Integrated Multi-Omics Analysis Reveals Fertilizer-Mediated Modulation of the Plant-Pathogen Interaction Pathway in Zanthoxylum armatum

Integrated Multi-Omics Analysis Reveals Fertilizer-Mediated Modulation of the Plant-Pathogen Interaction Pathway in Zanthoxylum armatum | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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. 31 October 2025 V1 Latest version Share on Integrated Multi-Omics Analysis Reveals Fertilizer-Mediated Modulation of the Plant-Pathogen Interaction Pathway in Zanthoxylum armatum Authors : Yuhan Wu , Danping Xu , Habib Ali , Zhiling Wang , and Zhihang Zhuo 0000-0002-2566-3172 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176190856.62672505/v1 225 views 181 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract In this study, we systematically analyzed the effects of microbial fertilizer (Z1), organic fertilizer (Z2) and bio-organic fertilizer (Z3) on the immune regulation and inter-root microecology of Zanthoxylum armatum based on the plant-pathogen interactions pathway (ko04626) in conjunction with the transcriptome, macro-genome and plant-soil physiological indices. The results showed that Z3 significantly up-regulated immunity key genes such as WRKY33 , PR1 , RBOHF and FLS2 ; meanwhile, it optimized the structure of inter-root beneficial bacterial flora, enriched functional groups such as actinomycetes and amoebae, and improved the metabolic activity of soil. Z1 was moderately activated and Z2 effects were weak. Transcriptome association analysis with physiological indicators revealed that WRKY33 and HSP83A were highly correlated with antioxidant enzyme activities and H 2 O 2 content. This study reveals the molecular mechanism of plant immunity regulated by fertilizers via inter-root microecology, which provides a theoretical basis for efficient ecological fertilization and eco-agriculture in Z. armatum . Integrated Multi-Omics Analysis Reveals Fertilizer-Mediated Modulation of the Plant-Pathogen Interaction Pathway in Zanthoxylum armatum Yuhan Wu 1, # , Danping Xu 1, # , Habib Ali 2, * , Zhiling Wang 1,3 , Zhihang Zhuo 1, * 1 College of Life Science, China West Normal University, Nanchong, 637002, China 2 Department of Agricultural Engineering, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, 64200, Pakistan 3 College of Forestry, Sichuan Agricultural University, Chengdu 611130, China # These authors contributed equally to this work and should be considered co-first authors. * Correspondence author: Zhihang Zhuo Email: [email protected] College of Life Science, China West Normal University, Nanchong, 637002, China Habib Ali Email: [email protected] Department of Agricultural Engineering, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, 64200, Pakistan Abstract: In this study, we systematically analyzed the effects of microbial fertilizer (Z1), organic fertilizer (Z2) and bio-organic fertilizer (Z3) on the immune regulation and inter-root microecology of Zanthoxylum armatum based on the plant-pathogen interactions pathway (ko04626) in conjunction with the transcriptome, macro-genome and plant-soil physiological indices. The results showed that Z3 significantly up-regulated immunity key genes such as WRKY33 , PR1 , RBOHF and FLS2 ; meanwhile, it optimized the structure of inter-root beneficial bacterial flora, enriched functional groups such as actinomycetes and amoebae, and improved the metabolic activity of soil. Z1 was moderately activated and Z2 effects were weak. Transcriptome association analysis with physiological indicators revealed that WRKY33 and HSP83A were highly correlated with antioxidant enzyme activities and H 2 O 2 content. This study reveals the molecular mechanism of plant immunity regulated by fertilizers via inter-root microecology, which provides a theoretical basis for efficient ecological fertilization and eco-agriculture in Z. armatum . Keywords: Zanthoxylum armatum ; Plant-pathogen interaction; Transcriptomic regulation; Rhizosphere microorganisms; multi-omics; 1. Introduction Zanthoxylum armatum belongs to the Rutaceae of small trees or shrubs, has a long history of cultivation in Southwest China (Sichuan, Chongqing, Guizhou, etc.), is an important economic tree species with both edible and medicinal value [1] . Z. armatum shoots and leaves are rich in nutrients and flavour and are widely consumed as a seasoning, while the rind, seeds and stems and leaves are used in traditional Chinese medicine for the treatment of headaches, fever and inflammation [2, 3] . Z. armatum has the characteristics of strong adaptability, high fertility, short production cycle, etc. It is the main economic tree species, drought-resistant tree species and soil and water conservation tree species in some areas of China, often used as soil and water conservation and ecological restoration plants, widely adapted to a variety of ecological types such as mountains and hills [4] . With the continuous expansion of Z. armatum cultivation area, improving its yield and quality has become a hot direction of current agricultural research. Fertilisation, as a major means to regulate plant nutrition and optimise soil physicochemical properties, significantly affects soil microstructure and plant growth and metabolism [5, 6] . Research has shown that new types of fertilisers such as microbial, organic and bio-organic fertilisers not only improve soil organic matter and nutrient supply capacity, but also enhance plant resilience and yield performance by altering inter-root microbial diversity and functional communities. Soil microorganisms play a central role in soil aggregate formation, organic matter transformation and nutrient cycling and are extremely sensitive to exogenous fertiliser application. Changes in their structure and function often indicate key points in the response of soil ecosystems to agricultural interventions [7] . Different fertilisation strategies differentially affect the efficiency of nutrient conversion (e.g. nitrogen and phosphorus cycling) and crop growth by regulating microbial diversity, abundance and metabolic activity of functional flora, specifically by promoting key ecological functions such as agglomerate formation, organic matter decomposition and pollutant degradation [8, 9] . Different fertilisation practices affect nutrient effectiveness by modulating soil microbial processes, which in turn optimise crop growth. Organic fertilisers enhance microbial biomass through the addition of carbon-rich organic matter and selectively enrich functional microbes to reshape the community structure [10, 11] . Organic fertilisers increase soil microbial activity by 16-20 per cent compared to inorganic fertilisers [12] . Long-term application significantly enhanced the physiological functions of Z. armatum leaves [13] . Studies have shown that formulated fertilization with nitrogen, phosphorus and potassium can promote the growth and development of Z. armatum [14] and enhance the photosynthetic efficiency of leaves [15] . All organic fertilisers increased the yield of Z. armatum , resulting in an increase in the input-output ratio of Z. armatum . For example, organic fertiliser application significantly increased soil microbiomass carbon, enzyme activity and nutrient effectiveness, which in turn promoted crop physiological activity and stress tolerance [16] . In addition to this, organic fertilisers had a significant effect on soil microorganisms, altering both inter- and non-inter-root bacterial communities of plants and increasing the abundance of microbial populations [17] . At the same time, plant pathogens are widely distributed in the soil, seriously threatening the health of crops, and how to intervene in the ”plant-pathogen” interactions by regulating the microbiome is a key direction to enhance the disease resistance of crops. Transcriptomics focuses on analysing gene expression patterns at the mRNA level, revealing which genes are actively transcribed under specific conditions. This information reveals the dynamics of gene regulation and identifies key pathways involved in plant growth, development, and response to environmental stimuli. RNA-seq technology can comprehensively capture the gene expression profiles of plant tissues under specific stress or management conditions, revealing key pathways (e.g., the MAPK signalling pathway, Plant-pathogeninteraction, etc.) [18, 19] . Macrogenomics is the study of the genomes of all microorganisms in a given environmental sample, the establishment of macrogenomic libraries, and the sequencing and analysis of functional genes to study the close relationship between the environment and