Characterization of a Pathogenic Escherichia coli Strain Derived from Oreochromis spp. Farms Using Whole-Genome Sequencing

Summary

The feasibility of whole-genome sequencing (WGS) strategies using benchtop instruments has simplified the genome interrogation of every microbe of public health relevance in a lab setting. A methodological adaptation of the workflow for bacterial WGS is described and a bioinformatics pipeline for analysis is also presented.

Abstract

Aquaculture is one of the fastest-growing food-producing sectors worldwide and tilapia (Oreochromis spp.) farming constitutes the major freshwater fish variety cultured. Because aquaculture practices are susceptible to microbial contamination derived from anthropogenic sources, extensive antibiotic usage is needed, leading to aquaculture systems becoming an important source of antibiotic-resistant and pathogenic bacteria of clinical relevance such as Escherichia coli (E. coli). Here, the antimicrobial resistance, virulence, and mobilome features of a pathogenic E. coli strain, recovered from inland farmed Oreochromis spp., were elucidated through whole-genome sequencing (WGS) and in silico analysis. Antimicrobial susceptibility testing (AST) and WGS were performed. Furthermore, phylogenetic group, serotype, multilocus sequence typing (MLST), acquired antimicrobial resistance, virulence, plasmid, and prophage content were determined using diverse available web tools. The E. coli isolate only exhibited intermediate susceptibility to ampicillin and was characterized as ONT:H21-B1-ST40 strain by WGS-based typing. Although only a single antimicrobial resistance-related gene was detected [mdf(A)], several virulence-associated genes (VAGs) from the atypical enteropathogenic E. coli (aEPEC) pathotype were identified. Additionally, the cargo of plasmid replicons from large plasmid groups and 18 prophage-associated regions were detected. In conclusion, the WGS characterization of an aEPEC isolate, recovered from a fish farm in Sinaloa, Mexico, allows insights into its pathogenic potential and the possible human health risk of consuming raw aquacultural products. It is necessary to exploit next-generation sequencing (NGS) techniques for studying environmental microorganisms and to adopt a one health framework to learn how health issues originate.

Introduction

Aquaculture is one of the fastest-growing food-producing sectors worldwide, and its production practices are intended to satisfy the rising food demand for human consumption. Global aquaculture production has tripled from 34 million tonnes (Mt) in 1997 to 112 Mt in 2017¹. The main species groups, contributing to nearly 75% of the production, were seaweed, carps, bivalves, catfish, and tilapia (Oreochromis spp.)¹. However, the appearance of diseases caused by microbial entities is unavoidable because of intensive fish farming, leading to potential economic losses².

Antibiotic usage in fish farming practices is well known for preventing and treating bacterial infections, the main limiting factor in productivity^3,4. Nonetheless, residual antibiotics accumulate in aquaculture sediments and water, exerting selective pressure and modifying the fish-associated and the residing bacterial communities^5,6,7,8. Consequently, the aquaculture environment serves as a reservoir for antimicrobial resistance genes (ARGs), and the further emergence and spread of antibiotic-resistant bacteria (ARB) in the surrounding milieu⁹. In addition to the bacterial pathogens commonly observed affecting fish farming practices, members of the Enterobacteriaceae family are often encountered, including human pathogen strains of Enterobacter spp., Escherichia coli, Klebsiella spp., and Salmonella spp.¹⁰. E. coli is the most common microorganism isolated from fish meal and water in fish farming^{11,12,13,14,15}.

E. coli is a versatile gram-negative bacterium that inhabits the gastrointestinal tract of mammals and birds as a commensal member of their intestinal microbiota. However, E. coli possess a highly adaptive capacity to colonize and persist in different environmental niches, including soil, sediments, food, and water¹⁶. Because of the gene gain and loss through the horizontal gene transfer (HGT) phenomenon, E. coli has rapidly evolved into a well-adapted antibiotic-resistant pathogen, able to cause a broad spectrum of diseases in humans and animals^{17, 18}. Based on the isolation origin, pathogenic variants are defined as intestinal pathogenic E. coli (InPEC) or extra-intestinal pathogenic E. coli (ExPEC). Furthermore, InPEC and ExPEC are subclassified into well-defined pathotypes according to disease manifestation, genetic background, phenotypic traits, and virulence factors (VFs)^16,17,19.

