- Open Access
Genome informatics and vaccine targets in Corynebacterium urealyticum using two whole genomes, comparative genomics, and reverse vaccinology
© Guimarães et al.; licensee BioMed Central Ltd. 2015
Published: 26 May 2015
Corynebacterium urealyticum is an opportunistic pathogen that normally lives on skin and mucous membranes in humans. This high Gram-positive bacteria can cause acute or encrusted cystitis, encrusted pyelitis, and pyelonephritis in immunocompromised patients. The bacteria is multi-drug resistant, and knowledge about the genes that contribute to its virulence is very limited. Two complete genome sequences were used in this comparative genomic study: C. urealyticum DSM 7109 and C. urealyticum DSM 7111.
We used comparative genomics strategies to compare the two strains, DSM 7109 and DSM 7111, and to analyze their metabolic pathways, genome plasticity, and to predict putative antigenic targets. The genomes of these two strains together encode 2,115 non-redundant coding sequences, 1,823 of which are common to both genomes. We identified 188 strain-specific genes in DSM 7109 and 104 strain-specific genes in DSM 7111. The high number of strain-specific genes may be a result of horizontal gene transfer triggered by the large number of transposons in the genomes of these two strains. Screening for virulence factors revealed the presence of the spaDEF operon that encodes pili forming proteins. Therefore, spaDEF may play a pivotal role in facilitating the adhesion of the pathogen to the host tissue. Application of the reverse vaccinology method revealed 19 putative antigenic proteins that may be used in future studies as candidate drug or vaccine targets.
The genome features and the presence of virulence factors in genomic islands in the two strains of C. urealyticum provide insights in the lifestyle of this opportunistic pathogen and may be useful in developing future therapeutic strategies.
The species Corynebacterium urealyticum was proposed in 1986, but this bacteria was first isolated between June 1983 and March 1984, when four patients were diagnosed with alkaline-encrusted cystitis. The published case report describes the isolates as belonging to the Corynebacterium group D2 [1, 2]. Chemotaxonomic studies and 16S rRNA sequence comparisons showed that C. urealyticum was more closely related to Corynebacterium lipophilic species such as Corynebacterium jeikeium, but could be differentiated from C. jeikeium based on its ability to hydrolyze urea . C. urealyticum is a Gram-positive, non-spore-forming, aerobic, and slow-growing bacteria. Its cell wall is composed of peptidoglycan, menaquinone, mycolic acids, and cellular fatty acids, which is the common composition of the cell walls of Corynebacterium species .
C. urealyticum is an opportunistic pathogen commonly isolated from the skin and mucous membranes of hospitalized patients. The pathogen mainly causes acute or encrusted cystitis, encrusted pyelitis, and pyelonephritis . Its urease activity is the main factor that contributes to the ability of C. urealyticum to colonize the urinary tract where its presence is associated with alkaline pH and the formation of ammonium magnesium phosphate stones [1, 2]. C. urealyticum is a multi-drug resistant bacterium and its treatment requires the administration of multiple drugs and additional invasive interventions [4, 5]. Currently, the complete genome sequences of only two C. urealyticum strains, DSM 7109 and DSM 7111, are publicly available. In these two genomes, the antibiotic resistance genes were located in mobile DNA, suggesting that the multidrug resistance was acquired through horizontal gene transfer [6, 7].
In this work, we compared the genome sequences of the two C. urealyticum multidrug resistance strains DSM 7109 and DSM 7111, focusing on differences in the gene content and metabolic pathways between the two strains. We also attempted to identify new candidate targets that can be used in the development of drugs or vaccines against this pathogen.
Genome sequences of C. urealyticum strains DSM 7109 and DSM 7111
C. urealyticum DSM 7109 was isolated from a patient with alkaline-encrusted cystitis and C. urealyticum DSM 7111 was isolated from the urine samples of a 9-year-old patient with an ectopic kidney. The genome sequences of both these strains were retrieved from the NCBI GenBank database (http://www.ncbi.nlm.nih.gov/genbank/)  [GenBank:NC_010545.1, GenBank: NC_020230.1].
The origin of chromosomal DNA replication (oriC) gene was predicted using the Ori-Finder web program . The Ori-Finder prediction was based on a combined process: (i) gene identification involving analysis of base composition through Z-curve method; and, (ii) occurrence of genes frequently close to oriCs (distribution of dnaA boxes along the genome) . The CRISPRs were predicted using the CRISPRfinder web program . These regions are important because they confer protection against bacteriophages. The comparative analysis was done using the EDGAR web-program that compares genome content based on the calculation of the BLAST score ratio by automatically adjusted cutoff for each selected dataset .
