Abstract
The risk posed by microorganisms in diverse environments has emerged as a notable concern. However, existing microbial risk assessment frameworks often lack breadth and coherence. Here, to address this constraint, we developed a cellular spike-in (including one Gram-positive bacterium (G+) and one Gram-negative bacterium (Gā)) method that enables absolute quantification of microorganisms in multiple environmental compartments (for example, wastewater, river water and marine water). This method was thoroughly evaluated for consistency, accuracy, feasibility and applicability. Furthermore, we investigated potential biases that might arise from DNA extraction to sequencing under different cell lysis conditions and, importantly, demonstrated that this spike-in absolute quantification method could correct such biases. We then applied this method to various samples to determine the absolute abundance (concentration) of microorganisms, pathogens and antibiotic resistance genes. On the basis of the results, we evaluated the removal efficiencies in terms of pathogens and antibiotic resistance genes in five wastewater treatment plants with different operational modes (for example, chemically enhanced primary treatment, secondary treatment, tertiary treatment and membrane bioreactor). Finally, we developed a risk assessment framework that simplifies complex absolute quantification data into accessible scores, enabling a comprehensive microbial risk evaluation and comparison across diverse environments. This analytical workflow could facilitate informed policymaking and decision-making by authorities based on risk assessment levels, advancing efforts to safeguard public health.
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Data availability
All the raw sequencing data generated in this study have been deposited in the National Center for Biotechnology Informatio Sequence Read Archive database under BioProject ID: PRJNA1158533.
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Acknowledgements
We thank the financial support of the Theme-based Research Scheme of the Research Grant Council of Hong Kong (grant no. T21-705/20-N) and the Shenzhen Science and Technology Innovation Bureau (no. SGDX20230821091559021). X.S., Y.Y., X.C., J.D. and S.L. thank the University of Hong Kong for their postgraduate studentship. C.W., X.X. and X.M. thank the University of Hong Kong for their postdoctoral fellowship. We also thank the Hong Kong Agriculture, Fisheries and Conservation Department and Hong Kong Environmental Protection Department for sample collection. The computations were performed using research computing facilities offered by Information Technology Services at the University of Hong Kong. We also thank the laboratory technician, V. Fung, for assisting with the experimental process.
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X.S.: conceptualization, methodology, formal analysis, visualization, writingāoriginal draft and investigation. Y.Y.: methodology, writingāreview and editing. C.W.: methodology, writingāreview and editing. X.X.: methodology, writingāreview and editing. X.M.: methodology, writingāreview and editing. X.C.: methodology, writingāreview and editing. J.D.: methodology, writingāreview and editing. S.L.: methodology, writingāreview and editing. T.Z.: supervision, resources, conceptualization, writingāreview and editing.
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Shi, X., Yang, Y., Wang, C. et al. Microbial risk assessment across multiple environments based on metagenomic absolute quantification with cellular internal standards. Nat Water 3, 473ā485 (2025). https://doi.org/10.1038/s44221-025-00421-y
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DOI: https://doi.org/10.1038/s44221-025-00421-y
