An attapulgite--Bacillus amyloliquefaciens biocontrol agent increases growth and medicinal metabolite content in Angelica sinensis by modulating hormonal and phenylpropanoid pathways

An attapulgite--Bacillus amyloliquefaciens biocontrol agent increases growth and medicinal metabolite content in Angelica sinensis by modulating hormonal and phenylpropanoid pathways | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 17 August 2025 V1 Latest version Share on An attapulgite--Bacillus amyloliquefaciens biocontrol agent increases growth and medicinal metabolite content in Angelica sinensis by modulating hormonal and phenylpropanoid pathways Authors : Yang Liu , Xiaofan Xie , Yuan Tian , Liang Yue , Xia Zhao , Qin Zhou , Yun Wang , … Show All … , Yubao Zhang , Ling Jin , Qingxia Guan , Zengxiang Guo , Lam-Son Tran , and Ruoyu Wang 0000-0002-0422-6408 [email protected] Show Fewer Authors Info & Affiliations https://doi.org/10.22541/au.175541778.86764056/v1 Published BMC Plant Biology Version of record Peer review timeline 222 views 119 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Beneficial interactions between host plants and rhizosphere microbes can increase plant growth, suppress plant disease, and promote sustainable production. In previous work, we demonstrated that a novel biocontrol agent consisting of attapulgite-coated Bacillus amyloliquefaciens FZB42 (APBA) effectively controlled Fusarium root rot in the well-known medicinal herb Angelica sinensis . Here, we sought to examine how APBA would affect the growth and medicinal metabolite content of A. sinensis under chemical and organic fertilizer regimes. Inoculation with B. amyloliquefaciens FZB42 in the greenhouse or application of APBA in the field significantly enhanced growth and yield of A. sinensis , regardless of whether it was applied with chemical or organic fertilizer. Levels of secondary metabolites such as shikimate and isoferulic acid were higher in APBA-treated plants, particularly when APBA was applied together with organic fertilizer. Transcriptome analysis revealed that APBA treatment altered the expression of genes encoding pathogenesis-related proteins, heat shock proteins, antioxidant enzymes, and various transcription factors, as well as genes associated with hormone signaling and primary and secondary metabolism. Some responses to APBA differed depending on the fertilizer regime: APBA had a greater effect on phenylpropanoid metabolite levels and upregulated expression of more hormone-related genes when applied with organic fertilizer. This study provides insights into the molecular responses of A. sinensis to a bacterial biocontrol agent, providing a foundation for further research and practical applications of B. amyloliquefaciens FZB42 in sustainable agriculture. 1 Introduction Angelica sinensis (Oliv.) Diels is a perennial herbaceous plant of the Apiaceae family. It is widely valued in traditional Chinese medicine for its dried root, which is used to promote blood circulation and as an anti-inflammatory agent. It is also a popular dietary supplement in China and other East Asian countries (Mu et al. , 2024). A. sinensis is primarily cultivated in the high-altitude mountains of southern Gansu Province, where Minxian County not only leads the country in production but also is renowned for the superior quality of its A. sinensis , which is commonly called ‘Min Gui’ and is considered to have superior geo-herbalism properties (Liu et al. , 2022). In 2018, Gansu Province had an A. sinensis cultivation area of about 44,000 ha, producing approximately 150,000 tons per year and generating an annual revenue of 2.2 billion yuan (Zhang et al. , 2021). The development of the Angelica industry has had a profound effect on the local economy, fostering a sustainable increase in farmers’ incomes, increasing the prosperity of county economies, and helping to revitalize rural industries. Given the desire for economic benefits and immediate returns, chemical fertilizers currently have a central role in typical field-cultivation practices for A. sinensis. Although chemical fertilizers are extensively applied to enhance yield and quality, their overuse has led to significant environmental issues, including soil acidification, soil compaction, and pollution of water bodies with nitrogen and phosphorus, all of which impede sustainable agricultural development (Guo et al. , 2010; Guo et al. , 2014; Gu et al. , 2015). In addition, climate change, continuous cropping, and the misuse and abuse of fungicides have created favorable conditions for severe plant disease outbreaks that reduce the yield and quality of A. sinensis . In response, the Chinese Ministry of Agriculture and Rural Affairs of China launched the “ Action Plan for Zero Growth in the Use of Chemical Fertilizers by 2020 ” in 2014 (Yang et al. , 2020; Xie et al. , 2022). The substitution of organic fertilizers for chemical fertilizers has proven effective for mitigating environmental damage and recycling organic waste, but excessive application of organic fertilizers also poses risks such as antibiotic pollution and the spread of antibiotic-resistant pathogens (Bloem et al. , 2017; Zhang et al. , 2020; Xu et al. , 2022). An promising alternative is the use of microbial agents for disease control and growth promotion. Biocontrol agents, particularly when combined with organic fertilizers, can enhance microbial activity and improve soil health, offering an economical and environmentally friendly approach for plant cultivation (Wang et al. , 2013; Fu et al. , 2017; Xiong et al. , 2017; Tao et al. , 2020).We recently developed a novel biocontrol agent (APBA) by coating the bacterial strain Bacillus amyloliquefaciens FZB42 with attapulgite, a magnesium aluminum phyllosilicate commonly used as an agricultural carrier. Application of APBA significantly reduced root rot disease in A. sinensis , improved multiple soil chemical properties, and induced changes in the composition of the rhizosphere microbial community (Liu et al. , 2021). Plant growth-promoting rhizobacteria (PGPRs) such as B. amyloliquefaciens are considered among the most promising alternatives for sustaining agricultural productivity without the rampant use of chemical fertilizers (Prasad et al. , 2019). The rhizosphere microbiota support plant nutrient uptake, disease resistance, and stress tolerance (Antoniou et al. , 2017; Chen et al. , 2020), and recent studies have shown that Bacillus spp., members of a versatile genus that includes many PGPRs, can act as biofertilizers, biocontrol agents, and growth promoters by stimulating secondary metabolite synthesis and increasing plant disease resistance (Borriss, 2011; Kumar et al. , 2011; Rosier et al. , 