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Article

Pan-Genome Analysis Reveals Host-Specific Functional Divergences in Burkholderia gladioli

by
Hyun-Hee Lee
1,†,
Jungwook Park
1,2,†,
Hyejung Jung
1 and
Young-Su Seo
1,*
1
Department of Integrated Biological Science, Pusan National University, Busan 46241, Korea
2
Environmental Microbiology Research Team, Nakdonggang National Institute of Biological Resources (NNIBR), Sangju 37242, Korea
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Microorganisms 2021, 9(6), 1123; https://doi.org/10.3390/microorganisms9061123
Submission received: 19 April 2021 / Revised: 17 May 2021 / Accepted: 20 May 2021 / Published: 22 May 2021
(This article belongs to the Section Systems Microbiology)
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Abstract

:
Burkholderia gladioli has high versatility and adaptability to various ecological niches. Here, we constructed a pan-genome using 14 genome sequences of B. gladioli, which originate from different niches, including gladiolus, rice, humans, and nature. Functional roles of core and niche-associated genomes were investigated by pathway enrichment analyses. Consequently, we inferred the uniquely important role of niche-associated genomes in (1) selenium availability during competition with gladiolus host; (2) aromatic compound degradation in seed-borne and crude oil-accumulated environments, and (3) stress-induced DNA repair system/recombination in the cystic fibrosis-niche. We also identified the conservation of the rhizomide biosynthetic gene cluster in all the B. gladioli strains and the concentrated distribution of this cluster in human isolates. It was confirmed the absence of complete CRISPR/Cas system in both plant and human pathogenic B. gladioli and the presence of the system in B. gladioli living in nature, possibly reflecting the inverse relationship between CRISPR/Cas system and virulence.

1. Introduction

The impact of environmental variability is an important issue in the evolution of microbial genomes, contributing to phylogenetic position, as well as the diversity of metabolic capabilities to which it is adapted [1,2]. Environmental pressure for genomic evolution can include pH, temperature, oxygen, nutrient availability, competition with other bacteria, and stress-inducing defense mechanisms, from habitats and hosts [2,3,4]. The changes in the microbial genome are related to several evolutionary events: (1) the loss of genes by deletion, (2) the modification of gene products by mutation, and (3) the acquisition of new genes by lateral gene transfer and duplication [5]. Functional diversity through genomic evolution can either confer on bacteria a unique survival strategy in harsh environments or the establishment of pathogenicity to dominate the hosts [3]. Hence, the genomic architecture of microbes shows signatures of a long journey of adaptive evolution for different specialized lifestyles. Even in the taxonomically distant lineage, microbes within similar lifestyles exhibit similar genomic inventory to acclimatize to common environmental conditions in each niche [3,6]. Nevertheless, information on bacterial genomic diversity regarding dynamic interactions between the microbe and habitat or host conditions is currently limited.
Rapid advances in next-generation sequencing (NGS) technology have drastically reduced the cost of genome sequencing, thereby accelerating the development of comparative genomics through numerous genomes of various organisms [7]. In contrast to traditional analyses focusing on a single genome, comparisons of whole genomes provide plentiful genetic information to elucidate genomic structural landmarks, novel gene repertoires, and phylogenetic relationships among different organisms [8,9]. The fundamental goal of comparative genomics is to build a pan-genome map based on the genomes, as first quoted by Tettelin et al. in 2005 [10]. The pan-genome has a conserved core genome shared among approximately all members of the target group, and an accessory and dispensable genome, which are either unique to individual organisms or present in variable regions among two or more but not all genomes [11,12]. While the core genome includes genes essential for its basic lifestyle and major phenotypic traits in all targets, those genes appearing in the accessory and dispensable genome are involved in the strain-specific phenotypes such as adaptability, pathogenicity, and stress responses for survival in particular niches [11]. In some studies, such comparative genomic analysis is effective in tracking the novel niche-specific adaptive strategy of microbes of interest [13,14,15,16].
The genus Burkholderia, Gram-negative, aerobic, motile, and rod-shaped bacteria, includes members that are characterized by high versatility and adaptability to various ecological niches [17,18]. This ecological versatility within and between species of Burkholderia is due to their genome plasticity, unusually large multiple genomes, and potential for intragenomic rearrangement between chromosomal replicons [19]. B. gladioli are also widely recognized as a bacterium that exhibits remarkable divergence of ecological niches even within species [20], and the first whole-genome sequence was reported in rice pathogenic B. gladioli BSR3 in 2011 [21]. Although B. gladioli was initially known as a plant pathogen of gladiolus-causing rot diseases [22], currently, B. gladioli is better known as the rice pathogen that consistently has threatened rice farming in Japan, China, and South Korea [23,24], inducing symptoms of seedling blight, panicle blight, and leaf-sheath browning in rice [25]. Moreover, B. gladioli are isolated not only in other plants (onions, iris, and mushrooms) [26,27] but are also found in diverse habitats, including soil, environmental water, [28,29], and even the respiratory tract of immunosuppressed humans [30,31].
The current study aimed to understand the focused biological pathways that were previously unknown but could be related to the successful adaptation of B. gladioli in each niche via functional pan-genomic analyses. For an unbiased investigation of the pan-genome’s biological function, we carefully selected and compared 14 B. gladioli genomes that can represent different lifestyles within the gladiolus, rice, humans, and natural environment. A phylogenomic analysis of B. gladioli strains was firstly conducted, showing the close relationship between the B. gladioli genome and ecological niche. Based on this relationship, we constructed the B. gladioli pan-genome consisting of core, accessory, dispensable, and unique genomes. Finally, the core and niche-associated genome of the B. gladioli pan-genome revealed core biological features regardless of the niche, and adaptive features reflecting habitat ubiquity of B. gladioli, respectively.

2. Materials and Methods

2.1. Public Genomic Resources

For comparative analysis, the genomic sequences for B. gladioli strains, including a B. gladioli KACC 11889 whole genome sequence, which was previously developed [16], were downloaded from the FTP site of Reference Sequence (RefSeq) database at NCBI (ftp://ftp.ncbi.nih.gov/genomes/, accessed on 10 February 2021). Subsequently, in selecting genomes for the comparative analyses, we excluded as many as possible bacterial genomes which were isolated from unclear or unshared sites with other strains and were previously studied [32]. Consequently, fourteen B. gladioli genomes, which were consisted of 4 complete genomes and 10 draft genomes, were carefully included in this study.

2.2. Bacterial Strain, Culture Conditions, and Genomic DNA Extraction

B. gladioli KACC 18962 was streaked on plates of Luria-Bertani (LB) agar and incubated at 30 °C for 2 days. After confirmation of pure culture, single colonies were transferred to fresh LB broth and incubated in a shaking incubator at 200 rpm and 30 °C.
Genomic DNA was extracted from B. gladioli KACC 18962 using a Wizard Genomic DNA purification kit (Promega, Madison, WI, USA) according to the manufacturer’s recommended protocol. The quantity and purity were determined using a NanoDrop™ 2000 spectrophotometer (Thermo-Fisher Scientific, Waltham, MA, USA).

2.3. Genome Sequencing and Assembly

The whole genome sequencing library was prepared with a SMRTbell template prep kit (Pacific Biosciences, Menlo Park, CA, USA), and then, single-molecule real-time (SMRT) sequencing was conducted on a PacBio RS II platform (Pacific Biosciences) using Macrogen (Seoul, South Korea). To construct more accurate contigs, HiSeq3000 (Illumina, San Diego, CA, USA) paired-end reads were applied for sequence compensation. De novo assembly with the hierarchical genome assembly process (HGAP) version 3 was implemented in the PacBio SMRT Analysis algorithm version 2.3.0 package [33]. Quality control of long reads was performed using a PreAssembler filter version 1 protocol from HGAP, and further quality improvements of genome sequences were achieved by polishing with Quiver [33]. To reinforce the genome assembly, HiSeq reads were mapped twice against the PacBio assembly using Pilon version 1.21 [34]. Gene prediction and annotation were initially performed by Prokka version 1.13 [35]. Next, the genome sequence was re-annotated with the NCBI prokaryotic genome annotation pipeline based on the best-placed reference protein set and GeneMarkS+ [36] and finally deposited in the NCBI RefSeq database [37].

2.4. Phylogenomic Analysis

For the phylogenomic analysis, whole-genome sequences of B. gladioli strains were compared by computing the average nucleotide identity (ANI) and the corresponding alignment coverage using the Python3 module pyani (version 0.2.10; option ‘-m ANIb’) [38]. The ANI values were visualized by heatmap.2 function in the gplots package in the R environment (https://CRAN.R-project.org/package=gplots, accessed on 10 February 2021). We also confirmed the phylogenetic distribution of the 14 B. gladioli in cladistic structure, which were previously well described by Jones et al. [32]. To cover the full diversity of the population, the representative 22 B. gladioli members were selected from different clades of 206 B. gladioli isolates and compared with 14 B. gladioli as in a study of Lin et al. [39]. The GenBank annotations were firstly converted to GFF3 format using BioPerl script (bp_genbank2gff3.pl). The GFF3 files were used as input for the Roary version 3.11.2 [40] to extract core genes and to align concatenated core genes with MAFFT using default parameters. In turn, core gene-based phylogenetic trees were generated using RAxML version 8.2.10 [41] with GTR + GAMMA model and bootstrap values from 1000 replications. The phylogenetic tree was visualized by MEGA X [42].

2.5. Pan-Genomic Analysis

All the orthologous pairs across 14 B. gladioli genomes were calculated using the pan-genome analysis pipeline (PGAP) version 1.2.1 [43] to identify pan-genomes, including the core, accessory, dispensable and unique genome. Under the GF method, the total protein sequences of each strain were mixed together and marked as the strain identifiers. BLAST searches were performed using the BLASTALL program [44] among the mixed protein sequences, and the minimum score value and E value applied in BLAST were set to 40 and 1.0 × 10−5, respectively. The filtered BLAST results were clustered by the Markov Cluster algorithm [45], which has been widely used in other studies on prokaryotic genomes to search for orthologs among multiple strains. For grouping the same genes into the same cluster, the global match regions have a minimum of 50% coverage and 50% identity on the protein sequences. The COG distribution in pan-genome was analyzed with the parameter “-function.” The pan-genome characteristic curves were drawn using PanGP version 1.0.1 [46]. Validation of the B. gladioli pan-genome was performed by anvi’o platform version 6.2 following the tutorial for microbial pan-genomics [47].

2.6. Functional Enrichment Analyses

Functional annotations of the B. gladioli pan-genome were performed using three databases: Clusters of orthologous groups (COG) [48], gene ontology (GO) [49], and Kyoto encyclopedia of genes and genomes (KEGG) [50]. For the annotation of 14 B. gladioli genes with COG, a whole genome BLASTP search was performed against a local version of the NCBI COG database (ftp://ftp.ncbi.nih.gov/pub/COG/COG2014/data, accessed on 10 February 2021) with the following parameters: E value < 1.0 × 10−5; identity more than 30%; coverage more than 30%. Only one annotation per protein was ranked by the E value, identity, and coverage. GO annotation was performed using a java-based Blast2GO software version 5.2.5 [51] with the default parameters. Blast2GO annotation was initiated by BLAST searches against the NCBI RefSeq non-redundant protein database followed by Interproscan [52] analyses to identify conserved protein domains. Finally, the GO IDs of all proteins were retrieved from the Blast2GO annotation database. This functional annotation categorized the B. gladioli proteins under all GO categories that include cellular components, molecular processes, and biological processes. The protein of each genome was also functionally annotated using pre-computed hidden Markov model profiles of KEGG ortholog by kofamscan version 1.2.0, which assigns KEGG orthology identifier to proteins via HMMER/HMMSEARCH. The kofamscan output in a mapper format was processed by the KEGG Mapper-Reconstruct Pathway utility with manual curation.
After COG/GO/KEGG functional annotation, enrichment analyses for each database were subsequently performed to find significantly over-represented biological annotations in genomes. The pan-, core and niche-associated genomes of B. gladioli were subjected to the COG, GO, and KEGG pathway enrichment analyses, respectively. All enrichment analyses were performed using a hypergeometric test with the proper function contained in the R statistical environment [53]. Specifically, the hypergeometric test considered the following statistics: the number of genes involved in the COG/GO/KEGG systems, the percentage of the systems covered by a specific category, and the expected number of target genes in the specific categories. The COGs, GO terms, and KEGG pathways with corrected p-values less than 0.05 were considered significantly enriched.

