Diversity and structural analysis of rhizosphere soil microbial communities in wild and cultivated Rhizoma Atractylodis Macrocephalae and their effects on the accumulation of active components

Soil sample collection

In this study, three different producing areas of RAM were selected as sampling points in Qimen and Shexian County of Huangshan City and Qiaocheng District of Bozhou City in Anhui Province. The sampling areas in Qimen and Shexian counties belong to the north subtropical monsoon climate (N29°35′, E117°57′). With an altitude of 1,000 m, the terrain is relatively high, and the climate is mild. It was located in a cloudy and foggy hillside woodland shrub forest, mainly growing in humus soil, showing a small individual population, only two or three plants grow together, and often mixed with a variety of plants. The samples collected are three or five year old samples. Qiaocheng District of Bozhou City has a temperate monsoon climate (N33°88′, E115°77′), with an altitude of 25 m and flat terrain. RAM samples are artificially cultivated products, planted in a large range, mostly annual or biennial medicinal materials.

Each sample was collected using a five-point sampling method, with each type of soil sample coming from at least three healthy, disease-free RAMs. All the samples are carefully removed from the surrounding soil sampling sites. During sampling, soil humus was removed first, and roots of healthy plants were taken by digging 0–20 cm vertically along the root of RAM. The non-rhizosphere soil attached to the root was gently shaken off by hand. Then the residual soil about 2 mm away from the roots was brushed away using a sterile spatula. The soil samples were mixed in equal amounts, placed in a sterile plastic bag, bag marked sampling site, quickly stored in an ice box, and brought back to the laboratory sieve (80 mesh), −80 °C stored for later use (Dong et al., 2021). RAM samples from different origins were marked as Qizhu (A1–A7), Shezhu (B1–B4), and Bozhu (C1–C5).

DNA extraction, PCR amplification

According to the manufacturer’s instructions, genomic DNA from rhizosphere soil of RAM from three producing areas was extracted using PowerSoil DNA Isolation Kit (MoBio Laboratories, Carlsbad, CA, USA ) (Li et al., 2022b). The bacterial 16S rRNA gene sequencing and the fungal ITS gene sequencing were proceed as described previously (Yuan et al., 2020).

High throughput sequencing

Paired-end sequencing was performed using Illumina Miseq PE300 high-throughput sequencing platform in Beijing Ovidson Gene Technology Co., Ltd. Original sequence uploaded to NCBI database (Li et al., 2022a).

Sequence processing

The off-machine data was split by the QIIME1 (v1.8.0) software according to the Barcode sequence, and the Pear (v0.9.6) software was used to filter and splice the data. After splicing, Vsearch (v2.7.1) software was used to remove sequences less than 230 bp in length, and chimera sequences were removed by the uchime method alignment according to the Gold Database (Wang et al., 2018). Finally, the Vsearch (v2.7.1) software uparse algorithm was used to perform OTU clustering (Operational Taxonomic Units) on the high-quality sequences, and the similarity threshold was 97% (Edgar, 2013). Compared with the Silva128 database using the RDP Classifier algorithm, a 70% confidence threshold was set to obtain the species classification information corresponding to each OTU (Cole et al., 2009). We then used QIIME1 (v1.8.0) software for alpha diversity index analysis (including Shannon, Simpson, and Chao1 indexes). Based on species annotation and relative abundance results, R (v3.6.0) software was used to perform a histogram analysis of species composition. QIIME1 (v1.8.0) was used to calculate the beta diversity distance matrix, and R (v3.6.0) software was used to obtain a clustering heat map and for PCoA analysis based on weighted Unifrace distance (Jiang et al., 2013). Intergroup heterogeneity analysis of metastats was performed using Mothur (v.1.34.4) software and LEfSe analysis was performed using Python (v2.7) software (Jami et al., 2013).

