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Study on the Dynamic Variation of the Secondary Metabolites in Viscum coloratum Using Targeted Metabolomics

  • Corresponding author: Yun Li, Department of Pharmacy, The Second Hospital of Dalian Medical University, Dalian, China. E-mail: dlmuxh@126.com; Dr. Yunli Zhao, School of Pharmacy, Shenyang Pharmaceutical University Shenyang 110016, P. R. China E-mail: Yunli76@163.com
  • Available Date: 24-Sep.-2022
  • Viscum coloratum (Kom.) Nakai is a well-known medicinal plant. However, the optimal harvest time for V. coloratum is unknown, and few studies have evaluated its compounds variation during storage, post-harvest quality control thus lacks a theoretical basis. Our study aimed to comprehensively evaluate the quality of V. coloratum in different growth stages, and determine the dynamic variation of metabolites. Ultra-performance liquid chromatography tandem mass spectrometry was used to quantify 29 compounds in V. coloratum harvested from six growth periods, and the associated biosynthetic pathway was explored. The accumulation of different types of compounds were analyzed based on their synthesis pathways. Grey relational analysis was used to evaluate the quality of V. coloratum across different months. The compounds variation during storage was analyzed using a high-temperature high-humidity accelerated test. The results showed that V. coloratum was better in March, followed by November, and was worst in July. During storage, compounds downstream of the biosynthesis pathway were degraded to produce their upstream compounds and low-molecular-weight organic acids first, leading to a large gap in the degradation time course between different compounds. Due to the rapid rate and large degree of degradation, five compounds were tentatively designated as “early warning components” for quality control. This report provides a reference for the analysis of the biosynthesis and degradation of metabolites in V. coloratum. It provides theoretical support for the rational application of V. coloratum resources and quality control during V. coloratum storage.
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Study on the Dynamic Variation of the Secondary Metabolites in Viscum coloratum Using Targeted Metabolomics

    Corresponding author: Yun Li, Department of Pharmacy, The Second Hospital of Dalian Medical University, Dalian, China. E-mail: dlmuxh@126.com; Dr. Yunli Zhao, School of Pharmacy, Shenyang Pharmaceutical University Shenyang 110016, P. R. China E-mail: Yunli76@163.com
  • 1. Department of Pharmaceutical Analysis, School of Pharmacy, Shenyang Pharmaceutical University, Shenyang, China
  • 2. Department of Pharmacy, The Second Hospital of Dalian Medical University, Dalian, China

Abstract: Viscum coloratum (Kom.) Nakai is a well-known medicinal plant. However, the optimal harvest time for V. coloratum is unknown, and few studies have evaluated its compounds variation during storage, post-harvest quality control thus lacks a theoretical basis. Our study aimed to comprehensively evaluate the quality of V. coloratum in different growth stages, and determine the dynamic variation of metabolites. Ultra-performance liquid chromatography tandem mass spectrometry was used to quantify 29 compounds in V. coloratum harvested from six growth periods, and the associated biosynthetic pathway was explored. The accumulation of different types of compounds were analyzed based on their synthesis pathways. Grey relational analysis was used to evaluate the quality of V. coloratum across different months. The compounds variation during storage was analyzed using a high-temperature high-humidity accelerated test. The results showed that V. coloratum was better in March, followed by November, and was worst in July. During storage, compounds downstream of the biosynthesis pathway were degraded to produce their upstream compounds and low-molecular-weight organic acids first, leading to a large gap in the degradation time course between different compounds. Due to the rapid rate and large degree of degradation, five compounds were tentatively designated as “early warning components” for quality control. This report provides a reference for the analysis of the biosynthesis and degradation of metabolites in V. coloratum. It provides theoretical support for the rational application of V. coloratum resources and quality control during V. coloratum storage.

    • Viscum coloratum (Komar.) Nakai, a semi-parasitic plant of Viscum L., is a medicinal plant that is widely distributed in Asia. Its dry stems and leaves have been used as traditional Chinese medicine (TCM) since ancient China. This plant was first recorded in Shen Nong's Materia Medica and is currently recorded in the Chinese Pharmacopoeia. Traditionally, V. coloratum is used to treat ailments such as arthralgia, soreness of the waist and knees, and threatened abortion. In many pharmacological studies in recent years, V. coloratum has been confirmed to have various pharmacological properties, such as in the treatment of cardiovascular system diseases[13], anti-tumor activity[47], immune regulation[8], anti-aging, anti-oxidation[9], inhibition of platelet aggregation[10], and anti-viral activities[11]. In recent decades, many metabolites have been extracted from V. coloratum, including flavonoids[1215], triterpenes[16,17], organic acids[17], lignans[18], and other compounds.

      The active compounds of TCM are usually the secondary metabolites, and secondary metabolism usually has a seasonal pattern, specific metabolic activities tend to be more active in a certain season. Therefore, the quality of medicinal materials can be guaranteed only when the medicinal plant grows to a certain period and are harvested within a certain time. For example, there are significant differences in the contents of steroid saponins in P. polyphylla var. chinensis across different growth years and harvest seasons. Yin et al.[19] found that the quality of P. polyphylla var. chinensis harvested in November was the best when it had been growing for at least eight years. Luo et al.[20] found that the content of cardiac glycosides in Sophora flavescens Alt. was the highest when harvested from October to December. Research by Xue et al.[21] showed that the total flavonoids and total phenolic acids in Artemisia argyi Levl. et Vant. exhibited the same change trend across six harvest periods. The type of medicinal plant, organ, growth characteristics, active ingredients, and dynamic changes in dry matter accumulation should be considered when determining the harvest time of a medicinal plant.

