IFN Regulatory Factor 3 Contributes to the Host Response during <i>Pseudomonas aeruginosa</i> Lung Infection in Mice
Bibliographic record
Abstract
Pseudomonas aeruginosa is a major opportunistic pathogen. However, host defense mechanisms involved in P. aeruginosa lung infection remain incompletely defined. The transcription factor IFN regulatory factor 3 (IRF3) is primarily associated with host defense against viral infections, and a role of IRF3 in P. aeruginosa infection has not been reported previously. In this study, we showed that IRF3 deficiency led to impaired clearance of P. aeruginosa from the lungs of infected mice. P. aeruginosa infection induced IRF3 translocation to the nucleus, activation of IFN-stimulated response elements (ISRE), and production of IFN-beta, suggesting that P. aeruginosa activates the IRF3-ISRE-IFN pathway. In vitro, macrophages from IRF3-deficient mice showed complete inhibition of CCL5 (RANTES) and CXCL10 (IP-10) production, partial inhibition of TNF, but no effect on CXCL2 (MIP-2) or CXCL1 (keratinocyte-derived chemokine) in response to P. aeruginosa stimulation. In vivo, IRF3-deficient mice showed complete inhibition of CCL5 production and partial or no effects on production of other cytokines and chemokines in the bronchoalveolar lavage fluids and lung tissues. Profiling of immune cells in the airways revealed that neutrophil and macrophage recruitment into the airspace was reduced, whereas B cell, T cell, NK cell, and NKT cell infiltration was unaffected in IRF3-deficient mice following P. aeruginosa lung infection. These data suggest that IRF3 regulates a distinct profile of cytokines and chemokines and selectively modulates neutrophil and macrophage recruitment during P. aeruginosa infection. Thus, IRF3 is an integral component in the host defense against P. aeruginosa lung infection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".