The MicroRNAome of Pregnancy: Deciphering miRNA Networks at the Maternal-Fetal Interface
Bibliographic record
Abstract
MicroRNAs (miRNAs) post-transcriptionally regulate a vast network of genes by inhibiting mRNA translation. Aberrant miRNA expression profiles have been implicated in pathologies and physiological processes including pregnancy and angiogenesis. Using our established model of implantation failure and spontaneous fetal loss in pigs (Sus scrofa), 236 miRNAs were profiled and compared between 1) non-pregnant and pregnant endometrium, 2) maternal and fetal tissues, and 3) viable and growth-arrested conceptus attachment sites by microarray and Real-Time PCR. Many significant differences in miRNA expression were observed between each of the aforementioned comparisons, and several were validated by PCR. Results indicated which miRNAs were important during pregnancy, which were elevated on the maternal or fetal side of the maternal-fetal interface, and they implicated the maternal expression of miR-10a, 27a, 29c, 323, 331-5p, 339-3p, 374b-5p, and 935 in the spontaneous loss observed in pigs. Several putative mRNA targets of the miRNAs (elevated in endometrium associated with arresting conceptuses) were assessed by quantitative Real-Time PCR and were depressed, supporting their regulation by miRNAs. Finally, targets were clustered by function to obtain ranked lists of gene networks that indicated which pathways/physiological processes might be important in non-pregnant (extracellular matrix factors) versus pregnant endometrium (nuclear transcription factor regulation), maternal (blood vessel development) versus fetal (neuronal differentiation) tissue, and healthy (extracellular matrix factors) versus arresting (GRAM domain) conceptus attachment sites. Overall, we demonstrate the presence of miRNAs on both sides of the maternal-fetal interface, implicate them in spontaneous fetal loss, and present a unique glimpse into the vast microRNAome of pregnancy.
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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".