Bacterial membrane vesicles deliver peptidoglycan to NOD1 in epithelial cells
Bibliographic record
Abstract
Gram-negative bacterial peptidoglycan is specifically recognized by the host intracellular sensor NOD1, resulting in the generation of innate immune responses. Although epithelial cells are normally refractory to external stimulation with peptidoglycan, these cells have been shown to respond in a NOD1-dependent manner to Gram-negative pathogens that can either invade or secrete factors into host cells. In the present work, we report that Gram-negative bacteria can deliver peptidoglycan to cytosolic NOD1 in host cells via a novel mechanism involving outer membrane vesicles (OMVs). We purified OMVs from the Gram-negative mucosal pathogens: Helicobacter pylori, Pseudomonas aeruginosa and Neisseria gonorrhoea and demonstrated that these peptidoglycan containing OMVs upregulated NF-kappaB and NOD1-dependent responses in vitro. These OMVs entered epithelial cells through lipid rafts thereby inducing NOD1-dependent responses in vitro. Moreover, OMVs delivered intragastrically to mice-induced innate and adaptive immune responses via a NOD1-dependent but TLR-independent mechanism. Collectively, our findings identify OMVs as a generalized mechanism whereby Gram-negative bacteria deliver peptidoglycan to cytosolic NOD1. We propose that OMVs released by bacteria in vivo may promote inflammation and pathology in infected hosts.
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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".