Artificial microbiome heterogeneity spurs six practical action themes and examples to increase study power-driven reproducibility
| Autor: | Fabio Cominelli, Alexander Rodriguez-Palacios, Betty Theriault, Sanja Ilic, Alexandria LaSalla, Mark S. Sundrud, Abigail R. Basson, Danielle Kulpins, Gretchen Lam, Erika L. Moen, Jun Miyoshi |
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| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
media_common.quotation_subject MEDLINE lcsh:Medicine Translational research computer.software_genre Article Translational Research Biomedical Mice 03 medical and health sciences 0302 clinical medicine Animals Laboratory Perception Animals Gastrointestinal models Microbiome Animal Husbandry lcsh:Science Cluster analysis media_common Multidisciplinary Microbiota lcsh:R Reproducibility of Results Housing Animal Data science Experimental models of disease 030104 developmental biology Sample size determination Sample Size lcsh:Q Professional association Psychology computer 030217 neurology & neurosurgery Data integration |
| Zdroj: | Scientific Reports Scientific Reports, Vol 10, Iss 1, Pp 1-19 (2020) |
| ISSN: | 2045-2322 |
| DOI: | 10.1038/s41598-020-60900-y |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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