Traditional culture and molecular techniques for pathogenic E. coli strains have allowed the rapid detection and identification of different pathotypes. However, they may be time-consuming, laborious, and frequently require high technical training¹⁹. Furthermore, no single method can be used to reliably study all pathogenic variants of E. coli because of the complexity of their genetic background. Currently, these drawbacks have been overcome with the advent of high-throughput sequencing (HTS) technologies. Whole-genome sequencing (WGS) approaches and bioinformatic tools have improved the exploration of microbial DNA affordably and at a large scale, facilitating the in-depth characterization of microbes in a single run, including closely related pathogenic variants^20,21,22. Depending on the biological questions, several bioinformatics tools, algorithms, and databases can be used to perform data analysis. For instance, if the main goal is to assess the presence of ARGs, VFs, and plasmids, tools such as ResFinder, VirulenceFinder, and PlasmidFinder, along with their associated databases, might be a good starting point. Carriço et al.²² provided a detailed overview of the different bioinformatics software and related databases applied for microbial WGS analysis, from raw data preprocessing to phylogenetic inference.

Several studies have demonstrated the broad utility of WGS for genome interrogation regarding antimicrobial resistance attributes, pathogenic potential, and tracking of the emergence and evolutionary relationships of clinically relevant variants of E. coli sourced from diverse origins^23,24,25,26. WGS has enabled the identification of molecular mechanisms underlying the phenotypic resistance to antimicrobials, including those rare or complex resistance mechanisms. This is through detecting acquired ARG variants, novel mutations in drug-target genes, or promoter regions^27,28. Moreover, WGS offers the potential to infer antimicrobial resistance profiles without requiring prior knowledge about the resistance phenotype of a bacterial strain²⁹. Alternatively, WGS has allowed the characterization of the mobile genetic elements (MGEs) carrying both antimicrobial resistance and virulence features, which has driven the bacterial genome evolution of existing pathogens. For instance, the application of WGS during the investigation of the German E. coli outbreak in 2011 resulted in uncovering the unique genomic features of an apparently novel E. coli pathotype; interestingly, those outbreak strains originated from the enteroaggregative E. coli (EAEC) group, which acquired the prophage encoding the Shiga toxin from the enterohemorrhagic E. coli (EHEC) pathotype³⁰.

This work presents a methodological adaptation of the workflow for bacterial WGS using a benchtop sequencer. Moreover, a bioinformatics pipeline is provided using web-based tools to analyze the resulting sequences and further support researchers with limited or no bioinformatics expertise. The described methods allowed elucidation of the antimicrobial resistance, virulence, and mobilome features of a pathogenic E. coli strain ACM5, isolated in 2011 from inland farmed Oreochromis spp. in Sinaloa, Mexico¹².

Protocol

NOTE: The E. coli strain ACM5 was recovered by processing and culturing the fish sample for fecal coliform (FC) determination¹². During the fish sampling, fish did not show clinical signs of disease, bacterial, or fungal infection, and a mean temperature of 22.3 °C prevailed. After isolation, the E. coli isolate was subjected to biochemical testing and cryopreserved in brain heart infusion (BHI) broth with DMSO (8% v/v) as a cryoprotective agent.

1. Reactivation of frozen E. coli ACM5 stock culture

1. From the frozen bacterial stock, open the tube and use a sterile loop, pipette tip, or toothpick to scrape the surface of the frozen bacterial culture.
2. Streak the bacteria onto a Luria-Bertani (LB) agar plate and incubate at 37 ± 2 °C for 24 h.

2. Determination of antibacterial susceptibility

NOTE: The antimicrobial susceptibility testing described here corresponds to the disk diffusion method based on the Clinical and Laboratory Standards Institute (CLSI) guidelines (M02 Ed13:2018)³¹. E. coli ATCC 25922 strain is required for quality control purposes.

