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Comparative Genomics, Pangenome, and Phylogenomic Analyses of Brenneria spp., and Delineation of Brenneria izadpanahii sp. nov.

    Affiliations
    Authors and Affiliations
    • Meysam Bakhshi ganje1
    • John Mackay2
    • Mogens Nicolaisen3
    • Masoud Shams-Bakhsh1 †
    1. 1Department of Plant Pathology, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
    2. 2Department of Plant Sciences, University of Oxford, Oxford, U.K.
    3. 3Faculty of Science and Technology, Department of Agroecology, Aarhus University, Forsøgsvej 1, 4200, Slagelse, Denmark
    Published Online:22 Sep 2020https://doi.org/10.1094/PHYTO-04-20-0129-FI

    Abstract

    Brenneria species are bacterial plant pathogens mainly affecting woody plants. Association of all members with devastating disorders (e.g., acute oak decline in Iran and United Kingdom) are due to adaptation and pathogenic behavior in response to host and environmental factors. Some species, including B. goodwinii, B. salicis, and B. nigrifluens, also show endophytic residence. Here we show that all species including novel Brenneria sp. are closely related. Gene-based and genome/pangenome-based phylogeny divide the genus into two distinct lineages, Brenneria clades A and B. The two clades were functionally distinct and were consistent with their common and special potential activities as determined via annotation of functional domains. Pangenome analysis demonstrated that the core pathogenicity factors were highly conserved, an hrp gene cluster encoding a type III secretion system was found in all species except B. corticis. An extensive repertoire of candidate virulence factors was identified. Comparative genomics indicated a repertoire of plant cell wall degrading enzymes, metabolites/antibiotics, and numerous prophages providing new insights into Brenneria−host interactions and appropriate targets for further characterization. This work not only documented the genetic differentiation of Brenneria species but also delineates a more functionally driven understanding of Brenneria by comparison with relevant Pectobacteriaceae thereby substantially enriching the extent of information available for functional genomic investigations.

    The family Pectobacteriaceae has become a major threat to trees worldwide. Most species from this genus seem ubiquitous and were isolated from a variety of distinctive forest habitats and urban trees (Brady et al. 2014; Denman et al. 2012; Hauben et al. 1998; Li et al. 2015, 2019; Zheng et al. 2017). The destructive capability and a causal association of Brenneria spp. with diseased woody plants have been demonstrated (Broberg et al. 2018; Hauben et al. 1998). Due to their wide spatiotemporal distribution and diverse habitats (Maes et al. 2009; Pettifor et al. 2020), the range of tree species affected and the severe pathogenic activity in the microbiota (e.g., acute oak decline [AOD] microbiome), their impacts can be devastating on forest health (Broberg et al. 2018; Sapp et al. 2016). Research aimed at deciphering their pathogenic behavior within their ecological niches has become crucial to shed light onto their ecological roles and their pathogenic activity.

    At the time of writing, the genus Brenneria comprises eight species with valid published names (www.bacterio.net/brenneria.html): B. alni, B. salicis, B. goodwinii, B. nigrifluens, B. rubrifaciens, B. roseae, B. corticis, and B. populi (Brady et al. 2014; Denman et al. 2012; Hauben et al. 1998; Li et al. 2015, 2019). Molecular marker-based taxonomy paradigms have changed with the transition from multilocus sequence analysis and typing (MLSA/MLST) 16S rRNA to genome-based phylogeny, with many Brenneria spp. strains being assigned into novel taxa as it was shown that they have been misidentified based on phenotypic-genotypic features. Adeolu et al. (2016) reconstructed phylogenetic and taxonomic relationships in Enterobacteriales and introduced the monophyletic Dickeya-Pectobacterium (Brenneria) clade based on a comprehensive study using genomic data. The relationship among Brenneria species is not completely clear because phylogenetic trees were based on MLSA of only four housekeeping genes at most and genome-based phylogenetic analyses were conducted only with a few species (Denman et al. 2012; Zhang et al. 2016). Given their phytopathological importance, the number of available Brenneria spp. genome sequences in GenBank has increased recently; however, their taxonomic placement are still controversial and additional investigations are needed to further clarify the taxonomy of the genus.

    High throughput sequencing has produced bacterial genome datasets enabling comparative genomic analyses and facilitating wider and more comprehensive assessments of similarities and differences between and within plant and animal pathogens (Toth et al. 2006). Doonan et al. (2019) demonstrated that canonical bacterial species contributed to the AOD syndrome by using comparative genomics and showed that orthologous gene inferences (e.g., identification of virulence gene orthogroups) can be used to evaluate the pathogenic potential of bacterial species and their impacts on host–microbe and microbe–microbe interactions. They identified B. goodwinii as a keystone taxa and primary pathogen that contributes vital pathogenicity repertoires in the polymicrobial consortium of AOD causal agents (Doonan et al. 2019). The communications among the community of species associated with the AOD, the interactions with other symbionts in the environment (B. goodwinii and Gibbsiella quercinecans in AOD syndrome), and the potential mechanisms and effects of Brenneria species on related pathobiome are diverse and incompletely understood.

    Despite the deep knowledge of the soft rot Pectobacteriaceae (Pectobacterium spp. and Dickeya spp.), genomic information of other members like Brenneria and Lonsdaleae (formerly Brenneria) is still scarce. Our objectives were to (i) conduct a comparative genomics investigation among Brenneria spp. using all available genome sequences; (ii) develop a novel taxonomic understanding of the genus; and (iii) clarify whether we have uncovered a new species of Brenneria in the north of Iran, represented by a Brenneria sp. isolate obtained from diseased oak trees. To this end, we inferred the phylogeny of the novel isolate and other strains of the family Pectobacteriaceae. A pangenome analysis emphasizing the predicted pathogenicity determinants was obtained by considering an increasing number of bacterial species, most of which represent new microflora and lack of a holistic comparison.

    MATERIALS AND METHODS

    Maintenance of bacterial strain, 16S rRNA/MLSA typing, genomic DNA extraction, library preparation, and genome sequencing.

    The novel isolate of Brenneria sp. (Iran isolate 50) was obtained from diseased oak trees (Quercus castaneifolia) in the north of Iran. It was stored in 40% glycerol stocks at −80°C and maintained on nutrient agar at 30°C. Genomic DNA for amplification was extracted by the alkali extraction method (Niemann et al. 1997) and stored at −20°C until use. The 16S rRNA gene and MLSA (for housekeeping genes gyrB, infB, and atpD) sequencing were conducted using primers and sequencing conditions according to Hauben et al. (1998) and Brady et al. (2008), respectively. The 16S rRNA gene and MLSA similarities evaluation were conducted by the EzTaxon-e (https://www.ezbiocloud.net/identify) for 16S rRNA gene in special and GenBank similarity engine (Blastn) for both. High-quality genomic DNA was extracted using Qiagen Genomic-tip 500/G following the manufacturer’s instructions (Qiagen, Germany). Genome sequencing of the novel isolate was accomplished with single molecule real-time (SMRT) technology of the Pacific Biosciences RSII platform (Macrogen Co., South Korea). The library was reconstructed with approximately 20-kb insert size using the SMRT Cell 8Pac V3, DNA Polymerase Binding Kit P6, DNA Sequencing Reagent 4.0 v2. The quality (DNA integrity) and quantity of the extracted genome and subsequent library were evaluated using 1% agarose gel electrophoresis and spectrophotometry/Nanodrop/DNA Bioanalyzer 12000 chip and DNA QC-Picogreen, respectively.

    Genome assembly of pacific biosciences RSII generated data.

    The de novo sequence assembly was performed using the hierarchical genome assembly 3 (HGAP3) workflow, incorporating the CELERA assembler and finished using the Quiver consensus polisher according to Doonan et al. (2019). Genome estimated completeness and contamination were verified with CheckM version 1.0.7 (Parks et al. 2015). Resultant assembly produced one contig and a <200× sequencing depth.

    Additional whole genome sequence data.

    Twenty-four draft genome sequences consisting of all valid published genomes from genus Brenneria (January 2020) and a few genomes from Lonsdalea spp., P. carotovorum ssp. carotovorum, and Dickeya spp. were retrieved from the NCBI GenBank database (Table 1). These genomes were incorporated into the workflow described below.

    TABLE 1. Genome metrics of Brenneria izadpanahii sp. nov. and other genomes available in GenBank database used in this study

    Genome annotation, identification of orthologs, and pangenome analysis.

    Aiming to assess the level of discrepancy among the genomes, whole genome sequence identity was computed using indices useful for species delineation; pairwise average nucleotide identity (ANI) according to BLAST (ANIb) in JSpeciesWS (Richter et al. 2016); ANI calculator (https://www.ezbiocloud.net/tools/ani) (Yoon et al. 2017) and OrthoANI values with the standalone orthologous average nucleotide identity tool (OAT) in Ezbiocloud (Lee et al. 2016). Also, digital DNA-DNA hybridization (dDDH) percentages were determined in silico with the genome-to-genome distance calculator (dGGDC 2.0) using the BLAST method and recommended formula 2 (Meier-Kolthoff et al. 2013) (http://ggdc.dsmz.de/ggdc.php#). Both ANI and dDDH values were below the accepted threshold in the case of the new species (≤95 and ≤70% for ANI and dDDH, respectively) (Table 2).

