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Updated: Jun 29, 2025

A Method to Define the Effects of Environmental Enrichment on Colon Microbiome Biodiversity in a Mouse Colon Tumor Model
Published on: February 28, 2018
Timing of standard chow exposure determines the variability of mouse phenotypic outcomes and gut microbiota profile
Megan M Knuth1,2, Carolina Vieira Campos1,3, Kirsten Smith1,4
1Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Abstract:
Standard chow diet contributes to reproducibility in animal model experiments since chows differ in nutrient composition, which can independently influence phenotypes. However, there is little evidence of the role of timing in the extent of variability caused by chow exposure. Here, we measured the impact of diet (5V5M, 5V0G, 2920X, and 5058) and timing of exposure (adult exposure (AE), lifetime exposure (LE), and developmental exposure (DE)) on growth & development, metabolic health indicators, and gut bacterial microbiota profiles across genetically identical C57BL6/J mice. Diet drove differences in macro- and micronutrient intake for all exposure models. AE had no effect on measured outcomes. However, LE mice exhibited significant sex-dependent diet effects on growth, body weight, and body composition. LE effects were mostly absent in the DE model, where mice were exposed to chow differences from conception to weaning. Both AE and LE models exhibited similar diet-driven beta diversity profiles for the gut bacterial microbiota, with 5058 diet driving the most distinct profile. Diet-induced beta diversity profiles were sex-dependent for LE mice. Compared to AE, LE drove 9X more diet-driven differentially abundant genera, majority of which were the result of inverse effects of 2920X and 5058. Our findings demonstrate that lifetime exposure to different chow diets has the greatest impact on reproducibility of experimental measures that are common components of preclinical mouse model studies. Importantly, weaning DE mice onto a uniform diet is likely an effective way to reduce unwanted phenotypic variability among experimental models.
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