High-Throughput Screening of Chemical Libraries for Modulators of Gene Promoter Activity of<i>HLA-B2705</i>: Environmental Pathogenesis and Therapeutics of Ankylosing Spondylitis
Bibliographic record
Abstract
OBJECTIVE: Ankylosing spondylitis (AS) is a highly heritable disease with HLA-B27 being the strongest susceptible gene. In order to survey the environmental triggers for arthritis development, we used a high-throughput technique to screen the effects of 12,264 chemicals on the HLA-B27 gene promoter. METHODS: Promoter reporter transfectants 293T-HLA-B27 and HeLa-HLA-B27 were tested using robotics with 12,264 chemicals. Chemicals that modulated HLA-B27 promoter activity > 150% or < 40% were selected for further evaluation of IC50/EC50 and cell viability. RESULTS: The primary screening using the 293T-HLA-B27 promoter reporter cell line yielded 5.1% hits that either suppressed (556 chemicals) or enhanced (68 chemicals) the HLA-B27 promoter activity. A secondary reconfirmation screening was carried out with these 624 candidates using HeLa-HLA-B27 promoter reporter cells under several different culture conditions. The yield of positive candidates was 130, of which 47 were derived from natural products. Based on the bio-information of those positive natural products, 21 chemicals were selected for analysis by dose-response IC50/EC50 experiments. Eight compounds showed potential pharmacological activities. Two suppressors are both derived from an herbal medicine (lei gong teng) that has been used for decades to treat immune diseases. The 6 activators all belonged to a group of chemicals known as flavonoids, widely distributed among dietary fruits and vegetables. CONCLUSION: Several common dietary products that contain certain flavonoids might be environmental risk factors for AS; the Chinese traditional herb lei gong teng might be a potential drug for patients who are HLA-B27-positive. These results provide new research directions for the pathogenesis and therapeutics of AS.
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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 | 0.001 |
| 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) | 0.002 | 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".