Changes in Climatic Conditions, Soil Texture, and Rhizospheric Microorganisms Affect the Quality and Suitable Production Zones of Stellaria dichotoma L. var. lanceolata Bge

doi:10.21203/rs.3.rs-4950657/v1

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Changes in Climatic Conditions, Soil Texture, and Rhizospheric Microorganisms Affect the Quality and Suitable Production Zones of Stellaria dichotoma L. var. lanceolata Bge

https://doi.org/10.21203/rs.3.rs-4950657/v1

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Background

Stellaria dichotoma L. var. lanceolata Bge. (S. lanceolata) is a psammophytic plant endemic to the northwest region of China and serves as a distinctive economic crop. It is the original plant species used in traditional Chinese medicine as Yinchaihu and also finds application in cosmetics production, predominantly growing in arid and semi-arid desert grasslands. In response to the significant changes in habitat and quality of S. lanceolata resulting from shifts in cultivation areas and indiscriminate introductions, this study aims to propose a more scientifically sound delineation of suitable production zones.

Results

The results indicated migration trends of S. lanceolata towards the central and eastern parts of Inner Mongolia and identified elev, bio_4, bio_13, bio_11, and S_clay as the primary influencing climate and soil environmental factors. Additionally, the rhizosphere microbial environment of S. lanceolata shifted significantly from high to medium suitability habitats. Meanwhile, increasing years of cultivation in introduction area broken the balance in fungal and bacterial diversity in the rhizosphere soil of S. lanceolata, leading to the enrichment of more pathogenic microbial communities, inducing diseases. It further demonstrated the suitability for high suitable zones of S. lanceolata from the perspective of rhizosphere microbiota. Metabolomic analysis revealed substantial changes in metabolic processes and substance accumulation during the migration from high to low suitable zones. Quality evaluations using near-infrared spectroscopy and determination of major component contents confirmed the superior quality of S. lanceolata in high suitable zones.

Conclusion

Overall, this study revealed the key climatic, soil, and rhizosphere microbial environmental factors influencing the quality formation of S. lanceolata and the selection of suitable production zones, offering guidance for sustainable development and production zone planning.

Stellaria dichotoma L. var. lanceolata Bge.

Climatic

Soil

Rhizospheric Microorganisms

Suitable zoning

Quality assessment

Stellaria dichotoma L. var. lanceolata Bge. (S. lanceolata) is a psammophytic plant species that thrives in the arid and semi-arid regions of Northwest China [1]. Due to its characteristics of being drought-tolerant, resilient to poor soil conditions, and possessing strong vitality, it plays a crucial role in maintaining the ecological balance of the desert steppe [2]. Additionally, S. lanceolata is an economically valuable medicinal plant, as its roots serve as the raw material for the traditional Chinese medicine known as "Yinchaihu" [3]. It is a representative medicinal material for clearing deficiency heat, possessing anti-inflammatory, anti-allergic, and anti-cancer effects [4]. It has also found extensive application in the cosmetics industry and serves as a vital ingredient [5, 6]. Additionally, studies have demonstrated its rich content of active components, such as cyclopeptides, flavonoids, alkaloids, and sterols [4]. Furthermore, since S. lanceolata is primarily distributed in regions adjacent to Ningxia, Inner Mongolia, and Shaanxi Province, it is considered to be an genuine (Dao-di) medicinal materials of these zones [1, 3].

Dao-di medicinal materials in Chinese refer to high-quality medicinal materials produced in specific geographical locations and habitats. Suitable and specific habitats is core factor determining the formation of medicinal materials [7]. Recent research shows that the habitat affects the accumulation of secondary metabolites in medicinal plants, thereby influencing efficacy and quality of medicinal materials [8]. In particular, recent studies have indicated a close relationship between the composition of soil microbial communities and the synthesis of bioactive components in medicinal materials [9, 10].

In the 1980s, S. lanceolata was first successfully introduced and domesticated in Ningxia province, gradually broadly cultivated from northern regions (wild distribution) to wind-sand arid zone of central Ningxia province. As of now, S. lanceolata has developed into a distinctive superior medicinal plant variety and important crop in Ningxia province, particularly in Tongxin County, which has become the largest planting area nationwide, supplying over 80% of S. lanceolata in the market. However, compared to wild S. lanceolata, the natural habitat of cultivated S. lanceolata has undergone significant changes, resulting in alterations in its characteristics, quality, composition, and efficacy [11]. In recent years, particularly with a fivefold increase in the price of S. lanceolata, its cultivation range has significantly expanded. Notably, the climate has transitioned from arid to regions with ample rainfall, while the soil has changed from poor sandy soil to fertile loam or even clay. This expansion poses greater challenges to ensuring the quality and sustainable development of S. lanceolata.

Ma et al. [12] previously utilized TCMGIS system to predict the suitable growth range of wild S. lanceolata and inferred that more than 90% of the northern regions of China were suitable zones. However, based on available research [1], wild S. lanceolata is mainly distributed in the narrow zones of adjacent to desert grasslands of Ningxia-Inner Mongolia-Shaanxi province, and its distribution area is not expended. Furthermore, we found that although some introduction zones are capable of growing S. lanceolata, continuous occurrences of root rot and death after planting for more than 3 years, particularly severe when planted in non-sandy soils, which does not occur in the natural growth zones of wild S. lanceolata. Therefore, a more scientific and targeted approach to the production zones division for S. lanceolata is of theoretical and practical significance for the sustainable development of S. lanceolata industry.

