Xiao-Fang Gong1,2†
Wasim Khan3†Ling Yang1Yu-Kun Chen2Juan Chen2Ling Zhang2Yong Zhang4Ying Zhu2Zhi-Ye Wang2Bing-Lin Zhang5*Lin-Gui Xue1*- 1School of Biological and Pharmaceutical Engineering, Lanzhou Jiaotong University, Lanzhou, China
- 2Institute of Biology, Gansu Academy of Sciences, Key Laboratory of Microbial Resources Exploitation and Utilization in Gansu Province, Lanzhou, China
- 3State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou, China
- 4College of Life Sciences and Engineering, Hexi University, Zhangye, China
- 5Yulong Snow Station of Cryosphere and Sustainable Development, State Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, China
Background: Rhizosphere microorganisms play a critical role in plant growth and medicinal quality, yet their altitudinal patterns and interactions with soil nutrients and bioactive compounds in Angelica sinensis (A. sinensis) remain poorly understood.
Methods: Using Illumina MiSeq sequencing, we analyzed bacterial, fungal, arbuscular mycorrhizal (AM) fungal, and archaeal diversity across an altitudinal gradient, alongside soil physicochemical characteristics and bioactive components.
Results: As cultivation elevation increased, bacterial and fungal diversity initially increased significantly and then stabilized (p < 0.05). In contrast, AM fungal and archaeal communities remained relatively stable. Bacterial communities varied significantly across altitudes (stress < 0.1, p = 0.001), as did soil nutrients and enzyme activities (p < 0.05). Bioactive components, except for ferulic acid, varied significantly with altitude. Redundancy analysis (RDA) confirmed that altitude and soil factors are key drivers of microbial community assembly. Mantel tests and structural equation modeling (SEM) demonstrated significant correlations between soil properties, microbial diversity, and medicinal properties of A. sinensis (p < 0.05).
Conclusion: The mid-to high elevation zone (2520–2717 m) was identified as optimal for both yield and bioactive compound accumulation. These findings deepen the understanding of how microbes adapt to different altitudes in medicinal plants and offer a framework for precise cultivation of A. sinensis, thereby supporting the high-altitude symbiosis theory.
Introduction
Angelica sinensis (Oliv.) Diels (Umbelliferae), a perennial herb with a 2000-year history of medicinal use, holds a unique status in traditional Chinese medicine (TCM) as demonstrated by its inclusion in the classical prescriptions summarized as “ten formulas and nine returns (Chinese Pharmacopoeia Commission, 2020). Its dried roots contain distinctive phthalides such as ligustilide and levistolide, as well as phenolic acids like coniferyl ferulate, which together exhibit multiple therapeutic effects, including regulation of blood formation, anti-inflammatory activity, and neuroprotection (Hook, 2014). A. sinensis prefers cold and humid environments (Xu et al., 2021a). Min County, located in Gansu Province, is the primary production area for A. sinensis in China. A. sinensis cultivated in Min County, known as “Min Gui”, is recognized as the gold standard for quality. This is largely due to its specialized high-altitude cultivation at elevations of 2200–2800 m, where cold and humid conditions promote superior accumulation of bioactive compounds (Xie et al., 2019; Liu et al., 2021a; Wang et al., 2012). However, current cultivation practices lack precision due to limited understanding of how elevation-driven microbiome and soil interactions affect medicinal quality. This study addresses this critical knowledge gap.
Rhizosphere microorganisms are recognized as the “second genome” of plants and are estimated to regulate nearly 80% of secondary metabolite biosynthesis (Wang et al., 2024a), play a crucial role in the growth and development of plants (Li et al., 2024a; Tong et al., 2021), and their community structure and metabolic activities have important effects on plant growth and secondary metabolite biosynthesis (Zhang et al., 2023; Li et al., 2022). Jiang et al. (2021) and Zhu et al. (2021) found that the rhizosphere soil bacterial community of A. sinensis has a significant influence on its growth, medicinal ingredients, and their types, under different cultivation conditions. The community composition and metabolic activity of rhizosphere microorganisms are affected by plant physiological status, geography, and climate (Feng et al., 2020). In northwest China, elevation critically impacts plant growth and medicinal quality by modulating temperature, precipitation, and light regimes (Zhang et al., 2024). While elevation-driven shifts in soil microbial communities exhibit predictable zonation patterns (Zhang et al., 2022a; Wei et al., 2024; Hou et al., 2024; Jiang et al., 2018), research on A. sinensis has largely focused on climatic and edaphic impacts (Liu et al., 2021a; Xi et al., 2021), and the effects of different elevations on the diversity and community structure of microbes in the A. sinensis rhizosphere soil have not been reported to date.
In this study, we systematically investigate interactions among an altitudinal gradient (2200–2800 m), rhizosphere microecosystems, and the biosynthesis of bioactive compounds in A. sinensis, addressing a significant knowledge gap in the research on alpine medicinal plants. Unlike previous elevation studies that focused primarily on individual environmental factors, we employ an integrated microbial community-soil properties-phytochemical approach to: (1) decipher how elevational zonation restructures the diversity and assembly of bacterial, total fungal, and archaeal communities via the distribution of soil nutrients and enzyme activities; (2) quantify the effects on the biosynthesis of key pharmacodynamic compounds; and (3) establish a predictive model linking soil physicochemical parameters and microbial community dynamics to the concentrations of the principal medicinal constituents of A. sinensis through SEM.
Materials and methods
Study site and sample collection
The experimental site was located in the A. sinensis cultivation area of Gansu Province, China. It is situated in a transitional zone from a temperate semi-humid climate to an alpine humid climate, characterized by cold and damp conditions, with an average annual temperature of 5.5°C, total annual precipitation of 635 mm, and average relative humidity of 68%. The soil type is chernozem, characterized by fine clay and low sand content. Six experimental sites at different altitudes ranging from 2210 to 2887 meters (m), including 2210 m (H1, N 34° 13′ 35″, E 104° 14′ 8″), 2425 m (H2, N 37° 1′ 1″, E 103° 7′ 38″), 2520 m (H3, N 34° 17′ 25″, E 104° 2′ 21″), 2615 m (H4, N 34° 31′ 22″, E 104° 28′ 50″), 2717 m (H5, N 34° 38′ 1″, E 103° 28′ 52″), and 2887 m (H6, N 34° 42′ 3″, E 104° 6′ 4″), were selected to transplant A. sinensis seedlings (variety of Mingui No. 1) with uniform transplanting density and under the same manual weeding regime. Samples were collected from H1 to H6 at the medicinal period (28 September 2024) of A. sinensis, using the five-point “S”-shaped sampling method, and five plants were combined to form one sample, and six samples were collected from each elevation site (H1–H6), resulting in a total of 30 samples. During sampling, A. sinensis plants with rhizosphere soil were placed in sterile, labeled, self-sealing bags and brought back to the laboratory in an icebox. Subsequently, the roots of A. sinensis were gently shaken to remove loosely adhering soil, and then the remaining soil tightly attached to the root surface was carefully brushed off with a sterile brush on a laminar flow bench to obtain rhizosphere soil samples (Zhu et al., 2021). Each rhizosphere soil sample was divided into three parts. One portion was placed in sterile self-sealing bags and stored at -80°C for high-throughput sequencing; one part was stored at 4°C for microbial biomass carbon (MC) and microbial biomass nitrogen (MN); the other part was air-dried at room temperature and passed through a 0.149-mm sieve to assess the soil physicochemical properties, soil nutrients, and soil enzymes. The contents of medicinal components in A. sinensis were measured after natural drying. All indicators were determined six replicates.
Physicochemical properties, soil nutrients, and enzyme activities of rhizosphere soil
Soil pH was measured using a pH meter, and the soil-to-water ratio was 1:2.5. The electrical conductivity (EC) was measured using an electrical conductivity meter (Lei-ci PHS-3C, Shanghai Yidian Scientific Instruments Co., Ltd, Shanghai, China) with a soil-to-water ratio of 1:2.5. Soil water content (SWC) was determined by the gravimetric method. Soil MC and MN were calculated using the fumigation-extraction method, MC by a carbon auto-analyzer (Phoenix 8000, Teledyne Tekmar, USA), and MN by a flow injection analyzer (FIAStar 5000, Foss, Denmark), respectively. Organic matter (OM) content of the rhizosphere soil was determined using the H2SO4-K2Cr2O7 oxidation method. Total nitrogen (TN) was determined using the Kjeldahl method, and Alkali-hydrolyzed nitrogen (AN) was determined by alkaline hydrolysis distillation method with a fully automatic Kjeldahl nitrogen analyzer (K1100, Hanon Instruments, Jinan, China). Available phosphorus (AP) was determined by the molybdenum-antimony colorimetric method using an eight-cell UV spectrophotometer (TU-1950, Beijing Puxi Technology Co., Ltd, Beijing, China). The available potassium (AK) in the soil was determined using the flame photometric method with atomic absorption spectrophotometry (A3AFG-00, Beijing Puxi Technology Co., Ltd, Beijing, China) (Bao, 2011). Urease (Ur) was measured by the sodium phenol-sodium hypochlorite colorimetric method. Catalase activity (Ca) was determined by the potassium permanganate titration method. Sucrase activity (Sa) was quantified using the 3,5-dinitrosalicylic acid colorimetric method. Acid Phosphatase (Acp) was assessed using phenylene disodium phosphate colorimetry. All soil enzyme indices were calculated by ultraviolet spectrophotometry (TU-1950, Beijing Puxi Technology Co., Ltd, Beijing, China).