the community characteristics, functions and evolution of all microorganisms in it [20, 21] . Macrogenomic technology can analyse the composition and function of inter-root microorganisms without the need to isolate microorganisms, providing a systematic view of the fertiliser-microbe-plant linkage [22] . Currently, macrogenomics is widely used in environmental microbiology and agricultural microecosystem research. In recent years, the rapid development of second-generation sequencing technologies has made transcriptome (RNA-seq) and metagenomics powerful tools for analysing plant-microbe interactions and fertilisation mechanisms [23, 24] . Based on the above technical advantages, the present study combines transcriptomic, macrogenomic and physiological indicators to carry out a comprehensive multi-indicator analysis, aiming to reveal the regulatory mechanism of fertilizer-mediated plant-pathogen interaction pathway. In the plant immune system, “Plant-pathogen interaction (ko04626)” is the core signaling pathway for plants to sense pathogens and initiate defense responses. It includes a series of typical immune processes such as pathogen-associated molecular pattern recognition (PAMP-Triggered Immunity, PTI), effector recognition (Effector-Triggered Immunity, ETI), calcium signaling pathway, ROS outbreak, WRKY transcription factor regulation, PR protein expression, and so on [25] . Genes in this pathway, such as WRKY33, RBOH, FLS2, CPK and PR1, play important roles in plant response to pathogen stress and micro-ecological regulation, and are becoming important targets for research on biofertilizers to enhance plant resistance [26] . In this study, Z. armatum was studied by setting three fertilization treatments: microbial fertilizer, organic fertilizer and bio-organic fertilizer. To systematically analyze the effects of different fertilizers on the inter-root soil microbial community of Z. armatum , the expression of genes related to plant immunity and the activation mechanism of the “plant-pathogen interaction pathway” by combining transcriptomic, macro-genomic and physiological indicators. The aim of this study is to reveal the multilevel regulatory mechanism among soil-microbe-plant and to provide theoretical basis for the enhancement of Z. armatum healthy planting and precise fertilizer management. 2. Materials and Methods 2.1 Plant material and soil properties The material for this study was Z. armatum seedlings, two-year old bare-root seedlings harvested from the Sichuan territory, with uniform growth and free from pests and diseases. The containers were polyethylene plastic pots (height 23 cm, average diameter 24 cm) with lightweight ceramic granules (0.8-1.5 cm) of about 3 cm thickness at the bottom as a drainage layer, and the soil used was a mixture of peat nutrient soil supplied by Pinsentop, Denmark. Fertilizers used in the experiment were microbial fertilizers (Z1), organic fertilizers (Z2) and bio-organic fertilizers (Z3), the organic fertilizers were provided by Meishan Yiji Agricultural Science and Technology Co. Ltd. in Sichuan Province, and the composite microbial fertilizers and the bio-organic fertilizers were provided by Sichuan Zhengyi Industry Co. Ltd. 2.2 Plant Cultivation and Handling The experimental potted plants were placed in the China West Normal University and a CK control group was set up. Fertilizer treatment was applied after one month of stable cultivation. Three fertiliser treatments were applied, including Z1 treatment (50 g of composite microbial fertiliser per potted plant), Z2 treatment (50 g of organic fertiliser per potted plant) and Z3 treatment (50 g of bio-organic fertiliser per potted plant), and all treatments were set in three parallels. After two months of growth, in order to ensure the consistency of the samples, fresh mature leaves at the same stage of nutritional development were collected from each treatment, and the surface of the leaves was sterilised twice with saline, then rinsed twice with sterile water and wiped dry with sterile filter paper, immediately frozen in liquid nitrogen and stored in a -80°C refrigerator for spare use. The inter-root soil of the same sample was collected from each treatment, and four sampling points with different orientations were randomly determined along 3-5 cm of the container side of each potted seedling. Subsequently, the top layer of floating soil was removed from the sampling points, and the depth of the soil taken was kept at 1-5 cm. The soil samples collected from the four points of the same plant were mixed thoroughly and then sieved for sampling, and the soil samples were removed from the plant residues, numbered and labelled, and then sealed in a sealed bag and placed into an ice box for temporary storage and brought back to the laboratory for processing as soon as possible. 2.3 Measurement of physiological indicators Physiological and biochemical parameters of Z. armatum leaves were determined including soluble protein content, soluble sugar content, hydrogen peroxide content, flavonoids content, amino acid content, superoxide dismutase activity, peroxidase activity, catalase activity, polyphenol oxidase activity, malondialdehyde content and alkaloid content. Soil physicochemical indicators were determined including organic matter, phosphatase, polyphenol oxidase, dehydrogenase, microbial carbon (MBC), microbial nitrogen (MBN), microbial phosphorus (MBP) and water content. All indicators were determined with reference to national or industry standard methods, using equipment such as ultraviolet-visible spectrophotometers and enzyme labellers. 2.4 Transcriptome sequencing and analysis 2.4.1 RNA extraction, sequencing, transcriptome assembly and annotation Total RNA was extracted from frozen Zanthoxylum armatum leaves using the TRIzol reagent (Invitrogen, USA) following the manufacturer’s instructions with slight modifications. Briefly, 50–100 mg of frozen leaf tissue was ground into a fine powder in a sterilized mortar with liquid nitrogen, followed by homogenization in 1 mL TRIzol reagent (sample volume <10% of TRIzol volume). The homogenate was transferred to a 1.5 mL centrifuge tube and incubated at 15–30 °C for 5 min to ensure complete dissociation of nucleoprotein complexes. Subsequently, 0.2 mL chloroform was added, the tube was tightly capped, shaken vigorously for 15 s, and incubated at 15–30 °C for 2–3 min. Samples were centrifuged at 12,000 r/s for 15 min at 4 °C, and the upper aqueous phase was transferred to a new tube. An equal volume of 0.5 mL isopropanol was added, followed by incubation for 10 min at room temperature and centrifugation at 12,000 r/s for 10 min at 4 °C. The resulting RNA pellet was washed with 1 mL of 75% ethanol, vortexed gently, and centrifuged at 7,500 r/s for 5 min at 4 °C. After discarding the supernatant, the pellet was air-dried for 5–10 min and resuspended in RNase-free water. RNA concentration and purity were measured, and samples were stored at −20 °C. RNA integrity was assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies, USA). The mRNA enrichment was performed by Oligo(dT) magnetic bead assay, followed by fragmentation, cDNA synthesis, end repair, addition of A-tail, junction ligation, amplification and purification for the construction of sequencing libraries. The library was initially quantified by Qubit 2.0 and then diluted to 1.5 ng/μL, Agilent 2100 was used to assess insert size, and qRT-PCR was used to detect the effective concentration (>1.5 nM). The qualified libraries were pooled according to the effective concentration and target data volume, and high-throughput sequencing was performed using the Illumina platform to obtain transcriptome data of the samples to be tested based on the principle of Sequencing by Synthesis. 