Gut Microbiome and Metabolome Signatures Associated with Heat Tolerance in Dairy Cows
Bibliographic record
Abstract
Heat stress significantly impairs dairy cow health and productivity, highlighting the need to understand the gut microbiome–metabolite interactions that contribute to heat tolerance. Here, we integrated metagenomic sequencing and untargeted metabolomics in twelve holstein cows selected from a previously phenotyped herd of 120 individuals, including six heat-tolerant (HT) and six heat-sensitive (HS) cows identified using entropy-weighted TOPSIS scoring. HT cows were enriched in genera such as Faecalimonas and UBA737, which were functionally linked to pathways of energy and lipid metabolism, whereas, HS cows harbored taxa associated with bacterial lipopolysaccharide and glycosphingolipid biosynthesis. A total of 135 metabolites were differentially abundant between groups. Among them, glycerol 2-phosphate and 24(28)-dehydroergosterol showed perfect classification performance (AUC = 1.000), and were mainly involved in membrane lipid remodeling and redox regulation. Integrated analysis revealed coordinated microbial–metabolite networks, exemplified by the Faecalimonas–LysoPS (16:0/0:0) and UBA737–Glycerol 2-phosphate axes, suggesting functional coupling between microbial composition and metabolic adaptation. Together, these findings demonstrate that HT cows harbor gut microbiota and metabolites favoring energy balance, membrane remodeling, and oxidative stress resilience, while HS cows display stress-related metabolic patterns. This study elucidates the microbial–metabolic mechanisms underlying thermal resilience and highlights potential biomarkers and metabolic pathways that could be applied in heat-tolerance breeding and precision management of dairy 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 | 0.001 | 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) | 0.000 | 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".