      On the other hand, due to the complex compositions and structures of metabolites, medicinal materials are easily affected by the surrounding environment during storage, leading to the deterioration and waste of medicinal materials. Generally, the factors affecting the quality of TCM can be divided into two categories: external factors and internal factors. External factors primarily refer to external conditions, such as temperature, humidity, light, air, and microorganisms. For example, physalis in Physalis alkekengi L. decreased significantly under moisture, light, and high temperature[22], while the content of chlorogenic acid in Eucommia ulmoides Oliver was reduced to varying degrees by temperature and light[23]. Internal factors refer to different types of compounds that are easily affected by various factors due to their different chemical structures, resulting in different forms of degradation or inactivation. For example, reduced anthraquinone compounds are easily oxidized, volatile components will volatilize under high temperature conditions, lignans are easily isomerized and inactivated under light, and glycosides are prone to hydrolysis[24]. The primary factor accounting for the deterioration of TCM is the decreased contents of active ingredients. External factors also impact the structure of the active ingredient, making it susceptible to degradation by external factors. Therefore, it is important to study the variation of the effective ingredients in TCM during storage.

      The history of V. coloratum as a TCM is ancient. Different harvesting times of V. coloratum have been recorded in TCM books from the past dynasties. It is recorded as “harvested on March 3 and dried in the shade” in Ming Yi Bie Lu and Xinxiu Materia Medica. The Compendium of Materia Medica states that V. coloratum is “collected in July and August” and the modern Chinese Materia Medica records it as “generally harvested in winter”. The 2020 edition of the Chinese Pharmacopoeia stipulates that “harvested from winter to next spring” Obviously, there are different opinions on the harvest time of mistletoe. However, the theoretical support for the determination of the optimal harvest period of V. coloratum is insufficient. In terms of the storage conditions of V. coloratum, the Chinese Pharmacopoeia only stipulates that it should be “placed in a dry place to prevent insects.” However, the metabolites will still be affected by other external factors. There are few reports on the changes in metabolites in V. coloratum during storage, there is still no theoretical basis for post-harvest quality control. Therefore, it is necessary for the quality control of TCM to determine the change rule of metabolites during both the growth and the storage of medicinal materials.

      At present, researches on the metabolites in mistletoe are constantly appearing. For example, Tatyana et al. [25] have studied non-polar and volatile compounds in V. coloratum and proved that the extract is cytotoxic to Ehrlich carcinoma cells. Dai et al.[26] isolated ten compounds from Viscum album and confirmed that three of them are cytotoxic. Our research group also conducted a lot of research on V. coloratum before, including separating compounds and studying the biological activities of some of them[27,28], establishing a fingerprinting method to control the quality of V. coloratum [29], and studying the relationship of compounds between the host and V. coloratum and so on[30,31]. We have also conducted research on how the origin and host affect the chemical composition of V. coloratum.[32] But for V. coloratum in a growth cycle, these studies focus on the compound information at a certain time point, and cannot reveal the changing law of metabolites in V. coloratum. Therefore, based on existing research, it is difficult to determine at which growth stage the quality of V. coloratum is better.

      Medicinal plant metabolomics adopts a variety of analytical methods to comprehensively analyze the low-molecular-weight metabolites of medicinal plants and qualitatively and quantitatively determine the effects of genes or the environment on metabolites as a whole. The purpose of medicinal plant metabolomics is to study the metabolic synthesis pathways, metabolite networks, and regulatory mechanisms to ultimately provide a foundation for medicinal plant variety selection, new drug development, and quality and safety evaluation[33]. Grey relational analysis (GRA) is a method to analyze the relational grade between discrete sequence data. The principle of evaluating sample quality is to establish an ideal reference sequence based on study data, calculate the correlation degree between the test sample and the ideal sequence, and then evaluate the quality of the sample. The greater the correlation between the sample and the ideal sequence, the better the sample quality[34].

      The main purpose of this study is to analyze the overall dynamic change rule of the compounds in mistletoe during growth and storage, and to provide a reference for the quality control of mistletoe and the study of biogenic synthesis pathways. Due to the complex components of TCM, a single or a few components cannot reflect the overall quality. Therefore, we selected 29 components of mistletoe as the research object. In this study, V. coloratum was harvested every two months from March 2019 to January 2020, and 29 components in 36 batches of V. coloratum harvested from six harvest periods were quantified. The dynamic changes in the selected compounds were analyzed by comprehensively considering the structure of the compounds and the biosynthetic pathways to provide theoretical support for determining the best harvest time for V. coloratum. GRA was used to evaluate the quality of V. coloratum across different months. In addition, a high-temperature and the high-humidity accelerated test was designed, and the bioactive components of V. coloratum from different acceleration times were quantified to explore their changes and provide a reference for the quality control of V. coloratum following harvesting.

