Muramylpeptide shedding modulates cell sensing of Shigella flexneri
Bibliographic record
Abstract
Bacterial infections trigger the activation of innate immunity through the interaction of pathogen-associated molecular patterns (PAMPs) with pattern recognition molecules (PRMs). The nucleotide-binding oligomerization domain (Nod) proteins are intracellular PRMs that recognize muramylpeptides contained in peptidoglycan (PGN) of bacteria. It is still unclear how Nod1 physically interacts with PGN, a structure internal to the Gram-negative bacterial envelope. To contribute to the understanding of this process, we demonstrate that, like Escherichia coli, Bordetella pertussis and Neisseria gonorrheae, the Gram-negative pathogen Shigella spontaneously releases PGN fragments and that this process can be increased by inactivating either ampG or mppA, genes involved in PGN recycling. Both Shigella mutants, but especially the strain carrying the mppA deletion, trigger Nod1-mediated NF-kappaB activation to a greater extent than the wild-type strain. Likewise, muramylpeptides spontaneously shed by Shigella are able per se to trigger a Nod1-mediated response consistent with the relative amount. Finally, we found that qualitative changes in muramylpeptide shedding can alter in vivo host responses to Shigella infection. Our findings support the idea that muramylpeptides released by pathogens during infection could modulate the immune response through Nod proteins and thereby influence the outcome of disease.
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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".