Nigericin Promotes NLRP3-Independent Bacterial Killing in Macrophages
Bibliographic record
Abstract
Altered microbiota has been associated with a number of diseases, including inflammatory bowel diseases, diabetes, and cancer. This dysregulation is thought to relate the host inflammatory response to enteric pathogens. Macrophages play a key role in host response to microbes and are involved in bacterial killing and clearance. This process is partially mediated through the potassium efflux-dependent, cytosolic, PYCARD-containing inflammasome protein complex. Surprisingly, we discovered an alternative mechanism for bacterial killing, independent of the NLRP3 inflammasome/PYCARD. Using the NLRP3 inflammasome-deficient Raw 264.7 and PYCARD-deficient J77 macrophages, which both lack PYCARD, we found that the potassium efflux activator nigericin enhances bacterial killing. Macrophage response to nigericin was examined by RT gene profiling and subsequent qPCR, which demonstrated altered expression of a series of genes involved in the IL-18 bacterial killing pathway. Based on our results we propose a model of bacterial killing, unrelated to NLRP3 inflammasome activation in macrophage cells. Improving understanding of the molecular pathways driving bacterial clearance within macrophage cells will aid in the development of novel immune-targeted therapeutics in a number of diseases.
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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) | 0.001 | 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".