Bacillus Intervention: Microbial Ecological Mechanisms for Controlling Root Rot in Coptis chinensis Franch

doi:10.21203/rs.3.rs-3933598/v1

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Research Article

Bacillus Intervention: Microbial Ecological Mechanisms for Controlling Root Rot in Coptis chinensis Franch

https://doi.org/10.21203/rs.3.rs-3933598/v1

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Background:

Coptis root rot (CRR) poses a significant threat to the yield and medicinal quality of Coptis chinensis (Cc), primarily attributed to the presence of Fusarium. This study investigates the potential of four Rhizosphere Bacillus bacteria as biological control agents to combat CRR. These bacteria, namely B. mycoides LB-021, B. pseudomycoides YEM-005, B. velezensis JM-1, and B. subtilis TR-064, were sourced from the rhizosphere of Cc roots. While their antagonistic efficacy has been demonstrated in controlled environments, the translation of their capabilities to field conditions and their impact on the microecological balance within Coptis roots require further exploration.

Results:

Bacillus introduction significantly reconfigures Cc root microbial communities, simplifying the network. Genera enrichment (Arthrobacter, Sphingobium, Pseudomonas, etc.) and Flavobacterium/Gemmatimonas reduction promote plant growth, disease resistance, and soil health. Bacillus triggers antibiotic synthesis (ansamycin, macrolides, etc.), fortifying plant defence against pathogens. Correlations with transcriptome/metabolome highlight Bacillus's influence on root genetics/chemistry. KEGG analysis reveals Bacillus impact on critical plant metabolic pathways.

Conclusions

In conclusion, this study delves into the dynamic relationship between plants and microorganisms in their natural environment, specifically focusing on the role of microbial communities in the rhizosphere. The introduction of Bacillus has a profound impact on the composition and behaviour of the root microbial community, enriching beneficial genera and inducing the synthesis of antibiotics and metabolites that enhance the defence ability of plants. The research emphasizes Bacillus's pivotal role in shaping molecular and metabolic responses, suggesting its eco-friendly potential in enhancing plant disease resistance as an alternative to chemical pesticides.

Bacillus intervention

Coptis root rot

Pre-protection

Repair

Microecology mechanism

Coptis chinensis (Cc) is a prominent source of alkaloids, including berberine, palmatine, coptisine, epiberberine, jatrorrhizine, columbamine, and magnoflorine. These alkaloids constitute the primary compounds in C. chinensis, which boasts a significant historical legacy as a traditional Chinese medicinal herb spanning over two millennia. However, the plant faces susceptibility to root rot, a soil-borne disease attributed to a potent plant-pathogenic fungus. This malady induces brown discoloration or necrosis in fibrous roots and rhizomes, culminating in wilting and eventual demise of the plant [1]. Beyond the evident impact on plant vitality, root rot detrimentally interferes with the plant's medicinal properties[2, 3], posing a formidable hindrance to its cultivation and overall industry advancement in China[4].

Root rot presents a significant global threat to agriculture, consistently reducing yields and jeopardizing crop survival[5]. The extent of damage is influenced by factors such as the causative agent, host susceptibility, and environmental conditions, potentially leading to the complete loss of fields. The intricacies of root rot diseases are intricately linked to environmental factors, involving a wide range of hosts and concealing underground symptoms and overwinter structures of numerous root rot pathogens. Effectively managing and controlling these diseases is challenging due to the complexity arising from dynamic interactions between the host, pathogen, and environment, forming what is commonly known as the disease triangle. The progression and success of infections rely on the delicate balance within this triangle. Chemical treatments, primarily used as preventive seed treatments for specific root rots or to eliminate the green bridge between crops, represent one of the limited options for intervention in this complex agricultural challenge. To address this challenge, current research explores beneficial microorganisms, such as bacteria and fungi, as eco-friendly approaches to control root rot diseases in plants [6]. This research aims to provide sustainable and environmentally friendly alternatives to combat root rot, contributing to the development of effective strategies for disease management in agriculture.

Despite the importance of Cc, a need for environmentally sustainable techniques for eradicating root rot prevails. In natural ecosystems[7], the intricate interplay among plants, symbiotic microorganisms, and pathogens is a pivotal mechanism for aiding plants in mitigating biotic and abiotic stresses[8, 9]. Analogous to the gut ecosystem[10], the rhizosphere microbiome is closely associated with the host's developmental stage[11] and nutrient uptake dynamics[12]. Harnessing the host root microbiota to combat disease offers several merits compared to alternative microbial disease control strategies. It establishes enduring symbiotic rapport with host plants and bolsters their disease-resistance capacity [13, 14]. Furthermore, this approach engenders persistent disease control effects while aligning with environmentally friendly requisites, thus aligning with the tenets of sustainable agricultural progress [15].

In a preceding investigation, we meticulously examined the diversity and functions of the rhizosphere microbiome in healthy and diseased Cc specimens via high-throughput sequencing [3]. Intriguingly, we observed an inverse correlation between Bacillus and the CRR pathogen Fusarium. Of note, four Bacillus bacterial strains from the healthy Cc exhibited discernible antagonistic properties against pathogenic fungi, such as F. sonani and F. avenaceum, which are responsible for CRR.

However, the translatability of these Bacillus strains' antagonistic attributes to field conditions, effectively curbing the onset of CRR, remains a puzzle. Moreover, the effectiveness of artificially introducing Bacillus to curtail the proliferation of Fusarium fungi and reinstating the microecological equilibrium of Coptis roots warrants elucidation. Hence, this study's primary goal is to ascertain Bacillus's efficacy in averting or ameliorating Cc root rot and to conjecture its underlying microecological mechanisms. Our investigation will examine shifts in microbial communities and functions alongside the host's transcriptome and metabolome responses to diverse treatments to shed light on these enigmas.

In vitro compatibility test of 4 Bacillus

An in vitro compatibility test was conducted involving 4 Bacillus strains (B. mycoides GJ-LB-021, B. pseudomycoides GJ-YEM-005, B. velezensis GJ-JM-1, and B. subtilis GJ-TR-064) which have antagonism potential against Fusarium solani (Fs) [3]. The experiment was performed on Nutrient Agar (NA) plates. Initially, 10 mL of NA media was poured into Petri plates. Subsequently, 500 µL of each overnight bacterial culture was added to 10 mL of semi-solid NA media [16], gently mixed, and poured onto a pre-prepared NA plate marked as the indicator bacterial strain. The tested strains were then inoculated (3 − 4 µL; overnight culture) at the centre of the plate, and this process was reciprocated. The plates were incubated at 37°C for 24 hours, following which the bacterial growth was evaluated. Compatibility between bacterial strains was determined based on the presence or absence of a clear zone surrounding each bacterial strain, indicating non-compatibility and compatibility,

Preparation of experimental microorganism liquid

Preparation of Fusarium solani (Fs) Spore Suspension:

The Fs fungi, stored in the refrigerator, were chosen and cultured on Potato Dextrose Agar Medium (PDA) using a line dilution method. Following a three-day incubation in darkness, single colony mycelia were transferred to Potato Dextrose Broth Medium (PDB). After four days of incubation, the culture medium was filtered through four layers of sterilized microscope paper to obtain the filtrate. The Fs spores were then harvested by centrifugation at 6000rpm for 3 minutes and washed twice with sterile PBS buffer(2.7 M NaCl, 54 mM KCl, 87 mM Na₂HPO₄, 30 mM KHPO₄, pH 7.4). The collected spores were dispersed in a sterile PBS buffer, and the spore suspension concentration was adjusted to 1 × 10⁷ cfu/mL.