1. Inoculum preparation by colony suspension method
   1. From the LB plate incubated in step 1.2, streak one or two colonies onto a Mueller-Hinton (MH) agar plate and incubate at 37 ± 2 °C for 18-24 h.
   2. Use a sterile loop to pick two or three colonies from the freshly sub-cultured E. coli isolate on MH agar plate and resuspend them in 3 mL of sterile MH broth or 0.85% (w/v) saline solution, mixing thoroughly by vortexing to obtain a uniform bacterial suspension.
   3. Using a UV-visible spectrophotometer (see Table of Materials), adjust the bacterial suspension to turbidity comparable to a 0.5 MacFarland standard (equivalent to approximately 1-2 x 10⁸ cells/mL). If the bacterial suspension is too light or too heavy, add more E. coli colonies or MH broth as appropriate. Use the prepared inoculum within 15 min of preparation.
2. Inoculation of the test agar plates
   1. Dip a sterile cotton swab into the adjusted bacterial suspension. Rotate the swab several times and press it firmly on the inside wall of the tube to remove excess fluid from the swab.
   2. Inoculate an MH agar plate by streaking the swab 3x over the entire agar surface, rotating the plate 60° each time. Swab the rim of the agar as well to ensure an even distribution of the inoculum.
   3. Leave the Petri dish lid ajar for 3-5 min to allow moisture to evaporate.
3. Application of the antimicrobial disk onto inoculated agar plates
   1. Distribute evenly and press each antimicrobial disk (see Table of Materials) down onto the surface of the inoculated agar plates to ensure complete contact. Invert the plates within 15 min after disk application and incubate at 35 ± 2 °C for 16-18 h.
      NOTE: A maximum of 12 and six disks must be used in Petri dishes of 150 mm and 100 mm, respectively.
4. After incubation, measure the zone of inhibition sizes using a Vernier caliper and interpret the resulting diameters according to the CLSI breakpoint criteria (M100 - Table 2A)³².

3. Genomic DNA (gDNA) extraction and quantification

1. Genomic DNA extraction
   1. Resuspend a loopful of freshly cultured E. coli colonies in 5 mL of LB broth and incubate overnight in a shaking incubator (180 rpm) at 37 ± 2 °C.
   2. Centrifuge the bacterial suspension at 3,500 x g for 5 min and carefully discard the supernatant.
   3. Extract gDNA following the DNA extraction kit guidelines (see Table of Materials).
   4. Check the purity of the gDNA by measuring optical density at 260/280 nm (ratio: >1.8) and 260/230 nm (ratio: 2.0-2.2) using a UV-visible spectrophotometer. Perform 0.8% agarose gel electrophoresis to verify the gDNA integrity.
2. Genomic DNA quantification
   NOTE: Use only thin-wall, clear, 0.5 mL PCR tubes approved by the fluorescence assay kit manufacturer (see Table of Materials).
   1. Set up the required number of tubes for samples and assay standards. Prepare the working assay solution by mixing component A and component B provided in the kit, following the manufacturer's guidelines.
   2. Prepare the assay standards by adding 190 µL of the working solution and 10 µL of each standard to the appropriate tube (two standards are required).
   3. Add 198 µL of the working solution and 2 µL of the DNA sample to the appropriate tube. Vigorously mix by vortexing all the tubes for 5 s. Be careful not to create bubbles.
   4. Incubate all tubes for 2 min at room temperature, protected from light. Measure the fluorescence of all tubes based on the manufacturer's guidelines using the fluorometer (see Table of Materials).
   5. Properly adjust the gDNA sample concentration for sequencing proceeding.

4. DNA library preparation

NOTE: DNA library preparation and sequencing were performed following the manufacturer's guidelines and protocols (see Table of Materials). The starting gDNA concentration is 4.0 ng.

