Next generation sequencing-based multigene panel for high throughput detection of food-borne pathogens

Accepted Manuscript Next generation sequencing-based multigene panel for high throughput detection of food-borne pathogens

Chiara Ferrario, Gabriele Andrea Lugli, Maria Cristina Ossiprandi, Francesca Turroni, Christian Milani, Sabrina Duranti, Leonardo Mancabelli, Marta Mangifesta, Giulia Alessandri, Douwe van Sinderen, Marco Ventura PII: DOI: Reference:

S0168-1605(17)30191-5 doi: 10.1016/j.ijfoodmicro.2017.05.001 FOOD 7574

To appear in:

International Journal of Food Microbiology

Received date: Revised date: Accepted date:

15 December 2016 28 April 2017 2 May 2017

Please cite this article as: Chiara Ferrario, Gabriele Andrea Lugli, Maria Cristina Ossiprandi, Francesca Turroni, Christian Milani, Sabrina Duranti, Leonardo Mancabelli, Marta Mangifesta, Giulia Alessandri, Douwe van Sinderen, Marco Ventura , Next generation sequencing-based multigene panel for high throughput detection of food-borne pathogens, International Journal of Food Microbiology (2017), doi: 10.1016/ j.ijfoodmicro.2017.05.001

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ACCEPTED MANUSCRIPT Revised FOOD-D-16-01265R2

Next Generation Sequencing-based multigene panel for high throughput detection of foodborne pathogens

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Running title: Illumina PCR panel for foodborne pathogens

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Chiara Ferrario1, Gabriele Andrea Lugli1, Maria Cristina Ossiprandi2, Francesca Turroni1, Christian Milani1, Sabrina Duranti1, Leonardo Mancabelli1, Marta Mangifesta2, Giulia

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Alessandri1, Douwe van Sinderen4 and Marco Ventura1

Laboratory of Probiogenomics, Department of Life Sciences, University of Parma, Parma, Italy1;

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Department of Medical-Veterinary Science, University of Parma, Parma, Italy2; GenProbio srl,

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Parma, Italy3; APC Microbiome Institute and School of Microbiology, Bioscience Institute,

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National University of Ireland, Cork, Ireland4

Corresponding author. Mailing address for Marco Ventura Department of Life Sciences,

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University of Parma, Parco Area delle Scienze 11a, 43124 Parma, Italy. Phone: ++39-521905666. Fax: ++39-521-905604. E-mail: [email protected]

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ACCEPTED MANUSCRIPT Abstract Contamination of food by chemicals or pathogenic bacteria may cause particular illnesses that are linked to food consumption, commonly referred to as foodborne diseases. Bacteria are present in/on various foods products, such as fruits, vegetables and ready-to-eat products.

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Bacteria that cause foodborne diseases are known as foodborne pathogens (FBPs). Accurate

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detection methods that are able to reveal the presence of FBPs in food matrices are in constant

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demand, in order to ensure safe foods with a minimal risk of causing foodborne diseases. Here, a multiplex PCR-based Illumina sequencing method for FBP detection in food matrices was

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developed. Starting from 25 bacterial targets and 49 selected PCR primer pairs, a primer

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collection called foodborne pathogen – panel (FPP) consisting of 12 oligonucleotide pairs was developed. The FPP allows a more rapid and reliable identification of FBPs compared to

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classical cultivation methods. Furthermore, FPP permits sensitive and specific FBP detection in

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about two days from food sample acquisition to bioinformatics-based identification. The FPP is able to simultaneously identify eight different bacterial pathogens, i.e. Listeria monocytogenes,

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Campylobacter jejuni, Campylobacter coli, Salmonella enterica subsp. enterica serovar

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enteritidis, Escherichia coli, Shigella sonnei, Staphylococcus aureus and Yersinia enterocolitica, in a given food matrix at a threshold contamination level of 101 cell/g. Moreover, this novel

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detection method may represent an alternative and/or a complementary approach to PCR-based techniques, which are routinely used for FBP detection, and could be implemented in (parts of) the food chain as a quality check.

Keywords: foodborne pathogen, primer design, Illumina MiSeq, food matrices

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ACCEPTED MANUSCRIPT 1. Introduction Foodborne diseases (FBDs) encompass a wide spectrum of illnesses representing a serious global public health problem (Lai et al., 2016; Ronholm et al., 2016). FBDs result from ingestion of food products contaminated with pathogenic microorganisms and/or their toxins, or chemicals.

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Pathogens responsible for FBDs are usually referred to as foodborne pathogens (FBPs) (Zhao et

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al., 2014). FBPs are commonly present in or on various foods, such as fruits, vegetables and

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ready-to-eat products that are consumed without any treatment to specifically remove such pathogens (Chung et al., 2010; Lee et al., 2014). The list of FBPs is continuously updated,

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eventhough until 1960s the most frequently encountered FBPs were Salmonella spp., Shigella

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spp., Clostridium botulinum and Staphylococcus aureus. In the 1980s and 1990s new pathogenic species/strains were added to this list, such as Campylobacter spp., Yersinia spp., Listeria

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monocytogenes, as well as the enterohaemorrhagic Escherichia coli O157:H7 strain (Newell et

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al., 2010).

In order to improve the microbiological safety of food it is essential to assess food matrices for

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the presence of FBPs (FDA, 2015; Tauxe, 1997). Conventional methods for the detection of

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(particular) bacteria rely on growing such bacteria on solid synthetic media, which have an approved diagnostic composition (Bai et al., 2010). Traditional methods are inexpensive, and

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allow the qualitative and quantitative assessment of microorganisms (Feng, 2001). However, these procedures are time-consuming and biased according to the specific cultivation requirements for most FBPs. In contrast to culture-based methods, PCR is faster, more sensitive, specific and it is widely used for the detection of certain bacteria (Postollec et al., 2011), in particular FBPs (Law et al., 2015). Notably, different culture-independent, PCR-based techniques have been developed, such as denaturing gradient gel electrophoresis (DGGE),

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ACCEPTED MANUSCRIPT quantitative PCR (qPCR) and loop-mediated isothermal amplification (LAMP) (Hoorfar, 2011; Mayo et al., 2014). However, next generation sequencing (NGS) has revolutionized food microbiology through the development of new high-throughput technologies such as 16S rRNAbased microbial profiling and shotgun sequencing, which have been used to investigate the

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microbiota composition of various foods (Mayo et al., 2014; Solieri et al., 2013). In the current

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study we developed a novel method for FBP detection based on parallel sequencing of multiple

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amplicons, an approach which is clearly different from the 16S rRNA-based microbial profiling method as the latter relies on sequencing of a single gene. This novel FBP detection approach is

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based on a primer collection called “foodborne pathogen-panel” (FPP), which generates species-

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specific amplicons designed for sequencing on an Illumina platform. FPP was evaluated by means of in silico and in vitro end-point PCR using DNA from selected FBPs, while specificity,

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sensitivity and the use of multiplex PCR were validated and optimized employing single- or

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mixed- DNA template experiments. Furthermore, to validate this new method, deliberately contaminated food products were investigated. Finally, this newly developed FPP approach was

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applied to trace FBPs in naturally contaminated food matrices.

