Metabolome-informed microbiome analysis refines metadata classifications and reveals unexpected medication transfer in captive cheetahs
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Date Issued
2020Author
Gauglitz, Julia M.Morton, James T.
Tripathi, Anupriya
Hansen, Shalisa
Gaffney, Michele
Carpenter, Carolina
Weldon, Kelly C.
Shah, Riya
Parampil, Amy
Fidgett, Andrea
Swafford, Austin D.
Knight, Rob
Dorrestein, Pieter C.
Subject Terms
CHEETAHSEXPERIMENTAL METHODS
GASTROENTEROLOGY
SAFARI PARK
GROOMING
HUSBANDRY
FECES
COMMUNITIES
SOCIAL BEHAVIOR
PHARMACOLOGY
Journal
mSystemsVolume
5Issue
2Start page
e00635End page
19, /msystems/5/2/msys.00635
Metadata
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http://msystems.asm.org/lookup/doi/10.1128/mSystems.00635-19Abstract
Even high-quality collection and reporting of study metadata in microbiome studies can lead to various forms of inadvertently missing or mischaracterized information that can alter the interpretation or outcome of the studies, especially with nonmodel organisms. Metabolomic profiling of fecal microbiome samples can provide empirical insight into unanticipated confounding factors that are not possible to obtain even from detailed care records. We illustrate this point using data from cheetahs from the San Diego Zoo Safari Park. The metabolomic characterization indicated that one cheetah had to be moved from the non-antibiotic-exposed group to the antibiotic-exposed group. The detection of the antibiotic in this second cheetah was likely due to grooming interactions with the cheetah that was administered antibiotics. Similarly, because transit time for stool is variable, fecal samples within the first few days of antibiotic prescription do not all contain detected antibiotics, and the microbiome is not yet affected. These insights significantly altered the way the samples were grouped for analysis (antibiotic versus no antibiotic) and the subsequent understanding of the effect of the antibiotics on the cheetah microbiome. Metabolomics also revealed information about numerous other medications and provided unexpected dietary insights that in turn improved our understanding of the molecular patterns on the impact on the community microbial structure. These results suggest that untargeted metabolomic data provide empirical evidence to correct records and aid in the monitoring of the health of nonmodel organisms in captivity, although we also expect that these methods may be appropriate for other social animals, such as cats.Type
ArticleRights
Copyright © 2020 Gauglitz et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.ae974a485f413a2113503eed53cd6c53
10.1128/mSystems.00635-19
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Except where otherwise noted, this item's license is described as Copyright © 2020 Gauglitz et al.
This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.