EVI1 Acts as an Inducible Negative-Feedback Regulator of NF-κB by Inhibiting p65 Acetylation
Bibliographic record
Abstract
Inflammation is a hallmark of many important human diseases. Appropriate inflammation is critical for host defense; however, an overactive response is detrimental to the host. Thus, inflammation must be tightly regulated. The molecular mechanisms underlying the tight regulation of inflammation remain largely unknown. Ecotropic viral integration site 1 (EVI1), a proto-oncogene and zinc finger transcription factor, plays important roles in normal development and leukemogenesis. However, its role in regulating NF-κB-dependent inflammation remains unknown. In this article, we show that EVI1 negatively regulates nontypeable Haemophilus influenzae- and TNF-α-induced NF-κB-dependent inflammation in vitro and in vivo. EVI1 directly binds to the NF-κB p65 subunit and inhibits its acetylation at lysine 310, thereby inhibiting its DNA-binding activity. Moreover, expression of EVI1 itself is induced by nontypeable Haemophilus influenzae and TNF-α in an NF-κB-dependent manner, thereby unveiling a novel inducible negative feedback loop to tightly control NF-κB-dependent inflammation. Thus, our study provides important insights into the novel role for EVI1 in negatively regulating NF-κB-dependent inflammation, and it may also shed light on the future development of novel anti-inflammatory strategies.
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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) | 0.001 | 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".