Next Article in Journal
Blocking Host Factors IAP and DDX3 Activates HIV-1 Transcription and Increases Apoptosis Sensitivity of HIV-1 Infected Cells
Previous Article in Journal
Spatiotemporal Dynamics of the 2025–2026 Measles Outbreak in Mexico: Jalisco as the Emerging Epicenter and the Impact of Intensive Immunization Efforts
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Pathogens
Volume 15
Issue 6
10.3390/pathogens15060573
pathogens-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Enumeration and Genomic Confirmation of Viable Shiga-Toxin-Producing Escherichia coli from Ground Beef by Integrating Serial Plating with Long-Read Sequencing

by
Katrina L. Counihan
* and
Shannon Tilman
Eastern Regional Research Center, United States Department of Agriculture, Agricultural Research Service, Wyndmoor, PA 19038, USA
*
Author to whom correspondence should be addressed.
Pathogens 2026, 15(6), 573; https://doi.org/10.3390/pathogens15060573
Submission received: 17 February 2026 / Revised: 11 May 2026 / Accepted: 21 May 2026 / Published: 27 May 2026
(This article belongs to the Section Bacterial Pathogens)
Versions Notes

Abstract

Consumption of food contaminated with Shiga toxin-producing Escherichia coli (STEC) causes thousands of illnesses in the United States annually. Long-read sequencing could reduce the time needed to test food for STEC, but sequencing is not quantitative and cannot differentiate between live and dead bacteria. Therefore, this study investigated combining serial plating with long-read sequencing to quantify only live STEC in a sample. Ground beef was inoculated with STEC and homogenized with a stomacher. The liquid was filtered to remove eukaryotic cells and then centrifuged to pellet the bacteria. Serial dilutions of the pellet were plated on selective agar, and single colonies were subsequently sequenced. Initial experiments revealed that processing samples at room temperature led to a 1 log increase in STEC levels from the initial inoculated concentration, confounding accurate enumeration. In subsequent experiments, samples and reagents were kept cold, and the amount of STEC recovered did not significantly differ from the amount inoculated. The DNA extracted from a single colony provided enough data to identify all virulence genes of interest multiple times. The amount of STEC in a sample could be quantified down to 1 cfu g−1. Quantification of STEC in food samples using this method would improve risk assessment and guide mitigation efforts in industry.

