search for


Comparative Genome Analysis of Rathayibacter tritici NCPPB 1953 with Rathayibacter toxicus Strains Can Facilitate Studies on Mechanisms of Nematode Association and Host Infection
The Plant Pathology Journal 2017;33:370-381
Published online August 1, 2017
© 2017 The Korean Society of Plant Pathology.

Jungwook Park1,†, Pyeong An Lee2,†, Hyun-Hee Lee1, Kihyuck Choi2, Seon-Woo Lee2,*, and Young-Su Seo1,*

1Department of Microbiology, Pusan National University, Busan 46241, Korea, 2Department of Applied Bioscience, Dong-A University, Busan 49315, Korea
Correspondence to: *Co-corresponding authors. SW Lee, Phone) +82-51-200-7551, FAX) +82-51-200-7505, E-mail) YS Seo, Phone) +82-51-510-2267, FAX) +82-51-514-1778, E-mail)
Received January 25, 2017; Revised April 12, 2017; Accepted April 23, 2017.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Rathayibacter tritici, which is a Gram positive, plant pathogenic, non-motile, and rod-shaped bacterium, causes spike blight in wheat and barley. For successful pathogenesis, R. tritici is associated with Anguina tritici, a nematode, which produces seed galls (ear cockles) in certain plant varieties and facilitates spread of infection. Despite significant efforts, little research is available on the mechanism of disease or bacteria-nematode association of this bacterium due to lack of genomic information. Here, we report the first complete genome sequence of R. tritici NCPPB 1953 with diverse features of this strain. The whole genome consists of one circular chromosome of 3,354,681 bp with a GC content of 69.48%. A total of 2,979 genes were predicted, comprising 2,866 protein coding genes and 49 RNA genes. The comparative genomic analyses between R. tritici NCPPB 1953 and R. toxicus strains identified 1,052 specific genes in R. tritici NCPPB 1953. Using the BlastKOALA database, we revealed that the flexible genome of R. tritici NCPPB 1953 is highly enriched in ‘Environmental Information Processing’ system and metabolic processes for diverse substrates. Furthermore, many specific genes of R. tritici NCPPB 1953 are distributed in substrate-binding proteins for extracellular signals including saccharides, lipids, phosphates, amino acids and metallic cations. These data provides clues on rapid and stable colonization of R. tritici for disease mechanism and nematode association.

Keywords : comparative genome, Rathayibacter toxicus, Rathayibacter tritici NCPPB 1953

As a Gram positive plant pathogen, the genus Rathayibacter is a member of coryneform bacteria belonging to the family Microbacteriaceae of phylum Actinobacteria, previously attributed to genus Clavibacter (Davis et al., 1984). Many coryneform phytopathogenic bacteria were initially classified as the genus Corynebacterium (Dowson, 1942), while many properties of the coryneform plant pathogenic bacteria were different from those of Corynebacterium sensu stricto (Evtushenko and Dorofeeva, 2012). Later, plant pathogenic Corynebacterium species were reclassified into the genus Clavibacter due to the presence of DAB, which is a particular component in peptidoglycan cell wall group B (Carlson and Vidaver, 1982; Davis et al., 1984). In 1993, Zgurskaya and colleagues proposed the genus Rathayibacter such as R. tritici, R. rathayi, and R. iranicus separating from the genus Clavibacter based on DNA-DNA hybridization, chemotaxonomic studies, and numerical analysis of bacterial phenotypes (Evtushenko and Dorofeeva, 2012; Zgurskaya et al., 1993). Separation of Rathayibacter species from Clavibacter was further supported by the analyses of 16S rRNA gene sequences (Rainey et al., 1994; Takeuchi and Yokota, 1994). The species Rathayibacter toxicus was also classified in this genus by Sasaki and colleagues in 1998 (Sasaki et al., 1998). Furthermore, these species of Rathayibacter have been shown to differ absolutely in many physiological phenotypes, including cell-wall compositions, multilocus enzyme profiles, and pigmentations (Davis et al., 1984; De Bruyne et al., 1992; Lee et al., 1997; Riley et al., 1988; Zgurskaya et al., 1993).

R. tritici has been identified as a causative agent of spike blight, also called yellow ear rot, yellow slime rot, or Tundu disease, in wheat and barley (Paruthi and Gupta, 1987). Plant diseases caused by R. tritici result in economic losses in many countries worldwide, including Australia (Riley and Reardon, 1995), China, Cyprus, Iran (Bradbury, 1986; Duveiller and Fucikovsky, 1997; Mehta, 2014) and Pakistan (Akhtar, 1987). Interestingly, R. tritici shows an association with Anguina tritici, a nematode, which produces seed galls (ear cockles) in certain plant varieties and facilitates spread of R. tritici infection (Paruthi and Bhatti, 1985). Intact seed galls produced by nematodes display the greatest grain loss due to development of R. tritici (Fattah, 1988). Similarly, a bacterium, R. toxicus, causing a gumming disease and ryegrass toxicity is associated with a nematode vector (Riley and Ophel, 1992). Compared to R. tritici, R. toxicus is commonly found in annual ryegrass with A. funesta as a nematode vector, rabbit-foot grass and annual browngrass with undescribed Anguina species. While host plants for R. toxicus are limited to ryegrasses in nature, it is likely that R. toxicus is not host specific experimentally (Agarkova et al., 2006). In addition to unique host plants for R. toxicus, R. toxicus is different from R. tritici for the production of glycolipid toxins, known as corynetoxins, in the infected ryegrass, and thus causes a lethal toxicosis in the animal that consumed the infected plants (Agarkova et al., 2006; Jago and Culvenor, 1987). Both in R. tritici and R. toxicus, bacterial association with a nematode vector could be used as a good model of interaction between plant pathogens and nematodes for successful pathogenesis. Despite significant efforts and interesting association between bacterial pathogen and nematodes, little research is available on the mechanism of disease or bacteria-nematode association of this bacterium due to lack of genomic information.

