Concerted control of gliogenesis by InR/TOR and FGF signalling in the <i>Drosophila</i> post-embryonic brain
Bibliographic record
Abstract
Glial cells are essential for the development and function of the nervous system. In the mammalian brain, vast numbers of glia of several different functional types are generated during late embryonic and early foetal development. However, the molecular cues that instruct gliogenesis and determine glial cell type are poorly understood. During post-embryonic development, the number of glia in the Drosophila larval brain increases dramatically, potentially providing a powerful model for understanding gliogenesis. Using glial-specific clonal analysis we find that perineural glia and cortex glia proliferate extensively through symmetric cell division in the post-embryonic brain. Using pan-glial inhibition and loss-of-function clonal analysis we find that Insulin-like receptor (InR)/Target of rapamycin (TOR) signalling is required for the proliferation of perineural glia. Fibroblast growth factor (FGF) signalling is also required for perineural glia proliferation and acts synergistically with the InR/TOR pathway. Cortex glia require InR in part, but not downstream components of the TOR pathway, for proliferation. Moreover, cortex glia absolutely require FGF signalling, such that inhibition of the FGF pathway almost completely blocks the generation of cortex glia. Neuronal expression of the FGF receptor ligand Pyramus is also required for the generation of cortex glia, suggesting a mechanism whereby neuronal FGF expression coordinates neurogenesis and cortex gliogenesis. In summary, we have identified two major pathways that control perineural and cortex gliogenesis in the post-embryonic brain and have shown that the molecular circuitry required is lineage specific.
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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".