Identification of genes influencing dendrite morphogenesis in developing peripheral sensory and central motor neurons
Bibliographic record
Abstract
BACKGROUND: Developing neurons form dendritic trees with cell type-specific patterns of growth, branching and targeting. Dendrites of Drosophila peripheral sensory neurons have emerged as a premier genetic model, though the molecular mechanisms that underlie and regulate their morphogenesis remain incompletely understood. Still less is known about this process in central neurons and the extent to which central and peripheral dendrites share common organisational principles and molecular features. To address these issues, we have carried out two comparable gain-of-function screens for genes that influence dendrite morphologies in peripheral dendritic arborisation (da) neurons and central RP2 motor neurons. RESULTS: We found 35 unique loci that influenced da neuron dendrites, including five previously shown as required for da dendrite patterning. Several phenotypes were class-specific and many resembled those of known mutants, suggesting that genes identified in this study may converge with and extend known molecular pathways for dendrite development in da neurons. The second screen used a novel technique for cell-autonomous gene misexpression in RP2 motor neurons. We found 51 unique loci affecting RP2 dendrite morphology, 84% expressed in the central nervous system. The phenotypic classes from both screens demonstrate that gene misexpression can affect specific aspects of dendritic development, such as growth, branching and targeting. We demonstrate that these processes are genetically separable. Targeting phenotypes were specific to the RP2 screen, and we propose that dendrites in the central nervous system are targeted to territories defined by Cartesian co-ordinates along the antero-posterior and the medio-lateral axes of the central neuropile. Comparisons between the screens suggest that the dendrites of peripheral da and central RP2 neurons are shaped by regulatory programs that only partially overlap. We focused on one common candidate pathway controlled by the ecdysone receptor, and found that it promotes branching and growth of developing da neuron dendrites, but a role in RP2 dendrite development during embryonic and early larval stages was not apparent. CONCLUSION: We identified commonalities (for example, growth and branching) and distinctions (for example, targeting and ecdysone response) in the molecular and organizational framework that underlies dendrite development of peripheral and central neurons.
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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 | 0.000 |
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".