Determination of phenolic volatile oil components in A. sinensis
The contents of the five phenolic volatile oils in A. sinensis roots were determined using high-performance liquid chromatography (HPLC). The chromatographic conditions were as follows: a MerkRP-C18 column (250.0 mm × 4.6 mm, 5 µm) was used with acetonitrile (B)-1% acetic acid (A) as the mobile phase, under gradient elution. The detection wavelength was 280 nm, column temperature was 30°C, flow rate was 1 mL/min, and injection volume was 20 µL (Luo et al., 2021).
DNA extraction, PCR amplification, and high-throughput sequencing of rhizosphere soil
The E.Z.N.A.® soil DNA kit (Omega Bio-tek, Norcross, GA, U.S.) was used to extract the total genomic DNA of soil samples according to the manufacturer’s instructions. The quality and concentration of DNA were determined by 1.0% agarose gel electrophoresis and a NanoDrop® ND-2000 spectrophotometer (Thermo Scientific Inc., USA) and samples were stored at -80°C before further use. Using the extracted DNA as a template, the hypervariable region (V3–V4) of the 16S rRNA gene of the bacterial community, the internal transcribed spacer (ITS) region of fungi, the hypervariable region (V4–V5) of the 18S rRNA gene of AM fungi, and the hypervariable region (V4–V5) of the 16S rRNA gene of archaea, were amplified using the primer pairs 338F-806R, ITS1F-ITS2R, AMV4-5NF-AMDGR and 524F10extF-Arch958RmodR, respectively (Table 1) (Shehata and Newmaster, 2023; Tian et al., 2022; Van et al., 2014; Liu et al., 2021b).
Table 1. The sequence information of primer pairs.
PCR was performed on an ABI GeneAmp® 9700 PCR thermocycler (Applied Biosystems, CA, USA). The PCR reaction system included 4 μL of 5 × TransStart FastPfu buffer, 2 μL of 2.5 mM dNTPs, 0.8 μL of each upstream (5 µM) and downstream (5 µM) primer, 0.4 μL of TransStart FastPfu DNA polymerase, and 10 ng of template DNA, supplemented to 20 μL with ddH2O. The PCR amplification cycling conditions were as follows, initial denaturation at 95°C for 3 min, followed by 29 cycles for bacterial primers, 35 cycles for fungal and archaeal primers, and 25 cycles for AM fungal primers, each consisting of denaturation at 95°C for 30 s, annealing at 55°C for 30 s, and extension at 72°C for 45 s. A final extension step was performed at 72°C for 10 min, and then stored at 4°C. Each sample was amplified three times. Subsequently, the PCR products from the same template were pooled, separated on 2% agarose gels, purified using the Axyprep DNA gel extraction kit (Axygen Biosciences, Union City, CA, USA), and then quantified using a Quantus ™ Fluorometer (Promega, USA). The purified amplification products were combined in equimolar amounts, and paired-end sequencing was performed using a MiSeq PE300 platform (Illumina, San Diego, USA) according to the standard protocols of Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China).
Data processing and statistical analysis
Fastp (https://github.com/OpenGene/fastp, version 0.19.6) was used to control the quality of the raw paired-end reads (Chen et al., 2018). Subsequently, FLASH version 1.2.11 (Magoč and Salzberg et al., 2011) was used to merge overlapping paired-end reads. Following this, UPARSE (http://drive5.com/uparse/, Version 7.1) was used to cluster quality-controlled concatenated sequences into Operational Taxonomic Units (OTUs) based on a similarity threshold of 97%, while eliminating chimeric sequences (Edgar, 2013). To mitigate the impact of sequencing depth on subsequent analyses of Alpha and Beta diversity, the sequence number of all samples was rarefied, and each sample still yielded an average coverage of 97.00%. The Ribosomal Database Project (RDP) classifier (http://github.com/rdpstaff/classifier, version 2.13) was then used for comparison with the Silva 16S rRNA (v138) database for bacterial and archaeal sequences, MaarjAM database for AM fungal sequences, and Unite 9.0 databases for fungal OTU species taxonomy annotation, respectively, with a confidence threshold of 70% to calculate the community composition of each sample at the different taxonomic classification levels (Wang et al., 2007).
Based on the OTU information, alpha (α) diversity indices, including the Shannon index and ACE richness, were calculated using Mothur v1.30.1 (Schloss et al., 2009). To evaluate the beta (β) diversity of microorganisms, we employed non-metric multidimensional scaling analysis (NMDS) based on Bray Curtis dissimilarity matrix, using the Vegan v2.5–3 package. In addition, LEfSe was used to calculate the multilevel species discriminant analysis of microbial communities and to identify taxa with significantly different relative abundances among elevations (Han et al., 2024; Ren et al., 2022). Soil physicochemical properties, nutrients, and medicinal ingredients were analyzed using SPSS 23 software, and Student’s t-test was used for pairwise comparisons, while one-way ANOVA was applied for multi-group comparisons across elevations. Redundancy Analysis (RDA) was used to investigate the correlations between rhizosphere soil microbial communities and soil factors. Moreover, the Mantel test was performed to analyze the correlations between the dominant rhizosphere soil microbial genera, soil factors, and medicinal ingredients (with high content and significant differences, p < 0.05). Diversity, composition, and species difference maps of bacteria and fungi were generated using R software. The Mantel test correlation analysis was conducted using ChiPlot software (https://www.chiplot.online/). Nonlinear regression modeling of the effects of elevation on root weight and bioactive component concentrations was performed using Origin 9.0 (Origin Lab, USA). Finally, SEM was adopted to evaluate the effects of elevation on soil characteristics (including soil indicators and soil microbial communities) and medicinal ingredients (which showed significant differences between different cultivation elevations, p < 0.05) with the piecewise SEM package.
Results
Diversity of microorganisms in the rhizosphere soil of A. sinensis at different cultivation elevations
The α-diversity index measures the diversity and richness of microbial communities. In this study, the Shannon index for bacteria in H1 was significantly lower compared to H2 through H6. Additionally, no significant differences were observed among the indices of H2 to H6. The ACE index showed an upward trend from H1 to H6. Specifically, H2 was significantly lower than H4, H5, and H6, while H1 was significantly lower than H3, H4, H5, and H6. However, no significant differences were found between H1 and H2, H2 and H3, or H3 and H6 (p < 0.05, Figure 1A). The Shannon index for fungi increased consistently from H1 to H6. H1 was significantly lower than H3 to H6, while no significant differences were found between H2 and H6. The ACE index demonstrated an increase from H1 to H6, with H6 being significantly higher than H1 to H5. Furthermore, the values from H3 to H5 were also higher than those of H1. However, no significant differences were observed between H1 and H2, as well as between H3 and H5 (p < 0.05, Figure 1B). For AM fungi, the Shannon index showed only limited variation among elevations, with a few pairwise comparisons showing significant differences, whereas no significant differences were detected between H1 and H2, H2 and H3, H4 and H5, or H5 and H6. The ACE index showed no significant differences among elevations (Figure 1C). For archaea, no significant differences were observed in either diversity (Shannon) or richness (ACE) indices across the six elevations (Figure 1D). These findings indicate that the diversity and richness of bacterial and fungal communities in the rhizosphere soil of A. sinensis show an overall increasing trend with elevation, whereas AM fungal and archaeal communities remain relatively stable. Thus, the response of α-diversity and richness to elevation is more pronounced for bacteria and fungi than for AM fungi and archaea.
Figure 1. Diversity of microbial communities in the rhizosphere soil of A. sinensis at different cultivation elevations. Significant differences among means were evaluated by one-way ANOVA followed by Fisher’s least significant difference (LSD) test (p ≤ 0.05; n = 6 per elevation); different lowercase letters indicate statistically significant differences at the at p ≤ 0.05; the same letters denote no significant differences. H1: 2210 m, H2: 2425 m, H3: 2520 m, H4: 2615 m, H5: 2717 m, and H6: 2887 m. (A–D) α-diversity (Shannon and ACE indices) for bacterial (A), fungal (B), AM fungal (C), and archaeal (D) communities. (E–H) β-diversity of the corresponding microbial communities based on Bray–Curtis dissimilarities (bacteria: E; fungi: F; AM fungi: G; archaea: H).
The β-diversity of bacterial, fungal, AM fungal, and archaeal communities in the rhizosphere soil of A. sinensis was analyzed at different cultivation elevations using Bray–Curtis dissimilarities and NMDS, with ANOSIM employed to test for differences among elevations. The NMDS results showed that, except for AM fungi, the grouping ellipses of H1–H6 were clearly separated in the ordination space for bacteria, fungi, and archaea (Figures 1E–H). All NMDS analyses had stress values < 0.2, and ANOSIM indicated significant differences in community structure among elevations for bacteria (R = 0.6916, p = 0.001), fungi (R = 0.6328, p = 0.001), and archaea (R = 0.5924, p = 0.001). These findings reveal notable elevational differences in bacterial community structure, with more moderate but still detectable variations in fungal and archaeal communities, while AM fungal communities showed only minor differences among elevations.
