Calpains promote neutrophil recruitment and bacterial clearance in an acute bacterial peritonitis model
Bibliographic record
Abstract
Activation of the innate immune system is critical for clearance of bacterial pathogens to limit systemic infections and host tissue damage. Here, we report a key role for calpain proteases in bacterial clearance in mice with acute peritonitis. Using transgenic mice expressing Cre recombinase primarily in innate immune cells (fes-Cre), we generated conditional capns1 knockout mice. Consistent with capns1 being essential for stability and function of the ubiquitous calpains (calpain-1, calpain-2), peritoneal cells from these mice had reduced levels of calpain-2/capns1, and reduced proteolysis of their substrate selenoprotein K. Using an acute bacterial peritonitis model, we observed impaired bacterial killing within the peritoneum and development of bacteremia in calpain knockout mice. These defects correlated with significant reductions in IL-1α release, neutrophil recruitment, and generation of reactive oxygen species in calpain knockout mice with acute bacterial peritonitis. Peritoneal macrophages from calpain knockout mice infected with enterobacteria ex vivo, were competent in phagocytosis of bacteria, but showed impaired clearance of intracellular bacteria compared with control macrophages. Together, these results implicate calpains as key mediators of effective innate immune responses to acute bacterial infections, to prevent systemic dissemination of bacteria that can lead to sepsis.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".