Human placental piwi-interacting RNA transcriptome is characterized by expression from the DLK1-DIO3 imprinted region
Bibliographic record
Abstract
The placenta is vital to embryonic development and requires a finely-tuned pattern of gene expression, achieved in part by its unique epigenetic landscape. Piwi-interacting RNAs (piRNAs) are a class of small-non-coding RNA with established roles as epigenetic regulators of gene expression, largely via methylation of targeted DNA sequences. The expression of piRNAs have mainly been described in germ cells, but a fraction have been shown to retain expression in adult somatic tissues. To aid in understanding the contribution of these regulators in the placenta, we provide the first description of the piRNA transcriptome in human placentas. We find 297 piRNAs to be preferentially expressed in the human placenta, a subset of which are expressed at higher levels relative to testes samples. We also observed a large proportion of placental piRNAs to be expressed from a single locus, as distinct from canonical cluster locations associated with transposable element silencing. Finally, we find that 15 of the highest-expressed placental piRNAs maps to the DLK1-DIO3 locus, suggesting a link to placental biology. Our findings suggest that piRNAs could contribute to the molecular networks defining placental function in humans, and a biological impact of piRNA expression beyond germ cells.
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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 | 0.001 |
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".