mRNA poly(A)-tail length is a battleground for coronavirus-host competition
Latifkar, A.; Levdansky, Y.; Balabaki, A.; Nyeo, S.; Valkov, E.; Bartel, D.
Show abstract
Most eukaryotic mRNAs contain a poly(A) tail, which in post-embryonic cells enhances their stability. Many cytoplasmic RNA viruses also harbor poly(A) tails on their genomic RNA and mRNAs. Here, we report that coronavirus infection causes cytoplasmic poly(A)-binding protein (PABPC) activity to become limiting, which preferentially destabilizes short-tailed host mRNAs, occurring before the action of virally encoded mRNA-decay factor nsp1. In this environment hostile to poly(A) tails, viral RNAs maintain a narrow tail-length distribution centering on 70-80 nucleotides across infection cycles. They do this through two mechanisms. First, viral tails are extended during RNA synthesis within double-membrane vesicles; second, viral tails are capped by a complex that includes PABPC1 and CSDE1 and slows tail shortening. Our findings suggest poly(A)-tail length is an arena of host- virus conflict, in which preserving tail lengths of viral mRNAs promotes their cytoplasmic dominance. HighlightsO_LIPABPC1 becomes limiting during coronavirus infection C_LIO_LILimiting PABPC1 promotes decay of short-tailed host mRNAs--independently of nsp1 C_LIO_LIThe tail lengths of coronaviral mRNAs are extended during their synthesis in DMVs C_LIO_LIViral tails are capped by PABPC1 and CSDE1, which protects against deadenylation C_LI
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Human brain cell types shape host-rabies virus transcriptional interactions revealing a preexisting pro-viral astrocyte subpopulation 97%
- HIV-1 Vpr drives a tissue residency-like phenotype during selective infection of resting memory T cells 97%
- Ribosome stalling caused by the Argonaute-microRNA-SGS3 complex regulates the production of secondary siRNAs in plants 96%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.