Multi-omics Insights into the Effects of Region and Growth Period on the Bioactive Compounds of Angelica sinensis (Oliv.) Diels

Multi-omics Insights into the Effects of Region and Growth Period on the Bioactive Compounds of Angelica sinensis (Oliv.) 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Diels Xiaofang Gong, Bao Chen, Ling Yang, Yong Zhang, Sijing Chang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7812601/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Feb, 2026 Read the published version in BMC Plant Biology → Version 1 posted 16 You are reading this latest preprint version Abstract Angelica sinensis, a traditional medicinal herb, exhibits efficacy quality variations strongly tied to geographical origin and the rhizosphere microbiome composition, yet the microbial drivers of its medicinally bioactive compounds synthesis in authentic versus adjacent regions remain poorly understood. Here, we integrated transcriptomic profiling of plant tissues with 16S rRNA (bacteria) and ITS (fungi) sequencing of rhizosphere soils across multiple growth stages in authentic and near-authentic producing regions. By coupling those dates with targeted metabolomics and soil property analysis. Our results revealed significant regional and growth-stage variations in bioactive compounds and soil properties. Specifically, we identified 2,367 DEGs, 417 bacterial ASVs, and 295 fungal ASVs with differential abundance. Key genera, including Vicinamibacter and Bacillus (bacteria) and Bisifusarium and Longitudinalis (fungi), were found to potentially play important roles in secondary metabolite production. Functional disparities (e.g., chitinolysis, fermentation pathways) were observed, and co-occurrence networks demonstrated tight linkages between plant genes and microbiota. Critically, soil parameters such as organic matter, total nitrogen, and soil alkaline phosphatase were identified as key factors influencing the microbial community structure. Furthermore, the rhizosphere microbiome appears to modulate nutrient absorption, thereby affecting bioactive compound accumulation. Collectively, our multi-omics analysis elucidates the mechanistic influence of region and growth stage on A. sinensis quality, offering new insights for optimizing its cultivation and efficacy across diverse regions. Angelica sinensis transcriptome sequencing 16S rRNA sequencing ITS sequencing microbiome rhizosphere Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Angelica sinensis (Oliv.) Diels (A. sinensis, Danggui in Chinese), a perennial herbaceous plant belonging to the Apiaceae family [ 1 ], is a medicinally important herb cultivated primarily in Asia, Africa, and certain regions of South America [ 2 ]. The key biobioactive 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 and relieve menstrual pain, support intestinal motility. It has also been employed in managing vascular cognitive impairment [ 3 ]. In China, A. sinensis is predominantly cultivated in Gansu, Qinghai, Sichuan, and Yunnan provinces. Among these, Min County in Gansu is historically regarded as the authentic production region (daodi in Chinese), renowned as for the superior pharmacological quality and consistent therapeutic efficacy of its A. sinensis [ 4 , 5 ]. However, the geographic expansion of cultivation into adjacent regions has prompted concerns regarding potential variations in the growth characteristics of A. sinensis and the stability of its bioactive constituents, particularly ferulic acid and other pharmacologically active compounds [ 6 , 7 ]. These observations underscore the critical need to elucidate the influence of regional environmental conditions and seasonal fluctuations on key metabolic pathways. Such investigations are essential not only for optimizing the standardization of traditional Chinese medicine (TCM) formulations but also for establishing ecologically sustainable cultivation practices [ 6 , 7 ]. The rhizosphere microbiome, often referred to as a plant’s “second genome” [ 8 , 9 ], plays an indisputable role in mediating complex root-microbe-soil interactions that influence plant health, stress resilience, and productivity [ 9 , 10 ]. Emerging evidence highlights that microbiome’s dual regulatory effects on plant systems, not only modulating primary physiological processes but also intricately interfacing with secondary metabolic pathways responsible for synthesizing pharmacologically active compounds in medicinal plants such as Salvia miltiorrhiza, Eucommia ulmoides, and Panax ginseng [ 11 – 13 ]. Furthermore, the rhizosphere microbial community acts as a keystone biological regulator, maintaining plant nutritional homeostasis and developmental stability across ontogenetic stages [ 8 , 14 ]. Metagenomic analyses reveal that root-associated microbiota undergo stage-specific restructuring in synchrony with phenological transitions, reflecting dynamic host-microbe co-adaptation [ 8 , 14 ]. However, most existing studies focus on single regions or static comparisons, neglecting the spatiotemporal dynamics of growth and development [ 8 , 14 ]. Deciphering the spatiotemporal dynamics and metabolic plasticity of rhizosphere microbiomes at critical phenological junctures (e.g., flowering initiation, fruit setting) holds transformative potential [ 8 , 14 ]. Such insights could enable targeted microbiome engineering to optimize soil-plant-microbe feedback loops, a cornerstone for developing precision agroecosystems with enhanced nutrient use efficiency and reduced chemical inputs [ 8 , 14 ]. Over the past decade, the high-throughput sequencing (HTS) has revolutionized our comprehension of plant-associated microbiomes, providing unprecedented insights into microbial community structure and its influence on plant health [ 15 , 16 ]. Meanwhile, transcriptomics, captures crucial host transcriptional responses, revealing interactions between host processes and microbial functions [ 16 , 17 ]. This knowledge facilitates the real-time modulation of plant-microbial interactions in response to environmental fluctuations [ 18 , 19 ]. These multi-omic approaches have also successfully delineated the evolutionary and regulatory mechanisms of key metabolic genes involved in the bioactive compound synthesis [ 6 , 20 ]. Despite growing studies on A. sinensis including studies on the tissue-specific pharmacological components [ 21 ], comparative transcriptomics of wild and cultivated roots [ 22 ], the molecular basis of early bolting [ 23 ], and the evolution of coumarin biosynthesis moleculer mechanism [ 31 ], a critical knowledge gap remains. Such as, (1) decipher the comparative analysis of rhizosphere microbiota between the authentic (daodi) and adjacent production areas to identify unique rhizosphere microbial assemblages associated with geo-authentic medicinal material formation, and (2) limited understanding of the co-dynamics of microbiota and metabolome throughout the entire growth cycle. Therefore, it is particularly important to use multi-omics strategies to study the differences in rhizosphere microbial communities during authentic formation and their potential regulatory mechanisms on modulating the biosynthesis of pharmacologically active compounds. In this study, we employed an integrated multi-omics approach combining transcriptomic, 16S rRNA gene, and ITS sequencing analyse to investigate A. sinensis from different production regions. Our study had two principal objectives. (1) Comparative analysis of gene expression and microbial communities: To systematically characterize gene expression differences in A. sinensis between its authentic (daodi) regions and adjacent production regions; and identify key differentially expressed genes (DEGs), bacterial and fungal taxa across distinct growth stages of A. sinensis. (2) Research on the underlying mechanisms of regulatory networks: To analyze the differential genes in A. sinensis from diverse geographical origins and developmental stages, alongside the interactions between rhizosphere bacteria and fungi, to further explore potential regulatory mechanisms. By addressing these objectives, our study could provide noval insights into the molecular and microbial factors contributing to variations in medicinal compound biosynthesis, The expected findings would serve as a valuable reference for understanding the geographical determinants of A. sinensis’s pharmacological quality, ultimately advancing research on this important traditional Chinese medicine. 2. Materials and Methods 2.1. Sample collection and definition grouping The samples of authentic A. sinensis (group A) and near-authentic A. sinensis (group B) were obtained from experimental base of the Min County Angelica sinensis research institute of Dingxi City (N34 ◦ 31’22”, E104 ◦ 28’50”), and experimental base of the Tanchang agricultural technology extension center of Longnan City, (N34 ◦ 3′35″, E104 ◦ 14′8″), respectively, Gansu Province, Northwest China. Besides, the A. sinensis seedlings transplanted from both two experimental sites were identified by Dr. Zengxiang Guo, researcher at the Min County Angelica sinensis research institute, as “Min Gui No.1”. In detail, the underground roots of A. sinensis over their growth (April-November) were collected at 3 periods: (1) the leaf clump period (May 28th, A5/B5), (2) the rhizome expansion period (July 28th, A7/B7), and (3) the period of drug formation (October 18th, A10/B10). The systematic sampling of each experimental field at each point of period was performed by using the 5-point S method. Six plants were randomly chosen at each point, and a total of 30 plants were then put into three groups to act as three replicates of a single sample. In sampling, whole plants of A. sinensis, along with rhizosphere soil, were gently dug up, put in sterile self-sealing bags, labeled, and transported in an ice box with ice packs. The A. sinensis root were cut into two parts; one part was quickly frozen in liquid nitrogen and kept in a -80 ℃ freezer until transcriptome sequencing. The other part was dried in the air and shade conditions to analyze the medicinal compounds. The rhizosphere soil samples were similarly divided into two portions: one portion was stored in -80 ℃ sterile self-sealing bags until high-throughput sequencing, and the other portion was dried naturally in the air to determine soil physicochemical properties, soil nutrients, and soil enzymes. A. sinensis is among the most important economic crops Min County and the adjacent counties in Gansu Province. It is the pillar of the economy of the area and the primary income of medicinal herb farmers in the region. 2.2. Determinations of major bioactive compounds of A. sinensis The major bioactive compounds of A. sinensis (shade-dried and sieved through a 0.074 mm mesh sieve) in both the authentic and near-authentic production areas were analyzed using a high-performance liquid chromatography system. Specifically, the chromatography was performed under the following conditions: MerkRP-C18 (250.0 mm x 4.6 mm, 5 um) column, acetonitrile (B)-1% acetic acid (A) mobile phase with gradient elution, detection wavelength at 280 nm, column temperature at 30 ℃, flow rate at 1mL/min, and injection volume at 20 Ul [ 24 ]. Next, the Wilcoxon test was employed to compare the differences in the 8 bioactive compounds of A. sinensis in groups A and B, namely ligustilide, ferulate, coniferylferulate, senkyunolide I, senkyunolide H, senkyunolide A, 3-n-butylphthalide (NBP), and levistilide A. Then, the ggplot2 package (v 3.3.2) [ 25 ] was employed to visualize the results. Afterward, based on groups A and B, the differences in these bioactive compounds of A. sinensis at different growth periods were also compared (A7 vs B7, A10 vs B10), respectively. 2.3. Determinations of soil physicochemical properties, soil nutrients and soil enzyme Soil (air-dried and sieved through a 0.149 mm mesh sieve) physicochemical properties including pH, electrical conductivity (EC), organic matter, total nitrogen (TN), available phosphorus (AP), available potassium (AK), and soil enzyme activities (urease, catalase (S_POD), sucrase (S_SC), acid phosphatase (S_ACP), neutral phosphatase (S_NP), and alkaline phosphatase (S_ALP) were analyzed as follows. Soil pH and EC were detected by a pH meter and a conductivity meter, respectively, using water to soil ratio of 2:5. Organic matter was determined by the H 2 SO 4 -K 2 Cr 2 O 7 oxidation method [[ 26 ]. TN was determined by the Kjeldahl method [ 27 ]. Available Phosphorus (AP) was determined by the molybdenum-antimony colorimetric method. AK was detected by the fame photometry method [ 28 ]. Urease was quantified using the sodium phenol-sodium hypochlorite colorimetric method. S_POD was measured by an ultraviolet spectrophotometry. 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 [ 29 ]. 2.4. Sample sequencing Total RNA of samples was extracted from the tissue of A. sinensis root using TRIzol® Reagent. The RNA quality was assessed by 5300 Bioanalyser (Agilent) and quantified using the ND-2000 (NanoDrop Technologies). Only the RNA sample (OD260/280 = 1.8 ~ 2.2, OD260/230 ≥ 2.0, RQN ≥ 6.5, 28S:18S ≥ 1.0, > 1µg) was used to construct sequencing library. Library preparation and sequencing RNA purification, reverse transcription, library construction, and sequencing were performed at Shanghai Majorbio Bio-pharm Biotechnology Co., Ltd. (Shanghai, China). The RNA-seq transcriptome library of A. sinensis was prepared following Illumina® Stranded mRNA Prep, Ligation (San Diego, CA) using 1µg of total RNA. Shortly, messenger RNA was isolated according to the poly A selection method by oligo (dT) beads and fragmented by fragmentation buffer first. Secondly, double-stranded cDNA was synthesized using a SuperScript double-stranded cDNA synthesis kit (Invitrogen, CA) with random hexamer primers. Then the synthesized cDNA was subjected to end-repair, phosphorylation, and adapter addition according to the library construction protocol. Libraries were size selected for cDNA target fragments of 300 bp on 2% Low Range Ultra Agarose followed by PCR amplified using Phusion DNA polymerase (NEB) for 15 PCR cycles. After quantified by Qubit 4.0, the sequencing library was performed on the NovaSeq X Plus platform (PE150) using the NovaSeq Reagent Kit. OR the sequencing library was performed on DNBSEQ-T7 platform (PE150) using DNBSEQ-T7RS Reagent Kit (FCL PE150) version 3.0. At the same time, the soil samples (stored at -80°C) of A. sinensis was employed for 16S ribosomal RNA (16S rRNA) and internally transcribed spacer (ITS) sequencing. Specifically, Genomic DNA was extracted from 0.25 g of A. sinensis rhizospheric soil samples using the E.Z.N.A. ® soil DNA Kit (Omega Bio-tek, Norcross, GA, USA) based on the instruction of the manufacturer. The quality and concentration of DNA were measured by 1.0% agarose gel electrophoresis and a NanoDrop® ND-2000 spectrophotometer (Thermo Scientific Inc., USA) and stored at − 80°C before further use. The hypervariable region (V3-V4) of the bacterial 16S rRNA gene was amplified with primer pairs (338F: 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 ]. PCR was performed on an ABI GeneAmp® 9700 PCR thermocycler (ABI, CA, USA). The PCR reaction systems was 4 µL of 5 × Fast Pfu buffer, 2 µL of 2.5 mM dNTPs, 0.8 µL each primer (5 µM), 0.4 µL Fast Pfu polymerase, 10 ng of template DNA, and ddH 2 O add to 20 µL. PCR amplify cation cycling were applied: 3 min initial denaturation at 95°C, followed by 30 s denaturing at 95°C (27 cycles for bacterial primer and 35 cycles for fungal primer), 30 s annealing at 55°C and 45 s extension at 72°C, then 10 min a final extension at 72°C, final end at 4°C. All amplifications were performed in three technical replicates. The PCR products were extracted from 2% agarose gels and purified by the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) and quantified using Quantus™ Fluorometer (Promega, USA). The purified amplification products were quantitatively mixed, and paired-end sequencing was performed with a MiSeq PE300 platform (Illumina, San Diego, USA) according to the standard protocols by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). 2.5. Processing and evaluation of sequencing data After obtaining the data from transcriptomics high-throughput sequencing, Trimmomatic software (v 0.39) [ 32 ] was employed to filter the low-quality data (LEADING: 3, TRAILING: 3, SLIDINGWINDOW: 4: 15, MINLEN: 36), removing contaminations and splice sequences, resulting in clean data. Subsequently, the default parameters of HISAT2 software (v 2.2.0) [ 33 ] were employed to compare clean data with the reference genome of A. sinensis [ 34 ]. After filtering the transcriptome data, to observe the gene expression in each sample, feature Count program of Rsubread package (v 1.12.0) [ 35 ] was employed to extract gene expression values (gene counts) (parameter: -t exon-g gene_name), and the eggNOG-emapper database ( http://eggnog-mapper.embl.de/ ) was utilized to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations corresponding to the genes. Immediately following this, the edgeR package (v 3.44.3) [ 36 ] was employed to normalize the gene expression matrix for each sample. The fact that all samples were at similar expression levels indicated that the data could be used for subsequent analysis. Additionally, based on 16S rRNA and ITS sequencing data, the DATA2 method in the QIIME2 package (v 2022.8) [[ 37 ] was used for clustering to group valid sequence clusters of sequenced samples into amplicon sequence variants (ASVs) [ 38 , 39 ]. 2.6. Differential expression analysis based on transcriptomics To acquire DEGs in different production areas (DEGs-AB), the Deseq2 package (v 1.26.0) [ 40 ] was employed to conduct differential expression analysis between groups A and B (P 1). Subsequently, the ggplot2 package (v 3.3.2) [ 25 ] was employed to draw a volcano map. Then, to further comprehend the pathways involved in the DEGs-AB, the clusterProfiler package (v 4.0.2) [ 41 ] was employed to conduct GO and KEGG analyses (adjust p < 0.05). All significant entries of GO and KEGG pathways were visualized, respectively. Afterward, to further understand the differences between production areas, samples from different production areas during the same growth period were analyzed for their differential expression and pathways involved were explored. Moreover, to gain genes playing significant roles in different growth periods within the same production area, differential expression analysis was conducted on samples from that area but at different growth periods (P 1). Next, the ggVenn package (v 1.7.3) [ 42 ] was employed to gain their shared DEGs, specific differential genes and involved to understand their roles during the growth of A. sinensis (adjust P < 0.05). Notably, the above enrichment results were sorted by adjusted p value from smallest to largest, visualizing top 10 GO entries and top 20 KEGG pathways, respectively. 