2018). For instance, Bacillus subtilis GB03 enhanced growth and secondary metabolite levels in Codonopsis pilosula (Zhao et al., 2016) , and the salt-tolerant strains Bacillus pumilus STR2 and Exiguobacterium oxidotolerans STR36 increased yield and secondary metabolite production in Bacopa monnieri under salt stress (Bharti et al. , 2013). Another study revealed that individual or combined application of Bacillus endophyticus TSH42 and Bacillus cereus TSH77 increased growth and curcumin content of Curcuma longa by as much as 18% (Chauhan et al. , 2017). Given the accumulating evidence that PGPRs enhance both growth and secondary metabolite production in medicinal plants, we ought to investigate whether the application of Bacillus amyloliquefaciens FZB42 in the form of APBA would confer comparable benefits to A. sinensis.Here, we performed greenhouse and field trials to examine the effects of B. amyloliquefaciens FZB42 inoculum (greenhouse) and APBA in combination with chemical or organic fertilizers (field) on the growth, physiology, gene expression, and metabolite contents of A. sinensis . RNA-sequencing (RNA-seq) and untargeted metabolomics data enabled us to identify differentially expressed genes (DEGs) and differentially abundant metabolites (DAMs) associated with APBA application, providing initial insights into the mechanisms by which this biocontrol agent stimulated plant growth and secondary metabolite synthesis. Our results offer a foundation for further research and practical applications of B. amyloliquefaciens FZB42 in sustainable agriculture. 2 Materials and Methods 2.1 Preparation of bacterial inoculum and the APBA biocontrol agent For the greenhouse study, B. amyloliquefaciens FZB42 and Escherichia coli DH5ɑ (as a non-PGPR control) were prepared by culturing each strain in Luria–Bertani (LB) liquid medium at 37°C with shaking at 200 rpm. Overnight cultures were centrifuged at 10,000 × g for 6 min, washed, and re-suspended in sterile water to a concentration of 5.0 × 10 7 CFU/mL for use as inoculum. B. amyloliquefaciens FZB42 was obtained from the Bacillus Genetic Stock Center (BGSC, strain 10A6), and E. coli DH5α was obtained from Takara Biotechnology (Dalian, China). For the field study, the APBA biocontrol agent was produced by combining B. amyloliquefaciens FZB42 with attapulgite clay using a wet process that yielded a solid particulate with 5.0×10 9 CFU of bacteria/g. Details of the manufacturing process are provided in (Liu et al., 2021) . 2.2 Plant materials and experimental design A. sinensis seeds were surface sterilized with 3% NaClO for 3–4 min, followed by 70% ethanol for 10 min, and then rinsed five times with sterile water. Disinfected seeds were placed in sterile petri dishes lined with double-sterilized filter paper and containing 10 mL of sterile water. After germination and growth for 5–10 days in a light incubator under a 16-h light/8-h dark photoperiod, seedlings with cotyledons were transferred to sterile soil (Pindstrup substrate and field soil, 3:1 v/v). For the greenhouse experiment, two seedlings were planted in each pot (28 × 25 × 32 cm), and irrigated with 50 mL of 1/2 Murashige and Skoog (MS) liquid medium for two weeks under greenhouse conditions. Plants were maintained at 27 °C with a 16/8 h light/dark photoperiod. After three weeks, uniform seedlings were inoculated with 1 mL of FZB42, E. coli DH5α, LB medium, or double-distilled water (CK) per plant. Each treatment consisted of six replicates, with each seedling considered an individual replicate. Plants were watered weekly after inoculation. Physiological parameters were measured at 30, 60, and 90 days after planting. For the field experiment, one-year-old A. sinensis seedlings were obtained from the Minxian Institute and transplanted in April 2019 to a field located in Anjiashan village (34°37′ N, 104°82′ E; elevation: 2350 m, in Minxian county, Gansu Province, China). The field experiment comprised 15 blocks, with five treatments and three replicate blocks per treatment. The treatments included: (1) soil amended with attapulgite particulate (‘AP’, serving as the control); (2) soil supplemented with chemical fertilizer (‘CF’) containing 17% N, 17% P2O5 and 17% K2O; (3) soil supplemented with organic fertilizer (‘OF’) in the form of fermented rapeseed cake (N 6.56%, P2O5 3.30%, K2O 1.3%, total organic matter > 75%), which represents the traditional fertilizer used by local herb farmers; (4) soil supplemented with both APBA and chemical fertilizer (‘BICF’); and (5) soil supplemented with both APBA and chemical organic fertilizer. (‘BIOF’). Transplanting was carried out in mid-April 2019, following the application of a base fertilizer to all plots. Subsequently, 16.7 kg of fermented rapeseed cake was applied per trial (2.47 kg/m3) for the OF treatment; and 3.3 kg of chemical fertilizer for the CF treatment. The AP treatment involved supplementation with 3.3 kg of AP (at 0.50 kg/m3). For the BICF and BIOF treatments, 0.33 kg of APBA was added to the CF and OF treatments, respectively, while maintaining the same fertilizer amounts. The experimental field measured 25 m × 27 m and was divided into 15 blocks (each 5.4 m × 8 m). Each block contained eight rows, with approximately 50 A. sinensis plants per row, spaced 60 cm between rows and 16 cm between plants. Recognizing that the direct addition of microbial agents to soil may reduce microbial activity, this study combined approach by integrating microbial agents with various fertilizers, aiming to enhance microbial activity. 2.3. Plant growth, harvest, and sample preparation For the greenhouse experiment, growth parameters were measured at 30, 60, and 90 days post-inoculation (dpi) using separate batches of seedlings for each date. The parameters were lateral root number, plant height, root dry weight, root fresh weight, shoot fresh weight, and shoot dry weight. For the field experiment, growth parameters were measured monthly until the final harvest in October. These consisted of lateral root number, plant height, taproot diameter, taproot length, shoot fresh weight, and root fresh weight. Each parameter was measured on 8 plants per treatment and date, with the exception of plant height, which was measured for 100 plants. At 180 days after treatment application (i.e., after six months of growth in the field), A. sinensis were harvested from six plots (1 m × 1 m) per treatment for yield measurements. The aboveground parts of each plant were removed, and the roots were completely excavated and shaken to dislodge any adhering soil before measurement of root and shoot fresh weight. Fresh A. sinensis root tissues were harvested from three plants per treatment in the field experiment for use in RNA-seq: the main root part, the middle part, and the lateral root part. The tissues were immediately placed in liquid N 2 and stored at –80°C for RNA extraction and metabolomics. 