and fatty acids (Figure S6, Table S4); scheme of the adopted process for C. wilfordii adventitious root (AR) proliferation (Figure S7) (PDF)

Author Contributions

# H.H. and E.B.J. contributed equally to this work as first authors.

Author Contributions

All authors have read and agreed to the published version of the manuscript. Conceptualization: Y.-H.J. and J.M.; resources: E.B.J., J.-D.L., Y.-H.J., and J.M.; methodology: H.H., E.B.J., J.-D.L., and H.B.H.; data curation, formal analysis, investigation, validation, visualization: H.H. and E.B.J; writing – original draft, writing – review & editing: H.H., E.B.J, W.-J.Y., and Y.-M.H.; project administration, supervision: Y.-M.H.

The authors declare no competing financial interest.

Supplementary Material

ao2c05833_si_001.pdf (1MB, pdf)

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