Differential gene expression in the peripheral blood of Chinese Sanhe cattle exposed to severe cold stress
Bibliographic record
Abstract
Livestock is an important food resource for the inhabitants of cold regions, such as northern Asia and alpine regions, where agriculture is limited. In these regions, cold stress largely affects livestock production, thereby reducing the productivity and survival of animals. Despite the importance of breeding cold-tolerant animals, few studies have investigated the effects of cold stress on cattle. Furthermore, whether severe cold stress alters gene expression or affects molecular genetic mechanisms remains unknown. Thus, we investigated gene expression changes in the peripheral blood samples of the Chinese Sanhe cattle exposed to severe cold. A total of 193 genes were found to exhibit significant alteration in expression (P < 0.05; fold change > 1.3), with 107 genes showing upregulation and 86 showing downregulation after cold exposure. The differences in the expression of 10 selected genes were further validated by real-time qRT-PCR. Further analyses showed that these differentially expressed genes (DEGs) were predominantly associated with important biological pathways and gene networks, such as lipid metabolism and cell death and survival, which are potentially associated with severe cold-stress resistance. Identification and description of these cold stress-induced DEGs might lead to the discovery of novel blood biomarkers that could be used to assess cold-stress resistance in cattle. To our knowledge, this is the first genomic evidence of differences in the transcript expression pattern in cattle exposed to severe cold stress. Our findings provide insights on the potential molecular mechanisms underlying cold-stress response in cattle.
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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".