Genetic recapitulation of human pre-eclampsia risk during convergent evolution of reduced placental invasiveness in eutherian mammals
Bibliographic record
Abstract
The relationship between phenotypic variation arising through individual development and phenotypic variation arising through diversification of species has long been a central question in evolutionary biology. Among humans, reduced placental invasion into endometrial tissues is associated with diseases of pregnancy, especially pre-eclampsia, and reduced placental invasiveness has also evolved, convergently, in at least 10 lineages of eutherian mammals. We tested the hypothesis that a common genetic basis underlies both reduced placental invasion arising through a developmental process in human placental disease and reduced placental invasion found as a derived trait in the diversification of Euarchontoglires (rodents, lagomorphs, tree shrews, colugos and primates). Based on whole-genome analyses across 18 taxa, we identified 1254 genes as having evolved adaptively across all three lineages exhibiting independent evolutionary transitions towards reduced placental invasion. These genes showed strong evidence of enrichment for associations with pre-eclampsia, based on genetic-association studies, gene-expression analyses and gene ontology. We further used in silico prediction to identify a subset of 199 genes that are likely targets of natural selection during transitions in placental invasiveness and which are predicted to also underlie human placental disorders. Our results indicate that abnormal ontogenies can recapitulate major phylogenetic shifts in mammalian evolution, identify new candidate genes for involvement in pre-eclampsia, imply that study of species with less-invasive placentation will provide useful insights into the regulation of placental invasion and pre-eclampsia, and recommend a novel comparative functional-evolutionary approach to the study of genetically based human disease and mammalian diversification.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".