To inhibit TrxR1 is to inactivate STAT3–Inhibition of TrxR1 enzymatic function by STAT3 small molecule inhibitors
Bibliographic record
Abstract
The transcription factor STAT3 plays a key role in cancer and immunity, being widely explored as a potential drug target for the development of novel immunomodulatory or anticancer therapeutics. The mechanisms of small molecule-derived inhibition of STAT3 appear, however, to be more complex than initially perceived. Our recent discovery, that some novel STAT3 inhibitors were bona fide inhibitors of the cytosolic selenoprotein oxidoreductase TrxR1 (TXNRD1), led us to explore the effects of a wide array of previously described STAT3 inhibitors on TrxR1 function. We found that 17 out of 23 tested STAT3 small molecule inhibitors indeed inhibited purified TrxR1 at the reported concentrations yielding STAT3 inhibition. All tested compounds were electrophilic as shown by direct reactivities with GSH, and several were found to also be redox cycling substrates of TrxR1. Ten compounds previously shown to inhibit STAT3 were here found to irreversibly inhibit cellular TrxR1 activity (Auranofin, Stattic, 5,15-DPP, Galiellalactone, LLL12, Napabucasin, BP1-102, STA-21, S3I-201 and Degrasyn (WP1130)). Our findings suggest that targeting of TrxR1 may be a common feature for many small molecules that inhibit cellular STAT3 function. It is possible that prevention of STAT3 activation in cells by several small molecules classified as STAT3 inhibitors can be a downstream event following TrxR1 inhibition. Therefore, the relationship between TrxR1 and STAT3 should be considered when studying inhibition of either of these promising drug targets.
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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 | 0.001 |
| 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 | 0.001 |
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".