The role of TMEM16A (ANO1) and TMEM16F (ANO6) in cell migration

Members of the TMEM16 family have recently been described as Ca(2+)-activated Cl(−) channels. They have been implicated in cancer and appear to be associated with poor patient prognosis. Here, we investigate the role of TMEM16 channels in cell migration, which is largely unknown. We focused on TMEM16A and TMEM16F channels that have the highest expression of TMEM16 channels in Ehrlich Lettre ascites (ELA) cells. Due to the lack of specific pharmacological modulators, we employed a miRNA approach and stably knocked down the expression of TMEM16A and TMEM16F channels, respectively. Migration analysis shows that TMEM16A KD clones are affected in their directional migration, whereas TMEM16F KD clones show a 40 % reduced rate of cell migration. Moreover, TMEM16A KD clones have a smaller projected cell area, and they are rounder than TMEM16F KD clones. The morphological changes are linearly correlated with the directionality of cells. TMEM16A and TMEM16F, thus, have an important function in cell migration—TMEM16A in directional migration, TMEM16F in determination of the speed of migration. We conclude that TMEM16A and TMEM16F channels have a distinct impact on the steering and motor mechanisms of migrating ELA cells

The scaffolding protein NHERF1 sensitizes EGFR-dependent tumor growth, motility and invadopodia function to gefitinib treatment in breast cancer cells.

Triple negative breast cancer (TNBC) patients cannot be treated with endocrine therapy or targeted therapies due to lack of related receptors. These patients overexpress EGFR but are resistant to Tyrosine Kinase Inhibitors (TKIs) and anti-EGFR therapies. Mechanisms suggested for resistance to TKIs include EGFR independence, mutations and alterations in EGFR and in its downstream signalling pathways. Ligand-induced endocytosis and degradation of EGFR play important roles in the down-regulation of the EGFR signal suggesting that its activity could be regulated by targeting its trafficking. Evidence in normal cells showing that the scaffolding protein Na+/H+ Exchanger Regulatory Factor 1 (NHERF1) can associate with EGFR to regulate its trafficking, led us to hypothesize that NHERF1 expression levels could regulate EGFR trafficking and functional expression in TNBC cells and, in this way, modulate its role in progression and response to treatment. We investigated the subcellular localization of NHERF1 and its interaction with EGFR in a metastatic basal like TNBC cell model, MDA-MB-231, and the role of forced NHERF1 overexpression and/or stimulation with EGF on the sensitivity to EGFR specific TKI treatment with gefitinib. Stimulation with EGF induces an interaction of NHERF1 with EGFR to regulate its localization, degradation and function. NHERF1 overexpression is sufficient to drive its interaction with EGFR in non-stimulated conditions, inhibits EGFR degradation and increases its retention time in the plasma membrane. Importantly, NHERF1 overexpression strongly sensitized the cell to the pharmacological inhibition by gefitinib of EGFR-driven growth, motility and invadopodia-dependent ECM proteolysis. The further determination of how the NHERF1-EGFR interaction is regulated may improve our understanding of TNBC resistance to the action of existing anticancer drugs

RedundancyMiner: De-replication of redundant GO categories in microarray and proteomics analysis

<p>Abstract</p> <p>Background</p> <p>The Gene Ontology (GO) Consortium organizes genes into hierarchical categories based on biological process, molecular function and subcellular localization. Tools such as GoMiner can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. Two or more of the categories are often redundant, in the sense that identical or nearly-identical sets of genes map to the categories. The redundancy might typically inflate the report of significant categories by a factor of three-fold, create an illusion of an overly long list of significant categories, and obscure the relevant biological interpretation.</p> <p>Results</p> <p>We now introduce a new resource, RedundancyMiner, that de-replicates the redundant and nearly-redundant GO categories that had been determined by first running GoMiner. The main algorithm of RedundancyMiner, MultiClust, performs a novel form of cluster analysis in which a GO category might belong to several category clusters. Each category cluster follows a "complete linkage" paradigm. The metric is a similarity measure that captures the overlap in gene mapping between pairs of categories.</p> <p>Conclusions</p> <p>RedundancyMiner effectively eliminated redundancies from a set of GO categories. For illustration, we have applied it to the clarification of the results arising from two current studies: (1) assessment of the gene expression profiles obtained by laser capture microdissection (LCM) of serial cryosections of the retina at the site of final optic fissure closure in the mouse embryos at specific embryonic stages, and (2) analysis of a conceptual data set obtained by examining a list of genes deemed to be "kinetochore" genes.</p

Gene expression changes associated with Barrett's esophagus and Barrett's-associated adenocarcinoma cell lines after acid or bile salt exposure

