Nod-like receptors are critical for gut-brain axis signaling
Bibliographic record
Abstract
ABSTRACT Gut-brain axis signaling is critical for maintaining health and homeostasis. Stressful life events can impact gut-brain signaling, leading to altered mood, cognition and intestinal dysfunction. Here we identify nucleotide binding oligomerization domain (Nod)-like receptors (NLR), Nod1 and Nod2, as novel regulators for gut-brain signaling. NLR are innate immune pattern recognition receptors expressed in the gut and brain, important in the regulation of gastrointestinal (GI) physiology. We found that mice deficient in both Nod1 and Nod2 (NodDKO) demonstrate signs of stress-induced anxiety, cognitive impairment and depression in the context of a hyperactive hypothalamic-pituitary-adrenal axis. These deficits were coupled with impairments in the serotonergic pathway in the brain, decreased hippocampal neurogenesis, and reduced neural activation. In addition, NodDKO mice had increased GI permeability and altered serotonin signaling in the gut following exposure to acute stress. Administration of the selective serotonin reuptake inhibitor, fluoxetine, abrogated behavioral impairments and restored serotonin signaling. We also identified that intestinal epithelial cell-specific deletion of Nod1 (VilCre + Nod1 f/f ), but not Nod2, increased susceptibility to stress-induced anxiety-like behavior and cognitive impairment following exposure to stress. Together these data suggest that intestinal epithelial NLR are novel modulators of gut-brain communication and may serve as potential novel therapeutic targets for the treatment of gut-brain disorders.
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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 | 0.001 |
| 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".