Phylodynamic inference of bacterial outbreak parameters using nanopore sequencing

Optimising nucleic acid recovery from rapid antigen tests for whole genome sequencing of respiratory viruses

BACKGROUND

Whole genome sequencing (WGS) of respiratory viruses from rapid antigen tests (RAT-WGS) is a novel approach to expanding genomic surveillance of respiratory infections. To date however, there are limited data on the genomic stability of these viruses on RATs. In this study, we investigated the effect of storage conditions and nucleic acid preservatives on the ability to enhance stability and improve recovery of respiratory virus genomes from RATs.

METHODS

A mixture of common respiratory viruses was used to inoculate RATs at different environmental temperatures (4°C, 20°C and 36°C), with two preservative reagents (RNALater and DNA/RNA shield) Nucleic acid was extracted from RATs at two different timepoints (72 h and seven days) and subject to real-time multiplex respiratory PCR to detect a range of respiratory viruses. WGS was performed using target-enrichment with the TWIST Comprehensive Viral Research Panel. Defined metrics from an automated in-house bioinformatic pipeline were used to assess and compare viral genome recovery under different conditions.

RESULTS

Nucleic acid degradation (indicated by relative change in PCR cycle threshold and WGS-based metrics) was most notable at 20 °C and 36 °C. Storage in either RNALater or DNA / RNA shield improved genome recovery for respiratory viruses across all temperature conditions, although this was most pronounced for RNALater. Subtyping of Influenza viruses demonstrated the applicability of RAT-WGS in downstream genomic epidemiological surveillance.

CONCLUSIONS

Under simulated conditions, RAT-WGS demonstrated that (i) viral genomes were generally stable at 4°C at 72 h and 1 week, (ii) RNALater has a more significant preservation of nucleic acids compared to DNA/RNA Shield and (iii) genome recovery can be achieved using a sequencing depth of 500,000 reads per sample in RNALater, across all respiratory viruses and conditions.

Closing the gap: Oxford Nanopore Technologies R10 sequencing allows comparable results to Illumina sequencing for SNP-based outbreak investigation of bacterial pathogens

Whole-genome sequencing has become the method of choice for bacterial outbreak investigation, with most clinical and public health laboratories currently routinely using short-read Illumina sequencing. Recently, long-read Oxford Nanopore Technologies (ONT) sequencing has gained prominence and may offer advantages over short-read sequencing, particularly with the recent introduction of the R10 chemistry, which promises much lower error rates than the R9 chemistry. However, limited information is available on its performance for bacterial single-nucleotide polymorphism (SNP)-based outbreak investigation. We present an open-source workflow, Prokaryotic Awesome variant Calling Utility (PACU) (https://github.com/BioinformaticsPlatformWIV-ISP/PACU), for constructing SNP phylogenies using Illumina and/or ONT R9/R10 sequencing data. The workflow was evaluated using outbreak data sets of Shiga toxin-producing Escherichia coli and Listeria monocytogenes by comparing ONT R9 and R10 with Illumina data. The performance of each sequencing technology was evaluated not only separately but also by integrating samples sequenced by different technologies/chemistries into the same phylogenomic analysis. Additionally, the minimum sequencing time required to obtain accurate phylogenetic results using nanopore sequencing was evaluated. PACU allowed accurate identification of outbreak clusters for both species using all technologies/chemistries, but ONT R9 results deviated slightly more from the Illumina results. ONT R10 results showed trends very similar to Illumina, and we found that integrating data sets sequenced by either Illumina or ONT R10 for different isolates into the same analysis produced stable and highly accurate phylogenomic results. The resulting phylogenies for these two outbreaks stabilized after ~20 hours of sequencing for ONT R9 and ~8 hours for ONT R10. This study provides a proof of concept for using ONT R10, either in isolation or in combination with Illumina, for rapid and accurate bacterial SNP-based outbreak investigation.

Emergence and clonal expansion of a qacA-harbouring sequence type 45 lineage of methicillin-resistant Staphylococcus aureus

The past decade has seen an increase in the prevalence of sequence type (ST) 45 methicillin-resistant Staphylococcus aureus (MRSA), yet the underlying drivers for its emergence and spread remain unclear. To better understand the worldwide dissemination of ST45 S. aureus, we performed phylogenetic analyses of Australian isolates, supplemented with a global population of ST45 S. aureus genomes. Our analyses revealed a distinct lineage of multidrug-resistant ST45 MRSA harbouring qacA, predominantly found in Australia and Singapore. Bayesian inference predicted that the acquisition of qacA occurred in the late 1990s. qacA was integrated into a structurally variable region of the chromosome containing Tn552 (carrying blaZ) and Tn4001 (carrying aac(6')-aph(2")) transposable elements. Using mutagenesis and in vitro assays, we provide phenotypic evidence that qacA confers tolerance to chlorhexidine. These findings collectively suggest both antimicrobial resistance and the carriage of qacA may play a role in the successful establishment of ST45 MRSA.

Genomic surveillance for antimicrobial resistance - a One Health perspective

Antimicrobial resistance (AMR) - the ability of microorganisms to adapt and survive under diverse chemical selection pressures - is influenced by complex interactions between humans, companion and food-producing animals, wildlife, insects and the environment. To understand and manage the threat posed to health (human, animal, plant and environmental) and security (food and water security and biosecurity), a multifaceted 'One Health' approach to AMR surveillance is required. Genomic technologies have enabled monitoring of the mobilization, persistence and abundance of AMR genes and mutations within and between microbial populations. Their adoption has also allowed source-tracing of AMR pathogens and modelling of AMR evolution and transmission. Here, we highlight recent advances in genomic AMR surveillance and the relative strengths of different technologies for AMR surveillance and research. We showcase recent insights derived from One Health genomic surveillance and consider the challenges to broader adoption both in developed and in lower- and middle-income countries.

