Back

Snapshot of in-cell protein contact sites reveals new host factors and hijacking of paraspeckles during influenza A virus infection

Kotova, I.; Muehlberg, L.; Gilep, K.; Yu, D.; Ziemianowicz, D.; Stanelle-Bertram, S.; Beck, S.; Baeg, K.; Duss, O.; Gabriel, G.; Liu, F.; Bogdanow, B.; Kosinski, J.

2025-03-11 microbiology
10.1101/2025.03.09.642134 bioRxiv
Show abstract

Influenza A virus (IAV) hijacks host cellular machinery, but many virus-IAV interactions and contacting protein sites remain uncharacterised, particularly those dependent on intact cellular architecture, such as membrane-associated or phase-separated compartments. Here, we applied in-cell cross-linking mass spectrometry (XL-MS), integrated with AlphaFold-based structural modelling and functional assays, to map protein-protein contact sites in IAV-infected human cells. This approach revealed previously unrecognised virus-host interactions linked to spatially organised processes, including the maturation pathway of HA through the membrane-bound ER- Golgi system, the novel interaction of M2 with the membrane-embedded LAT1 amino acid transporter, and the progressive disassembly of paraspeckles-phase-separated compartments in the nucleus. We validate M2-LAT1 interaction and paraspeckle disassembly in human primary lung epithelial cells and show that the paraspeckle disassembly constitutes a new and unique infection mechanism through which IAV releases RNA-binding proteins that support viral RNA replication. These findings advance the understanding of IAV manipulation of host cellular processes and illustrate how the integrative in-cell structural system biology approach captures native host-pathogen interactomes, infection pathways, and host cell perturbations.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"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.