In silico identification metabolic pathway construction
The metabolic pathways reconstruction of C. urealyticum was performed using the genome sequence file in FASTA format and the genome annotation file in GBK format. Metabolic pathways databases for strains DSM 7109 and DSM 7111 were created using the Pathway Tools 13 software (available at http://bioinformatics.ai.sri.com/ptools/), developed by SRI International . The Pathway Tools software contains algorithms that can predict the metabolic pathways of an organism from its genome by comparing it to a reference pathways database known as the MetaCyc Database . Construction of a metabolic pathways database was done using the BioCyc collection .
Prediction of genome plasticity of C. urealyticum DSM 7109 and DSM 7111 strains
Prediction of genomic islands in C. urealyticum genomes was done using PIPS software . PIPS detects genome signatures like C+G content, codon usage deviation, high concentrations of virulence factors, hypothetical proteins, the presence of transposases and tRNA flanking sequences, and absence of query regions in non-pathogenic organisms. C. glutamicum ATCC 13032 was used as the closely related non-pathogenic species to C. urealyticum in PIPS. The BRIG software  was used for plasticity comparisons among C. urealyticum (DSM 7109 and DSM 7111 strains), C. pseudotuberculosis 1002, C. diphtheriae NCTC 13129, C. ulcerans 809, and C. glutamicum ATCC 13032.
Prediction of putative antigenic targets of C. urealyticum
To identify antigenic targets, we used the strategy described by Barh et al.  with modifications. We also adopted the four rules as per the reverse vaccinology strategy of Rappuoli et al.  for final selection of the putative vaccine targets. Rule I: consider the antigenic proteins that are either secreted proteins, surface-exposed proteins, or membrane proteins so that they can be exposed to the host, and therefore can be promptly recognized by the host immune system ; Rule II: major histocompatibility complex (MHC) I and II binding properties with adhesion probability greater than 0.51 and absence of similarity to host proteins ; Rule III: protein conservation among different genomes ; and Rule IV: virulence factors are normally encoded within genomic islands . Rule IV does not exclude the targets from Rule III.
SurfG+ software  was used to predict targets according to the Rule I. This software classifies proteins according to their subcellular location using the presence or absence of signal peptides, retention signals, and transmembrane helices. To apply Rule II, the proteins predicted by surfG+ were analyzed using the Vaxign software . Because the aim of this work was to identify vaccine candidates, the predicted proteomes were screened for proteins that were potentially antigenic in both strains (Rule III). To achieve this, we used the Artemis Comparison Tool  with BLAST alignment comparison files and searched for antigenic proteins that show more than 70% similarity in 70% of their extensions in both strains. Base on Rule IV, we screened the detected antigenic proteins for antigenic targets harbored by shared genomic islands in the two strains.
Results and discussion
Genomic architecture and features of C. urealyticum strains DSM 7109 and DSM7111
General features of the genomes of C. urealyticum strains DSM 7109 and DSM 7111.
Genome size (bp)
C+G content (%)
Coding density (%)
3 × (16S - 23S - 5S)
3 × (16S - 23S - 5S)
Structural features of CRISPR loci predicted in C. urealyticum strains DSM 7109 and DSM 7111.
No. of CRISPR loci
No. of cas genes
No. of spacers
*CRISPR consensus sequence
Corynebacterium urealyticum DSM 7109
Corynebacterium urealyticum DSM 7111
Gene sharing among the two C. urealyticum strains
Orthologous genes were detected using the EDGAR software, which defines subsets of genes using the SRV method to predict orthologous genes in prokaryotic genomes . We found that the DSM 7109 and DSM 7111 genomes together encode 2,115 no-redundant coding gene sequences; 1,823 (86.2%) of these coding gene sequences were common to both strains, and 188 and 104 were specific to DSM 7109 and DSM 7111, respectively. Species-specific genes have been linked to niche adaptation of microorganisms. A previous study of 17 Escherichia coli strains found that less than 50% of the genes (2,200 genes in a total of 5,000 genes) were shared among these strains . Therefore, we can infer that the DSM 7109 and DSM 7111 genomes are very similar because they share a high proportion of their genes. A previous study of four Corynebacterium pseudotuberculosis strains  also reported a large number in shared genes; 1,851 (77.9%) genes in a total of 2,377 genes. Clearly, the numbers of core genes are likely to reduce when more strains of C. urealyticum are added, as was shown previously by Soares and colleagues in a study of 15 C. pseudotuberculosis strains were 1,504 (54.5%) genes in a total of 2,782 genes were shared .
