Sequencing of whole microbial genomes has become a standard procedure for cluster detection, source tracking, outbreak investigation and surveillance of many microorganisms. An increasing number of laboratories are currently in a transition phase from classical methods towards next generation sequencing, generating unprecedented amounts of data. Since the precision of downstream analyses depends significantly on the quality of raw data generated on the sequencing instrument, a comprehensive, meaningful primary quality control is indispensable. Here, we present AQUAMIS, a Snakemake workflow for an extensive quality control and assembly of raw Illumina sequencing data, allowing laboratories to automatize the initial analysis of their microbial whole-genome sequencing data. AQUAMIS performs all steps of primary sequence analysis, consisting of read trimming, read quality control (QC), taxonomic classification, de-novo assembly, reference identification, assembly QC and contamination detection, both on the read and assembly level. The results are visualized in an interactive HTML report including species-specific QC thresholds, allowing non-bioinformaticians to assess the quality of sequencing experiments at a glance. All results are also available as a standard-compliant JSON file, facilitating easy downstream analyses and data exchange. We have applied AQUAMIS to analyze ~13,000 microbial isolates as well as ~1000 in-silico contaminated datasets, proving the workflow’s ability to perform in high throughput routine sequencing environments and reliably predict contaminations. We found that intergenus and intragenus contaminations can be detected most accurately using a combination of different QC metrics available within AQUAMIS.

Metagenomics is a valuable diagnostic tool for enhancing microbial food safety because (i) it enables the untargeted detection of pathogens, (ii) it is fast since primary isolation of micro-organisms is not required, and (iii) it has high discriminatory power allowing for a detailed molecular characterization of pathogens. For shotgun metagenomics, total nucleic acids (NAs) are isolated from complex samples such as foodstuff. Along with microbial NAs, high amounts of matrix NAs are extracted that might outcompete microbial NAs during next-generation sequencing and compromise sensitivity for the detection of low abundance micro-organisms. Sensitive laboratory methods are indispensable for detecting highly pathogenic foodborne bacteria like Brucella spp., because a low infectious dose is sufficient to cause human disease through the consumption of contaminated dairy or meat products. In our study, we applied shotgun metagenomic sequencing for the identification and characterization of Brucella spp. in artificially and naturally contaminated raw milk from various ruminant species. With the depletion of eukaryotic cells prior to DNA extraction, Brucella was detectable at 10 bacterial cells ml⁻¹, while at the same time microbiological culture and isolation of the fastidious bacteria commonly failed. Moreover, we were able to retrieve the genotype of a Brucella isolate from a metagenomic dataset, indicating the potential of metagenomics for outbreak investigations using SNPs and core-genome multilocus sequence typing (cgMLST). To improve diagnostic applications, we developed a new bioinformatics approach for strain prediction based on SNPs to identify the correct species and define a certain strain with only low numbers of genus-specific reads per sample. This pipeline turned out to be more sensitive and specific than Mash Screen. In raw milk samples, we simultaneously detected numerous other zoonotic pathogens, antimicrobial resistance genes and virulence factors. Our study showed that metagenomics is a highly sensitive tool for biological risk assessment of foodstuffs, particularly when pathogen isolation is hazardous or challenging.

Francisella (F.) tularensis is a highly virulent, Gram-negative bacterial pathogen and the causative agent of the zoonotic disease tularemia. Here, we generated, analyzed and characterized a high quality circular genome sequence of the F. tularensis subsp. holarctica strain 12T0050 that caused fatal tularemia in a hare. Besides the genomic structure, we focused on the analysis of oriC, unique to the Francisella genus and regulating replication in and outside hosts and the first report on genomic DNA methylation of a Francisella strain. The high quality genome was used to establish and evaluate a diagnostic whole genome sequencing pipeline. A genotyping strategy for F. tularensis was developed using various bioinformatics tools for genotyping. Additionally, whole genome sequences of F. tularensis subsp. holarctica isolates isolated in the years 2008–2015 in Germany were generated. A phylogenetic analysis allowed to determine the genetic relatedness of these isolates and confirmed the highly conserved nature of F. tularensis subsp. holarctica.

