Time-series sewage metagenomics distinguishes seasonal, human-derived and environmental microbial communities potentially allowing source-attributed surveillance

Ágnes Becsei, Alessandro Fuschi, Saria Otani, Ravi Kant, Ilja Weinstein, Patricia Alba, József Stéger, Dávid Visontai, Christian Brinch, Miranda Graaf, Claudia M. E. Schapendonk, Antonio Battisti, Alessandra Cesare, Chiara Oliveri, Fulvia Troja, Tarja Sironen, Olli Vapalahti, Frédérique Pasquali, Krisztián Bányai, Magdolna Makó, Péter Pollner, Alessandra Merlotti, Marion Koopmans, Istvan Csabai, Daniel Remondini, Frank M. Aarestrup and Patrick Munk ()
Additional contact information
Ágnes Becsei: ELTE Eötvös Loránd University
Alessandro Fuschi: University of Bologna
Saria Otani: Technical University of Denmark
Ravi Kant: University of Helsinki
Ilja Weinstein: University of Helsinki
Patricia Alba: Istituto Zooprofilattico Sperimentale del Lazio e della Toscana
József Stéger: ELTE Eötvös Loránd University
Dávid Visontai: ELTE Eötvös Loránd University
Christian Brinch: Technical University of Denmark
Miranda Graaf: Erasmus MC
Claudia M. E. Schapendonk: Erasmus MC
Antonio Battisti: Istituto Zooprofilattico Sperimentale del Lazio e della Toscana
Alessandra Cesare: University of Bologna
Chiara Oliveri: University of Bologna
Fulvia Troja: University of Bologna
Tarja Sironen: University of Helsinki
Olli Vapalahti: University of Helsinki
Frédérique Pasquali: University of Bologna
Krisztián Bányai: HUN-REN Veterinary Medical Research Institute
Magdolna Makó: Fővárosi Csatornázási Művek Zrt.
Péter Pollner: Semmelweis University
Alessandra Merlotti: University of Bologna
Marion Koopmans: Erasmus MC
Istvan Csabai: ELTE Eötvös Loránd University
Daniel Remondini: University of Bologna
Frank M. Aarestrup: Technical University of Denmark
Patrick Munk: Technical University of Denmark

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract Sewage metagenomics has risen to prominence in urban population surveillance of pathogens and antimicrobial resistance (AMR). Unknown species with similarity to known genomes cause database bias in reference-based metagenomics. To improve surveillance, we seek to recover sewage genomes and develop a quantification and correlation workflow for these genomes and AMR over time. We use longitudinal sewage sampling in seven treatment plants from five major European cities to explore the utility of catch-all sequencing of these population-level samples. Using metagenomic assembly methods, we recover 2332 metagenome-assembled genomes (MAGs) from prokaryotic species, 1334 of which were previously undescribed. These genomes account for ~69% of sequenced DNA and provide insight into sewage microbial dynamics. Rotterdam (Netherlands) and Copenhagen (Denmark) show strong seasonal microbial community shifts, while Bologna, Rome, (Italy) and Budapest (Hungary) have occasional blooms of Pseudomonas-dominated communities, accounting for up to ~95% of sample DNA. Seasonal shifts and blooms present challenges for effective sewage surveillance. We find that bacteria of known shared origin, like human gut microbiota, form communities, suggesting the potential for source-attributing novel species and their ARGs through network community analysis. This could significantly improve AMR tracking in urban environments.

Date: 2024
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DOI: 10.1038/s41467-024-51957-8

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