Improved quantitative microbiome profiling for environmental antibiotic resistance surveillance

Area of Science:

• Environmental microbiology
• Antibiotic resistance (AR) research
• Quantitative microbial risk assessment (QMRA)

Background:

• Next-generation sequencing (NGS) relies on relative abundance data, limiting accurate quantification of microbiome and resistome changes.
• Relative data is unsuitable for Quantitative Microbial Risk Assessments (QMRA), hindering the evaluation of environmental antibiotic resistance exposure and transmission risks.

Purpose of the Study:

• To develop and apply a quantitative approach for profiling microbiomes and resistomes in environmental samples.
• To overcome limitations of relative abundance data in next-generation sequencing for microbiome and resistome analysis.
• To enhance the suitability of data for Quantitative Microbial Risk Assessments (QMRA).

Main Methods:

• Quantitative microbiome profiling (QMP) by parallelizing amplicon sequencing and 16S rRNA qPCR to estimate cell counts.
• Absolute resistome profiling using high-throughput qPCR.
• Application of these methods along an anthropogenically impacted river.
• Utilizing unified Hill number diversities for microbial community characterization.

Main Results:

• QMP effectively overcomes biases introduced by relative taxa abundance data.
• Unified Hill number diversities provide a superior method for characterizing microbial communities in QMP.
• The study successfully quantified antibiotic resistance in an impacted river environment.

Conclusions:

• The developed methods provide more quantitative data for environmental microbiome and resistome research.
• This approach is adaptable for various research questions, particularly enhancing QMRA applications.
• The findings offer improved tools for assessing environmental antibiotic resistance and microbial community dynamics.