2.4 Photosynthesis and chlorophyll content At 60 dpi, photosynthetic parameters were measured either the 5 th or the 6 th topmost leaves on the main stem using a LI-6400XT portable photosynthesis system (LI-COR, Lincoln, NE, USA). The measured parameters were photosynthetic rate, transpiration rate, intercellular CO 2 concentration, and stomatal conductance. Measurements were obtained between 9:00 a.m. and 11:30 a.m. For each treatment, three individual plants were selected, with each plant serving as a biological replicate. Leaf chlorophyll contents were measured using the acetone extraction method (Ashraf and Iram, 2005). The leaves (0.5g) were soaked in acetone at room temperature for 24 h in darkness, and the absorption of the resulting extract was measured at 663 nm and 646 nm using a spectrophotometer. Chlorophyll a and b concentrations were calculated as: Chl a = 12.21 × A 663 – 2.81 × A 646 Chl b = 20.13 × A 646 – 5.03 × A 663 2.5 Enzyme activity assays The activities of phenylalanine ammonia-lyase (PAL) and catalase (CAT) were measured as described by (Hassanpour and Pourhabibian, 2022). Fresh root samples (0.2 g) were homogenized in 2 mL of 50 mM Tris–HCl buffer (pH 8.8) with 15 mM β-mercaptoethanol. The homogenate was centrifuged at 15,000 × g for 15 min at 4°C. PAL activity was measured at 270 nm in a reaction mixture containing 100 µL enzyme extract, 100 mM Tris–HCl buffer (pH 8.8), and 10 mM phenylalanine, and incubated at 37°C for 1 h. CAT activity was measured at 240 nm by combining 0.625 mL enzyme extract with 50 mM potassium phosphate buffer (pH 7.0) and 0.075 mL 3% H 2 O 2 . All results were expressed as U g −1 min −1 . RNA extraction and Illumina sequencing Total RNA was extracted from root tissues of three biological replicates per treatment using the RNeasy Plant Mini Kit (TianGen Biotech, Beijing, China). RNA quality was assessed using a NanoDrop 2000 spectrophotometer (Thermo Scientific, USA) and by electrophoresis on a 1.5% agarose gel. Poly(A) mRNA was enriched using Oligo (dT) magnetic beads, and cDNA was synthesized using random hexamer primers. Sequencing adaptors were ligated to the short cDNA fragments, purified on the basis of size, and amplified via PCR to prepare cDNA libraries. Sequencing was performed on the Illumina HiSeq 2000 platform by Biomarker Technologies Corporation (Beijing, China) to obtain 150-bp paired-end reads. 2.7 Transcriptome analysis Adaptors, N bases, and low-quality reads were removed from the raw RNA-seq reads to obtain clean reads, which were then assembled into transcripts using Trinity software (v2.5.1) (Haas et al. , 2013). The clean data from each sample were then mapped to the assembled transcript sequences to obtain read counts. The Trinity unigene sequences were then used as BLAST queries to search multiple databases (NCBI nr, Swiss-Prot, Gene Ontology [GO], Clusters of Orthologous Genes (COG), EuKaryotic Orthologous Groups [KOG], eggNOG 4.5, and Kyoto Encyclopedia of Genes and Genomes [KEGG]) and assign annotations to each unigene. GO enrichment analysis was performed using the topGO R package (v2.28.0), and GO terms with a P value < 0.05 were considered to be significantly enriched. Significantly enriched biological process GO terms were then visualized using topGO. Gene expression levels were calculated as FPKM values (fragments per kilobase of exon per million fragments mapped) using DESeq2 software (v1.22.1) (Wang et al. , 2009). Genes with a false discovery rate (FDR)-adjusted p value < 0.01 and |log2(fold-change)| ≥ 1.5 were considered to be DEGs. The enrichment analysis of DEGs were performed using the KOBAS software (v2.0) and searched against the KEGG database using BLASTX (Xie et al. , 2011; Liu et al. , 2017). 2.8 Metabolome analysis During the harvesting period of A. sinensis, two plants were combined to form a single biological replicate, with six replicates per treatment used for metabolomics analysis. Non-targeted metabolomics analysis was performed by Biomarker Technologies as described in (Dunn et al. , 2011). Specifically, 25 mg of root sample (after removal of liquid nitrogen) was weighed and placed in an EP tube, and 500 μL of extraction solution (acetonitrile: methanol: water = 2: 2: 1) containing a mixture of isotopically-labelled internal standards was added. After 30 s of vortexing, the samples were homogenized at 35 Hz for 4 min and sonicated for 5 min in an ice-water bath. The homogenization and sonication cycle was repeated twice. Next, the samples were incubated at –40°C for 1 h and centrifuged at 12,000 rpm for 15 min at 4°C; 250 μL of the resulting supernatant was transferred to a fresh tube and dried in a vacuum concentrator at 37°C. The dried sample was reconstituted in 200 μL of 50% acetonitrile by sonication on ice for 10 min. The resulting solution was then centrifuged at 13,000 rpm for 15 min at 4°C, after which 75 μL of the supernatant was transferred to a fresh glass vial for LC–MS analysis. Quality control samples were prepared by mixing an equal aliquot of the supernatant from each sample. Ultra high-performance LC (UHPLC) separation was performed on a 1290 Infinity series UHPLC System (Agilent Technologies) equipped with a UPLC BEH amide column (2.1 × 100 mm, 1.7 μm; Waters). Mass spectrometric analysis was carried out on a Triple TOF 6600 MS system (AB Sciex) operated in information-dependent acquisition (IDA) mode to acquire MS/MS spectra. Metabolites were qualitatively and quantitatively analyzed based on secondary spectral information using both public metabolite databases and an in-house MS² spectral database. Metabolite data were analyzed by orthogonal partial least squares-discriminant analysis (OPLS–DA) using the base R package (Kuhl et al. , 2012). Differentially abundant metabolites (DAMs) between pairs of treatments were identified as those with VIP score ≥ 1 and |log2(fold-change)| ≥ 1. KEGG pathways significantly enriched in the DAMs were identified using the method described in (Kanehisa and Goto, 2000). 