The steady-state level of plasma membrane ceramide is regulated by neutral sphingomyelinase 2
Bibliographic record
Abstract
During the last 30 years, an increasing number of cellular functions have been reported to be regulated by the lipid ceramide. The diversity in the ceramide structure, leading to tens of ceramide species and the discrete distribution based on subcellular topology, could explain the wide variety of functions attributed to this bioactive lipid. One of these pools of ceramide resides in the plasma membrane, and several works have suggested that an increase in plasma membrane ceramide (PMCer) in response to stimulation leads to cell death and modulates cell adhesion and migration. However, there is a limitation in studying PMCer content in this location primarily due to the inability to quantify its mass. Our group recently developed a method to specifically quantitate PMCer. In this work, we interrogate what sphingolipid metabolizing enzymes are responsible for modulating the basal levels of plasma membrane ceramide. An in-silico prediction and experimental confirmation found an almost perfect correlation between the endogenous expression levels of neutral sphingomyelinase (nSMase2) and the amount of plasma membrane ceramide in unstimulated cells. Manipulating the expression levels of nSMase2, but not other candidate enzymes of ceramide metabolism, profoundly affected PMCer. Moreover, a physiologic induction of nSMase2 during cell confluence resulted in a nSMase2-dependent dramatic increase in PMCer. Together, these results identify nSMase2 as the primary enzyme to regulate plasma membrane ceramide.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".