Microbial composition analysis in the rhizosphere soil of A. sinensis at different cultivation elevations
The rhizosphere microbial composition of A. sinensis cultivated at different elevations was analyzed (Figure 2). The numbers of common and unique OTUs for bacteria, fungi, AM fungi, and archaea are shown in Figures 2A-D, respectively. The number of OTUs showed a general trend of initially increasing and then decreasing from H1 to H6. Except for fungi, the other three types of microbes exhibited their highest OTU numbers at H4. The composition of microbial communities at the phylum level across various cultivation elevations is shown in Figures 2E-G and Table 2. The dominant bacteria in the rhizosphere soil were similar across different elevations, including Pseudomonadota, Actinomycetota, Acidobacteriota, Chloroflexota, Bacillota, and Bacteroidota. The dominant fungi consisted of Ascomycota, Mortirellomycota, Basidiomycota, Chytridiomycota, and Olpidiomycota. Ascomycota had the highest relative abundance at all elevations (54%), with H1 showing the highest proportion (95.5%). AM fungal communities were composed almost entirely of Glomeromycota at all elevations (H1–H6). The dominant archaea comprised Thermoprotcota. The relative abundance of Thermoprotcota was the highest in H1-H6, with all exceeding 91%, and H3 has the highest proportion, at 98.3%. In addition, the dominant genera (top 10 in relative abundance) microbes in the rhizosphere soil of A. sinensis at different cultivation elevations is shown in Figure 2H-K, respectively. The dominant bacteria consisted of Arthrobacter, unclassified_Vicinamibacterales, Sphingomonas, unclassified_Vicinamibacteraceae, unclassified_Gemmatimonadaceae, Nitrospira, Niallia, and Blastococcus. The dominant fungi included Mortierella, Plectosphaerella, Bisifusarium, Pseudombrophila, Tausonia, Chordomyces, Fusarium, Trichocladium, Linnemannia, and Gibellulopsis. Compared to H3-H6, there were apparent differences in the compositional abundance of H1 and H2 especially for several dominant fungal genera. The dominant AM fungi mainly consisted of Claroideoglomus, Glomus, and Diversispora, and there were larger differences in the compositional abundance of H1 and H5 compared to other groups. Besides, the dominant archaea included unclassified_Nitrososphaeraceae, Candidatus_Nitrosocosmicus, and Candidatus_Nitrososphaera, and there were also noticeable differences in the abundance of different genera among the six groups. These results reveal that the composition of A. sinensis rhizosphere soil microbes varied significantly with elevation.
Figure 2. Composition of bacteria, fungi, AM fungi and archaea in the rhizosphere soil of A. sinensis at different cultivation elevations. H1: 2210 m, H2: 2425 m, H3: 2520 m, H4: 2615 m, H5: 2717 m, and H6: 2887 m; n = 6 per elevation; (A–D) Venn diagrams of OTU of bacteria (A), fungi (B), AM fungi (C) and archaea (D); (E–G) stacked bar plots showing the relative abundance of dominant phyla of bacteria (E), fungi (F),and archaea (G); (H–K) show the relative abundance of the top 10 most abundant genera of bacteria (H), fungi (I), AM fungi (J) and archaea (K) in each group, and the sum of relative abundances for these selected genera equals 100% per sample, and other low-abundance genera are not displayed.
Table 2. The differences of microbial species on phylum level in rhizosphere of A. sinensis at different cultivated elevations.
Differences in microbial composition at the species level of A. sinensis rhizosphere soil at different cultivation elevations
LEfSe analysis was applied to the taxonomic compositions of rhizosphere soil microbial communities to identify differences in species from phylum to genus for A. sinensis at different cultivation elevations. A LDA score above 4.0 was selected as the threshold for differential analysis (Figure 3). There were more species of bacteria significantly (p < 0.05) enriched in H1-H6 from phylum to genus, with the phylum of Actinomycctota enriched in H1. The phylum enriched in H2 was Bacillota. In H3, Choroflexota was an enriched phylum, belonging to the genus unclassified_Vicinamibacteraceae. No significant phylum- or genus-level bacterial biomarkers were detected at H4. The phylum enriched in H5 was Bacteroidota. The phylum enriched in H6 was Acidobacteriota, and unclassified_Vicinamibacterales at the genus level (Figure 3A). Similarly, fungal community composition also exhibited significant elevational niche partitioning (LDA = 4.0, LEfSe), with distinct phylum-genus associations. The phylum Ascomycota dominated in H1, harboring the discriminative genus of Chordomyces. There was no phylum enriched in H2, H3, and H5, but the enriched genus in H2 was Pseudombrophila, in H3 was Mortierella, Wardomyces, and Subulicystidium, while Titaea and Leptosphaeria were enriched in H5. The phylum Basidiomycota was enriched at H4, with the characteristic genus Tausonia. The phylum enriched in H6 was Mortierellomycota, and the genus enriched was Clonostachys (Figure 3B). However, there were fewer species of AM fungi enriched from phylum to genus, and only H4–H6 showed enrichment at the family and genus levels. H4 was enriched in the genus Glomus, H5 in Claroideoglomus, and H6 in Diversispora, indicating that AM fungi have fewer variations among different groups (Figure 3C). Additionally, the group of archaea (H1-H6) from phylum to genus was less enriched than bacteria and fungi, but more than AM fungi. At H1, Thermoproteota was exclusively enriched at the phylum level, with no genus-level specialization detected. At H2, Halobacteriota was enriched at the phylum level, again with no enriched genus. In H3 and H4, Candidatus Nitrososphaera and unclassified_Nitrososphaeraceae were enriched at the genus level, respectively. At H5, Thermoplasmatota (phylum) and the genus unclassified_Marine_Group_II were enriched (Figure 3D). Accordingly, there were significant differences in the enriched groups of bacteria, fungi, and archaea, whereas AM fungi showed comparatively limited enrichment in the rhizosphere microbiota of A. sinensis cultivated at different elevations. This indicates that the microbial community structure in the rhizosphere of A. sinensis is notably influenced by the altitude at which it is grown.
Figure 3. LEfSe analysis of rhizosphere soil microorganisms of A. sinensis at different cultivated elevations. LDA > 4. H1: 2210 m, H2: 2425 m, H3: 2520 m, H4:2615 m, H5: 2717 m, and H6: 2887 m; n = 6 per elevation; (A) bacteria, (B) fungi, (C) AM fungi, and (D) archaea.
Soil physicochemical properties, nutrients, and enzyme activities in the rhizosphere soil of A. sinensis at different cultivation elevations
Significant elevational variations (p < 0.05) were observed in rhizosphere soil properties of A. sinensis, including physicochemical parameters, nutrient availability, and enzyme activities (Table 3). Despite minimal latitudinal (LA) variation among sampling sites (range: 34.23°N-37.02°N), marked elevational differentiation emerged. Specifically, H2 showed significantly higher values compared to the other elevation groups (H1, H3-H6), while no significant differences were observed among the remaining sites. Consistent with the altitudinal pattern, the soil pH at H2 was significantly higher than that of other groups. Notably, unlike the generally uniform pH distribution typically observed along elevation gradients, H4 and H6 showed significantly lower values than all other sites (p < 0.05). However, no significant difference was detected between H4 and H6, or among the remaining groups (H1, H3, and H5), respectively. The soil EC varied across the altitudinal gradients, with H1 showing the highest value, while H4 and H5 exhibited the lowest. No significant differences were observed between H4 and H5 or among the remaining groups (H2, H3, and H6), respectively. SWC, OM, TN, and AN were all significantly higher in H6 than in other groups (p < 0.05), and there was marked inter-group heterogeneity. AP in H2 was significantly higher than in other groups, and there were significant differences between the groups (p < 0.05). The content of AK of H5 and H6 was significantly higher than that of H4, and H4 was significantly higher than that of the other groups (p < 0.05). The remaining groups did not differ substantially. The soil MN of H6 was significantly higher than others, H2 being significantly higher than H1 and H3-H5, and no significant difference was observed in H3 and H5 (p < 0.05). MC content of H2 and H4 was highest, followed by H5, which was significantly higher than the remaining groups (p < 0.05), with minimal variation among the remaining groups. Similarly, the soil Ur of H2 was significantly higher than that of other groups (p < 0.05), followed by H5, H4, and H6, which were significantly higher than H1 and H3. However, no significant difference was found between H4 and H6, or between H1 and H3. While the soil Ca of only H3 was significantly lower than that of other groups (p < 0.05), H1, H2, and H6 were significantly higher than that of H4 and H5, and there were no statistical differences in the three groups (H1, H2, and H6) or the other two groups (H4 and H5). Moreover, H6 outperformed all groups of Sa and Acp, and there were significant differences between other groups (p < 0.05).
Table 3. Physicochemical properties, nutrient contents and enzyme activities of rhizosphere soil of A. sinensis at different cultivation elevations.
Root weight and content of active ingredients of A. sinensis at different cultivation elevations
Significant elevational variations were observed in the root weight (RW) and concentrations of pharmacologically active constituents (p < 0.05), with distinct compound-specific distribution patterns (Table 4). Notably, RW exhibited a unimodal distribution pattern across cultivation elevations, peaking significantly at H4 (p < 0.05), with progressive increases from H1 to H4 followed by declines from H4 to H6. In addition, although ferulic acid (FA) showed no significant differences across elevations, the other medicinal components of A. sinensis varied significantly with cultivation elevation. Their concentrations were comparable among H4–H6 and were significantly higher than those in H1–H3 (p < 0.05), with no significant differences within H4–H6 or within H1–H3. Studies have shown that CF, a phenolic component with low stability, can easily convert into FA and coniferyl alcohol in A. sinensis. It can also undergo mutual conversion with FA in A. sinensis. The total amount of FA, including both the converted FA and that derived from CF, is referred to as “total FA”. Hence, it can be inferred that the total FA content in A. sinensis cultivated at higher altitudes is likely to be significantly higher. Consistent with this, CF levels were significantly lower at H1 and H2 compared to H3–H6, while H3 showed significantly higher CF levels than H4–H6 (p < 0.05). No significant differences were detected between H1 and H2, or among H4–H6. Similarly, levistolide A (LEA) levels were significantly higher at H5 compared to the other groups. H1 and H2 were significantly lower than H3, H4, and H6, while no significant differences were observed between H1 and H2 or among H3, H4, and H6. This indicates that the formation and accumulation of different pharmacodynamic components of A. sinensis responded differently to the cultivation elevation.