    Material and methods
    • Methanol and formic acid (HPLC grade) were obtained from Tianjin Concord Technology Co., Ltd. (Tianjin, China). Acetonitrile (HPLC grade) was purchased from Fisher Scientific (New Jersey, USA). Ultrapure water was purchased from Hangzhou Wahaha Group (Hangzhou, China).

      Shikimic acid (Shik), eleutheroside E (Eleu), and salicylic acid (Sali) were purchased from Shanghai Acmec Biochemical Co., Ltd. (Shanghai, China), succinic acid (Succ) and 4-hydroxycinnamic acid (Hydr) were purchased from WEIKEQI Biotechnology Co., Ltd. (Sichuan, China), protocatechuic acid (Prot) was purchased from Must Biotechnology Co., Ltd. (Chengdu, China), abscisic acid (ABA) was obtained from Mreda Technology Co., Ltd. (Beijing, China), and ferulic acid (Feru), chlorogenic acid (Chlo), paracetamol (APAP), Coumarin (Coum) and sulfamethoxazole (SMZ) were purchased from the National Institute of Food and Drug Control (Beijing, China).

      Phenylalanine (Phen), (1E,4E)-1,7-bis(4-hydroxyphenyl)hepta-1,4-dien-3-one (Dhdk), homoeriodictyol (Hedt-IV), 3,5-dihydroxy-1,7-bis(4-hydroxyphenyl)heptane (Dbhh), quercetin-3,3'-dimethyl ether (Quer), betulinic acid (Betu), oleanolic acid (Olea), homoeriodictyol-7-Ο-β-D- glucoside (Hedt-III), isorhamnetin-3-O-β-D-glucoside (Isor), rhamnazin-3-Ο-β-D-glucoside (Rham-I), syringenin 4-O-β-D-apiofuranosyl (1→2)-β-D-glucopyranosides (Syri-II), dihydrophaseic acid-4'-O-6''-(β-ribofuranosyl)-β-glucopyranoside (Dihy), ( + )-lyoniresinol-3α-O-β-D-glucopyranoside (Lyon), homoeriodictyol-7-O-β-D-apiosiyl-(1→2)-O-β-D-glucoside (Hedt-II), rhamnazin-3-Ο-β-D-(6''-β-hydroxy-β-methyglutaryl)-glucoside (Rham-II), rhamnazin-3-Ο-β-D-(6''-β-hydroxy-β-methyglutaryl)-glucoside-4'-O-β-D-glucoside (Rham-III), syringin (Syri), pachypodol (Pach), and 5-hydroxy-3,7,3'-trimethoxylflavonoid-4'-O-β-D-glucoside (Httf) were obtained from our previous separation and purification experiments, and the purity of all compounds exceeded 98% (HPLC grade).

      Syri, Pach, Httf, Coum, and Eleu were detected in positive ion mode, while the other compounds were detected in negative ion mode. In addition, hmoeriodictyol-7-O-β-D-apiosiyl-(1→5)-β-D-apiosyl-(1→2)-β-D-glucoside (Hedt-I) is another isolated flavonoid. Due to the lack of reference standard, Hedt-I was semi-quantitatively analyzed according to its theoretical ion fragments in order to analyze the content variation. Among these metabolites, Hedt-IV、Hedt-III、Hedt-II、Rham-I、Httf、Rham-II、Isor、Pach、Quer、Olea、Syri、Prot、Dhdk、Hedt-I、Rham-III、ABA、Dbhh、Shik、Sali、Phen、Chlo、Betu、Coum、Syri-II、Lyon、and Dihy were previously isolated or identified from mistletoe by our study group [27,28,31,35]. Eleu、Hydr、Succ、and Feru were isolated from mistletoe by other researchers [12,17,18]. Fig. 1 shows the structures of all analytes.

      Figure 1.  Chemical structures of 30 compounds and internal standard compounds

    • 0.50 mg/mL Stock solutions of each compound were obtained by dissolving the corresponding standard substance in 50% methanol. Positive mixed standard solutions and negative mixed standard solutions were prepared by diluting the appropriate amount of the stock solutions of the corresponding compounds in 50% methanol. The concentrations of each compound in the negative mixed standard solution were as follows: Succ 20.08 μg/mL, Shik 10.04 μg/mL, Phen 10.48 μg/mL, Hydr 3.97 μg/mL, Chlo 16.00 μg/mL, Feru 2.01 μg/mL, Prot 4.97 μg/mL, Hedt-II 21.04 μg/mL, Betu 4.18 μg/mL, Olea 10.72 μg/mL, Quer 5.50 μg/mL, Sali 4.00 μg/mL, Dbhh 12.00 μg/mL, Syri-II 20.12 μg/mL, Lyon 16.00 μg/mL, Rham-I 1.04 μg/mL, Rham-III 16.00 μg/mL, Hedt-IV 18.36 μg/mL, Hedt-III 27.05 μg/mL, Rham-II 17.41 μg/mL, ABA 1.02 μg/mL, Isor 11.52 μg/mL, Dhdk 1.12 μg/mL, and Dihy 16.74 μg/mL. The concentrations of each compound in the positive mixed standard solution were as follows: Syri 2.09 μg/mL, Pach 4.16 μg/mL, Httf 1.05 μg/mL, Coum 0.54 μg/mL, and Eleu 1.05 μg/mL.