Determination of polysaccharides, atractylone, and lactones components in the samples

As described in the materials and methods, three producing areas of RAM picking back to the laboratory. After removing the rhizosphere soil, the root of RAM root herbs was washed and sliced after low-temperature drying. After drying, the RAM medicinal materials were placed in a pulverizer to crush them and passed through a No. 3 sieve. The contents of lactone I, II, III and atractylone in different habitats were determined by HPLC. The content of polysaccharides was determined by the phenol-sulfuric acid method. We obtained 1 g of medicinal powder and placed it in a 10 mL volumetric flask, followed by the addition of methanol (chromatographic grade) to a constant volume to of 10 mL. Samples were cleaned with an ultrasonic machine (frequency 40 Hz, power 100 W, ultrasonic time 60 min). After cooling, the supernatant was centrifuged at 10,000 rpm for 10 min with a sterile syringe and then filtered through a 0.22 μM filter into a 2.0 mL sample bottle. We dissolve 1.3, 2.3, 2.28, and 3.6 mg of lactone I, II, III, and atractylone standard in 1 mL of methanol to achieve concentrations of 1.3, 2.3, and 2.28, and 3.6 mg/g, respectively. Chromatographic conditions: Dikma kromasil C18 (250 × 4.6 mm, 5 μm) column, acetonitrile (A)-water (B) as mobile phase, gradient elution: 0–16 min, 60–76% A, 16–18 min, 76–100% A, 18–30 min, 100% A. Volume flow rate was 1.0 mL/min and column temperature was 30 °C. The detection wavelengths were 220 nm (lactone I, III), 276 nm (lactone II), and the injection volume was 10 μL.

Statistical analysis

Statistical analysis was performed using SPSS 22.0 (SPSS, Inc., Chicago, IL, USA). One-way ANOVA was used to analyze the significant differences between the contents of polysaccharides, atractylone, and lactones from different origins. Means were analyzed using Tukey’s test. The Canoco 5 software was used to analyze the relationship between polysaccharides, atractylone, and lactones and rhizosphere soil microbial communities. P < 0.05 was considered to denote significant differences. All data were expressed as mean ± standard deviation.

Comprehensive analysis of sequencing data of rhizosphere microbial diversity of RAM from three habitats

To explore the rhizosphere microbial community of RAM, soil samples from three origins were collected and sequenced by Illumina MiSeq. After all sequence denoising and quality filtering, the quality-filtered sequences were clustered into 14,072 different OTUs at the 97% similarity level. All our samples are based on the statistics after flattening. The OUT-Venn map directly reflected the common and unique OTUs in all three samples. As shown in Fig. 1, a total of 8,379 and 5,693 OTUs for bacteria (a) and fungi (b) were obtained respectively. The total numbers of bacterial and fungi OTUs in the wild Qizhu sample were 5,714 and 3,256 respectively, which were higher than those of the cultivated Bozhu sample and wild shezhu sample. The total numbers of bacterial and fungi OTUs in Shezhu were the lowest among the three types. Among them, only 1,675 bacterial OTUs and 358 fungal OTUs were common in three soils.

Alpha diversity of bacterial and fungal communities in the rhizosphere soil of RAM from the three origins

Alpha diversity analysis was performed on species richness and uniformity within a single microbial ecosystem. Rarefaction curves showed that the higher the sequencing depth, the greater the likelihood of higher diversity observed. The OTU number curves of all samples tended to be flat, suggesting that the sequencing depth of all rhizosphere soil samples was reasonable. And it could reflect reliably the bacterial and fungal community structures and compositions in wild authentic Qizhu, Shezhu, and artificially cultivated Bozhu rhizosphere soil samples (Figs. 2A and 2B). In addition, the sample size was sufficient to reflect the richness of microbial communities (Figs. 2C and 2D). To check the relative species abundance, R software was used to draw the grade abundance curve. The abundances of Shezhu and Qizhu were high and uniform, while the abundances of Bozhu were significantly different between OTUs and low uniformity (Figs. 2E and 2F). Furthermore, the coverage indexes of all samples in Table 1 were greater than 96%, which also showed that the sequencing depth of all samples in this study was reasonable and further results were reliable.

There were many indexes reflecting the alpha diversity of microbial communities. Among them, chao1 and observed-species indexes reflected the richness of communities, and Shannon and Simpson indexes reflected the diversity and uniformity of community. The greater the Shannon index, the higher the community diversity, while the greater the Simpson index, the lower the community diversity. The microbial community richness and diversity indices are listed in Table 1, the Turkey test was used to calculate P values after a pairwise comparison of the values corresponding to each index, and there were no significant differences in the above indicators (P > 0.05).

