Cell Distribution Differences Of Matrix Metalloproteinase-9 and Tissue Inhibitor of Matrix Metalloproteinase-1 in Patients With Kawasaki Disease
Department of Pediatrics and Child Neurology, Oita University Faculty of Medicine, Hasama, Yufu, Oita, Japan. The Pediatric Infectious Disease Journal
(Impact Factor: 2.72).
09/2012; 31(9):973-4. DOI: 10.1097/INF.0b013e31825ba6b3
The interaction of matrix metalloproteinase (MMP)-9 and tissue inhibitor of matrix metalloproteinase-1 has been implicated in the formation of coronary aneurysms in Kawasaki disease. MMP-9 and tissue inhibitor of matrix metalloproteinase-1 were distributed predominantly in the granulocytes and platelets, respectively, in patients with Kawasaki disease. The plasma values of MMP-9 correlated positively with the circulating neutrophil count. Inhibiting the activity of granulocytes and maintaining the platelet activity might prevent coronary aneurysms.
Available from: Aditya Murthy
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ABSTRACT: Over the past 50 years, steady growth in the field of metalloproteinase biology has shown that the degradation of extracellular matrix components represents only a fraction of the functions performed by these enzymes and has highlighted their fundamental roles in immunity. Metalloproteinases regulate aspects of immune cell development, effector function, migration and ligand-receptor interactions. They carry out ectodomain shedding of cytokines and their cognate receptors. Together with their endogenous inhibitors TIMPs (tissue inhibitor of metalloproteinases), these enzymes regulate signalling downstream of the tumour necrosis factor receptor and the interleukin-6 receptor, as well as that downstream of the epidermal growth factor receptor and Notch, which are all pertinent for inflammatory responses. This Review discusses the metalloproteinase family as a crucial component in immune cell development and function.
Available from: Brian J Bennion
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ABSTRACT: Late-stage or post-market identification of adverse drug reactions (ADRs) is a significant public health issue and a source of major economic liability for drug development. Thus, reliable in silico screening of drug candidates for possible ADRs would be advantageous. In this work, we introduce a computational approach that predicts ADRs by combining the results of molecular docking and leverages known ADR information from DrugBank and SIDER. We employed a recently parallelized version of AutoDock Vina (VinaLC) to dock 906 small molecule drugs to a virtual panel of 409 DrugBank protein targets. L1-regularized logistic regression models were trained on the resulting docking scores of a 560 compound subset from the initial 906 compounds to predict 85 side effects, grouped into 10 ADR phenotype groups. Only 21% (87 out of 409) of the drug-protein binding features involve known targets of the drug subset, providing a significant probe of off-target effects. As a control, associations of this drug subset with the 555 annotated targets of these compounds, as reported in DrugBank, were used as features to train a separate group of models. The Vina off-target models and the DrugBank on-target models yielded comparable median area-under-the-receiver-operating-characteristic-curves (AUCs) during 10-fold cross-validation (0.60-0.69 and 0.61-0.74, respectively). Evidence was found in the PubMed literature to support several putative ADR-protein associations identified by our analysis. Among them, several associations between neoplasm-related ADRs and known tumor suppressor and tumor invasiveness marker proteins were found. A dual role for interstitial collagenase in both neoplasms and aneurysm formation was also identified. These associations all involve off-target proteins and could not have been found using available drug/on-target interaction data. This study illustrates a path forward to comprehensive ADR virtual screening that can potentially scale with increasing number of CPUs to tens of thousands of protein targets and millions of potential drug candidates.
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