Cistanche deserticola is an endangered plant used for medicine and food. Our purpose is to explore the differences in metabolism between inflorescences in non-medicinal parts and succulent stems in medicinal parts to strengthen the application and development of the non-medicinal parts of C. deserticola. We performed metabolomics analysis through LC-ESI-MS/MS on the inflorescences and succulent stems of three ecotypes (saline-alkali land, grassland, and sandy land) of C. deserticola. A total of 391 common metabolites in six groups were identified, of which isorhamnetin O-hexoside (inflorescence) and rosinidin O-hexoside (succulent stems) can be used as chemical markers to distinguish succulent stems and inflorescences. Comparing the metabolic differences between the three ecotypes,  we found that most of the different metabolites related to salt-alkali stress were flavonoids. In particular,  we mapped the biosynthetic pathway of phenylethanoid glycosides (PhGs) and showed the metabolic differences in the six groups. To better understand the pharmacodynamic mechanisms and targets of C. deserticola, we screened 88 chemical components and 15 potential disease targets through molecular docking. The active ingredients of C. deserticola have a remarkable docking effect on the targets of aging diseases such as osteoporosis, vascular disease, and atherosclerosis. To explore the use value of inflorescence, we analyzed the molecular docking of the unique flavonoid metabolites in the inflorescence with inflammation targets. The results showed that chrysoberyl and cynaroside had higher scores for inflammation targets. This study provides a scientific basis for the discovery and industrialization of the resource value of the non-medicinal parts of C. deserticola, and the realization of the sustainable development of C. deserticola. It also provides a novel strategy for exploring indications of Chinese herbs.

According to relevant studies,cistanche is a common herb that is known as "the miracle herb that prolongs life". Its main component is cistanoside, which has various effects such as antioxidant, anti-inflammatory, and immune function promotion. The mechanism between cistanche and skin whitening lies in the antioxidant effect of cistanche glycosides. Melanin in human skin is produced by the oxidation of tyrosine catalyzed by tyrosinase, and the oxidation reaction requires the participation of oxygen, so the oxygen-free radicals in the body become an important factor affecting melanin production. Cistanche contains cistanoside, which is an antioxidant and can reduce the generation of free radicals in the body, thus inhibiting melanin production.

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1. Introduction 

Cistanche deserticola is an edible and medicinal plant that is often called “desert ginseng”. 1 C. deserticola was first recorded in Shen Nong's Chinese Materia Medica about 1800 years ago and has been widely used as a traditionally considerable tonic in China and Japan for many years. The compounds that have been isolated from C. deserticola are phenylethanoid glycosides (PhGs), iridoids, lignans, fatty acids, alditols, carbohydrates,  and polysaccharides, among which PhGs is the main active ingredient.2 Modern pharmacology shows that the extracts of C. deserticola (such as phenylethanoid glycosides, polysaccharides,  etc.) have a wide range of medicinal functions, especially in improving sexual function, enhancing memory, immune regulation, liver protection, laxative activity, antioxidant activity,  etc.3–5 In addition to its medicinal value, C. deserticola has ecological value for desert control due to its ability to grow in arid environments, as well as under saline-alkali stress conditions.6 However, the wild sources of C. deserticola have been considered to be endangered in recent years due to rapidly growing market demand and over-exploitation. It has been listed as one of the class II plants needing protection in China.2 Consequently, it is urgent to effectively develop C. deserticola resources and to determine the best environment for the growth of C. deserticola.

Traditional medicinal parts of medicinal plants are widely used, while non-medicinal parts are often discarded. A large number of studies have shown that some non-medicinal parts such as Salvia miltiorrhiza, Paris polyphylla, and Crocus sativus have similar chemical compositions and pharmacological effects to medicinal parts. The research on non-medicinal parts is conducive to the expansion of medical resources, especially for the protection of endangered medicinal plants.7,8 Qiao et al. used GC-MS technology to identify 40 volatile components in C. deserticola inflorescence.9 Peng et al. used transcriptomics and metabolomics to comprehensively analyze the analgesic effects of different parts of citronella.10 Yang et al. isolated types of flavonoids from the aerial parts of Salvia miltiorrhiza and studied their antioxidant activity.8 The medicinal part of C. deserticola is a succulent stem, which causes a large number of inflorescences to be discarded every year, resulting in a huge waste of resources.