The Maxent model is a quantitative analysis tool utilized for predicting the potential geographical distribution and suitable habitats of species [13]. It predicts the probability distribution of the target object by computing the probability distribution of maximum entropy and analyzes the relationship between species distribution and environmental variables (climatic factors, spatial distribution, soil physicochemical properties, etc) [14]. Currently, it has been widely employed in numerous disciplines such as ecology, evolutionary biology, and plant protection, and is recognized for its strong predictive capability and accuracy [15]. Consequently, the Maxent model also provides new ideas and methods for genuine production zones division and studying the producing zones suitability of S. lanceolata.

This study was based on the distribution information and 33 environmental factors including climate and soil of S. lanceolata in wild production zones and uses the Maxent model to analyze the main environmental factors affecting the distribution of S. lanceolata. And it predicts the current potential distribution range and analyzes the development trends of future suitable regions based on predicted data for future climate change. Simultaneously, against the backdrop of different suitable division, we conducted that microbial community analysis of rhizosphere soil from different zonal and cultivation years of S. lanceolata. And we employed UHPLC-Q-TOF MS to detect metabolites of S. lanceolata from different production zones, analyzing its metabolic characteristics in response to different zonal habitats. Furthermore, we analyzed the near-infrared spectroscopy and major active ingredients content of S. lanceolata from different production zones, and comprehensively evaluated the medicinal material quality through multivariate statistical analysis. The present study aimed to comprehensively assess the production zones suitability of S. lanceolata from multiple perspectives, validated the reliability of production zones division, and provided reference for production zones construction and high-quality production of S. lanceolata.

2.1 Pecies occurrence records

This study collected distribution information of S. lanceolata in wild production zones, with some of the data obtained through searching of the Chinese Virtual Herbarium (http://www.cvh.ac.cn/) and National Specimen Information Infrastructure (http://www.nsii.org.cn/), while the rest was acquired through field resource survey. The ArcGIS software (10.4 version; ESRI, Redlands, USA) was used to screen the species distribution point data, and delete records with a straight-line distance of less than 2000 m. Ultimately, 70 valid information were obtained (Supplementary Table 1) for subsequent analysis.

2.2 Acquisition of environmental factors

The climate data used in the study, covering the current period (1970–2000), and future periods (2041ཞ2060 and 2081ཞ2100, referred to as 2050s and 2090s), were sourced from Worldclim (https://www.worldclim.org/), which included 19 bioclimatic variables (bio_1ཞbio_19) and an elevation dataset (elev) at a spatial resolution of 2.5 minutes. Using ArcGIS, slope and aspect data were extracted from the elevation dataset. Future climate scenario data were selected from two climate scenarios (SSP126 and SSP585) under the BCC-CSM2-MR model within the Sixth Coupled Model Intercomparison Project (CMIP6). SSP126 represented a low forcing, low greenhouse gas emission scenario, while SSP585 represented a high forcing, high greenhouse gas emission scenario. 11 soil variable datasets were downloaded from the Harmonized World Soil Database (HWSD, https://www.fao.org/) at a spatial resolution of 2.5 minutes. Soil and topographic factor data for the future period were based on the current data. In total, 33 environmental variable datasets were obtained (Supplementary Table 2).

2.3 Model construction and zoning prediction

The preprocessed S. lanceolata geographic distribution data and 33 environmental variable datasets were imported into the Maxent model, with 25% distribution spots set as testing data and 75% as training data. To ensure the stability and reliability of the results, the model was run 10 times repeatedly. Subsequently, the accuracy of the model was evaluated using the area under the Receiver Operating Characteristic curve (ROC curve), also called AUC (area under the curve) value. The AUC value ranged from 0.5 to 1, with values closer to 1 indicating more accurate predictions. An AUC value between 0.5 and 0.6 indicated very low prediction; 0.6 to 0.7 indicated poor results; 0.7 to 0.8 indicated fair results; 0.8 to 0.9 indicated good predictive accuracy, and 0.9 to 1 indicated excellent results [16]. The contribution of environmental variables was determined using the jackknife method and output format was set to logistic, while others remained at default settings. The average values obtained from the model were imported into ArcGIS for reclassification. Maximum test sensitivity plus specificity (MTSPS) was utilized to divide the distribution zones of S. lanceolata, resulting in four categories: unsuitability habitats (P < MTSPS), low suitability habitats (MTSPS ≤ P < 0.4), medium suitability habitats (0.4 ≤ P < 0.6), and high suitability habitats (P ≥ 0.6).

2.4 Samples collection

Based on the results of division predictions, the rhizosphere soil of S. lanceolata in high suitability zones (Lingwu City, Ningxia province, 106.591°E, 38.048°N)), and from different cultivated years in medium suitability zones (Tongxin County, Ningxia province, 106.313°E, 36.815°N), were collected for rhizosphere microbial analysis. The abbreviation for soil samples originating from high suitability zones is "HS" (more than 5 years of growth), while those from medium suitability zones are denoted as "MS." Soil samples from 1–2 years of cultivation were categorized as "MS I", those from 3–4 years as "MS II", and those from 5 years or more as "MS III". And the S. lanceolata samples from different suitable zones were collected, including 8 samples, 7 samples and 4 samples from high, medium and low suitability zones, respectively. The samples from the high suitability zones were all obtained from wild sources, while no wild samples were found in the medium and low suitability zones, thus cultivated samples aged 3–5 years (Supplementary Table 3 for detailed samples information) were collected. During the cultivation process of S. lanceolata, aside from sowing, there were almost no other field management processes. Six biological replicates were taken from each sampling spot for subsequent infrared spectroscopy analysis, content determination, and metabolomics analysis of S. lanceolata. All samples were identified by Professor Li Peng as roots of Stellaria dichotoma L. var. lanceolata Bge.

