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Comparative analysis of the rhizosphere microbiome and medicinally active ingredients of Atractylodes lancea from different geographical origins

  • Junjie Tang , Yun Han , Lingfeng Pei , Wei Gu EMAIL logo , Rongli Qiu , Sheng Wang , Qihan Ma , Yifu Gan and Min Tang
Published/Copyright: November 23, 2023
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Open Life Sciences
From the journal Open Life Sciences Volume 18 Issue 1

Abstract

This study aimed to explore the important role of the rhizosphere microbiome in the quality of Atractylodes lancea (Thunb.) DC. (A. lancea). The rhizosphere microbial community of A. lancea at two sampling sites was studied using metagenomic technology. The results of α-diversity analysis showed that the rhizosphere microbial richness and diversity were higher in the Maoshan area. The higher abundance of core microorganisms of the rhizosphere, especially Penicillium and Streptomyces, in the Maoshan area compared with those in the Yingshan area might be an important factor affecting the yield of A. lancea. Redundancy analysis illustrated that the available phosphorus had a significant effect on the rhizosphere microbial community structure of A. lancea. We also showed that the plant–microbe and microbe–microbe interactions were closer in the Maoshan area than in the Yingshan area, and Streptomyces were the main contributors to the potential functional difference between the two regions. A. lancea in the Maoshan area had a high content of atractylodin and atractylon, which might be related to the enhanced abundance of Streptomyces, Candidatus-Solibacter, and Frankia. Taken together, this study provided theoretical insights into the interaction between medicinal plants and the rhizosphere microbiome and provides a valuable reference for studying beneficial microbes of A. lancea.

Keywords: Atractylodes lancea ; microbiology; medicinally active ingredients; metagenomics

1 Introduction

The Atractylodes lancea (Thunb.) DC. rhizome, also known as “Maocangzhu” or “Nancangzhu,” is a well-known traditional Chinese medicine (TCM) with high medicinal value, which is traditionally used in clinics to treat night blindness, digestive disorders, and influenza [1,2,3]. Maoshan in Jiangsu Province is traditionally considered a genuine producing area; A. lancea from Maoshan is regarded as having the best quality, as evidenced by long-term clinical practice. A. lancea is rich in volatile oil, which is mainly composed of sesquiterpenoids and polyacetylene compounds [4,5]. However, the contents of atractylodin, atractylon, and β-eudesmol of A. lancea from different producing areas show large variations [6,7]. The rhizosphere microbiome, geographical location, and climate are important factors that affect the formation of TCM. The rhizosphere microbes exert an important influence on plant growth and secondary metabolite formation [8,9,10]. Therefore, one factor contributing to the differing contents of medicinally active ingredients could be variation in the rhizosphere microbiome from different geographical origins.

The composition of microbial communities in the rhizosphere is markedly different from that in bulk soil. This phenomenon is referred to as the “rhizosphere effect” [11]. Studies have revealed the significant influence of the rhizosphere microbiome on plant growth, which includes nitrogen-fixing bacteria, plant growth-promoting rhizobacteria, biological control bacteria, and disease-suppressing bacteria [12]. In addition, researchers have discovered that particular plant–microbial interactions promote the production of active ingredients in medicinal plants, possibly because certain microbes contain a gene pool encoding proteins that are involved in the biosynthesis of active ingredients, thereby providing the host with additional metabolic capacity [13,14]. For example, the unique microbiome in the seeds of Salvia miltiorrhiza Bge is rich in functions related to secondary metabolism, which might influence tanshinone biosynthesis [15]. Co-culture of immobilized spores of Aspergillus niger and Panax ginseng adventitious roots significantly increased the ginsenoside content in the roots of Panax ginseng [16].

Since the first metagenomic data were published, researchers have begun to analyze the rhizosphere microbial diversity, community structure, interactions, and gene functions based on metagenomic techniques [17,18,19,20]. However, there have been few studies on the rhizosphere microbiome of A. lancea, and a comparative evaluation of the rhizobiome composition in different regions is still lacking [21]. Metagenomics now provides an effective platform to explore the structure and function of the rhizosphere microbiome of A. lancea in different regions. Therefore, in the present study, a metagenomic approach was used to analyze the differences in the microbiome community structure, function, and medicinally active ingredients of A. lancea. This study aimed to link the better medicinal quality of A. lancea with its specific rhizosphere microbiome to provide a new understanding of how the rhizosphere microbiome can enhance the production and quality of A. lancea.

2 Materials and methods

2.1 Sample collection

The rhizosphere soil was collected from Maoshan (Mao), Jiangsu Province (119°16′50″E, 31°42′37″N, and altitude 72.10 m) and Yingshan (Ying), Hubei Province (115°46′56″E, 31°3′28″N, and altitude 416.41 m) in July 2018. Each sample was collected using the five-point sampling method, and based on the definition of rhizosphere soil (soil close to the root surface) [22], a sterile plastic shovel was used to collect rhizosphere soil samples. The five individual soil samples collected were mixed and homogenized into one pooled sample, and in each plot, samples were repeated three times, which were denoted M1, M2, and M3 from Maoshan; and Y1, Y2, and Y3 from Yingshan, respectively. Similarly, corresponding bulk soil samples were collected. All soil samples were stored on dry ice immediately after collection and stored at −80°C until further analysis. The rhizosphere soil was used for DNA extractions and bulk soil for measurements of physicochemical parameters. Fresh A. lancea were dried at 45°C in an oven (DHG-9140A, Jinghong, Shanghai, China) to constant weight, and then crushed and filtered through a ≤2 mm sieve for further detection.

2.2 Physicochemical parameters of the soil

The physicochemical properties of the soil were determined as follows. The pH was determined using a PHS-3E pH meter (INESA Scientific Instrument Co., Ltd, Shanghai, China). About 10.00 g of air-dried soil was sieved through a 1 mm sieve and placed in a conical bottle. Then, 25 mL of water without CO2 (boiling for 10 min and then cooling) was added and sealed with a sealing film. It was placed in an oscillator for 2 min; after standing for 30 min, the pH of the soil suspension was measured. The available nitrogen (AN), available phosphorus (AP), and available potassium (AK) were quantified using a TPY-8A Soil Nutrient tester (Topu, Zhejiang, China) according to the manufacturer’s instructions.

2.3 DNA extraction and sequencing

Total genomic DNA was extracted from the six pooled samples using the MP Soil DNA extraction kit (Yanhuibio, Shanghai, China) according to the manufacturer’s instructions. The concentration and purity of the extracted DNA were determined with TBS-380 and NanoDrop2000 (Thermo, MA, USA) at 260/230 and 260/280 nm, respectively. The quality of the DNA extract was checked by 1% agarose electrophoresis. The paired-end library was constructed using NEXTFLEX Rapid DNA-Seq (Bioo Scientific, Austin, TX, USA). Adapters containing the full complement of sequencing primer hybridization sites were ligated to the blunt end of the fragments. Paired-end sequencing was performed on Illumina Novaseq 6000 (Illumina Inc., San Diego, CA, USA) at Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China) using NovaSeq Reagent Kits according to the manufacturer’s instructions. Data processing was performed using the online Majorbio Cloud Platform (www.majorbio.com).

2.4 Bioinformatics analysis

Fastp software (version 0.20.0) was used to remove low-quality reads and those reads containing N among the raw sequences, while high-quality pair-end reads and single-end reads were retained. All cleaned sequences were assembled using MEGAHIT (version 1.1.2). Contigs with a length ≥300 bp were selected as the final assembling result, and they were used for further gene prediction and annotation. Open reading frames (ORFs) from each assembled contig were predicted using MetaGene. Then, a non-redundant gene catalog was constructed using CD-HIT (version 4.6.1). High-quality reads were aligned to the non-redundant gene catalogs to calculate gene abundance with 95% identity using SOAPaligner (version 2.21).

Representative sequences of the non-redundant gene catalog were aligned to the NR database using Diamond (version 0.8.35) for taxonomic annotations (e-value ≤ 1 × 10−5). Annotation of a cluster of orthologous groups (COGs) of proteins for the representative sequences was performed using Diamond against the eggNOG database (e-value ≤ 1 × 10−5). The KEGG annotation was conducted using Diamond against the Kyoto Encyclopedia of Genes and Genomes database (e-value ≤ 1 × 10−5).

