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

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.

SUBMITTER: Becsei A

PROVIDER: S-EPMC11364805 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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

Becsei Ágnes Á Fuschi Alessandro A Otani Saria S Kant Ravi R Weinstein Ilja I Alba Patricia P Stéger József J Visontai Dávid D Brinch Christian C de Graaf Miranda M Schapendonk Claudia M E CME Battisti Antonio A De Cesare Alessandra A Oliveri Chiara C Troja Fulvia F Sironen Tarja T Vapalahti Olli O Pasquali Frédérique F Bányai Krisztián K Makó Magdolna M Pollner Péter P Merlotti Alessandra A Koopmans Marion M Csabai Istvan I Remondini Daniel D Aarestrup Frank M FM Munk Patrick P

Nature communications 20240830 1

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 ca ...[more]

PMID: 39215001

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Project description:BackgroundKawasaki disease is an acute, febrile, systemic vasculitis of children that primarily affects medium-sized blood vessels with a tropism for the coronary arteries. Although the etiological factors remain unknown, infections have been suggested as the trigger of Kawasaki disease. We sought to calculate the fraction of Kawasaki disease potentially attributable to seasonal infections.MethodsThis cohort study used a population-based time series analysis from the French hospitalisation database (Programme de Médicalisation des Systèmes d'Information), which includes all inpatients admitted to any public or private hospital in France. We included all children aged 0-17 years hospitalised for Kawasaki disease in France over 13 years. The monthly incidence of Kawasaki disease per 10,000 children over time was analysed by a quasi-Poisson regression model. The model accounted for seasonality by using harmonic terms (a pair of sines and cosines with 12-month periods). The circulation of eight common seasonal pathogens (adenovirus, influenza, metapneumovirus, Mycoplasma pneumoniae, norovirus, rhinovirus, rotavirus, respiratory syncytial virus, and Streptococcus pneumonia) over the same period was included in the model to analyse the fraction of Kawasaki disease potentially attributable to each pathogen. Infections were identified on the basis of polymerase chain reaction or rapid antigen testing in hospital laboratories.FindingsBetween Jan 1, 2007, and Dec 31, 2019, we included 10,337 children with Kawasaki disease and 442,762 children with the selected infectious diseases. In the Kawasaki disease cohort, the median age [IQR] was 2 [0-4] years, 6164 [59.6%] were boys. Adenovirus infection was potentially responsible for 24.4% [21.5-27.8] (p < 0.001) of Kawasaki diseases, Norovirus for 6.7% [1.3-11.2] (p = 0.002), and RSV 4.6% [1.2-7.8] (p = 0.022). Sensitivity analyses found similar results.InterpretationThis cohort study of data from a comprehensive national hospitalisation database indicated that approximately 35% of Kawasaki diseases was potentially attributable to seasonal infections.FundingNone.

Dataset Information

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

Publications

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

Similar Datasets

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