1. Tagmentation, PCR amplification, and indexing
   1. In a 0.2 mL tube, add 2.5 µL of tagmentation buffer and 2 µL of input gDNA (2.0 ng/µL). Mix gently by pipetting.
   2. Add 1 µL of amplification buffer and mix gently by pipetting. Spin down at 280 x g for 1 min at room temperature.
   3. Place the samples in a thermocycler (see Table of Materials) and run the following PCR program: 55 °C for 5 min, then hold at 10 °C. When the samples reach 10 °C, proceed immediately to neutralize the reaction.
   4. Add 1 µL of neutralizing buffer to the sample tubes and mix gently by pipetting. Spin down at 280 x g for 1 min and incubate for 5 min at room temperature.
   5. Add 1.7 µL of each index adapter (i.e., indexes i7 and i5) and 3 µL of indexing PCR master mix. Mix gently by pipetting. Spin down at 280 x g for 1 min at room temperature.
   6. Place the samples in the thermocycler and perform a second PCR reaction as follows: 72 °C for 3 min, 95 °C for 30 s, 18 cycles of 95 °C for 10 s, 55 °C for 30 s, 72 °C for 30 s, 72 °C for 5 min, and hold at 10 °C.
2. Amplified library clean up
   1. Mix by vortexing the commercial magnetic bead solution (see Table of Materials) based on the manufacturer's guidelines.
   2. Transfer the tagmented/indexed gDNA sample to a new 1.5 mL tube and add 0.6 µL of magnetic beads for each µL of the final volume of gDNA sample (≈13 µL). Mix gently by pipetting and incubate for 5 min at room temperature.
   3. Place the sample tubes on a magnetic rack (see Table of Materials) for 2 min until the supernatant has cleared. Remove and discard the supernatant carefully without disturbing the beads.
   4. Add 200 µL of freshly prepared 80% ethanol without mixing. Incubate for 30 s until the eluate clears and carefully remove and discard the supernatant without disturbing the beads.
   5. Perform a second wash step. Add 200 µL of 80% ethanol to the beads and incubate for 30 s. After the eluate clears, remove and discard the supernatant and air-dry the beads for 10 min.
   6. Add 15 µL of 10 mM tris buffer (pH 8) to the beads and mix gently by pipetting. Incubate for 2 min at room temperature. Place the sample tubes again on the magnetic rack for 2 min, allowing the supernatant to clear.
   7. Carefully transfer 14 µL of the supernatant from the sample tube to a new 0.2 mL tube. This is the cleaned-up library.
      NOTE: At this point, the cleaned-up library can be stored at -20 °C for up to 7 days.
3. Library normalization
   1. Proceed to quantify the cleaned-up libraries by fluorescence assay as described in step 3.2, except for step 3.2.5.
   2. Visualize the cleaned-up libraries on a 1% agarose gel electrophoresis to determine the average fragment sizes.
   3. Calculate the molarity value of the library using the following equation:
      
   4. Using the molarity value, calculate the proper volumes of resuspension buffer (RSB) and cleaned-up libraries to dilute the individual library at a starting concentration of 10 nM.

5. Library pooling, denaturalizing, and sequencer initiating

1. Thaw the reagent cartridge following the manufacturer's directions. Extract the flow cell from the refrigerator (4 °C) and bring it to room temperature before sequencing.
2. Pool 5 µL of each normalized library (10 nM) into a low-bind 1.5 mL tube. Dilute the pooled library at 4 nM with the proper volume of RSB.
3. Add 5 µL of the pooled library (4 nM) and 5 µL of 0.2 N NaOH in a new low-bind 1.5 mL tube. Briefly mix by vortexing, spin down at 280 x g for 1 min, and incubate for 5 min at room temperature to denature the pooled library into single strands.
4. Gently mix 10 µL of denatured pooled library and 990 µL of prechilled hybridization buffer by pipetting and place on ice until the final dilution for sequencer loading is performed. The concentration of the denatured pooled library is 20 pM.
5. Thaw and prepare a control library (see Table of Materials) at a concentration of 4 nM by mixing 2 µL of control library (10 nM) and 3 µL of nuclease-free water.
6. Mix 5 µL of the control library (4 nM) and 5 µL of freshly prepared 0.2 N NaOH. Briefly mix by vortexing, spin down at 280 x g for 1 min, and incubate for 5 min at room temperature to denature the control library into single strands.
7. Gently mix 10 µL of denatured control library and 990 µL of prechilled hybridization buffer by pipetting and place on ice until the final dilution for sequencer loading is done. The concentration of the denatured control library is 20 pM.
8. In a new low-bind 1.5 mL tube, combine 594 µL of the denatured pooled library at 20 pM and 6 µL of the denatured control library at 20 pM. Mix properly.
9. Dilute the final library mix (20 pM) to a final loading concentration of 1.2 pM in a volume of 600 µL using the prechilled hybridization buffer.
10. Before loading the final library mix onto the reagent cartridge, perform an additional thermal pretreatment to achieve efficient loading into the flow cell. Incubate the final library mix for 2 min at 96 °C. Invert the tube to mix, and place the tube on ice for 5 min.
11. Load 500 µL of the final library mix into the designed reservoir on the reagent cartridge. Initiate sequencing following the guidelines. Load the flow cell and the reagent cartridge and set up the sequencing run.

