Design of a Targeted Amplicon Sequencing Panel for Detection of Foodborne Pathogens and its Application in Detection of Spiked Shiga Toxin-Producing Escherichia coli in Romaine Lettuce

Design of a Targeted Amplicon Sequencing Panel for Detection of Foodborne Pathogens and its Application in Detection of Spiked Shiga Toxin-Producing Escherichia coli in Romaine Lettuce | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Design of a Targeted Amplicon Sequencing Panel for Detection of Foodborne Pathogens and its Application in Detection of Spiked Shiga Toxin-Producing Escherichia coli in Romaine Lettuce Isha Patel, Mark Mammel, Jayanthi Gangiredla, Amit Mukherjee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6805163/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Contamination of leafy greens with foodborne pathogens like Shiga toxin–producing Escherichia coli (STEC) poses a public health concern as 40 documented outbreaks in the United States and Canada occurred between 2009–2018. Early detection and identification of foodborne pathogens helps mitigate outbreaks. One way to implement this is through the application of next-generation sequencing (NGS) methods which offer high throughput, resolution, and sensitivity for both detection and identification of foodborne pathogens. Here we demonstrate the use of a custom targeted amplicon sequencing (TAS) panel targeting 135 known human foodborne pathogens. Despite the scope and power of NGS methods, technical challenges remain in detecting low levels of pathogens in contaminated food. Using a quasimetagenomics approach, this study demonstrates that compared to whole metagenomic sequencing (WMS), targeted amplicon sequencing (TAS) is a rapid and sensitive NGS based method for detecting low levels of pathogens as demonstrated with spiked STEC in bagged chopped ready-to-eat (RTE) romaine lettuce. Results Here we evaluate the utility, specificity, and limit of detection of a targeted amplicon sequencing (TAS) approach for detection and identification of STEC in spiked RTE romaine lettuce. Romaine lettuce was inoculated with STEC at different concentrations. Post inoculation, cells were harvested using a modified version of the Bacteriological Analytical Methods (BAM), at 0.5 h, 5 h and 6 h from primary enrichments and DNA was isolated. DNA libraries were prepared for whole metagenome sequencing (WMS) as well as TAS. Data obtained indicate that TAS is more sensitive than WMS at not only detecting the pathogen at the species level, but also at detecting virulence markers such as stx1 and stx2. Conclusions The targeted sequencing approach described here provides a rapid and sensitive molecular method to detect and identify foodborne pathogenic bacteria. As proof of principle, we use STEC spiked RTE romaine lettuce to demonstrate the applicability of TAS in foodborne pathogen detection. Targeted amplicon sequencing Escherichia coli metagenomics foodborne pathogen Figures Figure 1 Figure 2 Figure 3 Introduction High-throughput or massively parallel sequencing technologies have opened opportunities to advance genomic research [1–3]. Among the varied applications of next-generation sequencing (NGS), advancements in the finer characterization of bacterial genomes and accurate taxonomic identifications of microorganisms in complex environments particularly in food and food related processes are useful in outbreak tracing [4, 5]. NGS methods are used in two independent ways. The first and most common shotgun sequencing method is whole genome sequencing (WGS) of a single cultured isolate. The data from WGS using core genome multilocus typing (cgMLST) and whole genome MLST (wgMLST) schemes are used for high-resolution pathogen typing, epidemiology, susceptibility prediction, and virulence factor determination [6, 7]. The second method is whole metagenome sequencing (WMS) which involves sequencing all of the microorganisms in a sample without the need of selectively purifying a single isolate. The high discriminatory power of NGS methods using shotgun sequencing has shown to be valuable and increasingly popular for microbial typing and pathogen detection from food and for source attribution related to outbreak investigations or regulatory applications [8, 9]. Although NGS methods provide resolution and sensitivity for high-throughput surveillance, there are still significant challenges in detecting low levels of pathogens or sub-populations of unculturable pathogens present in samples at low levels with WGS or WMS sequencing approaches [10–12]. These limits on specificity and sensitivity are due to the depth of sequencing being compromised depending on the number and complexity of the sample being sequenced. One way to overcome this problem is to use targeted amplicon sequencing (TAS) where specific target genes are chosen for PCR amplification and then the amplicons are subjected to NGS. This allows for the sequencing of selected target genes specific for pathogens of interest with increased sensitivity, better specificity, and ease of downstream analysis. TAS is now routinely used in detection of biothreat agents as well as in cancer diagnostics [13–15]. Metagenomics sequences all DNA in a sample, enabling broad microbial detection but complicating the identification of low-abundance organisms in food and environmental samples. In contrast, TAS strategically targets predefined loci of interest, thereby avoiding the need for deep sequencing, making it a cost-effective method. Another advantage is that unlike in WMS, TAS is independent of sequencing depth and platform, offering flexibility across technologies like Illumina and PacBio. Here, we used an MLST-like approach with core gene sequences to design a custom primer panel to amplify 10 core gene regions from each of the 135 foodborne pathogens in a single multiplex reaction for amplicon sequencing. The objective of this study was twofold: (1) to demonstrate the utility of TAS for identifying microbial content at the species level, quantifying intraspecies diversity down to strain level, and detecting low level pathogens that may be present in complex microbial communities in foods; and (2) to test, as a proof of principle, the detection of STEC spiked with different levels of colony forming units (CFU) on ready-to-eat (RTE) romaine lettuce. Specifically, we compare results of TAS with WMS using the same samples to determine the limit of detection of the pathogen and demonstrate that TAS is more sensitive than WMS. Methods Target Primer Panel Design: A total of 135 foodborne pathogens, including 21 bacterial foodborne pathogens affecting human health and 7 other bacterial species associated with animals, were selected for inclusion in the primer panel (Table S1 ). All sequences were from NCBI. Several species from each of the following foodborne viruses were also included: human adenovirus, Aichivirus A, human astrovirus, enterovirus, hepatitis A virus, hepatitis E virus, norovirus, rotavirus, and sapovirus. To represent the fungal kingdom, 23 molds and 12 yeast species were included in the design. Lastly, Cyclospora cayetanensis , a foodborne coccidian parasite was added to the set. For each of the pathogens, core gene regions were chosen, either from an existing MLST panel for that species or homologous genes from a MLST panel of a related species. 