Comparative Analysis of Quorum Sensing-Dependent Transcriptomic Responses in Phytopathogenic <i>Burkholderia</i> spp.

Minhee Kang; Hyejung Jung; Duyoung Lee; HaJin Seo; Jungwook Park; Young-Su Seo

doi:10.5423/PPJ.FT.03.2026.0038

Plant Pathol J > Volume 42(3); 2026 > Article

Kang, Jung, Lee, Seo, Park, and Seo: Comparative Analysis of Quorum Sensing-Dependent Transcriptomic Responses in Phytopathogenic Burkholderia spp.

Research Article

The Plant Pathology Journal 2026;42(3):322-335.

Published online: April 15, 2026

DOI: https://doi.org/10.5423/PPJ.FT.03.2026.0038

Comparative Analysis of Quorum Sensing-Dependent Transcriptomic Responses in Phytopathogenic Burkholderia spp.

Minhee Kang^1,^2,^†, Hyejung Jung^1,^3,^†, Duyoung Lee¹, HaJin Seo¹, Jungwook Park³, Young-Su Seo^1,^2,^*

¹Department of Integrated Biological Science, Pusan National University, Busan 46241, Korea

²Institute of Systems Biology, Pusan National University, Busan 46241, Korea

³Biotechnology Research Division, National Institute of Fisheries Science, Busan 46083, Korea

^*Corresponding author: Phone) +82-51-510-2267, FAX) +82-51-514-1778, E-mail: yseo2011@pusan.ac.kr

^† These authors contributed equally to this work.

Handling Editor : Hokyoung Son

(Received on March 25, 2026; Revised on April 7, 2026; Accepted on April 7, 2026)

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Rice is a crucial global crop of nutritional and economic importance. Burkholderia glumae, Burkholderia gladioli, and Burkholderia plantarii are the major phytopathogens that cause diseases in rice within the genus Burkholderia. These phytopathogenic Burkholderia spp. have an acylated homoserine lactone (AHL)-based quorum sensing (QS) system that regulates gene expression depending on the cell density. Differentially expressed genes (DEGs) between B. plantarii KACC 18965 wild-type and plaI deletion mutant were analyzed to understand the plaI-mediated QS system in B. plantarii, and transcriptome profiles were compared across three phytopathogenic Burkholderia spp. (B. glumae, B. gladioli, and B. plantarii) to investigate the impact of QS on their pathogenicity. Clusters of Orthologous Groups category and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses confirmed that diverse metabolic processes were regulated by QS in the three phytopathogenic Burkholderia spp. Additionally, characteristic differences in the regulation of gene expression across the three phytopathogenic Burkholderia spp. were observed in DEGs belonging to the bacterial secretion system (ko03070) pathway, using KEGG pathway analysis. The characterization and comparison of transcriptome profiles between phytopathogenic Burkholderia spp. in this study offers deeper insights into AHL-mediated QS systems in phytopathogenic Burkholderia spp. These results provide a foundation for future studies on the QS-based biology and pathogenicity of phytopathogenic Burkholderia spp.

Keywords: Burkholderia, quorum sensing, rice, transcriptome

Rice is a staple crop that accounts for one-fifth of the global energy supply and is particularly important in Asia, not only for nourishment but also for economic and social reasons. However, despite recent increases in demand owing to population growth, rice production has reached a plateau (Bin Rahman and Zhang, 2023; Sabri et al., 2023). Rice diseases account for more than a quarter of the annual rice yield losses. In addition to reducing yields, they also lead to the deterioration of grain quality and substantial economic losses (Gill et al., 2025). Therefore, the effective management and suppression of rice diseases are key strategies for enhancing rice yield potential.

The genus Burkholderia contains diverse types of bacteria, including pathogens, symbionts, and plant-beneficial bacteria. Among them, B. glumae, B. gladioli, and B. plantarii are considered the major phytopathogens that cause diseases in rice (Mannaa et al., 2018). Both B. glumae and B. gladioli cause bacterial panicle blight and seedling rot in rice and exist together in the field (Ham et al., 2011; Ura et al., 2006). Rice panicle blight poses a significant threat to global rice production, and the damage to rice production due to bacterial panicle blight has recently been increasing (Azzahra et al., 2024). B. plantarii, which causes seedling blight in rice, was first identified in 1985 (Azegami et al., 1985). Diverse virulence factors, including tropolone, lead to symptoms such as seedling wilt, root growth suppression, and chlorosis (Azegami et al., 1987).

