Design and evaluation of Actichip, a thematic microarray for the study of the actin cytoskeleton-0

<p><b>Copyright information:</b></p><p>Taken from "Design and evaluation of Actichip, a thematic microarray for the study of the actin cytoskeleton"</p><p>http://www.biomedcentral.com/1471-2164/8/294</p><p>BMC Genomics 2007;8():294-294.</p><p>Published online 29 Aug 2007</p><p>PMCID:PMC2077341.</p><p></p>3 replicates per RNA sample), the human oligonucleotide Operon set (4 replicates) and the Actichip arrays (10 replicates). Experiments were carried out using the same lots of RNAs extracted from human carcinoma MCF-7 cell line and from human skeletal muscle. Data were analysed as stated in the "Methods" section resulting in two groups of genes : "detected" and "not detected". The genes found similarly expressed or not expressed in less than 2/3 of the replicated assays were deemed "non reproducible". The histograms show the distribution of the three groups of genes for each platform relative to the samples. Results are expressed as percentages relative to the total number of genes simultaneously represented on each array platform

Le Bonhomme mayennais : journal de Lassay, Pré-en-Pail et cantons voisins ["puis" journal de Lassay, Pré-en-Pail, des cantons voisins et du département]...

11 septembre 18871887/09/11 (A4,N208)-1887/09/11.Appartient à l’ensemble documentaire : BNormand

Heatmap of transcription factors and Venn diagrams of microarrays data.

<p>(<b>A, B</b>) Heatmaps of all the differentially expressed transcription factors (source: Animal TFDB) from (<b>A</b>) human and (<b>B</b>) mouse microarrays data. (<b>C, D</b>) Venn diagrams showing the number of differentially expressed genes and transcription factors under LPS stimulation in (<b>C</b>) human PBMCs-derived macrophages and (<b>D</b>) mouse RAW264.7 macrophages.</p

Workflow for the identification of transcriptional regulators for a specific gene.

<p>The scheme shows the workflow of the different steps followed to identify potential transcriptional regulators for a given gene under LPS exposure. The upstream network was initially constructed using the PWMs from TRANSFAC^® and the prediction algorithm MATCH™. The literature network downstream of LPS was inferred using the Pathway Studio knowledge database. These networks were merged and the merged network was contextualised using the booleanised genome-wide expression data. Finally, the ranking scheme with simple paths and essentiality metric resulted in a set of potential transcriptional regulators which were tested using siRNA mediated gene silencing experiments.</p

siRNA <i>Irf1</i> mediated gene silencing in mouse RAW264.7 macrophages.

<p>(<b>A, B</b>) RAW264.7 cells were transfected with siRNA negative (siRNA NEG) or siRNA specific to <i>Irf1</i> (siRNA Irf1) 24 hours before treatment and RNA was extracted 2 hours after activation with LPS (10ng/ml). The bars show the mean of 3 biological replicates (± SEM) of (<b>A</b>) <i>Irf1</i> and (<b>B</b>) <i>Irg1</i> mRNA levels measured by real-time PCR normalised with L27 as the housekeeping gene. **p < 0.01, *p < 0.05. (<b>C</b>) Proteins were extracted from transfected cells after 4 hours of LPS stimulation. Western blot bands of IRF1, IRG1/CAD and β-ACTIN proteins are shown. (<b>D, E</b>) Metabolites were extracted from transfected cells after 4h of LPS stimulation and analysed by GC-MS. Itaconic acid levels in <i>Irf1</i> silenced cells were calculated as the percentage relative to the non-specific transfected cells in (<b>D</b>) control and (<b>E</b>) LPS activated cells. Error bars and statistical significance were calculated from 3 biological replicates (± SEM). *p < 0.05.</p

Transcription factors gene essentiality metric scores.

<p>Transcription factors gene essentiality metric scores.</p

Mitochondrial accumulation of IRG1/CAD under pro-inflammatory conditions.