    TABLE 2. Definition of the in silico average nucleotide identity and DNA-DNA hybridization values in the form (ANI/ANIb/orthoANI-dDDH) (first column only), ANI index for the rest of columns, of Brenneria izadpanahii sp. nov. and representative strains of the other described Pectobacteriaceae speciesa

    Automatic annotation of draft genomes and complete genome of Brenneria sp. achieved in this study was conducted using RAST (Overbeek et al. 2014). Also, prokaryotic genome assembly and annotation were performed using KBase, according to Arkin et al. (2018). New raw/annotated genomes (e.g., B. corticis) were imported into the KBase. A genome set was made for all genomes and then phylogenetic trees were constructed from the Brenneria sp. along with the 29 nearest neighbors using the Insert Genome into Species Tree app (version 2.1.10), which utilizes FastTree 2 (Price et al. 2010). A pangenome was constructed Build Pangenome with OrthoMCL app (Pangenome Orthomcl v0.0.7) using aforementioned genome set. Pangenomes were visualized using the Pangenome Circle Plot app (v1.2.0) and pangenome-based phylogenomic analysis was conducted by the Phylogenetic Pangenome Accumulation (v1.4.0) app. Genomes were further compared by annotating the functional domains in the whole genome set with DomainAnnotation (version 1.03) and viewing the results using the View Function Profile for Genomes app (version 1.0.1) with the domain namespace set to BSEED Roles.

    The online bacterial genome analysis service PATRIC (Pathosystems Resource Integration Center) was used for determination of the GC content, number of coding sequence regions (CDS), tRNA genes, pseudogenes, and others features, and the obtained information was used to reconstruct metabolic networks and subsystems. The clusters that were involved in diverse categories such as virulence and pathogenicity determinants, including type secretion systems (TSS), phytotoxins, iron uptake, polysaccharides biosynthesis, flagella encoding genes, and cell attachment were screened and compared across all Brenneria species. Circular genome maps were created using the circus software in PATRIC (Wattam et al. 2017). Also, the online service IslandViewer version 4.0 (an integrated interface for computational identification and visualization of genomic islands; http://www.pathogenomics.sfu.ca/islandviewer) was used for identification of pathogenicity islands within our bacterial genome (Bertelli et al. 2017).

    The CAZy and dbCAN2 meta server databases were used to annotate plant cell wall degrading enzymes (PCWDEs) (Lombard et al. 2014; Zhang et al. 2018). The presence of plasmids was screened using PlasmidFinder 2.0 (https://cge.cbs.dtu.dk/services/PlasmidFinder/) (Carattoli et al. 2014) for all the genomic sequences. The online service PHASTER (PHAge Search Tool Enhanced Release; http://phaster.ca/) was used for identification of prophage sequences within bacterial genomes (Arndt et al. 2016). The Prokka annotation output was used once as input data for in silico secondary metabolite and antibiotic profiling by searching against the antiSMASH database-bacterial version (https://antismash.secondarymetabolites.org/#!/start) (Blin et al. 2019) in addition to the genome wide comparisons and visualization of orthologous clusters that were performed using the online service OrthoVenn2 (https://orthovenn2.bioinfotoolkits.net/) (Xu et al. 2019).

    RESULTS

    Phylogenetic analysis, genomic DNA extraction, and qualification of library preparation.

    Concatenated phylogenetic trees placed Brenneria sp. from this study separately from other Brenneria clade members, indicating that the isolate from the north of Iran likely belongs to a novel species in the genus Brenneria. Sequence similarity assessments using 16S rRNA gene and MLSA (gyrB, infB, and atpD) showed that the novel Brenneria sp. is most similar (97 to 98%) to B. goodwinii along with all other Brenneria species (Fig. 1A). The quantification and qualification of produced library demonstrated the insert size was almost 17 kb.

    Fig. 1.

    Fig. 1. A, Bayesian 50% majority rule consensus tree based on concatenated partial 16S rRNA/gyrB, atpD, and infB gene sequences of Brenneria izadpanahii sp. nov. and all validly described species of the genus Brenneria in addition to phylogenetically related species under the GTR + G + I model. Bayesian posterior probabilities (BPP), maximum likelihood bootstrap (ML BS), and maximum parsimony bootstrap (MP BS) values >50% are given for appropriate clades in the form: BPP/ML/MP BS. Cronobacter sakazakii is included as an out-group. The scale bar indicates the fraction of substitutions per site. B, Phylogenomic tree of the Brenneria clade A and B species along with neighbor Pectobacteriaceae members generated by Insert Genome into Species Tree app in KBase using the 29 closest relatives in the public database. Numbers are local support values. C, Pangenome-based phylogenomic analysis of Brenneria clade A and B species. Orthologous gene set percentages within a pangenome partitioned into three categories: core (blue), singleton (red), and partial pangenome (pink). Pangenome created by OrthoMCL app distinguish which homologous genes are true orthologs and vertically inherited from those with lineage derived paralogous expansions by duplication.

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    Complete genome sequencing of B. izadpanahii sp. nov.

    The sequence data for B. izadpanahii (isolated from diseased oak trees at the north of Iran) was assembled into one contig and the genome size was determined to be 5,330,697 bp by using the hierarchical genome assembly 3 (HGAP3) workflow. Annotation of the complete genome predicted 4,743 CDS transcribed for B. izadpanahii. It identified 39 rRNA, 255 tRNA, 153 ribosomal protein, and 79 flagellum and flagellar motility-related genes. Subsystems and their related gene categories (e.g., metabolism), circular view of annotated subsystems within the B. izadpanahii genome, and pathogenicity islands screened by using IslandViewer tool are depicted in Figure 2A, B, and C, respectively.

    Fig. 2.

    Fig. 2. A, Subsystems and their related gene categories (e.g., metabolism). B, Circular view of annotated subsystems of the Brenneria izadpanahii sp. nov. genome generated in PATRIC (Pathosystems Resource Integration Center). C, Pathogenicity islands screening using Island Viewer tool.

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    The ANI/ANIb/orthoANI and dDDH values between B. izadpanahii and other Pectobacteriaceae members were estimated. The ANI values varied from 78 to 90% between different pairs of strains, showing B. goodwinii strain 141 with (89.98/90.35/90.57−41.10) identity/similarity lower than the proposed species boundary ANI cut-off value of 95 to 96% and ≤70% for dDDH was the most similar neighbor (Table 2).

    Whole genome phylogeny.

    All Brenneria clade members appeared to be closely related and the whole genome phylogeny suggests two distinct lineages (Fig. 1B). Clade A has five taxa including B. goodwinii, B. izadpanahii, B. alni, B. nigrifluens, and B. corticis that appear to share a recent common ancestor. Clade B includes the three others, B. rosease, B. rubrifaciens and B. salicis, sharing another recent common ancestor. The phylogeny also indicates that the Brenneria clades share a common recent ancestor with the Pectobacterium spp. and are more distantly related to Dickeya spp. and Lonsdalea spp. and share least with the Sodalia ssp. These results are in agreement with the rearrangements of Enterobacteriales and creation of the family Pectobacteriaceae (Adeolu et al. 2016).

    Pangenome analysis.

    The pangenome comparisons of Brenneria clades A and B and other relevant in Pectobacteriaceae indicated the distinctive status and genetic makeup of B. izadpanahii (Figs. 1C and 3). Summary statistics of the pangenome datasets and shared genes are presented in Table 3. Genomes in Brenneria clades (A and B) had a total of 47,402 genes. Of the genes in clade A, 28,745 are in homologous families among the pangenome, and 2,680 are in singleton families; while in clade B, 13,547 genes are in homologous families and 2,430 are in singleton families.

    Fig. 3.

    Fig. 3. Pangenome analysis of Brenneria A, clade A+B species, B, clade A, and C, clade B performed using the Build Pangenome with OrthoMCL app (Pangenome Orthomcl version 0.0.7). Red (medium gray) indicates base singletons; and sky blue (light gray) represents noncore genes with respect to B. izadpanahii (A and B) and B. salicis (C) as based genomes compared with the respective pangenomes. Dark blue (dark gray) represents core genes.

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    TABLE 3. Summary statistics of the pangenome datasets and shared genes within Brenneria clade A and B species retrieved from KBase

    Orthologous protein families and clusters were identified and categorized in both KBase and Orthovenn 2 and were visualized by online web server Orthovenn 2 for genome sets. Comparisons of the orthologous gene clusters within species of clades A and B and other relevant were determined through four versus four, five versus five, and six versus six genome sets (Fig. 4).

    Fig. 4.

    Fig. 4. Venn diagrams constructed using the OrthoVenn 2 online service showing the distribution of shared gene families (orthologous clusters) among different sets of strains. A, Brenneria clade A; B, Brenneria clade B; C, Brenneria clade A (− B. alni and + Pectobacterium carotovorum ssp. carotovorum); D, Brenneria clade B (+ P. carotovorum ssp. carotovorum); E, B. izadpanahii + B. goodwinii + B. salicis + P. carotovorum ssp. carotovorum; and F, B. izadpanahii + B. goodwinii + B. salicis + Lonsdaleae quercina ssp. quercina + P. carotovorum ssp. carotovorum + Dickeya dadantii.

