Post-translational modifications of NF-kappaB through phosphorylations enhance its transactivation potential. Much is known about the kinases that phosphorylate NF-kappaB, but little is known about the phosphatases that dephosphorylate it. By using a genome-scale siRNA screen, we identified the WIP1 phosphatase as a negative regulator of NF-kappaB signalling. WIP1-mediated regulation of NF-kappaB occurs in both a p38-dependent and independent manner. Overexpression of WIP1 resulted in decreased NF-kappaB activation in a dose-dependent manner, whereas WIP1 knockdown resulted in increased NF-kappaB function. We show that WIP1 is a direct phosphatase of Ser 536 of the p65 subunit of NF-kappaB. Phosphorylation of Ser 536 is known to be essential for the transactivation function of p65, as it is required for recruitment of the transcriptional co-activator p300. WIP1-mediated regulation of p65 regulated binding of NF-kappaB to p300 and hence chromatin remodelling. Consistent with our results, mice lacking WIP1 showed enhanced inflammation. These results provide the first genetic proof that a phosphatase directly regulates NF-kappaB signalling in vivo.

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Selection of relevant features for classification from a high dimensional data set by keeping their class discriminatory information intact is a classical problem in Machine Learning. The classification power of the features can be measured from the point of view of redundant information and correlations among them. Choosing minimal set of features optimizes time, space complexity related cost and simplifies the classifier design, resulting in better classification accuracy. In this paper, tomato (Solanum Lycopersicum L) leaves and fruiting habits were chosen with a futuristic goal to build a prototype model of leaf & fruit classification. By applying digital image processing techniques, tomato leaf and fruit images were pre-processed and morphological shape based features were computed. Next, supervised filter and wrapper based feature selection techniques were adopted to choose the optimal feature set leading to small within-class variance and large among-class distance which may be of utter importance in building the model for recognition system of the tomato leaf and fruiting habit genre.