Neutral sphingomyelinase activation precedes NADPH oxidase‐dependent damage in neurons exposed to the proinflammatory cytokine tumor necrosis factor‐α
Bibliographic record
Abstract
Inflammation accompanied by severe oxidative stress plays a vital role in the orchestration and progression of neurodegeneration prevalent in chronic and acute central nervous system pathologies as well as in aging. The proinflammatory cytokine tumor necrosis factor-α (TNFα) elicits the formation of the bioactive ceramide by stimulating the hydrolysis of the membrane lipid sphingomyelin by sphingomyelinase activities. Ceramide stimulates the formation of reactive oxygen species (ROS) and apoptotic mechanisms in both neurons and nonneuronal cells, establishing a link between sphingolipid metabolism and oxidative stress. We demonstrated in SH-SY5Y human neuroblastoma cells and primary cortical neurons that TNFα is a potent stimulator of Mg(2+) -dependent neutral sphingomyelinase (Mg(2+) -nSMase) activity, and sphingomyelin hydrolysis, rather than de novo synthesis, was the predominant source of ceramide increases. Mg(2+) -nSMase activity preceded an accumulation of ROS by a neuronal NADPH oxidase (NOX). Notably, TNFα provoked an NOX-dependent oxidative damage to sphingosine kinase-1, which generates sphingosine-1-phosphate, a ceramide metabolite associated with neurite outgrowth. Indeed, ceramide and ROS inhibited neurite outgrowth of dorsal root ganglion neurons by disrupting growth cone motility. Blunting ceramide and ROS formation both rescued sphingosine kinase-1 activity and neurite outgrowth. Our studies suggest that TNFα-mediated activation of Mg(2+) -nSMase and NOX in neuronal cells not only produced the neurotoxic intermediates ceramide and ROS but also directly antagonized neuronal survival mechanisms, thus accelerating neurodegeneration.
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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".