Samples of C. chinensis were cultivated in Shizhu, Chongqing (29°39′-30°33′ N, 107°59′-108°34′ E), which features extensive mountainous terrain, fertile soil, lush vegetation, abundant rainfall, a cool climate, low sunshine exposure, and a short frost-free period, making it one of the major producing areas of C. chinensis (Zhao et al., 2025). Within the study area, under the conditions of the same mountain range and uniform management practices, four sampling sites were established along different altitudes, with the soil at all sampling sites being yellow-brown loam. All plants shared the same genetic background, the same cultivation batch, and the same growth period (5-year growth period), and three replicate samples were set at each site (presented in Table 1). All plant samples were identified as C. chinensis Franch. (Ranunculaceae) by Researcher LI Longyun from Chongqing Academy of Chinese Materia Medica.

Plant samples were divided into two portions. One portion comprised rhizomes: the surface of the rhizome tissues was cleaned with Wahaha purified water, followed by disinfection with 75% ethanol for 5 minutes, and then stored in liquid nitrogen. The other portion was dried at 60°C upon returning to the laboratory. The resulting material was powdered and screened through a 150-mesh sieve. The moisture content of the processed samples was determined to be < 10%, and they were stored for subsequent determination of chemical components in C. chinensis. Rhizosphere soil was collected from each sampling site. After moisture determination, the soil was air-dried, passed through a 2 mm sieve, stored at room temperature, and subsequently used for the determination of soil physical and chemical properties.

Instruments, reagents, and reference standards information were shown in Tables 2–4.

Twenty-four fresh samples were measured for their plant height, number of leaves, leaf length, petiole length, fibrous root length, rhizome length, leaf width, rhizome diameter, and petiole diameter after being cleaned thoroughly.

Weigh 0.20 g of C. chinensis powder and introduce 50 mL of methanol-hydrochloric acid solution (100:1, v/v). The initial mass was recorded before an ultrasonic treatment (300 W, 80 kHz) for 30 minutes. After the total mass was restored to its original value by adding a methanol-hydrochloric acid solution, the mixture was centrifuged, and 1 mL of the supernatant was diluted to 100 mL in a volumetric flask. Measure the absorbance of the final solution at 270 nm.

Berberine was chosen as the evaluation indicator. A standard reference solution was prepared in methanol at a nominal concentration of 1 mg/mL and stored at 4°C until used. The content of berberine was calculated using the regression equations derived from calibration curves.

The evaluation of precision involved six consecutive analyses of the identical HSA1 solution, and the outcomes were presented as the relative standard deviation (RSD). Repeatability was evaluated by processing and analyzing six independent sample preparations of C. chinensis (HSA1) in parallel. Stability was determined by measuring the prepared HSA1 solution at designated intervals (0, 10, 20, 30, 40, and 50 minutes).

Weigh 0.20 g accurately of C. chinensis powder and introduce 50 mL of methanol-hydrochloric acid solution (100:1, v/v). The initial mass was recorded before subjecting to ultrasonic treatment (300 W, 80 kHz) for 30 minutes. The total mass was restored to its original value by adding the methanol-hydrochloric acid solution after cooling to room temperature. The mixture was centrifuged, and 2 mL of the supernatant was diluted to 10 mL in a volumetric flask. Methanol was used to dilute to the mark, followed by thorough shaking and filtration to obtain the final sample solution. The sample solutions were filtered with a 0.45 μm syringe filter. The HPLC analyses were performed with an Agilent 1200 Series. Welch Xtimate^® C18 column (250 mm × 4.6 mm, 5 μm) was used. The solution (ammonium bicarbonate 2.37 g dissolved in 992 mL water, then added 7 mL ammonium hydroxide and 1 mL triethylamine, shaken well) was used as mobile phase A, methanol was used as mobile phase B, the gradient program as follows: 90% A ~ 75% A and 10% B ~ 25% B for 0 ~ 15 min, 75% A ~ 70% A and 25% B ~ 30% B for 15 ~ 25 min, 70% A ~ 55% A and 30% B ~ 45% B for 25 ~ 40 min, and 55% A ~ 90% A and 45% B ~ 10% B for 40 ~ 45 min. Injection volume, 10 μL; detection wavelength, 275 nm; column temperature, 30°C; and flow rate, 1.0 mL/min.

Epiberberine, coptisine, palmatine, berberine, magnoflorine, groenlandicine, jatrorrhizine, and columbamine were chosen as the evaluation indexes. Prepare the mixed reference solution of eight alkaloids in methanol at approximately 1 mg/mL and store it at 4°C until required for use. The concentrations of the eight target analytes were quantified using regression equations derived from their respective calibration curves.

