Pesticide residues in crop-derived feedstocks represent a pervasive environmental stressor in agro-ecosystems, yet their role in driving the non-antibiotic co-selection of antimicrobial resistance (AMR) within the ruminant gut reservoir remains poorly understood. This study investigated the physiological trade-offs and indirect mechanisms of resistome expansion in a ruminant model exposed to environmentally relevant levels of cypermethrin. Integrated metagenomic and phenotypic assays revealed that cypermethrin exposure did not impair growth performance, but significantly increased daily feed intake and shifted fermentation profiles toward acetate. This metabolic compensation was supported by a reshaped core microbiome, characterized by increased abundance of fibrolytic consortia (e.g., Fibrobacter, Ruminococcus), enrichment of carbohydrate-active enzymes (GH3, GH5, GH13, and GH43), and upregulation of glycolysis and acetate-producing pathways. However, this metabolic adaptation came at a severe physiological cost, evidenced by systemic oxidative injury and hepatic dysfunction in the host. Crucially, cypermethrin acted as a potent non-antibiotic selective agent that expanded the ruminal resistome and mobilome, specifically, enriching efflux pumps (e.g., oqxA, MexB) confirmed target alteration genes (e.g., vanE). Consequently, dietary cypermethrin exposure forces microbial metabolic compensation at the expense of host hepatic health, while turning the ruminant gut into an overlooked repository for AMR. These findings highlight the critical ecological risks of pesticide-induced resistance co-selection, threatenting the One Health framework. Future research should incorporate multi-dose gradients, evaluate long-term exposure effects using sequential temporal sampling, and utilize non-invasive baseline monitoring across diverse ruminant species to fully elucidate these ecological risks.
Xiaoli Zhang, Shihao Han, Anlin Zhao et al.· Ecotoxicology and Environmen...· 0 citations
Early postnatal growth is a critical determinant of meat production efficiency and long-term genetic improvement in goats; however, the molecular mechanisms underlying individual variation in growth performance remain poorly understood. In this study, a total of 123 Hechuan white goats were included. First, a genome-wide association study (GWAS) for average daily gain (ADG) was performed using all 123 individuals. Subsequently, based on the coefficient of variation of ADG (CV = 65.6%), an extreme phenotype sampling (EPS) strategy was applied to select 39 individuals with extreme growth phenotypes for subsequent metabolomic, microbiome, and integrated mGWAS analyses.The results showed that ADG approximately followed a normal distribution across the 123 goats. GWAS identified 22 loci significantly associated with ADG, mapping to genes including DLK1, NCAPG2, LCORL, CNTNAP2, and SLC8A1, which are involved in pathways related to skeletal muscle development, cell cycle regulation, ion transport, and immune function. Metabolomic profiling detected 1,589 putative metabolites, revealing differential enrichment of lipid, amino acid, and bile acid metabolic pathways between fast- and slow-growing goats. Gut microbiome analysis demonstrated that Christensenellaceae_R-7_group and Monoglobus were significantly enriched in fast-growing individuals, whereas Desulfovibrio was more abundant in slow-growing goats.Integrated mGWAS analysis further revealed extensive effects of host genetic variation on gut microbiota and fecal metabolites. Specifically, 11 bacterial genera were significantly associated with host genomic variants, among which Desulfovibrio exhibited the highest number of associated loci. Integration of multiple variant types consistently linked Desulfovibrio, Eubacterium_hallii_group, and Candidatus_Saccharimonas with genes such as ARHGAP24 and IGF2BP2. In addition, 14 metabolites were significantly associated with host genetic variants, with Lysopc(14:1(9Z)/0:0) and glycocholic acid showing the strongest associations. Notably, the peak signal for Lysopc was located within HMGA2.Collectively, these findings define a coordinated host genome–gut microbiota–metabolite network underlying early growth variation in goats and provide a mechanistic foundation for precision breeding and targeted nutritional strategies in goat production systems.