Plasticity of the C. urealyticum DSM 7109 and DSM 7111 genomes
Genome plasticity has been used to provide insights into genome evolution through the study of horizontally acquired genomic regions. The transfer of blocks of genes (genomic islands) normally correlates with the acquirement of a given function, like virulence (pathogenicity islands), degradation of secondary compounds (metabolic islands), antibiotic resistance (resistance islands) and symbiotic relationships with Leguminosae (symbiotic islands). Furthermore, because genomic islands are acquired from a different organism, they are responsible for deviations in genomic signatures such as codon usage and G+C content once they reflect the genomic signature of the donor organism . We used the PIPS software  to predict genomic islands in both C. urealyticum strains. Twenty-six genomic islands were predicted in each genome (Figure 1). DSM 7109 had 556 genes present in the genomic islands and DSM 7111 had 496 genes. We identified 403 genes in the genomic islands that belonged to the shared genes dataset, meaning that the majority of genes that were acquired by horizontal gene transfer were commonly shared genes.
Genes present in genomic islands shared by related Corynebacterium species.
C. urealyticumDSM 7109
C. urealyticumDSM 7111
C. diphtheriaNCTC 13129
Putative aldehyde dehydrogenase
Fimbrial associated sortase (Surface protein transpeptidase)
Putative surface-anchored protein (Fimbrial subunit)
Fimbrial associated sortase (Surface protein transpeptidase)
tRNA-specific adenosine deaminase
Putative dicarboxylate uptake system
Prediction of candidate vaccine targets for C. urealyticum
Subcellular location of proteins from C. urealyticum strains DSM 7109 and DSM 7111.
Putative antigenic proteins identified using Vaxign and shared by two C. urealyticum strains.
DSM 7109 - Locus_Tag
DSM 7111 - Locus_Tag
Resuscitation-promoting factor RpfC
Putative secreted protein
Putative surface-anchored protein
Putative secreted protein
Putative secreted metallopeptidase
Putative secreted protein
Putative secreted protein
Trehalose corynomycolyl transferase A
Putative surface-anchored protein (fimbrial subunit)
Putative surface-anchored protein (fimbrial subunit)
Putative fluoride ion transporter CrcB
The rpfC gene (resuscitation-promoting factor) is a member of a protein family (rpfA, rpfB, rpfD, and rpfE) found in Actinobacteria. The protein encoded by rpfC plays a role in stimulating resuscitation of dormant cells and in the multiplication of normal viable bacteria. Studies in Mycobacterium luteus with a disrupted rpf gene were not possible because of the absence of a second functional copy of the gene, suggesting that this protein is essential for normal growth and reduces the lag phase of diluted fast-growers [32, 37].
The mepA gene (penicillin-insensitive murein endopeptidase) in E. coli encodes a protein the cleaves the D-alanyl-meso-2,6-diamino-pimelyl amide bond of peptidoglycans ; however, this protein is sensitive to metal-chelating agents such as lipoteichoic acid and deoxyribonucleic acid . Previous studies with metallopeptidases showed that animals infected with C. pseudotuberculosis reacted to the mepA protein, while non-infected animals did not. This protein has transmembrane domains, another strong indication that it may make a good molecular vaccine target [33, 40].
The lpps (lipoprotein) gene encodes a protein that is associated with cell envelopes and has four known lipoprotein functions: (i) structural function (murein lipoproteins); (ii) transport function (substrate-binding proteins of ABC transporters in Gram-positive bacteria); (iii) adhesion function; and (iv) enzymatic function. The lipoprotein present in C. urealyticum has an L,D-transpeptidase catalytic domain, which gives the bacteria the ability to resist beta-lactam antibiotics by inhibiting PBPs (penicillin-binding protein) [34, 41].
The cmtA (trehalose corynomycol transferase) gene encodes a protein that has catalytic function. It plays a role in the transfer of mycolic acids through trehalose monocorynomycolate on the cell wall arabinogalactan to another trehalose monocorynomycolate to produce trehalose dicorynomycolate .
