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Rumen Microbiota-Based Machine Learning Approach for Predicting Heat Stress and Identifying Associated Microbes.

Himani Joshi1, Michael Caprio2, Lindsey Reon1

  • 1Department of Animal and Dairy Sciences, Mississippi State University, Mississippi State, MS, 39762, USA.

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|November 25, 2025
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Summary

Heat stress negatively impacts dairy cows, altering their rumen microbes. Analyzing rumen bacteria and archaea can accurately predict heat stress, offering targets for mitigation strategies.

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Area of Science:

  • Animal Science
  • Microbiology
  • Environmental Science

Background:

  • Heat stress significantly challenges sustainable livestock production, impacting animal welfare and productivity.
  • Altered gastrointestinal microbial ecosystems in heat-stressed animals affect nutrient digestion and host production.
  • Inconsistencies in previous studies highlight the need for standardized microbiota analysis to identify reliable heat-stress-associated microbes.

Purpose of the Study:

  • To identify consistent rumen microbial taxa affected by heat stress in lactating Holstein cattle.
  • To evaluate the potential of rumen microbiota profiles for predicting heat stress.
  • To discover potential microbial biomarkers for heat stress mitigation.

Main Methods:

  • Collected and analyzed publicly available 16S rRNA gene amplicon sequencing data from eight studies.
  • Utilized a consistent bioinformatic pipeline for microbial composition analysis.
  • Developed and compared machine learning models (including random forest) for heat stress prediction using rumen microbiota and animal factors.

Main Results:

  • A distinct rumen microbiota signature was identified in heat-stressed lactating Holstein cattle.
  • Specific microbial taxa, including Lactobacillales, Ruminococcaceae UCG-001, and Methanomicrobium, were selected as potential biomarkers.
  • Machine learning models incorporating rumen microbiota significantly improved heat stress prediction accuracy (AUC: 0.851) compared to models without microbiota data (AUC: 0.440).

Conclusions:

  • Rumen microbiota composition is a reliable indicator of heat stress in dairy cows.
  • Identified microbial taxa can serve as biomarkers for heat stress.
  • Targeting specific rumen microbes presents a promising strategy for mitigating heat stress in dairy cattle.