2.7. Identification of Secondary Metabolite Biosynthetic Gene Clusters and CRISPR/Cas

Screening for secondary metabolite biosynthetic gene cluster (BGC) was performed on the antibiotics and secondary metabolite analysis shell (anti-SMASH, https://antismash.secondarymetabolites.org/, accessed on 10 February 2021) of the online web server [54]. For the screening, individual B. gladioli genome sequences were submitted to the anti-SMASH server in the GenBank.
The clustered regularly interspaced short palindromic repeat (CRISPR) regions and CRISPR-associated (Cas) proteins were identified by CRISPRCasFinder (https://crisprcas.i2bc.paris-saclay.fr/CrisprCasFinder, accessed on 10 February 2021) [55]. The whole genome sequences of B. gladioli strains were submitted to the CRISPRCasFinder tool in fasta format. For CRISPR array identification, 100 bp was selected as the size threshold of the flanking region, and the CRISPR array contained truncated repeats. Only those with an evidence level ≥1 were considered in this study. The spacer sequences, which were detected from CRISPRCasFinder, were subsequently entered into the CRISPRTarget tool [56] to predict the most likely targets of CRISPR RNA. A cut-off score of 30 was applied and we manually examined the results to exclude self-matching or accidental matching results.

3. Results and Discussion

B. gladioli are unique pathogenic bacteria that can cause diseases in both plants and humans, and they occupy significantly divergent ecological niches. Specifically, even strains of the same B. gladioli species exhibit these unique characteristics. Recent comparative genomic studies have demonstrated that bacterial adaptation to the environment in the host is closely related to biological capabilities, which are changed by the result of gene gain/loss or genome reduction/expansion [1,5]. Thus, the fundamental aims of this study are to identify concentrated biological functions of the B. gladioli, originating from different sources, and to infer the role of these functions, possibly contributing to successful adaptation in the surroundings.
To achieve this goal, we employed the pan-genomic analysis using a total of 14 B. gladioli isolates which have been historically, phenotypically, or genomically well characterized. Particularly, of fourteen B. gladioli, ten pathogenic B. gladioli isolates were classified by niches (gladiolus, rice, and human), and the others were integrated into the nature group. The gladiolus isolates included the pathovar reference strains of B. gladioli pv. gladioli (ATCC 10248 and NCTC 12378) [57,58,59], B. gladioli KACC 11889 [60] and B. gladioli ATCC 25417 [61,62] in which several virulence properties were experimentally investigated. Rice isolates comprised B. gladioli BSR3, one of the most notable rice pathogenic Burkholderia species [21,63,64,65], and B. gladioli KACC 18962, which was isolated in this study, presenting similar phenotype traits with strain BSR3. Human isolates were composed of opportunistic human pathogenic and multidrug-resistant B. gladioli (AU0032, AU26456, AU29541 and AU30473) in which antibiotic susceptibility profiling has been recently undertaken [66,67]. In addition, B. gladioli, which were isolated from water and soil for industrial application [28], were included in nature isolates.

3.1. Available Genomic Information for B. gladioli

We sequenced the genome of B. gladioli KACC 18962, which was isolated in rice cultured in South Korea using PacBio RS II sequencing technology. Table 1 summarizes several key features for the fully sequenced B. gladioli KACC 18962 genome and Figure 1 shows the circular replicons of strain KACC 18962. The complete genome of strain KACC 18962 consists of two chromosomes and one plasmid. The average coverage of PacBio reads was 190-fold for chromosome 1, 187-fold for the chromosome 2, and 190-fold for the plasmid with 92.0% and 83.0% of the reads exhibiting an average Phred quality score ≥Q20 and Q30, respectively (Table 1). A total of 7475 genes were identified from the genome, 173 of which were pseudogenes. The three replicons encode 7216 coding genes, 5 rRNA operons, and 67 tRNA loci. The sequences of B. gladioli KACC 18962 chromosome 1, chromosome 2, and plasmid genome have been deposited in GenBank under accession numbers CP045573, CP045574, and CP045575, respectively.
Finally, we collected the genome sequences of 14 B. gladioli strains and listed detailed information of them in Table 2. According to the NCBI database, the B. gladioli strains were isolated from gladiolus plant (ATCC 10248, KACC 11889, ATCC 25417 and NCTC 12378), rice plant (BSR3 and KACC 18962), human sputum (AU0032, AU26456, AU29541, and AU30473), and in nature (Coa14, MSMB1756, FDAARGOS_390, and FDAARGOS_391) (hereinafter “gladiolus isolates”, “rice isolates”, “human isolates”, and “nature isolates”, respectively). Of these, four strains, ATCC 10248, KACC 11889, BSR3 and KACC 18962, were fully sequenced. Genome sizes ranged between 8.4 Mb (strain NCTC 12378) and 9.3 Mb (strain ATCC 25417) for gladiolus isolates, 8.6 Mb (strain KACC 18962) and 9.1 Mb (strain BSR3) for rice isolates, 8.0 Mb (strain AU0032) and 8.4 Mb (strains AU29541) for human isolates, and 8.2 Mb (strain MSMB1756) and 8.8 Mb (strain FDAARGOS_390) for nature isolates. This genome size variation shows that gladiolus isolates have the largest variation in genome size with 0.9 Mb and human isolates have the smallest genome size variation with 0.4 Mb than the strains isolated elsewhere. This tendency was also observed in GC content and the counts of protein coding genes of gladiolus and human isolates. GC content variations were 67.3–68.0% (gladiolus isolates) and 68.2–68.3% (humans isolates). The number of protein coding genes for gladiolus isolates ranged between 7098 (strain NCTC 12378) and 7941 (strain ATCC 25417), and that for human isolates ranged between 7112 (strain AU0032) and 6769 (strain AU29541). This result indicated that there was no sharp distinction between B. gladioli strains in the genomic characteristics (i.e., genome size, coding density and GC content), and there was no clear association between the genomic architecture and isolation source. Therefore, we needed to inspect genome sequences in detail to find clues of niche-directed evolution in genomic contents.

3.2. Phylogenomic Analysis

To evaluate the adequacy of studying the relationship between B. gladioli’s genome and niche, a whole genome-based phylogenomic tree was constructed from 14 B. gladioli genomes via the average nucleotide identity ANI approach [69], the robust measurement of genomic relatedness between strains (Figure 2A and Table S1). The most notable result of this phylogenomic tree was highly close relationships of gladiolus isolates (Figure 2A). This result implies that niche could influence bacteria genome shaping especially in gladiolus-originated B. gladioli. Meanwhile, gladiolus and human isolates also relatively closed to each other, although pairwise ANI values across gladiolus isolates were more than 99.9%, and those between human isolates were over 98.9% (Table S1). ANI values between strain AU30473 of human isolates and gladiolus isolates (more than 99.1%) were slightly higher than those between strain AU30473 and other human isolates (over 98.9%). The close relationship between human isolates and gladiolus isolates offers a perspective on the origin and acquisition of biological functions that will be necessary for survival or adaptation in a new challenging niche like humans. According to this perspective, studies by Jones et al. [32] and Lipuma et al. [70], suggested that the main source of human-associated Burkholderia is the natural environment. In rice isolates, B. gladioli BSR3 and KACC 18962 were clearly distinguished from strains in gladiolus and human isolates while KACC 18962 shared high ANI scores with FDAARGOS_390 and FDAARGOS_391 of nature isolates (ANI values >99.0%). The nature isolates (MSMB1756, Coa14, FDAARGOS_390, and FDAARGOS_391) exhibited limited genome relatedness among each other, presenting ANI values ranging from 97.0–99.0%.
The ANI-based phylogenomic relationships also validated in cladistic structure, which were previously well described by Jones et al. [32]. We obtained 4392 concatenated core genes by comparing 14 B. gladioli genomes in this study with the genomes of 22 previously reported (Figure 2B). As a result, both gladiolus and human isolates were positioned in clade 3. In clade 3, gladiolus isolates still revealed notably close relationship with BCC0771 (B. gladioli pv. gladioli). However, rice and nature isolates failed again to be grouped into same clade, respectively. The rice isolate BSR3 and nature isolate MSMB1756 were solely belonged to clade 1B and 1A, respectively. The rice isolate KACC 18962 was classified into clade 2 with FDAARGOS_390, and FDAARGOS_391 of nature isolates. The rest 22 B. gladioli isolates were positioned identically as the previous classification [39]. In conclusion, the results highlight potential high relatedness between isolation sites and genome contents, although it is difficult to apply this association to all B. gladioli isolates.

3.3. Pan-Genome Analysis

We performed comprehensive pan-genome analysis for defining the sets of core, accessory, dispensable, and unique genomes. The core genome was defined as the set of genes, shared by all 14 B. gladioli strains. The accessory genome, which was available in one or up to n-1 of the genomes (n: total genomes), was composed of the dispensable genome (genes present in two or more strains) and the unique genome that contains the set of genes, observed in only one genome. First, cumulative curves, which were generated by PanGP [46] according to Heaps’ law, were presented in Figure 3A,B. Tettelin et al. [10] reported that a finite or infinite pan-genome can be determined by a prediction using Heaps’ law. Heaps’ law is devised as n = κNγ, where n is the pan-genome size, N is the number of genomes used, and κ and γ are the fitting parameters. For γ < 0, the pan-genome is closed and its size approaches a constant as more genomes are used, while for γ > 0, the pan-genome is open and its size increases as more genomes are included. As supported by the γ parameter from Heaps’ law, the B. gladioli pan-genome could be considered “open” state (γ = 0.37) with no sign of saturation, moderately expanding with the inclusion of new genomes [10]; whereas, the number of core genome and new genes remained relatively stable with the addition of new strains (Figure 3A,B). This phenomenon is common in species living in bacterial communities with large genomes and an open pan-genome [71]. For example, B. pseudomallei [72] and B. cepacia complex [73] genomes, which consist of large multiple circular chromosomal replicons, containing twice the amount of genetic material as Escherichia coli, are widely considered to have the “open” pan-genome. However, genomes of bacteria, which thrive only in a narrow range of niches, usually have a small size and “close” pan-genome [71,74].
The comparison of the protein-coding genes revealed that B. gladioli pan-genome contained a total repertoire of 11,403 gene clusters. The gene clusters in the accessory genome contributed a larger part of the pan-genome composition (6667 gene cluster, 58.5%) than those in the core genome (4736 gene clusters, 41.5%) (Figure 3C). These accessory genes were further classified into 3598 gene clusters in dispensable genome (shared by 2 to 13 strains), and 3069 gene clusters, which spread exclusively along independent strains as a unique genome. The accessory genome for gladiolus isolates (hereinafter “gladiolus-associated genome”) comprised 729 gene clusters that were missing from rice, human, and nature isolates. The accessory genomes for rice (hereinafter “rice-associated genome”), human (hereinafter “human-associated genome”), and nature (hereinafter “nature-associated genome”), which were not shared by isolates in other habitats, harbored 879, 735, and 1203 gene clusters. The Anvi’o program [47] was used to visualize the pan- and core genomes (Figure 3D). The Anvi’o visualization also highlighted the protein sets available in each one of the B. gladioli isolates and support genome conservation and differentiation. Below, we continuously investigated core genomes (universally conserved B. gladioli isolates) and the niche-associated genomes of gladiolus, rice, human, and nature isolates, which can contribute to adaptation in their respective habitat.
To assess the level of functional diversity within the B. gladioli core and accessory genomes, the COG analysis was employed (Table 3 and Figure S1). From 11,403 gene clusters, 6673 (59%) gene clusters were annotated in the COG functional categories; of these, 4736 gene clusters were part of the core genome shared by all 14 B. gladioli, and 2738 gene clusters were part of the accessory genome. The enrichment of COG category analysis presented that the gene clusters in the B. gladioli core genome were enriched for genes involved in COG class J (Translation, ribosomal structure and biogenesis), and class F (Nucleotide transport and metabolism) (Table 3). Genes composing accessory genomes were highly enriched in class L (replication, recombination, and repair), class Q (secondary metabolite biosynthesis, transport and catabolism), class W (extracellular structures), class U (intracellular trafficking, secretion, and vesicular transport), class V (defense mechanisms), class I (lipid transport and metabolism) and poorly characterized in some classes. Two independent pan-genome analyses of Burkholderia species, which occupy a large variety of hosts similar to B. gladioli species, identified COG functional categories for core and dispensable genomes [15,75]. Accordingly, the COG functional categories enriched for both core genomes of four Burkholderia genus (B. cenocepacia, B. thailandensis, and B. pseudomallei [15], and that of B. pseudomallei strains [75]) were also common with that of B. gladioli in our study, presenting ‘translation, ribosomal structure, and biogenesis’, and ‘nucleotide transport and metabolism’. However, the COG enrichment patterns for accessory genome resulting from these two studies were completely different from our result. Thus, the COG analysis result highlights that core genes can perform fundamental housekeeping functions regardless of their isolation sources, while accessory genes influence adaptation and survival in their own niche. Additionally, considering the open state of B. gladioli pan-genome, the result of COG enrichment analysis for B. gladioli accessory genome, especially COG class L (replication, recombination, and repair) and class Q (secondary metabolites biosynthesis, transport, and catabolism), also supports the perspective that larger genomes tend to accumulate functions like secondary metabolism [76] to allow organisms to reach a higher degree of ecological diversification.

