Toxicogenomic Mechanisms of 6-HO-BDE-47, 6-MeO-BDE-47, and BDE-47 in <i>E. coli</i>
Bibliographic record
Abstract
Cytotoxicity of 6-HO-BDE-47 and its two analogues, BDE-47 and 6-MeO-BDE-47, and the associated molecular mechanisms were assessed by use of a live cell reporter assay system which contains a library of 1820 modified green fluorescent protein (GFP) expressing promoter reporter vectors constructed from E. coli K12 strains. 6-HO-BDE-47 inhibited growth of E. coli with a 4 h median effect concentration (EC50) of 22.52 ± 2.20 mg/L, but neither BDE-47 nor 6-MeO-BDE-47 were cytotoxic. Thus, 6-HO-BDE-47 might serve as an antibiotic in some living organisms. Exposure to 6-HO-BDE-47 resulted in 65 (fold change >2) or 129 (fold change >1.5) genes being differentially expressed. The no observed transcriptional effect concentration (NOTEC) and median transcriptional effect concentration (TEC50) based on transcriptional end points, of 6-HO-BDE-47 were 0.0438 and 0.580 mg/L, respectively. The transcriptional responses were 514- and 39-fold more sensitive than the acute EC50 to inhibit cell growth. Most of the genes that were differentially expressed in response to 6-HO-BDE-47 were not modulated by BDE-47 or 6-MeO-BDE-47. These results suggest that cytotoxicity of 6-HO-BDE-47 to E. coli was via a mechanism that was different from that of either BDE-47 or 6-MeO-BDE-47. Gene expression associated with metabolic pathways was more responsive to 6-HO-BDE-47, which suggests that this pathway might be the primary target of this compound.
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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) | 0.001 | 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".