Epigenetic response in mice mastitis: Role of histone H3 acetylation and microRNA(s) in the regulation of host inflammatory gene expression during Staphylococcus aureus infection
Bibliographic record
Abstract
BACKGROUND: There is renewed interest towards understanding the host-pathogen interaction in the light of epigenetic modifications. Although epithelial tissue is the major site for host-pathogen interactions, there is handful of studies to show how epithelial cells respond to pathogens. Bacterial infection in the mammary gland parenchyma induces local and subsequently systemic inflammation that results in a complex disease called mastitis. Globally Staphylococcus aureus is the single largest mastitis pathogen and the infection can ultimately result in either subclinical or chronic and sometimes lifelong infection. RESULTS: In the present report we have addressed the differential inflammatory response in mice mammary tissue during intramammary infection and the altered epigenetic context induced by two closely related strains of S. aureus, isolated from field samples. Immunohistochemical and immunoblotting analysis showed strain specific hyperacetylation at histone H3K9 and H3K14 residues. Global gene expression analysis in S. aureus infected mice mammary tissue revealed a selective set of upregulated genes that significantly correlated with the promoter specific, histone H3K14 acetylation. Furthermore, we have identified several differentially expressed known miRNAs and 3 novel miRNAs in S. aureus infected mice mammary tissue by small RNA sequencing. By employing these gene expression data, an attempt has been made to delineate the gene regulatory networks in the strain specific inflammatory response. Apparently, one of the isolates of S. aureus activated the NF-κB signaling leading to drastic inflammatory response and induction of immune surveillance, which could possibly lead to rapid clearance of the pathogen. The other strain repressed most of the inflammatory response, which might help in its sustenance in the host tissue. CONCLUSION: Taken together, our studies shed substantial lights to understand the mechanisms of strain specific differential inflammatory response to S. aureus infection during mastitis. In a broader perspective this study also paves the way to understand how certain bacteria can evade the immune surveillance and cause sustained infection while others are rapidly cleared from the host body.
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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 | 0.001 |
| 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".