Bacteria detect the cell density in their surrounding environment and cooperatively change the transcriptional regulation of various genes through a quorum sensing (QS) mechanism (Schuster et al., 2013). The QS signaling system mediated by acylated homoserine lactones (AHLs) such as 3-oxohexanoyl-homoserine lactones (3-oxo-C6-HSLs), C4-homoserine lactones (C4-HSLs), C8-homoserine lactones (C8-HSLs) is the most familiar and actively studied system in gram-negative bacteria (Acet et al., 2021). In these systems, cell density recognition and transcriptional gene regulation occur via the binding of signaling molecules produced by LuxI-type synthases to LuxR-type receptors (Whiteley et al., 2017). In B. glumae, the sensing of TofI-generated QS signal molecules, C8-HSLs, by TofR regulates the expression of various genes via the QS system (Kim et al., 2004), and B. gladioli has been reported to harbor an AHL-based quorum sensing system, similar to B. glumae through previous studies (Elshafie et al., 2019; Kim et al., 2014; Takita et al., 2025). B. plantarii QS system that was first determined as a homolog of the luxI family, plaI, regulates QS via C8-HSLs (Solis et al., 2006).

The transcriptome, which is a complete set of cellular RNAs, provides in-depth insights into a variety of biological cell functions. Remarkable advances in next-generation sequencing technologies have enabled the generation and efficient utilization of large-scale sequencing data at low cost, and RNA sequencing (RNA-seq) techniques have correspondingly improved, allowing for more comprehensive transcriptome profiling (Satam et al., 2023; Tzec-Interián et al., 2025). RNA-seq offers advantages over hybridization-based methods by eliminating the need for predesigned probe sequences, thus enabling broader organism coverage with enhanced accuracy and dynamic ranges for gene expression measurements (Croucher and Thomson, 2010). In recent years, transcriptomic approaches are widely applied to understand molecular mechanisms of phytopathogens and to study interactions between pathogens and their host plants (Park et al., 2025; Shim et al., 2024).

Research on B. glumae and B. gladioli has been actively conducted, whereas research on B. plantarii is relatively less advanced. Despite the widespread observations of rice damage caused by B. plantarii, research on this pathogen has not progressed significantly. In this study, we analyze the transcriptome of B. plantarii KACC 18965 wild-type and the QS-deficient mutant, ΔplaI, and compare the similarities and differences in gene regulation between them to those in B. glumae and B. gladioli. This study aimed to elucidate QS-dependent gene expression in phytopathogenic Burkholderia spp. by analyzing the QS-dependent transcriptome of B. plantarii and comparing it with transcriptomes from other pathogenic rice bacteria, namely B. glumae and B. gladioli. This study enhances our understanding of QS-dependent gene regulation in Burkholderia spp. and leads to a better understanding of the pathogenicity of phytopathogenic Burkholderia spp.

Materials and Methods

Bacterial strains

The bacterial strains used in this study are listed in Table 1. B. glumae strains used in this study were the wild-type strain BGR1 (Jeong et al., 2003) and its derivative of BGR1 tofI::Ω, BGS2 (Kim et al., 2004). B. gladioli strains were the wild-type strain BSR3 (Seo et al., 2011) and its derivatives BSR3 tofI::lacZ, COK94 (Kim et al., 2014). B. plantarii strains were B. plantarii KACC 18965 wild-type and plaI deletion (ΔplaI) mutant (Kang et al., 2024).

RNA-seq data

RNA-seq data were downloaded for BGR1, BGS2, BSR3, and COK94 (GSE36485 and GSE60490) previously deposited in NCBI’s Gene Expression Omnibus (GEO). For all strains, RNA-seq count data at the 10-hour mark in the exponential growth phase were used to compare the gene expression profiles following QS activation. RNA sequencing of BGR1 and BGS2 was performed on Illumina Genome Analyzer II platform and RNA sequencing of BSR3 and COK94 was performed on an Illumina HiSeq2000 platform.

RNA extraction and sequencing

B. plantarii strains were cultured in Luria-Bertani (LB) broth (Duchefa Biochemie BV, Haarlem, Netherlands) for 10 h at 28°C to match the growth stage with other strains for extracting RNA (Supplementary Fig. 1). For stabilization, an equal amount of RNAprotect Bacteria Reagent (Qiagen, Valencia, CA, USA) was added to 4 mL of the bacterial culture, incubated at room temperature for 5 min, and centrifuged at 5,000 g for 10 min. The harvested pellets were dissolved in TE buffer (pH 8.0; Invitrogen, Carlsbad, CA, USA) and total RNA was extracted using a RNeasy Mini Kit (Qiagen), following the manufacturer’s protocol. Library preparation and RNA sequencing were performed by Macrogen (Seoul, Korea). Libraries were constructed using the TruSeq Stranded Total RNA kit (New England Biolabs, Ipswich, MA, USA) and paired-end sequencing was conducted using the Illumina NovaSeq 6000 platform (Illumina, San Diego, CA, USA).