6. Sequence data analysis

NOTE: Check Supplementary File 1 for further description of the general WGS data preprocessing, software, parameter settings, and sequence analysis of the E. coli genome.

1. Connect to the sequencing data server and download the FASTQ files.
2. Evaluate the initial quality of raw sequence data with third-party software. Remove remnant adapter sequences, low-quality bases (<Q30), and short reads (<50 bp) from the raw sequencing data (see Supplementary File 1).
3. Assemble the quality-checked sequencing data into contig- or scaffold-level using third-party software (see Supplementary File 1).
4. Perform genome annotation by submitting the FASTA file containing the assembled genome to the RAST server (https://rast.nmpdr.org/).
5. Upload the FASTA file to the Center for Genome Epidemiology (CGE) (http://www.genomicepidemiology.org/services/) and ClermonTyping (http://clermontyping.iame-research.center/index.php) web platforms to identify epidemiological features, ARGs, VAGs, and plasmids (see Supplementary File 1).
6. Upload the FASTA file to the PHASTER server to identify prophage sequences (https://phaster.ca/).

Representative Results

The antimicrobial susceptibility was determined by the disk diffusion method and interpreted by CLSI breakpoint criteria for 12 antibiotics spanning six distinct antimicrobial classes, that is, aminoglycosides, β-lactams, fluoroquinolones, nitrofurans, phenicols, and folate pathway antagonists. The E. coli ACM5 exhibited sensitivity to all antibiotics except one β-lactam drug. Four β-lactam drugs were tested: ampicillin, carbenicillin, cephalothin, and cefotaxime. Among these, a 14 mm inhibition halo for ampicillin was measured. Therefore, according to the CLSI interpretative categories for ampicillin (susceptible: ≥17 mm; intermediate: 14-16 mm; resistant: ≤13 mm) ³², E. coli ACM5 shows an intermediate susceptibility to ampicillin.

The E. coli ACM5 was subjected to WGS using the benchtop sequencer. Therefore, the DNA sample was prepared, multiplexed, and sequenced following the protocol described. Overall, the sequencing run held with mid-output configuration yielded 3.37 Gb of data with an error rate of 0.73%, and 89.71% of the bases scored a quality above Q30³³. Particularly, the sequencing of E. coli ACM5 generated a total of 1,490,594 paired end (PE) reads. The raw sequence data from this study were deposited in the sequence read archive (SRA) database at the national center for biotechnology information (NCBI) under BioProject number PRJNA715781.

After the initial quality check and filtering, 96.8% of the reads were conserved from raw sequencing data and assembled into scaffolds with a depth of coverage of 38x. The scaffold-level genome assembly generated 83 scaffolds (>300 bp), 5,272,433 bp in length and with 50.61% GC content. Genome annotation revealed 5,633 encoded features, of which 5,524 are protein-coding genes (CDSs) and 109 are RNA-related sequences (Figure 1). Additionally, genome annotation grouped CDSs into collections of functionally related proteins called subsystems, especially those playing functional roles in carbohydrates, proteins, amino acids and derivative metabolisms, and membrane transport (Figure 2).

In silico typing predicted E. coli ACM5 as an O-nontypeable (ONT) strain assigned to the ONT:H21 serotype. Additionally, it showed that it belongs to the phylogroup B1 and sequence type (ST) 40. A single acquired antimicrobial resistance determinant coding for a multidrug efflux pump of broad-spectrum was identified, that is, the mdf(A) gene (Figure 1).

Regarding virulence traits, several VAGs featured in the E. coli genome under study, including genes encoding for a heat-stable enterotoxin (astA), a serum resistance protein (iss), and the type III secretion system (T3SS) along with its secreted effector proteins (Figure 1). The bundle-forming pili (BFP) operon and the perABC gene cluster were not evidenced. Consequently, according to the predicted virulence profile, E. coli ACM5 was assigned to the aEPEC pathotype.