<p>Abstract</p> <p>Background</p> <p>Esophageal reflux and Barrett's esophagus represent two major risk factors for the development of esophageal adenocarcinoma. Previous studies have shown that brief exposure of the Barrett's-associated adenocarcinoma cell line, SEG-1, or primary cultures of Barrett's esophageal tissues to acid or bile results in changes consistent with cell proliferation. In this study, we determined whether similar exposure to acid or bile salts results in gene expression changes that provide insights into malignant transformation.</p> <p>Methods</p> <p>Using previously published methods, Barrett's-associated esophageal adenocarcinoma cell lines and primary cultures of Barrett's esophageal tissue were exposed to short pulses of acid or bile salts followed by incubation in culture media at pH 7.4. A genome-wide assessment of gene expression was then determined for the samples using cDNA microarrays. Subsequent analysis evaluated for statistical differences in gene expression with and without treatment.</p> <p>Results</p> <p>The SEG-1 cell line showed changes in gene expression that was dependent on the length of exposure to pH 3.5. Further analysis using the Gene Ontology, however, showed that representation by genes associated with cell proliferation is not enhanced by acid exposure. The changes in gene expression also did not involve genes known to be differentially expressed in esophageal adenocarcinoma. Similar experiments using short-term primary cultures of Barrett's esophagus also did not result in detectable changes in gene expression with either acid or bile salt exposure.</p> <p>Conclusion</p> <p>Short-term exposure of esophageal adenocarcinoma SEG-1 cells or primary cultures of Barrett's esophagus does not result in gene expression changes that are consistent with enhanced cell proliferation. Thus other model systems are needed that may reflect the impact of acid and bile salt exposure on the esophagus <it>in vivo</it>.</p

High-Throughput GoMiner, an 'industrial-strength' integrative gene ontology tool for interpretation of multiple-microarray experiments, with application to studies of Common Variable Immune Deficiency (CVID)

BACKGROUND: We previously developed GoMiner, an application that organizes lists of 'interesting' genes (for example, under-and overexpressed genes from a microarray experiment) for biological interpretation in the context of the Gene Ontology. The original version of GoMiner was oriented toward visualization and interpretation of the results from a single microarray (or other high-throughput experimental platform), using a graphical user interface. Although that version can be used to examine the results from a number of microarrays one at a time, that is a rather tedious task, and original GoMiner includes no apparatus for obtaining a global picture of results from an experiment that consists of multiple microarrays. We wanted to provide a computational resource that automates the analysis of multiple microarrays and then integrates the results across all of them in useful exportable output files and visualizations. RESULTS: We now introduce a new tool, High-Throughput GoMiner, that has those capabilities and a number of others: It (i) efficiently performs the computationally-intensive task of automated batch processing of an arbitrary number of microarrays, (ii) produces a human-or computer-readable report that rank-orders the multiple microarray results according to the number of significant GO categories, (iii) integrates the multiple microarray results by providing organized, global clustered image map visualizations of the relationships of significant GO categories, (iv) provides a fast form of 'false discovery rate' multiple comparisons calculation, and (v) provides annotations and visualizations for relating transcription factor binding sites to genes and GO categories. CONCLUSION: High-Throughput GoMiner achieves the desired goal of providing a computational resource that automates the analysis of multiple microarrays and integrates results across all of the microarrays. For illustration, we show an application of this new tool to the interpretation of altered gene expression patterns in Common Variable Immune Deficiency (CVID). High-Throughput GoMiner will be useful in a wide range of applications, including the study of time-courses, evaluation of multiple drug treatments, comparison of multiple gene knock-outs or knock-downs, and screening of large numbers of chemical derivatives generated from a promising lead compound

Infectious Disease Ontology

Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and information. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology (IDO) suite of interoperable ontology modules that together cover the entire infectious disease domain

Glucocorticoids with different chemical structures but similar glucocorticoid receptor potency regulate subsets of common and unique genes in human trabecular meshwork cells

