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

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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

MYEOV (myeloma overexpressed gene) drives colon cancer cell migration and is regulated by PGE2

<p>Abstract</p> <p>Introduction</p> <p>We have previously reported that Myeov (MYEloma OVerexpressed gene) expression is enhanced in colorectal cancer (CRC) and that it promotes CRC cell proliferation and invasion. The role of Myeov in CRC migration is unclear. ProstaglandinE2 (PGE ₂) is a known factor in promoting CRC carcinogenesis. The role of PGE ₂in modulating Myeov expression has also not been defined.</p> <p>Aim</p> <p>To assess the role of Myeov expression in CRC cell migration and to evaluate the role of PGE ₂in Myeov bioactivity.</p> <p>Methods</p> <p>siRNA mediated Myeov knockdown was achieved in T84 CRC cells. Knockdown was assessed using quantitative real time PCR. The effect of knockdown on CRC cell migration was assessed using a scratch wound healing assay. Separately, T84 cells were treated with PGE ₂(0.00025 μ M, 0.1 μ M and 1 μ M) from 30 min to 3 hours and the effect on Myeov gene expression was assessed using real time PCR.</p> <p>Results</p> <p>Myeov knockdown resulted in a significant reduction in CRC cell migration, observable as early as 12 hours (P < 0.05) with a 39% reduction compared to control at 36 hours (p < 0.01). Myeov expression was enhanced after treatment with PGE ₂, with the greatest effect seen at 60 mins for all 3 PGE ₂doses. This response was dose dependent with a 290%, 550% & 1,000% increase in Myeov expression for 0.00025 μ M, 0.1 μ M and 1 μ M PGE ₂respectively.</p> <p>Conclusion</p> <p>In addition to promoting CRC proliferation and invasion, our findings indicate that Myeov stimulates CRC cell migration, and its expression may be PGE ₂dependant.</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

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

In Silico Promoter Analysis can Predict Genes of Functional Relevance in Cell Proliferation: Validation in a Colon Cancer Model

Specific combinations of transcription-factor binding sites in the promoter regions of genes regulate gene expression, and thus key functional processes in cells. Analysis of such promoter regions in specific functional contexts can be used to delineate novel disease-associated genes based on shared phenotypic properties. The aim of this study was to utilize promoter analysis to predict cell proliferation-associated genes and to test this method in colon cancer cell lines. We used freely-available bioinformatic techniques to identify cell-proliferation-associated genes expressed in colon cancer, extract a shared promoter module, and identify novel genes that also contain this module in the human genome. An EGRF/ETSF promoter module was identified as prevalent in proliferation-associated genes from a colon cancer cDNA library. We detected 30 other genes, from the known promoters of the human genome, which contained this proliferation-associated module. This group included known proliferation-associated genes, such as HERG1 and MCM7, and a number of genes not previously implicated in cell proliferation in cancer, such as TSPAN3, Necdin and APLP2. Suppression of TSPAN3 and APLP2 by siRNA was performed and confirmed by RT-PCR. Inhibition of these genes significantly inhibited cell proliferation in colon cancer cell lines. This study demonstrates that promoter analysis can be used to identify novel cancer-associated genes based on shared functional processes.Cancer Research IrelandThe journal is out of print. Publisher taken over by Sage. Copyright for Journal is with Sage. Journal is licensed under Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 - A