Preparation of Biological Control Agent Liquid (BCA):

4 Bacillus strains, preserved in the refrigerator and exhibiting good compatibility, were selected for this process. They were inoculated on Nutrient Agar Medium (NA) using a scribing dilution method and cultured overnight at 37°C. A single colony from each was then inoculated into Nutrient Broth Medium (NB) and cultured overnight in a shaker incubator (37°C, 120 rpm). The thalli were harvested by centrifugation at 6000 rpm for 3 minutes and washed twice with sterile PBS buffer. Subsequently, the bacterial liquid's optical density at 600 nm (OD₆₀₀) was measured using a UV spectrophotometer. The OD₆₀₀ value of the bacterial liquid was adjusted to 1 using sterile PBS, and the four types of bacteria were mixed in equal proportions.

Collection and treatment of experimental materials

The field experiment was conducted on healthy Coptis chinensis (Cc) plants a year after transplantation, with official authorization from the Cc cultivation bases of Wawushan Pharmaceutical Co., Ltd., located in Hongya County. The specific site was situated at Group 2, Heishan Village, Gaomiao Town, Hongya County, Meishan City, Sichuan Province, China (coordinates: 29°29′10.91″ N, 103°9′39.9″ E) meticulously divided the experimental plot into twelve distinct sections, organized in a 4-line by 3-row grid. Each section accommodated approximately 180 to 200 healthy Coptis plants. A subset of these plants was designated as the blank control group (CK group) to establish the baseline.

The twelve plots were randomly divided into two main groups. In one group, Fusarium solani (Fs) and the Rhizosphere Bacillus bacteria (BCA) were introduced using root border irrigation method [17], with six plots assigned to this group. After seven days of inoculation (DPI, day past inoculation), 15 plants were randomly selected from each treatment group to assess the disease index. Five plants from each plot, including roots and adhering rhizosphere soil, were collected randomly for microbiome analysis. Another set of 5 plants had their roots cleaned with sterilized water and was collected for subsequent transcriptome and metabolomic analysis, labelled as Fs7d and BCA7d.

The remaining plants from the Fs7d and BCA7d groups underwent additional treatment, receiving root irrigation with BCA liquid and Fs liquid, each at a dosage of 10 ml per plant. This process was repeated for another seven days. Samples resulting from these treatments were categorized into two groups: Fs7dBCA7d and BCA7dFs7d. All samples were meticulously collected in 5mL cryopreservation tubes, rapidly frozen in liquid nitrogen, and stored in a refrigerator at -80°C for subsequent analysis.

Determination of disease index of Cc root rot in different treatments

The investigation method for Cc root rot disease was refined based on relevant literature [18], 15 plants were randomly chosen to observe the incidence of Cc root rot and calculate the disease index. The disease was categorized into five grades:

Grade 0: Rhizomes and fibrous roots were intact without any disease spots.

Grade 1: Scattered disease spots on fibrous roots.

Grade 2: Less than a quarter of the fibrous roots showed slight infection, and the leaves did not wilt.

Grade 3: Infection covered more than a quarter but less than half of the fibrous roots, and there was no wilting of leaves.

Grade 4: More than half but less than three-quarters of the fibrous roots were infected, and less than a quarter of the leaves began to wilt.

Grade 5: Over three-quarters of the fibrous roots were infected, rhizomes rotted, and over half of the leaves withered.

The disease index was calculated using the following formula:

$$\text{D}\text{i}\text{s}\text{e}\text{a}\text{s}\text{e} \text{I}\text{n}\text{d}\text{e}\text{x}=\frac{\sum \text{g}\text{r}\text{a}\text{d}\text{e}\times \text{n}\text{u}\text{m}\text{b}\text{e}\text{r} \text{o}\text{f} \text{p}\text{l}\text{a}\text{n}\text{t}\text{s} \text{o}\text{f} \text{t}\text{h}\text{a}\text{t} \text{g}\text{r}\text{a}\text{d}\text{e}}{\text{T}\text{h}\text{e} \text{h}\text{i}\text{g}\text{h}\text{e}\text{s}\text{t} \text{n}\text{u}\text{m}\text{b}\text{e}\text{r} \text{o}\text{f} \text{g}\text{r}\text{a}\text{d}\text{e} \times \text{T}\text{o}\text{t}\text{a}\text{l} \text{n}\text{u}\text{m}\text{b}\text{e}\text{r} \text{o}\text{f} \text{p}\text{l}\text{a}\text{n}\text{t}\text{s}}\times 100\%$$

Microbiome profiling of Cc roots

The total genomic DNA was extracted using the CTAB (Hexadecyl trimethyl ammonium Bromide) method [19]. The concentration and purity of DNA were detected with 1% agarose gel and Nanodrop (Bio-Rad, USA), then the DNA was diluted to 1 ng/µL with sterile water.

The 16S v3-v4 region of bacteria was amplified with the universal primers 338F:5'-ACTCCTACGGGAGGCAGCAG-3' and 806R:5'-GGACTACHVGGGTWTCTAAT-3' [20]. The ITS2 region of fungi was amplified with the universal primers ITS3_KYO2: 5'-GATGAAGAACGYAGYRAA-3' and ITS4༚5'-TCCTCCGCTTATTGATATGC-3' [21]. All PCR reactions were performed using 15 µL Phusion® High fidelity PCR (New England Biolabs, USA), 2 µM forward and reverse primers, and about 10 ng template DNA. The PCR procedure included initial denaturation at 98 ℃ for 1 min, followed by denaturation at 98 ℃ for 30 times, annealing at 50 ℃ for 30 s, and elongation at 72 ℃ for 30 s. The final temperature was 72 ℃ for 5 min.

Amplicons were extracted from 2% agarose gels and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, U.S.) according to the manufacturer's instructions and quantified using an ABI StepOnePlus Real-Time PCR System (Life Technologies, Foster City, USA). Purified amplicons were pooled in equimolar amounts and paired-end sequenced (2×250) on an Illumina NovaSeq platform according to standard protocols [22]. The raw reads were deposited into the NCBI Sequence Read Archive (SRA) database (Accession Number: PRJNA719928 (16S) and PRJNA720444 (ITS)).

