Regulation of RANTES Chemokine Gene Expression Requires Cooperativity Between NF-κB and IFN-Regulatory Factor Transcription Factors
Bibliographic record
Abstract
Virus infection of host cells activates a set of cellular genes, including cytokines, IFNs, and chemokines, involved in antiviral defense and immune activation. Previous studies demonstrated that virus-induced transcriptional activation of a member of the human CC-chemokine RANTES required activation of the latent transcription factors IFN-regulatory factor (IRF)-3 and NF-kappa B via posttranslational phosphorylation. In the present study, we further characterized the regulatory control of RANTES transcription during virus infection using in vivo genomic footprinting analyses. IRF-3, the related IRF-7, and NF-kappa B are identified as important in vivo binding factors required for the cooperative induction of RANTES transcription after virus infection. Using fibroblastic or myeloid cells, we demonstrate that the kinetics and strength of RANTES virus-induced transcription are highly dependent on the preexistence of IRFs and NF-kappa B. Use of dominant negative mutants of either I kappa B-alpha or IRF-3 demonstrate that disruption of either pathway dramatically abolishes the ability of the other to bind and activate RANTES expression. Furthermore, coexpression of IRF-3, IRF-7, and p65/p50 leads to synergistic activation of RANTES promoter transcription. These studies reveal a model of virus-mediated RANTES promoter activation that involves cooperative synergism between IRF-3/IRF-7 and NF-kappa B factors.
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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".