Alternative splicing expands the antiviral IFITM repertoire in Chinese horseshoe bats
Mak, N.; Zhang, D.; Li, X.; Rahman, K.; Datta, S. A. K.; Taylor, J.; Liu, J.; Shi, Z.; Temperton, N.; Irving, A. T.; Compton, A. A.; Sloan, R. D.
Show abstract
The interferon response is shaped by the evolutionary arms race between hosts and the pathogens they carry. The human interferon-induced transmembrane protein (IFITM) family consists of three antiviral IFITM genes that arose by gene duplication, they restrict virus entry and are key players of the interferon response. Yet, little is known about IFITMs in other mammals. Here, we identified an IFITM gene in Chinese horseshoe bat, a natural host of SARS-coronaviruses, that is alternatively spliced to produce two IFITM isoforms. These bat IFITMs have conserved structures in vitro and differential antiviral activities against influenza A virus and coronaviruses including SARS- and MERS-coronavirus. In parallel with human IFITM1-3, the bat IFITM isoforms localize to distinct cellular compartments. Further analysis of IFITM repertoires in 205 mammals reveals that alternative splicing is a ubiquitous strategy for IFITM diversification, albeit less widely adopted than gene duplication. These findings showcase an example of convergent evolution where species-specific selection pressures led to expansion of the IFITM family through multiple means, underscoring the importance of IFITM diversity as a component of innate immunity.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Influenza A Virus NS1 Limits Recognition of Double-Stranded Transposable Elements by Cytosolic RNA Sensors 95%
- Novel role of bone morphogenetic protein 9 (BMP9) in innate host responses to HCMV infection 95%
- Tick-borne flavivirus NS5 antagonizes interferon signaling by inhibiting the catalytic activity of TYK2 94%
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.