      A positive internal standard (IS) solution (20.4 ng/mL) was obtained by accurately weighing 5 mg SMZ and dissolving it in 50% methanol. Likewise, the negative IS solution was prepared by precisely weighing an appropriate amount of SMZ and APAP and dissolving it in 50% methanol (SMZ 204 ng/mL, APAP 5.2 μg/mL).

    • The V. coloratum plants were harvested every two months in Shenyang from March 2019 to January 2020, and identified by Prof. Yu Zhi-guo (Department of Pharmaceutical Analysis, School of Pharmacy, Shenyang Pharmaceutical University, Shenyang, China). The six host plants were marked before the first harvest to ensure the accuracy of the subsequent harvests. The samples with voucher were stored in the state Key Laboratory of Traditional Chinese Medicine (Shenyang Pharmaceutical University, China). Sample information is provided in Table 1.

      Harvest timeHost species
      2019.32019.52019.72019.92019.112020.1
      S3_1S5_1S7_1S9_1S11_1S1_1Populus ussuriensis Kom.
      S3_2S5_2S7_2S9_2S11_2S1_2Populus ussuriensis Kom.
      S3_3S5_3S7_3S9_3S11_3S1_3Populus ussuriensis Kom.
      S3_4S5_4S7_4S9_4S11_4S1_4Populus ussuriensis Kom.
      S3_5S5_5S7_5S9_5S11_5S1_5Populus ussuriensis Kom.
      S3_6S5_6S7_6S9_6S11_6S1_6Ulmus pumila L.

      Table 1.  Sample number and host species of Viscum coloratum at different harvest times

      The V. coloratum harvested in March 2019 was stored at 50°C and 70% RH for accelerated testing, and samples were obtained after 0, 1, 2, 3, and 6 months for content determination. The sampling time points were denoted as A0, A1, A2, A3, and A6.

      The collected samples were dried indoors at room temperature, crushed into powder, and passed through a 4-mesh sieve. An accurately weighed 0.2 g powder was transferred to a conical flask with 20 mL 50% methanol and sonicated for 30 min. Solvent loss was compensated with 50% methanol. The extraction solution was then centrifuged at 13000 rpm for 10 min, and 5 mL of clear supernatant was filtered using a 0.22 μm filter.

      The positive ion sample solution was obtained by diluting 2.5 mL filtered extracts and 1 mL positive IS solution with 50% methanol to 5 mL. Likewise, the negative ion sample solution was obtained by the dilution of 0.5 mL filtered extracts and 0.5 mL negative IS solution with 50% methanol to 10 mL. The positive ion and negative ion sample solutions were stored at 4°C prior to analysis.

    • An Agilent 1290 ultra-performance liquid chromatography (UPLC) system (CA, USA) was employed for quantification. The separation was conducted on a 2.1 mm × 100 mm, 1.7 μm ACQUITY UPLC BEH C18 column. Mobile phase A was acetonitrile with 0.1% formic acid added, and mobile phase B was water with 0.1% formic acid added. The gradient elution program for positive ion mode was as follows: 0 ~ 1 min, 5% A; 1 ~ 4.5 min, 5 ~ 53% A; 4.5 ~ 8 min, 53 ~ 75% A; 8 ~ 9 min, 75 ~ 99% A; 9 ~ 11 min, 99% A. The gradient elution program for negative ion mode was as follows: 0 ~ 9 min, 5 ~ 35% A; 9 ~ 18 min, 35 ~ 73% A; 18 ~ 23 min, 73% A; 23 ~ 23.5 min, 75 ~ 5% A; 23.5 ~ 24 min, 5% A. The flow rate was kept at 0.25 mL/min, and the injection volume was 5 μL. The column temperature was maintained at 35°C, and the sample room temperature was maintained at 4°C.

      All compounds were detected by an AB Sciex API 4000 mass spectrometer (Foster City, CA, USA). The MS parameters were set as follows: source voltage: positive ion mode, 4000 V, negative ion mode, −4000 V; source temperature (TEM), 550°C; ion source gas1 (Gas 1), 55 psi; ion source gas2 (Gas 2), 40 psi; curtain gas, 20 psi; colliding gas, 10 psi; and dwell time, 100 ms. The MS parameters of each compound were designed and optimized for multiple reaction monitoring (MRM) mode, including precursor ion (Q1), production ion (Q3), collision energy (CE), and declustering potential (DP). The detailed conditions are shown in Supplementary Table S1. The mass spectra of the all analytes are shown in Supplementary Fig. S1. All data were processed in AB Sciex Analyst 1.5.2 software.

    • Specificity was tested by analyzing a 50% methanol solution, a mixed stock standard solution, and a sample solution. The positive mixed standard solution was used as the positive linear standard solution SD8, and positive linear standard solution SD7-SD1 was prepared by diluting 5, 2.5, 1.25, 0.5, 0.2, 0.1, 0.05 mL of SD8 to 10 mL with 50% methanol. Similarly, the negative mixed standard solution was used as the negative linear standard solution SD9, and negative linear standard solution SD8-SD4 was prepared by diluting 5, 2.5, 1, 0.4, and 0.2 mL of SD8 to 10 mL with 50% methanol. The negative linear standard solution SD3-SD1 was obtained by diluting 4, 2, and 1 mL of SD4 to 10 mL with 50% methanol. A least squares linear regression model with 1/x as the weighting factor was used for the calculations from the peak area of each compound relative to the corresponding peak area of IS.

