The TrxG Complex Mediates Cytokine Induced De Novo Enhancer Formation in Islets
Bibliographic record
Abstract
To better understand how β-cells respond to proinflammatory cytokines we mapped the locations of histone 3 lysine 4 monomethylation (H3K4me1), a post-translational histone modification enriched at active and poised cis-regulatory regions, in IFNγ, Il-1β, and TNFα treated pancreatic islets. We identified 96,721 putative cis-regulatory loci, of which 3,590 were generated de novo, 3,204 had increased H3K4me1, and 5,354 had decreased H3K4me1 in IFNγ, Il-1β, and TNFα exposed islets. Roughly 10% of the de novo and increased regions were enriched for the repressive histone modification histone 3 lysine 27 trimethylation (H3K27me3) in untreated cells, and these were frequently associated with chemokine genes. We show that IFNγ, Il-1β, and TNFα exposure overcomes this repression and induces chemokine gene activation in as little as three hours, and that this expression persists for days in absence of continued IFNγ, Il-1β, and TNFα exposure. We implicate trithorax group (TrxG) complexes as likely players in the conversion of these repressed loci to an active state. To block the activity of these complexes, we suppressed Wdr5, a core component of the TrxG complexes, and used the H3K27me3 demethylase inhibitor GSK-J4. We show that GSK-J4 is particularly effective in blunting IFNγ, Il-1β, and TNFα-induced chemokine gene expression in β-cells; however, it induced significant islet-cell apoptosis and β-cell dysfunction. Wdr5 suppression also reduced IFNγ, Il-1β, and TNFα induced chemokine gene expression in β-cells without affecting islet-cell survival or β-cell function after 48hrs, but did begin to increase islet-cell apoptosis and β-cell dysfunction after four days of treatment. Taken together these data suggest that the TrxG complex is potentially a viable target for preventing cytokine induced chemokine gene expression in β-cells.
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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) | 0.002 | 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".