Location and growth period influence the bioactive compounds of Angelica sinensis (Oliv.) diels: multi-omics insights

Abstract

Angelica sinensis, a traditional medicinal herb, exhibits significant variations in efficacy quality linked to geographical origin and rhizosphere microbiome composition. However, the microbial factors driving the synthesis of its bioactive compounds in authentic (historically recognized for superior quality geoherbs, Min County) and adjacent regions remain poorly understood. This study integrated transcriptomic profiling of plant tissues with 16 S rRNA (bacteria) and ITS (fungi) sequencing of rhizosphere soils over multiple growth stages in authentic and near-authentic regions (the latter characterized by a similar climate but differing soil ecology). By combining these data with targeted metabolomics and soil property analysis, substantial regional and temporal variations in bioactive compound levels and soil properties were identified. Specifically, 2,367 differentially expressed genes (DEGs), 417 bacterial amplicon sequence variants (ASVs), and 295 fungal ASVs were detected with significant abundance shifts. Key genera, including Vicinamibacter and Bacillus (bacteria), and Bisifusarium and Longitudinalis (fungi), were linked to secondary metabolite production. Functional differences, such as those related to chitinolysis and fermentation pathways, were also observed. Co-occurrence networks revealed correlations between plant genes and microbial communities. Notably, soil parameters, including organic matter, total nitrogen, and soil alkaline phosphatase, were identified as key factors influencing microbial community structure. The rhizosphere microbiome was further associated with nutrient absorption, potentially impacting bioactive compound accumulation. This multi-omics analysis highlights the role of regional and growth-period variations in A. sinensis quality, offering valuable insights for optimizing its cultivation and efficacy across diverse regions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12870-026-08135-3.

Introduction

Angelica sinensis (Oliv.) Diels (Danggui in Chinese; Family: Apiaceae), a perennial herbaceous plant [1], is a medicinally significant herb primarily cultivated in Asia, Africa, and certain regions of South America [2]. The key bioactive compounds in A. sinensis include organic phenolic acids, predominantly ferulic acid, and volatile oils, notably ligustilide [1]. In traditional Chinese medicine (TCM), A. sinensis is widely used to promote blood circulation, tonify blood, regulate menstruation, alleviate menstrual pain, and support intestinal motility. It has also been utilized in managing vascular cognitive impairment [3].

In China, A. sinensis is primarily cultivated in the provinces of Gansu, Qinghai, Sichuan, and Yunnan. Min County in Gansu is historically recognized as the authentic production region (daodi in Chinese), renowned for its superior pharmacological quality and consistent therapeutic efficacy [4, 5]. A. sinensis is a vital economic crop in Min County and its neighboring counties in Gansu, serving as the region’s economic cornerstone and the primary source of income for medicinal herb farmers [6]. However, the geographic expansion of cultivation into adjacent regions has raised concerns about potential variations in the growth characteristics of A. sinensis and the stability of its bioactive compounds, particularly ferulic acid and other pharmacologically active constituents [7, 8]. These observations highlight the critical need to investigate the impact of regional environmental conditions and seasonal fluctuations on key metabolic pathways. Such research is essential not only for optimizing the standardization of TCM formulations but also for establishing ecologically sustainable cultivation practices [7, 8].

The rhizosphere microbiome, often referred to as a plant’s “second genome” [9, 10], plays a pivotal role in orchestrating root-microbe-soil interactions that are essential for plant health and secondary metabolic production in medicinal plants such as Salvia miltiorrhiza, Eucommia ulmoides, and Panax ginseng [10–13]. Existing research predominantly focuses on region-specific or static microbiome profiles, overlooking critical phenological transitions (e.g., flowering, fruiting) when microbial community dynamics exhibit peak metabolic plasticity [8, 14]. Understanding these spatiotemporal patterns could revolutionize precision agriculture through targeted microbiome engineering, optimizing nutrient cycles while reducing agrochemical reliance [8, 14]. Over the past decade, high-throughput sequencing (HTS) has transformed our understanding of plant-associated microbiomes, uncovering complex microbial community structures and their effects on plant health [15, 16]. Simultaneously, transcriptomics provides insights into host transcriptional responses, highlighting interactions between plant processes and microbial functions [16, 17]. These multi-omics approaches facilitate real-time monitoring of plant-microbial interactions under environmental changes [18, 19], and have revealed evolutionary and regulatory mechanisms driving bioactive compound biosynthesis [7, 20].

Although extensive research has been conducted on A. sinensis, covering topics such as tissue-specific pharmacological components [21], comparative transcriptomics of wild and cultivated roots [22], the molecular basis of early bolting [8, 23], and the evolution of coumarin biosynthesis mechanisms [24], critical knowledge gaps remain. Specifically: (1) a comparative analysis of the rhizosphere microbiota between authentic (daodi) and adjacent production regions is needed to identify microbial assemblages linked to geo-authentic quality, and (2) the co-dynamics between the rhizosphere microbiome and plant metabolome throughout the entire growth cycle remain poorly understood.

This study employed an integrated multi-omics approach to investigate A. sinensis from different production regions. Our objectives were: (1) to compare gene expression and microbial community differences in A. sinensis between authentic (daodi) regions and adjacent production areas across distinct growth periods, and (2) to analyze the interactions between rhizosphere bacteria, fungi, and plant genes to explore potential regulatory mechanisms influencing medicinal compound biosynthesis. This study provides a valuable reference for understanding the geographical determinants of A. sinensis’s pharmacological quality and establishes a foundation for advancing research on this important TCM.

Materials and methods

Sample collection and definition grouping

Authentic A. sinensis (group A) and near-authentic A. sinensis (group B) samples were collected from the experimental base of the Min County Angelica Sinensis Research Institute in Dingxi City (N34°31′22″, E104°28′50″) and the Tanchang Agricultural Technology Extension Center in Longnan City (N34°3′35″, E104°14′8″), both located in Gansu Province, Northwest China. The seedlings of A. sinensis were identified as “Min Gui No.1” from the Min County Angelica Sinensis Research Institute. Root samples of A. sinensis were collected during three distinct growth periods from April to November: (1) the leaf clump stage (May 28th, A5/B5), (2) the rhizome expansion stage (July 28th, A7/B7), and (3) the drug formation stage (October 18th, A10/B10). Sampling was performed using the five-point S method, where five representative points along an “S”-shaped path in each experimental field were selected. At each sampling point, six A. sinensis plants with their attached rhizosphere soil were carefully excavated. The collected plants and soil from all points were pooled and then randomly divided into three biological replicates. For each replicate, the plant roots were separated and subjected to initial tap water washing to remove soil debris, followed by five sterile water rinses. The roots were then disinfected using 30-second immersion in 75% ethanol, treated with 2% sodium hypochlorite for 5 min, and rinsed five times with sterile water. The sterilized roots were aseptically dissected to obtain internal tissues (2–3 mm beneath the epidermis), which were immediately flash-frozen in liquid nitrogen and stored at -80 ℃ until transcriptome sequencing. Another portion of the roots was cleaned of surface soil and shade-dried for bioactive compound analysis. Rhizosphere soil samples were processed similarly: one aliquot was stored at -80 ℃ for microbial DNA extraction and amplicon sequencing, while another was air-dried for analysis of soil physicochemical properties, nutrients, and enzyme activities. All samples were transported on ice to the laboratory. A summary of sample names, groupings, and key parameters is provided in Additional file 1.

