Retrograde BMP signaling activates neuronal gene expression through widespread deployment of a conserved BMP-responsive<i>cis</i>-regulatory<i>activation element</i>
Bibliographic record
Abstract
Retrograde Bone Morphogenetic Protein (BMP) signaling in neurons is essential for the differentiation and synaptic function of many neuronal subtypes. BMP signaling regulates these processes via Smad transcription factor activity, yet the scope and nature of Smad-dependent gene regulation in neurons are mostly unknown. Here, we applied a computational approach to predict Smad-binding cis-regulatory BMP-Activating Elements (BMP-AEs) in Drosophila, followed by transgenic in vivo reporter analysis to test their neuronal subtype enhancer activity in the larval central nervous system (CNS). We identified 34 BMP-AE-containing genomic fragments that are responsive to BMP signaling in neurons, and showed that the embedded BMP-AEs are required for this activity. RNA-seq analysis identified BMP-responsive genes in the CNS and revealed that BMP-AEs selectively enrich near BMP-activated genes. These data suggest that functional BMP-AEs control nearby BMP-activated genes, which we validated experimentally. Finally, we demonstrated that the BMP-AE motif mediates a conserved Smad-responsive function in the Drosophila and vertebrate CNS. Our results provide evidence that BMP signaling controls neuronal function by directly coordinating the expression of a battery of genes through widespread deployment of a conserved Smad-responsive cis-regulatory motif.
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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".