Trace levels of peptidoglycan in serum underlie the NOD-dependent cytokine response to endoplasmic reticulum stress
Bibliographic record
Abstract
NOD1 and NOD2 are intracellular sensors of bacterial peptidoglycan that belong to the Nod-like receptor family of innate immune proteins. In addition to their role as direct bacterial sensors, it was proposed that the nucleotide-binding oligomerization domain (NOD) proteins could detect endoplasmic reticulum (ER) stress induced by thapsigargin, an inhibitor of the sarcoplasmic or endoplasmic reticulum calcium ATPase family that pumps Ca 2+ into the ER, resulting in pro-inflammatory signaling. Here, we confirm that thapsigargin induces NOD-dependent pro-inflammatory signaling in epithelial cells. 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 signaling, and calcium chelators demonstrated a role for both intracellular and extracellular calcium in mediating thapsigargin-induced and NOD-dependent pro-inflammatory signaling, in part through the activation of plasma membrane-associated calcium release-activated channels. Moreover, 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 using HPLC and MS 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 signaling. The presence of peptidoglycan in animal serum suggests that a homeostatic function of NOD signaling may have been previously overlooked.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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 teacher head, 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".