Back

The proteasome and ERAD pathways can regulate hepatitis E virus pORF1 proteostasis and genome replication

Abualsaoud, K. M.; Buchanan, F. J.; Byford, O. M.; Bartholomew, E. M.; Williams, C. E.; Kailani, M.; Penrice-Randal, R.; Harris, M.; Herod, M. R.

2026-10-09 microbiology
10.64898/2026.10.08.757567 bioRxiv
Show abstract

Hepatitis E virus (HEV) is a major cause of acute viral hepatitis, with an estimated 20 million human infections annually. Its diverse host range presents a significant zoonotic threat, yet the molecular mechanisms underpinning HEV-host interactions remain poorly characterised. Here, we used transposon-based insertion screening of the HEV pORF1 polyprotein to identify a novel region within the viral PCP domain that permits HA-epitope insertion, enabling the first epitope tagging of this domain. Combined with V5 tagging of the hypervariable region (HVR), this approach enabled the generation of a replication-competent stable cell line expressing dual-tagged ORF1. Co-immunoprecipitation and TMT-based LC-MS proteomics revealed that pORF1 interacts with host proteins involved in endoplasmic reticulum-associated degradation (ERAD) and the 26S proteasome. Pharmacological inhibition demonstrated that both pathways are required for efficient HEV replication. Treatment with the FDA-approved proteasome inhibitor bortezomib altered pORF1 precursor abundance and led to loss of full-length pORF1 through enhanced proteolysis. Importantly, the kinetics of ORF1 degradation following proteasome inhibition correlated with immediate loss of HEV replication, demonstrating a functional link between pORF1 proteostasis and viral replication. Collectively, our findings identify the ERAD network as a previously unrecognised requirement for HEV replication and establish a role for the 26S proteasome in maintaining pORF1 proteostasis.

Matching journals

The top 3 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.