Egr-1 deficiency protects host against <i>Pseudomonas aeruginosa</i> lung infection
Bibliographic record
Abstract
Abstract Pseudomonas aeruginosa is an opportunistic pathogen that is the major cause of nosocomial infections in cystic fibrosis patients and immunocompromised individuals. The molecular mechanisms governing immune responses to P. aeruginosa infection remain incompletely defined. Early Growth Response 1 (Egr-1) is a zinc-finger transcription factor that binds to the GC-rich DNA consensus sequences in the promotor of target genes, important for cell growth, differentiation and survive. Aberrant expression of Egr-1 has been implicated in many inflammatory diseases. In this study, we demonstrate that Egr-1 deficiency protects host against P. aeruginosa infection in a mouse model of acute bacterial pneumonia. Egr-1 expression was rapidly and transiently induced both in vitro and in vivo upon P. aeruginosa infection. Egr-1-deficient mice displayed decreased disease score, reduced systemic levels of proinflammatory cytokines and impaired NF-κB and NFAT activation compared to wild-type mice. Interestingly, Egr-1 deficiency leads to enhanced bacterial clearance and increased nitric oxide production in lung whereas it has no impact on neutrophil recruitment. Further studies revealed that Egr-1-deficient neutrophils displayed elevated bacterial killing ability. Altogether, these findings suggest that Egr-1 promotes inflammatory responses by enhancing NF-κB and NFAT activation and plays a detrimental role in host defense against P. aeruginosa lung infection by negatively regulating nitric oxide production for bacterial clearance.
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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) | 0.001 | 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 | 0.000 |
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".