- Research article
- Open Access
Genomic characterization of the most barotolerant Listeria monocytogenes RO15 strain compared to reference strains used to evaluate food high pressure processing
BMC Genomics volume 21, Article number: 455 (2020)
High pressure processing (HPP; i.e. 100–600 MPa pressure depending on product) is a non-thermal preservation technique adopted by the food industry to decrease significantly foodborne pathogens, including Listeria monocytogenes, from food. However, susceptibility towards pressure differs among diverse strains of L. monocytogenes and it is unclear if this is due to their intrinsic characteristics related to genomic content. Here, we tested the barotolerance of 10 different L. monocytogenes strains, from food and food processing environments and widely used reference strains including clinical isolate, to pressure treatments with 400 and 600 MPa. Genome sequencing and genome comparison of the tested L. monocytogenes strains were performed to investigate the relation between genomic profile and pressure tolerance.
None of the tested strains were tolerant to 600 MPa. A reduction of more than 5 log10 was observed for all strains after 1 min 600 MPa pressure treatment. L. monocytogenes strain RO15 showed no significant reduction in viable cell counts after 400 MPa for 1 min and was therefore defined as barotolerant. Genome analysis of so far unsequenced L. monocytogenes strain RO15, 2HF33, MB5, AB199, AB120, C7, and RO4 allowed us to compare the gene content of all strains tested. This revealed that the three most pressure tolerant strains had more than one CRISPR system with self-targeting spacers. Furthermore, several anti-CRISPR genes were detected in these strains. Pan-genome analysis showed that 10 prophage genes were significantly associated with the three most barotolerant strains.
L. monocytogenes strain RO15 was the most pressure tolerant among the selected strains. Genome comparison suggests that there might be a relationship between prophages and pressure tolerance in L. monocytogenes.
Listeria monocytogenes is a well-known foodborne pathogen that may cause listeriosis, a severe infection with high hospitalization rate which can be fatal in humans and some animal species . Although being a relatively rare foodborne disease, increasing numbers of listeriosis cases have been reported in the EU/EEA countries since 2008 . L. monocytogenes is generally found in agricultural, aquacultural, and food processing environments . Listeriosis outbreaks have been associated with consumption of contaminated food, such as meat, fruits, vegetables, milk, cheese, and fish [3, 4]. The food industry has to process food in such a way to meet food safety microbiological criteria (Commission Regulation (EC) No 1441/2007 ), which include the presence in limited numbers (100 cfu/g) or absence of L. monocytogenes (absence in 25 g product). Food processing aimed at L. monocytogenes inactivation is challenging due to its pronounced ability to adapt to different environments, and to survive under various stress conditions . In addition, it is important to preserve the food quality and nutritional value during food processing. High pressure processing (HPP) is an alternative to thermal processes to reduce the concentration of pathogens, including L. monocytogenes, and maintain a high quality of the product. Only very small or no quality changes, such as loss of color, nutritional value, flavor, and texture, have been reported for HPP products  . Several studies showed that HPP products, such as meat, milk, juice, vegetables, and sauce are well accepted by the consumer, and reported that HPP preserves the taste . Depending on the food products, pressures currently used in the food industry are between 100 and 600 MPa, with a holding time ranging from one to several minutes . HPP has been tested and reported as an effective processing method for several products including fruits (and fruit juices) , meat , cheese , fish  and vegetables .
It has been shown that HPP has varying effects on different target organisms. For example, a pressure of 300 MPa is sufficient to inactivate most Gram-negative bacteria, while more than 400 MPa is needed for an inactivation of Gram-positive bacteria [15, 16]. It is also known that pressure tolerance can differ even among strains of the same species. Differences in pressure tolerance have been reported for strains of several species including Cronobacter sakazakii (previously Enterobacter sakazakii) , 24 different serotypes of Salmonella enterica , and L. monocytogenes [19, 20]. In L. monocytogenes strains, pressure tolerance varies between 300 and 500 MPa . To our knowledge, genomic profiling and comparison has not been performed for barotolerant and barosensitive L. monocytogenes strains. In this study, we selected 10 L. monocytogenes strains, isolated from either foods, food processing environments or clinical sources, to test their tolerance towards HPP at 400 and 600 MPa, compare their genomes, and investigate whether genetic traits may be associated with pressure tolerance.
Pressure treatment and reduction of viable cell counts
Reduction of viable cell counts (colony forming units, cfu) after pressure treatment at 400 and 600 MPa for 1 min showed that pressure tolerance differs between strains. The variance in log10 colony forming units per milliliter (cfu/ml) of the treated samples was significantly larger (p < 0.02, see ANOVA results in Fig. 1a) compared to control, indicating a high variance in the level of tolerance of L. monocytogenes. At 400 MPa for 1 min, the 10 strains exhibited an average log10 reduction of 0.57 cfu/ml, ranging from 0.05 log10 cfu/ml for the most barotolerant strain (RO15) to 2.07 log10 cfu/ml for the most pressure sensitive strain (EGD-e). Similar results were obtained at 600 MPa. Here, the 10 strains exhibited an average log10 cell number reduction of 7.06 cfu/ml, with a range of 5.42 to 8.27 log10 cfu/ml (Table 1). Strain RO15 was also the most barotolerant strain based on an initial screening including several other L. monocytogenes strains (Supplementary text 1, Table S1, Figure S1).
One-way analysis of variance (ANOVA), comparing the pressure (400 MPa) treated samples of each strain to the control samples, showed that the mean log10 cfu/ml for treated samples was significantly lower than the control for all strains (p < 0.02), except for strain RO15 (p = 0.15) (Fig. 1a). The same statistical analysis for HPP at 600 MPa indicated that all strains including strain RO15 had significantly (p < 0.01) lower log10 cfu/ml in treated samples compared to controls (Fig. 1b).
Genome sequencing, general features and RNA-Seq
We sequenced the genomes of seven L. monocytogenes strains for which no genome sequences were available in public databases. Genomes of strains RO15, 2HF33, MB5, AB199, AB120, C7, and RO4 were sequenced using Illumina MiSeq equipment and strain RO15 was sequenced using Pacbio RSII. Sequences of these seven strains were assembled and three additional strains (ScottA, F2365 and EGD-e, genome sequences of which are available in public databases) were used for comparative genome analysis. Assembly of PacBio long reads of strain RO15 resulted in one continuous 3,042,507 bp sized chromosome (Fig. 2) at an average 308-fold sequencing coverage. In addition to the chromosome, a contig with a complete circular prophage sequence of 38,811 bp was obtained, which was also found as part of the chromosome (2729417–2,770,759 bp). All genomes had a similar size, GC content and number of coding sequences (CDS) (Table 2).
