Nineteen climatic factors were used to characterize the ecological conditions of P. asiatica seeds cultivation regions, and their data were sourced from two authoritative databases: 1. WorldClim Global Climate Database (http://worldclim.org/version2; accessed on September 7th, 2025): Gridded data spatial resolution is 30 seconds (about 1 km²). Data preprocessing adopted the high-resolution climate layer construction methodology (Poggio et al., 2018) with reference to the foundational framework (Fick and Hijmans, 2017). Temperature-related variables are stored as “°C×10” (scaling factor 0.1 required to restore actual °C values), and precipitation-related variables are in “mm” (no scaling factor needed).2. CliMond Global Bioclimatic Modeling Database (Ramirez-Cabral et al., 2017; https://www.climond.info/; accessed on September 7th, 2025): Interpolated climate surfaces have a spatial resolution of 10′ (about 18 km²), consistent with its global conformal design. All climatic variables are used actual units (temperature: °C, precipitation: mm) without requiring scaling factors.

Based on the longitude, latitude, and altitude of each P. asiatica seed sampling sites, the 19 climatic factors corresponding to each production area were extracted using the GMPGIS (Geographic Information System for Global Medicinal Plants) platform. These factors (units consistent with the above databases) included: annual mean temperature (ANNT, °C), mean monthly temperature range (MOND, °C), isothermality (ISOT, dimensionless, ratio of mean diurnal range to annual temperature range), temperature seasonality (TEMP, coefficient of variation, dimensionless), maximum temperature of the warmest month (HTMT, °C), minimum temperature of the coldest month (CLMT, °C), annual temperature range (ANNR, °C), mean temperature of the wettest quarter (WETQ, °C), mean temperature of the driest quarter (DRYQ, °C), mean temperature of the warmest quarter (HTQQ, °C), mean temperature of the coldest quarter (CLQQ, °C), annual precipitation (ANNP, mm), precipitation of the wettest month (WETM, mm), precipitation of the driest month (DRYM, mm), precipitation seasonality (PREC, coefficient of variation, dimensionless), maximum precipitation of the wettest quarter (WETP, mm), precipitation of the driest quarter (DRYP, mm), precipitation of the warmest quarter (HTQP, mm), and precipitation of the coldest quarter (CLQP, mm) (Supplementary Table S2).

Fifteen key soil physicochemical indices were determined strictly in accordance with Chinese national standard methods, with specific determination methods and corresponding standard numbers as follows: total nitrogen (Kjeldahl method, LY/T 1228-2015); total phosphorus and available phosphorus (molybdenum-antimony colorimetric method, LY/T 1232–2015 and HJ 704-2014, respectively); total potassium and available potassium (flame photometric method, LY/T 1234–2015 and NY/T 889-2004, respectively); soil organic matter (potassium dichromate volumetric method, LY/T 1237-1999); available zinc, available iron, and available manganese (DTPA extraction method, NY/T 890-2004); available boron (curcumin colorimetric method, LY/T 1258-1999); exchangeable calcium and exchangeable magnesium (atomic absorption spectrophotometry, LY/T 1245-1999); soil pH (potentiometric method, HJ 962-2018); and soil electrical conductivity (electrode method, HJ 802-2016)(Supplementary Table S3).

Accurately 1.0 g of P. asiatica seeds powder (passed through a No. 3 Pharmacopoeia sieve) was weighed and transferred into a stoppered conical flask. Then, 20 mL of methanol was precisely added, and the total mass of the flask and its contents was recorded. The mixture was ultrasonically extracted at 250 W and 40 kHz for 1 h. After extraction, the flask was cooled to room temperature, and methanol was supplemented to restore the lost mass. The extracted solution was transferred to a centrifuge tube and centrifuged at 7000 r·min^-¹ for 10 min. The supernatant was carefully collected and filtered through a 0.45 μm microporous organic filter membrane to obtain the test solution, which was stored at 4 °C in the dark until HPLC analysis.

