Biology subjects

Kase, J.

Publications and source records attributed to Kase, J..

3 recordsLinked to original sources

bioRxiv2025

centriflaken: an automated data analysis pipeline for assembly and in silico analyses of foodborne pathogens from metagenomic samples

Rapid and comprehensive analysis of metagenomic data from samples associated with foodborne outbreaks is of critical importance in food safety. Equally important is the need for automated analysis pipelines that allow the rapid and effective construction of metagenomic assembled genomes (MAGs) to enable bacterial source-tracking from metagenomic data. Here, we present centriflaken, an automated precision metagenomics pipeline for detecting and characterizing Shiga toxin-producing Escherichia coli (STEC) from metagenomic data. centriflaken streamlines the process of generating metagenome-assembled genomes (MAGs) and conducting in silico analyses of STECs, significantly reducing the time and manual effort required for comprehensive pathogen profiling. centriflaken was validated using Oxford Nanopore long-read sequencing data from agricultural water enrichments, successfully reproducing results from our previous study that involved multiple manual bioinformatics steps (Maguire et al., 2021). The tools efficacy was further demonstrated through its application to ZymoBIOMICS microbial community standards and 21 additional irrigation water samples, completing STEC precision metagenomics analyses in less than 7 hours per sample. centriflakens versatility allows for the analysis of user-defined taxa beyond STEC, including other foodborne pathogens like Listeria monocytogenes or Salmonella. The pipeline generates comprehensive summary plots and tables, accessible through a MultiQC HTML report. Designed for portability, centriflaken packages all software dependencies within containers and virtual environments. This open-source tool is available on GitHub under the MIT license (https://github.com/CFSAN-Biostatistics/centriflaken), offering a powerful resource for rapid, automated pathogen detection and characterization in food safety applications. Author summaryMetagenomic sequencing, particularly using nanopore technology, generates vast amounts of data that are challenging to analyze, especially when searching for specific pathogens like Shiga toxin-producing Escherichia coli (STEC). This challenge is compounded when processing multiple samples simultaneously. The pipeline "centriflaken" was developed to streamline this complex process, automating the extraction of E. coli reads from metagenomic data and performing in-silico characterization of potential STEC present in the samples. centriflaken builds on the concept of precision metagenomics, employing a suite of automated data analysis workflows powered by Nextflow. The pipeline processes metagenomic data, generates metagenome-assembled genomes (MAGs), and conducts in-silico analyses as described in Maguire et al. (2021). By running steps in parallel, centriflaken enables users without extensive bioinformatics skills or STEC genomic knowledge to efficiently analyze complex metagenomic data. Key features of centriflaken include: 1) Automated workflow for STEC detection and characterization, 2) Parallel processing for improved efficiency, 3) User-friendly interface for non-specialists, 4) Scalability for analyzing multiple samples simultaneously, and 5) Compatibility with high-performance computing (HPC) clusters and cloud environments. This freely available software democratizes complex metagenomic analysis, making it accessible to a broader range of researchers and food safety professionals.

bioinformatics↗

bioRxiv2020

Precision long-read metagenomics sequencing for food safety by detection and assembly of Shiga toxin-producing Escherichia coli in irrigation water