Characterising the complete genome of R. tritici could help to resolve these gaps in our understanding. Here, we report the first whole genome sequence of R. tritici NCPPB 1953 isolated from wheat seeds. The whole genome of R. tritici NCPPB 1953 harbours one circular chromosome of 3,354,681 bp with 2,866 protein coding genes. The comparative genomic analysis between R. tritici NCPPB 1953 and R. toxicus strains showed a lot of 1,052 R. tritici NCPPB 1953-specific genes in flexible genome, which refers to genes present in two or more organisms or specific to a single organism in a pan-genome (Medini et al., 2005; Sternes and Borneman, 2016). We revealed that these genes are responsible for ‘Environmental Information Processing’ system and metabolic processes for diverse substrates, which might explain the rapid and stable growth of R. tritici rather than R. toxicus. Furthermore, distinctive genetic features of R. tritici NCPPB 1953 are distributed in substrate-binding proteins for extracellular signals. More research is required to better understand the pathogenesis of R. tritici and association with the nematode, and complete genomic information of R. tritici NCPPB 1953 will provide a valuable foundation for diverse biological experiments.

Materials and Methods

Growth conditions and genomic DNA preparation

Pure culture of R. tritici NCPPB 1953 was grown on nutrient broth yeast extract (NBY) media (8 g nutrient broth, 2 g yeast extract, 2 g K2HPO4, 0.5 g KH2PO4, 2.5 g glucose per liter, followed by autoclaving and supplementation with 1 ml of 1 M MgSO4·7H2O) media for 96 h at 25°C (Zgurskaya et al., 1993). For extraction of genomic DNA, the pellet of bacterial cells grown on the agar plate was harvested with sterilized water. DNA was subsequently isolated using the Promega Wizard Genomic DNA Purification Kit (Promega, Madison, WI, USA) following the standard protocol provided by the manufacturer. DNA was visualized in ethidium bromide-stained 0.7% agarose gel, and the concentration and purity of DNA were determined by a NanoDrop™ spectrophotometer.

Genome sequencing of R. tritici NCPPB 1953

Third generation DNA sequencing of the SMRT technology (Pacific Biosciences, Menlo Park, CA, USA) was used to establish the complete genome sequence of R. tritici NCPPB 1953 (Chin et al., 2013). A total of 150,292 raw reads containing an average of 7,236 bp were generated by a 20-kb insert SMRTbell standard library. To correct errors, long reads were selected as seeds, and other short reads were aligned into seeds by the basic local alignment with successive refinement step (Chaisson and Tesler, 2012). After filtration, post-filtered reads included 86,775 reads and represented an average of 11,144 bp with a quality of 0.852. The de novo assembly of post-filtered reads was conducted using the HGAP pipeline from SMRT-Analysis with default parameters (Chin et al., 2013). Circularization was verified, and overlapping ends were trimmed (Kopf et al., 2014). Assembly resulted in a single contig with a circular form of 3,354,681 bp. Subsequently, to evaluate assembly quality, all reads were mapped back to the chromosomal sequence according to the RS_Resequencing protocol. Final results indicate bases called value of 99.88%, consensus concordance of 100%, and 187.88-fold coverage.

Gene annotation of R. tritici NCPPB 1953

Genomic rRNAs and tRNAs were predicted using RNAmmer (Lagesen et al., 2007) and tRNAscan-SE (Lowe and Eddy, 1997), respectively. All open reading frames (ORFs) and pseudogenes were predicted using Glimmer (Delcher et al., 1999), GeneMarkHMM (Lukashin and Borodovsky, 1998) and Prodigal (Hyatt et al., 2010). Predicted ORFs with nucleotides were translated into amino acid sequences and then searched using the BLAST algorithm (Altschul et al., 1990) against the NCBI-NR (Benson et al., 2000), Pfam (Finn et al., 2016) and UniProt (UniProt Consortium, 2013) databases for a description of each protein. In addition, the KEGG (Kanehisa and Goto, 2000) and COG (Galperin et al., 2015) databases were used to construct functional categories in accordance with biological systems. Genes in internal clusters were detected using BLASTclust (Alva et al., 2016) with significant cutoffs of 70% coverage and 30% identity. Signal peptides and transmembrane helices were based on SignalP (Petersen et al., 2011) and TMHMM (Krogh et al., 2001), respectively. CRISPRFinder was used to identify the CRISPR (Grissa et al., 2007). Predicted genes were compared with the Pathogen-Host Interaction (PHI) database (Urban et al., 2015) using the BLASTp method and an e-value cutoff of 1.0 × 10−5 (Buiate et al., 2017). The annotation results were verified using the Artemis (Rutherford et al., 2000).

Nucleotide sequence accession number of R. tritici NCPPB 1953

The complete genome of R. tritici NCPPB 1953 has been deposited in GenBank (Benson et al., 2000) under accession number CP015515.

Phylogenetic analysis

To evaluate evolutionary distances among R. tritici NCPPB 1953 and closely related bacteria in the genus Rathayibacter, we collected all 16S rRNA sequences of 26 bacteria from the SILVA database. The ClustalW tool was used to align all sequences with default parameters (Larkin et al., 2007). Phylogenetic tree was conducted by the Maximum Likelihood algorithm based on the Tamura-Nei model as implemented in MEGA 7.0 tool (Kumar et al., 2016). The corresponding parameter of the Maximum Likelihood algorithm was set as ‘complete deletion’. The evolutionary test was performed by bootstrap analysis with 1,000 replications.

