MicroRNA-34a-5p regulates agouti-related peptide via krüppel-like factor 4 and is disrupted by bisphenol A in hypothalamic neurons
Bibliographic record
Abstract
Obesity is a complex disease marked by increased adiposity and impaired metabolic function. While diet and lifestyle are primary causes, endocrine-disrupting chemicals (EDCs), such as bisphenol A (BPA), significantly contribute to obesity. BPA, found in plastic consumer products, accumulates in the hypothalamus and dysregulates energy homeostasis by disrupting the neuropeptide Y (NPY)/agouti-related peptide (AgRP) and pro-opiomelanocortin (POMC) neurons. However, the precise molecular mechanisms of how BPA disrupts neuropeptide expression remains unclear. We hypothesized that microRNAs (miRNAs), which regulate approximately 60% of the human protein-coding genome and are crucial for hypothalamic energy regulation, may mediate the effects of BPA on Agrp. Using the TargetScanMouse 8.0 and DIANA microT bioinformatics tools, we identified miR-501-5p as a potential miRNA that directly regulates Agrp and the miR-34 family as miRNAs that indirectly regulate Agrp through its transcription factor krüppel-like factor 4 (KLF4). We found that in an immortalized NPY/AgRP-expressing cell line, mHypoE-41, miR-501-5p unexpectedly upregulated Agrp, while miR-34a-5p reduced Klf4 and Agrp mRNA levels. Serum starvation reduced miR-34a-5p levels and elevated Agrp mRNA levels, suggesting a potential role in AgRP regulation. Inhibiting the miR-34a-5p interaction with the Klf4 3'UTR using a specific target site blocker prevented the downregulation of both Klf4 and Agrp, suggesting miR-34a-5p alters Agrp mRNA levels via regulation of KLF4. BPA treatment increased Agrp and Klf4 expression while simultaneously decreasing miR-34a-5p levels, indicating miR-34a-5p may play a role in BPA-mediated dysregulation of Agrp. Overall, this study highlights indirect miRNA-based regulation of Agrp, which can also be dysregulated by obesogens, such as BPA.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
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".