Hepatitis E virus infection remodels the mature tRNAome in macrophages to orchestrate NLRP3 inflammasome response
Bibliographic record
Abstract
Hepatitis E virus (HEV) infection has been shown to activate NOD-like receptor family pyrin domain-containing 3 (NLRP3) inflammasome in macrophages, a key mechanism of causing pathological inflammation, but the mechanisms regulating this response remain poorly understood. Here, we report that the mature tRNAome dynamically responds to HEV infection in macrophages. This directs IL-1β expression, the hallmark of NLRP3 inflammasome activation, at mRNA and protein levels. Conversely, pharmacological inhibition of inflammasome activation abrogates HEV-provoked tRNAome remodeling, revealing a reciprocal interaction between the mature tRNAome and the NLRP3 inflammasome response. Remodeling the tRNAome results in improved decoding of codons directing leucine- and proline synthesis, which are the major amino acid constituents of IL-1β protein, whereas genetic or functional interference with tRNAome-mediated leucine decoding impairs inflammasome activation. Finally, we demonstrated that the mature tRNAome also actively responds to lipopolysaccharide (a key component of gram-negative bacteria)-triggered inflammasome activation, but the response dynamics and mode of actions are distinct from that induced by HEV infection. Our findings thus reveal the mature tRNAome as a previously unrecognized but essential mediator of host response to pathogens and represent a unique target for developing anti-inflammatory therapeutics.
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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".