In silico gene expression analysis – an overview

Efforts aimed at deciphering the molecular basis of complex disease are underpinned by the availability of high throughput strategies for the identification of biomolecules that drive the disease process. The completion of the human genome-sequencing project, coupled to major technological developments, has afforded investigators myriad opportunities for multidimensional analysis of biological systems. Nowhere has this research explosion been more evident than in the field of transcriptomics. Affordable access and availability to the technology that supports such investigations has led to a significant increase in the amount of data generated. As most biological distinctions are now observed at a genomic level, a large amount of expression information is now openly available via public databases. Furthermore, numerous computational based methods have been developed to harness the power of these data. In this review we provide a brief overview of in silico methodologies for the analysis of differential gene expression such as Serial Analysis of Gene Expression and Digital Differential Display. The performance of these strategies, at both an operational and result/output level is assessed and compared. The key considerations that must be made when completing an in silico expression analysis are also presented as a roadmap to facilitate biologists. Furthermore, to highlight the importance of these in silico methodologies in contemporary biomedical research, examples of current studies using these approaches are discussed. The overriding goal of this review is to present the scientific community with a critical overview of these strategies, so that they can be effectively added to the tool box of biomedical researchers focused on identifying the molecular mechanisms of disease

A novel technique for identifying the individual regions of the human colon at CT colonography

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NET1-mediated RhoA activation facilitates lysophosphatidic acid-induced cell migration and invasion in gastric cancer

The most lethal aspects of gastric adenocarcinoma (GA) are its invasive and metastatic properties. This aggressive phenotype remains poorly understood. We have recently identified neuroepithelial cell transforming gene 1 (NET1), a guanine exchange factor (GEF), as a novel GA-associated gene. Neuroepithelial cell transforming gene 1 expression is enhanced in GA and it is of functional importance in cell invasion. In this study, we demonstrate the activity of NET1 in driving cytoskeletal rearrangement, a key pathological mechanism in gastric tumour cell migration and invasion. Neuroepithelial cell transforming gene 1 expression was increased 10-fold in response to treatment with lysophosphatidic acid (LPA), resulting in an increase in active levels of RhoA and a 2-fold increase in cell invasion. Lysophosphatidic acid-induced cell invasion and migration were significantly inhibited using either NET1 siRNA or a RhoA inhibitor (C3 exoenzyme), thus indicating the activity of both NET1 and RhoA in gastric cancer progression. Furthermore, LPA-induced invasion and migration were also significantly reduced in the presence of cytochalasin D, an inhibitor of cytoskeletal rearrangements. Neuroepithelial cell transforming gene 1 knockdown resulted in AGS cell rounding and a loss of actin filament organisation, demonstrating the function of NET1 in actin organisation. These data highlight the importance of NET1 as a driver of tumour cell invasion, an activity mediated by RhoA activation and cytoskeletal reorganisation

