Genetic Background Influences Severity of Colonic Aganglionosis and Response to GDNF Enemas in the Holstein Mouse Model of Hirschsprung Disease
Bibliographic record
Abstract
Hirschsprung disease is a congenital malformation where ganglia of the neural crest-derived enteric nervous system are missing over varying lengths of the distal gastrointestinal tract. This complex genetic condition involves both rare and common variants in dozens of genes, many of which have been functionally validated in animal models. Modifier loci present in the genetic background are also believed to influence disease penetrance and severity, but this has not been frequently tested in animal models. Here, we addressed this question using Holstein mice in which aganglionosis is due to excessive deposition of collagen VI around the developing enteric nervous system, thereby allowing us to model trisomy 21-associated Hirschsprung disease. We also asked whether the genetic background might influence the response of Holstein mice to GDNF enemas, which we recently showed to have regenerative properties for the missing enteric nervous system. Compared to Holstein mice in their original FVB/N genetic background, Holstein mice maintained in a C57BL/6N background were found to have a less severe enteric nervous system defect and to be more responsive to GDNF enemas. This change of genetic background had a positive impact on the enteric nervous system only, leaving the neural crest-related pigmentation phenotype of Holstein mice unaffected. Taken together with other similar studies, these results are thus consistent with the notion that the enteric nervous system is more sensitive to genetic background changes than other neural crest derivatives.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".