Diabetes impairs an interleukin-1β-dependent pathway that enhances neurite outgrowth through JAK/STAT3 modulation of mitochondrial bioenergetics in adult sensory neurons
Bibliographic record
Abstract
BACKGROUND: A luminex-based screen of cytokine expression in dorsal root ganglia (DRG) and nerve of type 1 diabetic rodents revealed interleukin-1 (IL-1α) and IL-1β to be significantly depressed. We, therefore, tested the hypothesis that impaired IL-1α and IL-1β expression in DRG may contribute to aberrant axon regeneration and plasticity seen in diabetic sensory neuropathy. In addition, we determined if these cytokines could optimize mitochondrial bioenergetics since mitochondrial dysfunction is a key etiological factor in diabetic neuropathy. RESULTS: Cytokines IL-1α and IL-1β were reduced 2-fold (p<0.05) in DRG and/or nerve of 2 and 5 month streptozotocin (STZ)-diabetic rats. IL-2 and IL-10 were unchanged. IL-1α and IL-1β induced similar 2 to 3-fold increases in neurite outgrowth in cultures derived from control or diabetic rats (p<0.05). STAT3 phosphorylation on Tyr705 or Ser727 was depressed in DRG from STZ-diabetic mice and treatment of cultures derived from STZ-diabetic rats with IL-1β for 30 min raised phosphorylation of STAT3 on Tyr705 and Ser727 by 1.5 to 2-fold (p<0.05). shRNA-based or AG490 inhibition of STAT3 activity or shRNA blockade of endogenous IL-1β expression completely blocked neurite outgrowth. Cultured neurons derived from STZ-diabetic mice were treated for 24 hr with IL-1β and maximal oxygen consumption rate and spare respiratory capacity, both key measures of bioenergetic fidelity that were depressed in diabetic compared with control neurons, were enhanced 2-fold. This effect was blocked by AG490. CONCLUSIONS: Endogenous synthesis of IL-1β is diminished in nerve tissue in type 1 diabetes and we propose this defect triggers reduced STAT3 signaling and mitochondrial function leading to sup-optimal axonal regeneration and plasticity.
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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) | 0.001 | 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".