Dynamic transcriptome analysis of Maiwa yak corpus luteum during the estrous cycle
Bibliographic record
Abstract
Maiwa yak is a special breed of animal living on the Qinghai-Tibet Plateau, which has great economic value, but its fertility rate is low. The corpus luteum (CL) is a temporary tissue that plays a crucial role in maintaining the physiological cycle. However, little is known about the transcriptome profile in Maiwa yak CL. In the present study, the transcriptome of Maiwa yak CL at early (EYCL), middle (MYCL) and late-stages (LYCL) was studied employing high-throughput sequencing. A total of 25,922 transcripts were identified, including 22,277 known as well as 3,645 novel ones. Furthermore, 690 and 212 differentially expressed (DE) mRNAs were detected in the EYCL vs. MYCL and MYCL vs. LYCL groups, respectively. KEGG pathway enrichment analysis of DEGs illustrated that the most enriched pathway was PI3K-Akt pathway. Furthermore, twenty-six DEGs were totally found to be associated with different biological processes of CL development. One of these genes, PGRMC1, displayed a dynamical expression trend during the lifespan of yak CL. The knockdown of PGRMC1 in luteinized yak granulosa cells resulted in defective steroidogenesis. In conclusion, this study analyzed the transcriptome profiles in yak CL of different stages, and provided a novel database for analyzing the gene network in yak CL.HIGHLIGHTSThe manuscript analyzed the transcriptome profiles in yak CL during the estrous cycle.Twenty-six DEGs were found to be associated with the development or function of CL.One of the DEGs, PGRMC1, was found to be responsible for steroidogenesis in luteinized yak granulosa cells. The manuscript analyzed the transcriptome profiles in yak CL during the estrous cycle. Twenty-six DEGs were found to be associated with the development or function of CL. One of the DEGs, PGRMC1, was found to be responsible for steroidogenesis in luteinized yak granulosa cells.
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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 | 0.001 |
| 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".