Milk derived extracellular vesicle uptake in human microglia regulates the DNA methylation machinery
Bibliographic record
Abstract
Mammalian milk contains milk-derived extracellular vesicles (MEVs), a group of biological nanovesicles that transport macromolecules. Their ability to cross the blood brain barrier and the presence of cargo capable of modifying gene function have led to the hypothesis that MEVs may play a role in brain function and development. Here, we investigated the uptake of MEVs by human microglia cells in vitro and explored the functional outcomes of MEV uptake. We examined the expression of the miR-148/152 family, highly abundant MEV microRNAs, that directly suppress the translation of DNA methyltransferase (DNMT) enzymes crucial for catalyzing DNA methylation modifications. We also measured phenotypic and inflammatory gene expression in baseline homeostatic and IFN-γ primed microglia to determine if MEVs induce anti-inflammatory effects. We found that MEVs are taken up and localize in baseline and primed microglia. In baseline microglia, MEV supplementation reduced miR-148a-5P levels, increased DNMT1 transcript, protein abundance, and enzymatic activity, compared to cells that did not receive MEVs. In primed microglia, MEV supplementation decreased miR-148a-5P levels and increased DNMT1 protein abundance, but DNMT1 transcript and enzymatic levels remained unchanged. Contrary to predictions, MEV supplementation failed to attenuate pro-inflammatory IL1β expression in primed microglia. This study provides the first evidence of MEV uptake by a brain macrophage, suggesting a potential role in regulating epigenetic machinery and neuroimmune modulation.
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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".