2.5 Rhizosphere microbial community analysis

1.0 g soil sample was taken for microbial community whole-genome DNA extraction using DNeasy® PowerSoil® Pro Kit, followed by gel recovery of PCR products using AxyPrepDNA Gel Recovery Kit. The PCR products were quantified by QuantiFluor™-ST Blue Fluorescence Quantitation System (Promega) and subsequently not used for Illumina library construction and sequencing until meeting the concentration requirements. The bacterial 16S rRNA gene in the V3-V4 region was amplified using primers 338F and 806R; while the fungal ITS1-ITS2 region was amplified using primers ITS1F and ITS2R. After Illumina sequencing generated PE reads, sequences were assembled based on overlap relationships, followed by sequence quality control and filtering, sample differentiation, OTU cluster analysis, and taxonomic classification. Above sequencing work was supported by Shanghai Majorbio Pharmaceutical Technology Co., Ltd. Subsequent multivariate statistical analysis was conducted by OTU data.

2.6 Metabolomic analysis

S. lanceolata were pulverized into powder after being added to liquid nitrogen, and 200 mg was weighed and placed into a 2 mL centrifuge tube. Then, 70% methanol aqueous extraction solution was added, followed by thorough vortexing, extraction, vacuum drying, and storage at -80°C for later use. Prior to analysis, the samples were resuspended in 40% acetonitrile aqueous solution, vortexed thoroughly, centrifuged at 14,000 rpm for 10 min, and the supernatant was collected for metabolomic analysis. An Agilent 1290 Infinity LC ultra-high-performance liquid chromatography (UHPLC) was maintained at 25°C and eluted at a flow rate of 0.5 mL / min, where mobile phase A were water, 25 mM ammonium acetate and 25 mM ammonium hydroxide, and B was acetonitrile with an injection volume of 2.0 µL. A Triple TOF 6600 mass spectrometer was performed in both positive (pos) and negative (neg) ion modes via electrospray ionization (ESI). A standard substance database established by Shanghai Applied Protein Technology was employed for data mining. Metabolites were identified by matching retention time, molecular weight (within < 25 ppm error), secondary fragmentation spectrum, and collision energy information with those in the local database.

2.7 Near infrared spectrum analysis

The S. lanceolata samples collected were naturally dried to constant weight, pulverized, and sieved through a 40-mesh screen to obtain the herbal powder. 2.0 g powder was placed in a 10 mL centrifuge tube and compacted. Spectrum scanning was performed using BRUKER MATRIX-F (Bruker, Germany). The instrument probe was inserted into the centrifuge tube, directly contacting samples powder for measurement. Each sample was scanned six times, and average spectrum was obtained using OPUS software, constituting the NIR spectrum of samples. The parameters were set as follows: resolution: 4 cm^− 1, scan times: 16, scan range: 4, 000ཞ12, 000 cm^− 1. Subsequently, preprocessing was carried out using Origin 2020, including baseline subtraction, second derivative calculation, normalization, and Gaussian smoothing, as well as hierarchical cluster analysis.

2.8 Determination of components content

Methanol extract (MA) content: it was determined using the method alcohol-soluble extractives as outlined in the "Pharmacopoeia of the People's Republic of China (Part IV)" (2020 edition), general chapter "2201 determination of extractives".

Total sterols (TS) content: Using the vanillin-glacial acetic acid colorimetric method, with α-sitosterol as the reference standard, it was measured absorbance at 546 nm wavelength, and calculated the total sterols content.

Total flavonoids (TF) content: Using the aluminum nitrate colorimetric method, with rutin as the reference standard, it was measured absorbance at 496 nm wavelength, and calculated the total flavonoids content.

Dichotomines B (DB), Quercetin-3-O-β-D-glucoside (QG), Dihydroferulic acid (DA), and Paconol (PA) contents: 0.50 g S. lanceolata powder sieved through a 40-mesh screen was weighed accurately and placed in a 50 mL centrifuge tube. Then it was added with 20 mL 20% DMSO (DMSO : methanol = 1 : 4) and performed ultrasonic extraction for 30 minutes. Then, its supernatant was decanted and filtered through 0.22 µm organic membrane to obtain the test solution. Next, the contents of four substances in the samples were determined by High-Performance Liquid Chromatography (1260A, Agilent).

3.1 Accuracy analysis of prediction model

When conducting zoning predictions by Maxent model, AUC value (ranging from 0 to 1) could reflect the accuracy of model predictions. A larger AUC value indicated higher precision of model. In this study, AUC value was 0.984 (Fig. 1), indicating that predicted results under different climatic scenarios were highly reliable.

3.2 Dominant habitat factors analysis and current zoning prediction

The relative contributions of 33 habitat factors were analyzed using the Maxent model and jackknife method. As shown in Fig. 2A, elev, bio_4, bio_13, bio_11, and S_clay exhibited relatively high contribution rates, with a cumulative contribution rate reaching 80.5%. Additionally, the permutation importance scores for elev, bio_4, bio_13, and bio_11 were also high (Fig. 2B), indicating that these climatic factors were potential key factors influencing the distribution of wild S. lanceolata, while S_clay may play a more significant role as a soil environmental factor. Single-factor habitat suitability analysis was conducted for elev, bio_4, bio_13, bio_11, and S_clay to select optimal habitat range suitable for wild S. lanceolata distribution. The results revealed that elev, bio_4, bio_13, and bio_11 exhibited relatively regular normal distributions (Fig. 2C). Specifically, the medium suitability and above zones for Elev were 1050.60 ~ 1633.13m, the high suitability zones were 1258.65 ~ 1397.34m, and the optimum value was 1334.93m; for bio_4, the medium suitability and above zones were 1080.44 ~ 1239.28, the high suitability zones were 1121.03 ~ 1186.33, and the optimum value was 1152.80; for bio_13, the medium suitability and above zones were 46.13 ~ 105.40mm, the high suitability zones were 69.61ཞ96.45mm, and the optimum value was 87.51mm; for bio_11, the medium suitability and above zones were − 10.67~-5.16°C, the high suitability zones were − 8.38~-6.97°C, and the optimum value was − 7.68°C. S_clay, as the most influential soil factor, exhibited a medium suitability and above zones of 2.87%~7.80%, a high suitability zones of 2.87%~6.00%, and an optimum value of 4.05%.