The serotype of the strains was reported in previous studies [21,22,23,24,25] (Table 2). Although RO15 was reported as a serovar 4b strain using multiplex PCR in a previous study , our genome-based prediction suggests that it belongs to PCR-serogroup 1/2a. Sequences for ORF2110 and ORF2819 primers used for identification of serovar 4b strains  were not found in the genome of strain RO15 based on our sequence match analysis, whereas sequences for primers targeting lmo0737, which are indicators of serovar 1/2a strains  were present. Multilocus sequence typing (MLST) based on 7 loci, core genome MLST (cgMLST; based on Moura scheme ), and clonal complex (CC) of the strains were also assigned based on the genome sequences (Table 2). Lineage assignment showed that only ScottA and F2365 belonged to lineage I and all other strains were members of lineage II (Table 2).
In addition to genome sequencing, transcriptome analysis using RNA-seq for strains RO15 and ScottA provided a basic view of transcriptional activity of the genomes. Differential expression analysis indicated that virulence genes and heat shock genes were upregulated after high pressure treatment in strain ScottA (Table S2).
Methylated DNA motifs
High PacBio sequencing coverage allowed us to analyze DNA methylation modification motifs in strain RO15 in addition to assembling the genome. Using the long read modification data, 11 methylated sequence motifs were detected in the genome (Table 3). None of the detected motifs had a partner motif, i.e. a reverse-complementary sequence, but all detected motifs were only partially modified in the genome with less than 50% methylated motifs. While most of the motifs have already been deposited in the REBASE database  DADGYATYA, WNNTVVGCNTWNH, AHNBAACA, AGNNARNWW were novel, i.e. they have not been described as potential methylation sites previously. None of the detected motifs have been reported as recognition sequence motif for a restriction enzyme in the REBASE database.
We predicted one type II cytosine-5 DNA methyltransferase gene (OCPFDLNE_00657), and three type II N4-cytosine or N6-adenine DNA methyltransferase genes (OCPFDLNE_02168, OCPFDLNE_02626, OCPFDLNE_02808) in the strain RO15 genome. In addition, genes for a type IV methyl-directed restriction enzyme (OCPFDLNE_00324) and type II restriction enzymes (OCPFDLNE_00658, OCPFDLNE_02625, OCPFDLNE_02807) were predicted. These predicted methyltransferases and restriction enzymes had significant (e-value <1E-50) BLASTP hits in the REBASE protein sequences database . However, their recognition sequences are unknown. OCPFDLNE_02625 and OCPFDLNE_02807 type II restriction enzyme genes in prophage regions were expressed based on RNA-seq data.
Genomic comparison of the selected strains
In the genome assembly we also identified a circular phage as an independent contig in strain RO15. While looking after phage-originating genome parts, prophage prediction revealed five prophage regions (10.7 kb to 47.9 kb sized) in the RO15 genome (Fig. 2). Prophage (region) 5 had the same sequence as the separate circular phage. RNA-Seq count data suggested that most of the genes of prophage 1,3 and 5 in RO15 were transcribed. Subsequently, the prophage prediction was performed for all strains, which showed that all strains had at least one or more prophage regions in the genome with a maximum of six prophage regions in strain C7 (Table 4). One of the predicted prophage regions in ScottA was also transcribed based on the RNA-seq data.
Genome alignment of the selected nine strains against the strain RO15 (Fig. 3) showed large sequence gaps between strain RO15 and the other strains. The gaps were mainly related to prophage regions with prophage region 3 being specific for strain RO15. Prophage region 2 was only seen in the strain 2HF33. Prophage region 4 was partially seen in strains 2HF33, EGD-e, MB5 and ScottA. Similarly, prophage region 5 was partially seen in strains 2HF33, C7, and RO4. In addition, strains ScottA and F2365 had less aligned regions compared to the other strains. Average nucleotide identity (ANIb) results showed that ANIb scores of strains ScottA and F2365 with other strains were lower than 95 (Table S3). To study how the large sequence gaps are localized in the other publicly available genomic sequences of L. monocytogenes, all 200 complete L. monocytogenes genomes in RefSeq database  were downloaded and aligned to strain RO15 (Figure S2). The visualization of the alignment showed that prophages 2, 3 and 4 were seen only in the minority of the L. monocytogenes strains, while prophages 1 and 5 were seen in the majority of the L. monocytogenes strains. The comparison indicated that these prophage regions were common locations of variance between L. monocytogenes strains.
Annotation of CRISPR systems revealed that the RliB-CRISPR system was seen in all the strains, which is in line with previous RliB-CRISPR system studies [30, 31]. Interestingly, CRISPR I or CRISPR II system genes were present only in strains RO15, 2HF33, and MB5 (Fig. 4), which exhibited the lowest reduction in log10 cfu/ml with the 400 MPa pressure treatment (Table 1). Number of spacers in these strains were also significantly (p < 0.001) higher compared to barosensitive strains. The alignment of the spacer sequences of the CRISPR systems back to the genome itself revealed that only strains RO15, 2HF33, and MB5 contained self-targeting spacers with 100% identity. As expected, spacer sequences were aligned to the prophage regions, except one spacer sequence in RO15, which aligned to the addB gene (OCPFDLNE_02427) located in the chromosome encoding an ATP-dependent helicase.
Using prediction tools we detected anti-CRISPR genes in the prophage regions (Table 4) of strains RO15, 2HF33, MB5, RO4, and EGD-e. Homologues of all four previously annotated Listeria anti-CRISPR genes, i.e. acrIIA1, acrIIA2, acrIIA3, and acrIIA4 , were seen in strain 2HF33. Gene acrIIA4 was not seen in strains MB5, RO4, and EGD-e, but the rest of the anti-CRISPR genes were present. RO15 contains two copies of acrIIA1 (OCPFDLNE_02770, OCPFDLNE_02583) and acrIIA2 (OCPFDLNE_02582) (Fig. 2) and we observed expression of these anti-CRISPR genes in strain RO15.
As antibiotic resistance and pressure tolerance were linked in a previous study , we also searched antibiotic resistance genes in all strains to identify relation of antibiotic resistance genes and pressure tolerance. The same antibiotic resistance gene families were detected in all the 10 strains (Table S4). Multiple sequence alignment of the detected antibiotic resistance genes and the following average distance tree generation showed that amino acid sequence of the norB gene (encoding for a quinolone resistance protein) (OCPFDLNE_03068) was slightly different in barotolerant strain RO15 compared to the other strains (Figure S3). Similarly, for the lin gene (encoding for a lincomycin resistance protein) (OCPFDLNE_00980) amino acid difference was seen for strains 2HF33 and RO15 (Figure S3).