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ACCEPTED MANUSCRIPT 2. Materials and Methods 2.1 Bacterial strains and DNA extraction. Bacterial strains and culture media used in this work are listed in Table 1. All media and reagents were purchased from Oxoid (Oxoid, UK).

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Campylobacter coli, Campylobacter jejuni and Helicobacter pylori were grown at 42°C in a

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microaerophilic environment. Strains belonging to Clostridium spp. were grown under anaerobic

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conditions at 37°C. All other strains were cultivated at 37°C under aerobic conditions. Bacterial DNA was extracted from an overnight culture (about 109 cells/mL) using the

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GenElute™ Bacterial Genomic DNA Kit Protocol (Sigma, USA) following the manufacturer’s

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instructions. 2.2 Primer selection and design.

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Targeted bacterial species and species-specific primers retrieved from literature or designed for

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this study are reported in Table 2. When oligonucleotides retrieved from the current existing literature were not compatible with the Illumina amplicon sequencing protocol (product size, GC

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content; TruSeq® Custom Amplicon v1.5 Data sheet), they were re-designed using Primer3

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software (Untergasser et al., 2012). For multiplex PCR assay development, previously described criteria were taken into account (Rachlin et al., 2005; Shen et al., 2010). In order to design

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primers specific for FBPs that are suitable for multiplex reactions, oligonucleotides of about 20 bp were designed to achieve PCR products ranging in size from 150 to 250 bp. Moreover, only oligonucleotide pairs with similar melting temperatures were included in the selection step, allowing all PCR reactions to be performed in a single reaction following multiplex optimization (Cui et al., 2016; Shen et al., 2010; Sint et al., 2012). Selected primer pairs have an annealing temperature ranging from 53 to 56 °C.

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ACCEPTED MANUSCRIPT Primer specificity was checked using Primer Blast (Ye et al., 2012) with searches against the Non-redundant GenBank database. Only primer pairs that fully complied with the above mentioned criteria were selected for the subsequent step on the panel validation procedure (Fig. 1a). Primers were synthetized by

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Macrogen (Macrogen Inc., Korea).

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2.3 PCR amplification and design of the “foodborne pathogen - panel”.

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The specificity and sensitivity properties for each primer set were first validated by in silico PCR, end-point PCR and gel electrophoresis.

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Chromosomal DNA extracted from FBPs was employed as a template for end-point PCR using

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each of the 49 primer pairs included in the panel. Each 12.5 μL PCR reaction contained approximately 30 ng of genomic DNA, Platinum PCR SuperMix 1X (Invitrogen, USA) and 100

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pM of each oligo. Each PCR reaction consisted of an initial denaturation step of 4 min at 95°C,

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followed by 35 amplification cycles as follows: denaturation at 95 °C for 30 s, annealing at the temperature outlined in Table 2 for 30 s, 72°C for 40 s. Following these cycles, the PCR reaction

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was finalized by an elongation step at 72°C for 5 min. PCR reactions were performed on a Verity

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Thermocycler (Applied Biosystems, USA). Amplicons were analyzed by electrophoresis on 1.5 % agarose gel, and visualized by SYBR Safe DNA gel stain (Invitrogen).

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Based on the in vitro amplification tests results (Fig. 2), oligonucleotide pairs were selected or discarded (Table 2) for development of the FPP (Fig. 1a). In order to confirm the amplification results, single end-point PCRs were performed for each positive primer pair against all FBPs included in this study, by selecting a unique annealing temperature, consisting of 55 °C. After optimization of the temperature conditions, primers were mixed at different concentrations,

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ACCEPTED MANUSCRIPT ranging from 0.1 to 1 pM and PCR products were checked to evaluate the specificity and the presence of unexpected amplicons. Furthermore, sensitivity was assessed by amplifying single and mixed DNAs that were diluted tenfold from a starting concentration corresponding to about 109 cells/mL to as low as

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(theoretically) one cell/mL.

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2.4 In vitro panel validation.

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After temperature and primer concentration optimization, all properly diluted and mixed primer pairs that generated positive results (the final primer concentration is reported in Table 3) were

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tested. To evaluate the specificity of the multiplex PCR, a single selected FBP-derived DNA

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template (single-DNA template experiment) was tested in an amplification reaction using the

ATCC 31194 was selected (Fig. 1a).

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FPP. For this one-DNA template experiment Salmonella enterica subsp. enterica ser. enteritidis

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To simulate a real food matrix, where different bacterial species may be present, a mixture of 14 selected FBP DNAs (mixed-DNA template experiment) was used as a template in a PCR

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amplification reaction with the FPP. For such a mixed-DNA template experiment, we selected

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Listeria monocytogenes ATCC 13932, E. coli ATCC 35150, S. enterica subsp. enterica ser. enteritidis ATCC 31194, C. coli Cc1, C. jejuni ATCC 33291, Bacillus cereus ATCC 9634,

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Clostridium tyrobutiricum ATCC 25755, Shigella sonnei Ss3, Staphylococcus aureus ATCC 43300 and Yersinia enterocolitica CIP 6529. Moreover, other FBPs (Bacillus cereus ATCC 9634, Pseudomonas aeruginosa ATCC 27853, Aeromonas hydrophila ATCC 35654, Vibrio cholera ATCC14035 and Salmonella enterica subsp. enterica ser. thyphimurium ATCC1 4028) were included in the DNA bacterial mix to evaluate the presence of possible non-specific amplicons.