Differentially expressed gene (DEG) analysis

Raw reads of B. plantarii KACC 18965 RNA-seq were trimmed using Trim Galore (v.0.6.7) (Krueger, 2015), and the quality of the raw and trimmed reads was evaluated using FastQC (v.0.12.1) (Andrews, 2010). The processed reads were aligned against the B. plantarii reference genome (GCF_030644525.1), and the BAM files of each sample were created using STAR (v.2.7.11b) (Table 2) (Dobin et al., 2013). To quantify the expression levels, the number of reads mapped to the CDSs was calculated using HTSeq (v.2.0.9) (Anders et al., 2015). Read counts were normalized using the median-of-ratios method, and DEGs were identified by comparing gene expression levels between wild-types and ΔplaI mutants through DESeq2 package in R language (Love et al., 2014). Genes showing differential expression with a greater than two-fold change and an adjusted p-value of <0.05 were considered as DEGs in this study. For B. plantarii KACC 18965, log2 fold change (log2FC) values were calculated from normalized counts. For B. glumae BGR1 and B. gladioli BSR3, log2FC values were calculated using RPKM-normalized expression values with a pseudo-count of 1 added prior to log2 transformation because B. glumae BGR1 and B. gladioli BSR3 were analyzed using RPKM-normalized data previously deposited in NCBI.

Clusters of Orthologous Groups (COG) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis

COG and KEGG annotations were obtained using eggNOG-mapper (v.2.1.13) (Cantalapiedra et al., 2021). COG category enrichment analysis of the DEGs was performed using Fisher’s exact test. For each COG category, upregulated or downregulated DEGs were compared against the genomic background using a 2 × 2 contingency table: DEG in category, DEG not in category, non-DEG in category, and non-DEG not in category. P-values were adjusted using the Benjamini-Hochberg method, and enrichment ratios were calculated as follows: (proportion of DEGs in category)/(proportion of all genes in category). The most strongly enriched values were used to represent the category. KEGG pathway enrichment analysis was conducted using the enrichKEGG function of the R package clusterProfiler (Wu et al., 2021) based on the KEGG annotations (Kanehisa et al., 2016). KEGG pathway analysis was performed using the PathView R package (Luo and Brouwer, 2013).

Real-time quantitative PCR (qPCR)

Complementary DNA synthesis and real time qPCR were performed using an Enzynomics kit (Enzynomics, Daejeon, Korea) according to the manufacturer's protocol. Four representative genes from T2SS (gspM, gspL, gspK, gspE) and three representative genes from T3SS (sctJ, sctN, sctT) were selected for qPCR analysis. The primers listed in Supplementary Table 1 were used for qPCR to quantify the gene expression levels, and the 16S rRNA gene was used as a housekeeping gene for qPCR using the 785F and 907R universal primers (Ziesemer et al., 2015). qPCR was repeated using three independent biological replicates.

Data availability

Raw RNA-seq data were deposited in the NCBI Sequence Read Archive (SRA) database (http://www.ncbi.nlm.nih.gov/sra) under the BioProject accession number PRJNA1310657.

Results

Transcriptional profiling of B. plantarii KACC 18965 wild-type and plaI deletion mutant

To investigate genes regulated by QS in B. plantarii KACC 18965, RNA was extracted from three replicate samples of B. plantarii KACC 18965 wild-type and the plaI deletion mutant (ΔplaI) cultured for 10 h in LB broth media, and RNA-seq was conducted using the Illumina NovaSeq 6000 platform. After normalizing read counts using DESeq2’s median-of-ratios method, DEGs between wild-type and ΔplaI were analyzed to identify PlaI-regulated genes in B. plantarii KACC 18965. Principal component analysis and hierarchical clustering of the three B. plantarii KACC 18965 wild-type replicates and three ΔplaI replicates showed clear separation between two groups, indicating that each genotype formed a well-defined cluster (Fig. 1A and 1B). According to the DEG analysis results, a total of 1041 DEGs were found in ΔplaI compared to wild-type based on the absolute value of log2FC being ≥1 and the adjusted P-value being <0.05. Among the 1041 DEGs, 484 genes showed increased expression and 557 showed decreased expression (Fig. 1C and 1D).