Roary pan-genome pipeline suggested that the 10 L. monocytogenes strains contain a total of 4820 orthologous gene clusters. Of these genes, 2247 were core genes found in all studied strains and 2573 genes were accessory genes found in at least one strain. The core genome was used for constructing a phylogenetic tree, which indicated that the two serovar 4b strains (ScottA and F2365) are closely related to each other and cluster separately from the serovar 1/2a strains in the phylogenetic tree. In addition, there was also a clear difference in accessory genome for serovar 4b strains compared to serovar 1/2a strains (Fig. 5).
To test any significant association between pressure tolerance and (clusters of) genes in the accessory genome, we performed a pan-genome wide association analysis using Scoary v1.6.16 . Based on the pairwise comparison of reduction in log10 (cfu/ml) against strain RO15 using Student’s t-test (Table S5), a statistically significant (p < 0.05) difference compared to RO15 was seen in all strains except MB5 and 2HF33. Therefore, strains RO15, 2HF33, MB5 were used as barotolerant strains for the pan-genome association analysis. Scoary results showed that 13 gene clusters (Table S6) had p-values < 0.01 for the association with barotolerant strains, i.e., the genes were only seen in barotolerant strains. Of these, 10 gene clusters were located in the prophage regions, but most of the genes were annotated as hypothetical proteins (Table S6). These prophage genes were also searched for in 200 complete L. monocytogenes strains and found in some strains (Table S6). Interestingly, genes OCPFDLNE_02579 and OCPFDLNE_02580 were only seen in strains that harbored anti-CRISPR genes, Cas type I or II CRISPR system and self-targeting spacers (Table S6).
HPP is commonly used in the food industry to inactivate foodborne pathogens and spoilage organisms. Depending on the type of food product, up to ~ 600 MPa pressure is applied . It has also been shown that combinations of HPP and biocontrol agents, such as virulent bacteriophages and bacteriocins, have synergistic effects in the inactivation of the target pathogens in food [34, 35]. The pressure tolerance level varies between different species, strains, and even isolates [16, 19, 36, 37]. Here, we tested pressure tolerance of 10 L. monocytogenes strains, compared their genomes and predicted genome features related to pressure tolerance.
Pressure treatment using 600 MPa for 1 min caused more than 5 log10 reduction in all selected strains, which is generally considered sufficient for food safety regulations. According to the Food Safety and Inspection Service (United States Department of Agriculture), 5 log10 reduction is considered a full lethality treatment for L. monocytogenes . This suggests that the strains used in this study were relatively sensitive to 600 MPa pressure treatment. Similarly, an earlier study reported that pressure treatment at 500 MPa for 10 min provided sufficient reduction in viable counts to reach desired level of safety for all except one L. monocytogenes strains tested . However, the lower pressure levels, such as 400 MPa, would be more relevant for industrial applications, therefore, we focused more on the results obtained with the 400 MPa treatment. Among the strains tested, RO15 was the most barotolerant strain when processed at both 400 and 600 MPa, which is in line with the initial selection process of strains (Supplementary text 1). Based on ANOVA, RO15 was the only strain for which no statistically significant reduction was observed with the 400 MPa treatment (Fig. 1a). Therefore, we defined the strain RO15 as barotolerant. It has been shown that the level of inactivation by HPP can be affected by the chemical composition of the food product. Some food products, such as milk and cheese, have baroprotective effects on microorganisms during HPP treatment [39, 40]. However, experiments in this study were performed using TSBYE broth and thus do not take into account the effects of food composition on HPP. Temperature during pressure treatment can have an effect on microbial inactivation depending on treatment time, and it has been shown that increasing the temperature from 25 °C to 35 °C has a significant effect on the viability loss of L. monocytogenes . In this study, temperature during pressure treatment increased up to 33 °C for 400 MPa treatment and 38 °C for 600 MPa treatment due to adiabatic heating. However, since treatment time was only 1 min, the effect of temperature rise was expected to be minimal.
Serotypes of the selected strains were shown in the earlier studies based on agglutination method, multiplex-PCR and genome sequencing [21, 23,24,25] (Table 2). Here, we predicted the serotype of all strains based on their genome sequences. This confirmed the previous serotyping of all strains except RO15, which was reported to be a serovar 4b strain based on multiplex-PCR . Serotype 4b strains should contain serovar 4 marker genes (ORF2110 and ORF2819) and lack lmo0737 marker gene sequence as described by Doumith et al. . However, according to the genome sequence, serovar 4 marker genes were absent, and a lmo0737 homologue was present suggesting that the serotype of strain RO15 is indeed PCR-serogroup 1/2a. The phylogenetic tree that we created based on the core genome showed that there is considerable genetic variation between serovars. Serovar 4b strains (ScottA and F2365) clustered separately from the other strains (Fig. 4). Strain RO15 was not clustered together with other serovar 4b strains hence these results supported our gene-based prediction of PCR-serogroup 1/2a of strain RO15.
PacBio sequencing provides not only the genome sequence but also methylation data , which gives an opportunity to analyze restriction-modification systems and their recognition motifs. In this study, we did not observe a genuine methylated motif based on PacBio sequencing data of strain RO15. Similarly, PacBio methylation data of nine other published L. monocytogenes strains without genuine methylated motifs were also seen in REBASE PacBio list database , which showed that it is not uncommon to have only partially methylated motifs in L. monocytogenes.
Previous studies based on L. monocytogenes strain ScottA and LO28 barotolerant isolates showed that there is a phenotypic and genotypic variation between barotolerant isolates [36, 37, 43], which indicates that there are variety of factors which can lead to pressure tolerance. Therefore, it is a challenge to link a genomic profile with pressure tolerance in different strains. Nevertheless, pan-genome association analysis results suggest that there was a genotypic difference between barotolerant and barosensitive strains. A total of 13 genes were significantly (p < 0.01) associated with barotolerant strains. Interestingly, most of the barotolerant strain associated genes (Table S6) were located in the prophage regions in the studied genomes. In addition, extension search of these genes within all complete L. monocytogenes strain genomes showed that some phage genes (OCPFDLNE_02579 and OCPFDLNE_02580) were only seen in strains that contain inactivated CRISPR/Cas system (Table S6). It has been shown that prophages can provide increased biofilm formation and can be beneficial for stress coping in both L. monocytogenes and Escherichia coli [44, 45]. We also observed that barotolerant strains harbored a slightly higher number of prophages compared to barosensitive strains (based on average number of prophages in barotolerant and barosensitive strains). Therefore, we believe that prophages might play a role in barotolerance in L. monocytogenes. However, further phage gene deletion or reintroduction studies are needed to verify relation between phages and barotolerance.