The evaluation of precision involved six consecutive analyses of the identical HSA1 solution, and the outcomes were presented as the relative standard deviation (RSD). Repeatability was evaluated by processing and analyzing six independent sample preparations of C. chinensis (HSA1) in parallel. Stability was determined by measuring the prepared HSA1 solution at designated intervals (0, 2, 4, 8, 12, and 24 hours).

To the weighed sample, add 600 µL methanol, containing 2-amino-3-(2-chlorophenyl) propanoic acid (4 mg/L). Homogenize by sonication at 55 Hz, then centrifuge at 1200 rpm and 4°C for 10 min. Transfer the supernatant through a 0.22 µm microporous filter membrane for LC-MS analysis (Vasilev et al., 2016).

Construction of high-throughput sequencing libraries Macrogenomic sequencing was performed using the MGI T7 platform PE150 to obtain raw image data files of C. chinensis rhizome samples, which were converted into raw data (raw reads) through base calling analysis. Kraken2 software was used for macrogenomic species annotation based on reads. After filtering with fastp, effective sequencing data were obtained from the second-generation raw sequencing data. MEGAHIT software was used for metagenomic assembly of the second-generation data. minced software was used for CRISPR prediction of the genome; PhiSpy software was used to predict phages in the genome; IslandPath software was used to predict gene islands in the genome; functional annotation of target protein sequences was performed using multiple databases and alignment tools: GO annotations were assigned using BLASTP; COG annotations were generated with eggNOG-mapper employing DIAMOND BLASTP; ARDB, HI, CARD, VFDB, and CYPED annotations were conducted using BLASTP or DIAMOND BLASTP as appropriate; and CAZy database annotations were performed using HMMER; annotate the target protein sequence using diamond blastp based on the TCDB database; quantify the non-redundant gene set using the salmon software; perform differential analysis between paired samples using DESeq2 called by the trinityrnaseq software; perform statistical testing using the stats method in the scipy package in Python.

Microbial diversity was assessed by calculating α diversity indices and β diversity distances using the QIIME2 diversity module. Differential abundance analysis between paired samples was performed using both DESeq2 and edgeR implemented through the trinityrnaseq software.

The main physical and chemical properties of soil, including water content (WC), pH, Soil Organic Matter (SOM), Total Nitrogen (TN), Available Nitrogen (AN), Total Phosphorus (TP), Available Phosphorus (AP), Total Potassium (TK), Available Potassium (AK), urease (URE), sucrase (SUC), protease (PRO), catalase (CAT), were measured separately in accordance with the corresponding standard procedures.

Statistical analysis was performed using Excel 2016, with one-way analysis of variance (ANOVA) applied to determine significance. Results with P < 0.05 were considered statistically significant, and data are expressed as “mean ± standard deviation”. Figures were generated using GraphPad Prism 8.0 and arranged with Adobe Illustrator 2020.

The growth of C. chinensis exhibited significant variation across different altitudinal gradients (shown in Figure 1, Table 5). Significant morphological variations in C. chinensis were observed across the four altitudinal gradients. Branch number, plant height, leaf dimensions (width and length), and petiole characteristics (length and diameter) exhibited highly significant differences (P < 0.01). Rhizome diameter also showed a significant altitudinal variation (P < 0.05). In contrast, fibrous root length and rhizome length did not demonstrate significant differences across the elevation gradients.

The size of the medicinal rhizomes showed a decreasing trend with increasing altitude, suggesting that high altitudes may inhibit the growth of medicinal parts. Conversely, fibrous root length tended to increase with altitude, indicating that high-altitude environments may be more conducive to the growth of fibrous roots in C. chinensis.

With the increase in cultivation altitude, the contents of groenlandicine, coptisine, berberine, and total alkaloids in C. chinensis show a certain upward trend, and the total accumulation of active components in high-altitude areas is higher than that in low-altitude areas (Figure 2). However, the contents of magnoflorine, jatrorrhizine, columbamine, epiberberine, and palmatine hydrochloride do not show a regular trend related to altitude changes. Among them, the total alkaloid content increases from 15.97% at 907 m to 17.82% at 1698 m (P < 0.01).