WGS analysis also revealed two putative large plasmids belonging to different incompatibility (Inc) groups in the E. coli genome (i.e., the IncF and IncI plasmid groups). However, both lacked ARGs and VAGs. Moreover, 18 prophage-associated regions were identified; however, in contrast to plasmids, those harbor VAGs, including the iss gene, the sitABCD operon encoding for an iron/manganese transporter, and a few T3SS effector proteins (Figure 1).

Figure 1: Circular map of the draft genome of E. coli ACM5. Data from the outermost to the innermost circles are declared and colored as follows. Circle 1: Genome size scale in base pairs. Circles 2 and 3: Annotated CDSs transcribed on the forward (blue) and reverse (red) DNA strand, respectively. Circles 4 and 5: Ribosomal RNAs (black) and transfer RNAs (green), respectively. Circle 6: 83 scaffolds of the draft genome (gray). Circle 7: Annotated features: antimicrobial resistance determinants (red), virulence-associated genes (purple), and prophage regions (aqua). Circle 8: %GC content (black). Circle 9: GC skew. Please click here to view a larger version of this figure.

Figure 2: Subsystem distribution of aEPEC strain ACM5. The analysis is based on RAST SEED annotation. The pie chart organizes the subsystems by cellular process, and the protein-coding genes involved in the respective cellular process are indicated in parenthesis. Please click here to view a larger version of this figure.

Supplementary File 1. Description of WGS data preprocessing, software, parameter settings, and sequence analysis of the E. coli ACM5 genome. Please click here to download this File.

Discussion

This study presents an adaptation of the bacterial WGS workflow using a benchtop sequencer and a pipeline for genomic characterization of a pathogenic E. coli variant. Depending on the sequencing platform used, the turnaround times (TATs) for wet laboratory procedures (bacterial culturing, gDNA extraction, library preparation, and sequencing) and sequence analysis could vary, particularly if slow-growing bacteria are studied. Following the protocol for WGS described above, the TAT was within 4 days, which is comparable to what the literature currently states (<5 days) ³⁴.

The theoretical depth of coverage for genome sequencing of E. coli ACM5 was calculated at 30x. Nonetheless, the sequencing generated empirical data for the genome assembly at 38x depth. As previously recommended³⁵, this was sufficient to get an entire genome representation and follow downstream data analysis regarding the antimicrobial resistance, virulence, and mobilome features of E. coli ACM5. Respecting antimicrobial resistance, only the acquired determinants were investigated. E. coli ACM5 is an ampicillin-resistant isolate, but only the MdfA multidrug efflux pump encoding gene was observed. However, this does not confer resistance to β-lactams³⁶, and no other acquired determinant involved in β-lactam resistance was identified. Hence, it is likely that a combination of other molecular mechanisms, such as decreased cell membrane permeability and increased expression of distinct efflux pump systems, is underlying the observed ampicillin resistance³⁷.

Regarding virulence traits, the sole presence of the intimin-encoding gene (eae) is the hallmark genetic marker of aEPEC strains. The aEPEC pathotype is a subset of intestinal pathogenic E. coli strain carrying the locus of enterocyte effacement (LEE) pathogenicity island, where a T3SS and associated effector/translocator proteins are encoded, including Cif, EspA/B/D, EspF, intimin, and the translocated intimin receptor (Tir). The interplay between T3SS and its effectors is directly involved in the ability to promote the attaching and effacing (A/E) lesions on intestinal epithelial cells by typical enteropathogenic E. coli (EPEC) and EHEC, both pathotypes known as human foodborne illness causative agents^17,19. The astA gene encodes a heat-resistant enterotoxin named EAST1, and although it was first recognized and associated with EAEC strains, the EAST1 toxin is widely distributed among E. coli pathotypes³⁸. Here, WGS demonstrated that E. coli ACM5 possesses the astA gene, and despite the fact that EAST1-producing E. coli isolates have been associated with diarrheal illness in humans and animals, its pathogenic role in eliciting diarrhea remains controversial³⁹.

