Proteasomal Serine Hydrolases Are Up-Regulated by and Required for Influenza Virus Infection
Bibliographic record
Abstract
Interactions between viruses and their host cells are important determinants of virus replication and of immune responses to the virus. However, these interactions and resulting consequences of these interactions remain poorly defined. Numerous recent quantitative proteomic approaches have measured host proteins affected by virus infection. Here, we used activity-based protein profiling (ABPP) to measure functional alterations in host serine hydrolases after influenza A virus infection of Madin-Darby canine kidney and human A549 lung cells. We identified 62 serine proteases. We then combined the ABPP approach with stable isotope labeling to directly measure how serine hydrolase activities were affected by virus infection. Differentially regulated SHs mapped into a few key cellular pathway systems, most notably the proteasomal system. The specific serine protease inhibitors Aprotinin and Pefablock and specific proteasomal inhibitors Bortezomib and MG132 significantly inhibited influenza virus growth. Some inhibitors also down-regulated activities of several proteasomal proteins, including PSMA1, PSMA2, and PMSB3. Genetic knockdown of PMSA2 also attenuated influenza virus replication. These findings further our understanding of enzymatic cellular processes affected by influenza virus and may be beneficial in the search for additional antiviral therapeutic targets.
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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".