Mice Dually Disrupted for Nod2 and Mincle Manifest Early Bacteriological Control but Late Susceptibility During Mycobacterium tuberculosis Infection
Bibliographic record
Abstract
Pattern recognition receptors Mincle and NOD2 have been implicated in mycobacterial immunity. However, knockout (KO) animal infection studies with Mycobacterium tuberculosis (Mtb) have had mild/delayed phenotypes. Given that genetic susceptibility to infectious diseases can be polygenic, we hypothesized that murine double knockout (DKO) of Mincle and Nod2 would result in exacerbation of altered immunity to mycobacterial infection leading to a more extreme phenotype than either KO alone. To test this hypothesis, we monitored bacterial burden, immune responses and survival following in vivo infections with Mtb in DKO mice for comparison to wildtype (WT) and single KOs. Bacterial burden and immune responses were not significantly affected at 3 and 6 weeks after infection in all mutant mice. At later timepoints, Nod2-KO mice had reduced survival compared to wildtype mice, and Mincle-KO survival was intermediate. Unexpectedly, dual disruption had no further effect; rather, DKO mice phenocopied Nod2-KO mice. We observed that Mtb-related death, exclusively in mice with disrupted Nod2, was accompanied by greater pulmonary cell death and distinct large necrotic foci. Therefore, determining how these receptors contribute to mycobacterial resistance will require analysis of immunophenotypes and their consequences on host pathology.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".