      The SD5 was analyzed six times in a day for an intra-day precision test and was analyzed on three consecutive days for an inter-day precision test. The post-preparative stability was investigated by running a same sample solution analysis at 0, 2, 4, 6, 8, 12, and 24 h after the sample had been prepared. Six sample solutions of the S7_1 were extracted according to the above method and analyzed for a repeatability test. The relative standard deviation (RSD) was evaluated in the precision, stability, and repeatability tests. In the recovery test, a standard solution of the analytes was added to S7_1 of known content with amounts equivalent to those of the samples. The accuracy of the method was evaluated using the formula [detected amount (μg) − original amount (μg)] × 100%/spiked amount (μg).

    Results and discussion
    • The result of the selectivity test is shown in Supplementary Fig. S2 -Fig. S4 and indicated no apparent interferences from 50% methanol at each retention time. In this study, linear regression analysis was performed with the peak area of each compound relative to the corresponding peak area of IS (Y) and concentration (X, μg/mL). All correlation coefficients (R) were greater than 0.9990. The regression equations, correlation coefficients, linear ranges, limits of detection (LODs), and limits of quantification (LOQs) are all shown in Supplementary Table S2.

      All Mean, SD and RSD values from the precision, repeatability and stability test are shown in Supplementary Table S3 and S4. In the recovery test, the average recoveries of the 29 compounds ranged between 91.7% and 108.3%, and all the results are shown in Supplementary Table S5. The method validation results indicated that this method was reliable, accurate, and stable for the simultaneous determination of the 29 bioactive components in V. coloratum.

    • Twenty-nine compounds in V. coloratum from different harvest times were determined by the established method. All the quantitative results are shown in Supplementary Table S6.

      In order to facilitate the analysis of the change trend of compound content, the average content was used to draw a line graph of content change. The Pearson correlation analysis was performed between the change trend of compound content in each sample and the change trend of average content. Correlation coefficient (r) were collected in Supplementary Table S7. Overwhelming majority of the correlation coefficients were greater than 0.7 and most of them were grater then 0.8, indicating that the average content could reflect the variation trend of compound content in each sample.

    • The possible biosynthesis pathways of the compounds were reasonably speculated based on relevant literature [36,37], and the metabolic rules of the various compounds at different growth periods were analyzed in combination with the quantitative analysis results.

    • Visualization of the flavonoid content at different harvesting periods was performed by MeV (4.9.0), as shown in Fig. 2A. A line chart of the dynamic changes in flavonoids is shown in Fig. 3A. The possible metabolic pathways of related compounds are shown in Fig. 4A.

      Figure 2.  The heat map of content of flavonoids and phenylpropanoids in different months

      Figure 3.  Line chart of dynamic changes of metabolites in different months (μg/g)

      Figure 4.  Possible routes of the biosynthesis of flavonoids, ABA and phenylpropanoids in Viscum coloratum

      As indicated in Fig. 2A, except for a few compounds (Rham-I, Rham-II, Httf), the content of most compounds showed noticeable seasonal differences: Their content was generally higher in March, and gradually decreased with the increase of temperature. It generally reached the lowest level around July. After that, the content gradually increased with the decrease of temperature. Entering autumn and winter, the content increased significantly. Studies have shown [38] that the synthesis of flavonoids is affected by many factors, such as light, temperature, ultraviolet radiation, and soil moisture. Sufficient light, suitable ultraviolet radiation, low temperature, and low soil moisture will increase the activity of key enzymes, such as phenylalanine aminolyase (PAL), cinnamic acid 4-hydroxylase (C4H), 4-coumaric acid CoA Ligase (4CL)(Fig. 4A). In March, November, and January, low temperature directly stimulated related enzymes, and V. coloratum received sufficient light due to the shedding of the host plant leaves, resulting in more flavonoids being synthesized.

      Compared with November, the flavonoid content decreased slightly in January, especially that of Hedt-III. January is the period with the lowest temperatures throughout the year. Low-temperature stress is severe, and plants need to resist low temperature stress by consuming a large number of secondary metabolites. In addition, ultraviolet radiation is intense in the winter. Excessively long hours of ultraviolet radiation will reduce the synthesis of flavonoids [39]. Meanwhile, the harsh environment in winter leads to insufficient nutrient supply. Therefore, a combination of factors led to a decrease in the content of flavonoids in January.

      After the basic skeleton of a flavonoid is formed, various flavonoids can be synthesized through a series of modifications. Essential modifications include methylation and glycosylation, which are catalyzed by oxygen methyltransferases (OMTs) and glycosyltransferases (UGTs), respectively. The possible synthetic pathways are as follows: eriodictyol is methylated to obtain Hedt-IV, which is then glycosylated to obtain a series of dihydroflavonoid glycosides. On the other hand, eriodictyol is catalyzed by flavanone 3-hydroxylase (F3H) and flavonol synthase (FLS) to generate quercetin, and quercetin then undergoes methylation and glycosylation to form corresponding flavonoids or flavonoid glycosides (Fig. 4A).

      The content of Hedt-IV is much lower than that of the corresponding glycosides downstream, indicating that UGTs are more active than OMTs. After Hedt-IV is synthesized, it is glycosylated into corresponding glycosides in large amounts. The glycosylation increases the solubility and stability of compounds and promotes the synthesis and accumulation of compounds in plants.

