Complexity of the microRNA transcriptome of cow milk and milk-derived extracellular vesicles isolated via differential ultracentrifugation
Bibliographic record
Abstract
MicroRNAs (miRNAs) are small gene-regulatory noncoding RNA that are highly enriched in cow milk. They are encapsulated in different extracellular vesicle (EV) subsets that protect them from the extracellular milieu and the harsh conditions of the gastrointestinal tract during digestion. Here, we isolated pellets enriched in 4 different EV subsets, via differential ultracentrifugation of commercial cow milk: 12,000 × g (P12K), 35,000 × g (P35K), 70,000 × g (P70K), and 100,000 × g (P100K). Small RNA sequencing (sRNA-Seq) analyses revealed an unprecedented level of diversity in the complete miRNA repertoire and features of unfractionated cow milk and derived EV subsets. Although 5 miRNA sequences represented more than 50% of all miRNAs, milk EV exhibited heterogeneous content of miRNAs and isomeric variants (termed isomiR): P100K EV were enriched in reference miRNA sequences, and P12K and P35K EV in related isomiR. Incubation of milk EV with human cultured HeLa cells led to cellular enrichment in miRNA miR-223, which was concomitant with decreased expression of a reporter gene placed under the control of miR-223, thereby demonstrating the functionality of miR-223. These results suggest that cow milk EV may transfer their miRNAs to human cells and regulate recipient cell gene expression programming in a manner as complex as that of their miRNA transcriptome. The biological activity and relevance of the different milk EV subsets and bioactive mediators, including small noncoding RNA, in health and disease, warrants further investigation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".