A functional and transcriptomic analysis of NET1 bioactivity in gastric cancer

<p>Abstract</p> <p>Background</p> <p>NET1, a RhoA guanine exchange factor, is up-regulated in gastric cancer (GC) tissue and drives the invasive phenotype of this disease. In this study, we aimed to determine the role of NET1 in GC by monitoring the proliferation, motility and invasion of GC cells in which NET1 has been stably knocked down. Additionally, we aimed to determine NET1-dependent transcriptomic events that occur in GC.</p> <p>Methods</p> <p>An in vitro model of stable knockdown of NET1 was achieved in AGS human gastric adenocarcinoma cells via lentiviral mediated transduction of short-hairpin (sh) RNA targeting NET1. Knockdown was assessed using quantitative PCR. Cell proliferation was assessed using an MTS assay and cell migration was assessed using a wound healing scratch assay. Cell invasion was assessed using a transwell matrigel invasion assay. Gene expression profiles were examined using affymetrix oligonucleotide U133A expression arrays. A student's t test was used to determine changes of statistical significance.</p> <p>Results</p> <p>GC cells were transduced with NET1 shRNA resulting in a 97% reduction in NET1 mRNA (p < 0.0001). NET1 knockdown significantly reduced the invasion and migration of GC cells by 94% (p < 0.05) and 24% (p < 0.001) respectively, while cell proliferation was not significantly altered following NET1 knockdown. Microarray analysis was performed on non-target and knockdown cell lines, treated with and without 10 μM lysophosphatidic acid (LPA) allowing us to identify NET1-dependent, LPA-dependent and NET1-mediated LPA-induced gene transcription. Differential gene expression was confirmed by quantitative PCR. Shortlisted NET1-dependent genes included STAT1, TSPAN1, TGFBi and CCL5 all of which were downregulatd upon NET1 downregulation. Shortlisted LPA-dependent genes included EGFR and PPARD where EGFR was upregulated and PPARD was downregulated upon LPA stimulation. Shortlisted NET1 and LPA dependent genes included IGFR1 and PIP5K3. These LPA induced genes were downregulated in NET1 knockdown cells.</p> <p>Conclusions</p> <p>NET1 plays an important role in GC cell migration and invasion, key aspects of GC progression. Furthermore, the gene expression profile further elucidates the molecular mechanisms underpinning NET1-mediated aggressive GC cell behaviour.</p

Common polygenic variation in coeliac disease and confirmation of ZNF335 and NIFA as disease susceptibility loci

Coeliac disease (CD) is a chronic immune-mediated disease triggered by the ingestion of gluten. It has an estimated prevalence of approximately 1% in European populations. Specific HLA-DQA1 and HLA-DQB1 alleles are established coeliac susceptibility genes and are required for the presentation of gliadin to the immune system resulting in damage to the intestinal mucosa. In the largest association analysis of CD to date, 39 non-HLA risk loci were identified, 13 of which were new, in a sample of 12 014 individuals with CD and 12 228 controls using the Immunochip genotyping platform. Including the HLA, this brings the total number of known CD loci to 40. We have replicated this study in an independent Irish CD case–control population of 425 CD and 453 controls using the Immunochip platform. Using a binomial sign test, we show that the direction of the effects of previously described risk alleles were highly correlated with those reported in the Irish population, (P=2.2 × 10−16). Using the Polygene Risk Score (PRS) approach, we estimated that up to 35% of the genetic variance could be explained by loci present on the Immunochip (P=9 × 10−75). When this is limited to non-HLA loci, we explain a maximum of 4.5% of the genetic variance (P=3.6 × 10−18). Finally, we performed a meta-analysis of our data with the previous reports, identifying two further loci harbouring the ZNF335 and NIFA genes which now exceed genome-wide significance, taking the total number of CD susceptibility loci to 42

Net1 and Myeov: computationally identified mediators of gastric cancer

Gastric adenocarcinoma (GA) is a significant cause of mortality worldwide. The molecular mechanisms of GA remain poorly characterised. Our aim was to characterise the functional activity of the computationally identified genes, NET 1 and MYEOV in GA. Digital Differential Display was used to identify genes altered expression in GA-derived EST libraries. mRNA levels of a subset of genes were quantitated by qPCR in a panel of cell lines and tumour tissue. The effect of pro- and anti-inflammatory stimuli on gene expression was investigated. Cell proliferation and invasion were measured using in an in-vitro GA model following inhibition of expression using siRNA. In all, 23 genes not previously reported in association with GA were identified. Two genes, Net1 and Myeov, were selected for further analysis and increased expression was detected in GA tissue compared to paired normal tissue using quantitative PCR. siRNA-mediated downregulation of Net1 and Myeov resulted in decreased proliferation and invasion of gastric cancer cells in vitro. These functional studies highlight a putative role for NET1 and Myeov in the development and progression of gastric cancer. These genes may provide important targets for intervention in GA, evidenced by their role in promoting invasion and proliferation, key phenotypic hallmarks of cancer cells