3.4. Functional Analysis for Core and Niche-Associated Genome

To further clarify biological functions of core and niche-associated B. gladioli genome, we first performed GO enrichment analysis using core genome (Table S2). The top-ranked 30 GO terms (a ranking by p-value) were significantly enriched based on three classes (cellular component, molecular function, and biological process). The GO terms were mainly involved in non-metabolic functions as shown in the COG result (Table 3). The most significantly enriched GO terms were ribosome (GO:0005840) and translation (GO:0006412) and they were consistent with COG class J (translation, ribosomal structure, and biogenesis). We also identified fundamental cellular processes and signaling functions in core genomes such as cell division (GO:0051301), cell cycle (GO:0007049), bacterial-type flagellum-dependent cell motility (GO:0071973), chemotaxis (GO:0006935), and signal transduction (GO:0007165). These results showed the relevance to COGs (Figure S1), which were not significantly enriched but comprised the majority of the pan-genome as core genes, including genes for cell motility (66%), cell cycle control, cell division, chromosome partitioning (59%), and signal transduction mechanisms (61%). Consequently, GO enrichment analysis reveals fundamental cellular processes and signaling functions of the core genome of B. gladioli.
Subsequently, we compared the enriched KEGG pathways in niche-associated genomes to explore the difference between the B. gladioli strains in terms of their biological capabilities and to highlight particular niche adaptations (Figure 4). Notably, there were clear differences in functions that were uniquely conserved in each niche-associated genome. The gladiolus-associated genome uniquely presented categories related to metabolism (i.e., cysteine and methionine metabolism, and selenocompound metabolism) that were absent in other groups. Selenium is an essential micronutrient used by organisms across all three domains of life [77,78,79]. Plants can absorb selenium from soil and assimilate various selenocompounds like selenomethionine and selenocysteine [80]. Selenium that is assimilated by plants can contribute to stress tolerance, inhibition of ROS damage, promotion of plant growth, homeostasis of essential nutrient elements, and photosynthesis [81,82,83,84,85]. Moreover, many enzymes within both plant and bacteria require selenium and selenoamino acids for activities [86]. Hence, the competition between plant hosts and pathogen for limited selenium resources is a natural consequence to increase their fitness [87]. Taken together, this KEGG result indicates that selenium availability will play a uniquely critical role in determining who wins the battle between host gladiolus and pathogenic B. gladioli.
The rice-associated genome was heavily enriched in genes related to aromatic compounds, styrene, and aminobenzoate degradation pathways (Figure 4). Among various aromatic compounds, lignin is an abundant terrestrial aromatic macromolecule [88]. Once lignin is depolymerized to monomers and/or low-molecular-weight aromatics, surrounding bacteria assimilate them for carbon and energy sources through their well-adapted metabolic pathways [89]. Various studies have also reported Burkholderia harboring gene clusters for degradation of lignin-derived aromatics [90,91]; to date, the ability of B. gladioli to degrade aromatic compounds remains unclear. However, this KEGG analysis result strongly suggests that rice pathogenic B. gladioli have enough potential to effectively use the lignin-derived aromatic compounds. Additionally, the following facts describing: (1) the seed-borne nature of B. gladioli in rice as confirmed in several previous reports [25,92,93] and (2) lignin as a major component of rice husk [94] support our expectation. Interestingly, we also identified the degradation of aromatic compounds pathway in the nature-associated genome (Figure 4), and this result can be explained by a capacity of B. gladioli Coa14, which was isolated from the Coari Lake (Amazonas, Brazil), for using crude oil as the only carbon source [28]. The crude oil is a complex mixture of both hydrocarbons like aromatic hydrocarbons, and non-hydrocarbons can easily enter and accumulate in high concentrations in aquatic and terrestrial environments [95,96,97]. Therefore, it is possible that the degradation of aromatic compounds is not well observed at the B. gladioli species level but is a unique function of B. gladioli strains living in rice plants and in nature.
Genes composing the human-associated genome were highly distributed in mismatch repair, RNA degradation, DNA replication, homologous recombination, and biofilm formation—Vibrio cholerae of KEGG pathways (Figure 4). The mismatch repair is a system for identifying and repairing mispaired base during DNA replication and recombination, as well as repairing some forms of DNA damages during infection [98,99]. Upon infection, bacterial cell components stimulate pathogen recognition receptors in hosts, thereby provoking inflammation [100]. Pathogens experience DNA and RNA damage by chemical mediators of inflammation and require multiple DNA repair pathways for optimal survival in the stressful niche condition [98,100,101]. Conversely, this stress-induced repair system in the pathogen allows genetic diversification and accelerates the adaptation [102,103]. In several human pathogenic bacteria such as Helicobacter, Streptomyces, and Neisseria [104,105,106], it was reported that recombination occurs frequently and can promote their adaptation [107]. Zhou et al. also reported that recombination in Burkholderia cepacia complex (BCC), which causes diseases in cystic fibrosis with B. gladioli, significantly influences adaptation and diversification [73]. Besides, the biofilm formation pathway in the human-associated genome indicates the difficulty of B. gladioli to withstand several host-defense measures [108]. Therefore, the available evidence suggests that DNA repair, recombination, and formation of biofilm are possibly necessary for survival in the cystic fibrosis-niche when human pathogenic B. gladioli experience infection-associated DNA damage. However, the conservation of these biological functions remains to be seen in human isolates across different clades since the human isolates included in this study are specific to clade 3 (Figure 2B).
The nature-associated genome retained not only the degradation of aromatic compounds but also a few pathways (i.e., quorum sensing, plant-pathogenic interaction, bacterial secretion system, and lipopolysaccharide biosynthesis) (Figure 4). As mentioned above, a close phylogenomic relationship between rice and nature isolates (Figure 2), and sharing of degradation of the aromatic compound pathway with rice- and nature-associated genomes can provide evidence of the phenomenon that most gene transfer occurs between closely related organisms [109,110]. For example, comparative genome analysis of 144 prokaryotic genomes shows the frequent transfer of genetic information between closely related taxa or between bacteria inhabiting the same environment [110]. Consistent with this report, it is expected that some genes for plant-pathogen interaction can be transferred to soil-derived isolate B. gladioli MSMB1756 from close plant pathogenic bacteria. Alternatively, secretion system and lipopolysaccharide biosynthesis in the nature-associated genome (Figure 4) can be explained by their high variation among Burkholderia. Specifically, the type 4 secretion system (T4SS), which transports not only proteins but also nucleic acids [111], is known as the most versatile family of secretion systems as per Seo et al. who report high variability of T4SS among Burkholderia strains [16]. Additionally, there is considerable diversity in lipopolysaccharide (LPS) that is present on the bacterial surface and is produced by most gram-negative bacteria [112]. We identified that genes of the nature-associated genome were condensed in the biosynthesis of the S-layer of the cell envelope. From decades of research, it is evident that S-layers, which are closely associated with the LPS of the outer membrane, can remarkably differ even between closely related species [113]. Considering the enormous diversity of S-layer, S-layers can reflect the evolutionary adaptations of bacteria to natural habitats and provide bacteria with advantages in the selection of specific environmental and ecological conditions [114,115].

3.5. In Silico Analysis of Nonribosomal Peptide Synthetases

Contrary to primary metabolites, secondary metabolites are not required for normal cell growth or development but can offer a competitive advantage for survival and adaptation in its ecological niche [116]. Of secondary metabolites, nonribosomal peptide (NRP), which is synthesized by nonribosomal peptide synthetase (NRPS) working in a ribosome-independent assembly line fashion [117], has attracted intense interest by its antimicrobial activity [118,119]. In this regard, Burkholderia-produced NRP and NPRS BGCs have been recently explored in some Burkholderia members, including B. gladioli [32,39], B. ambifaria [120], B. pseudomallei [121], and B. thailandensis [122]. Hence, to explore NPRS BGCs of B. gladioli in this study, genome mining was conducted using antiSMASH. Analysis of 14 B. gladioli strain revealed a remarkable array of BGCs that encode for rhizomide A/B/C, sulfazecin, icosalide A/B, bongkrekic acid, aluminide, etnangien, enacyloxin IIa, ralsolamycin, micacocidin, and xenotetrapeptide (Figure 5). Compared with the distribution of BGCs in the previously studied B. gladioli [32], we also consistently identified the distribution of BGCs for bongkrekic and icosalide in genomes of B. gladioli within specific clades. As in the findings of Jones et al. [32], the BGC for bongkrekic was specifically contained in the genome of strain MSMB1756 in clade 1A and the genome of strain BSR3 in clade 1B. The BGC for icosalide was widely distributed in all 14 B. gladioli strains within clades 1A, 1B, 2, and 3 (Figure 2B and Figure 5). This result implies that some BGCs tend to be distributed in a clade-specific manner. We additionally detected several BGCs which were not investigated in other studies. Among them, rhizomide BGC was present in all the B. gladioli strains. The gladiolus and human isolates occupied additional rhizomide BGCs and particularly, human isolates, including AU0032, AU29541, and AU30473, were rich in rhizomide BGCs more than any other strains. By spectroscopic characterization, production of rhizomide was identified from soil bacterium Burkholderiales DSM 7029, and Paraburkholderia rhizoxinica, intracellular symbionts of the phytopathogenic fungus [123,124,125]. Additionally, Burkholderia-originated rhizomide exhibited various actives such as antitumor activity, protective activity against cucumber downy mildew, antibacterial activity for gram-positive bacteria [123]. Considering this, the conservation of rhizomide BGC in this study indicates that B. gladioli, especially that from human isolates, can also be a high potential producer for rhizomide. Interestingly, gladiolus isolates also uniquely conserved BGCs for etnangien, which is also well known for antibiotic activity against gram-positive bacteria by inhibiting nucleic acid polymerases [126]. An exception, ATCC 25417, BSR3, and KACC 18962 strains uniquely carried BGC for ralsolamycin, xenotetrapeptide, and enacycloxin IIa, respectively. It can be inferred from the exceptional acquisition of biosynthetic genes by the exchange of genetic information between contemporary organisms.

