Age and Microenvironment Outweigh Genetic Influence on the Zucker Rat Microbiome

Abstract

Animal models are invaluable tools which allow us to investigate the microbiome-host dialogue. However, experimental design introduces biases in the data that we collect, also potentially leading to biased conclusions. With obesity at pandemic levels animal models of this disease have been developed; we investigated the role of experimental design on one such rodent model. We used 454 pyrosequencing to profile the faecal bacteria of obese (n = 6) and lean (homozygous n = 6; heterozygous n = 6) Zucker rats over a 10 week period, maintained in mixed-genotype cages, to further understand the relationships between the composition of the intestinal bacteria and age, obesity progression, genetic background and cage environment. Phylogenetic and taxon-based univariate and multivariate analyses (non-metric multidimensional scaling, principal component analysis) showed that age was the most significant source of variation in the composition of the faecal microbiota. Second to this, cage environment was found to clearly impact the composition of the faecal microbiota, with samples from animals from within the same cage showing high community structure concordance, but large differences seen between cages. Importantly, the genetically induced obese phenotype was not found to impact the faecal bacterial profiles. These findings demonstrate that the age and local environmental cage variables were driving the composition of the faecal bacteria and were more deterministically important than the host genotype. These findings have major implications for understanding the significance of functional metagenomic data in experimental studies and beg the question; what is being measured in animal experiments in which different strains are housed separately, nature or nurture?

Shotgun metagenomics, from sampling to analysis

“…If samples originate in animal models, particularly those involving co-housed rodents, the roles of animal age and housing environment 22,23 , and the sex of the person handling the animals 24 , may have on microbial community profiles should be taken into account. It is usually possible to mitigate against potential confounders in the study design by housing animals individually to prevent the spread of microbes between cage mates (although this may introduce behavioural changes, potentially resulting in different biases), mixing animals derived from different experimental cohorts together within the same cage, or repeating experiments with mouse lines obtained from different vendors or with different genetic backgrounds 25 .…”

Ontogenetic Characterization of the Intestinal Microbiota of Channel Catfish through 16S rRNA Gene Sequencing Reveals Insights on Temporal Shifts and the Influence of Environmental Microbes

“…Ontogenetic and temporal changes in microbial beta diversity have been detected in other aquaculture fish species as well, such as rainbow trout [33], Atlantic cod [32], and coho salmon [31]. Similar microbiota shifts are commonly detected in other organisms too, including zebrafish [13, 30], humans [53–56], rodents [57, 58], and terrestrial mono-gastric livestock, such as pigs and chickens [59–60]. A common theme among such research is that the newly colonized gut microbiomes of adolescent or early life stage hosts tend to exhibit higher interindividual variability and greater sensitivity to alterations, as compared to the more mature microbiomes of older hosts.…”

Food-grade cationic antimicrobial ε-polylysine transiently alters the gut microbial community and predicted metagenome function in CD-1 mice

“…Previous studies showed that mice co-housed together exhibit similar gut microbial communities. 38,39 Thus, pooled fecal pellets from each cage were collected in 24-h metabolic cages and analyzed at three points: week 1 (baseline), week 5 (intermediate phase), and week 9 (final phase) (Fig. 1).…”

“…As mice are coprophagic it may contribute to the development of a common microbiome in cage mates. 39 In order to minimize the potential for this confounder, we collected and analyzed pooled feces samples. Thus, compositional change in the microbial community is more likely due to dietary treatment.…”