A CMV-H1 hybrid construct driving shLuc provides enhanced inhibition of a co-transfected target gene in the newborn (a and b) and in the adult (c) mouse brain

<p><b>Copyright information:</b></p><p>Taken from "A hybrid CMV-H1 construct improves efficiency of PEI-delivered shRNA in the mouse brain"</p><p></p><p>Nucleic Acids Research 2007;35(9):e65-e65.</p><p>Published online 10 Apr 2007</p><p>PMCID:PMC1888798.</p><p>© 2007 The Author(s)</p> () Dose dependence of CMV-H1-shLuc efficiency. The inhibition efficiency of CMV-H1-shLuc was tested at different doses ranging from 0.1 to 0.4 µg/µl. After co-transfection of pGL2-CMV and pRL-CMV along with 0.4 µg/µl of CMV-H1-shLuc (i.e. 0.8 µg/hemisphere), we observed up to 50% inhibition of the targeted luciferase expression. This level of inhibition was obtained at 48 h post-transfection. () Time course efficiency of CMV-H1-shLuc. Significant inhibition of the target gene with 0.4 µg/µl of CMV-H1-shLuc was seen at all times tested. The maximal level of inhibition (50%) was seen at 50 h post-transfection. () In the adult brain, H1-shLuc provided no inhibition of PP:RL ratio (grey bars) compared to controls (black bars). CMV-H1-shLuc leads to 25% inhibition of the target gene at 72 h post-transfection (white bars) and up to 112 h post-transfection (data not shown). Means ± SEM are shown. NS = ‘not significant’; * ≤ 0.05; ** ≤ 0.01; *** ≤ 0.001.  = 10 injected hemispheres per group

<i>Xenopus tropicalis</i> Genome Re-Scaffolding and Re-Annotation Reach the Resolution Required for <i>In Vivo</i> ChIA-PET Analysis

<div><p>Genome-wide functional analyses require high-resolution genome assembly and annotation. We applied ChIA-PET to analyze gene regulatory networks, including 3D chromosome interactions, underlying thyroid hormone (TH) signaling in the frog <i>Xenopus tropicalis</i>. As the available versions of <i>Xenopus tropicalis</i> assembly and annotation lacked the resolution required for ChIA-PET we improve the genome assembly version 4.1 and annotations using data derived from the paired end tag (PET) sequencing technologies and approaches (e.g., DNA-PET [gPET], RNA-PET etc.). The large insert (~10Kb, ~17Kb) paired end DNA-PET with high throughput NGS sequencing not only significantly improved genome assembly quality, but also strongly reduced genome “fragmentation”, reducing total scaffold numbers by ~60%. Next, RNA-PET technology, designed and developed for the detection of full-length transcripts and fusion mRNA in whole transcriptome studies (ENCODE consortia), was applied to capture the 5' and 3' ends of transcripts. These amendments in assembly and annotation were essential prerequisites for the ChIA-PET analysis of TH transcription regulation. Their application revealed complex regulatory configurations of target genes and the structures of the regulatory networks underlying physiological responses. Our work allowed us to improve the quality of <i>Xenopus tropicalis</i> genomic resources, reaching the standard required for ChIA-PET analysis of transcriptional networks. We consider that the workflow proposed offers useful conceptual and methodological guidance and can readily be applied to other non-conventional models that have low-resolution genome data.</p></div

Benefit of genome re-annotation with RNA-PET for ChIA-PET analysis.

<p>A. Large genomic view of the <i>bcl6</i> locus. Track order: Ensembl genes, RNA-PET-based models, ChIA-PET TR binding density, interaction PETs, RNA Pol-II binding density, RNA-Seq reads density with (+T₃) and without (-T₃) treatment with thyroid hormones. B. Close up on TR binding sites. Track order: Ensembl genes, RNA-PET-based genes, location of ChIP-qPCR probes, RNA-PET ditags, TR binding density and RNA Pol-II binding density. C: ChIP-qPCR validation of TR binding at locations shown in B. Ab: Antibody, T₃: 3’,5,3’ triiodothyronine treatment. D: Induction of <i>trpg1</i>, <i>lpp</i> and <i>bcl6</i> genes transcription assayed by RT-qPCR. E: Three-dimensional model of the locus topology.</p

RNA-PET efficiently captures transcripts ends.

<p>A. Overlap between RNA-Seq reads and Ensembl and RNA-PET-based models. B. Demarcation of gene model boundaries by RNA-PET. The histogram shows the relative size of Ensembl gene models in bins of various sizes. C. Enrichment of RNA-Pol II around Ensembl gene models and RNA-PET-based models. This shows that RNA-Pol II density fits well with RNA-PET based models, but not Ensembl models.</p

Examples of genome annotation improvements.

<p>Track order: Ensembl models, RNA-PET based models, RNA-PET ditags and RNA-Seq reads density. A, B, C: <i>sumo1</i>, <i>cadm2</i> and <i>kiaa1958</i> loci. D: Un-annotated gene split over scaffold_1031 and scaffold_1460.</p

Benefit of genome re-annotation with RNA-PET for ChIA-PET analysis.

<p>A. Genomic view of an un-annotated gene. Track order: Ensembl genes, RNA-PET-based models, ChIA-PET TR binding density, RNA Pol-II binding density, RNA-Seq reads density with (+T₃) and without (-T₃) THs treatment. B. Close up of TR binding sites. Track order: Ensembl genes, RNA-PET-based genes, location of ChIP-qPCR probes, RNA-PET PETs, TR binding density and RNA Pol-II binding density. C: ChIP-qPCR validation of TR binding at locations shown in B. Ab: antibody, T₃: 3’,5,3’ triiodothyronine treatment. D: Transcriptional induction assayed by RT-qPCR.</p