C-Peptide Signals via Gαi to Protect against TNF-α–Mediated Apoptosis of Opossum Kidney Proximal Tubular Cells
Bibliographic record
Abstract
Cell loss by apoptosis occurs in renal injury such as diabetic nephropathy. TNF-alpha is a cytokine that induces apoptosis and has been implicated in the pathogenesis of diabetic nephropathy. The aim was to investigate whether C-peptide or insulin could modulate TNF-alpha-mediated cell death in opossum kidney proximal tubular cells and to examine the mechanism(s) of any effects observed. C-peptide and insulin protect against TNF-alpha-induced proximal tubular cell toxicity and apoptosis. Cell viability was analyzed by methylthiazoletetrazolium assay; cell viability was reduced to 60.8 +/- 2.7% of control after stimulation with 300 ng/ml TNF-alpha. Compromised cell viability was reversed by pretreatment with 5 nM C-peptide or 100 nM insulin. TNF-alpha-induced apoptosis was detected by DNA nick-end labeling and by measuring histone associated DNA fragments using ELISA. By ELISA assay, 300 ng/ml TNF-alpha increased apoptosis by 145.8 +/- 4.9% compared with controls, whereas 5 nM C-peptide and 100 nM insulin reduced apoptosis to 81.6 +/- 4.8 and 77.4 +/- 3.1% of control, respectively. The protective effects of C-peptide and insulin were associated with activation of NF-kappaB. Activation of NF-kappaB by C-peptide was pertussis toxin sensitive and dependent on activation of Galpha(i). Phosphatidylinositol 3-kinase but not extracellular signal regulated mitogen-activated protein kinase mediated C-peptide and insulin activation of NF-kappaB. The cytoprotective effects of both C-peptide and insulin were related to increased expression of TNF receptor-associated factor 2, the product of an NF-kappaB-dependent survival gene. These data suggest that C-peptide and/or insulin activation of NF-kappaB-regulated survival genes protects against TNF-alpha-induced renal tubular injury in diabetes. The data further support the concept of C-peptide as a peptide hormone in its own right and suggest a potential therapeutic role for C-peptide.
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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".