Identification of Ovine Serum miRNAs Following Bacterial Lipopolysaccharide Challenge
Bibliographic record
Abstract
Host–pathogen interactions are complex and influenced by host genetic and epigenetic modifications. Recently, the significance of microRNAs (miRNAs) in pathogenic infection and the regulation of immune response has been highlighted. However, information on miRNAs’ role in the course of inflammation is still very limited in small ruminants. The present study was intended to identify changes in the expression of circulatory miRNAs post-lipopolysaccharide (LPS)-challenge. In this study, young ewes (n = 18) were challenged with Escherichia coli LPS (400 ng/kg i.v.) and blood samples were collected for serum miRNA isolation at two-time points; prior to challenge (T0), and 4 h (T4) post-challenge, reflecting the peak cortisol response. A total of 91 miRNAs were profiled, including 84 miRNAs on a commercial ovine miRNA-PCR array, and seven individual miRNAs. Forty five miRNAs were differentially expressed (DE) with 35 being up-regulated (Fold regulation, FR > 2) and 10 being down-regulated (FR < 1, p < 0.05) at T4. Among the up-regulated miRNAs, 14 were significantly (p < 0.05) induced, including oar-miRs: 369-3p, 495-3p, 376a-3p, 543-3p, 668-3p, 329a-3p, 655-3p, 411a-5p, and 154a-3p, which were located on ovine chromosome 18 forming four miRNA clusters within 10 kb. The elevated miRNAs belonged to different functional classes, playing roles in activating the hypothalamic-pituitary-adrenal axis; increasing cell survival and differentiation; and inducing inflammatory responses and targeted PI3K-Akt and MAPK signaling and chemokine signaling pathways. In summary, these results reveal the dynamic nature of ovine serum miRNAs during LPS-induced stress and highlight the potential role of identified miRNA-clusters on chromosome 18 to understand the regulation of the acute-phase response. Some of these identified circulating miRNAs may also serve as stress biomarkers for livestock in the future.
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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".