The bacterial isolates Bacillus haynesii IBHB1, B. amyloliquefaciens IBHB2, Stenotrophomonas maltophila IBHB3, B. subtilis IBHB4, Pseudomonas aeruginosa IBHB5, and B. subtilis NMB01 were obtained from the Department of Plant Pathology, Tamil Nadu Agricultural University, Coimbatore. The NCBI accession numbers based on 16S ribosomal DNA are given in Table 1 . Pure cultures of the bacteria were maintained in sterile Petri plates containing Luria–Bertani medium by the single streak method. The plates were incubated at a room temperature of 28°C ± 2°C.

Using the Quick-DNA Fungal/Bacterial kit (D6005) from Zymo Research, the genomic DNA of NMB01 was extracted, and whole-genome sequencing of the extracted DNA was performed by ONEOMICS Pvt Ltd. (https://oneomics.in/) using high-throughput Illumina sequencing technology. The quality of the raw sequencing reads was assessed using FastQC (version 0.11.9) (Andrews, 2010), which analyzed parameters such as sequence quality scores, GC content, per-base sequence quality, adapter content, and overrepresented sequences. The reads were then processed using Trimmomatic v 0.39 (Bolger et al., 2014) with default settings for quality trimming, and adapter sequences corresponding to P1 and other contaminants were removed. Trimmomatic, when used with default settings, applies a series of predefined trimming and quality control steps to sequencing reads. Adapter sequences were removed using the ILLUMINACLIP function, which detects and removes adapters specified in the TruSeq3-PE adapter files. This process allows up to two mismatches in the adapter seed sequence, with a palindrome clip threshold of 30 and a simple clip threshold of 10. Furthermore, the sliding window trimming removes bases with an average quality score below 15 within a sliding window of 4 bases. Low-quality bases at the beginning and end of reads were trimmed using Leading and Trailing trimming, both set to remove bases with quality scores below 3. Reads shorter than 36 bases after trimming were discarded based on the minimum read length setting. These default parameters ensured effective quality control while retaining the integrity of high-quality reads for downstream analysis. Genome assembly was carried out using SPAdes (version 3.11.1), a de-novo assembly algorithm tailored for bacterial genomes (Bankevich et al., 2012) with default parameters, and the quality-controlled reads were mapped to the assembled contigs using BWA-MEM v 0.7 17 to verify consistency and coverage. Post-assembly quality checks included QUAST 4.6.1 (Gurevich et al., 2013) to evaluate metrics like N50, number of contigs, and misassemblies and CheckM 1.0.9 (Parks et al., 2015) to assess genome completeness and contamination, ensuring the reliability of the assembled genome.

The assembly of B. subtilis NMB01 was submitted to the Comprehensive Genome Analysis Service provided by the Pathosystems Resource Integration Center (PATRIC) (Brettin et al., 2015). The annotation was carried out using the RASTtk pipeline (RASTtk) unique genome identifier of 1452.102 (Brettin et al., 2015) to categorize gene functions based on subsystems. The annotation process identified protein-coding genes, RNA genes, and functional categories, providing a detailed initial annotation of the genome. For a more comprehensive analysis, the annotated genome was further processed using PATRIC (Davis et al., 2020). The PATRIC platform facilitated comparative analysis with related microbial strains, including ortholog detection, phylogenetic reconstruction, and identification of unique biological processes and structural complexes through subsystem annotations. The genome annotation services in PATRIC used the k-mer-based antimicrobial resistance (AMR) gene detection method, which utilizes PATRIC’s curated collection of representative AMR gene sequence variants and assigns to each AMR gene functional annotation, a broad mechanism of antibiotic resistance. Virulence gene detection was performed by screening the genome against the VFDB, PATRIC_VF, and Victors DataBase using the Abricate tool and PATRIC’s integrated tools, with a minimum threshold of 90% coverage and identity (Chen et al., 2005). For secondary metabolite discovery, the genome was analyzed using AntiSMASH 5.1.0 (Blin et al., 2019) with relaxed strictness to identify putative biosynthetic gene clusters (BGCs). Functional annotation of these clusters was carried out through BLASTp (Altschul et al., 1997) and Pfam (Mistry et al., 2021) analysis to determine domain functions and similarities to known clusters. Furthermore, the metabolic pathways associated with the genome were mapped using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (Kanehisa et al., 2017). This combined approach utilizing RASTtk and PATRIC ensured high-quality genome annotation, comparative genomic insights, and identification of potential virulence factors and secondary metabolites.