10 genes per pathogen (except for E. coli we chose 12) were selected to account for any variations in the target that could lead to mis- or no priming and increase detection accuracy. In addition to the core genes for E. coli , Salmonella enterica , and Listeria monocytogenes , virulence genes (including E. coli stx1 and stx2 ) were targeted for each of these three species. The desired amplicon size was set to approximately 600 bp. Primer3 software v2.7.0 [16] was used to design primers for all species, setting the optimal primer length to 20 and optimal Tm to 60 o C. A consensus sequence from several strains of the species was used as input to maximize universality of the primers for that species. The pathogen and the genes that were used as target to design the primers are shown in Supplementary Table S1 . The primer sequences were provided to Swift Biosciences (now Integrated DNA Technologies, Coralville, IA) to design a custom kit using their multiplex pool Accel-Amplicon Panel approach. The panel, CP-FD6054-48 (pathogen specific 615 primers) was used in this study. Initial evaluation was done in silico with sequences from reference strains of foodborne pathogens. DNA Standards: Microbial Community DNA Standard (MCDS) containing 10 microbial species (Zymo Research Corporation, Irvine, CA, Product D6305) was used as a positive control to evaluate the performance of the panel. This standard was chosen to evaluate potential biases in NGS methods. Of the 10 strains included in the MCDS, eight are bacterial (3 Gram-negative and 5 Gram-positive) and two are yeasts. WMS and TAS libraries were prepared with 1 ng of DNA as described under the library preparation. Escherichia coli Samples: Three STEC strains carrying different Shiga toxin variants were chosen to test the performance of the panel (Table 1 ). The first was O157:H7 strain TW14359 or MI06-63 (GenBank CP001368), a clinical isolate that was associated with the 2006 E. coli outbreak associated with bagged spinach [17]. The other two strains were non-O157 STEC (O26:H11 and O121:H19). The selection of these strains was based on their serotypes, each of which has previously been associated with fresh produce outbreaks and have differing Shiga toxin gene repertoire (Table 1 ). Table 1 STEC strains used in this study. Strain Name Serotype stx Other Name Source Accesssion no. EC1611 O157:H7 2a, 2c MI06-63; TW14359 USA (MI), 2006 spinach outbreak CP001368 EC1763 O26:H11 1a TW14253 USA (MI), 1998 bovine feces NJTV01 EC1992 O121:H19 2a TW14945 USA (MI), 2002 human feces NJXV01 Summary of STEC strains, including strain name, serotype, Shiga toxin ( stx ) subtype(s), alternate names, source, and GenBank accession numbers. Metagenomic Sample Processing following spiking by STEC: Bagged chopped RTE romaine lettuce was bought from a local grocery store and stored at 4°C overnight. The three STEC strains were grown overnight in Buffered Peptone Water with pyruvate (mBPWp) broth. The overnight cultures were appropriately diluted in sterile saline solution. For each strain, three filter whirlpak bags were used to which 100 g of RTE romaine lettuce was added followed by addition of 225 ml of mBPWp medium and 100 µl of the diluted STEC containing either 1000 CFU, 100 CFU or 10 CFU. Unspiked romaine lettuce was used as a control. The bags were processed for enrichment of STEC as described by Leonard et al [18]. Briefly, the bags were incubated with shaking at 185 rpm at 37°C for 30 min and at this timepoint (0.5 h) 25 ml aliquots were withdrawn for processing as described below. The bags were then further incubated at 37°C for 4.5 hours without shaking and another 25 ml aliquot was withdrawn and acriflavine (10mg/l), cefsulodin (10mg/l), and vancomycin (8mg/l) were added, and the bags were then incubated at 42°C for another hour and another 25 ml aliquot was withdrawn. Bacteria from the 25ml sample aliquots withdrawn at 0.5 h, 5 h and 6 h were harvested by centrifugation at 5000 rpm for 10 minutes, the supernatant decanted and the pellets were stored at -20°C until genomic DNA extraction. DNA extraction, library preparation and next-generation sequencing: Genomic DNA was isolated from the metagenomic samples using DNeasy PowerSoil Pro kit (Qiagen, Carlsbad, CA). The DNA was quantified using a Qubit dsDNA BR assay kit and Qubit 3.0 fluorometer (Thermo Fisher Scientific, Waltham, MA). The WMS libraries were prepared using NexteraXT DNA sample preparation kit (Illumina, San Diego, CA). TAS libraries were prepared using a custom kit (CP-FD6054-48, Integrated DNA Technologies, Coralville, IA) in accordance with the manufacturer’s protocol. Briefly, multiplex PCR was performed on ~ 100 ng of sample DNA for 30 sec at 98°C, 4 cycles of 10 sec at 98°C, 5 min at 65°C, 1 min at 67°C, and 21 cycles of 10 sec at 98°C, 1 min at 64°C and ending with 1 min at 65°C and hold at 4°C. Size selection and clean-up were performed using AmpureXP beads (Beckman Coulter, Brea, CA) with a ratio of 1:1.2. Indexing sequencing adapters were then ligated to each library at 37°C for 20 minutes. A second clean-up step was performed using AmpureXP beads at a ratio of 1:0.85 and eluted with 20 µl of post-PCR TE buffer. Quantification of adapter ligated libraries was performed by qPCR using KAPA Library Quantification Kit (Roche Sequencing Solutions, Pleasanton, CA, USA). The library concentrations were measured using Qubit 3.0 fluorometer using Qubit dsDNA HS Assay kit (Thermo Fisher Scientific, Waltham, MA). To check the amplicon library size distribution, 2 µl of the library was run on 2200 TapeStation (Agilent Technologies, USA) using the High Sensitivity DNA kit. For both WMS and TAS, libraries were pooled at equimolar concentrations. The library pool from both methods was individually sequenced on the Illumina MiSeq Platform, using v2 chemistry generating paired-end 250 bp reads. Data Analysis: FastQC was used to check the quality of the raw reads obtained from each sequence run [19]. Reads with quality scores < Q20 were trimmed, and the Illumina adaptor sequences were removed using default parameters in Trimmomatic [20]. For both WMS and TAS, bacterial classification was done by MetaPhlAn [21] and Kraken2 [22] using GalaxyTrakr [23]. The number of Kraken classified reads were normalized per 1,000,000 reads sequenced for each sample to account for differences in sequencing depth across samples. The total read count ranged from 454,452 to 8,967,228 for WMS and from 36,630 to 3,953,726 for TAS methods respectively. ReCentrifuge was used for visualization of taxonomic abundances [24]. The E. coli MLST genes included in the TAS panel were aspC , clpX , fadD , icdA , lysP , mdh , uidA , adk , fumC , gyrB , purA , recA . The reads for each sequencing run were matched by BLAST [25] to a reference and a consensus sequence was formed for each gene after alignment with ClustalW version 2.1 (Slow/Accurate option, default parameters) [26]. Sequences for the 12 E. coli MLST genes were concatenated and phylogenetic analysis was done in MEGA v. 10.0.5 [27]. The tree was rooted with E. coli K-12 strain MG1655 (GenBank U00096.3). The evolutionary history was inferred using the Neighbor-joining method [28] and the evolutionary distances were computed using the p-distance method [29] with pairwise deletion of gaps/missing data, and other parameters were set to default. Results Evaluation of the TAS panel with ZymoBIOMICS Microbial Community DNA Standard The ZymoBIOMICS MCDS is composed of genomic DNA from eight bacterial species and two yeast. The bacterial species are Bacillus subtilis , Enterococcus faecalis , E. coli, Limosilactobacillus fermentum , L. monocytogenes , Pseudomonas aeruginosa , S. enterica , and Staphylococcus aureus and the two yeast are Saccharomyces cerevisiae and Cryptococcus neoformans . This standard has been routinely used in various studies as a control for optimizing polymerase chain reaction (PCR) methods and sequencing workflows, validating taxonomic classifications, and ensuring data quality in metagenomic research [30–33]. We used 1 ng of the MCDS for preparing libraries using the WMS and TAS protocols followed by sequencing. The WMS and TAS results are shown in Fig. 1 and the total number of reads were 1,534,650 and 2,309,624, respectively. The number of reads that mapped to each species are shown in Table 2 . The average read lengths for the data from WMS and TAS were 215 base pairs (bp) and 240 bp, respectively. The TAS panel had targets for five of the ten strains in the MCDS and the panel was able to amplify and allow for accurate identification of those five strains. For the foodborne pathogens, the gram-negative strains E. coli (47% GC) and S. enterica (52% GC) have high TAS reads compared to the gram-positive L. monocytogenes (38% GC) which has higher WMS read counts (Fig. 1 ). The other gram-positive species, E. faecalis (38% GC) and S. aureus (33% GC), also have higher WMS read counts. For the five strains that were not included in the panel, the WMS data accurately identified them as expected. Out of these five strains, the three bacteria, B. subtilis (44% GC) and L. fermentum (52% GC) are gram-positive while P. aeruginosa (66% GC), a gram-negative, all had higher WMS reads than TAS as expected. Even though these strains were not included in the panel design, they amplified genes most likely due to mis-priming or due to conservation of primers targeting a related species and non-specific amplification. Of the two yeasts, S. cerevisiae (38% GC) had more WMS reads and C. neoformans (48% GC) had more TAS reads probably because of the higher GC content which probably allowed for more non-specific amplification. Detection