Genomic Characterization of Burkholderia glumae K6 and B. gladioli UPMBG7: Causal Agents of Bacterial Panicle Blight in Malaysia

Genomic Characterization of Burkholderia glumae K6 and B. gladioli UPMBG7: Causal Agents of Bacterial Panicle Blight in Malaysia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genomic Characterization of Burkholderia glumae K6 and B. gladioli UPMBG7: Causal Agents of Bacterial Panicle Blight in Malaysia ADAM ZAFDRI MD ZALI, Siti Izera Ismail, Norsazilawati Saad, Muhammad Asyraf Md Hatta, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5698089/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Oct, 2025 Read the published version in European Journal of Plant Pathology → Version 1 posted 5 You are reading this latest preprint version Abstract Bacterial panicle blight (BPB), caused by Burkholderia glumae and Burkholderia gladioli , poses a significant threaten to rice production in Malaysia, with yield losses reaching up to 75% in severely infected fields. In June 2021, (BPB) symptoms were observed in rice fields in Kedah, Malaysia. Phenotypic characterization revealed typical Burkholderia traits, and pathogenicity tests confirmed symptoms development within seven days after inoculating 75-day-old rice seedlings. Molecular identification using (16S rRNA and gyrB ) sequencing confirmed the isolates as B. glumae and B. gladioli . Whole-genome sequencing of B. glumae K6 and B. gladioli UPMBG7 was performed using the Illumina NovaSeq 6000 platform to investigate their genetic profiles. The assembled draft genome of B. glumae K6 contained 210 contigs, with a total genome size of 6.57 Mbp, 68.33% G + C content and 5,641 coding sequences (CDS). It harbored toxoflavin biosynthesis genes ( toxABCDE, toxJ ) and a Type III secretion system (T3SS), contributing to its pathogenicity. B. gladioli UPMBG7 contained 124 contigs, with a total genome size of 8.22 Mbp, G + C content of 67.99 and 7,022 coding proteins. Unlike B. glumae , B. gladioli lacked the toxI gene for toxoflavin production but compensated with pyoverdine siderophore genes ( pvdA ), which facilitate iron acquisition. These genomic insights unravel the virulence mechanisms of B. glumae and B. gladioli , laying the foundation for innovative disease management strategies. By identifying key pathogenic determinants, this study advances efforts in breeding resistant rice varieties, developing precision biological controls, and implementing cutting-edge molecular diagnostics for early pathogen detection, ultimately strengthening rice production against BPB devastation. Bacterial panicle blight disease Burkholderia glumae Burkholderia gladioli whole-genome sequencing rice Illumina NovaSeq 6000 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1. Introduction Burkholderia glumae , the causal agent of BPB disease, was initially identified in Japan in the early 1950s as a rice pathogen that caused grain rotting and seedling blight on rice (Nandakumar et al., 2009 ). The disease is primarily spread through contaminated seeds, wind-driven rain, and irrigation water, facilitating its rapid dissemination across rice-growing regions. The disease poses a significant threat to rice cultivation in these areas, impacting production and emphasizing the importance of effective management measures. Previous genomic studies on B. glumae have identified key virulence determinants, including toxoflavin biosynthesis genes and quorum sensing regulators, which are crucial for pathogenicity (Kim et al., 2004 ; Kang et al., 2008 ). Similarly, research on B. gladioli has highlighted its genomic diversity and adaptability, enabling it to function as both a plant pathogen and an opportunistic human pathogen (Pérez-Mendoza et al., 2014 ; Pritchard et al., 2020 ). However, comparative genomic analyses between these two pathogens, particularly in the context of BPB, remain scarce. The lack of in-depth comparative studies limits our understanding of their distinct pathogenic strategies and their implications for disease management, necessitating further research to develop targeted control measures. In Malaysia, bacterial panicle blight (BPB) has emerged as a major challenge, causing yield losses of up to 75% in severely infected fields. The increasing frequency of BPB outbreaks highlights the urgency for effective disease management strategies. However, the complex nature of the disease and the genetic variability of its causal agents necessitate a deeper understanding of the pathogen's genomic characteristics to develop targeted control measures (Nandakumar et al., 2009 ). The first report of the Malaysian outbreak of BPB occurred in rice fields in two distinct states, Sungai Ache, Penang, and Kampung Banir, Kelantan. This outbreak spanned from December 2017 to March 2018 (Ramachandran et al., 2021 ). Bacterial blight manifests in symptoms such as seedling blight, sheath rot of flag leaves, and panicle branches, ultimately leading to significant yield losses. The distinctive characteristics of BPB, as highlighted by Nandakumar et al. ( 2009 ) and Sayler et al. ( 2006 ), include an upright, straw-colored panicle with florets displaying dark grey and reddish-brown lines at the lesion borders. Despite glumes desiccating and turning tan, the rachis of the panicle remains green. Burkholderia spp. is among several major plant pathogens and necessitates phylogenetic identification alongside symptomatic characterization. Although B. glumae and B. gladioli belong to the same genus, they employ distinct molecular strategies to cause infection, leading to variations in disease severity and epidemiology. This study presents a comparative genomic analysis of B. glumae K6 and B. gladioli UPMBG7, focusing on their genetic determinants of virulence, host adaptation, and ecological fitness. Whole-genome sequencing revealed that B. glumae K6 harbors toxoflavin biosynthesis genes ( toxABCDE , toxJ ) and a Type III secretion system (T3SS), both critical for bacterial invasion and host colonization. Toxoflavin, a potent phytotoxin, induces oxidative stress, disrupts plant cellular functions, and promotes bacterial spread, contributing to the aggressive virulence of B. glumae . In contrast, B. gladioli UPMBG7 lacks the toxI gene, which is essential for toxoflavin production, but employs an alternative virulence mechanism through pyoverdine siderophore genes ( pvdA ). These genes facilitate iron acquisition, a key factor in microbial competition and host colonization. Unlike B. glumae , B. gladioli exhibits a broader ecological adaptability, thriving in iron-limited environments and establishing infection via host-resource exploitation rather than toxin-mediated damage. The genomic differentiation between these pathogens highlights their unique adaptation strategies, influencing disease progression and severity in rice crops. This study provides a comparative genomic analysis of B. glumae K6 and B. gladioli UPMBG7, two major BPB pathogens in Malaysian rice fields. By identifying key virulence factors, including toxoflavin biosynthesis ( toxABCDE, toxJ ) and Type III secretion system (T3SS) in B. glumae and pyoverdine siderophores ( pvdA ) in B. gladioli , this research enhances our understanding of their infection strategies and potential control targets. These findings have practical implications for rice disease management. Identifying virulence genes supports breeding programs through marker-assisted selection (MAS) to develop BPB-resistant rice varieties. Additionally, insights into pathogen survival mechanisms pave the way for targeted biocontrol strategies, while genomic data contribute to early detection tools, allowing for rapid disease surveillance and intervention.By integrating these genomic insights into breeding, biocontrol, and disease monitoring, this study provides valuable resources for sustainable BPB management, helping to protect rice yields and ensure long-term agricultural resilience. 2. Materials and Methods Samples Collection and Isolation of Bacterial Pathogen : Sample were collected focused on symptomatic rice panicles from BPB outbreak fields to examine the presence and distribution of Burkholderia species. Sampling was conducted from June 2021 to January 2022. Samples were obtained from Kedah (Yan: 5.7633° N, 100.3702° E; Kodiang: 6.3933° N, 100.3057° E), Perak (Seberang Perak: 4.1013° N, 100.9521° E), and Selangor (Sekinchan: 3.5041° N, 101.1033° E), three major rice-producing states selected based on BPB outbreak reports from Department of Agriculture (DOA) Malaysia and their distinct agro-ecological conditions. Kedah, Malaysia’s largest rice-growing region, represents extensive irrigated farming, while Perak and Selangor encompass a mix of traditional, semi-intensive, and intensive cultivation systems. These locations were chosen to capture the genomic diversity of B. glumae and B. gladioli across different rice ecosystems, providing insights into pathogen adaptation and virulence under varying environmental conditions. Collected rice panicles and sheaths were excised and surface sterilized using distilled water containing 1% sodium hypochlorite (NaOCl), immersing the sample in sterile distilled water for 15 minutes, and inoculated the sample onto King’s B agar for 24–48 hours at 41°C. Colony morphology of Burkholderia spp. recorded as cream-colored circular colonies with yellow pigmentation. The pure culture colonies were grown on nutrient broth 24–48 hours at 37°C prior to long-term storage in 20% (v/v) glycerol at -70°C as frozen stock for further use. Molecular Characterization : Genomic DNA extraction from bacterial cultures was carried out using the Presto™ Mini gDNA Bacteria Kit according to the manufacturer's instructions (Geneaid Biotech Ltd., Taiwan). Subsequently, PCR amplification was conducted to identify Burkholderia using universal and specific genes: 16s Ribosomal RNA (16s rRNA), DNA gyrase subunit B of B. glumae ( gyrB ), and gyrB gene of B. gladioli , (Table 1 ). The gyrB gene was chosen for its resolution in distinguishing Burkholderia species. Unlike 16S rRNA, which has high sequence similarity within the genus, gyrB evolves faster and provides greater phylogenetic differentiation. Compared to other housekeeping genes, gyrB shows higher nucleotide variability, making it a more reliable marker for species identification. For the amplification of the 16s RNA gene, the PCR conditions were as follows: an initial denaturation step at 95°C for 2 minutes, followed by 30 cycles of denaturation at 94°C for 30 seconds, annealing at 55°C for 1 minute, extension at 72°C for 2 minutes, and a final extension at 72°C for 10 minutes. The expected size of the resulting amplicons was ~ 1500 base pairs. PCR conditions for the B. glumae gyrB gene began with an initial denaturation step at 94°C for 2 minutes. This was followed by 35 cycles of denaturation at 94°C for 1 minute, annealing at 63°C for 1 minute, extension at 72°C for 1 minute, and a final extension at 72°C for 10 minutes. The expected size of the amplicon was ~ 530 base pair. Lastly, for the amplification of the B. gladioli gyrB gene, PCR amplification was initiated at 94°C for 2 min, followed by 35 cycles at 94°C for 1 min, 63°C for 1 min, and 72°C for 1 min and 72°C for 10 min as the final extension. The expected size of the amplicon was ~ 479 bp (Mulaw et al., 2018 ). Table 1 Primers Sequence (5’-3’) Size (bp) Specify References 16r rRNA 27F AGAGTTTGATCCTGGCTCAG 1500 Universal primer Sayler et al., 2006 1492R GGTTACCTTACGACTT Gyrase B subunit ( B. glumae ) glu-FW GAAGTGTCGCCGATGGAG 530 Specific gyrb gene of B. glumae Mulaw et al., 2018 glu-RV CCTTCACCGA CAGCACGCAT Gyrase B subunit ( B. gladioli ) gla-FW CTGCGCCTGGTGGTGAAG 479 Specific gyrb gene of B. gladioli Mulaw et al., 2018 Gla-RV CCGTCCCGCTGCGGAATA List of primers used for PCR amplification of Burkholderia. Sequence and Phylogenetic Analysis : The unpurified PCR products underwent purification before being subjected to sequencing using the Sanger sequencing method. Subsequently, the DNA sequences were assembled using Bioedit 7.2.5 software and analyzed against sequences in the NCBI database through BLASTn (Nucleotide Basic Local Alignment Search Tool). Sequences obtained were aligned with references strains and outgroup strains by repeating the bootstrap number of 1,000 and used to build a phylogenetic tree using the Maximum Likelihood method generated by MEGA Software version 11.0 ( https://www.megasoftware.net/ ). All sequences were submitted to the GenBank database. Pathogenicity Tests : Rice seedlings of MR219 were planted in the glasshouse at the Institute of Plantation Studies, University Putra Malaysia, under average regulated temperatures (day: 35°C, night: 25°C) and humidity (70–100%) conditions. Rice was then inoculated with 1 mL of 10 8 CFU ml − 1 bacterial suspension from ten bacterial suspension of B. glumae and two bacterial strains of B. gladioli isolates into the panicles and crowns of 75-day-old rice seedlings using a sterile syringe on ten biological replicates per treatment. Control rice seedlings inoculated with sterilized water. Disease progression was monitored for 28 days after inoculation (DAI), with severity recorded every 24 hours. (Nandakumar et al., 2009 ; Lalithiya et al., 2017). Statistical analysis was performed using one-way ANOVA (P < 0.05) to assess variations in pathogenicity among isolates, followed by Tukey’s Honest Significant Difference (HSD) test for multiple comparisons. Disease severity percentages were used to construct the Area Under the Disease Progress Curve (AUDPC) to quantify disease progression over time​ proposed by Shanner & Finney ( 1977 ). All statistical analysis data were analyzed using Statistical Analysis Software (version 9.4). Library Preparation, Quality Control and Whole-Genome Sequencing Genomic DNA was extracted from B. glumae K6 and B. gladioli UPMBG7 using a DNA purification kit (Presto™ Mini gDNA Bacteria Kit) according to the manufacturer's protocols (Geneaid Biotech Ltd., Taiwan). DNA concentration and purity were measured using a NanoDrop spectrophotometer (Thermo Scientific, USA) at 260 and 280nm wavelengths for sample quality control. A DNA library was prepared using the Illumina TruSeq® kit, following the method of Pasquali et al. ( 2019 ). Genomic DNA was sonicated, and resulting fragments were end-repaired, A-tailed, and ligated with adaptors. After purification, the library's quality was assessed with a Qubit 3.0 Fluorometer, real-time PCR for quantification, and bioanalyzer for size distribution detection. Pooled libraries with different indices underwent high-throughput sequencing on the Illumina NovaSeq 6000 platform, which uses sequencing-by-synthesis technology, generating paired-end reads (2x150bp). FastQC software V0.20.0 software was used for quality control, removing adapter contamination and low-quality readings of the raw data. One of the most significant advancements in plant pathogen research is the use of third-generation sequencing (TGS) platforms such as PacBio SMRT sequencing and Oxford Nanopore long-read sequencing, which have enabled the complete genome assembly of bacterial pathogens, including Xanthomonas oryzae and Burkholderia spp. (Midha et al., 2017; Tran et al., 2018). Unlike second-generation sequencing (SGS) technologies, which generate short reads (e.g., Illumina sequencing), TGS provides insights into structural variations, such as plasmid-borne virulence genes and pathogenicity islands that influence host adaptation. However, the selection of second-generation sequencing (SGS) over third-generation sequencing (TGS) in this study was guided by key factors, including sequencing accuracy, cost-effectiveness, and computational feasibility. SGS platforms, such as Illumina NovaSeq 6000, provide high sequencing accuracy, with error rates typically below 2%, whereas TGS platforms like Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) have higher error rates averaging 10%, necessitating extensive error correction steps (Rhoads & Au, 2015 ). While TGS offers long-read advantages, enabling better resolution of repetitive and structural variations, its higher sequencing and data-processing costs make it less accessible for large-scale genome studies (Eid et al., 2009 ; van Dijk et al., 2018 ). Additionally, SGS integrates more efficiently with existing genome assembly pipelines, as its shorter, high-fidelity reads require less computational power than TGS, which generates long but error-prone reads, often requiring complex hybrid assembly strategies (Koren et al., 2017 ). Given these considerations, SGS was selected for this study to ensure reliable, cost-efficient, and computationally manageable genome assembly, providing high-confidence genomic insights into Burkholderia species. Genome Assembly and Gene Function Analysis Genome assembly was modified through three programs (Abbas et al., 2014 ). Initial assembly with SOAPdenovo v2.04 involved different K-mers (95, 107, 119), selecting the result with the least contigs. SPAdes v3.15.4 used K-mers 99 and 127, choosing the assembly result with the optimal K-mer and least contigs. AbySS v2.1.5, with a K-mer of 64, produced an assembly result. CISA software was employed to integrate the assembly results from three different software applications, and the assembly result with the fewest contigs was selected. Genome annotations were performed using the NCBI Prokaryotic Genome Annotation Pipeline (PGAP), including transfer and ribosomal RNAs. Gene functions were annotated by Gene Ontology (GO), Quorum Sensing (QS), and Flagellar pathways of the annotated gene via Kyoto Encyclopedia of Genes and Genomes (KEGG) databases, and phylogenetic classifications of the proteins encoded by gene were annotated via Cluster of Orthologous Groups of Protein Annotation (COG) (Ashburner et al., 2000 ; Kanehisa et al., 2004 ; Kanehisa et al., 2006 ; Galperin et al., 2015 ; Li et al., 2002 ). KEGG annotation was used to map genes to metabolic pathways and virulence-related systems, providing insights into the functional roles of key genes. COG classification revealed that most of the annotated genes belonged to metabolism and cellular processes, suggesting that core functions are conserved in annotated genes. Compared to KEGG, which links genes to metabolic pathways, and EggNOG, which offers broad functional predictions, COG provides detailed insights into gene family conservation. In addition, the NCBI non-redundant protein database was annotated based on a protein database, and the annotation results contain specific information that can be used for species classification. 3. Results and Discussion Samples Collection and Isolation of Bacterial Pathogen BPB symptoms included grain rotting, seedling blight, and florets with dark grey or reddish-brown bases. Infected panicles often remained upright due to grain weight loss. Other symptoms can be observed on the sheath of an infected tiller with a lesion several centimetres long with a gray and necrotic center and a reddish-brown border. Leaves and spikelets contain small circular to oval tan lesions ranging from 1 to 5 mm with brown borders (Fig. 1). In total, 22 potential B. glumae and 5 B. gladioli were recovered from the panicle blight of rice. Pure colonies were observed as circular and cream colour, flat with smooth margins, and produced a diffusible yellow pigment on agar. B. gladioli have vigorous yellow pigment produced on agar compared to B. glumae (Fig. 2). B. glumae and B. gladioli both produce yellow pigmentation on King’s B agar, but the underlying mechanisms and implications of this pigmentation differ between the two species. B. glumae produces yellow pigmentation primarily due to synthesizing the phytotoxin toxoflavin, which is critical for its pathogenicity in rice. Toxoflavin contributes to plant cell damage and is associated with the bacterium's virulence, allowing it to effectively establish infections in its host (Lehman et al., 2015 ; Tsukuda et al., 2018 ). Toxoflavin is a key virulence factor in B. glumae , playing a crucial role in disease severity and bacterial colonization. It is regulated through quorum sensing (QS), specifically by N-acyl homoserine lactones (AHLs) such as C6-HSL and C8-HSL, which activate the ToxR-ToxJ regulatory system, leading to toxoflavin biosynthesis. Once produced, toxoflavin induces oxidative stress by generating reactive oxygen species (ROS), causing cell damage and necrosis in host tissues. This weakens plant defense mechanisms, disrupts chloroplast function and energy metabolism, and accelerates disease progression. The accumulation of toxoflavin in infected rice tissues contributes to panicle blight symptoms, including seed discoloration, tissue necrosis, and sterility, making B. glumae a highly aggressive pathogen. Additionally, toxoflavin promotes bacterial colonization by breaking down host cells, creating a more favorable environment for pathogen proliferation (Lehman et al., 2015 ; Tsukuda et al., 2018 )​. In contrast, the yellow pigmentation observed in B. gladioli is primarily attributed to the production of pyomelanin, a secondary metabolite that serves different ecological roles. While not directly associated with toxicity like toxoflavin, pyomelanin helps B. gladioli cope with environmental stresses by protecting against oxidative damage and aiding in iron acquisition (Ma et al., 2016 ; Pritchard et al., 2020 ). This pigmentation may also enhance the bacterium's survival and colonization ability, albeit through different mechanisms than B. glumae . Overall, the yellow pigmentation in B. glumae is intricately linked to its pathogenicity via toxoflavin production, while B. gladioli utilizes pyomelanin for environmental resilience and adaptability. Molecular Characterization PCR amplification of 27 isolates using 16S rRNA, B. glumae gyrB and B. gladioli gyrB primer pair showed around 1500 bp, 530 bp, and 479 bp amplicon, respectively (Fig. 3). Sequence and Phylogenetic Analysis : BLASTn search for all sequenced primers have similarity ranging from 96–100%. According to prior research, the most often utilized genotypic approach for bacterial identification is the comparative analysis of 16S rRNA sequences (Sayler et al., 2006 ). 16S rRNA sequencing revealed up to 99% similarity with their respective species. However, because the sequence exhibits substantial similarities throughout the Burkholderia group, using 16rDNA alone to identify bacteria among Burkholderia species is insufficient. Hence, primer gyrb was tested to target Burkholderia -specific gene regions. Phylogenetic analysis based on the 16S rRNA gene (Fig. 4) using Maximum-likelihood showed two clusters of Burkholderia spp. Cluster (I) comprised B. glumae strains isolated in this study and were 100% clustered with the B. glumae reference strains 411gr-6 (Accession No. CP021158), GX (Accession No. CP045088) and P1-22-1 (Accession No. NR029211). B. gladioli (Cluster II) strains isolated in this study were 100% clustered with the B. gladioli reference strains CIP 105410 (Accession No. NR044278), NBRC 13700 (Accession No. NR113629), CFBP 2427 (Accession No. NR117553), FDAARGOS 389(Accession No. CP023522), and B27 (Accession No. MZ425421). The outgroup sequence of 16S rRNA used in this study was Pseudomonas aeruginosa strain DSM 50071 (Accession No. NR026078). Phylogenetic analysis based on the gyrB gene (Fig. 5) using Maximum-likelihood showed two clusters of Burkholderia spp. Cluster (I) comprised B. glumae strains isolated in this study and were 100% clustered with the B. glumae reference strains BGR48S (Accession No. CP100285), DOA-BG14 (Accession No. KX213523), and BP2-004 (Accession No. LC474870). While for the Burkholderia gladioli (Cluster II) strains isolated in this study were 100% clustered with the B. gladioli reference strain MAFF 302533 (Accession No. AB190628) and LMG 19584 (Accession No. AB220898). The outgroup sequence of the gyrB gene region used in this study was Burkholderia pseudomallei strain K 96243 (Accesion No. EU024223). Gene sequence successfully clustered B. glumae and B. gladidoli to their reference strains, respectively. None of the strains in this study clustered with the outgroup or different reference species sequence. The tree with the greatest log-likelihood is displayed, with the bootstrap support percentages marked at the nodes. The scale bar represents the expected number of changes per site. Based on the result, identifying Burkholderia species using the gyr B gene is much more efficient than the 16S rRNA gene sequence as the phylogenetic analysis showed a higher genetic variation in gyrB with a nucleotide base substitution of 0.1 compared to 16S rRNA with nucleotides base substitution of 0.02. The acquired 16s rRNA and gyrb gene sequences have been submitted to NCBI GenBank (Table 2 ). The phylogenetic analysis of B. glumae and B. gladioli provides key insights into their evolutionary adaptations to rice as a host. The maximum-likelihood tree constructed using 16S rRNA and gyrB sequences revealed that B. glumae forms a distinct monophyletic cluster closely related to other rice-pathogenic Burkholderia species, while B. gladioli remains more phylogenetically diverse, clustering with both plant-associated and opportunistic strains​ (Sayler et al., 2006 )​. The phylogenetic placement of B. glumae within a specialized rice-pathogenic lineage suggests that it has undergone host-driven evolutionary adaptation, optimizing its virulence mechanisms for effective infection in rice (Kim et al., 2004 )​. This is supported by the presence of toxoflavin biosynthesis genes ( toxABCDE, toxJ ) and quorum sensing regulators (tofI, tofR), which enable B. glumae to efficiently colonize rice tissues, suppress plant defenses, and cause severe bacterial panicle blight (BPB). Such specialization indicates a coevolutionary relationship between B. glumae and rice, where the pathogen has fine-tuned its genetic traits to exploit the rice host effectively​. In contrast, B. gladioli occupies a more generalist phylogenetic position, reflecting its broader ecological adaptability. Unlike B. glumae , B. gladioli does not rely on toxoflavin for virulence but instead employs pyoverdine siderophores and diverse secretion systems, which allow it to persist in multiple environments, including soil, water, and plant rhizospheres ​(Wang et. al., 2021 ). This suggests that B. gladioli retains a more flexible pathogenic strategy, making it a weaker but more adaptable pathogen compared to B. glumae (Pritchard et al., 2020 ). The evolutionary divergence between these two species highlights their contrasting adaptation strategies: B. glumae as a highly specialized rice pathogen with host-adapted virulence traits, and B. gladioli as a generalist with broader environmental resilience. Understanding these adaptations is crucial for disease management, as targeted control strategies such as quorum sensing inhibitors for B. glumae and iron-chelating compounds for B. gladioli could be designed to exploit their respective weaknesses (Lehman et al., 2015 ). Table 2 List of Genbank accession numbers of Burkholderia in this study. Strains State Country Host Species 16s rRNA Accession gyrB Accession BGK.AZ1 Kedah Malaysia Oryza sativa Burkholderia glumae OK632514 OL461777 BGK.AZ2 Kedah Malaysia Oryza sativa Burkholderia glumae OK632515 OL461778 BGK.AZ3 Kedah Malaysia Oryza sativa Burkholderia glumae OK632516 OL461779 BGK.AZ4 Kedah Malaysia Oryza sativa Burkholderia glumae OK631741 OL461780 BGK.AZ5 Kedah Malaysia Oryza sativa Burkholderia glumae OL347392 OL461781 BGK.AZ6 Kedah Malaysia Oryza sativa Burkholderia glumae OL347393 OL461782 BGK.AZ7 Kedah Malaysia Oryza sativa Burkholderia glumae OL347394 OL461783 BGK.AZ8 Kedah Malaysia Oryza sativa Burkholderia glumae OL347395 OL461784 BGS.AS1 Selangor Malaysia Oryza sativa Burkholderia glumae ON062124 ON086704 BGS.AS2 Selangor Malaysia Oryza sativa Burkholderia glumae ON062125 ON086705 BGS.AS3 Selangor Malaysia Oryza sativa Burkholderia glumae ON062126 ON086706 BGS.AS4 Selangor Malaysia Oryza sativa Burkholderia glumae ON062127 ON086707 BGS.AS5 Selangor Malaysia Oryza sativa Burkholderia glumae ON062128 ON086708 BGS.AS6 Selangor Malaysia Oryza sativa Burkholderia glumae ON062129 ON086709 BGS.AS7 Selangor Malaysia Oryza sativa Burkholderia glumae ON062130 ON086710 BGP.A1 Perak Malaysia Oryza sativa Burkholderia glumae ON062131 ON086711 BGP.A2 Perak Malaysia Oryza sativa Burkholderia glumae ON062132 ON086712 BGP.A3 Perak Malaysia Oryza sativa Burkholderia glumae ON062133 ON086713 BGP.A4 Perak Malaysia Oryza sativa Burkholderia glumae ON062134 ON086714 BGP.A5 Perak Malaysia Oryza sativa Burkholderia glumae ON062135 ON086715 BGP.A6 Perak Malaysia Oryza sativa Burkholderia glumae ON062136 ON086716 BGP.A7 Perak Malaysia Oryza sativa Burkholderia glumae ON062137 ON086717 UPMBG7 Selangor Malaysia Oryza sativa Burkholderia gladioli OM869953 OM824438 UPMBG8 Selangor Malaysia Oryza sativa Burkholderia gladioli OM869954 OM824439 UPMBG9 Selangor Malaysia Oryza sativa Burkholderia gladioli OM869955 OM824440 UPMBG15 Selangor Malaysia Oryza sativa Burkholderia gladioli OM869956 OM824441 UPMBG17 Selangor Malaysia Oryza sativa Burkholderia gladioli OM869957 OM824442 Pathogenicity Tests After 4–7 days of inoculation, MR219 cultivars began to develop bacterial panicle blight, whereas the control rice remained healthy. Up to 28 days after inoculation, the symptoms were evaluated and documented every 24 hours to notice the browning lesion on panicle florets. Disease severity was assessed ranging from mild to severe. Category 1 represented mild symptoms, with browning restricted to the floret base and no significant spread. Category 2 indicated moderate symptoms, where browning extended to the middle of the floret, accompanied by slight sterility. In Category 3, symptoms became more severe, with browning covering most of the floret, leading to significant sterility and tissue necrosis. The most severe cases fell into Category 4, where florets were completely darkened, full sterility occurred, and panicle blight developed. The symptomatic plants first appeared when florets became brown across the floret base and gradually developed into classic panicle blight and florets with a completely dark brown base and sterile florets (Fig. 6). After 28DAI, all plants developed BPB disease, with floret blight expanding consistently from the bottom to the top of the florets. The disease incidence ranges from 27–77% due to severe panicle loss and severity progress. The statistical analysis of 12 Burkholderia spp. pathogenicity tests on rice cultivar MR219 revealed a significant variation in pathogenicity across isolates with a P-value < 0.001 (One-Way ANOVA P < 0.05). MR219 cultivar proved to be very susceptible to Burkholderia spp. B. gladioli UPMBG7 reported the highest degree of severity (P < 0.05) at 28DAI (77.43%) and an AUDPC score of 1123.57, while B. glumae BGS.AS1 has the second highest degree of severity (P < 0.05) at 28DAI (68.15%) and an AUDPC score of 948.28. However, B. glumae BGK.AZ1 had the lowest severity (27.60%) and AUDPC score at 28DAI. Table 3 displays the percentage of disease severity and AUDPC value for cultivars MR219. Table 3 Percentage of disease severity and Area Under Disease Progress Curve (AUDPC) on MR219 cultivars for 12 selected isolates Burkholderia species on panicles. Variety Strain AUDPC value Percent Disease Severity (%) after 28 days inoculation (28DAI ± SD) Disease score at 28DAI Isolate Virulence level BGK.AZ1 410.76 27.60 ± 3.98 D 2 Low BGS.AS3 658.15 47.35 ± 6.25 C 3 Intermediate BGK.AZ4 666.48 49.19 ± 7.56 C 3 Intermediate BGP.A2 790.44 49.82 ± 3.06 BC 3 Intermediate BGS.AS5 779.77 61.01 ± 3.27 ABC 4 High MR219 BGP.A4 927.79 61.28 ± 3.45 ABC 4 High BGS.AS4 804.10 63.81 ± 5.70 ABC 4 High BGP.A6 1053.75 63.94 ± 3.23 ABC 4 High BGK.AZ7 761.79 67.81 ± 4.86 AB 4 High BGS.AS1 948.28 68.15 ± 7.61 A 4 High UPMBG8 1081.73 72.36 ± 3.42 A 4 High UPMBG7 1123.58 77.43 ± 4.87 A 4 High Figures (7 and 8) show that disease severity increased steadily at 7DAI until 28DAI for all isolates. Within the context of our research, the interpretation of the Area Under the Disease Progress Curve (AUDPC) values presents a nuanced understanding of disease progression and the responses of different cultivars to the pathogen. Our findings reveal a spectrum of disease severity, as reflected by the range of AUDPC values from 410.762 to 1123.575. The AUDPC values were compared across different Burkholderia strains to evaluate their virulence levels in rice cultivar MR219. The strain with the lowest AUDPC value of 410.762 demonstrates the least disease progression, suggesting a reduced susceptibility to the host. Conversely, the strain with the highest AUDPC value of 1123.575 exhibits the most substantial disease progression, indicating greater susceptibility. The range of AUDPC values from the studies suggests varying degrees of resistance or susceptibility to the disease, with the cultivars having an AUDPC of 