The composition characteristics of endophytic communities and their relationship with metabolites profile in Ephedra sinica under wild and cultivated conditions

Ephedra sinica is one of the most famous Chinese medicinal plants. The insufficient supply of wild resources has led to the increased use of cultivated products. However, the related medicinal quality differs significantly. Although the influence of external environment on the quality of E. sinica has been studied, the impact of endophytic microbes on it remains vague. This study characterized differential metabolites and microbial community compositions in wild and cultivated E. sinica by combining metabolomics with microbiomics, and explored the effect of endophytes on the formation of differential metabolites further. The results showed that the difference in quality between wild and cultivated E. sinica was mainly in the productions of alkaloids, flavonoids, and terpenoids. The associated endophytes had special compositional characteristics. For instance, the distribution and abundance of dominant endophytes varied between wild and cultivated E. sinica. Several endophytes had significant or highly significant correlations with the formations of ephedrine, pseudoephedrine, d-cathinone, methcathinone, coumarin, kaempferol, rhamnetin, or phenylacetic acid. This study will deepen our understanding of the plant-endophyte interactions and provide a strategy for the quality control of E. sinica products.


Introduction
Ephedra sinica is a valuable medicinal plant for several reasons: (i) it belongs to a scarce herbaceous gymnosperm; (ii) its leaves are severely degraded, and it is also called "leafless herb" (Zhang et al. 2020a); and (iii) both its stems and roots are well-known traditional Chinese medicines but with opposite pharmacological activities, which are recorded in the Chinese Pharmacopoeia as "Mahuang" and "Mahuanggen." According to the conventional experience and research, Mahuang is used for inducing diaphoresis and elevating blood pressure, which is mainly attributed to ephedrine, pseudoephedrine, methylephedrine, and tetramethylpyrazine. Mahuanggen is used for antiperspirant and antihypertensive, which is ascribed to the presence of feruloylhistamine, mahuannin, ephedrannin, and ephedradine A, B, C, and D (Miao et al. 2020). In modern medicine, E. sinica is widely used to treat bronchial asthma, angiocardiopathy, immune system disease, and so on (Miao et al. 2022;Seif et al. 2021). The extracts of E. sinica are also used as dietary supplements and diet products in Occident . Surprisingly, Mahuang has been frequently used to treat novel coronavirus pneumonia and has played an essential role in preventing and controlling the disease development, which is the fundamental component of "Maxing Shigan decoction" and "Lianhua Qingwen capsule/granule" in China Tang et al. 2023). Overall, the market's Responsible Editor: Zhihong Xu * Jin-Long Cui cjl717@163.com 1 demands for E. sinica are increasing. In addition to wild E. sinica, the cultivated resources are receiving more and more attention, but their qualities are different. In recent years, it has been discovered that metabolomics could uncover more fine-grained differences in diverse plant tissues, including the active ingredients, their precursors, and the trace but essential elements, all of which will provide opportunities to gain insight into the detailed formation of different plant chemical components (Cao et al. 2020). More research has substantiated that most wild and cultivated medicinal materials differed significantly in their chemical compositions. In addition to the external factors such as light, humidity, and altitude, there were also discrepancies between wild and cultivated medicinal materials even under the same phenological conditions, which is a big puzzle for scientists (Inngjerdingen et al. 2012). Recently, it has been noticed that plants' internal environment (Pandey et al. 2022) is also a significant factor affecting plant quality, among which the endophytes are the most important. Endophytes are those microorganisms that live inside plant tissues but do not cause apparent diseases (Strobel et al. 2003). After a long period of co-evolution, they developed into a "community of shared destiny." Meanwhile, endophytes are also dynamic, including "old species" that have been transmitted from their ancestors and "new species" that have been horizontally introduced from the environment (Frank et al. 2017). Their compositions are influenced not only by the genetics of species and environmental factors, but also by the anthropogenic factors of both irrigation and cultivation (Rasmussen et al. 2008). Endophytes, like the "organs" of plants, are the integral parts of plants. They directly produce metabolites into plant tissues (Cook et al. 2014) through gene exchange with the hosts (Motoyama et al. 2021), signal induction (Zhai et al. 2017), and biotransformation (Liu et al. 2021), which have substantial impacts on the formation of plant metabolites.
There are questions that the existing research has not yet answered. For instance, besides the traditional known active ingredients, what are the other differential metabolites that exist between wild and cultivated E. sinica? How do these differential metabolites synthesize? Are the endophytic compositions similar between the corresponding tissues of wild and cultivated E. sinica? What are the relationships among different endophytes in the communities? Do endophytes impact the formation of differential metabolites? Are there any endophytes that influence the formation of different compounds? There are many other questions as such. These issues have remained unclear up to now, but they will be explored in this study. Our study will be very helpful to understand the cause of metabolic differences and provide scientific references for the cultivation of E. sinica and their applications.