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ACCEPTED MANUSCRIPT The final volume of the PCR reaction was 25 μL, containing 5 μL 5X Colorless GoTaq Flexi Buffer (Promega, Italy), 2 μL MgCl2 solution 25 mM, 0.5 μL dNTPs 10 mM, 10.875 μL nuclease-free water, 0.125 μL of GoTaq G2 Hot Start Polymerase Taq (Promega) and 4 μL of genomic DNA.

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Following amplification, PCR products were visualized by gel electrophoresis and purified using

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the NucleoSpin Gel and PCR Clean-up kit (Macherey-Nagel GmbH & Co., Germany). The size

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analysis and were then subjected to Illumina sequencing.

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of purified DNA fragments was checked by Tape station 2200 (Agilent Technologies, USA)

2.5 Detection studies with naturally and seeded contaminated samples.

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Portions of representative food matrices such as poultry meat, caciotta cheese and swordfish fillet (about 15 g each) were purchased from a local supermarket in Parma, Italy. All samples

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were placed into sterile plastic bags prior to any processing. Samples were aseptically cut, taken

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from near adjacent areas to ensure uniformity in microbial population, and placed in sterile bags. DNA was extracted using the PowerFood Microbial DNA Isolation Kit (MoBio, USA) following

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the manufacturer’s instructions. Samples were tested with classical end-point PCR using FPP

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oligonucleotides, to verify that food matrices were not contaminated with test organisms. During FPP assay validation, samples that were deemed to be FBP-free were deliberately

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contaminated with varying amounts of FBPs. From each food matrix, four samples were taken, of which one represented the negative control, while the other three samples were deliberately contaminated with 101, 103 and 105 cells/ml of one or more FBPs. Moreover, matrix-specific strains were used: poultry was contaminated with S. enterica subsp. enterica ser. enteritidis ATCC 31194, C. jejuni ATCC 33291 and L. monocytogenes ATCC 13932. Caciotta cheese was

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ACCEPTED MANUSCRIPT inoculated with C. jejuni ATCC 33291, L. monocytogenes ATCC 13932 and S. sonnei Ss1. L. monocytogenes ATCC 13932 and C. jejuni ATCC 33291 were spread on swordfish fillets. Following growth on specific agar medium, for each pathogen selected for FBP seeding experiments, a pure colony was picked and dissolved in nine mL of saline solution (SS, 0.9 %

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NaCl) to reach 1-2 points of the McFarland scale (CLSI, 2009), which approximately

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corresponds to 3 - 6 x 108 cells/mL. Ten-fold dilutions were made to reach 101, 103 and 105

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cells/mL. Suspended cells were spread on food matrices. After 30 minutes, inoculated samples were mixed with 15 mL of SS in a blender (PBI, Italy) for four min. Following homogenization,

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DNA samples were extracted using the PowerFood Microbial DNA Isolation Kit (MoBio)

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following the supplier’s instructions. Furthermore, un-inoculated food samples were used for DNA extraction to evaluate contamination levels of such samples and the ability of the FPP to

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detect such contaminants. Experiments were performed in triplicate.

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2.6 Traditional microbiological analyses.

To confirm concentration of inoculated cells, dilutions in duplicate were plated on Tryptic Soy

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Agar (TSA) in aerobic conditions at 37°C. Only for Campylobacter counts, plates were

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incubated anaerobically at 42°C.

To evaluate the recovery of bacterial cells in food matrices, following 30 min of incubation with

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selected pathogenic bacteria, matrices were blended and ten-fold diluted in SS. Cell suspensions were incubated in TSA aerobically at 37°C. Moreover, negative controls were performed using un-inoculated food matrices, which were blended, diluted and incubated as indicated above. 2.7 MiSeq sequencing of amplicons. Since the Illumina amplicon sequencing technology (TruSeq Custom Amplicon) has been optimized for human, mouse, rat, bovine, maize, rice, pig, dog, soybean, chicken, and sheep, but

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ACCEPTED MANUSCRIPT not for bacteria, a custom library preparation protocol was applied. In order to reduce the cost of PCR primer synthesis, primers were synthetized without adapter and barcodes. Furthermore, all PCR products derived from multiplex PCR reactions with FPP were purified and barcoded using the TruSeq Nano DNA LT kit (Illumina, USA). PCR amplicons were directly subjected to end-

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repair PCR and after a size selection procedure employing magnetic beads (Illumina), indexes

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were attached to the blunt fragments. Barcoded DNA fragments were enriched and cleaned-up

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(Illumina TruSeq Nano DNA sample preparation guide, Part#15041110Rev.D, Illumina). From the concentration and the average size of each amplicon library, the amount of DNA

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fragments per microliter was calculated and libraries for each run were diluted to the proper

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concentration prior to clonal amplification. Sequencing of the amplicon libraries was carried out using the MiSeq Illumina system according to the supplier’s instructions. After sequencing, the sequence

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2.8 Bioinformatic analyses.

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(https://code.google.com/hosting/moved?project=ea-utils) to remove low quality sequences.

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sequenced

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In order to allow fast data analysis, we developed a bash script that automatically processes all

(http://probiogenomics.unipr.it/sw/foodborne_pathogen_analysis.zip). After an initial quality

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filtering based on average read quality and minimum read length, the paired-end reads were merged in order to reduce the presence of non-specific DNA amplifications. The resulting merged reads were used to identify reads associated with the 49 targeted FBP genes. This was performed by employing the software Bowtie 2 (Langmead and Salzberg, 2012; Li and Durbin, 2010) for read mapping on a database including all FBP genes targeted by corresponding primer pairs developed here, using a local alignment score setting (--score-min L,0,-0.3). The number of

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ACCEPTED MANUSCRIPT reads linked to each FBP gene was counted with HTseq-count (Anders et al., 2015) and a text

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file report was generated in the “output” folder.

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ACCEPTED MANUSCRIPT 3. Results 3.1 PCR Primer design for FBP identification. In order to develop a specific and reliable assay for FBP identification, suitable for Illumina MiSeq sequencing technology, we screened available literature for primer pairs that had

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previously been described for the detection of common FBPs. Moreover, FBP-specific

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oligonucleotides were designed when primer pairs retrieved from literature did not meet the

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criteria for Illumina sequencing (TruSeq® Custom Amplicon v1.5 Data sheet) (Fig. 1a). Amplicon size ranging from 150 to 250 bp, and an annealing temperature ranging from 53 to 56

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°C were selected.