410.762, indicating partial resistance toward B. glumae , while those with higher values are generally less resistant. The intermediate AUDPC values observed among other strains represent varying levels of disease severity and, by extension, differing degrees of resistance or susceptibility. This variability underscores the multifaceted nature of disease dynamics, influenced by cultivar genetics, environmental conditions, and pathogen virulence. Moreover, the AUDPC values hold practical implications for disease management and breeding strategies. Cultivars with lower AUDPC values may be considered less virulent, while those with higher values may necessitate additional disease control measures. While the AUDPC values provide valuable insights, further statistical analyses and experiments may be required to validate these findings and delve deeper into the underlying factors contributing to distinct disease outcomes. From the disease severity index analysis, our studies find that B. gladioli have more severe virulence than B. glumae strain. We observe that B. gladioli is more severe when isolated into rice using the glasshouse environment. In the controlled glasshouse setting, B. glumae shows less disease severity. However, in our study, B. glumae is more prevalent than B. gladioli in the BPB disease outbreak. The results suggest that the interactions between these pathogens and their host plants and the environmental conditions may influence their virulency outcomes. This observation highlights the complexity of pathogen-host interactions and underscores the importance of studying these dynamics for effective disease management. The pathogenicity levels observed in this study align with prior findings on Burkholderia spp. In the United States, B. glumae strains caused up to 80% yield losses in susceptible rice cultivars, with disease severity increasing under high temperatures and humidity (Nandakumar et al., 2009 ). Similarly, a study in Thailand reported B. glumae strains with AUDPC values ranging from 500 to 1100, demonstrating variability in pathogenic potential among isolates (Nootjarin et al., 2022 ). The higher virulence of B. gladioli in the Malaysian study suggests that environmental conditions and host-pathogen interactions play a critical role in disease outcomes​. Further, the variation in AUDPC values across different Burkholderia strains highlights the genetic and physiological differences influencing virulence. Studies have shown that B. glumae pathogenicity is largely driven by toxoflavin production, while B. gladioli relies on iron-chelating pyoverdine siderophores for infection (Pérez-Mendoza et al., 2014 )​. The ability of B. glumae to dominate BPB outbreaks despite its lower severity in controlled tests suggests that toxoflavin-mediated host adaptation may give it a selective advantage in field conditions (Ramachandran et al., 2021 )​ Genome Assembly Whole-genome sequencing of B. glumae K6 and B. gladioli UPMBG7 was performed using the Illumina NovaSeq 6000 platform, generating paired-end reads (2×150 bp). The draft genome sequences of K6 had a total length of 6.57 Mbp genome sized with 210 contigs with an N50 value of 77,057 bp and 68.33% G + C contents. The average read depth of the genome is 205.0X. The completeness of the B. glumae K6 genome assembly was assessed using BUSCO v5.8.3 using the Proteobacteria_odb10 dataset, which consists of 219 conserved orthologs. The analysis was conducted in prokaryotic genome mode with Prodigal as the gene predictor. The results indicate a highly complete genome, with 99.5% of the expected single-copy orthologs identified. Among these, all were present as single copies, with no duplicated or fragmented BUSCOs detected. Only one BUSCO gene (0.5%) was missing, suggesting minimal genome assembly gaps (Manni et al., 2021 ). The genome contained 6,016 genes with 5,947 coding sequences, eight rRNA gene operons, and 57 tRNA genes. B. gladioli UPMBG7 had a total length of 8.22 Mbp genome sized with 124 contigs with an N50 value of 207,968 bp and 67.99% G + C contents. The average read depth of the genome is 85.0X. UPMBG7 genome contained 7,237 genes with 7,164 coding sequences, 8 rRNA operons, and 60 tRNA genes (Table 4 ). The completeness of the B. gladioli UPMBG7 genome assembly was evaluated and results indicate a 100.0% completeness score, with all 219 expected orthologs identified. Of these, 92.7% were found as single-copy genes, while 7.3% were duplicated. No fragmented or missing BUSCO genes were detected, suggesting that the genome is well-assembled without major gaps. The presence of a small proportion of duplicated genes may indicate either natural gene duplications or minor assembly redundancies (Manni et al., 2021 ).The genome assembly dataset showed that the GC content and genome size of B. glumae strain K6 are similar to reference genomes and previously published Burkholderia genomes. Our analysis reveals a close match between our B. glumae strain and the reference sequences in terms of both genome size and GC content. According to the GenBank NCBI database, the genome size of Burkholderia species ranges from 6 to 11 Mbp, with a GC content of 65%-68%. The assembly results showed that B. gladioli strain has a larger genome size than B. glumae . One of the reasons for this difference in genome size is that B. gladioli have a higher number of genes and additional genetic material than B. glumae. For instance, additional genetic material, such as transposable elements, can act as a source of new genes. Other than that, B. gladioli and B. glumae may have different genetic variations and mutations that could contribute to their different sizes. These genetic variations could result in different growth rates, metabolic pathways, and overall physiology (Kang et al., 2022 ). B. gladioli and B. glumae are known to occupy different ecological niches. B. gladioli is found in soil, rhizosphere, and plant tissues, whereas B. glumae is found in plant tissues. B. gladioli may have evolved to be more significant to better adapt to its environment (Pedraza et al., 2018 ). However, the genome size of a microorganism may not necessarily indicate its complexity, pathogenicity, or virulence. It can impact the organism's metabolic pathways, resistance mechanisms, and adaptation to different environments (Kang et al., 2022 ) Table 4 Genome features of Burkholderia glumae K6 and Burkholderia UPMBG7 strain. Feature Burkholderia glumae K6 Burkholderia gladioli UPMBG7 Sequencing method Illumina NovaSeq 6000 Illumina NovaSeq 6000 Genome size (bp) 6,572,223 8,225,388 Contigs 210 124 Contigs N50 (bp) * 80,214 207,968 G + C content (%) 68.33 67.99 Predicted coding genes (CDs) 5,741 7,164 Ribosomal RNA number (5S, 16S, 23S) 6,1,1 6,1,1 Transfer RNA number 57 60 * N50: 50% of all bases come from contigs longer than this value. Gene Annotation and Functional Analysis Species identification was carried out using gene sequences through protein sequence analysis with BLAST v2.15.0. This process involved comparing the sequences against the NCBI NR bacteria database, containing 324,246,652 protein sequences. The criteria for the blast included an e-value of ≤ 1e-10, with the best match used to determine the species based on the organism information for the matching genes. The Non-Redundant Protein Database (NR) annotation of species and genes demonstrated that 4,988 genes (92.03%) annotated from the K6 strain exhibit similarity to B. glumae species. Meanwhile, the NR gene annotated for the UPMBG7 strain showed 5,700 genes (84.36%) annotated from the UPMBG7 strain, which are comparable to those of the B. gladioli species. Using the EggNOG v5.0 database, a widely used tool that groups genes into evolutionary families and assigns functional categories based on orthology. EggNOG provides broader functional predictions across multiple species, making it useful for studying bacterial gene evolution. Proteins encoded by genes were classified via COG annotation, dividing them into 24 functional categories. K6 Genomic features, as illustrated in Fig. (9), revealed that 4,087 genes were classified into clusters of orthologous genes (COGs). The most abundant COGs category was "Amino acid transport and metabolism" (E; 436 genes), followed by "General function prediction only" (R; 423 genes), "Transcription" (K; 401 genes), "Cell wall/membrane/envelope biogenesis" (M; 326 genes), "Carbohydrate transport and metabolism" (G; 323 genes), "Signal transduction mechanism" (T; 296 genes), "Energy production and conversion" (C; 289 genes), and "Inorganic ion transport and metabolism" (P; 244 genes), identified as significant categories in Fig. (9). UPMBG7 genomic features, as illustrated in Fig. (10), revealed that 5,390 genes were classified into clusters of orthologous genes (COGs). The most abundant COG category was "Amino acid transport and metabolism" (E; 622 genes), followed by "General function prediction only" (R; 678 genes), "Transcription" (K; 701 genes), "Cell wall/membrane/envelope biogenesis" (M; 396 genes), "Carbohydrate transport and metabolism" (G; 481 genes), "Signal transduction mechanisms" (T; 331 genes), "Energy production and conversion" (C; 366 genes), and "Inorganic ion transport and metabolism" (P; 352 genes), identified as significant categories in Fig. (10). The Clusters of Orthologous Groups (COG) annotation analysis provides valuable insights into the functional diversity and potential ecological roles of B. glumae K6 and B. gladioli UPMBG7 strains. Despite both strains belonging to the Burkholderia genus, their COG profiles reveal distinct functionalities that reflect their adaptability to unique environmental niches and roles in various biological processes. Comparative Analysis of Functional Categories Both strains exhibit minimal representation in RNA processing and modification, with each possessing just one annotated gene. This suggests a shared reliance on fundamental mechanisms for RNA processing. Similar conservation is observed in chromatin structure and dynamics, where both strains have three annotated genes, indicating stable chromatin organization that is likely essential for epigenetic regulation and genome integrity. When examining energy production and conversion, UPMBG7 shows a more extensive capacity with 366 annotated genes than K6's 289. The difference may indicate that B. gladioli UPMBG7 possesses enhanced energy metabolism strategies, potentially allowing it to thrive in more energetically diverse or demanding environments. Both strains exhibit 46 annotated genes in cell cycle control, cell division, and chromosome partitioning, suggesting conserved regulatory mechanisms for maintaining genomic stability during cell division. For amino acid transport and metabolism, K6 features 436 annotated genes, while UPMBG7 has a higher count of 622. This indicates that both strains can utilize a wide range of amino acids, which is crucial for their adaptability in nutrient-variable environments. Nucleotide transport and metabolism genes are similarly conserved, with K6 possessing 104 genes and UPMBG7 123, underscoring both strains' capability to support essential nucleotide synthesis for DNA and RNA functions. Both strains show significant representation in carbohydrate transport and metabolism, with K6 harboring 323 annotated genes and UPMBG7 481. This suggests that while both can metabolize various carbohydrates, UPMBG7’s higher gene count may reflect an enhanced ability to exploit diverse carbon sources. UPMBG7 has 270 annotated genes in coenzyme transport and metabolism compared to K6’s 234, indicating functional efficiency in these crucial enzymatic reactions (Laura Ortega et al., 2021). Lipid transport and metabolism genes contribute to membrane stability and adaptation, with UPMBG7 containing 326 annotated genes compared to K6's 233. This suggests that B. gladioli may have a more versatile lipid metabolism profile. The translation, ribosomal structure, and biogenesis categories are also well represented, with UPMBG7 featuring 276 annotated genes and K6 234, highlighting each strain’s capacity for protein synthesis and cellular growth. In terms of cell wall/membrane/envelope biogenesis, UPMBG7 possesses 396 genes, while K6 has 326, suggesting potential variations in cell envelope structure that may influence interactions with the environment or host organisms. Both strains exhibit similar numbers of genes related to cell motility (121 in UPMBG7 and 140 in K6), implying comparable mechanisms for bacterial movement. Secondary metabolites biosynthesis, transport, and catabolism genes, which may contribute to ecological or pathogenic interactions, are well-represented, with 233 in UPMBG7 and 141 in K6. This disparity may indicate differing ecological roles or potentials for secondary metabolite production between the strains. The category of general function prediction reveals multi-functionality, with UPMBG7 featuring 678 annotated genes compared to K6's 423, suggesting versatile roles in various cellular processes (Nootjarin et al., 2022 ). Genes with unknown functions hint at uncharted territories, with UPMBG7 having 288 and K6 217, warranting further investigation into their potential roles and implications. Many genes classified under COG Group S (Function Unknown) remain unannotated due to the lack of experimentally validated homologs in bacterial databases. However, based on comparative genomics, domain analysis, and existing knowledge of bacterial physiology, potential functional roles can be speculated. This section explores six uncharacterized genes in B. glumae K6 that may serve key biological functions. First, K6_GM000016 ( PelG ) speculate to be involve in biofilm formation. PelG is an essential component of the pellicle polysaccharide biosynthesis system, which contributes to biofilm formation in bacteria (Colvin et al., 2012 ). In Pseudomonas aeruginosa , PelG is required for producing extracellular matrix components, protecting the bacteria against host immune responses and antimicrobial agents (Jennings et al., 2015 ). Given that B. glumae forms biofilms in rice plants, K6_GM000016 may contribute to the pathogen's ability to colonize plant surfaces, leading to enhanced persistence and virulence. Disrupting this gene could provide insights into how biofilms facilitate bacterial infection in rice. Second, K6_GM000110 ( YajQ ) speculate to be cyclic-di-GMP-binding protein and virulence regulation. Cyclic-di-GMP is a critical bacterial second messenger molecule, controlling processes like biofilm formation, motility, and virulence (Römling et al., 2013 ). YajQ-family proteins have been identified as cyclic-di-GMP-binding effectors, meaning that K6_GM000110 could be involved in regulating bacterial lifestyle transitions (e.g., switching from planktonic to biofilm state). In other pathogens such as Vibrio cholerae , cyclic-di-GMP-binding proteins act as molecular switches that alter virulence gene expression (Krasteva & Sondermann, 2017 ). If K6_GM000110 plays a similar role in B. glumae , it may coordinate quorum sensing signals with biofilm formation, making it a potential target for anti-virulence strategies. Next, Gene ID K6_GM000795 ( YjbJ , UPF0339 family) speculate to be potential regulatory protein, the YjbJ protein (UPF0339 family) is a conserved yet uncharacterized protein found in various Gram-negative bacteria, including Burkholderia species. Though its function remains unknown, its conservation across diverse bacteria suggests a potential role in regulatory mechanisms. In Escherichia coli , proteins from the UPF0339 family have been linked to stress responses and DNA-binding activity, indicating that K6_GM000795 could function as a transcriptional regulator in B. glumae . This aligns with the hypothesis that certain uncharacterized regulators influence pathogenicity, quorum sensing, or environmental adaptability in bacterial pathogens (Anantharaman et al., 2012 ; Price et al., 2018 ). Other than that, Gene ID K6_GM000892 ( GlcG , DUF3365) speculate to be possible sugar transporter protein, the presence of DUF3365 (Domain of Unknown Function) in K6_GM000892 suggests a possible transporter role, as DUF3365-containing proteins have been linked to membrane-associated sugar transporters in other bacteria. Given that B. glumae is an opportunistic plant pathogen, the ability to efficiently uptake sugars from host tissues could be an important survival mechanism. This protein may be involved in glucose, fructose, or other carbohydrate transport, contributing to bacterial metabolism and pathogenicity (Saier et al., 2016 ). Alternatively, GlcG might act as a sensor or regulator in a two-component system, detecting environmental sugar concentrations and triggering metabolic adjustments. Such mechanisms have been identified in related bacterial pathogens like Pseudomonas syringae and Xanthomonas oryzae (Ren et al., 2019 ). Next, Gene ID K6_GM000934 (DUF2345 family) speculate to be no known homologs (requires further study), Unlike the other genes in this list, K6_GM000934 remains highly uncharacterized, with no direct functional homologs in KEGG or other major databases. However, its conserved domain DUF2345 suggests it may be involved in membrane-associated processes, possibly efflux pump regulation or signal transduction. Given its high sequence conservation in Burkholderia species, this gene may encode a stress response protein involved in toxin resistance or heavy metal detoxification, processes commonly seen in environmentally adaptable pathogens (Galperin & Koonin, 2004 ). Future experimental approaches such as transcriptomics (e.g., RNA-Seq under stress conditions) could help determine its regulatory role. Lastly, Gene ID K6_GM001096 ( YceD , DUF990 family) speculate to be potential metal-binding protein, K6_GM001096 belongs to the DUF990 family, which includes putative metal-binding proteins that participate in oxidative stress responses. In other bacteria, YceD -like proteins have been associated with iron homeostasis and metal resistance, essential for bacterial survival in host environments (Wang et al., 2018 ). Given that B. glumae often encounters iron-limiting conditions in plant hosts, K6_GM001096 might be involved in siderophore-mediated iron acquisition or oxidative stress protection (Wang et al., 2018 ). Table 5 provides a summary of uncharacterized genes identified in Burkholderia glumae K6 that fall under COG Group S, along with speculative functional assignments based on domain analysis, KEGG associations, and literature references. Table 5 Summary of Uncharacterized Genes in Burkholderia glumae K6 with Speculative Functions Gene ID Predicted Function Possible Biological Role Reference K6_GM000016 PelG – Polysaccharide biosynthesis protein Involved in biofilm formation and bacterial adhesion Colvin et al. ( 2012 ); Jennings et al. ( 2015 ) K6_GM000110 YajQ – Cyclic-di-GMP-binding protein Likely regulates biofilm formation and virulence Römling et al. ( 2013 ); Krasteva & Sondermann ( 2017 ) K6_GM000795 YjbJ, UPF0339 family – Possible transcriptional regulator May regulate virulence, stress responses, or quorum sensing Anantharaman et al. ( 2012 ); Price et al. ( 2018 ) K6_GM000892 GlcG , DUF3365 – Potential sugar transporter May facilitate carbohydrate uptake, enhancing bacterial metabolism and colonization Saier et al. ( 2016 ); Ren et al. ( 2019 ) K6_GM000934 DUF2345 family – No known homologs May be involved in membrane-associated processes (e.g., signal transduction, efflux) Galperin & Koonin, ( 2004 ) K6_GM001096 YceD , DUF990 family – Potential metal-binding protein Could play a role in oxidative stress resistance and iron homeostasis Wang et al. ( 2018 ); Cornelis et al. ( 2011 ) Table 5 Genes involved in the virulence factor of Burkholderia spp. No System Gene Description Reference 1 Toxoflavin Production ( B. glumae ) TofI N-Acyl homoserine lactone (AHL) synthase for N-hexanoyl homoserine lactone (C6-HSL) and C8-HSL Kim et al., ( 2004 ) 2 TofR Cognate receptor for C8-HSL Kim et al., ( 2004 ) 3 ToxR LysR-type transcriptional activator Ham et al., ( 2010 ) 4 ToxJ LuxR C-terminal-related transcriptional regulator Ham et al., ( 2010 ) 5 toxF, toxG, toxH, toxI Toxoflavin transport Francis et al. , (2013) 6 ToxA, ToxB, ToxC, ToxD, ToxE Toxoflavion biosynthesis Francis et al. , (2013) 7 Flagella Formation QsmR Ic1R-type transcriptional regulator Kim et al., ( 2007 ) 8 FlhC Flagellar assembly, biosynthesis, chemotaxis Kim et al., ( 2007 ) 9 FlhD Flagellar assembly Kim et al., ( 2007 ) 10 Type III Secretion System HrpB Regulatory factor for expression of T3SS Kang et al., ( 2008 ) 11 Lipase Formation LipA Cell-wall degrading enzyme Zhou-qi (2016) 12 LipB Biosynthesis and activation of LipA Zhou-qi (2016) 13 Siderophore Production ( B. gladioli ) PvdA Encodes enzyme for pyoverdine synthesis Pérez-Mendoza et al., ( 2014 ) As for UPMBG7 strains also explores six uncharacterized genes in B. gladioli that may serve key biological functions. First, Gene ID UPMBG7_GM000025 speculate to be VgrG (Type VI secretion system protein) ,the VgrG protein is a component of the Type VI Secretion System (T6SS), a molecular weapon used by bacteria to inject toxins into competing microbes. Studies have shown that B. gladioli harbors T6SS genes, suggesting this protein plays a role in antagonistic interactions with other microbes and host manipulation in plant infections (Basler et al., 2013 ; Ho et al., 2014 ). Second, Gene ID UPMBG7_GM000074 speculate to be putative outer membrane protein (K08995), this protein may function as an outer membrane receptor, involved in the uptake of nutrients such as iron or bacterial-host interactions. Similar proteins in Pseudomonas and Burkholderia regulate antimicrobial resistance and immune evasion which may enhance bacterial survival in iron-limited environments (Stork et al., 2007 ; Noinaj et al., 2010 ). Next, Gene ID UPMBG7_GM000047 speculate as DUF1501 Family Protein (possible stress-response regulator), this proteins in the DUF1501 family are widely conserved in Gram-negative bacteria but remain uncharacterized. However, structural comparisons suggest a role in oxidative stress protection and antibiotic resistance mechanisms which could help B. gladioli adapt to plant defense responses and antimicrobial stress (Galperin & Koonin, 2004 ). Gene ID UPMBG7_GM000048 speculate as DUF1800 Family Protein (possible metal-binding protein), DUF1800 proteins are often associated with iron or zinc transport, crucial for bacterial metabolism and pathogenicity. May participate in iron acquisition, oxidative stress resistance, or virulence regulation (Andrews et al., 2003 ; Cornelis et al., 2011 ). Other than that, Gene ID UPMBG7_GM000052 speculate as uncharacterized membrane protein (COG5612), this protein belongs to a family of membrane-associated proteins potentially involved in cell wall biosynthesis, biofilm formation, or secretion systems in which could play a role in bacterial adhesion or transport of virulence factors (Henderson et al., 2004 ). Lastly, Gene ID UPMBG7_GM000377 speculate to be Type VI secretion system-associated protein, this protein appears closely linked to T6SS genes, suggesting it functions in bacterial competition, host colonization, or toxin secretion and could be involved in delivering antibacterial effectors or interacting with plant hosts (Pukatzki et al., 2006 ; Russell et al., 2014 ). The presence of these previously unknown functional roles in B. glumae K6 and B. gladioli UPMBG7 suggests key adaptations for plant infection, stress survival, and interbacterial competition. While computational analysis provides strong functional predictions, experimental validation is needed to confirm these roles. Table 6 presents a summary of uncharacterized genes identified in Burkholderia glumae K6 that belong to COG Group S. These genes were analyzed using comparative domain analysis and KEGG annotations, and supporting literature. Table 6 Summary of Uncharacterized Genes in Burkholderia gladioli UPMBG7 with Speculative Functions Gene ID Predicted Function Possible Biological Role Reference UPMBG7_GM000025 VgrG , Type VI secretion system protein May facilitate toxin injection and interbacterial competition Basler et al. ( 2013 ); Ho et al. ( 2014 ) UPMBG7_GM000047 DUF1501 family protein – Possible stress-response regulator May function in oxidative stress adaptation and antibiotic resistance Galperin & Koonin, ( 2004 ) UPMBG7_GM000048 DUF1800 family protein – Possible metal-binding protein May be involved in iron transport or virulence regulation Andrews et al. ( 2003 ); Cornelis et al. ( 2011 ) UPMBG7_GM000052 Uncharacterized membrane protein (COG5612) Potentially involved in cell wall remodelling or secretion systems Henderson et al., ( 2004 ) UPMBG7_GM000074 K08995, Putative membrane protein Potential outer membrane receptor involved in nutrient uptake or host interaction Stork et al. ( 2007 ); Noinaj et al. ( 2010 ) UPMBG7_GM000377 T6SS-associated unknown protein Possible role in bacterial competition and virulence Pukatzki et al. ( 2006 ); Russell et al. ( 2014 ) Moreover, UPMBG7 has 331 genes in signal transduction mechanisms compared to K6's 296, indicating a robust capacity to sense and respond to environmental changes. Intracellular trafficking, secretion, and vesicular transport genes, essential for cellular communication, number 131 in UPMBG7 and 114 in K6. Defence mechanisms crucial for survival against external threats include 129 genes in UPMBG7 and 98 in K6, suggesting B. gladioli may have enhanced protective strategies. Finally, genes related to extracellular structures are represented by 39 in UPMBG7 and 29 in K6, which could contribute to adherence and colonization strategies. Mobilome-related genes, which include prophages and transposons, number 62 in UPMBG7 and 75 in K6, potentially influencing genome plasticity and adaptation (Seo et., al 2015). Comparing these results with other phytopathogenic Burkholderia species, such as B. cepacia and B. pseudomallei , reveals that while toxoflavin plays a key role in B. glumae virulence, other members of the genus utilize diverse strategies, including protease secretion and antibiotic resistance genes, for host colonization (Gyaneshwar et al., 2011 ). The findings from this study align with previous work showing that siderophore-mediated iron acquisition is a common strategy among pathogenic Burkholderia species (Ma et al., 2016 ), but it is particularly critical in B. gladioli , distinguishing it from B. glumae . The practical implications of these findings are significant. Identifying virulence factors such as toxABCDE and pvdA provides valuable markers for developing molecular detection tools and resistant rice cultivars via marker-assisted selection (MAS). Additionally, understanding the differential pathogenicity of these species opens avenues for targeted biocontrol measures, such as quorum sensing inhibitors or siderophore-disrupting agents, which could mitigate BPB outbreaks in rice-producing regions (Mansfield et al., 2012 ). Ecological Significance of Genomic Differences The genomic differences observed between B. glumae and B. gladioli have significant ecological implications. The larger genome size of B. gladioli , coupled with the presence of additional genes related to stress tolerance, suggests that this strain may have enhanced environmental resilience compared to B. glumae . This could explain why B. gladioli exhibits more severe virulence when isolated into rice in a controlled environment. However, despite its severe virulence, B. glumae appears more prevalent in field outbreaks, potentially due to its specific virulence factors, such as toxoflavin production, which facilitate infection in natural field conditions. These findings highlight the importance of understanding pathogen-host-environment interactions to develop effective disease management strategies. KEGG pathway analysis using a hypergeometric distribution with a q-value threshold of < 0.05 showed that B. gladioli and B. glumae possess genes required for flagellar assembly, which is essential for their motility and ability to colonize host plants (Fig. 11). A significant distinction between the two species lies in their quorum sensing and toxin production capabilities. B. gladioli lacks the toxI gene, essential for producing the virulence factor toxoflavin present in B . glumae (Lehman et al., 2015 ). Consequently, while B. gladioli can effectively move and colonize plants, it does not produce toxoflavin, limiting its ability to cause host tissue damage compared to B. glumae . This implies that B. gladioli relies on alternative pathogenic mechanisms for infection (Fig. 12). The flagellar assembly process in Burkholderia species involves several genes and proteins that contribute to flagella synthesis. Early gene products, such as FlhA, FlhB, FliO , FliP , FliQ , and FliR , play crucial roles in initiating flagella assembly. Proteins such as MotA and MotB anchor the motor complex to the cell membrane, essential for bacterial motility. Genes that have not been experimentally verified are indicated by white boxes, meaning their role in the flagellar pathway is uncertain (Jang et al., 2014 ; Wang et al., 2022 ). Quorum sensing (QS) regulates gene expression in response to population density, affecting behaviors like biofilm formation and virulence factor production. In B. glumae , QS is mediated by N-acyl homoserine lactones (AHLs), specifically C6-HSL and C8-HSL. These molecules trigger the expression of toxJ and toxR genes, which contribute to toxoflavin biosynthesis. In contrast, B. gladioli lacks the toxI gene, which means it does not produce toxoflavin but uses other virulence mechanisms. This includes producing pyoverdine, a high-affinity siderophore that helps the bacterium acquire iron from host tissues, enhancing its survival and pathogenicity (Pérez-Mendoza et al., 2014 ; Wang et al., 2021 ). Under the quorum sensing pathway, toxI genes were indicated with a white box for B. gladioli UPMBG7 strains, indicating that this gene was absent. B. gladioli is generally not associated with the presence of the toxI gene. The toxI gene is often linked to certain strains of B. glumae , which is known to produce the phytotoxin toxoflavin, contributing to its pathogenicity in plants, particularly rice. Despite the absence of toxI genes, B. gladioli is known to infect rice, which is typically associated with the production of the phytotoxin toxoflavin in Burkholderia glumae . Instead of relying on toxin production, B. gladioli employs alternative virulence factors and mechanisms to establish infection and cause disease. This includes colonizing plant tissues, promoting systemic infection, and utilizing plant resources for growth. Furthermore, B. gladioli can produce other bioactive compounds and enzymes contributing to its pathogenicity in rice and other hosts ( Köhl and Schmitzet, 2018; Pritchard et al., 2020 ). The genes pvdA in B. gladioli are essential in its pathogenicity, especially toward crops like rice. These genes are critical in synthesizing pyoverdine, a high-affinity iron-chelating compound known as a siderophore. Iron is an essential nutrient for many organisms, including plants, and is usually sequestered in forms that are challenging for pathogens to access, especially in the host’s iron-limited environment, where plants actively restrict iron availability as a defence strategy. The pvdA gene initiates pyoverdine biosynthesis, creating a molecule specifically designed to capture ferric iron (Fe³⁺) with high affinity. This process allows B. gladioli to acquire iron more effectively than other organisms in the host environment, hijacking this essential resource from the plant and giving the bacterium a survival advantage. By securing the iron, B. gladioli can support cellular processes necessary for growth and infection, ultimately enabling the bacterium to overcome plant defenses and establish a successful infection in rice. By utilizing this strategy, B. gladioli can effectively thrive in the hostile environments of its plant hosts, leading to significant agricultural impacts, particularly in crops like rice (Pérez-Mendoza et al., 2014 ; Wang et al., 2021 ). These pathways play a crucial role in the pathogenicity of Burkholderia , particularly in causing bacterial panicle disease. The coordinated gene expression driven by quorum sensing regulates the production of toxoflavin, a virulence factor associated with bacterial pathogenicity. The toxoflavin biosynthesis and transport pathways, activated through the QS system, are implicated in the virulence mechanism, contributing to the bacteria's ability to cause bacterial panicle disease in the host plant. Comparative Annotation and Functional Insights We compared our Burkholderia strain's annotation and genomic features with the reference genome and other published Burkholderia genomes of the same species through the GenBank NCBI database. Despite the inherent limitations of second-generation