Proteomics, ultrastructure, and physiology of hippocampal synapses in a fragile X syndrome mouse model reveal presynaptic phenotype

Fragile X syndrome (FXS), the most common form of hereditary mental retardation, is caused by a loss-of-function mutation of the Fmr1 gene, which encodes fragile X mental retardation protein (FMRP). FMRP affects dendritic protein synthesis, thereby causing synaptic abnormalities. Here, we used a quantitative proteomics approach in an FXS mouse model to reveal changes in levels of hippocampal synapse proteins. Sixteen independent pools of Fmr1 knock-out mice and wild type mice were analyzed using two sets of 8-plex iTRAQ experiments. Of 205 proteins quantified with at least three distinct peptides in both iTRAQ series, the abundance of 23 proteins differed between Fmr1 knock-out and wild type synapses with a false discovery rate (q-value) <5%. Significant differences were confirmed by quantitative immunoblotting. A group of proteins that are known to be involved in cell differentiation and neurite outgrowth was regulated; they included Basp1 and Gap43, known PKC substrates, and Cend1. Basp1 and Gap43 are predominantly expressed in growth cones and presynaptic terminals. In line with this, ultrastructural analysis in developing hippocampal FXS synapses revealed smaller active zones with corresponding postsynaptic densities and smaller pools of clustered vesicles, indicative of immature presynaptic maturation. A second group of proteins involved in synaptic vesicle release was up-regulated in the FXS mouse model. In accordance, paired-pulse and short-term facilitation were significantly affected in these hippocampal synapses. Together, the altered regulation of presynaptically expressed proteins, immature synaptic ultrastructure, and compromised short-term plasticity points to presynaptic changes underlying glutamatergic transmission in FXS at this stage of development. © 2011 by The American Society for Biochemistry and Molecular Biology, Inc

The Sloan Digital Sky Survey: Technical Summary

The Sloan Digital Sky Survey (SDSS) will provide the data to support detailed investigations of the distribution of luminous and non- luminous matter in the Universe: a photometrically and astrometrically calibrated digital imaging survey of pi steradians above about Galactic latitude 30 degrees in five broad optical bands to a depth of g' about 23 magnitudes, and a spectroscopic survey of the approximately one million brightest galaxies and 10^5 brightest quasars found in the photometric object catalog produced by the imaging survey. This paper summarizes the observational parameters and data products of the SDSS, and serves as an introduction to extensive technical on-line documentation.Comment: 9 pages, 7 figures, AAS Latex. To appear in AJ, Sept 200

The sudden presentation and progression of overt cervical metastases following treatment of head and neck cancers

Item does not contain fulltex

Power-law input-output transfer functions explain the contrast-response and tuning properties of neurons in visual cortex.

International audienceWe develop a unified model accounting simultaneously for the contrast invariance of the width of the orientation tuning curves (OT) and for the sigmoidal shape of the contrast response function (CRF) of neurons in the primary visual cortex (V1). We determine analytically the conditions for the structure of the afferent LGN and recurrent V1 inputs that lead to these properties for a hypercolumn composed of rate based neurons with a power-law transfer function. We investigate what are the relative contributions of single neuron and network properties in shaping the OT and the CRF. We test these results with numerical simulations of a network of conductance-based model (CBM) neurons and we demonstrate that they are valid and more robust here than in the rate model. The results indicate that because of the acceleration in the transfer function, described here by a power-law, the orientation tuning curves of V1 neurons are more tuned, and their CRF is steeper than those of their inputs. Last, we show that it is possible to account for the diversity in the measured CRFs by introducing heterogeneities either in single neuron properties or in the input to the neurons. We show how correlations among the parameters that characterize the CRF depend on these sources of heterogeneities. Comparison with experimental data suggests that both sources contribute nearly equally to the diversity of CRF shapes observed in V1 neurons