1. Introduction

An estimated 48 million people become ill annually in the United States (U.S.) due to foodborne pathogens [1]. Shiga toxin-producing Escherichia coli (STEC) is a foodborne bacterium that typically produces diarrhea and vomiting in infected individuals but may also cause severe pathologies such as hemorrhagic colitis or hemolytic uremic syndrome [2]. The incidence rate of STEC infections has been increasing since 2018 and is 6.6 per 100,000 people, with 20.4% of cases resulting in hospitalization and 0.4% being fatal [3]. Children under 5 are more likely to become ill and had an incidence rate of 17.8 per 100,000 people in 2022 [4]. Furthermore, the impact extends beyond the human health cost, as significant economic losses can result from pathogen contamination of food. In the U.S. during 2021, over 15 million pounds of meat were recalled, and STEC contamination was responsible for two of those recalls [5]. The estimated annual cost of foodborne pathogen contamination to the U.S. economy is approximately $17 billion [6].
Humans acquire STEC through contact with feces-contaminated environments, animals, or people [7]. Foodborne infections are most commonly associated with the consumption of STEC-contaminated meat and leafy greens [8]. Ingestion of unpasteurized raw milk or its products, such as cheese, is increasingly becoming a source of STEC infections, as well [9]. Animals, including cattle, are natural reservoirs for STEC and can shed the bacteria into the environment in their feces, contaminating soil and water [8,10]. Contaminated soil, water, or manure used as fertilizer can contaminate produce [10]. STEC present in the gastrointestinal tract or on the skin of cattle or swine can be transferred to the carcass during skinning and evisceration [7]. Both meat and produce can also be contaminated by processing equipment [8]. When raw beef becomes contaminated by STEC, it is considered “adulterated” by the U.S. Department of Agriculture Food Safety and Inspection Service (USDA FSIS) [11].
Testing meat for STEC adulteration is performed by the USDA FSIS, and isolation and identification are accomplished through a combination of culturing, molecular methods, O-antigen typing, and matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry [12]. A food sample is considered adulterated if it tests positive for the stx, eae, espK, and espV genes and one of seven targeted O-antigens associated with STEC infections in humans [12]. Intimin, the product of the eae gene, mediates attachment to intestinal epithelial cells in the host [2]. The interaction of intimin with other virulence factors results in the formation of attaching and effacing lesions, within which cytoskeletal rearrangement and microvilli destruction occurs, causing diarrheal symptoms [2,13]. Shiga toxin 1 and 2, products of the stx1 and stx2 genes, inhibit protein synthesis and stimulate cytokine production, both causing cytotoxicity in the host [2,14]. The espK and espV genes are secreted effectors of the Type III Secretion System that have been associated with severe disease in humans and are primarily found in enterohemorrhagic E. coli (EHEC), which are strains of STEC [15]. espK facilitates persistence in the intestine, and espV is an effector from the avirulence A family whose function in disease is unknown [15]. In 2024, the USDA FSIS updated its STEC reference method to include testing for espK and espV, as including these genes reduces the number of presumptive positives that require further screening [15,16]. The STEC serotype most frequently associated with foodborne outbreaks is O157:H7 [17]. The E. coli serotype is determined by the O-antigen, which is located on the outer membrane, and the H-antigen, which is present on the flagella [18]. The USDA FSIS identification method takes at least four days to complete [12]. The time required for foodborne pathogen identification could be reduced by integrating long-read, whole-genome sequencing with culturing.
Oxford Nanopore Technologies’ long-read sequencers could be beneficial for foodborne pathogen testing because pathogen detection can be completed in hours instead of days through real-time analysis [19]. The sequencers are small and portable, allowing whole-genome analysis to be performed outside of traditional laboratories, and sequencing costs are generally lower than second-generation sequencing. Additionally, the accuracy of raw reads from nanopore sequencing has improved to >99.9%, which is comparable to short-read sequencing [20]. Previous work in our lab investigated the practicality of long-read sequencing for identification and characterization of E. coli O157:H7 in ground beef [21]. Inoculated ground beef was homogenized with media in a stomacher. The liquid was removed for DNA extraction and sequencing. The presence of the eae, stx1, stx2, fliC, wzx, wzy, and rrsC genes in sequencing data indicated E. coli O157:H7 contamination. The fliC gene codes for the flagellar H-antigen, and wzx and wzy, encoding a translocase and polymerase, respectively, are involved in O-antigen production [22,23]. The ribosomal 16S rRNA gene, rrsC, identifies the presence of any E. coli to the species level. However, despite high inoculation levels (107 cfu g−1), all of the virulence genes needed to serotype E. coli could not be reliably identified in sequencing data from unenriched ground beef samples. The large quantity of bovine DNA outcompeted E. coli DNA for sequencing pores. Detection of very small concentrations of pathogen DNA within a high concentration of host DNA is a challenge that needs to be overcome.
An additional challenge for sequencing-based detection methods is the inability to differentiate between live and dead bacteria. Sequencing detects DNA from both live and dead organisms, but only live pathogens can infect and cause disease. Extracellular DNA and DNA in dead cells can persist for days to weeks [24,25]. Detecting DNA from dead bacteria could overestimate pathogen abundance [26] and potentially result in a costly and unnecessary food recall [27]. Therefore, culture-based methods remain the gold standard because bacterial growth indicates viability [28]. This issue must be resolved before sequencing can become a practical testing method.
Another disadvantage of both sequencing- and culture-based methods is that quantification of pathogens may be difficult or even impossible. Culture-based methods typically require an enrichment step to increase the pathogen concentration above the detection limit of the testing method being used, which prevents quantification of the initial pathogen concentration in the sample. Therefore, developing methods that bypass enrichment while retaining the viability selection of culturing is a key priority for advancing food safety testing. The most probable number procedure can estimate the number of bacteria in a sample, but it does not provide absolute quantification [29]. Sequencing-based quantification has been achieved through the addition of internal DNA standards of known concentration to samples. A correction factor can then be established to calculate the quantity of target DNA in a sample [30]. The amount of sequencing data produced has also been shown to correlate with DNA input, and further investigation may lead to additional quantification methods that do not rely on standards [31]. However, while these sequencing-based quantification methods show promise, they still cannot differentiate between live and dead bacteria, a fundamental advantage of our workflow, which maintains a culturing step.
The goal of this study was to determine whether serial dilution of E. coli O157:H7 contaminated ground beef onto selective media, followed by sequencing of single colonies, would result in both enumeration and identification. The objectives included evaluating whether processing temperature influenced the accuracy of bacterial recovery and determining if virulence genes of interest could be reliably detected in sequencing data from one colony.

2. Materials and Methods

2.1. Bacterial Culturing

Escherichia coli O157:H7 (ATCC strain 43895) was grown overnight in 10 mL tryptic soy broth (Oxoid Limited, Hampshire, UK) modified with novobiocin (mTSB; RPI Corporation, Mount Prospect, IL, USA) in an incubator at 42 °C ± 1 °C shaking at 180 rpm, which adheres to FSIS methods [12]. Optical density at a wavelength of 600 nm (OD 600) was measured with a Denovix DS-11 FX+ spectrophotometer (DeNovix Inc., Wilmington, DE, USA) to estimate bacteria concentration.