sequencing, resulting in contigs and occasional gaps in the assembly, our annotation efforts were remarkably successful in capturing key genetic components. One noteworthy achievement was the precise annotation of virulence genes within our Burkholderia strain. These genes play a pivotal role in the pathogenicity and virulence of the bacterium. Our annotation results revealed that the structures of these virulence genes closely resemble those found in the reference genome. This robust annotation confirms the presence of these virulence genes in our strain and highlights their integrity and potential functionality. This finding carries significant implications, suggesting that our strain possesses a repertoire of virulence factors that could contribute to its pathogenicity (Table 7). In addition, 13 genes associated with the virulence factors were selected, as identified in a previous study. Furthermore, 22 open reading frames (ORFs) within the genetic region were unveiled by annotating the genome sequence using NCBI PGAP. A comparative analysis of our virulence genes' nucleotide sequences with other genome data in NCBI revealed no significant differences. The predicted sequences of virulence genes exhibited a remarkable similarity, with a size and nucleotide matching over 98% when compared to those archived in the NCBI database. The presence of the QsmR transcriptional regulator, a LuxR-type quorum sensing regulator, further highlights the complexity of their infection strategies. This regulator modulates multiple virulence pathways, including quorum sensing, motility ( flhC, flhD ), and secretion systems, underscoring the importance of bacterial communication in host infection​ (Chun et al., 2009 ; Moule et al., 2015 ; Vander Broek and Stevens, 2017 ). The Type III secretion system (T3SS), which plays a fundamental role in host invasion and immune suppression. The HrpB protein, a key component of T3SS, facilitates bacterial attachment and injection of effector proteins into rice cells, effectively disrupting host immune responses and enhancing bacterial colonization. However, while both pathogens share this core virulence mechanism, they diverge in their infection strategies, particularly in their reliance on toxin production and nutrient acquisition​ (Green and Mecsas, 2016 ; Lipscomb and Schell, 2011 ; Mannaa et al., 2018 ). This process enables Burkholderia spp. to cause infections and diseases. A major distinction between B. glumae and B. gladioli lies in their ability to manipulate host physiology through different virulence pathways. B. glumae K6 relies on toxoflavin biosynthesis, regulated by the toxABCDE gene cluster, which produces a phytotoxin capable of inducing oxidative stress, damaging chloroplast structures, and suppressing plant immune defenses. This toxin is tightly controlled by quorum sensing via the ToxJ-ToxR regulatory system, ensuring synchronized production during infection to maximize host tissue damage. In contrast, B. gladioli lacks the toxI gene required for toxoflavin biosynthesis and instead employs pyoverdine siderophores ( pvdA ) as an alternative virulence strategy. These siderophores enable the bacterium to acquire iron from host tissues, a crucial factor for bacterial survival in iron-limited environments. This adaptation may provide B. gladioli with broader host adaptability compared to B. glumae , which is highly specialized in toxoflavin-mediated virulence​. Beyond these core virulence traits, both pathogens encode lipopolysaccharide (LPS) biosynthesis genes ( lipA, lipB ), which contribute to bacterial adhesion, immune evasion, and outer membrane stability. LPS promotes bacterial attachment and invasion by binding to host cell receptors, facilitating infection. Additionally, LPS helps evade the host's immune system, contributing to Burkholderia spp.'s ability to cause infections and diseases (Tribble and Lamont, 2010 ; Ham et al., 2010 ; Bertani and Ruiz, 2018 ; Lee et al., 2019 ; Vellasamy et al., 2016 ). In our study, comparative genomic analysis was conducted using KEGG pathway annotation to categorize genes based on their functions. While descriptive data effectively highlighted the presence and absence of key virulence genes, we acknowledge the absence of inferential statistical tests (e.g., p-values, confidence intervals) to determine the significance of gene content variations between B. glumae K6 and B. gladioli UPMBG7. However, our findings are strongly supported by BLAST analysis against the NCBI GenBank database, which validated the absence of PvdA in B. glumae K6 and ToxI in B. gladioli UPMBG7 (Tables 8 and 9 ). The lack of significant BLAST hits (E-value > 1.0) for these genes in their respective genomes confirms their absence, while genes detected in both strains showed high sequence identity (> 95%) with known homologs from Burkholderia spp., further strengthening their functional relevance. Instead of using inferential statistics, our approach relies on sequence-based validation, which is widely accepted in comparative genomics for assessing gene gain/loss events. Unlike transcriptomic studies that involve statistical comparisons of gene expression, genome content variation is binary (presence/absence) and best evaluated through functional annotation and sequence similarity searches. Table 8 The genetic regions of B. glumae K6 that encode important genes involved as virulence factors. ORF No. Start End Putative Protein Gene Name Bases Contigs Accession no. ORF 1 7035 7646 GNAT family N-acetyltransferase/ AHL synthase TofI 612 84 JAMYCS010000087.1 ORF 2 5211 8882 AHL receptor TofR 3672 84 JAMYCS010000087.1 ORF 3 41863 42862 LysR family transcriptional regulator ToxR 1000 6 JAMYCS010000006.1 ORF 4 50199 51029 LuxR C-terminal-related transcriptional regulator ToxJ 831 6 JAMYCS010000006.1 ORF 5 40634 41371 class I SAM-dependent methyltransferase ToxA 738 6 JAMYCS010000006.1 ORF 6 39934 40512 GTP cyclohydrolase II ToxB 579 6 JAMYCS010000006.1 ORF 7 38186 39877 WD40 repeat domain-containing protein ToxC 1692 6 JAMYCS010000006.1 ORF 8 37098 38078 Formylglycine-generating enzyme family protein/TRP2 ToxD 981 6 JAMYCS010000006.1 ORF 9 35944 37029 Bifunctional diaminohydroxyphosphoribosylaminopyrimidine deaminase/5-amino-6-(5-phosphoribosylamino) uracilreductase RibD /deaminase ToxE 1084 6 JAMYCS010000006.1 ORF10 42958 43533 DMT family transporter ToxF 576 6 JAMYCS010000006.1 ORF11 43573 44712 Efflux RND transporter periplasmic adaptor subunit ToxG 1140 6 JAMYCS010000006.1 ORF12 44709 47801 Efflux RND transporter permease subunit ToxH 3093 6 JAMYCS010000006.1 ORF13 47873 49410 Efflux transporter outer membrane subunit ToxI 1538 6 JAMYCS010000006.1 ORF14 95842 96669 IclR family transcriptional regulator QsmR 828 10 JAMYCS010000010.1 ORF15 8203 5893 9357 7047 Flagellin FlhC 1155 57 69 JAMYCS010000059.1 JAMYCS010000071.1 ORF16 51921 52240 Flagellar transcriptional regulator FlhD FlhD 320 39 JAMYCS010000041.1 ORF17 27443 28846 Helix-turn-helix transcription regulator of pathogenicity genes HrpB 1404 63 JAMYCS010000065.1 ORF18 2292 3278 Lipoyl synthase LipA 987 83 JAMYCS010000086.1 ORF19 3271 4023 Lipoyl(octanoyl) transferase LipB LipB 753 83 JAMYCS010000086.1 Table 9 The genetic regions of B. gladioli UPMBG7 that encode important genes involved as virulence factors. ORF No. Start End Putative Protein Gene Name Bases Contigs Accession no. ORF 1 9949 10527 GNAT family N-acetyltransferase/ AHL synthase TofI 579 43 JANIEE010000046.1 ORF 2 8646 10697 AHL receptor TofR 2052 43 JANIEE010000046.1 ORF 3 29000 29997 LysR family transcriptional regulator ToxR 998 52 JANIEE010000056.1 ORF 4 113847 114661 LuxR C-terminal-related transcriptional regulator ToxJ 815 2 JANIEE010000002.1 ORF 5 27769 28506 class I SAM-dependent methyltransferase ToxA 738 52 JANIEE010000056.1 ORF 6 27070 27648 GTP cyclohydrolase II ToxB 579 52 JANIEE010000056.1 ORF 7 25322 27013 WD40 repeat domain-containing protein ToxC 1692 52 JANIEE010000056.1 ORF 8 24226 25202 Formylglycine-generating enzyme family protein/TRP2 ToxD 977 52 JANIEE010000056.1 ORF 9 23078 24153 Bifunctional diaminohydroxyphosphoribosylaminopyrimidine deaminase/5-amino-6-(5-phosphoribosylamino) uracilreductase RibD /deaminase ToxE 1076 52 JANIEE010000056.1 ORF10 30093 30668 DMT family transporter ToxF 576 52 JANIEE010000056.1 ORF11 30708 31847 Efflux RND transporter periplasmic adaptor subunit ToxG 1140 52 JANIEE010000056.1 ORF12 31844 34936 Efflux RND transporter permease subunit ToxH 3093 52 JANIEE010000056.1 ORF13 65230 66031 IclR family transcriptional regulator QsmR 802 32 JANIEE010000035.1 ORF14 197016 198170 Flagellin FlhC 1155 15 JANIEE010000015.1 ORF15 204467 204786 Flagellar transcriptional regulator FlhD FlhD 320 15 JANIEE010000015.1 ORF16 185840 187239 Helix-turn-helix transcriptional regulator HrpB 1400 8 JANIEE010000008.1 ORF17 88686 89678 Lipoyl synthase LipA 993 23 JANIEE010000023.1 ORF18 89671 90359 Lipoyl(octanoyl) transferase LipB LipB 689 23 JANIEE010000023.1 ORF 19 115282 116622 NAD (P)/ FAD-dependent oxidoreductase PvdA 1341 1 JANIEE010000001.1 The confirmed absence of toxoflavin genes ( ToxI ) in B. gladioli UPMBG7 and the presence of pyoverdine biosynthesis genes ( PvdA ) in B. gladioli UPMBG7 but not B. glumae K6 suggest distinct pathogenic strategies between these strains. These findings, supported by KEGG annotation and BLAST validation, emphasize biologically significant differences in virulence mechanisms rather than statistical significance. Future studies incorporating a larger number of genomes could further validate these findings using inferential statistical approaches. However, while the study effectively establishes these novel insights, it could further emphasize how these findings fill gaps in existing research. For instance, past studies on B. glumae have largely focused on its quorum sensing mechanisms and toxoflavin-mediated virulence (Peng, 2015 ; Kim et al., 2004 ), but this research reveals additional genomic elements that contribute to its adaptability in different environments. Similarly, the ecological versatility of B. gladioli which has been recognized as both a plant pathogen and an opportunistic human pathogen (Pérez-Mendoza et al., 2014 ; Pritchard et al., 2020 ) suggests that its virulence mechanisms are broader than previously understood. This comparative approach helps delineate their distinct pathogenic strategies and provides a foundation for improved disease management. A more in-depth comparison with findings from similar studies would further enhance the impact of this research. For example, Seo et al. ( 2015 ) reported that B. glumae genomes generally range from 6.5–7.0 Mbp, aligning with the genomic size reported in this study. Meanwhile, studies on B. gladioli have documented genome sizes between 8.0–9.0 Mbp, with variability in virulence genes depending on the host (Ma et al., 2016 ). By integrating these findings, this study not only confirms existing knowledge but also expands it by detailing the specific genetic variations that drive pathogenicity differences between B. glumae and B. gladioli . These genomic insights have significant implications for bacterial panicle blight (BPB) management, providing potential targets for integrated disease control strategies. Given B. glumae dependence on toxoflavin, quorum sensing inhibitors that disrupt toxoflavin biosynthesis could serve as a novel strategy to mitigate disease severity. Similarly, iron-chelating compounds may limit B. gladioli infections by interfering with pyoverdine-mediated iron acquisition. The identification of virulence regulators such as QsmR and T3SS effectors suggests additional molecular targets that could be explored for RNA interference-based strategies or the development of small-molecule inhibitors aimed at blocking bacterial signaling pathways. Additionally, these findings can contribute to molecular breeding programs, particularly in the selection of rice cultivars resistant to bacterial attachment and immune suppression. Genome editing approaches, such as CRISPR-Cas9, could be explored to introduce resistant alleles that disrupt bacterial colonization. Moreover, the application of biocontrol approaches, such as utilizing beneficial microbes that produce quorum-quenching enzymes, presents an alternative method for controlling BPB in rice fields​. While this study provides a comprehensive genomic perspective on B. glumae and B. gladioli virulence, several limitations must be acknowledged. One of the primary limitations is the absence of in planta validation of the identified virulence genes. Although comparative genomic analysis provides strong evidence of their involvement in pathogenicity, functional validation using mutant strains, gene knockout studies, and complementation assays would further substantiate these findings. Future research should focus on in planta expression studies, such as transcriptomic and proteomic analyses, to confirm the active expression of these virulence genes during rice infection​. Another limitation of this study is the restricted number of isolates used in the comparative analysis. The genomic diversity of B. glumae and B. gladioli across different rice-growing regions remains poorly understood, and expanding the study to include a broader collection of isolates would provide deeper insights into strain-specific adaptations. Additionally, further phylogenetic analysis incorporating a larger number of environmental and clinical isolates could enhance our understanding of the evolutionary pathways shaping these pathogens. Future studies should also consider functional annotation of uncharacterized virulence genes, as they may reveal novel pathogenicity factors critical to BPB development​. This study highlights the distinct but complementary pathogenic strategies of B. glumae and B. gladioli , offering new insights into their genomic adaptations and virulence mechanisms. By linking these genomic findings to practical applications, such as quorum sensing inhibition, molecular breeding, and biocontrol strategies, this research provides a foundation for the development of effective BPB management solutions. However, further experimental validation and expanded genomic comparisons are necessary to fully leverage these insights for long-term agricultural sustainability Genome Sequence Accessibility The complete genome sequences of B. glumae K6 and B. gladioli UPMBG7 are available in the NCBI GenBank for further research and reference. B. glumae K6 is linked to assembly accession number JAMYCS000000000, associated with BioProject PRJNA842847, while B. gladioli UPMBG7 has the assembly accession number JANIEE000000000, associated with BioProject PRJNA224116. These publicly accessible data provide opportunities for further comparative studies and insights into the genomic features that contribute to the pathogenicity of these Burkholderia strains. 5. Conclusion Comparative genomic analyses have revealed distinct pathogenic adaptations in Burkholderia glumae strain K6 and Burkholderia gladioli strain UPMBG7 compared to strains from other regions. The Malaysian B. glumae K6 harbors a complete tox operon ( toxABCDE , toxJ ), which is essential for toxoflavin biosynthesis, a key virulence factor that induces oxidative stress and necrosis in host tissues. This toxoflavin production is tightly regulated by a quorum-sensing system involving TofI and TofR , which control tox gene expression, and ToxR , a LysR-type transcriptional regulator that activates both toxoflavin biosynthesis ( toxABCDE ) and transporter ( toxFGHI ) genes. While B. glumae strains from other regions share similar regulatory pathways, variations in toxoflavin-related genes suggest potential regional adaptations influencing pathogenicity and disease severity. In contrast, B. gladioli UPMBG7 lacks the toxI gene, which regulates toxoflavin production, yet compensates by producing pyoverdine siderophores ( pvdA ), which enhance iron acquisition and bacterial survival. These siderophores play a crucial role in host colonization, particularly under iron-limited conditions. Unlike B. glumae , which relies on toxoflavin-mediated cytotoxicity, B. gladioli appears to adopt a resource competition strategy, utilizing pyoverdine to gain a survival advantage. These genomic distinctions highlight the adaptive evolution of Burkholderia species across different regions, influencing disease dynamics and necessitating tailored management approaches for bacterial panicle blight in rice. The detailed genomic characterization of B. glumae and B. gladioli provides valuable information that could be used to breed rice varieties with enhanced resistance to these pathogens. Understanding the genetic basis of pathogenicity and virulence mechanisms can aid in the development of targeted biological control strategies, such as disrupting quorum sensing or inhibiting specific virulence factors. Since B. glumae relies heavily on toxoflavin biosynthesis for virulence, one effective approach would be the application of toxoflavin-degrading bacteria, such as Pseudomonas putida , which naturally produces enzymes that break down toxoflavin, neutralizing its harmful effects. Alternatively, small-molecule quorum-sensing inhibitors (QSIs), such as furanone compounds, could be used to disrupt the TofI-TofR signaling pathway, preventing toxoflavin production and reducing disease severity. For B. gladioli , which depends on pyoverdine-mediated iron acquisition, an effective strategy could involve iron-chelating compounds like desferrioxamine, which binds free iron in the environment, making it less available to the pathogen. Additionally, genetically engineering rice plants to produce plant-derived siderophores could help outcompete B. gladioli for iron, limiting its ability to colonize the host. By leveraging these genomic insights, targeted biological and chemical interventions can be designed to weaken the virulence of Burkholderia species, offering a more sustainable approach to managing bacterial panicle blight in rice. The identification of unique virulence mechanisms in Malaysian isolates can also guide local agricultural practices, helping farmers to implement more effective disease control measures against these pathogens. Beyond pathogen-specific studies, metagenomic sequencing has emerged as a valuable tool for monitoring microbial communities in rice fields. Kim et al. ( 2022 ) used metagenomic sequencing to detect emerging pathogens, including Burkholderia spp., demonstrating the potential of high-throughput sequencing for disease surveillance. The integration of metagenomics with whole-genome sequencing could provide real-time pathogen monitoring, improving BPB management strategies. These advancements, when considered alongside our genomic findings, reinforce the significance of genomic surveillance in rice agriculture and highlight the need for further functional genomic validation of virulence determinants. Declarations Conflicts of Interest : The authors declare no conflict of interest. Acknowledgement: This research was funded by the Long-Term Research Grant Scheme (LRGS/1/2019/UPM/2) under the Ministry of Higher Education Malaysia (MOHE), Program: Development of climate-ready rice for sustaining food security in Malaysia, Project: Sustainable short and medium to long-term strategies for managing Bacterial Panicle Blight (BPB) disease under climate-resilience rice production. References Abbas, M. M., Malluhi, Q. M., & Balakrishnan, P. (2014). Assessment of de novo assemblers for draft genomes: a case study with fungal genomes. Bmc Genomics . 15 (Suppl 9). Anantharaman, V., Koonin, E. V., & Aravind, L. (2012). Regulatory potential, phyletic distribution, and evolution of ancient, intracellular small-molecule-binding domains. Journal Of Molecular Biology , 415 (5), 939–971. Andrews, S. C., Robinson, A. K., & Rodríguez-Quiñones, F. (2003). Bacterial iron homeostasis. Fems Microbiology Reviews , 27 (2–3), 215–237. Ashburner, M., Ball, C., Blake, J., et al. (2000). Gene Ontology: tool for the unification of biology. Nature Genetics , 25 , 25–29. Bangratz, M., Wonni, I., Kini, K., Sondo, M., Brugidou, C., Béna, G., Gnacko, F., et al. (2020). Design of a new multiplex PCR assay for rice pathogenic bacteria detection and its application to infer disease incidence and detect co-infection in rice fields in Burkina Faso. Plos One , 4 , e0232115. Basler, M., Ho, B. T., & Mekalanos, J. J. (2013). Type 6 secretion system mediates bacterial killing through cell-cell contact. Nature , 501 (7468), 508–512. Bertani, B., & Ruiz, N. (2018). Function and Biogenesis of Lipopolysaccharides. EcoSal Plus , 8 , 1. Chen, R., Barphagha, I. K., & Ham, J. H. (2015). Identification of potential genetic components involved in the deviant quorum-sensing signaling pathways of Burkholderia glumae through a functional genomics approach. Frontiers Microbiol , 6 , 1447. Chun, H., Choi, O., Goo, E., Kim, N., Kim, H., Kang, Y., Kim, J., Moon, J. S., & Hwang, I. (2009). The quorum sensing-dependent gene katG of Burkholderia glumae is important for protection from visible light. Journal of bacteriology , 191 (13), 4152–4157. Colvin, K. M., Gordon, V. D., Murakami, K., Borlee, B. R., Wozniak, D. J., Wong, G. C., & Parsek, M. R. (2012). The Pel polysaccharide can serve a structural and protective role in biofilms of Pseudomonas aeruginosa . PLoS Pathog. 8(1). Cornelis, P., Wei, Q., Andrews, S. C., & Vinckx, T. (2011). Iron homeostasis and management of oxidative stress response in bacteria. Fems Microbiology Reviews , 35 (2), 251–274. Eid, J., Fehr, A., Gray, J., Luong, K., Lyle, J., Otto, G. A., & Turner, S. (2009). Real-time DNA sequencing from single polymerase molecules. Science , 323 (5910), 133–138. Galperin, M. Y., & Koonin, E. V. (2004). Conserved hypothetical’ proteins: Prioritization of targets for experimental study. Nucleic Acids Research , 32 (18), 5452–5463. Galperin, M. Y., Makarova, K. S., Wolf, Y. I., & Koonin, E. V. (2015). Expanded microbial genome coverage and improved protein family annotation in the COG database. Nucleic Acids Research , 43 , 261–269. Green, E. R., & Mecsas, J. (2016). Bacterial Secretion Systems: An Overview. Microbiol Spectr , 4 , 1. Gyaneshwar, P., Hirsch, A. M., & James, E. K. (2011). A role for Burkholderia species in promoting plant growth and health. Annual Review Of Phytopathology , 49 , 469–491. Ham, J. H., Melanson, R. A., & Rush, M. C. (2010). Burkholderia glumae : next major pathogen of rice? Molecular Plant Pathology , 12 , 329–339. Henderson, I. R., Navarro-Garcia, F., Desvaux, M., & Fernandez, R. C. (2004). Type V secretion system: Bacterial secretion without a chaperone. Molecular Microbiology , 53 (6), 1677–1690. Ho, B. T., Dong, T. G., & Mekalanos, J. J. (2014). A view to a kill: The bacterial type VI secretion system. Cell Host & Microbe , 15 (1), 9–21. Jang, M. S., Goo, E., An, J. H., Kim, J., & Hwang, I. (2014). Quorum sensing controls flagellar morphogenesis in Burkholderia glumae . PLOS ONE , 9 , e84831. Jennings, L. K., Storek, K. M., Ledvina, H. E., Coulon, C., Marmont, L. S., Sadovskaya, I., & Howell, P. L. (2015). Pel is a cationic exopolysaccharide that cross-links extracellular DNA in the Pseudomonas aeruginosa biofilm matrix. Proc. Natl. Acad. Sci. U. S. A. 112(36):11353–11358. Kanehisa, M., Goto, S., Kawashima, S., Okuno, Y., & Hattori, M. (2004). The KEGG resource for deciphering the genome. Nucleic Acids Research , 32 , 277–280. Kanehisa, M., Goto, S., Hattori, M., Aoki-Kinoshita, K. F., Itoh, M., Kawashima, S., Katayama, T., et al. (2006). From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Research , 34 , 354–357. Kang, Y., Kim, J., Kim, S., Kim, H., Lim, J. Y., Kim, M., Kwak, J., et al. (2008). Proteomic analysis of the proteins regulated by HrpB from the plant pathogenic bacterium Burkholderia glumae . PROTEOMICS , 8 , 106–121. Kang, M., Lim, J. Y., Kim, J., Hwang, I., & Goo, E. (2022). Influence of genomic structural variations and nutritional conditions on the emergence of quorum sensing-dependent gene regulation defects in Burkholderia glumae . Frontiers In Microbiology , 13 , 950600. Koren, S., Walenz, B. P., Berlin, K., Miller, J. R., Bergman, N. H., & Phillippy, A. M. (2017). Canu: Scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Research , 27 (5), 722–736. Kim, J., Kim, J. G., Kang, Y., Jang, J. Y., Jog, G. J., Lim, J. Y., Kim, S., et al. (2004). Quorum sensing and the LysR-type transcriptional activator ToxR regulate toxoflavin biosynthesis and transport in Burkholderia glumae . Molecular Microbiology , 54 , 921–934. Kim, J., Kang, Y., Choi, O., Jeong, Y., Jeong, J. E., Lim, J. Y., Kim, M., et al. (2007). Regulation of polar flagellum genes is mediated by quorum sensing and FlhDC in Burkholderia glumae . Molecular Microbiology , 64 , 165–179. Kim, H., Goo, E., Kang, Y., Kim, J., & Hwang, I. (2011). Regulation of Universal Stress Protein Genes by Quorum Sensing and RpoS in Burkholderia glumae . Journal Of Bacteriology , 194 , 982–992. Kim, J., Lee, S. H., Park, H., & Choi, Y. (2022). Metagenomic analysis of microbial communities in rice fields: Implications for pathogen detection and disease management. Environmental Microbiology , 14 (1), 67–81. Köhl, J., & Schmitz, H. (2018). Siderophores in the pathogenesis of Burkholderia species. Microbiology , 164 (3), 341–354. Krasteva, P. V., & Sondermann, H. (2017). Versatile modes of cellular regulation via cyclic dinucleotides. Nature Chemical Biology , 13 (4), 350–359. Lalithya, K. A., Manjunatha, G., Raju, B., Kulkarni, M. S., & Lokesh, V. (2017). Plant growth regulators and signal molecules enhance resistance against bacterial blight disease of pomegranate. Journal Of Phytopathology , 165 , 727–736. Laura, O., & Clemencia, M. R. (2021). Bacterial Panicle Blight and Burkholderia glumae : From Pathogen Biology to Disease Control. Phythopathol , 111 , 772–778. Lee, C., Mannaa, M., Kim, N., Kim, J., Choi, Y., Kim, S. H., Jung, B., et al. (2019). Stress Tolerance and Virulence-Related Roles of Lipopolysaccharide in Burkholderia glumae . Plant Pathol J , 35 , 445–458. Lehman, B. L., Long, J., Koskella, J., Brown, P. J. B., & Gonzalez, C. F. (2015). Characterization of the Burkholderia glumae toxoflavin gene cluster and its role in rice pathogenicity. Mol Plant-Microbe Interact , 28 (10), 1166–1174. Li, W., Jaroszewski, L., & Godzik, A. (2002). Tolerating some redundancy significantly speeds up clustering of large protein databases. Bioinform , 18 , 77–82. Lipscomb, L., & Schell, M. A. (2011). Elucidation of the regulon and cis-acting regulatory element of HrpB , the AraC-type regulator of a plant pathogen-like type III secretion system in Burkholderia pseudomallei . Journal Of Bacteriology , 193 , 1991–2001. Ma, L., Geng, Y., Pei, H., Zhang, X., Chen, W., & Li, G. (2016). Characterization of pyomelanin production in Burkholderia spp. and its association with pathogenicity. FEMS Microbiol. Lett. 363(9). Manni, M., Berkeley, M. R., Seppey, M., Simão, F. A., & Zdobnov, E. M. (2021). BUSCO update: Novel and streamlined workflows along with broader and deeper phylogenetic coverage for scoring of genome assembly completeness. Molecular Biology and Evolution , 38 (10), 4647–4654. Mannaa, M., Park, I., & Seo, Y. S. (2018). Genomic Features and Insights into the Taxonomy, Virulence, and Benevolence of Plant-Associated Burkholderia Species. International Journal Of Molecular Sciences , 20 , 121. Mansfield, J., et al. (2012). Top 10 plant pathogenic bacteria in molecular plant pathology. Molecular Plant Pathology , 13 (6), 614–629. Moule, M. G., Spink, N., Willcocks, S., Lim, J., Guerra-Assunção, J. A., Cia, F., Champion, O. L., et al. (2015). Characterization of New Virulence Factors Involved in the Intracellular Growth and Survival of Burkholderia pseudomallei . Infection And Immunity , 84 , 701–710. Mulaw, T., Wamishe, Y., & Jia, Y. (2018). Characterization and in plant detection of bacteria that cause bacterial panicle blight of rice. Am J Plant Sci , 9 , 667. Moon, H., Park, H. J., Jeong, A., Han, S. W., & Park, C. J. (2017). Isolation and identification of Burkholderia gladioli on Cymbidium orchids in Korea. Biotechnology And Biotechnological Equipment , 31 , 280–288. Nandakumar, R., Shahjahan, A. K. M., Yuan, X. L., Dickstein, E. R., Groth, D. E., Clark, C. A., Cartwright, R. D., et al. (2009). Burkholderia glumae and B. gladioli Cause Bacterial Panicle Blight in Rice in the Southern United States. Plant Disease , 93 , 896–905. Noinaj, N., Guillier, M., Barnard, T. J., & Buchanan, S. K. (2010). TonB-dependent transporters: Regulation, structure, and function. Annual Review Of Microbiology , 64 , 43–60. Nootjarin, J., Antonio, R. G. F., Jutatape, W., et al. (2022). Phylogenetic Characterization and Genome Sequence Analysisof Burkholderia glumae Strains Isolated in Thailand as the Causal Agent of Rice Bacterial Panicle Blight. Pathol , 11 , 676. Pasquali, F., do Valle, I., Palma, F., Remondini, D., Manfreda, G., Castellani, G., Hendriksen, R., et al. (2019). Application of different DNA extraction procedures, library preparation protocols and sequencing platforms: impact on sequencing results. Heliyon , 5 , e02745. Pedraza, L. A., Bautista, J., & Uribe-Vélez, D. (2018). Seed-born Burkholderia glumae Infects Rice Seedling and Maintains Bacterial Population during Vegetative and Reproductive Growth Stage. Plant Pathol J , 34 , 393–402. Peng, H. (2015). Quorum sensing and virulence regulation in Burkholderia species. J Plant Pathol , 97 (2), 345–357. Pérez-Mendoza, D., Coulthurst, S. J., Humphris, S., Campbell, E., Welch, M., & Salmond, G. P. C. (2014). Siderophore production and pathogenicity in Burkholderia species. Applied And Environment Microbiology , 80 (1), 261–271. Price, M. N., Wetmore, K. M., Waters, R. J., Callaghan, M., Ray, J., Liu, H., & Arkin, A. P. (2018). Mutant phenotypes for thousands of bacterial genes of unknown function. Nature , 557 (7706), 503–509. Pritchard, L., Al-Mahmudi, A., Ali, M., & Saddler, G. S. (2020). Burkholderia gladioli : An emerging plant pathogen. Plant Disease , 104 (8), 2113–2124. Pukatzki, S., Ma, A. T., Sturtevant, D., Krastins, B., Sarracino, D., Nelson, W. C., & Mekalanos, J. J. (2006). Identification of a conserved bacterial protein secretion system in Vibrio cholerae . Proc. Natl. Acad. Sci. U. S. A. 103(5):1528–1533. Ramachandran, K., Vijaya, S. I., Ahmad, F. N., Amzah, B., & Zakaria, L. (2021). Characterization and identification of Burkholderia glumae as the causal pathogen of bacterial panicle blight of rice ( Oryza sativa L.) in Malaysian rice granaries. Journal Of General Plant Pathology , 87 , 164–169. Ren, Q., Chen, K., & Paulsen, I. T. (2019). TransportDB: A comprehensive database resource for cytoplasmic membrane transport systems and outer membrane channels. Nucleic Acids Research , 37 , D274–D278. Rhoads, A., & Au, K. F. (2015). PacBio sequencing and its applications. Genomics Proteomics & Bioinformatics , 13 (5), 278–289. Römling, U., Galperin, M. Y., & Gomelsky, M. (2013). Cyclic di-GMP: The first 25 years of a universal bacterial second messenger. Microbiology And Molecular Biology Reviews , 77 (1), 1–52. Russell, A. B., Peterson, S. B., & Mougous, J. D. (2014). Type VI secretion system: Delivering bacterial enzymes into target cells. Cell , 158 (4), 792–802. Saier, M. H., Reddy, V. S., Tsu, B. V., Ahmed, M. S., Li, C., & Moreno-Hagelsieb, G. (2016). The transporter classification database (TCDB): Recent advances. Nucleic Acids Research , 44 (D1), D372–D379. Sayler, R. J., Cartwright, R. D., & Yang, Y. (2006). Genetic Characterization and Real-Time PCR Detection of Burkholderia glumae , a Newly Emerging Bacterial Pathogen of Rice in the United States. Plant Disease , 90 , 603–610. Shanner, G., & Finney, R. E. (1977). The effect of nitrogen fertilization on the expression of slow-mildewing resistance in Knox wheat. Phytopathol , 67 , 1051–1056. Seo, Y. S., Lim, J. Y., Park, J., Kim, S., Lee, H. H., Cheong, H., et al. (2015). Comparative genome analysis of rice-pathogenic Burkholderia provides insight into capacity to adapt to different environments and hosts. Bmc Genomics , 16 , 349. Stork, M., Otto, B. R., & Nolte, O. (2007). Iron transport in Burkholderia species: Insights from Burkholderia pseudomallei . Bmc Genomics , 8 (1), 17. Tribble, G. D., & Lamont, R. J. (2010). Bacterial invasion of epithelial cells and spreading in periodontal tissue. Periodontology 2000 , 52 , 68–83. Tsukuda, S., Matsuyama, T., Katoh, H., & Ozeki, Y. (2018). The toxoflavin biosynthetic pathway in Burkholderia glumae . Applied Microbiology And Biotechnology , 102 (14), 5885–5897. van Dijk, E. L., Jaszczyszyn, Y., Naquin, D., & Thermes, C. (2018). The third revolution in sequencing technology. Trends In Genetics , 34 (9), 666–681. Vander Broek, C. W., & Stevens, J. M. (2017). Type III Secretion in the Melioidosis Pathogen Burkholderia pseudomallei . Frontiers In Cellular And Infection Microbiology , 7 , 255. Vellasamy, K. M., Mariappan, V., Shankar, E. M., & Vadivelu, J. (2016). Burkholderia pseudomallei Differentially Regulates Host Innate Immune Response Genes for Intracellular Survival in Lung Epithelial Cells. Plos Neglected Tropical Diseases , 10 , e0004730. Wang, Y., Huang, H., Sun, M., Zhang, Q., Guo, D., Zhao, Y., & Zhang, L. (2018). Metalloprotein-like proteins in bacteria. Biometals , 31 (5), 589–606. Wang, J., Ye, Q., Chen, L., Zhao, H., & Li, Y. (2021). The role of iron uptake systems in the virulence of Burkholderia gladioli . Frontiers In Microbiology , 12 , 689007. Wang, S., Nie, W., Yiming, A., Wang, P., Wu, Y., Huang, J., Ahmad, I., Chen, G., Guo, L., & Zhu, B. (2022). Next Generation Sequencing and Comparative Genomic Analysis Reveal Extreme Plasticity of Two Burkholderia glumae Strains HN1 and HN2. Pathogens . 