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 designed this study to recover sewage genomes and develop a quantification and correlation workflow for these genomes and AMR over time. We used 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 recovered 2,332 metagenome-assembled genomes (MAGs) from prokaryotic species, 1,334 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) showed strong seasonal microbial community shifts, while Bologna, Rome, (Italy) and Budapest (Hungary) had 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. ### Competing Interest Statement The authors have declared no competing interest.

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.

ABSTRACTThe rapid diagnosis of infectious diseases has an essential impact on their control, treatment and recovery. Oxford Nanopore Technologies (ONT) sequencing opens up a new dimension in applying clinical metagenomics. In a large-scale pig farm in Hungary, four fattening and one piglet nasal swab pooled samples were sequenced using ONT for metagenomic analysis. Long reads covering 53.69% of the porcine cytomegalovirus genome were obtained in the piglet sample. The 650 bp long read matching theglycoprotein Bgene of the virus is sequentially most similar to Japanese, Chinese and Spanish isolates.

To understand antibiotic resistance in pathogenic bacteria, we need to monitor environmental microbes as reservoirs of antimicrobial resistance genes (ARGs). These bacteria are present in the air and can be investigated with the whole metagenome shotgun sequencing approach. This study aimed to investigate the feasibility of a method for metagenomic analysis of microbial composition and ARGs in the outdoor air. Air samples were collected with a Harvard impactor in the PM10 range at 50 m from a hospital in Budapest. From the DNA yielded from samples of PM10 fraction single-end reads were generated with an Ion Torrent sequencer. During the metagenomic analysis, reads were classified taxonomically. The core bacteriome was defined. Reads were assembled to contigs and the ARG content was analyzed. The dominant genera in the core bacteriome were Bacillus, Acinetobacter, Leclercia and Paenibacillus. Among the identified ARGs best hits were vanRA, Bla1, mphL, Escherichia coli EF-Tu mutants conferring resistance to pulvomycin; BcI, FosB, and mphM. Despite the low DNA content of the samples of PM10 fraction, the number of detected airborne ARGs was surprisingly high.

Összefoglaló. A COVID–19-járvány alatt bizonyossá vált, hogy az adattudományok, az adatok gyors megosztása és a nemzetközi összefogás a hatékony járványkezelés kulcsfontosságú eszközei. A járvány előtt létrejött Újonnan Felbukkanó fertőző betegségek Obszervatóriuma (Versatile Emerging infectious disease Observatory, VEO) nevű nemzetközi konzorcium célja egy olyan monitorozó rendszer kiépítése, amely a potenciálisan veszélyes kórokozókat még az előtt azonosítja, mielőtt azok tömeges megbetegedéseket okoznának, lehetőséget adva ezzel a gyors reagálásra. A járványok megelőzésére és kezelésére létrejött nemzetközi együttműködésekben, így a VEO-ban is a kórokozók, vagy a fertőzésnek kitett személyek genetikai szekvencia adatai kiemelkedő fontosságúak. Az ilyen típusú adatok kezelésével kapcsolatban az Európai Unióban többek között a Nagojai Jegyzőkönyv és a GDPR fogalmaz meg elveket, szabályokat. Summary. Data science is proved to be a key tool in the fight against the ongoing COVID-19 pandemic, but it requires a huge amount of data shared between international research groups. The Versatile Emerging infectious disease Observatory (VEO) EU collaboration was established to generate and distribute high quality data for an evidence-based early warning system for emerging infectious diseases. Through an iterative process between data scientists, disease experts, social scientists and citizen scientists, a collaborative platform will be created for storing, secure sharing and analyses of traditional and new data sources. Next generation sequencing (NGS) has revolutionized genomic research. This versatile technology is broadly applicable to pathogens and human hosts. Rapid sharing of pathogen genetic resources, including physical samples of cultured pathogens and additionally genetic sequencing data of pathogens, is crucial in support of research and outbreak response. Access to genetic resources is regulated by the Nagoya protocol which is an internationally binding treaty to ensure equal sharing of benefits arising from the use of genetic resources. So far the Nagoya protocol has been applied only to biological samples, but digital data from genetic sequencing doesn’t necessarily fall under the treaty. Effects of diseases can differ based on genetic backgrounds, as certain gene variants may provide protection against or susceptibility to viral diseases. Human genomic data is an important resource for medical research. The General Data Protection Regulation (GDPR) lists identifiable human genetic data as sensitive, which is a subset of personal data. Sharing and analysis of this kind of data are strictly regulated and they are also subject to ethical challenges. These concerns become less pronounced when analyzing environmental samples like sewage. Samples collected from wastewater treatment plants can be used as pooled samples, containing naturally anonymized genetic information of the human population, near the wastewater treatment plant.

Even though people worldwide tend to consume probiotic products for their beneficial health effects on a daily basis, recently, concerns were outlined regarding the uptake and potential intestinal colonisation of the bacteria that they carry. These bacteria are capable of executing horizontal gene transfer (HGT) which facilitates the movement of various genes, including antimicrobial resistance genes (ARGs), among the donor and recipient bacterial populations. Within our study, 47 shotgun sequencing datasets deriving from various probiotic samples (isolated strains and metagenomes) were bioinformatically analysed. We detected more than 70 ARGs, out of which rpoB mutants conferring resistance to rifampicin, tet(W/N/W) and potentially extended-spectrum beta-lactamase (ESBL) coding TEM-116 were the most common. Numerous ARGs were associated with integrated mobile genetic elements, plasmids or phages promoting the HGT. Our findings raise clinical and public health concerns as the consumption of probiotic products may lead to the transfer of ARGs to human gut bacteria.