Proximity of Transmembrane Segments 5 and 8 of the Glutamate Transporter GLT-1 Inferred from Paired Cysteine Mutagenesis

BACKGROUND: GLT-1 is a glial glutamate transporter which maintains low synaptic concentrations of the excitatory neurotransmitter enabling efficient synaptic transmission. Based on the crystal structure of the bacterial homologue Glt(Ph), it has been proposed that the reentrant loop HP2, which connects transmembrane domains (TM) 7 and 8, moves to open and close access to the binding pocket from the extracellular medium. However the conformation change between TM5 and TM8 during the transport cycle is not clear yet. We used paired cysteine mutagenesis in conjunction with treatments with Copper(II)(1,10-Phenanthroline)(3) (CuPh), to verify the predicted proximity of residues located at these structural elements of GLT-1. METHODOLOGY/PRINCIPAL FINDINGS: To assess the proximity of transmembrane domain (TM) 5 relative to TM8 during transport by the glial glutamate transporter GLT-1/EAAT2, cysteine pairs were introduced at the extracellular ends of these structural elements. A complete inhibition of transport by Copper(II)(1,10-Phenanthroline)(3) is observed in the double mutants I295C/I463C and G297C/I463C, but not in the corresponding single mutants. Glutamate and potassium, both expected to increase the proportion of inward-facing transporters, significantly protected against the inhibition of transport activity of I295C/I463C and G297C/I463C by CuPh. Transport by the double mutants I295C/I463C and G297C/I463C also was inhibited by Cd(2+). CONCLUSIONS/SIGNIFICANCE: Our results suggest that TM5 (Ile-295, Gly-297) is in close proximity to TM8 (Ile-463) in the mammalian transporter, and that the spatial relationship between these domains is altered during the transport cycle

Ensemble approach for generalized network dismantling

Finding a set of nodes in a network, whose removal fragments the network below some target size at minimal cost is called network dismantling problem and it belongs to the NP-hard computational class. In this paper, we explore the (generalized) network dismantling problem by exploring the spectral approximation with the variant of the power-iteration method. In particular, we explore the network dismantling solution landscape by creating the ensemble of possible solutions from different initial conditions and a different number of iterations of the spectral approximation.Comment: 11 Pages, 4 Figures, 4 Table

Genome-Wide Transcript Profiling of Endosperm without Paternal Contribution Identifies Parent-of-Origin–Dependent Regulation of AGAMOUS-LIKE36

Seed development in angiosperms is dependent on the interplay among different transcriptional programs operating in the embryo, the endosperm, and the maternally-derived seed coat. In angiosperms, the embryo and the endosperm are products of double fertilization during which the two pollen sperm cells fuse with the egg cell and the central cell of the female gametophyte. In Arabidopsis, analyses of mutants in the cell-cycle regulator CYCLIN DEPENDENT KINASE A;1 (CKDA;1) have revealed the importance of a paternal genome for the effective development of the endosperm and ultimately the seed. Here we have exploited cdka;1 fertilization as a novel tool for the identification of seed regulators and factors involved in parent-of-origin–specific regulation during seed development. We have generated genome-wide transcription profiles of cdka;1 fertilized seeds and identified approximately 600 genes that are downregulated in the absence of a paternal genome. Among those, AGAMOUS-LIKE (AGL) genes encoding Type-I MADS-box transcription factors were significantly overrepresented. Here, AGL36 was chosen for an in-depth study and shown to be imprinted. We demonstrate that AGL36 parent-of-origin–dependent expression is controlled by the activity of METHYLTRANSFERASE1 (MET1) maintenance DNA methyltransferase and DEMETER (DME) DNA glycosylase. Interestingly, our data also show that the active maternal allele of AGL36 is regulated throughout endosperm development by components of the FIS Polycomb Repressive Complex 2 (PRC2), revealing a new type of dual epigenetic regulation in seeds

Experimental investigation of three machine learning algorithms for ITS dataset

The present article is devoted to experimental investigation of the performance of three machine learning algorithms for ITS dataset in their ability to achieve agreement with classes published in the biologi cal literature before. The ITS dataset consists of nuclear ribosomal DNA sequences, where rather sophisticated alignment scores have to be used as a measure of distance. These scores do not form a Minkowski metric and the sequences cannot be regarded as points in a finite dimensional space. This is why it is necessary to develop novel machine learning ap proaches to the analysis of datasets of this sort. This paper introduces a k-committees classifier and compares it with the discrete k-means and Nearest Neighbour classifiers. It turns out that all three machine learning algorithms are efficient and can be used to automate future biologically significant classifications for datasets of this kind. A simplified version of a synthetic dataset, where the k-committees classifier outperforms k-means and Nearest Neighbour classifiers, is also presented

Consensus clustering and supervised classification for profiling phishing emails in internet commerce security

This article investigates internet commerce security applications of a novel combined method, which uses unsupervised consensus clustering algorithms in combination with supervised classification methods. First, a variety of independent clustering algorithms are applied to a randomized sample of data. Second, several consensus functions and sophisticated algorithms are used to combine these independent clusterings into one final consensus clustering. Third, the consensus clustering of the randomized sample is used as a training set to train several fast supervised classification algorithms. Finally, these fast classification algorithms are used to classify the whole large data set. One of the advantages of this approach is in its ability to facilitate the inclusion of contributions from domain experts in order to adjust the training set created by consensus clustering. We apply this approach to profiling phishing emails selected from a very large data set supplied by the industry partners of the Centre for Informatics and Applied Optimization. Our experiments compare the performance of several classification algorithms incorporated in this scheme. © 2010 Springer-Verlag Berlin Heidelberg