Regulatory network of miRNAs, lncRNAs and target genes associated with immune response in bovine mastitis
Bibliographic record
Abstract
Abstract Bovine mastitis is mainly caused by Escherichia coli and Streptococcus uberis. It is associated with complex multifactorial phenotypes, requiring an integrative approach to elucidate the molecular networks underlying variable disease outcome. The association of lncRNAs, miRNAs and their target as key players in immunity regulation has not been studied in bovine mastitis. We hypothesize that non-coding genes could mediate immune response in bovine mastitis and have potential as disease markers or drug targets. Through bioinformatic analyses, we investigated the networks of lncRNAs, miRNAs and mRNAs, and identified key regulatory elements driving immune response in bovine mastitis. Our analyses reveal 16 highly significant immune response genes including MYD88, IL-10, IL-4, ICAM1, CXCL8, IL-18 and CSF2. Notably, of the 2904 miRNAs reported, six - bta-miR-24-3p, bta-miR-149-5p, bta-miR-223, bta-miR-185, bta-miR-328, and bta-miR-874, were predicted to bind multiple regions of target genes. Likewise, eight out of 22 lncRNAs including NONBTAT001181.2, XR_003030515.1 and XR_003030515.1 bind 13 mRNA targets. Of interest, some of these are conserved in 15 different species, including Homo sapiens. Our functional analyses show that these lncRNAs and miRNAs may regulate pathogenesis of bovine mastitis through lipopolysaccharide-mediated signaling pathway, regulation of chemokine (C-X-C motif) ligand 2 production, regulation of IL-23 production, positive regulation of chemokine production and pattern recognition receptor activity. This interactome lays a foundation for molecular interconnectivity of regulatory elements in bovine mastitis, deserving further elucidation for potential vaccine and therapeutics.
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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".