Determinations of major bioactive compounds of A. sinensis

The major bioactive compounds in A. sinensis (shade-dried and sieved through a 0.074 mm mesh) were analyzed using high-performance liquid chromatography (HPLC). Chromatographic separation was performed under the following conditions: a Merk RP-C18 column (250.0 mm × 4.6 mm, 5 μm), with a mobile phase consisting of acetonitrile (B) and 0.1% acetic acid (A), using gradient elution (0–20 min, 19% B; 20–60 min, 19%-95% B; 60–75 min, 95%-100% B). The detection wavelength was set at 280 nm, column temperature at 30 ℃, flow rate at 1.0 mL/min, and injection volume at 10 µL [25]. Quantification was based on external standard curves with R² values > 0.99. The limits of detection (LOD) and quantification (LOQ) were determined as signal-to-noise ratios of 3 and 10, respectively. Differences in the eight bioactive compounds of A. sinensis between groups A and B, namely ligustilide, ferulate, coniferylferulate, senkyunolide I, senkyunolide H, senkyunolide A, 3-n-butylphthalide (NBP), and levistilide A, were analyzed using the Wilcoxon test. The results were visualized using the ggplot2 package (v 3.3.2) [26]. Additionally, differences in these bioactive compounds between groups A and B at various growth stages (A7 vs. B7, A10 vs. B10) were compared.

Determinations of soil physicochemical properties, soil nutrients, and soil enzymes

Air-dried soil, sieved through a 0.149 mm mesh, was used to determine physicochemical properties, including pH, electrical conductivity (EC), organic matter, total nitrogen (TN), available phosphorus (AP), available potassium (AK), and soil enzyme activities. These enzyme activities include urease, catalase (S_POD), sucrase (S_SC), acid phosphatase (S_ACP), neutral phosphatase (S_NP), and alkaline phosphatase (S_ALP). Soil pH and EC were measured using a pH meter and conductivity meter, respectively, with a water-to-soil ratio of 2:5. Organic matter was quantified by the H₂SO₄-K₂Cr₂O₇ oxidation method, while TN was determined using the Kjeldahl method. AP was measured by the molybdenum-antimony colorimetric method, and AK was assessed using flame photometry [27]. Urease activity was quantified by the sodium phenol-sodium hypochlorite colorimetric method. S_POD was measured by ultraviolet spectrophotometry, and S_SC was determined using the 3, 5-dinitrosalicylic acid colorimetric method. S_ACP, S_NP, and S_ALP were assessed by phenylene disodium phosphate colorimetry [28]. To visually compare differences in soil indicators across all samples, a heatmap was generated using the heatmap package (v 1.0.12) based on Z-score normalized soil physicochemical data [29].

Sample sequencing

Total RNA was extracted from A. sinensis root tissue using TRIzol^® Reagent. RNA quality was assessed with an Agilent 5300 Bioanalyzer and quantified using a NanoDrop ND-2000. Only high-quality RNA samples (OD260/280 = 1.8 ~ 2.2, OD260/230 ≥ 2.0, RIN ≥ 6.5, 28 S:18 S ≥ 1.0, > 1 µg) were used for library construction. Library preparation and sequencing were provided by Shanghai Majorbio Bio-pharm Biotechnology Co., Ltd. RNA-seq libraries were constructed following the Illumina^® Stranded mRNA Prep protocol, involving mRNA isolation, fragmentation (300 bp), cDNA synthesis, end-repair, adapter ligation, and 15-cycle PCR amplification. The libraries were quantified and sequenced on the NovaSeq 6000 platform (PE150).

For microbial analysis, genomic DNA was extracted from 0.25 g of rhizosphere soil using the E.Z.N.A. soil DNA Kit. DNA quality was assessed by 1.0% agarose gel electrophoresis and quantified using a NanoDrop^® ND-2000. 16 S rRNA gene sequencing (for bacteria) and ITS sequencing (for fungi) were performed to assess microbial community structure. These amplicon-based approaches differ from metagenomics, which provides functional gene information but at higher cost and complexity. This study focused on community composition and diversity, which are well-addressed by amplicon sequencing. The hypervariable region (V3-V4) of the 16 S rRNA gene was amplified with primer pairs (338 F: 5′-ACTCCTACGGGAGGCAGCAG-3′; 806R: 5′-GGACTACHVGGGTWTCTAAT-3′) [11, 30] and the fungal internal transcribed spacer (ITS) region was amplified with the primer pairs (ITS1F: 5′-CTTGGTCATTTAGAGGAAGTAA-3′; ITS2R: 5′-GCTGCGTTCTTCATCGATGC-3′) [31]. For specific information about PCR was provided in Supplementary Material 1. PCR products were purified, quantified, and paired-end sequenced on the Illumina MiSeq PE300 platform. The average sequencing depth was 50,000 reads per sample for both 16 S and ITS analyses.

Processing and evaluation of sequencing data

Raw RNA-seq reads were quality-trimmed and adapter-filtered using Trimmomatic (v 0.39) [32] with the following parameters: LEADING: 3, TRAILING: 3, SLIDINGWINDOW: 4:15, and MINLEN: 36. Subsequently, the default parameters of HISAT2 software (v2.2.0) [33] were used to align the clean data with the reference genome of A. sinensis [24]. Gene expression levels were quantified as read counts using feature Counts from the Rsubread package (v 1.12.0) [34]. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations were performed using the eggNOG-mapper database (http://eggnog-mapper.embl.de/). Gene expression normalization was conducted using the edgeR package (v 3.44.3) [35], confirming that all samples had similar expression levels, thus suitable for further analysis.

For microbial analysis, 16 S rRNA and ITS sequencing data were processed using the DATA2 method in QIIME2 (v 2022.8) [36] to cluster valid sequence clusters into amplicon sequence variants (ASVs) [37, 38]. Fastp (https://github.com/OpenGene/fastp, v 0.19.6) [39] was used for quality control of raw paired-end reads, followed by read merging with FLASH (version 1.2.11). UPARSE (http://drive5.com/uparse/, v 7.1) [40] was employed to cluster quality-controlled sequences into Operational Taxonomic Units based on a 97% similarity threshold and eliminate chimeric sequences. To mitigate the impact of sequencing depth on subsequent analyses of alpha and beta diversity, the sequence count of all samples was rarefied, ensuring an average coverage of 97.00% per sample. The Ribosomal Database Project (RDP) classifier (http://github.com/rdpstaff/classifier, v 2.13) was then used to compare bacterial sequences with the Silva 16 S rRNA (v 138) database, and fungal species taxonomy with the Unite 9.0 database, respectively. A confidence threshold of 70% was applied to calculate the community composition at various taxonomic classification levels [41].