2.5 Sample preparation and analytical procedure

About 1.0 g of the sample powder was weighed; then, it was sonicated with methanol (25.0 mL) for 60 min, cooled, made up for the weight loss with methanol, shaken well, and then filtered through a 0.45 μm filter. The three medicinally active ingredients of samples were determined using high-performance liquid chromatography (HPLC) (Waters e2695, USA). The separation was conducted using an Inertsil™ ODS-3 column (4.6 mm × 250 mm, 5 μm) at 30°C and the injection volume was 10 μL. Mobile phase A contained 0.1% formic acid in acetonitrile, and mobile phase B contained 0.1% formic acid in water at 0.8 mL/min flow rate with the following gradient elutions: 0–5 min, 52–60% A; 5–40 min, 60–75% A; 40–44 min, 75–80% A; 44–57 min, 80–52% A. Atractylodin, atractylon, and β-eudesmol were detected at 340, 220, and 203 nm, respectively.

For the generation of standard curves, the standards of atractylodin (Lot: B20128, HPLC ≥ 98%, Shanghai yuanye Bio-Technology Co., Ltd), atractylon (Lot: B24634, HPLC ≥ 95%, Shanghai yuanye Bio-Technology Co., Ltd), and β-eudesmol (Lot: B21969, HPLC ≥ 95%, Shanghai yuanye Bio-Technology Co., Ltd) were prepared at concentrations of 0.601, 0.620, and 0.920 mg/mL. After gradient dilution, 10 μL of the three standards were injected and detected at 340, 220, and 203 nm, respectively. The peak area values of each standard were recorded (Figure S1). The linear regression equations were then calculated using the sample concentration of standards as abscissa (X) and the peak areas as ordinate (Y) (Figure S2). The peak value of each active ingredient was substituted into the linear regression equation to obtain the concentration of the sample and then multiplied by dilution to calculate the amount of active ingredients in the sample (mg/g).

2.6 Data analysis

The Chao, Shannon, and Simpson indexes were used to evaluate the α-diversity of the sample [23,24,25], for which differences were tested by independent sample T-test (P ≤ 0.05 indicates statistical significance). The β-diversity of rhizosphere microorganisms among samples was analyzed by principal coordinate analysis (PCoA). A two-sided Welch’s T-test was applied to test the differences in microbial communities and functions among Mao and Ying samples (P ≤ 0.05 indicates statistical significance). Redundancy analysis (RDA) was used to evaluate the correlation between rhizosphere microorganisms and environmental factors. The data on the annual average temperature (AAT) and annual average precipitation (AAP) were obtained from www.xihe-energy.com. The heat maps of KEGG functional pathways were processed by z-score normalization based on relative abundances. The data of this study were analyzed using IBM SPSS Statistics (version 26.0). For the analysis of the three active ingredient contents of different samples, the independent sample T-test was conducted, and OriginPro 2023 (version 10.0.0.154) was used to plot the graphs.

3 Results

3.1 Sequencing results

The number of raw reads obtained from the six rhizosphere soil-pooled samples of A. lancea was between 51,675,168 and 58,520,292. After quality control, the amount of raw sequencing data per group was reduced to an average of 99.45%, which indicates that the sequencing quality is high. The assembly of reads using MEGAHIT software generated an average of 562,714 and 293,043 contigs from the Mao and Ying samples, respectively. Their N50 lengths ranged from 461 to 634 bp, with an N90 between 325 and 348 bp. Two values of N50 and N90 were used to evaluate the effect of splicing assembly, and the larger the two values, the better the effect of splicing assembly. The ORFs in these contigs were predicted and used to construct non-redundant gene sets. Thus, a total of 4,406,678 catalog genes were generated from the metagenomic libraries.

3.2 Diversity analysis of the rhizosphere microbiome

The Chao, Shannon, and Simpson indexes represent microbial community richness and diversity, respectively. According to the Chao analysis, the Mao and Ying samples had 11,922 and 10,682 OTUs (operational taxonomic units, which is a basic unit for species classification and relative abundance analysis), respectively. The OTU richness of the Mao sample was higher than that of the Ying sample but the difference was not significant (P ≥ 0.05) (Figure 1a). The Shannon index correlates positively with microbial community diversity, while the Simpson index correlates negatively with microbial community diversity. According to these two indices, the microbial community diversity in the Mao sample was higher than that in the Ying sample but not significant. PCoA based on the Bray–Curtis distance also revealed that the rhizosphere microbial communities in the Mao and Ying samples were obviously separated (Figure 1b). The two main coordinates explained 86.83% of the microbial community changes among all the samples, of which the PC1 and PC2 axes explained 69.70 and 17.13% of the results, respectively.

Figure 1 Diversity comparisons of the A. lancea rhizosphere microbiome. (a) α-Diversity in the rhizosphere microbiome was measured using the Chao, Shannon, and Simpson index. Chao: uses the chao1 algorithm to estimate the index of the number of OTUs contained in the sample. Shannon: one of the indices used to estimate microbial diversity in a sample; the higher the Shannon value, the higher the community diversity. Simpson: one of the indices used to estimate microbial diversity in a sample; the higher the Simpson index value, the lower the community diversity. (b) PCoA was based on the Bray–Curtis dissimilarity matrix to demonstrate the β-diversity of the rhizosphere microbiome.
Figure 1

Diversity comparisons of the A. lancea rhizosphere microbiome. (a) α-Diversity in the rhizosphere microbiome was measured using the Chao, Shannon, and Simpson index. Chao: uses the chao1 algorithm to estimate the index of the number of OTUs contained in the sample. Shannon: one of the indices used to estimate microbial diversity in a sample; the higher the Shannon value, the higher the community diversity. Simpson: one of the indices used to estimate microbial diversity in a sample; the higher the Simpson index value, the lower the community diversity. (b) PCoA was based on the Bray–Curtis dissimilarity matrix to demonstrate the β-diversity of the rhizosphere microbiome.

3.3 Comparative analysis of microbial communities

Comparing the catalog gene sets with the NR database, we found that the abundance of bacteria in all samples was the highest, above 98%, followed by archaea. Prokaryotes accounted for more than 99% of the species in each sample, and each sample was annotated to an average of 0.25% eukaryotes. Based on the dominant role of prokaryotes in the rhizosphere microorganisms of A. lancea, this study focused on prokaryotes. A total of 123 phyla were identified in the six samples. The dominant phyla were Proteobacteria (39.35%), Actinobacteria (33.85%), Acidobacteria (13.12%), Gemmatimonadetes (1.85%), Chloroflexi (1.58%), and Firmicutes (1.50%) (Figure 2a), which comprised 90% of all OTUs and were well represented. Comparing the relative abundance of prokaryotes among the two sampling sites at the phylum, family, and genus level revealed a marked difference in the microbial communities between the Mao and Ying samples. At the phylum level, there were significant differences in the abundance of Firmicutes, Planctomycetes, Thaumarchaeota, Euryarchaeota, Deinococcus-Thermu, and Armatimonadetes between the Mao and Ying samples (Figure 2b).

Figure 2 A comparison of procaryotic communities in the rhizosphere. (a) Relative abundance of the most abundant prokaryote at the phylum level. (b) Significant discrepancies in indicator prokaryotes between Mao and Ying groups at the phylum level. *P ≤ 0.05, **P ≤ 0.01.
Figure 2

A comparison of procaryotic communities in the rhizosphere. (a) Relative abundance of the most abundant prokaryote at the phylum level. (b) Significant discrepancies in indicator prokaryotes between Mao and Ying groups at the phylum level. *P ≤ 0.05, **P ≤ 0.01.

The main families included Acidobacteriaceae (9.57%), Bradyrhizobiaceae (9.16%), Streptomycetaceae (5.41%), Mycobacteriaceae (3.10%), and Hyphomicrobiaceae (3.03%) (Figure 3a). The families Streptomycetaceae, Streptosporangiaceae, Rhodospirillaceae, Micromonosporaceae, and Thermomonosporaceae were significantly more abundant in the Mao sample than in the Ying sample (P ≤ 0.05). The abundances of the families Sphingomonadaceae, Gaiellaceae, and Planctomycetaceae were significantly higher in the Ying sample than in the Mao sample (Figure 3b).

Figure 3 A comparison of procaryotic communities in the rhizosphere. (a) Relative abundance of the most abundant prokaryote at the family level. (b) Significant discrepancies in indicator prokaryotes between Mao and Ying groups at the family level. *P ≤ 0.05, **P ≤ 0.01.
Figure 3

A comparison of procaryotic communities in the rhizosphere. (a) Relative abundance of the most abundant prokaryote at the family level. (b) Significant discrepancies in indicator prokaryotes between Mao and Ying groups at the family level. *P ≤ 0.05, **P ≤ 0.01.

