High performance Legionella pneumophila source attribution using genomics-based machine learning classification.

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.IMPORTANCEIdentifying 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.

High-performance enrichment-based genome sequencing to support the investigation of hepatitis A virus outbreaks.

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.

Hidden Resistances: How Routine Whole-Genome Sequencing Uncovered an Otherwise Undetected blaNDM-1 Gene in Vibrio alginolyticus from Imported Seafood.

Vibrio alginolyticus causes vibriosis of marine vertebrates, invertebrates, and humans, and while there have been several reports of multidrug resistance in V. alginolyticus, carbapenem resistance is rare. V. alginolyticus strain AUSMDU00064140 was isolated in Melbourne, Australia, from imported prawns. Routine genomic surveillance detected the presence of a full-length blaNDM-1 gene, subsequently shown to be collocated with additional acquired antimicrobial resistance genes on a resistance cassette on the largest chromosome, flanked by mobilization gene annotations. Comparisons to a previously described V. alginolyticus plasmid, pC1349, revealed differing gene content and arrangements between the resistance cassettes. Phylogenetic analysis was performed against a local and global data set (n = 109), demonstrating that AUSMDU00064140 was distinct and did not cluster with any other strains. Despite the presence of the complete blaNDM-1 gene and positive phenotypic assays for carbapenemase production, carbapenem MICs were low (meropenem MIC ≤0.5 mg/liter). However, it is still possible that this gene may be transferred to another species in the environment or a host, causing phenotypic carbapenem resistance and presenting a risk of great public health concern. IMPORTANCE Carbapenems are last-line antimicrobials, vital for use in human medicine. Antimicrobial resistance determinants such as blaNDM (New Delhi metallo-ß-lactamase producing) genes conferring resistance to the carbapenem class of antimicrobials, are typically found in Enterobacterales (first described in 2009 from a Klebsiella pneumoniae isolate). Our study shows that Vibrio alginolyticus isolated from cooked prawn is able to harbor antimicrobial resistance (AMR) genes of public health concern, specifically a chromosomally located blaNDM-1 gene, and there is the potential for transmission of resistance genes. This may be linked with antimicrobial use in low- and middle-income settings, which has typically been high, unregulated, or not reported. Many countries, including Thailand, have implemented national strategic plans to incorporate the World Health Organization (WHO)'s Global Action Plan (2015) recommendations of a global One Health approach, including increased resources for surveillance of antimicrobial usage and AMR; however, efficient antimicrobial surveillance systems incorporating genomic and phenotypic testing of isolates are still lacking in many jurisdictions.

Validation of a single-step, single-tube reverse transcription loop-mediated isothermal amplification assay for rapid detection of SARS-CoV-2 RNA.

Introduction. The SARS-CoV-2 pandemic of 2020 has resulted in unparalleled requirements for RNA extraction kits and enzymes required for virus detection, leading to global shortages. This has necessitated the exploration of alternative diagnostic options to alleviate supply chain issues.Aim. To establish and validate a reverse transcription loop-mediated isothermal amplification (RT- LAMP) assay for the detection of SARS-CoV-2 from nasopharyngeal swabs.Methodology. We used a commercial RT-LAMP mastermix from OptiGene in combination with a primer set designed to detect the CDC N1 region of the SARS-CoV-2 nucleocapsid (N) gene. A single-tube, single-step fluorescence assay was implemented whereby 1 µl of universal transport medium (UTM) directly from a nasopharyngeal swab could be used as template, bypassing the requirement for RNA purification. Amplification and detection could be conducted in any thermocycler capable of holding 65 °C for 30 min and measure fluorescence in the FAM channel at 1 min intervals.Results. Assay evaluation by assessment of 157 clinical specimens previously screened by E-gene RT-qPCR revealed assay sensitivity and specificity of 87 and 100%, respectively. Results were fast, with an average time-to-positive (Tp) for 93 clinical samples of 14 min (sd±7 min). Using dilutions of SARS-CoV-2 virus spiked into UTM, we also evaluated assay performance against FDA guidelines for implementation of emergency-use diagnostics and established a limit-of-detection of 54 Tissue Culture Infectious Dose 50 per ml (TCID50 ml-1), with satisfactory assay sensitivity and specificity. A comparison of 20 clinical specimens between four laboratories showed excellent interlaboratory concordance; performing equally well on three different, commonly used thermocyclers, pointing to the robustness of the assay.Conclusion. With a simplified workflow, The N1 gene Single Tube Optigene LAMP assay (N1-STOP-LAMP) is a powerful, scalable option for specific and rapid detection of SARS-CoV-2 and an additional resource in the diagnostic armamentarium against COVID-19.

Prospective Whole-Genome Sequencing Enhances National Surveillance of Listeria monocytogenes.

Whole-genome sequencing (WGS) has emerged as a powerful tool for comparing bacterial isolates in outbreak detection and investigation. Here we demonstrate that WGS performed prospectively for national epidemiologic surveillance of Listeria monocytogenes has the capacity to be superior to our current approaches using pulsed-field gel electrophoresis (PFGE), multilocus sequence typing (MLST), multilocus variable-number tandem-repeat analysis (MLVA), binary typing, and serotyping. Initially 423 L. monocytogenes isolates underwent WGS, and comparisons uncovered a diverse genetic population structure derived from three distinct lineages. MLST, binary typing, and serotyping results inferred in silico from the WGS data were highly concordant (>99%) with laboratory typing performed in parallel. However, WGS was able to identify distinct nested clusters within groups of isolates that were otherwise indistinguishable using our current typing methods. Routine WGS was then used for prospective epidemiologic surveillance on a further 97 L. monocytogenes isolates over a 12-month period, which provided a greater level of discrimination than that of conventional typing for inferring linkage to point source outbreaks. A risk-based alert system based on WGS similarity was used to inform epidemiologists required to act on the data. Our experience shows that WGS can be adopted for prospective L. monocytogenes surveillance and investigated for other pathogens relevant to public health.