HPLC analysis was carried out using a Shimadzu LC-20A liquid chromatograph (Shimadzu Corporation, Kyoto, Japan) equipped with a UV-Vis detector. Chromato-graphic separation was achieved on an Agilent ZORBAX SB-C18 column (4.6 mm × 250 mm, 5 μm). The mobile phase was composed of phase A (0.1% acetic acid aqueous solution) and phase B (methanol), with a gradient elution program optimized as follows: 0.01–5.00 min, 5% B (isocratic elution); 5.00–25.00 min, 5%–25% B (linear gradient); 25.00–40.00 min, 25%–40% B (linear gradient); 40.00–45.00 min, 40%–60% B (linear gradient). The flow rate was maintained at 1.0 mL·min^-¹, the column temperature was set at 30 °C, the injection volume was 1 μL, and the detection wavelength was 254 nm. The calibration curves for target bioactive components were established with standard substances, and all showed good linearity (correlation coefficient r > 0.999), which was sufficient for quantitative analysis.

Accurately 5.0 g of P. asiatica seeds powder was weighed and transferred into a 20 mL headspace vial. The vial was immediately sealed with a polytetrafluoroethylene (PTFE)-lined butyl rubber stopper and an aluminum crimp cap to prevent the loss of volatile components, and the sealed sample was ready for headspace GC-MS analysis.

Headspace GC-MS analysis was performed using an Agilent 7890B gas chromato-graph coupled with an Agilent 5977A mass selective detector (Agilent Technologies, Santa Clara, CA, USA). The headspace equilibrium parameters were set as follows: equilibrium time of 0.5 min, and transfer line temperature maintained at 280 °C. The total GC cycle time was 48.5 min. Chromatographic separation was achieved on an Agilent 19091S-433 HP-5MS capillary column (30 m × 250 μm × 0.25 μm, 5% phenylmethylsiloxane stationary phase). The injection port temperature was set at 250 °C, and high-purity helium (purity ≥ 99.999%) was used as the carrier gas with a constant flow rate of 1.2014 mL/min. The split ratio was 10:1. The programmed temperature rise procedure was optimized as follows: initial temperature of 60 °C (held for 0 min); increased to 100 °C at a rate of 2 °C/min (held for 0 min); further increased to 140 °C at a rate of 5 °C/min (held for 0 min); finally increased to 210 °C at a rate of 4 °C/min (held for 3 min). Mass spectrometry conditions were: electron ionization (EI) source with electron energy of 70 eV; ion source temperature of 230 °C; quadrupole temperature of 150 °C; solvent delay time of 3 min; mass scan range of m/z 35–450.

Accurately 1.0 g of P. asiatica seeds powder was weighed and transferred into a stoppered conical flask. Then, 10 mL of methanol (chromatographic grade) was precisely added, and the total mass of the flask and its contents was recorded. The mixture was ultrasonically extracted at 250 W and 40 kHz for 1 h. After extraction, the flask was cooled to room temperature, and methanol was supplemented to restore the mass lost during ultrasonication. The extracted solution was transferred to a centrifuge tube and centrifuged at 7000 r·min^-¹ for 10 min. The supernatant was collected and filtered through a 0.45 μm microporous organic filter membrane, and the resulting filtrate was used as the test solution. All test solutions were stored at 4 °C until LC-MS analysis to avoid metabolite degradation.

LC-MS analysis was performed using a Shimadzu high-performance liquid chromatography (HPLC) system (equipped with LC30AD dual pumps, SIL30AC autosampler, and CTO30A column oven) coupled with an AB Sciex TripleTOF 5600+ mass spectrometer (AB Sciex, Framingham, MA, USA).

Chromatographic conditions were optimized as follows: column temperature maintained at 40 °C; injection volume of 2.0 μL; mobile phase composed of phase A (0.1% formic acid aqueous solution) and phase B (acetonitrile, LC-MS grade), with a gradient elution program based on Pump B: 0.01–5.00 min, 5%–10% B; 5.00–18.00 min, 10%–25% B; 18.00–23.00 min, 25%–65% B; 23.00–35.00 min, 65%–98% B; 35.00–37.00 min, 98% B (isocratic elution); 37.00–37.10 min, 98%–5% B; 37.10–40.00 min, 5% B (equilibration). The flow rate was set at 0.3 mL·min^-¹ throughout the analysis.