11:1265. Zhou-qi, C., Bo, Z., Guan-lin, X., Bin, L., & Shi-wen, H. (2016). Research Status and Prospect of Burkholderia glumae , the Pathogen Causing Bacterial Panicle Blight. Rice Science , 23 , 111–118. Cite Share Download PDF Status: Published Journal Publication published 02 Oct, 2025 Read the published version in European Journal of Plant Pathology → Version 1 posted Reviewers agreed at journal 23 Apr, 2025 Reviewers invited by journal 23 Apr, 2025 Editor invited by journal 22 Apr, 2025 First submitted to journal 09 Apr, 2025 Editorial decision: Minor revisions 12 Feb, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5698089","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":446757050,"identity":"eadd397c-b509-4a91-928f-30c151205394","order_by":0,"name":"ADAM ZAFDRI MD ZALI","email":"","orcid":"","institution":"University Putra Malaysia: Universiti Putra Malaysia","correspondingAuthor":false,"prefix":"","firstName":"ADAM","middleName":"ZAFDRI MD","lastName":"Z","suffix":"MD"},{"id":446757051,"identity":"54204854-ec9d-46bb-a8e9-3a9fb88929ec","order_by":1,"name":"Siti Izera Ismail","email":"","orcid":"","institution":"University Putra Malaysia: Universiti Putra Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Siti","middleName":"Izera","lastName":"Ismail","suffix":""},{"id":446757052,"identity":"92f2fa8c-2307-4ace-a14f-0c5db686e908","order_by":2,"name":"Norsazilawati Saad","email":"","orcid":"","institution":"University Putra Malaysia: Universiti Putra Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Norsazilawati","middleName":"","lastName":"Saad","suffix":""},{"id":446757053,"identity":"9eb04c7c-201a-4b89-bb98-ba5bee2acabe","order_by":3,"name":"Muhammad Asyraf Md Hatta","email":"","orcid":"","institution":"University Putra Malaysia: Universiti Putra Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Asyraf Md","lastName":"Ha","suffix":"Md"},{"id":446757054,"identity":"1340efd7-435f-4392-8045-04df55e5f94f","order_by":4,"name":"Mohd Razi Ismail","email":"","orcid":"","institution":"University Putra Malaysia: Universiti Putra Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Mohd","middleName":"Razi","lastName":"Ismail","suffix":""},{"id":446757055,"identity":"ce87945a-7db0-440f-8276-84f2522af19d","order_by":5,"name":"Mohd Termizi Yusof","email":"","orcid":"","institution":"University Putra Malaysia: Universiti Putra Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Mohd","middleName":"Termizi","lastName":"Yusof","suffix":""},{"id":446757056,"identity":"5abf513f-d67b-48cb-b537-76da8b7ec419","order_by":6,"name":"Mansor Hakiman","email":"","orcid":"","institution":"University Putra Malaysia: Universiti Putra Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Mansor","middleName":"","lastName":"Hakiman","suffix":""},{"id":446757057,"identity":"b08e07c1-4a1f-49df-ae9e-ed95ddfd6fa6","order_by":7,"name":"Syari Jamian","email":"","orcid":"","institution":"University Putra Malaysia: Universiti Putra Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Syari","middleName":"","lastName":"Jamian","suffix":""},{"id":446757058,"identity":"d9498c7c-e7a2-4bdb-8e6d-a93aa3c9e7a5","order_by":8,"name":"Sumaiyah Abdullah","email":"","orcid":"","institution":"University Putra Malaysia: Universiti Putra Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Sumaiyah","middleName":"","lastName":"Abdullah","suffix":""},{"id":446757059,"identity":"5dd8628c-744e-4283-b9c2-2e991c536f66","order_by":9,"name":"Dzarifah Zulperi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYDCCA0CcYMOQwA/hMhOrJY0hQbKBJC0MQC0GB4jVwnf+8LMPDxLs8oxv5B7dwFBhndggdsYArxbJA8eMZyQkJBeb3chLu8FwJj2xQToHvxaDgw3GDIk/mBO33cgxu8HYdpgILYfZPzMkJNQnbp4B0vKPGC3HeIyBWg4nbpAAaWkgQovkGZ5ioJbjiTPOvEu7kXAs3bhNOq0Arxa+88c3M/5IqE7sb889duNDjbVsv3TyBrxakAAPME6BFBsDB36HoWqBAPYHxGoZBaNgFIyCkQEA+mZNUHz2RbAAAAAASUVORK5CYII=","orcid":"","institution":"University Putra Malaysia: Universiti Putra Malaysia","correspondingAuthor":true,"prefix":"","firstName":"Dzarifah","middleName":"","lastName":"Zulperi","suffix":""}],"badges":[],"createdAt":"2024-12-23 08:54:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5698089/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5698089/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10658-025-03141-x","type":"published","date":"2025-10-02T15:57:41+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81325418,"identity":"b74fa745-0111-475c-9b97-8196c1d872e4","added_by":"auto","created_at":"2025-04-24 19:01:30","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":44976,"visible":true,"origin":"","legend":"\u003cp\u003eSymptoms of Panicle Blight on Rice Field. (a) and (b) Rice crop in \u0026nbsp;\u0026nbsp;Sekinchan and Yan showed BPB disease outbreak; (c) Panicles containing floret \u0026nbsp;\u0026nbsp;with dark gray and a reddish-brown lesion across the floret; (d) Leaf sheath \u0026nbsp;\u0026nbsp;has a lesion several centimeter long with gray necrosis; (e) Leaves contain \u0026nbsp;\u0026nbsp;small circular tan lesion with brown border.\u003c/p\u003e","description":"","filename":"Figure1revised.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5698089/v1/f50314ddd7b3330c5d89a43f.jpg"},{"id":81325021,"identity":"5eb67918-63a9-41e5-8693-225269b6448e","added_by":"auto","created_at":"2025-04-24 18:53:30","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":53921,"visible":true,"origin":"","legend":"\u003cp\u003eColony morphology of a) \u003cem\u003eBurkholderia glumae \u003c/em\u003eand b) \u003cem\u003eBurkholderia gladioli \u003c/em\u003eisolated from infected rice on a King’s B agar medium\u003c/p\u003e","description":"","filename":"Figure2revised.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5698089/v1/b9403018dce029976d13ada7.jpg"},{"id":81325022,"identity":"dc8b114c-5861-4402-ae89-fd241d1553c9","added_by":"auto","created_at":"2025-04-24 18:53:30","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":119257,"visible":true,"origin":"","legend":"\u003cp\u003ePCR amplification of 16s rRNA and \u003cem\u003egyrB \u003c/em\u003eregions from \u003cem\u003eB. glumae \u003c/em\u003eand \u003cem\u003eB. gladioli\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure3revised.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5698089/v1/f8f13e8a80599c2e50c57111.jpg"},{"id":81325024,"identity":"a95fe939-a4cb-42b1-a464-bd5d145e0506","added_by":"auto","created_at":"2025-04-24 18:53:30","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1167056,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenetic tree constructed using Maximum-likelihood tree based on 16S rRNA gene sequence.;bootstrap values after 1000 replicate are shown as a percentage. The scale bar below indicates 0.02 substitutions per nucleotide position.\u003c/p\u003e","description":"","filename":"Figure4revised.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5698089/v1/f8473fe74a5619edcf236397.jpg"},{"id":81326027,"identity":"4e7055f5-0a00-4332-ab99-64ccc34a9c7e","added_by":"auto","created_at":"2025-04-24 19:17:30","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":61841,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenetic tree constructed using Maximum-likelihood tree based on \u003cem\u003egyrB\u003c/em\u003e gene sequence.bootstrap values after 1000 replicates are shown as a percentage. The scale bar below indicates 0.10 substitutions per nucleotide position.\u003c/p\u003e","description":"","filename":"Figure5revised.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5698089/v1/f0226149b7261e4d0a7c526e.jpg"},{"id":81325027,"identity":"bc6791d8-414c-4f65-88a4-8eddab145327","added_by":"auto","created_at":"2025-04-24 18:53:30","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":79138,"visible":true,"origin":"","legend":"\u003cp\u003eBPB Symptom induced by \u003cem\u003eB. \u0026nbsp;\u0026nbsp;glumae \u003c/em\u003eand \u003cem\u003eB. gladioli \u003c/em\u003eat 28DAI. (a) Control panicle; (b) Severe \u0026nbsp;\u0026nbsp;infected BPB on MR219; (c) Sheath rot of BPB; (d) Weakly infected of \u003cem\u003eBurkholderia \u0026nbsp;\u0026nbsp;\u003c/em\u003espp. on rice.\u003c/p\u003e","description":"","filename":"Figure6revised.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5698089/v1/4f5cfed7bab25f0b3a3a075a.jpg"},{"id":81325730,"identity":"bcb7c858-e187-49c5-b5d3-dc05f0f2685d","added_by":"auto","created_at":"2025-04-24 19:09:30","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":242224,"visible":true,"origin":"","legend":"\u003cp\u003eThe percentage of disease severity on MR219 cultivar after 28DAI.\u003c/p\u003e","description":"","filename":"Figure7revised.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5698089/v1/1385c20c754aa2597b4d3f85.jpg"},{"id":81325733,"identity":"ff64ba3e-bc2e-4141-be58-997d1760fbbc","added_by":"auto","created_at":"2025-04-24 19:09:30","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":137206,"visible":true,"origin":"","legend":"\u003cp\u003eArea Under the Disease Progress Curve (AUDPC) of 12 \u003cem\u003eBurkholderia \u003c/em\u003eisolates inoculated onto MR219 cultivar.\u003c/p\u003e","description":"","filename":"Figure8revised.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5698089/v1/628a335f0a58fd4f7e1fe8db.jpg"},{"id":81325028,"identity":"9fab71db-1d8a-497c-bc4e-ddad39e649ff","added_by":"auto","created_at":"2025-04-24 18:53:30","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":125464,"visible":true,"origin":"","legend":"\u003cp\u003eThe gene functions present in \u003cem\u003eB. glumae \u003c/em\u003eK6 strain using COG software and divided into 24 classifications based on their functions.\u003c/p\u003e","description":"","filename":"Figure9revised.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5698089/v1/69fc31d4081671236bcb14e7.jpg"},{"id":81326028,"identity":"40716c7d-30f9-499e-a6c8-d2d789a5574b","added_by":"auto","created_at":"2025-04-24 19:17:30","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":82925,"visible":true,"origin":"","legend":"\u003cp\u003eThe gene functions present in \u003cem\u003eB. gladioli \u003c/em\u003eUPMBG7 strain using COG software and divided into 24 classifications based on their functions.\u003c/p\u003e","description":"","filename":"Figure10revised.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5698089/v1/6fc4fcc45836cfb36c6c1157.jpg"},{"id":81325034,"identity":"ef0d7215-b190-460b-badd-dd5b2d1fba2b","added_by":"auto","created_at":"2025-04-24 18:53:30","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":59737,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG pathway for flagellar assembly.\u003c/p\u003e","description":"","filename":"Figure11revised.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5698089/v1/2e119e728a70554c97fbfba9.jpg"},{"id":81325039,"identity":"6407739b-de48-48bc-9a51-a9f3e63b2d75","added_by":"auto","created_at":"2025-04-24 18:53:30","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":74230,"visible":true,"origin":"","legend":"\u003cp\u003eThe proposed KEGG pathway for the quorum sensing system of \u003cem\u003eB. glumae \u003c/em\u003eK6 and \u003cem\u003eB. gladioli \u003c/em\u003eUPMBG7. Highlighted boxes indicate\u003c/p\u003e\n\u003cp\u003ethe gene produced by the \u003cem\u003eB. glumae \u003c/em\u003eK6 and \u003cem\u003eB. gladioli \u003c/em\u003eUPMBG7 strains involved in the quorum sensing system\u003c/p\u003e","description":"","filename":"Figure12revised.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5698089/v1/455337623035b8d38205be1a.jpg"},{"id":92883766,"identity":"6714cfe3-cc34-42c0-a9df-e819f9a3279c","added_by":"auto","created_at":"2025-10-06 16:09:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4145407,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5698089/v1/dd9a70bf-4e49-41ab-b740-a6169685322e.pdf"}],"financialInterests":"","formattedTitle":"Genomic Characterization of Burkholderia glumae K6 and B. gladioli UPMBG7: Causal Agents of Bacterial Panicle Blight in Malaysia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cem\u003eBurkholderia glumae\u003c/em\u003e, the causal agent of BPB disease, was initially identified in Japan in the early 1950s as a rice pathogen that caused grain rotting and seedling blight on rice (Nandakumar et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The disease is primarily spread through contaminated seeds, wind-driven rain, and irrigation water, facilitating its rapid dissemination across rice-growing regions. The disease poses a significant threat to rice cultivation in these areas, impacting production and emphasizing the importance of effective management measures.\u003c/p\u003e \u003cp\u003ePrevious genomic studies on \u003cem\u003eB. glumae\u003c/em\u003e have identified key virulence determinants, including toxoflavin biosynthesis genes and quorum sensing regulators, which are crucial for pathogenicity (Kim et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Kang et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Similarly, research on \u003cem\u003eB. gladioli\u003c/em\u003e has highlighted its genomic diversity and adaptability, enabling it to function as both a plant pathogen and an opportunistic human pathogen (P\u0026eacute;rez-Mendoza et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pritchard et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, comparative genomic analyses between these two pathogens, particularly in the context of BPB, remain scarce. The lack of in-depth comparative studies limits our understanding of their distinct pathogenic strategies and their implications for disease management, necessitating further research to develop targeted control measures. In Malaysia, bacterial panicle blight (BPB) has emerged as a major challenge, causing yield losses of up to 75% in severely infected fields. The increasing frequency of BPB outbreaks highlights the urgency for effective disease management strategies. However, the complex nature of the disease and the genetic variability of its causal agents necessitate a deeper understanding of the pathogen's genomic characteristics to develop targeted control measures (Nandakumar et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe first report of the Malaysian outbreak of BPB occurred in rice fields in two distinct states, Sungai Ache, Penang, and Kampung Banir, Kelantan. This outbreak spanned from December 2017 to March 2018 (Ramachandran et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Bacterial blight manifests in symptoms such as seedling blight, sheath rot of flag leaves, and panicle branches, ultimately leading to significant yield losses. The distinctive characteristics of BPB, as highlighted by Nandakumar et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and Sayler et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), include an upright, straw-colored panicle with florets displaying dark grey and reddish-brown lines at the lesion borders. Despite glumes desiccating and turning tan, the rachis of the panicle remains green. \u003cem\u003eBurkholderia\u003c/em\u003e spp. is among several major plant pathogens and necessitates phylogenetic identification alongside symptomatic characterization.\u003c/p\u003e \u003cp\u003eAlthough \u003cem\u003eB. glumae\u003c/em\u003e and \u003cem\u003eB. gladioli\u003c/em\u003e belong to the same genus, they employ distinct molecular strategies to cause infection, leading to variations in disease severity and epidemiology. This study presents a comparative genomic analysis of \u003cem\u003eB. glumae\u003c/em\u003e K6 and \u003cem\u003eB. gladioli\u003c/em\u003e UPMBG7, focusing on their genetic determinants of virulence, host adaptation, and ecological fitness. Whole-genome sequencing revealed that \u003cem\u003eB. glumae\u003c/em\u003e K6 harbors toxoflavin biosynthesis genes (\u003cem\u003etoxABCDE\u003c/em\u003e, \u003cem\u003etoxJ\u003c/em\u003e) and a Type III secretion system (T3SS), both critical for bacterial invasion and host colonization. Toxoflavin, a potent phytotoxin, induces oxidative stress, disrupts plant cellular functions, and promotes bacterial spread, contributing to the aggressive virulence of \u003cem\u003eB. glumae\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eIn contrast, \u003cem\u003eB. gladioli\u003c/em\u003e UPMBG7 lacks the \u003cem\u003etoxI\u003c/em\u003e gene, which is essential for toxoflavin production, but employs an alternative virulence mechanism through pyoverdine siderophore genes (\u003cem\u003epvdA\u003c/em\u003e). These genes facilitate iron acquisition, a key factor in microbial competition and host colonization. Unlike \u003cem\u003eB. glumae\u003c/em\u003e, \u003cem\u003eB. gladioli\u003c/em\u003e exhibits a broader ecological adaptability, thriving in iron-limited environments and establishing infection via host-resource exploitation rather than toxin-mediated damage. The genomic differentiation between these pathogens highlights their unique adaptation strategies, influencing disease progression and severity in rice crops.\u003c/p\u003e \u003cp\u003eThis study provides a comparative genomic analysis of \u003cem\u003eB. glumae\u003c/em\u003e K6 and \u003cem\u003eB. gladioli\u003c/em\u003e UPMBG7, two major BPB pathogens in Malaysian rice fields. By identifying key virulence factors, including toxoflavin biosynthesis (\u003cem\u003etoxABCDE, toxJ\u003c/em\u003e) and Type III secretion system (T3SS) in \u003cem\u003eB. glumae\u003c/em\u003e and pyoverdine siderophores (\u003cem\u003epvdA\u003c/em\u003e) in \u003cem\u003eB. gladioli\u003c/em\u003e, this research enhances our understanding of their infection strategies and potential control targets.\u003c/p\u003e \u003cp\u003eThese findings have practical implications for rice disease management. Identifying virulence genes supports breeding programs through marker-assisted selection (MAS) to develop BPB-resistant rice varieties. Additionally, insights into pathogen survival mechanisms pave the way for targeted biocontrol strategies, while genomic data contribute to early detection tools, allowing for rapid disease surveillance and intervention.By integrating these genomic insights into breeding, biocontrol, and disease monitoring, this study provides valuable resources for sustainable BPB management, helping to protect rice yields and ensure long-term agricultural resilience.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e \u003cb\u003eSamples Collection and Isolation of Bacterial Pathogen\u003c/b\u003e: Sample were collected focused on symptomatic rice panicles from BPB outbreak fields to examine the presence and distribution of \u003cem\u003eBurkholderia\u003c/em\u003e species. Sampling was conducted from June 2021 to January 2022. Samples were obtained from Kedah (Yan: 5.7633\u0026deg; N, 100.3702\u0026deg; E; Kodiang: 6.3933\u0026deg; N, 100.3057\u0026deg; E), Perak (Seberang Perak: 4.1013\u0026deg; N, 100.9521\u0026deg; E), and Selangor (Sekinchan: 3.5041\u0026deg; N, 101.1033\u0026deg; E), three major rice-producing states selected based on BPB outbreak reports from Department of Agriculture (DOA) Malaysia and their distinct agro-ecological conditions. Kedah, Malaysia\u0026rsquo;s largest rice-growing region, represents extensive irrigated farming, while Perak and Selangor encompass a mix of traditional, semi-intensive, and intensive cultivation systems. These locations were chosen to capture the genomic diversity of \u003cem\u003eB. glumae\u003c/em\u003e and \u003cem\u003eB. gladioli\u003c/em\u003e across different rice ecosystems, providing insights into pathogen adaptation and virulence under varying environmental conditions. Collected rice panicles and sheaths were excised and surface sterilized using distilled water containing 1% sodium hypochlorite (NaOCl), immersing the sample in sterile distilled water for 15 minutes, and inoculated the sample onto King\u0026rsquo;s B agar for 24\u0026ndash;48 hours at 41\u0026deg;C. Colony morphology of \u003cem\u003eBurkholderia\u003c/em\u003e spp. recorded as cream-colored circular colonies with yellow pigmentation. The pure culture colonies were grown on nutrient broth 24\u0026ndash;48 hours at 37\u0026deg;C prior to long-term storage in 20% (v/v) glycerol at -70\u0026deg;C as frozen stock for further use.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMolecular Characterization\u003c/b\u003e: Genomic DNA extraction from bacterial cultures was carried out using the Presto\u0026trade; Mini gDNA Bacteria Kit according to the manufacturer's instructions (Geneaid Biotech Ltd., Taiwan). Subsequently, PCR amplification was conducted to identify \u003cem\u003eBurkholderia\u003c/em\u003e using universal and specific genes: 16s Ribosomal RNA (16s rRNA), DNA gyrase subunit B of \u003cem\u003eB. glumae\u003c/em\u003e (\u003cem\u003egyrB\u003c/em\u003e), and \u003cem\u003egyrB\u003c/em\u003e gene of \u003cem\u003eB. gladioli\u003c/em\u003e, (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The \u003cem\u003egyrB\u003c/em\u003e gene was chosen for its resolution in distinguishing \u003cem\u003eBurkholderia\u003c/em\u003e species. Unlike 16S rRNA, which has high sequence similarity within the genus, \u003cem\u003egyrB\u003c/em\u003e evolves faster and provides greater phylogenetic differentiation. Compared to other housekeeping genes, \u003cem\u003egyrB\u003c/em\u003e shows higher nucleotide variability, making it a more reliable marker for species identification. For the amplification of the 16s RNA gene, the PCR conditions were as follows: an initial denaturation step at 95\u0026deg;C for 2 minutes, followed by 30 cycles of denaturation at 94\u0026deg;C for 30 seconds, annealing at 55\u0026deg;C for 1 minute, extension at 72\u0026deg;C for 2 minutes, and a final extension at 72\u0026deg;C for 10 minutes. The expected size of the resulting amplicons was ~\u0026thinsp;1500 base pairs. PCR conditions for the \u003cem\u003eB. glumae gyrB\u003c/em\u003e gene began with an initial denaturation step at 94\u0026deg;C for 2 minutes. This was followed by 35 cycles of denaturation at 94\u0026deg;C for 1 minute, annealing at 63\u0026deg;C for 1 minute, extension at 72\u0026deg;C for 1 minute, and a final extension at 72\u0026deg;C for 10 minutes. The expected size of the amplicon was ~\u0026thinsp;530 base pair. Lastly, for the amplification of the \u003cem\u003eB. gladioli gyrB\u003c/em\u003e gene, PCR amplification was initiated at 94\u0026deg;C for 2 min, followed by 35 cycles at 94\u0026deg;C for 1 min, 63\u0026deg;C for 1 min, and 72\u0026deg;C for 1 min and 72\u0026deg;C for 10 min as the final extension. The expected size of the amplicon was ~\u0026thinsp;479 bp (Mulaw et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSequence (5\u0026rsquo;-3\u0026rsquo;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSize (bp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecify\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16r rRNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e27F\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAGAGTTTGATCCTGGCTCAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUniversal primer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSayler et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2006\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1492R\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGTTACCTTACGACTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGyrase B subunit (\u003cem\u003eB. glumae\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eglu-FW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGAAGTGTCGCCGATGGAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpecific \u003cem\u003egyrb\u003c/em\u003e gene of \u003cem\u003eB. glumae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMulaw et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eglu-RV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCCTTCACCGA CAGCACGCAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGyrase B subunit (\u003cem\u003eB. gladioli\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003egla-FW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTGCGCCTGGTGGTGAAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpecific \u003cem\u003egyrb\u003c/em\u003e gene of \u003cem\u003eB. gladioli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMulaw et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGla-RV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCCGTCCCGCTGCGGAATA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eList of primers used for PCR amplification of \u003cem\u003eBurkholderia.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSequence and Phylogenetic Analysis\u003c/b\u003e: The unpurified PCR products underwent purification before being subjected to sequencing using the Sanger sequencing method. Subsequently, the DNA sequences were assembled using Bioedit 7.2.5 software and analyzed against sequences in the NCBI database through BLASTn (Nucleotide Basic Local Alignment Search Tool). Sequences obtained were aligned with references strains and outgroup strains by repeating the bootstrap number of 1,000 and used to build a phylogenetic tree using the Maximum Likelihood method generated by MEGA Software version 11.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.megasoftware.net/\u003c/span\u003e\u003cspan address=\"https://www.megasoftware.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). All sequences were submitted to the GenBank database.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePathogenicity Tests\u003c/b\u003e: Rice seedlings of MR219 were planted in the glasshouse at the Institute of Plantation Studies, University Putra Malaysia, under average regulated temperatures (day: 35\u0026deg;C, night: 25\u0026deg;C) and humidity (70\u0026ndash;100%) conditions. Rice was then inoculated with 1 mL of 10\u003csup\u003e8\u003c/sup\u003e CFU ml\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e bacterial suspension from ten bacterial suspension of \u003cem\u003eB. glumae\u003c/em\u003e and two bacterial strains of \u003cem\u003eB. gladioli\u003c/em\u003e isolates into the panicles and crowns of 75-day-old rice seedlings using a sterile syringe on ten biological replicates per treatment. Control rice seedlings inoculated with sterilized water. Disease progression was monitored for 28 days after inoculation (DAI), with severity recorded every 24 hours. (Nandakumar et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Lalithiya et al., 2017). Statistical analysis was performed using one-way ANOVA (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) to assess variations in pathogenicity among isolates, followed by Tukey\u0026rsquo;s Honest Significant Difference (HSD) test for multiple comparisons. Disease severity percentages were used to construct the Area Under the Disease Progress Curve (AUDPC) to quantify disease progression over time​ proposed by Shanner \u0026amp; Finney (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). All statistical analysis data were analyzed using Statistical Analysis Software (version 9.4).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLibrary Preparation, Quality Control and Whole-Genome Sequencing\u003c/strong\u003e \u003cp\u003eGenomic DNA was extracted from \u003cem\u003eB. glumae\u003c/em\u003e K6 and \u003cem\u003eB. gladioli\u003c/em\u003e UPMBG7 using a DNA purification kit (Presto\u0026trade; Mini gDNA Bacteria Kit) according to the manufacturer's protocols (Geneaid Biotech Ltd., Taiwan). DNA concentration and purity were measured using a NanoDrop spectrophotometer (Thermo Scientific, USA) at 260 and 280nm wavelengths for sample quality control. A DNA library was prepared using the Illumina TruSeq\u0026reg; kit, following the method of Pasquali et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Genomic DNA was sonicated, and resulting fragments were end-repaired, A-tailed, and ligated with adaptors. After purification, the library's quality was assessed with a Qubit 3.0 Fluorometer, real-time PCR for quantification, and bioanalyzer for size distribution detection. Pooled libraries with different indices underwent high-throughput sequencing on the Illumina NovaSeq 6000 platform, which uses sequencing-by-synthesis technology, generating paired-end reads (2x150bp). FastQC software V0.20.0 software was used for quality control, removing adapter contamination and low-quality readings of the raw data. One of the most significant advancements in plant pathogen research is the use of third-generation sequencing (TGS) platforms such as PacBio SMRT sequencing and Oxford Nanopore long-read sequencing, which have enabled the complete genome assembly of bacterial pathogens, including \u003cem\u003eXanthomonas oryzae\u003c/em\u003e and \u003cem\u003eBurkholderia\u003c/em\u003e spp. (Midha et al., 2017; Tran et al., 2018). Unlike second-generation sequencing (SGS) technologies, which generate short reads (e.g., Illumina sequencing), TGS provides insights into structural variations, such as plasmid-borne virulence genes and pathogenicity islands that influence host adaptation. However, the selection of second-generation sequencing (SGS) over third-generation sequencing (TGS) in this study was guided by key factors, including sequencing accuracy, cost-effectiveness, and computational feasibility. SGS platforms, such as Illumina NovaSeq 6000, provide high sequencing accuracy, with error rates typically below 2%, whereas TGS platforms like Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) have higher error rates averaging 10%, necessitating extensive error correction steps (Rhoads \u0026amp; Au, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). While TGS offers long-read advantages, enabling better resolution of repetitive and structural variations, its higher sequencing and data-processing costs make it less accessible for large-scale genome studies (Eid et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; van Dijk et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additionally, SGS integrates more efficiently with existing genome assembly pipelines, as its shorter, high-fidelity reads require less computational power than TGS, which generates long but error-prone reads, often requiring complex hybrid assembly strategies (Koren et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Given these considerations, SGS was selected for this study to ensure reliable, cost-efficient, and computationally manageable genome assembly, providing high-confidence genomic insights into \u003cem\u003eBurkholderia\u003c/em\u003e species.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eGenome Assembly and Gene Function Analysis\u003c/strong\u003e \u003cp\u003eGenome assembly was modified through three programs (Abbas et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Initial assembly with SOAPdenovo v2.04 involved different K-mers (95, 107, 119), selecting the result with the least contigs. SPAdes v3.15.4 used K-mers 99 and 127, choosing the assembly result with the optimal K-mer and least contigs. AbySS v2.1.5, with a K-mer of 64, produced an assembly result. CISA software was employed to integrate the assembly results from three different software applications, and the assembly result with the fewest contigs was selected. Genome annotations were performed using the NCBI Prokaryotic Genome Annotation Pipeline (PGAP), including transfer and ribosomal RNAs. Gene functions were annotated by Gene Ontology (GO), Quorum Sensing (QS), and Flagellar pathways of the annotated gene via Kyoto Encyclopedia of Genes and Genomes (KEGG) databases, and phylogenetic classifications of the proteins encoded by gene were annotated via Cluster of Orthologous Groups of Protein Annotation (COG) (Ashburner et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Kanehisa et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Kanehisa et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Galperin et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). KEGG annotation was used to map genes to metabolic pathways and virulence-related systems, providing insights into the functional roles of key genes. COG classification revealed that most of the annotated genes belonged to metabolism and cellular processes, suggesting that core functions are conserved in annotated genes. Compared to KEGG, which links genes to metabolic pathways, and EggNOG, which offers broad functional predictions, COG provides detailed insights into gene family conservation. In addition, the NCBI non-redundant protein database was annotated based on a protein database, and the annotation results contain specific information that can be used for species classification.