Under the current climatic conditions, the zoning prediction results were depicted in Fig. 2D. The high suitability zones of wild S. lanceolata were approximately 164709.45 km², predominantly distributed in northern part of Ningxia province, central-southern regions of Inner Mongolia, and northern part of Shaanxi (adjacent zones of Ningxia province, Inner Mongolia, and Shaanxi), exhibiting a scattered distribution. Additionally, there were limited distributions in central-northern parts of Shanxi and central regions of Gansu. The medium suitability zones were mainly distributed in the surrounding regions of the high suitability zones, covering an area of approximately 281470.89 km². The low suitability zones were primarily found in high-altitude cold regions such as the Qinghai-Tibet Plateau and the Tianshan Mountains, covering an area of about 72481.10 km². The majority of remaining zones in China were classified as low suitability zones, with an area of approximately 9081338.57 km². It was noteworthy that within the high suitability zones, scattered non suitability zones. These zones were mostly located in hinterlands of Maowusu Desert, Ulan Buh Desert, and Kubuqi Desert, while S. lanceolata mainly inhabited the desert grasslands on the edges of above deserts, and was absent from the central zones of them [4, 17]. It may be attributed to the more extreme habitats in the central regions of these deserts (scarce precipitation, scorching summer temperatures, and dry, cold winters) [17], which surpassed S. lanceolata adaptive capacity to the environments. In summary, wild S. lanceolata can generally survive nationwide, but its high suitability and medium suitability zones were limited. This reflected S. lanceolata strong environmental adaptability, indicating its capability to thrive in most habitats. However, during the long-term natural evolution and selection process, wild S. lanceolata distribution primarily concentrated in high suitability zones.

3.3 Future zoning and development trend prediction

Based on above studies, we further predicted future zoning, which revealed a shift in suitability habitat range (high + medium suitability zones) of S. lanceolata towards the northeast under 4 habitat backgrounds, primarily concentrating in central and northern zones of Inner Mongolia (Fig. 3A, Supplementary Fig. 1). Ningxia Province, as the largest cultivation zone, still predominantly concentrates its high and medium suitability zones in the central and northern regions. However, there is a diminishing trend in comparison to the current climatic backdrop. Conversely, zones in Shaanxi Province and Inner Mongolia Province exhibit an expanding trend. Notably, under 50s-126 climate background, unsuitability zones within original high suitability zones noticeably expands. Conversely, under 90s-126 climate background, unsuitability zones within the same zones decreased while high and medium suitability zones expanded. Under future climate backgrounds with continued high carbon emissions (50s-585 and 90s-585), more pronounced regional shifts in different suitability areas occurred. Besides northeastward migration of high and medium suitability areas, significant changes also occurred in non suitability areas. Specifically, the unsuitable zones within the high suitability zones noticeably decreased, while extensive unsuitable zones emerged to the north and northeast of this zone. This reflects that under a high carbon emission background, climate change may lead to more significant shifts in the distribution of S. lanceolata.

Area analysis under different grading zoning under different climate backgrounds (Fig. 3B, Fig. 3C) revealed that compared to current climate background, except for 50s-126, the total suitability (high + medium suitability) areas exhibited an expanding trend, with medium suitability areas showing a similar trend across all scenarios, with the most significant expansion observed under 90s-585; however, high suitability areas diminished in size across 4 climate backgrounds, with the most pronounced reduction under 50s-126 and the least under 90s-585. Low suitability areas demonstrated a decreasing trend across all scenarios except for 50s-126, with the most substantial reduction notably under 90s-585. Conversely, non suitability areas manifested an expanding trend under 4 climate backgrounds, particularly prominent under 50s-126. In summary, high suitability areas for future S. lanceolata may decrease, yet overall, suitability areas may expand, especially under high carbon emission scenarios, where medium suitability areas significantly enlarged, potentially providing a more substantial environmental foundation for S. lanceolata propagation and producing zones construction.

3.7 Analysis of the Diversity of Rhizosphere Microorganisms in S. lanceolata across Different Suitable Zones and Planting Years

Using amplicon sequencing, we analyzed the characteristics of rhizospheric soil microbiota in high suitability zones, medium suitability zones for S. lanceolata planted in different planting years. The Shannon Index analysis (Fig. 4A) indicates that bacterial α-diversity is highest in the rhizospheric soil of the S. lanceolata's high suitable zones (HS). In contrast, fungal α-diversity in the HS is lowest compared to that of the medium suitable zones (MS). And more abundant bacterial α-diversity was showed in increasing planting years of S. lanceolata. Principal Co-ordinates Analysis (PCoA) demonstrated significant differences in both fungal and bacterial β-diversity between HS and MS I, MS II, and MS III, with PCoA scores primarily located in the third quadrant for HS, the first quadrant for MS III, and the fourth quadrant for MS I and II (Fig. 4B, Supplementary Fig. 2). Further analysis of community composition of rhizosphere soil microorganisms at genus level revealed that dominant fungal taxa in HS were Aspergillus, Acremonium, and Filobasidium, while Gibberella, Acremonium, Alternaria, and Fusarium predominated in MS (Fig. 4C). In terms of bacteria, Arthrobacter and Nocardioides were dominant genera (Supplementary Fig. 3).