Metabolomics analysis was conducted on the rhizomes of C. chinensis collected at different altitudes. Principal component analysis (PCA) revealed distinct clustering of replicate samples within each group, with the model explaining 54.7% of the total variance (Figure 3A). A total of 912 differentially expressed metabolites (DEMs) were identified in the comparisons among HSA, HSB, HSC, and HSD. Among these, the dominant categories were alkaloids and derivatives (29 compounds, accounting for 37.28% ~ 52.46%), organic acids and derivatives (79 compounds, accounting for 8.89% ~ 16.64%), phenylpropanoids and polyketides (137 compounds, accounting for 8.67% ~ 12.83%), and organoheterocyclic compounds (124 compounds, accounting for 4.97% ~ 6.78%), collectively accounting for 71.76% ~ 80.41% of all DEMs. KEGG pathway enrichment analysis revealed that these differential metabolites were predominantly enriched in amino acid metabolism pathways, particularly alanine, aspartate, glutamate, and arginine biosynthesis, as well as biosynthetic pathways including tyrosine and pyrimidine metabolism. Additionally, pathways related to genetic information transmission (e.g., aminoacyl-tRNA biosynthesis) and substance transmembrane transport (e.g., ABC transporter pathways) were also significantly enriched (Figure 3B). Heatmap results demonstrated distinct accumulation patterns of metabolites in C. chinensis rhizomes across the four altitudes, with differentially expressed metabolites showing clear variations in their accumulation trends at different altitudes (Figure 3C).

The pH was slightly acidic (ranging from 4.61 to 5.83), and the altitude of the HSA group was significantly lower than that of the other sampling sites. The water content (WC), total nitrogen (TN), available nitrogen (AN), available potassium (AK), and soil organic matter (SOM) in high-altitude areas were significantly higher than those in low-altitude areas, but they did not show an increasing trend with increasing altitude. However, unlike AK, total potassium (TK) showed a decreasing trend with increasing altitude. The total phosphorus (TP) content ranged from 1.09 to 2.29 g/kg, with significant differences among samples (P < 0.01): the HSA sample had the highest TP content, while the HSC sample had the lowest. The available phosphorus (AP) content varied highly significantly among samples (P < 0.01), and the AP content of the HSA sample was significantly higher than that of the other samples (shown in Table 6).

The urease (URE) activity ranged from 627.86 to 1026.61 U/g, and the URE activity showed an increasing trend with increasing altitude. The increase in urease activity may facilitate the conversion of soil organic nitrogen to available nitrogen (Kuscu, 2019), providing nitrogen nutrition for the growth of C. chinensis. The invertase (SUC) activity ranged from 599.16 to 1239.77 U/g, and the HSC sample had the highest SUC activity. The catalase (CAT) activity ranged from 26.00 to 31.77 U/g, and the HSA sample had the lowest CAT activity, which was significantly lower than that of the other three samples (P < 0.01). The acid protease (PRO) activity ranged from 2.78 to 4.40 U/g, with significant differences among samples: the HSD sample had the highest PRO activity, which was significantly higher than that of the other three samples (P < 0.01) (shown in Table 7).

The top 10 dominant microbial genera in rhizomes based on relative abundance were selected, and Spearman correlation analysis was employed to evaluate associations among these microbial genera, soil physicochemical properties, and the relative abundance of differentially expressed metabolites (DEMs). The results showed that Bradyrhizobium exhibited extremely significant positive correlations with total nitrogen (TN) and available nitrogen (AN), while showing an extremely significant negative correlation with total potassium (TK); Niastella was extremely significantly positively correlated with soil organic matter (SOM) and urease (URE) activity; Streptomyces presented an extremely significant negative correlation with available phosphorus (AP); Pseudomonas showed extremely significant positive correlations with TN and AN (visualized in Figure 9A).

As visualized in Figure 9B, organic sulfur compounds, phenols, lignans and neolignans, nucleosides, organic heterocyclic compounds, phenylpropanoids, polyketides, organic acids and their derivatives were all related to the distribution of microbial communities in C. chinensis. Associations between specific metabolite classes and microbial genera were identified through correlation analysis. Organic sulfur compounds exhibited a significant positive correlation with Niastella but a negative correlation with Mesorhizobium. In contrast, lignans, neolignans, phenylpropanoids, polyketides, and organic acids and derivatives were positively correlated with both Niastella and Mesorhizobium; organic heterocyclic compounds showed significant negative correlations with these two genera. Benzenoids demonstrated a positive correlation with Rhizophagus and negative correlations with Streptomyces and Flavobacterium. Lastly, nucleosides were negatively correlated with Variovorax. Sucrase activity, as well as the contents of groenlandicine, columbamine, coptisine, palmatine hydrochloride, berberine hydrochloride, and total alkaloids, were related to the distribution of microbial communities in the rhizomes of C. chinensis. The contents of groenlandicine, coptisine, berberine hydrochloride, and total alkaloids were strongly positively correlated with Rhizophagus, and significantly negatively correlated with Mesorhizobium and Variovorax; columbamine showed a strongly negative correlation with Streptomyces, Flavobacterium, and Rhizobium; palmatine hydrochloride was positively correlated with Paraburkholderia. Metabolites of C. chinensis were not only associated with the distribution of microbial communities but also showed significant correlations with key soil physicochemical properties. SOM was extremely significantly positively correlated with lignans, neolignans, phenylpropanoids and polyketides, while pH was extremely significantly negatively correlated with groenlandicine and coptisine; AP was extremely significantly positively correlated with organosulfur compounds.