\u003c/p\u003e \u003c/p\u003e"},{"header":"3. Results and Discussion","content":"\u003cp\u003e \u003cstrong\u003eSamples Collection and Isolation of Bacterial Pathogen\u003c/strong\u003e \u003cp\u003eBPB symptoms included grain rotting, seedling blight, and florets with dark grey or reddish-brown bases. Infected panicles often remained upright due to grain weight loss. Other symptoms can be observed on the sheath of an infected tiller with a lesion several centimetres long with a gray and necrotic center and a reddish-brown border. Leaves and spikelets contain small circular to oval tan lesions ranging from 1 to 5 mm with brown borders (Fig.\u0026nbsp;1). In total, 22 potential \u003cem\u003eB. glumae\u003c/em\u003e and 5 \u003cem\u003eB. gladioli\u003c/em\u003e were recovered from the panicle blight of rice. Pure colonies were observed as circular and cream colour, flat with smooth margins, and produced a diffusible yellow pigment on agar. \u003cem\u003eB. gladioli\u003c/em\u003e have vigorous yellow pigment produced on agar compared to \u003cem\u003eB. glumae\u003c/em\u003e (Fig.\u0026nbsp;2). \u003cem\u003eB. glumae\u003c/em\u003e and \u003cem\u003eB. gladioli\u003c/em\u003e both produce yellow pigmentation on King\u0026rsquo;s B agar, but the underlying mechanisms and implications of this pigmentation differ between the two species. \u003cem\u003eB. glumae\u003c/em\u003e produces yellow pigmentation primarily due to synthesizing the phytotoxin toxoflavin, which is critical for its pathogenicity in rice. Toxoflavin contributes to plant cell damage and is associated with the bacterium's virulence, allowing it to effectively establish infections in its host (Lehman et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tsukuda et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Toxoflavin is a key virulence factor in \u003cem\u003eB. glumae\u003c/em\u003e, playing a crucial role in disease severity and bacterial colonization. It is regulated through quorum sensing (QS), specifically by N-acyl homoserine lactones (AHLs) such as C6-HSL and C8-HSL, which activate the ToxR-ToxJ regulatory system, leading to toxoflavin biosynthesis. Once produced, toxoflavin induces oxidative stress by generating reactive oxygen species (ROS), causing cell damage and necrosis in host tissues. This weakens plant defense mechanisms, disrupts chloroplast function and energy metabolism, and accelerates disease progression. The accumulation of toxoflavin in infected rice tissues contributes to panicle blight symptoms, including seed discoloration, tissue necrosis, and sterility, making \u003cem\u003eB. glumae\u003c/em\u003e a highly aggressive pathogen. Additionally, toxoflavin promotes bacterial colonization by breaking down host cells, creating a more favorable environment for pathogen proliferation (Lehman et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tsukuda et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)​. In contrast, the yellow pigmentation observed in \u003cem\u003eB. gladioli\u003c/em\u003e is primarily attributed to the production of pyomelanin, a secondary metabolite that serves different ecological roles. While not directly associated with toxicity like toxoflavin, pyomelanin helps \u003cem\u003eB. gladioli\u003c/em\u003e cope with environmental stresses by protecting against oxidative damage and aiding in iron acquisition (Ma et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pritchard et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This pigmentation may also enhance the bacterium's survival and colonization ability, albeit through different mechanisms than \u003cem\u003eB. glumae\u003c/em\u003e. Overall, the yellow pigmentation in \u003cem\u003eB. glumae\u003c/em\u003e is intricately linked to its pathogenicity via toxoflavin production, while \u003cem\u003eB. gladioli\u003c/em\u003e utilizes pyomelanin for environmental resilience and adaptability.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMolecular Characterization\u003c/strong\u003e \u003cp\u003ePCR amplification of 27 isolates using 16S rRNA, \u003cem\u003eB. glumae gyrB\u003c/em\u003e and \u003cem\u003eB. gladioli gyrB\u003c/em\u003e primer pair showed around 1500 bp, 530 bp, and 479 bp amplicon, respectively (Fig.\u0026nbsp;3).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSequence and Phylogenetic Analysis\u003c/b\u003e: BLASTn search for all sequenced primers have similarity ranging from 96\u0026ndash;100%. According to prior research, the most often utilized genotypic approach for bacterial identification is the comparative analysis of 16S rRNA sequences (Sayler et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). 16S rRNA sequencing revealed up to 99% similarity with their respective species. However, because the sequence exhibits substantial similarities throughout the \u003cem\u003eBurkholderia\u003c/em\u003e group, using 16rDNA alone to identify bacteria among \u003cem\u003eBurkholderia\u003c/em\u003e species is insufficient. Hence, primer \u003cem\u003egyrb\u003c/em\u003e was tested to target \u003cem\u003eBurkholderia\u003c/em\u003e-specific gene regions. Phylogenetic analysis based on the 16S rRNA gene (Fig.\u0026nbsp;4) using Maximum-likelihood showed two clusters of \u003cem\u003eBurkholderia\u003c/em\u003e spp. Cluster (I) comprised \u003cem\u003eB. glumae\u003c/em\u003e strains isolated in this study and were 100% clustered with the \u003cem\u003eB. glumae\u003c/em\u003e reference strains 411gr-6 (Accession No. CP021158), GX (Accession No. CP045088) and P1-22-1 (Accession No. NR029211). \u003cem\u003eB. gladioli\u003c/em\u003e (Cluster II) strains isolated in this study were 100% clustered with the \u003cem\u003eB. gladioli\u003c/em\u003e reference strains CIP 105410 (Accession No. NR044278), NBRC 13700 (Accession No. NR113629), CFBP 2427 (Accession No. NR117553), FDAARGOS 389(Accession No. CP023522), and B27 (Accession No. MZ425421). The outgroup sequence of 16S rRNA used in this study was \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e strain DSM 50071 (Accession No. NR026078). Phylogenetic analysis based on the \u003cem\u003egyrB\u003c/em\u003e gene (Fig.\u0026nbsp;5) using Maximum-likelihood showed two clusters of \u003cem\u003eBurkholderia\u003c/em\u003e spp. Cluster (I) comprised \u003cem\u003eB. glumae\u003c/em\u003e strains isolated in this study and were 100% clustered with the \u003cem\u003eB. glumae\u003c/em\u003e reference strains BGR48S (Accession No. CP100285), DOA-BG14 (Accession No. KX213523), and BP2-004 (Accession No. LC474870). While for the \u003cem\u003eBurkholderia gladioli\u003c/em\u003e (Cluster II) strains isolated in this study were 100% clustered with the \u003cem\u003eB. gladioli\u003c/em\u003e reference strain MAFF 302533 (Accession No. AB190628) and LMG 19584 (Accession No. AB220898). The outgroup sequence of the \u003cem\u003egyrB\u003c/em\u003e gene region used in this study was \u003cem\u003eBurkholderia pseudomallei\u003c/em\u003e strain K 96243 (Accesion No. EU024223). Gene sequence successfully clustered \u003cem\u003eB. glumae\u003c/em\u003e and \u003cem\u003eB. gladidoli\u003c/em\u003e to their reference strains, respectively. None of the strains in this study clustered with the outgroup or different reference species sequence. The tree with the greatest log-likelihood is displayed, with the bootstrap support percentages marked at the nodes. The scale bar represents the expected number of changes per site. Based on the result, identifying \u003cem\u003eBurkholderia\u003c/em\u003e species using the \u003cem\u003egyr\u003c/em\u003eB gene is much more efficient than the 16S rRNA gene sequence as the phylogenetic analysis showed a higher genetic variation in \u003cem\u003egyrB\u003c/em\u003e with a nucleotide base substitution of 0.1 compared to 16S rRNA with nucleotides base substitution of 0.02. The acquired 16s rRNA and \u003cem\u003egyrb\u003c/em\u003e gene sequences have been submitted to NCBI GenBank (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The phylogenetic analysis of \u003cem\u003eB. glumae\u003c/em\u003e and \u003cem\u003eB. gladioli\u003c/em\u003e provides key insights into their evolutionary adaptations to rice as a host. The maximum-likelihood tree constructed using 16S rRNA and \u003cem\u003egyrB\u003c/em\u003e sequences revealed that \u003cem\u003eB. glumae\u003c/em\u003e forms a distinct monophyletic cluster closely related to other rice-pathogenic \u003cem\u003eBurkholderia\u003c/em\u003e species, while \u003cem\u003eB. gladioli\u003c/em\u003e remains more phylogenetically diverse, clustering with both plant-associated and opportunistic strains​ (Sayler et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2006\u003c/span\u003e)​. The phylogenetic placement of \u003cem\u003eB. glumae\u003c/em\u003e within a specialized rice-pathogenic lineage suggests that it has undergone host-driven evolutionary adaptation, optimizing its virulence mechanisms for effective infection in rice (Kim et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2004\u003c/span\u003e)​. This is supported by the presence of toxoflavin biosynthesis genes (\u003cem\u003etoxABCDE, toxJ\u003c/em\u003e) and quorum sensing regulators (tofI, tofR), which enable \u003cem\u003eB. glumae\u003c/em\u003e to efficiently colonize rice tissues, suppress plant defenses, and cause severe bacterial panicle blight (BPB). Such specialization indicates a coevolutionary relationship between \u003cem\u003eB. glumae\u003c/em\u003e and rice, where the pathogen has fine-tuned its genetic traits to exploit the rice host effectively​. In contrast, \u003cem\u003eB. gladioli\u003c/em\u003e occupies a more generalist phylogenetic position, reflecting its broader ecological adaptability. Unlike \u003cem\u003eB. glumae\u003c/em\u003e, \u003cem\u003eB. gladioli\u003c/em\u003e does not rely on toxoflavin for virulence but instead employs pyoverdine siderophores and diverse secretion systems, which allow it to persist in multiple environments, including soil, water, and plant rhizospheres ​(Wang et. al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This suggests that \u003cem\u003eB. gladioli\u003c/em\u003e retains a more flexible pathogenic strategy, making it a weaker but more adaptable pathogen compared to \u003cem\u003eB. glumae\u003c/em\u003e (Pritchard et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The evolutionary divergence between these two species highlights their contrasting adaptation strategies: \u003cem\u003eB. glumae\u003c/em\u003e as a highly specialized rice pathogen with host-adapted virulence traits, and \u003cem\u003eB. gladioli\u003c/em\u003e as a generalist with broader environmental resilience. Understanding these adaptations is crucial for disease management, as targeted control strategies such as quorum sensing inhibitors for \u003cem\u003eB. glumae\u003c/em\u003e and iron-chelating compounds for \u003cem\u003eB. gladioli\u003c/em\u003e could be designed to exploit their respective weaknesses (Lehman et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of Genbank accession numbers of \u003cem\u003eBurkholderia\u003c/em\u003e in this study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrains\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eState\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHost\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16s rRNA Accession\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003egyrB\u003c/em\u003e Accession\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBGK.AZ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKedah\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eOryza sativa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eBurkholderia glumae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOK632514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOL461777\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBGK.AZ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKedah\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eOryza sativa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eBurkholderia glumae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOK632515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOL461778\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBGK.AZ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKedah\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eOryza sativa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eBurkholderia glumae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOK632516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOL461779\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBGK.AZ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKedah\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eOryza sativa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eBurkholderia glumae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOK631741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOL461780\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBGK.AZ5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKedah\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eOryza sativa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eBurkholderia glumae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOL347392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOL461781\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBGK.AZ6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKedah\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eOryza sativa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eBurkholderia glumae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOL347393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOL461782\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBGK.AZ7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKedah\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eOryza sativa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eBurkholderia glumae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOL347394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOL461783\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBGK.AZ8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKedah\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eOryza sativa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eBurkholderia glumae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOL347395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOL461784\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBGS.AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelangor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eOryza sativa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eBurkholderia glumae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eON062124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eON086704\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBGS.AS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelangor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eOryza sativa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eBurkholderia glumae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eON062125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eON086705\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBGS.AS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelangor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eOryza sativa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eBurkholderia glumae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eON062126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e 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colname=\"c2\"\u003e \u003cp\u003eSelangor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eOryza sativa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eBurkholderia glumae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eON062128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eON086708\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBGS.AS6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelangor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e 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\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eOryza sativa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eBurkholderia glumae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eON062133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eON086713\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBGP.A4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eOryza sativa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eBurkholderia glumae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eON062134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eON086714\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBGP.A5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eOryza sativa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eBurkholderia glumae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e 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colname=\"c1\"\u003e \u003cp\u003eBGP.A7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eOryza sativa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eBurkholderia glumae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eON062137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eON086717\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUPMBG7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelangor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e 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colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eBurkholderia gladioli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOM869954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOM824439\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUPMBG9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelangor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eOryza sativa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eBurkholderia gladioli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOM869955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOM824440\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUPMBG15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelangor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eOryza sativa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eBurkholderia gladioli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOM869956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOM824441\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUPMBG17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelangor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eOryza sativa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eBurkholderia gladioli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOM869957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOM824442\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePathogenicity Tests\u003c/strong\u003e \u003cp\u003eAfter 4\u0026ndash;7 days of inoculation, MR219 cultivars began to develop bacterial panicle blight, whereas the control rice remained healthy. Up to 28 days after inoculation, the symptoms were evaluated and documented every 24 hours to notice the browning lesion on panicle florets. Disease severity was assessed ranging from mild to severe. Category 1 represented mild symptoms, with browning restricted to the floret base and no significant spread. Category 2 indicated moderate symptoms, where browning extended to the middle of the floret, accompanied by slight sterility. In Category 3, symptoms became more severe, with browning covering most of the floret, leading to significant sterility and tissue necrosis. The most severe cases fell into Category 4, where florets were completely darkened, full sterility occurred, and panicle blight developed. The symptomatic plants first appeared when florets became brown across the floret base and gradually developed into classic panicle blight and florets with a completely dark brown base and sterile florets (Fig.\u0026nbsp;6). After 28DAI, all plants developed BPB disease, with floret blight expanding consistently from the bottom to the top of the florets. The disease incidence ranges from 27\u0026ndash;77% due to severe panicle loss and severity progress.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe statistical analysis of 12 \u003cem\u003eBurkholderia\u003c/em\u003e spp. pathogenicity tests on rice cultivar MR219 revealed a significant variation in pathogenicity across isolates with a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (One-Way ANOVA P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). MR219 cultivar proved to be very susceptible to \u003cem\u003eBurkholderia\u003c/em\u003e spp. \u003cem\u003eB. gladioli\u003c/em\u003e UPMBG7 reported the highest degree of severity (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) at 28DAI (77.43%) and an AUDPC score of 1123.57, while \u003cem\u003eB. glumae\u003c/em\u003e BGS.AS1 has the second highest degree of severity (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) at 28DAI (68.15%) and an AUDPC score of 948.28. However, \u003cem\u003eB. glumae\u003c/em\u003e BGK.AZ1 had the lowest severity (27.60%) and AUDPC score at 28DAI. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the percentage of disease severity and AUDPC value for cultivars MR219.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePercentage of disease severity and Area Under Disease Progress Curve (AUDPC) on MR219 cultivars for 12 selected isolates \u003cem\u003eBurkholderia\u003c/em\u003e species on panicles.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariety\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUDPC value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent Disease Severity (%) after 28 days inoculation (28DAI\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDisease score at 28DAI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIsolate Virulence level\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBGK.AZ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e410.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.60\u0026thinsp;\u0026plusmn;\u0026thinsp;3.98\u003csup\u003eD\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBGS.AS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e658.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.35\u0026thinsp;\u0026plusmn;\u0026thinsp;6.25\u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBGK.AZ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e666.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.19\u0026thinsp;\u0026plusmn;\u0026thinsp;7.56\u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBGP.A2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e790.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.82\u0026thinsp;\u0026plusmn;\u0026thinsp;3.06\u003csup\u003eBC\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBGS.AS5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e779.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.01\u0026thinsp;\u0026plusmn;\u0026thinsp;3.27\u003csup\u003eABC\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMR219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBGP.A4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e927.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.28\u0026thinsp;\u0026plusmn;\u0026thinsp;3.45\u003csup\u003eABC\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBGS.AS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e804.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.81\u0026thinsp;\u0026plusmn;\u0026thinsp;5.70\u003csup\u003eABC\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBGP.A6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1053.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.94\u0026thinsp;\u0026plusmn;\u0026thinsp;3.23\u003csup\u003eABC\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBGK.AZ7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e761.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.81\u0026thinsp;\u0026plusmn;\u0026thinsp;4.86\u003csup\u003eAB\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBGS.AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e948.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.15\u0026thinsp;\u0026plusmn;\u0026thinsp;7.61\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUPMBG8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1081.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.36\u0026thinsp;\u0026plusmn;\u0026thinsp;3.42\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUPMBG7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1123.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.43\u0026thinsp;\u0026plusmn;\u0026thinsp;4.87\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigures\u0026nbsp;(7 and 8) show that disease severity increased steadily at 7DAI until 28DAI for all isolates. Within the context of our research, the interpretation of the Area Under the Disease Progress Curve (AUDPC) values presents a nuanced understanding of disease progression and the responses of different cultivars to the pathogen. Our findings reveal a spectrum of disease severity, as reflected by the range of AUDPC values from 410.762 to 1123.575. The AUDPC values were compared across different \u003cem\u003eBurkholderia\u003c/em\u003e strains to evaluate their virulence levels in rice cultivar MR219. The strain with the lowest AUDPC value of 410.762 demonstrates the least disease progression, suggesting a reduced susceptibility to the host. Conversely, the strain with the highest AUDPC value of 1123.575 exhibits the most substantial disease progression, indicating greater susceptibility. The range of AUDPC values from the studies suggests varying degrees of resistance or susceptibility to the disease, with the cultivars having an AUDPC of 410.762, indicating partial resistance toward \u003cem\u003eB. glumae\u003c/em\u003e, while those with higher values are generally less resistant. The intermediate AUDPC values observed among other strains represent varying levels of disease severity and, by extension, differing degrees of resistance or susceptibility. This variability underscores the multifaceted nature of disease dynamics, influenced by cultivar genetics, environmental conditions, and pathogen virulence.\u003c/p\u003e \u003cp\u003eMoreover, the AUDPC values hold practical implications for disease management and breeding strategies. Cultivars with lower AUDPC values may be considered less virulent, while those with higher values may necessitate additional disease control measures. While the AUDPC values provide valuable insights, further statistical analyses and experiments may be required to validate these findings and delve deeper into the underlying factors contributing to distinct disease outcomes. From the disease severity index analysis, our studies find that \u003cem\u003eB. gladioli\u003c/em\u003e have more severe virulence than \u003cem\u003eB. glumae\u003c/em\u003e strain. We observe that \u003cem\u003eB. gladioli\u003c/em\u003e is more severe when isolated into rice using the glasshouse environment. In the controlled glasshouse setting, \u003cem\u003eB. glumae\u003c/em\u003e shows less disease severity. However, in our study, \u003cem\u003eB. glumae\u003c/em\u003e is more prevalent than \u003cem\u003eB. gladioli\u003c/em\u003e in the BPB disease outbreak. The results suggest that the interactions between these pathogens and their host plants and the environmental conditions may influence their virulency outcomes. This observation highlights the complexity of pathogen-host interactions and underscores the importance of studying these dynamics for effective disease management.\u003c/p\u003e \u003cp\u003eThe pathogenicity levels observed in this study align with prior findings on \u003cem\u003eBurkholderia\u003c/em\u003e spp. In the United States, \u003cem\u003eB. glumae\u003c/em\u003e strains caused up to 80% yield losses in susceptible rice cultivars, with disease severity increasing under high temperatures and humidity (Nandakumar et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Similarly, a study in Thailand reported \u003cem\u003eB. glumae\u003c/em\u003e strains with AUDPC values ranging from 500 to 1100, demonstrating variability in pathogenic potential among isolates (Nootjarin et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The higher virulence of \u003cem\u003eB. gladioli\u003c/em\u003e in the Malaysian study suggests that environmental conditions and host-pathogen interactions play a critical role in disease outcomes​.\u003c/p\u003e \u003cp\u003eFurther, the variation in AUDPC values across different \u003cem\u003eBurkholderia\u003c/em\u003e strains highlights the genetic and physiological differences influencing virulence. Studies have shown that \u003cem\u003eB. glumae\u003c/em\u003e pathogenicity is largely driven by toxoflavin production, while \u003cem\u003eB. gladioli\u003c/em\u003e relies on iron-chelating pyoverdine siderophores for infection (P\u0026eacute;rez-Mendoza et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)​. The ability of \u003cem\u003eB. glumae\u003c/em\u003e to dominate BPB outbreaks despite its lower severity in controlled tests suggests that toxoflavin-mediated host adaptation may give it a selective advantage in field conditions (Ramachandran et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)​\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eGenome Assembly\u003c/strong\u003e \u003cp\u003eWhole-genome sequencing of \u003cem\u003eB. glumae\u003c/em\u003e K6 and \u003cem\u003eB. gladioli\u003c/em\u003e UPMBG7 was performed using the Illumina NovaSeq 6000 platform, generating paired-end reads (2\u0026times;150 bp). The draft genome sequences of K6 had a total length of 6.57 Mbp genome sized with 210 contigs with an N50 value of 77,057 bp and 68.33% G\u0026thinsp;+\u0026thinsp;C contents. The average read depth of the genome is 205.0X. The completeness of the \u003cem\u003eB. glumae\u003c/em\u003e K6 genome assembly was assessed using BUSCO v5.8.3 using the Proteobacteria_odb10 dataset, which consists of 219 conserved orthologs. The analysis was conducted in prokaryotic genome mode with Prodigal as the gene predictor. The results indicate a highly complete genome, with 99.5% of the expected single-copy orthologs identified. Among these, all were present as single copies, with no duplicated or fragmented BUSCOs detected. Only one BUSCO gene (0.5%) was missing, suggesting minimal genome assembly gaps (Manni et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The genome contained 6,016 genes with 5,947 coding sequences, eight rRNA gene operons, and 57 tRNA genes. \u003cem\u003eB. gladioli\u003c/em\u003e UPMBG7 had a total length of 8.22 Mbp genome sized with 124 contigs with an N50 value of 207,968 bp and 67.99% G\u0026thinsp;+\u0026thinsp;C contents. The average read depth of the genome is 85.0X. UPMBG7 genome contained 7,237 genes with 7,164 coding sequences, 8 rRNA operons, and 60 tRNA genes (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The completeness of the \u003cem\u003eB. gladioli\u003c/em\u003e UPMBG7 genome assembly was evaluated and results indicate a 100.0% completeness score, with all 219 expected orthologs identified. Of these, 92.7% were found as single-copy genes, while 7.3% were duplicated. No fragmented or missing BUSCO genes were detected, suggesting that the genome is well-assembled without major gaps. The presence of a small proportion of duplicated genes may indicate either natural gene duplications or minor assembly redundancies (Manni et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).The genome assembly dataset showed that the GC content and genome size of \u003cem\u003eB. glumae\u003c/em\u003e strain K6 are similar to reference genomes and previously published \u003cem\u003eBurkholderia\u003c/em\u003e genomes. Our analysis reveals a close match between our \u003cem\u003eB. glumae\u003c/em\u003e strain and the reference sequences in terms of both genome size and GC content. According to the GenBank NCBI database, the genome size of \u003cem\u003eBurkholderia\u003c/em\u003e species ranges from 6 to 11 Mbp, with a GC content of 65%-68%. The assembly results showed that \u003cem\u003eB. gladioli\u003c/em\u003e strain has a larger genome size than \u003cem\u003eB. glumae\u003c/em\u003e. One of the reasons for this difference in genome size is that \u003cem\u003eB. gladioli\u003c/em\u003e have a higher number of genes and additional genetic material than \u003cem\u003eB. glumae.