Bisphenol A Exposure May Induce Hepatic Lipid Accumulation via Reprogramming the DNA Methylation Patterns of Genes Involved in Lipid Metabolism
Bibliographic record
Abstract
Accumulating evidence suggests a role of bisphenol A (BPA) in metabolic disorders. However, the underlying mechanism is still unclear. Using a mouse BPA exposure model, we investigated the effects of long-term BPA exposure on lipid metabolism and the underlying mechanisms. The male mice exposed to BPA (0.5 μg BPA /kg/day, a human relevant dose) for 10 months exhibited significant hepatic accumulation of triglycerides and cholesterol. The liver cells from the BPA-exposed mice showed significantly increased expression levels of the genes related to lipid synthesis. These liver cells showed decreased DNA methylation levels of Srebf1 and Srebf2, and increased expression levels of Srebf1 and Srebf2 that may upregulate the genes related to lipid synthesis. The expression levels of DNA methyltransferases were decreased in BPA-exposed mouse liver. Hepa1-6 cell line treated with BPA showed decreased expression levels of DNA methyltransferases and increased expression levels of genes involved in lipid synthesis. DNA methyltransferase knockdown in Hepa1-6 led to hypo-methylation and increased expression levels of genes involved in lipid synthesis. Our results suggest that long-term BPA exposure could induce hepatic lipid accumulation, which may be due to the epigenetic reprogramming of the genes involved in lipid metabolism, such as the alterations of DNA methylation patterns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".