Metabolites, as the final products of various biochemical processes catalyzed by enzymes, provide useful molecular insights for the biochemistry of organisms at a given time.11 Metabolism is closely related to plant quality. Primary metabolites affect plant growth and development, and secondary metabolites can help plants resist environmental stress.12 Therefore, metabolomics technology is widely used in plant quality evaluation.13–15 We previously integrated the transcriptome and metabolome to evaluate the quality of the succulent stems of the three ecotypes of C. deserticola and explore the molecular mechanism of quality variation.16 We found that 20 -acetylacteoside can be used as a chemical marker to distinguish three ecotypes. Wenjing Liu et al. based on 1 H NMR non-targeting to LC-MS-based targeted metabolomics strategy, conducted an in-depth chemical group comparison of four succulent Cistanche species and identified echinacoside, acetonide, betaine, mannitol, 6-deoxycatalpol, sucrose, and 8- epi-organic acid can be used as chemical markers to distinguish four Cistanche species.17 Pingping Zou et al. applied 1 H NMR-based metabolomics to identify the upper and lower parts of C. deserticola stem and found that serial primary metabolites,  especially carbohydrates and tricarboxylic acid cycle metabolites, as the primary molecules governing the discrimination.18 HaiLi Qiao et al. illustrated that a higher content of esters and aromatics were found in flowers, which were significantly increased in comparison with the volatile compounds from buds through GC-MS analysis of the volatile components of the inflorescence of C. deserticola. 9 At present, the research on the quality variation between the succulent stem and inflorescence of C. deserticola from the perspective of metabolism is still lacking.

Existing studies have used network simulation of molecular docking to explore the targets and mechanisms of Chinese medicine in treating diseases.19–21 Jianling Liu et al. investigated the effective drug combinations based on system pharmacology among compounds from Cistanche tubulosa. They preliminarily screened 61 compounds and 43 targets related to neuroinflammation, of which verbascoside and tubuloside B could play key roles in neuroprotection.22 YingQi Li et al. integrated network pharmacology and zebrafish model to investigate dual effects components of Cistanche tubulosa for treating both osteoporosis and Alzheimer's disease.23 The chemical components of C. deserticola are complex and have a wide range of pharmacological effects. However, therapeutic mechanisms are not yet clear. It is of great significance to clarify disease targets and mechanisms for the further development of C. deserticola.

In this study, we used metabolomics to investigate the metabolic differences of the inflorescences and succulent stems of the three ecotypes (saline-alkali land, grassland, and sandy land) of C. deserticola and compared the grassland and sandy land ecotypes with the saline-alkali land ecotype to explore the metabolic variation in C. deserticola that are affected by salt alkali stress. Particularly, we identified and analyzed the metabolites of six groups involved in the biosynthesis of PhGs. We applied molecular docking to screen out the potential compounds and targets and drew network simulation diagrams, as well as GO and KEGG enrichment analyses. Our findings provide new insights into the metabolic differences between the inflorescence and succulent stems of the three ecotypes of C. deserticola.

2. Materials and methods

2.1 Plant Materials and sample collection

We collected the inflorescences (the sample serial number suffix is “1”) and succulent stems (the sample serial number suffix is “2”) for C. deserticola in the excavation stage (April to May 2017) from three different ecotypes: Ebinur Lake of Xinjiang (A1 & A2: saline-alkali land), Tula Village of Xinjiang (B1 & B2: grassland) and Alxa Left Banner of Inner Mongolia (C1 & C2:  sandy land) in northwestern China (Table 1 and Fig. 1a). The voucher specimens were deposited in the herbarium of the Institute of Medicinal Plant Development at the Chinese Academy of Medical Sciences in Beijing, China. Samples were collected in the field and stored in liquid nitrogen quickly. Aer cleaning with PBS, the succulent stem tissues were cut into small pieces and immediately stored at  80 degrees Celsius freezer until further processing. 18 samples (three biological replicates per habitat, two tissue parts per sample) were taken from the thick parts of the inflorescence and fleshy stems for metabolome analysis.