To investigate the relationship between B. subtilis NMB01 and other B. subtilis strains, a comparative genome analysis was conducted. For multi-genome analysis, the whole-genome sequences (WGS) of 19 B. subtilis strains—BYS2, ZD01, YB-04, XF1, UD1022, TR21, SG6, RS10, GUCC4, MC4, PMB102, BS16, BS16045, BSn5, GQJK2, HD15, J5, JCL16, and KC141—have been retrieved from the NCBI database. These strains were selected based on previous reports of their antimicrobial activity, underscoring their significance for this study. The specifics of their antimicrobial properties are provided in Supplementary Table S1 . Details of genome size, number of genes, and proteins of the above sequences were compared. Multiple genome alignment was performed by constructing Blast Ring Image Generator (BRIG analysis) (Alikhan et al., 2011) and MAUVE 2.4.0 (Darling et al., 2011), to compare the genome of NMB01 with the strains of B. subtilis. The open reading frame of NMB01 was compared with the other strains of B. subtilis.

The pan-genome analysis of B. subtilis NMB01 was carried out with 19 related strains of B. subtilis obtained from the NCBI database. The pan-genome analysis was performed utilizing the PGAP web server (Chen et al., 2018) to identify core genes (present in all strains), accessory genes (present in some strains), and unique genes (specific to NMB01). Clustering of all nucleic acid and protein sequences was analyzed via the GeneFamily (GF) method within the pan-genome analysis pipeline (PGAP). In the GF method, all protein sequences were pooled, followed by all-vs-all sequence alignment using BLASTALL (score: 40; e-value: 1e−10; coverage and identity: 0.5), and clustered using the Markov cluster algorithm (MCL) (Enright et al., 2002; Van Dongen, 2008). Pan-genome profiles were calculated based on orthologous clustering using Heap’s law or an exponential model (Tettelin et al., 2005; Rasko et al., 2008). Additionally, a phylogenetic tree was generated utilizing the pan-based neighbor-joining method and UPGMA algorithms in PHYLIP (Felsenstein, 1989), and the tree was represented using the Interactive Tree of Life (iTOL) v6.5.3 (https://itol.embl.de/). Based on the phylogenetic analysis, the closely related species of B. subtilis NMB01 included B. subtilis strains YB-04, PMB102, BS16045, HD15, RS10, and GUCC4. A proteome-wide comparison and annotation of clusters of orthologous groups (COGs) were conducted using the OrthoVenn web server (Wang et al., 2015). The analysis was performed using the FASTA sequences of all selected species, with the default e-value cutoff of 1e−5 and an inflation value (−I) of 1.5 to identify orthologous clusters.

Based on genome comparison, the number of flagellin genes was higher in NMB01. To examine the effect of flagellin in microbe-associated molecular pattern (MAMP)-triggered immunity, a qPCR study was conducted through real-time PCR in the Bio-Rad CFX96 manager. Samples were drawn at different time intervals (0, 3, 5, 7, and 9 days post-inoculation) following simultaneous inoculation of NMB01 and GBNV in tomato plants. A total volume of 10 µL reaction mixture contains 5 µL of SYBR Green master mix (KAPA SYBR @ FAST for Light Cycler 480, Cat. No. A1250), 10 pmol/µL concentration of forward and reverse primers for the N gene of GBNV, 2 µL of nuclease-free water, and 1 µL of template cDNA with an amplification cycle of 95°C for 10 min (initial denaturation) and 40 cycles of 95°C for 30 s, 60°C for 30 s, and 72°C for 30 s, followed by melting curve analysis. In the present study, actin was used as an internal control since the expression of actin remained stable during virus interaction. The major defense genes (MAPKK1, WRKY33B, PR1, PAL, and NPR1) involved in defense activation were selected to study the effect of NMB01 against GBNV. For each defense gene expression study, three biological replications and two technical replications were maintained. The primers used in this study are listed in Supplementary Table S2 .