Using LefSe analysis, a comparison of significantly different microbial taxa between HS and MS III was conducted. The results revealed enrichment of beneficial bacterial genera such as Mesorhizobium, Agromyces, Streptomyces, and Bacillus, as well as beneficial fungal genera including Acremonium and Penicillium, with higher abundance in HS. Conversely, MS III exhibited enrichment of beneficial bacterial genera such as Arthrobacter, Sphingomonas, and Microvirga, along with higher abundance of harmful fungal genera including Gibberella, Alternaria, Sarocladium, Cladosporium, Paraphoma, Phaeomycocentrospora, and Leptosphaerulina (Fig. 4D, Supplementary Fig. 4). Comparison across different planting years revealed that relative abundance of harmful fungal genera such as Aspergillus, Sarocladium, and Paraphoma increased with increasing planting years, while beneficial fungal genera including Arthrobacter, Nocardioides, Mesorhizobium, and Chaetomium gradually decreased in relative abundance (Fig. 4E, Supplementary Fig. 5).

3.6 Metabolomic analysis of S. lanceolata in different suitability zones.

To further investigate the impact of different suitability habitats on S. lanceolata, representative S. lanceolata from different suitability zones (HW: SM, YCW, and SBZ; MC: QYQ and HSP; LC: LJT and HZ) were subjected to metabolomic analysis. Adequate amounts of all S. lanceolata samples were pooled to prepare quality control (QC) samples. The metabolites within samples were separated and detected using UHPLC-Q-TOF MS, with 11 repeated measurements. As evidenced by Principal component analysis (PCA) (Fig. 5A), QC samples clustered tightly, indicating great reproducibility. Furthermore, samples from HW, including SM, YCW, and SBZ, exhibited minimal variability and similar principal component characteristics. In contrast, significant variability was observed between MC and LC, with relatively less inter groups differences between MC and LC. The acquired data were matched against database established by Shanghai Applied Protein Technology based on standard substances, incorporating information on metabolite retention times, molecular weights (with errors < 25 ppm), secondary fragmentation spectrum, collision energy, etc. A total of 1586 substances were identified, comprising 880 in positive mode and 706 in negative mode (detailed information provided in Supplementary Table 4). These substances were classified into 13 major categories, predominantly encompassing lipids and lipoids molecules, organic acids and derivatives, organic heterocyclic compounds, phenylpropanoids and polyketides, as well as benzene ring type compounds (Fig. 5B). Based on identified substances, systematic cluster analysis was performed on S. lanceolata from different suitability zones. The results (Fig. 5C) revealed that HW formed a distinct cluster, whereas MC formed another, at the same time, MC and LC formed a distinct cluster, aligning with trends observed in principal component analysis. This further reflected discernible differences in metabolite profiles between MC, LC, and HW.

To analyze the differential metabolites between MC and LC compared to HW, OPLS-DA analysis, FC analysis, and T-test analysis were separately conducted for MC vs HW and LC vs HW. Differential metabolites with a VIP > 1, FC < 0.67 or FC > 1.5, and a p < 0.05 were selected. Ultimately, 281 differential metabolites (up: 226; down: 55) were identified from MC vs HW, and 370 differential metabolites (up: 222; down: 148) from LC vs HW. These differential metabolites mainly comprised lipids and lioids molecules, polyketides, organic heterocyclic compounds, organic acids and derivatives (Fig. 5D). Venn diagram (Fig. 5E) revealed 143 commonly shared differential metabolites between two comparison groups, with 30 metabolites showing higher expression in HW compared to MC and LC, including various bioactive secondary metabolites such as Mangostine, β-glycyrrhetinic acid, Arctiin, and Cucurbitacin e, etc. They further reflected HW characteristics. And KEGG pathway enrichment analysis of differential metabolites from MC vs HW and LC vs HW demonstrated shared enrichment in Metabolic pathways, Biosynthesis of secondary metabolites, Carbon metabolism, and C5-Branched dibasic acid, etc. (Fig. 5F, Supplementary Fig. 6). Specifically, MC and LC exhibited advantageous enrichment in multiple metabolic pathways including Biosynthesis of secondary metabolites, while HW showed enrichment in C5-Branched dibasic acid metabolism pathway.

3.4 Near infrared spectrum analysis of S. lanceolata in different suitability zones.

Near infrared spectrum can reflect physical properties and compound characteristics of substance-rich materials, commonly employed for the authentication and quality evaluation of medicinal materials [18]. In this study, Near infrared spectrum analysis was conducted on 19 distinct sources S. lanceolata, resulting in acquisition of their spectral profiles. As illustrated in Fig. 6A, S. lanceolata characteristic spectral information primarily concentrated in long wavelength region of near infrared (4000 cm^− 1 ~ 9091 cm^− 1), with various sources of S. lanceolata exhibiting similar spectrum and characteristic peaks, albeit with discernible differences in peak intensities, reflecting their comparable chemical compositions while potentially differing in contents. To address the issue of significant information overlap due to broad bandwidths of near infrared spectrum and better elucidate inter samples variances, data preprocessing involving baseline correction, second derivative transformation, gaussian smoothing (7 points), and normalization analysis was applied to the near infrared long wavelength spectrum, followed by hierarchical cluster analysis. The results (Fig. 6B) revealed that samples collected from high suitability zones (ALS, SBZ, GSW, ALZ, SM, ZE, ZTL, and YCW) clustered together, while those from medium and low suitability zones (HSP, MD, QYQ, SYG, TFC, NY, ZJS, HZ, LD, LJT, and YP) formed another cluster. This delineation underscored the differing near infrared spectrum features of S. lanceolata from high, medium and low suitability zones, indicative of variances in S. lanceolata quality and intrinsic component contents across distinct suitability zones.

