The rearing environment persistently modulates mouse phenotypes from the molecular to the behavioural level

Abstract

The phenotype of an organism results from its genotype and the influence of the environment throughout development. Even when using animals of the same genotype, independent studies may test animals of different phenotypes, resulting in poor replicability due to genotype-by-environment interactions. Thus, genetically defined strains of mice may respond differently to experimental treatments depending on their rearing environment. However, the extent of such phenotypic plasticity and its implications for the replicability of research findings have remained unknown. Here, we examined the extent to which common environmental differences between animal facilities modulate the phenotype of genetically homogeneous (inbred) mice. We conducted a comprehensive multicentre study, whereby inbred C57BL/6J mice from a single breeding cohort were allocated to and reared in 5 different animal facilities throughout early life and adolescence, before being transported to a single test laboratory. We found persistent effects of the rearing facility on the composition and heterogeneity of the gut microbial community. These effects were paralleled by persistent differences in body weight and in the behavioural phenotype of the mice. Furthermore, we show that environmental variation among animal facilities is strong enough to influence epigenetic patterns in neurons at the level of chromatin organisation. We detected changes in chromatin organisation in the regulatory regions of genes involved in nucleosome assembly, neuronal differentiation, synaptic plasticity, and regulation of behaviour. Our findings demonstrate that common environmental differences between animal facilities may produce facility-specific phenotypes, from the molecular to the behavioural level. Furthermore, they highlight an important limitation of inferences from single-laboratory studies and thus argue that study designs should take environmental background into account to increase the robustness and replicability of findings.

Fig 1. Study design and effects of RF on gut microbiota diversity and composition.

(a) Schematic illustration of the multicentre study design—genetically homogeneous mice originating from a single inbred stock were reared until the age of 8 weeks in 5 different RFs before testing for phenotypic differences induced by the different rearing conditions in a single testing laboratory. (b) Effects of the RF were evaluated at 2 TPs, first, at the end of the rearing period in each of the 5 RFs (TP1) and during the testing period in the testing laboratory (TP2). Outcome measures assessed at both TP1 and TP2 included gut microbiota, body weight, and chromatin profiles using ATAC-seq, while behavioral tests (open field and light dark box tests) and physiological measures of stress (HPA axis reactivity test (SRT) and relative adrenal weight were limited to TP2). Values for α-diversity metrics for (c) male mice and (d) female mice from different RFs (n  =  6 mice/sex/RF/TP). (e-g) Ordination plots visualizing PCoA based on Bray–Curtis dissimilarity between samples of male (e) and female (g) mice from different RFs, split by TPs. (f-h) Differences between loadings of samples on the first 3 PCoA axes in male (f) and female (h) mice. Box plots show the first and third quartiles; horizontal line represents the median; whiskers represent the mean variability outside the upper and lower quartiles. Individual points represent outliers. TP1: 8 weeks of age (PND 56); TP2: 14.5 weeks of age (PND 104). The raw data underlying this figure are available in the Figshare repository https://doi.org/10.6084/m9.figshare.21082195. The 16S rRNA gene sequencing data are available from the ENA under accession number PRJEB49361. ATAC-seq, assay for transposase-accessible chromatin using sequencing; ENA, European Nucleotide Archive; HPA, hypothalamus–pituitary–adrenal; PCoA, principal coordinate analysis; PND, postnatal day; RF, rearing facility; SRT, stress reactivity test; TP, time point.

Fig 2. Effects of RF on the behavioral and physiological profile of the mice.

(a) Body weight persistently varied by RF in both males and females (n = 6 mice/sex/RF for TP1; n = 24 mice/sex/RF for TP2). (b) Behavior of the mice varied consistently by RF both in males and females. In the LDA plots, color indicates RF, and the circles represent classification based on discriminant function analysis (n = 12 mice/sex/RF). (c) RF did not affect plasma corticosterone levels in the SRT both in males and females (n = 12/mice/sex/RF), while relative adrenal gland weights (n = 24/mice/sex/RF) were affected only in males (d). Box plots include individual data points and show the first and third quartiles; horizontal line is the median; whiskers represent the variability outside the upper and lower quartiles. TP1: 8 weeks of age (PND 56); TP2: 14.5 weeks of age (PND 104). The raw data underlying this figure are available in the Figshare repository https://doi.org/10.6084/m9.figshare.21081949. LDA, linear discriminant function analysis; PND, postnatal day; RF, rearing facility; SRT, stress reactivity test.

Fig 3. Differences in neuronal chromatin accessibility between males from different RFs.

(a) Sample correlation matrices based on all sites and the 10% most variable ATAC-seq sites (n = 5 mice/RF/TP). (b) Manhatten distances between samples for all sites and open chromatin sites visualised by t-SNE. (c) ATAC-seq accessibility signal in response to the different RFs and TPs. Heatmap representation of the most and least accessible sites. The color represents the intensity of chromatin accessibility, from gain (yellow) to loss (dark blue), calculated by using row wise Z-scores (the values are scaled by subtracting the average across samples and by dividing by the standard deviation across samples). (d) Bar graphs representing the number of genes associated with the most and least accessible peaks. (e) Spidergraph representing the genomic features mapped by all (black), open (blue), or closed (red) sites. (f) Chromatin accessibility profiles of Col19a1, Dlg 2, Fzd9, and Lrrc4c. The raw data underlying this figure are available from the NCBI GEO database under accession number GSE191125. The analysis script is available at the GitHub repository https://github.com/MWSchmid/Jaric-et-al.-2022. ATAC-seq, assay for transposase-accessible chromatin using sequencing; GEO, Gene Expression Omnibus; RF, rearing facility; TP, time point.

Fig 4. Predicted biological processes, cellular components, and KEGG pathways affected by differential rearing environment.

GO analysis of genes showing differential chromatin accessibility between the different RF presented for each TP (a-b); KEGG pathway analysis of the genes showing differential chromatin accessibility between the different RFs for each TP. Fields marked with an asterisk depict comparisons that were statistically significant between TPs (two-sided Fisher’s exact test, adjusted for multiple testing, FDR < 0.05). FDR, false discovery rate; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; RF, rearing facility; TP, time point.