2.9 Real-time quantitative PCR We performed real-time quantitative PCR (RT–qPCR) to validate the expression patterns of 14 randomly selected DEGs, using the TUB gene as an internal reference for normalization. Due to the absence of a high-quality reference genome for A. sinensis, primers were designed based on homologous genomic sequences from a related Apiaceae species, Daucus carota subsp. sativus . Primers were designed using Primer 5.0 software and synthesized by Sangon Biotech (Shanghai, China); all primers are listed in Supplemental Table 4. First-strand cDNA was synthesized using the PrimeScript RT reagent Kit with gDNA Eraser (Perfect Real Time) (Takara Bio). RT–qPCR was performed on the Mx3000P qPCR system (Applied Biosystems) using SYBR Premix Ex Taq II (TliRNaseH Plus) (Takara) according to the manufacturer’s instructions. The cycling program was as follows: 95°C for 30 s; 40 cycles of 95°C for 5 s, 60°C for 30 s, and 72°C for 30 s; and a final extension at 72°C for 10 min. Each analytical run was repeated at least three times. The threshold cycle 2 −ΔΔCt method was used to calculate relative gene expression levels (Livak and Schmittgen, 2001). 2.10 Integration of metabolomic and transcriptomic data Pearson correlations were calculated between the levels of various metabolites and the expression levels of DEGs, and correlation coefficients ( r ) > 0.8 or < −0.8 with a P value < 0.05 were considered to represent important relationships between metabolite and transcript levels. To visualize the specific relationships among metabolites and transcripts, correlation network analysis was performed using Cytoscape software (v3.9.0). 2.11. Statistical Analyses The significance of differences in growth parameters, photosynthetic parameters, and enzyme activities among treatments was assessed by one-way analysis of variance (ANOVA) and Tukey’s HSD test ( p < 0.05) in SPSS v19.0 (SPSS Inc., Chicago, IL, USA). Figures were created with Origin 2021 and refined using Adobe Illustrator 2021. 3 Results 3.1 Growth responses of A. sinensis to B. amyloliquefaciens FZB42 and the APBA biocontrol agent In the greenhouse study, inoculation of A. sinensis seedlings with B. amyloliquefaciens FZB42 significantly increased lateral root number, root fresh weight, and root dry weight compared with those of water-treated control seedlings at 30 and 90 dpi ( p < 0.05; Supplementary Fig. 1). Shoot fresh weight was also greater in FZB42-treated seedlings than in water-treated controls at 30 dpi. In the field experiment, plants were treated with chemical fertilizer (CF), organic fertilizer (OF), the APBA biocontrol agent plus CF (BICF), the APBA biocontrol agent plus OF (BIOF), or attapulgite only (CK). The values of multiple growth parameters were higher in plants treated with BICF or BIOF than in plants treated with CF or OF, respectively. Lateral root number, plant height, root fresh weight, taproot diameter, and taproot length were all significantly higher in the BICF-treated and BIOF-treated plants than in the corresponding CF and OF plants on multiple dates, particularly later in the growing season (Fig. 1). For example, lateral root numbers were 19.2% and 22.0% higher in the BIOF and BICF treatments than in the corresponding OF and CF treatments, respectively, at 180 dpi ( p < 0.05; Fig. 1A). Plant height peaked at 120 dpi and was 19.8% and 8.8% higher in the BIOF and BICF treatments than in the OF and CF treatments ( p < 0.05; Fig. 1B). Shoot fresh weight was 1.26-fold higher in BICF than in CF at 60 dpi ( p < 0.05; Fig. 1C), and root fresh weight was 17.2% and 7.6% higher in BICF and BIOF than in OF and CF at 180 dpi ( p < 0.05; Fig. 1D). Taproot diameter was 8.5% and 16.0% greater in BIOF and BICF than in OF and CF at 150 dpi ( p < 0.05; Fig. 1E), and taproot length was 9.6% and 7.6% greater in BIOF and BICF than in OF and CF at 180 dpi ( p < 0.05; Fig. 1F). Thus, the addition of the APBA biocontrol agent significantly increased above- and belowground plant growth under both chemical and organic fertilizer regimes. Most importantly, the addition of APBA increased final plant yield. At 180 dpi, yield per unit area was 37.8% and 38.9% higher in BICF and BIOF than in OF and CF, respectively ( p 0.05; Supplementary Fig. 2). 3.2 Photosynthetic parameters and chlorophyll content of A. sinensis In the greenhouse experiment, inoculation with B. amyloliquefaciens FZB42 significantly increased leaf photosynthetic rate relative to that of water-treated control plants (although not relative to that of plants treated with LB medium alone) and increased the chlorophyll contents of A. sinensis leaves ( p < 0.05; Supplementary Figs. 3 and 4). In the field experiment, photosynthetic parameters were measured at 60 dpi. Photosynthetic rate was 23.0% and 27.9% higher in the BIOF and BICF plants than in the attapulgite-only controls (CK) ( p 0.05; Fig. 2A). Transpiration rates were 55.6% and 91.1% higher in BIOF and BICF plants than in CK plants ( p < 0.05; Fig. 2D); likewise, transpiration rates were 48.0% higher in BIOF than in OF plants and 50.9% higher in BICF than in CF plants ( p < 0.05; Fig. 2D). Stomatal conductance was lower in BIOF than OF plants ( p < 0.05; Fig. 2C), whereas intercellular CO 2 concentration was 75.0% higher ( p < 0.05; Fig. 2C). Together, these findings demonstrate that application of the BIOF and BICF treatments did not increase photosynthetic rates above those obtained with CF or OF alone, but they did significantly increase transpiration rates. 3.3 Activities of key enzymes in field-grown A. sinensis Because of the key roles of CAT in antioxidant defense and PAL in the biosynthesis of secondary metabolites, we examined the activities of these key enzymes in A. sinensis in the field experiment. Leaf PAL activity was 81.2% higher and leaf CAT activity was 89.4% higher in BICF plants than in CF plants ( p < 0.05; Fig. 3A,B). Leaf PAL and CAT activities were higher in OF and BIOF plants than in CF plants (all p 0.05; Fig. 3A,B). Thus, the addition of APBA increased leaf CAT and PAL activities only when plants were grown with chemical fertilizer; organic fertilizer application was associated with higher enzyme activities, and APBA application had no additional effect when plants were grown with organic fertilizer. 3.4 Transcriptome analysis 3.4.1 RNA-seq, assembly, and gene annotation To examine differences in gene expression associated with the application of APBA under chemical and organic fertilizer regimes, we performed transcriptome sequencing of replicate root samples from the CF, OF, BICF, and BIOF treatments. After removal of low-quality reads and adapter sequences, we obtained 93.66 Gb of clean data, with an average of 4.63 Gb per sample and 93.41% of bases meeting or exceeding the Q30 quality threshold. From these clean reads we assembled 442,446 transcripts and 95,609 unigenes. Unigenes had a mean length of 81,325 bp and an N50 length of 1517 bp, and transcripts had a mean length of 238,278 bp and an N50 length of 3439 bp, indicating the high quality of the assembly (Supplementary Table 1). A total of 52,943 unigenes (55.4%) were functionally annotated in at least one of eight databases: COG (13,576), GO (32,563), KEGG (16,672), KOG (25,400), Pfam (29,416), SwissProt (25,870), eggNOG (42,629), and NCBI nr (52,063) (Supplementary Table 2). Analysis of differential gene expression revealed 84 DEGs in the CF vs. BICF comparison (CF/BICF; 66 up- and 18 downregulated), 387 DEGs in the OF vs. BIOF comparison (OF/BIOF; 254 up- and 133 downregulated), and 216 DEGs in the BICF vs. BIOF comparison (BICF/BIOF; 80 up- and 136 downregulated) (Supplementary Table 3). 