Plant material collection and sample preparation
In July 2019, healthy wild and cultivated E. sinica were collected from Ordos (38° 9′ 38″ N, 107° 26′ 7″ E, ≈ 1305 m), Inner Mongolia of China. These samples were labeled as CS (stems of cultivated E. sinica), WS (stems of wild E. sinica), CR (roots of cultivated E. sinica), and WR (roots of wild E. sinica). Half of each sample was used for investigation of endophytes' composition and the other half for metabolite investigation. Voucher specimens were numbered as CS2019, CR2019, WS2019, and WR2019, then deposited at the Institute of Applied Chemistry, Shanxi University.

Investigation of the metabolite profiles
Plant samples for metabolomics were dried in the shade at 25 °C with a humidity of less than 40% and ground into powder with an 80-mesh sieve. Each 0.2 g aliquot was extracted through the supplementation of 15 mL of methanol-water solvent (4:1, v:v). After ultrasound treatment (210 W, 25 °C) for 40 min, the mixture was centrifuged at 4 °C at 12,000 r/min for 10 min. The supernatant was filtered through PTFE (0.22 µm) membrane for UPLC-QTOF/MS analysis. Ten replicates were created for each sample. Equal proportions of wild and cultivated samples were mixed separately to produce quality control samples.
Chromatographic analysis was performed on the Agilent 1290 UPLC system (Agilent Technologies, USA), equipped with a ZORBAX StableBond-C18 column (1.8 µm particle size, 4.6 × 50 mm; Agilent, USA) maintained at 40 °C. The mobile phase consisted of A (0.1% formic acid in water) and B (acetonitrile) at a flow rate of 0.25 mL/min, which were applied in the following linear gradient elution: 10-14% B at 0-5 min, 14-20% B at 5-8 min, 20-30% B at 8-12 min, 30-50% B at 12-16 min, 50-70% B at 16-20 min, 70-95% B at 20-24 min, and 95% at 24-30 min. Then, the separated components were detected by the mass spectrometer, which was performed with LC-Q/TOF-MS/MS equipped with an ESI source with JetStream technology. The instrumental parameters were as follows: a nebulizer pressure of 45 psi; capillary voltage of 4000 V; nozzle voltage of 500 V; sheath gas temperature and flow rate of 400 °C and 12 L/min, respectively; drying gas temperature and flow rate of 350 °C and 9 L/min, respectively; fragmentation voltage of 175 V; collision energy; slope = 6.66; and offset = 1. The positive ionization mode mass spectra were acquired simultaneously in a full-scan operation with a mass range of 80-1200 m/z. All the operations and acquisitions were controlled by Agilent MassHunter Acquisition software (version A.05.01; Agilent Technologies, USA), and data analysis was processed with MassHunter Qualitative Analysis software (version B.07.00; Agilent Technologies, USA).

