STAT3 is involved in IL-6-Mediated Downregulation of Hepatic Transporters in Mice
Bibliographic record
Abstract
Interleukin (IL)-6 decreases hepatic expression of numerous transporters. Although IL-6 signaling occurs through STAT3, the extent of the involvement of the STAT3 signaling pathway has not been elucidated. PURPOSE: Our objective was to investigate whether IL-6-mediated effects occur through STAT3, and whether PXR plays a role in this regulation. METHOD: PXR null (-/-) or wild-type (+/+) male mice were pre-dosed with a selective STAT3 inhibitor S3I-201 (7.5 mg/kg ip) or vehicle (n=5-8/group) 30 minutes before receiving a single dose of IL-6 (1 µg ip) or saline. Animals were sacrificed after 6 hours and liver samples were analyzed using qRT-PCR and western blotting. RESULTS: As compared to saline controls, IL-6 decreased the expression of Cyp3a, Abcb1a, Abcc3, and Slco1a4 20-70% similarly in PXR (+/+) and (-/-) mice at 6 hr, while downregulation of Abcb11, Abcc2, Slc10a1and Slco2b1 was only seen in PXR (+/+). Pre-administration of S3I-201 attenuated IL-6-mediated changes of most transporters in PXR (+/+) and PXR (-/-) mice. At early times after IL-6 administration (10-120 minutes), transcript levels of Socs3, PXR, Abcb1a, Abcc3, Abcb11, Slco1a4 and Slco2b1were increased in PXR (+/+) mice. CONCLUSIONS: Our findings demonstrate that IL-6 imposes a significant downregulation of numerous ABC and SLC transporters in liver primarily through activation of the STAT3 signaling pathway. Based on time-dependent changes in transporter expression, downregulation likely occurs downstream of STAT3 activation. As IL-6 is elevated in many diseases, understanding the underlying mechanism(s) involved in transporter dysregulation will allow us to predict potential drug-disease interactions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".