Table 4. Root weight and content of main effective components of A. sinensis at different cultivated elevations.
Interaction between rhizosphere soil microbial communities, soil environmental factors, and pharmacodynamic constituents
The redundancy analysis (RDA) of microbial community structures and soil environmental factors in the rhizosphere soil of A. sinensis was shown in Figure 4. The results indicated that RDA1 and RDA2 accounted for 23.61% and 18.65% (a total of 42.26%) of the total variation in bacterial communities, respectively (Figure 4A). For fungal communities, CCA1 and CCA2 explained 12.84% and 9.64% (a total of 22.48%) of the total variation, respectively (Figure 4B). Furthermore, RDA1 and RDA2 explained 62.92% and 11.14% of the total variation in AM fungal community structure, respectively, collectively accounting for 74.06% of the changes (Figure 4C). In contrast, for archaeal communities, the first two axes (RDA1 = 39.20%, RDA2 = 7.35%) collectively explained 46.55% of the changes (Figure 4D). These findings support the reliability of the RDA results. As shown in Figure 4, soil pH, AP, MC, MN, and Sa showed broadly similar associations with all microbial communities. However, the effects of other soil factors varied across different microbial groups. Specifically, EL had a pronounced impact on bacterial and fungal communities but exerted relatively weaker effects on AM fungi and archaea. LA, EC, and Ur demonstrated stronger influences on fungal, archaeal, and bacterial communities, while their impact on AM fungi was notably weaker. AK had a stronger impact on bacterial and archaeal communities, but exerted less influence on fungal and AM fungal communities. Additionally, AN and Acp significantly affected bacterial, fungal, and AM fungal communities, with a weaker effect observed only on archaea. Conversely, SWC, OM, and TN had substantial impacts on AM fungal communities but exhibited relatively weaker effects on other microbial groups, including bacteria, fungi, and archaea.
Figure 4. The correlation analysis between soil factors and microbial communities. (A) RDA of bacteria; (B) CCA of fungi; (C) RDA of AM fungi; (D) RDA of archaea.
The Mantel test was used to assess the correlation between microbial diversity indices, soil factors, root weight, and active ingredients of A. sinensis. The resulting heatmap was shown in Figure 5A. A significant correlation was observed between environmental factors and microbial diversity indices, particularly for bacterial and fungal indices. Specifically, EL exhibited positive correlations with Ur (p < 0.05), AM-s, Ar-a (p < 0.01), SWC, TN, AN, OM, Ar-s, Sa, as well as bacterial and fungal diversity indices (p < 0.001), while showing negative correlations with EC (p < 0.01), pH and AK (p < 0.001). LA demonstrated positive correlations with MN (p < 0.05), pH (p < 0.01), AP, Ur, and MC (p < 0.001), and negative correlations with AK, Ar-s (p < 0.05), and EC (p < 0.001). SWC exhibited a significantly negative correlation with pH (p < 0.01), while demonstrating strong positive associations with all other measured soil indices. Notably, SWC showed the most pronounced positive correlations with TN, AN, OM, Acp, Sa, MN, and F/Ar-a (p < 0.001). Additionally, pH, EC, and AK were negatively correlated with most of the soil factors. Notably, pH showed highly significant negative correlations with TN, AN, OM, Acp, Sa, B/F-a, and B/F/Ar-s (p < 0.001). EC exhibited highly significant negative correlations with Ur and MC, while being positively correlated with AK (p < 0.001); AK revealed highly significant negative correlations with Sa, Ur, B/F-s, and B/F-a (p < 0.001). Furthermore, TN, AN, OM, Acp, and Sa demonstrated positive correlations with most soil factors. In particular, TN was highly significantly positively correlated with AN, OM, Acp, Sa, MN, and F-a (p < 0.001); AN showed highly significant positive correlations with OM, Acp, Sa, B/F/Ar-s, and F/Ar-a (p < 0.001); OM exhibited highly significant positive correlations with Acp, Sa, MN, B/F-s, and F-a (p < 0.001); Acp showed highly significant positive correlations with MN and F-a (p < 0.001); Sa revealed highly significant positive correlations both with B/F-s and F-a indexes (p < 0.001). Ur was significantly positively correlated with MC (p < 0.001). Lastly, B-s and B-a showed significant positive correlation with F-s and F-a, and Ar-s and Ar-a (p < 0.001), while the correlation between AM-s and AM-a was mainly not significant (p < 0.05). Moreover, the correlation analysis results between the root weight and pharmacodynamic components of A. sinensis and environmental, soil factors, as well as microbial diversity indices revealed that RW, CF, LI, and LEA exhibited significant correlations with these factors. RW exhibited highly significant positive correlations with EL, SWC, TN, OM, MN (p < 0.001), pH, EC, AN, Ca, Acp, MC, F-a, Ar-s (p < 0.01), Sa, B-s, F-s, and Ar-a (p < 0.05). Among the pharmacodynamic components, CF demonstrated highly significant positive correlations with AK (p < 0.001), EC, Ca, Ur, Sa (p < 0.01), MC, and F-s (p < 0.05). LI was significantly positively correlated with EL, pH, AN, B-s, F-s, F-a, AM-s, Ar-s (p < 0.001), TN, AK, Acp, Sa (p < 0.01), LA, SWC, EC, AK, Ca, Ur, MN, and Ar-a (p < 0.05). LEA showed a significantly positive correlation with EL, Ur, B-s, B-a, F-s, F-a (p < 0.001), LA, pH, EC, AP, AK, MN, Ar-s (p < 0.01), and Sa (p < 0.05).
Figure 5. Interaction analysis between soil factors in the rhizosphere soil of A. sinensis and the content of medicinal components. The Pearson correlation coefficient was calculated in correlation analysis. (*p < 0.05; **p < 0.01; ***p < 0.001). (A) Mantel test heatmap of soil factors between pharmacologically active constituents; (B) Mantel test heatmap of differential microbial species of dominant genera between pharmacologically active constituents. B-, F-, AM- and Arc-: bacteria, fungi, AM fungi and archaea, respectively; RW: root weight; CF: coniferyl ferulate; LI: ligustilide; LEA: Levistolide A; B/F/AM/Ar-s: bacterial/fungal/AM fungal/archaeal Shannon; B/F/AM/Ar-a: bacterial/fungal/AM fungal/archaeal ACE; B-Ar: g_Arthrobacter; B-o-Vi: unclassified_Vicinamibacterales; B-Nit: g_Nitrospira; B-Sp: g_Sphingomonas: B-f-Vi: unclassified_Vicinamibacteraceae; B-Nia: g_Niallia; B-f-Ge: g:unclassified_f:Gemmatimonadaceae; B-Bla: g:Blastococcus; F-Mo: g_Mortierella; F-Ps: g_Pseudombrophila; F-Pl: g_Plectosphaerella; F-Ta: g_Tausonia; F-Ch: g_Chordomyces; F-Tr: g_Trichocladium; F-Ti: g_Titaea; F-Cl: g_Clonostachys; F-Wa: g_Wardomyces; F-Lo: g_Lophotrichus; AM-Gl: g_Glomus; AM-Cl: g_Claroideoglomus; AM-Di: Diversispora; A-CN: g_Candidatus_Nitrosocosmicus; A-f-Ni: unclassified_f_Nitrososphaeraceae; A-MG: g_norank_o_Marine_Group_II.
