Long-read transcriptome assembly reveals vast transcriptional complexity in the placenta associated with metabolic and endocrine function
Bibliographic record
Abstract
Abstract The placenta is critical for fetal development and mediates the effects of pregnancy complications on offspring metabolic health, yet it is often poorly characterized in genomic studies. Existing transcriptomic analyses rely on adult tissue-based references, which overlook developmentally important isoform diversity. We used largest-in-class long-read RNA-seq (N=72) to create a comprehensive placental transcriptome reference, identifying 37,661 high-confidence isoforms (14,985 novel) across 12,302 genes (2,759 novel). Contrary to characterizations of the placenta as a “transcriptomic void,” we found transcriptional breadth and complexity comparable to adult tissues, with extraordinary splicing diversity in genes controlling obesity, lactogen production and growth, including 108 distinct CSH1 (placental lactogen) isoforms. This improved reference offers two advantages: First, it reduced inferential uncertainty in isoform quantification by 30% and increased the yield of high-confidence transcripts. Applying this reference to short-read RNA-seq datasets (N=344) of gestational diabetes mellitus (GDM), we found that placental transcription mediated 36% of GDM effects on birth weight, with novel CSH1 isoforms identified as key mediators. We further uncovered ancestry-specific effects, with distinct CSH1 isoforms mediating larger effects in European (24.4%) than Asian (13.4%) populations. Our results establish that utilizing long-read-based, tissue-specific transcriptomic annotations is critical, enabling isoform-resolved analyses that provide greater sensitivity than conventional gene-level approaches for understanding placental function and context-specific variation across diverse biobanks.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".