2.2 Extraction and separation of metabolites

The freeze-dried sample was crushed using a mixer mill (MM 400, Retsch) with a zirconia bead for 1.5 min at 30 Hz. 100 mg powder was weighed and extracted overnight at 4°C with 1.0 mL 70% aqueous methanol. Following centrifugation at 10 000 g for 10 min, the extracts were absorbed before LC-MS analysis.

LC-ESI-MS/MS system (UPLC, Shim-pack UFLC SHIMADZU CBM30A system) was used to analyze the lyophilized sample extract. The analytical conditions were as follows: UPLC  column, Waters ACQUITY UPLC HSS T3 C18 (1.8 mm, 2.1 mm×100 mm); solvent, water (0.04% acetic acid): acetonitrile (0.04%  acetic acid); gradient program, 100 : 0 v/v at 0 min, 5: 95 v/v at 11.0 min, 5: 95 v/v at 12.0 min, 95: 5 v/v at 12.1 min and 95: 5  v/v at 15.0 min; flow rate, 0.40 mL min 1; temperature, 40°C;  and injection volume, 2 mL. The effluent was alternatively connected to an ESI-triple quadrupole-linear ion trap (Q TRAP)-MS. In this experiment, a quality control sample was prepared by  uniform mixing; during the analysis, quality control samples  were run every 10 injections to monitor the stability of the  analysis conditions.24–26

Linear Ion Trap (LIT) and triple quadrupole (QQQ) scans were acquired on a triple quadrupole-linear ion trap mass spectrometer (Q TRAP), API 6500 Q TRAP LC/MS/MS system,  equipped with an ESI turbo ion-spray interface, operating in positive ion mode and controlled by Analyst 1.6 software (AB Sciex). The ESI source operation parameters were as follows: an ion source, turbo spray; source temperature 500 °C; ion spray voltage (IS) 5500 V; ion source gas I (GSI), gas II (GSII), curtain gas (CUR) were set at 55, 60, and 25.0 psi, respectively; the collision gas (CAD) was high (12 psi). Instrument tuning and mass calibration were performed with 10 and 100 mmol L 1  polypropylene glycol solutions in QQQ and LIT modes, respectively. QQQ scans were acquired as MRM experiments with collision gas (nitrogen) set to 5 psi. Declustering potential (DP)  and collision energy (CE) for individual MRM transitions were performed with further optimization. A specific set of MRM  transitions was monitored for each period based on the metabolites eluted within this period.

2.3 Metabolite Identification and Quantification

Qualitative analysis of primary and secondary MS data was carried out by comparison of the precursor ions (Q1), fragment ions (Q3) values (isolation windows (±15 Da), dwell time (ms) or cycle time (1 second)), retention time (RT), and fragmentation patterns with those obtained by injecting standards using the same conditions if the standards were available or conducted using a self-compiled database MWDB (NetWare Biological Science and Technology Co., Ltd Wuhan, China) and publicly available metabolite databases if the standards were unavailable. Repeated signals of K+, Na+, NH4 +, and other large molecular weight substances were eliminated during identification. The quantitative analysis of metabolites was based on the MRM mode. The characteristic ions of each metabolite were screened through the QQQ mass spectrometer to obtain the signal strengths. Integration and correction of chromatographic peaks were performed using Multi Quant version 3.0.2 (AB SCIEX, Concord, Ontario, Canada). The corresponding relative metabolite contents were represented as chromatographic peak area integrals.

The VIP (variable important in projection) values of C. deserticola samples (three biological replicas) were calculated by SIMCA-P software (version 14.1, Sartorius Stedim Biotech, Ume˚a, Sweden) based on the principal component analysis and orthogonal partial least squares discriminant analysis. We set fold-change $2 or #0.5 and VIP value $1 as the threshold to screen the significantly different metabolites. Metabolite data were normalized, cluster heatmap analysis was performed on all samples, and the R program script was used to draw cluster heatmaps.

