Neutral sphingomyelinase activation precedes NADPH oxidase‐dependent damage in neurons exposed to the proinflammatory cytokine tumor necrosis factor‐α
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it