Mobile and Self-Sustained Data Storage in an Extremophile Genomic DNA

DNA has been pursued as a novel biomaterial for digital data storage. While large-scale data storage and random access have been achieved in DNA oligonucleotide pools, repeated data accessing requires constant data replenishment, and these implementations are confined in professional facilities. Here, a mobile data storage system in the genome of the extremophile Halomonas bluephagenesis, which enables dual-mode storage, dynamic data maintenance, rapid readout, and robust recovery. The system relies on two key components: A versatile genetic toolbox for the integration of 10-100 kb scale synthetic DNA into H. bluephagenesis genome and an efficient error correction coding scheme targeting noisy nanopore sequencing reads. The storage and repeated retrieval of 5 KB data under non-laboratory conditions are demonstrated. The work highlights the potential of DNA data storage in domestic and field scenarios, and expands its application domain from archival data to frequently accessed data.

Nanopore-only assemblies for genomic surveillance of the global priority drug-resistant pathogen, Klebsiella pneumoniae

Oxford Nanopore Technologies (ONT) sequencing has rich potential for genomic epidemiology and public health investigations of bacterial pathogens, particularly in low-resource settings and at the point of care, due to its portability and affordability. However, low base-call accuracy has limited the reliability of ONT data for critical tasks such as antimicrobial resistance (AMR) and virulence gene detection and typing, serotype prediction, and cluster identification. Thus, Illumina sequencing remains the standard for genomic surveillance despite higher capital and running costs. We tested the accuracy of ONT-only assemblies for common applied bacterial genomics tasks (genotyping and cluster detection, implemented via Kleborate, Kaptive and Pathogenwatch), using data from 54 unique Klebsiella pneumoniae isolates. ONT reads generated via MinION with R9.4.1 flowcells were basecalled using three alternative models [Fast, High-accuracy (HAC) and Super-accuracy (SUP), available within ONT's Guppy software], assembled with Flye and polished using Medaka. Accuracy of typing using ONT-only assemblies was compared with that of Illumina-only and hybrid ONT+Illumina assemblies, constructed from the same isolates as reference standards. The most resource-intensive ONT-assembly approach (SUP basecalling, with or without Medaka polishing) performed best, yielding reliable capsule (K) type calls for all strains (100 % exact or best matching locus), reliable multi-locus sequence type (MLST) assignment (98.3 % exact match or single-locus variants), and good detection of acquired AMR genes and mutations (88-100 % correct identification across the various drug classes). Distance-based trees generated from SUP+Medaka assemblies accurately reflected overall genetic relationships between isolates. The definition of outbreak clusters from ONT-only assemblies was problematic due to inflation of SNP counts by high base-call errors. However, ONT data could be reliably used to 'rule out' isolates of distinct lineages from suspected transmission clusters. HAC basecalling + Medaka polishing performed similarly to SUP basecalling without polishing. Therefore, we recommend investing compute resources into basecalling (SUP model), wherever compute resources and time allow, and note that polishing is also worthwhile for improved performance. Overall, our results show that MLST, K type and AMR determinants can be reliably identified with ONT-only R9.4.1 flowcell data. However, cluster detection remains challenging with this technology.

Long-Read Whole Genome Sequencing Elucidates the Mechanisms of Amikacin Resistance in Multidrug-Resistant Klebsiella pneumoniae Isolates Obtained from COVID-19 Patients

Klebsiella pneumoniae is a Gram-negative, encapsulated, non-motile bacterium, which represents a global challenge to public health as one of the major causes of healthcare-associated infections worldwide. In the recent decade, the World Health Organization (WHO) noticed a critically increasing rate of carbapenem-resistant K. pneumoniae occurrence in hospitals. The situation with extended-spectrum beta-lactamase (ESBL) producing bacteria further worsened during the COVID-19 pandemic, due to an increasing number of patients in intensive care units (ICU) and extensive, while often inappropriate, use of antibiotics including carbapenems. In order to elucidate the ways and mechanisms of antibiotic resistance spreading within the K. pneumoniae population, whole genome sequencing (WGS) seems to be a promising approach, and long-read sequencing is especially useful for the investigation of mobile genetic elements carrying antibiotic resistance genes, such as plasmids. We have performed short- and long read sequencing of three carbapenem-resistant K. pneumoniae isolates obtained from COVID-19 patients in a dedicated ICU of a multipurpose medical center, which belonged to the same clone according to cgMLST analysis, in order to understand the differences in their resistance profiles. We have revealed the presence of a small plasmid carrying aph(3′)-VIa gene providing resistance to amikacin in one of these isolates, which corresponded perfectly to its phenotypic resistance profile. We believe that the results obtained will facilitate further elucidating of antibiotic resistance mechanisms for this important pathogen, and highlight the need for continuous genomic epidemiology surveillance of clinical K. pneumoniae isolates.

Using Genomics to Understand the Epidemiology of Infectious Diseases in the Northern Territory of Australia

The Northern Territory (NT) is a geographically remote region of northern and central Australia. Approximately a third of the population are First Nations Australians, many of whom live in remote regions. Due to the physical environment and climate, and scale of social inequity, the rates of many infectious diseases are the highest nationally. Molecular typing and genomic sequencing in research and public health have provided considerable new knowledge on the epidemiology of infectious diseases in the NT. We review the applications of genomic sequencing technology for molecular typing, identification of transmission clusters, phylogenomics, antimicrobial resistance prediction, and pathogen detection. We provide examples where these methodologies have been applied to infectious diseases in the NT and discuss the next steps in public health implementation of this technology.