Synergistic Expression of the CXCL10 Gene in Response to IL-1β and IFN-γ Involves NF-κB, Phosphorylation of STAT1 at Tyr701, and Acetylation of Histones H3 and H4
Bibliographic record
Abstract
The CXCL10 gene encodes a peptide that chemoattracts a variety of leukocytes associated with type 1 and type 2 diabetes. The present study was undertaken to determine the molecular mechanisms required for expression of the CXCL10 gene in response to IL-1β and IFN-γ using rat islets and β cell lines. IL-1β induced the expression of the CXCL10 gene and promoter activity, whereas the combination of IL-1β plus IFN-γ was synergistic. Small interfering RNA-mediated suppression of NF-κB p65 markedly inhibited the ability of cytokines to induce the expression of the CXCL10 gene, whereas targeting STAT1 only diminished the synergy provided by IFN-γ. Furthermore, we found that a JAK1 inhibitor dose dependently reduced IFN-γ-controlled CXCL10 gene expression and promoter activity, concomitant with a decrease in STAT1 phosphorylation at Tyr(701). We further discovered that, although the Tyr(701) phosphorylation site is inducible (within 15 min of IFN-γ exposure), the Ser(727) site within STAT1 is constitutively phosphorylated. Thus, we generated single-mutant STAT1 Y701F and double-mutant STAT1 Y701F/S727A adenoviruses. Using these recombinant adenoviruses, we determined that overexpression of either the single- or double-mutant STAT1 decreased the IFN-γ-mediated potentiation of CXCL10 gene expression, promoter activity, and secretion of protein. Moreover, the Ser(727) phosphorylation was neither contingent on a functional Y701 site in β cells nor was it required for cytokine-mediated expression of the CXCL10 gene. We conclude that the synergism of IL-1β and IFN-γ to induce expression of the CXCL10 gene requires NF-κB, STAT1 phosphorylated at Tyr(701), recruitment of coactivators, and acetylation of histones H3 and H4.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".