Combined effects of intrinsic host gene (TRAPPC9) and extrinsic nutrient (folate) on resistance against S. aureus induced bovine mastitis
Bibliographic record
Abstract
Abstract Background: Drug-resistance and immunological escape of Staphylococcus aureus and its “superbug”, methicillin-resistant S. aureus (MRSA), have become one of main causes of bacterial infection in both human and animals. In dairy cattle, elimination of bovine mastitis induced by S. aureus is of importance because S. aureus-infected cows normally are culled passively. Methods: Here, we investigated the beneficial effects of bovine trafficking protein particle complex 9 (TRAPPC9) gene and folic acid supplementation in the control of mastitis induced by S. aureus or MRSA by a series of in vivo and in vitro experiments. Results: The data showed that the genetic mutations and DNA methylation of TRAPPC9 were highly linked with the mastitis resistance of dairy cows. Additionally, knockdown of bovine TRAPPC9 was significantly involved in the mRNA expression levels of interleukin’s genes (increased IL-1β and IL-6), and down-regulated the protein level of NF-κB-P65 in the mastitis cell model induced by MRSA. Meanwhile, dose-dependent folic acid addition can inhibit the invasion of MRSA into Mac-T cells and improve TRAPPC9 expression in dairy cows. Conclusions: Altogether, our data suggest that an appropriate dose of folic acid can significantly reduce the inflammation caused by MRSA partially through TRAPPC9 mediated NF-κB pathway. These findings provide new insights to control the drug-resistant pathogens and to restrict the overuse of antibiotics through combined effects of the intrinsic host gene and extrinsic nutrient.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".