Differential expression analysis based on transcriptomics

To identify differentially expressed genes (DEGs) between different production areas (DEGs-AB), the DESeq2 package (v 1.26.0) [42] was used for differential expression analysis between groups A and B, with a screening threshold of P < 0.05 and |log₂ fold change (FC)| > 1. The results were visualized with a volcano plot generated using the ggplot2 package (v 3.3.2) [26]. To further investigate the pathways involved in DEGs-AB, GO and KEGG analyses were performed using the clusterProfiler package (v 4.0.2) [43] with an adjusted P < 0.05. Differential gene expression and associated pathways between the two production regions were analyzed at each growth stage (P < 0.05, |log₂FC| > 1). For genes associated with developmental stages, differential expression analysis was performed between samples from different growth stages within the same production region (P < 0.05, |log₂FC| > 1). The shared and specific DEGs were identified using the ggVenn package (v 1.7.3) [44], which helped to understand their roles during the growth of A. sinensis (adjusted P < 0.05).

Weighted gene co-expression network analysis (WGCNA)

To identify genes associated with the major bioactive compounds of A. sinensis, WGCNA was performed. The goodSamplesGenes function of the WGCNA package (v 1.71) [45] was initially applied for hierarchical clustering of all samples from groups A and B to identify outliers. Soft threshold (power) and scale-free fit indices (R²) were computed to construct scale-free networks, and a power with mean connectivity converging to 0 was selected (R² = 0.85). The clustering tree was generated, and modules were identified by cutting the tree at a minimum module size of 200 genes using a shear tree algorithm (parameter = 0.2). Spearman’s correlation analysis was then performed to calculate the correlation coefficients (cor) between modules and major bioactive compounds of A. sinensis. Modules with |cor| > 0.3 and P < 0.05 were selected, and for cases where this was not satisfied, modules with a strong correlation with most of the 8 major bioactive compounds were prioritized. Additionally, the intersection of modular genes with DEGs between groups A and B was analyzed to identify key modular genes and explore the pathways they may be involved in.

Analysis of microbial community differences

To assess sequencing depth adequacy, rarefaction curves and observed features curves were plotted for all samples based on 16 S rRNA and ITS data. The plateauing of these curves indicated that sequencing depth was sufficient to capture microbial diversity. The ggVenn package (v 1.7.3) [44] was used to identify ASVs common or unique in groups A and B, highlighting the similarity and specificity between the two groups. Alpha diversity was assessed using the Observed, Shannon, PD, and Evenness indices, with differences between groups determined by statistical testing (P < 0.05). Beta diversity was analyzed based on Bray-Curtis distances, visualized using constrained principal coordinate analysis (CPCoA), and tested for significance using analysis of similarity (ANOSIM).

To statistically quantify the relative contributions of different factors (region and growth stage) to the observed variation in microbial community structure, Permutational Multivariate Analysis of Variance (PERMANOVA) was performed using the adonis2 function in the vegan package (v 2.6.4) [46] with 999 permutations. This analysis was based on Bray-Curtis distance matrices, and the proportion of variance explained (R²), as well as the associated P-value, were reported for each factor (region, growth stage) and their interaction.

Additionally, to partition the variation in microbial communities among three explanatory variables—soil physicochemical properties (as a matrix of continuous variables), geographical region (as a factor), and growth stage (as a factor)—variance partitioning analysis was performed using the varpart function in the vegan package (v 2.6.4). This approach allowed us to distinguish the independent and shared contributions of each set of variables to the total variation in microbial community structure.

The edgeR package (v 3.44.3) was then used to analyze differences in microbial community composition at the phylum and genus levels between groups A and B (P < 0.05, |log₂FC| > 1). Subsequently, the microbes responsible for differences in community structure were identified. These analyses were repeated to compare microbial communities across different growth stages within the same production area.

Identification of ASVs associated with the major bioactive compounds of A. sinensis

Furthermore, WGCNA was employed to identify microorganisms associated with the content of the major bioactive compounds of A. sinensis. First, the goodSamplesGenes function of the WGCNA package (v 1.71) was used to assess whether any ASVs in groups A and B should be excluded. Soft thresholds for constructing the scale-free network were determined (R² = 0.85). The clustering tree was then cut to obtain modules (minModuleSize = 200, parameter = 0.2), and the correlation of each module with the major bioactive compounds was calculated. Modules strongly correlated with the 8 major bioactive compounds of A. sinensis (|cor| > 0.3, P < 0.05) were selected. In cases where this threshold was not met, modules with strong correlations with most of the bioactive compounds were prioritized.

Symbiosis network analysis

To construct the gene-microbe interaction network with the major bioactive compounds of A. sinensis in different production areas, Spearman’s correlation between DEGs of groups A and B, bacterial ASVs, and fungal ASVs (from WGCNA) was calculated. This analysis resulted in a DEGs-A. sinensis major bioactive compounds-bacteria-fungi co-expression symbiotic network (|cor| > 0.9, adjusted P < 0.05). The correlations obtained indicate statistical associations, though they do not necessarily reflect direct ecological interactions or regulatory relationships.

Functional annotation of prokaryotic taxa (FAPROTAX) analysis

PAPROTAX, based on published and validated literature on culturable bacteria, facilitates functional annotation prediction for biogeochemical cycling processes, particularly the elemental cycling of carbon, hydrogen, nitrogen, phosphorus, and sulfur. To assess the functional characteristics of A. sinensis rhizosphere soils from different production areas, the FAPROTAX database (http://www.loucalab.com/archive/FAPROTAX) was used to predict the functional profiles of groups A and (B) Differential expression analysis of the functional data was conducted, with statistical comparison between the two groups using the t-test (P < 0.05). Additionally, FAPROTAX predictions were made for different growth stages within the same production area. This method predicts functional potential based on taxonomic composition rather than direct metagenomic evidence.

Association analysis

Next, Spearman’s correlation analysis was performed to explore the relationship between soil physicochemical properties and the content of the major bioactive compounds in A. sinensis. Redundancy analysis (RDA) was used to further examine the influence of soil physicochemical properties on microbial communities, with factors represented by arrows. The quadrant in which the arrow was positioned indicated the correlation type: a positive correlation within 90 degrees, a negative correlation beyond 90 degrees. The length of the line connecting the arrow to the origin indicated the strength of the correlation between the factor and both the community distribution and species distribution.

Statistical analysis

All statistical analyses were performed in R (v 4.1.1), using the Wilcoxon test and t-test, with P < 0.05 considered statistically significant.

Results

Differences in major bioactive compounds in A. sinensis between different production areas

The results revealed significant differences in the major bioactive compounds between authentic and near-authentic production areas, with 7 out of 8 compounds showing significant variation (P < 0.05). Specifically, ligustilide, coniferylferulate, NBP, and levistilide A were more abundant in the authentic production area (Additional file 2). Since A. sinensis is primarily used for its rhizome in medicine, the changes in these compounds during the rhizome expansion stage were further analyzed. During this stage, 5 compounds exhibited significant variations between production areas, with coniferylferulate being higher in the authentic region (Additional file 3). Notably, during the drug formation stages, 6 compounds displayed significant differences, with ligustilide, coniferylferulate, NBP, and levistilide A again being more prevalent in the authentic production area, contrasting with the findings from the rhizome expansion stage (Additional file 4).