      The content changes in Pach and Quer were similar, while the contents of Quer were lower. This may be because the structure of the two compounds is similar, and they are in adjacent positions in the flavonoid metabolism network. Furthermore, as a trimethylated flavonoid, Pach is more stable, while Quer can be further methylated into Pach or glycosylated into glycosides. In addition, the content of Pach increased slightly earlier then that of other compounds in autumn and winter, and decreased earlier too. This phenomenon suggests that the activity of OMTs related to Pach synthesis, or the expression of related genes may be more sensitive to environment conditions. The study on the related genes and enzyme activities in the biosynthetic pathway in V. coloratum will be carried out in the subsequent research of our research group.

    • ABA is one of the five major plant hormones. ABA can be converted into a series of secondary metabolites through oxidation, isomerization, and esterification [36].

      The main metabolic pathways are as follows: ABA is hydroxylated to produce 8'-OH-ABA, and 8'-OH-ABA is then rearranged and reduced to produce phaseicacid and dihydrosaffric acid. Dihydrosaffric acid is conjugated at the 4'-C to generate dihydrosaffric acid-4'-O-β-glucoside. Dihy may be the product of further glycosylation of dihydrosaffloric acid-4'-O-β-glucoside (Fig. 4B).

      The line chart of ABA and Dihy content is shown in Fig. 3C. The ABA content was highest in March and then gradually decreased. After September, the content began to rise. Dihy showed similar fluctuation trends.

      ABA plays a vital role in regulating plant growth and helping plants combat abiotic stresses such as osmotic stress, low-temperature stress, and salt stress [40]. In winter, the temperature drops and precipitation decreases. The accumulated content of endogenous ABA will increase at low temperatures. ABA can relieve the damage of low temperature stress to the cell membrane and reduce the content of malondialdehyde and gibberellin to improve the cold resistance of plants [41].

      Decreased precipitation will cause osmotic stress to plants, which leads to dehydration and reduced water absorption. In the case of osmotic stress, the accumulation of ABA can improve plant tolerance. ABA maintains water in plants by inducing stomatal closure. Conversely, ABA can protect plants from dehydration by inducing the expression of related genes to produce biological macromolecules that protect plant cells [42].

      These effects may be the main reason for the maximum accumulation of ABA in March. Additionally, ABA can also induce the ripening of seeds and fruits. September to November is the fruiting period of V. coloratum. This may also be a reason for the increase in ABA content in November.

    • Most phenylpropanoids are derived from phenylalanine or tyrosine. Phenylalanine (Phen) is first synthesized from phosphoenolpyruvate (PEP) and D-erythrose-4-phosphate via the shikimate pathway. Phen is catalyzed by phenylalanine aminolyase (PAL) to synthesize cinnamic acid, and cinnamic acid is then catalyzed by cinnamic acid-4-hydroxylase (C4H) to generate 4-hydroxycinnamic acid (Hydr). Then Hydr is modified by hydroxylation and methylation to form a series of cinnamic acid derivatives such as caffeic acid, ferulic acid (Feru), 5-hydroxyferulic acid, and sinapic acid (Fig. 4C).

      Among them, p-coumaryl alcohol, pinitol, and sinapyl alcohol can be synthesized from Hydr, Feru, and sinapinic acid through acetylation and reduction reactions. These hydroxycinnamoyl alcohol monomers can be catalyzed by peroxidase to generate free radicals, and the free radicals can be coupled to form dimer lignans with various structures [37].

      In this study, sinapyl alcohol could produce syringin (Syri) and Syri-II through glycosylation reaction. After forming sinapyl alcohol free radicals, two D-type free radicals coupled and undergo intramolecular nucleophilic attack to form syringaresinol. Eleutheroside E (Eleu) can be synthesized by the glycosylation of syringaresinin, while Lyon can be formed by the furan ring opening reaction, glycosylation, and intramolecular nucleophilic of Eleu (Fig. 4C).

      The heatmap of the contents of the involved compounds is shown in Fig. 2B. As can be seen from Fig. 3B, the content of Syri and Eleu was lower in July and September, but relatively higher in the other months. For syringin in particular, the content in November and January was higher than in the other months, suggesting that the synthesis of lignans and glycosylation of sinapyl alcohol maybe more active in November and January.

      The content of Syri-II was significantly higher than that of Syri, indicating that more syringin was further glycosylated into Syri-II in the glucosidation reaction pathway of sinapyl alcohol.

      For the other compounds, no obvious seasonality was found. That is probably because these low-molecule organic acids are located in the most upstream position in the metabolic network and could be converted into downstream products with complex structures through multiple pathways. On the other hand, the conversion between these compounds requires many steps. As the metabolic network is intricate, they will thus fail to show a clear seasonal accumulation law.

    • Since the content of various active ingredients from different months varied greatly, it was difficult to visually evaluate the quality of the medicinal materials. Therefore, GRA was used for comprehensive quantitative evaluation [43].

    • Different component contents may not be at the same order of magnitude, and thus the original data should be standardized. The specific method was as follows: the number of measured samples was denoted as n, and the evaluation index number of each sample was denoted as m. The original data matrix {Xik} (i = 1,2,3…n; k = 1,2,3…m) was n = 36 and m = 29 in this paper. Original data were standardized according to Formula (1).

