Differential expression of heat shock proteins in Drosophila embryonic cells following metal ion exposure

Drosophila embryonic cells were exposed to a number of metal ions that have been previously reported to act as teratogens in mammalian systems, including some known to induce heat shock (stress) proteins in a variety of model systems. This study examined the effects of these ions both on differentiation of muscles and neurons and on the induction of heat shock proteins. Metals such as arsenate, cadmium, and mercury all inhibited neuron and/or muscle differentiation in Drosophila embryonic cultures, while they also induced the entire set of heat shock proteins. Two metal ions, nickel and zinc, were shown to induce only the 22-and 23-K proteins, a pattern similar to that seen in “classical” teratogens reported previously. None of the metals tested induced only the 26-and 27-K proteins. These results suggest that there exist different regulatory mechanisms responsible for the heat shock response.     

Trehalose-6-phosphate-mediated phenotypic change in Acinetobacter baumannii

The stress protectant trehalose is synthesized in Acinetobacter baumannii from UPD-glucose and glucose-6-phosphase via the OtsA/OtsB pathway. Previous studies proved that deletion of otsB led to a decreased virulence, the inability to grow at 45°C and a slight reduction of growth at high salinities indicating that trehalose is the cause of these phenotypes. We have questioned this conclusion by producing ∆otsA and ∆otsBA mutants and studying their phenotypes. Only deletion of otsB, but not deletion of otsA or otsBA, led to growth impairments at high salt and high temperature. The intracellular concentrations of trehalose and trehalose-6-phosphate were measured by NMR or enzymatic assay. Interestingly, none of the mutants accumulated trehalose any more but the ∆otsB mutant with its defect in trehalose-6-phosphate phosphatase activity accumulated trehalose-6-phosphate. Moreover, expression of otsA in a ∆otsB background under conditions where trehalose synthesis is not induced led to growth inhibition and the accumulation of trehalose-6-phosphate. Our results demonstrate that trehalose-6-phosphate affects multiple physiological activities in A. baumannii ATCC 19606.     

Molecular docking analysis of beta-catenin with compounds derived from Lycopersicon esculentum

Beta-catenin is linked with colorectal cancer (CRC). Therefore, it is of interest to design and develop novel compounds to combat CRC. Hence, we document compounds (chlorogenic acid, gallic acid, protocatechuic acid, quercetin and vanillic acid) from Lycopersicon esculentum with optimal binding features for further consideration.     

Using Manifold Learning for Atlas Selection in Multi-Atlas Segmentation

Multi-atlas segmentation has been widely used to segment various anatomical structures. The success of this technique partly relies on the selection of atlases that are best mapped to a new target image after registration. Recently, manifold learning has been proposed as a method for atlas selection. Each manifold learning technique seeks to optimize a unique objective function. Therefore, different techniques produce different embeddings even when applied to the same data set. Previous studies used a single technique in their method and gave no reason for the choice of the manifold learning technique employed nor the theoretical grounds for the choice of the manifold parameters. In this study, we compare side-by-side the results given by 3 manifold learning techniques (Isomap, Laplacian Eigenmaps and Locally Linear Embedding) on the same data set. We assess the ability of those 3 different techniques to select the best atlases to combine in the framework of multi-atlas segmentation. First, a leave-one-out experiment is used to optimize our method on a set of 110 manually segmented atlases of hippocampi and find the manifold learning technique and associated manifold parameters that give the best segmentation accuracy. Then, the optimal parameters are used to automatically segment 30 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). For our dataset, the selection of atlases with Locally Linear Embedding gives the best results. Our findings show that selection of atlases with manifold learning leads to segmentation accuracy close to or significantly higher than the state-of-the-art method and that accuracy can be increased by fine tuning the manifold learning process.