Genetic screen to test microRNA function in peripheral glia morphology
Bibliographic record
Abstract
Abstract Glial cells perform many functions in the nervous system, including maintaining the blood-brain/nerve barriers and structurally supporting axons. While their functions are well-characterized, the complex molecular mechanisms important for their development are less known. Here, we investigated whether microRNA-mediated post-transcriptional regulation is involved during glial development, ensheathment and blood-nerve-barrier formation in Drosophila . In this study, we systematically knocked down 120 different microRNAs by competitive inhibition using microRNA-sponges and analyzed peripheral glial morphology. Knockdown of miRNA-125 in the blood-nerve barrier-forming glia (subperineurial glia) resulted in the most penetrant morphological defects. Since microRNA-125 is co-transcribed with miRNAs-let7 and −100 in a genetic cluster, our further verification for subperineurial glia function included miRNA-125 plus all other members of this cluster. However, the loss of each individual gene and the entire cluster did not lead to any morphological defects in the subperineurial glia. To test the efficiency of the microRNA sponge approach in subperineurial glia, we expressed a sponge targeting a microRNA established to be vital for blood-brain barrier formation (microRNA-285) and found no defects in brain lobes and peripheral nerves. Given that a scrambled-sponge control also generated morphological defects, this suggests that using miRNA sponge lines may not be an effective approach to study miRNA function in Drosophila peripheral glia.
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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".