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In order to ensure an unambiguous identification of FBPs, 49 representative genes were chosen to target a set of FBPs, whose genomes were used as templates for primer pair design. Where

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possible, at least two primer pairs were designed for each taxon. The gene targets were selected

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on the basis of being highly specific for the FBP investigated. The oligonucleotides developed in this work were designed using consensus sequences from database entries covering various

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sequenced isolates/strains of selected food-borne pathogens (Table 2).

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3.2 Foodborne pathogen - panel PCR specificity. As displayed in Figure 2, when all the individual FBP-targeting primer pairs were used in end-

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point PCR trials involving DNA extracted from all of the 25 FBP strains, about 39 % (19/49) of the specific primers generated the expected amplicon (Fig. 2). Specificity was assessed as follows: each oligonucleotide pair was tested using the DNA of the targeted FBP, as well as using DNA isolated from control species, in order to test that no cross amplification was taking place. Failure of the PCR to generate the expected amplicon may be explained by the presence of secondary structures in the template DNA.

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ACCEPTED MANUSCRIPT Therefore, based on the results achieved in the in vitro PCR validation step (Fig. 2), we reduced the number of genes and (consequently) species to be targeted (Table 2). The resulting panel of primer pairs, which is represented by 19 oligonucleotide couples, was named “foodborne pathogen - panel” or FPP. FPP experiments required the optimization of PCR amplification

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conditions such as primer concentration and annealing temperatures. For multiplex PCR

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experiments, the selected annealing temperature was 55°C, while optimal primer concentrations

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for each selected pair are variable (Table 3). 3.3 In vitro evaluation of the FPP.

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In order to investigate the accuracy of our detection method, we decided to apply the FPP to a

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simulated food matrix. Multiplex-PCR was firstly carried out using individual DNA samples of each FBP strain, in order to evaluate the reproducibility and specificity of the designed

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conditions (Fig. S1a). Amplification was achieved for all strains tested demonstrating the

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efficiency of the assay, with the exception for Bacillus cereus, Pseudomonas aeruginosa and Salmonella enterica subsp. enterica serovar tiphymurium; primers corresponding to these

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strains/species were subsequently excluded from the FPP (Table 3). Moreover, the sensitivity of

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FPP was assessed by the use of DNA template dilutions (Fig. S1b). Notably, amplifications produced positive results from 109 to 101 cells/mL.

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Genomic DNA from Salmonella enterica subsp. enterica ser. enteritidis ATCC 31194 (singleDNA template experiment) and a mix of 14 different DNA samples extracted from FBP strains (mixed-DNA template experiment) (Table 3) were used as a template to test FPP-mediated amplicons (Fig. 1a), which were then prepared for MiSeq Illumina sequencing (see Materials and Methods). In total, 709,008 and 674,862 sequence reads were generated by the Illumina MiSeq sequencing for the single- and mixed-DNA template experiments, respectively. The sequencing

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ACCEPTED MANUSCRIPT data were analyzed using a custom script (see Materials and Methods for details), obtaining the number of reads aligned to each FBP gene reported in Table 3. The achieved filtered mapped reads ranged from 95.5 % to 98.1 %, for the mixed- and single-DNA template experiment, respectively, perfectly aligning with the targeted FBP gene sequences, thus validating the

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employed primer sets. Seven oligonucleotide pairs did not produce specific reads, probably due

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to chimeric reads, and we therefore decided to omit these from the panel (Table 3).

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3.4 FPP validation with deliberately spoiled and naturally contaminated food matrices In order to validate the FPP described in this study, poultry meat, caciotta cheese and swordfish

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fillets were deliberately and simultaneously contaminated with specific FBPs at different

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contamination levels (101, 103 and 105 cells/mL), reproducing a simulated food environment. For poultry meat, we selected S. enterica subsp. enterica ser. enteritidis ATCC 31194, C. jejuni

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ATCC 33291 and L. monocytogenes ATCC 13932. Whereas, caciotta cheese was inoculated

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with C. jejuni ATCC 33291, L. monocytogenes and S. sonnei Ss3, while swordfish fillet was contaminated with L. monocytogenes ATCC 13932 and C. jejuni ATCC 33291.

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In total, sequence reads ranging in number from 95,059 to 422,836 were generated by Illumina

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MiSeq DNA sequencing. Primer sets targeting the FBPs yielded specific PCR products, as displayed by the presence of sequencing reads that mapped onto FBPs (Fig. 3), reaching more

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than 66.1 % of mapped reads of the total sequencing output in the cheese sample and perfectly aligned with the targeted gene sequences. These findings corroborate the high specificity of the primer pairs selected for the FBPs used in this experiment. In detail, Figure 3a displays the results using the FPP approach when food matrices were deliberately contaminated with FBPs. In caciotta cheese, all three FBP inoculated strains were faithfully detected. A higher (deliberate)

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ACCEPTED MANUSCRIPT FBP contamination level (105 cells/mL) resulted in a correspondingly higher number of sequence reads that matched with FBP-specific primers in this food matrix (Fig. 3a). With regards to fish and poultry samples, a very low number of reads was associated with the selected FBP genes. This result may be caused by (a high level of) PCR inhibitors in these food

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matrices. Despite the low number of specific reads, the FPP was able to detect each of the

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purposely added FBPs such as C. jejuni and L. monocytogenes, as well as C. jejuni, L.

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monocytogenes and S. enteritidis in swordfish and poultry meat, respectively (Fig. 3a). In order to validate the FPP protocol for FBP detection in a food chain control procedure, the

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assay was also applied to (naturally contaminated) food samples. Exploring the presence of FBPs

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in food matrices, a high contamination level was detected in all three food matrices explored. As shown in Figure 3b, c and d, amplification with the primer panel produced the expected

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amplicons. In particular, high read numbers were detected for caciotta cheese DNA, while a

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smaller number of reads were detected for fish and poultry samples, probably due to varying DNA extraction efficiencies from these food matrices (Fig. 3b). Notably, despite the encountered

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difficulties, the FPP was able to detect a high number of reads associated with E. coli and S.

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enterica, while a lower number of reads was shown to correspond to S. aureus and Y. enterocolitica. Regarding fish and poultry samples, FBP species usually associated with FBDs in

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these matrices were identified using the FPP approach and are illustrated in Figure 3c and 3d, respectively.