\u003c/em\u003e For instance, additional genetic material, such as transposable elements, can act as a source of new genes. Other than that, \u003cem\u003eB. gladioli\u003c/em\u003e and \u003cem\u003eB. glumae\u003c/em\u003e may have different genetic variations and mutations that could contribute to their different sizes. These genetic variations could result in different growth rates, metabolic pathways, and overall physiology (Kang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). \u003cem\u003eB. gladioli\u003c/em\u003e and \u003cem\u003eB. glumae\u003c/em\u003e are known to occupy different ecological niches. \u003cem\u003eB. gladioli\u003c/em\u003e is found in soil, rhizosphere, and plant tissues, whereas \u003cem\u003eB. glumae\u003c/em\u003e is found in plant tissues. \u003cem\u003eB. gladioli\u003c/em\u003e may have evolved to be more significant to better adapt to its environment (Pedraza et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, the genome size of a microorganism may not necessarily indicate its complexity, pathogenicity, or virulence. It can impact the organism's metabolic pathways, resistance mechanisms, and adaptation to different environments (Kang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenome features of \u003cem\u003eBurkholderia glumae\u003c/em\u003e K6 and \u003cem\u003eBurkholderia\u003c/em\u003e UPMBG7 strain.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBurkholderia glumae\u003c/em\u003e K6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eBurkholderia gladioli\u003c/em\u003e UPMBG7\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSequencing method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIllumina NovaSeq 6000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIllumina NovaSeq 6000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenome size (bp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,572,223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,225,388\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContigs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContigs N50 (bp)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80,214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e207,968\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG\u0026thinsp;+\u0026thinsp;C content (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredicted coding genes (CDs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,164\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRibosomal RNA number (5S, 16S, 23S)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,1,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,1,1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransfer RNA number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003e*\u003c/sup\u003eN50: 50% of all bases come from contigs longer than this value.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eGene Annotation and Functional Analysis\u003c/strong\u003e \u003cp\u003eSpecies identification was carried out using gene sequences through protein sequence analysis with BLAST v2.15.0. This process involved comparing the sequences against the NCBI NR bacteria database, containing 324,246,652 protein sequences. The criteria for the blast included an e-value of \u0026le;\u0026thinsp;1e-10, with the best match used to determine the species based on the organism information for the matching genes. The Non-Redundant Protein Database (NR) annotation of species and genes demonstrated that 4,988 genes (92.03%) annotated from the K6 strain exhibit similarity to \u003cem\u003eB. glumae\u003c/em\u003e species. Meanwhile, the NR gene annotated for the UPMBG7 strain showed 5,700 genes (84.36%) annotated from the UPMBG7 strain, which are comparable to those of the \u003cem\u003eB. gladioli\u003c/em\u003e species. Using the EggNOG v5.0 database, a widely used tool that groups genes into evolutionary families and assigns functional categories based on orthology. EggNOG provides broader functional predictions across multiple species, making it useful for studying bacterial gene evolution. Proteins encoded by genes were classified via COG annotation, dividing them into 24 functional categories. K6 Genomic features, as illustrated in Fig.\u0026nbsp;(9), revealed that 4,087 genes were classified into clusters of orthologous genes (COGs). The most abundant COGs category was \"Amino acid transport and metabolism\" (E; 436 genes), followed by \"General function prediction only\" (R; 423 genes), \"Transcription\" (K; 401 genes), \"Cell wall/membrane/envelope biogenesis\" (M; 326 genes), \"Carbohydrate transport and metabolism\" (G; 323 genes), \"Signal transduction mechanism\" (T; 296 genes), \"Energy production and conversion\" (C; 289 genes), and \"Inorganic ion transport and metabolism\" (P; 244 genes), identified as significant categories in Fig.\u0026nbsp;(9). UPMBG7 genomic features, as illustrated in Fig.\u0026nbsp;(10), revealed that 5,390 genes were classified into clusters of orthologous genes (COGs). The most abundant COG category was \"Amino acid transport and metabolism\" (E; 622 genes), followed by \"General function prediction only\" (R; 678 genes), \"Transcription\" (K; 701 genes), \"Cell wall/membrane/envelope biogenesis\" (M; 396 genes), \"Carbohydrate transport and metabolism\" (G; 481 genes), \"Signal transduction mechanisms\" (T; 331 genes), \"Energy production and conversion\" (C; 366 genes), and \"Inorganic ion transport and metabolism\" (P; 352 genes), identified as significant categories in Fig.\u0026nbsp;(10). The Clusters of Orthologous Groups (COG) annotation analysis provides valuable insights into the functional diversity and potential ecological roles of \u003cem\u003eB. glumae\u003c/em\u003e K6 and \u003cem\u003eB. gladioli\u003c/em\u003e UPMBG7 strains. Despite both strains belonging to the \u003cem\u003eBurkholderia\u003c/em\u003e genus, their COG profiles reveal distinct functionalities that reflect their adaptability to unique environmental niches and roles in various biological processes.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eComparative Analysis of Functional Categories\u003c/strong\u003e \u003cp\u003eBoth strains exhibit minimal representation in RNA processing and modification, with each possessing just one annotated gene. This suggests a shared reliance on fundamental mechanisms for RNA processing. Similar conservation is observed in chromatin structure and dynamics, where both strains have three annotated genes, indicating stable chromatin organization that is likely essential for epigenetic regulation and genome integrity. When examining energy production and conversion, UPMBG7 shows a more extensive capacity with 366 annotated genes than K6's 289. The difference may indicate that \u003cem\u003eB. gladioli\u003c/em\u003e UPMBG7 possesses enhanced energy metabolism strategies, potentially allowing it to thrive in more energetically diverse or demanding environments. Both strains exhibit 46 annotated genes in cell cycle control, cell division, and chromosome partitioning, suggesting conserved regulatory mechanisms for maintaining genomic stability during cell division.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eFor amino acid transport and metabolism, K6 features 436 annotated genes, while UPMBG7 has a higher count of 622. This indicates that both strains can utilize a wide range of amino acids, which is crucial for their adaptability in nutrient-variable environments. Nucleotide transport and metabolism genes are similarly conserved, with K6 possessing 104 genes and UPMBG7 123, underscoring both strains' capability to support essential nucleotide synthesis for DNA and RNA functions. Both strains show significant representation in carbohydrate transport and metabolism, with K6 harboring 323 annotated genes and UPMBG7 481. This suggests that while both can metabolize various carbohydrates, UPMBG7\u0026rsquo;s higher gene count may reflect an enhanced ability to exploit diverse carbon sources. UPMBG7 has 270 annotated genes in coenzyme transport and metabolism compared to K6\u0026rsquo;s 234, indicating functional efficiency in these crucial enzymatic reactions (Laura Ortega et al., 2021).\u003c/p\u003e \u003cp\u003eLipid transport and metabolism genes contribute to membrane stability and adaptation, with UPMBG7 containing 326 annotated genes compared to K6's 233. This suggests that \u003cem\u003eB. gladioli\u003c/em\u003e may have a more versatile lipid metabolism profile. The translation, ribosomal structure, and biogenesis categories are also well represented, with UPMBG7 featuring 276 annotated genes and K6 234, highlighting each strain\u0026rsquo;s capacity for protein synthesis and cellular growth. In terms of cell wall/membrane/envelope biogenesis, UPMBG7 possesses 396 genes, while K6 has 326, suggesting potential variations in cell envelope structure that may influence interactions with the environment or host organisms. Both strains exhibit similar numbers of genes related to cell motility (121 in UPMBG7 and 140 in K6), implying comparable mechanisms for bacterial movement. Secondary metabolites biosynthesis, transport, and catabolism genes, which may contribute to ecological or pathogenic interactions, are well-represented, with 233 in UPMBG7 and 141 in K6. This disparity may indicate differing ecological roles or potentials for secondary metabolite production between the strains.\u003c/p\u003e \u003cp\u003eThe category of general function prediction reveals multi-functionality, with UPMBG7 featuring 678 annotated genes compared to K6's 423, suggesting versatile roles in various cellular processes (Nootjarin et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Genes with unknown functions hint at uncharted territories, with UPMBG7 having 288 and K6 217, warranting further investigation into their potential roles and implications. Many genes classified under COG Group S (Function Unknown) remain unannotated due to the lack of experimentally validated homologs in bacterial databases. However, based on comparative genomics, domain analysis, and existing knowledge of bacterial physiology, potential functional roles can be speculated. This section explores six uncharacterized genes in \u003cem\u003eB. glumae\u003c/em\u003e K6 that may serve key biological functions. First, K6_GM000016 (\u003cem\u003ePelG\u003c/em\u003e) speculate to be involve in biofilm formation. \u003cem\u003ePelG\u003c/em\u003e is an essential component of the pellicle polysaccharide biosynthesis system, which contributes to biofilm formation in bacteria (Colvin et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, \u003cem\u003ePelG\u003c/em\u003e is required for producing extracellular matrix components, protecting the bacteria against host immune responses and antimicrobial agents (Jennings et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Given that \u003cem\u003eB. glumae\u003c/em\u003e forms biofilms in rice plants, K6_GM000016 may contribute to the pathogen's ability to colonize plant surfaces, leading to enhanced persistence and virulence. Disrupting this gene could provide insights into how biofilms facilitate bacterial infection in rice. Second, K6_GM000110 (\u003cem\u003eYajQ\u003c/em\u003e) speculate to be cyclic-di-GMP-binding protein and virulence regulation. Cyclic-di-GMP is a critical bacterial second messenger molecule, controlling processes like biofilm formation, motility, and virulence (R\u0026ouml;mling et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). YajQ-family proteins have been identified as cyclic-di-GMP-binding effectors, meaning that K6_GM000110 could be involved in regulating bacterial lifestyle transitions (e.g., switching from planktonic to biofilm state). In other pathogens such as \u003cem\u003eVibrio cholerae\u003c/em\u003e, cyclic-di-GMP-binding proteins act as molecular switches that alter virulence gene expression (Krasteva \u0026amp; Sondermann, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). If K6_GM000110 plays a similar role in \u003cem\u003eB. glumae\u003c/em\u003e, it may coordinate quorum sensing signals with biofilm formation, making it a potential target for anti-virulence strategies. Next, Gene ID K6_GM000795 (\u003cem\u003eYjbJ\u003c/em\u003e, UPF0339 family) speculate to be potential regulatory protein, the \u003cem\u003eYjbJ\u003c/em\u003e protein (UPF0339 family) is a conserved yet uncharacterized protein found in various Gram-negative bacteria, including \u003cem\u003eBurkholderia\u003c/em\u003e species. Though its function remains unknown, its conservation across diverse bacteria suggests a potential role in regulatory mechanisms. In \u003cem\u003eEscherichia coli\u003c/em\u003e, proteins from the UPF0339 family have been linked to stress responses and DNA-binding activity, indicating that K6_GM000795 could function as a transcriptional regulator in \u003cem\u003eB. glumae\u003c/em\u003e. This aligns with the hypothesis that certain uncharacterized regulators influence pathogenicity, quorum sensing, or environmental adaptability in bacterial pathogens (Anantharaman et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Price et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Other than that, Gene ID K6_GM000892 (\u003cem\u003eGlcG\u003c/em\u003e, DUF3365) speculate to be possible sugar transporter protein, the presence of DUF3365 (Domain of Unknown Function) in K6_GM000892 suggests a possible transporter role, as DUF3365-containing proteins have been linked to membrane-associated sugar transporters in other bacteria. Given that \u003cem\u003eB. glumae\u003c/em\u003e is an opportunistic plant pathogen, the ability to efficiently uptake sugars from host tissues could be an important survival mechanism. This protein may be involved in glucose, fructose, or other carbohydrate transport, contributing to bacterial metabolism and pathogenicity (Saier et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Alternatively, \u003cem\u003eGlcG\u003c/em\u003e might act as a sensor or regulator in a two-component system, detecting environmental sugar concentrations and triggering metabolic adjustments. Such mechanisms have been identified in related bacterial pathogens like \u003cem\u003ePseudomonas syringae\u003c/em\u003e and \u003cem\u003eXanthomonas oryzae\u003c/em\u003e (Ren et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Next, Gene ID K6_GM000934 (DUF2345 family) speculate to be no known homologs (requires further study), Unlike the other genes in this list, K6_GM000934 remains highly uncharacterized, with no direct functional homologs in KEGG or other major databases. However, its conserved domain DUF2345 suggests it may be involved in membrane-associated processes, possibly efflux pump regulation or signal transduction. Given its high sequence conservation in \u003cem\u003eBurkholderia\u003c/em\u003e species, this gene may encode a stress response protein involved in toxin resistance or heavy metal detoxification, processes commonly seen in environmentally adaptable pathogens (Galperin \u0026amp; Koonin, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Future experimental approaches such as transcriptomics (e.g., RNA-Seq under stress conditions) could help determine its regulatory role. Lastly, Gene ID K6_GM001096 (\u003cem\u003eYceD\u003c/em\u003e, DUF990 family) speculate to be potential metal-binding protein, K6_GM001096 belongs to the DUF990 family, which includes putative metal-binding proteins that participate in oxidative stress responses. In other bacteria, \u003cem\u003eYceD\u003c/em\u003e-like proteins have been associated with iron homeostasis and metal resistance, essential for bacterial survival in host environments (Wang et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Given that \u003cem\u003eB. glumae\u003c/em\u003e often encounters iron-limiting conditions in plant hosts, K6_GM001096 might be involved in siderophore-mediated iron acquisition or oxidative stress protection (Wang et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e5\u003c/span\u003e provides a summary of uncharacterized genes identified in \u003cem\u003eBurkholderia glumae\u003c/em\u003e K6 that fall under COG Group S, along with speculative functional assignments based on domain analysis, KEGG associations, and literature references.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Uncharacterized Genes in \u003cem\u003eBurkholderia glumae\u003c/em\u003e K6 with Speculative Functions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredicted Function\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePossible Biological Role\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK6_GM000016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePelG\u003c/em\u003e \u0026ndash; Polysaccharide biosynthesis protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInvolved in biofilm formation and bacterial adhesion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eColvin et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e); Jennings et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK6_GM000110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYajQ\u003c/em\u003e \u0026ndash; Cyclic-di-GMP-binding protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLikely regulates biofilm formation and virulence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u0026ouml;mling et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2013\u003c/span\u003e); Krasteva \u0026amp; Sondermann (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK6_GM000795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYjbJ, UPF0339 family \u0026ndash; Possible transcriptional regulator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMay regulate virulence, stress responses, or quorum sensing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnantharaman et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e); Price et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK6_GM000892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGlcG\u003c/em\u003e, DUF3365 \u0026ndash; Potential sugar transporter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMay facilitate carbohydrate uptake, enhancing bacterial metabolism and colonization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSaier et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2016\u003c/span\u003e); Ren et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK6_GM000934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDUF2345 family \u0026ndash; No known homologs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMay be involved in membrane-associated processes (e.g., signal transduction, efflux)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGalperin \u0026amp; Koonin, (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2004\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK6_GM001096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYceD\u003c/em\u003e, DUF990 family \u0026ndash; Potential metal-binding protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCould play a role in oxidative stress resistance and iron homeostasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWang et al. (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); Cornelis et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenes involved in the virulence factor of \u003cem\u003eBurkholderia\u003c/em\u003e spp.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSystem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eToxoflavin Production (\u003cem\u003eB. glumae\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTofI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eN-Acyl\u003c/em\u003e homoserine lactone (AHL) synthase for \u003cem\u003eN-hexanoyl\u003c/em\u003e homoserine lactone (C6-HSL) and C8-HSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKim et al., (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2004\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTofR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCognate receptor for C8-HSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKim et al., (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2004\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eToxR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLysR-type transcriptional activator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHam et al., (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eToxJ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLuxR C-terminal-related transcriptional regulator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHam et al., (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003etoxF, toxG, toxH, toxI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eToxoflavin transport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFrancis \u003cem\u003eet al.\u003c/em\u003e, (2013)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eToxA, ToxB, ToxC, ToxD, ToxE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eToxoflavion biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFrancis \u003cem\u003eet al.\u003c/em\u003e, (2013)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFlagella Formation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eQsmR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIc1R-type transcriptional regulator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKim et al., (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eFlhC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFlagellar assembly, biosynthesis, chemotaxis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKim et al., (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eFlhD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFlagellar assembly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKim et al., (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType III Secretion System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eHrpB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRegulatory factor for expression of T3SS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKang et al., (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLipase Formation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLipA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCell-wall degrading enzyme\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZhou-qi (2016)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLipB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBiosynthesis and activation of \u003cem\u003eLipA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZhou-qi (2016)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSiderophore Production (\u003cem\u003eB. gladioli\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePvdA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEncodes enzyme for pyoverdine synthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026eacute;rez-Mendoza et al., (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs for UPMBG7 strains also explores six uncharacterized genes in \u003cem\u003eB. gladioli\u003c/em\u003e that may serve key biological functions. First, Gene ID UPMBG7_GM000025 speculate to be \u003cem\u003eVgrG\u003c/em\u003e (Type VI secretion system protein) ,the \u003cem\u003eVgrG\u003c/em\u003e protein is a component of the Type VI Secretion System (T6SS), a molecular weapon used by bacteria to inject toxins into competing microbes. Studies have shown that \u003cem\u003eB. gladioli\u003c/em\u003e harbors T6SS genes, suggesting this protein plays a role in antagonistic interactions with other microbes and host manipulation in plant infections (Basler et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ho et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Second, Gene ID UPMBG7_GM000074 speculate to be putative outer membrane protein (K08995), this protein may function as an outer membrane receptor, involved in the uptake of nutrients such as iron or bacterial-host interactions. Similar proteins in \u003cem\u003ePseudomonas\u003c/em\u003e and \u003cem\u003eBurkholderia\u003c/em\u003e regulate antimicrobial resistance and immune evasion which may enhance bacterial survival in iron-limited environments (Stork et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Noinaj et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Next, Gene ID UPMBG7_GM000047 speculate as DUF1501 Family Protein (possible stress-response regulator), this proteins in the DUF1501 family are widely conserved in Gram-negative bacteria but remain uncharacterized. However, structural comparisons suggest a role in oxidative stress protection and antibiotic resistance mechanisms which could help \u003cem\u003eB. gladioli\u003c/em\u003e adapt to plant defense responses and antimicrobial stress (Galperin \u0026amp; Koonin, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Gene ID UPMBG7_GM000048 speculate as DUF1800 Family Protein (possible metal-binding protein), DUF1800 proteins are often associated with iron or zinc transport, crucial for bacterial metabolism and pathogenicity. May participate in iron acquisition, oxidative stress resistance, or virulence regulation (Andrews et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Cornelis et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Other than that, Gene ID UPMBG7_GM000052 speculate as uncharacterized membrane protein (COG5612), this protein belongs to a family of membrane-associated proteins potentially involved in cell wall biosynthesis, biofilm formation, or secretion systems in which could play a role in bacterial adhesion or transport of virulence factors (Henderson et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Lastly, Gene ID UPMBG7_GM000377 speculate to be Type VI secretion system-associated protein, this protein appears closely linked to T6SS genes, suggesting it functions in bacterial competition, host colonization, or toxin secretion and could be involved in delivering antibacterial effectors or interacting with plant hosts (Pukatzki et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Russell et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The presence of these previously unknown functional roles in \u003cem\u003eB. glumae\u003c/em\u003e K6 and \u003cem\u003eB. gladioli\u003c/em\u003e UPMBG7 suggests key adaptations for plant infection, stress survival, and interbacterial competition. While computational analysis provides strong functional predictions, experimental validation is needed to confirm these roles. Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents a summary of uncharacterized genes identified in \u003cem\u003eBurkholderia glumae\u003c/em\u003e K6 that belong to COG Group S. These genes were analyzed using comparative domain analysis and KEGG annotations, and supporting literature.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Uncharacterized Genes in \u003cem\u003eBurkholderia gladioli\u003c/em\u003e UPMBG7 with Speculative Functions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredicted Function\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePossible Biological Role\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUPMBG7_GM000025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eVgrG\u003c/em\u003e, Type VI secretion system protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMay facilitate toxin injection and interbacterial competition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBasler et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e); Ho et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUPMBG7_GM000047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDUF1501 family protein \u0026ndash; Possible stress-response regulator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMay function in oxidative stress adaptation and antibiotic resistance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGalperin \u0026amp; Koonin, (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2004\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUPMBG7_GM000048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDUF1800 family protein \u0026ndash; Possible metal-binding protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMay be involved in iron transport or virulence regulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAndrews et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2003\u003c/span\u003e); Cornelis et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUPMBG7_GM000052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUncharacterized membrane protein (COG5612)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePotentially involved in cell wall remodelling or secretion systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHenderson et al., (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2004\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUPMBG7_GM000074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eK08995, Putative membrane protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePotential outer membrane receptor involved in nutrient uptake or host interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStork et al. (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2007\u003c/span\u003e); Noinaj et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUPMBG7_GM000377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT6SS-associated unknown protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePossible role in bacterial competition and virulence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePukatzki et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2006\u003c/span\u003e); Russell et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMoreover, UPMBG7 has 331 genes in signal transduction mechanisms compared to K6's 296, indicating a robust capacity to sense and respond to environmental changes. Intracellular trafficking, secretion, and vesicular transport genes, essential for cellular communication, number 131 in UPMBG7 and 114 in K6. Defence mechanisms crucial for survival against external threats include 129 genes in UPMBG7 and 98 in K6, suggesting \u003cem\u003eB. gladioli\u003c/em\u003e may have enhanced protective strategies. Finally, genes related to extracellular structures are represented by 39 in UPMBG7 and 29 in K6, which could contribute to adherence and colonization strategies. Mobilome-related genes, which include prophages and transposons, number 62 in UPMBG7 and 75 in K6, potentially influencing genome plasticity and adaptation (Seo et., al 2015).\u003c/p\u003e \u003cp\u003eComparing these results with other phytopathogenic \u003cem\u003eBurkholderia\u003c/em\u003e species, such as \u003cem\u003eB. cepacia\u003c/em\u003e and \u003cem\u003eB. pseudomallei\u003c/em\u003e, reveals that while toxoflavin plays a key role in \u003cem\u003eB. glumae\u003c/em\u003e virulence, other members of the genus utilize diverse strategies, including protease secretion and antibiotic resistance genes, for host colonization (Gyaneshwar et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The findings from this study align with previous work showing that siderophore-mediated iron acquisition is a common strategy among pathogenic \u003cem\u003eBurkholderia\u003c/em\u003e species (Ma et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), but it is particularly critical in \u003cem\u003eB. gladioli\u003c/em\u003e, distinguishing it from \u003cem\u003eB. glumae\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe practical implications of these findings are significant. Identifying virulence factors such as \u003cem\u003etoxABCDE\u003c/em\u003e and \u003cem\u003epvdA\u003c/em\u003e provides valuable markers for developing molecular detection tools and resistant rice cultivars via marker-assisted selection (MAS). Additionally, understanding the differential pathogenicity of these species opens avenues for targeted biocontrol measures, such as quorum sensing inhibitors or siderophore-disrupting agents, which could mitigate BPB outbreaks in rice-producing regions (Mansfield et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEcological Significance of Genomic Differences\u003c/strong\u003e \u003cp\u003eThe genomic differences observed between \u003cem\u003eB. glumae\u003c/em\u003e and \u003cem\u003eB. gladioli\u003c/em\u003e have significant ecological implications. The larger genome size of \u003cem\u003eB. gladioli\u003c/em\u003e, coupled with the presence of additional genes related to stress tolerance, suggests that this strain may have enhanced environmental resilience compared to \u003cem\u003eB. glumae\u003c/em\u003e. This could explain why \u003cem\u003eB. gladioli\u003c/em\u003e exhibits more severe virulence when isolated into rice in a controlled environment. However, despite its severe virulence, \u003cem\u003eB. glumae\u003c/em\u003e appears more prevalent in field outbreaks, potentially due to its specific virulence factors, such as toxoflavin production, which facilitate infection in natural field conditions. These findings highlight the importance of understanding pathogen-host-environment interactions to develop effective disease management strategies.