2.2. Small-Scale Experiment

A 2% sorbitol-mTSB (w/v) solution was prepared by adding D-sorbitol (Sigma-Aldrich, Saint Louis, MO, USA) to mTSB. Seven treatments were tested in three independent experiments. All treatments consisted of 3 mL cold 2% sorbitol-mTSB solution in sterile 7 oz Whirl-Pak® filter bags (Austin, TX, USA). Three treatments were controls and included: media only, no meat inoculated with 103 cfu E. coli, and 1 g of uninoculated meat. The remaining four treatments were prepared with 1 g of ground beef inoculated with E. coli to achieve final concentrations of 103, 102, 101, or 100 cfu g−1, respectively. All bags were homogenized with a Bag Mixer stomacher (Spiral Biotech Inc., Norwood, MA, USA) for 120 s on normal speed. Samples were sequentially vacuum filtered through a 60 µm steri-flip filter (EMD Millipore Corporation, Burlington, MA, USA) and then a 20 µm steri-flip filter (EMD Millipore Corporation). Each filter was washed with 1 mL of mTSB after the sample had passed through. The tubes with the resulting filtrate were then refrigerated at 4 °C for 1 h. If present, residual fat was removed from the surface of the filtrate with a sterile spatula. Tubes were then centrifuged at 130× g for 10 min and any residual fat was removed from the surface of the filtrate. The supernatant was transferred to a clean tube and centrifuged at 13,000× g for 5 min. The supernatant was discarded, the pellet washed with 1 mL of phosphate-buffered saline, (PBS; Boston BioProducts, Milford, MA, USA) and then centrifuged at 13,000× g for 1 min. The supernatant was discarded and the pellet resuspended in 1 mL of PBS. Samples were serially diluted in Luria–Bertani (LB) broth (Thermo Fisher Scientific Chemicals, Inc., Fair Lawn, NJ, USA) and plated on sorbitol MacConkey agar (SMAC; Thermo Fisher Scientific Chemicals, Inc.) and incubated at 42 °C overnight. E. coli colonies were selected for each sample from the SMAC plates and DNA extracted using a Qiagen DNeasy PowerFood Microbial Kit (Qiagen, Germantown, MD, USA) according to the manufacturer’s instructions. DNA concentration and quality measurements were taken with a Denovix DS-11 FX+ spectrophotometer.

2.3. Cold Sample Experiment

An experiment was conducted utilizing reagents that had been refrigerated overnight and then kept on ice during sample processing. Treatments were prepared in triplicate in sterile 7 oz Whirl-Pak® filter bags containing 3 mL cold 2% sorbitol-mTSB. The treatments consisted of a media control, a positive control with no meat and 100 cfu E. coli, a meat control with 1 g of uninoculated ground beef, and 1 g of ground beef inoculated with either 101 or 100 cfu g−1 E. coli. The treatments were kept on ice until they were homogenized with a stomacher. The samples were processed as described above for the small-scale experiment, except that the samples were kept on ice between steps and centrifugation occurred at 4 °C. Colonies were counted for each plate to confirm spiked concentrations, but no DNA extractions were performed.

2.4. Large-Scale Experiment

Three independent experiments were conducted with 5 treatments prepared in sterile 7 oz Whirl-Pak® filter bags containing 30 mL cold 2% sorbitol-mTSB. Treatments included a media control, a positive control with no meat and 102 cfu E. coli, a meat control with 10 g of uninoculated ground beef, and 2 treatments each with 10 g of ground beef and either 101 or 100 cfu g−1 E. coli. The treatments were then processed as described above for the small-scale experiment, except that samples were kept on ice between processing steps and centrifugation occurred at 4 °C.

2.5. Sequencing

Sequencing libraries were prepared with the Rapid Barcoding Kit (SQK-RBK114, Oxford Nanopore Technologies [ONT], Oxford, United Kingdom) using the DNA extracted from the E. coli colonies according to the manufacturer’s instructions. Sequencing was performed on a GridION device (ONT) with R10.4.1 flow cells (ONT) for 20 h. The sequencing data were basecalled in real time with MinKNOW software (version 23.11.7, ONT) using fast basecalling and a minimum read length filter of 1 kb.

2.6. Bioinformatics

The Galaxy platform was used to analyze the fastq files [32]. First, sequencing adapters were removed with Porechop (version 0.2.4). The resulting data was run through Fastp (version 0.23.4) using a Phred quality cutoff of 8 and minimum length of 1 kb. After quality control, the data was assessed with Nanoplot (version 1.42.0). The sequences were queried against a BLAST database consisting of the genes fliC, eae, rrsC, stx1A, stx1B, stx2A, stx2B, wzx, wzy, espK, and espV using NCBI BLAST + blastn (version 2.14.1). The number of times each gene was detected was summarized with the tool Count Occurrences of Each Record (version 1.0.3).

2.7. Statistics

The mean and standard error were calculated for the triplicate selective agar plate counts from the experiments. Statistical differences (p < 0.05) between the amounts of E. coli inoculated and recovered were determined with a one sample t-test. The mean and standard deviation were calculated for the number of times each gene of interest was detected in sequencing data.

3. Results

3.1. Small-Scale Experiment

Escherichia coli O157:H7 was inoculated into ground beef at concentrations ranging from 100 to 103 cfu g−1. No E. coli was isolated from the media or meat control treatments. However, the amount of E. coli recovered after plating was significantly higher (p < 0.05) than the inoculated amount, with most samples having a 1 log increase in E. coli concentration (Table 1). Colonies were selected from the SMAC agar plates for each concentration, and the DNA extracted and sequenced. An average of 0.52 Gb of sequencing data was produced from each colony. The metrics for the sequencing run are presented in Supplementary Table S1. All genes of interest, fliC, eae, rrsC, stx1A, stx1B, stx2A, stx2B, wzx, wzy, espK, and espV were detected an average of 168 times each in all samples (Table 2).

