FLI1 Induces Plaque Psoriasis and Its Inhibition Attenuates Disease Progression
Bibliographic record
Abstract
Plaque Psoriasis: Plaque psoriasis is an inflammatory skin disorder affecting nearly 2% of the world population. Despite recent advances in psoriasis treatment, there is still a need for more effective therapies. The ETS transcription factor FLI1 plays critical roles in hematopoiesis, angiogenesis, immunity, and cancer. Emerging evidence suggests that FLI1 is intricately involved in inflammatory processes underlying psoriasis pathogenesis. Methods: RNAseq and bioinformatic analysis were used to identify the correlation between FLI1 levels and the expression of inflammatory genes associated with psoriasis. Over-expression of FLI1 in skin cells determined FLI1's role in inducing transcription of psoriasis-related inflammatory genes, including IL6, IL1A, IL1B, IL23, and TNFα. Inhibitors such as chelerythrine (CLT) were tested for their suppressive effects on these genes. Mouse models of plaque psoriasis were employed to assess the therapeutic potential of CLT and tacrolimus (TAC). Results: Over-expression of FLI1 in skin cells upregulated 24 psoriasis-associated genes, which were identified through RNAseq. Inhibitors of FLI1, such as CLT, suppressed these inflammatory genes in skin cells. In mouse models of plaque psoriasis induced by imiquimod (IMQ) or phorbol ester (TPA), treatment with the anti-FLI1 inhibitor CLT, administered either peritoneally or topically, significantly downregulated inflammatory genes and alleviated psoriasis symptoms. Similarly, TAC, a common immunosuppressive agent, effectively attenuated IMQ-induced psoriasis by acting as a potent anti-FLI1 compound. Conclusion: These findings demonstrate that FLI1 plays a central role in psoriasis development and highlight it as a potential therapeutic target for this skin disorder.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".