Matriptase Proteolytically Activates Influenza Virus and Promotes Multicycle Replication in the Human Airway Epithelium
Bibliographic record
Abstract
Influenza viruses do not encode any proteases and must rely on host proteases for the proteolytic activation of their surface hemagglutinin proteins in order to fuse with the infected host cells. Recent progress in the understanding of human proteases responsible for influenza virus hemagglutinin activation has led to the identification of members of the type II transmembrane serine proteases TMPRSS2 and TMPRSS4 and human airway trypsin-like protease; however, none has proved to be the sole enzyme responsible for hemagglutinin cleavage. In this study, we identify and characterize matriptase as an influenza virus-activating protease capable of supporting multicycle viral replication in the human respiratory epithelium. Using confocal microscopy, we found matriptase to colocalize with hemagglutinin at the apical surface of human epithelial cells and within endosomes, and we showed that the soluble form of the protease was able to specifically cleave hemagglutinins from H1 virus, but not from H2 and H3 viruses, in a broad pH range. We showed that small interfering RNA (siRNA) knockdown of matriptase in human bronchial epithelial cells significantly blocked influenza virus replication in these cells. Lastly, we provide a selective, slow, tight-binding inhibitor of matriptase that significantly reduces viral replication (by 1.5 log) of H1N1 influenza virus, including the 2009 pandemic virus. Our study establishes a three-pronged model for the action of matriptase: activation of incoming viruses in the extracellular space in its shed form, upon viral attachment or exit in its membrane-bound and/or shed forms at the apical surface of epithelial cells, and within endosomes by its membrane-bound form where viral fusion takes place.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".