2.7. Weighted gene co-expression network analysis (WGCNA) To gain the genes associated with the major bioactive compounds of A. sinensis, the WGCNA was conducted. The goodSamplesGenes function of WGCNA package (v 1.71) [ 43 ] was first applied to conduct hierarchical clustering analysis on all samples of groups A and B to check for outlier samples. Subsequently, to construct scale-free networks, soft threshold (power) and scale-free fit indices (R 2 ) were computed and the power with mean connectivity converging to 0 was selected (R 2 = 0.85). Then, the clustering tree was constructed, and the modules were acquired by cutting them according to the minModuleSize of genes 200, through a shear tree algorithm (parameter = 0.2). Finally, to gain the most relevant module genes for the major bioactive compounds of A. sinensis, the WGCNA package (v 1.71) was applied for Spearman's correlation analysis to calculate the correlation coefficients (cor) between modules and major bioactive compounds of A. sinensis. Modules were selected when their |cor| values were greater than 0.3 and their p values were less than 0.05, and when they could not be satisfied, modules with a strong correlation with most of the 8 major bioactive compounds were selected. Besides, the intersection of modular genes with DEGs between groups A and B was taken to gain key modular genes and explore the pathways in which they might be involved. 2.8. Analysis of microbial community differences Dilution curves were employed to confirm whether the sequencing data were sufficient to represent the diversity and abundance of species in the samples. Specifically, ASVs rarefaction curves and observed feature curves were plotted for all samples based on 16S rRNA sequencing and ITS sequencing data. Due to the ASVs rarefaction curves increased with the number of sequences (sequencing depth), the curve flattened out, indicating that the amount of sequencing data met the needs of the analysis. Additionally, similarly, observed feature curves were employed to observe the extent to which microbial species information was covered in the samples. Subsequently, the ggVenn package (v 1.7.3) was employed to obtain ASVs that were common or unique to the samples in groups A and B to demonstrate the similarity and specificity in the 2 groups. Afterward, to understand the species diversity of microbiota in the samples, an Alpha diversity analysis was carried out. In detail, the observed features index, Shannon, PD, and Evenness in Alpha index were adopted to visualize the diversity and differences (P < 0.05) of microorganisms in the samples of groups A and B. Moreover, diversity differences between groups A and B were determined by Beta diversity analysis. Specifically, the differences between the different subgroups were first demonstrated based on the constrained principal coordinate analysis (CPCoA) clustering plot of the euclidean distance matrix of all the sample bacteria. Then, analysis of similarity (ANOSIM) was employed to analyze the differences in Beta diversity in groups A and B. Later, the edgeR package (v 3.44.3) was employed to analyze differences in microbial community composition at the phylum and genus levels between groups A and B (P 1), followed by which microbes caused differences in the communities were further analyzed. Immediately after that, ASVs that were common and unique to different growth periods in the same production area were recognized. In addition, Alpha diversity, Beta diversity, differences in microbial community composition, and ASVs of differential microorganisms during different growth periods were further analyzed to observe the similarity and specificity of microbial communities during different growth periods in the same production area. 2.9. Identification of ASVs associated with the major bioactive compounds of A. sinensis Similarly, WGCNA analysis was employed to obtain microorganisms associated with the content of the major bioactive compounds of A. sinensis. In detail, the goodSamplesGenes function of WGCNA package (v 1.71) was employed to determine whether the ASVs of the samples in groups A and B needed to be eliminated, subsequently, soft thresholds were determined for the construction of the scale-free network (R 2 = 0.85). Next, the constructed clustering number was cut to obtain the modules (minModuleSize = 200, parameter = 0.2), and the correlation of the modules with the main active ingredient was calculated. Notably, modules with strong correlation with the 8 major bioactive compounds of A. sinensis were selected (|cor| >0.3, p < 0.05), and when they could not be satisfied, modules with strong correlation with most of the 8 major bioactive compounds were selected. 2.10. Symbiosis network analysis To obtain the gene and microbial interaction network with the major bioactive compounds of A. sinensis in different production areas, the Spearman correlation between DEGs of groups, bacterial ASVs, and fungal ASVs (WGCNA analysis) was calculated for the construction of DEGs-A. sinensis major bioactive compounds-bacteria-fungus co-expression symbiotic network (|cor| >0.9, adjust P < 0.05). 2.11. Functional Annotation of Prokaryotic Taxa (FAPROTAX) analysis The FAPROTAX was based on published and validated literature on culturable bacteria, which allowed biogeochemical cycling processes (especially elemental cycling of carbon, hydrogen, nitrogen, phosphorus, and sulfur) to be performed for functional annotation prediction. To obtain the functional characteristics of the rhizosphere soils of A. sinensis in different production areas, FAPROTAX database ( http://www.loucalab.com/archive/FAPROTAX ) was applied to predict the functional characteristics of groups A and B. The functional information of the obtained samples was analyzed for differential expression, and the t-test was employed to compare the differences between the 2 groups (P < 0.05). Moreover, FAPROTAX predictions were made for different growth periods in the same production area. 2.12. Association analysis Subsequently, the correlation between soil physicochemical property indexes and the content of the major bioactive compounds of A. sinensis was further explored by Spearman's correlation analysis. Then, redundancy analysis (RDA) was employed to further investigate the effects of soil physicochemical properties on microbial communities, in which the factors were represented by arrows, and the quadrant in which the arrow was located indicated the positive or negative correlation between the factor and the sorting axis, with a positive correlation of less than 90 degrees and a negative correlation of more than 90 degrees, and the length of the line connecting the arrow and the origin represented the correlation between the factor and the distribution of the community and the distribution of the species. 2.13. Statistical analysis The R software (v 4.1.1) was applied to conduct bioinformatics analysis. The Wilcoxon test and t-test were utilized to evaluate paired sample comparisons, with statistical significance defined at P < 0.05. 3. Results 3.1. Differences in major bioactive compounds in A. sinensis between different production areasThe results demonstrated that a significant effect of authentic and near-authentic production areas on the principal bioactive compounds of A. sinensis. Specifically, 7 out of the 8 bioactive compounds exhibited significant differences (P < 0.05). Among these, ligustilide, coniferylferulate, 3-n-butylphthalide (NBP), and levistilide A were more abundant in the authentic production area ( Additional file1 ). Since A. sinensis is primarily used for its rhizome in medicine, we further analyzed changes in these 8 bioactive compounds during the rhizome expansion period. At this stage, 5 compounds showed significant variations between production areas, with only coniferylferulate being higher in the authentic region ( Additional file2 ). Interestingly, during the drug formation period, 6 compounds displayed signifciant differences, with ligustilide, coniferylferulate, NBP, and levistilide A again being more prevalent in the authentic production area, a notable contrast to the rhizome expansion period ( Additional file3 ). 3.2. Differences in soil properties in A. sinensis between different production areas Moreover, soil properties also varied between the production areas. Notably, S_SC, S_ALP, and EC exhibited significant differences (P < 0.05) between the authentic and the near-authentic production areas ( Additional file4 ), suggesting that soil properties might contribute to the observed variations in the principal bioactive constituents of A. sinensis. Soil physicochemical properties further differed across growth periods within the same production area. For instance, urease (P < 0.001) and AP decreased from stages 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 file5 ). 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, whereas AP decreased ( Additional file6 ). Collectively, these dynamic changes in soil properties highlight a strong correlation between soil physicochemical properties and the accumulation of active compound in A. sinensis. 3.3. DEGs and their pathways between different production regions Comparative analysis revealed that 63%-73% of the 18 sequenced samples were successfully matched ( Additional file7 ). Gene expression levels were consistent across all these 18 samples after annotation, confirming their suitability for subsequent downstream analyses ( Additional file8 ). A total of 2,367 differentially expressed genes (DEGs) were identified between groups A and B, with 964 up-regulated and 1,403 down-regulated DEGs, respectively (Fig. 1 a). Functional enrichment analysis showed that these DEGs were significantly associated with 8 GO entries (adjusted P < 0.05). primarily related to secondary metabolite biosynthesis, anther wall tapetum development, and anion transmembrane transport (Fig. 1 b). Additionally, 21 KEGG pathways were significantly enriched (adjusted P < 0.05), including cortisol synthesis and secretion, steroid hormone biosynthesis, and phenylpropanoid biosynthesis (Fig. 1 c). These findings suggest that the DEGs may play a critical role in the synthesis of those secondary metabolites in A. sinensis. Subsequently, we identified DEGs between different production regions during the same growth period. Specifically, a total of 2,791 DEGs (DEGs-AB5) were detected between A5 and B5, with 1,061 were up-regulated and 1,730 down-regulated genes in A5 (Fig. 1 d); 3,759 DEGs (DEGs-AB7) were identified between A7 and B7, with 1,502 were up-regulated and 2,257 down-regulated genes in A7 (Fig. 1 e); and 4,746 DEGs-AB10 between A10 and B10, with 1,492 were up-regulated and 3,254 were down-regulated genes in A10 (Fig. 1 f). Notably, the number of DEGs increased during the rhizome expansion (A7/B7) and drug formation (A10/B10) periods compared to earlier stages, suggesting these DEGs played a pivotal role in regulating metabolite synthesis in A. sinensis. Functional analysis revealed while the enriched pathways varied across growth periods, DEGs were consistently associated with the secondary metabolite synthesis. This trend became more pronounced as the plant developed: during in the rhizome expansion (DEGs-AB7) and drug formation (DEGs-AB10), DEGs were predominately enriched in the secondary metabolic process (GO) and phenylpropanoid biosynthesis (KEGG) (Fig. 1 g-l). These findings underscore that production region and growth stage collectively influence gene expression patterns, particularly those governing secondary metabolite production in A. sinensis. 3.4. DEGs and their pathways involved during different growth periods We identified distinct patterns of DEGs during different growth periods within each production area. In the authentic production area, A7 vs A5: 7,815 DEGs-A7 vs A5 (3,661 up-regulated and 4,154 down-regulated genes were expressed in A7) (Fig. 2 a), A10 vs A5: 9,185 DEGs-A10 vs A5 (3,442 up-regulated and 5,743 down-regulated genes in A10) (Fig. 2 b); A10 vs A7: 7,300 DEGs-A10 vs A7 (2,465 up-regulated and 4,835 down-regulated in A10) (Fig. 2 c). Notably, the authentic production area showed 1,615 shared DEGs across all growth periods, and the fewest unique DEGs (957) in the A10 vs A7 comparison (Fig. 2 d). In near-authentic production area: B7 vs B5: 8,744 DEGs-B7 vs B5 (3,051 up-regulated and 5,693 down-regulated genes in B7) (Fig. 2 e); B10 vs B5: 9,244 DEGs-B10 vs B5 (3,683 up-regulated and 5,561 down-regulated genes in B10) (Fig. 2 f); and 9,511 DEGs-B10 vs B7 (5,136 up-regulated and 4,375 down-regulated genes in B10) (Fig. 2 g). The near-authentic production area exhibited 1,738 DEGs across the growth periods (Fig. 2 h). Subsequent analysis revealed significant enrichment of key biosynthetic pathways, particularly secondary metabolite process and phenylpropanoid biosynthesis, across different growth periods in both the authentic and near-authentic production areas. Intriguingly, we observed concurrent enrichment of defense-related pathways during various growth stages including the response to chitin and MAPK signaling pathway involving in signal transduction. These findings ( Additional file9a-f, Additional file10 ) suggest that the secondary metabolite accumulation in A. sinensis may closely linked to activated defense mechanisms, mirroring the patterns observed in other medicinal species. 3.5. 5,063 module genes related to major bioactive compounds in A. sinensis Following a quality control verification, we performed weighted gene co-expression network analysis (WGCNA) on all samples from groups A and B ( Additional file11a ). The analysis identified an optimal soft threshold power of 26 (scale-free topology model fit, R² = 0.85, Additional file11b ), yielding 10 distinct co-expression modules (including the grey module of unassigned genes) ( Additional file11c, d ). Notably, the MEbrown module showed significant correlations (absolute value > 0.3, P < 0.05) with 5 key bioactive compounds (ligustilide, coniferylferulate, senkyunolide H, NBP, and Levistilide A). This module contained 5,063 genes potentially involved in the biosynthesis of these active compounds ( Additional file11e ). Further intersection analysis with DEGs between groups A and B identified a total of 463 key module genes functionally enriched in cytochrome P450 activity, steroid hormone biosynthesis, and cortisol synthesis and secretion pathways ( Additional file11f ). 3.6. Differences in ASV of bacteria between production areas The Alpha diversity assessment showed that the rarefaction curves plateaued with increasing sequencing depth ( Additional file12 ), and demonstrating that (1) Sequencing coverage adequately captured sample biodiversity, (2) Additional sequencing would not yield new ASVs and (3) Observed features stabilized, confirming comprehensive microbial species representation ( Additional file12 ). The analysis of bacterial ASVs revealed the core and unique microbiome features as 729 shared bacterial ASVs across production areas, 1,659 ASVs unique to the authentic production areas and 1,889 ASVs unique to the near-authentic production areas (Fig. 3 a). The diversity metric comparisons exhibited no significant differences (P > 0.05) in the 4 indexes (observed features index, Shannon index, Pielou's evenness (PD) index, and Evenness index) were observed between the production areas (Fig. 3 b). These results indicate comparable bacterial richness and community homogeneity across production areas. However, the results from the Beta diversity analysis showed significant compositional differences (Bray-Curtis distance, P < 0.01) and distinct community structures between different production areas (Fig. 3 c). At the phylum level, the top three most abundant microbial groups across different production areas were Actinobacteriota, Proteobacteria, and Chloroflexi. Compared to the near-authentic production area, the authentic production area exhibited a lower abundance of Actinobacteriota, but a higher abundance of Acidobacteriota (Fig. 3 d). At the genus level, the microbial community abundance was similar between production areas, with uncultured taxa being the most prevalent. However, Vicinamibacter increased in abundance while Nocardioides decreased in the authentic production areas relative to the near-authentic production areas (Fig. 3 e). Differential expression analysis further identified amplicon sequence variants (ASVs) contributing to community differences. A total of 417 ASVs were differentially abundant between production areas, with 199 up-regulated and 218 down-regulated ASVs in the authentic production area compared to the near-authentic production area. These shifts were primarily driven by Proteobacteria, Actinobacteriota, Chloroflexi, and Acidobacteriota caused community changes (Fig. 3 f, g). 