3.5 Quality Evaluation of S. lanceolata in different suitability zones.

The contents of 7 major active ingredients (MA, TS, TF, DB, QG. DA and PA) were determined in S. lanceolata samples collected from different zones. The results (Fig. 7A) revealed trends in the average contents of TS, TF, DB, and QG among S. lanceolata from different zones, showing HW > MC > LC, with notably higher levels of QG (0.312 mg·kg^− 1) and TF (2.772 mg·kg^− 1) in HW compared to MC (0.233 mg·kg^− 1, 2.270 mg·kg^− 1) and LC (0.229 mg·kg^− 1, 2.122 mg·kg^− 1) (p < 0.05). Moreover, HW exhibited significantly higher DB (0.057 mg·kg^− 1) compared to LC (0.042 mg·kg^− 1). The average contents of ME and DA in S. lanceolata from different zones followed a trend of LC > MC > HW, in which LC showing significantly higher ME (37.0%) and DA (0.044 mg·kg^− 1) compared to HW (32.0% and 0.026 mg·kg^− 1). Furthermore, there were no significant differences in the contents of PA and TS.

To further analyze the relationship between zoning and contents of main active ingredients and quality of medicinal materials, principal component analysis was conducted on S. lanceolata 7 indicators from different zones. Based on the contributions of different indicators to different principal component factors, a comprehensive score equation was constructed to comprehensively evaluate S. lanceolata quality from different zones. As Fig. 7B showed, first three principal components had eigenvalues greater than 1 (2.95, 1.46, 1.01), with a cumulative variance contribution rate reaching 77.48% (42.21%, 20.85%, 14.42%), which could represent overall information of all S. lanceolata. PC1 and PC2 were selected to construct a biplot of principal component scores of S. lanceolata from different zones and load matrices of different indicators. The results (Fig. 7C) showed that the principal component scores of 8 S. lanceolata from HW were mainly concentrated in the first and fourth quadrants (ALS, SBZ, GSW, ALZ, SM, ZE, ZTL, and YCW), exhibiting a certain clustering trend, while S. lanceolata from MC and LC were mainly concentrated in the second and third quadrants (HSP, MD, SYG, TF, NY, ZJS, HZ, LD, LJT, and YP). Meanwhile, the loadings of TS, QG, DB, and TF tended to the principal component scores of HW S. lanceolata, indicating that 4 indicators were more reflective of the characteristics of HW S. lanceolata, whereas the loadings of DA, ME, and PA tended to the principal component scores of MC and LC samples, indicating that 3 indicators reflected more characteristics of MC and LC.

Further standardizing all relevant indicators of S. lanceolata, and calculating formulas for the scores of each principal component based on eigenvalues and loadings: F1 = -0.277*X₁ + 0.134*X₂ + 0.229*X₃ + 0.28*X₄ + 0.229*X₅ − 0.054*X₆ − 0.241*X₇、 F2 = -0.076*X₁ + 0.526*X₂ − 0.324*X₃ + 0.095*X₄ + 0.195*X₅ + 0.411*X₆ + 0.277*X₇、 F3 = 0.103*X₁ − 0.026*X₂ − 0.321*X₃ + 0.305*X₄ + 0.343*X₅ − 0.704*X₆ + 0.401*X₇ (X₁、 X₂、 X₃、 X₄、 X₅、 X₆ and X₇ represented ME、 TS、 TF、 DB、 QG、 PA and DA respectively). Finally, using variance contribution rate of each principal component as weights, the comprehensive evaluation equation for S. lanceolata was obtained by summing the scores of each principal component multiplied by their corresponding weights. The formula was F = -0.118*X₁ + 0.162*X₂ − 0.017*X₃ + 0.182*X₄ + 0.187*X₅ − 0.039*X₆ + 0.014*X₇. Calculating S. lanceolata scores from different zones using it, the results were shown in Fig. 7D. HW generally had higher scores, and overall scores were higher than MC and LC, indicating superior quality of HW.

4.1 The suitability zones of S. lanceolata may undergo migration in response to climate change.

As a xerophyte indigenous to temperate arid regions, S. lanceolata exhibited a notably narrow natural distribution, and primarily concentrated in desert grasslands of Ningxia province, Inner Mongolia, and adjacent to Shaanxi in China [12]. Predictions based on current climate habitats indicated that high and medium suitability areas for S. lanceolata were predominantly situated in these zones. Our result further underscored these area are the suitability areas for planting S. lanceolata. However, based on the future climate data from SSP126 and SSP585, as well as the zoning predictions from the Maxent Model, significant changes in the suitable habitats of S. lanceolata are anticipated. Ningxia province is the currently most widely recognized producing area and largest supply area of S. lanceolata. In the prediction of future, it was found that suitability zones in Ningxia province may gradually diminish, possibly due to less than ideal ecological restoration in the central and northern zones, further accelerated by industrialization [19]. Meanwhile, the central zones of Inner Mongolia have shown greater potential and can be considered as candidate zones for future development as more suitable production zones for S. lanceolata.

4.2 Ecological factors and rhizosphere microorganisms play crucial roles in the selection process of suitable zones for S. lanceolata.