Data preprocessing and annotation
Raw data were converted to mzData format using the MassHunter Qualitative Analysis software and uploaded to XCMS through a secure SSL connection. For the feature detection, the XCMS centWave algorithm was used with the following parameters: signal/noise threshold = 6, ppm = 30, peak width = (10, 60), and prefilter = (3, 500). Their feature alignment was performed with the default parameters in XCMS with bw = 5 and mzwid = 0.025. The retention time correction was performed with the standard obiwarp algorithm in XCMS with prfostep = 0.5. The putative identities of each ion were first given within XCMS by matching features in the METLIN database with the following parameters: sample biosource = PLANT, ppm = 10, and adducts =  3+ in positive ion mode. The SIMCA-P (version 14.1; Umetrics, Umeå, Sweden) software was used for multivariate statistical analysis of the data obtained via UPLC-MS analysis. A principal component analysis (PCA) was utilized to obtain an overview of the sample distribution and identify outliers. QC stands for quality control. The partial least squares-discrimination analysis (PLS-DA) was used to determine the significant components of the models and thus minimize the overfitting. The quality of the models was described by R2 (R2 X and R2 Y), indicating the degree of fitting, and Q2, showing the predictability of the model. We revised the description in related context, which make it clear. S-plot modeling and variable importance of projection (VIP) were utilized to explore the chemometric markers. Finally, potential biomarkers were identified according to their exact m/z value, MS/MS spectrum, and retention times. METLIN (http:// metlin. scrip ps. edu/), HMDB (https:// hmdb. ca/), MetFrag (https:// msbi. ipb-halle. de/ MetFr agBeta/), and the Agilent MassHunter Pesticides Personal Compound Database and Library (PCDL) were used for searching metabolites. Additionally, to further explore the biological significance of the different metabolites between wild and cultivated E. sinica, the metabolic pathways of different metabolites were identified and constructed by searching the Kyoto Encyclopedia of Genes and Genomes (KEGG) database.

Characteristic analysis of the microbial communities of associated endophytes
The bacterial and fungal communities associated with E. sinica were characterized using Illumina MiSeq sequencing to explore the alpha diversity, operational taxonomic unit (OTU) distribution, community composition, and species classification (Zhang et al. 2020b;Miao et al. 2022). To predict the function of endophytes through 16S rRNA and ITS sequences in KEGG using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) (Douglas et al. 2020). Enzyme-associated genes related to the main metabolic pathways of E. sinica were screened and displayed with GraphPad Prism 9 (GraphPad Software, San Diego, CA, USA). Additionally, to determine the contribution of endophytes in different environmental conditions to these enzyme-associated genes, non-parametric Spearman's correlation (two-tailed) and the R 3.2.2 package "pheatmap" were used to show the graphical data. Likewise, microbial intra-kingdom network analysis was examined by Spearman's correlation test using the R package "psych" based on the bacterial and fungal OTUs. Only those elements were conserved with statistically significant positive or negative correlations (Spearman's r > 0.6 or r < − 0.6; P value < 0.05). The network was visualized, and its topological parameters were computed by Gephi 0.9.2 (Bastian 2007). The experiment was established with three replicates.

Integrative analysis of the marker metabolites and specific endophytes
In order to accurately predict and explain the relationships between microorganisms (including predominant and shared endophytes) and metabolites in E. sinica, a correlation analysis (the Spearman algorithm combined with a two-tailed Student's t test) was performed to evaluate the relative influence of the microorganic community on chemical constituents of E. sinica using SPSS 16.0 software (Chicago, IL, USA), and then it was imagined with the R package "pheatmap." In the matrices, the correlation coefficients were marked in red and blue for positive and negative correlations, respectively. Spearman's r > 0.8 or r < − 0.8 and P < 0.05 (*) or P < 0.01 (**) indicated significant or extremely significant correlations.

Screening of differential metabolites
The results of the PCA score plot showed considerable differences in metabolic profiles between CS and WS ( Fig. 1a) as well as CR and WR (Fig. 1d). The model had high reliability and good fit based on the PLS-DA (Fig. 1b,  e). The S-VIP plot was constructed from OPLS-DA (Fig. 1c, f). Then, based on the screening values VIP > 1.0 and P < 0.05, a total of 654 and 288 different metabolites were found in the stems and roots of wild and cultivated E. sinica, respectively. However, only 47 and 23 different marker metabolites were ultimately identified in the stems and roots, correspondingly (Table S1).