3.7. Differences in ASV of bacteria during different growing periods We further analyzed changes or shifts in bacterial communities during different growth periods within the same production area. Analyses revealed that 123 ASVs were shared across all growth periods, while the rhizome expansion period (674 ASVs) and drug formation (672 ASVs) harbored similar unique ASVs, both significantly higher than the leaf clump period (368 ASVs) (Fig. 4 a). Alpha diversity varied most prominently between the leaf clump period and rhizome expansion periods, whereas no significant differences were observed in any of the four diversity indexes between the rhizome expansion and drug formation periods, highligting both the similarity and period specificity of the communities (Fig. 4 b). The Beta diversity analysis further distinguished these periods, with A5 (leaf clump) samples significantly separated from A7 (rhizome expansion) and A10 (drug formation), underscoring their compositional differences (Fig. 4 c). At the phylum and genus level, community abundance patterns were largely consistent across growth periods. Notably, the rhizome expansion and drug formation period exhibited particularly similar microbial profiles (Fig. 4 d, e). Differential expression analysis identified that a total of 264 significantly altered bacterial ASVs in A7 vs A5 (235 up-regulated and 29 down-regulated, Additional file13a ) and 241 ASVs in A10 vs A5 (212 up-regulated and 29 down-regulated, Additional file13b ). In contrast, A10 vs A7 showed fewer differential bacterial ASVs (61 in total: 24 up-regulated and 37 down-regulated, Additional file13c ). Strikingly, no ASVs were shared across all growth periods (Fig. 4 f), suggesting dynamic community restructuring. These shifts imply a potential linkage between rhizosphere microbiota and the accumulation of A. sinensis’s pharmacologically bioactive compounds, as evidenced by the progressive reduction in differential ASVs as growth advanced toward the drug formation period. In the near-authentic production area, 130 ASVs were shared across all growth periods, with the rhizome expansion and drug formation periods containing similarly higher numbers of unique ASVs compared to other periods (Fig. 5 a). Alpha diversity analysis revealed significant differences between the leaf clump period and both the rhizome expansion and drug formation periods across all indices except the Pielou's evenness. In contrast, no significant differences were observed between the rhizome expansion and drug formation periods (Fig. 5 b). Beta diversity analysis confirmed clear separation of microbial communities among growth periods (Fig. 5 c). While overall community structure remained similar across growth periods across growth periods, notable changes occurred at the genus level: the drug formation period showed an increased abundance of uncultured bacteria but decreased abundance of Nocardioides (Fig. 5 d, e). Differential expression analysis identified 327 significantly altered bacterial ASVs in B7 (rhizome expansion) vs B5 (leaf clump): 315 up-regulated and 12 down-regulated ( Additional file14a ), 319 differential bacterial ASVs in B10 (drug formation) vs B5: 299 up-regulated and 20 down-regulated ( Additional file14b ), and 233 differential bacterial ASVs in B10 vs B7, 98 up-regulated and 135 down-regulated ( Additional file14c ). Consistent with findings in the authentic production areas, no ASVs were shared across all growth periods in the near-authentic production areas, with particularly few ASVs persisting through both the rhizome expansion and drug formation periods (Fig. 5 f). These results demonstrated significant growth stage-dependent variation in A. sinensis bacterial communities. The particularly similar diversity and abundance patterns between the rhizome expansion and drug formation periods suggested these dominant bacterial communities might play important roles in plant development and secondary metabolite accumulation. 3.8. Variation of fungal ASVs between different production areas The ITS sequencing dilution curves confirmed adequate sequencing depth, demonstrating comprehensive coverage of fungal diversity ( Additional file15 ). Across the groups A and B, 481 ASVs were shared, with group A exhibiting more unique ASVs than the group B (Fig. 6 a). The Alpha displayed analysis revealed no significant differences in species richness and evenness between groups (all indices, P > 0.05, Fig. 6 b). In contrast, Beta diversity showed clear separation of fungal communities between groups (Fig. 6 c). Distinct from bacteria patterns, fungal communities differed notably at both the phylum and genus levels. Specifically in the group A: reduced abundances in: Bisifusarium, Longitudinalis and Verticillium, while increased abundance in Mycochlamys, Linnemannia and Mortierella, compared to the group B (Fig. 6 d, e). A total of 325 differentially expressed ASVs were identified (233 up-regulated and 92 down-regulated, in the group A vs B, prodominantly derived from Ascomycota (Fig. 6 f, g). 3.9. Differences in fungal community structure during different growth periods Core and unique ASV distribution analysis revealed 110 ASVs were shared across all growth periods, 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. 7 a). Notably, Alpha diversity showed no significant difference across the growth periods (P > 0.05 for all indices, Fig. 7 b). However, Beta diversity analysis revealed distinct community structures between periods (Fig. 7 c). At the phylum level, a progressive decline in Ascomycota abundance as the growth was progressed from A5 to A10 (Fig. 7 d). At the genus level, Mycochlamys’ abundance was highest in the A5 but significantly decreased in the A7 and A10 samples. The A7 and A10 showed some similarity, with notable increases in Mortierella, Plectosphaerella, and Tetracladium, but a decrease in Tausonia in the A10 samples (Fig. 7 e). Significant ASV changes were observed between periods as for A7 vs A5: 125 differential fungal ASVs (27 up-regulated and 98 down-regulated, Additional file 16a ), for A10 vs A5: 160 differential fungal ASVs (59 up-regulated and 101 down-regulated, Additional file 16b ), and for A10 vs A7: 32 fungal ASVs (23 up-regulated and 9 down-regulated, Additional file 16c ). Based on the above-mentioned results, the intersection analysis confirmed no fungal ASVs were shared across all growth periods. Mirroring the bacterial trends, endemic ASVs were gradually decreased as growth progressed, with only five unique ASVs remaining in A10 (Fig. 7 f). A total of 93 fungal ASVs were shared across all growth periods in the near-authentic production area. Notably, B10 samples contained the fewest unique ASVs (Fig. 8 a). Diversity analysis revealed no significant differences were observed in all four alpha diversity indices between B5 and B7 periods, while significant differences in Shannon's evenness and Pielou's evenness between B7 and B10 (Fig. 8 b), and a clear separation between B5 and B10 samples in beta diversity (Fig. 8 c). While the phylum-level composition remained relatively stable across the B5, B7 and B10 samples (Fig. 8 d), notable genus-level differences emerged as Bisifusarium and Longitudinalis became significantly more abundant in the fungal community during the B10 period (Fig. 8 e). In addition, significant fungal ASVs changes between growth periods included for B7 vs B5, 120 differential ASVs (40 up-regulated and 80 down-regulated, Additional file 17a ), for B10 vs B5, 141 differential ASVs, (21 up-regulated and 120 down-regulated, Additional file 17b ), and for B10 vs B7, 90 differential ASVs (12 up-regulated and 78 down-regulated, Additional file 17c ). The intersection analysis identified four shared ASVs maintained across all three growth periods, while similar numbers of period-specific ASVs for each the growth period (Fig. 8 f). In summary, these results demonstrated that fungal community structure undergoes significant reorganization during the A. sinensis growth, The drug formation period shows particularly distinct compositional changes, the key genera (Bisifusarium, Longitudinalis) may play important roles in secondary metabolite production, and the small core microbiome (4 ASVs) suggests strong environmental or host-mediated selection. 2.10. Bacterial and fungal ASVs associated with the main active ingredient of A. sinensis All bacterial ASVs from the group A and B were included in WGCNA after quality control verification ( Additional file 18a ). Network construction obtained optimal soft threshold power of 7 (scale-free topology fit R 2 = 0.85, Additional file 18b ), 6 co-expression modules through hierarchical clustering ( Figures S16 c, d ). The MEyellow module (containing 417 bacterial ASVs) showed strongest correlations with principal bioactive components in A. sinensis ( Additional file 18e ). Similarly, all fungal samples met inclusion criteria ( Additional file 18f ), and the fungal network was constructed with soft threshold of 12 (R 2 = 0.85) ( Additional file 18g ), and 6 modules including unclassified gray modules ( Additional file 18h, i ). Finally, the MEturquoise module (325 fungal ASVs) demonstrated strongest associations with the most active components in A. sinensis ( Additional file 18j ). Combining differentially expressed genes (DEGs) and WGCNA results, a comprehensive co-expression network encompassing was constructed with the DEGs from A. sinensis, key pharmaceutically bioactive 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. 9 a), revealing the complex relationship among the 4 elements. These results demonstrated a tight integration between microbial communities and plant metabolite production; revealed predominance of positive interactions in the microbiome-metabolome network; and identified specific bacterial (417 ASVs) and fungal (325 ASVs) modules that strongly associated with medicinal compound accumulation. 2.11. 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 significantly 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. 9 b). Six functional categories showed significant variation across growth periods (P < 0.05), with primary differences in fermentation, human pathogens all, and human pathogens pneumonia occurring between A5 vs A7 or vs A10 (Fig. 9 c). However, the most pronounced functional differences emerged in the near-authentic production area, with 9 significantly altered pathways (P < 0.05), such as the aerobic chemoheterotrophy with the highest relative abundance, human pathogen all, fermentation, dark hydrogen oxidation, nitrogen fixation and aromatic hydrocarbon degradation across all growth periods (Fig. 9 d). These functional differences highlighted distinct biogeochemical processing capabilities between production areas, period-specific microbial functions during plant development, Potential pathogen pressure variations across growth periods and the critical role of chemoheterotrophic metabolism in the rhizosphere soil interactions. 2.12. Positive correlation between soil physicochemical properties and the bioactive compounds of A. sinensis Correlation analysis identified significant relationships between soil physicochemical properties and bioactive compounds in A. sinensis (Fig. 9 e, Additional file19 ). The key findings were as follows. Soil enzymes (S_POD, S_ACP, S_NP), organic matter, and available potassium (AK) displayed strongly positive correlations with most bioactive compounds, and AK exhibited the strongest positive correlation with Levistilide A (r = 0.96, P < 0.0001). Available phosphorus (AP) was negatively correlated with coniferylferulate (r = -0.83, P < 0.001), which also showed negative correlations with most soil physicochemical properties. Besides, the ferulic acid significantly positively correlated 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. 9 e, Additional file19 ). These correlations provided valuable insights for promoting cultivation strategies to enhance target metabolite production in A. sinensis. The redundancy analysis (RDA) revealed distinct associations between soil physicochemical properties and microbial communities across different production areas. These associations showed positive correlations between both bacterial and fungal communities and S_ALP, TN or organic matter in the authentic production area, between bacterial communities and EC, AP or urease in the near-authentic production area, between fungal communities and S_SC, EC, AP, and urease in the near-authentic production area (Fig. 9 f, g). These findings suggest that soil characteristics differentially shape microbial communities in each production area, potentially influencing nutrient acquisition efficiency, secondary metabolite biosynthesis and medicinal compound accumulation in major bioactive compounds in A. sinensis. 4. Discussion 4.1. Primary bioactive compounds in A. sinensis are strongly influenced by its cultivation locations A. sinensis is a critically important medicinal plant, with phthalides, coumarins, lignans, and terpenoids as its primary bioactive compounds, which are strongly influenced by its cultivation location [ 6 ]. Our findings reveal significant differences in seven of the eight major active compounds of between the authentic and near-authentic cultivation areas. Among these key bioactive compounds, ligustilide, ferulic acid, NBP, and levistilide A are well-documented for their therapeutic roles [ 44 – 46 ]. Zhang et al. [ 5 ] reported that the geoherb samples contained significantly higher levels of senkyunolide I and NBP were compared to non-geoherb samples. Our findings align with this, showing elevated NBP levels in genuine cultivation regions. However, senkyunolide I exhibited an opposite trend, likely due to the relatively minor microenvironment differences between the genuine and nearby regions compared to non-genuine regions. Additionally, coniferylferulate, a phenolic compound with poor stability, readily converts into ferulic acid and coniferyl alcohol in A. sinensis and can interconvert with ferulic acid [ 35 ]. Thus, the sum of ferulic acid and its derivatives from coniferylferulate conversion is considered “total ferulate” [ 1 , 35 ]. In this study, the total ferulate was higher in the authentic cultivation regions than in nearby areas. Collectively, the significantly higher levels of ligustilide, total ferulic acid, NBP, and levistilide A in genuine A. sinensis may serves as key markers of its superior quality compared to the near-authentic samples. These findings support previous research underscoring the critical impact of cultivation region on A. sinensis quality [ 44 – 46 ]. Transcriptome analysis has proven valuable in elucidating gene regulatory network linked to the biosynthesis of bioactive compounds in traditional Chinese medicines (TCM) [ 18 , 22 ]. It also helps clarify the mechanisms underlying variations in the production and accumulation of major bioactive compounds across different tissues of A. sinensis root [ 47 ]. In this study, we identified numerous differentially expressed genes (DEGs) across different production areas and growth stages within the same production region. These DEGs are predominantly enriched in critical pathways including secondary metabolite biosynthesis and phenylpropanoid biosynthesis. The phenylpropanoid biosynthesis pathway directly contributes to the synthesis of phenolic and flavonoids, which are crucial for regulating the production of pharmacologically active compounds [ 48 ]. Notably, this pathway is also directly involved in the ferulic acid biosynthesis [ 38 ]. In addition, defense-related activation pathways including the chitin response may further stimulate the accumulation of secondary metabolites [ 12 , 49 ]. Interestingly, relatively fewer unique DEGs were detected during the root rhizome expansion stage and the medicinal maturity stage, suggesting that these genes may play a specialized role in active compound accumulation [ 50 , 51 ]. A key finding was the pronounced upregulating of cytochrome P450 enzymes, which mediate the furanocoumarin biosynthesis in A. sinensis from its authentic production area, particularly during the rhizome expansion period [ 42 ]. During this phase, genes associated with the secondary metabolite biosynthesis, phenylpropanoid biosynthesis, and cytochrome P450 were all significantly upregulated. In contrast, during the drug formation stage, most genes, except genes associated with chaperones, folding catalysts, and starch/sucrose metabolism, were markedly downregulated. We hypothesize that the elevated bioactive compound levels in authentic A. sinensis may result from the strong upregulation of biosynthesis genes during root enlargement. However, further research is needed to validate this mechanism. 4.2. Bacterial community dynamics and their impact on A. sinensis quality Root-associated microbiomes play a crucial role in the biosynthesis [ 11 , 12 ] and accumulation of specialized metabolites in medicinal plants [ 13 , 52 ]. Our analysis revealed that the bacterial community structure of A. sinensis remained highly consistent across different growth stages in authentic producing areas, with a progressive decline in the unique ASVs as the plant matures. Notably, key bacterial taxa, such as Acidobacteriota, Firmicutes, Vicinamibacter, and Bacillus, were significantly more abundant in the authentic producing regions compared to nearby areas. These bacterial groups are known to promote plant growth and suppressing plant pathogenic infections [ 8 , 53 ]. Strikingly, Bacillus has been demonstrated to enhance promote phthalide biosynthesis, specifically increasing butylidenephthalide accumulation in A. sinensis [ 54 ]. In contrast, Nocardioides was more abundant in the nearby producing area than in the authentic producing region, and its prevalence decreased during the A. sinensis growth in the authentic soils. This finding suggests that the indigenous bacterial composition of in authentic producing areas fosters a more favorable environment for both the growth and phytochemical quality of A. sinensis [ 31 , 55 ]. 