PLoS ONEBioinformaticsPeer-reviewed journal article
HSV-1 and influenza infection induce linear and circular splicing of the long NEAT1 isoform
Marie-Sophie Friedl, Lara DjakovićiD, Michael KlugeiD, Thomas HennigiD, Adam W. WhisnantiD, Simone Backes, Lars DölkeniD, Caroline C. FriedeliD
Abstract
The herpes simplex virus 1 (HSV-1) virion host shut-off (vhs) protein cleaves both cellular and viral mRNAs by a translation-initiation-dependent mechanism. Here, we show that vhs-mediated degradation of mRNAs leads to an accumulation of circular RNAs (circRNAs) relative to linear mRNAs during HSV-1 infection. Strikingly, we found that circular splicing of the long isoform (NEAT1_2) of the nuclear paraspeckle assembly transcript 1 (NEAT1) was massively induced during HSV-1 infection. In contrast to other circRNAs, induction of the NEAT1_2 circRNA was independent of vhs and occurred while NEAT1_2 was still bound to the chromatin. This was associated with induction of linear splicing of NEAT1_2 both within and downstream of the circRNA. NEAT1_2 splicing was absent in uninfected cells but can be induced by ectopic co-expression of the HSV-1 immediate-early proteins ICP22 and ICP27. Interestingly, NEAT1_2 circular and linear splicing was also up-regulated in influenza A virus (IAV) infection but absent in stress conditions, which disrupt transcription termination similar to but not by the same mechanisms as HSV-1 and IAV infection. Large-scale analysis of published RNA-seq data uncovered induction of NEAT1_2 splicing in cancer cells upon inhibition or knockdown of cyclin-dependent kinase 7 (CDK7) or the MED1 subunit of the Mediator complex phosphorylated by CDK7. Finally, CDK7 inhibition also disrupted transcription termination, highlighting a possible link between disruption of transcription termination and NEAT1_2 splicing.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: benchmarking
- research focus: database / resource
- approach: statistical model
- Citations
- 13
- FWCI
- 0.90
- Field percentile
- top 30%
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