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    Brenneria clade A and B species shared 2,391 and 2,369 proteins in their genome sequences, respectively (Fig. 4A and C). Clade A members (B. izadpanahii, B. goodwinii, B. alni, B. nigrifluens, and B. corticis) showed 534, 365, 438, 377, and 443 unique proteins among their genome sequences, and clade B (B. salicis, B. rubrifaciens, B. roseae ssp. americana, and B. roseae ssp. roseae) had 526, 292, 275, and 251 unique proteins, respectively. Similarities and relatedness of the orthologous clusters within the genomes were depicted as a heatmap (Fig. 5B). As expected in clades A and B phylogeny, B. izadpanahii-B. goodwinii, B. nigrifluens-B. corticis, and B. roseae ssp. americana-B. roseae ssp. roseae had most similarities. The orthoANI diagram from OTA tool and the distance matrices heatmap of the generated orthologous clusters from Orthovenn 2 showed highly consistent results although they are nucleotide and protein-based pipelines, respectively (Fig. 5A and B).

    Fig. 5.

    Fig. 5. A, Average nucleotide identity (ANI)-based neighbor joining phylogenetic tree of Brenneria clade A and B species in addition to Pectobacterium carotovorum ssp. carotovorum constructed using the OrthoANI calculator software. Different colors represent degree of identity. B, Heatmap showing intrastrain similarities of orthologous subsets from the Brenneria clade A and B species in addition to P. carotovorum ssp. carotovorum generated by the OrthoVenn 2 online service.

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    The clades identified in the phylogenetic analysis were also functionally distinct as determined via annotation of functional domains in the genomes using the SEED database. Genes in all categories (especially in the singletons) that were compared between the genomes included those specifically associated with transcription regulators, potentially associated virulence genes and carbohydrate, amino acid and lipid transport, and metabolism (Fig. 6). In comparison between B. izadpanahii and its closest neighbor, B. goodwinii 141, unique pathogenicity associated domains was notable such as transcriptional regulator, AraC family, type IV secretory pathway, VirD4 components, and many phage-related domains in B. izadpanahii and B. goodwinii 141, respectively.

    Fig. 6.

    Fig. 6. Pangenome functional domains annotation of the Brenneria clade A and B species identified in the phylogenomic analysis using the SEED database.

