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

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.