Sec22b regulates inflammatory responses by controlling the nuclear translocation of NF-κB
Bibliographic record
Abstract
Abstract Soluble NSF attachment receptor (SNARE) proteins regulate the vesicle transport machinery in phagocytic cells. Within the secretory pathway, Sec22b is an ER-Golgi intermediate compartment (ERGIC)-resident SNARE that controls phagosome maturation and function in macrophages and dendritic cells. The secretory pathway controls the release of cytokines and may also impact the secretion of nitric oxide (NO), which is synthesized by the Golgi-active inducible nitric oxide synthase (iNOS). Whether ERGIC SNARE Sec22b controls NO and cytokine secretion, is unknown. Using bone marrow-derived dendritic cells (BMDC), we demonstrated that iNOS colocalizes with ERGIC/Golgi markers, notably Sec22b and its partner syntaxin-5 (Stx5), in the cytoplasm and at the phagosome. Pharmacological blockade of the secretory pathway hindered NO and cytokine release, and inhibited NF-κB translocation to the nucleus. Importantly, RNAi-mediated silencing of Sec22b revealed that NO and cytokine production were abrogated at the protein and mRNA levels. This correlated with deregulated mitogen-activated protein kinase signalling and reduced nuclear translocation of NF-κB. We also found that Sec22b co-occurs with NF-κB in both the cytoplasm and nucleus, pointing to a role for this SNARE in the shuttling of NF-κB. Collectively, our data unveiled a novel function for the ER-Golgi, and its resident SNARE Sec22b, in the production and release of inflammatory mediators.
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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 | 0.001 |
| 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".