Different laboratories employ different Whole-Genome Sequencing (WGS) pipelines for Food and Waterborne disease (FWD) surveillance, casting doubt on the comparability of their results and hindering optimal communication at intersectoral and international levels. Through a collaborative effort involving eleven European institutes spanning the food, animal, and human health sectors, we aimed to assess the inter-pipeline clustering congruence across all resolution levels and perform an in-depth comparative analysis of cluster composition at outbreak level for four important foodborne pathogens: Listeria monocytogenes, Salmonella enterica, Escherichia coli, and Campylobacter jejuni. We found a general concordance between allele-based pipelines for all species, except for C. jejuni, where the different resolution power of allele-based schemas led to marked discrepancies. Still, we identified non-negligible differences in outbreak detection and demonstrated how a threshold flexibilization favors the detection of similar outbreak signals by different laboratories. These results, together with the observation that different traditional typing groups (e.g., serotypes) exhibit a remarkably different genetic diversity, represent valuable information for future outbreak case-definitions and WGS-based nomenclature design. This study reinforces the need, while demonstrating the feasibility, of conducting continuous pipeline comparability assessments, and opens good perspectives for a smoother international and intersectoral cooperation towards an efficient One Health FWD surveillance.

Background: We benchmarked sequencing technology and assembly strategies for short-read, long-read, and hybrid assemblers in respect to correctness, contiguity, and completeness of assemblies in genomes of Francisella tularensis. Benchmarking allowed in-depth analyses of genomic structures of the Francisella pathogenicity islands and insertion sequences. Five major high-throughput sequencing technologies were applied, including next-generation “short-read” and third-generation “long-read” sequencing methods. Results: We focused on short-read assemblers, hybrid assemblers, and analysis of the genomic structure with particular emphasis on insertion sequences and the Francisella pathogenicity island. The A5-miseq pipeline performed best for MiSeq data, Mira for Ion Torrent data, and ABySS for HiSeq data from eight short-read assembly methods. Two approaches were applied to benchmark long-read and hybrid assembly strategies: long-read-first assembly followed by correction with short reads (Canu/Pilon, Flye/Pilon) and short-read-first assembly along with scaffolding based on long reads (Unicyler, SPAdes). Hybrid assembly can resolve large repetitive regions best with a “long-read first” approach. Conclusions: Genomic structures of the Francisella pathogenicity islands frequently showed misassembly. Insertion sequences (IS) could be used to perform an evolutionary conservation analysis. A phylogenetic structure of insertion sequences and the evolution within the clades elucidated the clade structure of the highly conservative F. tularensis.

Food and waterborne disease (FWD) surveillance requires Whole-Genome Sequencing (WGS)-based systems following a One Health approach. However, different laboratories employ different WGS pipelines in their routine surveillance activities, casting doubt on the comparability of their results and hindering optimal communication at intersectoral and international levels. Through a collaborative effort involving eleven European institutes across seven countries and spanning the food, animal and human health sectors, we aimed to assess the inter-laboratory comparability of WGS clustering results for four important foodborne pathogens: Listeria monocytogenes , Salmonella enterica , Escherichia coli and Campylobacter jejuni . Each participating institute (n=9) applied its surveillance pipeline over the same WGS datasets (>2000 isolates per species), and, for each pipeline, genetic clusters were identified at each possible allele/SNP distance threshold. Inter-pipeline clustering congruence was assessed by calculating a “Congruence Score” (relying on Adjusted Wallace and Adjusted Rand coefficients) across all resolution levels, followed by an in-depth comparative analysis of cluster composition at outbreak level. An additional cluster congruence assessment was performed between WGS and traditional typing, which, depending on the species, included Sequence Type (ST), Clonal Complex (CC) and/or serotype. Our results revealed a general high concordance between allele-based pipelines at all resolution levels for all species, except for C. jejuni , where the different resolution power of available allele-based schemas led to marked discrepancies. Still, this study identified non-negligible differences in allele-based pipeline performance for outbreak cluster detection, suggesting that a threshold flexibilization is important for the detection of similar outbreak signals by different laboratories. These results, together with the observation that different STs, CCs and serotypes exhibit remarkably different genetic diversity, should inform future threshold selections for outbreak case definitions. In conclusion, this study provides valuable insights into the comparability of pipelines commonly used for routine genomics surveillance, and reinforces the need, while demonstrating the feasibility, of conducting continuous and comprehensive WGS pipeline comparability assessments. Ultimately, it opens good perspectives for a smoother international and intersectoral cooperation and communication towards a sustainable and efficient One Health FWD surveillance.