<p>Abstract</p> <p>Background</p> <p>In addition to their well-documented ocular therapeutic effects, glucocorticoids (GCs) can cause sight-threatening side-effects including ocular hypertension presumably via morphological and biochemical changes in trabecular meshwork (TM) cells. In the present study, we directly compared the glucocorticoid receptor (GR) potency for dexamethasone (DEX), fluocinolone acetonide (FA) and triamcinolone acetonide (TA), examined the expression of known GRα and GRβ isoforms, and used gene expression microarrays to compare the effects of DEX, FA, and TA on the complete transcriptome in two primary human TM cell lines.</p> <p>Methods</p> <p>GR binding affinity for DEX, FA, and TA was measured by a cell-free competitive radio-labeled GR binding assay. GR-mediated transcriptional activity was assessed using the GeneBLAzer beta-lactamase reporter gene assay. Levels of GRα and GRβ isoforms were assessed by Western blot. Total RNA was extracted from TM 86 and TM 93 cells treated with 1 μM DEX, FA, or TA for 24 hr and used for microarray gene expression analysis. The microarray experiments were repeated three times. Differentially expressed genes were identified by Rosetta Resolver Gene Expression Analysis System.</p> <p>Results</p> <p>The GR binding affinity (IC₅₀) for DEX, FA, and TA was 5.4, 2.0, and 1.5 nM, respectively. These values are similar to the GR transactivation EC₅₀of 3.0, 0.7, and 1.5 nM for DEX, FA, and TA, respectively. All four GRα translational isoforms (A-D) were expressed in TM 86 and TM 93 total cell lysates, however, the C and D isoforms were more highly expressed relative to A and B. All four GRβ isoforms (A-D) were also detected in TM cells, although GRβ-D isoform expression was lower compared to that of the A, B, or C isoforms. Microarray analysis revealed 1,968 and 1,150 genes commonly regulated by DEX, FA, and TA in TM 86 and TM 93, respectively. These genes included RGC32, OCA2, ANGPTL7, MYOC, FKBP5, SAA1 and ZBTB16. In addition, each GC specifically regulated a unique set of genes in both TM cell lines. Using Ingenuity Pathway Analysis (IPA) software, analysis of the data from TM 86 cells showed that DEX significantly regulated transcripts associated with RNA post-transcriptional modifications, whereas FA and TA modulated genes involved in lipid metabolism and cell morphology, respectively. In TM 93 cells, DEX significantly regulated genes implicated in histone methylation, whereas FA and TA altered genes associated with cell cycle and cell adhesion, respectively.</p> <p>Conclusion</p> <p>Human trabecular meshwork cells in culture express all known GRα and GRβ translational isoforms, and GCs with similar potency but subtly different chemical structure are capable of regulating common and unique gene subsets and presumably biologic responses in these cells. These GC structure-dependent effects appear to be TM cell-lineage dependent.</p

Adding a Little Reality to Building Ontologies for Biology

BACKGROUND: Many areas of biology are open to mathematical and computational modelling. The application of discrete, logical formalisms defines the field of biomedical ontologies. Ontologies have been put to many uses in bioinformatics. The most widespread is for description of entities about which data have been collected, allowing integration and analysis across multiple resources. There are now over 60 ontologies in active use, increasingly developed as large, international collaborations. There are, however, many opinions on how ontologies should be authored; that is, what is appropriate for representation. Recently, a common opinion has been the "realist" approach that places restrictions upon the style of modelling considered to be appropriate. METHODOLOGY/PRINCIPAL FINDINGS: Here, we use a number of case studies for describing the results of biological experiments. We investigate the ways in which these could be represented using both realist and non-realist approaches; we consider the limitations and advantages of each of these models. CONCLUSIONS/SIGNIFICANCE: From our analysis, we conclude that while realist principles may enable straight-forward modelling for some topics, there are crucial aspects of science and the phenomena it studies that do not fit into this approach; realism appears to be over-simplistic which, perversely, results in overly complex ontological models. We suggest that it is impossible to avoid compromise in modelling ontology; a clearer understanding of these compromises will better enable appropriate modelling, fulfilling the many needs for discrete mathematical models within computational biology

GeneSigDB: a manually curated database and resource for analysis of gene expression signatures

GeneSigDB (http://www.genesigdb.org or http://compbio.dfci.harvard.edu/genesigdb/) is a database of gene signatures that have been extracted and manually curated from the published literature. It provides a standardized resource of published prognostic, diagnostic and other gene signatures of cancer and related disease to the community so they can compare the predictive power of gene signatures or use these in gene set enrichment analysis. Since GeneSigDB release 1.0, we have expanded from 575 to 3515 gene signatures, which were collected and transcribed from 1604 published articles largely focused on gene expression in cancer, stem cells, immune cells, development and lung disease. We have made substantial upgrades to the GeneSigDB website to improve accessibility and usability, including adding a tag cloud browse function, facetted navigation and a ‘basket’ feature to store genes or gene signatures of interest. Users can analyze GeneSigDB gene signatures, or upload their own gene list, to identify gene signatures with significant gene overlap and results can be viewed on a dynamic editable heatmap that can be downloaded as a publication quality image. All data in GeneSigDB can be downloaded in numerous formats including .gmt file format for gene set enrichment analysis or as a R/Bioconductor data file. GeneSigDB is available from http://www.genesigdb.org