Local Differences in GABA Release Induced by Excitatory Amino Acids During Retina Development: Selective Activation of NMDA Receptors by Aspartate in the Inner Retina

Glutamate and GABA are the major excitatory and inhibitory neurotransmitters in the CNS. In the retina, it has been shown that glutamate and aspartate and their agonists kainate and NMDA promote the release of GABA. In the chick retina, at embryonic day 14 (E14), glutamate and kainate were able to induce the release of GABA from amacrine and horizontal cells as detected by GABA-immunoreactivity. NMDA also induced GABA release restricted to amacrine cell population and its projections to the inner plexiform layer (E14 and E18). Although aspartate reduced GABA immunoreactivity, specifically in amacrine cells of E18 retinas, it was not efficient to promote GABA release from retinas at E14. As observed in differentiated retinas, dopamine inhibited the GABA release promoted by NMDA and aspartate but not by kainate. Our data show that different retinal sites respond to distinct EAAs via different receptor systems.     

Lentiviral reporter constructs for fluorescence tracking of the temporospatial pattern of Smad3 signaling

Canonical TGF-beta is involved in cell differentiation, tissue maintenance, and wound healing, but also plays a central role in the pathogenesis of diseases such as cancer Here we describe a lentivirus-based reporter vector system expressing green fluorescent protein (GFP) or red fluorescent protein (RFP) under the control of a Smad3-responsive element (CAGA)12 that allows observation of the temporospatial pattern of endogeneous Smad3-mediated signaling on a cellular level. Use of this method will be valuable to identify cells with active Smad3 signaling and investigate the role of endogenous Smad3 signaling in complex systems such as co-cultures in vitro, or in tumors during tumor cell invasion and metastasis in vivo.     

A novel yeast expression system for the overproduction of quality-controlled membrane proteins

Saturation of the cell's protein folding capacity and accumulation of inactive incompletely folded protein often accompanying the overexpression of membrane proteins (MPs) presents an obstacle to their efficient purification in a functional form for structural studies. We present a novel strategy for optimization of functional MP expression in Saccharomyces cerevisiae. This approach exploits the unfolded protein response (UPR) pathway, a stress signaling mechanism that senses the accumulation of unfolded proteins in the endoplasmic reticulum. We demonstrate that a high level of UPR induction upon expression of a MP reflects impaired functional expression of that protein. Tuning the expression level of the protein so as to avoid or minimize UPR induction results in its increased functional expression. UPR status can therefore serve as a proxy variable for the extent of impaired expression of a MP that may even be applicable in the absence of knowledge of the protein's biological function.     

Structure and architecture of the maize genome

Maize (Zea mays or corn) plays many varied and important roles in society. It is not only an important experimental model plant, but also a major livestock feed crop and a significant source of industrial products such as sweeteners and ethanol. In this study we report the systematic analysis of contiguous sequences of the maize genome. We selected 100 random regions averaging 144 kb in size, representing about 0.6% of the genome, and generated a high-quality dataset for sequence analysis. This sampling contains 330 annotated genes, 91% of which are supported by expressed sequence tag data from maize and other cereal species. Genes averaged 4 kb in size with five exons, although the largest was over 59 kb with 31 exons. Gene density varied over a wide range from 0.5 to 10.7 genes per 100 kb and genes did not appear to cluster significantly. The total repetitive element content we observed (66%) was slightly higher than previous whole-genome estimates (58%-63%) and consisted almost exclusively of retroelements. The vast majority of genes can be aligned to at least one sequence read derived from gene-enrichment procedures, but only about 30% are fully covered. Our results indicate that much of the increase in genome size of maize relative to rice (Oryza sativa) and Arabidopsis (Arabidopsis thaliana) is attributable to an increase in number of both repetitive elements and genes.     

Profile-based string kernels for remote homology detection and motif extraction

We introduce novel profile-based string kernels for use with support vector machines (SVMs) for the problems of protein classification and remote homology detection. These kernels use probabilistic profiles, such as those produced by the PSI-BLAST algorithm, to define position-dependent mutation neighborhoods along protein sequences for inexact matching of k-length subsequences ("k-mers") in the data. By use of an efficient data structure, the kernels are fast to compute once the profiles have been obtained. For example, the time needed to run PSI-BLAST in order to build the profiles is significantly longer than both the kernel computation time and the SVM training time. We present remote homology detection experiments based on the SCOP database where we show that profile-based string kernels used with SVM classifiers strongly outperform all recently presented supervised SVM methods. We further examine how to incorporate predicted secondary structure information into the profile kernel to obtain a small but significant performance improvement. We also show how we can use the learned SVM classifier to extract "discriminative sequence motifs"--short regions of the original profile that contribute almost all the weight of the SVM classification score--and show that these discriminative motifs correspond to meaningful structural features in the protein data. The use of PSI-BLAST profiles can be seen as a semi-supervised learning technique, since PSI-BLAST leverages unlabeled data from a large sequence database to build more informative profiles. Recently presented "cluster kernels" give general semi-supervised methods for improving SVM protein classification performance. We show that our profile kernel results also outperform cluster kernels while providing much better scalability to large datasets.     

Characterization of Aerosolized Bacteria and Fungi From Desert Dust Events in Mali, West Africa

Millions of metric tons of African desert dust blow across the Atlantic Ocean each year, blanketing the Caribbean and southeastern United States. Previous work in the Caribbean has shown that atmospheric samples collected during dust events contain living microbes, including plant and opportunistic human pathogens. To better understand the potential downwind public health and ecosystem effects of the dust microbes, it is important to characterize the source population. We describe 19 genera of bacteria and 3 genera of fungi isolated from air samples collected in Mali, a known source region for dust storms, and over which large dust storms travel.