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Compost Microcosms as Microbially Diverse, Natural-like Environments for Microbiome Research in Caenorhabditis elegans
Published on: September 13, 2022
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Artificial microbiome heterogeneity spurs six practical action themes and examples to increase study power-driven
Abigail R Basson1,2, Alexandria LaSalla1, Gretchen Lam1
1Division of Gastroenterology & Liver Diseases, Case Western Reserve University School of Medicine, Cleveland, OH, USA.
Scientific Reports
|March 21, 2020
Summary
High housing density in mouse models may reduce costs but harms study reproducibility. This research offers a framework and tools to improve experimental design and statistical analysis for more reliable scientific findings.
Area of Science:
- * Preclinical research and translational science.
- * Laboratory animal science and experimental design.
- * Biostatistics and data analysis.
Background:
- * Mouse models are crucial in over 70,000 publications annually, yet reproducibility concerns are widespread.
- * High animal-cage density (more than 2 mice/cage) is common despite known negative impacts on data interpretation and study power.
- * This practice may stem from a perception of reduced housing costs, creating a 'cost-vs-science' discordance.
Purpose of the Study:
- * To investigate the reasons behind high animal-cage density practices in mouse research.
- * To develop a framework and statistical strategies to improve the reproducibility and power of studies using mouse models.
- * To provide tools for scientists to analyze cage-clustered data and estimate sample sizes.
Main Methods:
- * Surveyed three professional organizations in the USA regarding animal husbandry practices and perceptions.
- * Conducted scoping reviews and integrated expert opinions and implementability scores.
- * Developed a 'housing-density cost-calculator-simulator' and provided annotated statistical examples with code.
Main Results:
- * Identified heterogeneity in husbandry practices, with 'cost-vs-science' discordance being a key factor in scientists' reluctance to change housing density.
- * Prioritized confounding factors, including microbiome variations due to dirty cages.
- * Developed Six-Actionable Recommendation Themes (SART) as a framework for improved protocols and statistical strategies.
Conclusions:
- * The SART framework and provided tools can help scientists analyze cage-clustered data and increase study power.
- * Implementing these recommendations can enhance the reproducibility of basic and translational research.
- * The study supports better documentation for sample size estimations in grant proposals and publications.
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