3.5 Sensitivity of FPP. We tested the sensitivity and efficiency of FPP compared to culture dependent methods for the identification of FBPs. For this purpose, inoculated food samples were subjected to a classical cultivation methodology to evaluate if the inoculated FBPs strains were detectable. After

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ACCEPTED MANUSCRIPT intentional contamination and incubation at 37°C, the inoculum counts were determined to consistent with the number of inoculated FBPs cells (Table 4). For each of the five FBP strains inoculated at a different cell number, such inoculum values were validated (Table 4).

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Regarding naturally contaminated food matrices, fish samples showed a total bacterial count of

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about 1.4 x 105 CFU/g, while assessment of poultry samples resulted in 6.6 x 104 CFU/g. Cheese

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displayed a bacterial count that was higher than 108 CFU/g. When cell recovery after contamination, i.e., bacterial count after artificial contamination of food matrices, was evaluated,

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lower bacterial counts were obtained in comparison to the negative control for meat matrices,

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while comparable values were obtained for cheese samples (Table 4). This result may be due to

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bacterial competition thereby reducing bacterial growth in the food matrices tested.

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4. Discussion

In food industries, the most commonly employed methods for FBP detection are based on

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classical microbiology techniques (Lee et al., 2014). Such methodologies do not allow rapid and

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simultaneous detection of several different pathogens (Villamizar-Rodriguez et al., 2015). DNAbased methods for pathogen detection on the other hand save a considerable amount of time (de

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Boer et al., 2015; Law et al., 2015; Zhao et al., 2016). Here, we developed and validated an assay for the simultaneous detection of different FBPs in food matrices using an NGS sequencing approach. For this purpose, 25 bacterial species commonly responsible for FBDs (Kirk et al., 2015; Zhao et al., 2016) were selected as a starting point, in association with 49 corresponding gene targets that were identified from available databases and literature.

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ACCEPTED MANUSCRIPT The reproducibility and performance of the results achieved with FPP are not comparable with those achieved with classical methods based on cultivation of serial dilution (VillamizarRodriguez et al., 2015). The differences between these two approaches may be due to variable DNA extraction efficiencies among bacterial species as well as the presence of dead or injured

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cells, and selective outgrowth of species on TSA agar plates (de Boer et al., 2015). Moreover, in

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contrast to viable cell count approaches (involving plating), sequencing methods do not give a

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direct quantification of individual bacterial species, but only relative abundance of these in the same sample (de Boer et al., 2015). An advantage of the FPP method is the simultaneous

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identification of multiple FBPs, thus allowing direct detection in complex matrices without

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enrichment steps. Moreover, assessment of the sensitivity and specificity of FPP indicate that it is an accurate method, which can be compared with other DNA-based techniques such as qPCR

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and multiplex qPCR (Velusamy et al., 2010; Villamizar-Rodriguez et al., 2015).

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Recently, many new methods have been developed for FBP detection, such as those based on real-time PCR assay (Kawasaki et al., 2010), DNA microarray (Huang et al., 2014) and ELISAs

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(Shen et al., 2014). However, none of these is without its specific technical limitations (Law et

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al., 2014). Notably, the FPP protocol (Fig. 1b) is rapid, i.e., two days are required by FPP compared to up to four days when using classical methods, while it is also relative straight-

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forwards and reliable (in contrast to DNA microarray it is based on non-modified oligonucleotides) while the bioinformatic analysis will avoid false negative results. Taken altogether our findings highlight the usefulness of FPP as a method to simultaneously, rapidly and reliably detect various well-known FBPs. Application of the FPP protocol for FBP detection, using for example 40 samples, can be performed on a low cost Flow cell (Illumina), reducing analysis time and costs. This methodology may ultimately be implemented to serve as a

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ACCEPTED MANUSCRIPT quality check in (parts of) the food chain. Moreover, a possible improvement of FPP could involve the inclusion of a specific quantification step for the different FBPs, thus allowing the use of FPP not only for qualitative but also for quantification purposes.

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Acknowledgements

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This work was funded by the EU Joint Programming Initiative – A Healthy Diet for a Healthy

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Life (JPI HDHL, http://www.healthydietforhealthylife.eu/) and MIUR to MV. We thank GenProbio srl for financial support of the Laboratory of Probiogenomics. SD is supported by

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Fondazione Caritro, Trento, Italy. LM is supported by Fondazione Cariparma, Parma, Italy. DvS

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is a member of The APC Microbiome Institute funded by Science Foundation Ireland (SFI), through the Irish Government’s National Development Plan (Grant number SFI/12/RC/2273).

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The authors declare that they have no competing interests.

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ACCEPTED MANUSCRIPT 5. References

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ACCEPTED MANUSCRIPT Figure legends Figure 1. Foodborne pathogen-panel workflow. In panel a each step of the procedure for the method development is schematically illustrated from top to bottom. Panel b displays the protocol used for a practical application of FBP detection in food matrices, including the time to

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execute each step.

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Figure 2. Heat map showing the PCR amplification profile of the FBP-based primers. Cells are colored on a gradient from 0 % to 100 % positive matches on the basis of end-point PCR results.

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Annealing temperature, corresponding primer pairs and panel selection decision are indicated.

Figure 3. Heat map showing sequencing profile obtained from contaminated food matrices.

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Panel a reports the results obtained from sequencing the amplicons obtained from artificially

ED

contaminated food matrices. Each heat map shows the target species as well as the food matrix and the inoculum level (101 - 103 - 105 cells/mL). Panels b, c and d depict the sequencing results

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obtained from naturally contaminated food matrices of, respectively, cheese, fish and poultry

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meat. Each cell is colored from white to black, identifying the lowest and the highest value,

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respectively. Reads associated with each specific bacterial target are indicated.

