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dc.contributor.authorGauglitz, Julia M.
dc.contributor.authorMorton, James T.
dc.contributor.authorTripathi, Anupriya
dc.contributor.authorHansen, Shalisa
dc.contributor.authorGaffney, Michele
dc.contributor.authorCarpenter, Carolina
dc.contributor.authorWeldon, Kelly C.
dc.contributor.authorShah, Riya
dc.contributor.authorParampil, Amy
dc.contributor.authorFidgett, Andrea
dc.contributor.authorSwafford, Austin D.
dc.contributor.authorKnight, Rob
dc.contributor.authorDorrestein, Pieter C.
dc.contributor.editorCotter, Paul D.
dc.date.accessioned2020-05-15T19:24:55Z
dc.date.available2020-05-15T19:24:55Z
dc.date.issued2020
dc.identifier.doi10.1128/mSystems.00635-19
dc.identifier.urihttp://hdl.handle.net/20.500.12634/233
dc.description.abstractEven 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.
dc.language.isoen
dc.relation.urlhttp://msystems.asm.org/lookup/doi/10.1128/mSystems.00635-19
dc.rightsCopyright © 2020 Gauglitz et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCHEETAHS
dc.subjectEXPERIMENTAL METHODS
dc.subjectGASTROENTEROLOGY
dc.subjectSAFARI PARK
dc.subjectGROOMING
dc.subjectHUSBANDRY
dc.subjectFECES
dc.subjectCOMMUNITIES
dc.subjectSOCIAL BEHAVIOR
dc.subjectPHARMACOLOGY
dc.titleMetabolome-informed microbiome analysis refines metadata classifications and reveals unexpected medication transfer in captive cheetahs
dc.typeArticle
dc.source.journaltitlemSystems
dc.source.volume5
dc.source.issue2
dc.source.beginpagee00635
dc.source.endpage19, /msystems/5/2/msys.00635
refterms.dateFOA2020-05-15T19:24:56Z
html.description.abstractEven 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.


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Copyright © 2020 Gauglitz et al.
This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.
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.