At the genus level, Bradyrhizobium (7.62%), Streptomyces (4.30%), Candidatus_Koribacter (3.15%), Mycobacterium (3.10%), and Rhodoplanes (2.33%) were the top five abundant genera across all communities (Figure 4a). Genera with significant differences between the Mao and Ying samples included Streptomyces, Frankia, Gaiella, Sphingomonas, Actinomadura, and Strepta cidiphilus (Figure 4b). A total of 2,817 genera were identified in all samples, and those with abundances less than 0.01 were combined into the others.

Figure 4 A comparison of procaryotic communities in the rhizosphere. (a) Relative abundance of the most abundant prokaryote at the genus level. (b) Significant discrepancies in indicator prokaryotes between Mao and Ying groups at the genus level. *P ≤ 0.05, **P ≤ 0.01.
Figure 4

A comparison of procaryotic communities in the rhizosphere. (a) Relative abundance of the most abundant prokaryote at the genus level. (b) Significant discrepancies in indicator prokaryotes between Mao and Ying groups at the genus level. *P ≤ 0.05, **P ≤ 0.01.

3.4 Characteristic analysis of the core rhizosphere microbiome of A. lancea

Plant growth-promoting rhizobacteria refer to microorganisms that reside in the plant’s rhizosphere and rhizoplane that can directly or indirectly improve the growth conditions and quality of the host plant. Their main functions are nitrogen fixation, phosphorus solubilization, secretion of plant growth-promoting substances, and synthesis of iron production carriers [26]. The results of the independent sample T-test showed that there were significant differences in the relative abundances of rhizosphere core microorganisms of A. lancea between the two sites. The abundance of Bradyrhizobium in the rhizosphere of A. lancea was the highest (more than 7%), and its abundance in the Mao sample was higher than that of the Ying sample, followed by Burkholderia and Mesorhizobium. Moreover, the abundances of Azospirillum, Agrobacterium, Serratia, and Erwinia were higher in the Mao sample than in the Ying sample (Figure 5a). Azospirillum and Erwinia belong to asybiotic nitrogen-fixing bacteria, which not only promote root growth but also transport more water and minerals to the above-ground parts of the host plant. These bacteria are often used in the production of food crops because they do not need to form nodules with plants to perform biological nitrogen fixation [27].

Figure 5 Detailed relative abundance of the core rhizosphere microbiome involved in (a) plant growth-promoting rhizobacteria, (b) plant growth-promoting fungi, (c) disease suppression, and (d) biological control agent. *P ≤ 0.05.
Figure 5

Detailed relative abundance of the core rhizosphere microbiome involved in (a) plant growth-promoting rhizobacteria, (b) plant growth-promoting fungi, (c) disease suppression, and (d) biological control agent. *P ≤ 0.05.

Plant growth-promoting fungi are microorganisms that reside in the rhizosphere soil of various plants. They perform the following functions in plants: biocontrol potential through competition for nutrients, growth hormone production, and mineral solubilization [28]. Plant growth-promoting fungi in the A. lancea rhizosphere include Penicillium, Rhizoctonia, Talaromyces, Trichoderma, and Phytophthora. Penicillium had the highest relative abundance in the Mao sample, followed by Rhizoctonia, Talaromyces, and Trichoderma. The abundance of Penicillium in the Mao sample was 1.7 times higher than that in the Ying sample (Figure 5b).

Multiple disease suppression prokaryotes were observed in the rhizosphere of A. lancea, such as Proteobacteria, Actinobacteria, and Firmicutes (Figure 5c). Furthermore, ten biological control genera were identified (Figure 5d) [29]. Streptomyces, the relative abundance of which was 1.9 times higher in the Mao sample than in the Ying sample, can produce a variety of antibiotics to inhibit the growth and reproduction of plant pathogens and perform biological control functions via hyperparasitism, the production of secondary metabolites, and induction of plant resistance [30].

3.5 Environmental factors and their correlations with the microbial community

Previous studies have shown that habitat and soil type, rather than host genetic background, have greater effects on the overall structure of the microbiome. However, the habitat and soil characteristics that have specific effects on the microbial community remain unclear. The test results for the soil physicochemical factors are shown in Figure 6a. There were significant differences in the AN, AP, and AK contents between the Mao and Ying samples. The AAT at the Mao site was significantly higher than that at the Ying site; however, there was no significant difference in the AAP between them (Figure 6b and c). According to the RDA based on the environmental factors and the most abundant prokaryotes among the samples, the two groups were separated obviously at the family, genus, and species levels (Figure 6d–f). At the family level, RDA1 and RDA2 explained 64.33 and 20.94% of the variance values, respectively. AAP and AK were the main influencing factors for RDA1. At the genus level, the abundance of Streptomyces and Mycobacterium correlated positively with the AAT, while Solirubrobacter correlated positively with the AAP. At the species level, RDA1 and RDA2 explained 64.91 and 19.09% of the variance values, respectively. AP and AK correlated significantly with the changes in rhizosphere microbial community (Table 1). In particular, AP had significant effects on the rhizosphere microbial community structure of A. lancea at the three taxonomic levels.

Figure 6 Differences in the environmental factors between groups, including (a) AN, AP, AK, and pH; (b) AAT; and (c) AAP. (d–f) RDA for rhizosphere microbes with environmental factors at the family, genus, and species taxonomic levels. The solid line represents environmental factors with significance to indicators, while the dotted line represents non-significance. *P ≤ 0.05.
Figure 6

Differences in the environmental factors between groups, including (a) AN, AP, AK, and pH; (b) AAT; and (c) AAP. (d–f) RDA for rhizosphere microbes with environmental factors at the family, genus, and species taxonomic levels. The solid line represents environmental factors with significance to indicators, while the dotted line represents non-significance. *P ≤ 0.05.

Table 1

The RDA results of environmental factors and microbes (values in bold mean significant, P ≤ 0.05)

Environmental factors Family level Genus level Species level
R 2 P R 2 P R 2 P
AN 0.80446 0.08194 0.83882 0.07917 0.84386 0.09583
AP 0.91162 0.01806 0.90959 0.0375 0.97179 0.01111
AK 0.88133 0.03194 0.88041 0.0625 0.91693 0.04722
AAT 0.67033 0.18056 0.71339 0.14444 0.78061 0.10278
AAP 0.41188 0.42083 0.38662 0.4875 0.41664 0.45972

Phosphorus is one of the three essential elements for plant growth and is involved in the biosynthesis of many macromolecular substances such as nucleic acids and proteins. If the phosphorus supply is insufficient, plant growth will be inhibited, which will affect the absorption of other elements, leading to developmental delays, leaf shedding, and lower yields [31]. Plants mainly absorb water-soluble phosphorus from inorganic phosphorus in the soil, which leads to a low utilization efficiency of soil phosphorus [32]. Phosphate-solubilizing microorganisms (PSMs) are a group of microorganisms capable of hydrolyzing organic and inorganic phosphorus into soluble forms, thus increasing their bioavailability to plants. PSMs are quite abundant in the soil and are commonly associated with the rhizosphere of plants. There is also great diversity in PSMs, including Azospirillum, Bacillus, Pseudomonas, and Nitrosomonas [33], which can hydrolyze organophosphorus and inorganic phosphorus into soluble form by secreting extracellular enzymes or releasing phosphorus during substrate degradation, thus increasing the content of AP in the soil.

3.6 Function potential analysis of the microbiome

The main COG functional annotations of the six rhizosphere microbiome samples were amino acid transport and metabolism (9.76%), energy production and conversion (8.00%), carbohydrate transport and metabolism (6.15%), signal transduction mechanisms (6.08%), and replication, recombination, and repair (6.03%). A total of 6,730 KEGG orthologies were obtained from all six samples, participating in 385 KEGG pathways (level 3) according to their KEGG functional annotations. Metabolism (72.23%) was the most abundant function pathway at level 1, followed by genetic information processing (7.85%), environmental information processing (6.77%), cellular processes (6.18%), human diseases (4.10%), and organismal systems (2.88%) (Figure 7). The genes enriched in the top 40 KEGG pathways (level 3) accounted for 61% of the total genes. Overall, these results provided an overview of the rhizosphere microbiome function of A. lancea.

Figure 7 The annotated statistical map of the KEGG function of the rhizosphere microbiome of A. lancea.
Figure 7

The annotated statistical map of the KEGG function of the rhizosphere microbiome of A. lancea.