2.4 Molecular docking

2.4.1 Collection of chemical compounds. Through the preliminary experimental results of our research group and the literature search results, a total of 127 isolated compounds from the succulent stems of C. deserticola were collected and downloaded from the Chemical Book website or used ChemDraw to draw the 2D  molecular structure. In addition, we found 4 avoids (chrysoberyl, cynaroside, hesperetin, and homoeriodictyol)  detected only in the inflorescence through metabolome results. The 2D structure was converted into a three-dimensional structure with ChemDraw 3D software, and preliminary optimization was performed. Then the preliminary optimized three-dimensional structure was verified by Avogadro Software and further energy optimization was used to generate the nal compound file format required for subsequent molecular docking.

2.4.2 Collection of Target collection. We searched for disease protein targets through literature and the STITCH database. We obtained the corresponding gene targets by using the Uniport database and retrieved the PDB ID of the protein haplotype and the structure of small molecules by RCSB. When determining the positive drug, we used the literature and the Yaodu website to preliminarily identify 45  related disease targets that have been reported, including 10  diseases related to the succulent stems of C. deserticola in the literature. These ten diseases were atherosclerosis, osteoporosis, senile dementia, Alzheimer's disease, Parkinson, chronic constipation, torsades de pointes ventricular tachycardia,  vascular disease, myocardial injury, and rectal cancer. In addition, we collected 467 targets related to inflammation and obtained 2 important targets (6KBA and 7AWC) through screening, which were used for molecular docking analysis of inflorescence-specific flavinoids.

2.4.3 Molecular docking simulation. To evaluate the binding affinity of compounds in C. deserticola to candidate targets, we performed a molecular docking simulation through the software QuickVina 2.0, an open-source utility developed by the Alhossary research group. To verify the binding affinity between the targets and the compounds, we calculated a docking score through QuickVina 2.0. The docking scores that exceeded those of the positive drugs (data for every positive drug can be obtained from the corresponding targets in RCSB or literature) indicated a strong binding affinity between candidate targets and the corresponding compounds.27–30 We used PyMOL (Version 2.0 Schr¨odinger, LLC) to plot the docking results of the compound and the target.

2.4.4 Component-target-pathway network construction and GO/KEGG function analysis Component-target-pathway network construction was conducted using the network visualization software Cytoscape. In network interactions, nodes represent components, targets, and pathways, whereas edges represent the interaction of each other. We used the scoring value of molecular docking of the compound and the target gene as an indicator of the color of the connection. The greener the color, the higher the scoring value. A protein-protein interaction (PPI) network associated with gene targets was constructed and analyzed with STRING.31

To further find out the biological functions within the constructed network, we used the functional annotation module of the DAVID database29 to perform Gene Ontology (GO) and KEGG enrichment analyses on target genes.

3. Results

3.1Metabolic Profiles of C. deserticola

To obtain an overview of the metabolic changes of the three ecotypes C. deserticola inflorescences and succulent stems, widely targeted metabolome analysis was performed using LC-ESI-MS/MS. As shown in Fig. 1b, the inflorescences and succulent stems of C. deserticola from different ecotypes showed different separations, and the separation of different tissues was greater than that of different ecotypes. And the three replicate samples have similar PC scores, indicating that C. deserticola metabolites showed little separation between replicate samples. Moreover, the quality control (mix) samples clustered together in the center of the PCA scores plot. The petal diagram (Fig. 1c) and upset diagram (Fig. 1d) indicated that there were 391 common metabolites in the six groups, and the number of metabolites detected in the inflorescence was generally higher than that in the succulent stem. The number of metabolites detected in the saline-alkali inflorescence (A1) was the largest, with a total of 515, of which 18 metabolites were only detected in A1. The number of metabolites detected in grassland succulent stems (B2) was the least, with a total of 458, without its unique metabolites.

The relative contents of 578 metabolites were determined, including 35 metabolite categories (ESI File S1). The most abundant metabolites of the inflorescences and succulent stems in both three ecotypes were lipids, glycerolipids, amino acids, nucleotides, and their derivates, phenylethanoid glycosides (PhGs), and flavonoids (Fig. S3a,3b, and c). After normalization, the proportional content of each metabolite was determined by the average peak response area during UPLC-MS/MS, as shown in Fig. 1e with a heat map, and was further performed with hierarchical clustering analysis. More secondary metabolites showed high relative concentration levels in A1 and C2 than in other groups. Among the secondary metabolites in all three ecotypes, the relative content of phenylethanoid glycosides (PhGs) in the succulent stems was higher than in the inflorescences, while the relative content of flavonoids in the inflorescences was higher than the succulent stems.