3.4.2 GO terms and KEGG pathways enrichment analysis Forty-eight of the 84 DEGs in the CF/BICF comparison received GO annotations. The most common biological process terms were metabolic process (GO:0008152, 47.92% of genes), cellular process (GO:0009987, 52.08%), single organism process (GO:0044699, 43.75%), biological regulation (GO:0065007, 12.5%), signaling (GO:0023052, 8.33%), and response to stimulus (GO:0050896, 18.75%). The most common cellular component terms included cell (GO:0005623, 39.58%), cell part (GO:0044464, 39.58%), membrane (GO:0016620, 35.42%), and organelle (GO:0043226, 29.17%), and the most common molecular function terms were catalytic activity (GO:0003824, 52.08%) and binding activity (GO:0005486, 50%) (Fig. 4A). Twenty-two of the DEGs in the CF/BICF comparison were mapped to KEGG pathways, including starch and sugar metabolism (3 DEGs), carbon metabolism (2 DEGS), and glycolysis (1 DEG) (Fig. 5A), suggesting that the addition of APBA under the chemical fertilizer regime may have altered carbohydrate metabolism. One hundred ninety-six of the 387 DEGs in the OF/BIOF comparison received GO annotations. Among the most common GO terms were metabolic process (GO:0008152, 45.4%), cellular process (GO:0009987, 39.3%), single organism process (GO:0044699, 37.2%), negative regulation of cellular component organization (GO:0051129, 16.8%), biological regulation (GO:0065007, 16.8%), cellular component organization or biogenesis (GO:0044464, 4.1%), response to stimulus (GO:0050896, 14.3%), signaling (GO:0023052, 3.1%), reproductive process in single-celled organism (GO:0022413, 3.8%), and multicellular organismal process (GO:0032501, 3.8%) (Fig. 4B). DEGs in the OF/BIOF comparison were mapped to a number of metabolism-related KEGG pathways, including fatty acid metabolism (7 DEGs), biosynthesis of unsaturated fatty acids (6 DEGs), galactose metabolism (4 DEGs), starch and sucrose metabolism (4 DEGs), carbon metabolism (3 DEGs), and glutathione metabolism (3 DEGs) (Fig. 5B). Interestingly, in contrast to the CF/BICF DEGs, DEGs from the OF/BIOF comparison appeared to be involved in both fatty acid metabolism and carbohydrate metabolism. Additional KEGG pathways for these DEGs included plant-pathogen interaction and plant hormone signal transduction. Four of these KEGG pathways were significantly enriched: circadian rhythm regulation (Ko04712), plant hormone signal transduction (Ko04075), biosynthesis of unsaturated fatty acids (Ko01040), and protein processing in endoplasmic reticulum (Ko04141). One hundred forty of the DEGs from the BICF/BIOF comparison received GO annotations, including metabolic process (GO:0008152), cellular process (GO:0009987), transporter activity (GO:0005215), and several terms related to transcription factor activity (Fig. 4C). Sixty-four of the DEGs were mapped to KEGG pathways, primarily those associated with metabolism (50 DEGs), including carbon metabolism (4 DEGs), starch and sucrose metabolism (4 DEGs), glycolysis (4 DEGs), amino sugar and nucleotide sugar metabolism (4 DEGs), and biosynthesis of amino acids (3 DEGs) (Fig. 5C). 3.4.3 DEG expression patterns Energy metabolism: In the CF/BICF comparison, 13 DEGs were associated with energy metabolism (9 up- and 4 downregulated). Key upregulated genes in the BICF treatment included those encoding a sugar (and other) transporter (2.25-fold), AAA-ATPase (2.99-fold) and α-glucan phosphorylase (2.52-fold). In addition, genes encoding NADH ubiquinone reductase (Complex Ι), glucose-6-phosphate isomerase (G6PI), and triose phosphate transporter (TPT) were upregulated 3.4-, 2.2-, and 2.5-fold, respectively (Supplementary Fig. 5A). In the OF/BIOF comparison, 30 DEGs were associated with energy metabolism (28 upregulated). Strongly upregulated genes in the BIOF treatment included those encoding a sugar (and other) transporter (1.51-fold), NADH ubiquinone reductase (4.00-fold), phosphate transporter PHO1 (2.09-fold), and Calvin cycle protein CP12-2 (1.78-fold). Other upregulated energy-metabolism genes encoded oleosin 1, GTPase-activating protein AGD5, amino acid transporter AVT1A, mannose-6 phosphate isomerase 1, phosphatidylcholine transfer protein SFH13, glycolipid transfer protein (GLTP), and glycerol-3 phosphate transporter 1, which are involved in the synthesis and transport of lipids, amino acids, and sugars (Fig. 6A). In the BICF/BIOF comparison, 19 DEGs were associated with energy metabolism, including those encoding NADH ubiquinone reductase (Complex 1), polygalacturonase, non-specific lipid transfer protein 2, glutathione S-transferase, xyloglucan endo-transglycosylase, UDP-glycosyltransferase 91A1-like, and fatty acid desaturase (Supplementary Fig. 5B). Genes involved in energy metabolism and stress responses were notably upregulated in the BIOF treatment. Growth and development: In the CF/BICF comparison, 11 DEGs were related to plant growth and development. Genes encoding dienelactone hydrogenase (1.59-fold), carotenoid cleavage dioxygenase 8 (1.59-fold), and ethylene receptor 2 (0.85-fold) were upregulated under BICF treatment (Supplementary Fig. 5A). In OF/BIOF comparison, growth and development genes upregulated under BIOF included those encoding mitogen-activated protein kinase kinase kinases 17 and 18, abscisic acid receptor PYL8-like, serine aminopeptidase, auxin-responsive protein SAUR72 (0.9-fold), EIN3-binding F-box protein 1 (1.68-fold), and NDR1/HIN1-like protein 6 (0.99-fold) (Fig. 6A). In the BICF/BIOF comparison, 24 DEGs were involved in growth and development. Seventeen were upregulated under BIOF, including those encoding B-box zinc finger protein 23-like (BBX23), LSD1 zinc finger, pyruvate kinase, NDR1/HIN1-like protein 12, protein DMR6-like oxygenase 2, EIN3-binding F-box protein 1, abscisic acid receptor PYL4, and purple acid phosphatase 3 (PAP3) (Supplementary Fig. 5B). Notably, BBX23, which is involved in responses to abiotic stress, was upregulated 1.10-fold under BIOF treatment, and PAP3, which is associated with the production and clearance of reactive oxygen species, was upregulated 1.75-fold. Secondary metabolite synthesis: In the CF/BICF comparison, a gene encoding 4-hydroxy-3-methylbut-2-enyl diphosphate reductase, which catalyzes the synthesis of isopentenyl diphosphate and dimethylallyl diphosphate in the isoprenoid biosynthetic