    Fig. 6. KEY. Amino acids and derivatives: 1-Alanine biosynthesis; 2-Arginine biosynthesis extended; 3-Arginine deiminase pathway; 4-Arginine and ornithine degradation; 5-Aromatic amino acid degradation; 6-Aromatic amino acid interconversions with aryl acids; 7-Branched-chain amino acid biosynthesis; 8-Chorismate: Intermediate for synthesis of PAPA antibiotics, PABA, anthranilate, 3-hydroxyanthranilate and more; 9-Chorismate synthesis; 10-Common pathway for synthesis of aromatic compounds (DAHP synthase to chorismate); 11-Cysteine biosynthesis; 12-Glutamine, glutamate, aspartate, and asparagine biosynthesis; 13-Glycine and serine utilization; 14-Glycine cleavage system; 15-HMG CoA synthesis; 16-Histidine biosynthesis; 17-Histidine degradation; 18-Ketoisovalerate oxidoreductase; 19-L-2-amino-thiazoline-4-carboxylic acid-L-cysteine conversion; 20-Lysine biosynthesis DAP pathway; 21-Lysine degradation; 22-Methionine biosynthesis; 23-Methionine degradation; 24-Methionine salvage; 25-Phenylalanine and tyrosine branches from chorismate; 26-Polyamine metabolism; 27-Proline, 4-hydroxyproline uptake and utilization; 28-Proline synthesis; 29-Serine biosynthesis; 30-Threonine and homoserine biosynthesis; 31-Threonine degradation; 32-Urea decomposition; 33-Valine degradation; 34-2-Ketogluconate utilization; 35-2-Methylcitrate to 2-methylaconitate metabolism cluster; 36-Acetoin, butanediol metabolism; 37-Acetyl-CoA fermentation to butyrate; 38-Alpha-acetolactate operon; 39-Beta-glucoside metabolism; 40-Butanol biosynthesis; 41-CO2 uptake, carboxysome; 42-Calvin-Benson cycle; 43-Carboxysome; 44-Chitin and N-acetylglucosamine utilization; 45-D-Galacturonate and D-glucuronate utilization; 46-D-Sorbitol (D-glucitol) and L-sorbose utilization; 47-D-Tagatose and galactitol utilization; 48-D-Allose utilization; 49-D-Galactarate, D-glucarate, and D-Glycerate catabolism; 50-D-Galactonate catabolism; 51-D-Gluconate and ketogluconates metabolism; 52-D-Ribose utilization; 53-Deoxyribose and deoxynucleoside catabolism; 54-Di-Inositol-phosphate biosynthesis; 55-Dihydroxyacetone kinases; 56-Entner-Doudoroff pathway; 57-Ethanolamine utilization; 58-Fermentations: Mixed acid; 59-Formaldehyde assimilation: Ribulose monophosphate pathway; 60-Fructooligosaccharides (FOS) and raffinose utilization; 61-Fructose utilization; 62-Glycerol and glycerol-3-phosphate uptake and utilization; 63-Glycogen metabolism; 64-Glycolysis and gluconeogenesis; 65-Glycolysis and gluconeogenesis, including archaeal enzymes; 66-Hexose phosphate uptake system; 67-Inositol catabolism; 68-Isobutyryl-CoA to propionyl-CoA module; 69-L-Arabinose utilization; 70-L-Ascorbate utilization (and related gene clusters); 71-L-Fucose utilization; 72-L-Fucose utilization temp; 73-Lactate utilization; 74-Lacto-N-biose I and galacto-N-biose metabolic pathway; 75-Lactose and galactose uptake and utilization; 76-Lactose utilization; 77-Maltose and maltodextrin utilization; 78-Mannitol utilization; 79-Mannose metabolism; 80-Methylglyoxal metabolism; 81-N-Acetyl-galactosamine and galactosamine utilization; 82-One-carbon metabolism by tetrahydropterines; 83-Pentose phosphate pathway; 84-Photorespiration (oxidative C2 cycle); 85-Predicted carbohydrate hydrolases; 86-Propanediol utilization; 87-Propionate-CoA to succinate module; 88-Pyruvate/ferredoxin oxidoreductase; 89-Pyruvate alanine serine interconversions; 90-Pyruvate metabolism I: anaplerotic reactions, PEP; 91-Pyruvate metabolism II: acetyl-CoA, acetogenesis from pyruvate; 92-Ribitol, xylitol, arabitol, mannitol, and sorbitol utilization; 93-Serine-glyoxylate cycle; 94-Sucrose utilization; 95-TCA cycle; 96-Trehalose biosynthesis; 97-Trehalose uptake and utilization; 98-Unknown sugar utilization (cluster yphABCDEFG); 99-Xylose utilization. Cell division and cell cycle: 100-Bacterial cytoskeleton; 101-MukBEF chromosome condensation; 102-Two cell division clusters relating to chromosome partitioning. Cell wall and capsule: 103-CMP-N-acetylneuraminate biosynthesis; 104-Capsular heptose biosynthesis; 105-Colanic acid biosynthesis; 106-KDO2-lipid A biosynthesis; 107-LOS core oligosaccharide biosynthesis; 108-Lipid A-Ara4N pathway (polymyxin resistance); 109-Lipid A modifications; 110-Peptidoglycan biosynthesis; 111-Rhamnose containing glycans; 112-Sialic acid metabolism; 113-Teichuronic acid biosynthesis; 114-UDP-N-acetylmuramate from fructose-6-phosphate biosynthesis; 115-dTDP-rhamnose synthesis; 116-Linker unit-arabinogalactan synthesis; 117-Mycolic acid synthesis. Clustering-based subsystems: 118-Bacterial cell division; 119-CBSS-214092.1.peg.3450; 120-CBSS-262719.3.peg.410; 121-CBSS-343509.6.peg.2644; 122-CBSS-562.2.peg.5158 SK3 including; 123-Cluster-based subsystem grouping hypotheticals-perhaps proteosome related; 124-Conserved gene cluster associated with Met-tRNA formyltransferase; 125-LMPTP YwlE cluster; 126-NusA-TFII Cluster; 127-Putative hemin transporter; 128-Putative sulfate assimilation cluster; 129-Type III secretion systems, extended; 130-YeiH. Cofactors, vitamins, prosthetic groups, pigments: 131-Biotin biosynthesis; 132-Coenzyme A biosynthesis; 133-Coenzyme B12 biosynthesis; 134-Experimental type; 135-Flavodoxin; 136-Folate biosynthesis; 137-Heme and siroheme biosynthesis; 138-Lipoic acid metabolism; 139-Menaquinone and phylloquinone biosynthesis; 140-Molybdenum cofactor biosynthesis; 141-NAD and NADP cofactor biosynthesis global; 142-NAD regulation; 143-Plastoquinone biosynthesis; 144-Pyridoxin (vitamin B6) biosynthesis; 145-Pyrroloquinoline quinone biosynthesis; 146-Riboflavin, FMN, and FAD metabolism; 147-Thiamin biosynthesis; 148-Ubiquinone biosynthesis; 149-p-Aminobenzoyl-glutamate utilization. DNA metabolism: 150-2-phosphoglycolate salvage; 151-CRISPRs; 152-DNA-replication; 153-DNA repair base excision; 154-DNA repair, UvrABC system; 155-DNA repair, bacterial; 156-DNA repair, bacterial DinG, and relatives; 157-DNA repair, bacterial MutL-MutS system; 158-DNA repair, bacterial RecFOR pathway; 159-DNA repair, bacterial UvrD, and related helicases; 160-DNA structural proteins, bacterial; 161-DNA topoisomerases, Type I, ATP-independent; 162-Plasmid replication; 163-Restriction-modification system; 164-RuvABC plus a hypothetical; 165-YcfH. Fatty acids, lipids, and isoprenoids: 166-Fatty acid biosynthesis FASII; 167-Glycerolipid and glycerophospholipid metabolism in bacteria; 168-Isoprenoid biosynthesis; 169-Polyhydroxybutyrate metabolism; 170-Triacylglycerol metabolism; 171-Polyprenyl synthesis. Iron acquisition and metabolism: 172-Campylobacter iron metabolism; 173-Heme, hemin uptake and utilization systems in gram positives; 174-Hemin transport system; 175-Iron acquisition in Vibrio; 176-Siderophore aerobactin; 177-Siderophore enterobactin; 178-Transport of iron. Membrane transport: 179-ABC transporter alkylphosphonate (TC 3.A.1.9.1); 180-ABC transporter branched-chain amino acid (TC 3.A.1.4.1); 181-ABC transporter dipeptide (TC 3.A.1.5.2); 182-ABC transporter oligopeptide (TC 3.A.1.5.1); 183-ABC transporter peptide (TC 3.A.1.5.5); 184-Fructose and mannose inducible PTS; 185-Na(+) H(+) antiporter; 186-Ton and Tol transport systems; 187-Transport of manganese; 188-Transport of nickel and cobalt; 189-Transport of zinc; 190-Twin-arginine translocation system; 191-Type 4 conjugative transfer system, IncI1 type; 192-Type III secretion system orphans; 193-Type IV pilus; 194-Widespread colonization island; 195-pVir Plasmid of Campylobacter. Metabolism of aromatic compounds: 196-Aromatic amin catabolism; 197-Benzoate degradation; 198-Biphenyl degradation; 199-Catechol branch of beta-ketoadipate pathway; 200-Central meta-cleavage pathway of aromatic compound degradation; 201-Homogentisate pathway of aromatic compound degradation; 202-Phenylpropanoid compound degradation; 203-Protocatechuate branch of beta-ketoadipate pathway; 204-Quinate degradation; 205-Salicylate and gentisate catabolism. Miscellaneous: 206-Conserved gene cluster possibly involved in RNA metabolism; 207-Muconate lactonizing enzyme family; 208-YaaA; 209-YbbK; 210-ZZ gjo need homes. Motility and chemotaxis: 211-Bacterial chemotaxis; 212-Flagellar motility; 213-Flagellum; 214-Flagellum in Campylobacter. Nitrogen metabolism: 215-Allantoin utilization; 216-Amidase clustered with urea and nitrile hydratase functions; 217-Ammonia assimilation; 218-Cyanate hydrolysis; 219-Dissimilatory nitrite reductase; 220-Nitrate and nitrite ammonification; 221-Nitric oxide synthase; 222-Nitrogen fixation; 223-Nitrosative stress. Nucleosides and nucleotides: 224-De novo purine biosynthesis; 225-De novo pyrimidine synthesis; 226-Hydantoin metabolism; 227-Nudix proteins (nucleoside triphosphate hydrolases); 228-Purine utilization; 229-Purine conversions; 230-Purine nucleotide synthesis regulator; 231-Pyrimidine utilization; 232-Ribonucleotide reduction; 233-Xanthosine utilization (xap region). Phages, prophages, transposable elements, plasmids: 234-Phage capsid proteins; 235-Staphylococcal pathogenicity islands SaPI; 236-Staphylococcal phi-Mu50B-like prophages. Phosphorus metabolism: 237-Alkylphosphonate utilization; 238-Phosphate metabolism; 239-Phosphonate metabolism. Potassium metabolism: 240-Glutathione-regulated potassium-efflux system and associated functions; 241-Potassium homeostasis. Protein metabolism: 242-Glycine reductase, sarcosine reductase, and betaine reductase; 243-GroEL GroES; 244-N-linked glycosylation in bacteria; 245-Peptidyl-prolyl cis-trans isomerase; 246-Periplasmic disulfide interchange; 247-Programmed frameshift; 248-Proteasome bacterial; 249-Protein chaperones; 250-Protein degradation; 251-Protein secretion by ABC-type exporters; 252-Proteolysis in bacteria, ATP-dependent; 253-Putative TldE-TldD proteolytic complex; 254-Ribosomal protein S12p Asp methylthiotransferase; 255-Ribosome LSU bacterial; 256-Ribosome SSU bacterial; 257-Ribosome activity modulation; 258-Ribosome biogenesis bacterial; 259-Translation elongation factor G family; 260-Translation elongation factors eukaryotic and archaeal; 261-Translation initiation factors eukaryotic and archaeal; 262-Universal GTPases; 263-tRNA aminoacylation, Asp, and Asn; 264-tRNA aminoacylation, Glu, and Gln. RNA metabolism: 265-ATP-dependent RNA helicases, bacterial; 266-Polyadenylation bacterial; 267-Queuosine-archaeosine biosynthesis; 268-RNA polymerase bacterial; 269-RNA processing and degradation, bacterial; 270-Ribonuclease H; 271-Rrf2 family transcriptional regulators; 272-Transcription factors bacterial; 273-Transcription initiation, bacterial sigma factors; 274-Wyeosine-MimG biosynthesis; 275-tRNA processing. Regulation and cell signaling: 276-CytR regulation; 277-DNA-binding regulatory proteins, strays; 278-MazEF toxin-antitoxin (programmed cell death) system; 280-Murein hydrolase regulation and cell death; 279-Orphan regulatory proteins; 280-Sex pheromones in Enterococcus faecalis and other firmicutes; 281-Stringent response, (p)ppGpp metabolism; 282-cAMP signaling in bacteria. Respiration: 283-Anaerobic respiratory reductases; 284-Biogenesis of c-type cytochromes; 285-Biogenesis of cytochrome c oxidases; 286-Carbon monoxide induced hydrogenase; 287-F0F1-type ATP synthase; 288-Flavocytochrome C; 289-Formate dehydrogenase; 290-Formate hydrogenase; 291-Hydrogenases; 292-Membrane-bound Ni, Fe-hydrogenase; 293-Na(+)-translocating NADH-quinone; 294-NiFe hydrogenase maturation; 295-Respiratory complex I; 296-Respiratory dehydrogenases 1; 297-Soluble cytochromes and functionally related electron carriers; 298-Succinate dehydrogenase; 299-Terminal cytochrome oxidases; 300-Trimethylamine N-oxide (TMAO) reductase. Secondary metabolism: 301-Auxin biosynthesis; 302-Biflavanoid biosynthesis; 303-Cinnamic acid degradation; 304-Phenazine biosynthesis; 305-Steroid sulfates; 306-Tannin biosynthesis. Stress response: 307-Bacterial hemoglobins; 308-Choline and betaine uptake and betaine biosynthesis; 309-D-trosyl-tRNA(Tyr) deacylase; 310-Ectoine biosynthesis and regulation; 311-Flavohemoglobin; 312-Glutaredoxins; 313-Glutathione-dependent pathway of formaldehyde detoxification; 314-Glutathione: Biosynthesis and gamma-glutamyl cycle; 315-Glutathione: Nonredox reactions; 316-Glutathione: Redox cycle; 317-Glutathionylspermidine and trypanothione; 318-Heat shock dnaK gene cluster extended; 319-Hfl operon; 320-Oxidative stress; 321-Redox-dependent regulation of nucleus processes; 322-Rubrerythrin; 323-SigmaB stress response regulation; 324-Sugar-phosphate stress regulation; 325-Universal stress protein family. Sulfur metabolism: 326-Alkanesulfonate assimilation; 327-Alkanesulfonates utilization; 328-Sulfate reduction-associated complexes; 329-Taurine utilization; 330-Thioredoxin-disulfide reductase; 331-Utilization of glutathione as a sulfur source. Virulence, disease, and defense: 332-Type 4 secretion and conjugative transfer; 333-Aminoglycoside adenylyltransferases; 334-Arsenic resistance; 335-Bacteriocin-like peptides Blp; 336-Beta-lactamase; 337-Cobalt-zinc-cadmium resistance; 338-Copper homeostasis; 339-Copper homeostasis: Copper tolerance; 340-Fosfomycin resistance; 341-MLST; 342-Methicillin resistance in staphylococci; 343-Multidrug resistance, 2-protein version found in gram-positive bacteria; 344-Multidrug resistance, tripartite systems found in gram-negative bacteria; 345-Multidrug resistance efflux pumps; 346-Multidrug efflux pump in Campylobacter jejuni (CmeABC operon); 347-Resistance to fluoroquinolones; 348-Streptococcus pyogenes recombinatorial zone; 349-Zinc resistance.