      In the equation, Yik is the standardized data and Xk is the mean value of the k-th index in 36 samples.

    • The optimal reference sequence {Xwk} (k = 1,2,3…m) contained the maximum value of each index measured in 36 samples. The minimum value of each index measured in 36 samples was used as the worst reference sequence, namely {Xtk} (k = 1,2,3…m).

    • The optimal and worst reference sequence correlation coefficients were calculated according to Equations (2) and (3), respectively.

      min = min |YikYwk |, ∆max = max |YikYwk |, Ywk is the optimal reference sequence after standardization, and ρ is the resolution coefficient, generally taken as 0.5.

      min = min |YikYtk |, ∆max = max |YikYtk |, Ytk is the worst reference sequence after standardization, and ρ is the resolution coefficient, generally taken as 0.5.

      After the correlation coefficient was obtained, the correlation degree of the optimal reference sequence and the worst reference sequence was calculated according to Equations (4) and (5), respectively.

      The relative correlation degree was calculated according to Equation (6).

      The relative correlation degree of each sample was calculated by the above method, and the results are shown in Table 2.

      Sampleriri orderSampleriri order
      S3-40.45281S9-50.399619
      S3-60.44892S9-30.398820
      S3-30.44593S1-40.398321
      S11-60.44074S1-30.397622
      S3-10.43755S3-50.397423
      S11-40.43566S1-50.397324
      S1-60.43177S7-10.397325
      S11-30.42878S5-40.392826
      S9-40.42169S7-30.385927
      S5-20.415910S5-60.385928
      S11-10.415911S7-60.378929
      S5-50.415312S7-20.373530
      S9-10.414213S1-20.372331
      S5-10.413914S7-40.370732
      S5-30.410915S1-10.363833
      S11-20.406516S7-50.357434
      S9-60.400817S9-20.356735
      S3-20.400218S11-50.351836

      Table 2.  Relative degree of correlation of 36 samples

      According to the GRA, the relative correlation degree of the samples in March and November was higher, and was lower in July.

      The results indicated that the overall quality of V. coloratum in March might be best, followed by November, whereas the quality of V. coloratum in July is the worst.

      The impact of the harvest time on the biological activity of mistletoe (Viscum album L.) was investigated previously by some researchers. Önay-Uçar [44] studied the antioxidant activity of mistletoe harvested in February and July, and found that the antioxidant activity of mistletoe was higher in February. Vicaş et al. [45] analyzed the antioxidant activity of aqueous extracts from mistletoe harvested in May, July, and December, and found that the antioxidant activity was highest in December and lowest in May. Pietrzak and Nowak [46] reported that the chemical profile and biolofical activity of mistletoe are closely related to the harvest time. Mistletoe harvested in November–March had the highest total content of flavonoid and phenolic and high antioxidant activity, and autumn-winter period is the best time to harvest mistletoe. In addition, combined with climate data, they found that climatic conditions (such as temperature, light) may be the main reason for this seasonal difference. The results of these studies are similar to the findings of this paper.

      More over, we have investigated the impact of environmental factors on the antioxidant activity of mistletoe in previous study, and found that the antioxidant activity of mistletoe harvested in Changbai Mountain (Jilin province, China) was significantly better than that in Chengde Mountain Resort (Hebei province, China) [32]. In the past five years, the average monthly temperature in a year in Chengde Mountain Resort was −8.3 °C to 24.68 °C, while it was −15.96 °C to 21.92 °C in Changbai Mountain. Since temperature is one of the main factors of climate condition, this finding may also provide some circumstantial evidence for the results of present study. An in-depth study of the impact of harvest time on the biological activity (such as anti-liver fibrosis) of mistletoe will be carried out in our subsequent research.

    • Twenty-nine compounds in V. coloratum from different acceleration periods were determined by the established method. All the quantitative results are shown in Supplementary Table S8.

      After the accelerated test, the variation trend of compound contents could be divided into three categories: reduced content (Coum, Httf, Hedt- III, Phen, Betu, Olea, Shik, Eleu, Syri, Dihy, Syri- II, Rham- III, Aba, Hedt- I), increased content (Chlo, Dbhh, Succ, Prot, Sali, Hedt- IV, Rham- I, Rham- II, Feru, Hydr, Dhdk, Lyon), and less change in content (Quer, Pach, Hedt- II, Isor).

    • The line chart (Fig. 5A) indicate that the contents of most compounds dropped rapidly in the first month, following which the trend flattened. However, the content of Coum and Httf increased first, and then decreased. It may be due to the degradation of other metabolites to produce Coum and Httf at the beginning, following which Coum and Httf were then degraded.

      Figure 5.  Line chart of the content change of each compound in the accelerated test

      The degradation rate of each compound at each acceleration stage was summarized in Fig. 6. The degradation rate of Eleu, Syri, Betu, Phen, Olea, and Shik exceeded 50% in the first month. After 6 months, the degradation rate of most components exceeded 60%, among which the degradation rate of Syri and Eleu reached more than 90%.