The Mantel test was also used to analyze the correlation between the dominant differential genus of microbes and the characteristic indicators of A. sinensis (Figure 5B). The results showed a significant correlation between the dominant differential genus of microbes. Specifically, B-Ar in bacteria exhibited a significantly positive correlation with F-P l (p < 0.01), and A-CN in archaea (p < 0.001), but showed significant negative correlations with B-Nit, F-Mo (p < 0.05), F-Tr, and A-f-Ni (p < 0.001). B-o-Vi showed significant positive correlations with A-f-Ni (p < 0.05), AM-Di (p < 0.01), B-f-Vi, and F-Cl (p < 0.001), while showing significant negative correlations with B-Nia and F-Ps (p < 0.05), as well as B-Sp and B-f-Ge (p < 0.01). B-f-Vi was significantly positive correlation with F-Wa, and A-CN (p < 0.001), but was significantly negatively correlated with F-Ps (p < 0.05), B-Sp, B-f-Ge (p < 0.001). B-Sp showed significant positive correlations with F-Ta and F-Tr (p < 0.05), F-Lo (p < 0.01), as well as B-f-Ge and B-Nit (p < 0.001), while showing significant negative correlations with B-Bla, F-Wa, and A-CN (p < 0.001). B-f-Ge also showed a significantly positive correlation with B-Nia, F-Ta (p < 0.05), B-Nit (p < 0.01), F-Tr (p < 0.001), but was significantly negatively correlated with AM-Di (p < 0.05), F-Wa, A-CN (p < 0.01), B-Bla (p < 0.001). B-Nit showed a significantly positive correlation with F-Lo (p < 0.001), while was significantly negatively correlated with F-Ch, F-Wa (p < 0.05), F-Pl (p < 0.01), B-Bla, and A-CN (p < 0.001). B-Nia showed significant negative correlations with B-Bla, F-Cl, and A-f-Ni (p < 0.05). B-Bla exhibited significant positive correlations with F-Wa (p < 0.01), F-Ta, and A-CN (p < 0.001). Additionally, F-Mo in fungi demonstrated a significantly positive correlation with AM-Gl (p < 0.05) and F-Tr (p < 0.001), but was negatively correlated with F-Ch, AM-Cl (p < 0.01). There was significant positive correlation between F-Pl and AM-Cl (p < 0.05) and F-Ps showed a significantly negatively correlated with A-f-Ni (p < 0.05), and A-AN (p < 0.01). F-Ta was significantly positively correlated with F-Tr (p < 0.01). F-Ch demonstrated a significantly positive correlation with AM-Cl (p < 0.05), but was a negatively correlated with F-Tr, and A-f-Ni (p < 0.05). F-Tr showed a significant positive correlation with AM-Gl and a significant negative correlation with AM-Cl (p < 0.01). F-Ti was significantly positively correlated with F-Lo (p < 0.05), and A-MG (p < 0.001). F-Cl was significantly positively correlated with AM-Di (p < 0.05), and A-f-Ni (p < 0.01). There was a significant positive correlation between F-Wa and A-CN (p < 0.001). AM-Cl in AM fungi was significantly negatively correlated with AM-Di (p < 0.05) and AM-Gl (p < 0.001). Furthermore, AM-Di in AM fungi showed a significantly positive correlation with A-f-Ni in archaea (p < 0.01). Lastly, A-CN in archaea exhibited a significantly negative correlation with archaeal A-f-Ni (p < 0.05). In addition, the correlation between the characteristic indicators of A. sinensis and the dominant difference genus of microbes showed that RW, LI and LEA were closely related to the soil dominant difference flora. Specifically, B-Sp, B-f-Ge, B-Nit, B-Nia (p < 0.001), and B-Bla (p < 0.01) in bacteria, F-Tr, F-Cl (p < 0.001) in fungi, AM-Di (p < 0.05) in AMF, and A-f-Ni (p < 0.001) in archaea, were significantly positively correlated with RW. B-Nit (p < 0.001), and B-Bla (p < 0.01) in bacteria, F-Ps (p < 0.001) in fungi, A-CN (p < 0.01) in archaea, were significantly positively correlated with LEA. B-Sp (p < 0.05) and B-Bla (p < 0.01) in bacteria, and F-Wa (p < 0.05) in fungi, as well as A-CN (p < 0.01) in archaea, were significantly positively correlated with CF. Additionally, B-o-Vi (p < 0.001), B-f-Vi (p < 0.01), and B-Bla (p < 0.05) in bacteria, F-Mo, F-Ps (p < 0.05), F-Ch (p < 0.001) in fungi, and A-f-Ni (p < 0.001) in archaea were significantly positively correlated with LI.
Nonlinear regression modeling revealed a Gaussian distribution pattern for elevation-dependent variation of RW (Adj. R² = 0.846) and the bioactive components FA (Adj. R² = 0.963), CF (Adj. R² = 0.897), and LEA (Adj. R² = 0.883) in A. sinensis. In contrast, the response of LI (Adj. R² =0.859) to elevation was better described by a Gaussian Amp model (Figure 6). All measured parameters exhibited a unimodal trend, initially increasing and subsequently decreasing with increasing elevation, but with distinct elevational optima: RW, FA, and CF peaked at 2615 m, whereas LI and LEA reached their maxima at 2520 m. Notably, the post-peak decline in LI and LEA at higher elevations (> 2615 m) was less pronounced compared to other parameters, indicating that elevated altitudes are more conducive to the production and accumulation of bioactive constituents in A. sinensis.
Figure 6. Nonlinear regression modeling for elevation effects on root weight and bioactive compound content of A. sinensis. Nonlinear fitting of elevation between root weight (A), Ferulic acid (B), Coniferyl ferulate (C), Ligustilide (D), and Levistolide A (E).
Moreover, SEM was carried out to determine the main drivers influencing the content of active ingredients of A. sinensis. The variables that influence soil microbial communities in the RDA of soil indicators and the dominant phylum composed of microbial communities were chosen as predictors. The SEM analysis further indicated that elevation exerts both direct and indirect effects on the content of active ingredients. The indirect effect is primarily mediated through its influence on the soil microbial community, which subsequently affects the content of medicinal components. Alternatively, elevation may influence soil nutrients and enzyme activity via microorganisms, which in turn affect the content of primary active constituents (Figure 7, S1 and S2). Dominant phyla Actinomycetota, Thermoplasmatota, and Ascomycota (Figures 7B, C, S1B, S1C, S2B, and S2C), soil nutrient AK and OM (Figures 7D, S1D, and S2D), as well as soil enzyme activities (Figure 7E), were the most significant and influential factors affecting the content of all active ingredients. Therefore, the content of medicinal ingredients in A. sinensis is influenced by the synergistic effects of soil microorganisms, soil nutrients, and soil enzyme activities in growth environments along the elevational gradient.
Figure 7. Structural equation models (SEM) showing the direct and indirect effects of elevation, microbial composition, and soil indicators on the content of CF in A. sinensis. The arrows indicating the hypothesized direction of causality, with the yellow arrows indicating positive effects and blue arrows indicating negative effects. The width of the lines corresponds to the strength of the associations, and numbers adjacent to the lines represent path coefficients. *, ** and *** indicated p < 0.05, p < 0.01, and p < 0.001. The multilayered rectangles indicate the significant predictors of soil indicators and soil microbes. (A) SEM of soil factors between CF, predictors importance of influencing factors for content of CF, (B) bacterial communities, (C) fungal communities, (D) soil properties, and (E) soil enzyme activities ranked by random forest. Percentage increases in the MSE (mean squared error) of variables were used to estimate the importance of these predictors, and higher MSE% values implied more important predictors.
Discussion
Rhizosphere soil microbial properties under varying elevations
Elevation can significantly affect soil microbial diversity, community structure, and functions (Hou et al., 2024; Wei et al., 2024). Our findings demonstrate that elevation gradients induce nonlinear variation patterns in rhizosphere microbial diversity of A. sinensis (Figures 1A-H), with bacterial and fungal α-diversity exhibiting higher and more stable values at mid- to high elevations (2520–2887 m). This aligns with previous research findings indicating that the rhizosphere of traditional Chinese medicinal plants exhibits variations in microbial diversity along altitudinal gradients (Liu et al., 2024; Pu et al., 2022; Guo et al., 2020), However, it contrasts with other observations describing changes in rhizosphere bacterial and fungal diversity of Panax ginseng across different elevations in the Jiuzhai valley forest understory (Jin et al., 2024). This highlights the unique adaptation of A. sinensis to high-altitude regions (Xu et al., 2021a). Additionally, the stability of AM fungal and archaeal communities exhibited a negligible response to elevation (Figures 1C-H), suggesting their roles as keystone mutualists in buffering A. sinensis against altitudinal stressors (Liu et al., 2015; Liu et al., 2023; Xie et al., 2025; Nicol et al., 2005).
Furthermore, the relative abundances of dominant bacterial and fungal taxa responded strongly to elevational variation (Figure 2, Table 2). The dominant bacteria are likely stable and form a symbiotic community resulting from long-term interaction between A. sinensis and soil microorganisms (Mu et al., 2022; Zhu et al., 2021). This community performs a range of physiological functions, including iron chelation, phosphorus solubilization, and potassium mobilization (Karagöz et al., 2012). It contributes to the synergistic absorption of mineral elements by medicinal plants and plays both direct and indirect roles in promoting plant growth as well as the formation and accumulation of bioactive compounds in A. sinensis (Xie et al., 2022; Jiang et al., 2024). This, in turn, enabling plant adaptability to the environment (Li et al., 2024b), potentially mediated through root exudates that drive the recruitment of beneficial microbes (Qin et al., 2025).
Soil properties regulate the composition of microbial communities and bioactive components of A. sinensis
In this study, soil pH has a significant effect on the diversity of bacterial, fungal, and archaeal communities in the rhizosphere soil of A. sinensis (Table 3, Figures 4, 5A). This finding is consistent with previous studies (Jiang et al., 2020; Zhang et al., 2022b; Lauber et al., 2009), indicating that soil pH is a key factor influencing microbial community composition in the rhizosphere. Xu et al. (2021b) demonstrated that soil electrical conductivity is a crucial factor influencing soil microbial diversity and community structure. Higher soil EC can affect the growth and reproduction of microbial communities, reducing microbial abundance (Xu et al., 2021b; Aurélien et al., 2022). However, our results differ showing that microbial diversity and richness indices across different cultivation areas did not increase with decreasing soil EC levels (Table 3, Figures 4, 5A). This may be attributed to the fact that the soil conductivity of A. sinensis regions cultivated in this study is within the adaptive range of soil bacteria. Consequently, these variations do not impact the sensitivity or activity of the soil bacteria. Therefore, soil conductivity was not the primary factor driving changes in the soil bacterial and fungal communities in this study.
Tan et al. (2023) reported that soil microbial diversity and community composition serve as critical determinants of soil quality and exert a substantial influence on crop growth. Our study found that soil nutrients and enzyme activities, including soil OM, AN, AK, and soil sucrase, were significantly related to soil bacteria and fungi diversity and richness (Figure 5A). These results indicate that soil nutrients, microbial diversity, and community functions are interdependent and mutually influential (Yan et al., 2024; Li et al., 2024c). Specifically, soil nutrients provide a favorable environment for microbial metabolism, metabolic activity, and soil enzymes, while microbial activity alters soil chemistry, which in turn shapes microbial community structure (Meng and Qin, 2020; Wang et al., 2020). Furthermore, Shen et al. (2023) showed that variations in soil bacterial diversity may be associated with changes in plant growth and ambient temperatures. Across different elevations originating from the same source area, elevation acts as a direct environmental factor driving temperature variation (Li et al., 2024b). Our findings confirm that elevation is the primary environmental factor responsible for changes in soil properties and other environmental variables, which in turn affect bacterial diversity and the composition of bacterial and fungal communities in the A. sinensis rhizosphere (Figures 5A, 7A).