of E. coli using whole metagenomic sequencing. RTE romaine lettuce was spiked separately with three serotypes of STEC (O157:H7, O26:H11, and O121:H19) at 10, 100 and 1000 CFU/100 g lettuce. Unspiked romaine lettuce from the same lot was used as a control to examine if the microbial community in the romaine lettuce had E. coli . The bags were processed at 0.5 h, 5 h, and 6 h post inoculation except that the 6 h sample was not collected for the unspiked samples. The number and percentage of reads that mapped to E. coli and Shiga toxin genes were assessed for all three strains and the unspiked sample (Table 3 ). The WMS data for the unspiked samples at 0.5 h showed 77 reads, which identified Escherichia at the genus level but there were no reads for the E. coli species or virulence genes. The 5 h unspiked sample data had no Escherichia reads. The metagenomic community analysis for the unspiked samples showed that the more prevalent genus at 0.5 h was Pseudomonas and at 5 h was Priestia (Supplemental Fig. 1i and 1j). For the spiked samples, WMS generated an average of 1,749,068 reads with a minimum of 454,452 reads and a maximum of 8,967,228. Taxonomic classification using Kraken2 identified E. coli with an average of 89 reads that mapped to the species level at 10 CFU from the 0.5 h samples for all three strains. Our findings are similar to prior research on the detection of STEC from produce spiked at low CFUs [18]. The average number of total reads from the WMS data for the O157:H7 strain was 1,633,899, for the O26:H11 strain was 905,764 and for the O121:H19 strain was 2,802,784. At 0.5 h samples, stx1 and stx2 were not detected for the three strains. For the O157:H7 strain at 5 h, stx2 was detected for the 100 CFU and 1000 CFU spiked samples but at 6 h, we could detect stx2 at all three. For the O26:H11 strain at 5 h and 6 h, stx1 could be detected in all spiked samples. For the O121:H19 strain, stx2 was not detected at 10 CFU in both the 5 h and 6 h timepoints but could be detected at 100 and 1000 CFU. The taxonomic composition and abundance distribution of the bacterial communities in the spiked and unspiked samples were visualized using Krona charts (Supplemental Fig. 1). Detection of E. coli using target amplicon sequencing. The same DNA that was harvested at the timepoints mentioned above was used to set up a multiplex PCR as part of the custom targeted amplicon sequencing library preparation protocol. For the unspiked samples, TAS did not generate any data because the samples were not spiked and did not harbour pathogens included in the panel. For the spiked samples, TAS generated an average of 1,352,699 reads with a minimum of 36,630 and maximum of 3,953,726. Taxonomic classification using Kraken2 identified E. coli at the species level for all spiked samples and timepoints, with an average 1,317 reads that mapped at the species level for 10 CFU from the 0.5 h samples for all three strains (Table 3 ). The average number of reads for the O157:H7 was 800,072, the O26:H11 was 2,036,784 and for the O121:H19 strain was 1,221,240. For the O157:H7 and O121:H19 strains, stx2 and for the O26:H11 strain stx1 was detected for all spiked samples and timepoints tested. Phylogenetic analyses post whole metagenomic and target amplicon sequencing Phylogenetic analysis was performed to confirm that the spiked E. coli strains in the romaine lettuce samples were accurately identified and correctly clustered with their respective reference strains (Figs. 2 and 3 ). This was essential to evaluate the specificity and precision of the TAS approach, particularly in the context of complex metagenomic samples containing diverse microbial populations. By examining the clustering patterns, we verified that the TAS method could reliably distinguish and classify the spiked strains according to their genetic relatedness, in the presence of potential interference from non-target microbial DNA. Even in samples where data is missing due to low coverage, it properly clustered but with a long branch. For WMS, at 0.5 h there were not enough reads to include in the phylogenetic analysis. The only exception where we did not see a tight cluster in the WMS tree was at O121:H19 at 5 h and 10 CFU spiked sample (Fig. 2 ). Using both the WMS and TAS data, the strains clustered based on their serotypes as shown in Figs. 2 and 3 , respectively. Discussion This study describes the use of TAS in detection of foodborne pathogens and its comparison to WMS. We specifically compared the performance of WMS with TAS in detecting STEC in RTE romaine lettuce at different CFU and time points. Our findings demonstrate that sequencing depths achieved by both methods cover the microbial diversity in mock communities (Fig. 1 , Table 2 ). However, significant differences were observed in their sensitivity, specificity, and sequencing variabilities between the two sequencing methods. WMS provided broader taxonomic coverage as it offers a holistic, unbiased approach that captures the entire microbial community within a sample, accurately identifying species not targeted by TAS. This makes WMS particularly useful for quasimetagenomic characterization [34], enabling the detection of both STEC and other microbial taxa existing in the sample. Such insights are invaluable for understanding microbial ecology, potential cross-contamination, and community dynamics in food matrices. TAS exhibited higher sensitivity for low-abundance and GC-rich species (Fig. 1 , Table 2 ), which is attributed to the targeted design. The biases observed in TAS and WMS are most likely due to GC content. TAS demonstrated a preference for GC-rich genomes as seen in Table 2 , illustrated by higher read counts for E. coli (47% GC) and S. enterica (52% GC). In contrast, WMS captured species with low GC content, such as S. aureus (33% GC) and L. monocytogenes (38% GC). These findings align with previous studies highlighting the influence of GC content on sequencing efficiency, indicating the need for careful consideration when designing custom panels for pathogen detection [35]. In spiked romaine lettuce samples, TAS outperformed WMS in detecting low-abundance STEC serotypes (Table 3 ). TAS identified stx1 and stx2 genes across all time points and spiked samples tested as shown in Table 3 , highlighting its superior sensitivity while WMS could not detect stx1 and stx2 genes at 0.5 h with all three CFU. At 5h, while providing broader coverage of the microbial community, detection of species in low abundance using WMS was inconsistent. At 5 h, WMS could detect stx2 gene in the 100 CFU and 1000 CFU spiked samples for both O157:H7 and O121:H19 strains but not in the 10 CFU spiked samples. On the other hand, stx1 in the O26:H11 strain was detected at 5 h in 10, 100 and 1000 CFU using WMS. This indicates that pathogenic markers like stx1 and stx2 are less likely to be detected by WMS at earlier timepoints like 0.5 h or 5 h when spiked with 10 CFU. This is likely because WMS provides a broad snapshot of the entire microbial community and the abundance of the pathogens in the sample affects their detection, which can make detecting low-level pathogens challenging due to the background noise from other organisms. Additionally, if the pathogen grows poorly, its representation in the data would remain low. TAS in contrast bypasses the need for enrichment by molecularly amplifying the targeted regions of the pathogen. The differences in sequencing depth between WMS and TAS shown in Table 3 highlight why TAS is a better strategy to be used to detect low level pathogens that may be present in food samples and environmental samples. WMS relies on cultural target amplification to enhance detection of low-abundance targets whereas TAS employs molecular amplification by using specific primers to amplify genomic regions of interest and does not rely on culturing the target microorganisms. While TAS can overcome the limitations of culture dependence, the limitation is that it can only detect those pathogens that are included in the panel. However, the sensitivity of TAS makes it a powerful tool for food safety applications in outbreak investigations, foodborne illness surveillance, and routine food safety assessments. The phylogenetic analysis confirmed the robustness of TAS in accurately clustering spiked E. coli strains with their respective reference strains across different timepoints and CFU tested, even in the presence of complex microbial backgrounds. TAS demonstrated specificity producing