The Pro-Inflammatory Cytokines IL-1β and TNFα Are Neurotrophic for Enteric Neurons
Bibliographic record
Abstract
Intestinal inflammation causes initial axonal degeneration and neuronal death but subsequent axon outgrowth from surviving neurons restores innervation density to the target smooth muscle cells. Elsewhere, the pro-inflammatory cytokines TNFα and IL-1β cause neurotoxicity, leading us to test their role in promoting enteric neuron death. In a rat coculture model, TNFα or IL-1β did not affect neuron number but did promote significant neurite outgrowth to twofold that of control by 48 h, while other cytokines (e.g., IL-4, TGFβ) were without effect. TNFα or IL-1β activated the NFκB signaling pathway, and inhibition of NFκB signaling blocked the stimulation of neurite growth. However, nuclear translocation of NFκB in smooth muscle cells but not in adjacent neurons suggested a dominant role for smooth muscle cells. TNFα or IL-1β sharply increased both mRNA and protein for GDNF, while the neurotrophic effects of TNFα or IL-1β were blocked by the RET-receptor blocker vandetanib. Conditioned medium from cytokine-treated smooth muscle cells mimicked the neurotrophic effect, inferring that TNFα and IL-1β promote neurite growth through NFκB-dependent induction of glial cell line-derived neurotrophic factor (GDNF) expression in intestinal smooth muscle cells. In vivo, TNBS-colitis caused early nuclear translocation of NFκB in smooth muscle cells. Conditioned medium from the intact smooth muscle of the inflamed colon caused a 2.5-fold increase in neurite number in cocultures, while Western blotting showed a substantial increase in GDNF protein. Pro-inflammatory cytokines promote neurite growth through upregulation of GDNF, a novel process that may facilitate re-innervation of smooth muscle cells and a return to homeostasis following initial damage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".