Previous studies have shown that soil provides the essential nutrients for plants (Tong et al., 2021) and its nutritional status greatly influences plant growth (Tsachidou et al., 2019). Soil organic matter is an important source of soil nutrients and an essential component for the growth of traditional Chinese medicinal plants. It plays a significant role in enhancing the accumulation of their bioactive compounds (Zhang et al., 2019; Yu et al., 2024; Wang et al., 2024b). In addition, previous studies have shown that N, P, and K are essential for the growth and production of Chinese medicinal plants (Zhang et al., 2019). In this study, soil properties significantly influence root weight and the contents of coniferyl ferulate, levistolide A, and ligustilide of A. sinensis. OM and AK made the most substantial contributions to the soil effect on root weight and the accumulation of these bioactive compounds (Figures 5A, 7A; Supplementary Figures S1A, S2A). Additionally, AK was significantly and positively correlated with the contents of ligustilide, coniferyl ferulate, and Levistolide A (Figure 5A). It is speculated that root weight and bioactive compound contents are mainly affected by soil properties, with AK and OM being the main regulatory factors for the formation and accumulation of the bioactive compounds in A. sinensis.
Microbial regulation of bioactive components accumulation: agroecological implications for precision cultivation of A. sinensis
Rhizosphere microorganisms are crucial for synthesizing the main active natural components of medicinal plants such as Astragalus, and some microbes may be developed and utilized as potential medicinal resources (Ding et al., 2024). Furthermore, Sun et al. (2024) demonstrated that a reduction in rhizosphere bacterial diversity led to the inhibition of wogonoside. Consistent with previous research (Ding et al., 2024; Sun et al., 2024), our findings (Figure 5A) show that the diversity of microbial flora in the rhizosphere soil of A. sinensis plays a crucial role in driving the formation and accumulation of medicinal components in A. sinensis. Additionally, Bacillus species can produce substances inhibiting plant pathogens and thereby indirectly promote the growth of A. sinensis (Rajer et al., 2017). Pseudomonas (Cai et al., 2021), Sphingomonas (Zhu et al., 2021; Lombardino et al., 2022), Mortierella (Yan et al., 2024; He et al., 2025), and Vicinamibacterales (Wu et al., 2022) have been proven to promote the growth and accumulation of medicinal compounds in A. sinensis. This aligns with our findings (Figure 5B) indicating that various rhizosphere bacterial genera significantly and positively correlate with root weight and the content of medicinal components in A. sinensis. The mechanism involves soil microbial diversity enhancing nutrient contents and soil enzyme activities, thereby increasing nutrient availability for plants and promoting both plant growth and metabolite accumulation.
Although Bisifusarium and Plectosphaerella are pathogens of A. sinensis (Cheng et al., 2025), their relative abundances are greater at elevations below 2520 m, gradually decreasing as altitude increases above this level. Moreover, Wang et al. (2012) discovered that A. sinensis grows most effectively at medium altitudes rather than at low altitudes, likely due to its physiological traits, which are adapted to cooler conditions. Cultivating A. sinensis in mid- to high-altitude areas, specifically at elevations of 2520–2887 m, is most beneficial for the functioning of soil microbial communities in the rhizosphere and is the most suitable for the growth and medicinal content of A. sinensis. In addition, healthy and vigorously growing plants tend to recruit more dominant bacterial populations by releasing larger amounts of root exudates (Pang et al., 2021). This process enhances their ability to withstand biotic and abiotic stresses and directly or indirectly influences the production of secondary metabolites. Therefore, integrative analysis of soil community structure, nutrient dynamics, and plant growth performance across elevation gradients revealed that middle altitudes (2520–2717 m) are more favorable for A. sinensis growth, providing suitable conditions for the formation and accumulation of pharmacodynamic components.
Conclusion
This study systematically clarified, for the first time, the interaction among soil characteristics, microbial communities, and the bioactive compound content of A. sinensis across different altitudes. The study also addresses the limitations of traditional single-factor environmental analyses by providing a more integrated understanding of these complex relationships. The results indicate that soil properties are the main factors influencing the accumulation of bioactive compounds in A. sinensis. Among these, soil organic matter (OM) and available potassium (AK) act as key positive regulators of the formation and accumulation of these compounds. Notably, bacterial, fungal, and archaeal communities exhibited significant altitudinal differentiation, whereas AM fungi demonstrated a rare degree of vertical zonal stability. Dominant microbial phyla, including Pseudomonadota, Actinomycetota, Acidobacteriota, Ascomycota, Mortierelomycota, Glomeromycota, and Thermoproteota, were consistently detected in the rhizosphere of A. sinensis across different elevations. Furthermore, the diversity and richness of bacteria, fungi, and archaea in rhizosphere soil were positively correlated with the concentrations of coniferyl ferulate, ligustilide, and levistolide A. By integrating environmental variables and the rhizosphere microenvironment of A. sinensis, the mid- to high- elevation transition zone (2520–2717 m) was identified as the optimal cultivation region for maximizing both yield and quality. The altitude–soil–microorganism–plant bioactive compound framework established in this study provides original and valuable insights into enhancing the cultivation and medicinal efficacy of A. sinensis across varying altitudes, offering further evidence in support of the theory of microbial symbiosis in high-altitude medicinal plants.
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/, PRJNA1347083.
Author contributions
X-FG: Data curation, Investigation, Methodology, Project administration, Writing – original draft. WK: Formal Analysis, Methodology, Software, Writing – review & editing. LY: Investigation, Project administration, Writing – original draft. Y-KC: Data curation, Investigation, Writing – original draft. JC: Formal Analysis, Resources, Writing – original draft. LZ: Data curation, Investigation, Writing – original draft. YoZ: Formal Analysis, Resources, Writing – original draft. YiZ: Project administration, Resources, Writing – original draft. Z-YW: Funding acquisition, Resources, Writing – original draft. B-LZ: Methodology, Supervision, Validation, Visualization, Writing – review & editing. L-GX: Conceptualization, Supervision, Validation, Writing – review & editing.
Funding
The author(s) declared that financial support was received for this work and/or its publication. 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 number, 24JRRA1137); the Outstanding Youth Fund of the Gansu Academy of Sciences (grant number, 2024YQ-04); the Young Scientists Fund Project of Gansu Academy of Sciences (grant numbers, 2024QN-13) and the Gansu Longyuan Young Talent Project.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2025.1703258/full#supplementary-material
References
Aurélien, S., Lea, W., Laurent, P., and Sara, H. (2022). Loss in soil microbial diversity constrains microbiome selection and alters the abundance of N-cycling guilds in barley rhizosphere. Appl. Soil Ecology. 169, 104224. doi: 10.1016/J.APSOIL.2021.104224
Bao, S. D. (2011). Agrochemical analysis of soil [M] (Beijing: China Agricultural Publishing Press), 495.
Cai, Y., Liu, H., Kong, W. P., Zhong, C., Xie, J., Wang, Y. Q., et al. (2021). Study on community and diversity of endophyte and its correlation with active ingredient content in Polygonatum cyrtonema. Chin. Traditional Herbal Drugs 52, 4023–4030. doi: 10.7501/j.issn.0253-2670.2021.13.025
Chen, S., Zhou, Y., Chen, Y., and Gu, J. (2018). fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890. doi: 10.1093/bioinformatics/bty560
Cheng, Y., Zhang, X., Zhang, W., Dong, J., Ma, Y., Zhang, A., et al. (2025). Microbial community structure and diversity of endophytic bacteria and fungi in the healthy and diseased roots of angelica sinensis, and identification of pathogens causing root rot. Microorganisms 13, 417. doi: 10.3390/microorganisms13020417
Chinese Pharmacopoeia Commission (2020). Pharmacopoeia of the people’s republic of China 2020 edition Vol. 2020 (Beijing: Chinese Medicine Science and Technology Press), 139.
