Whole transcriptome sequencing reveals key genes and ceRNA regulatory networks associated with pimpled eggs in hens
Bibliographic record
Abstract
Eggshell is one of the most important indicators of egg quality, and due to low shell strength, pimple eggs (PE) are more susceptible to breakage, thus causing huge economic losses to the egg industry. At the current time, the molecular mechanisms that regulate the formation of pimple eggs are poorly understood. In this study, uterine tissues of PE-laying hens (n = 8) and normal egg (NE) -laying hens (n = 8) were analyzed by whole transcriptome sequencing, and a total of 619 differentially expressed mRNAs (DE mRNAs), 122 differentially expressed lncRNAs (DE lncRNAs) and 21 differentially expressed miRNAs (DE miRNAs) were obtained. Based on the targeting relationship among DE mRNAs, DE lncRNAs and DE miRNAs, we constructed a competitive endogenous RNA (ceRNA) network including 12 DE miRNAs, 19 DE lncRNAs, and 128 DE mRNAs. Considering the large amount of information contained in the network, we constructed a smaller ceRNA network to better understand the complex mechanisms of pimple egg formation. The smaller ceRNA network network contains 7 DE lncRNAs (LOC107056551, LOC121109367, LOC121108909, LOC121108862, LOC112530033, LOC121113165, LOC107054145), 5 DE miRNAs (gga-miR-6568-3p, gga-miR-31-5p, gga-miR-18b-3p, gga-miR-1759-3p, gga-miR-12240-3p) and 7 DE mRNAs (CABP1, DNAJC5, HCN3, HPCA, IBSP, KCNT1, OTOP3), and these differentially expressed genes may play key regulatory roles in the formation of pimpled eggs in hens. This study provides the overall expression profiles of mRNAs, lncRNAs and miRNAs in the uterine tissues of hens, which provides a theoretical basis for further research on the molecular mechanisms of pimpled egg formation, and has potential applications in improving eggshell quality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".