Biology of Streptococcus mutans-Derived Glucosyltransferases: Role in Extracellular Matrix Formation of Cariogenic Biofilms

The importance of Streptococcus mutans in the etiology and pathogenesis of dental caries is certainly controversial, in part because excessive attention is paid to the numbers of S. mutans and acid production while the matrix within dental plaque has been neglected. S. mutans does not always dominate within plaque; many organisms are equally acidogenic and aciduric. It is also recognized that glucosyltransferases from S. mutans (Gtfs) play critical roles in the development of virulent dental plaque. Gtfs adsorb to enamel synthesizing glucans in situ, providing sites for avid colonization by microorganisms and an insoluble matrix for plaque. Gtfs also adsorb to surfaces of other oral microorganisms converting them to glucan producers. S. mutans expresses 3 genetically distinct Gtfs; each appears to play a different but overlapping role in the formation of virulent plaque. GtfC is adsorbed to enamel within pellicle whereas GtfB binds avidly to bacteria promoting tight cell clustering, and enhancing cohesion of plaque. GtfD forms a soluble, readily metabolizable polysaccharide and acts as a primer for GtfB. The behavior of soluble Gtfs does not mirror that observed with surface-adsorbed enzymes. Furthermore, the structure of polysaccharide matrix changes over time as a result of the action of mutanases and dextranases within plaque. Gtfs at distinct loci offer chemotherapeutic targets to prevent caries. Nevertheless, agents that inhibit Gtfs in solution frequently have a reduced or no effect on adsorbed enzymes. Clearly, conformational changes and reactions of Gtfs on surfaces are complex and modulate the pathogenesis of dental caries in situ, deserving further investigation

Identifying potential cancer driver genes by genomic data integration

Cancer is a genomic disease associated with a plethora of gene mutations resulting in a loss of control over vital cellular functions. Among these mutated genes, driver genes are defined as being causally linked to oncogenesis, while passenger genes are thought to be irrelevant for cancer development. With increasing numbers of large-scale genomic datasets available, integrating these genomic data to identify driver genes from aberration regions of cancer genomes becomes an important goal of cancer genome analysis and investigations into mechanisms responsible for cancer development. A computational method, MAXDRIVER, is proposed here to identify potential driver genes on the basis of copy number aberration (CNA) regions of cancer genomes, by integrating publicly available human genomic data. MAXDRIVER employs several optimization strategies to construct a heterogeneous network, by means of combining a fused gene functional similarity network, gene-disease associations and a disease phenotypic similarity network. MAXDRIVER was validated to effectively recall known associations among genes and cancers. Previously identified as well as novel driver genes were detected by scanning CNAs of breast cancer, melanoma and liver carcinoma. Three predicted driver genes (CDKN2A, AKT1, RNF139) were found common in these three cancers by comparative analysis