Structural variation in a novel zinc finger protein and investigation of its role in Hirschsprung disease
Bibliographic record
Abstract
Abstract Zinc finger transcription factors play essential roles in neural crest cell development. Even subtle disruptions of the function of these genes could contribute significantly to complex developmental phenotypes such as the multigenic disorder Hirschsprung disease (HSCR), a congenital failure of enteric neurogenesis. Although germline mutations in the RET proto‐oncogene are the most common cause of familial disease, at least 8 genes including transcription factors have been implicated in sporadic HSCR to date. Thus, a further group of candidate genes are those involved in regulating expression of known HSCR genes. In this study, we have characterized the ZNF358 zinc finger gene, a human orthologue of the mouse gene zfend, which is expressed in early developmental stages in the gut and in the neural folds at the time of neural crest differentiation. ZNF358 represents a putative transcription factor with DNA binding activity confered by 9 Cys2His2 zinc fingers. Although we did not detect disease associated mutations in a panel of HSCR patients, we did identify a novel variable sequence within the coding region of ZNF358 that would result in a deletion of nine amino acids from a polyalanine domain. Our molecular model suggests that this variant could alter protein tertiary structure and the ability of ZNF358 to regulate transcription. Together, our data suggest that, while ZNF358 may be involved in the regulation of genes such as those necessary for development or differentiation of neural crest cells, it does not play an obvious role in HSCR.
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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 | 0.001 | 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".