Identification of novel nutrient-sensitive gene regulatory networks in amniocytes from fetuses with spina bifida
Bibliographic record
Abstract
Abstract Neural tube defects (NTDs) remain among the most common congenital anomalies. Contributing risk factors include genetics and nutrient deficiencies, however, a comprehensive assessment of nutrient-gene interactions in NTDs is lacking. We applied a nutrient-focused gene expression analysis pipeline to identify nutrient-sensitive gene regulatory networks in amniocyte gene expression data (GSE4182) from fetuses with NTDs (cases; n=3) and fetuses with no congenital anomalies (controls; n=5). Differentially expressed genes (DEGs) were screened for having nutrient cofactors. Nutrient-dependent transcriptional regulators (TRs) that regulated DEGs, and nutrient-sensitive miRNAs with a previous link to NTDs, were identified. Of the 880 DEGs in cases, 10% had at least one nutrient cofactor. DEG regulatory network analysis revealed that 39% and 52% of DEGs in cases were regulated by 22 nutrient-sensitive miRNAs and 10 nutrient-dependent TRs, respectively. Zinc- and B vitamin-dependent gene regulatory networks (Zinc: 10 TRs targeting 50.6% of DEGs; B vitamins: 4 TRs targeting 37.7% of DEGs, 9 miRNAs targeting 17.6% of DEGs) were dysregulated in cases. We identified novel, nutrient-sensitive gene regulatory networks not previously linked to NTDs, which may indicate new targets to explore for NTD prevention or to optimise fetal development.
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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 | 0.001 |
| 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".