Environmental factors such as temperature and precipitation exert a greater impact on the diversity of endophytes in leaves and stems (Cheng et al., 2024). Plant microecological communities construct specifically based on the ecosystem characteristics of different altitudes, and their diversity shows significant differences across altitudes (Reyes Ardila et al., 2024). Endophytes exhibit varying degrees of plant growth-promoting, colonization, and pathogen-resistant properties (Bashir et al., 2023). In this study, the dominant microbial phyla (Mucoromycota, Pseudomonadota, Actinobacteriota) in the rhizomes of C. chinensis from the Shizhu area are consistent with the dominant taxa reported in previous studies on medicinal plants (Alami et al., 2020; Liao et al., 2021), and the endophytic community of C. chinensis exhibits a unique altitude adaptation pattern.

Environmental filtering along the altitude gradient has promoted the enrichment of beneficial endophytic genera with specific functions in the rhizomes of C. chinensis, such as Bradyrhizobium, Rhizophagus, and Paraburkholderia. Among these, Bradyrhizobium, as a typical nitrogen-fixing microorganism, can convert atmospheric nitrogen into plant-available ammonium nitrogen, supplement the soil nitrogen pool, and enhance nitrogen supply capacity. Its positive correlation with soil nitrogen content may stem from the sufficient nitrogen providing the necessary nutritional basis for the reproduction and colonization of this genus. Meanwhile, Rhizophagus, as a typical arbuscular mycorrhizal fungus (AMF), showed no significant correlation between rhizosphere soil pH and its relative abundance in this study. However, previous studies have indicated that low pH inhibits its growth and functions (Wang et al., 2017), which may be one of the important reasons for the relatively low abundance of Rhizophagus in the low-altitude group (HSA).

It is confirmed that altitude-related environmental differences can alter plant alkaloid accumulation patterns by affecting the transcriptional activity of alkaloid biosynthetic genes, enzymatic reaction efficiency, and other factors (Lee et al., 2020; Koirala et al., 2023; Liu et al., 2023b). This study reveals the specific manifestations of this regulatory mechanism in C. chinensis. As 5altitude increases, with lower temperatures and more scarce nutrient conditions, plants are prompted to enhance the synthesis of defensive secondary metabolites, thereby improving their adaptability to extreme environments (Ma et al., 2021). As the core precursor for the synthesis of benzylisoquinoline alkaloids (BIAs), tyrosine’s enhanced metabolic flux directly provides sufficient substrates for the production of (S)-reticuline, thereby promoting the efficient synthesis of target alkaloids such as groenlandicine and berberine (He et al., 2018; Xu et al., 2024).

The differentially expressed metabolites (DEMs) across different altitudes are mainly involved in pathways such as tyrosine metabolism, which is one of the reasons for the increase in alkaloid content with rising altitude. The biosynthesis of benzylisoquinoline alkaloids (BIAs) initiates with the decarboxylation and transamination of tyrosine, a process highly dependent on nitrogen availability. As a fundamental nutrient for plant growth and metabolism, nitrogen supply directly influences the direction of alkaloid synthesis (Lee and Facchini, 2011). Previous studies have shown that Mesorhizobium carrying the nifH gene is a key nitrogen cycle-functional bacterium (Haukka et al., 1998; Lewin et al., 2024), capable of fixing atmospheric N₂ through symbiosis with plants (Haukka et al., 1998). However, under conditions of sufficient nitrogen supply, plants may preferentially utilize tyrosine for protein synthesis (e.g., enzymes, structural proteins) rather than secondary metabolites such as alkaloids (Park et al., 2021). This indicates that altitude can indirectly regulate the substrate allocation direction of alkaloid synthesis in C. chinensis by modulating soil nitrogen levels and the activity of related functional bacteria.

As a key geo-ecological factor, altitude affects environmental factors such as temperature, humidity, and light intensity (Holdsworth et al., 2025), thereby indirectly affecting soil physicochemical properties. The synergistic effect of these environmental factors and soil factors is the core driving force regulating plant secondary metabolism (Perrella et al., 2020). This study found that the rhizospheric soil physicochemical properties at different altitudes did not show a linear variation pattern with increasing altitude, but there were significant differences among different altitudes. This variation may be the outcome of the synergistic effect between soil environmental filtering and microbial communities, which indirectly regulates the growth of C. chinensis rhizomes and metabolite accumulation by affecting the composition of endophytic communities.