2.4.2 Sequence alignment, differential gene screening, enrichment analysis and real-time quantitative PCR (qPCR) Clean reads were aligned to the reference genome using HISAT2 for rapid and accurate mapping, generating alignment files in SAM format, which were subsequently converted to BAM format using SAMtools. Transcript assembly was performed with StringTie, which employs a network flow algorithm and optionally integrates de novo assembly to reconstruct transcripts. The assembled novel transcripts were annotated against the Pfam, SUPERFAMILY, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Based on the genomic locations of the mapped reads, the number of reads covering each gene (including newly predicted genes) from start to end positions was counted. Reads with a mapping quality score <10, unpaired mapped reads, and reads mapped to multiple genomic locations were filtered out. Read counting was performed using the featureCounts function in the Subread package. After the quantification of gene expression was completed, the expression data were statistically analysed to screen the samples for genes with significant differences in expression levels in different states. Firstly, the raw readcounts were normalised (normalization), mainly for the correction of sequencing depth, then the statistical model was used for the calculation of the probability of hypothesis testing ( p -value), and finally the multiple hypothesis testing correction was performed to obtain the FDR value (padj), the screening criterion for differential genes |log2(FoldChange)| >=1 & padj<= 0.05. We used clusterProfiler software to analyze the differential gene sets for GO functional enrichment, KEGG pathway enrichment, etc. GO functional enrichment and KEGG pathway enrichment used padj less than 0.05 as the threshold for significant enrichment. 2.4.3 qRT-PCR Validation Based on the transcriptome sequencing results, we selected six DEGs for quantitative real-time PCR (qRT-PCR) validation. Primers were designed using Primer Premier 5.0 software, and the amplification products were 80-300 bp in length. The literature was reviewed and ZaUBQ was selected as the internal reference gene [27, 28] . All reagents for qRT-PCR were obtained from TaKaRa. The RNA from three periods of Z. armatum was extracted with MiniBEST Plant RNA Extraction Kit, and cDNA was obtained by reverse transcription was done with PrimeScript TM RT reagent Kit. Finally, qPCR was performed by TB Green TM Premix Ex Taq TM Ⅱ. 2.5 Macrogenome Sequencing and Analysis 2.5.1 DNA extraction, library construction and on-board sequencing E.Z.N.A™ Mag-Bind Soil DNA Kit was used to extract DNA from the soil samples. The samples were mixed and homogenised with glass beads and then lysed to remove proteins and insoluble particles. The binding conditions were adjusted so that the DNA was adsorbed to the magnetic beads. The DNA was removed from the beads by two washes to remove the impurities, and finally the DNA was recovered by elution buffer. A total of 1 μg of genomic DNA from each sample was randomly sheared into library construction. The libraries were prepared through end repair, A-tailing, adapter ligation, purification, and PCR amplification. The fragment integrity and insert size of the libraries were assessed using an AATI system. Libraries meeting the expected size range were accurately quantified using qPCR to determine the effective concentration (>3 nM) to ensure sequencing quality. Qualified libraries were pooled according to their effective concentration and the target sequencing depth, and subjected to paired-end 150 bp (PE150) sequencing. 2.5.2 Data analysis Raw sequencing data obtained from the NovaSeq platform were preprocessed using fastp to remove: (i) reads containing adapter sequences; (ii) reads in which >50% of bases had a quality score ≤5; and (iii) reads in which the proportion of ambiguous bases (N) exceeded 10%. The resulting high-quality reads (clean data) were used for subsequent analyses. Assembly was performed using MEGAHIT, and the assembled scaffolds were split at ambiguous base (N) positions to generate contiguous sequences (scaftig) without Ns. Open reading frames (ORFs) were predicted from scaftigs (≥500 bp) using MetaGeneMark, and ORFs shorter than 100 nt were discarded. The predicted ORFs were dereplicated using CD-HIT to obtain a non-redundant initial gene catalogue. Clean reads from each sample were mapped to the initial gene catalogue using Bowtie2 to calculate the read counts per gene. Genes with ≤2 mapped reads in all samples were removed, and the remaining genes constituted the final non-redundant gene catalogue (unigenes) for downstream analysis. DIAMOND was used to compare the unigenes with the Micro_NR database (covering bacterial, fungal, archaeal and viral sequences in the NCBI NR library), and the results with e-value ≤ minimum e-value × 10 were filtered, and the LCA algorithm was used for species annotation. For functional annotation, the unigenes were also compared to the KEGG database via DIAMOND to obtain the corresponding functional information. 2.6 Data integration and correlation analysis In order to explore the potential correlation between physiological indicators and immune-related gene expression, Pearson correlation coefficient (PCC) was used to correlate gene expression with leaf physiological indicators and relative abundance of top ten with soil physiological indicators. During the analysis, key immunity genes that were significantly differentially expressed in the transcriptome data were first screened, and then their FPKM values were paired with physiological indicators of the corresponding samples for calculation. Correlation analyses were completed in statistical and visualisation software such as GraphPad Prism 9.0 (GraphPad Software Inc., USA), IBM SPSS Statistics 26.0, and Microsoft Excel 2019, and correlation heatmaps were plotted to demonstrate the strength and direction of correlation between variables. In the test of significance, p -value 3 Results 3.1 Transcriptomic analysis of leaves based on plant-pathogen interaction pathway 3.1.1 Acquisition and annotation of high-quality transcriptome In this study, RNA-seq sequencing was performed on Z. armatum samples using Illumina high-throughput sequencing platform, and 73.92 Gb clean reads were obtained from the raw data after strict quality control, Q20 and Q30 were more than 99% and 97%, respectively, with stable GC content (43.74%-44.24%) and reliable data quality (Table S1). To determine the origin of the sequencing fragments, the quality-controlled clean reads were aligned to the reference genome. A total of 417,289,740 reads were successfully mapped to the reference genome (Table S2). Transcript assembly and functional annotation were performed using StringTie, resulting in the identification of 26,691 expressed genes across the four experimental groups. Among them, 279 genes were co-expressed in the Z1, Z2, and Z3 groups, with the detailed expression distribution shown in Figure S1. 3.1.2 Analysis of differentially expressed genes (DEGs) under different fertiliser treatments After completing the quantitative analysis of gene expression, we statistically analysed the expression data to screen for genes with significant differences in expression levels under different treatment conditions. The results of the differentially expressed gene analysis with p -value ≤ 0.05 and |log2FoldChange| > 1 as significance thresholds are presented in Figure 1. In the comparison of the Z1 treatment group with the control (CK) (Figure 1A), 61 genes were up-regulated and 111 genes were down-regulated; in the comparison of the Z2 treatment group with the control (Figure 1B), 40 genes were up-regulated and 80 genes were down-regulated; and in the comparison of the Z3 treatment group with the control (Figure 1C), 85 genes were up-regulated and 173 genes were down-regulated. Among them, Z3 treatment induced the highest number of differential genes, indicating that it had the most significant regulatory effect on the plant expression network, which might improve the plant’s ability to respond to external stresses by activating immune-related pathways and signal transduction networks. In contrast, Z2 treatment induced relatively few expression changes, possibly indicating limited plant stimulation or plant adaptation, while Z1 treatment showed moderate activation coexisting with repression, presenting a moderately strong transcriptional response. 