Author response: A functional genomics screen in planarians reveals regulators of whole-brain regeneration
Bibliographic record
Abstract
Animals differ in the extent to which they can regenerate missing body parts after injury. Humans regenerate poorly after many injuries, especially when the brain becomes damaged after stroke, disease or trauma. On the other hand, planarians – small worms that live in fresh water – regenerate exceptionally well. A whole planarian can regenerate from small pieces of tissue. The ability of planarians to regenerate their nervous system relies on stem cells called neoblasts, which can migrate through the body and divide to replace lost cells. However, the specific mechanisms responsible for regenerating nervous tissue are largely unknown. Roberts-Galbraith et al. carried out a screen to identify genes that tell planarians whether to regenerate a new brain, what cells to make and how to arrange them. The study revealed over thirty genes that allow planarians to regenerate their brains after their heads have been amputated. These genes play several different roles in the animal. Some of the genes help neoblasts to make decisions about what kinds of cells they should become. One gene is needed to make an important connection in the planarian brain after injury. Another helps to restore the ability of the planarian to sense its food. The experiments also show that some key genes are switched on in a new cell type that might produce signals to support regeneration. Lastly, Roberts-Galbraith et al. found that the planarian nervous system contains cells called glia. Previous studies have shown that many of the cells in the human brain are glia and that these cells help nerve cells to survive and work properly. The discovery of glia in planarians means that it will be possible to use these worms to study how glia support brain regeneration and how glia themselves are replaced after injury. In the long term, this work might lead to discoveries that shed light on how tissue regeneration could be improved in humans.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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 teacher head, 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".