23

ACCEPTED MANUSCRIPT Table 1. Bacterial strains used in this study. Strainsb

Media

ATCC 35654

Bacillus cereus Campylobacter coli

ATCC 9634 Cc1

Tryptic Soy Agar (TSA) TSA Blood Agar

Campylobacter jejuni

ATCC33291

Blood Agar/ TSA

Clostridium difficile Clostridium perfringens Clostridium tyrobutirricum

VPI 10463 NCTC 8237 ATCC 25755

Blood Agar Blood Agar Blood Agar

Cronobacter sakazakii

ATCC 29544

TSA

Escherichia coli Helicobacter pylori Listeria monocytogenes Mycobacterium tubercolosis Pseudomonas aeruginosa Salmonella enterica subsp. enterica ser. enteritidis Salmonella enterica subsp. enterica ser. thyphimurium

ATCC 35150 (O157:H7) ATCC 43526 ATCC 13932 TMC 102 ATCC 27853

Shigella boydii

Sb1

Shigella flexneri

Sf2

Shigella sonnei

Ss3

Staphylococcus aureus Vibrio cholerae Vibrio parahaemolyticus Yersinia enterocolitica Yersinia pseudotubercolosis Escherichia coli STA+ LT+ Escherichia coli STA+ STB+ LT+ ETEC Escherichia coli 08 VTE+ ETEC Escherichia coli VTE+

ATCC 43300 ATCC 14035 ATCC 17802 CIP 6529 Yp 18S 2016/34570/1 2016/31720/1

TSA

Raw meat, raw milk, vegetables

2015/88866/1 2015/252433/1

TSA TSA

Raw meat, raw milk, vegetables Raw meat, raw milk, vegetables

CR

TSA TSA TSA TSA TSA TSA

Raw chicken, milk, seafood Cattle, milk, dairy products Raw vegetables, salad Poultry, meat eggs, seafood, fruit, vegetables Poultry, meat eggs, seafood, fruit, vegetables Water, eggs, fruits, dairy products, raw meat Water, eggs, fruits, dairy products, raw meat Water, eggs, fruits, dairy products, raw meat Salabs, backery products Seafood, shelfish Seafoods, oyster Pork meat Pork meat Raw meat, raw milk, vegetables

TSA

ATCC 31194

US

TSA

ATCC 14028

TSA

ED

M

AN

TSA

PT

a

TSA Blood Agar TSA

Seafood Spice and corn Raw meat, raw chicken, raw milk Shellfish, Raw meat, raw chicken, raw milk Raw meat Raw meat. Raw chicken Raw milk, cheese Vegetables, milk powder, cheese, eggs, meat Raw meat, raw milk, vegetables

T

Aeromonas hydrophila

Food sources

IP

Bacteriaa

TSA TSA

AC

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: STA: heat-stable enterotoxin type A; LT: heat-labile toxin; STB: heat-stable enterotoxin type B; ETEC: enterotoxigenic Escherichia coli; VTE: verotoxin Escherichia coli. b : ATCC: American Type Culture Collection, USA;VPI: Anaerobe Laboratory, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA; NCTC: National Collection of Type Cultures, Central Public Laboratory Service, London, UK; TMC: Trudeau Mycobacterial Culture Collection, Trudeau Institute, Denver, CO, USA; CIP: Collection of Institute Pasteur, Biological Resource Center of Institute Pasteur (CRBIP), Paris, France.

24

ACCEPTED MANUSCRIPT

Bacillus cereus

Campylobacter coli Campylobacter jejuni Clostridium perfringens Clostridium tyrobutyricum

Clostridium difficile

Cronobacter sakazii Escherichia coli (EHEC)

Escherichia coli (STEC/VTEC) Helicobacter pylori

55

+

53

-

53

-

53

-

55

+

This study

55

+

178 196

This study This study

55 53

+ -

CCATCACCTAAGGACTGTTC

170

53

-

Cper2rv

GATTGTACCAATTGCACAGG

170

(Wu et al., 2009)

55

+

Ctyr1rv

TTTACTGCACAACTCAAAGG

201

This study

53

-

GGTTTGAATCAGGCATCAAG

Ctyr2rv

GTTAATTGGTTAACCTCGTC

182

This study

55

+

CCTAATTTAGCAGCAGCTTC

Cdiff1rv

CTTGGATGGTTGATGAGTAC

154

(Du et al., 2014)

55

+

55

+

56

-

53

-

55

+

53

-

55

+

53

-

53

-

References

Panel selection

Aeromonas hydrophyla

Product Size

Organism

Temperature annealing (°C)

Table 2. List of primers used for the creation of the foodborne pathogen – panel.

Locus

Accession number

Haemolysin

KU845730.1

Ahyd1fw

TGGCCTTCTACCTCAACGTC

Ahyd1rv

ATCCGCACTATCTTGGCATC

196

Enterotoxin

CP013965.1

Ahyd2fw

CGCCATCAACAGCTCGCCCA

Ahyd2rv

GCCTGAGCTGACCCTCGCCA

185

Cereluide synthetase I

AB248763.2

Bcer1fw

ATCATAAAGGTGCGAACAAG

Bcer1rv

AGATCAACCGAATGCAACTG

186

Ces gene

DQ360825.1

Bcer2fw

ACGCCGAAAGTGATTATACC

Bcer2rv

ATAAAACCACTGAGATAGTG

171

CadF gene Aspartate kinase gene CdtB gene HipO gene Alpha enterotoxin fusion protein Plc gene

KP164620.1

Ccol1fw

TGTGAGACTACAGGAGCTGG

Ccol1rv

TTCCATGATGCAGATCATAG

149

(Singh et al., 2007) (Rather et al., 2014) (Kim et al., 2010) (Wehrle et al., 2010) This study

KP164645.1

Ccol2fw

GGGCTTGGTATGAAGAATAC

Ccol2rv

TCTTATCGCTTGCACTTCCT

198

KX495604.1 KP164639.1

Cjej1fw Cjej2fw

GCTCCTACATCTGTTCCTCC AAGTTATTGGAAGAGGTGGT

Cjej1rv Cjej2rv

GCGTTGATGTAGGAGCTAAT TTAATCGTTGCAATATCTGG

KU711833.1

Cper1fw

GATTGGCGTTCTTCTAACTC

Cper1rv

X13608.1

Cper2fw

TTCCATCTCCACCTCTTGAA

Enr gene

Y09960.1

Ctyr1fw

TGGTGTTCCACAAGAAGCTG

Fla gene NAD-specific glutamate dehydrogenase

AJ242662.1

Ctyr2fw

KU356679.1

Cdiff1fw

D E

Forward 5' - 3'

Reverse 5' - 3'

M

T P

E C

I R

C S

U N

A

T P

Toxin B

KC292190.1

Cdiff2fw

GAAGGTGGTTCAGGTCATAC

Cdiff2rv

CTGGTGTCCATCCTGTTTCC

199

GyrB gene Hypothetical protein Perosamine synthetase Shiga toxin subunit A Anti-termination protein Q Hypothetical protein Urease subunit alpha