3.1.3 KEGG enrichment analysis To further resolve the biological functions of the differentially expressed genes, we performed pathway enrichment analysis using the KEGG database, and Figure 1 shows the top 20 enriched pathways under different treatments. The analysis showed that differentially expressed genes were significantly enriched in the Plant-pathogen interaction pathway (ko04626) in the Z1-treated group compared with the control group (Figure 1D), suggesting that microbial fertiliser treatments may significantly activate pathogen recognition and resistance response in plants. In addition, the group was enriched in MAPK signaling pathway-plant, Protein processing in endoplasmic reticulum, Ether lipid metabolism and RNA degradation pathways, showing that plants are induced by microbial fertiliser aftershowed that plants undergoing induction by microbial fertilizers experienced significant changes in signal transduction, protein processing and metabolic regulation. In the Z2 group (Figure 1E), the most highly enriched pathway was Protein processing in endoplasmic reticulum, which also involved Monoterpenoid biosynthesis, Zeatin biosynthesis, OxidativeZeatin biosynthesis, Oxidative phosphorylation and Starch and sucrose metabolism were also involved in the metabolism-related pathways, suggesting that Z2 treatment mainly regulated intracellular homeostasis, energy metabolism and growth factor synthesis pathways, which showed a biased growth-regulatory response. Although the Plant-pathogen interaction pathway was not ranked in the top five in Z2, differential genes were also detected in this pathway, suggesting a trend towards a response. In group Z3 (Figure 1F), the Plant-pathogen interaction pathway was again significantly enriched and co-occurred with stress response pathways such as Glutathione metabolism and MAPK signaling pathway-plant, which further verified the activation of the plant defence system by bio-organic fertilizerThe effect of bio-organic fertiliser on the activation of plant defence system was further verified. In addition, the Z3 group showed significant enrichment in antioxidant- and secondary metabolism-related pathways such as Ascorbate and aldarate metabolism and Biosynthesis of various plant secondary metabolites, suggesting that plants may have strengthened their overallresistance network of the plant after exposure to bio-organic fertilisers. The Plant-pathogen interaction, Protein processing in endoplasmic reticulum, and RNA degradation pathways were commonly enriched across all three treatment groups, suggesting that these pathways may represent core mechanisms underlying Z. armatum ’s general response to fertilizer induction. In addition, some pathways were observed to be co-enriched between the two treatment groups, e.g., Monoterpenoid biosynthesis, Zeatin biosynthesis, and Circadian rhythm-plant were significantly enriched in the Z1 and Z2 groups; Flavonoid biosynthesis, MAPK signaling pathway-plant and Plant hormone signal transduction were co-existing in Z1 and Z3 groups; while Z2 and Z3 groups shared Starch and sucrose metabolism, Galactose metabolism and Ascorbate and aldarate metabolism. The above results indicated that the three fertiliser treatments triggered the enhancement of plant resistance and reprogramming of metabolic pathways to different degrees, especially the activation of plant-pathogen interaction pathway, which provided important clues for the in-depth investigation of plant-microbe-environment interactions. 3.1.4 Co-expression network analysis reveals the ko04626 pathway as a key regulatory centre To further reveal the regulatory effects of different fertiliser treatments on the defence-related pathway of Z. armatum. In this study, we focused on the key genes ( p -value ≤ 0.05 and |log2FoldChange| > 1) that were significantly differentially expressed in the Plant-pathogen interaction pathway (ko04626), and theexpression changes and functional categories were analysed under different treatments (Figure S2, Table S3, Table S4 and Table S5). As can be seen in Figure 2A and Table S3, a total of seven pathway genes were significantly enriched in the Z1 group compared with the control group, including the up-regulated expression of Zardc08593 (encoding CPK9, belonging to the NAC family), and the down-regulated expression of Zardc37059 (HSP83A, LBD family), Zardc42570 (HSP83A, G2-likefamily), Zardc07384 (CPK9), Zardc31684 (CML37, FAR1 family), Zardc28299 (WRKY33, WRKY family) and Zardc31018 (CML41, NAC family). These genes are involved in calcium signalling regulation (CPK/CML-like), transcriptional regulation (WRKY, G2-like) and molecular chaperone proteins (HSP-like) functions, suggesting that microbial fertiliser treatments significantly regulate primary pathogen recognition and defence activation mechanisms in plants. In the comparison of the Z2 group with the control group (Figure 2B and Table S4), the differentially expressed genes included the up-regulated expression of Zardc08593 (CPK9) and Zardc28299 (WRKY33), as well as the down-regulated expression of Zardc37059 (HSP83A). Notably, WRKY33 was co-enriched with PR1, suggesting that organic fertilizers may activate mid- and late-stage pathways of plant defence. In the Z3 group (Figure 2C and Table S5), significantly enriched pathway genes included a trend of down-regulation of Zardc42570 (HSP83A), Zardc37059 (HSP83A), Zardc31684 (CML37), and up-regulated expression of Zardc16693 (CCAMK, NAC family). In addition, enhanced expression of several ROS-related signalling pathway genes, such as Zardc31347 (RBOHF) and Zardc47296 (FLS2), was detected, suggesting that the bio-organic fertiliser facilitated the initiation of Ca²⁺ signalling and reactive oxygen species (ROS) bursts, among other things, early in the life of the plant after the recognition of pathogen-associated molecular patterns (PAMPs)immune response. Based on the functional annotation and gene family distribution of enriched genes in the three treatment groups, gene families such as WRKY, NAC, CML, CPK, and HSP were frequently identified, forming the core transcriptional regulatory and signal transduction network underlying Zanthoxylum armatum’s defense response to fertilization stimuli. Among them, WRKY33, a classical pathogen-responsive factor in plants, regulates the expression of the defense-related gene PR1 and subsequently activates systemic acquired resistance (SAR). Meanwhile, the receptor-like kinase FLS2 and respiratory burst oxidase homolog RBOHF mediate the recognition of pathogen-associated molecular patterns (PAMPs) and the production of reactive oxygen species (ROS), respectively, acting as key nodes in the PAMP-triggered immunity (PTI) pathway to initiate the plant’s primary immune response. As can be seen in Figure 2, the Z1 and Z3 treatment groups significantly activated multiple core nodes of the Plant-pathogen interaction pathway and involved a wide range of pathogen recognition (e.g., FLS2), signalling (e.g., CPK9, CML37/41), ROS burst (e.g., RBOHF), transcriptional regulation (e.g., WRKY33), and effector genes (e.g., PR1). The whole chain of response indicated that both microbial and bio-organic fertilizers could effectively enhance the pathogen recognition and defence response of Z. armatum . 3.1.5 Quantitative real-time PCR (qRT-PCR) assay We performed quantitative real-time PCR (qRT-PCR) assays to assess the accuracy of transcriptome sequencing. Six genes located in the Plant-pathogen interaction pathway (ko04626), Zardc37059, Zardc42570, Zardc31684, Zardc28299, Zardc06793, and Zardc32915, were selected, as shown in Figure 3, qRTPCR of thevalidation results were consistent with the FPKM values obtained from sequencing, proving that the RNA-seq data results were reliable. This result further confirms the accuracy of the bioinformatic predictions and provides reliable data for further studies on the genetics of Z. armatum leaves. 3.2 Soil macro-genomics analysis based on plant-pathogen interaction pathways 3.2.1 Sequencing data processing assembly and gene prediction In this study, metagenome sequencing was performed on rhizosphere soil microorganisms of Z. armatum in Z1, Z2, Z3 and control (CK), and 159.93 Gb of raw data were obtained. After strict filtering of low-quality sequences, junctions and host contamination, 158.68 Gb of high-quality data were retained, and the GC content was more than 63% in all of them (Table S6), which indicated that the quality of data was good. The clean reads were assembled by MEGAHIT, and the total length of Scaftigs was 8,636,610,115 bp, and only sequences of lengths greater than 500 bp were retained, for subsequent analyses (Table S7). ORF prediction of the assembled sequences was performed using MetaProdigal, followed by de-redundancy using CD-HIT, resulting in the construction of 14,176,482 non-redundant unigenes with an average length of 545.06 bp (Table S8). The rarefaction curve (Figure S3) showed that the number of genes tended to saturate gradually with the increase of sequencing volume, indicating that the sequencing depth was sufficient to cover the microbial community diversity. A total of 3,952,718 genes were identified across the four sample groups, 130,935 genes were shared among the Z1, Z2, and Z3 treatment groups. The numbers of group-specific genes were 38,581 (Z1), 19,274 (Z2), 52,213 (Z3), and 23,411 (CK), respectively (Figure S4). 