Accumulating clinical evidence suggests that hyperuricemia is associated with an increased risk of type 2 diabetes. However, it is still unclear whether elevated levels of uric acid can cause direct injury of pancreatic β-cells. In this study, we examined the effects of uric acid on β-cell viability and function. Uric acid solution or normal saline was administered intraperitoneally to mice daily for 4 weeks. Uric acid-treated mice exhibited significantly impaired glucose tolerance and lower insulin levels in response to glucose challenge than did control mice. However, there were no significant differences in insulin sensitivity between the two groups. In comparison to the islets in control mice, the islets in the uric acid–treated mice were markedly smaller in size and contained less insulin. Treatment of β-cells in vitro with uric acid activated the NF-κB signaling pathway through IκBα phosphorylation, resulting in upregulated inducible nitric oxide synthase (iNOS) expression and excessive nitric oxide (NO) production. Uric acid treatment also increased apoptosis and downregulated Bcl-2 expression in Min6 cells. In addition, a reduction in insulin secretion under glucose challenge was observed in the uric acid–treated mouse islets. These deleterious effects of uric acid on pancreatic β-cells were attenuated by benzbromarone, an inhibitor of uric acid transporters, NOS inhibitor L-NMMA, and Bay 11–7082, an NF-κB inhibitor. Further investigation indicated that uric acid suppressed levels of MafA protein through enhancing its degradation. Collectively, our data suggested that an elevated level of uric acid causes β-cell injury via the NF-κB-iNOS-NO signaling axis.

eIF3k, the smallest subunit of eukaryotic initiation factor 3 (eIF3), interacts with several other subunits of eIF3 and the 40 S ribosomal subunit. eIF3k is conserved among high eukaryotes, including mammals, insects, and plants, and it is ubiquitously expressed in human tissues. Interestingly, eIF3k does not exist in some species of yeast. Thus, eIF3k may play a unique regulatory role in higher organisms. Here we report the crystal structure of human eIF3k, the first high-resolution structure of an eIF3 component. This novel structure contains two distinct domains, a HEAT (named for Huntington, elongation factor 3, A subunit of protein phosphatase 2A, target of rapamycin) repeat-like HAM (HEAT analogous motif) domain and a winged-helix-like WH domain. Through structural comparison and sequence conservation analysis, we show that eIF3k has three putative protein-binding surfaces and has potential RNA binding activity. The structure provides key information for understanding the structure and function of the eIF3 complex.Translation initiation is a sophisticated cellular process, especially in eukaryotes. In general, translation initiation in eukaryotic organisms involves three steps (1); first, the methionyl-initiator tRNA (Met-tRNA i Met ) binds to the 40 S ribosomal subunit to form a 43 S preinitiation complex; second, the preinitiation complex binds to mRNA and scans to the AUG start codon in the mRNA; and third, the 60 S ribosomal subunit joins the mRNA-bound preinitiation complex to form an 80 S initiation complex, ready to commence translation. Each of these steps is stimulated by a number of proteins called eukaryotic initiation factors (eIFs).1 At least 11 eIFs have been identified, comprising over 25 polypeptides (2). In contrast, only three to five initiation factors are known in prokaryotes. This difference in protein complexity suggests that more protein-RNA and protein-protein interactions rather than RNA-RNA interactions are required for efficient translation initiation in eukaryotic cells.In mammalian cells, eIF3 is the largest initiation factor with an apparent molecular mass of about 600 kDa. It plays a central role in steps 1 and 2 of the translation initiation process (1, 3). For instance, eIF3 can bind to dissociated 40 S subunits and delay the reassociation with the 60 S ribosomal subunit for a long enough time to permit initiation. eIF3 also stabilizes the binding of the Met-tRNA i Met ⅐eIF2⅐GTP ternary complex to the 40 S subunits and promotes the formation of a 43 S preinitiation complex comprised of the 40 S subunit, the ternary complex, eIF1, eIF1A, and eIF3. In addition, eIF3 stimulates the binding of 5Ј-m 7 G-capped mRNA by interaction with the mRNA-associated factor eIF4G.eIF3 is a multisubunit protein complex. Various genes encoding eIF3 subunits have been cloned from mammals, plants, and yeasts. Twelve different subunits (eIF3a/p170, b/p116, c/p110, d/p66, e/p48, f/p47, g/p44, h/p40, i/p36, j/p45, k/p28, l/p69) have been identified in mammals, while in yeast only six subunits (eIF3...

Modeling and learning of brain activity patterns represent a huge challenge to the brain-computer interface (BCI) based on electroencephalography (EEG). Many existing methods estimate the uncorrelated instantaneous demixing of EEG signals to classify multiclass motor imagery (MI). However, the condition of uncorrelation does not hold true in practice, because the brain regions work with partial or complete collaboration. This work proposes a novel method, termed as a common Bayesian network (CBN), to discriminate multiclass MI EEG signals. First, with the constraints of a Gaussian mixture model on every channel, only related channels are selected to construct a normal Bayesian network. Second, the nodes that have both common and varying edges are selected to construct a CBN. Third, the probabilities on common edges are used to learn about the support vector machine for classification. To validate the proposed method, we conduct experiments on two well-known BCI datasets and perform a numerical analysis of the propose algorithm for EEG classification in a multiclass MI BCI. Experimental results show that the proposed CBN method not only has excellent classification performance, but also is highly efficient. Hence, it is suitable for the cases where a system is required to respond within a second.Index Terms-Bayesian network (BN), brain-computer interface (BCI), electroencephalography (EEG), learning algorithm, support vector machine (SVM).