A Smaug2-Based Translational Repression Complex Determines the Balance between Precursor Maintenance versus Differentiation during Mammalian Neurogenesis
Bibliographic record
Abstract
Here, we have asked about post-transcriptional mechanisms regulating murine developmental neurogenesis, focusing upon the RNA-binding proteins Smaug2 and Nanos1. We identify, in embryonic neural precursors of the murine cortex, a Smaug2 protein/nanos1 mRNA complex that is present in cytoplasmic granules with the translational repression proteins Dcp1 and 4E-T. We show that Smaug2 inhibits and Nanos1 promotes neurogenesis, with Smaug2 knockdown enhancing neurogenesis and depleting precursors, and Nanos1 knockdown inhibiting neurogenesis and maintaining precursors. Moreover, we show that Smaug2 likely regulates neurogenesis by silencing nanos1 mRNA. Specifically, Smaug2 knockdown inappropriately increases Nanos1 protein, and the Smaug2 knockdown-mediated neurogenesis is rescued by preventing this increase. Thus, Smaug2 and Nanos1 function as a bimodal translational repression switch to control neurogenesis, with Smaug2 acting in transcriptionally primed precursors to silence mRNAs important for neurogenesis, including nanos1 mRNA, and Nanos1 acting during the transition to neurons to repress the precursor state. SIGNIFICANCE STATEMENT: The mechanisms instructing neural stem cells to generate the appropriate progeny are still poorly understood. Here, we show that the RNA-binding proteins Smaug2 and Nanos1 are critical regulators of this balance and provide evidence supporting the idea that neural precursors are transcriptionally primed to generate neurons but translational regulation maintains these precursors in a stem cell state until the appropriate developmental time.
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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".