Cross-species hybridizations on a multi-species cDNA microarray to identify evolutionarily conserved genes expressed in oocytes
Bibliographic record
Abstract
BACKGROUND: Comparative genomic analysis using cDNA microarray is a new approach and a useful tool to identify important genetic sequences or genes that are conserved throughout evolution. Identification of these conserved sequences will help elucidate important molecular mechanisms or pathways common to many species. For example, the stockpiled transcripts in the oocyte necessary for successful fertilization and early embryonic development still remain relatively unknown. The objective of this study was to identify genes expressed in oocytes and conserved in three evolutionarily distant species. RESULTS: In this study we report the construction of a multi-species cDNA microarray containing 3,456 transcripts from three distinct oocyte-libraries from bovine, mouse and Xenopus laevis. Following the cross-species hybridizations, data analysis revealed that 1,541 positive hybridization signals were generated by oocytes of all three species, and 268 of these are preferentially expressed in the oocyte. Data reproducibility analyses comparing same-species to cross-species hybridization indicates that cross-species hybridizations are highly reproducible, thus increasing the confidence level in their specificity. A validation by RT-PCR using gene- and species-specific primers confirmed that cross-species hybridization allows the production of specific and reliable data. Finally, a second validation step through gene-specific microarray hybridizations further supported the validity of our cross-species microarray results. Results from these cross-species hybridizations on our multi-species cDNA microarray revealed that SMFN (Small fragment nuclease), Spin (Spindlin), and PRMT1 (Protein arginine methyltransferase 1) are transcripts present in oocytes and conserved in three evolutionarily distant species. CONCLUSION: Cross-species hybridization using a multi-species cDNA microarray is a powerful tool for the discovery of genes involved in evolutionarily conserved molecular mechanisms. The present study identified conserved genes in the oocytes of three distant species that will help understand the unique role of maternal transcripts 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".