The spaD and spaE genes are part of the spaDEF cluster that encodes adhesive pilus structures that are surface-anchored to the cell walls of Corynebacterium where they probably facilitate the adhesion of the pathogen to the host tissue. We detected the spaF gene in the DSM 7109 and DSM 7111 genomes, as well as the sortase encoding genes srtB and srtC. The genome organization of the spaDEF cluster in the two C. urealyticum strains is similar to cluster organization in C. diphitheriae NCTC 13129 and C. ulcerans 809 and BR-AD22 strains [30, 43]. However, the spaABC cluster of genes proposed as an essential virulence factor in C. diphtheria  was absent in both strains of C. urealyticum analyzed.
The crcB gene encodes a putative membrane protein, important for the reducing the fluoride concentration in cells, thus reducing its toxicity. Fluoride ions reduce cell growth, even when present in millimolar concentrations. Thus, we can infer that crcB gene is an efficient resistance mechanism .
Differences in metabolic pathways in the C. urealyticum genomes
Numbers of gene data types in C. urealyticum strains DSM 7109 and DSM 7111.
Numbers of pathways in C. urealyticum strains DSM 7109 and DSM 7111.
-Amines and Polyamines Biosynthesis
-Amino Acids Biosynthesis
-Aromatic Compounds Biosynthesis
-Cell Structures Biosynthesis
-Cofactors, Prosthetic Groups, Electron Carriers Biosynthesis
-Fatty Acids and Lipids Biosynthesis
-Metabolic Regulators Biosynthesis
-Nucleosides and Nucleotides Biosynthesis
-Secondary Metabolites Biosynthesis
-Amines and Polyamines Degradation
-Amino Acids Degradation
-Aromatic Compounds Degradation
-C1 Compounds Utilization and Assimilation
-Degradation/Utilization/Assimilation - Other
-Fatty Acid and Lipids Degradation
-Inorganic Nutrients Metabolism
-Nucleosides and Nucleotides Degradation
-Secondary Metabolites Degradation
Generation of Precursor Metabolites and Energy
On further analysis, we found that the DSM 7109 and DSM 7111 genomes had 25 and 57 unique metabolic pathways (Table S1, additional file 1), respectively, even though both the strains were isolated from humans and caused the same symptoms [6, 7].
To our knowledge, this is the first comparative genomic study using the complete genome sequences of two C. urealyticum strains, DSM 7109 and DSM 7111. Our analyses provided insights into the genome architecture and the gene content of this species. We found that the C. urealyticum DSM 7111 genome was 50 kb shorter than the C. urealyticum DSM 7109 genome. This difference in genome size may be linked to the large number of genomic islands (26 for each genome) predicted for both genomes. The genomic islands may have resulted from the horizontal transfer of genes, leading to the acquisition of many strain-specific genes. We detected a high number of strain-specific genes in the two genomes compared with the low number of species-specific genes that have been reported in previous studies of others species of Corynebacterium [25, 30]. The horizontal transfer of genes may also explain why C. urealyticum is multi-drug resistant; i.e., it has received virulence genes by horizontal transfer [4, 5].
C. urealyticum is a pathogenic opportunistic bacteria although it showed the spaDEF operon (virulence factor), with a structure similar to that of pathogenic species like C. diphtheriae and C. ulcerans. This operon encodes an adhesive pilus responsible for facilitating the adhesion of the pathogen to host cells [28, 43].
This comparative genomic study of two C. urealyticum strains provides a basis using reverse vaccinology to predict new antigenic targets. However, additional C. urealyticum strains will have to be studied to create effective vaccines against this bacterium.
We thank the CAPES/PDSE International Cooperation for financing an exchange scholarship (http://www.capes.gov.br/cooperacao-internacional) (grant number 9151125) and Graduate Cluster Industrial Biotechnology (CLIB, http://www.graduatecluster.net/) for support this research.
The publication of this work is funded by CNPq (National Counsel of Technological and Scientific Development), FAPEMIG (Foundation for Research Support of the State of Minas Gerais) and CAPES (Coordination for the Improvement of Higher Education Personnel) number SC-1000675.
This article has been published as part of BMC Genomics Volume 16 Supplement 5, 2015: Proceedings of the 10th International Conference of the Brazilian Association for Bioinformatics and Computational Biology (X-Meeting 2014). The full contents of the supplement are available online at http://www.biomedcentral.com/bmcgenomics/supplements/16/S5.
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