This study identified five primary factors (elev, bio_4, bio_13, bio_11, and S_clay) influencing the zoning of S. lanceolata, which also reflect the key habitat characteristics suitable for S. lanceolata. Among these, elevation is closely correlated with temperature and precipitation [20], with the optimal elevation for S. lanceolata ranging from 1050.60 to 1633.13 meters. Climatically, bio_4 within the range of 1080.44 ~ 1239.28 may better align with S. lanceolata growth cycle response to temperature. Higher bio_4 reflected significant seasonal variations, and temperature change range it adapting to was also large for its life cycle, which may affect its reproductive physiological processes such as flowering and fruiting. While S. lanceolata exhibited stress tolerance to an extent, its capacity to withstand aridity and other stressors was limited, rendering it unsuitable for growth in extremely arid desert core areas [2, 21]. This study suggested bio_13 suitable range of precipitation between 46.13 ~ 105.40 millimeters, indicating a more pronounced demand for water during July and August (the highest rainfall months in suitability areas). And bio_11 S. lanceolata adapting to temperature was − 10.67~-5.16 ℃, reflecting its cold tolerance during December to February (the coldest months in suitability areas). Additionally, S. clay can reflect the texture of the soil. In this study, S_clay suitability areas below 7.80% indicated that S. lanceolata is well adapted to sandy soil environments. And consistent with previous findings indicated a significant correlation between soil texture and accumulation of S. lanceolata metabolites[1].

Soil microbial environment was another key factor influencing medicinal plants growth, development, and accumulation of secondary metabolites [9]. Climate factors and soil physicochemical properties not only directly affected the growth and development of medicinal plants, but also soil microorganisms, especially composition and activity of rhizosphere microorganisms [22]. These microorganisms can, in turn, influence medicinal plants growth and synthesis and accumulation of secondary metabolites by secreting hormones, altering nutrient environments, or regulating they gene expression and metabolic pathways [23, 24]. Previous studies had found that S. lanceolata rhizosphere microorganisms exhibited different characteristics in different habitats, and were significantly correlated with its active ingredients [25, 26]. Our research had discovered that unique environment of high suitability zones enriched more abundant bacteria, while medium suitability zones exhibited greater fungal diversity. Generally, the greater bacteria diversity in the soil, the greater its ability to suppress plant diseases and reduce the risk of crop infection; whereas higher fungal diversity contributed to decomposition of organic matter and release of nutrients, providing a more ample nutritional environment for crops [27, 28].

Simultaneously, various highly enriched beneficial bacterial communities were discovered in the rhizosphere soil of high suitability zones. Among them, Mesorhizobium can convert atmospheric nitrogen into plant-accessible forms by nitrogen fixation [29], and Agromyces facilitated carbon cycling in the soil [30]; Bacillus enhanced plant drought resistance [31], and Streptomyces produced antibiotics to inhibit harmful flora, thereby enhancing plant resilience [32]. Additionally, highly enriched beneficial fungi such as Acremonium and Penicillium secreted organic acids to dissolve nitrogen, phosphorus, potassium, and other elements in the soil, thereby promoting plant absorption [33, 34]. These microbial communities could enhance S. lanceolata adaptability and disease resistance to arid and barren desert environments, suggesting their potential role as biological factors for S. lanceolata adaptation to high suitability habitats. At the same time, the unique habitats of highly suitable areas may promote their enrichment, collectively creating an environment conducive to S. lanceolata growth.

Furthermore, the rhizospheric soil of S. lanceolata in medium suitability zones is enriched with more pathogenic bacteria, such as Gibberella, Alternaria, Cladosporium, Sarocladium, Paraphoma, Phaeomycocentrospora and Leptosphaerulina, which may be an important factor contributing to the susceptibility to its diseases. For example, Gibberella was known to induce Fusarium wilt [35], and Alternaria was a major genus causing leaf spot disease [36], and Cladosporium can lead to pathogenesis in approximately 500 plants [37].

On the other hand, planting years can alter soil physicochemical properties (pH, organic matter, nitrogen, phosphorus, potassium contents, etc.) and microbial environment, which in turn affected its contents of active components [38, 39]. This study also revealed that fungal diversity in rhizosphere soil of high planting years S. lanceolata was more abundant, and with increasing planting years, the abundance of detrimental bacteria such as Aspergillus, Sarocladium, and Paraphoma increased, while beneficial ones including Arthrobacter, Nocardioides, Mesorhizobium, and Chaetomium decreased. Among them, besides Mesorhizobium, Arthrobacter can enhance plant salt tolerance [40]; Nocardioides can promote plant nutrient absorption, improve resistance to abiotic stress, and control plant pathogens, and can also produce plant hormones to stimulate its growth [41]; Chaetomium was used to control wilt disease [42]. In conclusion, with increasing planting years, it may be main factors leading to cultivated S. lanceolata disease exacerbation or death due to imbalance in fungal and bacterial diversity in rhizosphere soil, including enrichment of harmful bacteria and reduction of beneficial bacteria. Simultaneously, this also explains, from the perspective of rhizosphere microorganisms, why the medium and low suitability zones are not conducive to the long-term growth of S. lanceolata, while the high suitability zones can provide a more favorable ecological and microbial environment for S. lanceolata.

4.3 The quality of S. lanceolata is determined by its adaptability to the habitat.

Li et al. [1] identified environmental factors in the planting area of S. lanceolata in the southern mountainous region of Ningxia (low suitability zones), where higher rainfall, elevated temperatures, and more fertile soil were observed. However, in such habitats, although S. lanceolata can survive, the quality of S. lanceolata produced is poor, and issues such as root rot and mortality are more likely to occur [38]. This indicates that these regions may not be suitable for S. lanceolata cultivation.