      Figure 6.  The degradation rate of content-reducing components at different acceleration time

    • The components with increased contents could be roughly divided into three categories according to their change trends (Fig. 5B) as follows: Chlo, Dbhh, and Feru, which showed a rapid increase at first and then a decline after a plateau period; Succ, Lyon, Hydr, Dhdk, and Hedt-IV, which did not show a downward trend after increasing and plateauing; and Prot, Sali, Rham-I, and Rham-II, which showed an increasing trend during the accelerated test, but no plateau and decline trend.

    • Although the content of Isor and Hedt-II fluctuated slightly, they still showed an overall degradation trend (Fig. 5C).

      Most of the components with decreased contents were downstream metabolites with relative complex structures (such as Eleu, Olea and Betu), the flavonoid glycoside Hedt-I, and the flavonoid glycoside Rham-III. Most of the components with increased contents were low-molecular-weight organic acids upstream of the metabolic network and simple flavonoids, such as Chlo, Succ, Prot, Feru, and the flavonoid Hedt-IV.

      The results indicated that the complex structural components might first be degrade into some upstream compounds and some low-molecular-weight organic acids first, leading to the latter increasing first and decreasing later. For example, Hedt-I and Hedt-III could be deglycosylated to produce Hedt-IV; and Rham-III could be deglycosylated and hydrolyzed to form Rham-I and Rham-II. In other words, the degradation of complex compounds leads to a large gap in the degradation time course between different compounds.

      In addition, the degradation rate of components such as eleutheroside E, syringin and oleanolic acid reached 50% within one month and exceeded 80% after six months, indicating these components are extremely susceptible to temperature and humidity. Therefore, to ensure the quality of V. coloratum during storage, temperature and humidity should be strictly controlled.

      The degradation rate of Eleu, Syri, Rham-III, Betu, and Shik was over 70% after the accelerated test, with a high degradation speed and a large degree of degradation. It may be due to the glycosidic bonds, carboxyls, hydroxyls, and other structures in these components that are easily deglycosylated, deacidified, and dehydrated under high-temperature and high-humidity conditions. Therefore, these compounds were tentatively designated as “early warning components” for quality control, and special attention should be paid to these when performing drug quality control.

      In summary, in this paper, the biosynthetic pathways of 29 metabolites and their dynamic changes were investigated, and the quality of mistletoe in different months was evaluated; the degradation rules of metabolites during storage were analyzed and five “early warning components” were screened.

      Meanwhile, there are some questions that could be further investigated in the following research. First of all, although the differences in antioxidant activity are discussed, the pharmacological effects of mistletoe are extensive, and difference analysis in more pharmacological effects(such as anti-liver fibrosis and anti-tumor activity) need to be performed in future research to provide more support for the quality assessment of mistletoe in different harvesting periods. In addition, for the analysis of metabolites biosynthetic pathways and their seasonal changes, further studies on related enzyme activity or DNA expression are needed to explore the molecular mechanism of seasonal changes of metabolites. Our laboratory will conduct in-depth research on these questions.

    Conclusions
    • The knowledge about the dynamic changes of secondary metabolites in plants during the growth and storage periods is crucial for the rational application and storage management of traditional medicine resources.

      In this study, an UPLC-MS/MS method was established to simultaneously determine the contents of 29 components in V. coloratum. The associated biosynthetic pathways were reasonably speculated, and the synthesis and accumulation of different types of compounds in different months were analyzed. A comprehensive quality evaluation of V. coloratum in different growth stages was carried out using GRA. The results indicated that the overall quality of V. coloratum in March might be best, followed by November, whereas the quality of V. coloratum in July is the worst.

      The changes in bioactive compounds during storage were studied by accelerated tests. The results indicated that the components of V. coloratum were significantly affected by temperature and humidity, suggesting that there should be a particular focus on the temperature and humidity conditions during storage.

      The change law of the metabolites could be roughly summarized as follows: Compounds with relatively complex structures downstream of the biosynthesis pathway are degraded to produce their upstream compounds and some low-molecular-weight organic acids first, causing the content of the latter to increase first and then decrease. Concurrently, this causes a large gap in the degradation time course of different compounds. Compounds with complex structures degrade rapidly, and the degradation degree is relatively large, while compounds with relatively simple structures, such as small-molecular-weight organic acids, will be further degraded after a long period of increase and a plateau. The active components such as syringin, betulinic acid, eleutheroside E, shikimic acid, and rhamnazin-3-Ο-β-D-(6''-β-hydroxy-β-methyglutaryl)-glucoside-4'-O-β-D-glucoside have a rapid degradation rate and a large degree of degradation. Therefore, these compounds were tentatively designated as “early warning components” for quality control, and there should be a special focus on these when performing drug quality control.

      This study provides a reference for the analysis of the biosynthesis and degradation of the metabolites in V. coloratum. It provides theoretical support for the determination of optimal harvest time and quality control during V. coloratum storage. This paper may give a reference for studies on the rational application and quality control of TCMs.

    Acknowledgements
    • We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.

    Abbreviations:
    • TCM, traditional Chinese medicine; GRA, Grey relational analysis; LODs, limits of detection; LOQs, limits of quantification; RSD, relative standard deviation; PAL, phenylalanine aminolyase; C4H, cinnamic acid 4-hydroxylase; 4CL, 4-coumaric acid CoA Ligase; OMTs, oxygen methyltransferases; UGTs, glycosyltransferases; F3H, 3-hydroxylase; FLS, flavonol synthase; PEP, phosphoenolpyruvate

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