GDNF regenerates the missing enteric nervous system of Hirschsprung mice via non-canonical signaling in diverse subtypes of tissue-resident progenitors
Bibliographic record
Abstract
Hirschsprung disease (HSCR) is a deadly congenital disorder where the enteric nervous system (ENS) is absent from the distal bowel. Current surgical treatment is generally life-saving but is often accompanied by long-term bowel problems and comorbidities. As alternative, we are developing a regenerative therapy based on rectal administration of Glial cell-derived neurotrophic factor (GDNF). We previously showed that administering GDNF enemas to HSCR mice soon after birth is sufficient to permanently induce a new ENS from tissue-resident neural progenitors. Here, we elucidate the underlying mechanism using single-cell transcriptomics, signal transduction inhibitors and genetic cell lineage tracing tools. We found that the neurogenic effect of GDNF is mediated by NCAM1 (Neural cell adhesion molecule 1), rather than by its canonical signaling receptor RET (Rearranged during transfection). We also unveiled the existence of multiple neuronal differentiation pathways that involve a larger than expected repertoire of tissue-resident neural progenitors, including a surprising one not derived from the usual neural crest. These data support feasibility of GDNF-based therapy in most human patients, even those bearing a RET variant. This work also has far-reaching implications for the choice of ENS progenitor source to use when developing cell transplantation-based therapeutic approaches.
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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 | 0.001 |
| 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".