The fold changes in gene expression were calculated using the formula

The relative fold changes in the transcript level were represented graphically by converting the ΔΔCT value to 2^−(ΔΔCt) (Livak and Schmittgen, 2001). The statistical analysis for the relative fold change was performed using the TIBCO Spotfire analyst version 7.11.1 280 software.

The virus isolate DeTo (OR158681) was maintained in the local lesion host cowpea (VBN3) through sap inoculation. Initially, chlorotic lesions were observed 4 days after inoculation and later turned into necrotic lesions at 6 DPI ( Supplementary Figure S1 ). RT-PCR was carried out from the inoculated samples and the expected amplicon of 830 bp was obtained for the N gene primers of GBNV ( Supplementary Figure S2 ).

Among the bacteria tested, B. subtilis NMB01 was effective in reducing the lesion number to 0.86 per leaf which was 94.81% inhibition over the untreated plants. It was followed by B. subtilis IBHB4, in which the lesion number was 1.10 per leaf, which was 89.73% reduction over the inoculated control. The lowest inhibition of 76.69% was observed in B. amyloliquefaciens (IBHB2)-treated plants. On the other hand, the untreated inoculated plants expressed the maximum number of 16.56 lesions. Furthermore, the virus titer in bacterized (NMB01) cowpea plants was assessed through DAC-ELISA. The results revealed that the inoculated untreated plants had the highest OD value of 1.37 at A405 nm, whereas the NMB01-treated plants had a very low OD value of 0.62. Thus, the results confirmed that the application of NMB01 has reduced the GBNV titer in the local lesion host cowpea ( Table 1 ; Supplementary Figure S3 ).

Based on the preliminary screening in cowpea, the isolate NMB01 was selected and screened against GBNV in tomato upon simultaneous inoculation. The number of plants exhibiting the symptom was higher in the untreated plants with 80.00% disease incidence and had a disease severity of 3.12 with chlorotic and necrotic lesions in the leaves and stems compared to bacteria NMB01-treated plants. However, the incidence in NMB01-treated plants was 16% with a disease severity of 0.46. The delay in symptom expression was observed in NMB01-treated plants compared to the untreated plants. The symptom expression in the untreated plants appeared at 7 DPI and the complete drying of the whole plant was observed at 12 DPI. In the case of bacteria-treated plants, the symptom expression was observed only at 10 DPI and the plants were alive with mild necrotic rings ( Figure 1 ; Table 2 ).

The application of B. subtilis NMB01 has influenced the root growth characteristics of tomato. Compared with the untreated plants, the NMB01 treatment increased root length, surface area, root volume, and root diameter by 67.01%, 51.05%, 54.19%, and 89.61%, respectively. Furthermore, in the case of the untreated virus-inoculated plants, the reduction in root length, surface area, root volume, and root diameter was observed to an extent of 20%–38%. The number of forks (1,429), tips (799), and crossing (51) was also higher in NMB01-treated plants than the untreated plants with an average number of 313 forks, 414 tips, and 13 crossings ( Figures 2a–h , Supplementary Figure S4 ).

Tomato leaves bacterized with NMB01 had the lowest OD values of 0.221, 0.259, and 0.344 at 0, 5, and 10 DPI, respectively, whereas in the untreated inoculated control, the OD values were 0.216, 0.404, and 0.867, respectively. In the case of newly emerged leaves, the OD value was the lowest in the treated plants (0.242) compared to the untreated inoculated (0.413) control. The OD value was 0.199 in the healthy control. The virus titer in bacteria-treated plants was not increased until 10 DPI, which confirmed the antiviral nature of NMB01 ( Figure 3 ).