3.2. Cold Sample Experiment

The increase in E. coli concentration observed during the small-scale experiment prompted a study to determine if keeping samples cold during processing would prevent growth. Cold mTSB was used for dilutions, samples were placed on ice between processing steps, and centrifugation was conducted at 4 °C. Keeping samples cold prevented growth during sample preparation and there was no significant difference (p < 0.05) between the amount of E. coli inoculated into the sample and the amount recovered (Table 3).

3.3. Large-Scale Experiment

Escherichia coli O157:H7 was inoculated at 100 and 101 cfu g−1 into 10 g of ground beef, which was 10 times the amount used in the small-scale experiment. No E. coli was isolated from the media or meat control treatments. There was no significant difference (p < 0.05) in the concentration of E. coli inoculated into the treatments as compared to the amount observed on plate counts (Table 4). An average of 0.74 Gb of sequencing data was generated from each colony. Supplementary Table S2 shows the metrics of the sequencing run. All virulence genes of interest were detected an average of 206 times from each colony tested (Table 5).

4. Discussion

This study investigated whether the integration of serial plating with long-read sequencing could be used for foodborne pathogen testing. Host bovine DNA was eliminated from sequencing by extracting DNA from single colonies isolated on selective agar. Filtering the sample post-homogenization removed the larger eukaryotic bovine cells, and without the presence of lipid-rich ground beef, the remaining prokaryotic cells formed a tight pellet during centrifugation, allowing all supernatant to be removed prior to subsequent serial dilution and plating. Colonies exhibiting typical E. coli morphology on SMAC agar were selected for sequencing analysis. Contrary to concerns that a single bacterial colony might not yield enough DNA for sequencing, an average of 33 ng µL−1 of DNA was extracted, generating an average of 201,995 sequencing reads.
There is a need not only to identify pathogens in food samples but also to quantify their concentration to inform risk assessments and guide mitigation strategies. During the initial small-scale experiment, it was observed that the E. coli concentration on the serial dilution plates overestimated the inoculated amount. It was suspected that replication occurred during sample processing, as it was conducted at room temperature (~21 °C). Therefore, in subsequent experiments, reagents and samples were kept on ice, and centrifugation was performed at 4 °C. This modification prevented growth and ensured that the concentration of E. coli in the inoculum was accurately recovered. Current culture-based methods involve an enrichment step to ensure very low concentrations of pathogenic E. coli are detected [12], but this prevents absolute quantification of bacteria in the original sample. This study eliminated the enrichment step, and concentrations of E. coli as low as 1 cfu g−1 could be detected on selective agar, which is important because STEC are considered zero tolerance organisms in ground beef by FSIS [33]. Although enumeration of pathogens is not necessary to trigger a recall, it would improve risk management in the food industry. Quantification is important for assessing the effectiveness of sanitization procedures and evaluating quality assurance measures [34,35]. It can also be used to identify practices or products with high bacterial loads and trends in pathogen concentrations [36,37]. This information can help determine when to apply interventions or modify procedures to reduce bacterial loads.
The selective plating used in this study to isolate E. coli also overcomes one of the disadvantages of sequencing for foodborne pathogen testing, which is the lack of discrimination between live and dead cells. Interventions are often used to kill microorganisms in food, and the detection of DNA from these dead bacteria would result in a false positive and a potentially unnecessary recall [38]. Testing methods need to identify only live pathogens that may cause disease if consumed. Propidium monoazide treatment of bacteria prior to DNA extraction has been shown to prevent sequencing of dead bacteria, but it is still not quantitative [39]. The inclusion of serial plating in this study ensured that only live bacteria were selected for sequencing while also being quantitative.
Selecting only one morphologically typical colony for sequencing may be disadvantageous if multiple serotypes with indistinguishable morphologies are present in a sample. Current culture-based methods also require only one colony with typical morphology be selected for presumptive identification, isolation, and confirmation [12]. Reports of meat contamination with multiple serotypes are not common. A microbiological baseline study on veal found 12% of post-hide removal samples and 11% of post-chill samples tested positive for more than one non-STEC serotype [40]. Another study reported that 1% of E. coli outbreaks in the United States between 2010 and 2017 were attributed to both E. coli O157 and another non-O157 serotype [41]. However, the selection of only one isolate for testing may have missed other incidences involving multiple pathogenic serotypes. An advantage of sequencing over culture-based methods is that more colonies can be screened with less labor. Multiple colonies, either from the same sample or different samples, can be sequenced on the same flow cell. Each colony receives a unique barcode during library preparation, allowing sequencing data to be attributed back to a particular isolate. This is important because the pathogenic potential of E. coli depends on the specific virulence genes it carries [18]. Identification of stx, eae, and relevant serogroup genes within the same isolate is necessary to determine if the E. coli is STEC.
There were additional limitations to this study beyond the potential issue of detecting multiple serotypes, as discussed above. First, the samples from the cold experiment were not sequenced due to budget restraints. We did not anticipate sequencing data from E. coli processed under cold conditions to differ from those processed at room temperature because the genome, not transcripts, was being sequenced. However, this does represent a missed opportunity for additional data. This study also focused solely on E. coli in ground beef. Other pathogens or food matrices may perform differently due to variability in bacterial binding to a matrix, presence of background microflora, or bacteria that are viable but not culturable. Future experiments should also include mixtures of live and dead bacteria to further demonstrate the method’s ability to detect only live bacteria. Additionally, long-read sequencing technology changes rapidly, and revalidation of the method would be necessary when new library preparation kits, flow cell chemistries, or software are introduced.