Mass spectrometry conditions were configured with electrospray ionization (ESI) source operating in both positive and negative ion modes: ion source temperature (TEM) = 500.0 °C; spray voltage (ISVF) = 5.5 kV (positive mode) and 4.5 kV (negative mode); declustering potential (DP) = 100.0 eV (positive mode) and -100.0 eV (negative mode); curtain gas (CUR) flow rate = 40 mL·min^-¹; auxiliary gas 1 (GS1) and auxiliary gas 2 (GS2) flow rates = 50 mL·min^-¹ each.

Metabolomics data processing: The raw data was subjected to peak extraction, alignment, and redundancy removal using the MS-DIAL software (https://lipidmaps.org/resources/tools/16?task=4.3). Principal Component Analysis (PCA) was performed for preliminary multivariate statistical analysis to reflect the overall separation trend of samples; Redundancy Analysis (RDA) was used to explore the correlation between soil factors and metabolite profiles. The stability of the method was evaluated using Quality Control (QC) samples, with a Relative Standard Deviation (RSD) < 3%. Differential metabolites were screened using the Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) model with criteria of Variable Importance in Projection (VIP) > 1, t-test p-value (P) < 0.05, and Fold Change (FC) ≥ 2 or FC ≤ 0.5 (Huang et al., 2021). The core role of this screening step is to accurately distinguish metabolic characteristic differences among different sample groups, identify key secondary metabolites related to the research objectives (e.g., effects of soil/climatic factors, differences in sample characteristics), and provide core research targets for subsequent metabolic pathway analysis, clarification of physiological mechanisms, and verification of causal relationships. Moreover, all metabolites discussed in this article are secondary metabolites. Metabolic pathway annotation was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (https://www.genome.jp/kegg/; Kanehisa et al., 2017). Climate and soil factor data were standardized.

Statistical analysis and data visualization were performed using the following software and platforms: “Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine” (2012 Edition, used to calculate the similarity of HPLC samples), IBM SPSS Statistics 27, OriginPro 2024, Maiwei Cloud Platform (https://cloud.metware.cn/#/home), BioRender (https://www.biorender.dev), and Microsoft Excel (2019 Edition), all of which are publicly available.

High-performance liquid chromatography (HPLC) was employed to detect 26 batches of P. asiatica seed samples, leading to the acquisition of a control spectrum containing 23 common peaks (Supplementary Figure S1A). Through comparative analysis with reference substances and qualitative identification based on retention time, a total of five common chromatographic peaks were successfully characterized, namely geniposidic acid (10 min), ferulic acid (24.1 min), acteoside (27.2 min), luteoloside (29.5 min), and isoacteoside (31.4 min) (Supplementary Figure S1B). Subsequently, the similarity between the fingerprint spectra of the 26 batches of P. asiatica seeds samples and the established control spectrum was evaluated. The similarity values ranged from 0.977 to 1.000 (Supplementary Table S2), indicating that the overall chemical composition of P. asiatica seeds samples from different producing areas was relatively consistent, and the quality of the medicinal material remained stable across batches.

Notably, the contents of characteristic chemical components in P. asiatica seeds varied among different producing areas. The content ranges of the identified components were as follows (Supplementary Table S4):eniposidic acid (0.614%–1.215%), acteoside (0.698%–1.294%), isoacteoside (0.049%–0.123%), luteoloside (0.036%–0.093%), and ferulic acid (0.029%–0.067%). Among these components, the contents of acteoside and luteoloside in P. asiatica seeds from Jiangxi Province were generally higher than those from Sichuan Province, with a statistically notable difference observed for acteoside (p<0.05) (Supplementary Figure S2).

Volatile organic compounds (VOCs) are the primary contributors to the odor characteristics of medicinal materials, and variations in the types and concentrations of VOCs result in distinct odor profiles among P. asiatica seeds from different geographical origins. Gas chromatography-mass spectrometry (GC-MS) analysis identified a total of 53 VOCs in the tested samples, which were categorized into 5 groups with varying compound numbers (Figure 2A). Monoterpenoids accounted for the largest proportion (25), followed by sesquiterpenoids (12), lipids (7), aromatic compounds (6), and other compounds (3).

