Genetic disruption of the oncogenic HMGA2–PLAG1–IGF2 pathway causes fetal growth restriction
Bibliographic record
Abstract
PurposeFetal growth is a complex process involving maternal, placental and fetal factors. The etiology of fetal growth retardation remains unknown in many cases. The aim of this study is to identify novel human mutations and genes related to Silver-Russell syndrome (SRS), a syndromic form of fetal growth retardation, usually caused by epigenetic downregulation of the potent fetal growth factor IGF2.MethodsWhole-exome sequencing was carried out on members of an SRS familial case. The candidate gene from the familial case and two other genes were screened by targeted high-throughput sequencing in a large cohort of suspected SRS patients. Functional experiments were then used to link these genes into a regulatory pathway.ResultsWe report the first mutations of the PLAG1 gene in humans, as well as new mutations in HMGA2 and IGF2 in six sporadic and/or familial cases of SRS. We demonstrate that HMGA2 regulates IGF2 expression through PLAG1 and in a PLAG1-independent manner.ConclusionGenetic defects of the HMGA2-PLAG1-IGF2 pathway can lead to fetal and postnatal growth restriction, highlighting the role of this oncogenic pathway in the fine regulation of physiological fetal/postnatal growth. This work defines new genetic causes of SRS, important for genetic counseling.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".