It was recently shown that vascular endothelial growth factor (VEGF), a growth factor for endothelial cells, plays a pivotal role in rheumatoid arthritis. VEGF binds to specific receptors, known as VEGF-RI and VEGF-RII. We assessed the physical and histological effects of selective blockade of VEGF and its receptors in transgenic K/BxN mice, a model of rheumatoid arthritis very close to the human disease. Mice were treated with anti-mouse VEGF Ab, anti-mouse VEGF-RI and -RII Abs, and an inhibitor of VEGF-RI tyrosine kinase. Disease activity was monitored using clinical indexes and by histological examination. We found that synovial cells from arthritic joints express VEGF, VEGF-RI, and VEGF-RII. Treatment with anti-VEGF-RI strongly attenuated the disease throughout the study period, while anti-VEGF only transiently delayed disease onset. Treatment with anti-VEGF-RII had no effect. Anti-VEGF-RI reduced the intensity of clinical manifestations and, based on qualitative and semiquantitative histological analyses, prevented joint damage. Treatment with a VEGF-RI tyrosine kinase inhibitor almost abolished the disease. These results show that VEGF is a key factor in pannus development, acting through the VEGF-RI pathway. The observation that in vivo administration of specific inhibitors targeting the VEGF-RI pathway suppressed arthritis and prevented bone destruction opens up new possibilities for the treatment of rheumatoid arthritis.
We present a novel method that predicts transmembrane domains in proteins using solely information contained in the sequence itself. The PRED-TMR algorithm described, refines a standard hydrophobicity analysis with a detection of potential termini ('edges', starts and ends) of transmembrane regions. This allows one both to discard highly hydrophobic regions not delimited by clear start and end configurations and to confirm putative transmembrane segments not distinguishable by their hydrophobic composition. The accuracy obtained on a test set of 101 non-homologous transmembrane proteins with reliable topologies compares well with that of other popular existing methods. Only a slight decrease in prediction accuracy was observed when the algorithm was applied to all transmembrane proteins of the SwissProt database (release 35). A WWW server running the PRED-TMR algorithm is available at http://o2.db.uoa. gr/PRED-TMR/
MicroRNAs play critical roles in many physiological processes. Their dysregulations are also closely related to the development and progression of various human diseases, including cancer. Therefore, identifying new microRNAs that are associated with diseases contributes to a better understanding of pathogenicity mechanisms. MicroRNAs also represent a tremendous opportunity in biotechnology for early diagnosis. To date, several in silico methods have been developed to address the issue of microRNA-disease association prediction. However, these methods have various limitations. In this study, we investigate the hypothesis that information attached to miRNAs and diseases can be revealed by distributional semantics. Our basic approach is to represent distributional information on miRNAs and diseases in a high-dimensional vector space and to define associations between miRNAs and diseases in terms of their vector similarity. Cross validations performed on a dataset of known miRNA-disease associations demonstrate the excellent performance of our method. Moreover, the case study focused on breast cancer confirms the ability of our method to discover new disease-miRNA associations and to identify putative false associations reported in databases.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.