KU364513.1

Csak1fw

GATCGGTGTCGCCGGTTACG

Csak1rv

TCTCCGGTGGTCTGCACGGC

153

(Peterson et al., 2007) This study

KC602378.1

Csak2fw

TGTCATAGAGCGGAGCCAGG

Csak2rv

AGCGCGTCGCTTGCCAGCAG

202

This study

CCTCTCTTTCCTCTGCGGTC

180

TCCGTTGTCATGGAAACCG

184

C A

CP017249.1 CP015855.1 CP017251.1

KM085449.1 AY227442.1

EcEHEC1f w EcEHEC2f w EcSTEC1f w EcSTEC2f w Hpyl1fw

CTGCTATAGGATTAGCCCAG TGCTGTGGATATACGAGGGC

EcEHEC1r v EcEHEC2r v

TCAGCATGCCTTCAACAATC

EcSTEC1rv

ACGATGATGCGATGGTGATA

192

CCAATCAACAGTTTCGTCAA

EcSTEC2rv

GCAATAGCCTGTTCATCACG

166

AACCGGATGATGTGATGGAT

Hpyl1rv

CCTTCGTTGATAGTGATGTC

172

(Iyer, 2010) (Carey et al., 2009) (Olsen et al., 1995) (Son et al., 2015) This study

25

ACCEPTED MANUSCRIPT Toxin Listeria monocytogenes

Cell wall hydrolases A Peptidase

LC187396.1

Hpyl2fw

AAATACAACAAACACACCGC

Hpyl2rv

AGAAGCCCTGAGACCGTTCC

177

This study

53

-

JX126777.1

Lmon1fw

GTGTTGGTGCAACAGGAGTG

Lmon1rv

TAGTGGCGCTGGTGTTGATA

179

55

+

DQ857726.1

Lmon2fw

TGTGTTGGTGCAACAGGAGT

Lmon2rv

TAGTGGCGCTGGTGTTGATA

180

(Bai et al., 2010)

53

-

53

-

53 53

-

56

-

53

-

53

-

55

+

56

-

(Maruthai et al., 2015) This study This study (Park and Ricke, 2015) (Horton et al., 2016) (Maurisch at et al., 2015) (Moosavy et al., 2015) (Maurisch at et al., 2015)

Mycobacterium tubercolosis

RpoB gene

AY823310.1

Mtubfw

GTACGGTCGGCGAGCTGATC

Mtubrv

CCAGTCGGCGCTTGTGGGTC

Pseudomonas aeruginosa

Enterotoxin GyrB gene

CP015003.1 CP015003.1

Paer1fw Paer2fw

CGATGACTGATGACCGTGGG CCTGACCATCCGTCGCCACA

Paer1rv Paer2rv

TGTTGTGCCTGCTCGACCCG TTGAGGAAGGACAGCTCGCG

157 198

Hypothetical protein

CP007262.1

See1fw

CGAGCTTGATGACAAACCTG

See1rv

GCTTCGCTTTTCCAACTGCC

167

Thr operon leader peptide

CP001113.1

See2fw

GCGCCGCAGATCGTGTCAGC

See2rv

GATTGACCTGGACGGTAGCC

169

Pilus formation protein (safA)

EF113951.1

Sent1fw

GGTTGCTAACACGACACTG

Sent1rv

TGGGGCATTGGTATCAAAG

166

KX807610.1

Sent2fw

GCCGTACACGAGCTTATAGA

Sent2rv

TGACTCTCTGTAGCTCGACC

173

CP014982.1

Stiph1fw

CTGAACGTGGGTTATTTGAC

Stiph1rv

AGCGGCCAGGCGTTACCCAT

181

D00497.1

Stiph2fw

CATTACACCTTCAGCGGTAT

Stiph2rv

TGGTAAGAGAGCCTTATAGG

253

This study

56

-

AE014073.1

Sflex1fw

TCTTCCTCATATCGAGTCTC

Sflex1rv

TGGTGCTTGTTGAGCAACTC

169

This study

56

-

Hypothetical protein

CP011511.1

Sboy1fw

TGATGTCACTCTTTGCGAG

Sboy1rv

GTAAAGGATAACTACTTCAC

189

53

-

RshA gene

CP014099.1

Sson1fw

TCGAACTTCGATGCCAATCC

Sson1rv

CGGCAGACAGACGGATGCC G

188

53

-

BsuBI-PstI family restriction endonuclease

CP000038.1

Sson2fw

CTTGAAGGAGATTCGCTGCT

Sson2rv

ACTTCGATGACGGCCTTAGC

200

55

+

CP000253.1

Stau1fw

CACGACTAAATAAACGCTCA

Stau1rv

TCTCGTATGACCAGCTTCGG

160

55

+

CP013619.1

Stau2fw

GCCACGTGCAATATTAACAA

Stau2rv

AAGATGGTGTTGGCTTACCG

173

56

-

CP012011.1

Stau3fw

TGAAGCAAGTGCATTTACGA

Stau3rv

TAGCCAAGCCTTGACGAACT

161

55

+

KJ722607.1

Vch1fw

TTCCGTCCATATGTTGGTG

Vch1rv

AAGCGTTGAGGAACCAGCTA

172

This study

55

+

CP003069.1

Vch2fw

GGATAGGACCAATCTTGCTG

Vch2rv

CGCTCCAGATTCAATCAACA

170

This study

53

-

KX094895.1

Vpar1fw

TGCTTACGCGCTTAGCCTGA

Vpar1rv

CATCGTGAAAGTTGCTGCGG

167

This study

56

-

Salmonella enterica subsp. enterica

Salmonella enterica subsp. enterica serovar enteritidis Salmonella enterica subsp. enterica serovar tiphymurium Shigella flexneri Shigella boydii

Shigella sonnei

Fimbrial biosynthesis protein Putative cytoplasmic protein Sigma F factor of RNA polymerase Hypothetical protein

Membrane protein Staphylococcus aureus

Hypothetical protein Thermonuclease

Vibrio cholerae

Vibrio

OprF membrane domain protein Phosphotyrosine protein phosphatase GroEL gene

T P

E C

C A

D E

M

I R

C S

U N

A

T P

231

(Ranjbar et al., 2014) (Woubit et al., 2012) (Ranjbar et al., 2014) (Bai et al., 2010) (Bai et al., 2010) (Studer et al., 2008)