accurate phylogenetic relationships. (Fig. 3 ). WMS offers a broader taxonomic profiling and showed accurate clustering for all spiked samples at the 5 h and 6 h timepoints with the exception of the 100 CFU spiked sample for O121:H19 strain at 5 h (Fig. 2 ). As there were not enough reads for STEC in the 0.5 h timepoints for all strains at all spiked CFUs, they were excluded from the WMS tree. These findings reinforce the utility of TAS in pathogen surveillance, particularly when they are present in low amounts in the presence of the matrix microbiome. The complementary strengths of WMS and TAS suggest that hybrid approaches could provide both broad taxonomic coverage and precise strain-level identification, enhancing microbial surveillance in food safety. By combining the sensitivity of TAS for targeted gene detection with the broad coverage of WMS, a more comprehensive and efficient workflow for pathogen detection can be achieved. Conclusion The detection of pathogens like STEC in fresh produce remains a concern for public health and food safety. This study introduces the concept of use of TAS for risk assessment and shows its comparison with WMS for detecting STEC in RTE romaine lettuce, highlighting their respective strengths, limitations, and applicability to food safety. TAS demonstrated higher sensitivity and specificity for stx1 and stx2 detection, particularly at low CFU spiked levels, making it a powerful tool for targeted pathogen detection and risk assessment while WMS, with its broader microbial resolution complements TAS by offering valuable insights into microbial diversity and community dynamics. A combined approach leveraging the strengths of both methods holds promise for enhancing pathogen detection and food safety monitoring, ultimately safeguarding public health. Declarations Author statements Author contributions I.P. conceptualized the experiments, analyzed data, wrote, reviewed, and edited the manuscript. M.M. did the bioinformatic analysis to design the primer panel, data analysis, phylogenetic analysis and edited the manuscript. J.G. assisted with the data analysis and manuscript editing. A.M. critically reviewed the data and edited the manuscript. All authors have read and approved the final paper. Conflicts of interest The authors declare no conflicts of interest. Funding information Not Applicable. Ethical approval Not Applicable. Consent for publication Not Applicable. Acknowledgements We thank Dave Lacher and Gopal Gopinathrao for reviewing the manuscript. References Goodwin S, McPherson JD, McCombie WR: Coming of age: ten years of next-generation sequencing technologies . Nat Rev Genet 2016, 17 (6):333-351. Li B, Saingam P, Ishii S, Yan T: Multiplex PCR coupled with direct amplicon sequencing for simultaneous detection of numerous waterborne pathogens . Appl Microbiol Biotechnol 2019, 103 (2):953-961. 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Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, Valentin F, Wallace IM, Wilm A, Lopez R et al : Clustal W and Clustal X version 2.0 . Bioinformatics 2007, 23 (21):2947-2948. Kumar S, Stecher G, Li M, Knyaz C, Tamura K: MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms . Mol Biol Evol 2018, 35 (6):1547-1549. Saitou N, Nei M: The neighbor-joining method: a new method for reconstructing phylogenetic trees . Mol Biol Evol 1987, 4 (4):406-425. Nei M, and Kumar, S.: Molecular Evolution and Phylogenetics . New York: Oxford University Press; 2000. Callahan BJ, Wong J, Heiner C, Oh S, Theriot CM, Gulati AS, McGill SK, Dougherty MK: High-throughput amplicon sequencing of the full-length 16S rRNA gene with single-nucleotide resolution . Nucleic Acids Res 2019, 47 (18):e103. Jia Y, Zhao S, Guo W, Peng L, Zhao F, Wang L, Fan G, Zhu Y, Xu D, Liu G et al : Sequencing introduced false positive rare taxa lead to biased microbial community diversity, assembly, and interaction interpretation in amplicon studies . Environ Microbiome 2022, 17 (1):43. Petrone JR, Rios Glusberger P, George CD, Milletich PL, Ahrens AP, Roesch LFW, Triplett EW: RESCUE: a validated Nanopore pipeline to classify bacteria through long-read, 16S-ITS-23S rRNA sequencing . Front Microbiol 2023, 14 :1201064. Shvartsman E, Richmond MEI, Schellenberg JJ, Lamont A, Perciani C, Russell JNH, Poliquin V, Burgener A, Jaoko W, Sandstrom P et al : Comparative analysis of DNA extraction and PCR product purification methods for cervicovaginal microbiome analysis using cpn60 microbial profiling . PLoS One 2022, 17 (1):e0262355. Y. Enciso-Martinez GAG-A, M. A. Martinez-Tellez, C. J. Gonzalez-Perez, D. E. Valencia-Rivera, E. Barrios-Villa, et al.: Relevance of tracking the diversity of Escherichia coli pathotypes to reinforce food safety . International Journal of Food Microbiology 2022. Browne PD, Nielsen TK, Kot W, Aggerholm A, Gilbert MTP, Puetz L, Rasmussen M, Zervas A, Hansen LH: GC bias affects genomic and metagenomic reconstructions, underrepresenting GC-poor organisms . Gigascience 2020, 9 (2). Table 2 and 3 Table 2 and 3 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table2.docx Table3.docx SupplementaryTableS1.pdf SupplementaryFigS1aj.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6805163","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469789289,"identity":"b3109bc3-241c-4bc7-82de-7e3306d43762","order_by":0,"name":"Isha Patel","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYBACNgYeKIud+cAHIDvBgFgtEgzMbIkziNLCgNDCY0icFj7+swc/MO6wq+Nn5vnY8KaGIc+c/wDzi49teBwmkZcswXgmWUKymXdj45xjDMWWMxLYLGfi1cJjIMHYxixhcJh3+2MeNobEDTcY2Ix5zuDRwn/G+AdjW72E/WGeh808/4Bazh8goIUhxwxoy2EJA2YexmbeNqCWAwnMj3kq8Dksx8wi8cxxyRmH2Qwb5/ZJAB2W2MY4A48W+f4zxjc+7qjm529vftjw5psN0GGHD3/4QCioExvgTAkgZmyTIKABqKYBlc/8gaCWUTAKRsEoGEkAAKEeTWYfIYmHAAAAAElFTkSuQmCC","orcid":"","institution":"U.S. Food and Drug Administration","correspondingAuthor":true,"prefix":"","firstName":"Isha","middleName":"","lastName":"Patel","suffix":""},{"id":469789290,"identity":"96cfe68a-1906-46c4-b47e-f29424b4ea3f","order_by":1,"name":"Mark Mammel","email":"","orcid":"","institution":"U.S. Food and Drug 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11:24:07","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":749659,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigS1aj.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6805163/v1/9a9b1e384515ad1e94a49a89.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDesign of a Targeted Amplicon Sequencing Panel for Detection of Foodborne Pathogens and its Application in Detection of Spiked Shiga Toxin-Producing \u003cem\u003eEscherichia coli\u003c/em\u003e in Romaine Lettuce\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHigh-throughput or massively parallel sequencing technologies have opened opportunities to advance genomic research [1\u0026ndash;3]. Among the varied applications of next-generation sequencing (NGS), advancements in the finer characterization of bacterial genomes and accurate taxonomic identifications of microorganisms in complex environments particularly in food and food related processes are useful in outbreak tracing [4, 5]. NGS methods are used in two independent ways. The first and most common shotgun sequencing method is whole genome sequencing (WGS) of a single cultured isolate. The data from WGS using core genome multilocus typing (cgMLST) and whole genome MLST (wgMLST) schemes are used for high-resolution pathogen typing, epidemiology, susceptibility prediction, and virulence factor determination [6, 7]. The second method is whole metagenome sequencing (WMS) which involves sequencing all of the microorganisms in a sample without the need of selectively purifying a single isolate. The high discriminatory power of NGS methods using shotgun sequencing has shown to be valuable and increasingly popular for microbial typing and pathogen detection from food and for source attribution related to outbreak investigations or regulatory applications [8, 9].