A Versatile ΦC31 Based Reporter System for Measuring AP-1 and Nrf2 Signaling in Drosophila and in Tissue Culture

This paper describes the construction and characterization of a system of transcriptional reporter genes for monitoring the activity of signaling pathways and gene regulation mechanisms in intact Drosophila, dissected tissues or cultured cells. Transgenic integration of the reporters into the Drosophila germline was performed in a site-directed manner, using ΦC31 integrase. This strategy avoids variable position effects and assures low base level activity and high signal responsiveness. Defined integration sites furthermore enable the experimenter to compare the activity of different reporters in one organism. The reporter constructs have a modular design to facilitate the combination of promoter elements (synthetic transcription factor binding sites or natural regulatory sequences), reporter genes (eGFP, or DsRed.T4), and genomic integration sites. The system was used to analyze and compare the activity and signal response profiles of two stress inducible transcription factors, AP-1 and Nrf2. To complement the transgenic reporter fly lines, tissue culture assays were developed in which the same synthetic ARE and TRE elements control the expression of firefly luciferase

A domain-based approach to predict protein-protein interactions

<p>Abstract</p> <p>Background</p> <p>Knowing which proteins exist in a certain organism or cell type and how these proteins interact with each other are necessary for the understanding of biological processes at the whole cell level. The determination of the protein-protein interaction (PPI) networks has been the subject of extensive research. Despite the development of reasonably successful methods, serious technical difficulties still exist. In this paper we present DomainGA, a quantitative computational approach that uses the information about the domain-domain interactions to predict the interactions between proteins.</p> <p>Results</p> <p>DomainGA is a multi-parameter optimization method in which the available PPI information is used to derive a quantitative scoring scheme for the domain-domain pairs. Obtained domain interaction scores are then used to predict whether a pair of proteins interacts. Using the yeast PPI data and a series of tests, we show the robustness and insensitivity of the DomainGA method to the selection of the parameter sets, score ranges, and detection rules. Our DomainGA method achieves very high explanation ratios for the positive and negative PPIs in yeast. Based on our cross-verification tests on human PPIs, comparison of the optimized scores with the structurally observed domain interactions obtained from the iPFAM database, and sensitivity and specificity analysis; we conclude that our DomainGA method shows great promise to be applicable across multiple organisms.</p> <p>Conclusion</p> <p>We envision the DomainGA as a first step of a multiple tier approach to constructing organism specific PPIs. As it is based on fundamental structural information, the DomainGA approach can be used to create potential PPIs and the accuracy of the constructed interaction template can be further improved using complementary methods. Explanation ratios obtained in the reported test case studies clearly show that the false prediction rates of the template networks constructed using the DomainGA scores are reasonably low, and the erroneous predictions can be filtered further using supplementary approaches such as those based on literature search or other prediction methods.</p

A discriminative method for protein remote homology detection and fold recognition combining Top-n-grams and latent semantic analysis

<p>Abstract</p> <p>Background</p> <p>Protein remote homology detection and fold recognition are central problems in bioinformatics. Currently, discriminative methods based on support vector machine (SVM) are the most effective and accurate methods for solving these problems. A key step to improve the performance of the SVM-based methods is to find a suitable representation of protein sequences.</p> <p>Results</p> <p>In this paper, a novel building block of proteins called Top-<it>n</it>-grams is presented, which contains the evolutionary information extracted from the protein sequence frequency profiles. The protein sequence frequency profiles are calculated from the multiple sequence alignments outputted by PSI-BLAST and converted into Top-<it>n</it>-grams. The protein sequences are transformed into fixed-dimension feature vectors by the occurrence times of each Top-<it>n</it>-gram. The training vectors are evaluated by SVM to train classifiers which are then used to classify the test protein sequences. We demonstrate that the prediction performance of remote homology detection and fold recognition can be improved by combining Top-<it>n</it>-grams and latent semantic analysis (LSA), which is an efficient feature extraction technique from natural language processing. When tested on superfamily and fold benchmarks, the method combining Top-<it>n</it>-grams and LSA gives significantly better results compared to related methods.</p> <p>Conclusion</p> <p>The method based on Top-<it>n</it>-grams significantly outperforms the methods based on many other building blocks including N-grams, patterns, motifs and binary profiles. Therefore, Top-<it>n</it>-gram is a good building block of the protein sequences and can be widely used in many tasks of the computational biology, such as the sequence alignment, the prediction of domain boundary, the designation of knowledge-based potentials and the prediction of protein binding sites.</p

Genetic Evidence for Inhibition of Bacterial Division Protein FtsZ by Berberine

Background: Berberine is a plant alkaloid that is widely used as an anti-infective in traditional medicine. Escherichia coli exposed to berberine form filaments, suggesting an antibacterial mechanism that involves inhibition of cell division. Berberine is a DNA ligand and may induce filamentation through induction of the SOS response. Also, there is biochemical evidence for berberine inhibition of the cell division protein FtsZ. Here we aimed to assess possible berberine mechanism(s) of action in growing bacteria using genetics tools. Methodology/Principal Findings: First, we tested whether berberine inhibits bacterial growth through DNA damage and induction of the SOS response. The SOS response induced by berberine was much lower compared to that induced by mitomycin C in an SOS response reporter strain. Also, cell filamentation was observed in an SOS-negative E. coli strain. To test whether berberine inhibits FtsZ, we assessed its effects on formation of the cell division Z-rings, and observed a dramatic reduction in Z-rings in the presence of berberine. We next used two different strategies for RNA silencing of ftsZ and both resulted in sensitisation of bacteria to berberine, visible as a drop in the Minimum Inhibitory Concentration (MIC). Furthermore, Fractional Inhibitory Concentration Indices (FICIs) showed a high level of synergy between ftsZ silencing and berberine treatment (FICI values of 0.23 and 0.25 for peptide nucleic acid- and expressed antisense RNA-based silencing of ftsZ, respectively). Finally, over-expression of ftsZ led to a mild rescue effect in berberine-treated cells

