Biallelic variants in the conserved ribosomal protein chaperone gene <i>PDCD2</i> are associated with hydrops fetalis and early pregnancy loss
Bibliographic record
Abstract
Pregnancy loss is a major problem in clinical medicine with devastating consequences for families. Next generation sequencing has improved our ability to identify underlying molecular causes, though over half of all cases lack a clear etiology. Here, we began with clinical evaluation combined with exome sequencing across independent families to identify bi-allelic candidate genetic variants in the Programmed Cell Death 2 (PDCD2) gene in multiple fetuses with nonimmune hydrops fetalis (NIHF). PDCD2 is an evolutionarily conserved protein with no prior association with monogenic disorders. PDCD2 is known to act as a molecular chaperone for the ribosomal protein uS5, and this complex formation is important for incorporation of uS5 into the 40S subunit, a crucial step in ribosome biogenesis. Primary fibroblasts from an affected fetus and cell lines expressing PDCD2 patient variants demonstrated reduced levels of PDCD2, reduced PDCD2 binding to uS5, and altered ribosomal RNA processing. Xenopus tadpoles with Pdcd2 knockdown demonstrated developmental defects and edema, reminiscent of the NIHF seen in affected fetuses, and showed altered ribosomal RNA processing. Through genetic, biochemical, and in vivo approaches, we provide evidence that bi-allelic PDCD2 variants cause an autosomal recessive ribosomal biogenesis disorder resulting in pregnancy loss.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".