Plant–microbe and microbe–microbe interactions are likely to be important factors for the aggregation of the rhizosphere microbiome. The Mao and Ying samples were enriched and compared in different metabolic pathways (Figure 8a). The two-component system, a metabolic pathway known to be involved in plant–microbe and microbe–microbe interactions, had a higher proportion in the Mao sample than in the Ying sample. The two-component system regulates the majority of bacterial physiological functions, including chemotaxis, which makes bacteria more congregate. It may mean that the rhizosphere environment of A. lancea in Maoshan is more conducive to microbial aggregation. Tryptophan is not only an important amino acid for protein biosynthesis but also an important precursor for auxin biosynthesis. Tryptophan metabolism was enriched in the Mao sample compared with that in the Ying sample. We also found that compared with those in the Ying sample, the Mao sample had a greater proportion of amino acid anabolism pathways, including glycine, serine, threonine, valine, leucine, isoleucine, cysteine, methionine, arginine, and proline metabolism. The results indicated that the microbes in the Mao rhizosphere potentially could not obtain enough nutrients directly from root exudates of A. lancea, and needed to synthesize amino acids themselves or via other related ways. Subsequently, the 10 microbial genera with significant differences between the samples and the top 40 KEGG functional pathways were selected for functional contribution analysis (Figure 8b). The results showed that Streptomyces was the main driving factor of the two-component system and tryptophan metabolism, which was enriched in the Mao sample. In addition, the functional pathways affected by Streptomyces included carbon metabolism, biosynthesis of amino acids, carbon fixation pathways in prokaryotes, and pyruvate metabolism.

Figure 8 (a) Heat map analysis of the top 40 most abundant KEGG functional pathways using normalized abundance. The heat map is color-coded based on z-scores (zero mean normalization). (b) Pearson correlation analysis between genera with significant difference between Mao and Ying and metabolic pathway on level 3.
Figure 8

(a) Heat map analysis of the top 40 most abundant KEGG functional pathways using normalized abundance. The heat map is color-coded based on z-scores (zero mean normalization). (b) Pearson correlation analysis between genera with significant difference between Mao and Ying and metabolic pathway on level 3.

3.7 Correlation analysis of medicinally active ingredients with the rhizosphere microbiome

Rhizosphere microbes improve the utilization rate of nutrients in the soil, thereby affecting the growth and metabolic processes of medicinal plants, ultimately affecting the efficacy of medicinal materials [34]. The contents of atractylodin, atractylon, and β-eudesmol in A. lancea from the two regions were determined (Figure 9a). The results showed that the contents of atractylodin and atractylon in the Mao sample were significantly higher than those in the Ying sample, while the content of β-eudesmol was higher in the Ying sample. The results showed that there were differences in the active ingredients between the genuine producing area and the non-genuine producing area, which was consistent with the results of previous studies [35]. We performed a correlation analysis between the three medicinally active ingredients and the top ten abundant microbes at the phylum, family, and genus levels. At the phylum level (Figure 9b), the content of atractylodin and atractylon correlated positively with the abundance of Actinobacteria, Acidobacteria, and Verrucomicrobia, while the content of β-eudesmol correlated significantly and positively with the abundance of Firmicutes and Planctomycetes. At the family level (Figure 9c), the atractylon content correlated significantly with the abundance of Streptosporangiaceae and Thermomonosporaceae. At the genus level (Figure 9d), the content of β-eudesmol correlated positively with the abundance of Solirubrobacter, Conexibacter, and Pseudolabrys. In summary, microbes might play a very important role in plant secondary metabolism synthesis.

Figure 9 (a) Contents of atractylodin, atractylon, and β-eudesmol in the Mao and Ying samples. Correlation analysis between medicinally active ingredients of A. lancea and the rhizosphere microbiome at phylum (b), family (c), and genus (d) levels. *P ≤ 0.05.
Figure 9

(a) Contents of atractylodin, atractylon, and β-eudesmol in the Mao and Ying samples. Correlation analysis between medicinally active ingredients of A. lancea and the rhizosphere microbiome at phylum (b), family (c), and genus (d) levels. *P ≤ 0.05.

4 Discussion

4.1 Differences in the microflora composition between different regions of A. lancea

Through high-throughput sequencing, researchers found that the rhizosphere microorganisms of A. lancea were abundant and the relationship between microorganisms and plants was complex [21]. Furthermore, to the best of our knowledge, this is the first metagenomic report on rhizosphere microorganisms of A. lancea in different regions, and the quality is high compared with other studies. Similar to studies of medicinal plants such as A. membranaceus and Alisma, we also found that the composition and diversity of rhizosphere microbial communities are influenced by various environmental factors, including AN, AP, AK, and pH [36,37]. Although the contents of nutrients (AN, AP, and AK) in the Yingshan soil were significantly higher than those of the Maoshan soil, the α-diversity results showed no significant difference in the richness and diversity of the rhizosphere microbial communities between these two areas. PCoA could distinguish the two groups of samples, which means that soil and climate factors in this study had a greater impact on the dominant microbial flora than community diversity. Actinobacteria and Proteobacteria were the dominant phyla in the Mao and Ying samples, respectively. Studies have shown that the same species of plants recruit similar microorganisms to form rhizosphere environments on different soils [38]. From the perspective of genera, it can be seen that the abundance of most rhizosphere microorganisms in A. lancea is less than 0.01 (Figure 4a) but a rich and stable rhizosphere microbiome is formed.

In addition, Glomus, a group of arbuscular mycorrhizal fungi, was annotated in the Mao sample but not in the Ying sample. Studies have shown that inoculation of arbuscular mycorrhizal fungi could improve biomass, promote the accumulation of essential oil components, and reduce the occurrence of root rot of A. lancea [39,40], which might be one of the reasons for the better quality of medicinal materials in the Mao sample than in the Ying sample.

4.2 A. lancea contain various core rhizosphere microbiomes

The abundance of the prokaryotic community in each sample was above 99%; therefore, it was the dominant community in the rhizosphere soil of A. lancea. The samples contained 0.6% of archaea, which is an important component of the plant microbiome [41,42]. The main feature that distinguishes archaea from other microorganisms is that they can adapt to survive in extreme environments; therefore, they might help plants by enhancing their immune responses and adaptation to abiotic stresses [43]. Moreover, archaea are also involved in environmental nutrient cycling in plant ecosystems [44], and some archaea are also considered as plant growth-promoting archaea [45]. The identification of a certain abundance of archaea among the rhizosphere microbes of A. lancea is important for future studies on plant–archaeal interactions.

Bradyrhizobium (a type of plant growth-promoting rhizobacteria), which was more abundant in the Mao sample than in the Ying sample, can fix nitrogen in the environment and supply it to plants, thereby promoting plant growth directly or indirectly [46]. A. lancea plantations are faced with multiple challenges, including the impedance of growth by heat stress and drought [47,48]. Actinomadura and Acrophialophora, which are more abundant in the Mao samples, have been shown to increase A. lancea systemic tolerance to abiotic stresses, such as drought, thereby affecting the growth and accumulation of medicinal compounds [49]. In the rhizosphere of A. lancea, Penicillium in the Mao sample had the highest abundance. The biomass of A. lancea is affected by Streptomyces and other microorganisms [50]. Streptomyces is abundant in soil and contains many secondary metabolic biosynthesized gene clusters, such as that for abamectin synthesis, which has a strong effect on controlling plant diseases and insect pests [51,52]. We found that Streptomyces made the highest contribution to the functional difference of the rhizosphere microbiome between the two areas, and the abundance of Streptomyces in the Mao sample was significantly higher than that in the Ying sample.

4.3 Relationship between rhizosphere microorganisms and medicinally active ingredients

The main active ingredients of A. lancea are atractylodin, atractylon, and β-eudesmol. Atractylodin is a polyalkyne compound, while atractylon and β-eudesmol are sesquiterpenoids. Atractylodin is the quality control index of atractylodis rhizoma (Chinese herbal medicine) in Chinese Pharmacopoeia (2020). The significant difference in the content of the three components between the Mao and Ying samples might also be closely related to the rhizosphere microorganisms. The enrichment of metabolites secreted by rhizosphere microorganisms through plant roots is common in the growth and development of medicinal plants. Conversely, rhizosphere microorganisms can also increase the synthesis and accumulation of secondary metabolites by regulating nutrients and the activities of key enzymes to synthesize active ingredients [53,54]. We found that the contents of atractylodin and atractylon correlated positively with the abundance of Streptomyces, Candidatus-Solibacter, and Frankia. The content of β-eudesmol correlated positively with the abundance of Solirubrobacter in soil, suggesting that these microorganisms were directly or indirectly involved in the synthesis of secondary metabolites. The Streptomyces genus has been a rich source of bioactive natural products, medicinal chemicals, and novel drugs [55]. Sesquiterpenoids synthesized by Streptomyces species include roseosporol A and albaflavenoid [56,57].

