Improved quantitative microbiome profiling for environmental antibiotic resistance surveillance

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...

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Main Authors: Ott, Amelie, Quintela-Baluja, Marcos, Zealand, Andrew M., O’Donnell, Greg, Mohd. Haniffah, Mohd. Ridza, Graham, David W.
Format: Article
Language:English
Published: BioMed Central Ltd 2021
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Online Access:http://eprints.utm.my/id/eprint/95500/1/MohdRidzaMohd2021_ImprovedQuantitativeMicrobiomeProfiling.pdf
http://eprints.utm.my/id/eprint/95500/
http://dx.doi.org/10.1186/s40793-021-00391-0
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spelling my.utm.955002022-05-31T12:45:34Z http://eprints.utm.my/id/eprint/95500/ Improved quantitative microbiome profiling for environmental antibiotic resistance surveillance Ott, Amelie Quintela-Baluja, Marcos Zealand, Andrew M. O’Donnell, Greg Mohd. Haniffah, Mohd. Ridza Graham, David W. TA Engineering (General). Civil engineering (General) 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. BioMed Central Ltd 2021-12 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/95500/1/MohdRidzaMohd2021_ImprovedQuantitativeMicrobiomeProfiling.pdf Ott, Amelie and Quintela-Baluja, Marcos and Zealand, Andrew M. and O’Donnell, Greg and Mohd. Haniffah, Mohd. Ridza and Graham, David W. (2021) Improved quantitative microbiome profiling for environmental antibiotic resistance surveillance. Environmental Microbiomes, 16 (1). pp. 1-14. ISSN 2524-6372 http://dx.doi.org/10.1186/s40793-021-00391-0 DOI:10.1186/s40793-021-00391-0
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Ott, Amelie
Quintela-Baluja, Marcos
Zealand, Andrew M.
O’Donnell, Greg
Mohd. Haniffah, Mohd. Ridza
Graham, David W.
Improved quantitative microbiome profiling for environmental antibiotic resistance surveillance
description 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 Ott, Amelie
Quintela-Baluja, Marcos
Zealand, Andrew M.
O’Donnell, Greg
Mohd. Haniffah, Mohd. Ridza
Graham, David W.
author_facet Ott, Amelie
Quintela-Baluja, Marcos
Zealand, Andrew M.
O’Donnell, Greg
Mohd. Haniffah, Mohd. Ridza
Graham, David W.
author_sort Ott, Amelie
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 BioMed Central Ltd
publishDate 2021
url http://eprints.utm.my/id/eprint/95500/1/MohdRidzaMohd2021_ImprovedQuantitativeMicrobiomeProfiling.pdf
http://eprints.utm.my/id/eprint/95500/
http://dx.doi.org/10.1186/s40793-021-00391-0
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score 13.18916