The virus copy number was measured on different days after virus inoculation on tomato plants (3, 5, 7, and 9 DPI). From 3 to 9 days post-inoculation, the virus load consistently increased in both the bacteria NMB01-treated and untreated tomato plants challenged with GBNV. However, a minimal increase in the virus load was observed in the NMB01-treated plants compared to the untreated inoculated control from 5 DPI onward. In the untreated inoculated control, the viral copies continued to increase from 0 DPI and recorded the highest copy number of 55 × 10⁶, whereas in NMB01-treated plants, the copy number also increased from 0 to 9 DPI but the level was significantly lower (4.1 × 10⁶ at 9 DPI) than the untreated inoculated plants ( Figure 4 ).

Furthermore, whole-genome sequencing was carried out for NMB01 to identify the key gene’s role in the antiviral mechanism. The assembled genome was submitted to the Comprehensive Genome Analysis Service where the genome was annotated using the RAST tool kit (RASTtk). Accordingly, the taxonomy of the genome pertained to Cellular organisms > Bacteria > Terrabacteria group > Firmicutes > Bacilli > Bacillales > Bacillaceae > Bacillus > Bacillus subtilis. The genome was submitted to the NCBI database and the accession number was NZ_JALHRZ000000000. This genome had 28 contigs, with a total length of 3,985,883 bp and an average G+C content of 43.62%, 4,241 protein-coding sequences (CDS), 69 transfer RNA (tRNA) genes, and 3 ribosomal RNA (rRNA) genes. The assembly details are given in Table 3A .

During gene prediction, RASTtk annotates 672 hypothetical proteins and 3,569 proteins with functional assignments. The proteins with functional assignments included 1,047 proteins with enzyme commission (EC) numbers, 871 with gene ontology (GO) assignments, and 773 proteins that were mapped to KEGG pathways. PATRIC annotation included two types of protein families, and the genome had 3,991 proteins that belong to the genus-specific protein families (PLFams) and 4,082 proteins that belong to cross-genus protein families (PGFams) ( Table 3B ). The annotated genome of NMB01 is displayed as a circular graphical representation explaining GC content, GC skew, presence of contigs, CDS, RNA genes, virulence genes, and coding sequences displaying the antimicrobial genes ( Supplementary Figure S5 ).

Analysis of the subset was carried out through the PATRIC annotation to reveal the presence of superclasses with varying numbers of subsystems (SS) and associated families/genes. The result revealed that the majority of the genes accounted for metabolism processes followed by cellular processes and energy ( Supplementary Figure S6A ). Other subsystems such as protein processing and stress response, defense, and virulence also contributed significantly to the gene count. Subsystems like regulation and cell signaling, miscellaneous, and cell envelope had the fewest genes. Metabolism encompassed 98 subsystems with a total of 761 genes. Protein processing included 43 subsystems and 225 genes, while stress response, defense, and virulence comprised 33 subsystems with 128 genes. Cellular processes consisted of 29 subsystems containing 254 genes, and energy involved 25 subsystems with 214 genes. DNA processing had 17 subsystems and 88 genes. Membrane transport included 16 subsystems with 77 genes, and RNA processing covered 13 subsystems with 52 genes. The cell envelope category had 5 subsystems and 27 genes. Both miscellaneous and regulation and cell signaling categories had 4 and 3 subsystems, respectively, each with 11 genes ( Supplementary Figure S6B ). Using Blast2GO annotation (Conesa et al., 2005) in OmicsBox 2.0., a functional categorization by gene ontology (GO) terms was conducted. The analysis was based on the Blastx hits from the non-redundant database. In total, 17 GO terms related to biological processes, 5 GO terms related to cellular components, and 9 GO terms related to molecular function classes were identified.

