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Reveals gut microbiota dysbiosis and metabolic changes in neonatal calves with BRV-induced diarrhea, suggesting new diagnostic strategies.
Bovine rotavirus (BRV) is a major pathogen causing diarrhea in neonatal dairy calves, yet its impact on the gut microbiome and host metabolism remains incompletely understood. This study integrated 16S rRNA gene sequencing and untargeted metabolomics to characterize the fecal microbial and metabolic profiles of BRV-infected diarrheic calves compared to healthy controls. Fecal samples were collected from 16 Holstein calves (<30 days old), equally divided into a BRV-infected group (confirmed by RT-qPCR) and a healthy control group. Alpha diversity analysis revealed a significant reduction in microbial richness (Chao1, p = 0.012) in the BRV group. Beta diversity (Jaccard distance) showed distinct clustering between groups (PERMANOVA, p < 0.05). Linear discriminant analysis effect size (LEfSe) identified a significant enrichment of Escherichia (Proteobacteria) and Enterococcus (Firmicutes) in BRV calves, whereas healthy controls were enriched in Blautia and Faecalibacterium (both Firmicutes, with Faecalibacterium also commonly associated with healthy gut communities). Metabolomic analysis via UHPLC-MS/MS demonstrated a clear separation of metabolic profiles. BRV infection was associated with significant alterations in lipid metabolism pathways, notably elevated levels of lysophosphatidylcholines (LPCs) and lysophosphatidylethanolamines (LPEs). Integrated Spearman correlation analysis revealed extensive and significant associations (|ρ| > 0.6, FDR-adjusted p < 0.05) between differential bacterial genera (e.g., Enterococcus, Escherichia) and differential metabolites (e.g., glycerolipids, amino acid derivatives), suggesting a close linkage between gut microbial dysbiosis and metabolic remodeling during BRV infection. These findings provide a multi-omics perspective on the interplay between the gut microbiota and host metabolism in BRV-induced diarrhea, offering potential insights for developing microbiota-based diagnostic or therapeutic strategies.
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Yu et al. (2026) studied this question.
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