Th1 Cells Alter the Inflammatory Signature of IL-6 by Channeling STAT Transcription Factors to <i>Alu</i>-like Retroelements
Bibliographic record
Abstract
Cytokines that signal via STAT1 and STAT3 transcription factors instruct decisions affecting tissue homeostasis, antimicrobial host defense, and inflammation-induced tissue injury. To understand the coordination of these activities, we applied RNA sequencing, chromatin immunoprecipitation sequencing, and assay for transposase-accessible chromatin with high-throughput sequencing to identify the transcriptional output of STAT1 and STAT3 in peritoneal tissues from mice during acute resolving inflammation and inflammation primed to drive fibrosis. Bioinformatics focused on the transcriptional signature of the immunomodulatory cytokine IL-6 in both settings and examined how profibrotic IFN-γ-secreting CD4+ T cells altered the interpretation of STAT1 and STAT3 cytokine cues. In resolving inflammation, STAT1 and STAT3 cooperated to drive stromal gene expression affecting antimicrobial immunity and tissue homeostasis. The introduction of IFN-γ-secreting CD4+ T cells altered this transcriptional program and channeled STAT1 and STAT3 to a previously latent IFN-γ activation site motif in Alu-like elements. STAT1 and STAT3 binding to this conserved sequence revealed evidence of reciprocal cross-regulation and gene signatures relevant to pathophysiology. Thus, we propose that effector T cells retune the transcriptional output of IL-6 by shaping a regulatory interplay between STAT1 and STAT3 in inflammation.
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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 | 0.000 |
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".