3.6. Prediction of CRISPR/Cas System

In the past, the most representative roles of CRISPR systems are protecting bacteria from invasion by bacteriophages or foreign plasmid DNA [127,128]. CRISPR systems have recently received attention for association with virulence and biofilm formation in pathogenic bacteria, and modulation of their expression by various environmental stimuli [129]. For example, it was reported that QS-dependent regulation of CRISPR/Cas genes influences the bacterial immune system in B. plantarii PG1 (formerly B. glumae PG1) [130] and Pseudomonas aeruginosa [131]. With this respect, analysis of the CRISPR/Cas system was performed for B. gladioli genomes (Table 4) to find a link between the CRISPR/Cas system and the different surrounding environments of B. gladioli. CRISPRs were identified in most B. gladioli genomes except in AU29541, FDAARGOS_390, and FDAARGOS_391 strains, and the numbers of CRISPR arrays in one genome varied from one to four. It was assumed that these CRISPRs could not silence foreign DNA, because there were many orphan CRISPR arrays without associated Cas proteins. One interesting fact about these orphan CRISPR arrays is that spacer sequences of BSR3 matched to plasmid sequences of Cupriavidus taiwanensis. C. taiwanensis (originally called Ralstonia taiwanensis) is a nitrogen-fixing bacterium of the family Burkholderiaceae, forming root nodules on host plant [132]; meanwhile, C. taiwanensis were also isolated from the sputum of a cystic fibrosis patient and root nodule [133]. Additionally, the coexistence of Burkholderia and Cupriavidus has frequently been observed in plant roots and rhizosphere [134,135,136]. Therefore, the presence of spacer in BSR3 matching to C. taiwanensis indicates traces of interaction between Burkholderia and Cupriavidus not only in plant roots but also in the sputum of a cystic fibrosis patient and provisionally indicates cross-kingdom infection between plant and human [137,138]. Whereas, two B. gladioli strains (Coa14 and MSMB1756 of nature isolates) contained complete CRISPR/Cas systems and all those systems affiliated with type I-F that contained Cas proteins including Cas1, Cas3-Cas2, Csy1 to Csy3, and Cas6. In P. aeruginosa PA14, the type I-F CRISPR/Cas system functioned to eliminate invading DNA not RNA [139,140] and Buyukyoruk et al. also demonstrated an efficient barrier role of the type I-F CRISPR system by restricting dsDNA [141]. Ironically, we confirmed the spacer sequences of both Coa14 and MSMB1756 strains commonly matched to plasmid sequences of Streptomyces species (S. lavendulae, S. aureofaciens, S. rimosus, and S. katrae) and Ralstonia solanacearum (Table 4). Streptomyces members are widely considered the source of many tetracycline antibiotics and an important reservoir of antibiotic resistance genes in soil [142,143,144,145]. R. solanacearum is the most destructive phytopathogen that attacks many crops and other plants over a broad geographical range [146,147]. It is obvious that such antibiotic resistance and virulence genes in bacterial plasmids are important fitness traits that increase the survival ability of bacteria within the host and in nature. However, we couldn’t identify any antibiotic resistance genes or virulence-related genes in spacer sequences of both Coa14 and MSMB1756 strains. We speculate that the existence of sequences of Streptomyces and R. solanacearum in the spacer of B. gladioli was just vestiges of the interaction between distinct bacterial genera in nature. Furthermore, we suppose that the CRISPR/Cas system of B. gladioli may be an obstacle to acquiring antibiotic resistance and virulence genes. This inverse relationship between CRISPR/Cas and antibiotic resistance has been reported in Enterococcus species [148]. It was confirmed that a strong correlation between the absence of complete CRISPR-Cas loci and the emergence of multidrug-resistant enterococcal strains by analyzing a large genome data set. Hence, it is reasonable that none of the plant and human pathogenic B. gladioli have a complete and active CRISPR/Cas system.

4. Conclusions

Our pan-genomic study investigated the relationship between fourteen B. gladioli genomes and the ecological niche of their isolation. Specifically, we tried to prove the association between the bacteria’s phylogenomic distribution and habitat, and to find possible biological functions that could be shared by each niche-associated isolate. The pan-genomic analysis revealed that the enriched function of selenium availability in gladiolus pathogenic B. gladioli tends to be a uniquely important role during the competition between plant host and pathogen for the limited selenium resources. The aromatic compound degradation pathway in the genomes of B. gladioli isolates, which originate from rice and nature, was indicative of seed-borne nature and crude oil-accumulated nature, respectively. Enriched functions of human-associated genomes implied the possibility of promoting genetic diversification and accelerating the adaptation by stress-induced DNA repair systems and recombination, and of the possible protective role of biofilm formation as the host-defense measure in the cystic fibrosis-niche. We also identified the conservation of rhizome BGC in all B. gladioli strains and concentrated distribution of this cluster in human isolates. Finally, we confirmed the absence of a complete CRISPR/Cas system in both plant and human pathogenic B. gladioli and the presence of the system in B. gladioli living in nature, possibly reflecting the inverse relationship between CRISPR/Cas system and antibiotic resistance. Thus, this framework will provide snapshots of how ecological niches affect the versatility of gene contents in the genome and contribute to the metabolic diversity of bacteria.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/microorganisms9061123/s1, Figure S1: Clusters of orthologous group (COG) distribution of the Burkholderia gladioli core and accessory genomes, Table S1: The pairwise comparison of average nucleotide identity (ANI) values between 14 Burkholderia gladioli strains, Table S2: GO enrichment analysis of Burkholderia gladioli core genomes.

Author Contributions

Conceptualization, Y.-S.S.; methodology, H.-H.L., J.P. and H.J.; data curation, H.-H.L., J.P. and H.J.; writing—original draft preparation, H.-H.L., J.P. and Y.-S.S.; writing—review and editing, H.-H.L., J.P., H.J. and Y.-S.S.; project administration, Y.-S.S.; funding acquisition, Y.-S.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (NRF-2019R1A2C2006779) and by grants from the Strategic Initiative for Microbiomes in Agriculture and Food, Ministry of Agriculture, Food and Rural Affairs, Republic of Korea (no. 918019–04). This study was also financially supported by the 2020 Post-Doc. Development Program of Pusan National University.

Data Availability Statement

The genome sequences of B. gladioli KACC 18962 chromosome 1, chromosome 2, and plasmid genome are available in the NCBI GenBank under accession numbers CP045573, CP045574, and CP045575, respectively.

