Mouse model of the human serotonin transporter-linked polymorphic region

Genetic factors play a significant role in risk for mood and anxiety disorders. Polymorphisms in genes that regulate the brain monoamine systems, such as catabolic enzymes and transporters, are attractive candidates for being risk factors for emotional disorders given the weight of evidence implicating monoamines involvement in these conditions. Several common genetic variants have been identified in the human serotonin transporter (5-HTT) gene, including a repetitive sequence located in the promoter region of the locus called the serotonin transporter-linked polymorphic region (5-HTT-LPR). This polymorphism has been associated with a number of mental traits in both humans and primates, including depression, neuroticism, and harm avoidance. Some, but not all, studies found a link between the polymorphism and 5-HTT levels, leaving open the question of whether the polymorphism affects risk for mental traits via changes in 5-HTT expression. To investigate the impact of the polymorphism on gene expression, serotonin homeostasis, and behavioral traits, we set out to develop a mouse model of the human 5-HTT-LPR. Here we describe the creation and characterization of a set of mouse lines with single-copy human transgenes carrying the short and long 5-HTT-LPR variants. Although we were not able to detect differences in expression between the short and long variants, we encountered several technical issues concerning the design of our humanized mice that are likely to have influenced our findings. Our study serves as a cautionary note for future studies aimed at studying human transgene regulation in the context of the living mouse.

     

Quantifying the effects of long-range 13C-13C dipolar coupling on measured relaxation rates in RNA

Journal of Biomolecular NMR - Selective stable isotope labeling has transformed structural and dynamics analysis of RNA by NMR spectroscopy. These methods can remove 13C-13C dipolar couplings that...     

Splicing and the evolution of proteins in mammals

It is often supposed that a protein's rate of evolution and its amino acid content are determined by the function and anatomy of the protein. Here we examine an alternative possibility, namely that the requirement to specify in the unprocessed RNA, in the vicinity of intron–exon boundaries, information necessary for removal of introns (e.g., exonic splice enhancers) affects both amino acid usage and rates of protein evolution. We find that the majority of amino acids show skewed usage near intron–exon boundaries, and that differences in the trends for the 2-fold and 4-fold blocks of both arginine and leucine show this to be owing to effects mediated at the nucleotide level. More specifically, there is a robust relationship between the extent to which an amino acid is preferred/avoided near boundaries and its enrichment/paucity in splice enhancers. As might then be expected, the rate of evolution is lowest near intron–exon boundaries, at least in part owing to splice enhancers, such that domains flanking intron–exon junctions evolve on average at under half the rate of exon centres from the same gene. In contrast, the rate of evolution of intronless retrogenes is highest near the domains where intron–exon junctions previously resided. The proportion of sequence near intron–exon boundaries is one of the stronger predictors of a protein's rate of evolution in mammals yet described. We conclude that after intron insertion selection favours modification of amino acid content near intron–exon junctions, so as to enable efficient intron removal, these changes then being subject to strong purifying selection even if nonoptimal for protein function. Thus there exists a strong force operating on protein evolution in mammals that is not explained directly in terms of the biology of the protein.     

Insights into catalytic activity of industrial enzyme Co-nitrile hydratase. Docking studies of nitriles and amides

Nitrile hydratase (NHase) is an enzyme containing non-corrin Co³⁺ in the non-standard active site. NHases from Pseudonocardia thermophila JCM 3095 catalyse hydration of nitriles to corresponding amides. The efficiency of the enzyme is 100 times higher for aliphatic nitriles then aromatic ones. In order to understand better this selectivity dockings of a series of aliphatic and aromatic nitriles and related amides into a model protein based on an X-ray structure were performed. Substantial differences in binding modes were observed, showing better conformational freedom of aliphatic compounds. Distinct interactions with postranslationally modified cysteines present in the active site of the enzyme were observed. Modeling shows that water molecule activated by a metal ion may easily directly attack the docked acrylonitrile to transform this molecule into acryloamide. Thus docking studies provide support for one of the reaction mechanisms discussed in the literature. Figure Crystalographic structure of Pseudonocardia thermophila JCM 3095 nitrile hydratase (a) and the non-standard active site (b)     

Submit your interaction data the IMEx way: a step by step guide to trouble-free deposition

The ever-increasing generation of, and corresponding interest in, molecular interaction data has lead to the establishment of a number of high-quality molecular interaction databases which manually curate interaction data extracted from the literature. In order to effectively share the curation load, and ensure that data is stored in and accessible from multiple sources, these databases have united to form the IMEx consortium. All of the IMEx databases also accept direct deposition of interaction data from authors prior to publication, thus both assisting the scientist in preparing the dataset for publication and ensuring that its subsequent representation in the public domain databases is fully accurate. This article walks the potential submitter through the various routes by which data may be deposited with the databases and describes the tools which have been developed to assist in this process.     

Identifying Statistical Dependence in Genomic Sequences via Mutual Information Estimates

Questions of understanding and quantifying the representation and amount of information in organisms have become a central part of biological research, as they potentially hold the key to fundamental advances. In this paper, we demonstrate the use of information-theoretic tools for the task of identifying segments of biomolecules (DNA or RNA) that are statistically correlated. We develop a precise and reliable methodology, based on the notion of mutual information, for finding and extracting statistical as well as structural dependencies. A simple threshold function is defined, and its use in quantifying the level of significance of dependencies between biological segments is explored. These tools are used in two specific applications. First, they are used for the identification of correlations between different parts of the maize zmSRp32 gene. There, we find significant dependencies between the untranslated region in zmSRp32 and its alternatively spliced exons. This observation may indicate the presence of as-yet unknown alternative splicing mechanisms or structural scaffolds. Second, using data from the FBI''s combined DNA index system (CODIS), we demonstrate that our approach is particularly well suited for the problem of discovering short tandem repeats—an application of importance in genetic profiling.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22]     

New derivatives of 11-methyl-6-[2-(dimethylamino)ethyl]-6H-indolo[2,3-b]quinoline as cytotoxic DNA topoisomerase II inhibitors

Novel indolo[2,3-b]quinoline derivatives substituted at N-6 and C-2 or C-9 positions with (dimethylamino)ethyl chains linked to heteroaromatic core by ether, amide or amine bonds, were manufactured and evaluated in vitro for their cytotoxic activity against several cell lines of different origin including multidrug resistant sublines and tested for their ability to influence the cell cycle and inhibit topoisomerase II activity. It was found, that all compounds show cytotoxic activity against cell lines tested, including multidrug resistant LoVo/DX, MES-SA/DX5 and HL-60 sublines. The tested compounds induce the G(2)M phase cell cycle arrest in Jurkat cells, and inhibit topoisomerase II activity.     

Finding Protein Targets for Small Biologically Relevant Ligands across Fold Space Using Inverse Ligand Binding Predictions

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Highlights? Inverse ligand binding prediction is implemented with modern binding site predictors ? Use of just one protein-ligand complex allows accurate identification of other protein targets ? Inverse prediction is effective in finding structurally distant protein targets ? Consensus approach provides improvements