Improved quantitative microbiome profiling for environmental antibiotic resistance surveillance

Abstract Background Understanding environmental microbiomes and antibiotic resistance (AR) is hindered by over reliance on relative abundance data from next-generation sequencing. Relative data limits our ability to quantify changes in microbiomes and resistomes over space and time because sequencin...

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Autores principales:	Amelie Ott, Marcos Quintela-Baluja, Andrew M. Zealand, Greg O’Donnell, Mohd Ridza Mohd Haniffah, David W. Graham
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Quantitative microbiome Hill numbers Antibiotic resistance QMRA River water Southeast Asia Environmental sciences GE1-350 Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/74485208c7914f9ba19f0eed31b17181
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spelling	oai:doaj.org-article:74485208c7914f9ba19f0eed31b171812021-11-21T12:27:12ZImproved quantitative microbiome profiling for environmental antibiotic resistance surveillance10.1186/s40793-021-00391-02524-6372https://doaj.org/article/74485208c7914f9ba19f0eed31b171812021-11-01T00:00:00Zhttps://doi.org/10.1186/s40793-021-00391-0https://doaj.org/toc/2524-6372Abstract Background Understanding environmental microbiomes and antibiotic resistance (AR) is hindered by over reliance on relative abundance data from next-generation sequencing. Relative data limits our ability to quantify changes in microbiomes and resistomes over space and time because sequencing depth is not considered and makes data less suitable for Quantitative Microbial Risk Assessments (QMRA), critical in quantifying environmental AR exposure and transmission risks. Results Here we combine quantitative microbiome profiling (QMP; parallelization of amplicon sequencing and 16S rRNA qPCR to estimate cell counts) and absolute resistome profiling (based on high-throughput qPCR) to quantify AR along an anthropogenically impacted river. We show QMP overcomes biases caused by relative taxa abundance data and show the benefits of using unified Hill number diversities to describe environmental microbial communities. Our approach overcomes weaknesses in previous methods and shows Hill numbers are better for QMP in diversity characterisation. Conclusions Methods here can be adapted for any microbiome and resistome research question, but especially providing more quantitative data for QMRA and other environmental applications.Amelie OttMarcos Quintela-BalujaAndrew M. ZealandGreg O’DonnellMohd Ridza Mohd HaniffahDavid W. GrahamBMCarticleQuantitative microbiomeHill numbersAntibiotic resistanceQMRARiver waterSoutheast AsiaEnvironmental sciencesGE1-350MicrobiologyQR1-502ENEnvironmental Microbiome, Vol 16, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Quantitative microbiome Hill numbers Antibiotic resistance QMRA River water Southeast Asia Environmental sciences GE1-350 Microbiology QR1-502
spellingShingle	Quantitative microbiome Hill numbers Antibiotic resistance QMRA River water Southeast Asia Environmental sciences GE1-350 Microbiology QR1-502 Amelie Ott Marcos Quintela-Baluja Andrew M. Zealand Greg O’Donnell Mohd Ridza Mohd Haniffah David W. Graham Improved quantitative microbiome profiling for environmental antibiotic resistance surveillance
description	Abstract Background Understanding environmental microbiomes and antibiotic resistance (AR) is hindered by over reliance on relative abundance data from next-generation sequencing. Relative data limits our ability to quantify changes in microbiomes and resistomes over space and time because sequencing depth is not considered and makes data less suitable for Quantitative Microbial Risk Assessments (QMRA), critical in quantifying environmental AR exposure and transmission risks. Results Here we combine quantitative microbiome profiling (QMP; parallelization of amplicon sequencing and 16S rRNA qPCR to estimate cell counts) and absolute resistome profiling (based on high-throughput qPCR) to quantify AR along an anthropogenically impacted river. We show QMP overcomes biases caused by relative taxa abundance data and show the benefits of using unified Hill number diversities to describe environmental microbial communities. Our approach overcomes weaknesses in previous methods and shows Hill numbers are better for QMP in diversity characterisation. Conclusions Methods here can be adapted for any microbiome and resistome research question, but especially providing more quantitative data for QMRA and other environmental applications.
format	article
author	Amelie Ott Marcos Quintela-Baluja Andrew M. Zealand Greg O’Donnell Mohd Ridza Mohd Haniffah David W. Graham
author_facet	Amelie Ott Marcos Quintela-Baluja Andrew M. Zealand Greg O’Donnell Mohd Ridza Mohd Haniffah David W. Graham
author_sort	Amelie Ott
title	Improved quantitative microbiome profiling for environmental antibiotic resistance surveillance
title_short	Improved quantitative microbiome profiling for environmental antibiotic resistance surveillance
title_full	Improved quantitative microbiome profiling for environmental antibiotic resistance surveillance
title_fullStr	Improved quantitative microbiome profiling for environmental antibiotic resistance surveillance
title_full_unstemmed	Improved quantitative microbiome profiling for environmental antibiotic resistance surveillance
title_sort	improved quantitative microbiome profiling for environmental antibiotic resistance surveillance
publisher	BMC
publishDate	2021
url	https://doaj.org/article/74485208c7914f9ba19f0eed31b17181
work_keys_str_mv	AT amelieott improvedquantitativemicrobiomeprofilingforenvironmentalantibioticresistancesurveillance AT marcosquintelabaluja improvedquantitativemicrobiomeprofilingforenvironmentalantibioticresistancesurveillance AT andrewmzealand improvedquantitativemicrobiomeprofilingforenvironmentalantibioticresistancesurveillance AT gregodonnell improvedquantitativemicrobiomeprofilingforenvironmentalantibioticresistancesurveillance AT mohdridzamohdhaniffah improvedquantitativemicrobiomeprofilingforenvironmentalantibioticresistancesurveillance AT davidwgraham improvedquantitativemicrobiomeprofilingforenvironmentalantibioticresistancesurveillance
_version_	1718419013661360128

Improved quantitative microbiome profiling for environmental antibiotic resistance surveillance

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