Comparative genome analysis

For this analyses, three complete genome of R. tritici NCPPB 1953 (accession no. CP015515), R. toxicus WAC3373 (accession no. CP013292), and R. toxicus 70137 (accession no. CP010848) were obtained from GenBank (Benson et al., 2000). We employed the Mauve tool to compare whole genome sequences (Darling et al., 2004). The progressive Mauve algorithm was used to identify genomic rearrangements among whole genome sequences. The alignment was performed using default parameters.

Pan-genome analysis

Genomic information of R. tritici NCPPB 1953 was formatted for a reference database using the formatdb tool of BLAST (Altschul et al., 1990). The local BLASTp compared total protein genes of R. toxicus WAC3373 and R. toxicus 70137 to reference database of R. tritici NCPPB 1953 through amino acid sequences. The BLAST outputs were filtered with significant criteria of 50% coverage, 50% identity, and an e-value cutoff of 1.0 × 10−5 to exclude random hits (Anderson and Brass, 1998). By sorting e-value, the best matched subject was selected from total results per each query. Subsequently, to investigate biological meaning of R. tritici NCPPB 1953-specific genes, we used the BlastKOALA tool as an automatic annotation server for genome and metagenome sequences (Kanehisa et al., 2016). The BlastKOALA performed KEGG orthology assignments to characterize individual gene functions and reconstruction of KEGG pathways and modules in R. tritici NCPPB 1953. Also, we constructed networks of some genes related to transport system using the Cytoscape tool (

Results and Discussion

General features of R. tritici NCPPB 1953

R. tritici NCPPB 1953 is a Gram positive, non-motile, and rodshaped bacterium. Its size is 1.1–1.6 μm long and 0.5–0.6 μm wide (Fig. 1). On an aerobic NBY agar plate, R. tritici NCPPB 1953 formed round colonies and produced yellow pigment within 96 h at 25°C (Fig. 1D) and could grow in a pH range of 6–8. This strain was a non-motile bacterium and cells of R. tritici NCPPB 1953 were short, straight or slightly curved rods with blunt ends. Cells occurred singly, in pairs or sometimes in aggregates (Fig. 1A, B). This is likely because divided cells by binary fission failed to separate after septum formation. Cells were surrounded by a capsule and the capsular material sometimes exhibited distinct extracellular outer layers (Fig. 1C; black arrows). Bacterial colonies were often variable in size, probably because of capsules and bacterial cell aggregates.

Phylogenetic analysis of R. tritici NCPPB 1953 with closely-related bacteria species

Phylogenetic analysis was performed using the 16S rRNA genes of 26 different bacteria in the genus Rathayibacter: 3 strains, R. caricis; 3 strains, R. festucae; 3 strains, R. iranicus; 3 strains, R. rathayi; 5 strains, R. toxicus; 9 strains, R. tritici (Fig. 2). All 16S rRNA sequences were downloaded from the SILVA (Quast et al., 2013), providing information on small (16S/18S, SSU) and large (23S/28S, LSU) subunit ribosomal RNA databases. Pairwise distances were estimated using the Maximum Composite Likelihood approach (Supplementary Table 1). All positions containing gaps and missing data were eliminated. There were a total of 994 positions in the final dataset. As a result, R. tritici NCPPB 1953 was grouped with most of R. tritici strains. R. tritici ATCC 11402 and R. tritici DSM 7486T represented the closest bacteria as both distance value 0.000, followed by R. rathayi DSM 7485T (distance value 0.001), R. tritici ICPB70004 (distance value 0.001), and R. iranicus DSM 7484T (distance value 0.003) (Supplementary Table 1).

General genomic features of R. tritici NCPPB 1953

The complete genome of R. tritici NCPPB 1953 consists of a single circular form of 3,354,681 bp with 69.48% GC content. There are 2 rRNA operons, 43 tRNAs, and 64 pseudogenes (Table 1). The chromosome contains 2,866 protein-coding genes. Of these, 1,712 proteins are assigned to functional annotations, whereas 1,154 proteins are considered as hypothetical proteins. Table 2 shows the number of genes associated with general COG functional categories. The ‘General function prediction only’ (9.70%) represents the largest category, followed by ‘Amino acid transport and metabolism’ (8.27%), ‘Carbohydrate transport and metabolism’ (8.20%), ‘Transcription’ (6.84%), and ‘Translation, ribosomal structure, and biogenesis’ (6.42%). A complete genomic map of R. tritici NCPPB 1953 was constructed using the CGView tool in Fig. 3.

So far, it is hard to find virulence factors of R. tritici NCPPB 1953, since there is a lack of research on the pathogenesis and virulence mechanisms of R. tritici. We therefore performed detection of putative virulence genes in the R. tritici NCPPB 1953 genome using the PHI database that archives the list of experimentally verified pathogenicity, virulence, and effectors from diverse microbes (Urban et al., 2015). A total of 676 putative virulence genes were identified by the significant cutoff of 1.0 × 10−5e-value (Table 1). R. tritici NCPPB 1953 had about 200 more virulence genes than R. toxicus strains (447 virulence genes in WAC3373 and 445 virulence genes in 70137). Detailed information with putative virulence genes of R. tritici NCPPB 1953 was listed in Supplementary Table 2. Most of putative virulence genes in R. tritici NCPPB 1953 were distributed in many plant pathogenic bacteria and fungi. Magnaporthe oryzae, which causes a serious disease affecting rice (Wilson and Talbot, 2009), represented the largest category matched with 58 genes of R. tritici NCPPB 1953, followed by Xanthomonas citri (matched with 36 genes), Fusarium graminearum (matched with 32 genes), and Pseudomonas cichorii (matched with 21 genes). In particular, the X. citri mutant of the gene pstB (encoding the ABC phosphate transporter subunit), which are matched with 30 genes of R. tritici NCPPB 1953, revealed a complete absence of symptoms (Moreira et al., 2015). It was also reported that the gene ABC4 (matched with 16 genes of R. tritici NCPPB 1953) of ABC superfamily of membrane transporters is required for the pathogenicity of M. oryzae, helping the fungus to cope with the cytotoxic environment within host (Gupta and Chattoo, 2008). This data suggests that many virulence genes in R. tritici NCPPB 1953 are implicated to play a role in the membrane transporters to control efflux and influx of host metabolites for their pathogenicity.

