Evolutionary conservation of the oocyte transcriptome among vertebrates and its implications for understanding human reproductive function
Bibliographic record
Abstract
Cross-phylum and cross-species comparative transcriptomic analyses provide an evolutionary perspective on how specific tissues use genomic information. A significant mRNA subset present in the oocytes of most vertebrates is stabilized or stored for post-LH surge use. Since transcription is arrested in the oocyte before ovulation, this RNA is important for completing maturation and sustaining embryo development until zygotic genome activation. We compared the human oocyte transcriptome with an oocyte-enriched subset of mouse, bovine and frog (Xenopus laevis) genes in order to evaluate similarities between species. Graded temperature stringency hybridization on a multi-species oocyte cDNA array was used to measure the similarity of preferentially expressed sequences to the human oocyte library. Identity analysis of 679 human orthologs compared with each identified official gene symbol found in the subtractive (somatic-oocyte) libraries comprising our array revealed that bovine/human similarity was greater than mouse/human or frog/human similarity. However, based on protein sequence, mouse/human similarity was greater than bovine/human similarity. Among the genes over-expressed in oocytes relative to somatic tissue in Xenopus, Mus and Bos, a high level of conservation was found relative to humans, especially for genes involved in early embryonic development.
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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 | 0.000 | 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) | 0.001 | 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".