5. Conclusions

This study established a method for foodborne pathogen testing that integrates serial dilution and plating with long-read sequencing. This approach effectively resolves critical challenges encountered in previous methods, such as host DNA interference, by enabling the isolation and sequencing of pathogen DNA from single colonies. Additionally, it allows for accurate quantification of live pathogens at low concentrations (1 cfu g−1), eliminating the need for enrichment steps that prevent true enumeration, and provides a clear distinction between viable and non-viable cells, thereby reducing false positives. Multiple colonies can also be sequenced in a single run, and the whole process takes three days. This method could be a powerful tool for improved food safety risk assessment and guiding more targeted intervention strategies within the food industry.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/pathogens15060573/s1, Table S1: Metrics of the sequencing data from the small-scale experiment; Table S2: Metrics of the sequencing data from the large-scale experiment.

Author Contributions

Conceptualization, K.L.C.; methodology, K.L.C., S.T.; validation, K.L.C., S.T.; formal analysis, K.L.C.; investigation, S.T.; data curation, K.L.C.; writing—original draft preparation, K.L.C.; writing—review and editing, K.L.C.; supervision, K.L.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the U.S. Department of Agriculture, Agricultural Research Service, Food Safety program in-house project 8072-42000-093-00D. This research used the resources provided by the SCINet project and the AI Center of Excellence of the USDA Agricultural Research Service, ARS project number 0500-00093-001-00-D.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are openly available in USDA at https://doi.org/10.15482/USDA.ADC/31258642 [42].

Acknowledgments

Mention of trade names or commercial products in this article is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. USDA is an equal-opportunity employer.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Abbreviations

The following abbreviations are used in this manuscript:
STECShiga-toxin-producing Escherichia coli
U.S.United States
USDA FSISU.S. Department of Agriculture Food Safety and Inspection Service
MALDI-TOFMatrix-assisted laser desorption/ionization time-of-flight
mTSBTryptic soy broth modified with novobiocin
ODOptical density
PBSPhosphate-buffered saline
LBLuria–Bertani broth
SMACSorbitol MacConkey agar
CFUColony-forming unit