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    Pathogenicity gene clusters in Brenneria clades.

    Similar to other necrotrophic phytopathogens, pathogenicity determinants within the genus Brenneria including bacterial secretion systems are highly conserved (Doonan et al. 2019). These systems include PCWDEs, which may have a crucial role in disease occurrence, progress and suppression of the plant defense mechanisms. Sequence similarity searches may be employed in order to determine putative pathogenicity mechanisms in novel bacterial pathogens by identifying the core repertoire of virulence genes shared by related organisms (Broberg et al. 2018; Doonan et al. 2019). Significant potential virulence homologs were determined in silico in Brenneria clade A and B species and compared with other relevant Pectobacteriaceae emphasizing PCWDEs (particularly pectinases), functional secretion systems (in special type III T3SS), and associated effectors and harpins.

    We identified PCWDEs by using automated CAZym annotation performed in dbCAN2 meta-server database, which identifies closely related carbohydrate active enzymes including PCWDEs. The annotation results revealed that the family Pectobacteriaceae has a core enzyme repertoire in addition to a few specialists that might be involved in interactions with host plants and other symbionts (Table 4). The most common enzymes we identified were annotated as polysaccharide lyase (PL) family, PL22 (oligogalacturonide lyase [EC 4.2.2.6]); glycoside hydrolases (GH) family, GH102 (peptidoglycan lytic transglycosylase [EC 3.2.1.-]); and glycosyl transferase (GT) family, GT2—with variety of activities—were notable. Also, the family carbohydrate esterase (CE) only had three most frequent and top hits for CE1 in Dickeya dadantii, D. paradisaica, and P. carotovorum ssp. carotovorum PC1 in genome set. A pectate lyase, PL4 (EC 4.2.2.23), which degrades the plant cell wall component rhamnogalacturonan, was determined uniquely in B. izadpanahii and B. goodwinii (Table 4).

    TABLE 4. The carbohydrate-active enzymes database (CAZym) top 10 most frequent and hit repertoires of the Brenneria species and their relevant family Pectobacteriaceaea

    The T3SS is essential to the virulence of numerous phytopathogens, e.g., Pseudomonas syringae pathovars that use the system to enable host manipulation (Tampakaki et al. 2010). In Brenneria clade A, complete T3SS was detected in all members but surprisingly typical genes in the core type III nanomachinery were not identified in the B. corticis genome; the result was confirmed with the analysis system in PATRIC.

    Pathogenicity islands encoding the secretion of effector molecules via type III secretion system have been discovered in several gram-negative pathogens (Winstanley and Hart 2001). The hrpN harpin gene is key to the virulence of E. amylovora and secretes the DspA/E effector, both hrpN and dspA/E are encoded within Brenneria species. (Doonan et al. 2019). HrpG, HrpD, HrpA (TTSS pilin hrpA), HrpF, HrpW (harpin with pectate lyase domain), HrpB, HrpP (annotated as hypothetical protein in some cases), HrpQ, and HrpJ were found in all clade A and B members, except for B. corticis, which only shares both ATP-dependent helicase HrpA and ATP-dependent helicase HrpB with other members. Type III secretion protein HrpT (annotated as hypothetical protein in some cases) was found in all members except for B. alni, B. cortices, and B. nigrifluens.

    As described previously in AOD pathobiome, the major B. goodwinii homologous virulence factors, effector repertoire, were screened and hopAN1, hopX1, hopAJ2, hopA1, hopAW1, avrXccB, dspA/E, dspF, mxiE, and avrAxv, which are overall key to disease causation in bacterial phytopathogens, were detected (Doonan et al. 2019). We identified potential virulence factors to add to those of Doonan et al. (2019); they include hopV1 (B. alni), virulence factor VirK (B. goodwinii and B. izadpanahii), and protein of avirulence locus impE (B. izadpanahii) in Brenneria clade A species.

    In Brenneria clade B, homologous sequences were identified for virulence factors such as type III effector protein AvrE1 (B. roseae ssp. roseae, B. roseae ssp. americana, and B. salicis, annotated as hypothetical protein in B. rubrifaciens) and hopV1 within the B. salicis and B. rubrifaciens genomes. Sequences detected in both clades A and B of Brenneria included virulence factor MviM (B. salicis, B. goodwinii 141/171/OBR, B. izadpanahii, and B. roseae ssp. americana), virulence-associated protein VagC (B. corticis; B. roseae ssp. roseae), and virulence-associated protein C (VapC toxin protein) (B. corticis-two families, B. nigrifluens, B. roseae ssp. roseae, and B. roseae ssp. americana).

    In silico detection of the plasmids, phages, and secondary metabolites.

    The genome datasets were scanned for plasmids by the PlasmidFinder online service for Brenneria clades A and B and of relevant Pectobacteriaceae members, and the results indicated that they carry no detectable plasmid.

    Scanning the Brenneria spp. genomes investigated in this study for prophage sequences with the PHASTER online service detected several hypothetical prophage groups including PHAGE_Salmon_SEN1_NC_029003(3), PHAGE_Aeromo_phiO18P_NC_009542(16), and PHAGE_Gordon_Schwabeltier_NC_031255(1) (Table 5). We detected a few additional phage regions by using a GenBank formatted file (.gff) as a query that were not found when using FASTA formatted genomes files. Also, in our experience, the number of genomes contigs in .gff format could affect outputs. For example, using FASTA drafted genome of B. corticis, three prophage groups were determined, but using. gff format at least seven different prophage families were detected within the genome.

    TABLE 5. Prophages within the genome sequences of Brenneria spp. and relevant Pectobacteriaceae strains detected using the online service PHASTER

    Bacterial metabolite detection online tools were developed reinforcing the importance of their biological and ecological functions. Turnurbactin (up to 46% similarity with the reference data-Teredinibacter turnerae T7901-GenBank CP001614.2) and aryl polyene (100% similarity-Xenorhabdus doucetiae strain FRM16, reference sequence NZ_FO704550.1) were detected as the most encoded metabolites/antibiotic factors within the genome dataset using antiSMASH database-bacterial version (Table 6).

    TABLE 6. In silico secondary metabolite profiling by searching against the antiSMASH database-bacterial version within the genome sequences of Brenneria spp. and relevant Pectobacteriaceae strains

    DISCUSSION

    Only a few species within the genus Brenneria have been described so far despite increasing reports demonstrating that strains of Brenneria spp. are ubiquitous and commonly associated with woody plants presenting symptoms including bark canker and stem bleeding as the most visible (Brady et al. 2014; Denman et al. 2012; Hauben et al. 1998; Li et al. 2015, 2019; Zheng et al. 2017). The relatively small number of species analyzed is a likely consequence of the media and culturing conditions used in laboratory procedures and associated isolation biases. Also, screening of the population structure and evolutionary potential in genomic scales could be a useful tool since these bacteria may share close genetic relationships (e.g., B. goodwinii in U.K. [Kaczmarek et al. 2017]). So, combination of genomic indices provides added resolution to effectively identify species within the genus.

    A genome-based phylogeny using 50 clusters of orthologous groups (Fig. 1B) proposed rearrangement necessity of the family Pectobacteriaceae into two subfamilies, sharing most common recent ancestor in Brenneria-Pectobacterium and Dickeya-Lonsdalea, respectively. The B. izadpanahii-B. goodwinii 141, B. nigrifluens-B. corticis, and B. roseae ssp. roseae-B. roseae ssp. americana formed very tight pairs related by almost 90% for B. izadpanahii-B. goodwinii pair and 95 to 96% for another two pairs, respectively, with all ANI calculations and reciprocal relatedness of the dDDH values. These results are consistent with the 16S rRNA gene/MLSA sequence similarity data (Fig. 1A). All other comparisons between Brenneria clades A and B gave even lower similarity levels (below-threshold genomic similarity) and were scattered in several clades. Thus, the novel isolate should also be considered as another new species from a genomic point of view (Fig. 5A).

    With advances in high-throughput sequencing technologies bacterial taxonomy is undergoing many changes as a consequence and new innovative tools were developed (Caputo et al. 2019). Using pangenomic analyses, species can be redefined or new species definitions generated. The analysis pipeline in KBase was useful in providing a phylogenetic and functional genomic assessment of many whole bacterial genomes simultaneously. The Brenneria species were divided according to their host plants, the disease symptoms they cause, and other phenotypic or genotypic features. The taxonomic classification of Brenneria species has been the subject of controversy (Denman et al. 2012; Hauben et al. 1998). In a recent update, B. quercina was transferred to the new genus Lonsdalea (Brady et al. 2012). Here, we analyzed core/noncore and singleton genes in pangenomes once for genome-based taxonomy point of view in addition to deciphering their common and special potential activities.

    Both the core and dispensable genes are crucial for determining bacterial species diversity that are responsible for fundamental functions including replication, translation, and maintenance of cellular homeostasis and survivability, antimicrobial resistance, virulence traits, and development of novel gene functions, respectively (Adeniji et al. 2019).

