Associating population-level variability of the gut microbiome with host phenotypes

The gut microbiome (GM) affects host growth and development, behavior, and disease susceptibility. Biomedical research investigating the mechanisms by which the GM influences host phenotypes often involves collecting fecal samples from laboratory mice. Many environmental factors can affect the composition of the GM in mice, and while efforts are made to minimize this variation, biological and technical variability exists and may influence microbiome outcomes. Here we employed a hierarchical fecal sampling strategy to 1) quantify the effect size of biological and technical variation and 2) provide practical guidance for the development of microbiome studies involving laboratory mice. We found that while biological and technical sources of variation contribute significant variability to microbiome alpha and beta diversity outcomes, their effect size is 3- to 30-times lower than that of the experimental variable in the context of an experimental group with high intergroup variability. After quantifying variability of alpha diversity metrics at the technical and biological levels, we simulated whether sequencing multiple fecal samples from individual mice improves effect size in a two-group experimental design. Collecting five fecal samples per mouse increased effect size achieving the maximum 5 percent reduction in the required number of animals per group. While reducing the number of animals required, sequencing costs were dramatically increased. Our data suggest that the effect size of biological and technical factors may contribute appreciable variability to an experimental paradigm with relatively low mean differences. Additionally, repeated sampling improves statistical power, however, its application is likely impractical given the increased sequencing costs. Autism spectrum disorders (ASD) are complex human neurodiversities increasing in prevalence within the human population. In search of therapeutics to improve quality-of-life for ASD patients, the gut microbiome (GM) has become a promising target as a growing body of work supports roles for the complex community of microorganisms in influencing host behavior via the gut-brain-axis. However, whether naturally-occurring microbial diversity within the host GM affects these behaviors is often overlooked. Here we applied a model of population-level differences in the GM to a classic ASD model - the BTBR T+ Itpr3tf/J mouse - to assess how complex GMs affect host behavior. Leveraging the naturally occurring differences between supplier-origin GMs, our data demonstrate that differing, complex GMs selectively effect host ASD-related behavior - especially neonatal ultrasonic communication - and reveal a male-specific effect on behavior not typically observed in this strain. We then identified that the body weight of BTBR mice is influenced by the postnatal GM which was potentially mediated by microbiome-dependent effects on energy harvest in the gut. These data provide insight into how variability within the GM affects host behavior and growth, thereby emphasizing the need to incorporate microbial diversity within the host GM as an experimental factor in biomedical research. Horses and other equids are reliant on the gut microbiome for health, and studies have reported associations between certain clinical conditions and features of the fecal microbiome. However, research to date on the equine fecal microbiome has often relied on small sample sizes collected from single and relatively localized geographic regions. Previous work largely employs single timepoint analyses, or horses selected based on limited health criteria. To address these issues and expand our understanding of the core microbiome in health, and the changes associated with adverse outcomes, the Equine Gut Group (EGG) has collected and performed 16S rRNA sequencing on 2,362 fecal samples from 1,190 healthy and affected horses. Here we present the EGG database and demonstrate its utility in characterizing the equine microbiome in health and acute gastrointestinal disease. The EGG 16S rRNA database is a valuable resource to study the equine microbiome and its role in equine health.