Glia phagocytose neuronal sphingolipids to infiltrate developing synapses
Bibliographic record
Abstract
Abstract The complex morphologies of mature neurons and glia emerge through profound rearrangements of cell membranes during development. Despite being integral components of these membranes, it is unclear whether lipids might actively sculpt these morphogenic processes. By analyzing lipid levels in the developing fruit fly brain, we discover dramatic increases in specific sphingolipids coinciding with neural circuit establishment. Disrupting this sphingolipid bolus via genetic perturbations of sphingolipid biosynthesis and catabolism leads to impaired glial autophagy. Remarkably, glia can obtain sphingolipid precursors needed for autophagy by phagocytosing neurons. These precursors are then converted into specific long-chain ceramide phosphoethanolamines (CPEs), invertebrate analogs of sphingomyelin. These lipids are essential for glia to arborize and infiltrate the brain, a critical step in circuit maturation that when disrupted leads to reduced synapse numbers. Taken together, our results demonstrate how spatiotemporal tuning of sphingolipid metabolism during development plays an instructive role in programming brain architecture. Highlights Brain sphingolipids (SLs) remodel to very long-chain species during circuit maturation Glial autophagy requires de novo SL biosynthesis coordinated across neurons and glia Glia evade a biosynthetic blockade by phagolysosomal salvage of neuronal SLs Ceramide Phosphoethanolamine is critical for glial infiltration and synapse density
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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 | 0.000 |
| 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".