It was also interesting to see that two or more CRISPR-Cas systems, self-targeting spacers, and anti-CRISPR genes were seen together only in the three most barotolerant strains (RO15, MB5, and 2HF33). Since the existence of a self-targeting spacer indicates that the activity of the CRISPR/Cas systems is inhibited , it can be predicted that anti-CRISPR proteins detected in barotolerant strains inactivate CRISPR/Cas system. Previous studies have also shown that anti-CRISPR proteins are able to inhibit several types of CRISPR systems [32, 47]. The observed active transcription of anti-CRISPR genes in RO15 prophages based on RNA-seq may also support this prediction. It has been shown that bacteria tend to lose CRISPR/Cas systems, since they can be harmful to the host itself. Immunopathological effects through partially matching spacers were reported unless the phage carries anti-CRISPR genes . The anti-CRISPR genes found in prophages and inhibition of the CRISPR/Cas system may be the reason why barotolerant strains carry type I or type II CRISPR/Cas systems. We observed that more than 50% of the L. monocytogenes strains that harboring type I or type II CRISPR/Cas system also had anti-CRISPR genes (Table S6). CRISPR/Cas systems can have alternative roles in bacteria, such as roles in pathogenesis or role in modulating biofilm formation , however the role of inhibited CRISPR/Cas system is not known. More studies are required to support the relation between inhibited CRISPR/Cas systems and barotolerance.
RNA-seq revealed the general transcriptome profile of strains RO15 and ScottA. Significantly upregulated heat-shock genes after pressure treatment (chaperone and clp protease genes) in ScottA (Table S2) might indicate that proper folding of proteins plays an important role in cell recovery after pressure treatment. A previous study also showed that the expression levels of Clp proteins were significantly higher in mutant HPP-tolerant ScottA isolate compared to wild type ScottA .
A previous study concluded that antibiotic resistant L. monocytogenes strains are more tolerant to pressure at 400 MPa . Here, annotation and gene comparison results did not show any strain-specific antibiotic resistance gene within studied strains. Nevertheless, multiple sequence alignment of predicted antibiotic resistant genes showed that there were slight differences in amino acid sequences across the strains (Figure S3). However, it is not known that these amino acid differences cause antibiotic resistance advantage, and further studies are required to link the amino acid sequence variations of antibiotic resistance genes and pressure tolerance.
In this study we noticed that barotolerance is manifested at pressures lower than 600 MPa. Strain RO15 was identified as the most barotolerant strain for 400 MPa 1 min pressure treatment. Genome sequence of seven new strains and genome comparison of 10 strains revealed that the three most barotolerant strains have CRISPR-Cas genes and anti-CRISPR genes in their genomes. In addition, the average number of prophages was slightly higher in barotolerant strains compared to barosensitive strains. Furthermore, we have predicted 10 phage genes that might be related to pressure tolerance based on the pan-genome association test. Therefore, we conclude that prophages and inhibited prophage defense systems may be linked to pressure tolerance. The observation described above may argue for the use of phage cocktails as a pretreatment before HPP and will allow to reduce pressure and holding time.
Strains, growth conditions and pressure treatment
Strains Scott A (CIP103575) and EGD-e (CIP107776) were obtained from Centre de Ressources Biologiques de l’Institut Pasteur, Paris, France, and strain F2365 (LMG23356) from Laboratorium voor Microbiologie, UGent, Gent, Belgium. Strains RO4, RO15, AB120 and AB199 are from Dunarea de Jos University of Galati, Romania, and have been isolated in the Promise FP7 project either from food illegally introduced to Romania or from meat processing environments. All other strains were isolated from the environment as indicated in Table 2. The strains were stored on Microbank beads with cryopreservatives (Pro-Lab Diagnostics, Toronto, Canada) at − 80 °C prior to use. The bacteria were passaged twice in tryptic soy broth (TSB) supplemented with 0.6% (w/v) yeast extract (TSBYE; Oxoid, Basingstoke, Hampshire, England). Prior to pressure treatments, bacteria were grown overnight in 50 mL TSBYE at 37 °C with rotary agitation (150 rpm), resulting in cells in early stationary phase and with a target concentration of approx. 109 cfu/mL. Aliquots (10 mL) of the cultures were packaged in sous-vide plastic pouches and sealed without using vacuum, and 10 mL was transferred to 15 mL falcon tubes to serve as untreated controls.
HPP was carried out using the QFP 2 L-700 (Avure Technologies Inc., Columbus, USA). The cylindrical pressure vessel had 10 × 25.4 cm dimensions, 2 L capacity and 690 MPa upper pressure limit. Temperature of the pressure medium (water) was tracked with a K-type thermocouple located on the external surface of the samples. A holding time of 1 min was used, pressures of 400 and 600 MPa, and ambient vessel water temperature (20–22 °C). Due to adiabatic heating, water temperatures in the middle of the vessel at the end of pressure treatment had risen to 31–33 °C after the 400 MPa treatment, and 36–38 °C after the 600 MPa treatment. Straight after pressure treatment, 400 MPa pressurized and untreated samples were serially ten-fold diluted in TSBYE and plated in triplicate on tryptic soy agar with 0.6% yeast extract (TSAYE; Oxoid, Basingstoke, Hampshire, England) by using a spiral plater (Eddy Jet; IUL Instruments, Barcelona Spain). Pressurized samples at 600 MPa were additionally plated manually (100 μL) without being diluted. TSAYE plates were incubated at 37 °C for 48 h prior to counting the colonies and estimating bacterial inactivation. Two consecutive trials of the methodology were performed (Exp. 1 and 2).
DNA of each strain was extracted from 5 ml of a culture grown overnight in BHI broth at 37 °C with aeration on a rotary shaker. Wizard Genomic DNA purification kit (Promega, Madison, WI, USA) was used according to the manufacturer’s instructions.