Differences in soil properties in A. sinensis between different production areas

Soil properties showed significant variation between regions, with S_SC, S_ALP, and EC exhibiting marked differences (Additional file 5), suggesting that soil properties may contribute to the observed variations in the bioactive constituents of A. sinensis. Within the same production area, soil physicochemical properties also varied across growth stages. For example, urease activity (P < 0.001) and AP decreased from A5 to A10, while organic matter and EC increased (P < 0.0001). Soil TN increased in group A7 but decreased in A10 (P < 0.05) (Additional file 6). In the near-authentic production area, S_SC (P < 0.0001), S_ALP (P < 0.0001), organic matter, and pH (P < 0.01), TN (P < 0.0001), and AK (P < 0.01) increased from B5 to B10, while AP decreased (Additional file 7). As depicted in the heatmap (Additional file 8), all measured soil indicators exhibited distinct variation patterns across samples. These dynamic changes in soil properties highlight a strong correlation between soil physicochemical properties and the accumulation of bioactive compounds in A. sinensis.

DEGs and their pathways between different production regions

Comparative analysis revealed that 63%-73% of the 18 sequenced samples were successfully matched (Additional file 9). Gene expression levels were consistent across all these samples after annotation, confirming their suitability for downstream analyses (Additional file 10). Comparative transcriptomic analysis identified 2,367 DEGs between groups A and B (Fig. 1a). These DEGs were significantly enriched in biosynthetic pathways related to secondary metabolites, particularly phenylpropanoid and steroid hormone biosynthesis (Fig. 1b-c). The number and functional focus of regional DEGs varied across growth stages. A total of 2,791 DEGs were identified at the leaf clump stage (A5/B5), which increased to 3,759 at the rhizome expansion stage (A7/B7) and 4,746 at the drug formation stage (A10/B10) (Fig. 1d-f). Notably, the enrichment in phenylpropanoid biosynthesis became more pronounced in later stages (Fig. 1g-l), aligning with the active accumulation of bioactive compounds.

Fig. 1.

Differential analyses of transcriptome to gene variation in roots of A. sinensis. a volcano plot of differential genes (group A and B), blue down-regulated genes, red up-regulated genes, and gray non-significantly different genes; (b) GO enrichment analysis of differential genes group A/B; (c) KEGG enrichment analysis of difference genes group A/B; (d) volcano plot of differential analysis in A5-B5; (e) in A7-B7; (f) in A10-B10; (g) GO enrichment analysis of A5 vs. B5; (i) of A7 vs. B7; (k) of A10 vs. B10; (h) KEGG enrichment analysis of A5 vs. B5; (j) of A7 vs. B7; (l) of A10 vs. B10

DEGs and their pathways involved during different growth stages

Substantial transcriptional reprogramming occurred during plant development in each region. In the authentic region, comparisons between consecutive stages revealed 7,815 DEGs (A7 vs. A5) (Fig. 2a), 9,185 DEGs (A10 vs. A5) (Fig. 2b), and 7,300 DEGs (A10 vs. A7) (Fig. 2c). Notably, the authentic production area exhibited 1,615 shared DEGs across all growth stages, with the fewest unique DEGs (957) in the A10 vs. A7 comparison (Fig. 2d). The near-authentic production region showed a similar pattern of change: 8,744 DEGs (B7 vs. B5) (Fig. 2e), 9,244 DEGs (B10 vs. B5) (Fig. 2f), and 9,511 DEGs (B10 vs. B7) (Fig. 2g). In this region, 1,738 DEGs were identified across growth stages (Fig. 2h). Functional enrichment of these DEGs consistently highlighted pathways related to secondary metabolism and defense, such as the response to chitin and MAPK signaling, across both stages and regions (Additional file 11a-f, Additional file 12).

Fig. 2.

Differential analysis of transcriptome. a volcano plot of differential genes in A7 vs. A5, (b) in A10 vs. A5; (c) in A10 vs. A7; blue down-regulated genes, red up-regulated genes, and gray non-significantly different genes; (d) difference gene sharing analysis (group A); (e) volcano plot of differential genes in B7 vs. B5; (f) in B10 vs. B5; (g) in B10 vs. B7; (h) difference gene sharing analysis (group B)

5,063 module genes related to major bioactive compounds in A. sinensis

WGCNA, following quality control verification, was performed on all samples from groups A and B (Additional file 13a). The analysis identified an optimal soft threshold power of 26 (scale-free topology model fit, R² = 0.85, Additional file 13b), yielding 10 distinct co-expression modules, including the grey module of unassigned genes (Additional file 13c, d). Notably, the MEbrown module showed significant correlations (absolute value > 0.3, P < 0.05) with five key bioactive compounds (ligustilide, coniferylferulate, senkyunolide H, NBP, and levistilide A). This module contained 5,063 genes potentially involved in the biosynthesis of these compounds (Additional file 13e). Intersection analysis with DEGs between groups A and B identified 463 key module genes, functionally enriched in cytochrome P450 activity, steroid hormone biosynthesis, and cortisol synthesis and secretion pathways (Additional file 13f). These pathways suggest that the MEbrown module plays a central role in regulating the biosynthesis of major bioactive compounds in A. sinensis, potentially by modulating steroid hormone metabolic fluxes.

Differences in ASV of bacteria between production areas

Alpha diversity analysis showed that the rarefaction curves plateaued with increasing sequencing depth (Additional file 14). Bacterial communities shared 729 core ASVs between regions, with 1,659 and 1,889 ASVs unique to the authentic and near-authentic production regions, respectively (Fig. 3a). Alpha diversity did not significantly differ between regions (Fig. 3b), but beta diversity revealed clear separation (Fig. 3c), indicating distinct community compositions. At the phylum level, Actinobacteriota, Proteobacteria, and Chloroflexi were dominant. Region A exhibited lower Actinobacteriota but higher Acidobacteriota abundance compared to Region B (Fig. 3d). At the genus level, Vicinamibacter was more abundant, and Nocardioides was less abundant in Region A (Fig. 3e). Differential abundance analysis identified 417 ASVs (199 upregulated in A) contributing to these differences, primarily from Proteobacteria, Actinobacteriota, Chloroflexi, and Acidobacteriota (Fig. 3f, g).

Fig. 3.

Differential analysis of bacterial microbial communities (A/B). a shared and specific ASV analysis; b alpha diversity analysis of variance; c PCoA clustering diagram; d bar stacking diagram in phylum level; e in genus level; f volcano diagram for differential ASV; g manhattan diagram for differential ASV, different colors characterize different ASV counterpart gate levels

Differences in ASV of bacteria during different growing stages

Bacterial community composition exhibited dynamic shifts within each region across growth stages. In region A, 123 ASVs were shared across all stages (Fig. 4a). Alpha diversity showed the most significant difference between the leaf clump stage (A5) and later stages (Fig. 4b). Beta diversity clearly separated A5 from A7 and A10 (P < 0.01; Fig. 4c). Phylum- and genus-level profiles were similar between A7 and A10 (Fig. 4d, e). Differential ASVs were numerous in A7 vs. A5 (264, Additional file 15a) and A10 vs. A5 (241, Additional file 15b), but fewer in A10 vs. A7 (61, Additional file 15c). No ASVs were shared across all three pairwise comparisons (Fig. 4f), suggesting a dynamic restructuring of the community. The particularly similar diversity and abundance patterns between the rhizome expansion and drug formation stages suggest that these dominant bacterial communities might play pivotal roles in plant development and secondary metabolite accumulation.