The Illumina NovaSeq platform was used to identify the questionable sequence, USEARCH and VEARCH 2.14 merged the double-ended sequences, and the primers and quality control were removed. The sequences were de-redundant, OTU was generated according to 97% similarity, and the effective sequence of each sample was obtained. We called USEARCH for annotating species, 16S rDNA V3-V4 region sequence based on Silva (Version 138) [23, 24] and the ITS2 region sequence based on UNITE (Version 04.02.2020) [25]. After removing chimera sequences and annotating species, we removed chloroplasts and mitochondria, and the host ITS2 sequence and tag number did not exceed 5 sequences in total.

The Shannon α-diversity index was calculated in QIIME (version 1.9.1). The R project “ggplot2” package [26] (version 2.2.1) was used to draw a histogram. The Shannon index between the two groups was analyzed by Welch's T-test [27]. Muscle [28] (version 3.8.31) was used for sequence alignment, FastTree [29] (version 2.1) was used to construct a phylogenetic tree, and then we performed Bray-Curtis in the R project “Vegan” package [27] (version 2.3.5). Multivariate statistical analysis of PCoA and NMDS of Bray-Curtis distance was performed and plotted in the “ggplot2” package [26] with Welch's T-test.

PICRUSt2 [30, 31]( version 2.4.1) was used to predict the functional genes of the microbial community community function. The statistical analysis of STAMP software [32] (version2.1.3) was used to detect the significant difference in the abundance of functional genes/pathways. Welch's T-test (P < 0.05) and Benjamini-Hochberg's false discovery rate (FDR) multiple test correction were applied to generate extended error bar graphs or draw statistical stacked shock graphs.

The genera with relative abundances greater than 1% identified in the rhizosphere and endosphere of healthy and diseased Coptis plants were selected. We merged each group of fungi and bacteria and used the CoNet plug-in [33] of Cytoscape (v3.8.0) software [34, 35] for network analysis. The automatic threshold method was used to construct a microbial interaction network. A permutation test was performed to obtain the p-value and filter out invalid edges with a p-value greater than 0.01. To calculate the statistical significance of co-occurrence and mutual exclusion, we calculated the edge-specific permutation and bootstrap score distribution for 1000 iterations to obtain the final network [36] and visualized the network through Cytoscape (v3.8.0).

The fungi and bacteria species identified to genus level in each group were combined. The relative abundance of species was tested by ANOVA using the STAMP software (version 2.1.3) [32], and a bar chart was drawn.

Transcriptome profiling of Cc roots

Total RNA was extracted following the procedures described by Logemann et al. [37]. Frozen plant tissue was pulverized inside a 2 mL tube containing three 4 mm glass beads using a Qiagen TissueLyser II instrument. Samples were homogenized in 400 µL of Z6-buffer (8 M guanidinium- HCl, 20 mM MES, 20 mM EDTA, pH 7.0). Following the addition of 400 µL phenol: chloroform: isoamyl alcohol (25:24:1), samples were vortexed and centrifuged (20,000 g, 10 min) for phase separation. The aqueous phase was transferred to a new 1.5 mL tube, and 0.05 volumes of 1 N acetic acid and 0.7 volumes of 96% ethanol were added. The RNA was precipitated at − 20°C overnight. Following centrifugation (20,000 g, 10 min, 4°C), the pellet was washed with 200 µL sodium acetate (pH 5.2) and then 70% ethanol. The RNA was dried and dissolved in 30 µL of ultrapure water and stored at − 80°C until use.

Illumina mRNA-seq libraries were prepared from 1,000 ng RNA using the protocol described by Castrillo et al. [38]. Briefly, mRNA was purified from total RNA using Sera-mag oligo(dT) magnetic beads (GE Healthcare Life Sciences) and then fragmented in the presence of divalent cations (Mg2+) at 94 ℃ for 6 min. The resulting fragmented mRNA was used for first-strand cDNA synthesis using random hexamers (ThermoFisher Scientific) and the EnzScript reverse transcriptase (Enzymatics), followed by second-strand cDNA synthesis using DNA polymerase I (Enzymatics) and RNaseH (Enzymatics). Double-stranded cDNA was end-repaired using T4 DNA polymerase (Enzymatics), T4 poly-nucleotide kinase (Enzymatics), and Klenow polymerase (Enzymatics). The DNA fragments were then adenylated using Klenow exo-polymerase (Enzymatics) to allow the ligation of Illumina Truseq HT adapters. Following library preparation, quality control and quantification were performed using a 2100 Bioanalyzer instrument (Agilent) and the Quant-iT PicoGreen dsDNA Reagent (ThermoFisher Scientific). Libraries were sequenced on the Illumina Novaseq instrument to generate 50-bp single-end reads.

After CASAVA base recognition, all the high-throughput sequencing image data were transformed into sequence data (reads) to get a fastq file. Trim-galore (version 0.6.5) software removed the adapter in the sequence. After removing the low-quality and N-containing sequences, the sequences shorter than the length threshold (20bp) were removed. The software used is fastqc (version 0.11.9), and fastx_ Toolkit (version 0.0.14) removes low-quality fragments through data preprocessing. Use the qualimap [39] (version 2.2.1) to make statistics on the results of reads mapping and calculate the counts for simple follow-up analysis. This study's raw transcriptome sequence data were available at the National Center for Biotechnology Information Sequence Read Archive, project number PRJNA720442.

HISAT2 [40] (version 2.2.0) was used to compare the sequence with reference genome JADFTS000000000.1 (https://www.ncbi.nlm.nih.gov/nuccore/1936226600) [41]. The location information of the sequence on the reference genome was obtained. After comparing the quality control data to the reference genome, the alignment rate was calculated. Only the sequences that can be aligned to the reference genome independently (once) could participate in the subsequent analysis.

Stringtie [42] (version 2.1.2) software was used for transcriptional assembly and quantitative analysis. According to the annotation information of gene location in the genome, the number of sequences covered by each gene from start to end was counted. After a series of normalization treatments, the gene expression was obtained [43].

The difference in gene expression was analyzed by using the software DESeq2 (version 1.16.1). The difference of fold change (FC) was more than twice, and the p-value value of the test was less than 0.05 [44, 45]. KOBAS (http://kobas.cbi.pku.edu.cn/) was used to analyze KEGG pathway enrichment of different gene sets [46].

The cDNA was obtained by reverse transcription–polymerase chain reaction using a PrimeScript RT Reagent Kit (Takara Biotechnology Co., Ltd.). The qRT-PCR was performed with TB Green™ Premix EX Taq™ (Takara) using an ABI Quantstudio™ 3D digital PCR system (Life Technologies, Carlsbad, CA). The primers for qRT-PCR are listed in Supplementary Table S4. Transcription levels were calculated relative to the reference gene actin using the 2^-ΔΔCT method reported previously [47].