Library preparation, genome sequencing, de novo assembly, base modification detection and motif analysis
L. monocytogenes strain RO15 was sequenced using PacBio RSII (Pacific Bioscience, Menlo Park, CA, USA) and Illumina Miseq (Illumina, San Diego, CA, USA). Genomic DNA PacBio library was prepared according to the manufacturer’s Template Preparation and Sequencing Guide using DNA Template Prep Kit 2.0 and DNA/Polymerase Binding Kit P6. The Nextera XT protocol was used for the enzymatic genomic DNA fragmentation and Illumina sequencing library preparation. Pacbio reads were assembled using HGAP3 protocol  in SMRTPortal 2.3.0. Obtained assembly of chromosomal and phage sequences were checked and circularized using Gap4 program  and finally the chromosomal DNA sequence was set to start from dnaA gene. Cutadapt v1.8.1  was used with -m 200 and -q 25 options to trim Nextera adapter sequences and quality filtering. Trimmed reads were mapped against circularized chromosomal and phage sequences using bwa-mem . Short indels within homopolymeric regions were corrected using pilon v1.16 . Average sequencing coverages at the whole genome level were 308X in Pacbio data and 157X in Illumina data, respectively. DNA base modifications and motifs were analyzed using Modification and Motif analysis protocol implemented in SMRTPortal 2.3.0 with default parameters.
For L. monocytogenes strain RO4, AB199, and AB120, Nextera DNA Library (Illumina, San Diego, CA, USA) preparation and for strain 2HF33, MB5, and C7, Nextera XT DNA Library preparation was performed. Paired-end sequencing was performed using Illumina Miseq. RTA v1.18.54 and bcl2fastq v220.127.116.11 were used for base calling, demultiplexing and converting data to fastq format. The Cutadapt v1.8.1  was used with -m 200 and -q 25 options to trim Nextera adapter sequences and quality filtering. Reads aligning to PhiX genome, which was used as spike-in during Illumina sequencing, were removed by using bbmap v34.56 . Spades v3.13.0  with default options was used for assembling the reads and creating the contigs.
Pressure treatment for RNA-seq experiment and RNA extraction
For both strain RO15 and ScottA, 10 ml of cells were grown in TSBYE until early stationary phase (each triplicated with new cultures). Samples were packed in sterile sous-vide pouches and packaged without applying vacuum. They were put in the refrigerator 30 min before pressure treatment. After treatment at 200 and 400 MPa for 2, 8 and 60 min at 20–22 °C, samples were transferred to sterile 15 ml Falcon tubes. From each sample, 0.5 ml were transferred to a 2 ml Eppendorf tube prefilled with 1 ml RNA Protect (Qiagen, Hilden, Germany). They were vortexed, incubated for 5 min at room temperature, and frozen at − 80 °C. Cells previously stored in RNA Protect were pelleted by centrifugation for 10 min at 5000 g. RNA extraction was performed with NucleoSpin RNA kit (Macherey-Nagel, Düren, Germany) according to manufacturer’s instructions with some modifications in the cell disruption phase. The cell pellets were suspended with 700 μl RA1 buffer and 7 μl β-mercaptoethanol (Sigma-Aldrich, Saint Louis, MO, USA). Cells were then mechanically disrupted using Lysing Matrix B tubes (MP Biomedicals, Irvine, CA, USA) and FastPrep 24 tissue homogenizer (MP Biomedicals, Irvine, CA, USA) at 6 m/s for 3 × 30 s. Cells were rested on ice for 5 min between cycles. After spinning the cells briefly and transferring the supernatant to NucleoSpin filter column, manufacturer’s protocol was followed. Quantity and quality of RNA extractions were analyzed using the Agilent 2100 Bioanalyzer and RNA 6000 Nano kit (Agilent, Santa Clara, CA, USA).
RNA-seq library preparation
Ribosomal RNAs were removed from the total RNAs (ScottA 9.4 μl; RO15 14 μl) with Ribo-Zero rRNA Removal kit for bacteria (Illumina, San Diego, CA, USA) according to manufacturer’s instructions with 1/3 (ScottA) or 1/2 (RO15) volumes of kit solutions. rRNA-depleted RNA was purified with RNeasy MinElute Cleanup Kit (Qiagen, Hilden, Germany) according to modified protocol of the Ribo-Zero kit manual and eluted in 12 μl of RNA-free water. Eight μl of rRNA-depleted RNA was used to prepare RNA sequencing libraries with SENSE Total RNA-seq Library Prep Kit for Illumina (Lexogen, Vienna, Austria) according to manufacturer’s instructions. In reverse transcription and ligation phase, incubation time was extended to 2 h. After second-strand synthesis, purification and size-selection of the libraries were performed with 13 μl of Bead Diluent and 27 μl of Purification Solution. PCR amplification program was slightly modified by increasing the denaturation times: from 30 to 60 s in the beginning, and from 10 to 30 s during the cycles; cycle number was increased to 40. Concentration of amplified libraries was measured with Qubit fluorometer and dsDNA HS assay kit (Invitrogen, Waltham, MA, USA), and size distribution visualized with Fragment Analyzer and High Sensitivity NGS Fragment Analysis kit (Advanced Analytical, Parkersburg, WV, USA). Aliquots of amplified libraries were pooled twice for both strains. After pooling, libraries were concentrated using Amicon Ultra 100 K columns (Millipore, Burlington, MA, USA). To remove fragments under 200 bp, size selection of ScottA pools was performed using BluePippin and 2% agarose gel cassette (Sage Science, Beverly, MA, USA). In size selection of RO15 library pools, bead purification with 0.9 x AMPure XP beads (Beckman Coulter, Brea, CA, USA) and PEG/NaCl precipitation on MyOne™ carboxylic acid beads (Invitrogen, Waltham, MA, USA) were additionally used. Concentration of the pooled libraries was measured with Qubit fluorometer, and libraries were sequenced with NextSeq 500 (Illumina, San Diego, CA, USA).
RNA-seq data analysis
Quality filtering and adapter trimming for RNA-seq reads was done using Trimmomatic v0.36  using these parameters “TruSeq3-SE.fa:2:30:10 SLIDINGWINDOW:3:20 MINLEN:30”. SortMeRNA  was used to filter rRNA reads. Reads were mapped to the genomes using Bowtie2  default settings. Aligned reads were sorted using Samtools . HTSeq  with union mode was used for read counts. Differentially expressed gene analysis was done using DESeq2 R package . Genes with adjusted p-value (padj) ≤ 0.05 and |log2 Fold Change| ≥ 1 were considered as significantly differentially expressed.
