Markers of equine placental differentiation: insights from gene expression studies
Bibliographic record
Abstract
Development and the subsequent function of the fetal membranes of the equine placenta require both complex and precise regulation of gene expression. Advancements in recent years in bioinformatic techniques have allowed more extensive analyses into gene expression than ever before. This review starts by combining publically available transcriptomic data sets obtained from a range of embryonic, placental and maternal tissues, with previous knowledge of equine placental development and physiology, to gain insights into key gene families relevant to placentation in the horse. Covering the whole of pregnancy, the review covers trophectoderm, yolk sac, chorionic girdle cells, allantoamnion and allantochorion. In particular, 182 predicted 'early high impact' genes were identified (>100 transcripts per million (TPM) and >100 fold-change) that distinguish between progenitor trophectoderm, chorionic girdle tissue and allantochorion. Furthermore, 71 genes were identified as enriched in placental tissues (placental TPM > 10, with minimal expression in 12 non-placental TPM < 1), including excellent candidates for functional studies such as IGF1, apolipoproteins, VGLL1, GCM1, CDX2 and FABP4. It is pertinent that future studies should focus on single-cell transcriptomic approaches in order to determine how these changes in gene expression relate to tissue composition and start to better define trophoblast subpopulations in the equine placenta. Future functional characterisation of these genes and pathways will also be key not only to understanding normal placental development and fetal health but also their potential role in pathologies of pregnancy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".