Acknowledgments

We would like to thank Hokeun Son for his advice regarding the genomics analyses.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Dutta, C.; Paul, S. Microbial lifestyle and genome signatures. Curr. Genom. 2012, 13, 153–162. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Di Cenzo, G.C.; Finan, T.M. The divided bacterial genome: Structure, function, and evolution. Microbiol. Mol. Biol. Rev. 2017, 81. [Google Scholar] [CrossRef] [Green Version]
  3. Toft, C.; Andersson, S.G.E. Evolutionary microbial genomics: Insights into bacterial host adaptation. Nat. Rev. Genet. 2010, 11, 465–475. [Google Scholar] [CrossRef]
  4. Cases, I.; De Lorenzo, V.; Ouzounis, C.A. Transcription regulation and environmental adaptation in bacteria. Trends Microbiol. 2003, 11, 248–253. [Google Scholar] [CrossRef]
  5. Retchless, A.C.; Lawrence, J.G. Ecological adaptation in bacteria: Speciation driven by codon selection. Mol. Biol. Evol. 2012, 29, 3669–3683. [Google Scholar] [CrossRef] [Green Version]
  6. Fraser-Liggett, C.M. Insights on biology and evolution from microbial genome sequencing. Genome Res. 2005, 15, 1603–1610. [Google Scholar] [CrossRef] [Green Version]
  7. Zhang, J.; Chiodini, R.; Badr, A.; Zhang, G. The impact of next-generation sequencing on genomics. J. Genet. Genom. 2011, 38, 95–109. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Miller, W.; Makova, K.D.; Nekrutenko, A.; Hardison, R.C. Comparative genomics. Annu. Rev. Genom. Hum. Genet. 2004, 5, 15–56. [Google Scholar] [CrossRef] [PubMed]
  9. Tettelin, H.; Riley, D.; Cattuto, C.; Medini, D. Comparative genomics: The bacterial pan-genome. Curr. Opin. Microbiol. 2008, 11, 472–477. [Google Scholar] [CrossRef]
  10. Tettelin, H.; Masignani, V.; Cieslewicz, M.J.; Donati, C.; Medini, D.; Ward, N.L.; Angiuoli, S.V.; Crabtree, J.; Jones, A.L.; Durkin, A.S.; et al. Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: Implications for the microbial “pan-genome”. Proc. Natl. Acad. Sci. USA 2005, 102, 13950–13955. [Google Scholar] [CrossRef] [Green Version]
  11. Marschall, T.; Marz, M.; Abeel, T.; Dijkstra, L.; Dutilh, B.E.; Ghaffaari, A.; Kersey, P.; Kloosterman, W.P.; Mäkinen, V.; Novak, A.M.; et al. Computational pan-genomics: Status, promises and challenges. Brief. Bioinform. 2018, 19, 118–135. [Google Scholar] [CrossRef] [Green Version]
  12. Mira, A.; Martín-Cuadrado, A.B.; D’Auria, G.; Rodríguez-Valera, F. The bacterial pan-genome: A new paradigm in microbiology. Int. Microbiol. 2010, 13, 45–57. [Google Scholar] [CrossRef] [PubMed]
  13. Nunvar, J.; Capek, V.; Fiser, K.; Fila, L.; Drevinek, P. What matters in chronic Burkholderia cenocepacia infection in cystic fibrosis: Insights from comparative genomics. PLoS Pathog. 2017, 13. [Google Scholar] [CrossRef] [Green Version]
  14. Vandamme, P.; Peeters, C.; De Smet, B.; Price, E.P.; Sarovich, D.S.; Henry, D.A.; Hird, T.J.; Zlosnik, J.E.A.; Mayo, M.; Warner, J.; et al. Comparative genomics of Burkholderia singularis sp. nov., a low G+C content, free-living bacterium that defies taxonomic dissection of the genus Burkholderia. Front. Microbiol. 2017, 8. [Google Scholar] [CrossRef] [Green Version]
  15. Gislason, A.S.; Turner, K.; Domaratzki, M.; Cardona, S.T. Comparative analysis of the Burkholderia cenocepacia K56-2 essential genome reveals cell envelope functions that are uniquely required for survival in species of the genus Burkholderia. Microb. Genom. 2017, 3. [Google Scholar] [CrossRef] [Green Version]
  16. Seo, Y.-S.; Lim, J.Y.; Park, J.; Kim, S.; Lee, H.-H.; Cheong, H.; Kim, S.-M.; Moon, J.S.; Hwang, I. Comparative genome analysis of rice-pathogenic Burkholderia provides insight into capacity to adapt to different environments and hosts. BMC Genom. 2015, 16, 349. [Google Scholar] [CrossRef] [Green Version]
  17. O’Sullivan, L.A.; Mahenthiralingam, E. Biotechnological potential within the genus Burkholderia. Lett. Appl. Microbiol. 2005, 41, 8–11. [Google Scholar] [CrossRef] [PubMed]
  18. Compant, S.; Nowak, J.; Coenye, T.; Clément, C.; Ait Barka, E. Diversity and occurrence of Burkholderia spp. in the natural environment. FEMS Microbiol. Rev. 2008, 32, 607–626. [Google Scholar] [CrossRef] [Green Version]
  19. Mahenthiralingam, E.; Urban, T.A.; Goldberg, J.B. The multifarious, multireplicon Burkholderia cepacia complex. Nat. Rev. Microbiol. 2005, 3, 144–156. [Google Scholar] [CrossRef] [PubMed]
  20. Eberl, L.; Vandamme, P. Members of the genus Burkholderia: Good and bad guys. F1000Research 2016, 5, 1007. [Google Scholar] [CrossRef]
  21. Seo, Y.S.; Lim, J.; Choi, B.S.; Kim, H.; Goo, E.; Lee, B.; Lim, J.S.; Choi, I.Y.; Moon, J.S.; Kim, J.; et al. Complete genome sequence of Burkholderia gladioli BSR3. J. Bacteriol. 2011, 193, 3149. [Google Scholar] [CrossRef] [Green Version]
  22. McCulloch, L. A bacterial disease of gladiolus. Science 1921, 54, 115–116. [Google Scholar] [CrossRef] [Green Version]
  23. Nandakumar, R.; Rush, M.C.; Correa, F. Association of Burkholderia glumae and B. gladioli with panicle blight symptoms on rice in Panama. Plant Dis. 2007, 91, 767. [Google Scholar] [CrossRef]
  24. Nandakumar, R.; Shahjahan, A.K.M.; Yuan, X.L.; Dickstein, E.R.; Groth, D.E.; Clark, C.A.; Cartwright, R.D.; Rush, M.C. Burkholderia glumae and B. gladioli cause bacterial panicle blight in rice in the Southern United States. Plant Dis. 2009, 93, 896–905. [Google Scholar] [CrossRef] [Green Version]
  25. Ura, H.; Furuya, N.; Iiyama, K.; Hidaka, M.; Tsuchiya, K.; Matsuyama, N. Burkholderia gladioli associated with symptoms of bacterial grain rot and leaf-sheath browning of rice plants. J. Gen. Plant Pathol. 2006, 72, 98–103. [Google Scholar] [CrossRef]
  26. Hotta, H.; Yano, I.; Yabuuchi, E.; Kosako, Y.; Oyaizu, H.; Ezaki, T.; Hashimoto, Y.; Arakawa, M. Proposal of Burkholderia gen. nov. and transfer of seven species of the genus Pseudomonas homology group II to the new genus, with the type species Burkholderia cepacia (Palleroni and Holmes 1981) comb. nov. Microbiol. Immunol. 1992, 36, 1251–1275. [Google Scholar] [CrossRef]
  27. Chowdhury, P.R.; Heinemann, J.A. The general secretory pathway of Burkholderia gladioli pv. agaricicola BG164R is necessary for cavity disease in white button mushrooms. Appl. Environ. Microbiol. 2006, 72, 3558–3565. [Google Scholar] [CrossRef] [Green Version]
  28. Lopes, E.F.; Da Costa, J.G.; Wolf, I.R.; de Lima, J.P.A.; Astolfi-Filho, S. Draft genome sequence of Burkholderia gladioli Coa14, a bacterium with petroleum bioremediation potential isolated from Coari Lake, Amazonas, Brazil. Genome Announc. 2018, 6. [Google Scholar] [CrossRef] [Green Version]
  29. Collymore, C.; Giuliano, F.; Banks, E.K. Head tilt in immunodeficient mice due to contamination of drinking water by Burkholderia gladioli. J. Am. Assoc. Lab. Anim. Sci. 2019, 58, 246–250. [Google Scholar] [CrossRef]
  30. Brizendine, K.D.; Baddley, J.W.; Pappas, P.G.; Leon, K.J.; Rodriguez, J.M. Fatal Burkholderia gladioli infection misidentified as empedobacter brevis in a lung transplant recipient with cystic fibrosis. Transpl. Infect. Dis. 2012, 14. [Google Scholar] [CrossRef] [PubMed]
  31. Quon, B.S.; Reid, J.D.; Wong, P.; Wilcox, P.G.; Javer, A.; Wilson, J.M.; Levy, R.D. Burkholderia gladioli—A predictor of poor outcome in cystic fibrosis patients who receive lung transplants? A case of locally invasive rhinosinusitis and persistent bacteremia in a 36-year-old lung transplant recipient with cystic fibrosis. Can. Respir. J. 2011, 18. [Google Scholar] [CrossRef]
  32. Jones, C.; Webster, G.; Mullins, A.J.; Jenner, M.; Bull, M.J.; Dashti, Y.; Spilker, T.; Parkhill, J.; Connor, T.R.; Lipuma, J.J.; et al. Kill and cure: Genomic phylogeny and bioactivity of Burkholderia gladioli bacteria capable of pathogenic and beneficial lifestyles. Microb. Genom. 2021, 7, 1–13. [Google Scholar] [CrossRef]
  33. Chin, C.S.; Alexander, D.H.; Marks, P.; Klammer, A.A.; Drake, J.; Heiner, C.; Clum, A.; Copeland, A.; Huddleston, J.; Eichler, E.E.; et al. Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data. Nat. Methods 2013, 10, 563–569. [Google Scholar] [CrossRef]
  34. Walker, B.J.; Abeel, T.; Shea, T.; Priest, M.; Abouelliel, A.; Sakthikumar, S.; Cuomo, C.A.; Zeng, Q.; Wortman, J.; Young, S.K.; et al. Pilon: An integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE 2014, 9. [Google Scholar] [CrossRef] [PubMed]
  35. Seemann, T. Prokka: Rapid prokaryotic genome annotation. Bioinformatics 2014, 30, 2068–2069. [Google Scholar] [CrossRef]
  36. Besemer, J.; Lomsadze, A.; Borodovsky, M. GeneMarkS: A self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions. Nucleic Acids Res. 2001, 29, 2607–2618. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Pruitt, K.D.; Tatusova, T.; Maglott, D.R. NCBI reference sequences (RefSeq): A curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 2007, 35. [Google Scholar] [CrossRef] [Green Version]
  38. Pritchard, L.; Glover, R.H.; Humphris, S.; Elphinstone, J.G.; Toth, I.K. Genomics and taxonomy in diagnostics for food security: Soft-rotting enterobacterial plant pathogens. Anal. Methods 2016, 8, 12–24. [Google Scholar] [CrossRef]
  39. Lin, Y.T.; Lee, C.C.; Leu, W.M.; Wu, J.J.; Huang, Y.C.; Meng, M. Fungicidal activity of volatile organic compounds emitted by Burkholderia gladioli strain BBB-01. Molecules 2021, 26, 745. [Google Scholar] [CrossRef]
  40. Page, A.J.; Cummins, C.A.; Hunt, M.; Wong, V.K.; Reuter, S.; Holden, M.T.; Fookes, M.; Falush, D.; Keane, J.A.; Parkhill, J. Roary: Rapid large-scale prokaryote pan genome analysis. Bioinformatics 2015, 31, 3691–3693. [Google Scholar] [CrossRef] [PubMed]
  41. Stamatakis, A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 2014, 30, 1312–1313. [Google Scholar] [CrossRef]
  42. Kumar, S.; Stecher, G.; Li, M.; Knyaz, C.; Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 2018, 35, 1547–1549. [Google Scholar] [CrossRef] [PubMed]
  43. Zhao, Y.; Wu, J.; Yang, J.; Sun, S.; Xiao, J.; Yu, J. PGAP: Pan-genomes analysis pipeline. Bioinformatics 2012, 28, 416–418. [Google Scholar] [CrossRef] [Green Version]
  44. Altschul, S.F.; Gish, W.; Miller, W.; Myers, E.W.; Lipman, D.J. Basic local alignment search tool. J. Mol. Biol. 1990, 215, 403–410. [Google Scholar] [CrossRef]
  45. Enright, A.J.; Van Dongen, S.; Ouzounis, C.A. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res. 2002, 30, 1575–1584. [Google Scholar] [CrossRef]
  46. Zhao, Y.; Jia, X.; Yang, J.; Ling, Y.; Zhang, Z.; Yu, J.; Wu, J.; Xiao, J. PanGP: A tool for quickly analyzing bacterial pan-genome profile. Bioinformatics 2014, 30, 1297–1299. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Eren, A.M.; Esen, O.C.; Quince, C.; Vineis, J.H.; Morrison, H.G.; Sogin, M.L.; Delmont, T.O. Anvi’o: An advanced analysis and visualization platformfor ’omics data. PeerJ 2015, 2015. [Google Scholar] [CrossRef]
  48. Tatusov, R.L.; Fedorova, N.D.; Jackson, J.D.; Jacobs, A.R.; Kiryutin, B.; Koonin, E.V.; Krylov, D.M.; Mazumder, R.; Smirnov, S.; Nikolskaya, A.N.; et al. The COG database: An updated vesion includes eukaryotes. BMC Bioinform. 2003, 4. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  49. Carbon, S.; Douglass, E.; Dunn, N.; Good, B.; Harris, N.L.; Lewis, S.E.; Mungall, C.J.; Basu, S.; Chisholm, R.L.; Dodson, R.J.; et al. The gene ontology resource: 20 years and still GOing strong. Nucleic Acids Res. 2019, 47, D330–D338. [Google Scholar] [CrossRef] [Green Version]
  50. Kanehisa, M.; Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000, 28, 27–30. [Google Scholar] [CrossRef]
  51. Conesa, A.; Götz, S.; García-Gómez, J.M.; Terol, J.; Talón, M.; Robles, M. Blast2GO: A universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 2005, 21, 3674–3676. [Google Scholar] [CrossRef] [Green Version]
