文章基本信息

标题：Artificial microbiome heterogeneity spurs six practical action themes and examples to increase study power-driven reproducibility
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作者：Abigail R. Basson ; Alexandria LaSalla ; Gretchen Lam 等
期刊名称：Scientific Reports
电子版ISSN：2045-2322
出版年度：2020
卷号：10
期号：1
页码：1-19
DOI：10.1038/s41598-020-60900-y
出版社：Springer Nature
摘要：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.