PeerJ (Jul 2018)

Minimizing confounders and increasing data quality in murine models for studies of the gut microbiome

  • Jun Miyoshi,
  • Vanessa Leone,
  • Kentaro Nobutani,
  • Mark W. Musch,
  • Kristina Martinez-Guryn,
  • Yunwei Wang,
  • Sawako Miyoshi,
  • Alexandria M. Bobe,
  • A. Murat Eren,
  • Eugene B. Chang

DOI
https://doi.org/10.7717/peerj.5166
Journal volume & issue
Vol. 6
p. e5166

Abstract

Read online Read online

Murine models are widely used to explore host-microbe interactions because of the challenges and limitations inherent to human studies. However, microbiome studies in murine models are not without their nuances. Inter-individual variations in gut microbiota are frequent even in animals housed within the same room. We therefore sought to find an efficient and effective standard operating procedure (SOP) to minimize these effects to improve consistency and reproducibility in murine microbiota studies. Mice were housed in a single room under specific-pathogen free conditions. Soiled cage bedding was routinely mixed weekly and distributed among all cages from weaning (three weeks old) until the onset of the study. Females and males were separated by sex and group-housed (up to five mice/cage) at weaning. 16S rRNA gene analyses of fecal samples showed that this protocol significantly reduced pre-study variability of gut microbiota amongst animals compared to other conventional measures used to normalize microbiota when large experimental cohorts have been required. A significant and consistent effect size was observed in gut microbiota when mice were switched from regular chow to purified diet in both sexes. However, sex and aging appeared to be independent drivers of gut microbial assemblage and should be taken into account in studies of this nature. In summary, we report a practical and effective pre-study SOP for normalizing the gut microbiome of murine cohorts that minimizes inter-individual variability and resolves co-housing problems inherent to male mice. This SOP may increase quality, rigor, and reproducibility of data acquisition and analysis.

Keywords