<i>Salmonella typhimurium</i> infection increases p53 acetylation in intestinal epithelial cells
Bibliographic record
Abstract
The ability of Salmonella typhimurium to enter intestinal epithelial cells constitutes a crucial step in pathogenesis. Salmonella invasion of the intestinal epithelium requires bacterial type three secretion system. Type three secretion system is a transport device that injects virulence proteins, called effectors, to paralyze or reprogram the eukaryotic cells. Avirulence factor for Salmonella (AvrA) is a Salmonella effector that inhibits the host's inflammatory responses. The mechanism by which AvrA modulates host cell signaling is not entirely clear. p53 is situated at the crossroads of a network of signaling pathways that are essential for genotoxic and nongenotoxic stress responses. We hypothesized that Salmonella infection activates the p53 pathway. We demonstrated that Salmonella infection increased p53 acetylation. Cells infected with AvrA-sufficient Salmonella have increased p53 acetylation, whereas cells infected with AvrA-deficient Salmonella have less p53 acetylation. In a cell-free system, AvrA possessed acetyltransferase activity and used p53 as a substrate. AvrA expression increased p53 transcriptional activity and induced cell cycle arrest. HCT116 p53-/- cells had less inflammatory responses. In a mouse model of Salmonella infection, intestinal epithelial p53 acetylation was increased by AvrA expression. Our studies provide novel mechanistic evidence that Salmonella modulates the p53 pathway during intestinal inflammation and infection.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".