3.2.2 Relative abundance of species To further investigate whether activation of the plant-pathogen interaction (plant-pathogen interaction, ko04626) pathway under different fertiliser treatments was associated with changes in soil microbial community composition, we conducted taxonomic analysis of the species composition of rhizosphere soil microorganisms at the phylum, class, family, and order levels (Figure 4). At the Order level (Figure 4A), the major phyla in each treatment included Hyphomicrobiales, Acidimicrobiales, Gemmatimonadales, and Myxococcales. The relative abundances of Hyphomicrobiales and Acidimicrobiales significantly increased in Z2, while the composition of Z3 was similar to that of CK. At the Class level (Figure 4E), Alphaproteobacteria exhibited the highest abundance in Z1. This group is extensively involved in nitrogen fixation, IAA synthesis, and induced systemic resistance (ISR), potentially promoting plant responses in the “plant-pathogen interaction pathway” by enhancing rhizosphere signal perception and defense activation. Both Z2 and Z3 were dominated by Actinomycetes, with Z2 exhibiting the highest abundance. Actinomycetes not only possess potent organic matter decomposition capabilities but also produce various antibiotics and secondary metabolites that help suppress potential pathogens, thereby indirectly influencing the transmission of plant immune signals. Z3 treatment maintained a community composition similar to CK while retaining some defense-related groups, demonstrating strong ecological resilience and stability. At the Family level (Figure 4I), all four sample groups were dominated by taxa such as Gaiellaceae, Nocardioidaceae, Iamiaceae, and Gemmatimonadaceae. Z1 communities are dominated by “Others,” with relatively low proportions of individual phyla; Nocardioidaceae and Gaiellaceae exhibit higher abundance in Z2 and Z3. At the phylum level (Figure 4M), the results showed that the inter-root microbial communities of all samples were mainly composed of Actinomycetota, Pseudomonadota, Chloroflexota, Acidobacteriota, Gemmatimonadota and Bacteroidota. However, significant differences in the abundance of dominant phyla were observed between treatments, revealing a reconfiguration effect of fertiliser application on the inter-root microbial ecosystem. In the Z1 (microbial fertiliser) treatment (Figure 4B), the relative abundance of Pseudomonadota, a phylum that broadly encompasses a wide range of plant symbiotic or antagonistic bacterial taxa (e.g., Pseudomonas fluorescens and P. putida) with biocontrol, induced systemic resistance (ISR), and signalling molecules (e.g., flagellin), was significantly increased. Its increase may activate plant FLS2 -mediated PAMP-triggered immunity ( PTI ), which significantly up-regulates the expression of downstream immune-related genes such as WRKY33 , CPK9 and CML41 in Z1 treatment, and promotes the activation of Plant-pathogen interaction pathway. In addition, significant down-regulation of HSP83A and RBOHF was observed in the Z1 treatment, suggesting that this group may form an effective pathogen recognition barrier by using ”enhancement of basal immunity and reduction of oxidative stress” as a regulatory strategy. Z2 and Z3 treatments (Figure 4C and Figure 4D), on the other hand, significantly increased the abundance of Actinomycetota, a phylum that is an important soil antibiotic producer and organic matter decomposer. The enhancement of Actinomycetota may activate the expression of key genes in the plant immune pathway, such as PR1 , FLS2 , WRKY22 , CCAMK , etc., through abiotic induction or antibiotic-mediated indirect pathogen antagonism, resulting in the development of ”systemic acquired resistance (SAR)” to biotic stresses, and the expression of Plant-The activation of multiple signalling pathways in the plant-pathogen interaction pathway was demonstrated. In particular, several key response factors such as WRKY33 , RBOHF and CML37 were significantly regulated in the Z3 group, while enrichment and down-regulation of HSP90 series genes (e.g., HSP90-6 , HSP90-2 ) were observed, which may reflect that plants attenuate stress perception by regulating the expression of heat-excited proteins to focus more on the pathogen recognition signalling pathway. In addition, the relative abundance of Gemmatimonadota, Myxococcota and Bacteroidota increased in the Z1 group and decreased in Z2 and Z3, suggesting that these flora may have a contributing role in the activation of the Plant-pathogen interaction pathway. Deinococcota was significantly elevated in all treatment groups and this phylum has a strong capacity in antioxidant stress and may help plants to mitigate the reactive oxygen species burst (ROS burst) generated by pathogen recognition. Metagenomic clustering results indicate that different fertilization treatments significantly influence the structure of root-zone soil microbial communities. At the Class level (Figure 4F) and Phylum level (Figure 4N), Z3 treatment and the control (CK) clustered together, followed by Z2, while Z1 formed a separate cluster. This indicates that the bio-organic fertilizer (Z3) caused the least disturbance to the community structure, maintaining a state closer to natural conditions; The trend is consistent at the Order level (Figure 4B); However, at the Family level (Figure 4J), Z1 resembled CK, while Z2 and Z3 clustered together, suggesting differences in “overall community persistence” and “selectivity of core functional groups” among different fertilizers. Microbial α-diversity results further revealed community response patterns, assessed using the Chao1 index (Figure 4C,G,K,O) and Shannon index (Figure 4D,H,L,P). The Chao1 index increased across all fertilization treatments, indicating that fertilization enhanced community richness. However, the Shannon index significantly increased in Z1, while it significantly decreased in Z2 and Z3. This indicates that Z1 enhances community evenness and overall diversity while deviating from the original community structure; Z2 and Z3, though introducing new microbial communities, are dominated by a few dominant groups, leading to functional orientation in the communities. Combining the macrogenomic data with the expression of the ko04626 pathway genes in the transcriptome (Figure 2 and Table S3, Table S4, and Table S5), the present study initially revealed the synergistic mechanism of the specific microbial community structure on the recognition of plant pathogens and the signal transduction process. Microbial communities regulated by fertilisation not only play a key role in the maintenance of ecosystem functions, but also form a network of interactions with plant immune pathways through specific signalling molecules or metabolites, reflecting an ecological immune integration strategy among ”soil-microbe-plant”. 3.2.3 KEGG Functional Notes To further resolve the functional potential of the inter-root microbial community and its potential association with plant immune pathways, we performed a multilevel statistical analysis of the functional gene distribution characteristics of the four treatment groups based on the results of KEGG functional annotation of the macrogenome (Figure 5). The results showed (Figure S5) that the most annotated functional genes in all treatment groups were metabolism-related pathways, followed by Genetic Information Processing, Environmental Information Processing, Cellular Processes, Human Diseases, and Organismal Systems. Among them, the Plant-pathogen interaction pathway belongs to the ”Environmental adaptation” subcategory of the Organismal Systems category, suggesting that it is closely related to microbial environmental stress response. In the KEGG functional annotation clustering, distinct grouping patterns are evident across all hierarchical levels. At the primary KEGG level (Figure 5A), Z1 clusters together with CK, while Z2 and Z3 are relatively close but separated from the former two; The results at the secondary level were largely consistent (Figure 5B), with Z1 and CK still clustering together, while Z2 and Z3 formed another distinct category. At the tertiary functional classification level (Figure 5C), Z3 exhibited the highest functional spectrum similarity with CK, followed by clustering with Z1. Z2 remained isolated, indicating the most pronounced functional divergence. In the first KEGG level (Figure 5D), the relative abundance of genes in functional pathways such as metabolism and environmental information processing was generally higher in the Z2 and Z3 groups than in the control group, whereas the Z1 treatment group showed a certain degree of down-regulation in several metabolic and genetic processing pathways. This difference may reflect the differential effects of different fertiliser treatments on the metabolic potential of microorganisms and their ability to modulate host plant immunity. Further secondary hierarchical analyses (Figure 5E) showed that