Genome-wide analysis of the bHLH gene family in planarians identifies factors required for adult neurogenesis and neuronal regeneration
Bibliographic record
Abstract
In contrast to most well-studied model organisms, planarians have a remarkable ability to completely regenerate a functional nervous system from a pluripotent stem cell population. Thus, planarians provide a powerful model to identify genes required for adult neurogenesis in vivo. We analyzed the basic helix-loop-helix (bHLH) family of transcription factors, many of which are crucial for nervous system development and have been implicated in human diseases. However, their potential roles in adult neurogenesis or central nervous system (CNS) function are not well understood. We identified 44 planarian bHLH homologs, determined their patterns of expression in the animal and assessed their functions using RNAi. We found nine bHLHs expressed in stem cells and neurons that are required for CNS regeneration. Our analyses revealed that homologs of coe, hes (hesl-3) and sim label progenitors in intact planarians, and following amputation we observed an enrichment of coe(+) and sim(+) progenitors near the wound site. RNAi knockdown of coe, hesl-3 or sim led to defects in CNS regeneration, including failure of the cephalic ganglia to properly pattern and a loss of expression of distinct neuronal subtype markers. Together, these data indicate that coe, hesl-3 and sim label neural progenitor cells, which serve to generate new neurons in uninjured or regenerating animals. Our study demonstrates that this model will be useful to investigate how stem cells interpret and respond to genetic and environmental cues in the CNS and to examine the role of bHLH transcription factors in adult tissue regeneration.
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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 | 0.001 | 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".