Analysis of the Transcriptomes Downstream of Eyeless and the Hedgehog, Decapentaplegic and Notch Signaling Pathways in Drosophila melanogaster
Bibliographic record
Abstract
Tissue-specific transcription factors are thought to cooperate with signaling pathways to promote patterned tissue specification, in part by co-regulating transcription. The Drosophila melanogaster Pax6 homolog Eyeless forms a complex, incompletely understood regulatory network with the Hedgehog, Decapentaplegic and Notch signaling pathways to control eye-specific gene expression. We report a combinatorial approach, including mRNAseq and microarray analyses, to identify targets co-regulated by Eyeless and Hedgehog, Decapentaplegic or Notch. Multiple analyses suggest that the transcriptomes resulting from co-misexpression of Eyeless+signaling factors provide a more complete picture of eye development compared to previous efforts involving Eyeless alone: (1) Principal components analysis and two-way hierarchical clustering revealed that the Eyeless+signaling factor transcriptomes are closer to the eye control transcriptome than when Eyeless is misexpressed alone; (2) more genes are upregulated at least three-fold in response to Eyeless+signaling factors compared to Eyeless alone; (3) based on gene ontology analysis, the genes upregulated in response to Eyeless+signaling factors had a greater diversity of functions compared to Eyeless alone. Through a secondary screen that utilized RNA interference, we show that the predicted gene CG4721 has a role in eye development. CG4721 encodes a neprilysin family metalloprotease that is highly up-regulated in response to Eyeless+Notch, confirming the validity of our approach. Given the similarity between D. melanogaster and vertebrate eye development, the large number of novel genes identified as potential targets of Ey+signaling factors will provide novel insights to our understanding of eye development in D. melanogaster and humans.
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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) | 0.000 | 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 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".