BaeR protein acts as an activator of nuclear factor-kappa B and Janus kinase 2 to induce inflammation in murine cell lines
Bibliographic record
Abstract
BaeR, a response regulator protein, takes part in multidrug efflux, bacterial virulence activity, and other biological functions. Recently, BaeR was shown to induce inflammatory responses by activating the mitogen-activated protein kinases (MAPKs). In this study, we investigated additional pathways used by BaeR to induce an inflammatory response. BaeR protein was purified from Salmonella enterica Paratyphi A and subcloned into a pPosKJ expression vector. RAW 264.7 cells were treated with BaeR, and RNA was extracted by TRIzol reagent for RT-PCR. Cytokine gene expression was analyzed by using the comparative cycle threshold method, while western blotting and ELISA were used to assess protein expression. We confirmed that BaeR activates nuclear factor-kappa B (NF-κB), thereby inducing an inflammatory response and increases the production of interleukins (IL-)1β and IL-6. During this process, the Janus kinase 2 (JAK2)-STAT1 signaling pathway was activated, resulting in an increase in the release of interferons I and II. Additionally, COX-2 was activated and its expression increased with time. In conclusion, BaeR induced an inflammatory response through activation of NF-κB in addition to the MAPKs. Furthermore, activation of the JAK2-STAT1 pathway and COX-2 facilitated the cytokine binding activity, suggesting an additional role for BaeR in the modulation of the immune system of the host and the virulence activity of the pathogen.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 0.002 |
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".