Selective packaged circular RNAs in milk extracellular vesicles during <i>Staphylococcus aureus</i> infection may have potential against bacterial infection
Bibliographic record
Abstract
Extracellular vesicles (EVs) provide a novel intercellular communication mechanism to transfer biologically important molecules to target cells. Although several pieces of evidence have shown that EVs have potential to respond to bacterial infections, our knowledge about the role of circular RNA (circRNA), an important cargo of EV, behind this process remains poor. In particular, the mechanism by which circRNAs are packaged into EVs remains elusive during bacterial infection. In the present study, EVs from bovine milk samples with or without Staphylococcus aureus (S. aureus) infection were isolated. The presence of circRNAs in milk-derived EVs (MEVs) was validated for the first time by PCR amplification with convergent and divergent primers and the RNase R resistance test. Through high-throughput sequencing, the expression profile of circRNAs in EVs was found to be changed during S. aureus infection. Moreover, we demonstrated that circRNAs were selectively packaged into EVs. Finally, bioinformatic analyses predicted the involvement of differentially expressed circRNAs in immune functions. In summary, our findings offer an insight into the packaging mechanism of EV circRNAs and underscore the potential by which host used the EV circRNAs in response to pathogenic bacterial infections.
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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 | 0.001 | 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".