Interleukin-17A Increases Neurite Outgrowth from Adult Postganglionic Sympathetic Neurons
Bibliographic record
Abstract
Inflammation can profoundly alter the structure and function of the nervous system. Interleukin (IL)-17 has been implicated in the pathogenesis of several inflammatory diseases associated with nervous system plasticity. However, the effects of IL-17 on the nervous system remain unexplored. Cell and explant culture techniques, immunohistochemistry, electrophysiology, and Ca2+ imaging were used to examine the impact of IL-17 on adult mouse sympathetic neurons. Receptors for IL-17 were present on postganglionic neurons from superior mesenteric ganglia (SMG). Supernatant from activated splenic T lymphocytes, which was abundant in IL-17, dramatically enhanced axonal length of SMG neurons. Importantly, IL-17-neutralizing antiserum abrogated the neurotrophic effect of splenocyte supernatant, and incubation of SMG neurons in IL-17 (1 ng/ml) significantly potentiated neurite outgrowth. The neurotrophic effect of IL-17 was accompanied by inhibition of voltage-dependent Ca2+ influx and was recapitulated by incubation of neurons in a blocker of N-type Ca2+ channels (ω-conotoxin GVIA; 30 nM). IL-17-induced neurite outgrowth in vitro appeared to be independent of glia, as treatment with a glial toxin (AraC; 5 μM) did not affect the outgrowth response to IL-17. Moreover, application of the cytokine to distal axons devoid of glial processes enhanced neurite extension. An inhibitor of the NF-κB pathway (SC-514; 20 μM) blocked the effects of IL-17. These data represent the first evidence that IL-17 can act on sympathetic somata and distal neurites to enhance neurite outgrowth, and identify a novel potential role for IL-17 in the neuroanatomical plasticity that accompanies inflammation.
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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".