Comparative analysis of qPCR and metagenomics for detecting antimicrobial resistance in wastewater: a case study
Abstract Objective The World Health Organization (WHO) has declared antimicrobial resistance (AMR) as one of the top threats to global public health. While AMR surveillance of human clinical isolates is well-established in many countries, the increasing threat of AMR has intensified efforts to detec...
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| Language: | English |
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BMC
2025-01-01
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| Series: | BMC Research Notes |
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| Online Access: | https://doi.org/10.1186/s13104-024-07027-9 |
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| author | William Taylor Kristin Bohm Kristin Dyet Louise Weaver Isabelle Pattis |
| author_facet | William Taylor Kristin Bohm Kristin Dyet Louise Weaver Isabelle Pattis |
| author_sort | William Taylor |
| collection | DOAJ |
| description | Abstract Objective The World Health Organization (WHO) has declared antimicrobial resistance (AMR) as one of the top threats to global public health. While AMR surveillance of human clinical isolates is well-established in many countries, the increasing threat of AMR has intensified efforts to detect antibiotic resistance genes (ARGs) accurately and sensitively in environmental samples, wastewater, animals, and food. Using five ARGs and the 16S rRNA gene, we compared quantitative PCR (qPCR) and metagenomic sequencing (MGS), two commonly used methods to uncover the wastewater resistome. We compared both methods by evaluating ARG detection through a municipal wastewater treatment chain. Results Our results demonstrate that qPCR was more sensitive than MGS, particularly in diluted samples with low ARG concentrations such as oxidation pond water. However, MGS was potentially more specific and has less risk of off-target binding in concentrated samples such as raw sewage. MGS analysis revealed multiple subtypes of each gene which could not be distinguished by qPCR; these subtypes varied across different sample types. Our findings affect the conclusions that can be drawn when comparing different sample types, particularly in terms of inferring removal rates or origins of genes. We conclude that both methods appear suitable to profile the resistome of wastewater and other environmental samples, depending on the research question and type of sample. |
| format | Article |
| id | doaj-art-0c132c50e6cf4ed38d471f1e225d2d69 |
| institution | Kabale University |
| issn | 1756-0500 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Research Notes |
| spelling | doaj-art-0c132c50e6cf4ed38d471f1e225d2d692025-01-12T12:06:49ZengBMCBMC Research Notes1756-05002025-01-011811610.1186/s13104-024-07027-9Comparative analysis of qPCR and metagenomics for detecting antimicrobial resistance in wastewater: a case studyWilliam Taylor0Kristin Bohm1Kristin Dyet2Louise Weaver3Isabelle Pattis4Institute of Environmental Science and Research LtdInstitute of Environmental Science and Research LtdInstitute of Environmental Science and Research LtdInstitute of Environmental Science and Research LtdInstitute of Environmental Science and Research LtdAbstract Objective The World Health Organization (WHO) has declared antimicrobial resistance (AMR) as one of the top threats to global public health. While AMR surveillance of human clinical isolates is well-established in many countries, the increasing threat of AMR has intensified efforts to detect antibiotic resistance genes (ARGs) accurately and sensitively in environmental samples, wastewater, animals, and food. Using five ARGs and the 16S rRNA gene, we compared quantitative PCR (qPCR) and metagenomic sequencing (MGS), two commonly used methods to uncover the wastewater resistome. We compared both methods by evaluating ARG detection through a municipal wastewater treatment chain. Results Our results demonstrate that qPCR was more sensitive than MGS, particularly in diluted samples with low ARG concentrations such as oxidation pond water. However, MGS was potentially more specific and has less risk of off-target binding in concentrated samples such as raw sewage. MGS analysis revealed multiple subtypes of each gene which could not be distinguished by qPCR; these subtypes varied across different sample types. Our findings affect the conclusions that can be drawn when comparing different sample types, particularly in terms of inferring removal rates or origins of genes. We conclude that both methods appear suitable to profile the resistome of wastewater and other environmental samples, depending on the research question and type of sample.https://doi.org/10.1186/s13104-024-07027-9AMRAntimicrobial resistanceMetagenomicsQPCRWastewaterWastewater surveillance |
| spellingShingle | William Taylor Kristin Bohm Kristin Dyet Louise Weaver Isabelle Pattis Comparative analysis of qPCR and metagenomics for detecting antimicrobial resistance in wastewater: a case study BMC Research Notes AMR Antimicrobial resistance Metagenomics QPCR Wastewater Wastewater surveillance |
| title | Comparative analysis of qPCR and metagenomics for detecting antimicrobial resistance in wastewater: a case study |
| title_full | Comparative analysis of qPCR and metagenomics for detecting antimicrobial resistance in wastewater: a case study |
| title_fullStr | Comparative analysis of qPCR and metagenomics for detecting antimicrobial resistance in wastewater: a case study |
| title_full_unstemmed | Comparative analysis of qPCR and metagenomics for detecting antimicrobial resistance in wastewater: a case study |
| title_short | Comparative analysis of qPCR and metagenomics for detecting antimicrobial resistance in wastewater: a case study |
| title_sort | comparative analysis of qpcr and metagenomics for detecting antimicrobial resistance in wastewater a case study |
| topic | AMR Antimicrobial resistance Metagenomics QPCR Wastewater Wastewater surveillance |
| url | https://doi.org/10.1186/s13104-024-07027-9 |
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