Cathepsin B Is Involved in the Trafficking of TNF-α-Containing Vesicles to the Plasma Membrane in Macrophages
Bibliographic record
Abstract
TNF-alpha is a potent proinflammatory cytokine, essential for initiating innate immune responses against invading microbes and a key mediator involved in the pathogenesis of acute and chronic inflammatory diseases. To identify molecules involved in the production of TNF-alpha, we used a functional gene identification method using retroviral integration-mediated mutagenesis, followed by LPS-stimulated TNF-alpha production analysis in macrophages. We found that cathepsin B, a lysosomal cysteine proteinase, was required for optimal posttranslational processing of TNF-alpha in response to the bacterial cell wall component LPS. Mouse bone marrow-derived macrophages from cathepsin B-deficient mice and macrophages treated with the cathepsin B-specific chemical inhibitor CA074 methyl ester or small interfering RNA against cathepsin B secreted significantly less TNF-alpha than wild-type or nontreated macrophages. We further showed that the inhibition of cathepsin B caused accumulation of 26-kDa pro-TNF-containing vesicles. Ectopic expression of GFP-conjugated pro-TNF further suggests that pro-TNF failed to reach the plasma membrane without intracellular cathepsin B activity. Altogether, these data suggest that intracellular cathepsin B activity is involved in the TNF-alpha-containing vesicle trafficking to the plasma membrane.
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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) | 0.000 | 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".