In this metabolome analysis, 12 main active components of C. deserticola were detected, including 2′-acetylacteoside, acteoside, cistanoside A, coniferin, echinacoside, formononetin-7-O-glucoside, inosine, isoacteoside, ononin, pinoresinol, syringes, and uridine. A hierarchical clustering heat map (Fig. 1f) was drawn for the main active components of C. deserticola detected by the metabolome, showing that the relative content of the main active components in the succulent stem was higher than that in the inflorescence. Compared with different tissues, the active ingredients with relatively high content in inflorescence were 2′-acetylacteoside and coniferin, while the active ingredients with relatively high content in succulent stems were acteoside, cistanoside A, echinacoside, and isoacteoside. Compared with different ecotypes, the relatively high content of active ingredients in saline-alkali land was 2′-acetylacteoside, acteoside, coniferin, echinacoside, and isoacteoside. The relatively high content in grassland was echinacoside, and the relatively high contents in sandy land were cistanoside A.

3.2 Metabolic difference between inflorescence and succulent stem of C. deserticola

To understand the difference in metabolism between inflorescence and succulent stem of C. deserticola in three ecotypes, we screened the different metabolites. High predictability (Q2) of the OPLS-DA models was observed to generate a pairwise comparison between inflorescence versus Succulent stem in saline-alkali land (Q2 = 0.996), grassland (Q2 = 0.997), and sandy land (Q2 = 0.997) (Fig. S1a). The Q2 and R2 values were higher in the permutation test than in the OPLS-DA model (Fig. S1b). To identify potential variables, we set fold-change ≥2 or ≤0.5 and VIP value ≥1 as the threshold to screen the significantly different metabolites in each pair of comparisons. The top 10 different metabolites of the three ecotype inflorescences and succulent stems were shown in Table S1. Compared with succulent stems, the relatively high content of differential metabolites in inflorescences were flavonoids, such as flavonol, flavone, and flavone C-glycosides.

In saline-alkali land, compared with inflorescences, succulent stems had 43 up-regulated differential metabolites and 71 down-regulated differential metabolites (Fig. 2a). The heat map (Fig. 2b) showed that the relative content of the inflorescences was higher than that of the succulent stems. Comparing succulent stems with inflorescences, the main up-regulated metabolites were cyanidin 3-O-rutinoside (keracyanin), icariin (kaempferol 3,7-O-diglucoside 8-prenyl derivative), homovanillic acid, chlorogenic acid methyl ester, and rosinidin O-hexoside. The main down-regulated differential metabolites included N′, N′′-di-p-coumaroylspermidine, 8-C-hexosyl-luteolin O-hexoside, caffeic acid, isorhamnetin O-hexoside, and isorhamnetin 5-O-hexoside (Fig. 2c). KEGG metabolic pathway enrichment analysis (Fig. 2d) classified the differential metabolites identified from inflorescence and succulent stem into flavonoid biosynthesis, flavone and flavonol biosynthesis, isoflavonoid biosynthesis, phenylpropanoid biosynthesis, and ether lipid metabolism.

In grassland, compared with inflorescences, succulent stems had 35 up-regulated differential metabolites and 54 down-regulated differential metabolites (Fig. 2a). The heat map (Fig. 2b) showed that the relative content of the inflorescences was higher than that of the succulent stems. Comparing succulent stems with inflorescences, the main up-regulated metabolites were l-(+)-arginine, adipic acid, N-methylnicotinamide, 4-hydroxybenzoic acid, and dihydromyricetin. The main down-regulated differential metabolites included rosinidin O-hexoside, caffeic acid, isorhamnetin O-hexoside, selling 5-O-hexoside, and isorhamnetin 5-O-hexoside (Fig. 2c). KEGG metabolic pathway enrichment analysis (Fig. 2d) classified the differential metabolites identified from inflorescence and succulent stem into flavonoid biosynthesis, flavone and flavonol biosynthesis, diterpenoid biosynthesis, isoflavonoid biosynthesis, and circadian entrainment.