Out of 28 contigs, 10 contig regions were coded for secondary metabolites, namely, contigs 1, 2, 3, 4, 6, 7, 8, 13, 14, and 15. The number of regions predicted to encode secondary metabolites varied between the contigs. In contig 1, six regions were identified that coded for secondary metabolites including bacillibactin, subtilin, subtilosin, and thailanstatin with a similarity of 100% to the reference genus B. subtilis subsp. subtilis. For contig 2, three regions coded plipstatin and each one for contigs 3, 4, 6, 7, 8, 13, 14, and 15. For the other predicted metabolites, fengycin was detected in contig 8, surfactin in contigs 7 and 15, and plipstatin in contigs 14 and 15. The nucleotide range and the percent similarity are presented in Supplementary Table S3 . Furthermore, the genes present in the genome underwent several pathways, mainly for metabolisms, cellular processes, and environmental signaling. More than 180 pathways were identified for metabolisms and approximately 30–60 for other processes ( Supplementary Figure S7A ). Genes involved in secondary metabolite underwent various metabolic pathways, notably terpenoid backbone biosynthesis pathway, fatty acid metabolism, aminoacyl-tRNA biosynthesis pathway, ubiquinone and other terpenoid-quinone biosynthesis, biotin metabolism, phenylalanine metabolism, glycolysis/gluconeogenesis, nitrogen metabolism, pyruvate metabolism, peptidoglycan biosynthesis, amino sugar and nucleotide sugar metabolism, starch and sucrose metabolism, glyoxylate and dicarboxylate metabolism, and riboflavin metabolism. The total pathways are represented in Supplementary Figure S7B .

Bacillus subtilis NMB01 possesses a diverse repertoire of genes involved in various AMR mechanisms, which potentially contributed to its antimicrobial activity. The majority of these shared genes were associated with different mechanisms of antimicrobial resistance. It included genes related to antibiotic inactivation enzymes, proteins involved in altering cell wall charge, antibiotic target protection and replacement protein and efflux pump conferring antibiotic resistance, and regulators that modulate the expression of antibiotic resistance genes ( Table 4 ). The genes were also responsible for the survival of bacteria under extreme environmental conditions.

The genomic attributes [genome size, gene count, coding DNA sequences (CDSs), protein-coding genes, and G+C content] of B. subtilis NMB01 were analyzed and compared to 19 other B. subtilis strains available in the NCBI database. The total gene count across these strains ranged from 3,921 to 4,437. Among them, strain GUCC4 exhibited the highest number of total genes (4,437), of which 4,250 were coding genes. In contrast, the strain from our study, B. subtilis NMB01, possesses a total of 4,220 genes, with 4,044 CDSs ( Table 5 ).

To further explore the genomic characteristics of NMB01, a genome synteny analysis was performed. Through the analysis, aligned multiple genomes demonstrated a substantial degree of synteny between NMB01 in comparison with the other species. Using the MAUVE alignment tool, a synteny map was produced, highlighting local collinear blocks (LCBs) and various translocation regions. Each LCB signified instances of horizontal gene transfer among the genetically similar species. Homologous regions, represented as locally collinear blocks (LCBs) across the genomes, indicated that NMB01 shared extensive synteny with the majority of the analyzed strains. The consistent alignment of these LCBs suggested a high level of conservation, reinforcing the idea that NMB01 possessed a core genomic structure common to antimicrobial-producing B. subtilis strains (Figure 5).

The comparative genomic analysis of B. subtilis strains using BRIG provided an in-depth visualization of conserved and divergent regions among multiple related strains, with homology levels ranging from 50% to 100%. Notably, the circular genome comparison highlighted that strain NMB01 exhibited a high degree of similarity with the other 18 strains, which are known for their antimicrobial activity (Figure 6). This high similarity suggests that NMB01 shares a conserved genomic backbone with these strains, potentially encoding similar sets of genes involved in antimicrobial compound biosynthesis, regulatory pathways, and environmental adaptation.