Shiga toxin-producing Escherichia coli (STEC) contamination of agricultural water might be an important factor to recent foodborne illness and outbreaks involving leafy greens. Whole genome sequencing generation of closed bacterial genomes plays an important role in source tracking. We aimed to determine the limits of detection and classification of STECs by qPCR and nanopore sequencing using enriched irrigation water artificially contaminated with E. coli O157:H7 (EDL933). We determined the limit of STEC detection by qPCR to be 30 CFU/reaction, which is equivalent to 105 CFU/ml in the enrichment. By using Oxford Nanopores EPI2ME WIMP workflow and de novo assembly with Flye followed by taxon classification with a k-mer analysis software (Kraken), E. coli O157:H7 could be detected at 103 CFU/ml (68 reads) and a complete fragmented E. coli O157:H7 metagenome-assembled genome (MAG) was obtained at 105-108 CFU/ml. Using a custom script to extract the E. coli reads, a completely closed MAG was obtained at 107-108 CFU/ml and a complete, fragmented MAG was obtained at 105-106 CFU/ml. In silico virulence detection for E. coli MAGs for 105-108 CFU/ml showed that the virulotype was indistinguishable from the spiked E. coli O157:H7 strain. We further identified the bacterial species in the un-spiked enrichment, including antimicrobial resistance genes, which could have important implications to food safety. We propose this workflow could be used for detection and complete genomic characterization of STEC from a complex microbial sample and could be applied to determine the limit of detection and assembly of other foodborne bacterial pathogens. IMPORTANCEFoodborne illness caused by Shiga toxin-producing E. coli (STEC) ranges in severity from diarrhea to hemolytic uremic syndrome and produce-related incidence is increasing. The pervasive nature of E. coli requires not only detection, but also a complete genome to determine potential pathogenicity based on stx and eae genes, serotype, and other virulence factors. We have developed a pipeline to determine the limits of nanopore sequencing for STECs in a metagenomic sample. By utilizing the current qPCR in the FDA Bacteriological Analytical Manual (BAM) Chapter 4A, we can quantify the amount of STEC in the enrichment and then sequence and classify the STEC in less than half the time as current protocols that require a single isolate. These methods have wide implications for food safety, including decreased time to STEC identification during outbreaks, characterization of the microbial community, and the potential to use these methods to determine the limits for other foodborne pathogens.

genomics↗

bioRxiv2018

Virulence gene profiles and phylogeny of Shiga toxin-positive Escherichia coli strains isolated from FDA regulated foods during 2010-2017

Illnesses caused by Shiga toxin-producing Escherichia coli (STECs) can be life threatening, such as hemolytic uremic syndrome (HUS). The STECs most frequently identified by USDAs Microbiological Data Program (MDP) carried toxin gene subtypes stx1a and/or stx2a. Here we describe the genome sequences of 331 STECs isolated from foods regulated by the FDA 2010-2017, determining their genomic identity, serotype, sequence type, virulence potential, and prevalence of antimicrobial resistance. Isolates were selected from the MDP archive, routine food testing by field labs (ORA), food testing by a contract company, and our laboratory (ORS). Only 276 (83%) were confirmed as STECs by in silico analysis. Foods from which STECs were recovered included cilantro (6%), spinach (25%), lettuce (11%), and flour (9%). Phylogenetic analysis using core genome MLST revealed these STEC genomes were highly variable, with some clustering associated with ST types and serotypes. We detected 95 different sequence types (ST); several ST were previously associated with HUS: ST21 and ST29 (O26:H11), ST11 (O157:H7), ST33 (O91:H14), ST17 (O103:H2), and ST16 (O111:H-). in silico virulome analyses showed ~ 51% of these strains were potentially pathogenic [besides stx gene they also carried eae (25%) or 26% subA (26%)]. Virulence gene prevalence was also determined: stx1 only (19%) -variants a and c; stx2 only (66%) - variants a, b, c, d, e, and g; and stx1/sxt2 (15%). Our data form a new WGS database that can be used to support food safety investigations and monitor the recurrence/emergence of E. coli in foods.\n\nImportanceShiga toxin-producing Escherichia coli (STECs) are associated with foodborne outbreaks worldwide; however, surveillance has not previously included genomic analyses for phylogenetics, prevalence, or potential virulence. We constructed the first genomic database of isolates from FDA-regulated foods to help monitor the emergence of new pathogenic STECs. Although only ~30 STECs were isolated per year, 50% of these carried markers associated with pathogenesis either a combination of eae plus stx, or subA plus stx. Moreover, those strains also carried virulence genes associated with severe illnesses. Here we showed that WGS enabled comparisons across isolates to establish phylogeny, help in identification of antibiotic resistance by monitoring the presence of antimicrobial resistance genes, and determined the presence of known virulence genes that have been linked with illnesses. Future food safety investigations will benefit from improved source tracking and risk assessments made possible by these analyses and new WGS database.

microbiology↗