Genome-wide identification and functional prediction of circular RNAs in response to heat stress in Chinese Holstein cows
Bibliographic record
Abstract
Heat stress (HS) leads to substantial economic loss of dairy industry each year. The negative effect of HS in dairy cows is becoming one of the more urgent issue due to accelerating side-effects of global warming. Various genes are involved in HS response but the information about the role of noncoding RNAs, especially circular RNAs (circRNAs) is largely unknown. In our study, we aimed to investigate the different expression profile of circRNAs between HS and Non-heat-stressed condition (NC) of Chinese Holstein cow’s mammary gland. CircRNAs were identified using RNA sequencing and bioinformatics analysis. In total, 37405 circRNAs were detected and 95 were differentially expressed (DE), including 15 downregulated and 80 upregulated circRNAs in HS group compared to NC. Eight circRNAs were randomly selected to verify the RNA sequencing result. Further, Sanger sequencing validated the backsplicing site of the eight circRNAs. Moreover, results obtained from the Quantitative real time PCR (qRT-PCR) showed consistent expression trend with that of RNA sequencing. GO annotation and KEGG analysis suggested that these DE circRNAs probably involved in the energy metabolic regulation. Furthermore, we constructed ceRNA network and the result indicated that these DE circRNAs could regulate lactation through IGF1 and PRL signaling pathway.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".