4.3. Fungal community dynamics and their impact on A. sinensis quality For the 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 even more pronounced than bacterial variations. Previous studies have established that dominant phyla including Ascomycota, Mortierellomycota, and Basidiomycota play crucially ecological roles by enhancing soil nutrient availability [ 56 , 57 ], bolstering plant pathogen resistance [ 39 , 57 , 58 ], and promoting plant growth [ 56 , 59 ]. While Ascomycetes include many plant pathogens [ 57 ], they may also serves as key elicitors that stimulate medicinal plants to produce secondary metabolites [ 49 , 60 ]. The equilibrium of microbial taxonomic composition represents a critical factor influencing both plant fitness and secondary metabolite production [ 61 ], suggesting this balance directly affects phytochemical accumulation. For the fungal genera associated with authentic producing areas, several beneficial genera including Mycochlamys, Linnemannia, Mortierella, Tausonia, and Tetracladium, were more abundant in the authentic producing areas. These fungi contribute to plant growth promotion [ 59 , 62 ], root pathogens suppression [ 39 ], and enhanced medicinal compound production [ 48 ]. Notably, Mycochlamys, shows positive correlation with the ferulate acid accumulation [ 7 ]. Although it may facilitate Fusarium root invasion, no direct pathogenicity towards A. sinensis roots has been demonstrated [ 20 ]. In contrast, the near-authentic producing areas exhibited higher abundances of known pathogenic genera including Bisifusarium, Plectosphaerella, Longitudinalis, Verticillium, and Cladosporium. These are known plant pathogens affecting medicinal plants including A. sinensis, Panax ginseng and Withania somnifera [ 63 , 64 ], particularly during the medicinal growth stage. These findings demonstrate that the authentic producing areas maintain fungal communities more conducive to plant health and medicinal compound accumulation, while the near-authentic regions harbor higher proportions of pathogenic taxa that compromise plant fitness. These results align with and extend previous research on A. sinensis microbiome ecology [ 16 , 20 , 65 ]. Given that the established importance of stochastic processes in rhizosphere bacterial and fungal community assembly [ 31 , 66 ], we thus propose that the naturally selected microbial consortia in the authentic producing areas represent an optimized microbiome for producing high-quality A. sinensis. 4.4. Soil Properties and Their Impact on A. sinensis Quality Extensive research confirms that soil serves as the fundamental source of essential nutrients for plant growth, with its edaphic conditions directly influencing medicinal metabolic pathways and modulating bioactive compound production [ 30 , 67 ]. Our study reveals three key soil parameters, e.g. S_SC, S_ALP and EC, that show significant differences between the authentic and near-authentic production areas of A. sinensis. However, the most pronounced variations in soil characteristics emerged across different growth stages within both areas, suggesting substantial differences in growth patterns and metabolic activities between the authentic and near-authentic cultivated A. sinensis, and consequent modifications in rhizosphere soil properties, including physicochemical properties, nutrient profiles, and enzyme activities [ 68 , 69 ]. The microbial community dynamics consistent with these observations as follows. Bacterial and fungal α-diversity and richness show no significant spatial variations between regions. However, significant variations occurred cross growth stages in both regions, and these patterns parallel the observed physicochemical and nutrient gradients in soils. These align with established knowledge that variations in soil physicochemical properties, nutrient availability and enzyme activities that directly shape microbial community structure and function [ 70 , 71 ]. A total of 12 critical soil indicators demonstrated positive correlations with major pharmacologically active compounds in A. sinensis, including S_POD, S_ACP, S_NP, organic matter, TN, AP, and AK. Notably, four key bioactive compounds including ligustilide, ferulic acid, NBP, and Levistilide A exhibited particularly strong correlations with these soil properties. Furthermore, for the quality determinants in production areas, three soil quality parameters like S_ALP, TN, and organic matter exhibited both positive correlation with the authentic production areas and potential to influence medicinal compound accumulation. These likely mediate their effects through modulating nutrient uptake efficiency and shaping beneficial microbial communities [ 7 , 16 ]. These findings have ecological and agricultural implications by validating previous reports on microbial roles in agroecosystems bioactive compounds [ 4 , 10 , 41 , 65 ], demonstrating that microbes regulate soil fertility maintenance, plant growth promotion and bioactive compound synthesis. All of these can provide actionably management strategies for quality enhancement through targeted soil property modification and microbial community regulation. 4.5. Limitations and Future Perspectives This study integrates multi-omics approaches to elucidate the relationships among soil properties, microbial communities, and gene expression in A. sinensis, offering a foundational framework for developing region-specific sustainable production practices. However, several limitations should be acknowledged. First, the cultivation of A. sinensis from geo-authentic (daodi) and near-geo-authentic (near-daodi) regions in distinct soil types without cross-replication may introduce confounding factors, potentially compromising the accuracy of the results. Second, although key co-occurrence patterns and underlying mechanisms were identified, the findings do not establish direct causal relationships due to the observational nature of the study. To address these gaps, future research should prioritize controlled field experiments with the following designs: (1) systematic cultivation of A. sinensis genotypes (both daodi and near-daodi) across multiple replicated soil types; and (2) active manipulation of soil conditions and microbial assemblages to validate causal linkages. Such efforts would not only enhance the consistency of medicinal plant quality across diverse cultivation regions but also contribute to standardized, sustainable agricultural protocols for A. sinensis and other economically important species. 5. Conclusions We conducted the first comprehensive investigation into the differential gene expression in Angelica sinensis and the rhizosphere soil microbial community structure between its authentic production region and a near-authentic region, across different growth periods. Utilizing transcriptome sequencing, 16S rRNA sequencing, and ITS sequencing we performed a multi-omics analysis. Furthermore, we analyzed the correlation between soil physicochemical properties and the content of major pharmacologically bioactive compounds in A. sinensis. Our findings indicate that rhizosphere microbial (bacteria and fungi) communities likely influence nutrient absorption, thereby affecting the levels of key active compounds in the plant. Overall, the integrated multi-omics approach provides novel insights into the molecular basis for quality differences in A. sinensis between the authentic and non-authentic regions, and enhances our understanding of how regional factors and growth stages impact the bioactive compounds content. These discoveries could significantly advance strategies for promoting the accumulation of pharmacodynamic compounds and improving the medicinal quality of A. sinensis cultivated in both authentic and non-authentic regions. Abbreviations The following abbreviations are used in this manuscript (List them in an alphabetical order) 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. Clinical Trial number Not applicable. Consent for publication: Not applicable. Competing interest: The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. 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]; and Intellectual Property Plan Project Gansu Province of China [grant number, 22ZSCQ037]. Author Contribution 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. Acknowledgement We would like to express our sincere gratitude to all individuals and organizations who supported and assisted us throughout this research. Special thanks to the following authors: Zengxiang Guo (Min County Angelica sinensis research institute), Shifeng Xu (Agricultural technology extension center of Awu Town, Tanchang County), Jun Luo (Gansu University of Chinese Medicine), Qianqian Tong (School of life science of Lanzhou University), Ting Mao and Yanhao Fang (Institute of Biology, Gansu Academy of Sciences). In conclusion, we extend our thanks to everyone who has supported and assisted us along the way. Without your support, this research would not have been possible. 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(\u003cstrong\u003ea\u003c/strong\u003e) shared and specific ASV analysis; (\u003cstrong\u003eb\u003c/strong\u003e) alpha diversity difference analysis; (\u003cstrong\u003ec\u003c/strong\u003e) PCoA clustering diagram; (\u003cstrong\u003ed\u003c/strong\u003e) bar stacking diagram in phylum level; (\u003cstrong\u003ee\u003c/strong\u003e) in genus level; (f) differences ASV shared analysis.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7812601/v1/ae597044b15b808f906eddeb.png"},{"id":96708667,"identity":"ec670292-9546-4df7-9bb6-c26a1e48b20a","added_by":"auto","created_at":"2025-11-25 10:05:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":6762132,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential analysis of bacterial communities in group B during different growth periods. (\u003cstrong\u003ea\u003c/strong\u003e) shared and specific ASV analysis; (\u003cstrong\u003eb\u003c/strong\u003e) alpha diversity difference analysis; (\u003cstrong\u003ec\u003c/strong\u003e) PCoA clustering diagram; (\u003cstrong\u003ed\u003c/strong\u003e) bar stacking diagram in phylum level; (\u003cstrong\u003ee\u003c/strong\u003e) in genus level; (f) differences ASV shared analysis.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7812601/v1/7ea00ef75da83fd65c5a2167.png"},{"id":96708906,"identity":"6029817e-9c3a-4e47-9a9b-7027835862eb","added_by":"auto","created_at":"2025-11-25 10:06:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":9342165,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential analysis of fungal microbial communities (A/B). (\u003cstrong\u003ea\u003c/strong\u003e) shared and specific ASV analysis;(\u003cstrong\u003eb\u003c/strong\u003e) alpha diversity difference analysis; (c) PCoA clustering diagram; (\u003cstrong\u003ed\u003c/strong\u003e) bar stacking diagram in phylum level; (\u003cstrong\u003ee\u003c/strong\u003e) in genus level; (\u003cstrong\u003ef\u003c/strong\u003e) volcano diagram for differential ASV; (\u003cstrong\u003eg\u003c/strong\u003e) manhattan diagram for differential ASV.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7812601/v1/a6e90f69951d172a8dd9a4d9.png"},{"id":96708998,"identity":"5ae4c119-20c3-4af1-8b22-53b92c1f4f02","added_by":"auto","created_at":"2025-11-25 10:07:03","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":6158270,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential analysis of fungal communities in group A with different growth periods. (\u003cstrong\u003ea\u003c/strong\u003e) shared and specific ASV analysis;(\u003cstrong\u003eb\u003c/strong\u003e) alpha diversity difference analysis; (\u003cstrong\u003ec\u003c/strong\u003e) PCoA clustering diagram (A); (\u003cstrong\u003ed\u003c/strong\u003e) bar stacking diagram in phylum level; (\u003cstrong\u003ee\u003c/strong\u003e) in genes level; (\u003cstrong\u003ef\u003c/strong\u003e) volcano diagram for differential ASV in A7-A5; (\u003cstrong\u003eg\u003c/strong\u003e) in A10-A5; (\u003cstrong\u003eh\u003c/strong\u003e) in A10-A7; (\u003cstrong\u003ei\u003c/strong\u003e) differences ASV shared analysis.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-7812601/v1/37ab24caa523cd66fc891c88.png"},{"id":96629816,"identity":"dd6b197c-d125-4b47-8976-f8cd56f81f8c","added_by":"auto","created_at":"2025-11-24 12:31:31","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":6720234,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential analysis of fungal communities in group B with different growth periods. (\u003cstrong\u003ea\u003c/strong\u003e) shared and specific ASV analysis;(\u003cstrong\u003eb\u003c/strong\u003e) alpha diversity difference analysis; (\u003cstrong\u003ec\u003c/strong\u003e) PCoA clustering diagram; (\u003cstrong\u003ed\u003c/strong\u003e) bar stacking diagram in phylum level; (\u003cstrong\u003ee\u003c/strong\u003e) in genus level; (\u003cstrong\u003ef\u003c/strong\u003e) volcano diagram for differential ASV in B7-B5; (\u003cstrong\u003eg\u003c/strong\u003e) in B10-B5; (\u003cstrong\u003eh\u003c/strong\u003e) in B10-B7; (\u003cstrong\u003ei\u003c/strong\u003e) differences ASV shared analysis.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-7812601/v1/ee7ea7e2529eeefd478f2da2.png"},{"id":96708637,"identity":"8c5c15c7-bde1-42b9-bb2a-c86f128a5232","added_by":"auto","created_at":"2025-11-25 10:04:54","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":11460560,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation analysis between soil properties, microbesand active compounds content of A. sinensis. (\u003cstrong\u003ea\u003c/strong\u003e) 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; (\u003cstrong\u003eb\u003c/strong\u003e) 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.; (\u003cstrong\u003ec\u003c/strong\u003e) functional difference analysis of different growth periods in group A, the same letter indicates a difference not significant, the different letter between two indicates a significant difference; (\u003cstrong\u003ed\u003c/strong\u003e) functional difference analysis of different growth periods in group B; (\u003cstrong\u003ee\u003c/strong\u003e) 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; (\u003cstrong\u003ef\u003c/strong\u003e) RDA diagram of soil physical and chemical properties between bacterial community; (\u003cstrong\u003eg\u003c/strong\u003e) between fungal communities.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-7812601/v1/41b7ff86cdb0326d998276b8.png"},{"id":96629801,"identity":"1f8bffe9-da64-4ac8-a63b-364bb9ba15a7","added_by":"auto","created_at":"2025-11-24 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12:31:32","extension":"tif","order_by":18,"title":"","display":"","copyAsset":false,"role":"supplement","size":8009264,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile9.tif","url":"https://assets-eu.researchsquare.com/files/rs-7812601/v1/5ffa69f3fef0e9d1a9933316.tif"},{"id":96629835,"identity":"8598bf3b-db64-4e28-b94a-f44a1f805096","added_by":"auto","created_at":"2025-11-24 12:31:31","extension":"jpg","order_by":19,"title":"","display":"","copyAsset":false,"role":"supplement","size":18853,"visible":true,"origin":"","legend":"","description":"","filename":"Additonalfile21.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7812601/v1/4dc7e3538115799b4413f178.jpg"},{"id":96629842,"identity":"2ba40403-b2aa-41a3-bf6b-d69033e19767","added_by":"auto","created_at":"2025-11-24 12:31:31","extension":"jpg","order_by":20,"title":"","display":"","copyAsset":false,"role":"supplement","size":18378,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile20.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7812601/v1/accfb766276e69ca997457ed.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-omics Insights into the Effects of Region and Growth Period on the Bioactive Compounds of Angelica sinensis (Oliv.) Diels","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAngelica sinensis (Oliv.) Diels (A. sinensis, Danggui in Chinese), a perennial herbaceous plant belonging to the Apiaceae family [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], is a medicinally important herb cultivated primarily in Asia, Africa, and certain regions of South America [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The key biobioactive compounds in A. sinensis include organic phenolic acids (predominantly ferulic acid) and volatile oils (notably ligustilide) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In traditional Chinese medicine (TCM), A. sinensis is widely used to promote blood circulation, tonify blood, regulate menstruation and relieve menstrual pain, support intestinal motility. It has also been employed in managing vascular cognitive impairment [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn China, A. sinensis is predominantly cultivated in Gansu, Qinghai, Sichuan, and Yunnan provinces. Among these, Min County in Gansu is historically regarded as the authentic production region (daodi in Chinese), renowned as for the superior pharmacological quality and consistent therapeutic efficacy of its A. sinensis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, the geographic expansion of cultivation into adjacent regions has prompted concerns regarding potential variations in the growth characteristics of A. sinensis and the stability of its bioactive constituents, particularly ferulic acid and other pharmacologically active compounds [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These observations underscore the critical need to elucidate the influence of regional environmental conditions and seasonal fluctuations on key metabolic pathways. Such investigations are essential not only for optimizing the standardization of traditional Chinese medicine (TCM) formulations but also for establishing ecologically sustainable cultivation practices [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe rhizosphere microbiome, often referred to as a plant\u0026rsquo;s \u0026ldquo;second genome\u0026rdquo; [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], plays an indisputable role in mediating complex root-microbe-soil interactions that influence plant health, stress resilience, and productivity [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Emerging evidence highlights that microbiome\u0026rsquo;s dual regulatory effects on plant systems, not only modulating primary physiological processes but also intricately interfacing with secondary metabolic pathways responsible for synthesizing pharmacologically active compounds in medicinal plants such as Salvia miltiorrhiza, Eucommia ulmoides, and Panax ginseng [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Furthermore, the rhizosphere microbial community acts as a keystone biological regulator, maintaining plant nutritional homeostasis and developmental stability across ontogenetic stages [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Metagenomic analyses reveal that root-associated microbiota undergo stage-specific restructuring in synchrony with phenological transitions, reflecting dynamic host-microbe co-adaptation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHowever, most existing studies focus on single regions or static comparisons, neglecting the spatiotemporal dynamics of growth and development [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Deciphering the spatiotemporal dynamics and metabolic plasticity of rhizosphere microbiomes at critical phenological junctures (e.g., flowering initiation, fruit setting) holds transformative potential [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Such insights could enable targeted microbiome engineering to optimize soil-plant-microbe feedback loops, a cornerstone for developing precision agroecosystems with enhanced nutrient use efficiency and reduced chemical inputs [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Over the past decade, the high-throughput sequencing (HTS) has revolutionized our comprehension of plant-associated microbiomes, providing unprecedented insights into microbial community structure and its influence on plant health [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Meanwhile, transcriptomics, captures crucial host transcriptional responses, revealing interactions between host processes and microbial functions [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This knowledge facilitates the real-time modulation of plant-microbial interactions in response to environmental fluctuations [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These multi-omic approaches have also successfully delineated the evolutionary and regulatory mechanisms of key metabolic genes involved in the bioactive compound synthesis [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite growing studies on A. sinensis including studies on the tissue-specific pharmacological components [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], comparative transcriptomics of wild and cultivated roots [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], the molecular basis of early bolting [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and the evolution of coumarin biosynthesis moleculer mechanism [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], a critical knowledge gap remains. Such as, (1) decipher the comparative analysis of rhizosphere microbiota between the authentic (daodi) and adjacent production areas to identify unique rhizosphere microbial assemblages associated with geo-authentic medicinal material formation, and (2) limited understanding of the co-dynamics of microbiota and metabolome throughout the entire growth cycle. Therefore, it is particularly important to use multi-omics strategies to study the differences in rhizosphere microbial communities during authentic formation and their potential regulatory mechanisms on modulating the biosynthesis of pharmacologically active compounds.