Assessment of microRNA profiles in small extracellular vesicles isolated from bovine colostrum with different immunoglobulin G concentrations
Bibliographic record
Abstract
The consumption of bovine colostrum by newborn calves during the first days of life is essential to ensure the transfer of passive immunity. In addition to critical IgG, colostrum also contains non-IgG biomolecules, including microRNA (miRNA). The present study investigated the profiles of miRNA in small extracellular vesicles (sEV) isolated from bovine colostrum with high (256.5 ± 5.7 mg/mL, mean ± standard deviation, n = 4) and low (62.8 ± 3.6 mg/mL, n = 4) concentrations of IgG. Different combination of sEV extraction methods and bioinformatic pipelines (miRDeep2 and sRNAbench) for miRNA analysis were evaluated. Results showed that miRCURY exosome Cell/Urine/CSF and miRNeasy Mini kits yielded the highest RNA concentration. The miRNA-seq data analysis showed miRDeep2 yielded more comprehensive miRNAome compared with sRNAbench (527 versus 392 unique miRNA), whereas 389 shared miRNA were identified using both approaches. The profiles of top 50 miRNA were the same using both approaches, and their abundance contributed to 91.7% and 94.3% of total abundance of miRNA using miRDeep2 and sRNAbennch, respectively. These core miRNA were predicted to target 2,655 genes, which regulate 78 KEGG (Kyoto Encyclopedia of Genes and Genomes) level-3 pathways including PI3K-Akt and MAPK signaling pathway, axon guidance, and focal adhesion. The expression profiles of sEV-associated miRNA were similar between high- and low-IgG colostrum samples, despite the fact that the abundance of miR-27a-3p was higher in colostrum with high concentrations of IgG. In conclusion, a core miRNAome in bovine colostrum may play a role in regulating health and developmental stages in neonatal calves, independent of IgG concentration.
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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 | 0.001 |
| 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 | 0.000 |
| 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".