Trace levels of peptidoglycan in serum underlie the NOD-dependent cytokine response to endoplasmic reticulum stress
Bibliographic record
Abstract
Abstract NOD1 and NOD2 are intracellular sensors of bacterial peptidoglycan that belong to the Nod-like receptor (NLR) family of innate immune proteins. In addition to their role as direct bacterial sensors, it was recently proposed that NOD proteins could detect endoplasmic reticulum (ER) stress induced by thapsigargin, an inhibitor of the sarcoplasmic or endoplasmic reticulum calcium ATPase family (SERCA) that pumps Ca 2+ into the ER, resulting in pro-inflammatory signalling. Here, we confirm that thapsigargin induces pro-inflammatory signalling in epithelial cells in a NOD-dependent manner. However, the effect was specific to thapsigargin, as tunicamycin and the subtilase cytotoxin SubAB from Shiga toxigenic Escherichia coli, which induce ER stress by other mechanisms, did not induce cytokine expression. The calcium ionophore A23187 also induced NOD-dependent signalling, and the calcium chelator BAPTA-AM blunted thapsigargin-dependent pro-inflammatory signalling, showing NOD proteins responded to a rise in intracellular Ca 2+ . Since intracellular Ca 2+ directly affects vesicular trafficking, we tested if thapsigargin-induced NOD activation required endocytosis. Our results demonstrate that both endocytosis and the addition of serum to the cell culture medium were required for thapsigargin-mediated NOD activation. Finally, we analyzed cell culture grade fetal calf serum as well as serum from laboratory mice by high-pressure liquid chromatography and mass spectrometry, and identified the presence of various peptidoglycan fragments. We propose that cellular perturbations that affect intracellular Ca 2+ can trigger internalization of peptidoglycan trace contaminants found in culture serum, thereby stimulating pro-inflammatory signalling. The presence of peptidoglycan in animal serum suggests that a homeostatic function of NOD signalling may have been previously overlooked.
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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".