typical metabolic pathways such as carbohydrate metabolism, amino acid metabolism, energy metabolism, and cofactor and vitamin metabolism were highly enriched in all groups and were core components of the basic metabolic functions of microorganisms. The relative abundance in both Z2 and Z3 treatment groups in these pathways was higher than that of the control group, suggesting that the Z2 and Z3 treatments may be more conducive to the enhancement of the metabolic activity of the microbial community, which contributes to its production of more signalling molecules related to plant interactions, such as sphingolipids, fatty acids, secondary metabolites, etc., which may trigger the immune response of the plants through the plant-pathogen interactions pathway (ko04626). At the tertiary functional classification level (Figure 5F), all treatment groups showed significant enrichment in typical microbial signal transduction systems, including ABC transporter proteins (ko02010), the two-component system (ko02020), and quorum sensing (ko02024). These pathways play crucial roles in signal exchange between rhizosphere microorganisms and plants. Notably, the two-component system and quorum sensing mechanisms often influence plant pathogen recognition by regulating pathogen-associated secretion systems, chemotaxis, and toxin expression. This regulation subsequently promotes activation of WRKY transcription factors (e.g., WRKY33 ), calmodulin-dependent protein kinases (e.g., CPK9 , CCAMK ), and other downstream components through pathways such as the FLS2 (flagellin receptor)-mediated PAMP recognition, thereby triggering plant defense responses. Combined with the aforementioned transcriptome analysis results (Figure 2), we found that the expression of Plant-pathogen interaction pathway-related genes (e.g. PR1 , WRKY33 , CPK9 ) was significantly up-regulated in the Z2 and Z3 treatments, which was highly consistent with the enhancement of microbial signalling pathway function as described above. In contrast, the Z1 treatment, despite a slightly lower microbial metabolic abundance, showed the activity of the quorum sensing and two-component system, and the corresponding activation of early signalling recognition factors such as FLS2 and CML41 . This suggests that under different microbial regulatory backgrounds, the host plant may activate the ko04626 pathway by sensing the signaling molecules produced by microorganisms, forming an integrated response system of microbial signaling-plant recognition-gene regulation. Figure S6 shows that β-diversity results further validate this differentiation trend. At the KEGG level 1, PCoA results indicate that Z1/CK and Z2/Z3 are clearly distinguished on the principal coordinate axes. NMDS analysis shows Z3 is closest to CK, while Z1 exhibits significant differentiation from Z2. PCA similarly reveals Z1 adjacent to CK, with Z2 and Z3 clustered on the opposite side. At the secondary KEGG level, Z1 and CK still exhibit high similarity, while Z2 and Z3 are relatively close but display more dispersed communities. At the KEGG Level 3, Z3 showed the closest functional similarity to CK, while Z2 remained significantly separated from the other treatments throughout. Overall, Z1 (microbial fertilizer) exhibits a high degree of similarity to CK in functional terms, indicating that it causes relatively minor disruption to the soil functional community. Z2 (organic fertilizer) exhibits distinct differentiation at the tertiary functional level, suggesting it significantly reshapes the functional structure; Z3 (bio-organic fertilizer) exhibits the strongest correlation with CK across all hierarchical levels, indicating that while maintaining overall stability, Z3 may regulate plant immune functions through microbial interactions, thereby serving as a bridge in the “plant-pathogen interaction pathway (ko04626)”. 3.7 Physiological indicators Fertilization significantly affected multiple physiological indicators in both the leaves and rhizosphere soil of Z. armatum (Figure 6). In terms of soil properties, fertilization significantly increased key nutrient contents such as organic matter, microbial biomass, carbon, nitrogen, and phosphorus, with the greatest improvements observed in Z1 and Z3. This demonstrates their potential to enhance soil fertility and microbial activity. In contrast, phosphatase and soil polyphenol oxidase exhibited the highest levels in the control group (CK) but decreased after fertilization. This indicates that while fertilization supplements exogenous nutrients, it also reduces the soil’s reliance on enzyme activities associated with organic matter decomposition. Dehydrogenase activity significantly increased in the Z1 treatment, indicating that microbial metabolic activity was strongly stimulated. Overall, the CK group without fertilization exhibited significantly higher levels than the fertilized group in multiple indicators, including amino acids, soluble proteins, hydrogen peroxide and its associated scavenging enzymes (CAT), as well as polyphenol oxidase (PPO). This indicates that under nutrient-limited conditions, plants maintain physiological homeostasis by enhancing the accumulation of osmotic regulatory substances and activating reactive oxygen species metabolic pathways. However, in terms of antioxidant defense, the superoxide dismutase (SOD) and peroxidase (POD) activities in the fertilized group were significantly enhanced, particularly under Z1 and Z3 treatments. This suggests that fertilization helps activate the plant’s ROS scavenging system, thereby improving its stress tolerance. Regarding secondary metabolites, fertilization significantly promoted the accumulation of flavonoids and alkaloids (particularly in Z2 and Z3), potentially enhancing plant immunity and disease resistance. However, flavonoid levels decreased significantly under Z1 treatment, indicating that different fertilizer types exert varying regulatory effects on secondary metabolism. MDA results showed elevated levels in the CK group, with significantly higher levels in the Z3 group, indicating accelerated lipid peroxidation under Z3 conditions, potentially associated with its stronger activation of defense metabolism. Overall, plants under unfertilized conditions exhibited typical nutrient stress responses, while fertilization treatments reshaped plant–soil interaction balance by improving soil nutrient supply and enhancing plant antioxidant defenses and secondary metabolite accumulation. Different fertilizer types demonstrated differential effects in regulating metabolic pathways and soil microbiome. 3.8 Physiological indicators significantly correlated with pathway gene expression Spearman correlation analyses were conducted between the top ten major taxa in relative abundance and key soil physiological indicators (organic matter, phosphatase, polyphenol oxidase, dehydrogenase activity, microbial carbon, microbial nitrogen, microbial phosphorus, and water content) at the phylum level based on metagenomic data. The results showed that dehydrogenase activity was significantly and positively correlated (P < 0.05) with five phyla, namely p__Actinomycetota, p__Pseudomonadota, p__Gemmatimonadota, p__Myxococcota, and p__Bacteroidota (Figure 7A). The significant correlation of dehydrogenase, an important indicator of microbial metabolic activity, with the above phyla suggests that these microbial taxa may play a central role in organic matter degradation and inter-root energy metabolism. Combining the transcriptome expression profiling data with leaf physiological indicators, we further analysed the correlation between key differentially expressed genes (DEGs), WRKY33 (Zardc28299) and HSP83A (Zardc37059), in the plant–pathogen interaction (ko04626) pathway, and antioxidant and metabolic indices of the plants (Figure 7B). The results showed that the expression of Zardc28299 was significantly and positively correlated with superoxide dismutase (SOD), catalase (CAT), polyphenol oxidase (PPO), H₂O₂ content, and amino acid content (P < 0.05), whereas the Zardc37059 pathway was significantly correlated with SOD, CAT, H₂O₂, amino acid, and flavonoid content. All of these physiological indicators are closely related to the plant’s ability to cope with oxidative stress and immune defences, suggesting that the above differentially expressed genes may play a synergistic role in regulating the plant’s disease resistance response and abiotic stress defence. Taken together, based on the bidirectional association between microbial function and plant response, a potential mechanism can be tentatively proposed: under the treatment of organic or bio-organic fertilizers, the dominant inter-root microorganisms (e.g. Actinomycetota and Pseudomonadota) further stimulate the activation of plant immune signalling pathways by increasing the activity of soil dehydrogenases, which promotes the enhancement of soil metabolism activity. For example, up-regulation of genes such as WRKY33 and HSP83A may enhance plant defences against pathogens by modulating the ROS scavenging system (SOD/CAT/PPO) and regulating the accumulation of secondary metabolites such as flavonoids and amino acids. This result suggests that changes in soil microbial communities can enhance systemic resistance responses in plants by mediating transcriptional signalling networks. This process constitutes a regulatory chain of ”microbial functional metabolism → soil enzyme activity → plant immune gene activation → physiological index response”, which profoundly reveals the possible pathways of ”microbial-plant interactions - enhancement of resistance ” under different fertilisation treatments. 