The comparative genomic analysis between R. tritici NCPPB 1953 and R. toxicus strains

R. toxicus shares major biological properties compared to R. tritici. For examples, a gumming disease was reported as a common plant disease caused by both R. tritici and R. toxicus (Agarkova et al., 2006; Arif et al., 2016). Also, R. toxicus uses several species of Anguina as the vector system to carry themselves into the plant host like R. tritici (Price et al., 1979; Riley, 1992). Riley and Reardon (1995) discussed the potential of R. tritici as a biological control for R. toxicus because of co-occurrence of two bacteria. Thus, R. toxicus is the important source for comparative analysis with R. tritici and we intended to find distinctive genetic features of R. tritici NCPPB 1953 through comparative genomic analysis between R. tritici and R. toxicus. Among R. toxicus strains, there are two complete genomes in the GenBank (Benson et al., 2000) including R. toxicus WAC3373 and R. toxicus 70137 under accession number CP013292 and CP010848, respectively. Based on the pairwise BLAST comparison of the 16S rRNA gene sequences, R. tritici NCPPB 1953 was closely related to R. toxicus, resulting in 100% coverage and 98% identity in both comparisons with R. toxicus WAC3373 and R. toxicus 70137. A similar distribution of rRNA and tRNA genes was maintained as follows: 6 rRNAs and 43 tRNAs, R. tritici NCPPB 1953; 6 rRNAs and 45 tRNAs, R. toxicus WAC3373; 6 rRNAs and 45 tRNAs, R. toxicus 70137.

However, there are different features between R. tritici and R. toxicus in the genome-wide level. GC contents of whole genome sequence showed a large discrepancy between R. tritici (GC content 69.5%) and R. toxicus (GC contents 61.5% in WAC3373 and 61.5% in 70137). Above all, R. tritici NCPPB 1953 harbours a larger genomic size (about 3.35 Mbp) and more genes (2,979 genes), when compared to R. toxicus WAC3373 (about 2.35 Mbp and 2,137 genes) and R. toxicus 70137 (about 2.33 Mbp and 2,120 genes). Indeed, we revealed the dynamic genomic rearrangement of whole genome sequence between R. tritici and R. toxicus using the Mauve aligner (Darling et al., 2004). In Fig. 4, R. tritici NCPPB 1953 shared a total of 58 locally collinear blocks (LCBs), which are highly homologous regions among genome sequences, in comparison to R. toxicus. Although these LCBs covered most parts of the whole genome in R. tritici NCPPB 1953, most of LCBs showed sporadic inversion and irregular genomic rearrangement caused by local and/or large-scale changes in genetic loci. In addition to the variable rearrangement, we confirmed additional genetic differences in R. tritici NCPPB 1953 such as non-matched regions and some LCBs of low similarities. Medini et al. (2005) reported that conserved core regions of microbial genome are responsible for basic aspects of the biology related to essential phenotypes and growth, whereas each flexible genome allows bacteria to obtain supplementary biochemical functions as selective advantages. Consequently, this data suggests that R. tritici NCPPB 1953 have needed additional genetic elements and functions, which could provide effective physiological activity in accordance with specific environment and conditions.

Functional analyses of flexible genome in R. tritici NCPPB 1953 compared to R. toxicus strains

To investigate the biological meaning of distinctive genetic elements in R. tritici NCPPB 1953, we performed the pangenome analysis of R. tritici NCPPB 1953 compared to R. toxicus WAC3373 and R. toxicus 70137, leading to identification of 1,052 R. tritici NCPPB 1953-specific genes (Supplementary Fig. 1). Furthermore, 1,052 genes were identified through the BlastKOALA algorithm to infer high-level functions of gene clusters (Kanehisa et al., 2016). As a result, the highest enriched system was ‘Environmental Information Processing’ (72 genes) related to sensing signals and responses to diverse stimuli (Fig. 5). Also, many metabolic processes for substrates were observed in enriched systems as follows: ‘Carbohydrate metabolism’, 28 genes; ‘Amino acid metabolism’, 21 genes; ‘Nucleotide metabolism’, 15 genes; ‘Metabolism of cofactors and vitamins’, 14 genes; ‘Energy metabolism’, 12 genes (Fig. 5). These results indicate that flexible genome in R. tritici NCPPB 1953 have been mainly developed to sense and process diverse substrates from the environment.

