<i>Mycobacterium paratuberculosis</i>is recognized by Toll-like receptors and NOD2
Bibliographic record
Abstract
Mycobacterium paratuberculosis has been suggested to be involved in the pathogenesis of Crohn's disease (CD). The importance of microorganisms in CD is supported by the association of CD with mutations in the intracellular pathogen recognition receptor (PRR) nucleotide-binding oligomerization domain 2 (NOD2). The aim of this study is to investigate the PRR involved in the recognition of M. paratuberculosis. Methods used include in vitro stimulation of transfected cell lines, murine macrophages, and human PBMC. M. paratuberculosis stimulated human TLR2 (hTLR2)-Chinese hamster ovary (CHO) cells predominantly and hTLR4-CHO cells modestly. Macrophages from TLR2 and TLR4 knockout mice produced less cytokines compared with controls after stimulation with M. paratuberculosis. TLR4 inhibition in human PBMC reduced cytokine production only after stimulation with live M. paratuberculosis. TLR-induced TNF-alpha, IL-1beta, and IL-10 production is mediated through MyD88, whereas Toll-IL-1R domain-containing adaptor inducing IFN-beta (TRIF) promoted the release of IL-1beta. hNOD2-human embryo kidney (HEK) cells, but not hNOD1-HEK cells, responded to stimulation with M. paratuberculosis. PBMC of individuals homozygous for the 3020insC NOD2 mutation showed a 70% defective cytokine response after stimulation with M. paratuberculosis. These results demonstrate that TLR2, TLR4, and NOD2 are involved in the recognition of M. paratuberculosis by the innate immune system.
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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) | 0.003 | 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".