\u003c/p\u003e \u003cp\u003eAlthough NGS methods provide resolution and sensitivity for high-throughput surveillance, there are still significant challenges in detecting low levels of pathogens or sub-populations of unculturable pathogens present in samples at low levels with WGS or WMS sequencing approaches [10\u0026ndash;12]. These limits on specificity and sensitivity are due to the depth of sequencing being compromised depending on the number and complexity of the sample being sequenced. One way to overcome this problem is to use targeted amplicon sequencing (TAS) where specific target genes are chosen for PCR amplification and then the amplicons are subjected to NGS. This allows for the sequencing of selected target genes specific for pathogens of interest with increased sensitivity, better specificity, and ease of downstream analysis. TAS is now routinely used in detection of biothreat agents as well as in cancer diagnostics [13\u0026ndash;15]. Metagenomics sequences all DNA in a sample, enabling broad microbial detection but complicating the identification of low-abundance organisms in food and environmental samples. In contrast, TAS strategically targets predefined loci of interest, thereby avoiding the need for deep sequencing, making it a cost-effective method. Another advantage is that unlike in WMS, TAS is independent of sequencing depth and platform, offering flexibility across technologies like Illumina and PacBio.\u003c/p\u003e \u003cp\u003eHere, we used an MLST-like approach with core gene sequences to design a custom primer panel to amplify 10 core gene regions from each of the 135 foodborne pathogens in a single multiplex reaction for amplicon sequencing. The objective of this study was twofold: (1) to demonstrate the utility of TAS for identifying microbial content at the species level, quantifying intraspecies diversity down to strain level, and detecting low level pathogens that may be present in complex microbial communities in foods; and (2) to test, as a proof of principle, the detection of STEC spiked with different levels of colony forming units (CFU) on ready-to-eat (RTE) romaine lettuce. Specifically, we compare results of TAS with WMS using the same samples to determine the limit of detection of the pathogen and demonstrate that TAS is more sensitive than WMS.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eTarget Primer Panel Design:\u003c/p\u003e \u003cp\u003eA total of 135 foodborne pathogens, including 21 bacterial foodborne pathogens affecting human health and 7 other bacterial species associated with animals, were selected for inclusion in the primer panel (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). All sequences were from NCBI. Several species from each of the following foodborne viruses were also included: human adenovirus, Aichivirus A, human astrovirus, enterovirus, hepatitis A virus, hepatitis E virus, norovirus, rotavirus, and sapovirus. To represent the fungal kingdom, 23 molds and 12 yeast species were included in the design. Lastly, \u003cem\u003eCyclospora cayetanensis\u003c/em\u003e, a foodborne coccidian parasite was added to the set. For each of the pathogens, core gene regions were chosen, either from an existing MLST panel for that species or homologous genes from a MLST panel of a related species. 10 genes per pathogen (except for \u003cem\u003eE. coli\u003c/em\u003e we chose 12) were selected to account for any variations in the target that could lead to mis- or no priming and increase detection accuracy. In addition to the core genes for \u003cem\u003eE. coli\u003c/em\u003e, \u003cem\u003eSalmonella enterica\u003c/em\u003e, and \u003cem\u003eListeria monocytogenes\u003c/em\u003e, virulence genes (including \u003cem\u003eE. coli stx1\u003c/em\u003e and \u003cem\u003estx2\u003c/em\u003e) were targeted for each of these three species. The desired amplicon size was set to approximately 600 bp. Primer3 software v2.7.0 [16] was used to design primers for all species, setting the optimal primer length to 20 and optimal Tm to 60\u003csup\u003eo\u003c/sup\u003eC. A consensus sequence from several strains of the species was used as input to maximize universality of the primers for that species. The pathogen and the genes that were used as target to design the primers are shown in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The primer sequences were provided to Swift Biosciences (now Integrated DNA Technologies, Coralville, IA) to design a custom kit using their multiplex pool Accel-Amplicon Panel approach. The panel, CP-FD6054-48 (pathogen specific 615 primers) was used in this study. Initial evaluation was done in silico with sequences from reference strains of foodborne pathogens.\u003c/p\u003e \u003cp\u003eDNA Standards:\u003c/p\u003e \u003cp\u003eMicrobial Community DNA Standard (MCDS) containing 10 microbial species (Zymo Research Corporation, Irvine, CA, Product D6305) was used as a positive control to evaluate the performance of the panel. This standard was chosen to evaluate potential biases in NGS methods. Of the 10 strains included in the MCDS, eight are bacterial (3 Gram-negative and 5 Gram-positive) and two are yeasts. WMS and TAS libraries were prepared with 1 ng of DNA as described under the library preparation.\u003c/p\u003e \u003cp\u003e \u003cem\u003eEscherichia coli\u003c/em\u003e Samples:\u003c/p\u003e \u003cp\u003eThree STEC strains carrying different Shiga toxin variants were chosen to test the performance of the panel (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The first was O157:H7 strain TW14359 or MI06-63 (GenBank CP001368), a clinical isolate that was associated with the 2006 \u003cem\u003eE. coli\u003c/em\u003e outbreak associated with bagged spinach [17]. The other two strains were non-O157 STEC (O26:H11 and O121:H19). The selection of these strains was based on their serotypes, each of which has previously been associated with fresh produce outbreaks and have differing Shiga toxin gene repertoire (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSTEC strains used in this study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrain Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSerotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003estx\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOther Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAccesssion no.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC1611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO157:H7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2a, 2c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMI06-63; TW14359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSA (MI), 2006 spinach outbreak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCP001368\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC1763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO26:H11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTW14253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSA (MI), 1998 bovine feces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNJTV01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC1992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO121:H19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTW14945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSA (MI), 2002 human feces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNJXV01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eSummary of STEC strains, including strain name, serotype, Shiga toxin (\u003cem\u003estx\u003c/em\u003e) subtype(s), alternate names, source, and GenBank accession numbers.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMetagenomic Sample Processing following spiking by STEC:\u003c/p\u003e \u003cp\u003eBagged chopped RTE romaine lettuce was bought from a local grocery store and stored at 4\u0026deg;C overnight. The three STEC strains were grown overnight in Buffered Peptone Water with pyruvate (mBPWp) broth. The overnight cultures were appropriately diluted in sterile saline solution. For each strain, three filter whirlpak bags were used to which 100 g of RTE romaine lettuce was added followed by addition of 225 ml of mBPWp medium and 100 \u0026micro;l of the diluted STEC containing either 1000 CFU, 100 CFU or 10 CFU. Unspiked romaine lettuce was used as a control. The bags were processed for enrichment of STEC as described by Leonard et al [18]. Briefly, the bags were incubated with shaking at 185 rpm at 37\u0026deg;C for 30 min and at this timepoint (0.5 h) 25 ml aliquots were withdrawn for processing as described below. The bags were then further incubated at 37\u0026deg;C for 4.5 hours without shaking and another 25 ml aliquot was withdrawn and acriflavine (10mg/l), cefsulodin (10mg/l), and vancomycin (8mg/l) were added, and the bags were then incubated at 42\u0026deg;C for another hour and another 25 ml aliquot was withdrawn. Bacteria from the 25ml sample aliquots withdrawn at 0.5 h, 5 h and 6 h were harvested by centrifugation at 5000 rpm for 10 minutes, the supernatant decanted and the pellets were stored at -20\u0026deg;C until genomic DNA extraction.