PlaI-dependent COG functional analysis in B. plantarii

COG functional analysis was performed on 1041 DEGs of B. plantarii KACC 18965. Compared to B. plantarii KACC 18965 wild-type, the ΔplaI mutant exhibited relatively more upregulated genes within COG categories of amino acid transport and metabolism (E), carbohydrate transport and metabolism (G), energy production and conversion (C), and inorganic ion transport and metabolism (P) (Fig. 2A). In contrast, the ΔplaI mutant exhibited relatively more downregulated genes within COG categories of transcription (K), secondary metabolites biosynthesis, transport and catabolism (Q), lipid transport and metabolism (I), and intracellular trafficking, secretion, and vesicular transport (U) compared to the wild-type (Fig. 2A).

To validate the significance of the COG functional analysis for the DEGs, COG enrichment analysis was performed by comparing the proportion of DEGs in each COG category against all annotated genes. According to the COG enrichment analysis, DEGs belonging to COG categories of secondary metabolites biosynthesis, transport and catabolism (Q), intracellular trafficking, secretion, and vesicular transport (U), and lipid transport and metabolism (I) were significantly enriched and downregulated in the ΔplaI mutant compared to the wild-type (Fig. 2B). In contrast, DEGs belonging to the carbohydrate transport and metabolism (G) COG category were significantly enriched and upregulated in the ΔplaI mutant compared to the wild-type (Fig. 2B).

COG functional category distribution of DEGs in B. glumae and B. gladioli

To gain further insight into the QS mechanism in phytopathogenic Burkholderia spp., COG functional analysis was conducted on the DEGs in B. glumae BGR1 and B. gladioli BSR3 for comparison to B. plantarii KACC 18965.

The QS mutant B. glumae ΔtofI (BGS2) exhibited relatively more upregulated genes within COG categories related to energy production and conversion (C), amino acid transport and metabolism (E), inorganic ion transport and metabolism (P), and transcription (K) compared to the B. glumae wild-type BGR1 (Fig. 2C). In contrast, BGS2 exhibited relatively more downregulated genes within COG categories related to signal transduction mechanisms (T) and cell motility (N) than BGR1 (Fig. 2C). In case of B. gladioli, DEGs were downregulated overall in COG categories, excluding the signal transduction mechanisms (T) category in the QS mutant B. gladioli ΔtofI (COK94) compared to the B. gladioli wild-type BSR3 (Fig. 2E).

COG enrichment analysis was also performed on B. glumae and B. gladioli to validate the significance of COG functional analysis of DEGs. According to COG enrichment analysis, DEGs belonging to the COG categories of cell motility (N), signal transduction mechanisms (T), and secondary metabolite biosynthesis, transport, and catabolism (Q) were significantly enriched and downregulated in BGS2 compared to those in BGR1 (Fig. 2D). In contrast, DEGs belonging to the COG categories of inorganic ion transport and metabolism (P), translation, ribosomal structure, and biogenesis (J) were significantly enriched and upregulated in BGS2 compared to those in BGR1 (Fig. 2D). In the case of B. gladioli, DEGs belonging to the intracellular trafficking, secretion, and vesicular transport (U) categories were significantly enriched and downregulated in the COK94 compared to BSR3 (Fig. 2F). Although the COG categories of secondary metabolite biosynthesis, transport, and catabolism (Q) and energy production and conversion (C) contained more downregulated DEGs in absolute numbers, statistically significant enrichment was observed among the upregulated DEGs in COK94 than in BSR3 (Fig. 2F).

KEGG pathway enrichment of B. plantarii, B. glumae, and B. gladioli

KEGG pathway enrichment analysis (P < 0.05) was also performed on DEGs identified between the wild-type and the mutant with a defect in the biosynthesis of QS signal molecules of B. plantarii KACC 18965, B. glumae BGR1, and B. gladioli BSR3. KEGG pathway enrichment analysis revealed that 85 KEGG pathways were enriched in both B. plantarii and B. glumae individually, and 79 KEGG pathways were enriched in B. gladioli. Among the top 10 enriched KEGG pathways ranked by the number of DEGs, B. plantarii, B. glumae, and B. gladioli exhibited the highest representation in the pathways of microbial metabolism in diverse environments and biosynthesis of secondary metabolites (Supplementary Fig. 2).

Since KEGG enrichment analysis of the complete pathway set yielded an excessive number of metabolism-related pathways, KEGG enrichment analysis was performed again after excluding pathways classified within the metabolism KEGG category to enable a more focused functional characterization. Consequently, genes associated with pathways such as quorum sensing, the two-component system, and biofilm formation were significantly enriched and upregulated or downregulated across the three spp. In addition, genes associated with the bacterial secretion system pathway were enriched and downregulated in B. plantarii. Genes associated with the flagellar assembly pathway were enriched and downregulated, and genes associated with the ribosome pathway were enriched and upregulated in B. glumae (Fig. 3).