In general, the environment with host condition is far away from the optimal growth conditions for bacteria than experimental conditions with rich media due to multiple stresses (Shimizu, 2013; Yadeta and Thomma, 2013). The acquisition of nutrients of plant pathogens in hostile environment is a fundamental challenge against an extremely low level of organic and inorganic compounds (Yadeta and Thomma, 2013). Pathogens have to need the ability to sense, process, and respond to a variety of substrates and stresses for survival (Shimizu, 2013). Until now, diverse mechanisms for sensing and processing exogenous metabolites within host have been intensively studied in many pathogens (Divon et al., 2006; Jones and Wildermuth, 2011; Wooldridge and Williams, 1993). Our module enrichments showed that most genes in the ‘Environmental Information Processing’ system are associated with recognition and transport of many substrates, including saccharides, lipids, phosphates, amino acids, metallic cations, minerals, organic ions, and vitamins (Table 3). Flexible genome of R. tritici NCPPB 1953 was particularly distributed in substrate-binding proteins of transport system (Fig. 6), which is a key determinant of the extracellular signals and high affinity of uptake systems (Maqbool et al., 2015). In this regard, there are interesting reports on the flexible growth and colonization of R. tritici than R. toxicus (Bradbury, 1973; Riley and Ophel, 1992). In 1992, Riley and Ophel reported that R. tritici grows more rapidly than R. toxicus in culture (Riley and Ophel, 1992). It was reported that R. tritici successfully colonizes in plant hosts such as the gummosis of seed heads rather than R. toxicus (Bradbury, 1973). Therefore, we suggests that distinctive genetic features in flexible genome help R. tritici NCPPB 1953 to establish the stable growth and colonization in diverse environments through effective recognition of substrates. Further studies could lead to a better understanding of interactions between R. tritici and nematode as well as R. tritici and plant hosts.


This research was supported by the Research of Animal and Plant Quarantine Agency, South Korea and with the support of “Cooperative Research Program for Agriculture Science & Technology Development (PJ01110502)”, Rural Development Administration, South Korea.

Supplementary Information
Fig. 1. Image of transmission electron microscope (TEM) photomicrograph (A–C) and the appearance of colony morphology on solid media (D) of Rathayibacter tritici NCPPB 1953. Cells were grown in NBY agar plate for 96 h. The magnification rates of TEM for A–C were 15,000×, 25,000×, and 10,000×, respectively. Bacterial capsules with extracellular outer layer were observed (black arrows).
Fig. 2. 16S rRNA phylogenetic tree showing the relationships of Rathayibacter tritici NCPPB 1953 (shown in bold print) with other members in the genus Rathayibacter. Sequences were aligned using ClustalW and the phylogeny was inferred from the alignment of 1,267 bp of 16S rRNA using the Maximum Likelihood method based on the Tamura-Nei model with MEGA 7.0 software. The bootstrap consensus tree from 1,000 replicates was performed to assess the support of the clusters. The scale bar indicates 0.01 nucleotide change per nucleotide position. The GenBank accession numbers are shown in parentheses.
Fig. 3. Graphical circular map of Rathayibacter tritici NCPPB 1953 chromosome displaying relevant genetic features. From centre to outside: 1, positive (green) and negative (yellow) of GC skew; 2, GC content (black); 3, CDSs on reverse strand (blue); 4, CDSs on forward strand (red); 5, pseudogenes (grey); 6, tRNAs (brown); 7, rRNAs (pink). This figure was built using the CGView tool.
Fig. 4. Whole genome alignment of Rathayibacter tritici NCPPB 1953, Rathayibacter toxicus WAC3373, and Rathayibacter toxicus 70137. The top, middle, and bottom sequences represent each genomic sequence of R. tritici NCPPB 1953, R. toxicus WAC3373, and R. toxicus 70137, respectively. Identically coloured boxes by same lines are locally collinear blocks, indicating homologous and conservative regions in both genomes. Coloured bars inside boxes are related to the level of sequence similarities. Numbers above the map show nucleotide positions.
Fig. 5. Summary of biological systems in Rathayibacter tritici NCPPB 1953-specific genes. A distribution of the KEGG systems is displayed as a pie-chart. The number of gene counts is shown inside each pie of categories. Total 11 KEGG terms are displayed.
Fig. 6. The network of Rathayibacter tritici NCPPB 1953 in KEGG transporter. Pathway maps of KEGG transporter system were constructed by the Cytoscape tool. The KEGG database was used to download the data source. There are two color types in the rectangle node: red, genes belonging to flexible genome of R. tritici NCPPB 1953; green, genes belonging to the core genome among R. tritici NCPPB 1953, R. toxicus WAC3373, and R. toxicus 70137.

Genome statistics in Rathayibacter tritici NCPPB 1953 and Rathayibacter toxicus strains

AttributeR. tritici NCPPB 1953R. toxicus WAC3373R. toxicus 70137
Genome size (bp)3,354,6812,346,0322,328,288
DNA coding (bp)2,830,3301,970,8331,957,820
DNA G + C (bp)2,330,8831,442,2411,431,482
DNA scaffolds111
Total genes2,9792,1372,120
Protein coding genes2,8661,9991,993
RNA genes495151
Pseudo genes647570
Genes in internal clusters780309303
Genes with functional prediction1,7121,1741,158
Genes assigned to COGs2,2381,4971,499
Genes with Pfam domains2,1591,5751,572
Genes with signal peptides218117117
Genes with transmembrane helices694497495
CRISPR repeats022
Putative virulence genes676447445

A distribution of genes associated with COG categories in Rathayibacter tritici NCPPB 1953

J1846.42Translation, ribosomal structure and biogenesis
A10.03RNA processing and modification
L1264.40Replication, recombination, and repair
B00.00Chromatin structure and dynamics
D351.22Cell cycle control, cell division, chromosome partitioning
V682.37Defense mechanisms
T1133.94Signal transduction mechanisms
M1495.20Cell wall/membrane biogenesis
N160.56Cell motility
U200.70Intracellular trafficking and secretion
O1093.80Posttranslational modification, protein turnover, chaperones
C1103.84Energy production and conversion
G2358.20Carbohydrate transport and metabolism
E2378.27Amino acid transport and metabolism
F913.18Nucleotide transport and metabolism
H1475.13Coenzyme transport and metabolism
I1164.05Lipid transport and metabolism
P1304.54Inorganic ion transport and metabolism
Q762.65Secondary metabolites biosynthesis, transport, and catabolism
R2789.70General function prediction only
S1304.54Function unknown
-62821.91Not in COGs

