Protein Tyrosine Phosphatase 1B Is a Regulator of the Interleukin-10–Induced Transcriptional Program in Macrophages
Bibliographic record
Abstract
Both pro- and anti-inflammatory cytokines activate the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway; however, they elicit distinct transcriptional programs. Posttranslational modifications of STAT proteins, such as tyrosine phosphorylation, are critical to ensure the differential expression of STAT target genes. Although JAK-STAT signaling is dependent on reversible tyrosine phosphorylation, whether phosphatases contribute to the specificity of STAT-dependent gene expression is unclear. We examined the role of protein tyrosine phosphatase 1B (PTP1B) in regulating the interleukin-10 (IL-10)-dependent, STAT3-mediated anti-inflammatory response. We found that IL-10-dependent STAT3 phosphorylation and anti-inflammatory gene expression were enhanced in macrophages from PTP1B(-/-) mice compared to those in macrophages from wild-type mice. Consistent with this finding, the IL-10-dependent suppression of lipopolysaccharide-induced macrophage activation was increased in PTP1B(-/-) macrophages compared to that in wild-type macrophages, as was the IL-10-dependent increase in the cell surface expression of the anti-inflammatory cytokine receptor IL-4Rα. Furthermore, RNA sequencing revealed the expression of genes encoding proinflammatory factors in IL-10-treated PTP1B(-/-) macrophages, which correlated with increased phosphorylation of STAT1, which is not normally highly activated in response to IL-10. These findings identify PTP1B as a central regulator of IL-10R-STAT3 and IL-10R-STAT1 signaling, and demonstrate that phosphatases can tailor the quantitative and qualitative properties of cytokine-induced transcriptional responses.
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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".