Delineating differential regulatory signatures of the human transcriptome in the choriodecidua and myometrium at term labor†,‡
Bibliographic record
Abstract
Preterm deliveries remain the leading cause of neonatal morbidity and mortality. Current therapies target only myometrial contractions and are largely ineffective. As labor involves multiple coordinated events across maternal and fetal tissues, identifying fundamental regulatory pathways of normal term labor is vital to understanding successful parturition and consequently labor pathologies. We aimed to identify transcriptomic signatures of human normal term labor of two tissues: in the fetal-facing choriodecidua and the maternal myometrium. Microarray transcriptomic data from choriodecidua and myometrium following term labor were analyzed for functional hierarchical networks, using Cytoscape 2.8.3. Hierarchically high candidates were analyzed for their regulatory casual relationships using Ingenuity Pathway Analysis. Selected master regulators were then chemically inhibited and effects on downstream targets were assessed using real-time quantitative PCR (RT-qPCR). Unbiased network analysis identified upstream molecular components in choriodecidua including vimentin, TLR4, and TNFSF13B. In the myometrium, candidates included metallothionein 2 (MT2A), TLR2, and RELB. These master regulators had significant differential gene expression during labor, hierarchically high centrality in community cluster networks, interactions amongst the labor gene set, and strong causal relationships with multiple downstream effects. In vitro experiments highlighted MT2A as an effective regulator of labor-associated genes. We have identified unique potential regulators of the term labor transcriptome in uterine tissues using a robust sequence of unbiased mathematical and literature-based in silico analyses. These findings encourage further investigation into the efficacy of predicted master regulators in blocking multiple pathways of labor processes across maternal and fetal tissues, and their potential as therapeutic approaches.
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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".