O-GlcNAc Modification of NFκB p65 Inhibits TNF-α-Induced Inflammatory Mediator Expression in Rat Aortic Smooth Muscle Cells

BACKGROUND: We have shown that glucosamine (GlcN) or O-(2-acetamido-2-deoxy-D-glucopyranosylidene)amino-N-phenylcarbamate (PUGNAc) treatment augments O-linked-N-acetylglucosamine (O-GlcNAc) protein modification and attenuates inflammatory mediator expression, leukocyte infiltration and neointima formation in balloon injured rat carotid arteries and have identified the arterial smooth muscle cell (SMC) as the target cell in the injury response. NFκB signaling has been shown to mediate the expression of inflammatory genes and neointima formation in injured arteries. Phosphorylation of the p65 subunit of NFκB is required for the transcriptional activation of NFκB. This study tested the hypothesis that GlcN or PUGNAc treatment protects vascular SMCs against tumor necrosis factor (TNF)-α induced inflammatory stress by enhancing O-GlcNAcylation and inhibiting TNF-α induced phosphorylation of NFκB p65, thus inhibiting NFκB signaling. METHODOLOGY/PRINCIPAL FINDINGS: Quiescent rat aortic SMCs were pretreated with GlcN (5 mM), PUGNAc (10(-4) M) or vehicle and then stimulated with TNF-α (10 ng/ml). Both treatments inhibited TNF-α-induced expression of chemokines [cytokine-induced neutrophil chemoattractant (CINC)-2β and monocyte chemotactic protein (MCP)-1] and adhesion molecules [vascular cell adhesion molecule (VCAM)-1 and P-Selectin]. Both treatments inhibited TNF-α induced NFκB p65 activation and promoter activity, increased NFκB p65 O-GlcNAcylation and inhibited NFκB p65 phosphorylation at Serine 536, thus promoting IκBα binding to NFκB p65. CONCLUSIONS: There is a reciprocal relationship between O-GlcNAcylation and phosphorylation of NFκB p65, such that increased NFκB p65 O-GlcNAc modification inhibits TNF-α-Induced expression of inflammatory mediators through inhibition of NFκB p65 signaling. These findings provide a mechanistic basis for our previous observations that GlcN and PUGNAc treatments inhibit inflammation and remodeling induced by acute endoluminal arterial injury

Modeling working memory: An interference model of complex span

This article introduces a new computational model for the complex-span task, the most popular task for studying working memory. SOB-CS is a two-layer neural network that associates distributed item representations with distributed, overlapping position markers. Memory capacity limits are explained by interference from a superposition of associations. Concurrent processing interferes with memory through involuntary encoding of distractors. Free time in-between distractors is used to remove irrelevant representations, thereby reducing interference. The model accounts for benchmark findings in four areas: (1) effects of processing pace, processing difficulty, and number of processing steps; (2) effects of serial position and error patterns; (3) effects of different kinds of item-distractor similarity; and (4) correlations between span tasks. The model makes several new predictions in these areas, which were confirmed experimentally