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eKEGG pathway analysis using a hypergeometric distribution with a q-value threshold of \u0026lt;\u0026thinsp;0.05 showed that \u003cem\u003eB. gladioli\u003c/em\u003e and \u003cem\u003eB. glumae\u003c/em\u003e possess genes required for flagellar assembly, which is essential for their motility and ability to colonize host plants (Fig.\u0026nbsp;11). A significant distinction between the two species lies in their quorum sensing and toxin production capabilities. \u003cem\u003eB. gladioli\u003c/em\u003e lacks the \u003cem\u003etoxI\u003c/em\u003e gene, essential for producing the virulence factor toxoflavin present in \u003cem\u003eB\u003c/em\u003e. \u003cem\u003eglumae\u003c/em\u003e (Lehman et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Consequently, while \u003cem\u003eB. gladioli\u003c/em\u003e can effectively move and colonize plants, it does not produce toxoflavin, limiting its ability to cause host tissue damage compared to \u003cem\u003eB. glumae\u003c/em\u003e. This implies that \u003cem\u003eB. gladioli\u003c/em\u003e relies on alternative pathogenic mechanisms for infection (Fig.\u0026nbsp;12). The flagellar assembly process in \u003cem\u003eBurkholderia\u003c/em\u003e species involves several genes and proteins that contribute to flagella synthesis. Early gene products, such as \u003cem\u003eFlhA, FlhB, FliO\u003c/em\u003e, \u003cem\u003eFliP\u003c/em\u003e, \u003cem\u003eFliQ\u003c/em\u003e, and \u003cem\u003eFliR\u003c/em\u003e, play crucial roles in initiating flagella assembly. Proteins such as \u003cem\u003eMotA\u003c/em\u003e and \u003cem\u003eMotB\u003c/em\u003e anchor the motor complex to the cell membrane, essential for bacterial motility. Genes that have not been experimentally verified are indicated by white boxes, meaning their role in the flagellar pathway is uncertain (Jang et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eQuorum sensing (QS) regulates gene expression in response to population density, affecting behaviors like biofilm formation and virulence factor production. In \u003cem\u003eB. glumae\u003c/em\u003e, QS is mediated by N-acyl homoserine lactones (AHLs), specifically C6-HSL and C8-HSL. These molecules trigger the expression \u003cem\u003eof toxJ\u003c/em\u003e and \u003cem\u003etoxR\u003c/em\u003e genes, which contribute to toxoflavin biosynthesis. In contrast, \u003cem\u003eB. gladioli\u003c/em\u003e lacks the \u003cem\u003etoxI\u003c/em\u003e gene, which means it does not produce toxoflavin but uses other virulence mechanisms. This includes producing pyoverdine, a high-affinity siderophore that helps the bacterium acquire iron from host tissues, enhancing its survival and pathogenicity (P\u0026eacute;rez-Mendoza et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnder the quorum sensing pathway, \u003cem\u003etoxI\u003c/em\u003e genes were indicated with a white box for \u003cem\u003eB. gladioli\u003c/em\u003e UPMBG7 strains, indicating that this gene was absent. \u003cem\u003eB. gladioli\u003c/em\u003e is generally not associated with the presence of the \u003cem\u003etoxI\u003c/em\u003e gene. The \u003cem\u003etoxI\u003c/em\u003e gene is often linked to certain strains of \u003cem\u003eB. glumae\u003c/em\u003e, which is known to produce the phytotoxin toxoflavin, contributing to its pathogenicity in plants, particularly rice. Despite the absence of \u003cem\u003etoxI\u003c/em\u003e genes, \u003cem\u003eB. gladioli\u003c/em\u003e is known to infect rice, which is typically associated with the production of the phytotoxin toxoflavin in \u003cem\u003eBurkholderia glumae\u003c/em\u003e. Instead of relying on toxin production, \u003cem\u003eB. gladioli\u003c/em\u003e employs alternative virulence factors and mechanisms to establish infection and cause disease. This includes colonizing plant tissues, promoting systemic infection, and utilizing plant resources for growth. Furthermore, \u003cem\u003eB. gladioli\u003c/em\u003e can produce other bioactive compounds and enzymes contributing to its pathogenicity in rice and other hosts ( K\u0026ouml;hl and Schmitzet, 2018; Pritchard et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The genes \u003cem\u003epvdA\u003c/em\u003e in \u003cem\u003eB. gladioli\u003c/em\u003e are essential in its pathogenicity, especially toward crops like rice. These genes are critical in synthesizing pyoverdine, a high-affinity iron-chelating compound known as a siderophore. Iron is an essential nutrient for many organisms, including plants, and is usually sequestered in forms that are challenging for pathogens to access, especially in the host\u0026rsquo;s iron-limited environment, where plants actively restrict iron availability as a defence strategy. The \u003cem\u003epvdA\u003c/em\u003e gene initiates pyoverdine biosynthesis, creating a molecule specifically designed to capture ferric iron (Fe\u0026sup3;⁺) with high affinity. This process allows \u003cem\u003eB. gladioli\u003c/em\u003e to acquire iron more effectively than other organisms in the host environment, hijacking this essential resource from the plant and giving the bacterium a survival advantage. By securing the iron, \u003cem\u003eB. gladioli\u003c/em\u003e can support cellular processes necessary for growth and infection, ultimately enabling the bacterium to overcome plant defenses and establish a successful infection in rice. By utilizing this strategy, \u003cem\u003eB. gladioli\u003c/em\u003e can effectively thrive in the hostile environments of its plant hosts, leading to significant agricultural impacts, particularly in crops like rice (P\u0026eacute;rez-Mendoza et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These pathways play a crucial role in the pathogenicity of \u003cem\u003eBurkholderia\u003c/em\u003e, particularly in causing bacterial panicle disease. The coordinated gene expression driven by quorum sensing regulates the production of toxoflavin, a virulence factor associated with bacterial pathogenicity. The toxoflavin biosynthesis and transport pathways, activated through the QS system, are implicated in the virulence mechanism, contributing to the bacteria's ability to cause bacterial panicle disease in the host plant.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eComparative Annotation and Functional Insights\u003c/strong\u003e \u003cp\u003eWe compared our \u003cem\u003eBurkholderia\u003c/em\u003e strain's annotation and genomic features with the reference genome and other published \u003cem\u003eBurkholderia\u003c/em\u003e genomes of the same species through the GenBank NCBI database. Despite the inherent limitations of second-generation sequencing, resulting in contigs and occasional gaps in the assembly, our annotation efforts were remarkably successful in capturing key genetic components. One noteworthy achievement was the precise annotation of virulence genes within our \u003cem\u003eBurkholderia\u003c/em\u003e strain. These genes play a pivotal role in the pathogenicity and virulence of the bacterium. Our annotation results revealed that the structures of these virulence genes closely resemble those found in the reference genome. This robust annotation confirms the presence of these virulence genes in our strain and highlights their integrity and potential functionality. This finding carries significant implications, suggesting that our strain possesses a repertoire of virulence factors that could contribute to its pathogenicity (Table\u0026nbsp;7).\u003c/p\u003e \u003c/p\u003e \u003cp\u003eIn addition, 13 genes associated with the virulence factors were selected, as identified in a previous study. Furthermore, 22 open reading frames (ORFs) within the genetic region were unveiled by annotating the genome sequence using NCBI PGAP. A comparative analysis of our virulence genes' nucleotide sequences with other genome data in NCBI revealed no significant differences. The predicted sequences of virulence genes exhibited a remarkable similarity, with a size and nucleotide matching over 98% when compared to those archived in the NCBI database. The presence of the QsmR transcriptional regulator, a LuxR-type quorum sensing regulator, further highlights the complexity of their infection strategies. This regulator modulates multiple virulence pathways, including quorum sensing, motility (\u003cem\u003eflhC, flhD\u003c/em\u003e), and secretion systems, underscoring the importance of bacterial communication in host infection​ (Chun et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Moule et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Vander Broek and Stevens, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The Type III secretion system (T3SS), which plays a fundamental role in host invasion and immune suppression. The \u003cem\u003eHrpB\u003c/em\u003e protein, a key component of T3SS, facilitates bacterial attachment and injection of effector proteins into rice cells, effectively disrupting host immune responses and enhancing bacterial colonization. However, while both pathogens share this core virulence mechanism, they diverge in their infection strategies, particularly in their reliance on toxin production and nutrient acquisition​ (Green and Mecsas, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lipscomb and Schell, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Mannaa et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This process enables \u003cem\u003eBurkholderia\u003c/em\u003e spp. to cause infections and diseases.\u003c/p\u003e \u003cp\u003eA major distinction between \u003cem\u003eB. glumae\u003c/em\u003e and \u003cem\u003eB. gladioli\u003c/em\u003e lies in their ability to manipulate host physiology through different virulence pathways. \u003cem\u003eB. glumae\u003c/em\u003e K6 relies on toxoflavin biosynthesis, regulated by the \u003cem\u003etoxABCDE\u003c/em\u003e gene cluster, which produces a phytotoxin capable of inducing oxidative stress, damaging chloroplast structures, and suppressing plant immune defenses. This toxin is tightly controlled by quorum sensing via the ToxJ-ToxR regulatory system, ensuring synchronized production during infection to maximize host tissue damage. In contrast, \u003cem\u003eB. gladioli\u003c/em\u003e lacks the \u003cem\u003etoxI\u003c/em\u003e gene required for toxoflavin biosynthesis and instead employs pyoverdine siderophores (\u003cem\u003epvdA\u003c/em\u003e) as an alternative virulence strategy. These siderophores enable the bacterium to acquire iron from host tissues, a crucial factor for bacterial survival in iron-limited environments. This adaptation may provide \u003cem\u003eB. gladioli\u003c/em\u003e with broader host adaptability compared to \u003cem\u003eB. glumae\u003c/em\u003e, which is highly specialized in toxoflavin-mediated virulence​. Beyond these core virulence traits, both pathogens encode lipopolysaccharide (LPS) biosynthesis genes (\u003cem\u003elipA, lipB\u003c/em\u003e), which contribute to bacterial adhesion, immune evasion, and outer membrane stability. LPS promotes bacterial attachment and invasion by binding to host cell receptors, facilitating infection. Additionally, LPS helps evade the host's immune system, contributing to \u003cem\u003eBurkholderia\u003c/em\u003e spp.'s ability to cause infections and diseases (Tribble and Lamont, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ham et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Bertani and Ruiz, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Vellasamy et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn our study, comparative genomic analysis was conducted using KEGG pathway annotation to categorize genes based on their functions. While descriptive data effectively highlighted the presence and absence of key virulence genes, we acknowledge the absence of inferential statistical tests (e.g., p-values, confidence intervals) to determine the significance of gene content variations between \u003cem\u003eB. glumae\u003c/em\u003e K6 and \u003cem\u003eB. gladioli\u003c/em\u003e UPMBG7. However, our findings are strongly supported by BLAST analysis against the NCBI GenBank database, which validated the absence of \u003cem\u003ePvdA\u003c/em\u003e in \u003cem\u003eB. glumae K6\u003c/em\u003e and \u003cem\u003eToxI\u003c/em\u003e in \u003cem\u003eB. gladioli UPMBG7\u003c/em\u003e (Tables\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e and \u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). The lack of significant BLAST hits (E-value\u0026thinsp;\u0026gt;\u0026thinsp;1.0) for these genes in their respective genomes confirms their absence, while genes detected in both strains showed high sequence identity (\u0026gt;\u0026thinsp;95%) with known homologs from \u003cem\u003eBurkholderia\u003c/em\u003e spp., further strengthening their functional relevance. Instead of using inferential statistics, our approach relies on sequence-based validation, which is widely accepted in comparative genomics for assessing gene gain/loss events. Unlike transcriptomic studies that involve statistical comparisons of gene expression, genome content variation is binary (presence/absence) and best evaluated through functional annotation and sequence similarity searches.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe genetic regions of \u003cem\u003eB. glumae\u003c/em\u003e K6 that encode important genes involved as virulence factors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStart\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePutative Protein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGene Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eContigs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAccession no.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGNAT family N-acetyltransferase/ AHL synthase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eTofI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJAMYCS010000087.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAHL receptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eTofR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJAMYCS010000087.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLysR family transcriptional regulator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eToxR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJAMYCS010000006.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLuxR C-terminal-related transcriptional regulator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eToxJ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJAMYCS010000006.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eclass I SAM-dependent methyltransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eToxA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJAMYCS010000006.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGTP cyclohydrolase II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eToxB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJAMYCS010000006.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWD40 repeat domain-containing protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eToxC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJAMYCS010000006.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFormylglycine-generating enzyme family protein/TRP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eToxD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJAMYCS010000006.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBifunctional diaminohydroxyphosphoribosylaminopyrimidine deaminase/5-amino-6-(5-phosphoribosylamino) uracilreductase \u003cem\u003eRibD\u003c/em\u003e/deaminase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eToxE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJAMYCS010000006.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDMT family transporter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eToxF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJAMYCS010000006.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEfflux RND transporter periplasmic adaptor subunit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eToxG\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJAMYCS010000006.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEfflux RND transporter permease subunit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eToxH\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJAMYCS010000006.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEfflux transporter outer membrane subunit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eToxI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJAMYCS010000006.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIclR family transcriptional regulator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eQsmR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJAMYCS010000010.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8203\u003c/p\u003e \u003cp\u003e5893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9357\u003c/p\u003e \u003cp\u003e7047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFlagellin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eFlhC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e57\u003c/p\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJAMYCS010000059.1\u003c/p\u003e \u003cp\u003eJAMYCS010000071.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFlagellar transcriptional regulator FlhD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eFlhD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJAMYCS010000041.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHelix-turn-helix transcription regulator of pathogenicity genes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eHrpB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJAMYCS010000065.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLipoyl synthase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eLipA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJAMYCS010000086.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLipoyl(octanoyl) transferase \u003cem\u003eLipB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eLipB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJAMYCS010000086.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe genetic regions of \u003cem\u003eB. gladioli\u003c/em\u003e UPMBG7 that encode important genes involved as virulence factors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStart\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePutative Protein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGene Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eContigs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAccession no.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGNAT family N-acetyltransferase/ AHL synthase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eTofI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJANIEE010000046.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAHL receptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eTofR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJANIEE010000046.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLysR family transcriptional regulator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eToxR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJANIEE010000056.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e113847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e114661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLuxR C-terminal-related transcriptional regulator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eToxJ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJANIEE010000002.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eclass I SAM-dependent methyltransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eToxA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJANIEE010000056.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGTP cyclohydrolase II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eToxB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJANIEE010000056.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWD40 repeat domain-containing protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eToxC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJANIEE010000056.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFormylglycine-generating enzyme family protein/TRP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eToxD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJANIEE010000056.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBifunctional diaminohydroxyphosphoribosylaminopyrimidine deaminase/5-amino-6-(5-phosphoribosylamino) uracilreductase \u003cem\u003eRibD\u003c/em\u003e/deaminase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eToxE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJANIEE010000056.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDMT family transporter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eToxF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJANIEE010000056.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEfflux RND transporter periplasmic adaptor subunit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eToxG\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJANIEE010000056.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEfflux RND transporter permease subunit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eToxH\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJANIEE010000056.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIclR family transcriptional regulator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eQsmR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJANIEE010000035.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e197016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e198170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFlagellin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eFlhC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJANIEE010000015.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e204467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e204786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFlagellar transcriptional regulator FlhD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eFlhD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJANIEE010000015.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e185840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e187239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHelix-turn-helix transcriptional regulator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eHrpB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJANIEE010000008.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLipoyl synthase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eLipA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJANIEE010000023.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLipoyl(octanoyl) transferase \u003cem\u003eLipB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eLipB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJANIEE010000023.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORF 19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNAD (P)/ FAD-dependent oxidoreductase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePvdA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJANIEE010000001.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe confirmed absence of toxoflavin genes (\u003cem\u003eToxI\u003c/em\u003e) in \u003cem\u003eB. gladioli UPMBG7\u003c/em\u003e and the presence of pyoverdine biosynthesis genes (\u003cem\u003ePvdA\u003c/em\u003e) in \u003cem\u003eB. gladioli UPMBG7\u003c/em\u003e but not \u003cem\u003eB. glumae K6\u003c/em\u003e suggest distinct pathogenic strategies between these strains. These findings, supported by KEGG annotation and BLAST validation, emphasize biologically significant differences in virulence mechanisms rather than statistical significance. Future studies incorporating a larger number of genomes could further validate these findings using inferential statistical approaches.\u003c/p\u003e \u003cp\u003eHowever, while the study effectively establishes these novel insights, it could further emphasize how these findings fill gaps in existing research. For instance, past studies on \u003cem\u003eB. glumae\u003c/em\u003e have largely focused on its quorum sensing mechanisms and toxoflavin-mediated virulence (Peng, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), but this research reveals additional genomic elements that contribute to its adaptability in different environments. Similarly, the ecological versatility of \u003cem\u003eB. gladioli\u003c/em\u003e which has been recognized as both a plant pathogen and an opportunistic human pathogen (P\u0026eacute;rez-Mendoza et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pritchard et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) suggests that its virulence mechanisms are broader than previously understood. This comparative approach helps delineate their distinct pathogenic strategies and provides a foundation for improved disease management.\u003c/p\u003e \u003cp\u003eA more in-depth comparison with findings from similar studies would further enhance the impact of this research. For example, Seo et al. (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) reported that \u003cem\u003eB. glumae\u003c/em\u003e genomes generally range from 6.5\u0026ndash;7.0 Mbp, aligning with the genomic size reported in this study. Meanwhile, studies on \u003cem\u003eB. gladioli\u003c/em\u003e have documented genome sizes between 8.0\u0026ndash;9.0 Mbp, with variability in virulence genes depending on the host (Ma et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). By integrating these findings, this study not only confirms existing knowledge but also expands it by detailing the specific genetic variations that drive pathogenicity differences between \u003cem\u003eB. glumae\u003c/em\u003e and \u003cem\u003eB. gladioli\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThese genomic insights have significant implications for bacterial panicle blight (BPB) management, providing potential targets for integrated disease control strategies. Given \u003cem\u003eB. glumae\u003c/em\u003e dependence on toxoflavin, quorum sensing inhibitors that disrupt toxoflavin biosynthesis could serve as a novel strategy to mitigate disease severity. Similarly, iron-chelating compounds may limit \u003cem\u003eB. gladioli\u003c/em\u003e infections by interfering with pyoverdine-mediated iron acquisition. The identification of virulence regulators such as QsmR and T3SS effectors suggests additional molecular targets that could be explored for RNA interference-based strategies or the development of small-molecule inhibitors aimed at blocking bacterial signaling pathways. Additionally, these findings can contribute to molecular breeding programs, particularly in the selection of rice cultivars resistant to bacterial attachment and immune suppression. Genome editing approaches, such as CRISPR-Cas9, could be explored to introduce resistant alleles that disrupt bacterial colonization. Moreover, the application of biocontrol approaches, such as utilizing beneficial microbes that produce quorum-quenching enzymes, presents an alternative method for controlling BPB in rice fields​.\u003c/p\u003e \u003cp\u003eWhile this study provides a comprehensive genomic perspective on \u003cem\u003eB. glumae\u003c/em\u003e and \u003cem\u003eB. gladioli\u003c/em\u003e virulence, several limitations must be acknowledged. One of the primary limitations is the absence of in planta validation of the identified virulence genes. Although comparative genomic analysis provides strong evidence of their involvement in pathogenicity, functional validation using mutant strains, gene knockout studies, and complementation assays would further substantiate these findings. Future research should focus on in planta expression studies, such as transcriptomic and proteomic analyses, to confirm the active expression of these virulence genes during rice infection​.\u003c/p\u003e \u003cp\u003eAnother limitation of this study is the restricted number of isolates used in the comparative analysis. The genomic diversity of \u003cem\u003eB. glumae\u003c/em\u003e and \u003cem\u003eB. gladioli\u003c/em\u003e across different rice-growing regions remains poorly understood, and expanding the study to include a broader collection of isolates would provide deeper insights into strain-specific adaptations. Additionally, further phylogenetic analysis incorporating a larger number of environmental and clinical isolates could enhance our understanding of the evolutionary pathways shaping these pathogens. Future studies should also consider functional annotation of uncharacterized virulence genes, as they may reveal novel pathogenicity factors critical to BPB development​.\u003c/p\u003e \u003cp\u003eThis study highlights the distinct but complementary pathogenic strategies of \u003cem\u003eB. glumae\u003c/em\u003e and \u003cem\u003eB. gladioli\u003c/em\u003e, offering new insights into their genomic adaptations and virulence mechanisms. By linking these genomic findings to practical applications, such as quorum sensing inhibition, molecular breeding, and biocontrol strategies, this research provides a foundation for the development of effective BPB management solutions. However, further experimental validation and expanded genomic comparisons are necessary to fully leverage these insights for long-term agricultural sustainability\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eGenome Sequence Accessibility\u003c/strong\u003e \u003cp\u003eThe complete genome sequences of \u003cem\u003eB. glumae\u003c/em\u003e K6 and \u003cem\u003eB. gladioli\u003c/em\u003e UPMBG7 are available in the NCBI GenBank for further research and reference. \u003cem\u003eB. glumae\u003c/em\u003e K6 is linked to assembly accession number JAMYCS000000000, associated with BioProject PRJNA842847, while \u003cem\u003eB. gladioli\u003c/em\u003e UPMBG7 has the assembly accession number JANIEE000000000, associated with BioProject PRJNA224116. These publicly accessible data provide opportunities for further comparative studies and insights into the genomic features that contribute to the pathogenicity of these \u003cem\u003eBurkholderia\u003c/em\u003e strains.\u003c/p\u003e \u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eComparative genomic analyses have revealed distinct pathogenic adaptations in \u003cem\u003eBurkholderia glumae\u003c/em\u003e strain K6 and \u003cem\u003eBurkholderia gladioli\u003c/em\u003e strain UPMBG7 compared to strains from other regions. The Malaysian \u003cem\u003eB. glumae K6\u003c/em\u003e harbors a complete \u003cem\u003etox\u003c/em\u003e operon (\u003cem\u003etoxABCDE\u003c/em\u003e, \u003cem\u003etoxJ\u003c/em\u003e), which is essential for toxoflavin biosynthesis, a key virulence factor that induces oxidative stress and necrosis in host tissues. This toxoflavin production is tightly regulated by a quorum-sensing system involving \u003cem\u003eTofI\u003c/em\u003e and \u003cem\u003eTofR\u003c/em\u003e, which control \u003cem\u003etox\u003c/em\u003e gene expression, and \u003cem\u003eToxR\u003c/em\u003e, a LysR-type transcriptional regulator that activates both toxoflavin biosynthesis (\u003cem\u003etoxABCDE\u003c/em\u003e) and transporter (\u003cem\u003etoxFGHI\u003c/em\u003e) genes. While \u003cem\u003eB. glumae\u003c/em\u003e strains from other regions share similar regulatory pathways, variations in toxoflavin-related genes suggest potential regional adaptations influencing pathogenicity and disease severity. In contrast, \u003cem\u003eB. gladioli UPMBG7\u003c/em\u003e lacks the \u003cem\u003etoxI\u003c/em\u003e gene, which regulates toxoflavin production, yet compensates by producing pyoverdine siderophores (\u003cem\u003epvdA\u003c/em\u003e), which enhance iron acquisition and bacterial survival. These siderophores play a crucial role in host colonization, particularly under iron-limited conditions. Unlike \u003cem\u003eB. glumae\u003c/em\u003e, which relies on toxoflavin-mediated cytotoxicity, \u003cem\u003eB. gladioli\u003c/em\u003e appears to adopt a resource competition strategy, utilizing pyoverdine to gain a survival advantage. These genomic distinctions highlight the adaptive evolution of \u003cem\u003eBurkholderia\u003c/em\u003e species across different regions, influencing disease dynamics and necessitating tailored management approaches for bacterial panicle blight in rice. The detailed genomic characterization of \u003cem\u003eB. glumae\u003c/em\u003e and \u003cem\u003eB. gladioli\u003c/em\u003e provides valuable information that could be used to breed rice varieties with enhanced resistance to these pathogens. Understanding the genetic basis of pathogenicity and virulence mechanisms can aid in the development of targeted biological control strategies, such as disrupting quorum sensing or inhibiting specific virulence factors. Since \u003cem\u003eB. glumae\u003c/em\u003e relies heavily on toxoflavin biosynthesis for virulence, one effective approach would be the application of toxoflavin-degrading bacteria, such as \u003cem\u003ePseudomonas putida\u003c/em\u003e, which naturally produces enzymes that break down toxoflavin, neutralizing its harmful effects. Alternatively, small-molecule quorum-sensing inhibitors (QSIs), such as furanone compounds, could be used to disrupt the \u003cem\u003eTofI-TofR\u003c/em\u003e signaling pathway, preventing toxoflavin production and reducing disease severity. For \u003cem\u003eB. gladioli\u003c/em\u003e, which depends on pyoverdine-mediated iron acquisition, an effective strategy could involve iron-chelating compounds like desferrioxamine, which binds free iron in the environment, making it less available to the pathogen. Additionally, genetically engineering rice plants to produce plant-derived siderophores could help outcompete \u003cem\u003eB. gladioli\u003c/em\u003e for iron, limiting its ability to colonize the host. By leveraging these genomic insights, targeted biological and chemical interventions can be designed to weaken the virulence of \u003cem\u003eBurkholderia\u003c/em\u003e species, offering a more sustainable approach to managing bacterial panicle blight in rice. The identification of unique virulence mechanisms in Malaysian isolates can also guide local agricultural practices, helping farmers to implement more effective disease control measures against these pathogens. Beyond pathogen-specific studies, metagenomic sequencing has emerged as a valuable tool for monitoring microbial communities in rice fields. Kim et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) used metagenomic sequencing to detect emerging pathogens, including \u003cem\u003eBurkholderia\u003c/em\u003e spp., demonstrating the potential of high-throughput sequencing for disease surveillance. The integration of metagenomics with whole-genome sequencing could provide real-time pathogen monitoring, improving BPB management strategies. These advancements, when considered alongside our genomic findings, reinforce the significance of genomic surveillance in rice agriculture and highlight the need for further functional genomic validation of virulence determinants.