The key limitation of this methodological approach should be acknowledged; the initial contig-level assembly resulted in a fragmented draft genome, and therefore was further subjected to scaffolding against a closer reference genome. The implemented sequencing read length configuration (2 x 150 bp) and the presence of vast, repetitive elements throughout the genome of E. coli are the most presumable explanations for the fragmented contig-level assembly. Short-read datasets cannot correctly resolve, and therefore reconstruct large repetitive elements, causing potential misassemblies and several breakpoints during the assembly process. Hence, the implementation of long-read sequencing technologies would aid in overcoming these limitations and obtaining closed genomes⁴⁰.

Several critical steps must be considered throughout the WGS protocol. High-quality gDNA is the first checkpoint required to ensure high-quality sequencing data. Secondly, in the library preparation, extra caution must be taken during the tagmentation process, especially in the handling of multiple samples and the addition of index primers, to avoid cross-contamination, and in controlling the incubation time, to properly fragment the DNA samples. Moreover, quantification and normalization steps are essential to eliminate any bias that DNA libraries could introduce into the final sequencing data, since an incorrect equimolar ratio in the pooled library can favor a substantially unequal distribution in the number of reads per sample. The method presented here is straightforward and represents a cost-effective alternative, principally, for the optimal usage of the reagents contained in the DNA library preparation kits. The protocol for WGS described above has been applied not only to E. coli genome sequencing but also to other unrelated bacterial species such as Vibrio parahaemolyticus⁴¹. According to the Food and Agriculture Organization of the United Nations (FAO) and the World Health Organization (WHO), NGS methodologies can play a determining role in food safety by providing rapid identification and characterization of microorganisms and antimicrobial resistance (AMR) with accuracy that was previously not possible⁴². Therefore, these methodologies constitute a tool for surveillance and source tracking of pathogens, not only in the clinical context but also in the risk assessment of foodborne pathogens, revealing insights into the ecological and physiological properties of these microorganisms⁴³.

Materials

Name	Company	Catalog Number	Comments
Accublock Mini digital dry bath	Labnet	D0100	Dry bath for incubation of tubes
Agencourt AMPure XP	Beckman Coulter	A63881	Magnetic beads in solution for DNA library purification
DeNovix DS-11	DeNovix Inc.		UV-Vis spectophotometer to check the quality of the gDNA extracted
DNA LoBind Tubes	Eppendorf	0030108418	1.5 mL PCR tubes for DNA library pooling
DynaMag-2 Magnet	Invitrogen, Thermo Fisher Scientific	12321D	Magnetic microtube rack used during magnetic beads-based DNA purification
Gram-negative Multibac I.D.	Diagnostic reseach (Mexico)	PT-35	Commercial standard antibiotic disks for antimicrobial susceptibility testing
MiniSeq Mid Output Kit (300-cycles)	Illumina	FC-420-1004	Reagent cartdrige for paired-end sequencing (2x150)
MiniSeq System Instrument	Illumina	SY-420-1001	Benchtop sequencer used for Next-generation sequencing
MiniSpin centrifuge	Eppendorf	5452000816	Standard centrifuge for tubes
Nextera XT DNA Library Preparation Kit	Illumina	FC-131-1024	Reagents to perform DNA libraries for sequencing. Includes Box 1 and Box 2 reagents for 24 samples
Nextera XT Index Kit v2	Illumina	FC-131-2001, FC-131-2002, FC-131-2003, FC-131-2004	Index set A, B, C, D
PhiX Control v3	Illumina	FC-110-3001	DNA library control for sequencing
Precision waterbath	LabCare America	51221081	Water bath shaker used for bacterial culture
Qubit 1X dsDNA HS Assay Kit	Invitrogen, Thermo Fisher Scientific	Q33231	Reagents for fluorescence-based DNA quantification assay
Qubit 2.0 Fluorometer	Invitrogen, Thermo Fisher Scientific	Q32866	Fluorometer used for fluorescence assay
Qubit Assay tubes	Invitrogen, Thermo Fisher Scientific	Q32856	0.5 mL PCR tubes for fluorescence-based DNA quantification assay
SimpliAmp Thermal Cycler	Applied Biosystems, Thermo Fisher Scientific	A24811	Thermocycler used for DNA library amplification
Spectronic GENESYS 10 Vis	Thermo	335900	Spectophotometer used for bacterial suspension in antimicrobial susceptibility testing
ZymoBIOMICS DNA Miniprep Kit	Zymo Research Inc.	D4300	Kit for genomic DNA extraction (50 preps)