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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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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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 |
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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 |
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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. |
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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 |
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BioMed Central Ltd |
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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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