Fig. 4.

Differential analysis of bacterial communities in group A during different growth stages. a shared and specific ASV analysis; b alpha diversity difference analysis; c PCoA clustering diagram; d bar stacking diagram in phylum level; e in genus level; f differences ASV shared analysis

In region B, 130 ASVs were shared across all stages (Fig. 5a). Alpha diversity differed between B5 and later stages (Fig. 5b). Beta diversity separated communities by stages (Fig. 5c). At the genus level, Nocardioides decreased at B10 (Fig. 5d, e). Differential ASVs numbered 327 (B7 vs. B5) (Additional file 16a), 319 (B10 vs. B5) (Additional file 16b), and 233 (B10 vs. B7) (Additional file 16c). Similar to region A, no ASVs were shared across all growth stage comparisons in B (Fig. 5f). These patterns indicate strong growth stage-specific restructuring of bacterial communities in both regions, with convergence in community profiles during the rhizome expansion and drug formation stages.

Fig. 5.

Differential analysis of bacterial communities in group B during different growth stages. a shared and specific ASV analysis; b alpha diversity difference analysis; c PCoA clustering diagram; d bar stacking diagram in phylum level; e in genus level; f differences ASV shared analysis

Variation of fungal ASVs between different production areas

The ITS sequencing dilution curves confirmed adequate sequencing depth, demonstrating comprehensive coverage of fungal diversity (Additional file 17). Across groups A and B, 481 ASVs were shared, with group A exhibiting more unique ASVs than group B (Fig. 6a). Alpha diversity analysis revealed no significant differences in species richness and evenness between groups (all indices, P > 0.05, Fig. 6b). In contrast, beta diversity showed clear separation of fungal communities between groups (Fig. 6c). Distinct from bacterial patterns, fungal communities differed notably at both the phylum and genus levels. In group A, abundances of Bisifusarium, Longitudinalis, and Verticillium were reduced, while Mycochlamys, Linnemannia, and Mortierella showed increased abundance compared to group B (Fig. 6d, e). A total of 325 differentially expressed ASVs were identified (233 up-regulated and 92 down-regulated) in the group A vs. B, predominantly derived from Ascomycota (Fig. 6f, g).

Fig. 6.

Differential analysis of fungal microbial communities (A/B). a shared and specific ASV analysis; b alpha diversity difference analysis; c PCoA clustering diagram; d bar stacking diagram in phylum level; e in genus level; f volcano diagram for differential ASV; g manhattan diagram for differential ASV

Differences in fungal community structure during different growth stages

Core and unique ASV distribution analysis revealed that 110 ASVs were shared across all growth stages, while 410, 159, and 235 ASVs were unique to A5 (leaf clump), A7 (rhizome expansion), and A10 (drug formation) in the authentic production area, respectively (Fig. 7a). Notably, alpha diversity showed no significant differences across the growth stages (P > 0.05 for all indices, Fig. 7b). However, beta diversity analysis revealed distinct community structures between stages (Fig. 7c). At the phylum level, Ascomycota abundance progressively declined as growth progressed from A5 to A10 (Fig. 7d). At the genus level, Mycochlamys abundance was highest in A5 but significantly decreased in A7 and A10. A7 and A10 samples shared some similarities, with notable increases in Mortierella, Plectosphaerella, and Tetracladium, but a decrease in Tausonia in the A10 samples (Fig. 7e). Significant ASV changes were observed between stages: for A7 vs. A5, 125 differential fungal ASVs (27 up-regulated and 98 down-regulated, Additional file 18a); for A10 vs. A5, 160 differential fungal ASVs (59 up-regulated and 101 down-regulated, Additional file 18b); and for A10 vs. A7, 32 fungal ASVs (23 up-regulated and 9 down-regulated, Additional file 18c). Intersection analysis confirmed that no fungal ASVs were shared across all growth stages. Mirroring bacterial trends, endemic ASVs gradually decreased as growth progressed, with only five unique ASVs remaining in A10 (Fig. 7f).

Fig. 7.

Differential analysis of fungal communities in group A with different growth stages. a shared and specific ASV analysis; (b) alpha diversity difference analysis; (c) PCoA clustering diagram (A); (d) bar stacking diagram in phylum level; (e) in genes level; (f) volcano diagram for differential ASV in A7-A5; (g) in A10-A5; (h) in A10-A7; (i) differences ASV shared analysis

In the near-authentic production area, 93 fungal ASVs were shared across all growth stages. Notably, B10 samples contained the fewest unique ASVs (Fig. 8a). Diversity analysis revealed no significant differences in all four alpha diversity indices between B5 and B7 stages, while significant differences in Shannon’s evenness and Pielou’s evenness were observed between B7 and B10 (Fig. 8b), with clear separation between B5 and B10 samples in beta diversity (Fig. 8c). Although the phylum-level composition remained relatively stable across the B5, B7, and B10 samples (Fig. 8d), notable genus-level differences emerged as Bisifusarium and Longitudinalis became significantly more abundant in the fungal community during the B10 stage (Fig. 8e). Significant fungal ASV changes between growth stages included 120 differential ASVs (40 up-regulated and 80 down-regulated) for B7 vs. B5 (Additional file 19a), 141 differential ASVs (21 up-regulated and 120 down-regulated) for B10 vs. B5 (Additional file 19b), and 90 differential ASVs (12 up-regulated and 78 down-regulated) for B10 vs. B7 (Additional file 19c). The intersection analysis identified four shared ASVs maintained across all three growth stages, with similar numbers of stage-specific ASVs for each growth stage (Fig. 8f). In summary, these results demonstrate that fungal community structure undergoes significant reorganization during A. sinensis growth. The drug formation stage shows particularly distinct compositional changes, with key genera (Bisifusarium and Longitudinalis) likely playing important roles in secondary metabolite production. The small core microbiome (4 ASVs) suggests strong environmental or host-mediated selection.

Fig. 8.

Differential analysis of fungal communities in group B with different growth stages. a shared and specific ASV analysis; (b) alpha diversity difference analysis; (c) PCoA clustering diagram; (d) bar stacking diagram in phylum level; (e) in genus level; (f) volcano diagram for differential ASV in B7-B5; (g) in B10-B5; (h) in B10-B7; (i) differences ASV shared analysis

Core fungal phylum across all samples

The phylum Ascomycota emerged as a core component of the A. sinensis rhizosphere microbiome, present in all samples regardless of geographical origin or growth stage (Additional file 20). As a conserved, foundational element, Ascomycota likely plays a critical role in maintaining plant fitness. Its ubiquitous presence suggests a potential function in fostering an ecological environment that supports secondary metabolism, positioning it as a key microbiome constituent that contributes to the plant’s medicinal properties. This finding emphasizes the concept of a stable core microbiome working alongside a dynamic, condition-responsive microbial community to collectively enhance plant health and specialized functions.

Quantifying the relative importance of region and growth stage on Microbiome assembly

PERMANOVA results highlighted the growth stage as the primary factor, accounting for 46.69% of the variation in fungal community structure. The geographical region showed a stronger impact on fungi than on bacteria, contributing 33.20% of fungal variation and indicating fungi’s heightened sensitivity to geographic differences (Additional file 21). The model’s explanatory power was robust, with growth stage and region collectively accounting for 79.89% of the total variation, leaving 20.11% unexplained. The exceptionally high pseudo-F value for region (F = 19.80) revealed significant differentiation between groups relative to within-group variation.

