Improved quantitative microbiome profiling for environmental antibiotic resistance surveillance

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 consi...

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Bibliographic Details
Published in:Environmental microbiome 2021-11, Vol.16 (1), p.21-21, Article 21
Main Authors: Ott, Amelie, Quintela-Baluja, Marcos, Zealand, Andrew M, O'Donnell, Greg, Haniffah, Mohd Ridza Mohd, Graham, David W
Format: Article
Language:English
Subjects:
Anthropogenic factors
Antibiotic resistance
Antibiotics
Drug resistance
Drug resistance in microorganisms
Hill numbers
Microbial activity
Microbiomes
Next-generation sequencing
QMRA
Quantitative microbiome
Relative abundance
Risk assessment
River water
Rivers
RNA
rRNA 16S
Southeast Asia
Tetracycline
Tetracyclines
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Summary: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. 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. Methods here can be adapted for any microbiome and resistome research question, but especially providing more quantitative data for QMRA and other environmental applications.
ISSN:2524-6372
2524-6372
DOI:10.1186/s40793-021-00391-0