Optimization and validation of a consolidated Set of TaqMan qPCR assays for the surveillance of clinically relevant antibiotic resistance genes in environmental matrices.

Sasikaladevi Rathinavelu, Karin Beck, Denise Lea Wälchli, Helmut Bürgmann

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Article in MethodsX, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

Sasikaladevi RathinaveluEawag, Swiss Federal Institute of Aquatic Science and Technology, 6047 Kastanienbaum, Switzerland.

Karin BeckEawag, Swiss Federal Institute of Aquatic Science and Technology, 6047 Kastanienbaum, Switzerland.

Denise Lea WälchliEawag, Swiss Federal Institute of Aquatic Science and Technology, 6047 Kastanienbaum, Switzerland.

Helmut BürgmannEawag, Swiss Federal Institute of Aquatic Science and Technology, 6047 Kastanienbaum, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The continued rise of antibiotic resistance and adoption of the One Health approach necessitates reliable methods for detection and quantification of antibiotic resistance genes (ARGs) in complex environmental matrices. Here we present a consolidated set of TaqMan quantitative PCR assays for quantification of clinically relevant and emerging ARGs in complex environmental matrices.•We systematically designed five new primer sets, six TaqMan probes and verified and adapted four previously published relevant primer/probe sets from literature and evaluated their specificity

Indexed as

aadAAntimicrobial resistance genesaph(3″)-IbblaTEMClinically relevantDetectionEnvironmentermFgBlocksmcr-1mecAMetagenomeqnrSqPCRQuantificationSurveillanceTaqMan assaytetAtetBWastewater

Identifiers

PMID40994895

DOI10.1016/j.mex.2025.103600

PMCPMC12455071

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.