Genome annotation, prophage prediction and genome alignment
Assembled genomes were annotated using Prokka v1.13  with default options. To improve functional annotation PANNZER2 annotation web server  was used. CRISPR repeat regions were detected using CRISPRCasFinder version 4.2.19 . Anti-CRISPR genes were annotated using BLAST alignment against known anti-CRISPR genes . The prophage prediction for all genomes was done using PHASTER web tool . Serotype of the selected strains were predicted by checking the marker primers  in the genome using EMBOSS primersearch v 6.6.0 . Multilocus sequence typing (MLST) based on 7 loci (MLST), clonal complex (CC), core genome MLST (cgMLST) and lineages of strains were assigned based on MLST and the cgMLST schemes developed by Moura et al. [27, 68] by uploading the sequences to BIGSdb-Lm webserver (https://bigsdb.pasteur.fr/listeria/listeria.html). Antibiotic resistance genes were detected using Resistance Gene Identifier (RGI) tool with CARD database . Heuristic neighbor-joining phylogeny tree based on concatenated sequences of the MLST gene fragments was created using FastTree v2.1 . Whole genome alignment and ring figure was created using CGView Comparison Tool . The ANIb scores were calculated using JSpeciesWS webtool .
Pan-genome and Pan-GWAS analysis
The pan-genome analysis was done using Roary Pan-genome Pipeline  with default settings, to create alignment of core genes using PRANK  the “-e” setting was used. The core genes alignment was used for phylogenetic tree construction using FastTree v2.1 . Pan-genome-wide association analysis was done using Scoary v1.6.16  with default settings.
Availability of data and materials
All sequencing data and assembled genomes have been deposited in the European Nucleotide Archive (ENA) under accession code PRJEB35939. Strains RO4, RO15, AB120 and AB199 are from Dunarea de Jos University of Galati, Romania, and are available upon request to email@example.com. Strains C7, MB5, and 2HF33 are from Nofima – Norwegian Institute of Food, and are available upon request to firstname.lastname@example.org.
Analysis of variance
Average nucleotide identity
Colony forming units
High pressure processing
Multilocus sequence typing
Schlech WF, Lavigne PM, Bortolussi RA, Allen AC, Haldane EV, Wort AJ, et al. Epidemic Listeriosis — evidence for transmission by food. N Engl J Med. 1983;308:203–6.
European Centre for Disease Prevention and Control. Listeriosis - Annual Epidemiological Report for 2016. 2018. https://www.ecdc.europa.eu/sites/portal/files/documents/AER_for_2016-listeriosis.pdf. Accessed 10 Nov 2019.
Buchanan RL, Gorris LGM, Hayman MM, Jackson TC, Whiting RC. A review of Listeria monocytogenes: an update on outbreaks, virulence, dose-response, ecology, and risk assessments. Food Control. 2017;75:1–13.
Farber JM, Peterkin PI. Listeria monocytogenes, a food-borne pathogen. Microbiol Rev. 1991;55:36.
COMMISSION REGULATION (EC) No 1441/2007 of 5 December 2007 amending Regulation (EC) No 2073/2005 on microbiological criteria for foodstuffs Official Journal of the EC. no. L322/12, 2007. https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2007:322:0012:0029:EN:PDF Accessed 22 Apr 2020.
Bucur FI, Grigore-Gurgu L, Crauwels P, Riedel CU, Nicolau AI. Resistance of Listeria monocytogenes to stress conditions encountered in food and food processing environments. Front Microbiol. 2018;9:2700.
Abera G. Review on high-pressure processing of foods. Cogent Food Agric. 2019;5:1568725.
Olsen NV, Grunert KG, Sonne A-M. Consumer acceptance of high-pressure processing and pulsed-electric field: a review. Trends Food Sci Technol. 2010;21:464–72.
Jiménez-Colmenero F, Borderias AJ. High-pressure processing of myosystems. Uncertainties in methodology and their consequences for evaluation of results. Eur Food Res Technol. 2003;217:461–5.
Daher D, Le Gourrierec S, Pérez-Lamela C. Effect of high pressure processing on the microbial inactivation in fruit preparations and other vegetable based beverages. Agriculture. 2017;7:72.
Yuste J, Mor-Mur M, Capellas M, Pla R. Listeria innocua and aerobic mesophiles during chill storage of inoculated mechanically recovered poultry meat treated with high hydrostatic pressure. Meat Sci. 1999;53:251–7.
Evert-Arriagada K, Trujillo AJ, Amador-Espejo GG, Hernández-Herrero MM. High pressure processing effect on different Listeria spp. in a commercial starter-free fresh cheese. Food Microbiol. 2018;76:481–6.
Montiel R, Martín-Cabrejas I, Gaya P, Medina M. Reuterin and high hydrostatic pressure treatments on the inactivation of Listeria monocytogenes and effect on the characteristics of cold-smoked Salmon. Food Bioprocess Technol. 2014;7:2319–29.
Woo H-J, Park J-B, Kang J-H, Chun HH, Song KB. Combined treatment of high hydrostatic pressure and cationic surfactant washing to inactivate Listeria monocytogenes on fresh-cut broccoli. J Microbiol Biotechnol. 2019;29:1240–7.
Arroyo G, Sanz PD, Préstamo G. Effect of high pressure on the reduction of microbial populations in vegetables. J Appl Microbiol. 1997;82:735–42.
Wuytack EY, Diels AMJ, Michiels CW. Bacterial inactivation by high-pressure homogenisation and high hydrostatic pressure. Int J Food Microbiol. 2002;77:205–12.
González S, Flick GJ, Arritt FM, Holliman D, Meadows B. Effect of high-pressure processing on strains of Enterobacter sakazakii. J Food Prot. 2006;69:935–7.
Tamber S. Population-wide survey of salmonella enterica response to high-pressure processing reveals a diversity of responses and tolerance mechanisms. Appl Environ Microbiol. 2017;84. https://doi.org/10.1128/AEM.01673-17.
Bruschi C, Komora N, Castro SM, Saraiva J, Ferreira VB, Teixeira P. High hydrostatic pressure effects on Listeria monocytogenes and L. innocua: evidence for variability in inactivation behaviour and in resistance to pediocin bacHA-6111-2. Food Microbiol. 2017;64:226–31.
Alpas H, Bozoglu F. Efficiency of high pressure treatment for destruction of Listeria monocytogenes in fruit juices. FEMS Immunol Med Microbiol. 2003;35:269–73.
Ciolacu L, Nicolau AI, Wagner M, Rychli K. Listeria monocytogenes isolated from food samples from a Romanian black market show distinct virulence profiles. Int J Food Microbiol. 2015;209:44–51.
Lunestad BT, Truong TTT, Lindstedt B-A. A multiple-locus variable-number tandem repeat analysis (MLVA) of Listeria monocytogenes isolated from Norwegian salmon-processing factories and from listeriosis patients. Epidemiol Infect. 2013;141:2101–10.