    A majority of the core genes irrespective of their functions and location in the genome are useful for phylogenomic reassignments among Brenneria and elsewhere. This pangenomic analysis enabled us to conclude that B. izadpanahii, which exhibit as many differences between them as with other Brenneria species, is a distinct Brenneria species. The conserved genomic regions of the Brenneria strains were probably due to their common ancestry, either by duplication or by speciation of the genes (Fig. 1C). While the phylogenomic position of the Dickeya-Pectobacterium clade was clarified in these analyses, only further investigations using a larger collection of strains will shed light on the genetic content and taxonomic status of the family Pectobacteriaceae.

    Although the existence of T3SS cluster within a putative pathogen is typically considered a substantial factor in establishing the causality of disease (Arnold and Jackson 2011), it should be noted that the functional hrp systems as a minor virulence component in the Pectobacteriaceae (Pectobacterium and Dickeya) (Pérombelon 2002) may reflect a hemibiotrophic lifestyle of Brenneria ssp. (Glazebrook 2005). While B. corticis with high similarity in genome metrics and functional domains with the B. nigrifluens (a well-known pathogenic member of the genus) does not carry the T3SS cluster and potential effectors, only further investigations could decipher exact nature of the bacterium in related pathosystem. The whole genome sequence analyses Lee et al. (2020) showed that the nonpathogenic Xanthomonas campestris strain nE1 does not contain the entire pathogenicity island (hrp gene cluster; type III secretion system) and all type III effector protein genes. Experimental evidence demonstrated that this nonpathogenic strain (nE1) could acquire the entire pathogenicity island from the endemic X. campestris pv. campestris and X. campestris pv. raphani strains by conjugation, while type III effector genes were not cotransferred (Lee et al. 2020). This results in raising questions in the case of symptomatic trees where several bacterial species occasionally are found as complex networks in the microbiome and the possible transfer events of pathogenicity determinants between the species is not impossible.

    The analysis of prophage regions within Brenneria genomes may be fundamental to understanding their evolution and the potential for transfer for virulence factors between strains or species such as reported for ECA41 and hopAR1 in potato and cherry, respectively (Evans et al. 2010; Hulin et al. 2018), and influencing ecological fitness of their bacterial hosts (e.g., genes encoding metal ion transporters) (Matos et al. 2013; Ohnishi et al. 2001). Thirty-seven intact prophages have been characterized in Pectobacterium spp. and Dickeya spp. genomes and 6 of 37 were located near genes coding for pectate lyase degrading enzyme (Czajkowski 2019). The relatively high number of intact prophages found in the present analysis may suggest that the interaction of Brenneria and (bacterio) phages at the microbiome scale is underestimated. The potential effects of phages in AOD is undescribed but is suggested by the upregulation of bacteriophages genes in the AOD pathobiome in which B. goodwinii was the dominant taxa and was an essential virulence component.

    The factors that determine the interactions within phyllosphere microbial communities are poorly understood whether they are between different bacteria or between bacteria and other microorganisms. The production of microbial secondary metabolites and antibiotics might be the principal mechanisms by which endogenous bacteria and fungi antagonize each other (Vorholt 2012).

    Aryl polyene are yellow pigments produced by bacteria living in widely varied environments such as soil, human intestines, or other ecological niches. Embedded in the membrane of the bacteria, they protect against oxidative stress or reactive oxygen species. (Grammbitter et al. 2019). The forces of biochemical adaptive evolution operate at the level of genes, manifesting in complex phenotypes and the global biodiversity of proteins and metabolites. Secondary metabolites characterizations aiming to decipher how these factors shape adaptive landscapes is an interesting era of bacterial plant pathogen studies. To our knowledge, the first evidence of derived type II polyketide synthases bacterial pigment across the family Pectobacteriaceae was documented in our in silico analysis.

    The majority of detectable metabolites within the Brenneria genomes were related to the iron acquisition (Table 6). In response to iron limitation, many bacteria and some fungi produce siderophores. Turnerbactin is a triscatecholate siderophore that was first described in Teredinibacter turnerae, a shipworm endosymbiont found in decaying wood floating on water. This compound is crucial in obtaining iron, bacterial competition, and to the survival of the symbiont in nature (Han et al. 2013). The number of gene clusters related to iron acquisition and transfer within the Brenneria clades members (of various types) suggests they may be crucial to the successful colonization of their plant host. The in silico evidence presented here suggest that these factors could be important in epiphytic maintenance of bacterial species and that experimental evidence should be sought to discover their role in planta.

    Our findings provide the first genome/pangenome-based approach conducted on the genus Brenneria and allowed the classification of our new isolate into novel species, whose description is given below. The genetic variations cataloged here provide new insights into the evolutionary history of the genus, generating hypotheses about phylogenetic relationships, common and new pathogenicity scenarios, and how possibly they may synchronize their functions as a dominant complex community of the Brenneria. Functionally distinctive domains (in singletons category) within the Brenneria species genomes that specifically massive enzymatic potential activity, transcriptional regulatory, and ABC transporters in many types encoded by B. izadpanahii and huge number of phage-related genes in B. goodwinii are only small examples that show the diversification and adaptive capabilities in the biology of Brenneria, and each species may act as a puzzle pieces. Other members encompass a variety batch of pathogenicity repertoires that enable them to settle an offensive front line against the host defense barriers and become dominant taxa in the microbiome. These data are consistent with our observations in the Iranian diseased oak pathosystem that isolation of multiple Brenneria species (B. goodwinii, B. roseae ssp. roseae, B. nigrifluens—only two isolates—, and Brenneria sp.—now named B. izadpanahii) from individual symptomatic oak trees indicated the complexity of the syndromes that Brenneria species are involved (Bakhshi ganje et al. 2020). In the light of high-throughput sequencing technologies and improved metagenomics (e.g., metagenomic based- strain-level identification [Mechan Llontop et al. 2020]), metatranscriptomics, and metaproteomics analyses, studies of tree and forest ecosystem microbiomes have increased and consequently lead to developing an understanding of complicated communications between the species.

    Description of B. izadpanahii sp. nov.

    B. izadpanahii sp. nov. (i.zad.pa.nah’i.i. N.L. gen. n. izadpanahii, named in honor of Professor Keramatollah Izadpanah, for his 6 decades of efforts in plant pathology education and research in Iran). Cells are gram stain-negative and facultatively anaerobic. Colonies are pale cream, circular, opaque, convex with entire margins, smooth and approximately 0.5 to 1.0 mm in diameter after incubation for 2 days at 30°C on nutrient agar. Growth occurs at 10 to 40°C and the optimum growth temperature is 30°C. It is negative for activities of lysine decarboxylase, arbutin (β-galactosidase), arginine dihydrolase, nitrate reductase, ornithine decarboxylase, urease, oxidase, d-arabinose, and l-rhamnose. It is positive for fermentation of inositol, amygdalin, sucrose, glucose, mannitol, and arabinose. Positive for acid production from glycerol, L-arabinose, D-xylose, aesculin ferric citrate, D-glucose, D-fructose, β-d-methyl glycoside, inositol, D-mannitol, raffinose, melibiose, sucrose, trehalose, and D-galactose. It is positive for assimilation of Tween 80, N-acetyl-D-glucosamine, L-arabinose, D-fructose, D-galactose, a-D-glucose, m-inositol, D-mannitol, D-mannose, melibiose, raffinose, sucrose, trehalose, glycerol, glucose-1-phosphate, and glucose-6-phosphate. The DNA G+C content is 53.66 mol%. The type strain is Iran isolate 50 (BioProject PRJNA616088; accession number CP050854 = Iran 52 = Iran 29), isolated from symptomatic bark of Q. castaneifolia canker in north of Iran.

    The author(s) declare no conflict of interest.

