Glutaredoxin-1 up-regulation induces soluble vascular endothelial growth factor receptor 1, attenuating post-ischemia limb revascularization

Glutaredoxin-1 (Glrx) is a cytosolic enzyme that regulates diverse cellular function by removal of GSH adducts from S-glutathionylated proteins including signaling molecules and transcription factors. Glrx is up-regulated during inflammation and diabetes, and Glrx overexpression inhibits VEGF-induced EC migration. The aim was to investigate the role of up-regulated Glrx in EC angiogenic capacities and in vivo revascularization in the setting of hind limb ischemia. Glrx-overexpressing EC from Glrx transgenic (TG) mice showed impaired migration and network formation and secreted higher levels of soluble VEGF receptor 1 (sFlt), an antagonizing factor to VEGF. After hind limb ischemia surgery Glrx TG mice demonstrated impaired blood flow recovery, associated with lower capillary density and poorer limb motor function compared with wild type littermates. There were also higher levels of anti-angiogenic sFlt expression in the muscle and plasma of Glrx TG mice after surgery. Noncanonical Wnt5a is known to induce sFlt. Wnt5a was highly expressed in ischemic muscles and EC from Glrx TG mice, and exogenous Wnt5a induced sFlt expression and inhibited network formation in human microvascular EC. Adenoviral Glrx-induced sFlt in EC was inhibited by a competitive Wnt5a inhibitor. Furthermore, Glrx overexpression removed GSH adducts on p65 in ischemic muscle and EC and enhanced NF-κB activity, which was responsible for Wnt5a-sFlt induction. Taken together, up-regulated Glrx induces sFlt in EC via NF-κB-dependent Wnt5a, resulting in attenuated revascularization in hind limb ischemia. The Glrx-induced sFlt explains part of the mechanism of redox-regulated VEGF signaling.

Independent component analyses for quantifying neuronal ensemble interactions

The goal of this study was to compare how multivariate statistical methods for dimension reduction account for correlations between simultaneously recorded neurons. Here, we describe applications of principal component analysis (PCA) and independent component analysis (ICA) (Cardoso J-F, Souloumiac A. IEE-Proc F 1993;140:362-70; Hyvarinen A, Oja E. Neural Comput 1997;9:1483-92; Lee TW, Girolami M, Sejnowski TJ. Neural Comp 1999;11:417-41) to neuronal ensemble data. Simulated ensembles of neurons were used to compare how well the methods above could account for correlated neuronal firing. The simulations showed that 'population vectors' defined by PCA were broadly distributed over the neuronal ensembles; thus, PCA was unable to identify independent groupings of neurons that shared common sources of input. By contrast, the ICA methods were all able to identify groupings of neurons that emerged due to correlated firing. This result suggests that correlated neuronal firing is reflected in higher-order correlations between neurons and not simply in the neurons' covariance. To assess the significance of these methods for real neuronal ensembles, we analyzed data from populations of neurons recorded in the motor cortex of rats trained to perform a reaction-time task. Scores for PCA and ICA were reconstructed on a bin-by-bin basis for single trials. These data were then used to train an artificial neural network to discriminate between single trials with either short or long reaction-times. Classifications based on scores from the ICA-based methods were significantly better than those based on PCA. For example, scores for components defined with an ICA-based method, extended ICA (Lee et al., 1999), classified more trials correctly (80.58+/-1.25%) than PCA (73.14+/-0.84%) for an ensemble of 26 neurons recorded in the motor cortex (ANOVA: P < 0.005). This result suggests that behaviorally relevant information is represented in correlated neuronal firing and can be best detected when higher-order correlations between neurons are taken into account.