Composition and structure of rhizosphere soil microbial community

Through high-throughput sequencing, a total of 16 phyla, 29 classes, 49 orders, 51 families, and 39 genera of bacteria and eight phyla, 17 classes, 37 orders, 65 families, and 71 genera of fungi were identified in the rhizosphere soil of wild genuine Qizhu and Shezhu samples and artificially cultivated Bozhu samples (Fig. 3). The microbial communities in both the wild and artificial cultivated soil samples were extremely species-rich, but there were some differences in their structure and the relative abundances of Microorganism taxa.

At the phylum level, the main bacterial phyla in the rhizosphere samples of Qizhu, Shezhu, and Bozhu were Proteobacteria, Acidobacteriota, Actinobacteria, Chloroflexi, Verrucomicrobiota, Bacteroidetes, Myxomycota, Firmicutes, and Planctomycetota (Fig. 3A). Proteobacteria and Acidobacteriota were the dominant groups in the rhizosphere samples of RAM from three different habitats, accounting for more than 50%. However, the abundances of Proteobacteria and Acidobacteriota were significantly different among the three samples. The relative abundance of Proteobacteria in the wild Qizhu sample was 34.1%, which was higher than that in Shezhu and Bozhu (29.3% and 27.0%) (P < 0.05). The relative abundances of Acidobacteriota were 29.4% and 28.5% in Shezhu and Bozhu respectively, which were higher than those in Qizhu (24.0%). In addition, the relative abundances of Verrucomicrobiota, and Planctomycetota in Qizhu were higher than those in Shezhu and Bozhu (P < 0.05). In Shezhu, the numbers of Actinobacteriota and Chloroflexi were significantly greater than those in Qizhu and Bozhu. The relative abundance of Bacteroidetes in the rhizosphere soil of the artificially cultivated Bozhu sample was 5.7%, which was higher than that of the rhizosphere soil samples of Qizhu and Shezhu (2.4%). Among the fungi, Ascomycota, Basidiomycota, Mortierellmycota, Rozellomycota, Glomeromycota, and Chytridiomycota were the main fungal phyla in the rhizosphere samples of Qizhu, Shezhu, and Bozhu (Fig. 3B). Among them, Basidiomycota and Ascomycota were the dominant groups shared by the three samples, with relatively high relative abundance, accounting for more than 80%. The relative abundances of Basidiomycota in Qizhu and Shezhu were similar but significantly higher than those in the rhizosphere soil sample of Bozhu. In Bozhu, the relative abundance of Ascomycota (60.5%) was significantly higher than that in Qizhu and Shezhu (41.3% and 46.7%) (P < 0.05). In addition, the relative abundance of Rozellomycota in Shezhu was higher than that in Qizhu and Bozhu rhizosphere soil samples (P < 0.05).

At the class level, for bacterial groups, Alphaproteobacteria and Acidobacteriae were the dominant groups shared by Qizhu, Shezhu, and Bozhu, accounting for more than 40%. Alphaproteobacteria had the highest relative abundance (24%) in Qizhu, which was higher than that in Shezhu and Bozhu (19.6% and 13.8%, respectively). The relative abundances of Acidobacteriae in Qizhu and Shezhu were 17.6% and 18.4%, respectively, which were significantly higher than that in Bozhu (0.8%) (P < 0.05). Vicinamibacteria was the dominant group in Bozhu, and its relative abundance was significantly higher than that in Qizhu and Shezhu. In addition, the relative abundance of Verrucomicrobiae in Qizhu was higher than that in the rhizosphere samples of Shezhu and artificially cultivated of Bozhu (P < 0.05). In Shezhu, the number of Thermoleophilia was higher than that in Qizhu and Bozhu. The relative abundance of Gammaproteobacteria in the artificially cultivated Bozhu sample was 13.1%, higher than that in Qizhu and Shezhu rhizosphere soil samples (Fig. 3C). Among the fungal groups, Sordariomycetes and Agaricomycetes were the most abundant in Qizhu and Shezhu, accounting for about 50% of Qizhu and 40% of Shezhu. The relative abundance of Sordariomycetes was the highest (42.7%) in the artificially cultivated Bozhu sample, which was significantly higher than that in Qizhu and Shezhu (20% and 19.6%) (P < 0.05). At the same time, the relative abundances of Tremellomycetes and Archaeorhizomycetes in the rhizosphere soil samples of Shezhu were significantly higher than those in the rhizosphere soil samples of Qizhu and Bozhu (Fig. 3D).

