Biology subjects

Szarvas, J.

Publications and source records attributed to Szarvas, J..

4 recordsLinked to original sources

bioRxiv2023

Large-scale phenotypic and genomic characterization of Listeria monocytogenes susceptibility to quaternary ammonium compounds

Listeria monocytogenes is a significant concern for the food industry due to its ability to persist in the food processing environment. Decreased susceptibility to disinfectants is one of the factors that contribute to the persistence of L. monocytogenes. The objective of this study was to explore the diversity of L. monocytogenes susceptibility to quaternary ammonium compounds (QACs) using 1,671 L. monocytogenes isolates. This was used to determine the phenotype-genotype concordance and characterize genomes of the QAC sensitive and tolerant isolates for stress resistance, virulence and plasmid replicon genes. Distribution of QAC tolerance genes among 37,897 publicly available L. monocytogenes genomes were also examined. The minimum inhibitory concentration to QACs was determined by the broth microdilution method and non-sequenced isolates (n=1,244) were whole genome sequenced. Genotype-phenotype concordance was 99% for benzalkonium chloride, DDAC and a commercial QAC based sanitizer. Prevalence of QAC tolerance genes was 23% and 28% in our L. monocytogenes collection and in the global dataset, respectively. qacH was the most prevalent gene in our collection (61%), with 19% prevalence in the global dataset. Notably, bcrABC was most common (72%) globally, while 25% in our collection. Prevalence of emrC and emrE was comparable in both datasets, 7% and 2%, respectively. Replicon genes, indicative of plasmid harborage, were detected in 44% of the isolates and associated with the QAC tolerant phenotype. The presented analysis is based on the biggest L. monocytogenes collection in diversity and quantity for characterization of the L. monocytogenes QAC tolerance at both phenotypic and genomic levels. IMPORTANCEContamination of Listeria monocytogenes within the food processing environment is of concern to the food industry due to challenges in eradicating the pathogen once it becomes persistent in the environment. Genetic markers associated with increased tolerance to disinfectants have been identified, which alongside factors favor the persistence of L. monocytogenes in the production environment. By employing a comprehensive large-scale phenotypic testing and genomic analysis our study significantly enhances the understanding of the prevalence of quaternary ammonium compound (QAC) tolerant L. monocytogenes and the genetic determinants associated with the increased tolerance. Furthermore, we report on the prevalence of QAC tolerance genes among 37,897 publicly available L. monocytogenes sequences and their distribution within clonal complexes, isolation sources and geographical locations. As the propagation of QAC tolerance showed not be evenly distributed globally this highlights that understanding the development of L. monocytogenes disinfectant tolerance can be monitored using publicly available WGS data.

microbiology↗

bioRxiv2023

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 learned. As a component of the Platform, the SARS-CoV-2 Data Hubs enabled the extension and set up of infrastructure that we intend to use more widely in the future for pathogen surveillance and pandemic preparedness.

bioinformatics↗

bioRxiv2021

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 based on the random forest machine learning method. We trained the models with 8,519 plasmids from 359 different bacterial species per taxonomic level, where the models achieved 0.662 and 0.867 Matthews correlation coefficients 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 can be used online through Center for Genomic Epidemiology (https://cge.cbs.dtu.dk/services/PlasmidHostFinder/). ImportanceAntimicrobial resistance is a global health threat to humans and animals causing high mortality and morbidity, and effectively ending decades of success in fighting against bacterial infections. Plasmids confer extra genetic capabilities to the host organisms through accessory genes, which can encode antimicrobial resistance and virulence factors. In addition to lateral inheritance, plasmids can be transferred horizontally between bacterial taxa. Therefore, detecting the host range of plasmids is crucial for understanding and predicting the dissemination trajectories of extrachromosomal genes and bacterial evolution, as well as for taking effective counter measures against antimicrobial resistance.

bioinformatics↗

bioRxiv2019

Large scale automated phylogenomical analysis of bacterial whole-genome isolates and the Evergreen platform

Public health authorities whole-genome sequence thousands of pathogenic isolates each month for microbial diagnostics and surveillance of pathogenic bacteria. The computational methods have not kept up with the deluge of data and 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. To decrease the computational burden, a two level clustering strategy is employed. The data is first divided into sets by matching each isolate to a closely related reference genome. The reads then are aligned to the reference to gain a consensus sequence and SNP based genetic distance is calculated between the sequences in each set. Isolates are clustered together with a threshold of 10 SNPs. Finally, phylogenetic trees are inferred from the non-redundant sequences and the clustered isolates are placed on a clade with the cluster representative sequence. The method was benchmarked and found to be accurate in grouping outbreak strains together, while discriminating from non-outbreak strains. The pipeline was applied in Evergreen Online, which processes publicly available sequencing data from foodborne bacterial pathogens on a daily basis, updating the phylogenetic trees as needed. It has so far placed more than 100,000 isolates into phylogenies, and has been able to keep up with the daily release of data. The trees are continuously published on https://cge.cbs.dtu.dk/services/Evergreen

bioinformatics↗