Cell‐cell interactions between genetically distinct bacteria, known as coaggregation, are important for the formation of mixed‐species biofilms such as dental plaque. Interactions lead to gene regulation in the partner organisms that may be critical for adaptation and survival in mixed‐species biofilms. Here, gene regulation responses to coaggregation between Streptococcus gordonii and Fusobacterium nucleatum were studied using dual RNA‐Seq. Initially, S. gordonii was shown to coaggregate strongly with F. nucleatum in buffer or human saliva. Using confocal laser scanning microscopy and transmission electron microscopy, cells of different species were shown to be evenly distributed throughout the coaggregate and were closely associated with one another. This distribution was confirmed by serial block face sectioning scanning electron microscopy, which provided high resolution three‐dimensional images of coaggregates. Cell‐cell sensing responses were analysed 30 minutes after inducing coaggregation in human saliva. By comparison with monocultures, 16 genes were regulated following coaggregation in F. nucleatum whereas 119 genes were regulated in S. gordonii. In both species, genes involved in amino acid and carbohydrate metabolism were strongly affected by coaggregation. In particular, one 8‐gene operon in F. nucleatum encoding sialic acid uptake and catabolism was up‐regulated 2‐ to 5‐fold following coaggregation. In S. gordonii, a gene cluster encoding functions for phosphotransferase system‐mediated uptake of lactose and galactose was down‐regulated up to 3‐fold in response to coaggregation. The genes identified in this study may play key roles in the development of mixed‐species communities and represent potential targets for approaches to control dental plaque accumulation.

The oral streptococci are spherical Gram-positive bacteria categorized under the phylum Firmicutes which are among the most common causative agents of bacterial infective endocarditis (IE) and are also important agents in septicaemia in neutropenic patients. The Streptococcus mitis group is comprised of 13 species including some of the most common human oral colonizers such as S. mitis, S. oralis, S. sanguinis and S. gordonii as well as species such as S. tigurinus, S. oligofermentans and S. australis that have only recently been classified and are poorly understood at present. We present StreptoBase, which provides a specialized free resource focusing on the genomic analyses of oral species from the mitis group. It currently hosts 104 S. mitis group genomes including 27 novel mitis group strains that we sequenced using the high throughput Illumina HiSeq technology platform, and provides a comprehensive set of genome sequences for analyses, particularly comparative analyses and visualization of both cross-species and cross-strain characteristics of S. mitis group bacteria. StreptoBase incorporates sophisticated in-house designed bioinformatics web tools such as Pairwise Genome Comparison (PGC) tool and Pathogenomic Profiling Tool (PathoProT), which facilitate comparative pathogenomics analysis of Streptococcus strains. Examples are provided to demonstrate how StreptoBase can be employed to compare genome structure of different S. mitis group bacteria and putative virulence genes profile across multiple streptococcal strains. In conclusion, StreptoBase offers access to a range of streptococci genomic resources as well as analysis tools and will be an invaluable platform to accelerate research in streptococci. Database URL: http://streptococcus.um.edu.my.

Many oral bacteria form macroscopic clumps known as coaggregates when mixed with a different species. It is thought that these cell-cell interactions are critical for the formation of mixed-species biofilms such as dental plaque. Here, we assessed the impact of coaggregation between two key initial colonizers of dental plaque, Streptococcus gordonii and Veillonella parvula , on gene expression in each partner. These species were shown to coaggregate in buffer or human saliva. To monitor gene regulation, coaggregates were formed in human saliva and, after 30 minutes, whole-transcriptomes were extracted for sequencing and Dual RNA-Seq analysis. In total, 272 genes were regulated in V. parvula , including 39 genes in oxidoreductase processes. In S. gordonii , there was a high degree of inter-sample variation. Nevertheless, 69 genes were identified as potentially regulated by coaggregation, including two phosphotransferase system transporters and several other genes involved in carbohydrate metabolism. Overall, these data indicate that responses of V. parvula to coaggregation with S. gordonii are dominated by oxidative stress-related processes, whereas S. gordonii responses are more focussed on carbohydrate metabolism. We hypothesize that these responses may reflect changes in the local microenvironment in biofilms when S. gordonii or V. parvula immigrate into the system.

Helicobacter pylori colonizes the stomach, causing gastritis, peptic ulcers and gastric carcinoma. Drugs for treatment of H. pylori relieve from gastritis or pain but are not specific to H. pylori. Therefore, there is an immediate requirement for new therapeutic molecules to treat H. pylori. Current study investigates identification of drug targets in the strain HPAG1 of H. pylori by in silico genome analysis. Genome of HPAG1 was reconstructed for metabolic pathways and compared with Homosapien sapiens to identify genes which are unique to H. pylori. These unique genes were subjected to gene property analysis to identify the potentiality of the drug targets. Among the total number of genes analysed in H. pylori strain HPAG1, nearly 542 genes qualified as unique molecules and among them 29 were identified to be potential drug targets. Co/Zn/Cd efflux system membrane fusion protein, Ferric sidephore transport system and biopolymer transport protein EXbB were found to be critical drug targets to H. pylori HPAG1. Five genes (superoxide dismutase, HtrA protease/chaperone protein, Heatinducible transcription repressor HrcA, HspR, transcriptional repressor of DnaK operon, Cobalt-zinccadmium resistance protein CzcA) of the 29 predicted drug targets are already experimentally validated either genetically or biochemically lending credence to our unique approach.

BackgroundYersinia is a Gram-negative bacteria that includes serious pathogens such as the Yersinia pestis, which causes plague, Yersinia pseudotuberculosis, Yersinia enterocolitica. The remaining species are generally considered non-pathogenic to humans, although there is evidence that at least some of these species can cause occasional infections using distinct mechanisms from the more pathogenic species. With the advances in sequencing technologies, many genomes of Yersinia have been sequenced. However, there is currently no specialized platform to hold the rapidly-growing Yersinia genomic data and to provide analysis tools particularly for comparative analyses, which are required to provide improved insights into their biology, evolution and pathogenicity.DescriptionTo facilitate the ongoing and future research of Yersinia, especially those generally considered non-pathogenic species, a well-defined repository and analysis platform is needed to hold the Yersinia genomic data and analysis tools for the Yersinia research community. Hence, we have developed the YersiniaBase, a robust and user-friendly Yersinia resource and analysis platform for the analysis of Yersinia genomic data. YersiniaBase has a total of twelve species and 232 genome sequences, of which the majority are Yersinia pestis. In order to smooth the process of searching genomic data in a large database, we implemented an Asynchronous JavaScript and XML (AJAX)-based real-time searching system in YersiniaBase. Besides incorporating existing tools, which include JavaScript-based genome browser (JBrowse) and Basic Local Alignment Search Tool (BLAST), YersiniaBase also has in-house developed tools: (1) Pairwise Genome Comparison tool (PGC) for comparing two user-selected genomes; (2) Pathogenomics Profiling Tool (PathoProT) for comparative pathogenomics analysis of Yersinia genomes; (3) YersiniaTree for constructing phylogenetic tree of Yersinia. We ran analyses based on the tools and genomic data in YersiniaBase and the preliminary results showed differences in virulence genes found in Yersinia pestis and Yersinia pseudotuberculosis compared to other Yersinia species, and differences between Yersinia enterocolitica subsp. enterocolitica and Yersinia enterocolitica subsp. palearctica.ConclusionsYersiniaBase offers free access to wide range of genomic data and analysis tools for the analysis of Yersinia. YersiniaBase can be accessed at http://yersinia.um.edu.my.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0422-y) contains supplementary material, which is available to authorized users.