  52. Jones, P.; Binns, D.; Chang, H.Y.; Fraser, M.; Li, W.; McAnulla, C.; McWilliam, H.; Maslen, J.; Mitchell, A.; Nuka, G.; et al. InterProScan 5: Genome-scale protein function classification. Bioinformatics 2014, 30, 1236–1240. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Qureshi, R.; Sacan, A. Weighted set enrichment of gene expression data. BMC Syst. Biol. 2013, 7, S10. [Google Scholar] [CrossRef] [Green Version]
  54. Blin, K.; Shaw, S.; Steinke, K.; Villebro, R.; Ziemert, N.; Lee, S.Y.; Medema, M.H.; Weber, T. AntiSMASH 5.0: Updates to the secondary metabolite genome mining pipeline. Nucleic Acids Res. 2019, 47, W81–W87. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Couvin, D.; Bernheim, A.; Toffano-Nioche, C.; Touchon, M.; Michalik, J.; Néron, B.; Rocha, E.P.C.; Vergnaud, G.; Gautheret, D.; Pourcel, C. CRISPRCasFinder, an update of CRISRFinder, includes a portable version, enhanced performance and integrates search for Cas proteins. Nucleic Acids Res. 2018, 46, W246–W251. [Google Scholar] [CrossRef] [Green Version]
  56. Biswas, A.; Gagnon, J.N.; Brouns, S.J.J.; Fineran, P.C.; Brown, C.M. CRISPRTarget: Bioinformatic prediction and analysis of crRNA targets. RNA Biol. 2013, 10, 817–827. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  57. Johnson, S.L.; Bishop-Lilly, K.A.; Ladner, J.T.; Daligault, H.E.; Davenport, K.W.; Jaissle, J.; Frey, K.G.; Koroleva, G.I.; Bruce, D.C.; Coyne, S.R.; et al. Complete genome sequences for 59 Burkholderia isolates, both pathogenic and near neighbor. Genome Announc. 2016, 3, e00159-15. [Google Scholar] [CrossRef] [Green Version]
  58. Chen, H.; Sun, T.; Bai, X.; Yang, J.; Yan, F.; Yu, L.; Tu, Q.; Li, A.; Tang, Y.; Zhang, Y.; et al. Genomics-driven activation of silent biosynthetic gene clusters in Burkholderia gladioli by screening recombineering system. Molecules 2021, 26, 700. [Google Scholar] [CrossRef]
  59. Clode, F.E.; Kaufmann, M.E.; Malnick, H.; Pitt, T.L. Evaluation of three oligonucleotide primer sets in PCR for the identification of Burkholderia cepacia and their differentiation from Burkholderia gladioli. J. Clin. Pathol. 1999, 52, 173–176. [Google Scholar] [CrossRef] [Green Version]
  60. Lee, J.; Park, J.; Kim, S.; Park, I.; Seo, Y.S. Differential regulation of toxoflavin production and its role in the enhanced virulence of Burkholderia gladioli. Mol. Plant Pathol. 2016, 17, 65–76. [Google Scholar] [CrossRef] [PubMed]
  61. Daligault, H.; Davenport, K.; Minogue, T.; Bishop-Lilly, K.; Broomall, S.; Bruce, D.; Chain, P.; Coyne, S.; Frey, K.; Gibbons, H.; et al. Whole-genome assemblies of 56 Burkholderia species. Genome Announc. 2014, 2, e01106-14. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  62. Li, R.; Oliver, R.A.; Townsend, C.A. Identification and characterization of the sulfazecin monobactam biosynthetic gene cluster. Cell Chem. Biol. 2017, 24, 24–34. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Fory, P.A.; Triplett, L.; Ballen, C.; Abello, J.F.; Duitama, J.; Aricapa, M.G.; Prado, G.A.; Correa, F.; Hamilton, J.; Leach, J.E.; et al. Comparative analysis of two emerging rice seed bacterial pathogens. Phytopathology 2014, 104, 436–444. [Google Scholar] [CrossRef] [PubMed]
  64. Naughton, L.M.; An, S.Q.; Hwang, I.; Chou, S.H.; He, Y.Q.; Tang, J.L.; Ryan, R.P.; Dow, J.M. Functional and genomic insights into the pathogenesis of Burkholderia species to rice. Environ. Microbiol. 2016, 18, 780–790. [Google Scholar] [CrossRef]
  65. Kim, S.; Park, J.; Choi, O.; Kim, J.; Seo, Y.S. Investigation of quorum sensing-dependent gene expression in Burkholderia gladioli BSR3 through RNA-seq analyses. J. Microbiol. Biotechnol. 2014, 24, 1609–1621. [Google Scholar] [CrossRef] [Green Version]
  66. Zeiser, E.T.; Becka, S.A.; Wilson, B.M.; Barnes, M.D.; LiPuma, J.J.; Papp-Wallace, K.M. “Switching partners”: Piperacillin-Avibactam is a highly potent combination against multidrug-resistant Burkholderia cepacia complex and Burkholderia gladioli cystic fibrosis isolates. J. Clin. Microbiol. 2019, 57, e00181-19. [Google Scholar] [CrossRef] [Green Version]
  67. Zeiser, E.T.; Becka, S.A.; Barnes, M.D.; Taracila, M.A.; LiPuma, J.J.; Papp-Wallace, K.M. Resurrecting old -lactams: Potent inhibitory activity of temocillin against multidrug-resistant Burkholderia species isolates from the United States. Antimicrob. Agents Chemother. 2019, 63, e02315-18. [Google Scholar] [CrossRef] [Green Version]
  68. Stothard, P.; Wishart, D.S. Circular genome visualization and exploration using CGView. Bioinformatics 2005, 21, 537–539. [Google Scholar] [CrossRef] [Green Version]
  69. Kim, M.; Oh, H.S.; Park, S.C.; Chun, J. Towards a taxonomic coherence between average nucleotide identity and 16S rRNA gene sequence similarity for species demarcation of prokaryotes. Int. J. Syst. Evol. Microbiol. 2014, 64, 346–351. [Google Scholar] [CrossRef]
  70. Lipuma, J.J. The changing microbial epidemiology in cystic fibrosis. Clin. Microbiol. Rev. 2010, 23, 299–323. [Google Scholar] [CrossRef] [Green Version]
  71. Diene, S.M.; Merhej, V.; Henry, M.; El Filali, A.; Roux, V.; Robert, C.; Azza, S.; Gavory, F.; Barbe, V.; La Scola, B.; et al. The rhizome of the multidrug-resistant enterobacter aerogenes genome reveals how new “Killer Bugs” are created because of a sympatric lifestyle. Mol. Biol. Evol. 2013, 30, 369–383. [Google Scholar] [CrossRef] [Green Version]
  72. Tuanyok, A.; Leadem, B.R.; Auerbach, R.K.; Beckstrom-Sternberg, S.M.; Beckstrom-Sternberg, J.S.; Mayo, M.; Wuthiekanun, V.; Brettin, T.S.; Nierman, W.C.; Peacock, S.J.; et al. Genomic islands from five strains of Burkholderia pseudomallei. BMC Genom. 2008, 9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  73. Zhou, J.; Ren, H.; Hu, M.; Zhou, J.; Li, B.; Kong, N.; Zhang, Q.; Jin, Y.; Liang, L.; Yue, J. Characterization of Burkholderia cepacia complex core genome and the underlying recombination and positive selection. Front. Genet. 2020, 11. [Google Scholar] [CrossRef]
  74. Georgiades, K.; Raoult, D. Defining pathogenic bacterial species in the genomic era. Front. Microbiol. 2011, 1. [Google Scholar] [CrossRef] [Green Version]
  75. Siew, H.S.; Yu, Y.; Chi, H.L.; Karuturi, R.K.M.; Wuthiekanun, V.; Tuanyok, A.; Hui, H.C.; Ong, C.; Paramalingam, S.S.; Tan, G.; et al. The core and accessory genomes of Burkholderia pseudomallei: Implications for human melioidosis. PLoS Pathog. 2008, 4. [Google Scholar] [CrossRef] [Green Version]
  76. Konstantinidis, K.T.; Tiedje, J.M. Trends between gene content and genome size in prokaryotic species with larger genomes. Proc. Natl. Acad. Sci. USA 2004, 101, 3160–3165. [Google Scholar] [CrossRef] [Green Version]
  77. Romero, H.; Zhang, Y.; Gladyshev, V.N.; Salinas, G. Evolution of selenium utilization traits. Genome Biol. 2005, 6. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  78. Zhang, Y.; Turanov, A.A.; Hatfield, D.L.; Gladyshev, V.N. In silico identification of genes involved in selenium metabolism: Evidence for a third selenium utilization trait. BMC Genom. 2008, 9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  79. Zhang, Y.; Romero, H.; Salinas, G.; Gladyshev, V.N. Dynamic evolution of selenocysteine utilization in bacteria: A balance between selenoprotein loss and evolution of selenocysteine from redox active cysteine residues. Genome Biol. 2006, 7. [Google Scholar] [CrossRef] [Green Version]
  80. Whanger, P.D. Selenocompounds in plants and animals and their biological significance. J. Am. Coll. Nutr. 2002, 21, 223–232. [Google Scholar] [CrossRef]
  81. Feng, R.; Wei, C.; Tu, S. The roles of selenium in protecting plants against abiotic stresses. Environ. Exp. Bot. 2013, 87, 58–68. [Google Scholar] [CrossRef]
  82. Pilon-Smits, E.A.; Quinn, C.F.; Tapken, W.; Malagoli, M.; Schiavon, M. Physiological functions of beneficial elements. Curr. Opin. Plant Biol. 2009, 12, 267–274. [Google Scholar] [CrossRef] [PubMed]
  83. Kaur, N.; Sharma, S.; Kaur, S.; Nayyar, H. Selenium in agriculture: A nutrient or contaminant for crops? Arch. Agron. Soil Sci. 2014, 60, 1593–1624. [Google Scholar] [CrossRef]
  84. Chauhan, R.; Awasthi, S.; Tripathi, P.; Mishra, S.; Dwivedi, S.; Niranjan, A.; Mallick, S.; Tripathi, P.; Tripathi, R.D.; Chauhan, R.; et al. Selenite modulates the level of phenolics and nutrient element to alleviate the toxicity of arsenite in rice (Oryza sativa L.). Ecotoxicol. Environ. Saf. 2017, 138, 47–55. [Google Scholar] [CrossRef] [PubMed]
  85. Balk, J.; Pilon, M. Ancient and essential: The assembly of iron-sulfur clusters in plants. Trends Plant Sci. 2011, 16, 218–226. [Google Scholar] [CrossRef]
  86. Rahmanto, A.S.; Davies, M.J. Selenium-containing amino acids as direct and indirect antioxidants. IUBMB Life 2012, 64, 863–871. [Google Scholar] [CrossRef] [PubMed]
  87. Sumner, S.E.; Markley, R.L.; Kirimanjeswara, G.S. Role of selenoproteins in bacterial pathogenesis. Biol. Trace Elem. Res. 2019, 192, 69–82. [Google Scholar] [CrossRef]
  88. Brink, D.P.; Ravi, K.; Lidén, G.; Gorwa-Grauslund, M.F. Mapping the diversity of microbial lignin catabolism: Experiences from the eLignin database. Appl. Microbiol. Biotechnol. 2019, 103, 3979–4002. [Google Scholar] [CrossRef] [Green Version]
  89. Bugg, T.D.H.; Winfield, C.J. Enzymatic cleavage of aromatic rings: Mechanistic aspects of the catechol dioxygenases and later enzymes of bacterial oxidative cleavage pathways. Nat. Prod. Rep. 1998, 15, 513–530. [Google Scholar] [CrossRef]
  90. Romero-Silva, M.J.; Méndez, V.; Agulló, L.; Seeger, M. Genomic and functional analyses of the gentisate and protocatechuate ring-cleavage pathways and related 3-hydroxybenzoate and 4-hydroxybenzoate peripheral pathways in Burkholderia xenovorans LB400. PLoS ONE 2013, 8. [Google Scholar] [CrossRef] [Green Version]
  91. Pérez-Pantoja, D.; Donoso, R.; Agulló, L.; Córdova, M.; Seeger, M.; Pieper, D.H.; González, B. Genomic analysis of the potential for aromatic compounds biodegradation in Burkholderiales. Environ. Microbiol. 2012, 14, 1091–1117. [Google Scholar] [CrossRef]
  92. Cottyn, B.; Regalado, E.; Lanoot, B.; De Cleene, M.; Mew, T.W.; Swings, J. Bacterial populations associated with rice seed in the tropical environment. Phytopathology 2001, 91, 282–292. [Google Scholar] [CrossRef] [Green Version]
  93. Nandakumar, R.; Bollich, P.A.; Shahjahan, A.K.M.; Groth, D.E.; Rush, M.C. Evidence for the soilborne nature of the rice sheath rot and panicle blight pathogen, Burkholderia gladioli. Can. J. Plant Pathol. 2008, 30, 148–154. [Google Scholar] [CrossRef]
  94. Ma’Ruf, A.; Pramudono, B.; Aryanti, N. Lignin isolation process from rice husk by alkaline hydrogen peroxide: Lignin and silica extracted. In Proceedings of the AIP Conference Proceedings; American Institute of Physics Inc.: Yogyakarta, Indonesia, 2017; Volume 1823, p. 020013. [Google Scholar]
  95. Seo, J.S.; Keum, Y.S.; Li, Q.X. Bacterial degradation of aromatic compounds. Int. J. Environ. Res. Public Health 2009, 6, 278–309. [Google Scholar] [CrossRef]
  96. Ali, N.; Dashti, N.; Khanafer, M.; Al-Awadhi, H.; Radwan, S. Bioremediation of soils saturated with spilled crude oil. Sci. Rep. 2020, 10. [Google Scholar] [CrossRef] [Green Version]
  97. Andreoni, V.; Gianfreda, L. Bioremediation and monitoring of aromatic-polluted habitats. Appl. Microbiol. Biotechnol. 2007, 76, 287–308. [Google Scholar] [CrossRef] [PubMed]
  98. Amoroso, A.; Crespan, E.; Wimmer, U.; Hubscher, U.; Maga, G. DNA polymerases and oxidative damage: Friends or foes? Curr. Mol. Pharmacol. 2010, 1, 162–170. [Google Scholar] [CrossRef] [PubMed]
  99. Dorer, M.S.; Sessler, T.H.; Salama, N.R. Recombination and DNA repair in Helicobacter pylori. Annu. Rev. Microbiol. 2011, 65, 329–348. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  100. Sahan, A.Z.; Hazra, T.K.; Das, S. The pivotal role of DNA repair in infection mediated-inflammation and cancer. Front. Microbiol. 2018, 9. [Google Scholar] [CrossRef] [Green Version]