Metabolome profiling of Cc roots

About 100 mg Cc root tissue samples from each group were harvested, weighed, ground with deep-frozen liquid nitrogen, put into an EP tube, and then extracted with methanol/water (80/20, v/v). A certain amount of supernatant was diluted with mass spectrometry grade water until the methanol content was 53%, then the supernatant was collected (15000 g, 4℃, 20mini) and analyzed by LC-MS [48]. Blank sample: 53% methanol-water solution instead of the experimental sample, the pretreatment process is the same as the experimental sample. 20µl was taken from each sample and mixed well as a quality control (QC) sample. During the test, 1 QC sample was inserted in every 8 analysis samples to test the repeatability of the whole analysis process.

A Q Exactive™ HF MS (Thermo Fisher, Germany) was used in the flow-injection mode (a Vanquish UHPLC Infinity from Thermo Fisher, Germany, was used). A Hypesil Gold C18 reverse-phase column (100×2.1 mm, 1.9µm particle size, Thermo Fisher, USA). Positive mode binary gradient of transport eluent A (0.1% formic acid) and transport eluent B (methanol), and negative mode binary gradient of transport eluent A (5 mM ammonium acetate (pH9.0)) and transport eluent B (methanol) with the same chromatographic gradient elution procedure (0 to 1.5min, hold at 98%; 1.5 to 12.0min, 2–100%B; 12.0 to 14.0 min, hold at 100%B; 14.0 to 14.1 min, 100–2% B; 14.1 to 17.0 min, hold at 98%. Column temperature: 40 ℃. Flow rate: 0.2 mL/min.). The scanning range was selected as m/z 100–1500; the settings for the ESI source were as follows: Spray Voltage:3.2kV; Sheath gas flow rate:40arb; Aux flow rate:10arb; Capillary Temp:320 ℃. Polarity: positive; negative; MS/MS secondary scan: data-dependent scans.

The off-machine data (. Raw) file was imported into CD 3.1 search software for processing, and each metabolite's retention time, mass charge ratio, and other parameters were screened. Then, the retention time deviation of 0.2 min and mass deviation of 5 ppm were set to align the peaks of different samples to make the identification more accurate. Then, the mass deviation of 5 ppm, signal intensity deviation of 30%, signal-to-noise ratio of 3, minimum signal intensity, and minimum signal intensity of 5 ppm were set simultaneously, the peak area was quantified, and then the target ions were integrated. Then, the molecular formula was predicted by molecular ion peak and fragment ions and compared with mzcloud (https://www.mzcloud.org/). The background ions were removed with blank samples, and the original quantitative results were standardized. Finally, the identification and relative quantitative results of metabolites were obtained.

For metabolite analyses, statistical analyses were performed in R using the MetaboAnalystR package [49] (version 3.0). OPLS-DA analysis was also performed in the same way using the MetaboAnalystR package. Differential gene expression patterns were assessed using the Bio-conductor package DESeq2 [45](version 1.18.1), with the P value for the Benjamini-Hochberg false discovery threshold being adjusted to 0.05 or less and the log2 fold-change ratio to 1 or greater. The metabolic pathway of each compound was identified by the MetaboAnalyst (www.metaboanalyst.ca/) analysis platform. Then, the metabolic pathway analysis of differentially identified proteins was conducted according to the KEGG Pathway Database [50] (http://www.genome.jp/kegg/), representing knowledge in molecular interactions and reaction networks.

Weighted gene correlation network analysis

A mantel test was performed using the Pearson correlation coefficient to establish connections with multi-omics data. The Spearman correlation coefficient was used to analyze the correlations between regularly varying microbial genera and metabolome and transcriptome enzymes with a threshold of P < 0.05.

To identify the interactions between the plant microbes, the Weighted gene coexpression network analysis (WGCNA) [51], a systematic biology method to describe the coexpression patterns between genes (or metabolites), was used to determine the cooccurrence modules among the host microbes at the genus level of regularly changing genus, which was performed and visualized using the online platform imageGP [52]. Soft thresholding power was calculated to dynamically prune branches off the dendrogram dynamically depending on the cluster’s shape. The adjacency matrix was transformed into a Topological Overlap Matrix (TOM) to minimise spurious associations during module identification. Then, the tax in the identified modules was mapped into the interactions for constructing the cooccurrence networks of regularly changing genus communities. A correlation analysis was conducted on the module eigengene with specific data for each genus. The Pearson correlation coefficient between each gene (or metabolite) and genus was also calculated for the most relevant module (positive correlation and negative correlation) corresponding to each genus, and the gene (or metabolite) value was obtained.

Quantitative real-time PCR validation. To validate the RNA-seq results, 10 DEGs were selected and analyzed by quantitative real-time polymerase chain reaction (RT-qPCR). All primers were designed in Primer version 5.0 software, and their sequences are listed in Supplementary Table S4. An 800-ng aliquot of the same RNA used for RNA-seq was reverse-transcribed into first-strand cDNAs with HiScript II QRT SuperMix for qPCR (Tsingke Biotech Co., Ltd. Beijing, China). RT-qPCR was performed in 2×RealStar Green Fast Mix (BioRad, Shanghai, China) on a CFX96 Touch Real-Time PCR system (Thermo Fisher Scientific). The 18S gene of Cc was used as an internal reference for normalization. Relative transcript levels were calculated with the 2^–∆∆C_T method [15].

Four strains of Bacillus showed good compatibility and controlled the root rot of Coptis chinensis

The absence of inhibition zones across the strains (Fig. 1) indicates their harmonious compatibility. This observation establishes the groundwork for amalgamating them into a composite bacterial solution, which can pave the way for more advanced rounds of experimentation.

The disease index of Cc root rot after treatment with various microorganisms for 7 days was performed. The findings revealed that a specific quantity of Fs led to mild root rot with a disease index of 6.67%, whereas BCA did not cause any disease. Inoculation of Fs after BCA or BCA after Fs infection was observed to decrease the disease index of Cc root rot to some extent (with the same disease index of 4.00%).

The composition of the root microbial community changed significantly

High-throughput sequencing technology was used to remove low-quality barcode and primer sequences from the obtained sequences. The resulting sequences were combined with two ends, and the primers and redundant sequences were eliminated. Sequences with tag numbers less than 10ten were also removed to generate ASVs. The silva138 was used to remove chimaeras. The 16S sequence was annotated using the Silva 16S v123.fa database and the sequence was annotated according to unit 8.2 databases. Sequences annotated as eukaryotes, chloroplast, and mitochondria were eliminated, resulting in 9422 16S ASVs. These ASVs were distributed among the CK group, BCA7d group, Fs7d group, BCA7dFs7d group, and Fs7dBCA7d group. The specific ASV numbers were 65, 64, 71, 57, and 89, respectively (Fig. 3A). Furthermore, 1762 additional ASVs were produced, including 1563, 1560, 1416, 1508, and 1522 ASVs in the CK group, BCA7d group, Fs7d group, BCA7dFs7d group, and Fs7dBCA7d group, respectively. The specific ASV numbers for each group were 32, 1, 2, 7, and 2, respectively (Fig. 3B). Principal component analysis (PCA) showed some differences among groups. (Fig. 3C, Fig. 3D).