\u003c/p\u003e \u003cp\u003eDNA extraction, library preparation and next-generation sequencing:\u003c/p\u003e \u003cp\u003eGenomic DNA was isolated from the metagenomic samples using DNeasy PowerSoil Pro kit (Qiagen, Carlsbad, CA). The DNA was quantified using a Qubit dsDNA BR assay kit and Qubit 3.0 fluorometer (Thermo Fisher Scientific, Waltham, MA). The WMS libraries were prepared using NexteraXT DNA sample preparation kit (Illumina, San Diego, CA). TAS libraries were prepared using a custom kit (CP-FD6054-48, Integrated DNA Technologies, Coralville, IA) in accordance with the manufacturer\u0026rsquo;s protocol. Briefly, multiplex PCR was performed on ~\u0026thinsp;100 ng of sample DNA for 30 sec at 98\u0026deg;C, 4 cycles of 10 sec at 98\u0026deg;C, 5 min at 65\u0026deg;C, 1 min at 67\u0026deg;C, and 21 cycles of 10 sec at 98\u0026deg;C, 1 min at 64\u0026deg;C and ending with 1 min at 65\u0026deg;C and hold at 4\u0026deg;C. Size selection and clean-up were performed using AmpureXP beads (Beckman Coulter, Brea, CA) with a ratio of 1:1.2. Indexing sequencing adapters were then ligated to each library at 37\u0026deg;C for 20 minutes. A second clean-up step was performed using AmpureXP beads at a ratio of 1:0.85 and eluted with 20 \u0026micro;l of post-PCR TE buffer. Quantification of adapter ligated libraries was performed by qPCR using KAPA Library Quantification Kit (Roche Sequencing Solutions, Pleasanton, CA, USA). The library concentrations were measured using Qubit 3.0 fluorometer using Qubit dsDNA HS Assay kit (Thermo Fisher Scientific, Waltham, MA). To check the amplicon library size distribution, 2 \u0026micro;l of the library was run on 2200 TapeStation (Agilent Technologies, USA) using the High Sensitivity DNA kit. For both WMS and TAS, libraries were pooled at equimolar concentrations. The library pool from both methods was individually sequenced on the Illumina MiSeq Platform, using v2 chemistry generating paired-end 250 bp reads.\u003c/p\u003e \u003cp\u003eData Analysis:\u003c/p\u003e \u003cp\u003eFastQC was used to check the quality of the raw reads obtained from each sequence run [19]. Reads with quality scores\u0026thinsp;\u0026lt;\u0026thinsp;Q20 were trimmed, and the Illumina adaptor sequences were removed using default parameters in Trimmomatic [20]. For both WMS and TAS, bacterial classification was done by MetaPhlAn [21] and Kraken2 [22] using GalaxyTrakr [23]. The number of Kraken classified reads were normalized per 1,000,000 reads sequenced for each sample to account for differences in sequencing depth across samples. The total read count ranged from 454,452 to 8,967,228 for WMS and from 36,630 to 3,953,726 for TAS methods respectively. ReCentrifuge was used for visualization of taxonomic abundances [24]. The \u003cem\u003eE. coli\u003c/em\u003e MLST genes included in the TAS panel were \u003cem\u003easpC\u003c/em\u003e, \u003cem\u003eclpX\u003c/em\u003e, \u003cem\u003efadD\u003c/em\u003e, \u003cem\u003eicdA\u003c/em\u003e, \u003cem\u003elysP\u003c/em\u003e, \u003cem\u003emdh\u003c/em\u003e, \u003cem\u003euidA\u003c/em\u003e, \u003cem\u003eadk\u003c/em\u003e, \u003cem\u003efumC\u003c/em\u003e, \u003cem\u003egyrB\u003c/em\u003e, \u003cem\u003epurA\u003c/em\u003e, \u003cem\u003erecA\u003c/em\u003e. The reads for each sequencing run were matched by BLAST [25] to a reference and a consensus sequence was formed for each gene after alignment with ClustalW version 2.1 (Slow/Accurate option, default parameters) [26]. Sequences for the 12 \u003cem\u003eE. coli\u003c/em\u003e MLST genes were concatenated and phylogenetic analysis was done in MEGA v. 10.0.5 [27]. The tree was rooted with \u003cem\u003eE. coli\u003c/em\u003e K-12 strain MG1655 (GenBank U00096.3). The evolutionary history was inferred using the Neighbor-joining method [28] and the evolutionary distances were computed using the p-distance method [29] with pairwise deletion of gaps/missing data, and other parameters were set to default.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eEvaluation of the TAS panel with ZymoBIOMICS Microbial Community DNA Standard\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe ZymoBIOMICS MCDS is composed of genomic DNA from eight bacterial species and two yeast. The bacterial species are \u003cem\u003eBacillus subtilis\u003c/em\u003e, \u003cem\u003eEnterococcus faecalis\u003c/em\u003e, \u003cem\u003eE. coli, Limosilactobacillus fermentum\u003c/em\u003e, \u003cem\u003eL. monocytogenes\u003c/em\u003e, \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, \u003cem\u003eS. enterica\u003c/em\u003e, and \u003cem\u003eStaphylococcus aureus\u003c/em\u003e and the two yeast are \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e and \u003cem\u003eCryptococcus neoformans\u003c/em\u003e. This standard has been routinely used in various studies as a control for optimizing polymerase chain reaction (PCR) methods and sequencing workflows, validating taxonomic classifications, and ensuring data quality in metagenomic research [30\u0026ndash;33]. We used 1 ng of the MCDS for preparing libraries using the WMS and TAS protocols followed by sequencing. The WMS and TAS results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and the total number of reads were 1,534,650 and 2,309,624, respectively. The number of reads that mapped to each species are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The average read lengths for the data from WMS and TAS were 215 base pairs (bp) and 240 bp, respectively. The TAS panel had targets for five of the ten strains in the MCDS and the panel was able to amplify and allow for accurate identification of those five strains. For the foodborne pathogens, the gram-negative strains \u003cem\u003eE. coli\u003c/em\u003e (47% GC) and \u003cem\u003eS. enterica\u003c/em\u003e (52% GC) have high TAS reads compared to the gram-positive \u003cem\u003eL. monocytogenes\u003c/em\u003e (38% GC) which has higher WMS read counts (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The other gram-positive species, \u003cem\u003eE. faecalis\u003c/em\u003e (38% GC) and \u003cem\u003eS. aureus\u003c/em\u003e (33% GC), also have higher WMS read counts. For the five strains that were not included in the panel, the WMS data accurately identified them as expected. Out of these five strains, the three bacteria, \u003cem\u003eB. subtilis\u003c/em\u003e (44% GC) and \u003cem\u003eL. fermentum\u003c/em\u003e (52% GC) are gram-positive while \u003cem\u003eP. aeruginosa\u003c/em\u003e (66% GC), a gram-negative, all had higher WMS reads than TAS as expected. Even though these strains were not included in the panel design, they amplified genes most likely due to mis-priming or due to conservation of primers targeting a related species and non-specific amplification. Of the two yeasts, \u003cem\u003eS. cerevisiae\u003c/em\u003e (38% GC) had more WMS reads and \u003cem\u003eC. neoformans\u003c/em\u003e (48% GC) had more TAS reads probably because of the higher GC content which probably allowed for more non-specific amplification.\u003c/p\u003e \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eDetection of\u003c/strong\u003e \u003cstrong\u003eE. coli\u003c/strong\u003e \u003cstrong\u003eusing whole metagenomic sequencing.