Modules related to ‘Environmental Information Processing’ in Rathayibacter tritici NCPPB 1953-specific genes

KEGG IDModule Gene counts 
rtn_M00196 Multiple sugar transport system8
rtn_M00206Cellobiose transport system2
rtn_M00207Putative multiple sugar transport system1
rtn_M00209Osmoprotectant transport system1
rtn_M00212Ribose transport system2
rtn_M00216Multiple sugar transport system4
rtn_M00218Fructose transport system2
rtn_M00221Putative simple sugar transport system3
rtn_M00233Glutamate transport system1
rtn_M00236Putative polar amino acid transport system 1
rtn_M00238D-Methionine transport system1
rtn_M00240Iron complex transport system1
rtn_M00243Manganese/iron transport system1
rtn_M00252Lipooligosaccharide transport system2
rtn_M00254ABC-2 type transport system6
rtn_M00299Spermidine/putrescine transport system2
rtn_M00436Sulfonate transport system2
rtn_M00813Lantibiotic transport system2
  1. Agarkova, IV, Vidaver, AK, Postnikova, EN, Riley, IT, and Schaad, NW (2006). Genetic characterization and diversity of Rathayibacter toxicus. Phytopathology. 96, 1270-1277.
  2. Akhtar, MA (1987). Outbreaks and new records. Pakistan. Bacterial gumming disease of wheat. FAO Plant Prot Bull. 35, 102.
  3. Altschul, SF, Gish, W, Miller, W, Myers, EW, and Lipman, DJ (1990). Basic local alignment search tool. J Mol Biol. 215, 403-410.
    Pubmed CrossRef
  4. Alva, V, Nam, SZ, Söding, J, and Lupas, AN (2016). The MPI bioinformatics Toolkit as an integrative platform for advanced protein sequence and structure analysis. Nucleic Acids Res. 44, W410-W415.
    Pubmed KoreaMed CrossRef
  5. Anderson, I, and Brass, A (1998). Searching DNA databases for similarities to DNA sequences: when is a match significant?. Bioinformatics. 14, 349-356.
    Pubmed CrossRef
  6. Arif, M, Busot, GY, Mann, R, Rodoni, B, Liu, S, and Stack, JP (2016). Emergence of a new population of Rathayibacter toxicus: an ecologically complex, geographically isolated bacterium. PLoS One. 11, e0156182.
    Pubmed KoreaMed CrossRef
  7. Benson, DA, Karsch-Mizrachi, I, Lipman, DJ, Ostell, J, Rapp, BA, and Wheeler, DL (2000). GenBank. Nucleic Acids Res. 28, 15-18.
  8. Bradbury, JF, and Commonwealth Mycological Institute and C.A.B. International Mycological Institute (1973). Corynebacterium tritici. CMI descriptions of pathogenic fungi and bacteria. Kew, UK: Commonwealth Mycological Institute, pp. 371-380
  9. Bradbury, JF (1986). Guide to plant pathogenic bacteria. Slough, UK: CAB International Mycological Institute, pp. 332
  10. Buiate, EA, Xavier, KV, Moore, N, Torres, MF, Farman, ML, Schardl, CL, and Vaillancourt, LJ (2017). A comparative genomic analysis of putative pathogenicity genes in the host-specific sibling species Colletotrichum graminicola and Colletotrichum sublineola. BMC Genomics. 18, 67.
    Pubmed KoreaMed CrossRef
  11. Carlson, RR, and Vidaver, AK (1982). Taxonomy of Corynebacterium plant pathogens, including a new pathogen of wheat, based on polyacrylamide gel electrophoresis of cellular proteins. Int J Syst Bacteriol. 32, 315-326.
  12. Chaisson, MJ, and Tesler, G (2012). Mapping single molecule sequencing reads using basic local alignment with successive refinement (BLASR): application and theory. BMC Bioinformatics. 13, 238.
    Pubmed KoreaMed CrossRef
  13. Chin, CS, Alexander, DH, Marks, P, Klammer, AA, Drake, J, Heiner, C, Clum, A, Copeland, A, Huddleston, J, Eichler, EE, Turner, SW, and Korlach, J (2013). Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data. Nat Methods. 10, 563-569.
    Pubmed CrossRef
  14. Darling, AC, Mau, B, Blattner, FR, and Perna, NT (2004). Mauve: multiple alignment of conserved genomic sequence with rearrangements. Genome Res. 14, 1394-1403.
    Pubmed KoreaMed CrossRef
  15. Davis, MJ, Gillaspie, AG, Vidaver, AK, and Harris, RW (1984). Clavibacter: a new genus containing some phytopathogenic coryneform bacteria, including Clavibacter xyli subsp. xyli sp. nov., subsp. nov. and Clavibacter xyli subsp. cynodontis subsp. nov., pathogens that cause ratoon stunting disease of sugarcane and bermudagrass stunting disease. Int J Syst Bacteriol. 34, 107-117.
  16. De Bruyne, E, Swings, J, and Kersters, K (1992). Enzymatic relatedness amongst phytopathogenic coryneform bacteria and its potential use for their identification. Syst Appl Microbiol. 15, 393-401.
  17. Delcher, AL, Harmon, D, Kasif, S, White, O, and Salzberg, SL (1999). Improved microbial gene identification with GLIMMER. Nucleic Acids Res. 27, 4636-4641.
    Pubmed KoreaMed CrossRef