\u003c/p\u003e\u003cp\u003eIn this study, we employed an integrated multi-omics approach combining transcriptomic, 16S rRNA gene, and ITS sequencing analyse to investigate A. sinensis from different production regions. Our study had two principal objectives. (1) Comparative analysis of gene expression and microbial communities: To systematically characterize gene expression differences in A. sinensis between its authentic (daodi) regions and adjacent production regions; and identify key differentially expressed genes (DEGs), bacterial and fungal taxa across distinct growth stages of A. sinensis. (2) Research on the underlying mechanisms of regulatory networks: To analyze the differential genes in A. sinensis from diverse geographical origins and developmental stages, alongside the interactions between rhizosphere bacteria and fungi, to further explore potential regulatory mechanisms. By addressing these objectives, our study could provide noval insights into the molecular and microbial factors contributing to variations in medicinal compound biosynthesis, The expected findings would serve as a valuable reference for understanding the geographical determinants of A. sinensis\u0026rsquo;s pharmacological quality, ultimately advancing research on this important traditional Chinese medicine.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Sample collection and definition grouping\u003c/h2\u003e\u003cp\u003eThe samples of authentic A. sinensis (group A) and near-authentic A. sinensis (group B) were obtained from experimental base of the Min County Angelica sinensis research institute of Dingxi City (N34\u003csup\u003e◦\u003c/sup\u003e31\u0026rsquo;22\u0026rdquo;, E104\u003csup\u003e◦\u003c/sup\u003e28\u0026rsquo;50\u0026rdquo;), and experimental base of the Tanchang agricultural technology extension center of Longnan City, (N34\u003csup\u003e◦\u003c/sup\u003e3\u0026prime;35\u0026Prime;, E104\u003csup\u003e◦\u003c/sup\u003e14\u0026prime;8\u0026Prime;), respectively, Gansu Province, Northwest China. Besides, the A. sinensis seedlings transplanted from both two experimental sites were identified by Dr. Zengxiang Guo, researcher at the Min County Angelica sinensis research institute, as \u0026ldquo;Min Gui No.1\u0026rdquo;. In detail, the underground roots of A. sinensis over their growth (April-November) were collected at 3 periods: (1) the leaf clump period (May 28th, A5/B5), (2) the rhizome expansion period (July 28th, A7/B7), and (3) the period of drug formation (October 18th, A10/B10). The systematic sampling of each experimental field at each point of period was performed by using the 5-point S method. Six plants were randomly chosen at each point, and a total of 30 plants were then put into three groups to act as three replicates of a single sample. In sampling, whole plants of A. sinensis, along with rhizosphere soil, were gently dug up, put in sterile self-sealing bags, labeled, and transported in an ice box with ice packs. The A. sinensis root were cut into two parts; one part was quickly frozen in liquid nitrogen and kept in a -80 ℃ freezer until transcriptome sequencing. The other part was dried in the air and shade conditions to analyze the medicinal compounds. The rhizosphere soil samples were similarly divided into two portions: one portion was stored in -80 ℃ sterile self-sealing bags until high-throughput sequencing, and the other portion was dried naturally in the air to determine soil physicochemical properties, soil nutrients, and soil enzymes. A. sinensis is among the most important economic crops Min County and the adjacent counties in Gansu Province. It is the pillar of the economy of the area and the primary income of medicinal herb farmers in the region.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Determinations of major bioactive compounds of A. sinensis\u003c/h2\u003e\u003cp\u003eThe major bioactive compounds of A. sinensis (shade-dried and sieved through a 0.074 mm mesh sieve) in both the authentic and near-authentic production areas were analyzed using a high-performance liquid chromatography system. Specifically, the chromatography was performed under the following conditions: MerkRP-C18 (250.0 mm x 4.6 mm, 5 um) column, acetonitrile (B)-1% acetic acid (A) mobile phase with gradient elution, detection wavelength at 280 nm, column temperature at 30 ℃, flow rate at 1mL/min, and injection volume at 20 Ul [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Next, the Wilcoxon test was employed to compare the differences in the 8 bioactive compounds of A. sinensis in groups A and B, namely ligustilide, ferulate, coniferylferulate, senkyunolide I, senkyunolide H, senkyunolide A, 3-n-butylphthalide (NBP), and levistilide A. Then, the ggplot2 package (v 3.3.2) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] was employed to visualize the results. Afterward, based on groups A and B, the differences in these bioactive compounds of A. sinensis at different growth periods were also compared (A7 vs B7, A10 vs B10), respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Determinations of soil physicochemical properties, soil nutrients and soil enzyme\u003c/h2\u003e\u003cp\u003eSoil (air-dried and sieved through a 0.149 mm mesh sieve) physicochemical properties including pH, electrical conductivity (EC), organic matter, total nitrogen (TN), available phosphorus (AP), available potassium (AK), and soil enzyme activities (urease, catalase (S_POD), sucrase (S_SC), acid phosphatase (S_ACP), neutral phosphatase (S_NP), and alkaline phosphatase (S_ALP) were analyzed as follows. Soil pH and EC were detected by a pH meter and a conductivity meter, respectively, using water to soil ratio of 2:5. Organic matter was determined by the H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e-K\u003csub\u003e2\u003c/sub\u003eCr\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e7\u003c/sub\u003e oxidation method [[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. TN was determined by the Kjeldahl method [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Available Phosphorus (AP) was determined by the molybdenum-antimony colorimetric method. AK was detected by the fame photometry method [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Urease was quantified using the sodium phenol-sodium hypochlorite colorimetric method. S_POD was measured by an ultraviolet spectrophotometry. 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 [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Sample sequencing\u003c/h2\u003e\u003cp\u003eTotal RNA of samples was extracted from the tissue of A. sinensis root using TRIzol\u0026reg; Reagent. The RNA quality was assessed by 5300 Bioanalyser (Agilent) and quantified using the ND-2000 (NanoDrop Technologies). Only the RNA sample (OD260/280\u0026thinsp;=\u0026thinsp;1.8\u0026thinsp;~\u0026thinsp;2.2, OD260/230\u0026thinsp;\u0026ge;\u0026thinsp;2.0, RQN\u0026thinsp;\u0026ge;\u0026thinsp;6.5, 28S:18S\u0026thinsp;\u0026ge;\u0026thinsp;1.0, \u0026gt;\u0026thinsp;1\u0026micro;g) was used to construct sequencing library. Library preparation and sequencing RNA purification, reverse transcription, library construction, and sequencing were performed at Shanghai Majorbio Bio-pharm Biotechnology Co., Ltd. (Shanghai, China). The RNA-seq transcriptome library of A. sinensis was prepared following Illumina\u0026reg; Stranded mRNA Prep, Ligation (San Diego, CA) using 1\u0026micro;g of total RNA. Shortly, messenger RNA was isolated according to the poly A selection method by oligo (dT) beads and fragmented by fragmentation buffer first. Secondly, double-stranded cDNA was synthesized using a SuperScript double-stranded cDNA synthesis kit (Invitrogen, CA) with random hexamer primers. Then the synthesized cDNA was subjected to end-repair, phosphorylation, and adapter addition according to the library construction protocol. Libraries were size selected for cDNA target fragments of 300 bp on 2% Low Range Ultra Agarose followed by PCR amplified using Phusion DNA polymerase (NEB) for 15 PCR cycles. After quantified by Qubit 4.0, the sequencing library was performed on the NovaSeq X Plus platform (PE150) using the NovaSeq Reagent Kit. OR the sequencing library was performed on DNBSEQ-T7 platform (PE150) using DNBSEQ-T7RS Reagent Kit (FCL PE150) version 3.0.\u003c/p\u003e\u003cp\u003eAt the same time, the soil samples (stored at -80\u0026deg;C) of A. sinensis was employed for 16S ribosomal RNA (16S rRNA) and internally transcribed spacer (ITS) sequencing. Specifically, Genomic DNA was extracted from 0.25 g of A. sinensis rhizospheric soil samples using the E.Z.N.A. \u0026reg; soil DNA Kit (Omega Bio-tek, Norcross, GA, USA) based on the instruction of the manufacturer. The quality and concentration of DNA were measured by 1.0% agarose gel electrophoresis and a NanoDrop\u0026reg; ND-2000 spectrophotometer (Thermo Scientific Inc., USA) and stored at \u0026minus;\u0026thinsp;80\u0026deg;C before further use. The hypervariable region (V3-V4) of the bacterial 16S rRNA gene was amplified with primer pairs (338F: 5\u0026prime;-ACTCCTACGGGAGGCAGCAG-3\u0026prime;; 806R: 5\u0026prime;-GGACTACHVGGGTWTCTAAT-3\u0026prime;) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and the fungal internal transcribed spacer (ITS) region was amplified with the primer pairs (ITS1F :5\u0026prime;-CTTGGTCATTTAGAGGAAGTAA-3\u0026prime;; ITS2R: 5\u0026prime;-GCTGCGTTCTTCATCGATGC-3\u0026prime;) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. PCR was performed on an ABI GeneAmp\u0026reg; 9700 PCR thermocycler (ABI, CA, USA). The PCR reaction systems was 4 \u0026micro;L of 5 \u0026times; Fast Pfu buffer, 2 \u0026micro;L of 2.5 mM dNTPs, 0.8 \u0026micro;L each primer (5 \u0026micro;M), 0.4 \u0026micro;L Fast Pfu polymerase, 10 ng of template DNA, and ddH\u003csub\u003e2\u003c/sub\u003eO add to 20 \u0026micro;L. PCR amplify cation cycling were applied: 3 min initial denaturation at 95\u0026deg;C, followed by 30 s denaturing at 95\u0026deg;C (27 cycles for bacterial primer and 35 cycles for fungal primer), 30 s annealing at 55\u0026deg;C and 45 s extension at 72\u0026deg;C, then 10 min a final extension at 72\u0026deg;C, final end at 4\u0026deg;C. All amplifications were performed in three technical replicates. The PCR products were extracted from 2% agarose gels and purified by the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) and quantified using Quantus\u0026trade; Fluorometer (Promega, USA). The purified amplification products were quantitatively mixed, and paired-end sequencing was performed with a MiSeq PE300 platform (Illumina, San Diego, USA) according to the standard protocols by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Processing and evaluation of sequencing data\u003c/h2\u003e\u003cp\u003eAfter obtaining the data from transcriptomics high-throughput sequencing, Trimmomatic software (v 0.39) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] was employed to filter the low-quality data (LEADING: 3, TRAILING: 3, SLIDINGWINDOW: 4: 15, MINLEN: 36), removing contaminations and splice sequences, resulting in clean data. Subsequently, the default parameters of HISAT2 software (v 2.2.0) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] were employed to compare clean data with the reference genome of A. sinensis [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. After filtering the transcriptome data, to observe the gene expression in each sample, feature Count program of Rsubread package (v 1.12.0) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] was employed to extract gene expression values (gene counts) (parameter: -t exon-g gene_name), and the eggNOG-emapper database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://eggnog-mapper.embl.de/\u003c/span\u003e\u003cspan address=\"http://eggnog-mapper.embl.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations corresponding to the genes. Immediately following this, the edgeR package (v 3.44.3) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] was employed to normalize the gene expression matrix for each sample. The fact that all samples were at similar expression levels indicated that the data could be used for subsequent analysis. Additionally, based on 16S rRNA and ITS sequencing data, the DATA2 method in the QIIME2 package (v 2022.8) [[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] was used for clustering to group valid sequence clusters of sequenced samples into amplicon sequence variants (ASVs) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Differential expression analysis based on transcriptomics\u003c/h2\u003e\u003cp\u003eTo acquire DEGs in different production areas (DEGs-AB), the Deseq2 package (v 1.26.0) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] was employed to conduct differential expression analysis between groups A and B (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |log2fold change (FC)| \u0026gt;1). Subsequently, the ggplot2 package (v 3.3.2) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] was employed to draw a volcano map. Then, to further comprehend the pathways involved in the DEGs-AB, the clusterProfiler package (v 4.0.2) [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] was employed to conduct GO and KEGG analyses (adjust p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). All significant entries of GO and KEGG pathways were visualized, respectively. Afterward, to further understand the differences between production areas, samples from different production areas during the same growth period were analyzed for their differential expression and pathways involved were explored.\u003c/p\u003e\u003cp\u003eMoreover, to gain genes playing significant roles in different growth periods within the same production area, differential expression analysis was conducted on samples from that area but at different growth periods (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |log2 fold change (FC)| \u0026gt;1). Next, the ggVenn package (v 1.7.3) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] was employed to gain their shared DEGs, specific differential genes and involved to understand their roles during the growth of A. sinensis (adjust P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Notably, the above enrichment results were sorted by adjusted p value from smallest to largest, visualizing top 10 GO entries and top 20 KEGG pathways, respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7. Weighted gene co-expression network analysis (WGCNA)\u003c/h2\u003e\u003cp\u003eTo gain the genes associated with the major bioactive compounds of A. sinensis, the WGCNA was conducted. The goodSamplesGenes function of WGCNA package (v 1.71) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] was first applied to conduct hierarchical clustering analysis on all samples of groups A and B to check for outlier samples. Subsequently, to construct scale-free networks, soft threshold (power) and scale-free fit indices (R\u003csup\u003e2\u003c/sup\u003e) were computed and the power with mean connectivity converging to 0 was selected (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.85). Then, the clustering tree was constructed, and the modules were acquired by cutting them according to the minModuleSize of genes 200, through a shear tree algorithm (parameter\u0026thinsp;=\u0026thinsp;0.2). Finally, to gain the most relevant module genes for the major bioactive compounds of A. sinensis, the WGCNA package (v 1.71) was applied for Spearman's correlation analysis to calculate the correlation coefficients (cor) between modules and major bioactive compounds of A. sinensis. Modules were selected when their |cor| values were greater than 0.3 and their p values were less than 0.05, and when they could not be satisfied, modules with a strong correlation with most of the 8 major bioactive compounds were selected. Besides, the intersection of modular genes with DEGs between groups A and B was taken to gain key modular genes and explore the pathways in which they might be involved.