Contribution of Phagocytosis and Intracellular Sensing for Cytokine Production by <i>Staphylococcus aureus</i> -Activated Macrophages
Bibliographic record
Abstract
Toll-like receptors (TLRs) are involved in the sensing of microbially derived compounds. We analyzed the contribution of these receptors to cytokine production by macrophages following stimulation with whole bacteria. Using knockout mice, we confirmed that the TLR4 and TLR2 contribution was predominant in the induction of tumor necrosis factor alpha and interleukin-10 by gram-negative bacteria. In contrast, the absence of TLR2 and/or TLR4 or TLR9 did not affect the response to gram-positive bacteria. In the absence of TLR2, phagocytosis was essential for cytokine production in response to heat-killed Staphylococcus aureus (HKSA). Because intracellular sensing was important in the absence of TLR2, we evaluated the contribution of Nod1 and Nod2, intracytoplasmic sensors of peptidoglycan-derived muropeptides, to the response to HKSA. By transfecting RAW 264.7 macrophages with dominant negative (DN) forms of Nod1 and Nod2, we showed that both molecules inhibited NF-kappaB activation in response to HKSA. The unexpected interference of DN Nod1 in the response of macrophages to gram-positive bacteria was confirmed with a Nod2 agonist (muramyl dipeptide) in transfection experiments with HEK293T cell. Taken together, these results show the contribution of phagocytosis and Nod molecules to the response to HKSA in macrophages and also identify possible cross talk between Nod1 and Nod2.
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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) | 0.001 | 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".