  18. Divon, HH, Ziv, C, Davydov, O, Yarden, O, and Fluhr, R (2006). The global nitrogen regulator, FNR1, regulates fungal nutrition-genes and fitness during Fusarium oxysporum pathogenesis. Mol Plant Pathol. 7, 485-497.
    Pubmed CrossRef
  19. Dowson, WJ (1942). On the generic name of the gram-positive bacterial plant pathogens. Trans Br Mycol Soc. 25, 311-314.
  20. Duveiller, E, and Fucikovsky, L (1997). Other plant pathogenic bacteria reported on wheat. The bacterial diseases of wheat: concepts and methods of disease management, Duveiller, E, Fucikovsky, L, and Rudolph, K, ed. Distrito Federal, Brazil: CIMMYT, pp. 66
  21. Evtushenko, LI, and Dorofeeva, LV (2012). Genus XXII. Rathayibacter. Bergey’s manual of systematic bacteriology, Whitman, W, Goodfellow, M, Kämpfer, P, Busse, HJ, Trujillo, M, Ludwig, W, Suzuki, K, and Parte, A, ed. New York, NY, USA: Springer, pp. 953-964
  22. Fattah, FA (1988). Effects of inoculation methods on the incidence of ear-cockle and ‘tundu’ on wheat under field conditions. Plant Soil. 109, 195-198.
  23. Finn, RD, Coggill, P, Eberhardt, RY, Eddy, SR, Mistry, J, Mitchell, AL, Potter, SC, Punta, M, Qureshi, M, Sangrador-Vegas, A, Salazar, GA, Tate, J, and Bateman, A (2016). The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res. 44, D279-D285.
    KoreaMed CrossRef
  24. Galperin, MY, Makarova, KS, Wolf, YI, and Koonin, EV (2015). Expanded microbial genome coverage and improved protein family annotation in the COG database. Nucleic Acids Res. 43, D261-D269.
    KoreaMed CrossRef
  25. Grissa, I, Vergnaud, G, and Pourcel, C (2007). CRISPRFinder: a web tool to identify clustered regularly interspaced short palindromic repeats. Nucleic Acids Res. 35, W52-W57.
    Pubmed KoreaMed CrossRef
  26. Gupta, A, and Chattoo, BB (2008). Functional analysis of a novel ABC transporter ABC4 from Magnaporthe grisea. FEMS Microbiol Lett. 278, 22-28.
  27. Hyatt, D, Chen, GL, Locascio, PF, Land, ML, Larimer, FW, and Hauser, LJ (2010). Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 11, 119.
    Pubmed KoreaMed CrossRef
  28. Jago, MV, and Culvenor, CC (1987). Tunicamycin and corynetoxin poisoning in sheep. Aust Vet J. 64, 232-235.
    Pubmed CrossRef
  29. Jones, AM, and Wildermuth, MC (2011). The phytopathogen Pseudomonas syringae pv. tomato DC3000 has three high-affinity iron-scavenging systems functional under iron limitation conditions but dispensable for pathogenesis. J Bacteriol. 193, 2767-2775.
    Pubmed KoreaMed CrossRef
  30. Kanehisa, M, and Goto, S (2000). KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27-30.
  31. Kanehisa, M, Sato, Y, and Morishima, K (2016). BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J Mol Biol. 428, 726-731.
  32. Kopf, M, Klähn, S, Voss, B, Stüber, K, Huettel, B, Reinhardt, R, and Hess, WR (2014). Finished genome sequence of the unicellular cyanobacterium Synechocystis sp. strain PCC 6714. Genome Announc. 2, e00757-14.
    Pubmed KoreaMed CrossRef
  33. Krogh, A, Larsson, B, von Heijne, G, and Sonnhammer, EL (2001). Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol. 305, 567-580.
    Pubmed CrossRef
  34. Kumar, S, Stecher, G, and Tamura, K (2016). MEGA7: Molecular Evolutionary Genetics Analysis version 7.0 for bigger datasets. Mol Biol Evol. 33, 1870-1874.
    Pubmed CrossRef
  35. Lagesen, K, Hallin, P, Rødland, EA, Staerfeldt, HH, Rognes, T, and Ussery, DW (2007). RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res. 35, 3100-3108.
    Pubmed KoreaMed CrossRef
  36. Larkin, MA, Blackshields, G, Brown, NP, Chenna, R, Mc-Gettigan, PA, McWilliam, H, Valentin, F, Wallace, IM, Wilm, A, Lopez, R, Thompson, JD, Gibson, TJ, and Higgins, DG (2007). Clustal W and Clustal X version 2.0. Bioinformatics. 23, 2947-2948.
    Pubmed CrossRef
  37. Lee, IM, Bartoszyk, IM, Gundersen-Rindal, DE, and Davis, RE (1997). Phylogeny and classification of bacteria in the genera Clavibacter and Rathayibacter on the basis of 16s rRNA gene sequence analyses. Appl Environ Microbiol. 63, 2631-2636.
    Pubmed KoreaMed
  38. Lowe, TM, and Eddy, SR (1997). tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 25, 955-964.
    Pubmed KoreaMed CrossRef
  39. Lukashin, AV, and Borodovsky, M (1998). GeneMark.hmm: new solutions for gene finding. Nucleic Acids Res. 26, 1107-1115.
    Pubmed KoreaMed CrossRef
  40. Maqbool, A, Horler, RS, Muller, A, Wilkinson, AJ, Wilson, KS, and Thomas, GH (2015). The substrate-binding protein in bacterial ABC transporters: dissecting roles in the evolution of substrate specificity. Biochem Soc Trans. 43, 1011-1017.
    Pubmed CrossRef
  41. Medini, D, Donati, C, Tettelin, H, Masignani, V, and Rappuoli, R (2005). The microbial pan-genome. Curr Opin Genet Dev. 15, 589-594.