In sandy land, compared with inflorescences, succulent stems had 40 up-regulated differential metabolites and 87 down-regulated differential metabolites (Fig. 2a). The heat map (Fig. 2b) showed that the relative content of the inflorescences was higher than that of the succulent stems. Comparing succulent stems with inflorescences, the main up-regulated metabolites were O-feruloyl 4-hydroxylcoumarin, syringing, rosinidin O-hexoside, 3-(4-hydroxyphenyl) propionic acid, and homovanillic acid. The main down-regulated differential metabolites included chrysoeriol O-rhamnosyl-O-glucuronic acid, C-hexosyl-apigenin O-caffeoylhexoside, selling O-malonylhexoside, isorhamnetin O-hexoside, and 8-C-hexosyl-luteolin O-hexoside (Fig. 2c). KEGG metabolic pathway enrichment analysis (Fig. 2d) classified the differential metabolites identified from inflorescence and succulent stem into flavone and flavonol biosynthesis, flavonoid biosynthesis, isoflavonoid biosynthesis, diterpenoid biosynthesis, and degradation of aromatic compounds.

3.3Metabolic differences related to saline-alkali stress in three ecotypes of C. deserticola

To grasp the unique metabolic characteristics of the three ecotypes of the saline-alkali land of C. deserticola, we screened the different metabolites in saline-alkali land versus grassland and sandy land versus saline-alkali land. High predictability (Q2) of the OPLS-DA models was observed to generate a pairwise comparison between saline-alkali land versus grassland of inflorescence (Q2 = 0.997) and succulent stem (Q2 = 0.991). Meanwhile, high predictability (Q2) of the OPLS-DA models between sandy land versus saline-alkali land of inflorescence (Q2 = 0.988) and succulent stem (Q2 = 0.995). The Q2 and R2 values were higher in the permutation test than in the OPLS-DA model (Fig. S2†). To identify potential variables, we set fold-change ≥2 or ≤0.5 and VIP value ≥1 as the threshold to screen the significantly different metabolites in each pair of comparisons. Table 2 showed the different metabolites of inflorescences and succulent stems related to saline-alkali stress (saline-alkali land vs. grassland and sandy land vs. saline-alkali land), sorted by metabolite category, and demonstrated that the most metabolites class was flavonoid. Among them, the relative content of anthocyanins, flavonoids, flavonol, flavanone, catechin, and their derivatives, and isoflavone are the highest in saline-alkali land. Furthermore, the heatmap (Fig. 3d) showed that the groups with higher relative content of differential metabolites of flavonoids were A1 and C1. The relative content of anthocyanins was the highest in the A2 group, and the relative content of flavonoids and flavonols was the highest in the A1 group.

The volcano maps (Fig. 3a) showed that the number of up-regulated differential metabolism in saline-alkali soils is higher than that of grassland and sandy soils, whether in inflorescences or succulent stems. The top 20 differential metabolites of each comparison were shown in Fig. 3b. In saline-alkali land vs. grassland, the KEGG pathways of differential metabolites of inflorescence were mainly enriched in flavonoid biosynthesis, flavonol, and flavonol biosynthesis, diterpenoid biosynthesis, isoflavonoid biosynthesis, and biosynthesis of phenylpropanoids. Besides, the KEGG pathways of the different metabolites of the succulent stem were mainly enriched in the dopaminergic synapse, purine metabolism, flavonoid biosynthesis, pyrimidine metabolism, and circadian entrainment. In saline-alkali land vs. grassland, the KEGG pathways of differential metabolites of inflorescence were mainly enriched in isoflavonoid biosynthesis, biosynthesis of secondary metabolites, flavone, and flavonol biosynthesis, antineoplastics agents from natural products, and asthma. Moreover, the KEGG pathways of the different metabolites of the succulent stem were mainly enriched in aminoacyl-tRNA biosynthesis, protein digestion and absorption, central carbon metabolism in cancer, biosynthesis of amino acids, and biosynthesis of phenylpropanoids (Fig. 3c).

For more info: david.deng@wecistanche.com / WhatApp:86 13632399501