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Circular DNA tumor viruses make circular RNAs

ABSTRACT: Epstein-Barr virus (EBV) and Kaposi’s sarcoma herpesvirus (KSHV) cause ~2% of all human cancers. RNase R-resistant RNA sequencing revealed that both gammaherpesviruses encode multiple, uniquely stable, circular RNAs (circRNA). EBV abundantly expressed both exon-only and exon-intron circRNAs from the BART locus (circBARTs) formed from a spliced BART transcript and excluding the EBV miRNA region. CircBARTs were expressed in all verified EBV latency types, including EBV- positive post-transplant lymphoproliferative disease (PTLD), Burkitt lymphoma, nasopharyngeal carcinoma, and AIDS-associated lymphoma tissues and cell lines. Only cells infected with the B95-8 EBV strain, with a 12-kb BART locus deletion, were negative for EBV circBARTs. Less abundant levels of EBV circRNAs originating from LMP2 and BHLF1-encoding genes were also identified. CircRNA sequencing of KSHV- infected PEL cells revealed a KSHV-encoded circRNA from the vIRF4 locus (circvIRF4) that was constitutively expressed. In addition, KSHV polyadenylated nuclear (PAN) RNA locus generated a swarm (>100) of multiply backspliced, low-abundance RNase R- resistant circRNAs originating in both sense and antisense directions consistent with a novel hyper-backsplicing mechanism. In EBV and KSHV co-infected cells, exon-only EBV circBARTS were located more in the cytoplasm, whereas the intron-retaining circBARTs were found in the nuclear fraction. KSHV circvIRF4 and circPANs were detected in both nuclear and cytoplasmic fractions. Among viral circRNAs tested, none were found in polysome fractions from KSHV-EBV co-infected BC1 cells although low abundance protein translation from viral circRNAs could not be excluded. CircRNAs are a new class of viral transcripts expressed in gammaherpesvirus-related tumors that might contribute to viral oncogenesis.

ORGANISM(S): Homo sapiens

PROVIDER: GSE117798 | GEO | 2018/07/28

REPOSITORIES: GEO

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Project description:In platelets, splicing and translation occur in the absence of a nucleus. However, the integrity and stability of mRNAs derived from megakaryocyte progenitor cells remain poorly quantified on a transcriptome-wide level. As circular RNAs (circRNAs) are resistant to degradation by exonucleases, their abundance relative to linear RNAs can be used as a surrogate marker for mRNA stability in the absence of transcription. Here we show that circRNAs are enriched in human platelets 17-188 fold relative to nucleated tissues, and 14-26 fold relative to samples digested with RNAseR to selectively remove linear RNA. We compare RNAseq read depths inside and outside circRNAs to provide in silico evidence of transcript circularity, show that exons within circRNAs are enriched ~13X in platelets relative to nucleated tissues, and identify 3162 genes significantly enriched for circRNAs including some where all RNAs appear to be derived from circular molecules. We also confirm that this is a feature of other anucleate cells through transcriptome sequencing of mature erythrocytes, demonstrate that circRNAs are not enriched in megakaryocytes, and that linear RNAs decay more rapidly than circRNAs in platelet preparations. Collectively, these results suggest that circulating platelets have lost on aveage over 90% of their progenitor mRNAs, and that translation in platelets occurrs against the backdrop of a highly degraded transcriptome. Finally, we find that transcripts classified as products of reverse transcriptase template switching are both enriched in platelets and resistant to decay, countering the recent suggestion that up to 50% of rearranged RNAs are artefacts. A single rRNA depleted total RNA sample was sequenced. This together with 25 publicly available rRNA depleted total RNA samples (including 3 from platelets) were analysed using PTESFinder v 1 (http://sourceforge.net/projects/ptesfinder-v1/) to identify back-splice junctions, characteristic of circRNA transcripts. The contribution of circRNA producing exons was analysed on a gene by gene basis as follows: All circRNA transcripts identified in any sample were first pooled to define exons which can contribute to circRNA generation using custom scripts (available on request). For each sample, expression estimates (RPKMI) across all circRNA producing exons were computed for each locus using the total size of exons (in bp) and the read counts mapping to them. Similarly, total size and exonic read counts for exons for which no circRNA were detected in any sample were used to compute expression estimates (RPKME) for non-circRNA producing exons for each locus. Abundance ratios (RPKMI/RPKME and RPKMI/RPKMI+RPKME) were calculated and compared between Platelets and human tissues using Wilcoxon signed-rank test. Please note that the '25sample_info_accn_no.txt' contains the accession numbers and tissue/cell type information for 25 samples analyzed together.

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Circular DNA tumor viruses make circular RNAs

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