    LITERATURE CITED

    • Adeniji, A. A., and Babalola, O. O. 2019. Bacillus velezensis: Phylogeny, useful applications, and avenues for exploitation. Appl. Microbiol. Biotechnol. 103:3669-3682. Crossref, Medline, ISI, Google Scholar
    • Adeolu, M., Alnajar, S., Naushad, S., and Gupta, R. S. 2016. Genome-based phylogeny and taxonomy of the ‘Enterobacteriales’: Proposal for Enterobacterales ord. nov. divided into the families Enterobacteriaceae, Erwiniaceae fam. nov., Pectobacteriaceae fam. nov., Yersiniaceae fam. nov., Hafniaceae fam. nov., Morganellaceae fam. nov., and Budviciaceae fam. nov. Int. J. Syst. Evol. Microbiol. 66:5575-5599. https://doi.org/10.1099/ijsem.0.001485 Crossref, Medline, ISI, Google Scholar
    • Arkin, A. P., Cottingham, R. W., Henry, C. S., Harris, N. L., Stevens, R. L., Maslov, S., Dehal, P., Ware, D., Perez, F., Canon, S., and Sneddon, M. W. 2018. KBase: The United States Department of Energy Systems Biology Knowledgebase. Nat. Biotechnol. 36:566-569. https://doi.org/10.1038/nbt.4163 Crossref, Medline, ISI, Google Scholar
    • Arndt, D., Grant, J. R., Marcu, A., Sajed, T., Pon, A., Liang, Y., and Wishart, D. S. 2016. PHASTER: A better, faster version of the PHAST phage search tool. Nucleic Acids Res. 44:W16-W21. ISI, Google Scholar
    • Arnold, D. L., and Jackson, R. W. 2011. Bacterial genomes: Evolution of pathogenicity. Curr. Opin. Plant Biol. 14:385-391. https://doi.org/10.1016/j.pbi.2011.03.001 Crossref, Medline, ISI, Google Scholar
    • Bakhshi ganje, M., Shams‐Bakhsh, M., Mackay, J., and Rahimian, H. 2020. Identification and characterization of bacterial strains associated with diseased oak trees in Northern Iran. For. Pathol. 50:e12571. https://doi.org/10.1111/efp.12571 Google Scholar
    • Bertelli, C., Laird, M. R., Williams, K. P., Simon Fraser University Research Computing Group, Lau, B. Y., Hoad, G., Winsor, G. L., and Brinkman, F. S. L. 2017. IslandViewer 4: Expanded prediction of genomic islands for larger-scale datasets. Nucleic Acids Res. 45:W30-W35. https://doi.org/10.1093/nar/gkx343 Crossref, Medline, ISI, Google Scholar
    • Blin, K., Shaw, S., Steinke, K., Villebro, R., Ziemert, N., Lee, S. Y., Medema, M. H., and Weber, T. 2019. antiSMASH 5.0: Updates to the secondary metabolite genome mining pipeline. Nucleic Acids Res. 47:W81-W87. https://doi.org/10.1093/nar/gkz310 Crossref, Medline, ISI, Google Scholar
    • Brady, C., Cleenwerck, I., Venter, S., Vancanneyt, M., Swings, J., and Coutinho, T. 2008. Phylogeny and identification of Pantoea species associated with plants, humans and the natural environment based on multilocus sequence analysis (MLSA). Syst. Appl. Microbiol. 31:447-460. https://doi.org/10.1016/j.syapm.2008.09.004 Google Scholar
    • Brady, C., Hunter, G., Kirk, S., Arnold, D., and Denman, S. 2014. Description of Brenneria roseae sp. nov. and two subspecies, Brenneria roseae subspecies roseae ssp. nov. and Brenneria roseae subspecies americana ssp. nov. isolated from symptomatic oak. Syst. Appl. Microbiol. 37:396-401. https://doi.org/10.1016/j.syapm.2014.04.005 Google Scholar
    • Brady, C. L., Cleenwerck, I., Denman, S., Venter, S. N., Rodríguez-Palenzuela, P., Coutinho, T. A., and De Vos, P. 2012. Proposal to reclassify Brenneria quercina (Hildebrand and Schroth 1967) Hauben et al. 1999 into a new genus, Lonsdalea gen. nov., as Lonsdalea quercina comb. nov., descriptions of Lonsdalea quercina subsp. quercina comb. nov., Lonsdalea quercina subsp. iberica subsp. nov. and Lonsdalea quercina subsp. britannica subsp. nov., emendation of the description of the genus Brenneria, reclassification of Dickeya dieffenbachiae as Dickeya dadantii subsp. dieffenbachiae comb. nov., and emendation of the description of Dickeya dadantii. Int. J. Syst. Evol. Microbiol. 62:1592-1602. https://doi.org/10.1099/ijs.0.035055-0 Google Scholar
    • Broberg, M., Doonan, J., Mundt, F., Denman, S., and McDonald, J. E. 2018. Integrated multi-omic analysis of host−microbiota interactions in acute oak decline. Microbiome 6:21. https://doi.org/10.1186/s40168-018-0408-5 Crossref, Medline, ISI, Google Scholar
    • Caballero, J. I., Zerillo, M. M., Snelling, J., Cranshaw, W., Boucher, C., and Tisserat, N. 2014. Genome sequences of strain ATCC 29281 and pin and northern red oak isolates of Lonsdalea quercina subsp. quercina. Genome Announc. 2(3). Google Scholar
    • Caputo, A., Fournier, P. E., and Raoult, D. 2019. Genome and pan-genome analysis to classify emerging bacteria. Biol. Direct 14:5. https://doi.org/10.1186/s13062-019-0234-0 Crossref, Medline, ISI, Google Scholar
    • Carattoli, A., Zankari, E., Garcìa-Fernandez, A., Larsen, M. V., Lund, O., Villa, L., Aarestrup, F. M., and Hasman, H. 2014. In silico detection and typing of plasmids using PlasmidFinder and plasmid multilocus sequence typing. Antimicrobiol. Agents Chemother. 58:3895-3903. https://doi.org/10.1128/AAC.02412-14 Crossref, Medline, ISI, Google Scholar
    • Czajkowski, R. 2019. May the phage be with you? Prophage-like elements in the genomes of soft rot Pectobacteriaceae: Pectobacterium spp. and Dickeya spp. Front. Microbiol. 10:138. https://doi.org/10.3389/fmicb.2019.00138 Crossref, Medline, ISI, Google Scholar
    • Denman, S., Brady, C., Kirk, S., Cleenwerck, I., Venter, S., Coutinho, T., and De Vos, P. 2012. Brenneria goodwinii sp. nov., associated with acute oak decline in the UK. Int. J. Syst. Evol. Microbiol. 62:2451-2456. https://doi.org/10.1099/ijs.0.037879-0 Crossref, Medline, ISI, Google Scholar
    • Doonan, J., Denman, S., Pachebat, J. A., and McDonald, J. E. 2019. Genomic analysis of bacteria in the acute oak decline pathobiome. Microbiol. Genom. 5:1-15. ISI, Google Scholar
    • Evans, T. J., Coulthurst, S. J., Komitopoulou, E., and Salmond, G. P. 2010. Two mobile Pectobacterium atrosepticum prophages modulate virulence. FEMS Microbiol. Lett. 304:195-202. Google Scholar
    • Glasner, J. D., Yang, C. H., Reverchon, S., Hugouvieux-Cotte-Pattat, N., Condemine, G., Bohin, J. P., Van Gijsegem, F., Yang, S., Franza, T., Expert, D., and Plunkett, G. 2011. Genome sequence of the plant-pathogenic bacterium Dickeya dadantii 3937. J. Bacteriol. 193:2076-2077. Crossref, Medline, ISI, Google Scholar
    • Glazebrook, J. 2005. Contrasting mechanisms of defense against biotrophic and necrotrophic pathogens. Annu. Rev. Phytopathol. 43:205-227. https://doi.org/10.1146/annurev.phyto.43.040204.135923 Crossref, Medline, ISI, Google Scholar
    • Grammbitter, G. L., Schmalhofer, M., Karimi, K., Shi, Y. M., Schöner, T. A., Tobias, N. J., Morgner, N., Groll, M., and Bode, H. B. 2019. An uncommon type II PKS catalyzes biosynthesis of aryl polyene pigments. J. Am. Chem. Soc. 141:16615-16623. https://doi.org/10.1021/jacs.8b10776 Crossref, Medline, ISI, Google Scholar
    • Han, A. W., Sandy, M., Fishman, B., Trindade-Silva, A. E., Soares, C. A., Distel, D. L., Butler, A., and Haygood, M. G. 2013. Turnerbactin, a novel triscatecholate siderophore from the shipworm endosymbiont Teredinibacter turnerae T7901. PLoS One 8:e76151. ISI, Google Scholar
    • Hauben, L., Moore, E. R., Vauterin, L., Steenackers, M., Mergaert, J., Verdonck, L., and Swings, J. 1998. Phylogenetic position of phytopathogens within the Enterobacteriaceae. Syst. Appl. Microbiol. 21:384-397. https://doi.org/10.1016/S0723-2020(98)80048-9 Crossref, Medline, ISI, Google Scholar
    • Hulin, M. T., Armitage, A. D., Vicente, J. G., Holub, E. B., Baxter, L., Bates, H. J., Mansfield, J. W., Jackson, R. W., and Harrison, R. J. 2018. Comparative genomics of Pseudomonas syringae reveals convergent gene gain and loss associated with specialization onto cherry (Prunus avium). New Phytol. 219:672-696. https://doi.org/10.1111/nph.15182 Crossref, Medline, ISI, Google Scholar
    • Kaczmarek, M., Mullett, M. S., McDonald, J. E., and Denman, S. 2017. Multilocus sequence typing provides insights into the population structure and evolutionary potential of Brenneria goodwinii, associated with acute oak decline. PLoS One 12:e0178390. https://doi.org/10.1371/journal.pone.0178390 Crossref, Medline, Google Scholar
    • Lee, I., Kim, Y. O., Park, S. C., and Chun, J. 2016. OrthoANI: An improved algorithm and software for calculating average nucleotide identity. Intl. J. Syst. Evol. Microbiol. 66:1100-1103. https://doi.org/10.1099/ijsem.0.000760 Crossref, Medline, ISI, Google Scholar
