PSXIV-9 Identification of Differential Methylation Regions as Candidate Discriminant Markers of Bovine Subclinical Mastitis Caused by Staphylococcus Aureus
Bibliographic record
Abstract
Abstract Staphylococcus aureus (S. aureus) is one of the most prevalent pathogens of bovine subclinical mastitis and a challenging pathogen in dairy production. DNA methylation involvement in regulating mammary gland inflammatory defense has been documented. This study adopted whole genome bisulfite sequencing and RNA-sequencing technologies to profile the genome-wide DNA methylation and transcriptome landscapes, respectively, of milk somatic cells (MSCs) from 15 cows with S. aureus-induced subclinical mastitis (SACs) and 13 healthy cows (HCs). Bioinformatics processing of data with standard tools indicated that the global DNA methylation patterns of MSCs demonstrated inverse correlations between gene expression and DNA methylation levels at promoter, first exon and first intron regions, suggesting potential regulatory roles of DNA methylation in transcriptional activities. Totally, 26,289 differential methylation regions (DMRs) were identified between SACs and HCs (1,000 bp window, 1,000 bp step, q-value < 0.05, >20% methylation difference and ≥3 differentially methylated cytosines). A total of 887, 334 and 3,414 DMRs were overlapped with promoter, first exon, and first intron regions, respectively, including 363 DMRs harboring transcription start sites. Among them, the methylation levels of 956 DMRs were strongly correlated with the expression levels of their overlapped genes (|Spearman’s correlation coefficient| >0.5, adjusted-p-value< 0.05). Functional annotation of the overlapped genes indicated significant (adjusted-p-value < 0.05) enrichment in 18 GO terms and 1 KEGG pathway related to cellular activities, notably in cell migration, epithelial cell differentiation and tight junction, among others (Figure 1). These results suggest the involvement of DNA methylation in mammary gland defense against S. aureus-invasion. Applying DIABLO method to integrate correlated DMRs and genes, a total of 20 DMR and 10 gene candidate markers were found to discriminate SACs from HCs. The identified candidate markers could serve as reference for enhancing breeding for mastitis resistance and for developing new mastitis management strategies.
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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 | 0.001 | 0.001 |
| 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".