Linnan MJ, Mascola L, Lou XD, Goulet V, May S, Salminen C, et al. Epidemic Listeriosis associated with Mexican-style cheese. N Engl J Med. 1988;319:823–8.
Fleming DW, Cochi SL, MacDonald KL, Brondum J, Hayes PS, Plikaytis BD, et al. Pasteurized milk as a vehicle of infection in an outbreak of listeriosis. N Engl J Med. 1985;312:404–7.
Doumith M, Buchrieser C, Glaser P, Jacquet C, Martin P. Differentiation of the major Listeria monocytogenes Serovars by multiplex PCR. J Clin Microbiol. 2004;42:3819–22.
Glaser P, Frangeul L, Buchrieser C, Rusniok C, Amend A, Baquero F, et al. Comparative genomics of Listeria species. Science. 2001;294:849–52.
Moura A, Criscuolo A, Pouseele H, Maury MM, Leclercq A, Tarr C, et al. Whole genome-based population biology and epidemiological surveillance of Listeria monocytogenes. Nat Microbiol. 2016;2:1–10.
Roberts RJ, Vincze T, Posfai J, Macelis D. REBASE--a database for DNA restriction and modification: enzymes, genes and genomes. Nucleic Acids Res. 2015;43(Database issue):D298–9.
O’Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D, McVeigh R, et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 2016;44:D733–45.
Sesto N, Touchon M, Andrade JM, Kondo J, Rocha EPC, Arraiano CM, et al. A PNPase dependent CRISPR System in Listeria. PLoS Genet. 2014;10:e1004065.
Di H, Ye L, Yan H, Meng H, Yamasak S, Shi L. Comparative analysis of CRISPR loci in different Listeria monocytogenes lineages. Biochem Biophys Res Commun. 2014;454:399–403.
Rauch BJ, Silvis MR, Hultquist JF, Waters CS, McGregor MJ, Krogan NJ, et al. Inhibition of CRISPR-Cas9 with Bacteriophage Proteins. Cell. 2017;168:150–158.e10.
Brynildsrud O, Bohlin J, Scheffer L, Eldholm V. Rapid scoring of genes in microbial pan-genome-wide association studies with Scoary. Genome Biol. 2016;17:238.
Komora N, Maciel C, Pinto CA, Ferreira V, Brandão TRS, Saraiva JMA, et al. Non-thermal approach to Listeria monocytogenes inactivation in milk: the combined effect of high pressure, pediocin PA-1 and bacteriophage P100. Food Microbiol. 2020;86:103315.
Ahmadi H, Anany H, Walkling-Ribeiro M, Griffiths MW. Biocontrol of Shigella flexneri in ground beef and Vibrio cholerae in seafood with bacteriophage-assisted high hydrostatic pressure (HHP) treatment. Food Bioprocess Technol. 2015;8:1160–7.
Karatzas KAG, Bennik MHJ. Characterization of a Listeria monocytogenes Scott a isolate with high tolerance towards high hydrostatic pressure. Appl Environ Microbiol. 2002;68:3183–9.
Van Boeijen IKH, Chavaroche AAE, Valderrama WB, Moezelaar R, Zwietering MH, Abee T. Population diversity of Listeria monocytogenes LO28: phenotypic and genotypic characterization of variants resistant to high hydrostatic pressure. Appl Environ Microbiol. 2010;76:2225–33.
FSIS. FSIS Compliance Guideline: Controlling Listeria monocytogenes in Post-lethality Exposed Ready-to-Eat Meat and Poultry Products. 2014. https://www.fsis.usda.gov/wps/wcm/connect/d3373299-50e6-47d6-a577-e74a1e549fde/Controlling-Lm-RTE-Guideline.pdf? MOD=AJPERES. Accessed 10 Oct 2019.
Dogan C, Erkmen O. High pressure inactivation kinetics of Listeria monocytogenes inactivation in broth, milk, and peach and orange juices. J Food Eng. 2004;62:47–52.
Komora N, Bruschi C, Ferreira V, Maciel C, Brandão TRS, Fernandes R, et al. The protective effect of food matrices on Listeria lytic bacteriophage P100 application towards high pressure processing. Food Microbiol. 2018;76:416–25.
Alpas H, Kalchayanand N, Bozoglu F, Ray B. Interactions of high hydrostatic pressure, pressurization temperature and pH on death and injury of pressure-resistant and pressure-sensitive strains of foodborne pathogens. Int J Food Microbiol. 2000;60:33–42.
Flusberg BA, Webster D, Lee J, Travers K, Olivares E, Clark TA, et al. Direct detection of DNA methylation during single-molecule, real-time sequencing. Nat Methods. 2010;7:461–5.
Karatzas KAG, Wouters JA, Gahan CGM, Hill C, Abee T, Bennik MHJ. The CtsR regulator of Listeria monocytogenes contains a variant glycine repeat region that affects piezotolerance, stress resistance, motility and virulence. Mol Microbiol. 2003;49:1227–38.
Verghese B, Lok M, Wen J, Alessandria V, Chen Y, Kathariou S, et al. comK prophage junction fragments as markers for Listeria monocytogenes genotypes unique to individual meat and poultry processing plants and a model for rapid niche-specific adaptation, biofilm formation, and persistence. Appl Environ Microbiol. 2011;77:3279–92.
Wang X, Kim Y, Ma Q, Hong SH, Pokusaeva K, Sturino JM, et al. Cryptic prophages help bacteria cope with adverse environments. Nat Commun. 2010;1:147.
Borges AL, Davidson AR, Bondy-Denomy J. The discovery, mechanisms, and evolutionary impact of anti-CRISPRs. Annu Rev Virol. 2017;4:37–59.
Yin Y, Yang B, Entwistle S. Bioinformatics Identification of Anti-CRISPR Loci by Using Homology, Guilt-by-Association, and CRISPR Self-Targeting Spacer Approaches. mSystems. 2019;4. https://doi.org/10.1128/mSystems.00455-19.
Rollie C, Chevallereau A, Watson BNJ, Chyou T, Fradet O, McLeod I, et al. Targeting of temperate phages drives loss of type I CRISPR–Cas systems. Nature. 2020;578:149–53.
Sampson TR, Weiss DS. Alternative roles for CRISPR/Cas Systems in Bacterial Pathogenesis. PLoS Pathog. 2013;9:e1003621.
Chin C-S, Alexander DH, Marks P, Klammer AA, Drake J, Heiner C, et al. Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data. Nat Methods. 2013;10:563–9.