References

  1. Facts About Food Poisoning. 2025. Available online: https://www.cdc.gov/food-safety/data-research/facts-stats/ (accessed on 20 May 2026).
  2. Pakbin, B.; Bruck, W.; Rossen, J. Virulence factors of enteric pathogenic Escherichia coli: A review. Int. J. Mol. Sci. 2021, 22, 9922. [Google Scholar] [CrossRef] [PubMed]
  3. Shah, H.; Jervis, R.; Wymore, K.; Rissman, T.; LaClair, B.; Boyle, M.M.; Smith, K.; Lathrop, S.; McGuire, S.; Trevejo, R.; et al. Reported incidence of infections caused by pathogens transmitted commonly through food: Impact of increased use of culture-independent diagnostic tests—Foodborne Diseases Active Surveillance Network, 1996–2023. Mobidity Mortal. Wkly. Rep. 2024, 73, 584–593. [Google Scholar] [CrossRef] [PubMed]
  4. FoodNet Fast. 2026. Available online: https://wwwn.cdc.gov/foodnetfast/ (accessed on 14 April 2026).
  5. Summary of Recall Cases in Calendar Year 2021. Food Safety. 2022. Available online: https://www.fsis.usda.gov/food-safety/recalls-public-health-alerts/annual-recall-summaries/summary-recall-cases-calendar-8 (accessed on 16 January 2025).
  6. Hoffmann, S.; Ahn, J. Economic Cost of Major Foodborne Illnesses Increased $2 Billion from 2013 to 2018. 2021. Available online: https://www.ers.usda.gov/amber-waves/2021/april/economic-cost-of-major-foodborne-illnesses-increased-2-billion-from-2013-to-2018/ (accessed on 11 March 2025).
  7. Haque, M.; Bosilevac, J.; Chaves, B. A review of Shiga-toxin producing Escherichia coli (STEC) contamination in the raw pork production chain. Int. J. Food Microbiol. 2022, 377, 109832. [Google Scholar] [CrossRef]
  8. de Carvalho, R.; Gonzalez, A.; Ramos, G. Quantitative microbiological risk assessment for Shiga toxin-producing Escherichia coli in food: A review. Food Res. Int. 2025, 214, 116641. [Google Scholar] [CrossRef]
  9. Williams, E.; Van Doren, J.; Leonard, C.; Datta, A. Prevalence of Listeria monocytogenes, Salmonella spp., Shiga toxin-producing Escherichia coli, and Campylobacter spp. in raw milk in the United States between 2000 and 2019: A systemic review and meta-analysis. J. Food Prot. 2023, 86, 100014. [Google Scholar] [CrossRef]
  10. Chevez, Z.; Dunn, L.; da Silva, A.; Rodrigues, C. Prevalence of STEC virulence markers and Salmonella as a function of abiotic factors in agricultural water in the southeastern United States. Front. Microbiol. 2024, 15, 1320168. [Google Scholar] [CrossRef]
  11. Food Safety and Inspection Service USDA. Expansion of FSIS Shiga Toxin-Producing Escherichia coli (STEC) Testing to Additional Raw Beef Products; Federal Register: Washington, DC, USA, 2022; pp. 69242–69249. [Google Scholar]
  12. MLG 5C.04; Detection, Isolation, and Identification of Top Seven Shiga Toxin-Producing Escherichia coli (STEC) from Meat Products, Carcass, and Environmental Sponges. United States Department of Agriculture Food Safety Inspection Service: Washington, DC, USA, 2024.
  13. Sperandio, V.; Mellies, J.; Nguyen, W.; Shin, S.; Kaper, J. Quorum sensing controls expression of the type III secretion gene transcription and protein secretion in enterohemorrhagic and enteropathogenic Escherichia coli. Proc. Natl. Acad. Sci. USA 1999, 96, 15196–15201. [Google Scholar] [CrossRef]
  14. Ashton, P.; Perry, N.; Ellis, R.; Petrovska, L.; Wain, J.; Grant, K.; Jenkins, C.; Dallman, T. Insight into Shiga toxin genes encoded by Escherichia coli O157 from whole genome sequencing. PeerJ 2015, 3, e739. [Google Scholar] [CrossRef]
  15. Bosilevac, J.; Katz, T.; Manis, L.; Rozier, L.; Day, M. Using pathogenic Escherichia coli Type III secreted effectors espK and espV as markers to reduce the risk of potentially enterohemorrhagic Shiga toxin-producing Escherichia coli in beef. Foods 2025, 14, 382. [Google Scholar] [CrossRef]
  16. Tran, M.; Delannoy, S.; Fach, P. Enhancing detection of STEC in the meat industry: Insights into virulence of priority STEC. Front. Microbiol. 2025, 16, 1543686. [Google Scholar] [CrossRef] [PubMed]
  17. Delahoy, M.; Shah, H.; Weller, D.; Ray, L.; Smith, K.; McGuire, S.; Trevejo, R.; Walter, E.; Wymore, K.; Rissman, T.; et al. Preliminary incidence and trends of infections caused by pathogens transmitted commonly through food—Foodborne diseases active surveillance network, 10 U.S. sites, 2022. Morb. Mortal. Wkly. Rep. 2023, 72, 701–706. [Google Scholar] [CrossRef]
  18. Fratamico, P.; DebRoy, C.; Liu, Y.; Needleman, D.; Baranzoni, G.; Feng, P. Advances in molecular serotyping and subtyping of Escherichia coli. Front. Microbiol. 2016, 7, 644. [Google Scholar] [CrossRef]
  19. Jain, M.; Olsen, H.; Paten, B.; Akeson, M. The Oxford Nanopore MinION: Delivery of nanopore sequencing to the genomics community. Genome Biol. 2016, 17, 239. [Google Scholar] [CrossRef] [PubMed]