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8. Analysis of microbial community differences\u003c/h2\u003e\u003cp\u003eDilution curves were employed to confirm whether the sequencing data were sufficient to represent the diversity and abundance of species in the samples. Specifically, ASVs rarefaction curves and observed feature curves were plotted for all samples based on 16S rRNA sequencing and ITS sequencing data. Due to the ASVs rarefaction curves increased with the number of sequences (sequencing depth), the curve flattened out, indicating that the amount of sequencing data met the needs of the analysis. Additionally, similarly, observed feature curves were employed to observe the extent to which microbial species information was covered in the samples. Subsequently, the ggVenn package (v 1.7.3) was employed to obtain ASVs that were common or unique to the samples in groups A and B to demonstrate the similarity and specificity in the 2 groups.\u003c/p\u003e\u003cp\u003eAfterward, to understand the species diversity of microbiota in the samples, an Alpha diversity analysis was carried out. In detail, the observed features index, Shannon, PD, and Evenness in Alpha index were adopted to visualize the diversity and differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) of microorganisms in the samples of groups A and B. Moreover, diversity differences between groups A and B were determined by Beta diversity analysis. Specifically, the differences between the different subgroups were first demonstrated based on the constrained principal coordinate analysis (CPCoA) clustering plot of the euclidean distance matrix of all the sample bacteria. Then, analysis of similarity (ANOSIM) was employed to analyze the differences in Beta diversity in groups A and B. Later, the edgeR package (v 3.44.3) was employed to analyze differences in microbial community composition at the phylum and genus levels between groups A and B (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |log2FC| \u0026gt;1), followed by which microbes caused differences in the communities were further analyzed. Immediately after that, ASVs that were common and unique to different growth periods in the same production area were recognized. In addition, Alpha diversity, Beta diversity, differences in microbial community composition, and ASVs of differential microorganisms during different growth periods were further analyzed to observe the similarity and specificity of microbial communities during different growth periods in the same production area.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9. Identification of ASVs associated with the major bioactive compounds of A. sinensis\u003c/h2\u003e\u003cp\u003eSimilarly, WGCNA analysis was employed to obtain microorganisms associated with the content of the major bioactive compounds of A. sinensis. In detail, the goodSamplesGenes function of WGCNA package (v 1.71) was employed to determine whether the ASVs of the samples in groups A and B needed to be eliminated, subsequently, soft thresholds were determined for the construction of the scale-free network (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.85). Next, the constructed clustering number was cut to obtain the modules (minModuleSize\u0026thinsp;=\u0026thinsp;200, parameter\u0026thinsp;=\u0026thinsp;0.2), and the correlation of the modules with the main active ingredient was calculated. Notably, modules with strong correlation with the 8 major bioactive compounds of A. sinensis were selected (|cor| \u0026gt;0.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and when they could not be satisfied, modules with strong correlation with most of the 8 major bioactive compounds were selected.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.10. Symbiosis network analysis\u003c/h2\u003e\u003cp\u003eTo obtain the gene and microbial interaction network with the major bioactive compounds of A. sinensis in different production areas, the Spearman correlation between DEGs of groups, bacterial ASVs, and fungal ASVs (WGCNA analysis) was calculated for the construction of DEGs-A. sinensis major bioactive compounds-bacteria-fungus co-expression symbiotic network (|cor| \u0026gt;0.9, adjust P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.11. Functional Annotation of Prokaryotic Taxa (FAPROTAX) analysis\u003c/h2\u003e\u003cp\u003eThe FAPROTAX was based on published and validated literature on culturable bacteria, which allowed biogeochemical cycling processes (especially elemental cycling of carbon, hydrogen, nitrogen, phosphorus, and sulfur) to be performed for functional annotation prediction. To obtain the functional characteristics of the rhizosphere soils of A. sinensis in different production areas, FAPROTAX database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.loucalab.com/archive/FAPROTAX\u003c/span\u003e\u003cspan address=\"http://www.loucalab.com/archive/FAPROTAX\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was applied to predict the functional characteristics of groups A and B. The functional information of the obtained samples was analyzed for differential expression, and the t-test was employed to compare the differences between the 2 groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Moreover, FAPROTAX predictions were made for different growth periods in the same production area.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.12. Association analysis\u003c/h2\u003e\u003cp\u003eSubsequently, the correlation between soil physicochemical property indexes and the content of the major bioactive compounds of A. sinensis was further explored by Spearman's correlation analysis. Then, redundancy analysis (RDA) was employed to further investigate the effects of soil physicochemical properties on microbial communities, in which the factors were represented by arrows, and the quadrant in which the arrow was located indicated the positive or negative correlation between the factor and the sorting axis, with a positive correlation of less than 90 degrees and a negative correlation of more than 90 degrees, and the length of the line connecting the arrow and the origin represented the correlation between the factor and the distribution of the community and the distribution of the species.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.13. Statistical analysis\u003c/h2\u003e\u003cp\u003eThe R software (v 4.1.1) was applied to conduct bioinformatics analysis. The Wilcoxon test and t-test were utilized to evaluate paired sample comparisons, with statistical significance defined at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1. Differences in major bioactive compounds in A. sinensis between different production areasThe results demonstrated that a significant effect of authentic and near-authentic production areas on the principal bioactive compounds of A. sinensis. Specifically, 7 out of the 8 bioactive compounds exhibited significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among these, ligustilide, coniferylferulate, 3-n-butylphthalide (NBP), and levistilide A were more abundant in the authentic production area (\u003cb\u003eAdditional file1\u003c/b\u003e). Since A. sinensis is primarily used for its rhizome in medicine, we further analyzed changes in these 8 bioactive compounds during the rhizome expansion period. At this stage, 5 compounds showed significant variations between production areas, with only coniferylferulate being higher in the authentic region (\u003cb\u003eAdditional file2\u003c/b\u003e). Interestingly, during the drug formation period, 6 compounds displayed signifciant differences, with ligustilide, coniferylferulate, NBP, and levistilide A again being more prevalent in the authentic production area, a notable contrast to the rhizome expansion period (\u003cb\u003eAdditional file3\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e3.2. Differences in soil properties in A. sinensis between different production areas\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eMoreover, soil properties also varied between the production areas. Notably, S_SC, S_ALP, and EC exhibited significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between the authentic and the near-authentic production areas (\u003cb\u003eAdditional file4\u003c/b\u003e), suggesting that soil properties might contribute to the observed variations in the principal bioactive constituents of A. sinensis. Soil physicochemical properties further differed across growth periods within the same production area. For instance, urease (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and AP decreased from stages A5 to A10, while organic matter and EC increased (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Soil TN increased in group A7 but decreased in A10 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (\u003cb\u003eAdditional file5\u003c/b\u003e). In the near-authentic production area, S_SC (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), S_ALP (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), organic matter and pH (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), TN (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and AK (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) increased from B5 to B10, whereas AP decreased (\u003cb\u003eAdditional file6\u003c/b\u003e). Collectively, these dynamic changes in soil properties highlight a strong correlation between soil physicochemical properties and the accumulation of active compound in A. sinensis.\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.3. DEGs and their pathways between different production regions\u003c/h2\u003e\u003cp\u003eComparative analysis revealed that 63%-73% of the 18 sequenced samples were successfully matched (\u003cb\u003eAdditional file7\u003c/b\u003e). Gene expression levels were consistent across all these 18 samples after annotation, confirming their suitability for subsequent downstream analyses (\u003cb\u003eAdditional file8\u003c/b\u003e). A total of 2,367 differentially expressed genes (DEGs) were identified between groups A and B, with 964 up-regulated and 1,403 down-regulated DEGs, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Functional enrichment analysis showed that these DEGs were significantly associated with 8 GO entries (adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). primarily related to secondary metabolite biosynthesis, anther wall tapetum development, and anion transmembrane transport (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Additionally, 21 KEGG pathways were significantly enriched (adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), including cortisol synthesis and secretion, steroid hormone biosynthesis, and phenylpropanoid biosynthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). These findings suggest that the DEGs may play a critical role in the synthesis of those secondary metabolites in A. sinensis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSubsequently, we identified DEGs between different production regions during the same growth period. Specifically, a total of 2,791 DEGs (DEGs-AB5) were detected between A5 and B5, with 1,061 were up-regulated and 1,730 down-regulated genes in A5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed); 3,759 DEGs (DEGs-AB7) were identified between A7 and B7, with 1,502 were up-regulated and 2,257 down-regulated genes in A7 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee); and 4,746 DEGs-AB10 between A10 and B10, with 1,492 were up-regulated and 3,254 were down-regulated genes in A10 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef). Notably, the number of DEGs increased during the rhizome expansion (A7/B7) and drug formation (A10/B10) periods compared to earlier stages, suggesting these DEGs played a pivotal role in regulating metabolite synthesis in A. sinensis. Functional analysis revealed while the enriched pathways varied across growth periods, DEGs were consistently associated with the secondary metabolite synthesis. This trend became more pronounced as the plant developed: during in the rhizome expansion (DEGs-AB7) and drug formation (DEGs-AB10), DEGs were predominately enriched in the secondary metabolic process (GO) and phenylpropanoid biosynthesis (KEGG) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eg-l). These findings underscore that production region and growth stage collectively influence gene expression patterns, particularly those governing secondary metabolite production in A. sinensis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.4. DEGs and their pathways involved during different growth periods\u003c/h2\u003e\u003cp\u003eWe identified distinct patterns of DEGs during different growth periods within each production area. In the authentic production area, A7 vs A5: 7,815 DEGs-A7 vs A5 (3,661 up-regulated and 4,154 down-regulated genes were expressed in A7) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), A10 vs A5: 9,185 DEGs-A10 vs A5 (3,442 up-regulated and 5,743 down-regulated genes in A10) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb); A10 vs A7: 7,300 DEGs-A10 vs A7 (2,465 up-regulated and 4,835 down-regulated in A10) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Notably, the authentic production area showed 1,615 shared DEGs across all growth periods, and the fewest unique DEGs (957) in the A10 vs A7 comparison (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). In near-authentic production area: B7 vs B5: 8,744 DEGs-B7 vs B5 (3,051 up-regulated and 5,693 down-regulated genes in B7) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee); B10 vs B5: 9,244 DEGs-B10 vs B5 (3,683 up-regulated and 5,561 down-regulated genes in B10) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef); and 9,511 DEGs-B10 vs B7 (5,136 up-regulated and 4,375 down-regulated genes in B10) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg). The near-authentic production area exhibited 1,738 DEGs across the growth periods (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eh).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSubsequent analysis revealed significant enrichment of key biosynthetic pathways, particularly secondary metabolite process and phenylpropanoid biosynthesis, across different growth periods in both the authentic and near-authentic production areas. Intriguingly, we observed concurrent enrichment of defense-related pathways during various growth stages including the response to chitin and MAPK signaling pathway involving in signal transduction. These findings (\u003cb\u003eAdditional file9a-f, Additional file10\u003c/b\u003e) suggest that the secondary metabolite accumulation in A. sinensis may closely linked to activated defense mechanisms, mirroring the patterns observed in other medicinal species.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.5. 5,063 module genes related to major bioactive compounds in A. sinensis\u003c/h2\u003e\u003cp\u003eFollowing a quality control verification, we performed weighted gene co-expression network analysis (WGCNA) on all samples from groups A and B (\u003cb\u003eAdditional file11a\u003c/b\u003e). The analysis identified an optimal soft threshold power of 26 (scale-free topology model fit, R\u0026sup2; = 0.85, \u003cb\u003eAdditional file11b\u003c/b\u003e), yielding 10 distinct co-expression modules (including the grey module of unassigned genes) (\u003cb\u003eAdditional file11c, d\u003c/b\u003e). Notably, the MEbrown module showed significant correlations (absolute value\u0026thinsp;\u0026gt;\u0026thinsp;0.3, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with 5 key bioactive compounds (ligustilide, coniferylferulate, senkyunolide H, NBP, and Levistilide A). This module contained 5,063 genes potentially involved in the biosynthesis of these active compounds (\u003cb\u003eAdditional file11e\u003c/b\u003e). Further intersection analysis with DEGs between groups A and B identified a total of 463 key module genes functionally enriched in cytochrome P450 activity, steroid hormone biosynthesis, and cortisol synthesis and secretion pathways (\u003cb\u003eAdditional file11f\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.6. Differences in ASV of bacteria between production areas\u003c/h2\u003e\u003cp\u003eThe Alpha diversity assessment showed that the rarefaction curves plateaued with increasing sequencing depth (\u003cb\u003eAdditional file12\u003c/b\u003e), and demonstrating that (1) Sequencing coverage adequately captured sample biodiversity, (2) Additional sequencing would not yield new ASVs and (3) Observed features stabilized, confirming comprehensive microbial species representation (\u003cb\u003eAdditional file12\u003c/b\u003e). The analysis of bacterial ASVs revealed the core and unique microbiome features as 729 shared bacterial ASVs across production areas, 1,659 ASVs unique to the authentic production areas and 1,889 ASVs unique to the near-authentic production areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The diversity metric comparisons exhibited no significant differences (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in the 4 indexes (observed features index, Shannon index, Pielou's evenness (PD) index, and Evenness index) were observed between the production areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). These results indicate comparable bacterial richness and community homogeneity across production areas. However, the results from the Beta diversity analysis showed significant compositional differences (Bray-Curtis distance, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and distinct community structures between different production areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAt the phylum level, the top three most abundant microbial groups across different production areas were Actinobacteriota, Proteobacteria, and Chloroflexi. Compared to the near-authentic production area, the authentic production area exhibited a lower abundance of Actinobacteriota, but a higher abundance of Acidobacteriota (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). At the genus level, the microbial community abundance was similar between production areas, with uncultured taxa being the most prevalent. However, Vicinamibacter increased in abundance while Nocardioides decreased in the authentic production areas relative to the near-authentic production areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). Differential expression analysis further identified amplicon sequence variants (ASVs) contributing to community differences. A total of 417 ASVs were differentially abundant between production areas, with 199 up-regulated and 218 down-regulated ASVs in the authentic production area compared to the near-authentic production area. These shifts were primarily driven by Proteobacteria, Actinobacteriota, Chloroflexi, and Acidobacteriota caused community changes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef, g).