5 Conclusions

There were differences in the diversity, structure, and function of the rhizosphere microbial communities of A. lancea in the genuine producing area and the non-genuine producing area. There are various reasons for these differences and not only soil factors. The differences in rhizosphere microorganisms might lead to a difference in the quality of A. lancea. The rhizosphere microorganisms of A. lancea are rich in species, some of which have great potential as the key microorganisms for disease prevention and control, stress response, and quality improvement, thus providing a valuable foundation for high-quality planting of A. lancea in the future.

# Co-first authors: Junjie Tang and Yun Han.

  1. Funding information: This work was supported by the Key project at central government level: The ability establishment of sustainable use for valuable Chinese medicine resources (Grant No. 2060302), research of assurance ability improvement of Chinese medicinal resources (2023), project of Forestry Science and Technology Innovation and Promotion of Jiangsu (Grant No. LYKJ Changzhou [2020] 04), Suzhou science and technology development plan (agricultural science and technology innovation) project (Grant No. SNG2020063) and Suzhou Science and Technology Development Plan (Grant No. SYSD2020248).

  2. Author contributions: All authors contributed to the study conception and design. J.J.T. and Y.H. contributed equally to this work. W.G. and R.L.Q. designed and revised the manuscript. L.F.P., Y.F.G., and M.T. performed the experiments and collected data. J.J.T. and Y.H. wrote the manuscript. S.W. and Q.H.M. analyzed the data. All authors have read and approved the manuscript.

  3. Conflict of interest: Authors state no conflict of interest.

  4. Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Received: 2023-08-17
Revised: 2023-10-18
Accepted: 2023-10-22
Published Online: 2023-11-23