QS-dependent gene regulation of bacterial secretion systems across three Burkholderia spp

To explore the genes regulated by QS in each spp. in more detail, we investigated the expressional changes of DEGs belonging to KEGG pathways, excluding the metabolism category, and the most characteristic differences between the three spp. were observed in the bacterial secretion system. In B. plantarii KACC 18965, all 17 DEGs belonging to the bacterial secretion system were found to be downregulated in the ΔplaI mutant compared to the wild-type (Fig. 4, Table 3). We also examined four type II secretion system (T2SS) representative genes and three type III secretion system (T3SS) representative genes via qPCR, and it was confirmed that the expression levels of representative T2SS and T3SS genes were reduced in ΔplaI mutant compared to the wild-type (Fig. 5). In contrast, 9 out of 21 DEGs were upregulated and 12 out of 21 DEGs were downregulated in BGS2 compared to BGR1, and all 13 DEGs were upregulated in COK94 compared to BSR3. In B. plantarii KACC 18965 ΔplaI mutant, most of the downregulated DEGs belonged to a T2SS or T3SS, and additionally, two vgrG genes of a type VI secretion system (T6SS) were downregulated. In BGS2 and COK94, most of the DEGs belonging to the bacterial secretion system pathway were also part of the T2SS, T3SS, or T6SS, and some type IV secretion system (T4SS) genes were identified (Fig. 4, Supplementary Table 2).

Discussion

Burkholderia spp. exhibit exceptional genetic plasticity and adaptability to various environmental conditions (O'Sullivan and Mahenthiralingam, 2005). Among the genus Burkholderia, B. plantarii, B. glumae, and B. gladioli represent major phytopathogens that are phylogenetically closely related (Mannaa et al., 2018). Previous studies by Kim et al. explored the QS mechanisms of B. glumae BGR1 and B. gladioli BSR3 using RNA-Seq analysis. These RNA-seq studies utilized the Gene Ontology (GO) and KEGG enrichment results as the basis for downstream functional interpretations (Kim et al., 2013, 2014). In this study, we employed COG functional analysis to investigate QS regulation in B. plantarii KACC 18965, B. glumae BGR1, and B. gladioli BSR3 because of the insufficient GO annotations in B. plantarii. In addition, comparison of transcriptional profiles revealed conserved or species-specific QS regulatory patterns across the closely related phytopathogenic Burkholderia spp., B. plantarii, B. glumae, and B. gladioli.

According to the results of the COG functional analysis, DEGs involved in energy metabolism, inorganic ion transport and metabolism, and carbohydrate metabolism showed the highest number of DEGs across all three Burkholderia spp., B. plantarii, B. glumae, and B. gladioli, although there were differences in the direction of upregulation or downregulation (Fig. 2). This corresponds to the results of the KEGG pathway analysis, which showed that the number of DEGs corresponding to the “microbial metabolism in diverse environments” (KEGG map/ko01120) pathway was the highest among the analyzed DEGs (Supplementary Fig. 2). “Microbial metabolism in diverse environments” encompasses various modules such as the pentose phosphate pathway (M00004), reductive pentose phosphate cycle (Calvin-Benson cycle, M00165), and reductive citrate cycle (Arnon-Buchanan cycle, M00173) (Kanehisa et al., 2023). Bacterial spp. belonging to the genus Burkholderia display metabolic versatility owing to their large genome size, which provides extensive coding capacity (O'Sullivan and Mahenthiralingam, 2005). The present study suggests that the regulation of diverse metabolic responses upon exposure to environmental stimuli by the QS may also contribute to the high adaptability to a wide range of environments in these Burkholderia spp.