Species annotation identified 34 bacterial phyla, with the top 20 species by abundance rank shown in Fig. 3E. Proteobacteria, Actinobacteria, and Acidobacteria were the top three species, accounting for over 80% of the identified phyla. The top 10 species included Chloroflex, Bacteroidetes, Nitrospirae, Gemmatimonades, Firmicutes, Verrucomicrobia, and Latesicibacteria, accounting for over 97% of the identified phyla. Nine fungal phyla were also identified, as shown in Fig. 3F. The proportion of species not identified to the phylum level varied widely from 18–48.4%. Ascomycota, Basidiomycota, and Glomeromycota were the top three fungal phyla, accounting for 49.92–81.26% of the identified phyla.

Shannon index was used to evaluate the α-diversity of root bacteria community of Cc in different treatments. It was found that there was no significant difference in the α-diversity of the bacteria communities among Fs7d, BCA7d, Fs7dBCA7d, and CK. Bacterial communities α-diversity (Fig. 4A) of BCA7dFs7d, compared with Fs7d and Fs7dBCA7d, increased significantly (P < 0.05), compared with CK increased highly significant (P < 0.01). Fungal community α-diversity (Fig. 4B) of Fs7d significantly increased (P < 0.05) compared to CK. Compared to BCA7d and CK, the diversity of BCA7dFs7d decreased significantly (P < 0.05). There was no significant difference in fungal community α-diversity among BCA7d, Fs7dBCA7d, and CK.

Principal Coordinates Analysis (PCoA) and Nonmetric Multidimensional Scaling (NMDS) analysis based on Bray-Curtis distance (Fig. 4C, D, E, F) revealed that the microbial community β-diversity of BCA7dFs7d was significantly different from that of other treatments. However, there was no significant difference among other treatments.

The network of root microbial community changed significantly

The node size shows the relative abundance of the species in the corresponding fungal or bacterial kingdom; The node color represents the corresponding phylum of the node; The color of the edge represents the mode of interaction type (red represents mutual exclusion, green represents copresence), and the thickness of the edge represents the intensity of action.

The construction of microbial networks for distinct microbial genera groups was facilitated by utilizing the CoNet plug-in within the Cytoscape 3.8.0 platform. Several preliminary steps were undertaken to establish these networks. Firstly, Amplicon Sequence Variants (ASVs) that could not be confidently classified to the genus level were excluded from consideration. Additionally, ASVs attributed to the same genus were merged to enhance clarity and coherence. In alignment with stringent criteria, fungal and bacterial genera accounting for less than 0.3% of relative abundance within each group were excluded from the subsequent analysis. The resultant microbial network is visually depicted in Fig. 5, while the intricate attributes of each microbial network group are meticulously documented in Supplementary Table S2.

Within the control group (CK), 110 distinct genera were identified, linked by 830 edges. In contrast, the BCA7d group exhibited 102 genera intricately connected by 240 edges. The Fs7d group displayed 110 genera interlinked through 1072 edges. The hybrid BCA7dFs7d group manifested 103 genera intertwined by 316 edges, while the Fs7dBCA7d group showcased 93 genera interconnected by 416 edges. These enumerations underscore the distinctive complexities inherent in each microbial network, furthering our comprehension of their respective compositions and interactions.

Compared to the CK group, the BCA7d group exhibited a reduced average number of neighbours, network density, characteristic path length, and clustering coefficient. However, this trend shifted following the introduction of Fs inoculation, with these network metrics demonstrating an increase 7 days after that. These findings underscore the notable influence of BCA and Fs introduction on the intricate structure and connectivity of the root microbiome's microbial network.

Microorganisms' Influence on Cc roots: Transcriptome & Metabolic Changes

The MA diagram portraying the transcriptome divergence analysis of Cc roots subjected to various microorganism treatments is presented in Fig. 6 (left). Notably, the outcomes reveal the pattern: 248 genes exhibited up-regulation, while 198 genes displayed down-regulation in Cc roots precisely 7 days after Fs inoculation within everyday Cc roots. In contrast, after a 7-day interval of BCA inoculation in ordinary Cc roots, 308 genes demonstrated upregulation, and 131 genes evinced downregulation. A distinctive shift occurred after 7 days of Fs infection, where 719 genes were upregulated, and 337 genes were downregulated in Cc roots. Furthermore, after 7 days of BCA pre-inoculation followed by re-inoculation with Fs, 473 genes were upregulated, accompanied by 435 genes exhibiting downregulation in Cc roots. The findings from this analysis underscore the considerable transformative potential of distinct microorganism inoculations on the transcriptomic landscape of Cc roots. This transformative potential is further accentuated by the discernible variations in gene up-regulation and down-regulation based on the sequence of inoculations.

Given the intricate nature of root metabolites within Cc, the conducted experiment employed the OPLS-DA model for in-depth analysis to filter out background noise unrelated to the sample. The robust separation of root metabolite samples from Cc, treated with diverse microorganisms, serves as a demonstrative testament to the stability and high reliability of the model (Fig. 6, Right). After subjecting the model to 100 permutation tests, it is observed that the R2Y values consistently exceed 0.991, indicative of their proximation to unity. There are minor exceptions in cases such as BCA7dFs7d vs. BCA7d and Fs7dBCA7d vs. BCA7d, where the actual observed Q2 (0.281, 0.174) indicated by the arrow deviates towards the left side of the random distribution. This deviation, corroborated by a large p-value, implies a lack of significant distinction between the two mentioned groups. Conversely, the remaining comparison groups depict the actual observed Q2 positioned towards the right side of the random distribution, signifying non-randomness and statistical significance. This substantiates the model's substantial predictive capability and underscores significant metabolic disparities between distinct groups.

Due to the complexity of root metabolites in Cc, this experiment used the OPLS-DA model to analyze and filter out background noise unrelated to the sample. The obvious separation of root metabolites samples from Cc treated with different microorganisms indicates that the model is stable and highly reliable, as shown in (Right). After 100 permutation tests, the R2Y values of the model are all greater than 0.991, which is very close to 1. Except for BCA7dFs7d vs BCA7d, Fs7dBCA7d vs BCA7d. The actual observation Q2 (0.281, 0.174) indicated by the arrow is located on the left side of the random distribution, with a large p-value and no significant difference between the two groups. The observed Q2 indicated by the arrows in other comparison groups is on the right side of the random distribution (the observed value is significantly greater than the random value), indicating that Q2 is not random but significant. The model's predictive ability is significant, and there are significant differences in metabolites between groups.