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  77. Screening of the best time window for MSC transplantation to treat acute myocardial infarction with SDF-1α antibody-loaded targeted ultrasonic microbubbles: An in vivo study in miniswine
  78. Inhibition of TAZ impairs the migration ability of melanoma cells
  79. Molecular complexity analysis of the diagnosis of Gitelman syndrome in China
  80. Effects of maternal calcium and protein intake on the development and bone metabolism of offspring mice
  81. Identification of winter wheat pests and diseases based on improved convolutional neural network
  82. Ultra-multiplex PCR technique to guide treatment of Aspergillus-infected aortic valve prostheses
  83. Virtual high-throughput screening: Potential inhibitors targeting aminopeptidase N (CD13) and PIKfyve for SARS-CoV-2
  84. Immune checkpoint inhibitors in cancer patients with COVID-19
  85. Utility of methylene blue mixed with autologous blood in preoperative localization of pulmonary nodules and masses
  86. Integrated analysis of the microbiome and transcriptome in stomach adenocarcinoma
  87. Berberine suppressed sarcopenia insulin resistance through SIRT1-mediated mitophagy
  88. DUSP2 inhibits the progression of lupus nephritis in mice by regulating the STAT3 pathway
  89. Lung abscess by Fusobacterium nucleatum and Streptococcus spp. co-infection by mNGS: A case series
  90. Genetic alterations of KRAS and TP53 in intrahepatic cholangiocarcinoma associated with poor prognosis
  91. Granulomatous polyangiitis involving the fourth ventricle: Report of a rare case and a literature review
  92. Studying infant mortality: A demographic analysis based on data mining models
  93. Metaplastic breast carcinoma with osseous differentiation: A report of a rare case and literature review
  94. Protein Z modulates the metastasis of lung adenocarcinoma cells
  95. Inhibition of pyroptosis and apoptosis by capsaicin protects against LPS-induced acute kidney injury through TRPV1/UCP2 axis in vitro
  96. TAK-242, a toll-like receptor 4 antagonist, against brain injury by alleviates autophagy and inflammation in rats
  97. Primary mediastinum Ewing’s sarcoma with pleural effusion: A case report and literature review
  98. Association of ADRB2 gene polymorphisms and intestinal microbiota in Chinese Han adolescents
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  100. Study on the cytokines related to SARS-Cov-2 in testicular cells and the interaction network between cells based on scRNA-seq data
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  102. HP1 induces ferroptosis of renal tubular epithelial cells through NRF2 pathway in diabetic nephropathy
  103. Intravaginal estrogen management in postmenopausal patients with vaginal squamous intraepithelial lesions along with CO2 laser ablation: A retrospective study
  104. Hepatocellular carcinoma cell differentiation trajectory predicts immunotherapy, potential therapeutic drugs, and prognosis of patients
  105. Effects of physical exercise on biomarkers of oxidative stress in healthy subjects: A meta-analysis of randomized controlled trials
  106. Identification of lysosome-related genes in connection with prognosis and immune cell infiltration for drug candidates in head and neck cancer
  107. Development of an instrument-free and low-cost ELISA dot-blot test to detect antibodies against SARS-CoV-2
  108. Research progress on gas signal molecular therapy for Parkinson’s disease
  109. Adiponectin inhibits TGF-β1-induced skin fibroblast proliferation and phenotype transformation via the p38 MAPK signaling pathway
  110. The G protein-coupled receptor-related gene signatures for predicting prognosis and immunotherapy response in bladder urothelial carcinoma
  111. α-Fetoprotein contributes to the malignant biological properties of AFP-producing gastric cancer
  112. CXCL12/CXCR4/CXCR7 axis in placenta tissues of patients with placenta previa
  113. Association between thyroid stimulating hormone levels and papillary thyroid cancer risk: A meta-analysis
  114. Significance of sTREM-1 and sST2 combined diagnosis for sepsis detection and prognosis prediction
  115. Diagnostic value of serum neuroactive substances in the acute exacerbation of chronic obstructive pulmonary disease complicated with depression
  116. Research progress of AMP-activated protein kinase and cardiac aging
  117. TRIM29 knockdown prevented the colon cancer progression through decreasing the ubiquitination levels of KRT5
  118. Cross-talk between gut microbiota and liver steatosis: Complications and therapeutic target
  119. Metastasis from small cell lung cancer to ovary: A case report
  120. The early diagnosis and pathogenic mechanisms of sepsis-related acute kidney injury
  121. The effect of NK cell therapy on sepsis secondary to lung cancer: A case report
  122. Erianin alleviates collagen-induced arthritis in mice by inhibiting Th17 cell differentiation
  123. Loss of ACOX1 in clear cell renal cell carcinoma and its correlation with clinical features
  124. Signalling pathways in the osteogenic differentiation of periodontal ligament stem cells
  125. Crosstalk between lactic acid and immune regulation and its value in the diagnosis and treatment of liver failure
  126. Clinicopathological features and differential diagnosis of gastric pleomorphic giant cell carcinoma
  127. Traumatic brain injury and rTMS-ERPs: Case report and literature review
  128. Extracellular fibrin promotes non-small cell lung cancer progression through integrin β1/PTEN/AKT signaling
  129. Knockdown of DLK4 inhibits non-small cell lung cancer tumor growth by downregulating CKS2
  130. The co-expression pattern of VEGFR-2 with indicators related to proliferation, apoptosis, and differentiation of anagen hair follicles
  131. Inflammation-related signaling pathways in tendinopathy
  132. CD4+ T cell count in HIV/TB co-infection and co-occurrence with HL: Case report and literature review
  133. Clinical analysis of severe Chlamydia psittaci pneumonia: Case series study
  134. Bioinformatics analysis to identify potential biomarkers for the pulmonary artery hypertension associated with the basement membrane
  135. Influence of MTHFR polymorphism, alone or in combination with smoking and alcohol consumption, on cancer susceptibility
  136. Catharanthus roseus (L.) G. Don counteracts the ampicillin resistance in multiple antibiotic-resistant Staphylococcus aureus by downregulation of PBP2a synthesis
  137. Combination of a bronchogenic cyst in the thoracic spinal canal with chronic myelocytic leukemia
  138. Bacterial lipoprotein plays an important role in the macrophage autophagy and apoptosis induced by Salmonella typhimurium and Staphylococcus aureus
  139. TCL1A+ B cells predict prognosis in triple-negative breast cancer through integrative analysis of single-cell and bulk transcriptomic data
  140. Ezrin promotes esophageal squamous cell carcinoma progression via the Hippo signaling pathway
  141. Ferroptosis: A potential target of macrophages in plaque vulnerability
  142. Predicting pediatric Crohn's disease based on six mRNA-constructed risk signature using comprehensive bioinformatic approaches
  143. Applications of genetic code expansion and photosensitive UAAs in studying membrane proteins
  144. HK2 contributes to the proliferation, migration, and invasion of diffuse large B-cell lymphoma cells by enhancing the ERK1/2 signaling pathway
  145. IL-17 in osteoarthritis: A narrative review
  146. Circadian cycle and neuroinflammation
  147. Probiotic management and inflammatory factors as a novel treatment in cirrhosis: A systematic review and meta-analysis
  148. Hemorrhagic meningioma with pulmonary metastasis: Case report and literature review
  149. SPOP regulates the expression profiles and alternative splicing events in human hepatocytes
  150. Knockdown of SETD5 inhibited glycolysis and tumor growth in gastric cancer cells by down-regulating Akt signaling pathway
  151. PTX3 promotes IVIG resistance-induced endothelial injury in Kawasaki disease by regulating the NF-κB pathway
  152. Pancreatic ectopic thyroid tissue: A case report and analysis of literature
  153. The prognostic impact of body mass index on female breast cancer patients in underdeveloped regions of northern China differs by menopause status and tumor molecular subtype
  154. Report on a case of liver-originating malignant melanoma of unknown primary
  155. Case report: Herbal treatment of neutropenic enterocolitis after chemotherapy for breast cancer
  156. The fibroblast growth factor–Klotho axis at molecular level
  157. Characterization of amiodarone action on currents in hERG-T618 gain-of-function mutations
  158. A case report of diagnosis and dynamic monitoring of Listeria monocytogenes meningitis with NGS
  159. Effect of autologous platelet-rich plasma on new bone formation and viability of a Marburg bone graft
  160. Small breast epithelial mucin as a useful prognostic marker for breast cancer patients
  161. Continuous non-adherent culture promotes transdifferentiation of human adipose-derived stem cells into retinal lineage
  162. Nrf3 alleviates oxidative stress and promotes the survival of colon cancer cells by activating AKT/BCL-2 signal pathway
  163. Favorable response to surufatinib in a patient with necrolytic migratory erythema: A case report
  164. Case report of atypical undernutrition of hypoproteinemia type
  165. Down-regulation of COL1A1 inhibits tumor-associated fibroblast activation and mediates matrix remodeling in the tumor microenvironment of breast cancer
  166. Sarcoma protein kinase inhibition alleviates liver fibrosis by promoting hepatic stellate cells ferroptosis
  167. Research progress of serum eosinophil in chronic obstructive pulmonary disease and asthma
  168. Clinicopathological characteristics of co-existing or mixed colorectal cancer and neuroendocrine tumor: Report of five cases
  169. Role of menopausal hormone therapy in the prevention of postmenopausal osteoporosis
  170. Precisional detection of lymph node metastasis using tFCM in colorectal cancer
  171. Advances in diagnosis and treatment of perimenopausal syndrome
  172. A study of forensic genetics: ITO index distribution and kinship judgment between two individuals
  173. Acute lupus pneumonitis resembling miliary tuberculosis: A case-based review
  174. Plasma levels of CD36 and glutathione as biomarkers for ruptured intracranial aneurysm
  175. Fractalkine modulates pulmonary angiogenesis and tube formation by modulating CX3CR1 and growth factors in PVECs
