Alternative activation of macrophages by IL-4 enhances the proteolytic capacity of their phagosomes through synergistic mechanisms
Bibliographic record
Abstract
Alternatively activated macrophages, generated in a T-helper 2 environment, have demonstrated roles in wound repair and tissue remodeling in addition to being charged with immune tasks. Because the hydrolytic chemistries of the phagosomal lumen are central to many of these functions, we investigated their modification after alternative activation with IL-4 and IL-13. Most significantly, we found striking up-regulation of the proteolytic levels within the phagosome of IL-4-activated macrophages. Two synergistic mechanisms were determined to underlie this up-regulation. First, IL-4-activated macrophages displayed increased expression of cathepsin S and L, providing greater proteolytic machinery to the phagosome despite unchanged rates of lysosomal contribution. Secondly, decreased phagosomal NADPH oxidase (NOX2) activity, at least partially resulting from decreased expression of the NOX2 subunit gp91(phox), resulted in a more reductive lumenal microenvironment, which in turn, enhanced activities of local cysteine cathepsins. Decreased NOX2 activity additionally increased the phagosome's ability to reduce disulfides, further enhancing the efficiency of the macrophage to degrade proteins containing disulfide bonds. Together, these changes initiated by IL-4 act synergistically to rapidly and dramatically enhance the macrophage's ability to degrade phagocytosed protein, which, we reason, better equips this cell for its roles in wound repair and tissue remodeling.
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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) | 0.001 | 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 | 0.001 |
| 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".