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRTE romaine lettuce was spiked separately with three serotypes of STEC (O157:H7, O26:H11, and O121:H19) at 10, 100 and 1000 CFU/100 g lettuce. Unspiked romaine lettuce from the same lot was used as a control to examine if the microbial community in the romaine lettuce had \u003cem\u003eE. coli\u003c/em\u003e. The bags were processed at 0.5 h, 5 h, and 6 h post inoculation except that the 6 h sample was not collected for the unspiked samples. The number and percentage of reads that mapped to \u003cem\u003eE. coli\u003c/em\u003e and Shiga toxin genes were assessed for all three strains and the unspiked sample (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The WMS data for the unspiked samples at 0.5 h showed 77 reads, which identified \u003cem\u003eEscherichia\u003c/em\u003e at the genus level but there were no reads for the \u003cem\u003eE. coli\u003c/em\u003e species or virulence genes. The 5 h unspiked sample data had no \u003cem\u003eEscherichia\u003c/em\u003e reads. The metagenomic community analysis for the unspiked samples showed that the more prevalent genus at 0.5 h was \u003cem\u003ePseudomonas\u003c/em\u003e and at 5 h was \u003cem\u003ePriestia\u003c/em\u003e (Supplemental Fig. 1i and 1j). For the spiked samples, WMS generated an average of 1,749,068 reads with a minimum of 454,452 reads and a maximum of 8,967,228. Taxonomic classification using Kraken2 identified \u003cem\u003eE. coli\u003c/em\u003e with an average of 89 reads that mapped to the species level at 10 CFU from the 0.5 h samples for all three strains. Our findings are similar to prior research on the detection of STEC from produce spiked at low CFUs [18]. The average number of total reads from the WMS data for the O157:H7 strain was 1,633,899, for the O26:H11 strain was 905,764 and for the O121:H19 strain was 2,802,784. At 0.5 h samples, \u003cem\u003estx1\u003c/em\u003e and \u003cem\u003estx2\u003c/em\u003e were not detected for the three strains. For the O157:H7 strain at 5 h, \u003cem\u003estx2\u003c/em\u003e was detected for the 100 CFU and 1000 CFU spiked samples but at 6 h, we could detect \u003cem\u003estx2\u003c/em\u003e at all three. For the O26:H11 strain at 5 h and 6 h, \u003cem\u003estx1\u003c/em\u003e could be detected in all spiked samples. For the O121:H19 strain, \u003cem\u003estx2\u003c/em\u003e was not detected at 10 CFU in both the 5 h and 6 h timepoints but could be detected at 100 and 1000 CFU. The taxonomic composition and abundance distribution of the bacterial communities in the spiked and unspiked samples were visualized using Krona charts (Supplemental Fig. 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetection of\u003c/strong\u003e \u003cstrong\u003eE. coli\u003c/strong\u003e \u003cstrong\u003eusing target amplicon sequencing.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe same DNA that was harvested at the timepoints mentioned above was used to set up a multiplex PCR as part of the custom targeted amplicon sequencing library preparation protocol. For the unspiked samples, TAS did not generate any data because the samples were not spiked and did not harbour pathogens included in the panel. For the spiked samples, TAS generated an average of 1,352,699 reads with a minimum of 36,630 and maximum of 3,953,726. Taxonomic classification using Kraken2 identified \u003cem\u003eE. coli\u003c/em\u003e at the species level for all spiked samples and timepoints, with an average 1,317 reads that mapped at the species level for 10 CFU from the 0.5 h samples for all three strains (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The average number of reads for the O157:H7 was 800,072, the O26:H11 was 2,036,784 and for the O121:H19 strain was 1,221,240. For the O157:H7 and O121:H19 strains, \u003cem\u003estx2\u003c/em\u003e and for the O26:H11 strain \u003cem\u003estx1\u003c/em\u003e was detected for all spiked samples and timepoints tested.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhylogenetic analyses post whole metagenomic and target amplicon sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePhylogenetic analysis was performed to confirm that the spiked \u003cem\u003eE. coli\u003c/em\u003e strains in the romaine lettuce samples were accurately identified and correctly clustered with their respective reference strains (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). This was essential to evaluate the specificity and precision of the TAS approach, particularly in the context of complex metagenomic samples containing diverse microbial populations. By examining the clustering patterns, we verified that the TAS method could reliably distinguish and classify the spiked strains according to their genetic relatedness, in the presence of potential interference from non-target microbial DNA. Even in samples where data is missing due to low coverage, it properly clustered but with a long branch. For WMS, at 0.5 h there were not enough reads to include in the phylogenetic analysis. The only exception where we did not see a tight cluster in the WMS tree was at O121:H19 at 5 h and 10 CFU spiked sample (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Using both the WMS and TAS data, the strains clustered based on their serotypes as shown in Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, respectively.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study describes the use of TAS in detection of foodborne pathogens and its comparison to WMS. We specifically compared the performance of WMS with TAS in detecting STEC in RTE romaine lettuce at different CFU and time points. Our findings demonstrate that sequencing depths achieved by both methods cover the microbial diversity in mock communities (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, significant differences were observed in their sensitivity, specificity, and sequencing variabilities between the two sequencing methods. WMS provided broader taxonomic coverage as it offers a holistic, unbiased approach that captures the entire microbial community within a sample, accurately identifying species not targeted by TAS. This makes WMS particularly useful for quasimetagenomic characterization [34], enabling the detection of both STEC and other microbial taxa existing in the sample. Such insights are invaluable for understanding microbial ecology, potential cross-contamination, and community dynamics in food matrices. TAS exhibited higher sensitivity for low-abundance and GC-rich species (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which is attributed to the targeted design. The biases observed in TAS and WMS are most likely due to GC content. TAS demonstrated a preference for GC-rich genomes as seen in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, illustrated by higher read counts for \u003cem\u003eE. coli\u003c/em\u003e (47% GC) and \u003cem\u003eS. enterica\u003c/em\u003e (52% GC). In contrast, WMS captured species with low GC content, such as \u003cem\u003eS. aureus\u003c/em\u003e (33% GC) and \u003cem\u003eL. monocytogenes\u003c/em\u003e (38% GC). These findings align with previous studies highlighting the influence of GC content on sequencing efficiency, indicating the need for careful consideration when designing custom panels for pathogen detection [35].\u003c/p\u003e \u003cp\u003eIn spiked romaine lettuce samples, TAS outperformed WMS in detecting low-abundance STEC serotypes (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). TAS identified \u003cem\u003estx1\u003c/em\u003e and \u003cem\u003estx2\u003c/em\u003e genes across all time points and spiked samples tested as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, highlighting its superior sensitivity while WMS could not detect \u003cem\u003estx1\u003c/em\u003e and \u003cem\u003estx2\u003c/em\u003e genes at 0.5 h with all three CFU. At 5h, while providing broader coverage of the microbial community, detection of species in low abundance using WMS was inconsistent. At 5 h, WMS could detect \u003cem\u003estx2\u003c/em\u003e gene in the 100 CFU and 1000 CFU spiked samples for both O157:H7 and O121:H19 strains but not in the 10 CFU spiked samples. On the other hand, \u003cem\u003estx1\u003c/em\u003e in the O26:H11 strain was detected at 5 h in 10, 100 and 1000 CFU using WMS. This indicates that pathogenic markers like \u003cem\u003estx1\u003c/em\u003e and \u003cem\u003estx2\u003c/em\u003e are less likely to be detected by WMS at earlier timepoints like 0.5 h or 5 h when spiked with 10 CFU. This is likely because WMS provides a broad snapshot of the entire microbial community and the abundance of the pathogens in the sample affects their detection, which can make detecting low-level pathogens challenging due to the background noise from other organisms. Additionally, if the pathogen grows poorly, its representation in the data would remain low. TAS in contrast bypasses the need for enrichment by molecularly amplifying the targeted regions of the pathogen. The differences in sequencing depth between WMS and TAS shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e highlight why TAS is a better strategy to be used to detect low level pathogens that may be present in food samples and environmental samples. WMS relies on cultural target amplification to enhance detection of low-abundance targets whereas TAS employs molecular amplification by using specific primers to amplify genomic regions of interest and does not rely on culturing the target microorganisms. While TAS can overcome the limitations of culture dependence, the limitation is that it can only detect those pathogens that are included in the panel. However, the sensitivity of TAS makes it a powerful tool for food safety applications in outbreak investigations, foodborne illness surveillance, and routine food safety assessments.