  20. Nanopore Sequencing Accuracy. 2024. Available online: https://nanoporetech.com/platform/accuracy/ (accessed on 2 February 2024).
  21. Counihan, K.L.; Kanrar, S.; Tilman, S.; Capobianco, J.; Armstrong, C.M.; Gehring, A. Detection of Escherichia coli O157:H7 in ground beef using long-read sequencing. Foods 2024, 13, 828. [Google Scholar] [CrossRef] [PubMed]
  22. Hong, Y.; Reeves, P. Diversity of O-antigen repeat unit structures can account for the substantial sequence variation of wzx translocases. J. Bacteriol. 2014, 196, 1713–1722. [Google Scholar] [CrossRef]
  23. Wang, L.; Rothemund, D.; Curd, H.; Reeves, P. Sequence diversity of the Escherichia coli H7 fliC genes: Implication for a DNA-based typing scheme for E. coli O157:H7. J. Clin. Microbiol. 2000, 38, 1786–1790. [Google Scholar] [CrossRef]
  24. Josephson, K.; Gerba, C.; Pepper, I. Polymerase chain reaction detection of nonviable bacterial pathogens. Appl. Environ. Microbiol. 1993, 59, 3513–3515. [Google Scholar] [CrossRef]
  25. Carini, P.; Marsden, P.; Leff, J.; Morgan, E.; Strickland, M.; Fierer, N. Relic DNA is abundant in soil and obscures estimates of soil microbial diversity. Nat. Microbiol. 2017, 2, 16242. [Google Scholar] [CrossRef] [PubMed]
  26. Rudi, K.; Moen, B.; Dromtorp, S.; Holck, A. Use of ethidium monoazide and PCR in combination for quantification of viable and dead cells in complex samples. Appl. Environ. Microbiol. 2005, 71, 1018–1024. [Google Scholar] [CrossRef]
  27. Liu, Y.; Mustapha, A. Detection of viable Escherichia coli O157:H7 in ground beef by propidium monoazide real-time PCR. Int. J. Food Microbiol. 2014, 170, 48–54. [Google Scholar] [CrossRef]
  28. Emerson, J.; Adams, R.; Román, C.B.; Brooks, B.; Coil, D.; Dahlhausen, K.; Ganz, H.; Hartmann, E.; Hsu, T.; Justice, N.; et al. Schrodinger’s microbes: Tools for distinguishing the living from the dead in microbial ecosystems. Microbiome 2017, 5, 86. [Google Scholar] [CrossRef] [PubMed]
  29. Appendix 2.05. Microbiology Laboratory Guidebook. 2014. Available online: https://www.fsis.usda.gov/news-events/publications/microbiology-laboratory-guidebook (accessed on 18 May 2025).
  30. Yang, Y.; Che, Y.; Liu, L.; Wang, C.; Yin, X.; Deng, Y.; Yang, C.; Zhang, T. Rapid absolute quantification of pathogens and ARGs by nanopore sequencing. Sci. Total Environ. 2022, 809, 152190. [Google Scholar] [CrossRef]
  31. Harper, S.; Counihan, K.L.; Kanrar, S.; Paoli, G.; Tilman, S.; Gehring, A. Investigating the quantification capabilities of a nanopore-based sequencing platform for food safety application via external standards of lambda DNA and lambda spiked beef. Foods 2024, 13, 3304. [Google Scholar] [CrossRef]
  32. The Galaxy Community. The Galaxy platform for accessible, reproducible, and collaborative data analyses: 2024 update. Nucleic Acids Res. 2024, 52, W83–W94. [Google Scholar] [CrossRef]
  33. United States Department of Agriculture Food Safety Inspection Service (Ed.) Sampling Verification Activities for Shiga Toxin-Producing Escherichia coli in Raw Beef Products; United States Department of Agriculture Food Safety Inspection Service: Washington, DC, USA, 2024. [Google Scholar]
  34. Moore, G.; Griffith, C. A comparison of traditional and recently developed methods for monitoring surface hygiene within the food industry: An industry trial. Int. J. Environ. Health Res. 2002, 12, 317–329. [Google Scholar] [CrossRef] [PubMed]
  35. Hoorfar, J. Rapid detection, characterization and enumeration of foodborne pathogens. Acta Pathol. Microbiol. Immunol. Scand. 2011, 119, 1–24. [Google Scholar] [CrossRef] [PubMed]
  36. Dhatt, U. Evaluation of Enumeration Methods for Campylobacter and Salmonella in Poultry. Master’s Thesis, The University of Georgia, Athens, GA, USA, 2024. [Google Scholar]
  37. McEntire, J.; Acheson, D.; Siemens, A.; Eilert, S.; Robach, M. The public health value of reducing Salmonella levels in raw meat and poultry. Food Prot. Trends 2014, 34, 386–392. [Google Scholar]
  38. Sohaib, M.; Anjum, F.; Arshad, M.; Rahman, U. Postharvest intervention technologies for safety enhancement of meat and meat based products; a critical review. J. Food Sci. Technol. 2016, 53, 19–30. [Google Scholar] [CrossRef]
  39. Counihan, K.L.; Tilman, S.; Chen, C.; He, Y. Detection of live shiga toxin-producing Escherichia coli with long-read sequencing. Int. J. Mol. Sci. 2025, 26, 2228. [Google Scholar] [CrossRef]
  40. United States Department of Agriculture Food Safety and Inspection Service. The Nationwide Microbiological Baseline Data Collection Program: Beef-Veal Carcass Survey; United States Department of Agriculture Food Safety and Inspection Service: Washington, DC, USA, 2020. [Google Scholar]
