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During the first epidemic wave of SARS-CoV-2, Australia closed its borders; during March 28, 2020-November 1, 2021, international arriving passengers were required to undergo mandatory supervised quarantine (1). This initial [...]

Reconstructing the evolutionary origins of Mycobacterium tuberculosis, the causative agent of human tuberculosis, has helped identify bacterial factors that have led to the tubercle bacillus becoming such a formidable human pathogen. Here we report the discovery and detailed characterization of an exceedingly slow growing mycobacterium that is closely related to M. tuberculosis for which we have proposed the species name Mycobacterium spongiae sp. nov., (strain ID: FSD4b-SM). The bacterium was isolated from a marine sponge, taken from the waters of the Great Barrier Reef in Queensland, Australia. Comparative genomics revealed that, after the opportunistic human pathogen Mycobacterium decipiens, M. spongiae is the most closely related species to the M. tuberculosis complex reported to date, with 80% shared average nucleotide identity and extensive conservation of key M. tuberculosis virulence factors, including intact ESX secretion systems and associated effectors. Proteomic and lipidomic analyses showed that these conserved systems are functional in FSD4b-SM, but that it also produces cell wall lipids not previously reported in mycobacteria. We investigated the virulence potential of FSD4b-SM in mice and found that, while the bacteria persist in lungs for 56 days after intranasal infection, no overt pathology was detected. The similarities with M. tuberculosis, together with its lack of virulence, motivated us to investigate the potential of FSD4b-SM as a vaccine strain and as a genetic donor of the ESX-1 genetic locus to improve BCG immunogenicity. However, neither of these approaches resulted in superior protection against M. tuberculosis challenge compared to BCG vaccination alone. The discovery of M. spongiae adds to our understanding of the emergence of the M. tuberculosis complex and it will be another useful resource to refine our understanding of the factors that shaped the evolution and pathogenesis of M. tuberculosis. Author summary: Tuberculosis, caused by Mycobacterium tuberculosis, is still one of the world's deadliest infectious diseases. However, the origins and rise of M. tuberculosis as a successful pathogen are not well understood. Here, we report the isolation and characterisation of a marine sponge-derived mycobacterium (M. spongiae) from the Great Barrier Reef that has striking genotypic similarity to M. tuberculosis, with 80% average nucleotide identity. We further show by proteomic and lipidomic analyses that M. spongiae shares virulence factors and unique cell wall lipids with the tubercle bacillus. In spite of these conserved genotypic and phenotypic features, M. spongiae was not virulent in a mouse model of infection, leading us to investigate its potential as a vaccine strain or genetic donor for enhancing the current BCG vaccine for tuberculosis. Our findings contribute to understanding the evolutionary origins of M. tuberculosis and provide further insights into its pathogenesis. [ABSTRACT FROM AUTHOR]

Healthcare workers are at increased risk of occupational transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We report 2 instances of healthcare workers contracting SARS-CoV-2 despite no known breach of personal protective equipment. Additional specific equipment cleaning was initiated. Viral genomic sequencing supported this transmission hypothesis and our subsequent response. [ABSTRACT FROM AUTHOR]

Second SARS-CoV-2 infections twelve months after initial infections in Australia, confirmed by genomic analysis Keywords: COVID-19 EN COVID-19 199 201 3 03/11/22 20220301 NES 220301 Second infections with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are thought to affect fewer than 1% of people with resolved coronavirus disease 2019 (COVID-19).1 Reinfections as soon as 26 days after the initial diagnosis have been reported, in some cases with increased disease severity.1,2 No confirmed cases of second SARS-CoV-2 infections have been reported in Australia, but public awareness of the possibility is needed to encourage continued testing and vaccination. All three patients had also been diagnosed with COVID-19 in July 2020, during a period of high community transmission in Victoria; the source of these earlier infections was unknown. [Extracted from the article]

Fundamental to effective Legionnaires’ disease outbreak control is the ability to rapidly identify the environmental source(s) of the causative agent, Legionella pneumophila. Genomics has revolutionized pathogen surveillance, but L. pneumophila has a complex ecology and population structure that can limit source inference based on standard core genome phylogenetics. Here, we present a powerful machine learning approach that assigns the geographical source of Legionnaires’ disease outbreaks more accurately than current core genome comparisons. Models were developed upon 534 L. pneumophila genome sequences, including 149 genomes linked to 20 previously reported Legionnaires’ disease outbreaks through detailed case investigations. Our classification models were developed in a cross-validation framework using only environmental L. pneumophila genomes. Assignments of clinical isolate geographic origins demonstrated high predictive sensitivity and specificity of the models, with no false positives or false negatives for 13 out of 20 outbreak groups, despite the presence of within-outbreak polyclonal population structure. Analysis of the same 534-genome panel with a conventional phylogenomic tree and a core genome multi-locus sequence type allelic distance-based classification approach revealed that our machine learning method had the highest overall classification performance—agreement with epidemiological information. Our multivariate statistical learning approach maximizes the use of genomic variation data and is thus well-suited for supporting Legionnaires’ disease outbreak investigations. IMPORTANCE Identifying the sources of Legionnaires’ disease outbreaks is crucial for effective control. Current genomic methods, while useful, often fall short due to the complex ecology and population structure of Legionella pneumophila, the causative agent. Our study introduces a high-performing machine learning approach for more accurate geographical source attribution of Legionnaires’ disease outbreaks. Developed using cross-validation on environmental L. pneumophila genomes, our models demonstrate excellent predictive sensitivity and specificity. Importantly, this new approach outperforms traditional methods like phylogenomic trees and core genome multi-locus sequence typing, proving more efficient at leveraging genomic variation data to infer outbreak sources. Our machine learning algorithms, harnessing both core and accessory genomic variation, offer significant promise in public health settings. By enabling rapid and precise source identification in Legionnaires’ disease outbreaks, such approaches have the potential to expedite intervention efforts and curtail disease transmission. [ABSTRACT FROM AUTHOR]