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.7. Differences in ASV of bacteria during different growing periods\u003c/h2\u003e\u003cp\u003eWe further analyzed changes or shifts in bacterial communities during different growth periods within the same production area. Analyses revealed that 123 ASVs were shared across all growth periods, while the rhizome expansion period (674 ASVs) and drug formation (672 ASVs) harbored similar unique ASVs, both significantly higher than the leaf clump period (368 ASVs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Alpha diversity varied most prominently between the leaf clump period and rhizome expansion periods, whereas no significant differences were observed in any of the four diversity indexes between the rhizome expansion and drug formation periods, highligting both the similarity and period specificity of the communities (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). The Beta diversity analysis further distinguished these periods, with A5 (leaf clump) samples significantly separated from A7 (rhizome expansion) and A10 (drug formation), underscoring their compositional differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). At the phylum and genus level, community abundance patterns were largely consistent across growth periods. Notably, the rhizome expansion and drug formation period exhibited particularly similar microbial profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed, e). Differential expression analysis identified that a total of 264 significantly altered bacterial ASVs in A7 vs A5 (235 up-regulated and 29 down-regulated, \u003cb\u003eAdditional file13a\u003c/b\u003e) and 241 ASVs in A10 vs A5 (212 up-regulated and 29 down-regulated, \u003cb\u003eAdditional file13b\u003c/b\u003e). In contrast, A10 vs A7 showed fewer differential bacterial ASVs (61 in total: 24 up-regulated and 37 down-regulated, \u003cb\u003eAdditional file13c\u003c/b\u003e). Strikingly, no ASVs were shared across all growth periods (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef), suggesting dynamic community restructuring. These shifts imply a potential linkage between rhizosphere microbiota and the accumulation of A. sinensis\u0026rsquo;s pharmacologically bioactive compounds, as evidenced by the progressive reduction in differential ASVs as growth advanced toward the drug formation period.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the near-authentic production area, 130 ASVs were shared across all growth periods, with the rhizome expansion and drug formation periods containing similarly higher numbers of unique ASVs compared to other periods (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Alpha diversity analysis revealed significant differences between the leaf clump period and both the rhizome expansion and drug formation periods across all indices except the Pielou's evenness. In contrast, no significant differences were observed between the rhizome expansion and drug formation periods (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Beta diversity analysis confirmed clear separation of microbial communities among growth periods (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). While overall community structure remained similar across growth periods across growth periods, notable changes occurred at the genus level: the drug formation period showed an increased abundance of uncultured bacteria but decreased abundance of Nocardioides (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed, e). Differential expression analysis identified 327 significantly altered bacterial ASVs in B7 (rhizome expansion) vs B5 (leaf clump): 315 up-regulated and 12 down-regulated (\u003cb\u003eAdditional file14a\u003c/b\u003e), 319 differential bacterial ASVs in B10 (drug formation) vs B5: 299 up-regulated and 20 down-regulated (\u003cb\u003eAdditional file14b\u003c/b\u003e), and 233 differential bacterial ASVs in B10 vs B7, 98 up-regulated and 135 down-regulated (\u003cb\u003eAdditional file14c\u003c/b\u003e). Consistent with findings in the authentic production areas, no ASVs were shared across all growth periods in the near-authentic production areas, with particularly few ASVs persisting through both the rhizome expansion and drug formation periods (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef). These results demonstrated significant growth stage-dependent variation in A. sinensis bacterial communities. The particularly similar diversity and abundance patterns between the rhizome expansion and drug formation periods suggested these dominant bacterial communities might play important roles in plant development and secondary metabolite accumulation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.8. Variation of fungal ASVs between different production areas\u003c/h2\u003e\u003cp\u003eThe ITS sequencing dilution curves confirmed adequate sequencing depth, demonstrating comprehensive coverage of fungal diversity (\u003cb\u003eAdditional file15\u003c/b\u003e). Across the groups A and B, 481 ASVs were shared, with group A exhibiting more unique ASVs than the group B (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). The Alpha displayed analysis revealed no significant differences in species richness and evenness between groups (all indices, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). In contrast, Beta diversity showed clear separation of fungal communities between groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). Distinct from bacteria patterns, fungal communities differed notably at both the phylum and genus levels. Specifically in the group A: reduced abundances in: Bisifusarium, Longitudinalis and Verticillium, while increased abundance in Mycochlamys, Linnemannia and Mortierella, compared to the group B (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed, e). A total of 325 differentially expressed ASVs were identified (233 up-regulated and 92 down-regulated, in the group A vs B, prodominantly derived from Ascomycota (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef, g).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e3.9. Differences in fungal community structure during different growth periods\u003c/h2\u003e\u003cp\u003eCore and unique ASV distribution analysis revealed 110 ASVs were shared across all growth periods, 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.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea). Notably, Alpha diversity showed no significant difference across the growth periods (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for all indices, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb). However, Beta diversity analysis revealed distinct community structures between periods (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec). At the phylum level, a progressive decline in Ascomycota abundance as the growth was progressed from A5 to A10 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed). At the genus level, Mycochlamys\u0026rsquo; abundance was highest in the A5 but significantly decreased in the A7 and A10 samples. The A7 and A10 showed some similarity, with notable increases in Mortierella, Plectosphaerella, and Tetracladium, but a decrease in Tausonia in the A10 samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ee). Significant ASV changes were observed between periods as for A7 vs A5: 125 differential fungal ASVs (27 up-regulated and 98 down-regulated, \u003cb\u003eAdditional file 16a\u003c/b\u003e), for A10 vs A5: 160 differential fungal ASVs (59 up-regulated and 101 down-regulated, \u003cb\u003eAdditional file 16b\u003c/b\u003e), and for A10 vs A7: 32 fungal ASVs (23 up-regulated and 9 down-regulated, \u003cb\u003eAdditional file 16c\u003c/b\u003e). Based on the above-mentioned results, the intersection analysis confirmed no fungal ASVs were shared across all growth periods. Mirroring the bacterial trends, endemic ASVs were gradually decreased as growth progressed, with only five unique ASVs remaining in A10 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ef).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eA total of 93 fungal ASVs were shared across all growth periods in the near-authentic production area. Notably, B10 samples contained the fewest unique ASVs (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea). Diversity analysis revealed no significant differences were observed in all four alpha diversity indices between B5 and B7 periods, while significant differences in Shannon's evenness and Pielou's evenness between B7 and B10 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb), and a clear separation between B5 and B10 samples in beta diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec). While the phylum-level composition remained relatively stable across the B5, B7 and B10 samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ed), notable genus-level differences emerged as Bisifusarium and Longitudinalis became significantly more abundant in the fungal community during the B10 period (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ee). In addition, significant fungal ASVs changes between growth periods included for B7 vs B5, 120 differential ASVs (40 up-regulated and 80 down-regulated, \u003cb\u003eAdditional file 17a\u003c/b\u003e), for B10 vs B5, 141 differential ASVs, (21 up-regulated and 120 down-regulated, \u003cb\u003eAdditional file 17b\u003c/b\u003e), and for B10 vs B7, 90 differential ASVs (12 up-regulated and 78 down-regulated, \u003cb\u003eAdditional file 17c\u003c/b\u003e). The intersection analysis identified four shared ASVs maintained across all three growth periods, while similar numbers of period-specific ASVs for each the growth period (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ef). In summary, these results demonstrated that fungal community structure undergoes significant reorganization during the A. sinensis growth, The drug formation period shows particularly distinct compositional changes, the key genera (Bisifusarium, Longitudinalis) may play important roles in secondary metabolite production, and the small core microbiome (4 ASVs) suggests strong environmental or host-mediated selection.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e2.10. Bacterial and fungal ASVs associated with the main active ingredient of A. sinensis\u003c/h2\u003e\u003cp\u003eAll bacterial ASVs from the group A and B were included in WGCNA after quality control verification (\u003cb\u003eAdditional file 18a\u003c/b\u003e). Network construction obtained optimal soft threshold power of 7 (scale-free topology fit R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.85, \u003cb\u003eAdditional file 18b\u003c/b\u003e), 6 co-expression modules through hierarchical clustering (\u003cb\u003eFigures \u003cspan refid=\"MOESM16\" class=\"InternalRef\"\u003eS16\u003c/span\u003ec, d\u003c/b\u003e). The MEyellow module (containing 417 bacterial ASVs) showed strongest correlations with principal bioactive components in A. sinensis (\u003cb\u003eAdditional file 18e\u003c/b\u003e). Similarly, all fungal samples met inclusion criteria (\u003cb\u003eAdditional file 18f\u003c/b\u003e), and the fungal network was constructed with soft threshold of 12 (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.85) (\u003cb\u003eAdditional file 18g\u003c/b\u003e), and 6 modules including unclassified gray modules (\u003cb\u003eAdditional file 18h, i\u003c/b\u003e). Finally, the MEturquoise module (325 fungal ASVs) demonstrated strongest associations with the most active components in A. sinensis (\u003cb\u003eAdditional file 18j\u003c/b\u003e). Combining differentially expressed genes (DEGs) and WGCNA results, a comprehensive co-expression network encompassing was constructed with the DEGs from A. sinensis, key pharmaceutically bioactive 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.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ea), revealing the complex relationship among the 4 elements. These results demonstrated a tight integration between microbial communities and plant metabolite production; revealed predominance of positive interactions in the microbiome-metabolome network; and identified specific bacterial (417 ASVs) and fungal (325 ASVs) modules that strongly associated with medicinal compound accumulation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e2.11. Differences in the functional characteristics of the rhizosphere soil of A. sinensis\u003c/h2\u003e\u003cp\u003eThe Functional Annotation of Prokaryotic Taxa (FAPROTAX) analysis in groups A and B revealed significantly functional differences between production areas (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in five key metabolic pathways: chitinolysis, aromatic hydrocarbon degradation, aliphatic non methane hydrocarbon degradation, ureolysis, and invertebrate parasites (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eb). Six functional categories showed significant variation across growth periods (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with primary differences in fermentation, human pathogens all, and human pathogens pneumonia occurring between A5 vs A7 or vs A10 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ec). However, the most pronounced functional differences emerged in the near-authentic production area, with 9 significantly altered pathways (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), such as the aerobic chemoheterotrophy with the highest relative abundance, human pathogen all, fermentation, dark hydrogen oxidation, nitrogen fixation and aromatic hydrocarbon degradation across all growth periods (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ed). These functional differences highlighted distinct biogeochemical processing capabilities between production areas, period-specific microbial functions during plant development, Potential pathogen pressure variations across growth periods and the critical role of chemoheterotrophic metabolism in the rhizosphere soil interactions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e2.12. Positive correlation between soil physicochemical properties and the bioactive compounds of A. sinensis\u003c/h2\u003e\u003cp\u003eCorrelation analysis identified significant relationships between soil physicochemical properties and bioactive compounds in A. sinensis (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ee, \u003cb\u003eAdditional file19\u003c/b\u003e). The key findings were as follows. Soil enzymes (S_POD, S_ACP, S_NP), organic matter, and available potassium (AK) displayed strongly positive correlations with most bioactive compounds, and AK exhibited the strongest positive correlation with Levistilide A (r\u0026thinsp;=\u0026thinsp;0.96, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Available phosphorus (AP) was negatively correlated with coniferylferulate (r = -0.83, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which also showed negative correlations with most soil physicochemical properties. Besides, the ferulic acid significantly positively correlated with soil sucrase (S_SC, r\u0026thinsp;=\u0026thinsp;0.84, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), TN (r\u0026thinsp;=\u0026thinsp;0.76, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), AP (r\u0026thinsp;=\u0026thinsp;0.72, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and EC (r\u0026thinsp;=\u0026thinsp;0.65, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ee, \u003cb\u003eAdditional file19\u003c/b\u003e). These correlations provided valuable insights for promoting cultivation strategies to enhance target metabolite production in A. sinensis.\u003c/p\u003e\u003cp\u003eThe redundancy analysis (RDA) revealed distinct associations between soil physicochemical properties and microbial communities across different production areas. These associations showed positive correlations between both bacterial and fungal communities and S_ALP, TN or organic matter in the authentic production area, between bacterial communities and EC, AP or urease in the near-authentic production area, between fungal communities and S_SC, EC, AP, and urease in the near-authentic production area (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ef, g). These findings suggest that soil characteristics differentially shape microbial communities in each production area, potentially influencing nutrient acquisition efficiency, secondary metabolite biosynthesis and medicinal compound accumulation in major bioactive compounds in A. sinensis.