BackgroundCorynebacterium 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.ResultsWe 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.ConclusionsThe 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.

BackgroundThe bacterium Corynebacterium pseudotuberculosis (Cp) causes caseous lymphadenitis (CLA), mastitis, ulcerative lymphangitis, and oedema in a number of hosts, comprising ruminants, thereby intimidating economic and dairy industries worldwide. So far there is no effective drug or vaccine available against Cp. Previously, a pan-genomic analysis was performed for both biovar equi and biovar ovis and a Pathogenicity Islands (PAIS) analysis within the strains highlighted a large set of proteins that could be relevant therapeutic targets for controlling the onset of CLA. In the present work, a structural druggability analysis pipeline was accomplished along 15 previously sequenced Cp strains from both biovar equi and biovar ovis.Methods and resultsWe computed the whole modelome of a reference strain Cp1002 (NCBI Accession: NC_017300.1) and then the homology models of proteins, of 14 different Cp strains, with high identity (≥ 85%) to the reference strain were also done. Druggability score of all proteins pockets was calculated and only those targets that have a highly druggable (HD) pocket in all strains were kept, a set of 58 proteins. Finally, this information was merged with the previous PAIS analysis giving two possible highly relevant targets to conduct drug discovery projects. Also, off-targeting information against host organisms, including Homo sapiens and a further analysis for protein essentiality provided a final set of 31 druggable, essential and non-host homologous targets, tabulated in table S4, additional file 1. Out of 31 globally druggable targets, 9 targets have already been reported in other pathogenic microorganisms, 3 of them (3-isopropylmalate dehydratase small subunit, 50S ribosomal protein L30, Chromosomal replication initiator protein DnaA) in C. pseudotuberculosis.ConclusionOverall we provide valuable information of possible targets against C. pseudotuberculosis where some of these targets have already been reported in other microorganisms for drug discovery projects, also discarding targets that might be physiologically relevant but are not amenable for drug binding. We propose that the constructed in silico dataset might serve as a guidance for the scientific community to have a better understanding while selecting putative therapeutic protein candidates as druggable ones as effective measures against C. pseudotuberculosis.

BackgroundMicroRNAs (miRNAs) have increasingly been found to regulate diseases at a significant level. The interaction of miRNA and diseases is a complex web of multilevel interactions, given the fact that a miRNA regulates upto 50 or more diseases and miRNAs/diseases work in clusters. The clear patterns of miRNA regulations in a disease are still elusive.MethodsIn this work, we approach the miRNA-disease interactions from a network scientific perspective and devise two approaches - maximum weighted matching model (a graph theoretical algorithm which provides the result by solving an optimization equation of selecting the most prominent set of diseases) and motif-based analyses (which investigates the motifs of the miRNA-disease network and selects the most prominent set of diseases based on their maximum number of participation in motifs, thereby revealing the miRNA-disease interaction dynamics) to determine and prioritize the set of diseases which are most certainly impacted upon the activation of a group of queried miRNAs, in a miRNA-disease network.Results and ConclusionOur tool, DISMIRA implements the above mentioned approaches and presents an interactive visualization which helps the user in exploring the networking dynamics of miRNAs and diseases by analyzing their neighbors, paths and topological features. A set of miRNAs can be used in this analysis to get the associated diseases for the input group of miRs with ranks and also further analysis can be done to find key miRs or diseases, shortest paths etc. DISMIRA can be accessed online for free at http://bnet.egr.vcu.edu:8080/dismira.

BackgroundCorynebacterium 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.ResultsWe 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.ConclusionsThe 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.

BackgroundMicroRNAs (miRNAs) have increasingly been found to regulate diseases at a significant level. The interaction of miRNA and diseases is a complex web of multilevel interactions, given the fact that a miRNA regulates upto 50 or more diseases and miRNAs/diseases work in clusters. The clear patterns of miRNA regulations in a disease are still elusive.MethodsIn this work, we approach the miRNA-disease interactions from a network scientific perspective and devise two approaches - maximum weighted matching model (a graph theoretical algorithm which provides the result by solving an optimization equation of selecting the most prominent set of diseases) and motif-based analyses (which investigates the motifs of the miRNA-disease network and selects the most prominent set of diseases based on their maximum number of participation in motifs, thereby revealing the miRNA-disease interaction dynamics) to determine and prioritize the set of diseases which are most certainly impacted upon the activation of a group of queried miRNAs, in a miRNA-disease network.Results and ConclusionOur tool, DISMIRA implements the above mentioned approaches and presents an interactive visualization which helps the user in exploring the networking dynamics of miRNAs and diseases by analyzing their neighbors, paths and topological features. A set of miRNAs can be used in this analysis to get the associated diseases for the input group of miRs with ranks and also further analysis can be done to find key miRs or diseases, shortest paths etc. DISMIRA can be accessed online for free at http://bnet.egr.vcu.edu:8080/dismira.

BackgroundThe bacterium Corynebacterium pseudotuberculosis (Cp) causes caseous lymphadenitis (CLA), mastitis, ulcerative lymphangitis, and oedema in a number of hosts, comprising ruminants, thereby intimidating economic and dairy industries worldwide. So far there is no effective drug or vaccine available against Cp. Previously, a pan-genomic analysis was performed for both biovar equi and biovar ovis and a Pathogenicity Islands (PAIS) analysis within the strains highlighted a large set of proteins that could be relevant therapeutic targets for controlling the onset of CLA. In the present work, a structural druggability analysis pipeline was accomplished along 15 previously sequenced Cp strains from both biovar equi and biovar ovis.Methods and resultsWe computed the whole modelome of a reference strain Cp1002 (NCBI Accession: NC_017300.1) and then the homology models of proteins, of 14 different Cp strains, with high identity (≥ 85%) to the reference strain were also done. Druggability score of all proteins pockets was calculated and only those targets that have a highly druggable (HD) pocket in all strains were kept, a set of 58 proteins. Finally, this information was merged with the previous PAIS analysis giving two possible highly relevant targets to conduct drug discovery projects. Also, off-targeting information against host organisms, including Homo sapiens and a further analysis for protein essentiality provided a final set of 31 druggable, essential and non-host homologous targets, tabulated in table S4, additional file 1. Out of 31 globally druggable targets, 9 targets have already been reported in other pathogenic microorganisms, 3 of them (3-isopropylmalate dehydratase small subunit, 50S ribosomal protein L30, Chromosomal replication initiator protein DnaA) in C. pseudotuberculosis.ConclusionOverall we provide valuable information of possible targets against C. pseudotuberculosis where some of these targets have already been reported in other microorganisms for drug discovery projects, also discarding targets that might be physiologically relevant but are not amenable for drug binding. We propose that the constructed in silico dataset might serve as a guidance for the scientific community to have a better understanding while selecting putative therapeutic protein candidates as druggable ones as effective measures against C. pseudotuberculosis.