4. Discussion In this study, we focused on the plant-pathogen interaction (ko04626) pathway, combining transcriptomic, macro-genomic and plant-soil physiological indicators to investigate the inter-root microbial community composition, plant immune regulation of Z. armatum after the application of different types of fertilisers (microbial fertiliser Z1, organic fertiliser Z2 and bio-organic fertiliser Z3). response and environmental adaptation mechanisms were systematically interpreted. The central role of the plant-pathogen interaction pathway in the synergistic defence mechanism of inter-root ecosystems was revealed by integrating expression analyses, functional enrichment, pathway activation patterns, and correlation of environmental factor responses. Transcriptome analysis showed that all three fertilisation treatments activated several key genes in the ko04626 pathway to varying degrees, but there were significant differences in the expression profiles and direction of regulation. Subsequent qRT-PCR validation results were consistent with the RNA-seq data, further confirming the accuracy of the data. Under Z1 treatment, the expression of typical defence-related genes in the ko04626 pathway, such as WRKY33 , CPK9 , CML37 , and HSP83A , was significantly changed, especially WRKY33 (Zardc28299) and CML37 (Zardc31684) showed a trend of down-regulation, indicating that the plants may be able to adapt to microbial fertiliser by tuning down the expression of sensitive immune genesto avoid overconsumption of resources by moderately activating stimuli brought about by microbial fertiliser. WRKY transcription factors play key roles in regulating plant growth, development and responses to adversity [29] . WRKY transcription factors regulate plant defence against pathogens including fungi, bacteria, oomycetes and viruses by regulating downstream pathogen resistance genes or interacting with other regulators WRKY [25] . This low-activation type of strategy may be better suited to non-stress environments and help maintain the balance between growth and defence. In comparison, Z3 treatment induced a more extensive and drastic gene response, with significant up-regulation of key immune regulators such as WRKY33 , PR1 , RBOHF , FLS2 , and CCAMK , indicating that the plants constructed a multi-level and strongly coupled immune defence system under the treatment of bio-organic fertilizers. Up-regulation of RBOHF suggests that Z3 induces a ROS burst which in turn triggers a rapid defence response [30] ; FLS2 acts as a PAMP recognition receptor and its high expression enhances the sensitivity of plant recognition of pathogenic bacterial flagellins [31] ; Activation of transcription factors and effector proteins such as WRKY33 and PR1 further drives the formation of systemic acquired resistance ( SAR ) [32] . The number and expression amplitude of genes activated by Z2 treatment were lower than those of Z1 and Z3, indicating that although organic fertilizers have some biological activity, they have limited ability to regulate plant defence pathways, and may be deficient in microbial signaling substances transmission or plant sensory ability. In terms of pathway function, WRKY33 is recognised as a transcriptional hub regulating plant disease resistance and stress tolerance, and its expression level showed significant changes in all treatment groups and was highly correlated with a variety of leaf antioxidant enzymes (SOD, CAT, POD, PPO) and reactive oxygen species content (H₂O₂), suggesting that this gene is not only involved in pathogen recognition, but also in the plant’s scavenging of ROS and maintenance of cellular homeostasisSystemic regulatory processes [25, 33] . In particular, WRKY33 expression was significantly and positively correlated with CAT and PPO activities in the Z3 treatment, as well as with H₂O₂ accumulation, suggesting that this gene plays a bridging role in coordinating between pathogen recognition and ROS metabolism. This regulation helps the plant to maintain the balance of ”moderate defence activation - oxidative damage control” and to avoid damage to its own cells due to excessive defence responses. Furthermore, PR1 was specifically up-regulated in Z2 and Z3 as a classical marker of systemic acquired resistance ( SAR ), suggesting that the treatment group induced a potential long-term defence memory in the plant [34] . Zardc37059 ( HSP83A ) was also significantly up-regulated in Z3 and correlated with a variety of antioxidant metrics, suggesting that the functions of heat-excited proteins to assist in protein fold repair and stabilise signalling pathway components are also significantly activated under pathogenic stress. The lower expression of HSP83A in the Z2 and Z1 treatment groups may be one of the important reasons for their inadequate immune system activation. Macrogenomic analysis further reveals the association between soil microbial community structure and plant defence pathways [35] . At the taxonomic level of phylum, the soil microbial communities of all treatments and controls were dominated by Actinomycetota, Pseudomonadota, Chloroflexota, Acidobacteriota, etc., which is consistent with Huang Rui et al.’s (2022)This is consistent with the findings of Huang R. et al. [36] . Z3 treatment significantly enriched Actinomycetota, Pseudomonadota, Myxococcota and other plant-beneficial functional phyla, which have been shown to play important roles in soil pathogen antagonism and inter-root induced resistance. The phylum Ascomycota is known for its complex physiology and metabolic capabilities, which play a crucial role in the carbon cycle [37] . Actinomycetes participate in the metabolism of nitrogen and phosphorus in the soil and promote the decomposition of fast-acting phosphorus and nitrogen [38] . Z3 treatment promotes the activation of plant immune pathways by producing antibiotics, inducers, or modulating phytohormones. Correlation analysis showed that dehydrogenase activity, an indicator of overall microbial metabolic activity, was significantly and positively correlated with the abundance of the above phyla, suggesting that Z3 provides plants with diverse signalling substances and metabolic aids by boosting soil metabolic activity [39] . These microbial signals may act as ”inter-root inducers” to activate key plant immune factors such as WRKY , CML , PR , etc., and drive the activation of the plant-pathogen pathway system to achieve an ”inter-root-plant” co-regulatory response [40] . Although the Z1 treatment group also regulated some microbial structures, its inhibition of Actinomycetota suggests that the direction of regulation may not be favourable to immune activation, and the dominant microorganisms in the Z2 treatment group were weakly transformed, with a more homogeneous ecological signaling chain. Notably, the joint transcriptome-physiological index analysis revealed a differential strategy of different treatments on the construction of antioxidant system in plants [41] . For example, both Zardc28299 ( WRKY33 ) and Zardc37059 ( HSP83A ) were significantly correlated with superoxide dismutase (SOD), catalase (CAT), polyphenol oxidase (PPO), H 2 O 2 concentration and amino acid [42] . Combined with the high expression levels under Z3 treatment, it suggests that this treatment not only regulates strongly in pathogen recognition, but also enhances the plant’s non-specific antistress defence system, which is important for maintaining cellular homeostasis. This paper combines macrogenomic and transcriptomic for comprehensive analysis to reveal the mechanism of microbial and plant response to biofertiliser [43, 44] . From an