Biology subjects

Reska, T.

Publications and source records attributed to Reska, T..

5 recordsLinked to original sources

bioRxiv2026

A metagenomic framework for rapid Listeria monocytogenes surveillance in food production environments

Listeria monocytogenes remains a major foodborne pathogen with high mortality and costly persistence in food-processing environments. Established diagnostics rely on selective enrichment and single-colony isolation, which could introduce strong biases by favouring fast-growing strains or those more tolerant to enrichment broth inhibitors, while suppressing slow-growing, viable-but-nonculturable, and other co-occurring strains. This can obscure true pathogen diversity and may contribute to discrepancies between strains detected in food production environments and those associated with disease. To quantify the bias introduced by established culture-based diagnostics and to assess the potential advantage of metagenomics-based pathogen detection directly from the original sample matrix, we developed and evaluated a rapid nanopore sequencing-based metagenomic framework. We designed an artificial metagenomic community of several Listeria strains, comprising L. monocytogenes lineages I-III (including hypervirulent, persistent, and low-virulence strains), other Listeria spp., and a realistic background microbiome representative of food-processing environments. We then used this mock community to spike standard surveillance sponges and compared three workflows: (i) direct nanopore metagenomic sequencing of the original sample matrix, (ii) quasi-metagenomic sequencing after 4 h, 12 h, 24 h, or 48 h of selective enrichment, and (iii) ISO-based culture followed by whole-genome sequencing of a single presumptive L. monocytogenes isolate. We found that the culture-based approach recovered only a limited subset of strains, consistently underrepresenting diversity and failing to detect multi-strain contamination. These findings were reflected by the quasi-metagenomic results, where we found relative L. monocytogenes enrichment to be strain-dependent, indicating selective enrichment bias favouring specific strains. Metagenomics captured the full spectrum of spiked Listeria strains, enabling comprehensive strain-level resolution at all inoculation levels. We only observed relative enrichment of the L. monocytogenes strains by quasi-metagenomics compared with metagenomics after 48 h of selective enrichment. While driven primarily by the additional enrichment of L. innocua, these results suggest that quasi-metagenomics improves L. monocytogenes recovery only at the cost of a substantial reduction in speed. We finally showed that the sensitivity and accuracy of metagenomics could be improved by utilising different environmental sampling materials. We did not find any significant performance improvements from nanopore sequencing-based enrichment of L. monocytogenes through adaptive sampling approaches. We conclude that integrating long-read metagenomics into routine surveillance shows great promise to improve detection and source attribution in food safety systems.

microbiology↗

bioRxiv2025

Real-time genomic pathogen, resistance, and host range characterization from passive water sampling of wetland ecosystems

Wetland ecosystems provide interfaces for the transmission of microbial pathogens and antimicrobial resistances (AMR) between migratory birds, wild and domestic animals, and human populations. The efficient surveillance of wetlands is, however, challenging, since the typically low concentration of pathogens typically requires the sampling of large volumes of water and subsequent targeted detection, which is inherently limited to a few pathogens or AMR genes of interest. Here, we present a holistic, accessible, and cost-efficient framework to characterize the pathogen and resistance load of water sources together with their potential associated hosts by combining passive water sampling through torpedo-shaped devices with nanopore sequencing technology. We used this framework to characterize anthropogenically influenced and natural wetland ecosystems along the East Atlantic Flyway, where we obtained robust assessments of the microbial communities from long-read metagenomic and RNA virome data, and showed that anthropogenically impacted wetland ecosystems consistently exhibited higher relative abundances of pathogens and AMR genes. By focusing on avian influenza viruses (AIV), we finally highlight the additional need for targeted screening and whole-genome sequencing of pathogens of interest; we detected and characterized AIV at a third of the monitored sites, and used environmental DNA (eDNA) to explore potential animal hosts to better understand the role of wetland ecosystems as One Health interfaces. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=191 HEIGHT=200 SRC="FIGDIR/small/674394v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@1a006f2org.highwire.dtl.DTLVardef@12824c2org.highwire.dtl.DTLVardef@8554bdorg.highwire.dtl.DTLVardef@17e800e_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2024