Variance partitioning further clarified the dominant role of growth stage, explaining 71.62% of fungal community variation (Additional file 22). Fungal communities exhibited clear geographical specificity, with region accounting for 26.57% of their variation, in contrast to just 3.71% for bacteria.

Bacterial and fungal ASVs associated with the main bioactive compound of A. sinensis

All bacterial ASVs from groups A and B were included in the WGCNA after quality control verification (Additional file 23a). Network construction identified an optimal soft threshold power of 7 (scale-free topology fit R² = 0.85, Additional file 23b) and revealed six co-expression modules through hierarchical clustering (Additional file 23c, d). The MEyellow module, containing 417 bacterial ASVs, exhibited the strongest correlations with the principal bioactive components in A. sinensis (Additional file 23e). Similarly, all fungal samples met the inclusion criteria (Additional file 23f), and the fungal network was constructed with a soft threshold of 12 (R² = 0.85) (Additional file 23 g), yielding six modules, including the unclassified gray module (Additional file 23 h, i). The MEturquoise module, consisting of 325 fungal ASVs, demonstrated the strongest associations with the key bioactive components in A. sinensis (Additional file 23j). Combining DEGs and WGCNA results, a comprehensive co-expression network was constructed, incorporating DEGs from A. sinensis, key pharmaceutically active components, bacterial ASVs (MEyellow module), and fungal ASVs (MEturquoise module). This network contained a total of 2,343 nodes and 18,140 interaction pairs (16,558 positive correlations, Fig. 9a), revealing the complex relationships among the four components. These results highlight a tight integration between microbial communities and plant metabolite production, a predominance of positive interactions in the microbiome-metabolome network, and the identification of specific bacterial (417 ASVs) and fungal (325 ASVs) modules that strongly correlate with medicinal compound accumulation.

Fig. 9.

The correlation analysis between soil properties, microbes and bioactive compounds content of A. sinensis. a gene-bacteria-fungi symbiotic network analysis, bacterial AVS in green, fungal AVS in red, genes in blue, and active compounds content of A. sinensis in yellow; (b) functional difference analysis in A vs. B, different colors indicate different subgroups, and the method of difference statistics is paired samples, ns indicates that the difference is not significant.; (c) functional difference analysis of different growth stages in group A, the same letter indicates a difference not significant, the different letter between two indicates a significant difference; (d) functional difference analysis of different growth stages in group B; (e) correlation analysis between soil physical and chemical properties and bioactive compounds content, vertical coordinates indicate the soil physicochemical properties, horizontal coordinates indicate the content of bioactive compounds of A. sinensis, each grid color and size table shown Spearmen correlation the redder the color, the larger the shape of the circle Spearmen correlation coefficient absolute value the larger; (f) RDA diagram of soil physical and chemical properties between bacterial community; (g) between fungal communities

Differences in the functional characteristics of the rhizosphere soil of A. sinensis

The Functional Annotation of Prokaryotic Taxa (FAPROTAX) analysis in groups A and B revealed significant functional differences between production areas (P < 0.05) in five key metabolic pathways: chitinolysis, aromatic hydrocarbon degradation, aliphatic non-methane hydrocarbon degradation, ureolysis, and invertebrate parasites (Fig. 9b). Six functional categories showed significant variation across growth stages (P < 0.05), with primary differences in fermentation, human pathogens (all), and human pathogens (pneumonia) observed between A5 vs. A7 or A10 (Fig. 9c). However, the most pronounced functional differences emerged in the near-authentic production area, with nine significantly altered pathways (P < 0.05), including aerobic chemoheterotrophy with the highest relative abundance, human pathogens (all), fermentation, dark hydrogen oxidation, nitrogen fixation, and aromatic hydrocarbon degradation across all growth stages (Fig. 9d). These functional differences highlight distinct biogeochemical processing capabilities between production areas, stage-specific microbial functions during plant development, potential pathogen pressure variations across growth stages, and the critical role of chemoheterotrophic metabolism in rhizosphere soil interactions.

Positive correlation between soil physicochemical properties and the bioactive compounds of A. sinensis

Correlation analysis revealed significant relationships between soil physicochemical properties and bioactive compounds (Fig. 9e, Additional file 24). Soil enzymes (S_POD, S_ACP, S_NP), organic matter, and AK exhibited strong positive correlations with most bioactive compounds, with AK showing the highest positive correlation with Levistilide A (r = 0.96, P < 0.0001). AP was negatively correlated with coniferylferulate (r = -0.83, P < 0.001), which also displayed negative correlations with most soil physicochemical properties. Additionally, ferulic acid showed significant positive correlations with soil sucrase (S_SC, r = 0.84, P < 0.01), TN (r = 0.76, P < 0.01), AP (r = 0.72, P < 0.01), and EC (r = 0.65, P < 0.05) (Fig. 9e, Additional file 24). These correlations provide key insights for developing cultivation strategies aimed at enhancing target metabolite production in A. sinensis. RDA further revealed distinct associations between soil physicochemical properties and microbial communities across production areas. Positive correlations were observed between both bacterial and fungal communities and S_ALP, TN, or organic matter in the authentic production area, and between bacterial communities and EC, AP, or urease in the near-authentic production area. Fungal communities in the near-authentic production area showed associations with S_SC, EC, AP, and urease (Fig. 9f, g). These results indicate that soil characteristics differentially shape microbial communities in each production area, potentially influencing nutrient acquisition efficiency, secondary metabolite biosynthesis, and the accumulation of medicinal compounds in A. sinensis.

Discussion

Cultivation location influenced the primary bioactive compounds in A. sinensis

The pharmacological quality of A. sinensis is intricately tied to its geographical origin, primarily due to variations in key bioactive compounds such as phthalides, coumarins, lignans, and terpenoids [6]. Our study reveals significant differences in seven out of eight major bioactive compounds between authentic and near-authentic cultivation areas. Among these compounds, ligustilide, ferulic acid, NBP, and levistilide A are well-documented for their therapeutic properties [47–49]. Zhang et al. [5] reported that geoherb samples contained notably higher levels of senkyunolide I and NBP compared to non-geoherb samples, aligning with our findings for NBP. However, senkyunolide I exhibited an opposite trend, likely due to the relatively minor microenvironmental differences between the genuine and nearby regions compared to non-genuine areas. Furthermore, “total ferulate,” defined as the sum of ferulic acid and its derivatives from coniferylferulate conversion [1, 34], was higher in authentic cultivation regions than in nearby areas. The significantly elevated levels of ligustilide, total ferulic acid, NBP, and levistilide A in genuine A. sinensis may serve as key markers for distinguishing its superior quality from near-authentic samples. These findings support prior research emphasizing the critical role of cultivation region in determining A. sinensis quality [47–49].

