The antimicrobial drug pyrimethamine inhibits STAT3 transcriptional activity by targeting the enzyme dihydrofolate reductase
Bibliographic record
Abstract
Cancer is often characterized by aberrant gene expression patterns caused by the inappropriate activation of transcription factors. Signal transducer and activator of transcription 3 (STAT3) is a key transcriptional regulator of many protumorigenic processes and is persistently activated in many types of human cancer. However, like many transcription factors, STAT3 has proven difficult to target clinically. To address this unmet clinical need, we previously developed a cell-based assay of STAT3 transcriptional activity and performed an unbiased and high-throughput screen of small molecules known to be biologically active in humans. We identified the antimicrobial drug pyrimethamine as a novel and specific inhibitor of STAT3 transcriptional activity. Here, we show that pyrimethamine does not significantly affect STAT3 phosphorylation, nuclear translocation, or DNA binding at concentrations sufficient to inhibit STAT3 transcriptional activity, suggesting a potentially novel mechanism of inhibition. To identify the direct molecular target of pyrimethamine and further elucidate the mechanism of action, we used a new quantitative proteome profiling approach called proteome integral solubility alteration coupled with a metabolomic analysis. We identified human dihydrofolate reductase as a target of pyrimethamine and demonstrated that the STAT3-inhibitory effects of pyrimethamine are the result of a deficiency in reduced folate downstream of dihydrofolate reductase inhibition, implicating folate metabolism in the regulation of STAT3 transcriptional activity. This study reveals a previously unknown regulatory node of the STAT3 pathway that may be important for the development of novel strategies to treat STAT3-driven cancers.
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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) | 0.000 | 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 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".