Mobilisation and analyses of publicly available SARS-CoV-2 data for pandemic responses.

The COVID-19 pandemic has seen large-scale pathogen genomic sequencing efforts, becoming part of the toolbox for surveillance and epidemic research. This resulted in an unprecedented level of data sharing to open repositories, which has actively supported the identification of SARS-CoV-2 structure, molecular interactions, mutations and variants, and facilitated vaccine development and drug reuse studies and design. The European COVID-19 Data Platform was launched to support this data sharing, and has resulted in the deposition of several million SARS-CoV-2 raw reads. In this paper we describe (1) open data sharing, (2) tools for submission, analysis, visualisation and data claiming (e.g. ORCiD), (3) the systematic analysis of these datasets, at scale via the SARS-CoV-2 Data Hubs as well as (4) lessons learnt. This paper describes a component of the Platform, the SARS-CoV-2 Data Hubs, which enable the extension and set up of infrastructure that we intend to use more widely in the future for pathogen surveillance and pandemic preparedness.

The first complete genome sequence of Staphylococcus aureus ST5477.

This study presents the first complete genome of Staphylococcus aureus ST5477, one of the most common sequence types (ST) from bovine in eastern Africa. The genome consists of a 2,723,132-bp circular chromosome and a 3,044-bp plasmid. This strain was collected in 2017 from cow milk in Tanzania.

Whole genomes from bacteria collected at diagnostic units around the world 2020.

The Two Weeks in the World research project has resulted in a dataset of 3087 clinically relevant bacterial genomes with pertaining metadata, collected from 59 diagnostic units in 35 countries around the world during 2020. A relational database is available with metadata and summary data from selected bioinformatic analysis, such as species prediction and identification of acquired resistance genes.

SourceFinder: a Machine-Learning-Based Tool for Identification of Chromosomal, Plasmid, and Bacteriophage Sequences from Assemblies.

High-throughput genome sequencing technologies enable the investigation of complex genetic interactions, including the horizontal gene transfer of plasmids and bacteriophages. However, identifying these elements from assembled reads remains challenging due to genome sequence plasticity and the difficulty in assembling complete sequences. In this study, we developed a classifier, using random forest, to identify whether sequences originated from bacterial chromosomes, plasmids, or bacteriophages. The classifier was trained on a diverse collection of 23,211 chromosomal, plasmid, and bacteriophage sequences from hundreds of bacterial species. In order to adapt the classifier to incomplete sequences, each complete sequence was subsampled into 5,000 nucleotide fragments and further subdivided into k-mers. This three-class classifier succeeded in identifying chromosomes, plasmids, and bacteriophages using k-mer distributions of complete and partial genome sequences, including simulated metagenomic scaffolds with minimum performance of 0.939 area under the receiver operating characteristic curve (AUC). This classifier, implemented as SourceFinder, has been made available as an online web service to help the community with predicting the chromosomal, plasmid, and bacteriophage sources of assembled bacterial sequence data (https://cge.food.dtu.dk/services/SourceFinder/). IMPORTANCE Extra-chromosomal genes encoding antimicrobial resistance, metal resistance, and virulence provide selective advantages for bacterial survival under stress conditions and pose serious threats to human and animal health. These accessory genes can impact the composition of microbiomes by providing selective advantages to their hosts. Accurately identifying extra-chromosomal elements in genome sequence data are critical for understanding gene dissemination trajectories and taking preventative measures. Therefore, in this study, we developed a random forest classifier for identifying the source of bacterial chromosomal, plasmid, and bacteriophage sequences.

PlasmidHostFinder: Prediction of Plasmid Hosts Using Random Forest.

Plasmids play a major role facilitating the spread of antimicrobial resistance between bacteria. Understanding the host range and dissemination trajectories of plasmids is critical for surveillance and prevention of antimicrobial resistance. Identification of plasmid host ranges could be improved using automated pattern detection methods compared to homology-based methods due to the diversity and genetic plasticity of plasmids. In this study, we developed a method for predicting the host range of plasmids using machine learning-specifically, random forests. We trained the models with 8,519 plasmids from 359 different bacterial species per taxonomic level; the models achieved Matthews correlation coefficients of 0.662 and 0.867 at the species and order levels, respectively. Our results suggest that despite the diverse nature and genetic plasticity of plasmids, our random forest model can accurately distinguish between plasmid hosts. This tool is available online through the Center for Genomic Epidemiology (https://cge.cbs.dtu.dk/services/PlasmidHostFinder/). IMPORTANCE Antimicrobial resistance is a global health threat to humans and animals, causing high mortality and morbidity while effectively ending decades of success in fighting against bacterial infections. Plasmids confer extra genetic capabilities to the host organisms through accessory genes that can encode antimicrobial resistance and virulence. In addition to lateral inheritance, plasmids can be transferred horizontally between bacterial taxa. Therefore, detection of the host range of plasmids is crucial for understanding and predicting the dissemination trajectories of extrachromosomal genes and bacterial evolution as well as taking effective countermeasures against antimicrobial resistance.

Global Distribution of mcr Gene Variants in 214K Metagenomic Samples.

Since the initial discovery of a mobilized colistin resistance gene (mcr-1), several other variants have been reported, some of which might have circulated a while beforehand. Publicly available metagenomic data provide an opportunity to reanalyze samples to understand the evolutionary history of recently discovered antimicrobial resistance genes (ARGs). Here, we present a large-scale metagenomic study of 442 Tbp of sequencing reads from 214,095 samples to describe the dissemination and emergence of nine mcr gene variants (mcr-1 to mcr-9). Our results show that the dissemination of each variant is not uniform. Instead, the source and location play a role in the spread. However, the genomic context and the genes themselves remain primarily unchanged. We report evidence of new subvariants occurring in specific environments, such as a highly prevalent and new variant of mcr-9. This work emphasizes the importance of sharing genomic data for the surveillance of ARGs in our understanding of antimicrobial resistance. IMPORTANCE The ever-growing collection of metagenomic samples available in public data repositories has the potential to reveal new details on the emergence and dissemination of mobilized colistin resistance genes. Our analysis of metagenomes deposited online in the last 10 years shows that the environmental distribution of mcr gene variants depends on sampling source and location, possibly leading to the emergence of new variants, although the contig on which the mcr genes were found remained consistent.