Preliminary principal component analysis (PCA) was performed based on the VOC fingerprint spectra of P. asiatica seeds samples (Figure 2B). The score plot demonstrated clear separation between samples from Jiangxi and Sichuan production areas, indicating significant differences in the volatile component profiles of P. asiatica seeds from these two regions. To further characterize the specific differential components among origins, orthogonal partial least squares discriminant analysis (OPLS-DA) was conducted (Figure 2C). Cross-validation (n=200) revealed that the Y-intercept of Q² was negative, with both R²X and Q² exceeding 0.5, confirming the reliability of the model and the absence of overfitting (Supplementary Figures S3A). Differential metabolites were screened based on the criteria of variable importance in projection (VIP) > 1.0, P < 0.05, and fold change (FC) ≥ 2 or FC ≤ 0.5 (Huang et al., 2021), leading to the identification of 13 key differential compounds (Supplementary Table S5).Notably, lipid compounds in Jiangxi-sourced P. asiatica seeds were generally upregulated, whereas terpenoid compounds were predominantly downregulated (Figure 2D). Among the detected VOCs, (+)-alpha-pinene and linoleic acid were the dominant components in their respective categories (Figure 2E).Of particular interest, (-)-camphene and (Z)-carveol were specifically detected in Sichuan-sourced samples.

Metabolic profiling data of P. asiatica seeds samples were acquired in both electrospray ionization (ESI) positive and negative ion modes. The raw data were subjected to peak extraction and normalization using MS-DIAL software, leading to the identification of 1032 metabolites that were categorized into 12 classes with distinct quantities. Lipids constituted the most abundant class (217 metabolites), followed by terpenoids (132) and other compounds (129). Glycosides (115), steroids (103), and aromatic compounds (98) formed the second tier, while flavonoids (72), amino acids and their derivatives (70), and alkaloids (62) were present in moderate amounts. The remaining classes—phenylpropanoids (16), quinones (14), and tannins (4)—were the least abundant (Figure 3A).

Principal component analysis (PCA) was performed based on the 1032 detected metabolites to evaluate the overall metabolic differences among samples (Figure 3B). In the unsupervised PCA model, samples from the two geographical origins were distinctly clustered and well separated, with the first two principal components (PC1 and PC2) explaining 24.27% and 13.41% of the total variance, respectively. After further analysis using the supervised orthogonal partial least squares discriminant analysis (OPLS-DA) model (Figure 3C; Supplementary Figure S3B), the separation effect between groups was more pronounced, with samples from Jiangxi and Sichuan forming two clear, discrete clusters. This result confirmed distinct differences in the chemical composition of P. asiatica seeds from different producing areas. Forty-eight key differential metabolites were screened using the criteria of variable importance in projection (VIP) > 1.0, t-test result (P < 0.05), and fold change (FC) ≥ 2 or FC ≤ 0.5 (Supplementary Table S6; Figure 3D). These differential metabolites were predominantly classified into lipids, terpenoids, and steroids—three classes of compounds closely associated with the bioactivity of P. asiatica seeds. Statistical analysis revealed that among these 48 metabolites, 18 were upregulated and 30 were downregulated in Jiangxi-sourced P. asiatica seeds compared to P. asiatica seeds from Sichuan. Notably, terpenoid compounds, a major class of bioactive constituents in P. asiatica seeds, exhibited a general downregulated trend in samples from the Jiangxi origin (Figure 3E).