The Function of the root microbial community changed significantly

The KEGG functional prediction analysis yielded compelling insights into the impact of different microorganism inoculations on the functional diversity of the Cc rhizome microbiome, as illustrated in Fig. 7. Notably, pre-inoculation with BCA followed by subsequent Fs inoculation exhibited particularly pronounced effects, elucidated in Fig. 8. Figure 8. Within the microbiome of Cc infected with Fs, a notable enhancement in the degradation capacity of the harmful substance xylene was evident. Conversely, BCA inoculation facilitated the degradation of various detrimental substances encompassing xylene, dioxin, and phthalate esters. Additionally, introducing BCA to the rhizome of Cc proved conducive to restoring the rhizome's soil microenvironment in scenarios involving root rot disease.

After a 7-day interval post BCA inoculation, followed by subsequent Fs infection, a substantial escalation was observed in the biosynthesis of various antibiotics, including ansamycin, macrolide, polyketone, streptomycin, neomycin, kanamycin, gentamicin, acarbose, and validamycin. Furthermore, there was an augmentation in fatty acid biosynthesis within the microbiome of Cc. These findings collectively imply that pre-inoculation or BCA inoculation post-Fs infection can augment the Cc microbiome's capacity for harmful substance degradation and the secretion of multiple antibiotics, thus fortifying its resistance against Fs infection.

Microbial inoculation shaped dynamic changes in Cc root microbiota

Through ANOVA analysis using STAMP software, distinct patterns in the relative abundance of Cc root microbiota were revealed. Irrespective of the presence of Fs, post-BCA inoculation witnessed a significant increase in the relative abundance of 11 genera: Arthrobacter, Sphingobium, Pseudomonas, Bradyrhizobium, Polaromonas, Agromyces, Ensifer, Massilia, Rivibacter, Paenisporosarcina and Frigoribacterium. Following Fs inoculation, notable rises in relative abundance were noted for Flavobacterium and Gemmatimonas genera. However, the subsequent introduction of BCA resulted in a substantial decline in their relative abundance. These observations collectively highlight the significant impact of microbial inoculation on specific genera's relative abundances, shaping the complex dynamics of Cc root microbiota composition.

Correlation of Microbial Genera with Metabolome and Transcriptome Variation

Differential metabolites and differentially expressed genes of five groups of Cc root samples were related to each of 11 regularly varying microbial genera using Mantel-test analysis. Edge width corresponds to the Mantel’s r statistic for the corresponding distance correlations, and edge color denotes the statistical significance. Pairwise comparisons of regularly varying microbial genera are shown, with a color gradient denoting Pearson’s correlation coefficient.

The Mantel test methodology was employed to assess the impact of systematically altering species on the transcriptome and metabolome of Cc roots after diverse microorganism inoculations. The outcomes of this analysis revealed a compelling correlation among all included data points, substantiating an intriguing interrelationship within the dataset (Fig. 10). Bacillus, Sphingobium are significantly associated with differentially expressed genes; Ensifer, Pseudomonas, and Polaromonas are significantly associated with differential metabolites.

Regularly changing genera are significantly related to the root transcriptome and metabolome of Coptis chinensis

To delve into the intricate relationship between the rhizome transcriptome and metabolome of Cc roots and the dynamic alterations in species resulting from diverse microorganism inoculations, we extended our analysis by conducting a WGCNA. This analysis aimed to uncover correlations between the host's transcriptome, metabolome, and the Regularly changing genus, along with two additional microbial genera (Bacillus and Fusarium). Within this context, a comprehensive evaluation was conducted on a pool of 7667 host differential genes expressed across 25 samples. As a result, a total of 10 distinct gene modules were identified. Notably, the brown and red modules displayed noteworthy correlations, alongside a significant correlation with the Bacillus genus (depicted in Fig. 11A, C). Likewise, a parallel investigation encompassed the evaluation of 326 host differential metabolites expressed across the same set of 25 samples. This endeavour led to the identification of a total of 9 unique metabolite modules. The turquoise, yellow, and brown modules emerged as notably correlated.

In contrast, akin to the gene module analysis, a significant correlation with the Bacillus genus was also observed (as depicted in Fig. 11B, D). These intricate interconnections and correlations, as the WGCNA reveals, offer a deeper understanding of the multifaceted interactions between the transcriptomic and metabolomic landscapes of Cc roots and the dynamic shifts in microbial populations post-inoculation. The core genes and core metabolites significantly associated with Bacillus subtilis are shown in supplementary tables S2 and S3.

Bacillus regulates the root microbial community structure by affecting the transcription and metabolism of Coptis root

Utilizing Arabidopsis thaliana as the reference species, an investigation into the module significantly associated with Bacillus was carried out, focusing on core genes and metabolites (supplementary tables S2 and S3). KEGG enrichment analysis was conducted on these components. The gene-level KEGG enrichment analysis (Fig. 12A) indicated that Bacillus had a notable influence on pivotal pathways underpinning the advancement of both primary and secondary plant metabolism. These pathways encompassed significant processes such as Biosynthesis of secondary metabolites, Starch and sucrose metabolism, Pyruvate metabolism, Phenylpropanoid biosynthesis, Purine metabolism, Glycolysis/Gluconeogenesis, Fatty acid biosynthesis/elongation and metabolism, Glycosaminoglycan degradation, Brassinosteroid biosynthesis, Biotin metabolism, Amino sugar, and nucleotide sugar metabolism, Diterpenoid biosynthesis, Glucosinolate biosynthesis, Oxidative phosphorylation, Steroid biosynthesis, Tryptophan metabolism, Glycerolipid metabolism, Plant-pathogen interaction, and Plant hormone signal transduction, among others.

Concurrently, the metabolite-level KEGG enrichment analysis (Fig. 12B) similarly demonstrated that Bacillus played a crucial role in influencing pathways essential for enhancing primary and secondary plant metabolism. These pathways encompassed Biosynthesis of unsaturated fatty acids, Cysteine and methionine metabolism, Arachidonic acid metabolism, Tyrosine metabolism, Metabolism of xenobiotics by cytochrome P450, and Steroid hormone biosynthesis. These metabolites contain a large amount of unsaturated fatty acids (such as 9-HpOTrE, FAHFA (18:2/18:1), Methyl linoleate, FAHFA (18:2/16:0), Elaidic Acid, Arachidonic Acid, Adrenic Acid, Methyl oleate, 11Z,14Z-Eicosadienoic Acid, FAHFA ༈18:1/20:)3, trans-Petroselinic Acid, 8Z,11Z,14Z-Eicosatrienoic Acid, FAHFA (16:0/22:5), Hexadecanamide, 11Z,14Z,17Z-Eicosatrienoic Acid, 16-Hydroxyhexadecanoic Acid༉, flavonoids (Syringetin-3-O-glucoside, Kaempferol, Irigenin, Kaempferide, Naringerin, Rhapontigenin 3'-O-glucoside, Hesperetin, Isoliquiritin apioside, Eupatilin), critical metabolites in the biosynthesis of phenylpropanoid (5-O-p-Coumaroyl shikimic Acid, 5-O-Caffeoyl shikimic Acid, Arabidopyl shikimic Acid), which are mutually confirmed by gene KEGG enrichment analysis.