<p>(<b>A</b>) Immunofluorescence images show maximum projections from 11 confocal planes. Scale bars indicate 20μm. Additionally magnified image regions and 2x magnified inlets are highlighted with yellow boxes. (<b>B</b>) IRG1/CAD was detected in nuclei, mitochondria and cytoplasmic vesicular structures. To quantify mitochondrial accumulation of IRG1/CAD, nuclear and mitochondrial proportions of single cell IRG1/CAD positive pixels were analysed. (<b>C</b>) Bars represent the median proportion of mitochondrial IRG1/CAD in untreated cells, LPS, IFNγ and LPS with IFNγ treated cells. Error bars show median absolute deviations. Statistical testing was done via two sided Wilcoxon rank sum tests. ***p < 0.001 (Wilcoxon rank sum test).</p

All possible paths from LPS to <i>IRG1</i> from the contextualised network.

<p>(<b>A</b>) Mouse and (<b>B</b>) human networks hierarchical layouts after calculating all possible paths between LPS and <i>IRG1</i> from the contextualised networks. <i>IRF1</i>, interferon regulatory factor 1; CEBPB, CCAAT/enhancer binding protein (C/EBP) beta; CEBPD, CCAAT/enhancer binding protein (C/EBP) delta; STAT1, signal transducer and activator of transcription 1; JUNB, Jun B proto-oncogene; PRDM1, PR domain containing 1 with ZNF domain; STAT4, signal transducer and activator of transcription 4; FOS, FBJ murine osteosarcoma viral oncogene homolog; ETS2, v-ets avian erythroblastosis virus E26 oncogene homolog 2; VDR, vitamin D (1,25-dihydroxyvitamin D3) receptor; RARA, retinoic acid receptor alpha; RUNX1, runt-related transcription factor 1. Solid line: activation; dashed line: inhibition.</p

siRNA <i>IRF1</i> mediated gene silencing in human PBMCs-derived macrophages.

<p>PBMCs-derived macrophages were transfected with siRNA negative (siRNA NEG) or siRNA specific to <i>IRF1</i> (siRNA IRF1) 24 hours before treatment and RNA was extracted 6 hours after activation with LPS (10μg/ml) in independent donors (D1-D3). The bars show the mean of (<b>A</b>) <i>IRF1</i> and (<b>B</b>) <i>IRG1</i> mRNA levels of 3 technical replicates (± SEM) measured by real-time PCR normalised with L27 as the housekeeping gene. *p < 0.05.</p

Comprehensive Analysis of Glycolytic Enzymes as Therapeutic Targets in the Treatment of Glioblastoma

<div><p>Major efforts have been put in anti-angiogenic treatment for glioblastoma (GBM), an aggressive and highly vascularized brain tumor with dismal prognosis. However clinical outcome with anti-angiogenic agents has been disappointing and tumors quickly develop escape mechanisms. In preclinical GBM models we have recently shown that bevacizumab, a blocking antibody against vascular endothelial growth factor, induces hypoxia in treated tumors, which is accompanied by increased glycolytic activity and tumor invasiveness. Genome-wide transcriptomic analysis of patient derived GBM cells including stem cell lines revealed a strong up-regulation of glycolysis-related genes in response to severe hypoxia. We therefore investigated the importance of glycolytic enzymes in GBM adaptation and survival under hypoxia, both in vitro and in vivo. We found that shRNA-mediated attenuation of glycolytic enzyme expression interfered with GBM growth under normoxic and hypoxic conditions in all cellular models. Using intracranial GBM xenografts we identified seven glycolytic genes whose knockdown led to a dramatic survival benefit in mice. The most drastic effect was observed for <i>PFKP</i> (PFK1, +21.8%) and <i>PDK1</i> (+20.9%), followed by <i>PGAM1</i> and <i>ENO1</i> (+14.5% each), <i>HK2</i> (+11.8%), <i>ALDOA</i> (+10.9%) and <i>ENO2</i> (+7.2%). The increase in mouse survival after genetic interference was confirmed using chemical inhibition of PFK1 with clotrimazole. We thus provide a comprehensive analysis on the importance of the glycolytic pathway for GBM growth in vivo and propose PFK1 and PDK1 as the most promising therapeutic targets to address the metabolic escape mechanisms of GBM.</p></div