In this study, a shift from high suitability zones to medium suitability zones, and then to low suitability zones results in more pronounced changes in metabolites within the S. lanceolata. These findings indicate that environmental changes impact the physiological metabolism and substance accumulation processes of S. lanceolata. And the near-infrared spectra and active ingredients of S. lanceolata from high suitability zones exhibit significant differences compared to those from low suitability zones, with the S. lanceolata from high suitability zones demonstrating superior characteristics, particularly in terms of contents of TF, QG and DB. Furthermore, a comprehensive evaluation based on the content of seven active components indicates that the quality of S. lanceolata in high suitable zones is superior to that in medium and low suitability zones. This further demonstrates that high suitability zones are more conducive to the formation of high-quality S. lanceolata.

The common understanding in the production process of traditional Chinese medicinal materials is that "favorable conditions lead to higher yields, while adverse conditions lead to better quality" [43, 44]. The definitions of "favorable" and "adverse" conditions vary for different medicinal plants with distinct growth characteristics, and it is the appropriate adverse conditions that can enhance the quality [45]. Previous studies have indicated that moderate drought stress and salt stress can effectively increase the content of active ingredients in S. lanceolata [2, 21]. However, the habitat of low suitability zones may impose inappropriate environmental stresses on S. lanceolata grown in arid, high-temperature difference, and infertile areas, thereby affecting the physiological metabolism and accumulation of components in S. lanceolata, ultimately leading to a reduction in the quality of medicinal materials.

In summary, as a medicinal plant that grew in unique habitats, S. lanceolata exhibited specific selection and adaptability to the environment. Therefore, in the development of S. lanceolata producing, greater emphasis should be placed on organic integration of producing zones suitability with modern producing techniques and objectives in order to produce higher quality medicinal materials. Furthermore, attention to changes in the microbial environment in the soil, maintaining a balance and stability in fungal and bacteria diversity, will be more conducive to sustainable development of S. lanceolata production.

Currently, Ningxia province, Inner Mongolia, and the adjacent regions of Shaanxi were the most suitable zones for S. lanceolata within China, while in the future, S. lanceolata suitability zones exhibited a trend of shifting towards the central and eastern parts of Inner Mongolia. Elev, bio_4, bio_13, bio_11, and S_clay, were the main climate and soil environmental factors to determine suitability zoning. The richer bacterial diversity in the rhizosphere soil of high suitability zones benefits S. lanceolata by maintaining strong disease resistance, with highly enriched Mesorhizobium, Agromyces, and Bacillus aiding S. lanceolata's adaptation to complex environments. In contrast, planting S. lanceolata in medium suitability zones leads to an imbalance in bacterial and fungal diversity over time, raising the risk of disease and mortality. Furthermore, from high to medium and low suitability zones, significant changes in the metabolic levels and characteristics of S. lanceolata have occurred. Finally, through near-infrared and active component analysis, we confirmed that the quality of S. lanceolata in high suitability zones is superior. These research findings indicate that S. lanceolata exhibits specific selection and adaptability to climate and soil environmental factors in high suitability zones, which is conducive to the formation of high-quality S. lanceolata. In contrast, unsuitable habitats may impose inappropriate environmental stress on S. lanceolata, affecting its substance metabolism and overall quality. In conclusion, this study had positive reference significance for construction of S. lanceolata producing zones and sustainable development.

Conflict of interest statement

The authors declare that there is no conflict of interest.

Funding

This work was supported by the Open Competition Program of Top Ten Critical Priorities of Agricultural Science and Technology Innovation for the 14th Five-Year Plan of Guangdong Province (grant number 2022SDZG07) and Ningxia Natural Science Foundation (No. 2021AAC03103).

Author contributions

Zhenkai Li: Sample collection, ingredient testing, data curation, formal analysis, methodology, writing-original draft. Yang Yang: Maxent model analysis. Lu Feng: Microbiome analysis. Haishan Li: Ingredient testing, language translation. Zhiheng Dai: Visualisation. Tianle Cheng: Information collection. Shuying Liu: Near infrared analysis. Xin Luo: Ingredient testing. Yukun Wang: Supervision. Ling Ma: Guidance on data analysis. Li Peng: Resources, funding acquisition, methodology. Hong Wu: Conceptualization, funding acquisition, supervision, writing – review & editing.

Acknowledgments

The authors acknowledge the financial support provided by the Open Competition Program of Top Ten Critical Priorities of Agricultural Science and Technology Innovation for the 14th Five-Year Plan of Guangdong Province (grant number 2022SDZG07) and Ningxia Natural Science Foundation (2021AAC03103).

Supplementary material

The Supplementary Material for this article can be found in the annex.

Declaration of Generative AI and AI-assisted technologies in the writing process.

During the preparation of this work the author(s) used ChatGPT in order to translation. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.

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Changes in Climatic Conditions, Soil Texture, and Rhizospheric Microorganisms Affect the Quality and Suitable Production Zones of Stellaria dichotoma L. var. lanceolata Bge

Status:

Version 1

Abstract

Background

Results

Conclusion

Figures

1 Introduction

2 Materials and methods

2.1 Pecies occurrence records

2.2 Acquisition of environmental factors

2.3 Model construction and zoning prediction

2.4 Samples collection

2.5 Rhizosphere microbial community analysis

2.6 Metabolomic analysis

2.7 Near infrared spectrum analysis

2.8 Determination of components content

3 Results and analysis

3.1 Accuracy analysis of prediction model

3.2 Dominant habitat factors analysis and current zoning prediction

3.3 Future zoning and development trend prediction

3.6 Metabolomic analysis of S. lanceolata in different suitability zones.

3.4 Near infrared spectrum analysis of S. lanceolata in different suitability zones.

3.5 Quality Evaluation of S. lanceolata in different suitability zones.

4 Discussion

4.1 The suitability zones of S. lanceolata may undergo migration in response to climate change.

4.3 The quality of S. lanceolata is determined by its adaptability to the habitat.

5 Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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Version 1