ArticleEnvironmental microbiome2021
Improved quantitative microbiome profiling for environmental antibiotic resistance surveillance.
Article in Environmental microbiome, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Metagenomic insights into microbial diversity, xenobiotic and plastic-degrading enzymes in sediments of river Yamuna at Agra.Frontiers in microbiology · 2026Article
- Metagenomic insights into antibiotic resistance genes and virulence factors in sediments of river Yamuna.Frontiers in microbiology · 2026Article
- Development of a quantitative PMA-16S rRNA gene sequencing workflow for absolute abundance measurements of seawater microbial communities.Environmental microbiome · 2025Article
- Antibiotic treatments to mothers during the perinatal period leaving hidden trouble on infants.European journal of pediatrics · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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.
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Registered trials
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