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Environmental Cadmium Exposure Perturbs Gut Microbial Dysbiosis in Ducks.

Veterinary sciences | 2023

Ore extraction, chemical production, and agricultural fertilizers may release significant amounts of heavy metals, which may eventually accumulate widely in the environment and organisms over time, causing global ecological and health problems. As a recognized environmental contaminant, cadmium has been demonstrated to cause osteoporosis and renal injury, but research regarding the effects of cadmium on gut microbiota in ducks remains scarce. Herein, we aimed to characterize the adverse effects of cadmium on gut microbiota in ducks. Results indicated that cadmium exposure dramatically decreased gut microbial alpha diversity and caused significant changes in the main component of gut microbiota. Moreover, we also observed significant changes in the gut microbial composition in ducks exposed to cadmium. A microbial taxonomic investigation showed that Firmicutes, Bacteroidota, and Proteobacteria were the most preponderant phyla in ducks regardless of treatment, but the compositions and abundances of dominant genera were different. Meanwhile, a Metastats analysis indicated that cadmium exposure also caused a distinct increase in the levels of 1 phylum and 22 genera, as well as a significant reduction in the levels of 1 phylum and 36 genera. In summary, this investigation demonstrated that cadmium exposure could disturb gut microbial homeostasis by decreasing microbial diversity and altering microbial composition. Additionally, under the background of the rising environmental pollution caused by heavy metals, this investigation provides a crucial message for the assessment of environmental risks associated with cadmium exposure.

Pubmed ID: 37999472 RIS Download

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Associated grants

  • Agency: National Natural Science Foundation of Henan,
    Id: 232300420029

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RRID:SCR_014609

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023. Algorithm for high-dimensional biomarker discovery and explanation that identifies genes, pathways, or taxa characterizing the differences between two or more biological conditions. The algorithm identifies features that are statistically different among biological classes, then performs additional tests to assess whether these differences are consistent with respect to expected biological behavior. Statistical significance and biological relevance are emphasized.

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