Characterization of miRNAs in Extracellular Vesicles Released From Atlantic Salmon Monocyte-Like and Macrophage-Like Cells
Bibliographic record
Abstract
transfer of molecular cargo including genetic material like miRNAs. In mammals, it has previously been established that EV-mediated transfer of miRNAs can alter the development or function of immune cells, such as macrophages. Our previous research revealed that Atlantic salmon head kidney leukocytes (HKLs) change their morphology, phagocytic ability and miRNA profile from primarily "monocyte-like" at Day 1 to primarily "macrophage-like" at Day 5 of culture. Therefore, we aimed to characterize the miRNA cargo packaged in EVs released from these two cell populations. We successfully isolated EVs from Atlantic salmon HKL culture supernatants using the established Vn96 peptide-based pull-down. Isolation was validated using transmission electron microscopy, nanoparticle tracking analysis, and Western blotting. RNA-sequencing identified 19 differentially enriched (DE) miRNAs packaged in Day 1 versus Day 5 EVs. Several of the highly abundant miRNAs, including those that were DE (e.g. ssa-miR-146a, ssa-miR-155 and ssa-miR-731), were previously identified as DE in HKLs and are associated with macrophage differentiation and immune response in other species. Interestingly, the abundance relative of the miRNAs in EVs, including the most abundant miRNA (ssa-miR-125b), was different than the miRNA abundance in HKLs, indicating selective packaging of miRNAs in EVs. Further study of the miRNA cargo in EVs derived from fish immune cells will be an important next step in identifying EV biomarkers useful for evaluating immune cell function, fish health, or response to disease.
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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) | 0.000 | 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 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".