To investigate the distribution of DEGs by focusing more on the functional region in the three Burkholderia spp., KEGG enrichment analysis was performed, excluding pathways classified within the metabolism KEGG category. Genes associated with pathways of QS, two-component system, and biofilm formation were significantly enriched and upregulated or downregulated across the three Burkholderia spp. (Fig. 3). Although various KEGG pathways contain QS-dependent regulated genes, DEGs belonging to the bacterial secretion system, particularly the T2SS, T3SS, and T6SS, were characteristically regulated by QS across all three Burkholderia spp., although the directionality of regulation differed among the individual strains. Focusing more specifically on QS-regulated genes within bacterial secretion systems, in B. plantarii, most DEGs belonged to the T2SS and T3SS and downregulated in the ΔplaI mutant compared to the wild-type. In B. glumae, DEGs associated with the T2SS and twin-arginine targeting (Tat) were downregulated, but most DEGs associated with Sec_SRP or T3SS were upregulated in the ΔtofI mutant BGS2 compared to the wild-type. The DEGs associated with the T6SS were both upregulated and downregulated in B. glumae. In B. gladioli, DEGs associated with T2SS, Sec_SRP, T3SS, and T6SS were all upregulated in the ΔtofI mutant COK94 compared to the wild-type (Fig. 4, Table 3, Supplementary Table 2). The T2SS affects not only pathogenicity but also ecological adaptability, biofilm formation, and colonization in assorted bacteria (Cianciotto, 2005; Naskar et al., 2021). The T3SS is widely recognized as playing important roles in the virulence of bacterial pathogens through mechanisms such as injecting effector proteins or suppressing host immunity (Deng et al., 2017). Notably, the core components of the T3SS injectisome, which injects virulent proteins into host cells, are structurally similar to those of flagella (Diepold and Armitage, 2015). Examination of the genomic locations of fliN, flhB, and fliH in B. glumae BGR1 and B. gladioli BSR3 revealed their proximity to the flagellar biosynthesis or assembly genes. Based on these findings, although fliN, flhB, and fliH were classified as bacterial secretion system genes based on their structural similarity, they do not truly belong to the bacterial secretion system. The T6SS has been implicated in virulence, stress tolerance, and interspecies competition (Yu et al., 2021). Indeed, previous phenotype experiments based on the plaI deletion of B. plantarii KACC 18965 and the qPCR results targeting T2SS and T3SS representative genes in this study have shown that the ΔplaI mutant exhibits reduced antibacterial activity and decreased expression of genes belonging to the T2SS and T3SS compared to the wild-type (Fig. 5) (Kang et al., 2024). While the directional trends were consistent, RNA-seq and qPCR results differed in expression levels for some gene such as gspM or sctT. The annotation status of reference genome and forms of analyzing and normalizing the data may influence the results of transcriptome quantification (Aguiar et al., 2023; Everaert et al., 2017). Additionally, previous proteomic analysis on B. glumae reported that the ΔtofI mutant BGS2 showed a significant decrease in the amount of extracellular proteins secreted by the T2SS compared to the wild-type (Goo et al., 2010).

Based on the results of COG enrichment analysis, DEGs were significantly enriched and downregulated in intracellular trafficking, secretion, and vesicular transport (U) in B. plantarii and B. gladioli. However, none of the DEGs belonging to the COG U category were enriched in B. glumae. The COG U category primarily includes T2SS, T3SS, T4SS, and T6SS components (NCBI COG database). COG enrichment results for B. plantarii correspond to the results of the KEGG pathway analysis for B. plantarii, which revealed that the expression of several genes belonging to the T2SS, T3SS, and vgrG of T6SS was significantly reduced in the ΔplaI mutant compared to the wild-type (Fig. 4, Table 3). However, for B. glumae and B. gladioli, the results of the KEGG pathway and COG enrichment analyses did not show a consistent trend. The reason for the differences between COG analysis and KEGG pathway analysis results seems to be the incompleteness of COG categories, KEGG annotation, and KEGG pathway analysis on all analyzed DEGs, regardless of enrichment.

In conclusion, we analyzed the transcriptome profiles of B. plantarii and compared the QS-dependent COG and KEGG enrichment across three phytopathogenic Burkholderia spp. DEGs analysis revealed that the QS of B. plantarii regulates diverse metabolic processes, including bacterial secretion systems and biofilm formation, which are crucial for bacterial pathogenicity and survival. Furthermore, by comparing the transcriptomes of the three Burkholderia spp., QS was found to regulate bacterial secretion systems, which could potentially affect the disease-causing ability, intermicrobial competition, and environmental adaptability, despite the direction of expression regulation differing for each strain.

This study has several methodological limitations that should be taken into account in interpreting these finding. This transcriptomic study was conducted on transcripts sampled at the 10-hour time point. Given that QS activity is cell density-dependent, our findings reflect the gene expression patterns associated with the early phases of QS in Burkholderia spp. Therefore, transcriptome analysis at additional time points is necessary for a comprehensive understanding of the QS regulatory mechanism. In addition, the transcriptomic datasets used in this study were generated using different experimental designs and normalization strategies as mentioned in the Materials and Methods section. These methodological discrepancies limit the direct quantitative comparison of absolute gene expression levels among strains. To mitigate this limitation, the comparative analysis was designed to emphasize relative expression trends and directional changes rather than absolute expression values in this study.