Transcriptomic analysis revealed that these phytochemical differences are closely linked to distinct gene expression patterns. DEGs between regions and across growth stages were consistently enriched in phenylpropanoid and secondary metabolite biosynthesis pathways. The phenylpropanoid biosynthesis pathway is essential for synthesizing phenolic compounds and flavonoids, which regulate the production of pharmacologically active compounds [50, 51]. Notably, this pathway is directly involved in ferulic acid biosynthesis [38]. Intriguingly, defense-related pathways, such as the response to chitin, were also enriched, suggesting that secondary metabolite accumulation may be co-regulated with plant defense responses, a pattern observed in other medicinal plants [12, 52]. Fewer unique DEGs were detected during the root rhizome expansion and medicinal maturity stages, indicating that these genes play a specialized role in active compound accumulation [53, 54]. A key observation was the pronounced upregulation of cytochrome P450 enzymes, which mediate furanocoumarin biosynthesis in authentic A. sinensis, particularly during the rhizome expansion stage [44]. During this stage, genes associated with secondary metabolite biosynthesis, phenylpropanoid biosynthesis, and cytochrome P450 were significantly upregulated. In contrast, during the drug formation stage, most genes—except those related to chaperones, folding catalysts, and starch/sucrose metabolism—were markedly downregulated. These results suggest that the elevated bioactive compound levels in authentic A. sinensis may be attributed to the strong upregulation of biosynthesis genes during root enlargement. However, further research is necessary to validate this mechanism.

Bacterial community dynamics and their impact on A. sinensis quality

Root-associated microbiomes play a pivotal role in the biosynthesis [11, 12] and accumulation of specialized metabolites in medicinal plants [13, 55]. Our analysis revealed that key bacterial taxa, such as Acidobacteriota, Firmicutes, Vicinamibacter, and Bacillus, were significantly more abundant in the authentic production regions compared to nearby areas. These bacterial groups are known to enhance plant growth by improving antioxidant enzyme activities and increasing chlorophyll content in plant leaves [56], while also suppressing pathogenic infections by modulating cell wall integrity signaling pathways and various cellular processes [8, 57, 58]. Bacillus species, in particular, are well-documented for their plant growth-promoting and biocontrol properties [59]. Members of Acidobacteriota and Vicinamibacter have also been linked to beneficial soil functions and plant health by promoting plant defense against bacterial wilt by enhancing antagonism against pathogens in diverse ecosystems [60, 61]. However, their specific roles in the A. sinensis rhizosphere warrant further investigation. Notably, Bacillus has been shown to promote phthalide biosynthesis, specifically increasing NBP accumulation in A. sinensis [62]. This is likely due to the action of shikimate O-hydroxycinnamoyl transferase, which facilitates NBP accumulation [63]. In our study, Bacillus was associated with the MEbrown gene module, suggesting that Bacillus may produce this enzyme gene, thereby promoting NBP accumulation, but further research is needed to confirm this. In contrast, Nocardioides was more abundant in the near-authentic production area than in the authentic region. As previously reported, Nocardioides is a stress-tolerant microbial genus valued for its dual functions in pesticide degradation and plant growth promotion [64]. This observation suggests that the indigenous bacterial composition in traditional geo-authentic production areas fosters a more favorable environment for both the growth and phytochemical quality of A. sinensis [31, 65].

Fungal community dynamics and their impact on A. sinensis quality

The differences in fungal community structure between the authentic and near-authentic producing areas were more pronounced than those observed in the bacterial communities, with significant implications for the quality of A. sinensis. Previous studies have highlighted the critical ecological roles of dominant phyla, including Ascomycota, Mortierellomycota, and Basidiomycota, in enhancing soil nutrient availability, bolstering plant pathogen resistance, and promoting plant growth [38, 66–69]. While Ascomycetes include many plant pathogens [67], they may also serve as key elicitors that stimulate medicinal plants to produce secondary metabolites [52, 70]. The microbial taxonomic composition equilibrium is a critical factor influencing both plant fitness and secondary metabolite production [71], suggesting that this balance directly impacts phytochemical accumulation. Genera such as Mycochlamys, Linnemannia, Mortierella, Tausonia, and Tetracladium were more abundant in the authentic production areas. Notably, Mycochlamys showed a positive correlation with ferulic acid accumulation [7]. Mortierella contributes to plant growth promotion by providing a nitrogen source, which may explain the observed upregulation of nitrogen metabolism-related pathways in A. sinensis in the traditional geo-authentic regions [69, 72]. Furthermore, Mortierella may suppress root pathogens by generating plant hormones and activating plant defense transcription responses [38, 71], while enhancing medicinal compound production through enzymes that facilitate bioactive compound formation [51]. Although Mortierella may promote Fusarium root invasion, no direct pathogenicity toward A. sinensis roots has been demonstrated [20]. In contrast, the near-authentic production areas exhibited higher abundances of known pathogenic genera, including Bisifusarium, Plectosphaerella, Longitudinalis, Verticillium, and Cladosporium. These genera are established plant pathogens affecting medicinal plants like A. sinensis, Panax ginseng, and Withania somnifera, particularly during the medicinal growth stage [73, 74]. These results suggest that authentic production areas support fungal communities more conducive to plant health and medicinal compound accumulation, while near-authentic regions harbor higher proportions of pathogenic taxa that compromise plant fitness. These results not only align with but also extend previous research on A. sinensis microbiome ecology [16, 20, 75]. Given the established importance of stochastic processes in rhizosphere bacterial and fungal community assembly [31, 76], it is hypothesized that the distinct microbial consortia found in authentic areas, associated with higher medicinal compound content, represent a locally adapted microbiome that contributes to the superior quality of A. sinensis in these regions. The balance of microbial taxonomic composition is a key factor influencing plant fitness and secondary metabolite production [72], indicating that this equilibrium directly impacts phytochemical accumulation.

The bacterial and fungal communities displayed both overlapping and distinct responses to regional variations and growth stages. While both communities were enriched with beneficial microorganisms in traditional geo-authentic production regions and shared structural similarities during the root and stem expansion and medicinal material formation stages, fungal communities exhibited greater sensitivity to regional differences. Notably, certain pathogenic fungi were significantly more abundant in non-traditional geo-authentic production areas, a pattern not observed in bacterial communities. This disparity may stem from more direct functional interactions between fungi and plant hosts, particularly in the induction of secondary metabolites, with Ascomycota serving as a potential activator [67, 70]. Future research should focus on exploring the bacterial–fungal interaction networks and their synergistic regulatory mechanisms influencing medicinal material quality.