pathway, was upregulated 0.87-fold under BICF (Supplementary Fig. 5A). In the OF/BIOF comparison, 5 DEGs were related to secondary metabolite synthesis. These included genes encoding cytochrome P450 76A2, geranylgeranyl diphosphate reductase, strictosidine synthase (STR), heavy metal-associated isoprenylated plant protein 3, and phytoene synthase 1 (PSY). STR, which catalyzes the condensation of tryptamine and secologanin to form strictosidine, a key intermediate in monoterpene indole alkaloid biosynthesis, was upregulated 1.1-fold in BIOF (Fig. 6B). PSY is a key enzyme in the carotenoid biosynthetic pathway whose activity is required for the synthesis of lycopene, a tetraterpene known for its antioxidant and health-promoting properties. In the BICF/BIOF comparison, 2 DEGs associated with secondary metabolism were upregulated in BIOF: caffeoyl shikimate esterase (CSE, 1.8-fold) and STR (1.1-fold). CSE is involved in the biosynthesis of ferulic acid, which contributes to antioxidant activity (Supplementary Fig. 5B). Stress response: In the CF/BICF comparison, 4 DEGs were associated with stress responses. Genes encoding peroxisomal (S)-2-hydroxy acid oxidase (0.9-fold), catalase (3.6-fold) and osmotin-like protein TPM-1 (1.1-fold) were upregulated under BICF (Supplementary Fig. 5A). The upregulation of catalase gene expression is consistent with the higher rates of CAT activity observed in BICF plants (Fig. 3B). In the OF/BIOF comparison, 22 DEGs related to stress responses and the antioxidant system were upregulated under BIOF (Fig. 6B). These included genes encoding heat shock protein 90 (HSP90), the wound-induced protein WIP1, ethylene-responsive transcription factor 110, abscisic acid-insensitive 5, defensin-like protein 5, dehydration-responsive element binding protein 2A (DREB2A), bidirectional sugar transporter SWEET11-like, chaperone protein DnaJ-like, low temperature-induced protein lt101.2, basic proline-rich protein-like, stem-specific protein TSJT1-like (TSJT1), and universal stress protein PHOS34, among others. DREB2A, which participates in biotic and abiotic stress responses, was upregulated 1.69-fold. The bidirectional sugar transporter SWEET11, whose expression promotes drought and salt-stress tolerance, was upregulated 2.67-fold. Antioxidant-related genes encoding alternative oxidase, peroxidase, and polyphenol oxidase I were upregulated 2.8-, 1.6-, and 4.5-fold, respectively. In the BICF/BIOF comparison, nine stress-response DEGs were upregulated under BIOF, including HSP20, HSP90, WIP1, calcium binding-like isoform X2, TSJT1, DnaJ 39, PAP3, and universal stress protein PHOS32 (Supplementary Fig. 5B). Transcription Factors: A number of transcription factors from various families were differentially expressed in the CF/BICF, OF/BIOF, and BICF/BIOF comparisons (Figure 6B and Supplementary Figure 5). Interestingly, several transcription factor genes associated with stress responses were upregulated in BIOF compared with BICF, including genes encoding the ethylene-responsive transcription factor ERF071, inducer of CBF expression 1 (ICE1), and heat-stress transcription factor B-2c (Supplementary Fig. 5B). 3.5 Validation of DEGs by RT–qPCR To validate the RNA-seq results, we selected 14 DEGs for RT–qPCR analysis. Twelve were upregulated with fold changes ranging from 0.75 to 2.27, and 2 were downregulated with fold changes of–1.79 and –1.95. The RT–qPCR results were broadly consistent with the RNA-seq results for 12 of the 14 DEGs, providing evidence for the accuracy of the RNA-seq dataset (Fig. 7). 3.6 Metabolomic analysis 3.6.1 Quality control of metabolomic data We performed non-targeted metabolomics of root samples from the CF, OF, BICF, and BIOF treatments and identified 2467 total metabolites from 36 subclasses across all the samples (Supplementary Table 5). Spearman rank correlation coefficients of 0.75–0.95 between samples indicated the high reproducibility of the data (Supplementary Fig. 6). Orthogonal partial least squares discriminant analysis (OPLS–DA) yielded Q 2 values of 0.726 (CF/BICF), 0.619 (OF/BIOF), and 0.713 (BICF/BIOF), indicating that the model showed good predictive ability (Fig. 8A), and substitution tests further confirmed that the ability of the model to distinguish between treatment groups was not due to random chance (Fig. 8B). In positive ion mode, 272 DAMs were identified in the CF/BICF comparison (242 down- and 30 upregulated), 101 in the OF/BIOF comparison (45 down- and 56 upregulated), and 182 in the BICF/BIOF comparison (30 down- and 152 upregulated) (Fig. 9). 3.6.2 Differentially abundant metabolites Seventy three of the identified DAMs received annotations (Supplementary Table 6); the annotated DAMs from the CF/BICF and OF/BIOF comparisons are shown in Figure 10, and those from the BICF/BIOF comparison are shown in Supplementary Figure 7. A number of metabolites were upregulated in both the CF/BICF and OF/BIOF comparisons. For example, levels of citrate, a key TCA cycle intermediate, were significantly higher in BICF and BIOF plants than in corresponding CF and OF plants (Figure 10). Levels of pantothenate were also higher in both BICF and BIOF plants; pantothenate is the precursor for synthesis of coenzyme A and acyl-carrier protein, two cofactors with central roles in the TCA cycle and the synthesis of fatty acids, isoprenoids, and other secondary metabolites (Ereck et al. , 2006). Both L-citrulline and its upstream precursors N2-acetyl-L-ornithine and ornithine were more abundant in BICF and BIOF plants, suggesting that APBA application was associated with increased synthesis of these non-protein amino acids (Aslam et al. , 2021). Finally, kynurenic acid and, in BIOF only, its related metabolite 3-hydroxykynurenine were more abundant in BICF and BIOF plants than in CF and OF plants. A product of tryptophan metabolism, kynurenic acid has been suggested to inhibit auxin biosynthesis, although its roles in plants are poorly understood (Wróbel-Kwiatkowska et al. , 2024). Together, these results demonstrate that multiple metabolites with central roles in energy metabolism and amino acid synthesis were upregulated by application of APBA. By contrast, a number of DAMs were identified only in the OF/BIOF and/or BICF/BIOF comparisons but not in the CF/BICF comparison, suggesting that their synthesis was promoted specifically by APBA treatment only in the context of organic fertilization. These included the phenolic secondary metabolites 3-hydroxy-4-methoxycinnamic acid (isoferulic acid) and trans-2-hydroxycinnamic acid (2-coumaric acid), as well as shikimate, a key precursor for aromatic amino acid synthesis, and by extension, the synthesis of numerous plant secondary metabolites (flavonoids, phenylpropanoids, etc.). In addition, the levels of numerous amino acids were higher in BIOF than in BICF plants (Supplementary Figure 7), perhaps suggesting improved nitrogen nutrition in plants from the BIOF treatment, although that possibility remains to be tested. Joint analysis of transcriptome and metabolome 3.7.1 Correlations between genes and metabolites To explore the relationships between DEGs and DAMs in A. sinensis roots, we calculated Pearson correlation coefficients between all gene–metabolite pairs; pairs with | r | > 0.8 and p < 0.05 were considered to be significantly correlated. Numerous gene–metabolite pairs showed positive correlations in the CF/BICF and OF/BIOF comparisons (Fig. 11A–B), suggesting that some of these genes may be involved in regulating the levels of their correlated metabolites. KEGG enrichment analysis identified 35 and 21 pathways for the CF/BICF and OF/BIOF comparisons, respectively (Supplementary Tables 9 and 10). The integrated transcriptomic and metabolomic data showed significant enrichment in amino sugar and nucleotide sugar metabolism, starch and sucrose metabolism, and pyruvate metabolism (Fig. 11C–D). 3.7.2 Effects of BIOF treatment on the phenylpropanoid pathway To investigate in greater detail how the BIOF treatment influenced the contents of medicinal secondary metabolites and related intermediates in A. sinensis roots , we specifically examined the phenylpropanoid biosynthetic pathway, two of whose branches are crucial for the synthesis of phenolic acids and flavonoids. DEGs and DAMs associated with this pathway in the OF/BIOF comparison are shown in Fig. 12. In the phenolic acid pathway, the content of 3-hydroxy-4-methoxycinnamic acid (isoferulic acid), the predominant medicinal compound detected, was significantly higher (1.54-fold) in BIOF roots than in OF roots and significantly higher (1.46-fold) in BIOF roots than in BICF roots. BIOF roots also had higher levels of the isoferulic-acid precursors phenylalanine and coumaric acid. Interestingly, BICF roots showed a 0.67-fold decrease in isoferulic acid content compared with CF roots, suggesting that the effect of APBA on isoferulic acid depends on the type of fertilizer applied. In the flavonoid biosynthetic pathway, levels of the precursors shikimate, phenylalanine, and coumaric acid were significantly higher in the BIOF treatment than the OF treatment. Flavonoid content was also 1.40-fold higher in BIOF plants compared with OF plants. These results suggest that application of APBA together with organic fertilizer promotes the accumulation of secondary metabolites and medicinal compounds in A. sinensis roots. 4. Discussion In this study, we demonstrated that the biocontrol agent B. amyloliquefaciens FZB42 significantly enhanced above- and belowground growth of A. sinensis under greenhouse and field conditions. In field trials, A. sinensis yields were significantly higher when plants were treated with the APBA biocontrol agent, regardless of the type of fertilizer used, and the BIOF treatment produced the highest yields of fresh roots, averaging 4.836 kg/m 2 . These results underscore the potential for APBA to serve as an effective biological inoculant for improving biomass production and stress tolerance of traditional Chinese medicinal plants. The combination of APBA with fertilizers resulted in the upregulation of multiple genes related to energy metabolism. In CF vs. BICF plants, these were mainly associated with carbon metabolism (e.g., NADH ubiquinone reductase, glucose-6-phosphate isomerase, triose phosphate transporter), whereas in OF vs. BIOF plants and/or BIOCF vs. BIOF plants, both carbohydrate-related genes and genes associated with lipid metabolism (e.g., oleosin 1,3-ketoacyl-CoA synthase, fatty acid desaturase) were upregulated. This result suggests that some effects of APBA on energy metabolism differed depending on the type of fertilizer used. Numerous genes upregulated in the BIOF treatment relative to the OF treatment were associated with phytohormone signaling. These included genes encoding SAUR72, which participates in IAA signal transduction; the ABA receptor PYL8, which promotes lateral root growth by enabling binding of MYB77 to the promoters of auxin-responsive genes (Zhao et al., 2014) ; the ABA-related transcription factor ABI5, which mediates seed germination, seedling growth, and abiotic stress responses (Skubacz et al. , 2016); and tEBF1, which negatively regulates ethylene signaling (Wang et al. , 2023). Also upregulated in the BIOF plants were genes encoding NDR1/HIN1-Like protein 6, which is thought to enhance disease resistance by activating the JA signaling pathway (Varet et al. , 2002) and pathogenesis-related protein 2, whose induction via the salicylic acid pathway mediates systemic acquired resistance (Anisimova et al. , 2021). Upregulation of the latter two genes may help to explain the increased resistance to Fusarium root rot documented in our previous work (Liu et al., 2021), a possibility that warrants further study. Penetrin-like proteins (PnOLPs), known for their roles in plant defense, were upregulated 1.1-fold in BICF plants, suggesting another possible mechanism by which APBA may promote disease resistance in A. sinensis , as PnOLP1 enhances defense against Fusarium infection in Notoginseng (Zhao et al. , 2020). Interestingly, fewer genes associated with hormonal signaling were differentially expressed in the CF/BICF comparison. The BIOF treatment increased the expression of multiple genes linked to secondary metabolite synthesis, including genes encoding STR, which is essential for monoterpene indole alkaloid biosynthesis; phytoene synthase, a key enzyme in lycopene biosynthesis (Zhou et al. , 2022); CSE, which participates in ferulic acid biosynthesis (Wang et al. , 2019); and geranyl geranyl diphosphate reductase, a key enzyme of terpenoid metabolism. Consistent with the gene expression results, levels of multiple secondary metabolites were also higher in BIOF plants than in OF plants, and similar trends were observed in the BICF/BIOF comparison. These metabolites included 3-hydroxy-4-methoxycinnamic acid (isoferulic acid), trans-2-hydroxycinnamic acid (2-coumaric acid), shikimate, homovanillic acid, kynurenic acid, adipic acid, and L-pipecolic acid. Notably, isoferulic acid—a key medicinal compound in A. sinensis known for its immune-regulating, anti-inflammatory, anticancer, and antioxidant properties (Su et al. , 2015)—was upregulated 1.54-fold in BIOF plants compared with OF plants. In addition, quinolinic acid (4-hydroxyquinoline-2-carboxylic acid), a neuroprotective and anticonvulsant compound (Walczak et al. , 2020), was upregulated 1.91-fold in BIOF plants compared with OF