Staden R, Judge DP, Bonfield JK. Managing sequencing projects in the GAP4 environment. In: Krawetz SA, Womble DD, editors. Introduction to bioinformatics: a theoretical and practical approach. Totowa: Humana Press; 2003. p. 327–44. https://doi.org/10.1007/978-1-59259-335-4_20.
Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.j. 2011;17:10–2.
Li H, Durbin R. Fast and accurate short read alignment with burrows-wheeler transform. Bioinformatics. 2009;25:1754–60.
Walker BJ, Abeel T, Shea T, Priest M, Abouelliel A, Sakthikumar S, et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS One. 2014;9:e112963.
Bushnell B. BBMap: A Fast, Accurate, Splice-Aware Aligner. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); 2014. https://www.osti.gov/biblio/1241166-bbmap-fast-accurate-splice-aware-aligner. Accessed 4 Dec 2019.
Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.
Kopylova E, Noé L, Touzet H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics. 2012;28:3211–7.
Langmead B, Salzberg SL. Fast gapped-read alignment with bowtie 2. Nat Methods. 2012;9:357–9.
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–9.
Anders S, Pyl PT, Huber W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31:166–9.
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.
Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.
Törönen P, Medlar A, Holm L. PANNZER2: a rapid functional annotation web server. Nucleic Acids Res. 2018;46:W84–8.
Couvin D, Bernheim A, Toffano-Nioche C, Touchon M, Michalik J, Néron B, et al. CRISPRCasFinder, an update of CRISRFinder, includes a portable version, enhanced performance and integrates search for Cas proteins. Nucleic Acids Res. 2018;46:W246–51.
Arndt D, Grant JR, Marcu A, Sajed T, Pon A, Liang Y, et al. PHASTER: a better, faster version of the PHAST phage search tool. Nucleic Acids Res. 2016;44:W16–21.
Rice P, Longden I, Bleasby A. EMBOSS: the European molecular biology open software suite. Trends Genet. 2000;16:276–7.
Ragon M, Wirth T, Hollandt F, Lavenir R, Lecuit M, Le Monnier A, et al. A new perspective on Listeria monocytogenes evolution. PLoS Pathog. 2008;4:e1000146.
Jia B, Raphenya AR, Alcock B, Waglechner N, Guo P, Tsang KK, et al. CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database. Nucleic Acids Res. 2017;45:D566–73.
Price MN, Dehal PS, Arkin AP. FastTree 2 – approximately maximum-likelihood trees for large alignments. PLoS One. 2010;5:e9490.
Grant JR, Arantes AS, Stothard P. Comparing thousands of circular genomes using the CGView comparison tool. BMC Genomics. 2012;13:202.
Richter M, Rosselló-Móra R, Oliver Glöckner F, Peplies J. JSpeciesWS: a web server for prokaryotic species circumscription based on pairwise genome comparison. Bioinformatics. 2016;32:929–31.
Page AJ, Cummins CA, Hunt M, Wong VK, Reuter S, Holden MTG, et al. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics. 2015;31:3691–3.
Löytynoja A. Phylogeny-aware alignment with PRANK. Methods Mol Biol. 2014;1079:155–70.
We want to thank Kirsi Lipponen and Paula Collin for performing the NGS procedures for this project. Ingrid B. Eri, Karin Tranøy and Laila Budal (Nofima) are acknowledged for technical laboratory assistance. The sequencing service for strains MB5, C7, and 2HF33 was provided by the Norwegian Sequencing Centre (www.sequencing.uio.no), a national technology platform hosted by the University of Oslo. Prof. Daniela Borda (Faculty of Food Science and Engineering, Dunarea de Jos University of Galati) is acknowledged for her assistance with the high pressure processing experiments conducted in the equipment purchased in the frame of the RE-SPIA project. We wish to acknowledge CSC – IT Center for Science, Finland, for computational resources. We would like to thank reviewers for helpful comments and suggestions to improve our manuscript.
This study was supported within the ERA-IB2 consortium “SafeFood” (ID: ERA-IB-16- 247 014) by grants from Academy of Finland to PA (307856 and 311717), by a grant of the Executive Agency for Higher Education, Research, Development and Innovation Funding in Romania to AIN (International and European Cooperation – 250 Subprogramme 3.2 – Horizon 2020 – Contract No. 15/2017) and by a grant of the German Ministry for Education and Research to CUR (Grant No: 031B0268). ICD supported by the MBDP doctoral program. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics approval and consent to participate
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
L. monocytogenes strain selection, including Table S1, Figure S1.
Gene count data and differentially expressed gene list for both strains RO15 and ScottA based on RNA-Seq.
. Table shows Average Nucleotide Identity (ANI) (based on BLAST) calculation results of each strain. For strains ScottA and F2365 the ANI score with other strains was lower than 95. ANI score between ScottA and F2365 was higher than 95. For the other strains ANI scores between each other were more than 98.
. Genome comparison of 200 L. monocytogenes strains mapped against strain RO15. The genomes were aligned using BLAST and visualized as ring figures. a) Each ring represents the whole genome alignment to strain RO15 (100 most similar genomes (based on BLAST) were displayed in (a) for simplification), b) ring figure zoomed in the prophage 1 region, c) ring figure zoomed in the prophage 2 region, d) ring figure zoomed in the prophage 3 region, e) ring figure zoomed in the prophage 4 region, f) ring figure zoomed in the prophage 5 region. The Refseq identifiers of 200 L. monocytogenes strains can be seen in figure. Genome features can be seen in Table S6. Red colored BLAST hits represent 90–98% identity, blue colored BLAST hits represent 80–90% identity. Black colored BLAST hits represent 100% identity. White gaps represent < 80% identity.
shows identified antibiotic resistance genes and percentage identity of matching region in all strains using CARD database. Figure S3 shows average distance trees from alignments of protein sequences of identified antibiotic resistance genes.
T-test against RO15 log10 reduction.
Barotolerant strains specific genes based on pan-genome wide association analysis. Table shows genes that have p < 0.01 based on pan-genome wide association analysis for barotolerant strains. Gene IDs of strain RO15 was used for this table. Orthologs of these genes with 95% identity were also seen in strains MB5 and 2HF33. In addition, the list of other publicly available L. monocytogenes genomes, where these genes were seen, are also provided.
About this article
Cite this article
Duru, I.C., Andreevskaya, M., Laine, P. et al. Genomic characterization of the most barotolerant Listeria monocytogenes RO15 strain compared to reference strains used to evaluate food high pressure processing. BMC Genomics 21, 455 (2020). https://doi.org/10.1186/s12864-020-06819-0
- High-pressure processing (HPP)
- Foodborne pathogen
- Comparative genomics