  101. Guillemet, E.; Leréec, A.; Tran, S.L.; Royer, C.; Barbosa, I.; Sansonetti, P.; Lereclus, D.; Ramarao, N. The bacterial DNA repair protein Mfd confers resistance to the host nitrogen immune response. Sci. Rep. 2016, 6. [Google Scholar] [CrossRef] [Green Version]
  102. Stich, M.; Manrubia, S.C.; Lázaro, E. Variable mutation rates as an adaptive strategy in replicator populations. PLoS ONE 2010, 5. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  103. Wielgoss, S.; Schneider, D.; Barrick, J.E.; Tenaillon, O.; Cruveiller, S.; Chane-Woon-Ming, B.; Médigue, C.; Lenski, R.E. Mutation rate inferred from synonymous substitutions in a long-term evolution experiment with Escherichia coli. G3 Genes Genomes Genet 2011, 1, 183–186. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  104. Suerbaum, S.; Maynard Smith, J.; Bapumia, K.; Morelli, G.; Smith, N.H.; Kunstmann, E.; Dyrek, I.; Achtman, M. Free recombination within Helicobacter pylori. Proc. Natl. Acad. Sci. USA 1998, 95, 12619–12624. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  105. Doroghazi, J.R.; Buckley, D.H. Widespread homologous recombination within and between Streptomyces species. ISME J. 2010, 4, 1136–1143. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  106. Kong, L.C.; Tap, J.; Aron-Wisnewsky, J.; Pelloux, V.; Basdevant, A.; Bouillot, J.L.; Zucker, J.D.; Doré, J.; Clément, K. Gut microbiota after gastric bypass in human obesity: Increased richness and associations of bacterial genera with adipose tissue genes. Am. J. Clin. Nutr. 2013, 98, 16–24. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  107. Cooper, T.F. Recombination speeds adaptation by reducing competition between beneficial mutations in populations of Escherichia coli. PLoS Biol. 2007, 5, 1899–1905. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  108. Van Acker, H.; Sass, A.; Bazzini, S.; De Roy, K.; Udine, C.; Messiaen, T.; Riccardi, G.; Boon, N.; Nelis, H.J.; Mahenthiralingam, E.; et al. Biofilm-grown Burkholderia cepacia complex cells survive antibiotic treatment by avoiding production of reactive oxygen species. PLoS ONE 2013, 8. [Google Scholar] [CrossRef] [Green Version]
  109. Lawrence, J.G.; Hendrickson, H. Lateral gene transfer: When will adolescence end? Mol. Microbiol. 2003, 50, 739–749. [Google Scholar] [CrossRef]
  110. Beiko, R.G.; Harlow, T.J.; Ragan, M.A. Highways of gene sharing in prokaryotes. Proc. Natl. Acad. Sci. USA 2005, 102, 14332–14337. [Google Scholar] [CrossRef] [Green Version]
  111. Wallden, K.; Rivera-Calzada, A.; Waksman, G. Type IV secretion systems: Versatility and diversity in function. Cell. Microbiol. 2010, 12, 1203–1212. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  112. Bertani, B.; Ruiz, N. Function and biogenesis of lipopolysaccharides. EcoSal Plus 2018, 8. [Google Scholar] [CrossRef] [PubMed]
  113. Zhu, C.; Guo, G.; Ma, Q.; Zhang, F.; Ma, F.; Liu, J.; Xiao, D.; Yang, X.; Sun, M. Diversity in S-layers. Prog. Biophys. Mol. Biol. 2017, 123, 1–15. [Google Scholar] [CrossRef] [PubMed]
  114. Sleytr, U.B.; Schuster, B.; Egelseer, E.M.; Pum, D. S-layers: Principles and applications. FEMS Microbiol. Rev. 2014, 38, 823–864. [Google Scholar] [CrossRef]
  115. Sleytr, U.B.I. Basic and applied S-layer research: An overview. FEMS Microbiol. Rev. 1997, 20, 5–12. [Google Scholar] [CrossRef] [Green Version]
  116. Hibbing, M.E.; Fuqua, C.; Parsek, M.R.; Peterson, S.B. Bacterial competition: Surviving and thriving in the microbial jungle. Nat. Rev. Microbiol. 2010, 8, 15–25. [Google Scholar] [CrossRef] [Green Version]
  117. Marahiel, M.A. Working outside the protein-synthesis rules: Insights into non-ribosomal peptide synthesis. J. Pept. Sci. 2009, 15, 799–807. [Google Scholar] [CrossRef] [PubMed]
  118. Li, Y.X.; Zhong, Z.; Zhang, W.P.; Qian, P.Y. Discovery of cationic nonribosomal peptides as gram-negative antibiotics through global genome mining. Nat. Commun. 2018, 9. [Google Scholar] [CrossRef]
  119. Esmaeel, Q.; Pupin, M.; Jacques, P.; Leclère, V. Nonribosomal peptides and polyketides of Burkholderia: New compounds potentially implicated in biocontrol and pharmaceuticals. Environ. Sci. Pollut. Res. 2018, 25, 29794–29807. [Google Scholar] [CrossRef]
  120. Lin, Z.; Falkinham, J.O.; Tawfik, K.A.; Jeffs, P.; Bray, B.; Dubay, G.; Cox, J.E.; Schmidt, E.W. Burkholdines from Burkholderia ambifaria: Antifungal agents and possible virulence factors. J. Nat. Prod. 2012, 75, 1518–1523. [Google Scholar] [CrossRef]
  121. Franke, J.; Ishida, K.; Ishida-Ito, M.; Hertweck, C. Nitro versus hydroxamate in siderophores of pathogenic bacteria: Effect of missing hydroxylamine protection in malleobactin biosynthesis. Angew. Chem. Int. Ed. 2013, 52, 8271–8275. [Google Scholar] [CrossRef]
  122. Carr, G.; Seyedsayamdost, M.R.; Chandler, J.R.; Greenberg, E.P.; Clardy, J. Sources of diversity in bactobolin biosynthesis by Burkholderia thailandensis E264. Org. Lett. 2011, 13, 3048–3051. [Google Scholar] [CrossRef] [PubMed]
  123. Wang, X.; Zhou, H.; Chen, H.; Jing, X.; Zheng, W.; Li, R.; Sun, T.; Liu, J.; Fu, J.; Huo, L.; et al. Discovery of recombinases enables genome mining of cryptic biosynthetic gene clusters in Burkholderiales species. Proc. Natl. Acad. Sci. USA 2018, 115, E4255–E4263. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  124. Tang, B.; Yu, Y.; Zhang, Y.; Zhao, G.; Ding, X. Complete genome sequence of the glidobactin producing strain [Polyangium] brachysporum DSM 7029. J. Biotechnol. 2015, 210, 83–84. [Google Scholar] [CrossRef] [PubMed]
  125. Lackner, G.; Moebius, N.; Partida-Martinez, L.; Hertweck, C. Complete genome sequence of Burkholderia rhizoxinica, an endosymbiont of Rhizopus microsporus. J. Bacteriol. 2011, 193, 783–784. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  126. Irschik, H.; Schummer, D.; Höfle, G.; Reichenbach, H.; Steinmetz, H.; Jansen, R. Etnangien, a macrolide-polyene antibiotic from Sorangium cellulosum that inhibits nucleic acid polymerases. J. Nat. Prod. 2007, 70, 1060–1063. [Google Scholar] [CrossRef]
  127. Jansen, R.; Van Embden, J.D.A.; Gaastra, W.; Schouls, L.M. Identification of genes that are associated with DNA repeats in prokaryotes. Mol. Microbiol. 2002, 43, 1565–1575. [Google Scholar] [CrossRef]
  128. Mojica, F.J.; Díez-Villaseñor, C.; Soria, E.; Juez, G. Biological significance of a family of regularly spaced repeats in the genomes of archaea, bacteria and mitochondria. Mol. Microbiol. 2000, 36, 244–246. [Google Scholar] [CrossRef]
  129. Louwen, R.; Staals, R.; Endtz, H.; van Baarlen, P.; van der Oost, J. The role of CRISPR-Cas systems in virulence of pathogenic bacteria. Microbiol. Mol. Biol. Rev. 2014, 78, 74–88. [Google Scholar] [CrossRef] [Green Version]
  130. Gao, R.; Krysciak, D.; Petersen, K.; Utpatel, C.; Knapp, A.; Schmeisser, C.; Daniel, R.; Voget, S.; Jaeger, K.E.; Streit, W.R. Genome-wide RNA sequencing analysis of quorum sensing-controlled regulons in the plant-associated Burkholderia glumae PG1 strain. Appl. Environ. Microbiol. 2015, 81, 7993–8007. [Google Scholar] [CrossRef] [Green Version]
  131. Høyland-Kroghsbo, N.M.; Paczkowski, J.; Mukherjee, S.; Broniewski, J.; Westra, E.; Bondy-Denomy, J.; Bassler, B.L. Quorum sensing controls the Pseudomonas aeruginosa CRISPR-Cas adaptive immune system. Proc. Natl. Acad. Sci. USA 2017, 114, 131–135. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  132. Marchetti, M.; Catrice, O.; Batut, J.; Masson-Boivin, C. Cupriavidus taiwanensis bacteroids in Mimosa pudica indeterminate nodules are not terminally differentiated. Appl. Environ. Microbiol. 2011, 77, 2161–2164. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  133. Chen, W.M.; Laevens, S.; Lee, T.M.; Coenye, T.; De Vos, P.; Mergeay, M.; Vandamme, P. Ralstonia taiwanensis sp. nov., isolated from root nodules of Mimosa species and sputum of a cystic fibrosis patient. Int. J. Syst. Evol. Microbiol. 2001, 51, 1729–1735. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  134. Estrada-de los Santos, P.; Vacaseydel-Aceves, N.B.; Martínez-Aguilar, L.; Cruz-Hernández, M.A.; Mendoza-Herrera, A.; Caballero-Mellado, J. Cupriavidus and Burkholderia species associated with agricultural plants that grow in alkaline soils. J. Microbiol. 2011, 49, 867–876. [Google Scholar] [CrossRef] [PubMed]
  135. Klonowska, A.; Melkonian, R.; Miché, L.; Tisseyre, P.; Moulin, L. Transcriptomic profiling of Burkholderia phymatum STM815, Cupriavidus taiwanensis LMG19424 and Rhizobium mesoamericanum STM3625 in response to Mimosa pudica root exudates illuminates the molecular basis of their nodulation competitiveness and symbiotic ev. BMC Genom. 2018, 19, 1–22. [Google Scholar] [CrossRef]
  136. Barrett, C.F.; Parker, M.A. Coexistence of Burkholderia, Cupriavidus, and Rhizobium sp. nodule bacteria on two Mimosa spp. in Costa Rica. Appl. Environ. Microbiol. 2006, 72, 1198–1206. [Google Scholar] [CrossRef] [Green Version]
  137. Kim, J.; Yoon, S.; Park, Y.; Kim, S.; Ryu, C. Crossing the kingdom border: Human diseases caused by plant pathogens. Environ. Microbiol. 2020, 22, 2485–2495. [Google Scholar] [CrossRef] [PubMed]
  138. Kirzinger, M.W.B.; Nadarasah, G.; Stavrinides, J. Insights into cross-kingdom plant pathogenic bacteria. Genes 2011, 2, 980–997. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  139. Gong, B.; Shin, M.; Sun, J.; Jung, C.H.; Bolt, E.L.; Van Der Oost, J.; Kim, J.S. Molecular insights into DNA interference by CRISPR-associated nuclease-helicase Cas3. Proc. Natl. Acad. Sci. USA 2014, 111, 16359–16364. [Google Scholar] [CrossRef] [Green Version]
  140. Rollins, M.C.F.; Chowdhury, S.; Carter, J.; Golden, S.M.; Wilkinson, R.A.; Bondy-Denomy, J.; Lander, G.C.; Wiedenheft, B. Cas1 and the Csy complex are opposing regulators of Cas2/3 nuclease activity. Proc. Natl. Acad. Sci. USA 2017, 114, E5113–E5121. [Google Scholar] [CrossRef] [Green Version]
  141. Buyukyoruk, M.; Wiedenheft, B. Type I-F CRISPR–Cas provides protection from DNA, but not RNA phages. Cell Discov. 2019, 5. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  142. Watve, M.G.; Tickoo, R.; Jog, M.M.; Bhole, B.D. How many antibiotics are produced by the genus Streptomyces? Arch. Microbiol. 2001, 176, 386–390. [Google Scholar] [CrossRef]
  143. Petković, H.; Cullum, J.; Hranueli, D.; Hunter, I.S.; Perić-Concha, N.; Pigac, J.; Thamchaipenet, A.; Vujaklija, D.; Long, P.F. Genetics of Streptomyces rimosus, the oxytetracycline producer. Microbiol. Mol. Biol. Rev. 2006, 70, 704–728. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  144. Kumar, P.S.; Al-Dhabi, N.A.; Duraipandiyan, V.; Balachandran, C.; Kumar, P.P.; Ignacimuthu, S. In vitro antimicrobial, antioxidant and cytotoxic properties of Streptomyces lavendulae strain SCA5. BMC Microbiol. 2014, 14. [Google Scholar] [CrossRef] [Green Version]
  145. Schlatter, D.C.; Kinkel, L.L. Global biogeography of Streptomyces antibiotic inhibition, resistance, and resource use. FEMS Microbiol. Ecol. 2014, 88, 386–397. [Google Scholar] [CrossRef] [Green Version]
  146. Genin, S.; Denny, T.P. Pathogenomics of the Ralstonia solanacearum species complex. Annu. Rev. Phytopathol. 2012, 50, 67–89. [Google Scholar] [CrossRef] [PubMed]
  147. Schell, M.A. Control of virulence and pathogenicity genes of Ralstonia solanacearum by an elaborate sensory network. Annu. Rev. Phytopathol. 2000, 38, 263–292. [Google Scholar] [CrossRef] [PubMed]
  148. Palmer, K.L.; Gilmore, M.S. Multidrug-resistant enterococci lack CRISPR-cas. mBio 2010, 1. [Google Scholar] [CrossRef] [PubMed] [Green Version]
