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Improved quantitative microbiome profiling for environmental antibiotic resistance surveillance

Lookup NU author(s): Amelie Ott, Marcos Quintela-Baluja, Dr Andrew Zealand, Dr Greg O'Donnell, Professor David Graham

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

BackgroundUnderstanding 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.ResultsHere 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.ConclusionsMethods here can be adapted for any microbiome and resistome research question, but especially providing more quantitative data for QMRA and other environmental applications.


Publication metadata

Author(s): Ott A, Quintela M, Zealand AM, ODonnell G, Hanifah MRM, Graham DW

Publication type: Article

Publication status: Published

Journal: Environmental Microbiome

Year: 2021

Volume: 16

Print publication date: 18/11/2021

Online publication date: 18/11/2021

Acceptance date: 04/11/2021

Date deposited: 20/11/2021

ISSN (electronic): 2524-6372

Publisher: Springer Nature

URL: https://doi.org/10.1186/s40793-021-00391-0

DOI: 10.1186/s40793-021-00391-0


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Funding

Funder referenceFunder name
British Council Newton Fund Institutional Links grant (331945729)
HT-qPCR analysis was funded by the Key Collaborative Research Program of the Alliance of International Science Organizations (ANSO-CR-KP-2020-03).
Newcastle University SAgE Singapore Scholarships programme
UK EPSRC Impact Acceleration Award (EP/K503885/1)

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