  41. Tack, D.; Kisselburgh, H.; Richardson, L.; Geissler, A.; Griffin, P.; Payne, D.; Gleason, B. Shiga toxin-producing Escherichia coli outbreaks in the United States, 2010–2017. Microorganisms 2021, 9, 1529. [Google Scholar] [CrossRef]
  42. Counihan, K.; Tilman, S. Data from: Combination of Serial Plating and Long-Read Sequencing to Detect and Quantify Live Shiga-Toxin Producing Escherichia coli; Ag Data Commons: Beltsville, MD, USA, 2026. [Google Scholar] [CrossRef]
Table 1. The concentration of Escherichia coli (cfu) inoculated into the treatments and the mean ± standard error E. coli (cfu) recovered on agar plates after processing in the small-scale experiments.
Table 1. The concentration of Escherichia coli (cfu) inoculated into the treatments and the mean ± standard error E. coli (cfu) recovered on agar plates after processing in the small-scale experiments.
E. coli Inoculated (cfu)Mean ± SE E. coli Recovered (cfu)
10313,167.0 ± 693.6
102864.3 ± 81.8
101181.3 ± 19.3
10022.0 ± 1.7
Table 2. The mean ± standard deviation of the number of times each gene of interest was detected in sequencing data from the small-scale experiment.
Table 2. The mean ± standard deviation of the number of times each gene of interest was detected in sequencing data from the small-scale experiment.
Escherichia coli (cfu g−1)fliCeaeespKespVrrsCstx1Astx1Bstx2Astx2Bwzxwzy
103 *90.7 ± 13.8142.0 ± 22.5100.5 ± 0.762.5 ± 3.5624.3 ± 59.896.7 ± 11.482.7 ± 9.5101.7 ± 9.393.3 ± 11.046.3 ± 10.434.7 ± 12.1
10394.7 ± 6.0121.3 ± 4.2157.5 ± 10.664.0 ± 6.7708.0 ± 25.479.0 ± 11.171.3 ± 8.472.7 ± 19.196.7 ± 9.959.0 ± 7.037.0 ± 8.9
10265.3 ± 7.589.7 ± 2.9115.5 ± 2.150.0 ± 7.1671.0 ± 81.650.7 ± 6.845.0 ± 5.6105.3 ± 2.168.0 ± 18.731.0 ± 3.629.7 ± 7.6
101171.3 ± 28.5147.7 ± 42.870.0 ± 28.338.0 ± 4.2 1227.0 ± 45.0107.3 ± 13.7101.7 ± 15.9207.0 ± 28.6170.7 ± 24.879.7 ± 15.775.0 ± 15.5
100108.0 ± 10.5290.3 ± 40.758.0 ± 1.454.0 ± 15.6917.0 ± 64.9150.0 ± 17.0140.7 ± 12.1134.0 ± 15.7 115.0 ± 6.1112.7 ± 15.0104.0 ± 16.4
* Positive control with no ground beef.
Table 3. The mean ± standard error of Escherichia coli recovered on agar plates after processing in the cold experiment.
Table 3. The mean ± standard error of Escherichia coli recovered on agar plates after processing in the cold experiment.
E. coli Inoculated (cfu)Mean ± SE E. coli Recovered (cfu)
1031433.0 ± 120.2
102133.3 ± 8.8
10120.3 ± 2.4
1004.7 ± 1.2
Table 4. The mean ± standard error of Escherichia coli recovered on agar plates after processing in the large-scale experiment.
Table 4. The mean ± standard error of Escherichia coli recovered on agar plates after processing in the large-scale experiment.
E. coli Inoculated (cfu)Mean ± SE E. coli Recovered (cfu)
10131.0 ± 5.2
1003.3 ± 1.8
Table 5. The mean ± standard deviation of the number of times each gene of interest was detected in sequencing data from the large-scale experiment.
Table 5. The mean ± standard deviation of the number of times each gene of interest was detected in sequencing data from the large-scale experiment.
Escherichia coli (cfu g−1)fliCeaeespKespVrrsCstx1Astx1Bstx2Astx2Bwzxwzy
102 *67.7 ± 12.979.7 ± 13.669.5 ± 12.067.0 ± 8.5711.0 ± 70.2 47.7 ± 9.959.7 ± 8.783.0 ± 6.281.0 ± 8.225.0 ± 8.728.7 ± 7.6
101118.0 ± 13.1131.0 ± 26.9106.0 ± 2.3142.5 ± 17.71191.0 ± 88.2104.7 ± 15.5116.3 ± 21.8198.3 ± 17.6210.7 ± 28.276.0 ± 9.068.3 ± 6.7
100161.3 ± 17.2240.7 ± 13.3158.0 ± 7.176.0 ± 18.41444.0 ± 65.7146.0 ± 18.4116.3 ± 21.1144.0 ± 21.4135.3 ± 21.6100.7 ± 10.486.3 ± 17.2
* Positive control with no ground beef.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Counihan, K.L.; Tilman, S. Enumeration and Genomic Confirmation of Viable Shiga-Toxin-Producing Escherichia coli from Ground Beef by Integrating Serial Plating with Long-Read Sequencing. Pathogens 2026, 15, 573. https://doi.org/10.3390/pathogens15060573

AMA Style

Counihan KL, Tilman S. Enumeration and Genomic Confirmation of Viable Shiga-Toxin-Producing Escherichia coli from Ground Beef by Integrating Serial Plating with Long-Read Sequencing. Pathogens. 2026; 15(6):573. https://doi.org/10.3390/pathogens15060573

Chicago/Turabian Style

Counihan, Katrina L., and Shannon Tilman. 2026. "Enumeration and Genomic Confirmation of Viable Shiga-Toxin-Producing Escherichia coli from Ground Beef by Integrating Serial Plating with Long-Read Sequencing" Pathogens 15, no. 6: 573. https://doi.org/10.3390/pathogens15060573

APA Style

Counihan, K. L., & Tilman, S. (2026). Enumeration and Genomic Confirmation of Viable Shiga-Toxin-Producing Escherichia coli from Ground Beef by Integrating Serial Plating with Long-Read Sequencing. Pathogens, 15(6), 573. https://doi.org/10.3390/pathogens15060573

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop