Phosphoproteomic Analysis of <i>Salmonella</i> -Infected Cells Identifies Key Kinase Regulators and SopB-Dependent Host Phosphorylation Events
Bibliographic record
Abstract
Salmonella enterica is a bacterial pathogen that causes gastroenteritis and typhoid fever. Virulence is achieved by two type III secretion systems that translocate effector proteins into host cells, where they mimic or block host protein function. Effectors translocated into host cells by the first type III secretion system facilitate invasion and stimulate intracellular signaling cascades leading to inflammation. Here, we performed global temporal analysis of host signaling events induced during the initial stages of Salmonella infection and identified the dynamics of host protein phosphorylation as well as differences between growth factor-stimulated and bacteria-induced signaling. Informatics analysis predicted that sites with altered phosphorylation in infected cells were targeted by the serine-threonine kinases Akt, protein kinase C, and Pim and that up to half of the host phosphorylation events detected after Salmonella infection required the effector protein SopB. Our data reveal extensive manipulation of host phosphorylation cascades by this Salmonella effector and provide a detailed map of the events leading to intestinal inflammation, which is the crucial host response that enables Salmonella to proliferate in the intestine.
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) | 0.002 | 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".