To clarify the potential correlation between habitat climatic conditions and the chemical composition differences of P. asiatica seeds, 19 climatic factors from 26 sampling sites (covering the core producing areas of Jiangxi and Sichuan) were collected and subjected to statistical analysis (Supplementary Table S2). The results revealed notable regional differentiation in climatic factors between the two production areas (Figure 4A), which may serve as a key driver of the metabolic variations of P. asiatica seeds. In terms of precipitation-related factors, the precipitation of the driest month, annual precipitation, precipitation of the driest quarter, and precipitation of the coldest quarter in Jiangxi were all markedly higher than those in Sichuan (all p < 0.01). This indicates that Jiangxi has more abundant overall moisture conditions, and its seasonal precipitation distribution is more conducive to maintaining a humid growth environment for P. asiatica seeds. In contrast, Sichuan showed two distinct climatic characteristics: the coefficient of variation of seasonal precipitation and the minimum temperature of the coldest month were notably higher than those in Jiangxi (both p < 0.05). These results suggest that Sichuan has weaker seasonal stability of precipitation and lower extreme low temperatures in winter, which may impose more fluctuating environmental constraints on the growth of P. asiatica seeds. Overall, the temperature in Jiangxi Province is higher, and precipitation is greater and more evenly distributed. In Sichuan, the temperature is relatively mild, but there is a significant seasonal variation in precipitation.

To explore the potential association between soil nutrient supply and the geographical variations in chemical composition of P. asiatica seeds from the core production areas of Jiangxi and Sichuan, 15 soil physico-chemical indicators across 26 sampling sites were determined and subjected to statistical analysis. The results revealed notable regional differentiation in soil factors between the two areas (Figure 4B). The average contents of available iron (AFe), available manganese (AMn), and available phosphorus (AP) in the soil of Jiangxi production area were markedly higher than those in Sichuan production area (all p < 0.05). This suggests that the soil in Jiangxi has higher bioavailability of iron, manganese, and phosphorus, which can more effectively meet the demand of P. asiatica seeds for these mineral nutrients essential for plant growth and secondary metabolism. In contrast, the contents of available boron (AB), exchangeable magnesium (Ex-Mg), and the pH value in the soil of Sichuan production area were considerably higher than those in Jiangxi (all p < 0.01). These findings indicate that the soil in Sichuan has better bioavailability of boron and higher exchangeability of magnesium, forming a soil background that was significantly distinct from that of Jiangxi.

To clarify the climate-driven mechanisms underlying the content variations of the five core bioactive components identified by HPLC, Pearson correlation analysis combined with Mantel test was used (significance was defined as P < 0.05, and extreme significance as P < 0.01) to explore the specific response patterns of these components to 19 climatic factors (Figure 4C).

The results are summarized as follows: geniposidic acid was correlated solely with the precipitation of the hottest season, indicating that its content is weakly regulated by climatic factors. In contrast, acteoside had highly notable correlations with multiple climatic factors, including the maximum temperature of the hottest month, average temperature of the hottest quarter, precipitation of the driest month, coefficient of variation of seasonal precipitation, annual average temperature, temperature seasonality (isothermality), and average temperature of the driest quarter. These findings suggest that seasonal fluctuations in hydrothermal conditions are the core climatic factors regulating the accumulation of acteoside. Luteoloside was significantly correlated with annual average temperature, temperature seasonality (isothermality), maximum temperature of the hottest month, average temperature of the driest quarter, average temperature of the hottest quarter, precipitation of the driest month, and coefficient of variation of seasonal precipitation. Its accumulation is mainly regulated by annual average temperature and the combination of hydrothermal conditions during dry and wet seasons. Ferulic acid was only correlated with the average temperature of the coldest quarter, showing that its response specificity to climatic factors was relatively low. Notably, isoacteoside exhibited no correlation with any of the tested climatic factors, implying that its content may be more strongly regulated by genetic factors or non-climatic environmental cues.

Using P. asiatica seeds from regions with distinct climatic conditions as research subjects, gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) techniques were used to identify and screen differential metabolites. Pearson correlation analysis combined with Mantel test was further used to explore the specific response patterns of these differential metabolites to 19 climatic factors, with significance defined as P < 0.05 and extreme significance as P < 0.01.

To elucidate the soil-driven mechanisms underlying the content variations of the five core bioactive components detected by HPLC, Pearson correlation analysis, Mantel test, and redundancy analysis (RDA) were combined to investigate the association patterns between these five core bioactive components and 15 soil physico-chemical properties. Statistical significance was defined as P < 0.05, and extreme significance as P < 0.01.

To gain insights into the molecular mechanisms underlying the metabolomic differences of P. asiatica seeds between Jiangxi and Sichuan provinces, KEGG pathway enrichment analysis was performed on the differential metabolites screened by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS), respectively.