Sulfur mustard analog 2-chloroethyl ethyl sulfide increases triglycerides by activating DGAT1-dependent biogenesis and inhibiting PGC1ɑ-dependent fat catabolism in immortalized human bronchial epithelial cells
Bibliographic record
Abstract
Using sulfur mustard analog 2-chloroethyl ethyl sulfide (CEES), we established an in vitro model by poisoning cultured immortalized human bronchial epithelial cells. Nile Red staining revealed lipids accumulated 24 h after a toxic dose of CEES (0.9 mM). Lipidomics analysis showed most of the increased lipids were triglycerides (TGs), and the increase in TGs was further confirmed using a Triglyceride-Glo™ Assay kit. Protein and mRNA levels of DGAT1, an important TG biogenesis enzyme, were increased following 0.4 mM CEES exposure. Under higher dose CEES (0.9 mM) exposure, protein and mRNA levels of PPARγ coactivator-1ɑ (PGC-1ɑ), a well-known transcription factor that regulates fatty acid oxidation, were decreased. Finally, application with DGAT1 inhibitor A 922500 or PGC1ɑ agonist ZLN005 was able to block the CEES-induced TGs increase. Overall, our dissection of CEES-induced TGs accumulation provides new insight into energy metabolism dysfunction upon vesicant exposure.HIGHLIGHTSIn CEES (0.9 mM)-injured cells:Triglycerides (TGs) were abundant in the accumulated lipids.Expression of DGAT1, not DGAT2, was increased.Expression of PGC1ɑ, not PGC1β, was reduced.DGAT1 inhibitor or PGC1ɑ agonist blocked the CEES-mediated increase in TGs. In CEES (0.9 mM)-injured cells: Triglycerides (TGs) were abundant in the accumulated lipids. Expression of DGAT1, not DGAT2, was increased. Expression of PGC1ɑ, not PGC1β, was reduced. DGAT1 inhibitor or PGC1ɑ agonist blocked the CEES-mediated increase in TGs.
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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 | 0.001 |
| 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".