Danish Whole-Genome-Sequenced Candida albicans and Candida glabrata Samples Fit into Globally Prevalent Clades.

Candida albicans and Candida glabrata are opportunistic fungal pathogens with increasing incidence worldwide and higher-than-expected prevalence in Denmark. We whole-genome sequenced yeast isolates collected from Danish Clinical Microbiology Laboratories to obtain an overview of the Candida population in the country. The majority of the 30 C. albicans isolates were found to belong to three globally prevalent clades, and, with one exception, the remaining isolates were also predicted to cluster with samples from other geographical locations. Similarly, most of the eight C. glabrata isolates were predicted to be prevalent subtypes. Antifungal susceptibility testing proved all C. albicans isolates to be susceptible to both azoles and echinocandins. Two C. glabrata isolates presented azole-resistant phenotypes, yet all were susceptible to echinocandins. There is no indication of causality between population structure and resistance phenotypes for either species.

Rapid Open-Source SNP-Based Clustering Offers an Alternative to Core Genome MLST for Outbreak Tracing in a Hospital Setting.

Traditional genotyping methods for infection control of antimicrobial-resistant bacteria in healthcare settings have been supplemented by whole-genome sequencing (WGS), often relying on a gene-based approach, e.g., core genome multilocus sequence typing (cgMLST), to cluster-related samples. In this study, we compared clusters of methicillin-resistant Staphylococcus aureus (MRSA) and Enterococcus faecium analyzed with the commercial cgMLST software Ridom SeqSphere+ and with an open-source single-nucleotide polymorphism (SNP)-based phylogenetic analysis pipeline (PAPABAC). A total of 5,655 MRSA and 2,572 E. faecium patient isolates, collected between 2013 and 2018, were processed. Clusters of 1,844 MRSA and 1,355 E. faecium isolates were compared to cgMLST results, and epidemiological data were included when available. The phylogenies inferred by the two different technologies were highly concordant, and the MRSA SNP tree re-captured known hospital-related outbreaks and epidemiologically linked samples. PAPABAC has the advantage over Ridom SeqSphere+ to generate stable, referable clusters without the need for sequence assembly, and it is a free-of-charge, open-source alternative to the commercial software.

Phylogenetic Investigation of Norovirus Transmission between Humans and Animals.

Norovirus infections are a leading cause of acute gastroenteritis worldwide, affecting people of all ages. There are 10 norovirus genogroups (GI-GX) that infect humans and animals in a host-specific manner. New variants and genotypes frequently emerge, and their origin is not well understood. One hypothesis is that new human infections may be seeded from an animal reservoir, as human noroviruses have occasionally been detected in animal species. The majority of these sequences were identified as older GII.4 variants, but a variety of other GIIs and GIs have been detected as well. While these sequences share at least 94% nt similarity with human strains, most of them are >98% identical to human strains. The fact that these strains were detected in animals after they had been detected through human surveillance to be already circulating in humans suggests human-to-animal transmission.

Large scale automated phylogenomic analysis of bacterial isolates and the Evergreen Online platform.

Public health authorities whole-genome sequence thousands of isolates each month for microbial diagnostics and surveillance of pathogenic bacteria. The computational methods have not kept up with the deluge of data and the need for real-time results. We have therefore created a bioinformatics pipeline for rapid subtyping and continuous phylogenomic analysis of bacterial samples, suited for large-scale surveillance. The data is divided into sets by mapping to reference genomes, then consensus sequences are generated. Nucleotide based genetic distance is calculated between the sequences in each set, and isolates are clustered together at 10 single-nucleotide polymorphisms. Phylogenetic trees are inferred from the non-redundant sequences and the clustered isolates are added back. The method is accurate at grouping outbreak strains together, while discriminating them from non-outbreak strains. The pipeline is applied in Evergreen Online, which processes publicly available sequencing data from foodborne bacterial pathogens on a daily basis, updating phylogenetic trees as needed.

Metaphylogenetic analysis of global sewage reveals that bacterial strains associated with human disease show less degree of geographic clustering.

Knowledge about the difference in the global distribution of pathogens and non-pathogens is limited. Here, we investigate it using a multi-sample metagenomics phylogeny approach based on short-read metagenomic sequencing of sewage from 79 sites around the world. For each metagenomic sample, bacterial template genomes were identified in a non-redundant database of whole genome sequences. Reads were mapped to the templates identified in each sample. Phylogenetic trees were constructed for each template identified in multiple samples. The countries from which the samples were taken were grouped according to different definitions of world regions. For each tree, the tendency for regional clustering was determined. Phylogenetic trees representing 95 unique bacterial templates were created covering 4 to 71 samples. Varying degrees of regional clustering could be observed. The clustering was most pronounced for environmental bacterial species and human commensals, and less for colonizing opportunistic pathogens, opportunistic pathogens and pathogens. No pattern of significant difference in clustering between any of the organism classifications and country groupings according to income were observed. Our study suggests that while the same bacterial species might be found globally, there is a geographical regional selection or barrier to spread for individual clones of environmental and human commensal bacteria, whereas this is to a lesser degree the case for strains and clones of human pathogens and opportunistic pathogens.