Metabolome-Informed Microbiome Analysis Refines Metadata Classifications and Reveals Unexpected Medication Transfer in Captive Cheetahs

ABSTRACT 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 fec...

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Autores principales:	Julia M. Gauglitz, James T. Morton, Anupriya Tripathi, Shalisa Hansen, Michele Gaffney, Carolina Carpenter, Kelly C. Weldon, Riya Shah, Amy Parampil, Andrea L. Fidgett, Austin D. Swafford, Rob Knight, Pieter C. Dorrestein
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2020
Materias:	metabolome metagenome microbiome cheetah medication antibiotics Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/5bae96fe42614cd489a65552737f785b
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spelling	oai:doaj.org-article:5bae96fe42614cd489a65552737f785b2021-12-02T18:15:45ZMetabolome-Informed Microbiome Analysis Refines Metadata Classifications and Reveals Unexpected Medication Transfer in Captive Cheetahs10.1128/mSystems.00635-192379-5077https://doaj.org/article/5bae96fe42614cd489a65552737f785b2020-04-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.00635-19https://doaj.org/toc/2379-5077ABSTRACT 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. IMPORTANCE Metabolome-informed analyses can enhance omics studies by enabling the correct partitioning of samples by identifying hidden confounders inadvertently misrepresented or omitted from carefully curated metadata. We demonstrate here the utility of metabolomics in a study characterizing the microbiome associated with liver disease in cheetahs. Metabolome-informed reinterpretation of metagenome and metabolome profiles factored in an unexpected transfer of antibiotics, preventing misinterpretation of the data. Our work suggests that untargeted metabolomics can be used to verify, augment, and correct sample metadata to support improved grouping of sample data for microbiome analyses, here for nonmodel organisms in captivity. However, the techniques also suggest a path forward for correcting clinical information in microbiome studies more broadly to enable higher-precision analyses.Julia M. GauglitzJames T. MortonAnupriya TripathiShalisa HansenMichele GaffneyCarolina CarpenterKelly C. WeldonRiya ShahAmy ParampilAndrea L. FidgettAustin D. SwaffordRob KnightPieter C. DorresteinAmerican Society for MicrobiologyarticlemetabolomemetagenomemicrobiomecheetahmedicationantibioticsMicrobiologyQR1-502ENmSystems, Vol 5, Iss 2 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	metabolome metagenome microbiome cheetah medication antibiotics Microbiology QR1-502
spellingShingle	metabolome metagenome microbiome cheetah medication antibiotics Microbiology QR1-502 Julia M. Gauglitz James T. Morton Anupriya Tripathi Shalisa Hansen Michele Gaffney Carolina Carpenter Kelly C. Weldon Riya Shah Amy Parampil Andrea L. Fidgett Austin D. Swafford Rob Knight Pieter C. Dorrestein Metabolome-Informed Microbiome Analysis Refines Metadata Classifications and Reveals Unexpected Medication Transfer in Captive Cheetahs
description	ABSTRACT 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. IMPORTANCE Metabolome-informed analyses can enhance omics studies by enabling the correct partitioning of samples by identifying hidden confounders inadvertently misrepresented or omitted from carefully curated metadata. We demonstrate here the utility of metabolomics in a study characterizing the microbiome associated with liver disease in cheetahs. Metabolome-informed reinterpretation of metagenome and metabolome profiles factored in an unexpected transfer of antibiotics, preventing misinterpretation of the data. Our work suggests that untargeted metabolomics can be used to verify, augment, and correct sample metadata to support improved grouping of sample data for microbiome analyses, here for nonmodel organisms in captivity. However, the techniques also suggest a path forward for correcting clinical information in microbiome studies more broadly to enable higher-precision analyses.
format	article
author	Julia M. Gauglitz James T. Morton Anupriya Tripathi Shalisa Hansen Michele Gaffney Carolina Carpenter Kelly C. Weldon Riya Shah Amy Parampil Andrea L. Fidgett Austin D. Swafford Rob Knight Pieter C. Dorrestein
author_facet	Julia M. Gauglitz James T. Morton Anupriya Tripathi Shalisa Hansen Michele Gaffney Carolina Carpenter Kelly C. Weldon Riya Shah Amy Parampil Andrea L. Fidgett Austin D. Swafford Rob Knight Pieter C. Dorrestein
author_sort	Julia M. Gauglitz
title	Metabolome-Informed Microbiome Analysis Refines Metadata Classifications and Reveals Unexpected Medication Transfer in Captive Cheetahs
title_short	Metabolome-Informed Microbiome Analysis Refines Metadata Classifications and Reveals Unexpected Medication Transfer in Captive Cheetahs
title_full	Metabolome-Informed Microbiome Analysis Refines Metadata Classifications and Reveals Unexpected Medication Transfer in Captive Cheetahs
title_fullStr	Metabolome-Informed Microbiome Analysis Refines Metadata Classifications and Reveals Unexpected Medication Transfer in Captive Cheetahs
title_full_unstemmed	Metabolome-Informed Microbiome Analysis Refines Metadata Classifications and Reveals Unexpected Medication Transfer in Captive Cheetahs
title_sort	metabolome-informed microbiome analysis refines metadata classifications and reveals unexpected medication transfer in captive cheetahs
publisher	American Society for Microbiology
publishDate	2020
url	https://doaj.org/article/5bae96fe42614cd489a65552737f785b
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Metabolome-Informed Microbiome Analysis Refines Metadata Classifications and Reveals Unexpected Medication Transfer in Captive Cheetahs

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