\u003c/p\u003e \u003cp\u003eThe phylogenetic analysis confirmed the robustness of TAS in accurately clustering spiked \u003cem\u003eE. coli\u003c/em\u003e strains with their respective reference strains across different timepoints and CFU tested, even in the presence of complex microbial backgrounds. TAS demonstrated specificity producing accurate phylogenetic relationships. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). WMS offers a broader taxonomic profiling and showed accurate clustering for all spiked samples at the 5 h and 6 h timepoints with the exception of the 100 CFU spiked sample for O121:H19 strain at 5 h (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). As there were not enough reads for STEC in the 0.5 h timepoints for all strains at all spiked CFUs, they were excluded from the WMS tree. These findings reinforce the utility of TAS in pathogen surveillance, particularly when they are present in low amounts in the presence of the matrix microbiome. The complementary strengths of WMS and TAS suggest that hybrid approaches could provide both broad taxonomic coverage and precise strain-level identification, enhancing microbial surveillance in food safety. By combining the sensitivity of TAS for targeted gene detection with the broad coverage of WMS, a more comprehensive and efficient workflow for pathogen detection can be achieved.\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eThe detection of pathogens like STEC in fresh produce remains a concern for public health and food safety. This study introduces the concept of use of TAS for risk assessment and shows its comparison with WMS for detecting STEC in RTE romaine lettuce, highlighting their respective strengths, limitations, and applicability to food safety.\u003c/p\u003e\u003cp\u003eTAS demonstrated higher sensitivity and specificity for \u003cem\u003estx1\u003c/em\u003e and \u003cem\u003estx2\u003c/em\u003e detection, particularly at low CFU spiked levels, making it a powerful tool for targeted pathogen detection and risk assessment while WMS, with its broader microbial resolution complements TAS by offering valuable insights into microbial diversity and community dynamics. A combined approach leveraging the strengths of both methods holds promise for enhancing pathogen detection and food safety monitoring, ultimately safeguarding public health.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthor statements\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eI.P. conceptualized the experiments, analyzed data, wrote, reviewed, and edited the manuscript. M.M. did the bioinformatic analysis to design the primer panel, data analysis, phylogenetic analysis and edited the manuscript. J.G. assisted with the data analysis and manuscript editing. A.M. critically reviewed the data and edited the manuscript. All authors have read and approved the final paper.\u003c/p\u003e\n\u003cp\u003eConflicts of interest\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003eFunding information\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003eEthical approval\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe thank Dave Lacher and Gopal Gopinathrao for reviewing the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGoodwin S, McPherson JD, McCombie WR: \u003cstrong\u003eComing of age: ten years of next-generation sequencing technologies\u003c/strong\u003e. \u003cem\u003eNat Rev Genet \u003c/em\u003e2016, \u003cstrong\u003e17\u003c/strong\u003e(6):333-351.\u003c/li\u003e\n\u003cli\u003eLi B, Saingam P, Ishii S, Yan T: \u003cstrong\u003eMultiplex PCR coupled with direct amplicon sequencing for simultaneous detection of numerous waterborne pathogens\u003c/strong\u003e. \u003cem\u003eAppl Microbiol Biotechnol \u003c/em\u003e2019, \u003cstrong\u003e103\u003c/strong\u003e(2):953-961.\u003c/li\u003e\n\u003cli\u003eSuminda GGD, Bhandari S, Won Y, Goutam U, Kanth Pulicherla K, Son YO, Ghosh M: \u003cstrong\u003eHigh-throughput sequencing technologies in the detection of livestock pathogens, diagnosis, and zoonotic surveillance\u003c/strong\u003e. \u003cem\u003eComput Struct Biotechnol J \u003c/em\u003e2022, \u003cstrong\u003e20\u003c/strong\u003e:5378-5392.\u003c/li\u003e\n\u003cli\u003eF. 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Enciso-Martinez GAG-A, M. A. Martinez-Tellez, C. J. Gonzalez-Perez, D. E. Valencia-Rivera, E. Barrios-Villa, et al.: \u003cstrong\u003eRelevance of tracking the diversity of Escherichia coli pathotypes to reinforce food safety\u003c/strong\u003e. \u003cem\u003eInternational Journal of Food Microbiology \u003c/em\u003e2022.\u003c/li\u003e\n\u003cli\u003eBrowne PD, Nielsen TK, Kot W, Aggerholm A, Gilbert MTP, Puetz L, Rasmussen M, Zervas A, Hansen LH: \u003cstrong\u003eGC bias affects genomic and metagenomic reconstructions, underrepresenting GC-poor organisms\u003c/strong\u003e. \u003cem\u003eGigascience \u003c/em\u003e2020, \u003cstrong\u003e9\u003c/strong\u003e(2).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 2 and 3","content":"\u003cp\u003eTable 2 and 3 are available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Targeted amplicon sequencing, Escherichia coli, metagenomics, foodborne pathogen","lastPublishedDoi":"10.21203/rs.3.rs-6805163/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6805163/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eContamination of leafy greens with foodborne pathogens like Shiga toxin\u0026ndash;producing \u003cem\u003eEscherichia coli\u003c/em\u003e (STEC) poses a public health concern as 40 documented outbreaks in the United States and Canada occurred between 2009\u0026ndash;2018. Early detection and identification of foodborne pathogens helps mitigate outbreaks. One way to implement this is through the application of next-generation sequencing (NGS) methods which offer high throughput, resolution, and sensitivity for both detection and identification of foodborne pathogens. Here we demonstrate the use of a custom targeted amplicon sequencing (TAS) panel targeting 135 known human foodborne pathogens. Despite the scope and power of NGS methods, technical challenges remain in detecting low levels of pathogens in contaminated food. Using a quasimetagenomics approach, this study demonstrates that compared to whole metagenomic sequencing (WMS), targeted amplicon sequencing (TAS) is a rapid and sensitive NGS based method for detecting low levels of pathogens as demonstrated with spiked STEC in bagged chopped ready-to-eat (RTE) romaine lettuce.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHere we evaluate the utility, specificity, and limit of detection of a targeted amplicon sequencing (TAS) approach for detection and identification of STEC in spiked RTE romaine lettuce. Romaine lettuce was inoculated with STEC at different concentrations. Post inoculation, cells were harvested using a modified version of the Bacteriological Analytical Methods (BAM), at 0.5 h, 5 h and 6 h from primary enrichments and DNA was isolated. DNA libraries were prepared for whole metagenome sequencing (WMS) as well as TAS. Data obtained indicate that TAS is more sensitive than WMS at not only detecting the pathogen at the species level, but also at detecting virulence markers such as \u003cem\u003estx1\u003c/em\u003e and \u003cem\u003estx2.\u003c/em\u003e\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe targeted sequencing approach described here provides a rapid and sensitive molecular method to detect and identify foodborne pathogenic bacteria. As proof of principle, we use STEC spiked RTE romaine lettuce to demonstrate the applicability of TAS in foodborne pathogen detection.\u003c/p\u003e","manuscriptTitle":"Design of a Targeted Amplicon Sequencing Panel for Detection of Foodborne Pathogens and its Application in Detection of Spiked Shiga Toxin-Producing Escherichia coli in Romaine Lettuce","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-12 11:08:02","doi":"10.21203/rs.3.rs-6805163/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d7cea304-18a5-49c9-8971-29b5ec8001b8","owner":[],"postedDate":"June 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-24T07:54:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-12 11:08:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6805163","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6805163","identity":"rs-6805163","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}