USP18 as a novel regulator of PD-1 and IFNAR1-mediated immune dysfunction (IRM11P.629)
Bibliographic record
Abstract
Abstract Immune dysfunction induced by cancer and chronic viral infection contributes to the pathology and progression of each of these diseases. Recent studies have identified a novel player associated with poorly functioning immune cells during chronic viral infection, the ubiquitin-specific protease Usp18. Usp18 is a negative regulator of innate anti-viral responses through direct inhibition of Type I IFN receptor signaling. However, the role of Usp18 in maintaining functional immunity is still unclear. To investigate the role of Usp18 in immunity, we have utilized Usp18-deficient mice and assessed immune function during infection with the Th1 pathogen Listeria monocytogenes (Lm). We find that Usp18 deficiency results in severe susceptibility to acute Lm infection and an inability to develop functional adaptive immunity to Lm. Severe susceptibility to Lm infection in the Usp18-/- mouse is associated with poor inflammatory cytokine responses and blunted splenic T cell function. The immune tolerant state in Usp18-/- mice is partially regulated by the PD-1 inhibitory receptor as blockade partially restores innate responses to Lm. However, blockade of IFNAR1 signaling resulted in significant reversal of this immunosuppressive state and a restoration of T cell function. This study identifies Usp18 and IFNAR1 signaling as critical mediators of immune dysfunction, a finding that may have broad implications in the treatment of cancer and chronic viral infection.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".