Integrative blood transcriptomic and metabolomic profiling reveals biomarkers of natural heat tolerance in Holstein cows
Bibliographic record
Abstract
Heat stress poses a major threat to dairy cattle productivity, particularly in high-producing Holstein cows. To identify robust biomarkers of thermotolerance, we employed an integrative strategy combining physiological phenotyping, blood metabolite profiling, and transcriptomic analysis. A total of 120 lactating Holstein cows were evaluated under natural summer heat conditions using rectal temperature, respiratory rate, salivation index, serum HSP70, cortisol, potassium levels, and milk production. These 7 indicators were weighted via an entropy-based Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model to classify heat-resistant (HR) and heat-sensitive (HS) individuals. Subsequent transcriptomic analysis identified 330 differentially expressed genes (DEG), with PDGFRA upregulated and TIMP3 and CCL5 downregulated in HR cows, suggesting reduced inflammation and extracellular matrix stress. Untargeted metabolomics revealed 220 differentially expressed metabolites, with HR cows exhibiting lower levels of anti-inflammatory compounds such as 6-gingerol and phosphatidylinositol phosphate (PIP [18:1 (11Z)/6-keto-PGF1α]), and higher levels of inflammatory lipids. Two plasma metabolites, 3-methoxytyramine and (3Z)-phytochromobilin, showed strong discriminative power for thermotolerance (area under the curve >0.88). Multi-omics integration uncovered 411 significant gene-metabolite correlations enriched in heat-related pathways, including sphingolipid signaling and arachidonic acid metabolism. The identified biomarkers demonstrated their utility for rapid, noninvasive screening of heat-tolerant cows. These findings provide novel insights into the molecular mechanisms of heat resilience and offer a foundation for biomarker-assisted selection in climate-resilient dairy breeding.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".