Scoping review addresses metagenomic methods for pathogen detection, emphasizing standardization challenges and improvements.
Pathogens in the environment may pose a threat to our ecosystem and public health by causing infectious disease outbreaks. Early detection and identification are crucial for effective surveillance, outbreak prevention, and source attribution. However, analyzing environmental samples (e.g., air, soil, water, biowaste) is challenging due to their complex composition. Testing for each pathogen, known and undiscovered ones, is not possible yet. Metagenomic shotgun sequencing offers a promising approach for pathogen-agnostic DNA detection in these matrices. This review provides guidance and recommendations for experimental design, DNA extraction, library preparation, sequencing, and bioinformatics, and underscores the need for standardized protocols and inter-laboratory studies. This scoping review addresses metagenomic methodologies for pathogen detection in environmental matrices by highlighting current practices, challenges and limitations, and provides guidance for researchers and practitioners. Following the PRISMA guidelines, we identified 81 relevant studies from 6,034 initial records. Most studies utilized Illumina short-read sequencing, with fewer using long-read platforms like Oxford Nanopore Technologies or Pacific Biosciences. DNA extraction protocols varied, with a trade-off between DNA yield and preserving community structure. Few studies reported inter-laboratory comparisons or standardized workflows. Selection of bioinformatics tools and reference databases significantly influenced taxonomic classification, yet reporting of analytical parameters was often incomplete. This review highlights the need for appropriate controls and increased transparency in reporting applied methods and settings. Methodological diversity and unreported gaps hinder reproducibility and comparability, while a systematic approach in environmental metagenomics holds great promise for pathogen and ecosystem monitoring.
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Maas et al. (2026) studied this question.
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