Meanwhile, a previous study has shown that deletion of the plaI gene alone in B. plantarii does not significantly attenuate pathogenicity (Kang et al., 2024), and the present transcriptome analysis also revealed no substantial change in gene expression related to toxin biosynthesis in B. plantarii. A recent study by Takita et al. has shown that the plaI3/plaR3-QS system, but not the plaI1/plaR1-QS system, plays a crucial role in the biosynthesis of tropolone, a virulence factor of B. plantarii in B. plantarii MAFF 301723 (Takita et al., 2026). Research conducted on B. gladioli MAFF 302385 revealed that the glaL1/glaR1-QS system, which is an AHL-based QS system of B. gladioli MAFF 302385, is involved in diverse virulence factors, such as swarming motility and biofilm formation, but is not involved in the biosynthesis of toxoflavin, a phytotoxin produced by B. gladioli (Takita et al., 2025). Based on these previous studies and the results of the present study, it appears that pathogenicity in phytopathogenic Burkholderia spp. is achieved through collaboration with multiple QS systems or other mechanisms, rather than a single C8-HSL-based QS system. Nonetheless, this transcriptomic study of QS-dependent gene regulation in Burkholderia spp. showed that the C8-HSL-mediated QS system regulates the expression of numerous genes and is involved in the pathogenicity of phytopathogenic Burkholderia spp. Therefore, future studies investigating other QS systems or toxin production mechanisms, building upon this transcriptomic study targeting the C8-HSL-mediated QS system of Burkholderia spp., will further elucidate the social behavior and pathogenic mechanisms of phytopathogenic Burkholderia spp.

Notes

Conflicts of Interests

No potential conflict of interest relevant to this article was reported.

Acknowledgments

This research was supported by the Basic Science Research Program of the National Research Foundation of Korea (NRF), funded by the Ministry of Education (RS-2024-00446591), Republic of Korea.

Electronic Supplementary Material

Supplementary materials are available at The Plant Pathology Journal website (http://www.ppjonline.org/).

PPJ-FT-03-2026-0038-Supplementary-Fig-1.pdf

PPJ-FT-03-2026-0038-Supplementary-Fig-2.pdf

PPJ-FT-03-2026-0038-Supplementary-Table-1.pdf

PPJ-FT-03-2026-0038-Supplementary-Table-2.pdf

Fig. 1

Quality assessment of RNA-seq samples and DEG overviews of B. plantarii KACC 18965 wild-type and ΔplaI mutant. (A) Hierarchical clustering heatmap of B. plantarii KACC 18965 wild-type (Bp65_WT) and ΔplaI mutant (Bp65_ΔplaI) strains. (B) Principal component analysis (PCA) of B. plantarii KACC 18965 wild-type (control) and ΔplaI mutant (mutant) strains. (C) Volcano plot showing differential expression analysis between B. plantarii KACC 18965 wild-type and ΔplaI mutant. Each point represents a gene, with x-axis indicating log2FC and y-axis showing log10 (adjusted P-value). Red points indicate significantly upregulated/downregulated genes (|log2FC|>1, adjusted P < 0.05), and gray points represent non-significant genes. (D) Bar plot showing the number of upregulated (red) and downregulated (green) DEGs between B. plantarii KACC 18965 wild-type and ΔplaI mutant. DEGs, differentially expressed genes.

Fig. 2

COG functional analysis of DEGs in three Burkholderia spp. Comparative analysis of COG functional categories for DEGs between wild-type and QS-deficient mutants across three Burkholderia spp. (A, C, E) Bar plots showing the top 10 most represented COG categories among DEGs in B. plantarii KACC 18965 (A), B. glumae BGR1 (C), and B. gladioli BSR3 (E), respectively. Categories are ranked by number of DEGs. (B, D, F) Dot plots showing COG enrichment analysis results for B. plantarii KACC 18965 (B), B. glumae BGR1 (D), and B. gladioli BSR3 (F). Dot size represents number of DEGs in each category. In all panels (A-F), red bars/dots indicate upregulated DEGs, and green bars/dots indicate downregulated DEGs. Functional categories are as follows: E, Amino acid transport and metabolism; G, Carbohydrate transport and metabolism; C, Energy production and conversion; P, Inorganic ion transport and metabolism; K, Transcription; Q, Secondary metabolites biosynthesis, transport and catabolism; M, Cell wall/membrane/envelope biogenesis; I, Lipid transport and metabolism; U, Intracellular trafficking, secretion, and vesicular transport; T, Signal transduction mechanisms; H, Coenzyme transport and metabolism; J, Translation, ribosomal structure and biogenesis; L, Replication, recombination and repair; N, Cell motility. COG, Clusters of Orthologous Groups; DEGs, differentially expressed genes; QS, quorum sensing.

