Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UNIPROT:P47989 (xanthine oxidase)
 8,633 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The mechanisms of context-specific differences in signal transduction, such as those that occur among different cell types, are not fully understood. One possibility is that differences in the abundance of proteins change signaling outputs because these proteins compete for binding to hub proteins at critical network branch points. Focusing on the ErbB signaling, we created a protein interaction network that included information about protein domains and analyzed the role of competing protein interactions. By leveraging three-dimensional protein structures to infer steric interactions among binding partners for a common binding domain or linear motif (node) and including information about protein abundance and interaction affinities, we identified a large number of competitive, mutually exclusive (XOR) protein interactions. Modeling changes in protein abundance with different patterns of partner proteins and XOR nodes (XOR motifs) revealed that each motif conferred a different response. We experimentally investigated the XOR motif containing the hub protein Ras and its binding partners RIN1 (Ras and Rab interactor 1) and CRAF (v-raf-leukemia viral oncogene 1). Consistent with the computational prediction, overexpression of RIN1 in cultured cells decreased the phosphorylation of CRAF and its downstream targets. Thus, our analyses provide evidence that variation in the abundance of proteins that compete for binding to XOR nodes could contribute to context-specific signaling plasticity.
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PMID:Integration of protein abundance and structure data reveals competition in the ErbB signaling network. 2434 80

Correction of human myeloid cell function is crucial for the prevention of inflammatory and allergic reactions as well as leukaemia progression. Caffeine, a naturally occurring food component, is known to display anti-inflammatory effects which have previously been ascribed largely to its inhibitory actions on phosphodiesterase. However, more recent studies suggest an additional role in affecting the activity of the mammalian target of rapamycin (mTOR), a master regulator of myeloid cell translational pathways, although detailed molecular events underlying its mode of action have not been elucidated. Here, we report the cellular uptake of caffeine, without metabolisation, by healthy and malignant hematopoietic myeloid cells including monocytes, basophils and primary acute myeloid leukaemia mononuclear blasts. Unmodified caffeine downregulated mTOR signalling, which affected glycolysis and the release of pro-inflammatory/pro-angiogenic cytokines as well as other inflammatory mediators. In monocytes, the effects of caffeine were potentiated by its ability to inhibit xanthine oxidase, an enzyme which plays a central role in human purine catabolism by generating uric acid. In basophils, caffeine also increased intracellular cyclic adenosine monophosphate (cAMP) levels which further enhanced its inhibitory action on mTOR. These results demonstrate an important mode of pharmacological action of caffeine with potentially wide-ranging therapeutic impact for treating non-infectious disorders of the human immune system, where it could be applied directly to inflammatory cells.
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PMID:Caffeine affects the biological responses of human hematopoietic cells of myeloid lineage via downregulation of the mTOR pathway and xanthine oxidase activity. 2638 6

Naringenin is one of the most abundant dietary flavonoids exerting several beneficial biological activities. Synthetic modification of naringenin is of continuous interest. During this study our aim was to synthesize a compound library of oxime and oxime ether derivatives of naringenin, and to investigate their biological activities. Two oximes and five oxime ether derivatives were prepared; their structure has been elucidated by NMR and high-resolution mass spectroscopy. The antiproliferative activity of the prepared compounds was evaluated by MTT assay against human leukemia (HL-60) and gynecological cancer cell lines isolated from cervical (HeLa, Siha) and breast (MCF-7, MDA-MB-231) cancers. Tert-butyl oxime ether derivative exerted the most potent cell growth inhibitory activity. Moreover, cell cycle analysis suggested that this derivative caused a significant increase in the hypodiploid (subG1) phase and induced apoptosis in Hela and Siha cells, and induced cell cycle arrest at G2/M phase in MCF-7 cells. The proapoptotic potential of the selected compound was confirmed by the activation of caspase-3. Antioxidant activities of the prepared molecules were also evaluated with xanthine oxidase, DPPH and ORAC assays, and the methyl substituted oxime ether exerted the most promising activity.
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PMID:Synthesis and In Vitro Antitumor Activity of Naringenin Oxime and Oxime Ether Derivatives. 3105 51

Analysis of high-dimensional data is a challenge in machine learning and data mining. Feature selection plays an important role in dealing with high-dimensional data for improvement of predictive accuracy, as well as better interpretation of the data. Frequently used evaluation functions for feature selection include resampling methods such as cross-validation, which show an advantage in predictive accuracy. However, these conventional methods are not only computationally expensive, but also tend to be over-optimistic. We propose a novel cross-entropy which is based on beta distribution for feature selection. In beta distribution-based cross-entropy (BetaDCE) for feature selection, the probability density is estimated by beta distribution and the cross-entropy is computed by the expected value of beta distribution, so that the generalization ability can be estimated more precisely than conventional methods where the probability density is learnt from data. Analysis of the generalization ability of BetaDCE revealed that it was a trade-off between bias and variance. The robustness of BetaDCE was demonstrated by experiments on three types of data. In the exclusive or-like (XOR-like) dataset, the false discovery rate of BetaDCE was significantly smaller than that of other methods. For the leukemia dataset, the area under the curve (AUC) of BetaDCE on the test set was 0.93 with only four selected features, which indicated that BetaDCE not only detected the irrelevant and redundant features precisely, but also more accurately predicted the class labels with a smaller number of features than the original method, whose AUC was 0.83 with 50 features. In the metabonomic dataset, the overall AUC of prediction with features selected by BetaDCE was significantly larger than that by the original reported method. Therefore, BetaDCE can be used as a general and efficient framework for feature selection.
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PMID:Beta Distribution-Based Cross-Entropy for Feature Selection. 3326 82


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