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Figure 1. Circular view of complete genome of rice pathogenic Burkholderia gladioli KACC 18962. The key describes the single circles in the top-down outermost-innermost direction. The circular maps were generated by CGView [68].
Figure 1. Circular view of complete genome of rice pathogenic Burkholderia gladioli KACC 18962. The key describes the single circles in the top-down outermost-innermost direction. The circular maps were generated by CGView [68].
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Figure 2. Phylogenomic distribution of Burkholderia gladioli isolates. (A) Comparison of the average nucleotide identity (ANI) values between each genome of the 14 strains of B. gladioli. Heatmaps display the percentage average nucleotide identities between the 14 B. gladioli strains. Color bars above and to the left of the heatmaps correspond to isolation source of each B. gladioli strain. The color keys represent the identity of strains with lower (white), and higher (red) ANI values. Hierarchal cluster dendrograms were generated based on similar ANIb values across each strain. The ANI was calculated by the Python3 module pyani using blastn alignment. (B) Core genome phylogeny of B. gladioli isolates. A total of 4392 core genes were identified using 14 B. gladioli strains which are listed in (A) and 22 B. gladioli isolates, which were analyzed in previous studies [39]. The concatenated core genes were aligned and used to construct phylogeny with 1000 rapid bootstraps. Classification of clades 1A, 1B, 2, and 3 was adopted from the previous study by Jones et al. [32]. The scale bar displays the number of base substitutions per site.
Figure 2. Phylogenomic distribution of Burkholderia gladioli isolates. (A) Comparison of the average nucleotide identity (ANI) values between each genome of the 14 strains of B. gladioli. Heatmaps display the percentage average nucleotide identities between the 14 B. gladioli strains. Color bars above and to the left of the heatmaps correspond to isolation source of each B. gladioli strain. The color keys represent the identity of strains with lower (white), and higher (red) ANI values. Hierarchal cluster dendrograms were generated based on similar ANIb values across each strain. The ANI was calculated by the Python3 module pyani using blastn alignment. (B) Core genome phylogeny of B. gladioli isolates. A total of 4392 core genes were identified using 14 B. gladioli strains which are listed in (A) and 22 B. gladioli isolates, which were analyzed in previous studies [39]. The concatenated core genes were aligned and used to construct phylogeny with 1000 rapid bootstraps. Classification of clades 1A, 1B, 2, and 3 was adopted from the previous study by Jones et al. [32]. The scale bar displays the number of base substitutions per site.
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Figure 3. Pan-genome analysis of Burkholderia gladioli strains (A) Pan- and core genome size accumulation. The colored boxes respectively denote the pan-genome (blue) and core genome (orange) sizes for each genome for comparison. The curve is the least square fit of the power law for the average values. (B) New gene family accumulation. The orange bars illustrate the number of expected new genes detected with every increase in the number of B. gladioli genomes. (C) Distribution of gene cluster numbers in the B. gladioli pan-genome. A circular plot schematic representation depicts the number of gene clusters in core, accessory, dispensable, and unique genomes. From inside to outside: Core genome, accessory genome, niche-associated accessory genome, which is further divided into gladiolus-associated (red), rice- associated (green), human- associated (orange), and nature- associated (blue) genome, and unique genome at the strain level. (D) Comparative overview of pan- and core genomes obtained from the Anvi’o tool. The different B. gladioli isolate groups are displayed by red (gladiolus isolates), green (rice isolates), orange (human isolates), and blue (nature isolates). Each track indicates a genome which is presented by number and color variations (dark/light) shows presence/absence of genes per genome.
Figure 3. Pan-genome analysis of Burkholderia gladioli strains (A) Pan- and core genome size accumulation. The colored boxes respectively denote the pan-genome (blue) and core genome (orange) sizes for each genome for comparison. The curve is the least square fit of the power law for the average values. (B) New gene family accumulation. The orange bars illustrate the number of expected new genes detected with every increase in the number of B. gladioli genomes. (C) Distribution of gene cluster numbers in the B. gladioli pan-genome. A circular plot schematic representation depicts the number of gene clusters in core, accessory, dispensable, and unique genomes. From inside to outside: Core genome, accessory genome, niche-associated accessory genome, which is further divided into gladiolus-associated (red), rice- associated (green), human- associated (orange), and nature- associated (blue) genome, and unique genome at the strain level. (D) Comparative overview of pan- and core genomes obtained from the Anvi’o tool. The different B. gladioli isolate groups are displayed by red (gladiolus isolates), green (rice isolates), orange (human isolates), and blue (nature isolates). Each track indicates a genome which is presented by number and color variations (dark/light) shows presence/absence of genes per genome.
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Figure 4. KEGG enrichment analysis of Burkholderia gladioli niche-associated genomes. The gladiolus-associated genome was derived from gladiolus isolates (ATCC 10248, KACC 11889, ATCC 25417, and NCTC 12378), the rice- and human-associated genomes were derived from rice isolates (BSR3 and KACC 18962), and human isolates (AU0032, AU26456, AU29541, and AU30473), respectively. The nature-associated genome was derived from nature isolates (Coa14, MSMB1756, FDAARGOS_390, and FDAARGOS_391). The y-axis represents enriched KEGG pathways. The x-axis shows the count of genes in each niche-associated genome. KEGG pathways with a p-value < 0.05 were considered significantly enriched. P-value from the hypergeometric test is displayed next to each bar.
Figure 4. KEGG enrichment analysis of Burkholderia gladioli niche-associated genomes. The gladiolus-associated genome was derived from gladiolus isolates (ATCC 10248, KACC 11889, ATCC 25417, and NCTC 12378), the rice- and human-associated genomes were derived from rice isolates (BSR3 and KACC 18962), and human isolates (AU0032, AU26456, AU29541, and AU30473), respectively. The nature-associated genome was derived from nature isolates (Coa14, MSMB1756, FDAARGOS_390, and FDAARGOS_391). The y-axis represents enriched KEGG pathways. The x-axis shows the count of genes in each niche-associated genome. KEGG pathways with a p-value < 0.05 were considered significantly enriched. P-value from the hypergeometric test is displayed next to each bar.
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Figure 5. The number of nonribosomal peptide synthetase (NRPS) genes predicted from Burkholderia gladioli genomes. NPRS biosynthetic gene cluster (BGC) in genomes of 14 B. gladioli strains were identified thorough antiSMASH.
Figure 5. The number of nonribosomal peptide synthetase (NRPS) genes predicted from Burkholderia gladioli genomes. NPRS biosynthetic gene cluster (BGC) in genomes of 14 B. gladioli strains were identified thorough antiSMASH.
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Table
Table 1. Genome statistics of Burkholderia gladioli KACC 18962.
Table 1. Genome statistics of Burkholderia gladioli KACC 18962.
FeatureChromosome 1Chromosome 2Plasmid 1
Genome sequencing levelComplete
Total number of reads10,350,496
Total yield (bp)1,562,924,896
Bases with a phred value > 20 (%)92.02
Bases with a phred value > 30 (%)82.95
Sequencing depth190187190
Genome size (bp)4,358,6393,979,285281,218
Genome G + C content (%)67.5668.6261.1
No. of genes39823250243
No. of coding genes38203169227
No. of pseudogenes936515
No. of RNA genes (16S/5S/23S)3/3/32/2/20
No. of tRNA genes56101
Other RNA400
Table
Table 2. Genomes information of the 14 strains of Burkholderia gladioli used in this study.
Table 2. Genomes information of the 14 strains of Burkholderia gladioli used in this study.
B. gladioli StrainsSourceAssemblySize
(Mb)		GC
(%)		LevelProteins
ATCC 10248GladiolusGCA_000959725.18.967.6Complete7514
KACC 11889GladiolusGCA_002208175.18.967.7Complete7221
ATCC 25417GladiolusGCA_000756855.19.367.3Scaffold7941
NCTC 12378GladiolusGCA_900446225.18.468.0Contig7098
BSR3RiceGCA_000194745.19.167.4Complete7639
KACC 18962RiceThis study8.667.8Complete7216
AU0032Sputum aGCA_002980975.18.068.2Contig6769
AU26456SputumGCA_002981405.18.168.2Contig6874
AU29541SputumGCA_002981475.18.468.2Contig7112
AU30473SputumGCA_002981875.18.168.3Contig6928
Coa14WaterGCA_002917905.18.568.0Contig7189
MSMB1756SoilGCA_001527485.18.268.1Contig6946
FDAARGOS_390Nature bGCA_002554225.18.867.6Contig7470
FDAARGOS_391NatureGCA_002554395.18.468.0Contig7058
aHomo sapiens; b unspecified in NCBI database.
Table
Table 3. Clusters of orthologous groups (COG) enriched in core and dispensable genomes of Burkholderia gladioli.
Table 3. Clusters of orthologous groups (COG) enriched in core and dispensable genomes of Burkholderia gladioli.
CoreCOGNo. Clusterp-Value
Class
JTranslation, ribosomal structure and biogenesis1721.52 × 10−4
FNucleotide transport and metabolism914.79 × 10−4
Dispensable
ClassCOGNo. clusterP-value
LReplication, recombination and repair2340
QSecondary metabolites biosynthesis, transport and catabolism2970
-Unclassified39290
WExtracellular structures295.52 × 10−12
UIntracellular trafficking, secretion, and vesicular transport1476.91 × 10−7
VDefense mechanisms536.36 × 10−4
RGeneral function prediction only5413.76 × 10−3
ILipid transport and metabolism1761.27 × 10−2
Table
Table 4. Predicted CRISPR/Cas system in Burkholderia gladioli.
Table 4. Predicted CRISPR/Cas system in Burkholderia gladioli.
StrainCRISPR CountTypeCas Gene CountCas GeneNo. SpacersSpacer Match
ATCC 102484---1,1,1,1ND a
KACC 118894---1,1,1,1ND
ATCC 254172---1,1ND
NCTC 123784---1,1,1,1ND
BSR32---1,2Cupriavidus taiwanensis
KACC 189621---1ND
AU00321---1ND
AU264562---1,1ND
AU29541------
AU304732---1,1ND
Coa143Type I-F6Cas1,Cas3-Cas2,Csy1,Csy2,Csy3,Cas61,1,39Streptomyces spp. b
Ralstonia solanacearum
MSMB17562Type I-F6Cas1,Cas3-Cas2,Csy1,Csy2,Csy3,Cas61,46Streptomyces spp.
Ralstonia solanacearum
FDAARGOS_390------
FDAARGOS_391------
a Not determined; b S. lavendulae, S. aureofaciens, S. rimosus, and S. katrae.
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Lee, H.-H.; Park, J.; Jung, H.; Seo, Y.-S. Pan-Genome Analysis Reveals Host-Specific Functional Divergences in Burkholderia gladioli. Microorganisms 2021, 9, 1123. https://doi.org/10.3390/microorganisms9061123

AMA Style

Lee H-H, Park J, Jung H, Seo Y-S. Pan-Genome Analysis Reveals Host-Specific Functional Divergences in Burkholderia gladioli. Microorganisms. 2021; 9(6):1123. https://doi.org/10.3390/microorganisms9061123

Chicago/Turabian Style

Lee, Hyun-Hee, Jungwook Park, Hyejung Jung, and Young-Su Seo. 2021. "Pan-Genome Analysis Reveals Host-Specific Functional Divergences in Burkholderia gladioli" Microorganisms 9, no. 6: 1123. https://doi.org/10.3390/microorganisms9061123

APA Style

Lee, H. -H., Park, J., Jung, H., & Seo, Y. -S. (2021). Pan-Genome Analysis Reveals Host-Specific Functional Divergences in Burkholderia gladioli. Microorganisms, 9(6), 1123. https://doi.org/10.3390/microorganisms9061123

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