    • Lee, Y. A., Yang, P. Y., and Huang, S. C. 2020. Characterization, phylogeny, and genome analyses of nonpathogenic Xanthomonas campestris strains isolated from brassica seeds. Phytopathology 110:981-988. Link, Google Scholar
    • Li, Y., Fang, W., Xue, H., Liang, W. X., Wang, L. F., Tian, G. Z., and Piao, C. G. 2015. Brenneria populi sp. nov., isolated from symptomatic bark of Populus × euramericana canker. Int. J. Syst. Evol. Microbiol. 65:432-437. https://doi.org/10.1099/ijs.0.066068-0 Crossref, Medline, ISI, Google Scholar
    • Li, Y., Zheng, M. H., Wang, H. M., Lin, C. L., and Wang, X. Z. 2019. Brenneria corticis sp. nov., isolated from symptomatic bark of Populus × euramericana canker. Intl. J. Syst. Evol. Microbiol. 69:63-67. Google Scholar
    • Lombard, V., Golaconda Ramulu, H., Drula, E., Coutinho, P. M., and Henrissat, B. 2014. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 42:D490-D495. https://doi.org/10.1093/nar/gkt1178 Crossref, Medline, ISI, Google Scholar
    • Maes, M., Huvenne, H., and Messens, E. 2009. Brenneria salicis, the bacterium causing watermark disease in willow, resides as an endophyte in wood. Environ. Microbiol. 11:1453-1462. https://doi.org/10.1111/j.1462-2920.2009.01873.x Crossref, Medline, ISI, Google Scholar
    • Matos, R. C., Lapaque, N., Rigottier-Gois, L., Debarbieux, L., Meylheuc, T., Gonzalez-Zorn, B., Repoila, F., de Fatima Lopes, M., and Serror, P. 2013. Enterococcus faecalis prophage dynamics and contributions to pathogenic traits. PLoS Genet. 9:e1003539. https://doi.org/10.1371/journal.pgen.1003539 Crossref, Medline, ISI, Google Scholar
    • Mechan Llontop, M. E., Sharma, P., Aguilera Flores, M., Yang, S., Pollok, J., Tian, L., Huang, C., Rideout, S., Heath, L. S., Li, S., and Vinatzer, B. A. 2020. Strain-level identification of bacterial tomato pathogens directly from metagenomic sequences. Phytopathology 110:768-779. Link, ISI, Google Scholar
    • Meier-Kolthoff, J. P., Auch, A. F., Klenk, H. P., and Göker, M. 2013. Genome sequence-based species delimitation with confidence intervals and improved distance functions. BMC Bioinform. 14:60. https://doi.org/10.1186/1471-2105-14-60 Crossref, Medline, ISI, Google Scholar
    • Niemann, S., Pühler, A., Tichy, H. V., Simon, R., and Selbitschka, W. 1997. Evaluation of the resolving power of three different DNA fingerprinting methods to discriminate among isolates of a natural Rhizobium meliloti population. J. Appl. Microbiol. 82:477-484. https://doi.org/10.1046/j.1365-2672.1997.00141.x Crossref, Medline, ISI, Google Scholar
    • Ohnishi, M., Kurokawa, K., and Hayashi, T. 2001. Diversification of Escherichia coli genomes: Are bacteriophages the major contributors? Trends Microbiol. 9:481-485. https://doi.org/10.1016/S0966-842X(01)02173-4 Crossref, Medline, ISI, Google Scholar
    • Overbeek, R., Olson, R., Pusch, G. D., Olsen, G. J., Davis, J. J., Disz, T., Edwards, R. A., Gerdes, S., Parrello, B., Shukla, M., and Vonstein, V. 2014. The SEED and the rapid annotation of microbial genomes using subsystems technology (RAST). Nucleic Acids Res. 42:D206-D214. https://doi.org/10.1093/nar/gkt1226 Crossref, Medline, ISI, Google Scholar
    • Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P., and Tyson, G. W. 2015. CheckM: Assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25:1043-1055. https://doi.org/10.1101/gr.186072.114 Crossref, Medline, ISI, Google Scholar
    • Pérombelon, M. C. M. 2002. Potato diseases caused by soft rot Erwinias: An overview of pathogenesis. Plant Pathol. 51:1-12. Crossref, ISI, Google Scholar
    • Pettifor, B. J., Doonan, J., Denman, S., and McDonald, J. E. 2020. Survival of Brenneria goodwinii and Gibbsiella quercinecans, associated with acute oak decline, in rainwater and forest soil. Syst. Appl. Microbiol. 43:126052. https://doi.org/10.1016/j.syapm.2019.126052 Crossref, Medline, ISI, Google Scholar
    • Poret-Peterson, A. T., McClean, A. E., Chen, L., and Kluepfel, D. A. 2019. Complete genome sequences of Brenneria rubrifaciens strain 6D370 and Brenneria nigrifluens strain ATCC 13028, causative agents of bark cankers in walnut. Microbiol. Resour. Announc. 8:e00597-19. Crossref, Medline, ISI, Google Scholar
    • Price, M. N., Dehal, P. S., and Arkin, A. P. 2010. FastTree 2–Approximately maximum-likelihood trees for large alignments. PLoS One 5:e9490. https://doi.org/10.1371/journal.pone.0009490 Crossref, Medline, ISI, Google Scholar
    • Pritchard, L., Humphris, S., Saddler, G. S., Elphinstone, J. G., Pirhonen, M., and Toth, I. K. 2013. Draft genome sequences of 17 isolates of the plant pathogenic bacterium Dickeya. Genome Announc. 26:1(6). Google Scholar
    • Richter, M., Rosselló-Móra, R., Oliver Glöckner, F., and Peplies, J. 2016. JSpeciesWS: A web server for prokaryotic species circumscription based on pairwise genome comparison. Bioinformatics 32:929-931. https://doi.org/10.1093/bioinformatics/btv681 Crossref, Medline, ISI, Google Scholar
    • Sapp, M., Lewis, E., Moss, S., Barrett, B., Kirk, S., Elphinstone, J. G., and Denman, S. 2016. Metabarcoding of bacteria associated with the acute oak decline syndrome in England. Forests 7:95. https://doi.org/10.3390/f7050095 Crossref, ISI, Google Scholar
    • Tampakaki, A. P., Skandalis, N., Gazi, A. D., Bastaki, M. N., Panagiotis, F. S., Charova, S. N., Kokkinidis, M., and Panopoulos, N. J. 2010. Playing the “Harp”: Evolution of our understanding of hrp/hrc genes. Annu. Rev. Phytopathol. 48:347-370. https://doi.org/10.1146/annurev-phyto-073009-114407 Crossref, Medline, ISI, Google Scholar
    • Toth, I. K., Pritchard, L., and Birch, P. R. J. 2006. Comparative genomics reveals what makes an enterobacterial plant pathogen. Annu. Rev. Phytopathol. 44:305-336. https://doi.org/10.1146/annurev.phyto.44.070505.143444 Crossref, Medline, ISI, Google Scholar
    • Vorholt, J. A. 2012. Microbial life in the phyllosphere. Nat. Rev. Microbiol. 10:828-840. https://doi.org/10.1038/nrmicro2910 Crossref, Medline, ISI, Google Scholar
    • Wattam, A. R., Davis, J. J., Assaf, R., Boisvert, S., Brettin, T., Bun, C., Conrad, N., Dietrich, E. M., Disz, T., Gabbard, J. L., and Gerdes, S. 2017. Improvements to PATRIC, the all-bacterial bioinformatics database and analysis resource center. Nucleic Acids Res. 45:D535-D542. ISI, Google Scholar
    • Winstanley, C., and Hart, C. A. 2001. Type III secretion systems and pathogenicity islands. J. Med. Microbiol. 50:116-126. https://doi.org/10.1099/0022-1317-50-2-116 Crossref, Medline, ISI, Google Scholar
    • Xu, L., Dong, Z., Fang, L., Luo, Y., Wei, Z., Guo, H., Zhang, G., Gu, Y. Q., Coleman-Derr, D., Xia, Q., and Wang, Y. 2019. OrthoVenn2: A web server for whole-genome comparison and annotation of orthologous clusters across multiple species. Nucleic Acids Res. 47:W52-W58. https://doi.org/10.1093/nar/gkz333 Crossref, Medline, ISI, Google Scholar
    • Yoon, S. H., Ha, S. M., Lim, J. M., Kwon, S. J., and Chun, J. 2017. A large-scale evaluation of algorithms to calculate average nucleotide identity. Antonie van Leeuwenhoek 110:1281-1286. https://doi.org/10.1007/s10482-017-0844-4 Crossref, Medline, ISI, Google Scholar
    • Zhang, H., Yohe, T., Huang, L., Entwistle, S., Wu, P., Yang, Z., Busk, P. K., Xu, Y., and Yin, Y. 2018. dbCAN2: A meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 46:W95-W101. https://doi.org/10.1093/nar/gky418 Crossref, Medline, ISI, Google Scholar
    • Zhang, Y., Fan, Q., and Loria, R. 2016. A re-evaluation of the taxonomy of phytopathogenic genera Dickeya and Pectobacterium using whole-genome sequencing data. Syst. Appl. Microbiol. 39:252-259. Google Scholar
    • Zheng, M. H., Piao, C. G., Xue, H., Guo, M. W., and Li, Y. 2017. Brenneria populi subsp. brevivirga subsp. nov. isolated from symptomatic bark of Populus × euramericana canker, and description of Brenneria populi subsp. populi subsp. nov. Int. J. Syst. Evol. Microbiol. 67:3633-3638. https://doi.org/10.1099/ijsem.0.002183 Crossref, Medline, ISI, Google Scholar

    The author(s) declare no conflict of interest.

    Funding: Financial support provided by Tarbiat Modares University.