26

ACCEPTED MANUSCRIPT parahaemoliticu s

Yersinia enterocolitica

Yersinia pseudotubercul osis

ToxS gene

AB372521.1

Vpar2fw

TTCGACTCCACATTCACTCG

Vpar2rv

TCAGTGGTTGGTTGTACTGG

149

FoxA gene

CP009846.1

Yent1fw

CACGGCGGTGATGTGAACAA

Yent1rv

TACTACGCCACCAGGGATGC

165

KM253257.1

Yent2fw

ACTCGATGATAACTGGGGAG

Yent2rv

CCCCCAGTAATCCATAAAGG

170

U09235.1

Yent3fw

ATGCTGTCTTCATTTGGAGC

Yent3rv

TCCCAATCACTACTGACTTC

143

HE805222.1

Ypse1fw

TGTCTGCGGTTCTTGAATCC

Ypse1rv

TGTACCAGCGCGCCGGTGTC

Attachment invasion locus Yst precursor gene Invasin Chain length determinant family protein

AF461770.1

Ypse2fw

ATAACCTTGATAACCCAATG

Ypse2rv

I R

T P

GGGAGGATATATGAGACCA C

This study (Wang et al., 2014) (Olsen et al., 1995) (Onori et al., 2014)

53

-

55

+

55

+

55

+

181

This study

53

-

172

(Fukushi ma et al., 2011)

53

-

C S

U N

A

D E

M

T P

E C

C A

27

ACCEPTED MANUSCRIPT Table 3. MiSeq data obtained from the in vitro panel validation experiment of the foodborne pathogen

SingleDNA template reads

Mixed–DNA template reads

Panel selection

Primer concentration (pM)

Hemolysin

Ahyd1

0

0

-

0.8

Campylobacter coli

Outer membrane porin F precursor

Ccol1

0

2299

+

0.16

Campylobacter coli

DNA replication regulator Cytolethal distending toxin subunit CdtB Cpa gene for phospholipase C (alpha-toxin)

Ccol2

0

1760

+

0.8

Cjej1

1

24358

+

0.16

Cper2

0

0

-

0.16

A-type flagellin

Ctyr2

0

0

-

0.16

Clostridium difficile

Glutamate dehydrogenase

Cdiff1

0

0

-

0.16

Clostridium difficile

Toxin B

Cdiff2

0

6

-

0.16

Escherichia coli

Perosamine synthetase homolog

EcEHEC1

4

69308

+

0.16

Escherichia coli

Antitermination protein

EcSTEC1

0

0

-

0.16

Peptidase

Lmon1

5

51032

+

0.16

Fimbrial biosynthesis protein

Sent2

347078

17427

+

0.16

Sson2

0

17946

+

0.16

Stau1

146

8996

+

0.16

Thermonuclease precursor

Stau3

80

2339

+

0.16

Membrane protein

Vch1

0

0

-

0.8

Yersinia enterocolitica

TonB-dependent siderophore receptor family

Yent1

5

19903

+

0.16

Yersinia enterocolitica

Attachment invasion locus protein

Yent2

20

36710

+

0.16

Yersinia enterocolitica

Heat-stable enterotoxin A

Yent3

68

74013

+

0.16

Ni

-

-

Ni

Ni

-

-

Ni

Ni

-

-

Ni

Clostridium perfringens Clostridium tyrobutyrricum

Listeria monocytogenes Salmonella enterica subsp. enterica serovar enteritidis

BsuBI-PstI family restriction endonuclease Ferredoxin-dependent glutamate synthase

Shigella sonnei

M

Staphylococcus aureus Staphylococcus aureus

PT

ED

Vibrio cholerae

Bacillus cereus Salmonella enterica subsp. enterica serovar typhimurium

AC

ni: not included.

CE

Pseudomonas aeruginosa

IP

Campylobacter jejuni

AN

Aeromonas hydrophila

T

Primer pairs

Bacterial species

US

Genes target

CR

- panel.

28

ACCEPTED MANUSCRIPT Table 4. Viable cell counts of artificially contaminated food matrices, obtained using classical microbiological methods.

ED

PT

CE

Cheese

IP

T

1.9x104

7.4x101 2.4x104 2.2x104 7.4x103 2.4x106 2.2x106 7.4x105 / 2.4x102 8.4x101 7.4x101 2.4x104 8.4x103 7.4x103 2.4x106 8.4x105 7.4x105

1.7x104 6.6x104 6.6x103

6.8x103

7.4x103

>108 >108 >108 5.7x107

AC

Poultry

Recovery (CFU/g) 1.4x105 1.1x105

CR

/ Listeria monocytogenes Campylobacter jejuni Listeria monocytogenes Campylobacter jejuni Listeria monocytogenes Campylobacter jejuni / Listeria monocytogenes Samlonella enterica subsp. enterica ser. enteritidis Campylobacter jejuni Listeria monocytogenes Samlonella enterica subsp. enterica ser. Eeteritidis Campylobacter jejuni Listeria monocytogenes Samlonella enterica subsp. enterica ser. enteritidis Campylobacter jejuni / Listeria monocytogenes Shigella sonnei Campylobacter jejuni Listeria monocytogenes Shigella sonnei Campylobacter jejuni Listeria monocytogenes Shigella sonnei Campylobacter jejuni

US

Fish

Inoculum (CFU/mL) / 2.4x102 7.4x101 2.4x104 7.4x103 2.4x106 7.4x105 / 2.4x102 2.2x102

AN

Bacterial species

M

Matrices

29

AC

CE

PT

ED

M

AN

US

CR

IP

T

ACCEPTED MANUSCRIPT

30

AC

CE

PT

ED

M

AN

US

CR

IP

T

ACCEPTED MANUSCRIPT

31

AC

CE

PT

ED

M

AN

US

CR

IP

T

ACCEPTED MANUSCRIPT

32

ACCEPTED MANUSCRIPT Next Generation Sequencing-based multigene panel for high throughput detection of food-borne pathogens

Chiara Ferrario, Gabriele Andrea Lugli, Maria Cristina Ossiprandi, Francesca Turroni, Christian

IP

CR

Sinderen and Marco Ventura

T

Milani, Sabrina Duranti, Leonardo Mancabelli, Marta Mangifesta, Giulia Alessandri, Douwe van

Highlights

A PCR-based Illumina sequencing method for foodborne pathogen was developed



The foodborne pathogen panel is faster than classical microbiological methods



The panel simultaneously identify eight different pathogens in food matrix

AC

CE

PT

ED

M

AN

US



33