Fig. 3

Composition of DEGs in top 10 enriched KEGG pathways excluding metabolism category across three Burkholderia spp. Stacked bar plots displaying the number of upregulated (red) and downregulated (green) DEGs in top 10 enriched KEGG pathways for B. plantarii KACC 18965 (A), B. glumae BGR1 (B), and B. gladioli BSR3 (C) QS-deficient mutants compared to wild-type, respectively. Each bar ranked by total DEG count, with segments indicating contribution of upregulated and downregulated DEGs. DEGs, differentially expressed genes; KEGG, Kyoto Encyclopedia of Genes and Genomes; QS, quorum sensing.

Fig. 4

Comparative regulation patterns of genes belonging to bacterial secretion systems in three Burkholderia spp. (A) Schematic diagram of T2SS, T3SS, and T6SS. (B) Regulation of DEGs belonging to T2SS, T3SS, and T6SS pathways in B. plantarii, B. glumae and B. gladioli, respectively. Each cell is colored according to regulation direction: red for upregulation and green for downregulation. T2SS, type II secretion system; T3SS, type III secretion system; T6SS, type VI secretion system; DEGs, differentially expressed genes.

Fig. 5

Validation of RNA-seq results using qPCR. Expression patterns for representative genes belonging to T2SS and T3SS in B. plantarii were analyzed using qPCR. gspM (GIY62_10380), gspL (GIY62_10385), gspK (GIY62_10390), and gspE (GIY62_10425) were components of T2SS, whereas sctJ (GIY62_29655), sctN (GIY62_29670), and sctT (GIY62_29680) were components of T3SS. Bars represent mean ± standard deviation of three biological replicates for qPCR results. qPCR, quantitative PCR; T2SS, type II secretion system; T3SS, type III secretion system.

Table 1

Bacterial strains used in this study

Bacterial strain	Description	Source
Burkholderia plantarii
KACC 18965	Wild-type, Rif^R	KACC
KACC 18965 ΔplaI	B. plantarii KACC 18965 derivative, deletion of 503 bp within GIY62_33880	Kang et al., 2024
Burkholderia glumae
BGR1	Wild-type, Rif^R	Jeong et al., 2003
BGS2	BGR1 tofI::Ω	Kim et al., 2004
Burkholderia gladioli
BSR3	Wild-type	Seo et al., 2011
COK94	BSR3 tofI::lacZ	Kim et al., 2014

Table 2

Summary statistics of B. plantarii transcriptome

Sample	Total reads	Mapped reads	Mapped ratio
KACC 18965 WT_1	24,838,330	23,077,826	92.91%
KACC 18965 WT_2	24,912,634	23,052,488	92.53%
KACC 18965 WT_3	24,664,717	23,012,610	93.30%
KACC 18965 ΔplaI _1	24,694,339	21,834,894	88.42%
KACC 18965 ΔplaI _2	24,791,002	22,936,956	92.52%
KACC 18965 ΔplaI _3	24,845,608	23,053,573	92.79%

Table 3

Expression profiling of DEGs associated with bacterial secretion system in B. plantarii KACC 18965

Locus ID	Gene name	Annotation	Log2FC
Burkholderia plantarii KACC 18965
GIY62_10420	gspF	Type II secretion system inner membrane protein GspF	−1.03
GIY62_10425	gspE	Type II secretion system ATPase GspE	−1.23
GIY62_10380	gspM	Type II secretion system protein M	−1.19
GIY62_10385	gspL	Type II secretion system protein GspL	−1.19
GIY62_10390	gspK	Type II secretion system minor pseudopilin GspK	−1.08
GIY62_18050	vgrG	Type VI secretion system tip protein VgrG	−1.04
GIY62_24795	vgrG	Type VI secretion system tip protein VgrG	−1.54
GIY62_29615	sctS	Type III secretion system export apparatus subunit SctS	−1.08
GIY62_29620	sctR	Type III secretion system export apparatus subunit SctR	−1.51
GIY62_29625	sctQ	Type III secretion system cytoplasmic ring protein SctQ	−1.30
GIY62_29635	sctV	Type III secretion system export apparatus subunit SctV	−1.22
GIY62_29640	sctU	Type III secretion system export apparatus subunit SctU	−1.20
GIY62_29655	sctJ	Type III secretion inner membrane ring lipoprotein SctJ	−1.81
GIY62_29665	sctL	Type III secretion system stator protein SctL	−1.75
GIY62_29670	sctN	Type III secretion system ATPase SctN	−2.01
GIY62_29680	sctT	Type III secretion system export apparatus subunit SctT	−1.30
GIY62_29690	sctC	Type III secretion system outer membrane ring subunit SctC	−1.95

DEGs, differentially expressed genes.

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