Fatty Acids Modify the MicroRNA Content of Exosomes Released by Hypothalamic Astrocytes and the Response of POMC Neurons to These Exosomes
Bibliographic record
Abstract
Abstract It is now clear that hypothalamic astrocytes participate in maintaining metabolic homeostasis. Both nutrients and metabolic hormones can directly impact on these glial cells to modify their release of gliotransmitters, metabolic factors, growth factors, etc, as well as their physical interaction with neighboring neurons. Another mechanism by which astrocytes could communicate with neurons is through their release of exosomes. We have previously shown (by RNAseq analysis) that exposure to palmitic acid (PA) dramatically modifies the miRNA content of exosomes released by hypothalamic astrocytes. Here our objectives were: 1) To determine if the miRNA changes in hypothalamic astrocyte-derived exosomes in response to oleic acid (OA) differ from those seen in response to PA and 2) Analyze the response of POMC neurons to exosomes derived from astrocytes exposed to either PA or OA. Primary hypothalamic astrocyte cultures were treated with PA (0.5 mM), OA (0.5 mM) or vehicle (V) for 24 hours. Exosomes were purified from the media and used for miRNA analysis and to treat a POMC neuronal cell line (mHypoA-POMC/GFP-1). Both OA and PA modified miRNA levels in exosomes compared to those detected in V exosomes, but these modifications differed between the two fatty acids. Furthermore, the response of POMC neurons to exosomes from vehicle (E-V), OA (E-OA) and PA (E-PA) treated astrocytes for 24 hours differed significantly. The expression of POMC mRNA was significantly decreased in response to E-V and increased in response to both E-OA and E-PA, although the increase in POMC mRNA was significantly greater in E-PA treated neurons. These results suggest that hypothalamic astrocytes can directly communicate with neurons involved in metabolic control through exosomes and that the messages contained within these exosomes are modulated by the nutrient environment.
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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".