Expression Profile of miRNA from High, Middle, and Low Stress-Responding Sheep during Bacterial Endotoxin Challenge
Bibliographic record
Abstract
Animals respond to stress by activating a wide array of physiological and behavioral responses that are collectively referred to as the stress response. MicroRNAs (miRNAs) are small, noncoding RNAs that play key roles in the regulation of homeostasis. There are many reports demonstrating examples of stress-induced miRNA expression profiles. The aim of this study was to determine the circulatory miRNA profile of variable stress-responding lambs (n = 112) categorized based on their cortisol levels as high (HSR, 336.2 ± 27.9 nmol/L), middle (MSR, 147.3 ±9.5 nmol/L), and low (LSR, 32.1 ± 10.4 nmol/L) stress responders post-LPS challenge (400 ng/kg iv). Blood was collected from the jugular vein at 0 (T0) and 4 h (T4) post-LPS challenge, and miRNAs were isolated from four animals from each group. An array of 84 miRNAs and 6 individual miRNAs were evaluated using qPCR. Among 90 miRNAs, there were 48 differentially expressed (DE) miRNAs (log fold change (FC) > 2 < log FC) in the HSR group, 46 in the MSR group, and 49 in the LSR group compared with T0 (control) samples. In the HSR group, three miRNAs, miR-485-5p, miR-1193-5p, and miR-3957-5p were significantly (p < 0.05) upregulated, while seven miRNAs, miR-376b-3p, miR-376c-3p, miR-411b-5p, miR-376a-3p, miR-376b-3p, miR-376c-3p, and miR-381-3p, were downregulated (p < 0.05) as compared to the LSR and MSR groups. Functional analysis of DE miRNAs revealed their roles in Ras and MAPK signaling, cytokine signaling, the adaptive immune system, and transcription pathways in the HSR phenotype, implicating a hyper-induced acute-phase response. In contrast, in the LSR group, enriched pathways included glucagon signaling metabolic regulation, the transportation of amino acids and ions, and the integration of energy metabolism. Taken together, these results indicate variation in the acute-phase response to an immune stress challenge, and these miRNAs are implicated in regulating responses within cortisol-based phenotypes.
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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".