PSLBII-18 Transcriptomic comparison of embryonic tissues between high and low fertility sired pregnancies
Bibliographic record
Abstract
Abstract Pregnancy loss is considered one of the main causes of reproductive inefficiency in the beef cattle industry. The objective was to determine the differentially expressed genes (DEGs) between the conceptus and uterus of pregnancies produced by sires of known fertility. We hypothesized that the paternal genome would have a significant effect on transcript abundance between these tissues. The sires used have been previously reported to have significantly different levels of field fertility; however, they had passed all normal quality control standards for frozen thawed semen. Bos indicus heifers (n = 45) were subjected to estrus synchronization and embryo transfer with IVP embryos from either a High Fertility (HF) or a Low Fertility sire (LF), pregnancies were confirmed at slaughter. Samples were collected from the trophectoderm (TE) and caruncle (CAR) on d 25 and 36 of gestation for transcriptome analysis. Total RNA was isolated from tissue samples using the RNeasy kit (QIAGEN; Hilden, Germany) per manufacturer’s instructions. Sequencing was conducted using an Illumina platform. Sequences were aligned to the reference genome ARS-UCD1.2. DEGs between sires and tissue were determined using edge-R package from R. False discovery rate was 0.1. On d 25, out of 15,755 genes expressed in the trophectoderm, 19 genes were downregulated, and 9 genes were upregulated in the LF. In the CAR, 9 genes were downregulated and 7 were upregulated. On d 36, in the TE the LF resulted in 53 downregulated genes and 20 upregulated genes. In CAR there were 18 downregulated genes for the LF whereas 22 genes were upregulated. Gene ontology analysis reported DEGs in the LF compared with the HF were associated with immunology and reproduction. Of particular interest BoLA NC-1
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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".