Back

ISG15 drives immune pathology and respiratory failure during viral infection

Shaabani, N.; Zak, J.; Johnson, J. L.; Huang, Z.; Nguyen, N.; Lazar, D. C.; Vartabedian, V. F.; Honke, N.; Prinz, M.; Knobeloch, K.-P.; Arimoto, K.-i.; Zhang, D.-E.; Catz, S. D.; Teijaro, J. R.

2020-04-14 immunology
10.1101/2020.04.13.039321 bioRxiv
Show abstract

Cytokine storm during respiratory viral infection is an indicator of disease severity and poor prognosis. Type 1 interferon (IFN-I) production and signaling has been reported to be causal in cytokine storm-associated pathology in several respiratory viral infections, however, the mechanisms by which IFN-I promotes disease pathogenesis remain poorly understood. Here, using Usp18-deficient, USP18 enzymatic-inactive and Isg15-deficient mouse models, we report that lack of deISGylation during persistent viral infection leads to severe immune pathology characterized by hematological disruptions, cytokine amplification, lung vascular leakage and death. This pathology requires T cells but not T cell-intrinsic deletion of Usp18. However, lack of Usp18 in myeloid cells mimicked the pathological manifestations observed in Usp18-/- or Usp18C61A mice which were dependent on Isg15. We further mechanistically demonstrate that interrupting the ISGylation/deISGylation circuit increases extracellular levels of ISG15 which is accompanied by inflammatory neutrophil accumulation to the lung. Importantly, neutrophil depletion reversed morbidity and mortality in Usp18C61A mice. In summary, we reveal that the enzymatic function of Usp18 is crucial for regulating extracellular release of ISG15. This is accompanied by altered neutrophil differentiation, cytokine amplification and mortality following persistent viral infection. Moreover, our results suggest that extracellular ISG15 may drive the inflammatory pathology observed and could be both a prospective predictor of disease outcome and a therapeutic target during severe respiratory viral infections.

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.