ecological perspective, the integrity of the plant immune system and the synergistic response of inter-root microbial functions not only help individual plants to resist pathogen stress, but also serve as an important basis for maintaining community stability and enhancing ecosystem resistance to disturbances [45] . The ”microbial-plant immune pathway-antioxidant response-environmental adaptation” chain revealed in this study provides new regulatory strategies and theoretical support for vegetation restoration in ecologically fragile areas such as desert margins and slopes. In particular, the multi-layer defence system activated by bio-organic fertilizer treatment and the synergistic construction of inter-root functional bacterial communities are expected to become a low-cost and eco-friendly vegetation management solution. In this study, we systematically assessed the combined effects of microbial (Z1), organic (Z2) and bio-organic (Z3) fertiliser application on Z. armatum plant immunomodulation, inter-root microbial community structure, and soil and leaf physiological status. By integrating transcriptomic and macrogenomic data, this study revealed the molecular dynamics involved in the plant-pathogen interaction pathway (ko04626) under different fertilisation treatments. The results showed that several key immune-related genes (e.g. WRKY33 , RBOHF , PR1 , FLS2 and CCAMK ) were significantly up-regulated in the Z3-treated group, and their expression levels were correlated with a number of physiological indices, such as antioxidant enzyme activities (SOD, CAT, POD), H₂O₂ content, and amino acid metabolism, reflecting the strong relationship between gene expression and the physiological status of plants. The results showed that there was a close relationship between gene expression and plant physiological status. Macrogenomic analyses further showed that Z3 treatment significantly enriched beneficial inter-root microbial taxa with pro-resistance functions, such as Actinomycetota, Pseudomonadota and Myxococcota, and also increased the activities of key functional enzymes, such as dehydrogenases and polyphenol oxidases, in the soil, possibly through microbial metabolites acting as signalling molecules or inducing factorsthat indirectly drive the activation of plant immune responses. The correlation network analysis showed that plants, microorganisms and soil physicochemical factors were highly coupled with each other, in which the ”plant-pathogen interaction” pathway was the central node, integrating several system modules, such as environment sensing, pathogen recognition, signal transduction and defence response. In summary, bio-organic fertilizer (Z3) treatment significantly enhanced the recognition and response ability of Z. armatum to potential biotic stress signals, optimised the structure and function of the inter-root micro-ecosystem, and enhanced the overall plant health and ecological adaptability. This study not only elucidated the differences in the regulatory mechanisms of plant immune pathways by different fertilisation strategies, but also provided theoretical support and practical reference for the development of eco-agriculture, vegetation restoration and soil and water conservation. Declaration of competing interest 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 The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2025) in National Genomics Data Center (Nucleic Acids Res 2025), China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: 1CRA030152) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa . References: [1]. Feng, X., et al., Discrimination and characterization of the volatile organic compounds in eight kinds of huajiao with geographical indication of China using electronic nose, HS-GC-IMS and HS-SPME-GC–MS. Food Chemistry, 2022. 375: p. 131671. [2]. Mushtaq, M.N., et al., Tambulin is a major active compound of a methanolic extract of fruits of Zanthoxylum armatum DC causing endothelium-independent relaxations in porcine coronary artery rings via the cyclic AMP and cyclic GMP relaxing pathways. Phytomedicine, 2019. 53: p. 163-170. [3]. 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DEF, Scatter plot of KEGG enrichment analysis under different treatments. Figure 2. Pathway map of ath04626 under different treatments. Figure 3. Quantitative real-time PCR (qRT-PCR) validation of 6 selected DEGs based on transcriptomic data. Comparing qRT-PCR with FPKM values, the height variations in the graph indicate the gene expression levels. Figure 4 Taxonomic annotation and structural analysis of Micro_NR microorganisms in rhizosphere soil under different fertilization treatments. (A,E,I,M) Relative abundance distribution of microbial communities at the Order, Class, Family, and Phylum levels under different fertilization treatments. (B,F,J,N) Clustering analysis of relative abundance at Order, Class, Family, and Phylum levels of microbial communities under different fertilization treatments. (C,D,G,H,K,L,O,P) Comparison of α-diversity among treatments using the Chao1 richness estimator and Shannon diversity index at the order level. Figure 5 KEGG Functional Annotation Diagram. (A,D) Clustering diagram and histogram of relative abundance for KEGG functional annotations at the first level. (B,E) Clustering diagram and histogram of relative abundance for KEGG functional annotations at the second level. (C,F) Clustering diagram and histogram of relative abundance for KEGG functional annotations at the third level. Figure 6 Effects of different treatments on the physiological properties of Z. armatum leaves and the physicochemical properties of soil. Soil physicochemical properties include: (A) Conductivity, (B) Polyphenol Oxidase (PPO), (C) Moisture Content, (D) Phosphatase, (E) Dehydrogenase, (F) Microbial Biomass Nitrogen (MBN), (G) Myelin Basic Protein (MBP), (H) Microbial Biomass Carbon (MBC), and (I) Organic Matter. Leaf physiological parameters under various treatments include: (J) Amino Acid, (K) Malondialdehyde (MDA), (L) Superoxide Dismutase (SOD), (M) Polyphenol Oxidase (PPO), (N) H 2 O 2 value, (O) Catalase (CAT), (P) Peroxidase (POD), (Q) Soluble Protein, (R) Soluble Sugars, (S) Bioflavonoids, and (T) Alkaloid. Different letters indicate statistically significant differences among treatments (P < 0.05) based on one-way ANOVA followed by Duncan’s test. Figure 7 Correlation Analysis Heatmap. A indicates the heat map of correlation between the top ten major taxa in abundance at the Phylum level of the macrogenome and soil physiological indicators, and B indicates the heat map of correlation between key differentially expressed genes in the ko04626 pathway of the transcriptome and leaf physiological indicators. By integrating transcriptomic, metagenomic, and physiological data, the study revealed that bio-organic fertilizer (Z3) significantly activated key defense genes (such as WRKY33 and PR1) in plant-pathogen interaction pathways. It also enhanced antioxidant enzyme activity and H₂O₂ accumulation, promoted amino acid metabolism, and effectively strengthened plant immune responses. Concurrently, Z3 treatment markedly optimized the structure of beneficial rhizosphere microorganisms, enriched multiple antibacterial phyla, elevated key soil enzyme activities, and established a highly coupled soil-microbe-plant functional network. This facilitated plants’ sensitive recognition and rapid response to stress factors. These findings provide a sustainable fertilization strategy for improving Z. armatum quality and stress tolerance. Information & Authors Information Version history V1 Version 1 31 October 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords zanthoxylum armatum genome multi-omics plant-pathogen interaction rhizosphere microorganisms transcriptome Authors Affiliations Yuhan Wu China West Normal University View all articles by this author Danping Xu China West Normal University View all articles by this author Habib Ali Khwaja Fareed University of Engineering & Information Technology Faculty of Engineering and Technology View all articles by this author Zhiling Wang Sichuan Agricultural University View all articles by this author Zhihang Zhuo 0000-0002-2566-3172 [email protected] China West Normal University View all articles by this author Metrics & Citations Metrics Article Usage 225 views 181 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Yuhan Wu, Danping Xu, Habib Ali, et al. Integrated Multi-Omics Analysis Reveals Fertilizer-Mediated Modulation of the Plant-Pathogen Interaction Pathway in Zanthoxylum armatum. Authorea . 31 October 2025. 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