These findings collectively underscore Bacillus' significant involvement in promoting various critical pathways associated with plant primary and secondary metabolism development. This has substantial implications for the interactions between the microbial components and the host plant's metabolic processes.

Plants exist in a dynamic relationship with various microorganisms within their natural environment. While certain microbes can provoke plant harm and prompt defensive responses, others contribute beneficially to overall plant performance. The microbial communities closely associated with plant roots are pivotal in conferring distinct functions to their host plants. It intricately influences aspects such as plant growth, health, and overall productivity [53]. The rhizosphere, in particular, has emerged as a focal point of investigation, revealing compelling insights into the complex interactions between plants and microorganisms. A mounting body of evidence underscores the phenomenon wherein plants actively recruit protective bacteria and bolster microbial activities to counteract the proliferation of pathogens. This intricate network of interactions underscores the multifaceted strategies plants employ to ensure their well-being in the presence of various microorganisms [54, 55].

Our research findings present compelling evidence regarding the substantial impact of Bacillus introduction on the intricate facets of the composition, structural arrangement, and functional dynamics inherent within the root microbial community of Cc roots. A vital outcome of this interaction is the reduction in the complexity of the microbial network. Remarkably, the presence of Bacillus leads to the pronounced enrichment of specific genera, such as Arthrobacter, Sphingobium, Pseudomonas, Bradyrhizobium, Polaromonas, Agromyces, Ensifer, Massilia, Rivibacter, Paenisporosarcina, and Frigoribacterium. Simultaneously, this enrichment is correlated with a decrease in the relative abundance of Flavobacterium and Gemmatimonas. Arthrobacter plays a significant role as a plant growth-promoting and biocontrol agent, enhancing plant resistance against pests and diseases while promoting growth [56, 57]. Their presence in the soil maintains a healthy ecological balance by suppressing harmful microorganisms, increasing soil microbial diversity, and creating a healthier plant growth environment [57]. Sphingobium positively influences plants and soil environments through various pathways, such as stimulating plant growth [58], regulating metabolism [59], maintaining soil health [60], and degrading pollutants [61]. Ensifer establishes symbiotic relationships in non-leguminous plant roots, enhancing nutrient absorption [62], secreting growth-promoting molecules, and providing nitrogen, improving overall plant growth and physiological state [63]. Pseudomonas degrades harmful substances, enhances soil nutrient efficiency, modulates plant metabolism, and boosts stress resistance [64] while producing antibiotics to inhibit pathogenic growth and maintain plant health [65]. Bradyrhizobium engages in nitrogen-fixing symbiosis, providing nitrogen nutrition, enhancing growth [66], and stabilizing the rhizosphere microbial community [67]. Polaromonas and Agromyces bacteria influence plant growth by augmenting soil nutrients [68, 69], impacting microbial interactions [70, 71] within the root ecosystem. Ensifer affects plant roots and overall ecology through nitrogen fixation, growth promotion, rhizosphere interaction [72], and alleviation of heavy metal stress [73]. Paenisporosarcina participates in organic matter decomposition and nitrogen fixation [74], contributing to soil health [75]. Massilia consumes fungal secretions, controlling pathogenic fungi and inhibiting their growth [76, 77]. Frigoribacterium may act as antagonists to plant pathogens or promoters of plant growth [78]. Flavobacteria provide nutrients in plant roots to promote growth and combat pathogenic microorganisms, helping plant health and disease resistance [79]. Gemmatimonas bacteria engage in plant symbiosis, organic matter decomposition [80], enhancement of plant nutrient uptake [81], maintaining soil health, and promoting plant growth [82]. In summary, introducing Bacillus triggers a significant reconfiguration in the root microbial community, fostering the enrichment of beneficial genera and their pivotal roles in enhancing plant growth, combatting diseases, and fortifying soil health.

Furthermore, the introduction of Bacillus prompts a notable shift in the root microorganisms' behaviour, particularly in their synthesis of antibiotics. This transformative effect translates into the biosynthesis of various antibiotics, including ansamycin, macrolides, polyketones, streptomycin, neomycin, kanamycin, gentamicin, acarose, and vatamycin. This repertoire of synthesized antibiotics can play a pivotal role in fortifying the plant's defence mechanisms against potential pathogens. The orchestrated interplay between microbial communities and the induction of antibiotic synthesis represents a noteworthy aspect of Bacillus-mediated modulation in the root ecosystem of Cc.

The findings from our study reveal significant correlations between Bacillus and the transcriptome and the metabolome of Cc root. Through Mantel-test analysis, we established robust statistical links between these bacterial species and the genetic and chemical profiles of the root. Further analysis using WGCNA substantiated the noteworthy influence of Bacillus. The outcomes demonstrated a clear and significant correlation between Bacillus and the alterations observed in both the transcriptome and metabolome of the Cc root. This underlines the pivotal role of Bacillus in shaping the plant's molecular and metabolic responses.

Subsequent KEGG enrichment analysis, focusing on critical genes and metabolites within significantly correlated modules, uncovered the profound impact of Bacillus on critical pathways governing primary and secondary plant metabolism within the Cc root system. Essential genes KEEG enrichment pathways encompass a spectrum of vital biochemical processes, encompassing secondary metabolite biosynthesis, starch and sucrose metabolism, pyruvate metabolism, Phenylpropanoid biosynthesis, Purine metabolism, Glycolysis/Gluconeogenesis, Fatty acid biosynthesis/elongation and metabolism, Glycosaminoglycan degradation, Brassinosteroid biosynthesis, Biotin metabolism, Amino sugar and nucleotide sugar metabolism, Diterpenoid biosynthesis, Glucosinolate biosynthesis, Oxidative phosphorylation, Steroid biosynthesis, Tryptophan metabolism, Glycerolipid metabolism, Plant-pathogen interaction, and Plant hormone signal transduction, among others. Moreover, Key metabolites KEEG enrichment analysis highlighted Bacillus' involvement in pathways associated with unsaturated fatty acid biosynthesis, cysteine and methionine metabolism, arachidonic acid metabolism, tyrosine metabolism, cytochrome P450 metabolism of exogenous substances, and steroid hormone biosynthesis.

