Tumor Necrosis Factor–Neuropeptide Y Cross Talk Regulates Inflammation, Epithelial Barrier Functions, and Colonic Motility
Bibliographic record
Abstract
BACKGROUND: Neuro-immune interactions play a significant role in regulating the severity of inflammation. Our previous work demonstrated that neuropeptide Y (NPY) is upregulated in the enteric nervous system during murine colitis and that NPY knockout mice exhibit reduced inflammation. Here, we investigated if NPY expression during inflammation is induced by tumor necrosis factor (TNF), the main proinflammatory cytokine. METHODS: Using primary enteric neurons and colon explant cultures from wild type and NPY knockout (NPY(-/-)) mice, we determined if NPY knockdown modulates TNF release and epithelial permeability. Further, we assessed if NPY expression is inducible by TNF in enteric neuronal cells and mouse model of experimental colitis, using the TNF inhibitors-etanercept (blocks transmembrane and soluble TNF) and XPro1595 (blocks soluble TNF only). RESULTS: We found that enteric neurons express TNF receptors (TNFR1 and R2). Primary enteric neurons from NPY(-/-) mice produced less TNF compared with wild type. Further, TNF activated NPY promoter in enteric neurons through phospho-c-Jun. NPY(-/-) mice had decreased intestinal permeability. In vitro, NPY increased epithelial permeability through phosphatidyl inositol-3-kinase (PI3-K)-induced pore-forming claudin-2. TNF inhibitors attenuated NPY expression in vitro and in vivo. TNF inhibitor-treated colitic mice exhibited reduced NPY expression and inflammation, reduced oxidative stress, enhanced neuronal survival, and improved colonic motility. XPro1595 had more protective effects on neuronal survival and motility compared with etanercept. CONCLUSIONS: We demonstrate a novel TNF-NPY cross talk that modulates inflammation, barrier functions, and colonic motility during inflammation. It is also suggested that selective blocking of soluble TNF may be a better therapeutic option than using anti-TNF antibodies.
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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".