plants, and flavonoids, known for their anti-inflammatory and antioxidant activities (Xu et al. , 2020), were upregulated 1.40-fold in BIOF plants compared with OF plants. Enhanced biosynthesis of phenylpropanoids such as p -coumaric acid and isoferulic acid helps to mitigate the oxidative damage caused by environmental stress (Yao et al. , 2021; Jia et al. , 2022), as shown in celery (Zhang et al. , 2019), peanuts (Wang et al. , 2021), and mulberries (Gan et al. , 2021). Thus, the combination of APBA with organic fertilizer may be particularly effective at stimulating production of the bioactive secondary metabolites that both protect A. sinensis from oxidative damage and make it a valuable medicinal herb. Previous research has shown that PGPRs can enhance the activity of enzymes such as superoxide dismutase, peroxidase, and CAT, which help to scavenge the reactive oxygen species (ROS) generated under abiotic stress (Hu et al. , 2016). Strains of Bacillus spp. antagonistic to the rice pathogen Pyricularia oryzae significantly enhanced the activity of antioxidant enzymes in rice leaves and roots, reducing oxidative damage from the pathogen (Rais et al. , 2017). In the present study, CAT activity was significantly higher and catalase gene expression was upregulated in BICF plants relative to CF plants, suggesting that APBA application increased the antioxidant capacity of A. sinensis . Similarly, upregulation of genes encoding heat-shock proteins, a Dna-J-like chaperone, and a heat-shock transcription factor in BIOF plants suggested an increased capacity to respond to high-temperature stress. Transcription factors play a role in regulating plant growth, development, and stress responses. For example, the zinc finger protein gene ZAT10 , whose A. sinensis homolog was upregulated 1.44-fold in the BIOF treatment, has been reported to play both positive and negative regulatory roles in stress responses. ZAT10 enhances tolerance to salt, heat, and osmotic stresses in A. thaliana by upregulating ROS-defense genes (Mittler et al. , 2006). Another transcription factor gene, SRM1 , upregulated 1.24-fold in the BIOF treatment, regulates the stress hormone ABA, affecting seed germination and vegetative growth under saline conditions (Wang et al. , 2015). SRM1 may therefore be a useful target for breeding of salt-tolerant medicinal plants. ICE1 , whose overexpression in A. thaliana increases frost resistance by enhancing proline accumulation and reducing electrolyte leakage and malondialdehyde content (Zhou et al. , 2020), was upregulated 1.35-fold in BIOF plants, suggesting that APBA may also promote cold resistance in A. sinensis through its effects on ICE1 regulation. 5 Conclusions This study provides insights into the metabolic responses of A. sinensis to the application of a bacterial biocontrol agent under field conditions. In addition to increasing growth and yield, the APBA biocontrol agent also altered the expression of key genes involved in growth, development, and stress responses. Transcriptome results suggested that APBA application upregulated the expression of genes related to pathogen and stress responses, perhaps by stimulating JA, ABA, and SA signaling. The combination of APBA with organic fertilizer appeared to have the greatest effect on the production of secondary metabolites, which are the basis of A. sinensis ’ medicinal effects. Increased production of phenylpropanoid compounds, especially isoferulic acid, may also have increased the plant’s ability to resist oxidative stress. These findings demonstrate that application of a biocontrol agent can increase the yield and quality of A. sinensis while supporting sustainable agricultural practices, providing guidance for the development of similar strategies in other medicinal crops. Acknowledgments This research was supported by the International Partnership Program of the Chinese Academy of Sciences for Grand Challenges (315GJHZ2024123GC); the Science and Technology Planning Project of Gansu Province, major special project (21ZD4NA019); the International Science and Technology Cooperation Base of Gansu Province (2020-0413-GHC-0036); the Natural Science Foundation of Gansu Province, China (21JR7RA042); and the Science and Technology Planning Project of Gansu Province (23JRRA575). We would like to thank A&L Scientific Editing (www.alpublish.com) for their assistance in content and English language editing during the preparation of this manuscript. Declaration of Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability Data will be made available on request. 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Supplementary Material File (figures.docx) Download 3.29 MB Information & Authors Information Version history V1 Version 1 17 August 2025 Peer review timeline Published BMC Plant Biology Version of Record 22 Dec 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords angelica sinensis biocontrol agent metabolome physiology growth secondary metabolism Authors Affiliations Yang Liu Northwest Institute of Eco-Environment and Resources View all articles by this author Xiaofan Xie Northwest Institute of Eco-Environment and Resources View all articles by this author Yuan Tian Northwest Institute of Eco-Environment and Resources View all articles by this author Liang Yue Northwest Institute of Eco-Environment and Resources View all articles by this author Xia Zhao Northwest Institute of Eco-Environment and Resources View all articles by this author Qin Zhou Northwest Institute of Eco-Environment and Resources View all articles by this author Yun Wang Northwest Institute of Eco-Environment and Resources View all articles by this author Yubao Zhang Northwest Institute of Eco-Environment and Resources View all articles by this author Ling Jin Gansu University of Chinese Medicine View all articles by this author Qingxia Guan Seed Station of Longxi county View all articles by this author Zengxiang Guo Angelica Research Institute of Minxian View all articles by this author Lam-Son Tran Texas Tech University View all articles by this author Ruoyu Wang 0000-0002-0422-6408 [email protected] Northwest Institute of Eco-Environment and Resources View all articles by this author Metrics & Citations Metrics Article Usage 222 views 119 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Yang Liu, Xiaofan Xie, Yuan Tian, et al. An attapulgite--Bacillus amyloliquefaciens biocontrol agent increases growth and medicinal metabolite content in Angelica sinensis by modulating hormonal and phenylpropanoid pathways. Authorea . 17 August 2025. DOI: https://doi.org/10.22541/au.175541778.86764056/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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