Multiple Host Kinases Contribute to Akt Activation during Salmonella Infection
Bibliographic record
Abstract
SopB is a type 3 secreted effector with phosphatase activity that Salmonella employs to manipulate host cellular processes, allowing the bacteria to establish their intracellular niche. One important function of SopB is activation of the pro-survival kinase Akt/protein kinase B in the infected host cell. Here, we examine the mechanism of Akt activation by SopB during Salmonella infection. We show that SopB-mediated Akt activation is only partially sensitive to PI3-kinase inhibitors LY294002 and wortmannin in HeLa cells, suggesting that Class I PI3-kinases play only a minor role in this process. However, depletion of PI(3,4) P2/PI(3-5) P3 by expression of the phosphoinositide 3-phosphatase PTEN inhibits Akt activation during Salmonella invasion. Therefore, production of PI(3,4) P2/PI(3-5) P3 appears to be a necessary event for Akt activation by SopB and suggests that non-canonical kinases mediate production of these phosphoinositides during Salmonella infection. We report that Class II PI3-kinase beta isoform, IPMK and other kinases identified from a kinase screen all contribute to Akt activation during Salmonella infection. In addition, the kinases required for SopB-mediated activation of Akt vary depending on the type of infected host cell. Together, our data suggest that Salmonella has evolved to use a single effector, SopB, to manipulate a remarkably large repertoire of host kinases to activate Akt for the purpose of optimizing bacterial replication in its host.
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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 | 0.000 |
| 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".