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 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.

Automated summary

This study combines quantitative microbiome profiling (QMP) and absolute resistome profiling to quantify antibiotic resistance along a river. QMP overcomes biases from relative abundance data and demonstrates the benefits of using Hill numbers for characterizing environmental microbial communities. These methods can provide more quantitative data for Quantitative Microbial Risk Assessments (QMRA) and other environmental applications.