The pan-genome analysis of B. subtilis NMB01, along with 19 other strains, depicted the pan-genome as a blue curve and the core genome as a green curve. When the number of gene families was plotted against increasing genome number, a rise in the pan-genome size and a decrease in core-genome size were observed. This implicated that the pan-genome for the studied isolate was an “open form” concept of Heap’s law (Tettelin et al., 2005), indicating that the number of gene families expanded with the addition of B. subtilis genomes, suggesting that B. subtilis can easily acquire new genes ( Figures 7a, b ). The comparison of core orthologous genes across the strains identified 1,640 core genes (19%) and 4,885 dispensable genes (55%). The unique genes accounting for 26% across the 20 genomes ranged from 38 in B. subtilis ZD01 to 340 in B. subtilis J5 ( Supplementary Figure S8 ). Using a pan-genome-based neighbor-joining method, the 20 B. subtilis were categorized into two clusters, labeled A and B. Among these, strains BS916 and J5 formed a distinct cluster. Strain NMB01 was placed in cluster B, closely related to the YB-04, HD15, and PMB102 strains ( Figures 7c, d ). Furthermore, the genes associated with MAMP, glucanase, amylase, xylanase, glucosidase, thiol peroxidase, and chitin-binding proteins were compared. In our strain NMB01, we identified the presence of the following genes: 2 glucanase genes, 2 xylanase genes, 2 amylase genes, 9 glucosidase genes, 2 thiol peroxidase genes, and 15 ABC transporter genes. The highest number of arabinose genes (n = 15) found in our study isolate was followed by BSn5 and XF1 (n = 1), while it was absent in the rest of the species compared. Notably, our isolate also possessed the highest number of MAMP genes (n = 15). Of these, six were associated with elongation factors, nine with peptidoglycan, and three with flagellin. Interestingly, the flagellin gene count in isolate NMB01, as well as in isolates RS10 and BS16045, was higher than the other compared isolates ( Figures 8 , 9 ; Supplementary Tables S4 , S5 ).

Orthologous cluster analysis revealed the presence of 4,547 clusters of orthologous proteins. These clusters contained proteins from 20 B. subtilis species, and the proteins within each cluster were related by a common ancestral gene. Out of the 4,547 clusters, 3,482 are single-copy clusters, i.e., each cluster containing one protein from each species. There were 908 singletons representing 3.10% of all clusters. The 59 overlaps suggested the shared orthologous proteins across these clusters, indicating potential functional similarities or common evolutionary origins. In comparison to other strains, NMB01 contained 4,241 proteins, 3,965 clusters, and 255 singletons ( Figures 10a, b ; Table 6 ).

The relative gene expression between bacteria NMB01-treated and untreated control plants of tomato was assessed using the comparative 2^−ΔΔCT method. The actin gene was used as a reference gene. Inoculation with GBNV prompted an increase in the expression of the MAPKK gene, beginning at 0 DPI. At this initial time point, the relative fold increase in MAPKK expression was 1.309-fold compared to mock-inoculated plants. This upregulation continued, with transcript levels rising to a peak of 1.9-fold at 5 DPI, slightly tapering to 1.8-fold at 7 DPI, and then declined at 9 DPI ( Figure 11a ). Conversely, in the untreated plants, the MAPKK transcript levels presented a different pattern. At 0 DPI, the expression level was 0.659-fold, which slightly increased to 0.725-fold at 3 DPI. However, this was followed by a sharp decline to −0.2-fold at 5 DPI, further decreasing to −1.239-fold subsequently. These results revealed a stark divergence in the MAPKK gene expression in response to GBNV inoculation between the treated and untreated plants. The treated plants disclosed a significant initial upregulation, which was sustained over several days before tapering off. The untreated plants, however, exhibited a slight initial increase in expression, followed by a dramatic downregulation.