The community composition structure at each taxonomic level was clustered according to the abundance distribution of the taxa and the degree of similarity between samples. According to the clustering results, the taxa and samples were sorted separately and represented using a heat map. Through clustering, high-abundance and low-abundance taxa were generally distinguished, and a color gradient was used to reflect the similarity of community composition between samples. At the genus level, the most abundant 20 genera of bacteria and 16 genera of fungi in 16 samples from three producing areas were analyzed by a heat map. Bacterial community abundance analysis showed (Fig. 4A) that the dominant bacteria in Qizhu were Bradyrhizobium, Bacillus, Candidatus-udaeobacter, Candidatus-solibacter, and Burkholderia-caballeronia-paraburkholderia, accounting for 4.2%, 4.1%, 3.4%, 2.0%, and 2.9% respectively. These relative abundances were significantly higher than those in the rhizosphere soil samples of Shezhu and Bozhu.

Among them, the relative abundances of Bradyrhizobium, Bacillus, and Candidatus-udaeobacter were 14, 5.3, and 34 times higher, respectively, than those of Bozhu rhizosphere soil (P < 0.05). The dominant bacteria in the Shezhu sample were Bradyrhizobium, Candidatus-solibacter, Acidibacter, and Bryobacter, with relative abundances of 2.6%, 1.7%, 1.6%, and 1.4% respectively, which were significantly higher than those in the rhizosphere soil of Bozhu. Meanwhile, the relative abundance of Bryobacter was also higher than that of Qizhu. RB41, Sphingomonas and MND1 were the dominant genera in the Bozhu rhizosphere soil sample, and their relative abundances were 5.6%, 3.7%, and 2.7%, respectively, representing significantly higher values than those in Qizhu and Shezhu rhizosphere soil samples. The relative abundances of RB41 and MND1 in the Qizhu and Shezhu samples were less than 1% (P < 0.05). It was worth noting that Burkholderia-caballeronia-paraburkholderia had a high relative abundance (2.9%) in Qizhu samples, which was 4.8 times higher than that in Shezhu rhizosphere soil samples, and there was almost no enrichment of this genus in the Bozhu sample (P < 0.05). For fungi (Fig. 4B), among the rhizosphere soil sample of Qizhu, the dominant genera were Saitozyma, Exidia, Clavulina, and Russula, and their relative abundances were 9.4%, 5.8%, 3.8%, and 2.8% respectively, which were higher than those of the rhizosphere soil samples of Sezhu and Bozhu (P < 0.05). In Qizhu, Saitozyma enrichment was 2.8 times that of Shezhu and 47 times that of Bozhu, while Russula enrichment was 5.6 times that of Shezhu, and 28 times that of Bozhu. Additionally, Exidia and Clavulina had a high abundance in Qizhu, and their relative abundances in Shezhu were less than 0.1%, while these two bacteria were not detected in Bozhu. Simultaneously, Archaeo, Inocybe, Fusarium, Tausonia, and Solicoccozyma were the dominant genera in the rhizosphere soil sample of Shezhu, and their relative abundances were 9.0%, 8.0%, 5.4%, 5.7%, and 4.4%, respectively, representing higher values than those in rhizosphere soil samples of Qizhu and Bozhu (P < 0.05). In the rhizosphere soil sample of Bozhu, Mortierella, Botryotrichum, Chaetomium, Acremonium, Trichoderma, and Plectosphaerella were dominant, and their relative abundances were 9.8%, 5.6%, 3.6%, 3.2%, 3.2%, and 2.6%, respectively, representing higher values than those in Qizhu and Shezhu, but the relative abundances of other genera were lower. Therefore, the relative abundances of bacteria and fungi in rhizosphere soils of wild Qizhu and Shezhu and cultivated Bozhu were significantly different at the genus level.

