Artificial microbiome heterogeneity spurs six practical action themes and examples to increase study power-driven reproducibility

Abigail R Basson,Alexandria Lasalla,Gretchen Lam,Jun Miyoshi,Danielle Kulpins,Mark S Sundrud,Erika L Moen,Sanja Ilic,Alexander Rodriguez-Palacios,Fabio Cominelli,Betty R Theriault

doi:10.1038/s41598-020-60900-y

Abstract

With >70,000 yearly publications using mouse data, mouse models represent the best engrained research system to address numerous biological questions across all fields of science. Concerns of poor study and microbiome reproducibility also abound in the literature. Despite the well-known, negative-effects of data clustering on interpretation and study power, it is unclear why scientists often house >4 mice/cage during experiments, instead of ≤2. We hypothesized that this high animal-cage-density practice abounds in published literature because more mice/cage could be perceived as a strategy to reduce housing costs. Among other sources of ‘artificial’ confounding, including cyclical oscillations of the ‘dirty-cage/excrement microbiome’, we ranked by priority the heterogeneity of modern husbandry practices/perceptions across three professional organizations that we surveyed in the USA. Data integration (scoping-reviews, professional-surveys, expert-opinion, and ‘implementability-score-statistics’) identified Six-Actionable Recommendation Themes (SART) as a framework to re-launch emerging protocols and intuitive statistical strategies to use/increase study power. ‘Cost-vs-science’ discordance was a major aspect explaining heterogeneity, and scientists’ reluctance to change. With a ‘housing-density cost-calculator-simulator’ and fully-annotated statistical examples/code, this themed-framework streamlines the rapid analysis of cage-clustered-data and promotes the use of ‘study-power-statistics’ to self-monitor the success/reproducibility of basic and translational research. Examples are provided to help scientists document analysis for study power-based sample size estimations using preclinical mouse data to support translational clinical trials, as requested in NIH/similar grants or publications.

Highlights

With >70,000 yearly publications using mouse data, mouse models represent the best engrained research system to address numerous biological questions across all fields of science
18% of studies housed mice at lower densities of ≤2 MxCg, which is ideal because it increases study power by decreasing cage effects (Fig. 2a–c)
This study and proposed framework were motivated by the identification of a wide heterogeneity in published methods relevant to diet, microbiome, and the pathogenesis of inflammatory bowel diseases and digestive health in humans, where mice models are critical to study diseases biology, translational interventions, or to inform clinical trials for humans[75]

Summary

Introduction

Despite the well-known, negativeeffects of data clustering on interpretation and study power, it is unclear why scientists often house >4 mice/cage during experiments, instead of ≤2. We hypothesized that this high animal-cage-density practice abounds in published literature because more mice/cage could be perceived as a strategy to reduce housing costs. To appropriately select statistics controlling for cage-clustered data, scientists must be aware of study details, namely, which data points belong to which mice and respective cages in a dataset or published figure These details are often omitted during analysis and in publications, and misconceptions on heterogeneity, husbandry and analysis may exist among leading research organizations. The negative impact of clustering is reduced when more cages, with fewer mice per cage, are used per group (‘less mice-per-cage is more’)

Methods

Results

Conclusion