Differential metabolites and associated metabolic pathways
The selected differential metabolites could be deemed responsible for distinguishing chemical compositions between wild and cultivated E. sinica, which were identified by their mass spectra and mass fragmentation information. Their identification was described as an example of M148T6 (Fig. 2). The differential metabolite M148T6 was identified as pseudoephedrine based on the spectral data of MS/MS match in the HMDB and METLIN databases, combined with m/z values of 148.1116 and its fragmentation pattern (m/z 91.0538 for C 7 H 7 , m/z 95.0473 for C 7 H 11 , m/z 107.0709 for C 7 H 7 O, m/z 117.0691 for C 7 H 9 , m/z 119.0816 for C 9 H 11 , m/z 132.0802 for C 9 H 10 N, m/z 133.0877 for C 9 H 9 O, m/z 135.0920 for C 9 H 11 O, m/z 148.1121 for C 10 H 14 N, and m/z 150.1182 for C 9 H 12 NO). The results of other differential compounds, including names, m/z values, retention times, adduct ions, and fragment ions, are summarized in Table S1, and their structures are shown in Fig. S1.
The visual network indicated that those differential metabolites involved at least eleven metabolic pathways (Fig. 3): map00360 (phenylalanine metabolism), map00940 (phenylpropanoid biosynthesis), map00944 (flavone and flavonol biosynthesis), map00941 (flavonoid biosynthesis), map00900 (terpenoid backbone biosynthesis), map00960 (tropane, piperidine, and pyridine alkaloid biosynthesis), map01062 (biosynthesis of terpenoids and steroids), map00400 (phenylalanine, tyrosine, and tryptophan biosynthesis), map00350 (tyrosine metabolism), map00380 (tryptophan metabolism), and map00790 (folate biosynthesis). l-Phenylalanine was dehydrogenated by phenylalanine ammonia-lyase to obtain transcinnamic acid. Then, l-phenylalanine and trans-cinnamic acid underwent continuous hydrogenation and methylation to produce phenolic acid metabolites, such as phenylacetic acid, dihydro-3-coumaric acid, ferulic acid, and protocatechualdehyde. The existence of organic acids was a unique feature of plant metabolism. Flavonoids are secondary metabolites derived from phenylpropanoids. In total, eighteen metabolites in stems and six metabolites in roots participated in flavone and flavonol biosynthesis. In addition to flavonoids, coumarin was another benzopyrone of the phenylalanine metabolism. The biosynthetic pathway of terpenoids is involved in the deoxyxylulose phosphate pathway. This pathway forms 1-deoxyd-xylulose 5-phosphate (DXP) from pyruvate and d-aldehyde 3-phosphate as starting materials. After several transformation steps, they reacted to isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP). IPP and DMAPP were condensed head to tail under the action of the enzyme to form geranyl diphosphate (GPP), a critical synthetic precursor of monomers, which further synthesizes limonene, β-myrcene, and α-pinene. Anthranilic acid involved in phenylalanine, tyrosine, and tryptophan biosynthesis may be transformed from chorismate and l-kynurenine from tropane piperidine and pyridine alkaloid biosynthesis and tryptophan metabolism, respectively.

