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Biomedical subjects

Kush Parmar

Publications and source records attributed to Kush Parmar.

2 recordsLinked to original sources

Kruppel-like factor 2 (KLF2) regulates endothelial thrombotic function.

The vascular endothelium maintains blood fluidity by inhibiting blood coagulation, inhibiting platelet aggregation, and promoting fibrinolysis. Endothelial cells lose these nonthrombogenic properties on exposure to proinflammatory stimuli. We recently identified the Kruppel-like factor KLF2 as a novel regulator of endothelial proinflammatory activation. Here it is found that KLF2 differentially regulates key factors involved in maintaining an antithrombotic endothelial surface. Overexpression of KLF2 strongly induced thrombomodulin (TM) and endothelial nitric oxide synthase (eNOS) expression and reduced plasminogen activator inhibitor-1 (PAI-1) expression. Furthermore, overexpression of KLF2 inhibited the cytokine-mediated induction of tissue factor (TF). In contrast, siRNA mediated knockdown of KLF2 reduced antithrombotic gene expression while inducing the expression of pro-coagulant factors. The functional importance of KLF2 was verified by in vitro clotting assays. By comparison to control infected cells, KLF2 overexpression increased blood clotting time as well as flow rates under basal and inflammatory conditions. In contrast, siRNA-mediated knockdown of KLF2 reduced blood clotting time and flow rates. These observations identify KLF2 as a novel transcriptional regulator of endothelial thrombotic function. The full text of this article is available online at http://circres.ahajournals.org.

Animals↗

A cross-genomic approach for systematic mapping of phenotypic traits to genes.

We present a computational method for de novo identification of gene function using only cross-organismal distribution of phenotypic traits. Our approach assumes that proteins necessary for a set of phenotypic traits are preferentially conserved among organisms that share those traits. This method combines organism-to-phenotype associations,along with phylogenetic profiles,to identify proteins that have high propensities for the query phenotype; it does not require the use of any functional annotations for any proteins. We first present the statistical foundations of this approach and then apply it to a range of phenotypes to assess how its performance depends on the frequency and specificity of the phenotype. Our analysis shows that statistically significant associations are possible as long as the phenotype is neither extremely rare nor extremely common; results on the flagella,pili, thermophily,and respiratory tract tropism phenotypes suggest that reliable associations can be inferred when the phenotype does not arise from many alternate mechanisms.

Bacterial Proteins↗