The relative fold level of the phenylalanine ammonia lyase (PAL) gene in plants treated with bacteria NMB01 showed a consistent increase from the day of inoculation to 10 DPI. Initially, at 0 DPI, the PAL gene expression level was at −0.83-fold. This expression increased to 1.16-fold at 3 DPI and further rose to 1.22-fold at 5 DPI. The upward trend continued, reaching a peak at 10 DPI with a maximum expression level of 2.32-fold. Notably, there was no decline in the PAL gene expression levels throughout this period. In contrast, the untreated control plants also showed an increase in PAL gene expression, but the levels were consistently lower compared to the treated plants. The initial expression level in the untreated plants was 0.74-fold. At 10 DPI, the expression increased to 1.00-fold. However, despite this increase, the expression levels in the untreated plants did not reach the same as those in the treated plants at any observed time point. The PAL gene, involved in the phenylpropanoid pathway, also exhibited elevated transcript levels in bacteria-treated plants, suggesting an augmented production of defense-related compounds ( Figure 11b ).

Upon virus inoculation, the WRKY33 gene exhibited a dynamic expression pattern in the treated plants. Initially, the expression level was upregulated to 0.48-fold, but this upregulation was transient and declined at 3 DPI. At 5 DPI, the WRKY33 expression reached its peak at 1.31-fold but subsequently decreased at 7 and 9 DPI. At 7 and 10 DPI, the expression levels dropped to 0.15-fold and −1.31-fold, respectively. In contrast, the WRKY 33 transcript levels in the untreated, virus-infected plants were significantly lower. Immediately following infection, the expression level was 0.53-fold, which then sharply declined to −0.04-fold at 3 DPI. This decline continued and was −2.75-fold at 10 DPI. These observations suggested a differential regulatory mechanism of the WRKKY 33 gene in response to virus inoculation, with the treated plants resulting in an initial upregulation followed by fluctuating expression levels. However, the untreated plants displayed a consistent and obvious downregulation of WRKY33 over the observed period ( Figure 11c ). Likewise, the MAPKK and WRKY genes, being the integral components of signal transduction pathways in plant immunity, demonstrated significant transcriptional increases of defense proteins. MAPKKs play a vital role in amplifying defense signals, while WRKY33 transcription factors are key regulators of defense gene expression.

The transcript level of PR1 in bacteria NMB01-treated plants increased immediately after virus inoculation to 0.37-fold. This upregulation continued, reached 1.3-fold at 5 DPI. However, at 9 DPI, the expression level declined to −0.41-fold. In contrast, the untreated plants also exhibited an increase in PR1 expression immediately after virus infection, reaching 0.33-fold. This expression level was maintained up to 5 DPI, but subsequently, it declined sharply at 9 DPI to −2.19-fold. The PR1 gene, commonly associated with systemic acquired resistance, showed a marked increase in the treated plants, indicating an enhanced defense response ( Figure 11d ).

As anticipated, the level of NPR1 was upregulated upon GBNV inoculation in the treated plants. NPR1, a central regulator of salicylic acid-mediated defense responses, was similarly upregulated, highlighting its role in coordinating the immune response. The transcript level of NPR1 increased progressively from 1.25-fold at 0 DPI to 1.74-fold at 7 DPI. However, at 9 DPI, this upregulation declined slightly to 1.35-fold. In contrast, plants inoculated with GBNV alone unveiled a downregulation of NPR1 transcripts over the same period. Starting at 0 DPI, the NPR1 transcript level was lower at 0.46-fold and continued to decrease, reaching 0.33-fold at 10 DPI. It indicated that the initial response to GBNV inoculation in the treated plants involved an increase in NPR1 levels and was maintained up to 7 DPI. On the other hand, the NPR1 levels in plants inoculated with GBNV alone consistently declined, signifying a higher virus infection compared to the treated plants ( Figure 11e ).

Overall, the results revealed that inoculation with GBNV in NMB01-treated plants elicited a significant upregulation in the transcript levels of several key genes, including PR1, PAL, NPR1, MAPKK, and WRKY. This obvious increase in gene expression is likely attributable to the interaction with the flagellin gene, which in turn activates MAMP-triggered immunity.