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.

Highlights

  • Understanding environmental microbiomes and antibiotic resistance (AR) is hindered by over reliance on relative abundance data from next-generation sequencing

  • Quantifying the extent of environmental AR over space and time is difficult because methods are not standardized, with researchers using different measures of AR across studies [6]

  • Mean concentrations with standard deviations are reported per sampling point across seasons

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Summary

Introduction

Understanding environmental microbiomes and antibiotic resistance (AR) is hindered by over reliance on relative abundance data from next-generation sequencing. AR pathogens spread through hospitals, and enter the environment via insufficiently treated sewage [3, 4]. Linking microbiome characteristics from DNA sequencing with quantitative ARG data is an unfulfilled aspiration for studying environmental AR [10, 11]. This restricts our ability to perform realistic Quantitative Microbial Risk Assessments (QMRA) needed to quantify true risks of environment AR exposures [12, 13]

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