The shikimate pathway synthesises the aromatic amino acids Tyr, Phe, and Trp in plastids and cytosol [83]. Essential genes KEEG enrichment shows that Bacillus significantly affects tryptophan metabolism in the root system of Cc. At least three critical shikimic acid metabolic pathway compounds were found in crucial metabolites, such as 5-O-p-Coumaroyl shikimic Acid, 5-O-Caffeoyl shikimic Acid, and Arabidophyl shikimic Acid (supplementary table S3). A considerable share of carbon flow (≥ 30%) is directed through the shikimate pathway to produce pigments, defence compounds, and the cell wall component lignin [84]. At least nine flavonoids (Syringetin-3-O-glucoside, Kaempferol, Irigenin, Kaempferide, Naringerin, Rhapontigenin 3'-O-glucoside, Hesperetin, Isoliquiritin apioside, Eupatilin) were found in key metabolites. Plant-specialized metabolites can act as nutrient sources, signalling molecules, or toxins for individual microbial strains, thereby shaping the overall composition of the microbiome [85, 86]. Many studies have shown that plant root flavonoid exudates can enrich plant rhizosphere microorganisms [87] and act as plant antimicrobial toxins to inhibit the growth of pathogens [88].

Fatty acids are an essential class of metabolites in plant cells and play important roles in regulating the biological functions of plant roots. The release of fatty acids can alter plant rhizosphere microorganisms' community structure and diversity, affecting their functions and ecosystem processes [89]. Venugopal et al. [90] reported the roles of plant hormones (such as jasmonic Acid) derived from fatty acids in defending against plant physiological stress. Furthermore, several studies have revealed that fatty acids and their derivatives directly inhibit the growth of plant pathogens within the rhizosphere and improve the surrounding environment of plant rhizosphere to reduce the occurrence of crop diseases and promote crop growth [91, 92]. At least 16 unsaturated fatty acids (such as 9-HpOTrE, FAHFA (18:2/18:1), Methyl linoleate, FAHFA (18:2/16:0), Elaidic Acid, Arachidonic Acid, Adrenic Acid, Methyl oleate, 11Z,14Z-Eicosadienoic Acid, FAHFA ༈18:1/20:)3, trans-Petroselinic Acid, 8Z,11Z,14Z-Eicosatrienoic Acid, FAHFA (16:0/22:5), Hexadecanamide, 11Z,14Z,17Z-Eicosatrienoic Acid, 16-Hydroxyhexadecanoic Acid༉ were found in critical metabolites (supplementary table S3). Cc recruits beneficial microorganisms in the rhizosphere by synthesizing these unsaturated fatty acids and flavonoids to inhibit the outbreak of pathogens.

In summary, this study delves into the dynamic relationship between plants and microorganisms in their natural environment, mainly focusing on the role of microbial communities in the rhizosphere. It highlights the intricate strategies plants employ to interact with microorganisms, including the recruitment of protective bacteria and the modulation of microbial activities. The introduction of Bacillus significantly impacts the composition and behaviour of the root microbial community, reducing complexity and the enrichment or inactive of specific genera. It is worth noting that Bacillus can induce Cc root microorganisms to synthesize various antibiotics and induce Cc root to synthesize a variety of unsaturated fatty acids and flavonoids to enhance the defence ability of plants (Fig. 13). The study establishes robust correlations between Bacillus and the transcriptome and metabolome of Cc roots, emphasizing Bacillus's pivotal role in shaping molecular and metabolic responses. KEGG enrichment analysis unveils the profound influence of Bacillus on essential plant metabolic pathways. This research suggests the eco-friendly potential of beneficial microbes in enhancing plant disease resistance as an alternative to chemical pesticides.

This study illuminates the intricate interplay between plants and microorganisms, emphasizing the pivotal role of microbial communities in the rhizosphere. Bacillus introduction significantly impacts the composition and behaviour of the root microbial community, reducing complexity while enriching specific genera crucial for enhancing plant growth, combating diseases, and fortifying soil health. Notably, Bacillus synthesizes diverse antibiotics, unsaturated fatty acids, and flavonoids within Cc roots, empowering plant defence mechanisms. Robust correlations between Bacillus and the transcriptome and metabolome of Cc roots underscore its profound influence on molecular and metabolic responses. KEGG enrichment analysis unveils Bacillus's significant impact on vital plant metabolic pathways. This research underscores the eco-friendly potential of leveraging beneficial microbes to enhance plant disease resistance, offering a sustainable alternative to chemical pesticides (Fig. 13).

Acknowledgements

Guangyu Yan and Wenzhong Sheng （Hongya County Wawushan Pharmaceutical Co., Ltd） are gratefully acknowledged for their support during the field sampling campaigns.

Authors’ contributions

H.L., T.Z. and Y.M. designed the study. H.L., W.K. S.C. and J.D. performed the field sampling. H.L., W.K. and S.C. did data analysis, H.L. drafted the manuscript, W.L., B.X. and H.L. revised the manuscript; all authors read and approved the final version.

Funding

This work was funded by the National Natural Science Foundation of China (NSFC) under project number U19A2011 as part of the joint fund projects.

Availability of data and materials

The original 16S and ITS rRNA gene sequence data of this study has been stored in the NCBI Sequence Read Archive (SRA) database under the BioProject number PRJNA1072968 and PRJNA1072972. The original transcriptome sequence data of this study has been stored in the NCBI Sequence Reading Archive (SRA) with the BioProject number PRJNA1073058. Metabolomics data have been deposited to the EMBL-EBI MetaboLights database with the identifier MTBLS9511.

Ethics approval and consent to participate

This manuscript does not report on data collected from humans or animals.

Consent for publication

All authors have read and approved the paper for submission.

Competing interests

The authors declare no competing interests.

Author details

1.State Key Laboratory of Characteristic Chinese Medicine Resources in Southwest China, Chengdu University of Traditional Chinese Medicine, Chengdu, China.

2.Economic Crop Research Institute of Sichuan Academy of Agricultural Sciences, Chengdu, China.

3.School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China.

4. Traditional Chinese Medicine Hospital of Ya'an, Ya'an, China.

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Bacillus Intervention: Microbial Ecological Mechanisms for Controlling Root Rot in Coptis chinensis Franch

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Abstract

Background:

Results:

Conclusions

Figures

Background

Materials and methods

Preparation of experimental microorganism liquid

Preparation of Biological Control Agent Liquid (BCA):

Collection and treatment of experimental materials

Determination of disease index of Cc root rot in different treatments

Microbiome profiling of Cc roots

Transcriptome profiling of Cc roots

Metabolome profiling of Cc roots

Weighted gene correlation network analysis

Results

The composition of the root microbial community changed significantly

The network of root microbial community changed significantly

Microorganisms' Influence on Cc roots: Transcriptome & Metabolic Changes

The Function of the root microbial community changed significantly

Microbial inoculation shaped dynamic changes in Cc root microbiota

Correlation of Microbial Genera with Metabolome and Transcriptome Variation

Discussion

Conclusions

Declarations

References

Additional Declarations

Supplementary Files

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