Importance: This proof-of-concept study introduces a hybrid capture oligo panel for whole-genome sequencing of all six human pathogenic hepatitis A virus (HAV) subgenotypes, exhibiting a higher sensitivity than some conventional genotyping assays. The ability of hybrid capture to enrich multiple targets allows for a single, streamlined workflow, thus facilitating the potential harmonization of molecular surveillance of HAV with other enteric viruses. Even challenging sample matrices can be accommodated, making them suitable for broad implementation in clinical and public health laboratories. This innovative approach has significant implications for enhancing multijurisdictional outbreak investigations as well as our understanding of the global diversity and transmission dynamics of HAV., Competing Interests: The authors declare no conflict of interest.

Background: Multiresistant organisms (MROs) pose a critical threat to public health. Population-based programs for control of MROs such as carbapenemase-producing Enterobacterales (CPE) have emerged and evaluation is needed. We assessed the feasibility and impact of a statewide CPE surveillance and response program deployed across Victoria, Australia (population 6.5 million)., Methods: A prospective multimodal intervention including active screening, carrier isolation, centralized case investigation, and comparative pathogen genomics was implemented. We analyzed trends in CPE incidence and clinical presentation, risk factors, and local transmission over the program's first 3 years (2016-2018)., Results: CPE case ascertainment increased over the study period to 1.42 cases/100 000 population, linked to increased screening without a concomitant rise in active clinical infections (0.45-0.60 infections/100 000 population, P = .640). KPC-2 infection decreased from 0.29 infections/100 000 population prior to intervention to 0.03 infections/100 000 population in 2018 (P = .003). Comprehensive case investigation identified instances of overseas community acquisition. Median time between isolate referral and genomic and epidemiological assessment for local transmission was 11 days (IQR, 9-14). Prospective surveillance identified numerous small transmission networks (median, 2; range, 1-19 cases), predominantly IMP and KPC, with median pairwise distance of 8 (IQR, 4-13) single nucleotide polymorphisms; low diversity between clusters of the same sequence type suggested genomic cluster definitions alone are insufficient for targeted response., Conclusions: We demonstrate the value of centralized CPE control programs to increase case ascertainment, resolve risk factors, and identify local transmission through prospective genomic and epidemiological surveillance; methodologies are transferable to low-prevalence settings and MROs globally., (© The Author(s) 2020. Published by Oxford University Press for the Infectious Diseases Society of America.)

Late in 2020, two genetically-distinct clusters of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with mutations of biological concern were reported, one in the United Kingdom and one in South Africa. Using a combination of data from routine surveillance, genomic sequencing and international travel we track the international dispersal of lineages B.1.1.7 and B.1.351 (variant 501Y-V2). We account for potential biases in genomic surveillance efforts by including passenger volumes from location of where the lineage was first reported, London and South Africa respectively. Using the software tool grinch (global report investigating novel coronavirus haplotypes), we track the international spread of lineages of concern with automated daily reports, Further, we have built a custom tracking website (cov-lineages.org/global_report.html) which hosts this daily report and will continue to include novel SARS-CoV-2 lineages of concern as they are detected., Competing Interests: No competing interests were disclosed., (Copyright: © 2021 O'Toole Á et al.)

The COVID-19 pandemic has exposed the dependence of diagnostic laboratories on a handful of large corporations with market monopolies on the worldwide supply of reagents, consumables, and hardware for molecular diagnostics. Global shortages of key consumables for RT-qPCR detection of SARS-CoV-2 RNA have impaired the ability to run essential, routine diagnostic services. Here, we describe a workflow for rapid detection of SARS-CoV-2 RNA in upper respiratory samples including nasal swabs and saliva, utilizing low-cost equipment and readily accessible reagents. Using repurposed Creality3D Ender-3 three-dimensional (3D) printers, we built a semiautomated paramagnetic bead RNA extraction platform. The hardware for the system was built for $300 USD, and the material cost per reaction was $1 USD. Named the Ender VX500 , instrument performance when paired with RT-qPCR for SARS-CoV-2 detection in nasal and saliva specimens was two virus copies per microliter. There was a high-performance agreement (assessed using 458 COVID-19 nasal swab specimens) with the Aptima SARS-CoV-2 assay run on the Hologic Panther, a commercial automated RNA extraction and detection platform. Inter- and intrainstrument precision was excellent (coefficients of variation (CoV) of 1.10 and 0.66-1.32%, respectively) across four instruments. The platform is scalable with throughput ranging from 23 specimens on a single instrument run by one user in 50 min to 364 specimens on four instruments run by four users in 190 min. Step-by-step instructions and protocols for building and running the Ender VX500 have been made available without restriction.