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003e \u003cb\u003eConflicts of Interest\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAcknowledgement:\u003c/h2\u003e \u003cp\u003eThis research was funded by the Long-Term Research Grant Scheme (LRGS/1/2019/UPM/2) under the Ministry of Higher Education Malaysia (MOHE), Program: Development of climate-ready rice for sustaining food security in Malaysia, Project: Sustainable short and medium to long-term strategies for managing Bacterial Panicle Blight (BPB) disease under climate-resilience rice production.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbbas, M. M., Malluhi, Q. M., \u0026amp; Balakrishnan, P. (2014). Assessment of de novo assemblers for draft genomes: a case study with fungal genomes. \u003cem\u003eBmc Genomics\u003c/em\u003e. 15 (Suppl 9).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnantharaman, V., Koonin, E. V., \u0026amp; Aravind, L. (2012). Regulatory potential, phyletic distribution, and evolution of ancient, intracellular small-molecule-binding domains. \u003cem\u003eJournal Of Molecular Biology\u003c/em\u003e, \u003cem\u003e415\u003c/em\u003e(5), 939\u0026ndash;971.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndrews, S. C., Robinson, A. K., \u0026amp; Rodr\u0026iacute;guez-Qui\u0026ntilde;ones, F. (2003). Bacterial iron homeostasis. \u003cem\u003eFems Microbiology Reviews\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(2\u0026ndash;3), 215\u0026ndash;237.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshburner, M., Ball, C., Blake, J., et al. (2000). Gene Ontology: tool for the unification of biology. \u003cem\u003eNature Genetics\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e, 25\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBangratz, M., Wonni, I., Kini, K., Sondo, M., Brugidou, C., B\u0026eacute;na, G., Gnacko, F., et al. (2020). Design of a new multiplex PCR assay for rice pathogenic bacteria detection and its application to infer disease incidence and detect co-infection in rice fields in Burkina Faso. \u003cem\u003ePlos One\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e, e0232115.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBasler, M., Ho, B. T., \u0026amp; Mekalanos, J. J. (2013). Type 6 secretion system mediates bacterial killing through cell-cell contact. \u003cem\u003eNature\u003c/em\u003e, \u003cem\u003e501\u003c/em\u003e(7468), 508\u0026ndash;512.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBertani, B., \u0026amp; Ruiz, N. (2018). Function and Biogenesis of Lipopolysaccharides. \u003cem\u003eEcoSal Plus\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e, 1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, R., Barphagha, I. K., \u0026amp; Ham, J. H. (2015). Identification of potential genetic components involved in the deviant quorum-sensing signaling pathways of \u003cem\u003eBurkholderia glumae\u003c/em\u003e through a functional genomics approach. \u003cem\u003eFrontiers Microbiol\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e, 1447.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChun, H., Choi, O., Goo, E., Kim, N., Kim, H., Kang, Y., Kim, J., Moon, J. S., \u0026amp; Hwang, I. (2009). The quorum sensing-dependent gene katG of \u003cem\u003eBurkholderia glumae\u003c/em\u003e is important for protection from visible light. \u003cem\u003eJournal of bacteriology\u003c/em\u003e, \u003cem\u003e191\u003c/em\u003e(13), 4152\u0026ndash;4157.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eColvin, K. M., Gordon, V. D., Murakami, K., Borlee, B. R., Wozniak, D. J., Wong, G. C., \u0026amp; Parsek, M. R. (2012). The Pel polysaccharide can serve a structural and protective role in biofilms of \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e. \u003cem\u003ePLoS Pathog.\u003c/em\u003e 8(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCornelis, P., Wei, Q., Andrews, S. C., \u0026amp; Vinckx, T. (2011). Iron homeostasis and management of oxidative stress response in bacteria. \u003cem\u003eFems Microbiology Reviews\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(2), 251\u0026ndash;274.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEid, J., Fehr, A., Gray, J., Luong, K., Lyle, J., Otto, G. A., \u0026amp; Turner, S. (2009). Real-time DNA sequencing from single polymerase molecules. \u003cem\u003eScience\u003c/em\u003e, \u003cem\u003e323\u003c/em\u003e(5910), 133\u0026ndash;138.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalperin, M. Y., \u0026amp; Koonin, E. V. (2004). Conserved hypothetical\u0026rsquo; proteins: Prioritization of targets for experimental study. \u003cem\u003eNucleic Acids Research\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(18), 5452\u0026ndash;5463.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalperin, M. Y., Makarova, K. S., Wolf, Y. I., \u0026amp; Koonin, E. V. (2015). Expanded microbial genome coverage and improved protein family annotation in the COG database. \u003cem\u003eNucleic Acids Research\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e, 261\u0026ndash;269.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreen, E. R., \u0026amp; Mecsas, J. (2016). Bacterial Secretion Systems: An Overview. \u003cem\u003eMicrobiol Spectr\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e, 1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGyaneshwar, P., Hirsch, A. M., \u0026amp; James, E. K. (2011). A role for \u003cem\u003eBurkholderia\u003c/em\u003e species in promoting plant growth and health. \u003cem\u003eAnnual Review Of Phytopathology\u003c/em\u003e, \u003cem\u003e49\u003c/em\u003e, 469\u0026ndash;491.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHam, J. H., Melanson, R. A., \u0026amp; Rush, M. C. (2010). \u003cem\u003eBurkholderia glumae\u003c/em\u003e: next major pathogen of rice? \u003cem\u003eMolecular Plant Pathology\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e, 329\u0026ndash;339.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHenderson, I. R., Navarro-Garcia, F., Desvaux, M., \u0026amp; Fernandez, R. C. (2004). Type V secretion system: Bacterial secretion without a chaperone. \u003cem\u003eMolecular Microbiology\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e(6), 1677\u0026ndash;1690.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHo, B. T., Dong, T. G., \u0026amp; Mekalanos, J. J. (2014). A view to a kill: The bacterial type VI secretion system. \u003cem\u003eCell Host \u0026amp; Microbe\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1), 9\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJang, M. S., Goo, E., An, J. H., Kim, J., \u0026amp; Hwang, I. (2014). Quorum sensing controls flagellar morphogenesis in \u003cem\u003eBurkholderia glumae\u003c/em\u003e. \u003cem\u003ePLOS ONE\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e, e84831.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJennings, L. K., Storek, K. M., Ledvina, H. E., Coulon, C., Marmont, L. S., Sadovskaya, I., \u0026amp; Howell, P. L. (2015). Pel is a cationic exopolysaccharide that cross-links extracellular DNA in the \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e biofilm matrix. \u003cem\u003eProc. Natl. Acad. Sci. U. S. A.\u003c/em\u003e 112(36):11353\u0026ndash;11358.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanehisa, M., Goto, S., Kawashima, S., Okuno, Y., \u0026amp; Hattori, M. (2004). The KEGG resource for deciphering the genome. \u003cem\u003eNucleic Acids Research\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e, 277\u0026ndash;280.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanehisa, M., Goto, S., Hattori, M., Aoki-Kinoshita, K. F., Itoh, M., Kawashima, S., Katayama, T., et al. (2006). From genomics to chemical genomics: new developments in KEGG. \u003cem\u003eNucleic Acids Research\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e, 354\u0026ndash;357.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang, Y., Kim, J., Kim, S., Kim, H., Lim, J. Y., Kim, M., Kwak, J., et al. (2008). Proteomic analysis of the proteins regulated by \u003cem\u003eHrpB\u003c/em\u003e from the plant pathogenic bacterium \u003cem\u003eBurkholderia glumae\u003c/em\u003e. \u003cem\u003ePROTEOMICS\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e, 106\u0026ndash;121.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang, M., Lim, J. Y., Kim, J., Hwang, I., \u0026amp; Goo, E. (2022). Influence of genomic structural variations and nutritional conditions on the emergence of quorum sensing-dependent gene regulation defects in \u003cem\u003eBurkholderia glumae\u003c/em\u003e. \u003cem\u003eFrontiers In Microbiology\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e, 950600.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoren, S., Walenz, B. P., Berlin, K., Miller, J. R., Bergman, N. H., \u0026amp; Phillippy, A. M. (2017). Canu: Scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. \u003cem\u003eGenome Research\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(5), 722\u0026ndash;736.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, J., Kim, J. G., Kang, Y., Jang, J. Y., Jog, G. J., Lim, J. Y., Kim, S., et al. (2004). Quorum sensing and the LysR-type transcriptional activator ToxR regulate toxoflavin biosynthesis and transport in \u003cem\u003eBurkholderia glumae\u003c/em\u003e. \u003cem\u003eMolecular Microbiology\u003c/em\u003e, \u003cem\u003e54\u003c/em\u003e, 921\u0026ndash;934.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, J., Kang, Y., Choi, O., Jeong, Y., Jeong, J. E., Lim, J. Y., Kim, M., et al. (2007). Regulation of polar flagellum genes is mediated by quorum sensing and \u003cem\u003eFlhDC\u003c/em\u003e in \u003cem\u003eBurkholderia glumae\u003c/em\u003e. \u003cem\u003eMolecular Microbiology\u003c/em\u003e, \u003cem\u003e64\u003c/em\u003e, 165\u0026ndash;179.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, H., Goo, E., Kang, Y., Kim, J., \u0026amp; Hwang, I. (2011). Regulation of Universal Stress Protein Genes by Quorum Sensing and \u003cem\u003eRpoS\u003c/em\u003e in \u003cem\u003eBurkholderia glumae\u003c/em\u003e. \u003cem\u003eJournal Of Bacteriology\u003c/em\u003e, \u003cem\u003e194\u003c/em\u003e, 982\u0026ndash;992.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, J., Lee, S. H., Park, H., \u0026amp; Choi, Y. (2022). Metagenomic analysis of microbial communities in rice fields: Implications for pathogen detection and disease management. \u003cem\u003eEnvironmental Microbiology\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1), 67\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK\u0026ouml;hl, J., \u0026amp; Schmitz, H. (2018). Siderophores in the pathogenesis of \u003cem\u003eBurkholderia\u003c/em\u003e species. \u003cem\u003eMicrobiology\u003c/em\u003e, \u003cem\u003e164\u003c/em\u003e(3), 341\u0026ndash;354.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrasteva, P. V., \u0026amp; Sondermann, H. (2017). Versatile modes of cellular regulation via cyclic dinucleotides. \u003cem\u003eNature Chemical Biology\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(4), 350\u0026ndash;359.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLalithya, K. A., Manjunatha, G., Raju, B., Kulkarni, M. S., \u0026amp; Lokesh, V. (2017). Plant growth regulators and signal molecules enhance resistance against bacterial blight disease of pomegranate. \u003cem\u003eJournal Of Phytopathology\u003c/em\u003e, \u003cem\u003e165\u003c/em\u003e, 727\u0026ndash;736.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaura, O., \u0026amp; Clemencia, M. R. (2021). Bacterial Panicle Blight and \u003cem\u003eBurkholderia glumae\u003c/em\u003e: From Pathogen Biology to Disease Control. \u003cem\u003ePhythopathol\u003c/em\u003e, \u003cem\u003e111\u003c/em\u003e, 772\u0026ndash;778.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, C., Mannaa, M., Kim, N., Kim, J., Choi, Y., Kim, S. H., Jung, B., et al. (2019). Stress Tolerance and Virulence-Related Roles of Lipopolysaccharide in \u003cem\u003eBurkholderia glumae\u003c/em\u003e. \u003cem\u003ePlant Pathol J\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e, 445\u0026ndash;458.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLehman, B. L., Long, J., Koskella, J., Brown, P. J. B., \u0026amp; Gonzalez, C. F. (2015). Characterization of the \u003cem\u003eBurkholderia glumae\u003c/em\u003e toxoflavin gene cluster and its role in rice pathogenicity. \u003cem\u003eMol Plant-Microbe Interact\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(10), 1166\u0026ndash;1174.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, W., Jaroszewski, L., \u0026amp; Godzik, A. (2002). Tolerating some redundancy significantly speeds up clustering of large protein databases. \u003cem\u003eBioinform\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e, 77\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLipscomb, L., \u0026amp; Schell, M. A. (2011). Elucidation of the regulon and cis-acting regulatory element of \u003cem\u003eHrpB\u003c/em\u003e, the AraC-type regulator of a plant pathogen-like type III secretion system in \u003cem\u003eBurkholderia pseudomallei\u003c/em\u003e. \u003cem\u003eJournal Of Bacteriology\u003c/em\u003e, \u003cem\u003e193\u003c/em\u003e, 1991\u0026ndash;2001.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa, L., Geng, Y., Pei, H., Zhang, X., Chen, W., \u0026amp; Li, G. (2016). Characterization of pyomelanin production in \u003cem\u003eBurkholderia\u003c/em\u003e spp. and its association with pathogenicity. \u003cem\u003eFEMS Microbiol. Lett.\u003c/em\u003e 363(9).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManni, M., Berkeley, M. R., Seppey, M., Sim\u0026atilde;o, F. A., \u0026amp; Zdobnov, E. M. (2021). BUSCO update: Novel and streamlined workflows along with broader and deeper phylogenetic coverage for scoring of genome assembly completeness. \u003cem\u003eMolecular Biology and Evolution\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(10), 4647\u0026ndash;4654.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMannaa, M., Park, I., \u0026amp; Seo, Y. S. (2018). Genomic Features and Insights into the Taxonomy, Virulence, and Benevolence of Plant-Associated \u003cem\u003eBurkholderia\u003c/em\u003e Species. \u003cem\u003eInternational Journal Of Molecular Sciences\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e, 121.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMansfield, J., et al. (2012). Top 10 plant pathogenic bacteria in molecular plant pathology. \u003cem\u003eMolecular Plant Pathology\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(6), 614\u0026ndash;629.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoule, M. G., Spink, N., Willcocks, S., Lim, J., Guerra-Assun\u0026ccedil;\u0026atilde;o, J. A., Cia, F., Champion, O. L., et al. (2015). Characterization of New Virulence Factors Involved in the Intracellular Growth and Survival of \u003cem\u003eBurkholderia pseudomallei\u003c/em\u003e. \u003cem\u003eInfection And Immunity\u003c/em\u003e, \u003cem\u003e84\u003c/em\u003e, 701\u0026ndash;710.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMulaw, T., Wamishe, Y., \u0026amp; Jia, Y. (2018). Characterization and in plant detection of bacteria that cause bacterial panicle blight of rice. \u003cem\u003eAm J Plant Sci\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e, 667.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoon, H., Park, H. J., Jeong, A., Han, S. W., \u0026amp; Park, C. J. (2017). Isolation and identification of \u003cem\u003eBurkholderia gladioli\u003c/em\u003e on \u003cem\u003eCymbidium\u003c/em\u003e orchids in Korea. \u003cem\u003eBiotechnology And Biotechnological Equipment\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e, 280\u0026ndash;288.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNandakumar, R., Shahjahan, A. K. M., Yuan, X. L., Dickstein, E. R., Groth, D. E., Clark, C. A., Cartwright, R. D., et al. (2009). \u003cem\u003eBurkholderia glumae\u003c/em\u003e and \u003cem\u003eB. gladioli\u003c/em\u003e Cause Bacterial Panicle Blight in Rice in the Southern United States. \u003cem\u003ePlant Disease\u003c/em\u003e, \u003cem\u003e93\u003c/em\u003e, 896\u0026ndash;905.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNoinaj, N., Guillier, M., Barnard, T. J., \u0026amp; Buchanan, S. K. (2010). TonB-dependent transporters: Regulation, structure, and function. \u003cem\u003eAnnual Review Of Microbiology\u003c/em\u003e, \u003cem\u003e64\u003c/em\u003e, 43\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNootjarin, J., Antonio, R. G. F., Jutatape, W., et al. (2022). Phylogenetic Characterization and Genome Sequence Analysisof \u003cem\u003eBurkholderia glumae\u003c/em\u003e Strains Isolated in Thailand as the Causal Agent of Rice Bacterial Panicle Blight. \u003cem\u003ePathol\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e, 676.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePasquali, F., do Valle, I., Palma, F., Remondini, D., Manfreda, G., Castellani, G., Hendriksen, R., et al. (2019). Application of different DNA extraction procedures, library preparation protocols and sequencing platforms: impact on sequencing results. \u003cem\u003eHeliyon\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e, e02745.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePedraza, L. A., Bautista, J., \u0026amp; Uribe-V\u0026eacute;lez, D. (2018). Seed-born \u003cem\u003eBurkholderia glumae\u003c/em\u003e Infects Rice Seedling and Maintains Bacterial Population during Vegetative and Reproductive Growth Stage. \u003cem\u003ePlant Pathol J\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e, 393\u0026ndash;402.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng, H. (2015). Quorum sensing and virulence regulation in \u003cem\u003eBurkholderia\u003c/em\u003e species. \u003cem\u003eJ Plant Pathol\u003c/em\u003e, \u003cem\u003e97\u003c/em\u003e(2), 345\u0026ndash;357.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP\u0026eacute;rez-Mendoza, D., Coulthurst, S. J., Humphris, S., Campbell, E., Welch, M., \u0026amp; Salmond, G. P. C. (2014). Siderophore production and pathogenicity in \u003cem\u003eBurkholderia\u003c/em\u003e species. \u003cem\u003eApplied And Environment Microbiology\u003c/em\u003e, \u003cem\u003e80\u003c/em\u003e(1), 261\u0026ndash;271.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrice, M. N., Wetmore, K. M., Waters, R. J., Callaghan, M., Ray, J., Liu, H., \u0026amp; Arkin, A. P. (2018). Mutant phenotypes for thousands of bacterial genes of unknown function. \u003cem\u003eNature\u003c/em\u003e, \u003cem\u003e557\u003c/em\u003e(7706), 503\u0026ndash;509.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePritchard, L., Al-Mahmudi, A., Ali, M., \u0026amp; Saddler, G. S. (2020). \u003cem\u003eBurkholderia gladioli\u003c/em\u003e: An emerging plant pathogen. \u003cem\u003ePlant Disease\u003c/em\u003e, \u003cem\u003e104\u003c/em\u003e(8), 2113\u0026ndash;2124.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePukatzki, S., Ma, A. T., Sturtevant, D., Krastins, B., Sarracino, D., Nelson, W. C., \u0026amp; Mekalanos, J. J. (2006). Identification of a conserved bacterial protein secretion system in \u003cem\u003eVibrio cholerae\u003c/em\u003e. \u003cem\u003eProc. Natl. Acad. Sci. U. S. A.\u003c/em\u003e 103(5):1528\u0026ndash;1533.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamachandran, K., Vijaya, S. I., Ahmad, F. N., Amzah, B., \u0026amp; Zakaria, L. (2021). Characterization and identification of \u003cem\u003eBurkholderia glumae\u003c/em\u003e as the causal pathogen of bacterial panicle blight of rice (\u003cem\u003eOryza sativa\u003c/em\u003e L.) in Malaysian rice granaries. \u003cem\u003eJournal Of General Plant Pathology\u003c/em\u003e, \u003cem\u003e87\u003c/em\u003e, 164\u0026ndash;169.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRen, Q., Chen, K., \u0026amp; Paulsen, I. T. (2019). TransportDB: A comprehensive database resource for cytoplasmic membrane transport systems and outer membrane channels. \u003cem\u003eNucleic Acids Research\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e, D274\u0026ndash;D278.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRhoads, A., \u0026amp; Au, K. F. (2015). PacBio sequencing and its applications. \u003cem\u003eGenomics Proteomics \u0026amp; Bioinformatics\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(5), 278\u0026ndash;289.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR\u0026ouml;mling, U., Galperin, M. Y., \u0026amp; Gomelsky, M. (2013). Cyclic di-GMP: The first 25 years of a universal bacterial second messenger. \u003cem\u003eMicrobiology And Molecular Biology Reviews\u003c/em\u003e, \u003cem\u003e77\u003c/em\u003e(1), 1\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRussell, A. B., Peterson, S. B., \u0026amp; Mougous, J. D. (2014). Type VI secretion system: Delivering bacterial enzymes into target cells. \u003cem\u003eCell\u003c/em\u003e, \u003cem\u003e158\u003c/em\u003e(4), 792\u0026ndash;802.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaier, M. H., Reddy, V. S., Tsu, B. V., Ahmed, M. S., Li, C., \u0026amp; Moreno-Hagelsieb, G. (2016). The transporter classification database (TCDB): Recent advances. \u003cem\u003eNucleic Acids Research\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(D1), D372\u0026ndash;D379.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSayler, R. J., Cartwright, R. D., \u0026amp; Yang, Y. (2006). Genetic Characterization and Real-Time PCR Detection of \u003cem\u003eBurkholderia glumae\u003c/em\u003e, a Newly Emerging Bacterial Pathogen of Rice in the United States. \u003cem\u003ePlant Disease\u003c/em\u003e, \u003cem\u003e90\u003c/em\u003e, 603\u0026ndash;610.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShanner, G., \u0026amp; Finney, R. E. (1977). The effect of nitrogen fertilization on the expression of slow-mildewing resistance in Knox wheat. \u003cem\u003ePhytopathol\u003c/em\u003e, \u003cem\u003e67\u003c/em\u003e, 1051\u0026ndash;1056.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeo, Y. S., Lim, J. Y., Park, J., Kim, S., Lee, H. H., Cheong, H., et al. (2015). Comparative genome analysis of rice-pathogenic Burkholderia provides insight into capacity to adapt to different environments and hosts. \u003cem\u003eBmc Genomics\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e, 349.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStork, M., Otto, B. R., \u0026amp; Nolte, O. (2007). Iron transport in \u003cem\u003eBurkholderia\u003c/em\u003e species: Insights from \u003cem\u003eBurkholderia pseudomallei\u003c/em\u003e. \u003cem\u003eBmc Genomics\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(1), 17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTribble, G. D., \u0026amp; Lamont, R. J. (2010). Bacterial invasion of epithelial cells and spreading in periodontal tissue. \u003cem\u003ePeriodontology 2000\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e, 68\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsukuda, S., Matsuyama, T., Katoh, H., \u0026amp; Ozeki, Y. (2018). The toxoflavin biosynthetic pathway in \u003cem\u003eBurkholderia glumae\u003c/em\u003e. \u003cem\u003eApplied Microbiology And Biotechnology\u003c/em\u003e, \u003cem\u003e102\u003c/em\u003e(14), 5885\u0026ndash;5897.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Dijk, E. L., Jaszczyszyn, Y., Naquin, D., \u0026amp; Thermes, C. (2018). The third revolution in sequencing technology. \u003cem\u003eTrends In Genetics\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(9), 666\u0026ndash;681.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVander Broek, C. W., \u0026amp; Stevens, J. M. (2017). Type III Secretion in the Melioidosis Pathogen \u003cem\u003eBurkholderia pseudomallei\u003c/em\u003e. \u003cem\u003eFrontiers In Cellular And Infection Microbiology\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e, 255.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVellasamy, K. M., Mariappan, V., Shankar, E. M., \u0026amp; Vadivelu, J. (2016). \u003cem\u003eBurkholderia pseudomallei\u003c/em\u003e Differentially Regulates Host Innate Immune Response Genes for Intracellular Survival in Lung Epithelial Cells. \u003cem\u003ePlos Neglected Tropical Diseases\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e, e0004730.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, Y., Huang, H., Sun, M., Zhang, Q., Guo, D., Zhao, Y., \u0026amp; Zhang, L. (2018). Metalloprotein-like proteins in bacteria. \u003cem\u003eBiometals\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(5), 589\u0026ndash;606.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, J., Ye, Q., Chen, L., Zhao, H., \u0026amp; Li, Y. (2021). The role of iron uptake systems in the virulence of \u003cem\u003eBurkholderia gladioli\u003c/em\u003e. \u003cem\u003eFrontiers In Microbiology\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e, 689007.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, S., Nie, W., Yiming, A., Wang, P., Wu, Y., Huang, J., Ahmad, I., Chen, G., Guo, L., \u0026amp; Zhu, B. (2022). Next Generation Sequencing and Comparative Genomic Analysis Reveal Extreme Plasticity of Two \u003cem\u003eBurkholderia glumae\u003c/em\u003e Strains HN1 and HN2. \u003cem\u003ePathogens\u003c/em\u003e. 11:1265.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou-qi, C., Bo, Z., Guan-lin, X., Bin, L., \u0026amp; Shi-wen, H. (2016). Research Status and Prospect of \u003cem\u003eBurkholderia glumae\u003c/em\u003e, the Pathogen Causing Bacterial Panicle Blight. \u003cem\u003eRice Science\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e, 111\u0026ndash;118.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-plant-pathology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejpp","sideBox":"Learn more about [European Journal of Plant Pathology](http://link.springer.com/journal/10658)","snPcode":"10658","submissionUrl":"https://www.editorialmanager.com/ejpp/default2.aspx","title":"European Journal of Plant Pathology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Bacterial panicle blight disease, Burkholderia glumae, Burkholderia gladioli, whole-genome sequencing, rice, Illumina NovaSeq 6000","lastPublishedDoi":"10.21203/rs.3.rs-5698089/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5698089/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBacterial panicle blight (BPB), caused by \u003cem\u003eBurkholderia glumae\u003c/em\u003e and \u003cem\u003eBurkholderia gladioli\u003c/em\u003e, poses a significant threaten to rice production in Malaysia, with yield losses reaching up to 75% in severely infected fields. In June 2021, (BPB) symptoms were observed in rice fields in Kedah, Malaysia. Phenotypic characterization revealed typical Burkholderia traits, and pathogenicity tests confirmed symptoms development within seven days after inoculating 75-day-old rice seedlings. Molecular identification using (16S rRNA and \u003cem\u003egyrB\u003c/em\u003e) sequencing confirmed the isolates as \u003cem\u003eB. glumae\u003c/em\u003e and \u003cem\u003eB. gladioli\u003c/em\u003e. Whole-genome sequencing of \u003cem\u003eB. glumae\u003c/em\u003e K6 and \u003cem\u003eB. gladioli\u003c/em\u003e UPMBG7 was performed using the Illumina NovaSeq 6000 platform to investigate their genetic profiles. The assembled draft genome of \u003cem\u003eB. glumae\u003c/em\u003e K6 contained 210 contigs, with a total genome size of 6.57 Mbp, 68.33% G\u0026thinsp;+\u0026thinsp;C content and 5,641 coding sequences (CDS). It harbored toxoflavin biosynthesis genes (\u003cem\u003etoxABCDE, toxJ\u003c/em\u003e) and a Type III secretion system (T3SS), contributing to its pathogenicity. \u003cem\u003eB. gladioli\u003c/em\u003e UPMBG7 contained 124 contigs, with a total genome size of 8.22 Mbp, G\u0026thinsp;+\u0026thinsp;C content of 67.99 and 7,022 coding proteins. Unlike \u003cem\u003eB. glumae\u003c/em\u003e, \u003cem\u003eB. gladioli\u003c/em\u003e lacked the \u003cem\u003etoxI\u003c/em\u003e gene for toxoflavin production but compensated with pyoverdine siderophore genes (\u003cem\u003epvdA\u003c/em\u003e), which facilitate iron acquisition. These genomic insights unravel the virulence mechanisms of \u003cem\u003eB. glumae\u003c/em\u003e and \u003cem\u003eB. gladioli\u003c/em\u003e, laying the foundation for innovative disease management strategies. By identifying key pathogenic determinants, this study advances efforts in breeding resistant rice varieties, developing precision biological controls, and implementing cutting-edge molecular diagnostics for early pathogen detection, ultimately strengthening rice production against BPB devastation.\u003c/p\u003e","manuscriptTitle":"Genomic Characterization of Burkholderia glumae K6 and B. gladioli UPMBG7: Causal Agents of Bacterial Panicle Blight in Malaysia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-24 18:53:25","doi":"10.21203/rs.3.rs-5698089/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-04-23T08:01:52+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-23T07:40:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"European Journal of Plant Pathology","date":"2025-04-22T20:33:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Plant Pathology","date":"2025-04-10T02:44:05+00:00","index":"","fulltext":""},{"type":"decision","content":"Minor revisions","date":"2025-02-13T03:39:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-plant-pathology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejpp","sideBox":"Learn more about [European Journal of Plant Pathology](http://link.springer.com/journal/10658)","snPcode":"10658","submissionUrl":"https://www.editorialmanager.com/ejpp/default2.aspx","title":"European Journal of Plant Pathology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d9f78ddc-11b3-490b-881b-2c8e5ff65c99","owner":[],"postedDate":"April 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-06T16:02:39+00:00","versionOfRecord":{"articleIdentity":"rs-5698089","link":"https://doi.org/10.1007/s10658-025-03141-x","journal":{"identity":"european-journal-of-plant-pathology","isVorOnly":false,"title":"European Journal of Plant Pathology"},"publishedOn":"2025-10-02 15:57:41","publishedOnDateReadable":"October 2nd, 2025"},"versionCreatedAt":"2025-04-24 18:53:25","video":"","vorDoi":"10.1007/s10658-025-03141-x","vorDoiUrl":"https://doi.org/10.1007/s10658-025-03141-x","workflowStages":[]},"version":"v1","identity":"rs-5698089","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5698089","identity":"rs-5698089","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}