    Pubmed CrossRef
  42. Mehta, YR (2014). Spike diseases caused by bacteria. Wheat diseases and their management, Mehta, YR, ed. New York, NY, USA: Springer, pp. 105-121
  43. Moreira, LM, Facincani, AP, Ferreira, CB, Ferreira, RM, Ferro, MI, Gozzo, FC, de Oliveira, JC, Ferro, JA, and Soares, MR (2015). Chemotactic signal transduction and phosphate metabolism as adaptive strategies during citrus canker induction by Xanthomonas citri. Funct Integr Genomics. 15, 197-210.
  44. Paruthi, IJ, and Bhatti, DS (1985). Estimation of loss in yield and incidence of Anguina tritici on wheat in Haryana (India). Int Nematol Netw Newsl. 2, 13-16.
  45. Paruthi, IJ, and Gupta, DC (1987). Incidence of ‘Tundu’ in barley and kanki in wheat field infested with Anguina tritici. Haryana Agric Univ J Res. 17, 78-79.
  46. Petersen, TN, Brunak, S, von Heijne, G, and Nielsen, H (2011). SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods. 8, 785-786.
    Pubmed CrossRef
  47. Price, PC, Fisher, JM, and Kerr, A (1979). On Anguina funesta n. sp. and its association with Corynebacterium sp., in infecting Lolium rigidum. Nematologica. 25, 76-85.
  48. Quast, C, Pruesse, E, Yilmaz, P, Gerken, J, Schweer, T, Yarza, P, Peplies, J, and Glöckner, FO (2013). The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590-D596.
    KoreaMed CrossRef
  49. Rainey, F, Weiss, H, Prauser, H, and Stackebrandt, E (1994). Further evidence for the phylogenetic coherence of actinomycetes with Group B-peptidoglycan and evidence for the phylogenetic intermixing of the genera Microbacterium and Aureobacterium as determined by 16S rDNA analysis. FEMS Microbiol Lett. 118, 135-139.
  50. Riley, IT (1992). Anguina tritici is a potential vector of Clavibacter toxicus. Australas Plant Pathol. 21, 147-149.
  51. Riley, IT, and Ophel, KM (1992). Clavibacter toxicus sp. nov., the bacterium responsible for annual ryegrass toxicity in Australia. Int J Syst Bacteriol. 42, 64-68.
  52. Riley, IT, and Reardon, TB (1995). Isolation and characterization of Clavibacter tritici associated with Anguina tritici in wheat from Western Australia. Plant Pathol. 44, 805-810.
  53. Riley, IT, Reardon, TB, and McKay, AC (1988). Genetic analysis of plant pathogenic bacteria in the genus Clavibacter using allozyme electrophoresis. J Gen Microbiol. 134, 3025-3030.
  54. Rutherford, K, Parkhill, J, Crook, J, Horsnell, T, Rice, P, Rajandream, MA, and Barrell, B (2000). Artemis: sequence visualization and annotation. Bioinformatics. 16, 944-945.
    Pubmed CrossRef
  55. Sasaki, J, Chijimatsu, M, and Suzuki, K (1998). Taxonomic significance of 2,4-diaminobutyric acid isomers in the cell wall peptidoglycan of actinomycetes and reclassification of Clavibacter toxicus as Rathayibacter toxicus comb. nov. Int J Syst Bacteriol. 48, 403-410.
    Pubmed CrossRef
  56. Shimizu, K (2013). Regulation systems of bacteria such as Escherichia coli in response to nutrient limitation and environmental stresses. Metabolites. 4, 1-35.
    Pubmed KoreaMed CrossRef
  57. Sternes, PR, and Borneman, AR (2016). Consensus pan-genome assembly of the specialised wine bacterium Oenococcus oeni. BMC Genomics. 17, 308.
    Pubmed KoreaMed CrossRef
  58. Takeuchi, M, and Yokota, A (1994). Phylogenetic analysis of the genus Microbacterium based on 16S rRNA gene sequences. FEMS Microbiol Lett. 124, 11-16.
    Pubmed CrossRef
  59. UniProt Consortium (2013). Update on activities at the Universal Protein Resource (UniProt) in 2013. Nucleic Acids Res. 41, D43-D47.
    KoreaMed CrossRef
  60. Urban, M, Pant, R, Raghunath, A, Irvine, AG, Pedro, H, and Hammond-Kosack, KE (2015). The Pathogen-Host Interactions database (PHI-base): additions and future developments. Nucleic Acids Res. 43, D645-D655.
    KoreaMed CrossRef
  61. Wilson, RA, and Talbot, NJ (2009). Under pressure: investigating the biology of plant infection by Magnaporthe oryzae. Nat Rev Microbiol. 7, 185-195.
    Pubmed CrossRef
  62. Wooldridge, KG, and Williams, PH (1993). Iron uptake mechanisms of pathogenic bacteria. FEMS Microbiol Rev. 12, 325-348.
    Pubmed CrossRef
  63. Yadeta, KA, and Thomma, BPHJ (2013). The xylem as battleground for plant hosts and vascular wilt pathogens. Front Plant Sci. 4, 97.
    Pubmed KoreaMed CrossRef
  64. Zgurskaya, HI, Evtushenko, LI, Akimov, VN, and Kalakoutskii, LV (1993). Rathayibacter gen. nov., including the species Rathayibacter rathayi comb. nov., Rathayibacter tritici comb. nov., Rathayibacter iranicus comb. nov., and six strains from annual grasses. Int J Syst Bacteriol. 43, 143-149.

August 2017, 33 (4)
  • Crossref Similarity Check logo