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e4.1. Primary bioactive compounds in A. sinensis are strongly influenced by its cultivation locations\u003c/p\u003e\u003cp\u003eA. sinensis is a critically important medicinal plant, with phthalides, coumarins, lignans, and terpenoids as its primary bioactive compounds, which are strongly influenced by its cultivation location [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Our findings reveal significant differences in seven of the eight major active compounds of between the authentic and near-authentic cultivation areas. Among these key bioactive compounds, ligustilide, ferulic acid, NBP, and levistilide A are well-documented for their therapeutic roles [\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Zhang et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] reported that the geoherb samples contained significantly higher levels of senkyunolide I and NBP were compared to non-geoherb samples. Our findings align with this, showing elevated NBP levels in genuine cultivation regions. However, senkyunolide I exhibited an opposite trend, likely due to the relatively minor microenvironment differences between the genuine and nearby regions compared to non-genuine regions. Additionally, coniferylferulate, a phenolic compound with poor stability, readily converts into ferulic acid and coniferyl alcohol in A. sinensis and can interconvert with ferulic acid [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Thus, the sum of ferulic acid and its derivatives from coniferylferulate conversion is considered \u0026ldquo;total ferulate\u0026rdquo; [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In this study, the total ferulate was higher in the authentic cultivation regions than in nearby areas. Collectively, the significantly higher levels of ligustilide, total ferulic acid, NBP, and levistilide A in genuine A. sinensis may serves as key markers of its superior quality compared to the near-authentic samples. These findings support previous research underscoring the critical impact of cultivation region on A. sinensis quality [\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eTranscriptome analysis has proven valuable in elucidating gene regulatory network linked to the biosynthesis of bioactive compounds in traditional Chinese medicines (TCM) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. It also helps clarify the mechanisms underlying variations in the production and accumulation of major bioactive compounds across different tissues of A. sinensis root [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In this study, we identified numerous differentially expressed genes (DEGs) across different production areas and growth stages within the same production region. These DEGs are predominantly enriched in critical pathways including secondary metabolite biosynthesis and phenylpropanoid biosynthesis. The phenylpropanoid biosynthesis pathway directly contributes to the synthesis of phenolic and flavonoids, which are crucial for regulating the production of pharmacologically active compounds [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Notably, this pathway is also directly involved in the ferulic acid biosynthesis [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In addition, defense-related activation pathways including the chitin response may further stimulate the accumulation of secondary metabolites [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Interestingly, relatively fewer unique DEGs were detected during the root rhizome expansion stage and the medicinal maturity stage, suggesting that these genes may play a specialized role in active compound accumulation [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. A key finding was the pronounced upregulating of cytochrome P450 enzymes, which mediate the furanocoumarin biosynthesis in A. sinensis from its authentic production area, particularly during the rhizome expansion period [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. During this phase, genes associated with the secondary metabolite biosynthesis, phenylpropanoid biosynthesis, and cytochrome P450 were all significantly upregulated. In contrast, during the drug formation stage, most genes, except genes associated with chaperones, folding catalysts, and starch/sucrose metabolism, were markedly downregulated. We hypothesize that the elevated bioactive compound levels in authentic A. sinensis may result from the strong upregulation of biosynthesis genes during root enlargement. However, further research is needed to validate this mechanism.\u003c/p\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Bacterial community dynamics and their impact on A. sinensis quality\u003c/h2\u003e\u003cp\u003eRoot-associated microbiomes play a crucial role in the biosynthesis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and accumulation of specialized metabolites in medicinal plants [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Our analysis revealed that the bacterial community structure of A. sinensis remained highly consistent across different growth stages in authentic producing areas, with a progressive decline in the unique ASVs as the plant matures. Notably, key bacterial taxa, such as Acidobacteriota, Firmicutes, Vicinamibacter, and Bacillus, were significantly more abundant in the authentic producing regions compared to nearby areas. These bacterial groups are known to promote plant growth and suppressing plant pathogenic infections [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Strikingly, Bacillus has been demonstrated to enhance promote phthalide biosynthesis, specifically increasing butylidenephthalide accumulation in A. sinensis [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. In contrast, Nocardioides was more abundant in the nearby producing area than in the authentic producing region, and its prevalence decreased during the A. sinensis growth in the authentic soils. This finding suggests that the indigenous bacterial composition of in authentic producing areas fosters a more favorable environment for both the growth and phytochemical quality of A. sinensis [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Fungal community dynamics and their impact on A. sinensis quality\u003c/h2\u003e\u003cp\u003eFor the 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 even more pronounced than bacterial variations. Previous studies have established that dominant phyla including Ascomycota, Mortierellomycota, and Basidiomycota play crucially ecological roles by enhancing soil nutrient availability [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], bolstering plant pathogen resistance [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], and promoting plant growth [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. While Ascomycetes include many plant pathogens [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], they may also serves as key elicitors that stimulate medicinal plants to produce secondary metabolites [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. The equilibrium of microbial taxonomic composition represents a critical factor influencing both plant fitness and secondary metabolite production [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], suggesting this balance directly affects phytochemical accumulation.\u003c/p\u003e\u003cp\u003eFor the fungal genera associated with authentic producing areas, several beneficial genera including Mycochlamys, Linnemannia, Mortierella, Tausonia, and Tetracladium, were more abundant in the authentic producing areas. These fungi contribute to plant growth promotion [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], root pathogens suppression [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], and enhanced medicinal compound production [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Notably, Mycochlamys, shows positive correlation with the ferulate acid accumulation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Although it may facilitate Fusarium root invasion, no direct pathogenicity towards A. sinensis roots has been demonstrated [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In contrast, the near-authentic producing areas exhibited higher abundances of known pathogenic genera including Bisifusarium, Plectosphaerella, Longitudinalis, Verticillium, and Cladosporium. These are known plant pathogens affecting medicinal plants including A. sinensis, Panax ginseng and Withania somnifera [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], particularly during the medicinal growth stage. These findings demonstrate that the authentic producing areas maintain fungal communities more conducive to plant health and medicinal compound accumulation, while the near-authentic regions harbor higher proportions of pathogenic taxa that compromise plant fitness. These results align with and extend previous research on A. sinensis microbiome ecology [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Given that the established importance of stochastic processes in rhizosphere bacterial and fungal community assembly [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], we thus propose that the naturally selected microbial consortia in the authentic producing areas represent an optimized microbiome for producing high-quality A. sinensis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Soil Properties and Their Impact on A. sinensis Quality\u003c/h2\u003e\u003cp\u003eExtensive research confirms that soil serves as the fundamental source of essential nutrients for plant growth, with its edaphic conditions directly influencing medicinal metabolic pathways and modulating bioactive compound production [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Our study reveals three key soil parameters, e.g. S_SC, S_ALP and EC, that show significant differences between the authentic and near-authentic production areas of A. sinensis. However, the most pronounced variations in soil characteristics emerged across different growth stages within both areas, suggesting substantial differences in growth patterns and metabolic activities between the authentic and near-authentic cultivated A. sinensis, and consequent modifications in rhizosphere soil properties, including physicochemical properties, nutrient profiles, and enzyme activities [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. The microbial community dynamics consistent with these observations as follows. Bacterial and fungal α-diversity and richness show no significant spatial variations between regions. However, significant variations occurred cross growth stages in both regions, and these patterns parallel the observed physicochemical and nutrient gradients in soils. These align with established knowledge that variations in soil physicochemical properties, nutrient availability and enzyme activities that directly shape microbial community structure and function [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. A total of 12 critical soil indicators demonstrated positive correlations with major pharmacologically active compounds in A. sinensis, including S_POD, S_ACP, S_NP, organic matter, TN, AP, and AK. Notably, four key bioactive compounds including ligustilide, ferulic acid, NBP, and Levistilide A exhibited particularly strong correlations with these soil properties. Furthermore, for the quality determinants in production areas, three soil quality parameters like S_ALP, TN, and organic matter exhibited both positive correlation with the authentic production areas and potential to influence medicinal compound accumulation. These likely mediate their effects through modulating nutrient uptake efficiency and shaping beneficial microbial communities [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These findings have ecological and agricultural implications by validating previous reports on microbial roles in agroecosystems bioactive compounds [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], demonstrating that microbes regulate soil fertility maintenance, plant growth promotion and bioactive compound synthesis. All of these can provide actionably management strategies for quality enhancement through targeted soil property modification and microbial community regulation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\u003ch2\u003e4.5. Limitations and Future Perspectives\u003c/h2\u003e\u003cp\u003eThis study integrates multi-omics approaches to elucidate the relationships among soil properties, microbial communities, and gene expression in A. sinensis, offering a foundational framework for developing region-specific sustainable production practices. However, several limitations should be acknowledged. First, the cultivation of A. sinensis from geo-authentic (daodi) and near-geo-authentic (near-daodi) regions in distinct soil types without cross-replication may introduce confounding factors, potentially compromising the accuracy of the results. Second, although key co-occurrence patterns and underlying mechanisms were identified, the findings do not establish direct causal relationships due to the observational nature of the study.\u003c/p\u003e\u003cp\u003eTo address these gaps, future research should prioritize controlled field experiments with the following designs: (1) systematic cultivation of A. sinensis genotypes (both daodi and near-daodi) across multiple replicated soil types; and (2) active manipulation of soil conditions and microbial assemblages to validate causal linkages. Such efforts would not only enhance the consistency of medicinal plant quality across diverse cultivation regions but also contribute to standardized, sustainable agricultural protocols for A. sinensis and other economically important species.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eWe conducted the first comprehensive investigation into the differential gene expression in Angelica sinensis and the rhizosphere soil microbial community structure between its authentic production region and a near-authentic region, across different growth periods. Utilizing transcriptome sequencing, 16S rRNA sequencing, and ITS sequencing we performed a multi-omics analysis. Furthermore, we analyzed the correlation between soil physicochemical properties and the content of major pharmacologically bioactive compounds in A. sinensis. Our findings indicate that rhizosphere microbial (bacteria and fungi) communities likely influence nutrient absorption, thereby affecting the levels of key active compounds in the plant. Overall, the integrated multi-omics approach provides novel insights into the molecular basis for quality differences in A. sinensis between the authentic and non-authentic regions, and enhances our understanding of how regional factors and growth stages impact the bioactive compounds content. These discoveries could significantly advance strategies for promoting the accumulation of pharmacodynamic compounds and improving the medicinal quality of A. sinensis cultivated in both authentic and non-authentic regions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eThe following abbreviations are used in this manuscript\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e(List them in an alphabetical order)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003cp\u003eThis 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.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eClinical Trial number\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interest:\u003c/h2\u003e\u003cp\u003eThe authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThis 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]; and Intellectual Property Plan Project Gansu Province of China [grant number, 22ZSCQ037].\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, 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.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to express our sincere gratitude to all individuals and organizations who supported and assisted us throughout this research. Special thanks to the following authors: Zengxiang Guo (Min County Angelica sinensis research institute), Shifeng Xu (Agricultural technology extension center of Awu Town, Tanchang County), Jun Luo (Gansu University of Chinese Medicine), Qianqian Tong (School of life science of Lanzhou University), Ting Mao and Yanhao Fang (Institute of Biology, Gansu Academy of Sciences). In conclusion, we extend our thanks to everyone who has supported and assisted us along the way. Without your support, this research would not have been possible.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe raw RNA-seq data generated in this study have been deposited in the NCBI Sequence Read Archive (SRA) under the BioProject accession number PRJNA1259572. The data and materials presented in this study are available on request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLeonardi M, Martelletti P, Burstein R, Fornari A, Grazzi L, Guekht A, et al. The World Health Organization Intersectoral Global Action Plan on Epilepsy and Other Neurological Disorders and the headache revolution: from headache burden to a global action plan for headache disorders. J Headache Pain. 2024;25:4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWei WL, Zeng R, Gu CM, Qu Y, Huang LF. 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Soil Biol Biochem. 2019;136:107521.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Angelica sinensis, transcriptome sequencing, 16S rRNA sequencing, ITS sequencing, microbiome, rhizosphere","lastPublishedDoi":"10.21203/rs.3.rs-7812601/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7812601/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAngelica sinensis, a traditional medicinal herb, exhibits efficacy quality variations strongly tied to geographical origin and the rhizosphere microbiome composition, yet the microbial drivers of its medicinally bioactive compounds synthesis in authentic versus adjacent regions remain poorly understood. Here, we integrated transcriptomic profiling of plant tissues with 16S rRNA (bacteria) and ITS (fungi) sequencing of rhizosphere soils across multiple growth stages in authentic and near-authentic producing regions. By coupling those dates with targeted metabolomics and soil property analysis. Our results revealed significant regional and growth-stage variations in bioactive compounds and soil properties. Specifically, we identified 2,367 DEGs, 417 bacterial ASVs, and 295 fungal ASVs with differential abundance. Key genera, including Vicinamibacter and Bacillus (bacteria) and Bisifusarium and Longitudinalis (fungi), were found to potentially play important roles in secondary metabolite production. Functional disparities (e.g., chitinolysis, fermentation pathways) were observed, and co-occurrence networks demonstrated tight linkages between plant genes and microbiota. Critically, soil parameters such as organic matter, total nitrogen, and soil alkaline phosphatase were identified as key factors influencing the microbial community structure. Furthermore, the rhizosphere microbiome appears to modulate nutrient absorption, thereby affecting bioactive compound accumulation. Collectively, our multi-omics analysis elucidates the mechanistic influence of region and growth stage on A. sinensis quality, offering new insights for optimizing its cultivation and efficacy across diverse regions.\u003c/p\u003e","manuscriptTitle":"Multi-omics Insights into the Effects of Region and Growth Period on the Bioactive Compounds of Angelica sinensis (Oliv.) 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