Beta-diversity analysis of the rhizosphere microbial community of RAM from three production areas

To observe similaries and differences between samples, principal component analyses (PCoA) were carried out on the differences between the three soils. PCoA revealed substantial inter- and intra- group variation in microbial communities depending on the samples (Figs. 5A and 5B). Principal component 1 (PC1) interpreted both inter-group and intra-group variations, while principal component 2 (PC2) mainly explained the intra-group variation. The microbial community structures (bacteria and fungi) of rhizosphere samples of Qizhu and Shezhu had relatively great intra-group variation, but there was relatively small intra-group variation in the compositions of microbial communities in Bozhu. Moreover, the bacterial community structures in Qizhu and Shezhu differed from those in Bozhu. There were clear seen that there were significant differences in the structure of bacteria and fungi in rhizosphere soil between wild and cultivated RAM.

Content analysis of polysaccharides, atractylone, and lactone I, II, and III

Powders of RAM samples from the Qimen, Shexian, and Bozhou cities of the Anhui Province were obtained. Three portions of each sample were used, and three needles were repeated for each portion. The contents of polysaccharides, atractylone, and lactone I, II, and III from three samples were determined and shown in Table 2. The results showed that the contents of lactone I, II, III, and atractylone were significantly different among the three habitats (P < 0.05). The contents of lactone I, II, III, atractylone, and polysaccharides were the highest in Shezhu, which had lactone I contents that were 1.54 and 1.82 times higher than those in Qizhu and Bozhu, respectively. The contents of lactone II in Shezhu were 1.38 and 2.53 times higher than those in Qizhu and Bozhu, respectively. The contents of lactone III in Shezhu were 1.46 times and 1.89 times higher than that in Qizhu and Bozhu, respectively. Atractylone and polysaccharide contents in Shezhu were 1.13 times and 1.15 times higher than those in Qizhu, and 3.14 times and 1.12 times higher than those in Bozhu. The contents of atractylone and lactone I, II, and III in Qizhu were significantly higher than those in Bozhu (P < 0.05). Polysaccharide contents in Qizhu and Bozhu showed had no significant differences (P > 0.05).

Correlation between rhizosphere microorganisms and the accumulation of polysaccharides, atractylone, and lactones in RAM

The correlation coefficients between the accumulation of polysaccharides, atractylone, and lactones in RAM and the top 16 bacteria and fungi at the genus level were calculated using redundancy analysis (RDA) and shown in Fig. 6. Among them, the microbial community composition at the genus level was the response variable and the chemical components were the explanatory variables. We used Pearson or Spearman’s correlation analysis to show significant P values for explanatory variables. Among the P values of explanatory variables, those of ateactylone, lactone II, and lactone III in bacteria were 0.002, 0.002, and 0.04, respectively, denoting significant differences. There were no significant differences between lactone I and polysaccharide enrichment among bacteria (P > 0.05). Among fungi, there were significant differences in atractylone and lactone-II (P < 0.05), while there were no significant differences in the other three components (P > 0.05). The correlation between explanatory variables and responsibility variables can be combined with the second point above, and the correlation between explanatory variables and response variables can be analyzed according to the angle relationship between explanatory variables and response variables in Fig. 6 RDA. Conexibacter, Bryobacter, Acidothermus, Candidatus-Solibacter, Candidatus-Koribacter, Bradyrhizobium, Burkholderia-Caballeronia-Paraburkholderia, Candidatus-Udaeobacter, and Candidatus-Xiphinematobacter in bacteria were positively correlated with atractylone and lactone II contents (Fig. 6A). Except for Candidatus-Xiphinematobacter, the above bacterial genera were positively correlated with lactone I and III compounds. In addition, polysaccharide content was positively correlated with Conexibacter, Bryobacter, Sphingomonas, and MNDI. According to the RDA statistical data table of bacterial strains in Table 3, except for Sphingomonas and MNDI, the contents of these bacteria in Qizhu and Shezhu were higher than those in artificially cultivated RAM samples. Meanwhile, RB41, Steroidobacter, Aamlibacter, and Lysobacter were significantly negatively correlated with the accumulation of these five compounds. In Table 3, the relative abundance of these four strains in Bozhou was the highest, being higher than that of Qizhu and Shezhu rhizosphere soil samples. Among fungi (Fig. 6B), Briansuttonomyces, Apiotrichum, Podospora, Fusarium, Solicoccozyma, Inolyma, and Laccuria were all positively correlated with the accumulation of the five compounds. Saitozyma was positively correlated with atractylone, lactone I, II, and III. Tausonia and Chaeotomium were positively correlated with polysaccharides contents. At the same time, in the RDA statistical data table of fungal strains in Table 4, the relative abundance of these genera in Shezhu was higher than that in Qizhu and Bozhu, and their relative abundance in Bozhu samples was the lowest. On the contrary, Botryotrichum, Plectosphaerella, and Neosetophoma were negatively correlated with the content of the above five compounds. As shown In Table 4, the contents of these three fungi in Bozhu were the highest, being higher than those in Qizhu and Shezhu rhizosphere soil. These results showed that rhizosphere bacteria and fungi were involved in the accumulation of polysaccharides, atractylone, and lactones.