Endophytic community characteristics in wild and cultivated E. sinica
Microbial diversity based on high-throughput sequencing revealed that the number of endophytic bacterial OTUs was the same order of magnitude between WS and CS, which was also similar between WR and CR. The number of endophytic bacterial OTUs in WR or CR was 2 orders of magnitude higher than that in WS or CS. Nevertheless, it was entirely different for endophytic fungi. The number of endophytic fungi in WS, CS, WR, and CR was the same order of magnitude (Table 1). The results of the Venn diagram showed that the number of species of endophytes in each tissue was different. The shared OTUs between CSB and WSB, between CRB and WRB (CSB, WSB, CRB, and WRB are endophytic bacteria in CS, WS, CR, and WR, respectively), between CSF and WSF, and between CRF and WRF (which are endophytic fungi in CS, WS, CR, and WR, respectively) were 8, 500, 1452, and 1019 species, respectively. The similarity was 18.60%, 33.62%, 22.47%, and 21.82%, respectively (Fig. S2). The coverage of bacteria and fungi was 0.93-0.96 and 0.98-0.99, respectively, and the ecological indexes of these endophytic OTUs are shown in Table 1.
Undoubtedly, Proteobacteria was the most predominant endophytic bacterial phylum across all samples. For instance, Pseudomonas exhibited high relative abundance in WS (84.23%) and CS (91.01%) (Fig. 4), while Streptococcus Fig. 3 Pathway of differential metabolites between wild and cultivated Ephedra sinica. The orange boxes represent unique differential metabolites in roots; the green boxes represent unique differential metabolites in stems; the blue boxes represent the common differen-tial metabolites in both stems and roots; the Peru KOs represent bacterial genes; the steel blue KOs represent fungal genes; the red KOs represent the common genes in both bacteria and fungi Table 1 Diversity of endophytic bacteria and fungi in each sample of Ephedra sinica (mean ± SD) (n = 3) OTU richness was observed in the endophytes of wild and cultivated Ephedra sinica. Richness = observed number of OTUs, Shannon = Shannon index estimator, ACE = ACE richness estimator, Chao1 = Chao1 richness estimator, coverage = Good's coverage estimator, and Simpson = Simpson index estimator. CSB and WSB are endophytic bacteria in the stems of cultivated and wild E. sinica, respectively; CRB and WRB are endophytic bacteria in the roots of cultivated and wild E. sinica, respectively; CSF and WSF are endophytic fungi in the stems of cultivated and wild E. sinica, respectively; CRF and WRF are endophytic fungi in the roots of cultivated and wild E. sinica, respectively (2.25%) was unique in WS (Fig. S3a). In addition, the shared bacteria between WR and CR (relative abundance > 1%) were mainly Steroidobacter, Pseudomonas, Sandaracinus, Povalibacter, Pseudonocardia, Devosia, and Acidobacteria sp. (Fig. 4). Nevertheless, eleven endophytic bacteria were significantly enriched in WR, including Phytohabitans, Nocardioides, Actinophytocola, Streptomyces, Ohtaekwangia, Asticcacaulis, Mesorhizobium, Phyllobacterium, Rhizobium, Sphingomonas, and Variovorax, while Acidobacteria sp. and Kofleria were significantly enriched in CR (Fig. S3b). For endophytic fungi, the majority of OTUs were allocated to Ascomycota, except for the unknown species (Fig. 4). Both WS and CS contained Alternaria (0.2% vs. 0.07%) and Passalora (0.17% vs. 0.09%), but the exclusive fungus of the former was Didymellaceae sp. (0.02%), and the unique fungi of the latter were Fusarium (0.02%), Malbranchea (0.01%), and Colletotrichum (0.01%) (Fig. 4). The shared fungi of WR and CR were species from Fusarium, Onygenales sp., Ilyonectria, Paraphoma, Pleosporales sp., Alternaria, and some unknown species of Ascomycota. However, their abundance fluctuated. For example, Fusarium (0.61%) in WR was 20 times higher than that in CR (0.03%). In addition, Aporospora (0.2%), Entodesmium (0.06%), Auriculariales sp. (0.01%), and Gibberella (0.01%) were only present in WR, and Arthrographis (0.02%) was just detected in CR (Fig. 4).

Interrelation among endophytic microorganisms
The interaction differences of intra-kingdom (bacteria-bacteria; fungi-fungi) were assessed through a co-occurrence network under the wild and cultivated conditions (Fig. 5). Specifically, the topological properties of nodes were utilized to characterize complex patterns of microbiota. Overall, the correlations among microorganisms occurring within co-occurrence networks were predominantly positive (ranging from 60.98 to 100%), indicating that commensalism or mutualism dominated the interactions among microorganisms ( Fig. 5 and Table S2). Remarkably, the microbial network exhibited much more complex and intricate co-occurrence network patterns in the stems or roots of the wild E. sinica compared to the cultivated species network (more nodes, edges, average clustering coefficient (ACC), and average degree) (except CSF vs. WSF). The modularity value (an index indicating that OTUs are well connected in their modules rather than the others) of each network in wild E. sinica (both stems and roots) was greater than 0.4, which suggested that the synergistic effect of these colonies was more robust than that in cultivated E. sinica (Table S2).