Nanopore- and AI-empowered metagenomic viability inference

The ability to differentiate between viable and dead microorganisms in metagenomic data is crucial for various microbial inferences, ranging from assessing ecosystem functions of environmental microbiomes to inferring the virulence of potential pathogens from metagenomic analysis. While established viability-resolved genomic approaches are labor-intensive as well as biased and lacking in sensitivity, we here introduce a new fully computational framework that leverages nanopore sequencing technology to assess microbial viability directly from freely available nanopore signal data. Our approach utilizes deep neural networks to learn features from such raw nanopore signal data that can distinguish DNA from viable and dead microorganisms in a controlled experimental setting of UV-induced Escherichia cell death. The application of explainable AI tools then allows us to pinpoint the signal patterns in the nanopore raw data that allow the model to make viability predictions at high accuracy. Using the model predictions as well as explainable AI, we show that our framework can be leveraged in a real-world application to estimate the viability of obligate intracellular Chlamydia, where traditional culture-based methods suffer from inherently high false negative rates. This application shows that our viability model captures predictive patterns in the nanopore signal that can be utilized to predict viability across taxonomic boundaries. We finally show the limits of our models generalizability through antibiotic exposure of a simple mock microbial community, where a new model specific to the killing method had to be trained to obtain accurate viability predictions. While the potential of our computational frameworks generalizability and applicability to metagenomic studies needs to be assessed in more detail, we here demonstrate for the first time the analysis of freely available nanopore signal data to infer the viability of microorganisms, with many potential applications in environmental, veterinary, and clinical settings. Author summaryMetagenomics investigates the entirety of DNA isolated from an environment or a sample to holistically understand microbial diversity in terms of known and newly discovered microorganisms and their ecosystem functions. Unlike traditional culturing of microorganisms, genomic approaches are not able to differentiate between viable and dead microorganisms since DNA might persist under different environmental circumstances. The viability of microorganisms is, however, of importance when making inferences about a microorganisms metabolic potential, a pathogens virulence, or an entire microbiomes impact on its environment. As existing viability-resolved genomic approaches are labor-intensive, expensive, and lack sensitivity, we here investigate our hypothesis if freely available nanopore sequencing signal dat that captures DNA molecule information beyond the DNA sequence might be leveraged to infer such viability. This hypothesis assumes that DNA from dead microorganisms accumulates certain damage signatures that reflect microbial viability and can be read from nanopore signal data using fully computational frameworks. We here show first evidence that such a computational framework might be feasible by training a deep model on controlled experimental data to predict viability at high accuracy, exploring what the model has learned, and using it in a real-world application by application to a bacterial species of veterinary relevance. We finally show that a specific model has to be trained to accurately predict viability after antibiotic exposure of a mock microbial community. While the generalizability of our computational framework therefore needs to be assessed in much more detail, we here demonstrate that freely available data might be usable for relevant viability inferences in environmental, veterinary, and clinical settings.

bioinformatics↗

bioRxiv2024

Latest RNA and DNA nanopore sequencing allows for rapid avian influenza profiling

Avian influenza virus (AIV) currently causes a panzootic with extensive mortality in wild birds, poultry, and wild mammals, thus posing a major threat to global health and underscoring the need for efficient monitoring of its distribution and evolution. Here, we utilized a well-defined AIV strain to systematically investigate AIV characterization through rapid, portable nanopore sequencing by (i) benchmarking the performance of fully portable RNA extraction and viral detection; (ii) comparing the latest DNA and RNA nanopore sequencing approaches for in-depth AIV profiling; and (iii) evaluating the performance of various computational pipelines for viral consensus sequence creation and phylogenetic analysis. Our results show that the latest RNA-specific nanopores can accurately genomically profile AIV from native RNA while additionally detecting RNA epigenetic modifications. We further identified an optimal laboratory and bioinformatic pipeline for reconstructing viral consensus genomes from nanopore sequencing data at various rarefaction thresholds, which we validated by application to real-world environmental samples for AIV monitoring in livestock. Author SummaryWe tested portable, rapid, and easy-to-use technology to obtain more information about the potentially zoonotic RNA virus avian influenza virus, or AIV. AIV has spread globally via the migratory paths of wild birds, and endangers domestic birds, mammals, and human populations given past evidence of infections of different animal species. We here used novel genomic technology that is based on nanopores to explore the genomes of the virus; we established optimized ways of creating the viral genome by comparing different laboratory and computational approaches and the performance of nanopores that either sequence the viral RNA directly or the converted DNA. We then applied the optimized protocol to dust samples which were collected from a duck farm in France during an AIV outbreak. We showed that we were able to use the resulting data to reconstruct the relationship between the virus responsible for the outbreak and previously detected AIV. Altogether, we showed how novel easy-to-use genomic technology can support the surveillance of potentially zoonotic pathogens by accurately recreating the viral genomes to better understand evolution and transmission of these pathogens.

genomics↗

bioRxiv2023

Air monitoring by nanopore sequencing

While the air microbiome and its diversity are essential for human health and ecosystem resilience, comprehensive air microbial diversity monitoring has remained rare, so that little is known about the air microbiomes composition, distribution, or functionality. Here we show that nanopore sequencing-based metagenomics can robustly assess the air microbiome in combination with active air sampling through liquid impingement and tailored computational analysis. We provide fast and portable laboratory and computational approaches for air microbiome profiling, which we leverage to robustly assess the taxonomic composition of the core air microbiome of a controlled greenhouse environment and of a natural outdoor environment. We show that long-read sequencing can resolve species-level annotations and specific ecosystem functions through de novo metagenomic assemblies despite the low amount of fragmented DNA used as an input for nanopore sequencing. We then apply our pipeline to assess the diversity and variability of an urban air microbiome, using Barcelona, Spain, as an example; this randomized experiment gives first insights into the presence of highly stable location-specific air microbiomes within the citys boundaries, and showcases the robust microbial assessments that can be achieved through automatable, fast, and portable nanopore sequencing technology.

molecular biology↗