  176. Novel risk prediction models for deep vein thrombosis after thoracotomy and thoracoscopic lung cancer resections, involving coagulation and immune function
  177. Exploring the diagnostic markers of essential tremor: A study based on machine learning algorithms
  178. Evaluation of effects of small-incision approach treatment on proximal tibia fracture by deep learning algorithm-based magnetic resonance imaging
  179. An online diagnosis method for cancer lesions based on intelligent imaging analysis
  180. Medical imaging in rheumatoid arthritis: A review on deep learning approach
  181. Predictive analytics in smart healthcare for child mortality prediction using a machine learning approach
  182. Utility of neutrophil–lymphocyte ratio and platelet–lymphocyte ratio in predicting acute-on-chronic liver failure survival
  183. A biomedical decision support system for meta-analysis of bilateral upper-limb training in stroke patients with hemiplegia
  184. TNF-α and IL-8 levels are positively correlated with hypobaric hypoxic pulmonary hypertension and pulmonary vascular remodeling in rats
  185. Stochastic gradient descent optimisation for convolutional neural network for medical image segmentation
  186. Comparison of the prognostic value of four different critical illness scores in patients with sepsis-induced coagulopathy
  187. Application and teaching of computer molecular simulation embedded technology and artificial intelligence in drug research and development
  188. Hepatobiliary surgery based on intelligent image segmentation technology
  189. Value of brain injury-related indicators based on neural network in the diagnosis of neonatal hypoxic-ischemic encephalopathy
  190. Analysis of early diagnosis methods for asymmetric dementia in brain MR images based on genetic medical technology
  191. Early diagnosis for the onset of peri-implantitis based on artificial neural network
  192. Clinical significance of the detection of serum IgG4 and IgG4/IgG ratio in patients with thyroid-associated ophthalmopathy
  193. Forecast of pain degree of lumbar disc herniation based on back propagation neural network
  194. SPA-UNet: A liver tumor segmentation network based on fused multi-scale features
  195. Systematic evaluation of clinical efficacy of CYP1B1 gene polymorphism in EGFR mutant non-small cell lung cancer observed by medical image
  196. Rehabilitation effect of intelligent rehabilitation training system on hemiplegic limb spasms after stroke
  197. A novel approach for minimising anti-aliasing effects in EEG data acquisition
  198. ErbB4 promotes M2 activation of macrophages in idiopathic pulmonary fibrosis
  199. Clinical role of CYP1B1 gene polymorphism in prediction of postoperative chemotherapy efficacy in NSCLC based on individualized health model
  200. Lung nodule segmentation via semi-residual multi-resolution neural networks
  201. Evaluation of brain nerve function in ICU patients with Delirium by deep learning algorithm-based resting state MRI
  202. A data mining technique for detecting malignant mesothelioma cancer using multiple regression analysis
  203. Markov model combined with MR diffusion tensor imaging for predicting the onset of Alzheimer’s disease
  204. Effectiveness of the treatment of depression associated with cancer and neuroimaging changes in depression-related brain regions in patients treated with the mediator-deuterium acupuncture method
  205. Molecular mechanism of colorectal cancer and screening of molecular markers based on bioinformatics analysis
  206. Monitoring and evaluation of anesthesia depth status data based on neuroscience
  207. Exploring the conformational dynamics and thermodynamics of EGFR S768I and G719X + S768I mutations in non-small cell lung cancer: An in silico approaches
  208. Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer
  209. Incidence of different pressure patterns of spinal cerebellar ataxia and analysis of imaging and genetic diagnosis
  210. Pathogenic bacteria and treatment resistance in older cardiovascular disease patients with lung infection and risk prediction model
  211. Adoption value of support vector machine algorithm-based computed tomography imaging in the diagnosis of secondary pulmonary fungal infections in patients with malignant hematological disorders
  212. From slides to insights: Harnessing deep learning for prognostic survival prediction in human colorectal cancer histology
  213. Ecology and Environmental Science
  214. Monitoring of hourly carbon dioxide concentration under different land use types in arid ecosystem
  215. Comparing the differences of prokaryotic microbial community between pit walls and bottom from Chinese liquor revealed by 16S rRNA gene sequencing
  216. Effects of cadmium stress on fruits germination and growth of two herbage species
  217. Bamboo charcoal affects soil properties and bacterial community in tea plantations
  218. Optimization of biogas potential using kinetic models, response surface methodology, and instrumental evidence for biodegradation of tannery fleshings during anaerobic digestion
  219. Understory vegetation diversity patterns of Platycladus orientalis and Pinus elliottii communities in Central and Southern China
  220. Studies on macrofungi diversity and discovery of new species of Abortiporus from Baotianman World Biosphere Reserve
  221. Food Science
  222. Effect of berrycactus fruit (Myrtillocactus geometrizans) on glutamate, glutamine, and GABA levels in the frontal cortex of rats fed with a high-fat diet
  223. Guesstimate of thymoquinone diversity in Nigella sativa L. genotypes and elite varieties collected from Indian states using HPTLC technique
  224. Analysis of bacterial community structure of Fuzhuan tea with different processing techniques
  225. Untargeted metabolomics reveals sour jujube kernel benefiting the nutritional value and flavor of Morchella esculenta
  226. Mycobiota in Slovak wine grapes: A case study from the small Carpathians wine region
  227. Elemental analysis of Fadogia ancylantha leaves used as a nutraceutical in Mashonaland West Province, Zimbabwe
  228. Microbiological transglutaminase: Biotechnological application in the food industry
  229. Influence of solvent-free extraction of fish oil from catfish (Clarias magur) heads using a Taguchi orthogonal array design: A qualitative and quantitative approach
  230. Chromatographic analysis of the chemical composition and anticancer activities of Curcuma longa extract cultivated in Palestine
  231. The potential for the use of leghemoglobin and plant ferritin as sources of iron
  232. Investigating the association between dietary patterns and glycemic control among children and adolescents with T1DM
  233. Bioengineering and Biotechnology
  234. Biocompatibility and osteointegration capability of β-TCP manufactured by stereolithography 3D printing: In vitro study
  235. Clinical characteristics and the prognosis of diabetic foot in Tibet: A single center, retrospective study
  236. Agriculture
  237. Biofertilizer and NPSB fertilizer application effects on nodulation and productivity of common bean (Phaseolus vulgaris L.) at Sodo Zuria, Southern Ethiopia
  238. On correlation between canopy vegetation and growth indexes of maize varieties with different nitrogen efficiencies
  239. Exopolysaccharides from Pseudomonas tolaasii inhibit the growth of Pleurotus ostreatus mycelia
  240. A transcriptomic evaluation of the mechanism of programmed cell death of the replaceable bud in Chinese chestnut
  241. Melatonin enhances salt tolerance in sorghum by modulating photosynthetic performance, osmoregulation, antioxidant defense, and ion homeostasis
  242. Effects of plant density on alfalfa (Medicago sativa L.) seed yield in western Heilongjiang areas
  243. Identification of rice leaf diseases and deficiency disorders using a novel DeepBatch technique
  244. Artificial intelligence and internet of things oriented sustainable precision farming: Towards modern agriculture
  245. Animal Sciences
  246. Effect of ketogenic diet on exercise tolerance and transcriptome of gastrocnemius in mice
  247. Combined analysis of mRNA–miRNA from testis tissue in Tibetan sheep with different FecB genotypes
  248. Isolation, identification, and drug resistance of a partially isolated bacterium from the gill of Siniperca chuatsi
  249. Tracking behavioral changes of confined sows from the first mating to the third parity
  250. The sequencing of the key genes and end products in the TLR4 signaling pathway from the kidney of Rana dybowskii exposed to Aeromonas hydrophila
  251. Development of a new candidate vaccine against piglet diarrhea caused by Escherichia coli
  252. Plant Sciences
  253. Crown and diameter structure of pure Pinus massoniana Lamb. forest in Hunan province, China
  254. Genetic evaluation and germplasm identification analysis on ITS2, trnL-F, and psbA-trnH of alfalfa varieties germplasm resources
  255. Tissue culture and rapid propagation technology for Gentiana rhodantha
  256. Effects of cadmium on the synthesis of active ingredients in Salvia miltiorrhiza
  257. Cloning and expression analysis of VrNAC13 gene in mung bean
  258. Chlorate-induced molecular floral transition revealed by transcriptomes
  259. Effects of warming and drought on growth and development of soybean in Hailun region
  260. Effects of different light conditions on transient expression and biomass in Nicotiana benthamiana leaves
  261. Comparative analysis of the rhizosphere microbiome and medicinally active ingredients of Atractylodes lancea from different geographical origins
  262. Distinguish Dianthus species or varieties based on chloroplast genomes
  263. Comparative transcriptomes reveal molecular mechanisms of apple blossoms of different tolerance genotypes to chilling injury
  264. Study on fresh processing key technology and quality influence of Cut Ophiopogonis Radix based on multi-index evaluation
  265. An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology
  266. Erratum
  267. Erratum to “Protein Z modulates the metastasis of lung adenocarcinoma cells”
  268. Erratum to “BRCA1 subcellular localization regulated by PI3K signaling pathway in triple-negative breast cancer MDA-MB-231 cells and hormone-sensitive T47D cells”
  269. Retraction
  270. Retraction to “Protocatechuic acid attenuates cerebral aneurysm formation and progression by inhibiting TNF-alpha/Nrf-2/NF-kB-mediated inflammatory mechanisms in experimental rats”
https://doi.org/10.1515/biol-2022-0769
Keywords for this article
Atractylodes lancea; microbiology; medicinally active ingredients; metagenomics
Creative Commons
BY 4.0

