Poldip2, a novel regulator of Nox4 and cytoskeletal integrity in vascular smooth muscle cells.

RATIONALE: NADPH oxidases (Noxes) regulate vascular physiology and contribute to the pathogenesis of vascular disease. In vascular smooth muscle cells (VSMCs), the interactions of individual Nox homologs with regulatory proteins are poorly defined. OBJECTIVE: The objective of this study was to identify novel NADPH oxidase regulatory proteins. METHODS AND RESULTS: Using a yeast 2-hybrid screen, we identified a novel p22phox binding partner, Poldip2, and demonstrated that it associates with p22phox, NADPH oxidase (Nox)1, and Nox4 and colocalizes with p22phox at sites of Nox4 localization. Poldip2 increases Nox4 enzymatic activity by 3-fold and positively regulates basal reactive oxygen species production in VSMCs (O2(.-): 86.3+/-15.6% increase; H2O2: 40.7+/-4.5% increase). Overexpression of Poldip2 activates Rho (180.2+/-24.8% increase), strengthens focal adhesions, and increases stress fiber formation. These phenotypic changes are blocked by dominant negative Rho. In contrast, depletion of either Poldip2 or Nox4 results in a loss of these structures, which is rescued by adding back active Rho. Cell migration, which requires dynamic cytoskeletal remodeling, is impaired by either excess (70.1+/-14.7% decrease) or insufficient Poldip2 (63.5+/-5.9% decrease). CONCLUSIONS: These results suggest that Poldip2 associates with p22phox to activate Nox4, leading to regulation of focal adhesion turnover and VSMC migration, thus linking reactive oxygen species production and cytoskeletal remodeling. Poldip2 may be a novel therapeutic target for vascular pathologies with a significant VSMC migratory component, such as restenosis and atherosclerosis.

A Method for EHR Phenotype Management in an i2b2 Data Warehouse.

Electronic health record (EHR) data is valuable for finding patients for clinical research and analytics but is complex to query. EHR phenotyping involves the curation and dissemination of best practices for querying commonly studied populations. Phenotyping software computes patterns in clinical and administrative data and may add the found patterns as derived variables to a database that researchers can query. This paper describes a method for managing EHR phenotypes in a data warehouse as the warehouse is incrementally updated with new and changed data. We have implemented this method in proof-of-concept form as an extension to the Eureka! Clinical Analytics phenotyping software system and evaluated the implementation's performance. The method shows promise for realizing the efficient addition, modification, and removal of derived variables representing phenotypes in a data warehouse.

The RCSB Protein Data Bank: a redesigned query system and relational database based on the mmCIF schema.

The Protein Data Bank (PDB) is the central worldwide repository for three-dimensional (3D) structure data of biological macromolecules. The Research Collaboratory for Structural Bioinformatics (RCSB) has completely redesigned its resource for the distribution and query of 3D structure data. The re-engineered site is currently in public beta test at http://pdbbeta.rcsb.org. The new site expands the functionality of the existing site by providing structure data in greater detail and uniformity, improved query and enhanced analysis tools. A new key feature is the integration and searchability of data from over 20 other sources covering genomic, proteomic and disease relationships. The current capabilities of the re-engineered site, which will become the RCSB production site at http://www.pdb.org in late 2005, are described.

The distribution and query systems of the RCSB Protein Data Bank.

The Protein Data Bank (PDB; http://www.pdb.org) is the primary source of information on the 3D structure of biological macromolecules. The PDB's mandate is to disseminate this information in the most usable form and as widely as possible. The current query and distribution system is described and an alpha version of the future re-engineered system introduced.

Mechanism of hydrogen peroxide-induced cell cycle arrest in vascular smooth muscle.

Reactive oxygen species such as hydrogen peroxide (H(2)O(2)) can positively and negatively modulate vascular smooth muscle cell (VSMC) growth. To investigate these paradoxical effects of H(2)O(2), we examined its effect on apoptosis, cell cycle progression, and cell cycle proteins. High concentrations of H(2)O(2) (500 microM to 1 mM) induced apoptosis, whereas moderate concentrations (100 microM) caused cell cycle arrest in G1. H(2)O(2) (100 microM) blocked serum-stimulated cyclin-dependent kinase-2 (CDK2) activity, but not CDK4 activity, suggesting that cell cycle arrest occurred in part by inhibiting CDK2 activity. The serum-induced increase in cyclin A mRNA was also completely suppressed by H(2)O(2), whereas cyclin D1 mRNA was not affected. In addition, H(2)O(2) caused a dramatic increase in expression of the cell cycle inhibitor p21 mRNA (9.67 +/- 0.94-fold at 2 h) and protein (8.75 +/- 0.08-fold at 8 h), but no change in p27 protein. Finally, H(2)O(2 )transiently increased p53 protein levels (3.16 +/- 1.2-fold at 2 h). Thus, whereas high levels of H(2)O(2) induce apoptosis, moderate concentrations of H(2)O(2) coordinate a set of molecular events leading to arrest of VSMCs at the G1/S checkpoint of the cell cycle. These results provide insight into the mechanisms underlying positive and negative regulation of VSMC growth by H(2)O(2) in vascular disease.

Intrinsic evaluation of text mining tools may not predict performance on realistic tasks.

Biomedical text mining and other automated techniques are beginning to achieve performance which suggests that they could be applied to aid database curators. However, few studies have evaluated how these systems might work in practice. In this article we focus on the problem of annotating mutations in Protein Data Bank (PDB) entries, and evaluate the relationship between performance of two automated techniques, a text-mining-based approach (MutationFinder) and an alignment-based approach, in intrinsic versus extrinsic evaluations. We find that high performance on gold standard data (an intrinsic evaluation) does not necessarily translate to high performance for database annotation (an extrinsic evaluation). We show that this is in part a result of lack of access to the full text of journal articles, which appears to be critical for comprehensive database annotation by text mining. Additionally, we evaluate the accuracy and completeness of manually annotated mutation data in the PDB, and find that it is far from perfect. We conclude that currently the most cost-effective and reliable approach for database annotation might incorporate manual and automatic annotation methods.