Stimulation of the cytosolic receptor for peptidoglycan, Nod1, by infection with Chlamydia trachomatis or Chlamydia muridarum
Bibliographic record
Abstract
Infection of epithelial cells by the intracellular pathogen, Chlamydia trachomatis, leads to activation of NF-kappaB and secretion of pro-inflammatory cytokines. We find that overexpression of a dominant-negative Nod1 or depletion of Nod1 by RNA interference inhibits partially the activation of NF-kappaB during chlamydial infection in vitro, suggesting that Nod1 can detect the presence of Chlamydia. In parallel, there is a larger increase in the expression of pro-inflammatory genes following Chlamydia infection when primary fibroblasts are isolated from wild-type mice than from Nod1-deficient mice. The Chlamydia genome encodes all the putative enzymes required for proteoglycan synthesis, but proteoglycan from Chlamydia has never been detected biochemically. Since Nod1 is a ubiquitous cytosolic receptor for peptidoglycan from Gram-negative bacteria, our results suggest that C. trachomatis and C. muridarum do in fact produce at least the rudimentary proteoglycan motif recognized by Nod1. Nonetheless, Nod1 deficiency has no effect on the efficiency of infection, the intensity of cytokine secretion, or pathology in vaginally infected mice, compared with wild-type controls. Similarly, Rip2, a downstream mediator of Nod1, Toll-like receptor (TLR)-2, and TLR4, increases only slightly the intensity of chlamydial infection in vivo and has a very mild effect on the immune response and pathology. Thus, Chlamydia may not produce sufficient peptidoglycan to stimulate Nod1-dependent pathways efficiently in infected animals, or other receptors of the innate immune system may compensate for the absence of Nod1 during Chlamydia infection in vivo.
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 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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".