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  134. Bioinformatics analysis to identify potential biomarkers for the pulmonary artery hypertension associated with the basement membrane
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  138. Bacterial lipoprotein plays an important role in the macrophage autophagy and apoptosis induced by Salmonella typhimurium and Staphylococcus aureus
  139. TCL1A+ B cells predict prognosis in triple-negative breast cancer through integrative analysis of single-cell and bulk transcriptomic data
  140. Ezrin promotes esophageal squamous cell carcinoma progression via the Hippo signaling pathway
  141. Ferroptosis: A potential target of macrophages in plaque vulnerability
  142. Predicting pediatric Crohn's disease based on six mRNA-constructed risk signature using comprehensive bioinformatic approaches
  143. Applications of genetic code expansion and photosensitive UAAs in studying membrane proteins
  144. HK2 contributes to the proliferation, migration, and invasion of diffuse large B-cell lymphoma cells by enhancing the ERK1/2 signaling pathway
  145. IL-17 in osteoarthritis: A narrative review
  146. Circadian cycle and neuroinflammation
  147. Probiotic management and inflammatory factors as a novel treatment in cirrhosis: A systematic review and meta-analysis
  148. Hemorrhagic meningioma with pulmonary metastasis: Case report and literature review
  149. SPOP regulates the expression profiles and alternative splicing events in human hepatocytes
  150. Knockdown of SETD5 inhibited glycolysis and tumor growth in gastric cancer cells by down-regulating Akt signaling pathway
  151. PTX3 promotes IVIG resistance-induced endothelial injury in Kawasaki disease by regulating the NF-κB pathway
  152. Pancreatic ectopic thyroid tissue: A case report and analysis of literature
  153. The prognostic impact of body mass index on female breast cancer patients in underdeveloped regions of northern China differs by menopause status and tumor molecular subtype
  154. Report on a case of liver-originating malignant melanoma of unknown primary
  155. Case report: Herbal treatment of neutropenic enterocolitis after chemotherapy for breast cancer
  156. The fibroblast growth factor–Klotho axis at molecular level
  157. Characterization of amiodarone action on currents in hERG-T618 gain-of-function mutations
  158. A case report of diagnosis and dynamic monitoring of Listeria monocytogenes meningitis with NGS
  159. Effect of autologous platelet-rich plasma on new bone formation and viability of a Marburg bone graft
  160. Small breast epithelial mucin as a useful prognostic marker for breast cancer patients
  161. Continuous non-adherent culture promotes transdifferentiation of human adipose-derived stem cells into retinal lineage
  162. Nrf3 alleviates oxidative stress and promotes the survival of colon cancer cells by activating AKT/BCL-2 signal pathway
  163. Favorable response to surufatinib in a patient with necrolytic migratory erythema: A case report
  164. Case report of atypical undernutrition of hypoproteinemia type
  165. Down-regulation of COL1A1 inhibits tumor-associated fibroblast activation and mediates matrix remodeling in the tumor microenvironment of breast cancer
  166. Sarcoma protein kinase inhibition alleviates liver fibrosis by promoting hepatic stellate cells ferroptosis
  167. Research progress of serum eosinophil in chronic obstructive pulmonary disease and asthma
  168. Clinicopathological characteristics of co-existing or mixed colorectal cancer and neuroendocrine tumor: Report of five cases
  169. Role of menopausal hormone therapy in the prevention of postmenopausal osteoporosis
  170. Precisional detection of lymph node metastasis using tFCM in colorectal cancer
  171. Advances in diagnosis and treatment of perimenopausal syndrome
  172. A study of forensic genetics: ITO index distribution and kinship judgment between two individuals
  173. Acute lupus pneumonitis resembling miliary tuberculosis: A case-based review
  174. Plasma levels of CD36 and glutathione as biomarkers for ruptured intracranial aneurysm
  175. Fractalkine modulates pulmonary angiogenesis and tube formation by modulating CX3CR1 and growth factors in PVECs
  176. Novel risk prediction models for deep vein thrombosis after thoracotomy and thoracoscopic lung cancer resections, involving coagulation and immune function
  177. Exploring the diagnostic markers of essential tremor: A study based on machine learning algorithms
  178. Evaluation of effects of small-incision approach treatment on proximal tibia fracture by deep learning algorithm-based magnetic resonance imaging
  179. An online diagnosis method for cancer lesions based on intelligent imaging analysis
  180. Medical imaging in rheumatoid arthritis: A review on deep learning approach
  181. Predictive analytics in smart healthcare for child mortality prediction using a machine learning approach
  182. Utility of neutrophil–lymphocyte ratio and platelet–lymphocyte ratio in predicting acute-on-chronic liver failure survival
  183. A biomedical decision support system for meta-analysis of bilateral upper-limb training in stroke patients with hemiplegia
  184. TNF-α and IL-8 levels are positively correlated with hypobaric hypoxic pulmonary hypertension and pulmonary vascular remodeling in rats
  185. Stochastic gradient descent optimisation for convolutional neural network for medical image segmentation
  186. Comparison of the prognostic value of four different critical illness scores in patients with sepsis-induced coagulopathy
  187. Application and teaching of computer molecular simulation embedded technology and artificial intelligence in drug research and development
  188. Hepatobiliary surgery based on intelligent image segmentation technology
  189. Value of brain injury-related indicators based on neural network in the diagnosis of neonatal hypoxic-ischemic encephalopathy
  190. Analysis of early diagnosis methods for asymmetric dementia in brain MR images based on genetic medical technology
  191. Early diagnosis for the onset of peri-implantitis based on artificial neural network
  192. Clinical significance of the detection of serum IgG4 and IgG4/IgG ratio in patients with thyroid-associated ophthalmopathy
  193. Forecast of pain degree of lumbar disc herniation based on back propagation neural network
  194. SPA-UNet: A liver tumor segmentation network based on fused multi-scale features
  195. Systematic evaluation of clinical efficacy of CYP1B1 gene polymorphism in EGFR mutant non-small cell lung cancer observed by medical image
  196. Rehabilitation effect of intelligent rehabilitation training system on hemiplegic limb spasms after stroke
  197. A novel approach for minimising anti-aliasing effects in EEG data acquisition
  198. ErbB4 promotes M2 activation of macrophages in idiopathic pulmonary fibrosis
  199. Clinical role of CYP1B1 gene polymorphism in prediction of postoperative chemotherapy efficacy in NSCLC based on individualized health model
  200. Lung nodule segmentation via semi-residual multi-resolution neural networks
  201. Evaluation of brain nerve function in ICU patients with Delirium by deep learning algorithm-based resting state MRI
  202. A data mining technique for detecting malignant mesothelioma cancer using multiple regression analysis
  203. Markov model combined with MR diffusion tensor imaging for predicting the onset of Alzheimer’s disease
  204. Effectiveness of the treatment of depression associated with cancer and neuroimaging changes in depression-related brain regions in patients treated with the mediator-deuterium acupuncture method
  205. Molecular mechanism of colorectal cancer and screening of molecular markers based on bioinformatics analysis
  206. Monitoring and evaluation of anesthesia depth status data based on neuroscience
  207. Exploring the conformational dynamics and thermodynamics of EGFR S768I and G719X + S768I mutations in non-small cell lung cancer: An in silico approaches
  208. Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer
  209. Incidence of different pressure patterns of spinal cerebellar ataxia and analysis of imaging and genetic diagnosis
  210. Pathogenic bacteria and treatment resistance in older cardiovascular disease patients with lung infection and risk prediction model
  211. Adoption value of support vector machine algorithm-based computed tomography imaging in the diagnosis of secondary pulmonary fungal infections in patients with malignant hematological disorders
  212. From slides to insights: Harnessing deep learning for prognostic survival prediction in human colorectal cancer histology
  213. Ecology and Environmental Science
  214. Monitoring of hourly carbon dioxide concentration under different land use types in arid ecosystem
  215. Comparing the differences of prokaryotic microbial community between pit walls and bottom from Chinese liquor revealed by 16S rRNA gene sequencing
  216. Effects of cadmium stress on fruits germination and growth of two herbage species
  217. Bamboo charcoal affects soil properties and bacterial community in tea plantations
  218. Optimization of biogas potential using kinetic models, response surface methodology, and instrumental evidence for biodegradation of tannery fleshings during anaerobic digestion
  219. Understory vegetation diversity patterns of Platycladus orientalis and Pinus elliottii communities in Central and Southern China
  220. Studies on macrofungi diversity and discovery of new species of Abortiporus from Baotianman World Biosphere Reserve
  221. Food Science
  222. Effect of berrycactus fruit (Myrtillocactus geometrizans) on glutamate, glutamine, and GABA levels in the frontal cortex of rats fed with a high-fat diet
  223. Guesstimate of thymoquinone diversity in Nigella sativa L. genotypes and elite varieties collected from Indian states using HPTLC technique
  224. Analysis of bacterial community structure of Fuzhuan tea with different processing techniques
  225. Untargeted metabolomics reveals sour jujube kernel benefiting the nutritional value and flavor of Morchella esculenta
  226. Mycobiota in Slovak wine grapes: A case study from the small Carpathians wine region
  227. Elemental analysis of Fadogia ancylantha leaves used as a nutraceutical in Mashonaland West Province, Zimbabwe
  228. Microbiological transglutaminase: Biotechnological application in the food industry
  229. Influence of solvent-free extraction of fish oil from catfish (Clarias magur) heads using a Taguchi orthogonal array design: A qualitative and quantitative approach
  230. Chromatographic analysis of the chemical composition and anticancer activities of Curcuma longa extract cultivated in Palestine
  231. The potential for the use of leghemoglobin and plant ferritin as sources of iron
  232. Investigating the association between dietary patterns and glycemic control among children and adolescents with T1DM
  233. Bioengineering and Biotechnology
  234. Biocompatibility and osteointegration capability of β-TCP manufactured by stereolithography 3D printing: In vitro study
  235. Clinical characteristics and the prognosis of diabetic foot in Tibet: A single center, retrospective study
  236. Agriculture
  237. Biofertilizer and NPSB fertilizer application effects on nodulation and productivity of common bean (Phaseolus vulgaris L.) at Sodo Zuria, Southern Ethiopia
  238. On correlation between canopy vegetation and growth indexes of maize varieties with different nitrogen efficiencies
  239. Exopolysaccharides from Pseudomonas tolaasii inhibit the growth of Pleurotus ostreatus mycelia
  240. A transcriptomic evaluation of the mechanism of programmed cell death of the replaceable bud in Chinese chestnut
  241. Melatonin enhances salt tolerance in sorghum by modulating photosynthetic performance, osmoregulation, antioxidant defense, and ion homeostasis
  242. Effects of plant density on alfalfa (Medicago sativa L.) seed yield in western Heilongjiang areas
  243. Identification of rice leaf diseases and deficiency disorders using a novel DeepBatch technique
  244. Artificial intelligence and internet of things oriented sustainable precision farming: Towards modern agriculture
  245. Animal Sciences
  246. Effect of ketogenic diet on exercise tolerance and transcriptome of gastrocnemius in mice
  247. Combined analysis of mRNA–miRNA from testis tissue in Tibetan sheep with different FecB genotypes
  248. Isolation, identification, and drug resistance of a partially isolated bacterium from the gill of Siniperca chuatsi
  249. Tracking behavioral changes of confined sows from the first mating to the third parity
  250. The sequencing of the key genes and end products in the TLR4 signaling pathway from the kidney of Rana dybowskii exposed to Aeromonas hydrophila
  251. Development of a new candidate vaccine against piglet diarrhea caused by Escherichia coli
  252. Plant Sciences
  253. Crown and diameter structure of pure Pinus massoniana Lamb. forest in Hunan province, China
  254. Genetic evaluation and germplasm identification analysis on ITS2, trnL-F, and psbA-trnH of alfalfa varieties germplasm resources
  255. Tissue culture and rapid propagation technology for Gentiana rhodantha
  256. Effects of cadmium on the synthesis of active ingredients in Salvia miltiorrhiza
  257. Cloning and expression analysis of VrNAC13 gene in mung bean
  258. Chlorate-induced molecular floral transition revealed by transcriptomes
  259. Effects of warming and drought on growth and development of soybean in Hailun region
  260. Effects of different light conditions on transient expression and biomass in Nicotiana benthamiana leaves
  261. Comparative analysis of the rhizosphere microbiome and medicinally active ingredients of Atractylodes lancea from different geographical origins
  262. Distinguish Dianthus species or varieties based on chloroplast genomes
  263. Comparative transcriptomes reveal molecular mechanisms of apple blossoms of different tolerance genotypes to chilling injury
  264. Study on fresh processing key technology and quality influence of Cut Ophiopogonis Radix based on multi-index evaluation
  265. An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology
  266. Erratum
  267. Erratum to “Protein Z modulates the metastasis of lung adenocarcinoma cells”
  268. Erratum to “BRCA1 subcellular localization regulated by PI3K signaling pathway in triple-negative breast cancer MDA-MB-231 cells and hormone-sensitive T47D cells”
  269. Retraction
  270. Retraction to “Protocatechuic acid attenuates cerebral aneurysm formation and progression by inhibiting TNF-alpha/Nrf-2/NF-kB-mediated inflammatory mechanisms in experimental rats”
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