BackgroundHeat stress (HS) can disrupt the gut microbiome, yet most livestock studies rely on taxonomic summaries that overlook the ecological structure of microbial communities. Enterosignatures (ES) as latent, co-occurring microbial assemblages learned from metagenomic data, offer a framework to capture these dynamics but have scarcely been applied in livestock HS research.MethodsShotgun metagenomes were obtained from 25 lactating sows, belonging to two genetic lines (TOL, n = 13; SEN, n = 12), which were divergently selected based on genomic breeding values (GEBVs) for heat tolerance, and exposed to HS conditions. Results were decomposed using non-negative matrix factorization (NMF), yielding 8 taxonomic (T-ES) and 5 functional (F-ES) subcommunities. Functional profiles (based on KEGG Orthology, KOs) were mapped to metagenome-assembled genomes (MAGs) to integrate metabolic attributes within each ES.ResultsTemporal shifts dominated T-ES variation, with limited genetic-line effects. T-ES 1 (p = 5.42 × 10−4, Cohen’s d = 0.723) and T-ES 7 (p = 0.007, Cohen’s d = 0.303) showed increases from day 4 to day 14. Despite modest overall genetic line effects, TOL animals progressively transitioned toward phylogenetically diverse and balanced communities, whereas SEN animals shifted toward imbalanced states characterized by enrichment of taxa with pathobiont potential or single-taxon dominance. Other T-ES displayed small to moderate effects, and T-ES 8 showed a potentially noteworthy genetic line-specific effect size at late lactation (Cohen’s d = 0.960; 95% CI: −1.80 to −0.10), though omnibus tests were non-significant (p = 0.757), and the wide confidence interval underscores substantial uncertainty at this sample size. No F-ES reached statistical significance (p > 0.05); moderate effect sizes (up to d = 0.638) suggest possible functional restructuring warranting investigation in larger cohorts.ConclusionThis work presents the first use of ES to track microbiome responses to HS in lactating sows. ES revealed latent taxonomic and functional subcommunities with clear temporal reorganization, offering insights not detectable with standard clustering or diversity metrics. Although genetic-line effects were modest, several ES showed biologically relevant shifts, supporting ES as a hypothesis-generating exploratory framework for linking microbial ecology to physiological adaptation under HS conditions, while warranting validation in larger, controlled trials.
Gini et al. (Wed,) studied this question.