Discussion
The quality of Chinese medicinal materials depends on the variable content and quantity of chemical compositions (Zhang et al. 2022). Due to technical constraints, the limited and fragmented knowledge of the disparity in quality between wild and cultivated E. sinica was mainly reflected in alkaloids with high relative contents (Sun et al. 2012;Miao et al. 2022). In our study, 47 and 23 differential metabolites were found through holistic analyses of the metabolome in the stems and roots of the wild and cultivated varieties. Among them, the content of alkaloids (ephedrine, pseudoephedrine, and dl-norephedrine), flavonoids (such as luteolin, kaempferol, quercetin, tricin, and vitexin), and organic acids (such as anthranilic acid, quinaldic acid, phenylacetic acid, and aminobenzoic acid) in CS was significantly higher than that in WS. In comparison, the terpenoids, including limonene, β-myrcene, farnesal, thymol, and α-pinene, were produced higher in WS than in CS. In addition, a lot of phenylpropanoids (chavicol, ( +)-syringaresinol, cinnamaldehyde, and cinnamyl alcohol) and alkaloids (norpseudoephedrine, propanoylagmatine, and piperettine) were accumulated in WR, whereas higher contents of flavonoids (such as afzelechin, biochanin A, proanthocyanidin A2, and delphinidin 3-glucosylglucoside) and organic acids (e.g., terephthalic acid) were accumulated in CR (Table S1). The accumulation had an essential contribution to the pharmaceutical effect of E. sinica (Oshima et al. 2019).
The effects of endophytes on the differentiation of chemical constituents between wild and cultivated E. sinica were studied under the same phenological conditions. It was found that there were discrepancies in the number of OTUs and community distribution of endophytes between wild and cultivated E. sinica, which might be caused by the agricultural activities and the natural selection of host plants. Furthermore, both wild and cultivated varieties had specific endophytic communities, indicating the selective effects on particular endophytes. For example, Streptococcus was only presented in WS, while the unique endophytic fungi in WS, CS, WR, and CR were Didymellaceae spp., Fusarium, Aporospora, and Arthrographis, respectively. However, Suleiman et al. (2017) reported that plants might have robust core microbiome compositions that were less prone to the alteration due to a variation in land use, soil type, or edaphic factors. This means that there was a highly conserved and co-evolved microbiome preserved during the domestication of E. sinica. These observations are consistent with the other studies (Abdullaeva et al. 2020;Guo et al. 2021). Pseudomonas were the dominant bacterial genus in both WS and CS, which were correlated both significantly and positively with the most differential metabolites, such as d-cathinone, methcathinone, coumarin, kaempferol, and rhamnetin. Hence, Pseudomonas most likely played an essential role in metabolites forming in the stems of E. sinica. Common predominant bacteria of WR and CR included Steroidobacter, Pseudomonas, Sandaracinus, Povalibacter, and Pseudonocardia, all of which could promote growth and protect plants from host plant diseases as previously reported (Kinkel et al. 2012;Sah et al. 2021). Moreover, a high proportion of Sphingomonas in the roots of wild E. sinica enhanced the drought resistance of host plants by releasing volatile organic compounds (Luo et al. 2020). The co-occurrence networks also clarified that endophytes improved the stress resistance of wild E. sinica by modulating its phenotypic plasticity, which was essential for its healthy growth.
The gene functional prediction results showed that there were microbial genes involved in differential metabolite accumulation in wild or cultivated E. sinica. Therefore, endophytes are very critical as the hosts' endo-environment for their growth, development, and metabolite formation of medicinal plants. For example, the endophytic bacterial OTU243 (Escherichia/Shigella) produced monoamine oxidase (K00274 MAO) putatively (Ramesh et al. 2016), which was highly expressed in CSB and had a close correlation with phenylacetic acid (r = 0.621). Therefore, OTU243 (Escherichia/Shigella) might be involved in forming phenylacetic acid, a metabolite of E. sinica (Zhai et al. 2017;Motoyama et al. 2021;Liu et al. 2021). However, the more elaborate mechanisms of action are not well understood, and more evidence is required. Such reports have sprung up in recent years including several studies revealed that Salvia miltiorrhiza-microbe interactions might enhance the biomass production of S. miltiorrhiza, which could affect the metabolic pathway for tanshinone production (Huang et al. 2018). In summary, further exploration of the effects of endophytes on the metabolism is of great significance for the precise cultivation of E. sinica.