Metformin and the gastrointestinal tract

Metformin is an effective agent with a good safety profile that is widely used as a first-line treatment for type 2 diabetes, yet its mechanisms of action and variability in terms of efficacy and side effects remain poorly understood. Although the liver is recognised as a major site of metformin pharmacodynamics, recent evidence also implicates the gut as an important site of action. Metformin has a number of actions within the gut. It increases intestinal glucose uptake and lactate production, increases GLP-1 concentrations and the bile acid pool within the intestine, and alters the microbiome. A novel delayed-release preparation of metformin has recently been shown to improve glycaemic control to a similar extent to immediate-release metformin, but with less systemic exposure. We believe that metformin response and tolerance is intrinsically linked with the gut. This review examines the passage of metformin through the gut, and how this can affect the efficacy of metformin treatment in the individual, and contribute to the side effects associated with metformin intolerance

Interferon-γ Regulates the Proliferation and Differentiation of Mesenchymal Stem Cells via Activation of Indoleamine 2,3 Dioxygenase (IDO)

The kynurenine pathway (KP) of tryptophan metabolism is linked to antimicrobial activity and modulation of immune responses but its role in stem cell biology is unknown. We show that human and mouse mesenchymal and neural stem cells (MSCs and NSCs) express the complete KP, including indoleamine 2,3 dioxygenase 1 (IDO) and IDO2, that it is highly regulated by type I (IFN-β) and II interferons (IFN-γ), and that its transcriptional modulation depends on the type of interferon, cell type and species. IFN-γ inhibited proliferation and altered human and mouse MSC neural, adipocytic and osteocytic differentiation via the activation of IDO. A functional KP present in MSCs, NSCs and perhaps other stem cell types offers novel therapeutic opportunities for optimisation of stem cell proliferation and differentiation

Illuminating the life of GPCRs

The investigation of biological systems highly depends on the possibilities that allow scientists to visualize and quantify biomolecules and their related activities in real-time and non-invasively. G-protein coupled receptors represent a family of very dynamic and highly regulated transmembrane proteins that are involved in various important physiological processes. Since their localization is not confined to the cell surface they have been a very attractive "moving target" and the understanding of their intracellular pathways as well as the identified protein-protein-interactions has had implications for therapeutic interventions. Recent and ongoing advances in both the establishment of a variety of labeling methods and the improvement of measuring and analyzing instrumentation, have made fluorescence techniques to an indispensable tool for GPCR imaging. The illumination of their complex life cycle, which includes receptor biosynthesis, membrane targeting, ligand binding, signaling, internalization, recycling and degradation, will provide new insights into the relationship between spatial receptor distribution and function. This review covers the existing technologies to track GPCRs in living cells. Fluorescent ligands, antibodies, auto-fluorescent proteins as well as the evolving technologies for chemical labeling with peptide- and protein-tags are described and their major applications concerning the GPCR life cycle are presented

Development and application of an interaction network ontology for literature mining of vaccine-associated gene-gene interactions

Abstract Background Literature mining of gene-gene interactions has been enhanced by ontology-based name classifications. However, in biomedical literature mining, interaction keywords have not been carefully studied and used beyond a collection of keywords. Methods In this study, we report the development of a new Interaction Network Ontology (INO) that classifies >800 interaction keywords and incorporates interaction terms from the PSI Molecular Interactions (PSI-MI) and Gene Ontology (GO). Using INO-based literature mining results, a modified Fisher’s exact test was established to analyze significantly over- and under-represented enriched gene-gene interaction types within a specific area. Such a strategy was applied to study the vaccine-mediated gene-gene interactions using all PubMed abstracts. The Vaccine Ontology (VO) and INO were used to support the retrieval of vaccine terms and interaction keywords from the literature. Results INO is aligned with the Basic Formal Ontology (BFO) and imports terms from 10 other existing ontologies. Current INO includes 540 terms. In terms of interaction-related terms, INO imports and aligns PSI-MI and GO interaction terms and includes over 100 newly generated ontology terms with ‘INO_’ prefix. A new annotation property, ‘has literature mining keywords’, was generated to allow the listing of different keywords mapping to the interaction types in INO. Using all PubMed documents published as of 12/31/2013, approximately 266,000 vaccine-associated documents were identified, and a total of 6,116 gene-pairs were associated with at least one INO term. Out of 78 INO interaction terms associated with at least five gene-pairs of the vaccine-associated sub-network, 14 terms were significantly over-represented (i.e., more frequently used) and 17 under-represented based on our modified Fisher’s exact test. These over-represented and under-represented terms share some common top-level terms but are distinct at the bottom levels of the INO hierarchy. The analysis of these interaction types and their associated gene-gene pairs uncovered many scientific insights. Conclusions INO provides a novel approach for defining hierarchical interaction types and related keywords for literature mining. The ontology-based literature mining, in combination with an INO-based statistical interaction enrichment test, provides a new platform for efficient mining and analysis of topic-specific gene interaction networks.http://deepblue.lib.umich.edu/bitstream/2027.42/110806/1/13326_2014_Article_197.pd