<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

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

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

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

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