Food Protection Trends - July/August 2023 - 332

TABLE 2. Primer design for E. coli PCR identification and confirmation
Target
Sequence (5′-3′)
clpB
uidA
ybbW
Forward: CATACGAATGCTGGATGCTG
Reverse: TTTGAAGAACGTTTAAAAGGCG
Forward: ACCACGGTGATATCGTCCAC
Reverse: TACAAGAAAGCCGGGCAAT
Forward: AATCTGGCCGGGATTTTT
Reverse: 5TGGCTCCGGCAATAATACAT
449, 454, and/or 447 bp, and to further confirm the presence
of E. coli, positive samples were streaked on MacConkey agar
(BD Difco).
DNA preparation
DNA extraction, library preparation, sequencing, and
in silico analysis were performed as described by Domesle
et al. (20), with some modifications. Genomic DNA was
extracted using DNeasy blood & tissue kits (Qiagen, Hilden,
Germany). DNA concentrations were measured with a Qubit
fluorometer 3.0 by using the dsDNA HS assay kit, according
to the manufacturer's instructions (Thermo Fisher). DNA
extracts were stored at −20°C until WGS analysis.
WGS characterization
The Nextera XT library preparation kit (Illumina, San
Diego, CA, USA) was used for preparing paired-end libraries,
and WGS was carried on a MiSeq sequencer using a 2 ×
300-cycle MiSeq reagent V3 kit (Illumina). Trimming
and de novo assemblies were obtained with Shovill version
0.9 (https://github.com/tseemann/shovill), available in
the GalaxyTrakr pipeline (http://galaxytrakr.org/) (2).
Default parameters were used in all analyses (80). The NCBI
Prokaryotic Genomes Automatic Annotation pipeline
(https://www.ncbi.nlm.nih.gov/genome/annotation_
prok/) was used to annotate draft genomes of each isolate.
Characterization was performed using an in silico multilocus
sequence typing (MLST) for E. coli, based on genome
sequences of seven housekeeping genes, and core genome
MLST analysis by using EnteroBase version 1.1.2 (https://
enterobase.warwick.ac.uk/).
Isolate characterization
The genetic relatedness of the isolates sequenced was
compared with sequences of more than 200,000 E. coli
isolates from various environmental and clinical sources
available in the NCBI Pathogen Detection database.
Through this database, the antimicrobial resistance
(AMR) profiles of each isolate were identified through the
332 Food Protection Trends July/August
AMRFinderPlus 3.10 tool (https://www.ncbi.nlm.nih.
gov/pathogens/antimicrobial-resistance/AMRFinder/).
Further characterization of E. coli isolates was conducted
in silico through the Center for Genomic Epidemiology
services. Virulence factors were predicted through the
alignment of draft genome assembles per isolate against
the database through VirulenceFinder 2.0 (version 2.0.3)
(http://cge.cbs.dtu.dk/services/VirulenceFinder/) https://
bio.tools/virulencefinder. Virulent factors that passed the
default threshold of ≥ 90% nucleotide identity and ≥ 60%
coverage were accepted as present in the isolate. Rapid
serotype identification was completed using SerotypeFinder
2.0 (version 2.0.1), with accepted serotype genes falling
within the default threshold of ≥ 85% nucleotide identity
and ≥ 60% coverage per isolate. A phylogenetic tree was
constructed based on the core genome MLST as defined by
Ridom SeqSphere (version 8.4) (Ridom GmbH, Münster,
Germany), where genomes were grouped based on serotype
identification with distance centered on the seven E. coli
Warwick genes. A retrospective literature study was used
to further group isolates centered around animal reservoirs
associated with specific serotypes. Articles obtained as
suitable for this study were peer-reviewed original research
articles.
Accession numbers
The sequences of all isolates used in this research are
available under the BioProjects 357722 and 832234
on the NCBI website: https://www.ncbi.nlm.nih.gov/
bioproject/357722 and https://www.ncbi.nlm.nih.gov/
bioproject/832234).
RESULTS AND DISCUSSION
Prevalence of presumptive E. coli in sampled water
sources
In total, 570 presumptive E. coli isolates were collected
during the 1-year sampling period from nine different
agricultural water sources. Pond D had overall the highest
counts (P < 0.05) among sampling sites and seasons, with
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