Functional profiling and visualization of the sphingolipid metabolic network in vivo
Bibliographic record
Abstract
Abstract Sphingolipids govern diverse cellular processes; their dysregulation underlies numerous diseases. Despite extensive characterizations, understanding the orchestration of the sphingolipid network within living organisms remains challenging. We established a versatile genetic platform of CRISPR-engineered reporters of 52 sphingolipid regulators, recapitulating endogenous gene activity and protein distribution. This platform further allows conditional protein degradation for functional characterization. In addition, we developed the biosensor OlyA w to detect ceramide phosphoethanolamine and visualize membrane raft dynamics in vivo. Using this platform, we established comprehensive profiles of the sphingolipid metabolic network in the brain at the transcriptional and translational levels. The highly heterogeneous patterns indicate extensive coordination between distinct cell types and regions, suggesting the brain functions as a coherent unit to execute specific steps of sphingolipid metabolism. As a proof-of-concept application, we showed cell type-specific requirements of sphingomyelinases, including CG6962/dSMPD4 and CG3376/aSMase , degrading distinct subcellular pools of ceramide phosphoethanolamine to maintain brain function. These findings establish a foundation for future studies on brain sphingolipid metabolism and showcase the utilization of this genetic platform in elucidating in vivo mechanisms of sphingolipid metabolism.
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.
How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".