Ding, J. J., Zhou, G. J., Chen, X. J., Xu, W., Gao, X. M., Zhang, Y. Z., et al. (2024). Analysis of microbial diversity and community structure of rhizosphere soil of three astragalus species grown in special high-cold environment of northwestern yunnan, China. Microorganisms 12, 539. doi: 10.3390/microorganisms12030539
Edgar, R. C. (2013). UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998. doi: 10.1038/nmeth.2604
Feng, J. Y., Jue, S. T., Zhu, Y. J., Tang, C. X., He, Y., and Xu, J. M. (2020). Crop-dependent root-microbe-soil interactions induce contrasting natural attenuation of organochlorine lindane in soils. Environ. pollut. 257, 113580. doi: 10.1016/j.envpol.2019.113580
Guo, Y., Ren, C., Yi, J., Doughty, R., and Zhao, F. (2020). Contrasting responses of rhizosphere bacteria, fungi and arbuscular mycorrhizal fungi along an elevational gradient in a temperate montane forest of China. Front. Microbiol. 11. doi: 10.3389/fmicb.2020.02042
Han, C., Shi, C., Liu, L., Han, J., Yang, Q., Wang, Y., et al. (2024). Majorbio Cloud 2024: Update single-cell and multiomiworkflows. Imeta 3, e217. doi: 10.1002/imt2.217
He, D., Gao, W., Zhang, Z., Xing, J., Han, G., Wang, H., et al. (2025). Microecological recombination of Angelica sinensis driven by the transplanting of “alpine seedling-cellar planting-dam cultivation. PeerJ 13, e19208. doi: 10.7717/peerj.19208
Hook, I. L. (2014). Danggui to Angelica sinensis root: are potential benefits to European women lost in translation? A review. J. Ethnopharmacol. 152, 1–13. doi: 10.1016/j.jep.2013.12.018
Hou, Z. N., Zhang, X. J., Wang, R. H., Li, A., Li, X. T., and Wei, Y. Q. (2024). Changes of soil microbial communities during litter decomposition in alpine forests at different elevations. J. China Agric. University. 29, 36–46. doi: 10.11841/j.issn.1007-4333.2024.02.04
Jiang, X. F., Guo, F. X., Chen, Y., and Guo, J. G. (2024). Effects of cropping pattern on soil fungal community and functional groups in rhizosphere soil of Angelica sinensis. Soil Fertilizer Sci. China. 01, 28–36. doi: 10.11838/sfsc.1673-6257.22802
Jiang, X. F., Guo, F. X., Chen, Y., Guo, J. G., and Liu, X. F. (2021). Effects of cropping patterns on bacterial community diversity and metabolic function in rhizosphere of Angelica sinensis. J. Appl. Ecology. 32, 4254–4262. doi: 10.13287/j.1001-9332.202112.031
Jiang, C. L., Wei, J. M., Bai, S. S., Qiang, Y., Cui, L. J., and Liu, S. P. (2020). Characteristics of rhizosphere soil fungi community of schisandra sphenanthera rehd. Et Wils. J. Yanbian Univ. (Natural Sci. Edition). 39, 15–20. doi: 10.13876/J.cnki.ydnse.2020.04.015
Jiang, J. Y., Yang, W. Z., Kang, C. Z., Liu, Y. H., Wang, S., Sun, K., et al. (2018). Assessment of soil fertility for cultivation of Chinese herbal medicines. China J. Chin. Materia Medica. 43, 847–852. doi: 10.19540/j.cnki.cjcmm.20180105.003
Jin, M. Z., Wu, L. M., Yang, K. F., Li, R. R., and Zhang, Y. Y. (2024). Analysis of soil characteristics of understory ginseng at different altitudes in jiuzhai valley. Acta Agriculturae Boreali-occidentalis Sin. 33, 1531–1540. doi: 10.7606/j.issn.1004-1389.2024.08.001
Karagöz, K., Ateş, F., Karagöz, H., Kotan, R., and Çakmakçı, R. (2012). Characterization of plant growth-promoting traits of bacteria isolated from the rhizosphere of grapevine grown in alkaline and acidic soils. Eur. J. Soil Biol. 50, 144–150. doi: 10.1016/j.ejsobi.2012.01.007
Lauber, C. L., Hamady, M., Knight, R., and Fierer, N. (2009). Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl. Environ. Microbiol. 75, 5111–5120. doi: 10.1128/AEM.00335-09
Li, Y. Z., Bao, X. L., Zhu, X. F., Deng, F. B., Yang, Y. L., Zhao, Y., et al. (2024c). Parent material influences soil properties to shape bacterial community assembly processes, diversity, and enzyme-related functions. Sci. Total Environ. 927, 172064. doi: 10.1016/j.scitotenv.2024.172064
Li, Y. F., Wang, J. W., Chen, Y. L., He, B. M., and La, Q. (2024b). Elevation distribution pattern and driving factors of soil bacterial diversity on Mount Gangbala, Tibet, China. Acta Ecologica Sinica. 44, 712–722. doi: 10.20103/j.stxb.202208222403
Li, Y. M., Wang, Y. M., Qiu, G. W., Yu, H. J., Liu, F. M., Wang, G. L., et al. (2024a). Conservation tillage facilitates the accumulation of soil organic carbon fractions by affecting the microbial community in an eolian sandy soil. Front. Microbiol. 15. doi: 10.3389/fmicb.2024.1394179
Li, Y., Zhong, H. R., Li, Y. S., Weize, R. S., Li, W. B., and Liu, Y. (2022). Community structure and diversity analysis of rhizosphere bacterial of Nardostachys jatamansi and endophytic bacteria in medicinal parts based on high-throughput sequencing technology. Chin. Traditional Herbal Drugs 53, 835–847. doi: 10.7501/j.issn.0253-2670.2022.03.024
Liu, L., Hart, M. M., Zhang, J. L., Cai, X. B., Gai, J. P., Christie, P., et al. (2015). Altitudinal distribution patterns of AM fungal assemblages in a Tibetan alpine grassland. FEMS Microbiol. Ecol. 91, fiv078. doi: 10.1093/femsec/fiv078
Liu, S., Vasar, M., Öpik, M., and Koorem, K. (2023). Disturbance induces similar shifts in arbuscular mycorrhizal fungal communities from grassland and arable field soils. Mycorrhiza 33, 153–164. doi: 10.1007/s00572-023-01108-6
Liu, R. P., Wang, X. Z., Yang, W. J., and Liu, X. Z. (2021a). Effects of altitudes on direct autumn-sowing Angelica sinensis growth dynamics and quality. Gansu Agric. Sci. Technology. 52, 50–55. doi: 10.3969/j.issn.1001-1463.2021.07.010
Liu, B., Yang, J., Lu, W., Wang, H., Song, X., Yu, S., et al. (2024). Altitudinal variation in rhizosphere microbial communities of the endangered plant Lilium tsingtauense and the environmental factors driving this variation. Microbiol. Spectr. 12, e0096624. doi: 10.1128/spectrum.00966-24
Liu, P. Y., Zhang, H. K., Song, Z. L., Huang, Y. Y., and Hu, X. K. (2021b). Seasonal dynamics of bathyarchaeota-dominated benthic archaeal communities associated with seagrass (Zostera japonica) meadows. J. Mar. Sci. Engineering. 9, 1304. doi: 10.3390/jmse9111304
Lombardino, J., Bijlani, S., Singh, N. K., Wood, J. M., Barker, R., Gilroy, S., et al. (2022). Genomic characterization of potential plant growth-promoting features of sphingomonas strains isolated from the international space station. Microbiol. Spectr. 10, e0199421. doi: 10.1128/spectrum.01994-21
Luo, J., Wang, Y. Q., Wu, G. T., Wang, Y., Wang, Z. H., Li, S. N., et al. (2021). Dynamic accumulation and transformation of ferulic acid and ligustilide in root of angelica sinensis. Chin. Traditional Herbal Drugs 52, 6663–6668. doi: 10.7501/j.issn.0253-2670.2021.21.024
Magoč, T. and Salzberg, S. L. (2011). FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963. doi: 10.1093/bioinformatics/btr507
Meng, Y. J. and Qin, P. (2020). Coupling relationship between microbial community diversity and soil nutrients in different wetlands in coastal area of pearl river delta. Res. Soil Water Conserv. 27, 77–84. doi: 10.13869/j.cnki.rswc.2020.06.011
Mu, P., Jin, Q., Liu, Z. B., Deng, Y. Z., and Liu, N. (2022). Application on construction technology of synthetic microbial communities in field of medicinal plant cultivation. Chin. Traditional Herbal Drugs 53, 2506–2516. doi: 10.7501/j.issn.0253-2670.2022.08.028
Nicol, G. W., Tscherko, D., Embley, T. M., and Prosser, J. I. (2005). Primary succession of soil Crenarchaeota across a receding glacier foreland. Environ. Microbiol. 7, 337–347. doi: 10.1111/j.1462-2920.2005.00698.x
Pang, Z. Q., Chen, J., Wang, T. H., Gao, C. S., Li, Z. M., Guo, L. T., et al. (2021). Linking plant secondary metabolites and plant microbiomes: A review. Front. Plant Sci. 12. doi: 10.3389/fpls.2021.621276
Pu, T., Liu, J., Dong, J., Qian, J., Zhou, Z., Xia, C., et al. (2022). Microbial community diversity and function analysis of Aconitum carmichaelii Debeaux in rhizosphere soil of farmlands in Southwest China. Front. Microbiol. 13. doi: 10.3389/fmicb.2022.1055638
Qin, J., Dodd, I. C., Bian, C., Li, G., and Jin, L. (2025). Deficit irrigation differentially modulates rhizosphere microbial community and metabolites of two potato genotypes differing in drought tolerance. J. Environ. Manage. 373, 123836. doi: 10.1016/j.jenvman.2024.123836
Rajer, F. U., Wu, H. J., Xie, Y. L., Xie, S., Raza, W., Tahir, H. A. S., et al. (2017). Volatile organiccom-pounds produced by a soil-isolate, bacillus subtilis FA26 induce adverse ultra-structural changes to the cells of clavibacter michiganensis ssp. sepedonicus, the causal a-gent of bacterial ring rot of potato. Microbiology 163, 523–530. doi: 10.1099/mic.0.000451