Function prediction of bacteria and fungi in rhizosphere soil of wild and cultivated RAM

PICRUSt software was used to predict the function of microbial communities (Figs. 7A and 7B). For the convenience of the display, only the pathways with the most enrichment in the samples were shown. In the PICRUSt method, Qizhu was more enriched in the following functions: Carbohydrate metabolism, Xenobiotics biodegradation and metabolism, Lipid metabolism, Cell motility, Membrane transport, and Signal transduction. In Shezhu, the abundance of these functional groups was not very prominent, basically between Qizhu and Bozhu rhizosphere soil samples. Bozhu has relatively rich functions in Amino acid metabolism, Metabolism of cofactors and vitamins, Metabolism of terpenoids and polyketides, Energy metabolism, Replication and repair, Nucleotide metabolism, etc (Fig. 7A). However, there was little difference in abundance between the various functional groups of the three samples.

The sequences of the fungi isolated from the wild and reintroduced rhizospheric soil were compared with those in the FUNGuild database to predict and analyze differences in their functions (Chen et al., 2020; Del Pilar Martínez-Diz et al., 2019). Figure 7B was divided into different functional groups according to the fungal groups in wild and cultivated rhizosphere soils. A total of 20 functional groups were identified in this study, including Animal Pathogen, Plant Pathogen, Ectomycorrhizal, Dung Saprotroph, Undefined Saprotroph, Dung Saprotroph, Wood Saprotroph, Plant Saprotroph, Soil Saprotroph, Endomycorrhizal, Endophyte, Arbuscular Mycorrhizal, Fungal Parasite, Lichenizeda and Unknown. The results showed that there were many fungal groups with an unidentified trophic mode (defined as having an unknown trophic mode) in wild and cultivated rhizosphere soils. Both the wild and reintroduced rhizospheric soils hosted many unknown groups, whose relative abundance in the Qizhu rhizospheric soil was the highest (53%), being higher than those in the rhizosphere soil of Shezhu and cultivated Bozhu (45.9% and 44%). The results also showed that except for unidentified, the relative abundance of Ectomycorrhizal species in the rhizosphere soil of wild Qizhu was the highest, with a relative abundance of 7.8%, which was significantly higher than those of the rhizosphere soil of Shzhu and Bozhu (0.6% and 0.5%, respectively) (P < 0.05). The second was Endophyte-Undefined Saprotroph, with a relative abundance of 6.3%, which was also significantly higher than those of Shezhu and Bozhu rhizosphere soils (0.1% and 0.001%, respectively) (P < 0.05). The relative abundance of Bryophyte Parasite-Dung Saprotroph-Ectomycorrhizal-Fungal Parasite-Leaf-Saprotroph-Plant Parasite-Undefined Saprotroph-Wood Saprotroph in the rhizosphere soil of Shezhu high (11.2%), being significantly higher than those in Qizhu and Bozhu rhizosphere soils (6.2% and 4.4%, respectively) (P < 0.05). Followed by Animal Pathogen-Endophyte-Lichen Parasite-Plant Pathogen-Soil Saprotroph-Wood Saprotroph 5.8%, higher than Qizhu and Bozhu samples (2.4% and 1.2%). For the rhizosphere soil of Bozhu, the relative abundance of Undefined Saprotroph was high (17.1%), being significantly higher than that of wild Qizhu and Shezhu (8.5% and 10.9%, respectively) (P < 0.05). The second is Endophyte-Litter Saprotroph-Soil Saprotroph-Undefined Saprotroph, with a relative abundance of 10.5%, which is higher than those of Qizhu and Shezhu samples (8.8% and 0.5%, respectively).