Identification of divergent placental profiles in clinically distinct pregnancy complications revealed by the transcriptome
Bibliographic record
Abstract
INTRODUCTION: Pregnancy complications, including preeclampsia (PE), preterm birth (PTB), and intra-uterine growth restriction (IUGR) have individually been associated with inflammation but the combined comparative analysis of their placental profiles at the transcriptomic and histological levels is lacking. METHODS: Bulk RNA-sequencing of human placental biopsies from uncomplicated term pregnancies (CTL) and pregnancies complicated with early-onset (EO), and late-onset (LO) PE, as well as PTB and term IUGR were used to characterize individual molecular profiles. We also applied immune-cell-specific cellular deconvolution to address local immune cell compositions and analyzed placental lesions by histology to further characterize these complications. RESULTS: Transcriptome analysis revealed that clinically distinct complications differentiated themselves in unique ways compared to CTLs. Only TMEM136 was commonly modulated. Compared to CTLs, we found that PTB and IUGR were the most distinct, with LOPE being the least distinct. PTB and IUGR revealed differently enhanced inflammatory pathways, where PTB had general inflammatory responses and IUGR had immune cell activation. This inflammation was reflected in the histological profile for PTB only, whereas structural lesions were elevated in all complications. Placental lesions additionally had corresponding enhancement in inflammatory and structural biological processes. We observed that having co-complications, particularly for PTB with or without IUGR, impacted placental transcriptomes. Lastly, cellular deconvolution uncovered shared immune features among the complications. DISCUSSION: Overall, we provide evidence that these pregnancy complications are not only distinct in their clinical manifestations but also in their placental profiles, which could be leveraged to understand their underlying mechanisms and could offer therapeutic targets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".