Identification and profiling of microRNAs during yak’s testicular development
Bibliographic record
Abstract
BACKGROUND: Normal testicular development is highly crucial for male reproduction and is a precondition for spermatogenesis that is the production of spermatozoa in the testes. MiRNAs have been implicated in several testicular biological processes, including cell proliferation, spermatogenesis, hormone secretion, metabolism and reproductive regulation. In the present study, we used deep sequencing data to study the functions of miRNAs during testicular development and spermatogenesis, by analyzing the expression patterns of small RNAs in 6-, 18- and 30-month-old yak testis tissues. RESULTS: A total of 737 known and 359 novel miRNAs were obtained from 6-, 18- and 30-month-old yak testes. In all, we obtained 12, 142 and 139 differentially expressed (DE) miRNAs in 30- vs. 18-, 18- vs. 6-, and 30- vs. 6-month-old testes, respectively. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of all DE miRNA target genes revealed BMP2, TGFB2, GDF6, SMAD6, TGFBR2 and other target genes as participants in different biological processes, including TGF-β, GnRH, Wnt, PI3K-Akt, MAPK signaling pathways and several other reproductive pathways. In addition, quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) was used to detect the expression of seven randomly selected miRNAs in 6-, 18- and 30-month-old testes, and the results were consistent with the sequencing data. CONCLUSIONS: The differential expression of miRNAs in yak testes at different development stages was characterized and investigated using deep sequencing technology. We believe that the results will contribute to further understanding the functions of miRNAs in regulating the development of yak testes and improving the reproductive performance of male yaks.
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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".