An Insight into MptpB Inhibitors as a Key Strategy to Treat MDR and XDRTuberculosis.

Tuberculosis (TB) is a chronic, air-borne infectious disease caused by Mycobacterium tuberculosis (Mtb), which prominently affects the lungs and usually manifests in other organs. TB is preventable and curable but what makes it challenging is the emergence of resistance to the available treatment options. MDR-continued TB's expansion is one of the world's most pressing and difficult problems. Mtb revives via the reciprocity between Mycobacterium and host signalling pathways. Mtb secretes a virulence component called Mycobacterium tuberculosis protein tyrosine phosphatase (MptpB), which helps to survive against host macrophages. It indicates that targeting secreted virulence factors offers more benefits to circumvent the emergence of resistance. Many effective inhibitors of MptpA and MptpB have been discovered, providing a solid foundation for future research and development. Aside from possessing a structurally unique binding site in the Mtb enzyme, MptpB's minimal resemblance to other human phosphatases provides a broad platform for improving selectivity over host PTPs. We believe that addressing several parts of infection processes in the host and bacteria with combination therapy is the greatest way to reduce treatment burden and medication resistance. We have discussed the recent potent, selective, and efficacious MptpB inhibitors, such as natural and marine-based, isoxazole- linked carboxylic acid-based, oxamic acid-based, and lactone-based inhibitors, as potential strategies for treating TB.

GWAS Central: a comprehensive resource for the comparison and interrogation of genome-wide association studies.

To facilitate broad and convenient integrative visualization of and access to GWAS data, we have created the GWAS Central resource (http://www.gwascentral.org). This database seeks to provide a comprehensive collection of summary-level genetic association data, structured both for maximal utility and for safe open access (i.e., non-directional signals to fully preclude research subject identification). The resource emphasizes on advanced tools that allow comparison and discovery of relevant data sets from the perspective of genes, genome regions, phenotypes or traits. Tested markers and relevant genomic features can be visually interrogated across up to 16 multiple association data sets in a single view, starting at a chromosome-wide view and increasing in resolution down to individual bases. In addition, users can privately upload and view their own data as temporary files. Search and display utility is further enhanced by exploiting phenotype ontology annotations to allow genetic variants associated with phenotypes and traits of interest to be precisely identified, across all studies. Data submissions are accepted from individual researchers, groups and consortia, whereas we also actively gather data sets from various public sources. As a result, the resource now provides over 67 million P-values for over 1600 studies, making it the world's largest openly accessible online collection of summary-level GWAS association information.

Observ-OM and Observ-TAB: Universal syntax solutions for the integration, search, and exchange of phenotype and genotype information.

Genetic and epidemiological research increasingly employs large collections of phenotypic and molecular observation data from high quality human and model organism samples. Standardization efforts have produced a few simple formats for exchange of these various data, but a lightweight and convenient data representation scheme for all data modalities does not exist, hindering successful data integration, such as assignment of mouse models to orphan diseases and phenotypic clustering for pathways. We report a unified system to integrate and compare observation data across experimental projects, disease databases, and clinical biobanks. The core object model (Observ-OM) comprises only four basic concepts to represent any kind of observation: Targets, Features, Protocols (and their Applications), and Values. An easy-to-use file format (Observ-TAB) employs Excel to represent individual and aggregate data in straightforward spreadsheets. The systems have been tested successfully on human biobank, genome-wide association studies, quantitative trait loci, model organism, and patient registry data using the MOLGENIS platform to quickly setup custom data portals. Our system will dramatically lower the barrier for future data sharing and facilitate integrated search across panels and species. All models, formats, documentation, and software are available for free and open source (LGPLv3) at http://www.observ-om.org.

Knowledge engineering for health: a new discipline required to bridge the "ICT gap" between research and healthcare.

Despite vast amount of money and research being channeled toward biomedical research, relatively little impact has been made on routine clinical practice. At the heart of this failure is the information and communication technology "chasm" that exists between research and healthcare. A new focus on "knowledge engineering for health" is needed to facilitate knowledge transmission across the research-healthcare gap. This discipline is required to engineer the bidirectional flow of data: processing research data and knowledge to identify clinically relevant advances and delivering these into healthcare use; conversely, making outcomes from the practice of medicine suitably available for use by the research community. This system will be able to self-optimize in that outcomes for patients treated by decisions that were based on the latest research knowledge will be fed back to the research world. A series of meetings, culminating in the "I-Health 2011" workshop, have brought together interdisciplinary experts to map the challenges and requirements for such a system. Here, we describe the main conclusions from these meetings. An "I4Health" interdisciplinary network of experts now exists to promote the key aims and objectives, namely "integrating and interpreting information for individualized healthcare," by developing the "knowledge engineering for health" domain.