Ren, Y., Yu, G., Shi, C., Liu, L., Guo, Q., Han, C., et al. (2022). Majorbio Cloud: A one-stop, comprehensive bioinformatic platform for multiomics analyses. Imeta 1, e12. doi: 10.1002/imt2.12
Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., et al. (2009). Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541. doi: 10.1128/AEM.01541-09
Shehata, H. R. and Newmaster, S. G. (2023). The power of DNA based methods in probiotic authentication. Front. Microbiol. 14. doi: 10.3389/FMICB.2023.1158440
Shen, Y. W., Feng, J. G., Zhou, D. Y., He, K. Y., and Zhu, B. (2023). Impacts of aboveground litter and belowground roots on soil greenhouse gas emissions: Evidence from a DIRT experiment in a pine plantation. Agric. For. Meteorology 343, 109792. doi: 10.1016/J.AGRFORMET.2023.109792
Sun, Y., Liu, H., Peng, J. W., Shen, M. C., Hu, Y., Yu, D. S., et al. (2024). Rhizosphere microbes influence wogonoside accumulation in perennial Scutellaria baicalensis. Pedosphere 34, 553–566. doi: 10.1016/J.PEDSPH.2023.04.008
Tan, X., Dong, Z., Zhang, L. M., Yuan, Z. X., and Li, C. X. (2023). Structural Diversity and its temporal variation in the soil bacterial community under plantations of Taxodium distichum in the riparian zone of the Three Gorges Reservoir Area. Huan Jing Ke Xue. 44, 1748–1757. doi: 10.13227/j.hjkx.202202021
Tian, L. X., Yu, S. P., Zhang, L., Dong, K. J., and Feng, B. L. (2022). Mulching practices manipulate the microbial community diversity and network of root-associated compartments in the Loess Plateau. Soil Tillage Res. 223, 105476. doi: 10.1016/j.still.2022.105476
Tong, R. C., Whitehead, C. S., and Fawole, O. A. (2021). Effects of conventional and bokashi hydroponics on vegetative growth, yield and quality attributes of bell peppers. Plants (Basel). 10, 1281. doi: 10.3390/plants100712
Tsachidou, B., Scheuren, M., Gennen, J., Debbaut, V., Toussaint, B., Hissler, C., et al. (2019). Biogas residues in substitution for chemical fertilizers: A comparative study on a grassland in the Walloon Region. Sci. Total Environ. 666, 212–225. doi: 10.1016/j.scitotenv.2019.02.238
Van, G. M., Busschaert, P., Honnay, O., and Lievens, B. (2014). Evaluation of six primer pairs targeting the nuclear rRNA operon for characterization of arbuscular mycorrhizal fungal (AMF) communities using 454 pyrosequencing. J. Microbiol. Methods 106, 93–100. doi: 10.1016/j.mimet.2014.08.006
Wang, Q., Garrity, G. M., Tiedje, J. M., and Cole, J. R. (2007). Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267. doi: 10.1128/AEM.00062-07
Wang, H. Z., Jin, L., and Zhang, E. H. (2012). Effect of altitudes on the photosynthate accumulation and distribution pattern of Angelic sinensis. J. Chin. Medicinal Materials. 35, 1191–1194. doi: 10.13863/j.issn1001-4454.2012.08.005
Wang, G., Li, Z. F., Yang, B. Y., Yang, H. Q., Zhang, Y. J., Zeng, Q. P., et al. (2024a). The effect of white grub (Maladera Verticalis) larvae feeding on rhizosphere microbial characterization of aerobic rice (Oryza sativa L.) in Puer City, Yunnan Province, China. BMC Microbiol. 24, 123–123. doi: 10.1186/S12866-024-03265-W
Wang, Z. H., Liu, S., Yang, G., Lu, Z. Y., Zhu, R. Q., Li, Y., et al. (2024b). Effects of organic fertilizer from traditional Chinese medicine residues on growth and soil microbial community of Salvia miltiorrhiza by metagenomic technique. China J. Chin. materia medica. 49, 1206–1216. doi: 10.19540/j.cnki.cjcmm.20231213.101
Wang, Q., Wu, P. H., Yang, M., Mu, M. J., Huang, Y., Guo, D. Q., et al. (2020). Correlation analysis between rhizome quality and rhizosphere soil factors of Paris polyphylla var. yunnanensis. Chin. Traditional Herbal Drugs 51, 5839–5849. doi: 10.7501/j.issn.0253-2670.2020.22.022
Wei, Y. Q., Hou, Z. N., Chai, S. Y., Wang, R. H., Li, X. T., Li, A., et al. (2024). Effects of freeze-thaw on soil microbial community structure in alpine forest of Mount Segrila. Acta Ecologica Sinica. 44, 1–12. doi: 10.20103/j.stxb.202304040671
Wu, X., Rensing, C., Han, D., Xiao, K. Q., Dai, Y., Tang, Z., et al. (2022). Genome-resolved metagenomics reveals distinct phosphorus acquisition strategies between soil microbiomes. mSystems 7, e0110721. doi: 10.1128/msystems.01107-21
Xi, S. Y., Cao, H. K., Guo, Y. X., Ma, X. H., Zhu, T. T., and Jin, L. (2021). Quantitative analysis of total content differentiation and driving factors of phenolic acids in Angelica sinensis radix of gansu Dao-di herbs from perspective of geographical space. China J. Chin. Materia Medica. 46, 5781–5791. doi: 10.19540/j.cnki.cjcmm.20210903.102
Xie, Z. J., Gong, C. W., Mi, Y. W., and Wang, G. X. (2019). Effects of sowing time on the morphology and material accumulation of annual Angelica sinensis of direct sowing. J. Chin. Veterinary Med. 38, 70–72. doi: 10.13823/j.cnki.jtcvm.2019.04.021
Xie, T. P., Liu, N., Liu, Y. M., Qu, X., Fu, S. Q., and Jing, M. (2022). Effects of chemical fertilizer reduction and application of plant growth regulators from traditional Chinese medicine on the quality and its bacterial community in rhizosphere soil. Biotechnol. Bulletin. 38, 79–91. doi: 10.13560/j.cnki.biotech.bull.1985.2021-0467
Xie, Y., Song, X., Lu, Y., Hu, X., Pan, S., Xu, W., et al. (2025). Different forms of nitrogen uptake in tobacco promoted by the arbuscular mycorrhizal fungi. Front. Plant Sci. 16. doi: 10.3389/fpls.2025.1600887
Xu, X., He, X. L., Heng, T., and Feng, Y. (2021b). Effects of subsurface pipe and shaft on soil salinity in cotton field in arid saline-alkali land under drip irrigation and mulch. Agric. Res. Arid Areas. 39, 95–104. doi: 10.7606/j.issn.1000-7601.2021.06.11
Xu, X. Q., Zhu, T. T., Xi, S. Y., Chen, B., Zhang, X. B., and Jin, L. (2021a). Quality regionalization of Angelica sinensis in genuine producing area. Chin. J. Exp. Traditional Med. Formulae. 27, 132–139. doi: 10.13422/j.cnki.syfjx.20210211
Yan, Z. H., Jin, H., Yang, X. Y., Min, D., Xu, X. X., Hua, C. P., et al. (2024). Effect of rhizosphere soil microbial communities and environmental factors on growth and the active ingredients of Angelica sinensis in Gansu Province, China. Folia Microbiologica. 70, 1–15. doi: 10.1007/s12223-024-01210-y
Yu, Z. M., Lv, P., Wang, L. M., Zhang, J. Z., Xu, M. R., and Ma, W. J. (2024). Correlation analysis of pharmacodynamic components of paeoniae radix rubra and soil nutrient factors in heilongjiang province. Chin. J. Soil Science. 55, 716–728. doi: 10.19336/j.cnki.trtb.2023062001
Zhang, Z., Chang, L., Liu, X. X., Wang, J., Ge, X. H., Cheng, J. S., et al. (2023). Rapeseed domestication affects the diversity of rhizosphere microbiota. Microorganisms 11, 724–724. doi: 10.3390/MICROORGANISMS11030724
Zhang, K. X., Chen, Y. S., Ren, Y., Liu, S., and Wu, F. M. (2022a). Response of biological characteristics and medicinal quality of wild Polygonum viviparum community to altitude and aspect. Chin. Pharm. J. 57, 181–191. doi: 10.11669/cpj.2022.03.004
Zhang, J. C., Lin, C. H., Yang, J. Y., Li, C. Z., Wang, Y., Yang, J. W., et al. (2019). Chemical properties and microelement characteristics of soil for growth of wild bletilla striata in guizhou. Southwest China J. Agric. Science. 32, 1839–1844. doi: 10.16213/j.cnki.scjas.2019.8.024
Zhang, M., Wang, X. L., Ma, Y. S., Wang, Y. L., Qin, J. P., and Dong, R. Z. (2024). Characteristics of the aboveground biomass and diversity along altitudinal gradients in alpine meadows of the upper reaches of the Yarlung Zangbo River. Acta Botanica Boreali-Occidentalia Sinica. 44, 154–163. doi: 10.7606/j.issn.1000-4025.20230381
Zhang, Y. J., Xun, J., Lu, R. C., Ye, C., Huang, H. C., Yang, M., et al. (2022b). Modification of quicklime on acid soil under forest and their effect on the growth of Panax notoginseng. Chin. J. Appl. Ecology. 33, 972–980. doi: 10.13287/j.1001-9332.202204.018
Keywords: Angelica sinensis, elevation, high throughput sequencing, microbial community, microbial diversity, pharmacodynamic component, soil factor
Citation: Gong X-F, Khan W, Yang L, Chen Y-K, Chen J, Zhang L, Zhang Y, Zhu Y, Wang Z-Y, Zhang B-L and Xue L-G (2026) Altitude-mediated soil microbe-nutrient dynamics shape medicinal properties of Angelica sinensis. Front. Plant Sci. 16:1703258. doi: 10.3389/fpls.2025.1703258
Received: 11 September 2025; Accepted: 23 December 2025; Revised: 09 December 2025;
Published: 28 January 2026.
Edited by:
Ana I. F. Ribeiro-Barros, University of Lisbon, PortugalReviewed by:
Wei-meng Feng, Henan University of Chinese Medicine, ChinaChinedu Obieze, Université Laval, Canada
Copyright © 2026 Gong, Khan, Yang, Chen, Chen, Zhang, Zhang, Zhu, Wang, Zhang and Xue. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Lin-Gui Xue, eHVlbGc2MjIxOEBzaW5hLmNvbQ==; Bing-Lin Zhang, emhhbmdibEBsemIuYWMuY24=
†These authors have contributed equally to this work