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Abstract Background The COVID-19 pandemic remains a global public health concern. Advances in sequencing technologies has allowed for high numbers of SARS-CoV-2 whole genome sequence (WGS) data and rapid sharing of sequences through global repositories to enable almost real-time genomic analysis of the pathogen. WGS data has been used previously to group genetically similar viral pathogens to reveal evidence of transmission, including methods that identify distinct clusters on a phylogenetic tree. Identifying clusters of linked cases can aid in the regional surveillance and management of the disease. In this study, we present a novel method for producing stable genomic clusters of SARS-CoV-2 cases, cov2clusters, and compare the accuracy and stability of our approach to previous methods used for phylogenetic clustering using real-world SARS-CoV-2 sequence data obtained from British Columbia, Canada. Results We found that cov2clusters produced more stable clusters than previously used phylogenetic clustering methods when adding sequence data through time, mimicking an increase in sequence data through the pandemic. Our method also showed high accuracy when predicting epidemiologically informed clusters from sequence data. Conclusions Our new approach allows for the identification of stable clusters of SARS-CoV-2 from WGS data. Producing high-resolution SARS-CoV-2 clusters from sequence data alone can a challenge and, where possible, both genomic and epidemiological data should be used in combination.

Abstract Background Pathogen whole genome sequencing (WGS) is being incorporated into public health surveillance and disease control systems worldwide and has the potential to make significant contributions to infectious disease surveillance, outbreak investigation and infection prevention and control. However, to date, there are limited data regarding (i) the optimal models for integration of genomic data into epidemiological investigations and (ii) how to quantify and evaluate public health impacts resulting from genomic epidemiological investigations. Methods We developed the Pathogen Genomics in Public HeAlth Surveillance Evaluation (PG-PHASE) Framework to guide examination of the use of WGS in public health surveillance and disease control. We illustrate the use of this framework with three pathogens as case studies: Listeria monocytogenes, Mycobacterium tuberculosis and SARS-CoV-2. Results The framework utilises an adaptable whole-of-system approach towards understanding how interconnected elements in the public health application of pathogen genomics contribute to public health processes and outcomes. The three phases of the PG-PHASE Framework are designed to support understanding of WGS laboratory processes, analysis, reporting and data sharing, and how genomic data are utilised in public health practice across all stages, from the decision to send an isolate or sample for sequencing to the use of sequence data in public health surveillance, investigation and decision-making. Importantly, the phases can be used separately or in conjunction, depending on the need of the evaluator. Subsequent to conducting evaluation underpinned by the framework, avenues may be developed for strategic investment or interventions to improve utilisation of whole genome sequencing. Conclusions Comprehensive evaluation is critical to support health departments, public health laboratories and other stakeholders to successfully incorporate microbial genomics into public health practice. The PG-PHASE Framework aims to assist public health laboratories, health departments and authorities who are either considering transitioning to whole genome sequencing or intending to assess the integration of WGS in public health practice, including the capacity to detect and respond to outbreaks and associated costs, challenges and facilitators in the utilisation of microbial genomics and public health impacts.

Abstract Mutation acquisition is a major mechanism of bacterial antibiotic resistance that remains insufficiently characterised. Here we present RM-seq, a new amplicon-based deep sequencing workflow based on a molecular barcoding technique adapted from Low Error Amplicon sequencing (LEA-seq). RM-seq allows detection and functional assessment of mutational resistance at high throughput from mixed bacterial populations. The sensitive detection of very low-frequency resistant sub-populations permits characterisation of antibiotic-linked mutational repertoires in vitro and detection of rare resistant populations during infections. Accurate quantification of resistance mutations enables phenotypic screening of mutations conferring pleiotropic phenotypes such as in vivo persistence, collateral sensitivity or cross-resistance. RM-seq will facilitate comprehensive detection, characterisation and surveillance of resistant bacterial populations (https://github.com/rguerillot/RM-seq).

Abstract Background Large-scale genomic studies of within-host diversity in Staphylococcus aureus bacteraemia (SAB) are needed to understanding bacterial adaptation underlying persistence and thus refining the role of genomics in management of SAB. However, available comparative genomic studies of sequential SAB isolates have tended to focus on selected cases of unusually prolonged bacteraemia, where secondary antimicrobial resistance has developed. Methods To understand bacterial genetic diversity during SAB more broadly, we applied whole genome sequencing to a large collection of sequential isolates obtained from patients with persistent or relapsing bacteraemia. After excluding genetically unrelated isolates, we performed an in-depth genomic analysis of point mutations and chromosome structural variants arising within individual SAB episodes. Results We show that, while adaptation pathways are heterogenous and episode-specific, isolates from persistent bacteraemia have a distinctive molecular signature, characterised by a low mutation frequency and high proportion of non-silent mutations. Analysis of structural genomic variants revealed that these often overlooked genetic events are commonly acquired during SAB. We discovered that IS256 insertion may represent the most effective driver of within-host microevolution in selected lineages, with up to three new insertion events per isolate even in the absence of other mutations. Genetic mechanisms resulting in significant phenotypic changes, such as increases in vancomycin resistance, development of small colony phenotypes, and decreases in cytotoxicity, included mutations in key genes (rpoB, stp, agrA) and an IS256 insertion upstream of the walKR operon. Conclusions This study provides for the first time a large-scale analysis of within-host genomic changes during invasive S. aureus infection and describes specific patterns of adaptation that will be informative for both understanding S. aureus pathoadaptation and utilising genomics for management of complicated S. aureus infections.