Soil properties and their impact on A. sinensis quality

Extensive research confirms that soil is the primary source of essential nutrients for plant growth, with its edaphic conditions directly influencing medicinal metabolic pathways and modulating bioactive compound production [30, 77]. The present study identified three key soil parameters—S_SC, S_ALP, and EC—that differ significantly between the authentic and near-authentic production areas of A. sinensis. However, the most substantial variations in soil characteristics were observed across different growth stages within both areas, suggesting notable differences in growth patterns and metabolic activities between authentic and near-authentic cultivated A. sinensis. These variations corresponded with modifications in rhizosphere soil properties, including physicochemical properties, nutrient profiles, and enzyme activities [78]. The microbial community dynamics aligned with these findings. While bacterial and fungal α-diversity and richness did not show significant spatial variations between regions, considerable differences were observed across growth stages in both regions. These patterns mirrored the changes in soil physicochemical properties, nutrient gradients, and enzyme activities, reinforcing the established understanding that variations in soil conditions shape microbial community structure and function [79, 80]. Twelve critical soil indicators were found to positively correlate with major pharmacologically active compounds in A. sinensis, including S_POD, S_ACP, S_NP, organic matter, TN, AP, and AK. Four key bioactive compounds—ligustilide, ferulic acid, NBP, and Levistilide A—demonstrated particularly strong correlations with these soil properties. Additionally, soil parameters such as S_ALP, TN, and organic matter exhibited positive correlations with medicinal compound accumulation in the authentic production areas, suggesting that these soil factors may influence bioactive compound levels by altering nutrient availability and selecting for specific microbial assemblages [8, 16]. These findings have both ecological and agricultural implications, reinforcing previous studies on the role of microbes in agroecosystems and bioactive compound production [4, 43, 75]. Microbes play a key role in regulating soil fertility, promoting plant growth, and facilitating bioactive compound synthesis. These insights offer actionable strategies for quality enhancement through targeted modification of soil properties and regulation of microbial communities.

The intrinsic relationship between the functional characteristics of rhizosphere microorganisms and the quality formation of authentic medicinal materials

FAPROTAX functional prediction analysis reveals significant differences in the functional potential of rhizosphere microbial communities between authentic and near-authentic (nearby) regions. Microbial communities in authentic areas exhibit notable enrichment in functions such as chitin degradation and aromatic hydrocarbon degradation [48, 49]. These findings not only confirm structural differences in microbial communities but also unveil the ecological roles of the microbiome in the formation of authentic medicinal herbs from a functional standpoint. Specifically, the enhanced chitin degradation is likely linked to the induction of plant systemic resistance. Chitin, a key component of pathogenic fungal cell walls, breaks down into products that serve as elicitors to trigger plant defense responses [81, 82]. This mechanism could enable local A. sinensis to optimize resource allocation, redirecting photosynthetic products towards the synthesis of bioactive compounds instead of relying on passive defense mechanisms. This offers a novel ecological explanation for the accumulation of higher-order metabolites in medicinal plants cultivated in native environments. Additionally, the enhancement of organic matter transformation functions, such as aromatic hydrocarbon degradation, highlights the microbiome’s critical role in shaping the rhizosphere microenvironment. In authentic areas, microbial communities may continuously release low-molecular-weight nutrients by degrading complex soil organic matter [83]. This slow-release nutrient mechanism likely supports the sustained activity of secondary metabolic pathways in medicinal plants, promoting bioactive compound production rather than rapid vegetative growth. These findings expand the functional scope of the rhizosphere microbiome beyond basic nutrient cycling, incorporating plant resistance induction and secondary metabolism regulation. This provides functional insights into how soil microorganism-plant interactions influence the quality of medicinal herbs. Importantly, these microbial functional traits are intricately linked to the unique soil physicochemical properties of authentic regions—such as high organic matter content and alkaline phosphatase activity—jointly forming the microecological foundation underlying the superior quality of authentic medicinal materials.

Limitations and future perspectives

This multi-omics study provides valuable insights into the complex interactions influencing the quality of A. sinensis. However, several limitations must be acknowledged. First, the experimental design confounds geographical region with inherent soil type, as plants were cultivated in their native soils without reciprocal transplantation. This limitation hinders the ability to disentangle the specific effects of soil properties from other regional factors. Second, the observational nature of this multi-omics analysis highlights strong associations but cannot establish causal relationships among soil parameters, microbial communities, plant gene expression, and metabolite accumulation.

To establish causality, future research should employ controlled manipulative experiments. Focus should be placed on: (1) reciprocal soil-plant experiments, where genotypes from both authentic and near-authentic regions are grown in cross-replicated soils from each region, and (2) direct manipulation of the microbiome through inoculation or sterilization to assess its specific impact on plant physiology and chemistry. Additionally, validation of key transcriptomic results through qRT-PCR is recommended to reinforce the gene expression findings. Such mechanistic studies are critical for developing reliable, region-adapted strategies to optimize the cultivation and medicinal quality of A. sinensis.

Conclusions

This study represents the first integrated multi-omics analysis of A. sinensis from authentic and near-authentic regions, combining transcriptome, microbiome (16 S rRNA and ITS), and metabolome data. The results demonstrate that region-specific soil properties shape distinct rhizosphere microbial communities, which are correlated with differential expression of key biosynthetic genes and subsequent accumulation of pharmacologically active compounds. These findings provide a mechanistic understanding of the geoherbalism of A. sinensis and establish a foundation for optimizing its cultivation quality through targeted management of soil-microbe interactions.

Abbreviation

AK

Available Potassium

ANOSIM

Analysis of Similarity

AP

Available Phosphorus

ASVs

Amplicon Sequence Variants

CPCoA

Constrained Principal Coordinate Analysis

DEGs

Differentially Expressed Genes

EC

Electrical Conductivity

GO

Gene Ontology

HTS

High-throughput Sequencing

ITS

Internally Transcribed Spacer

KEGG

Kyoto Encyclopedia of Genes and Genomes

NBP

3-n-butylphthalide

RDA

Redundancy Analysis

S_ACP

Soil Enzyme Activities (Acid Phosphatase)

S_ALP

Soil Enzyme Activities (Alkaline Phosphatase)

S_NP

Soil Enzyme Activities (Neutral Phosphatase)

S_POD

Soil Enzyme Activities (Catalase)

S_SC

Soil Enzyme Activities (Sucrase)

TCM

Traditional Chinese Medicines

TN

Total Nitrogen

WGCNA

Weighted Gene Co-expression Network Analysis

Authors’ contributions

Conceptualization, X. G. and L. X.; methodology, B. C. and L. Y.; software, B. C. and T. Y.; validation, Y. Z. and S. C.; formal analysis, X. G. and Y. C.; investigation, X. G., B. C. and T. Y.; resources, Z. W.; data curation, X. G. and B. C.; writing-original draft preparation, X. G.; writing-review and editing, B. C. and X.H.; visualization, X. G. and B. C.; supervision, L. X.; project administration, Z. W. and Y. Z.; funding acquisition, Z. W.

Funding

This work was supported by the Natural Science Foundation of Gansu Province of China [grant numbers, 24JRRA1134]; the Science and Technology Program of Gansu Province of China [grant numbers, 24JRRA1137]; the Outstanding Youth Fund of the Gansu Academy of Sciences [grant numbers, 2024YQ-04]; the Young Scientists Fund Project of Gansu Academy of Sciences [grant numbers, 2024QN-13]; the Gansu Longyuan Young Talent Project; and Intellectual Property Plan Project Gansu Province of China [grant number, 22ZSCQ037].

Data availability

The raw RNA-seq and microbial-seq data generated in this study have been deposited in the NCBI Sequence Read Archive (SRA) under the BioProject accession number PRJNA1259572 and PRJNA1404805, respectively. The data and materials presented in this study are available on request from the corresponding author.

Declarations

Ethics approval and consent to participate

This study was conducted under the authorization of the participating organizations, Min County Angelica research institute and agricultural technology extension center of Awu Town, Tanchang County.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Clinical trial number

Not applicable.