Population Structure of Non-ST6 Listeria monocytogenes Isolated in the Red Meat and Poultry Value Chain in South Africa

Meat products have been implicated in many listeriosis outbreaks globally, however there is a dearth of information on the diversity of L. monocytogenes isolates circulating in food products in South Africa. The aim of this study was to investigate the population structure of L. monocytogenes isolated in the meat value chain within the South African market. Based on whole-genome sequence analysis, a total of 217 isolates were classified into two main lineage groupings namely lineages I (n = 97; 44.7%) and II (n = 120; 55.3%). The lineage groups were further differentiated into IIa (n = 95, 43.8%), IVb (n = 69, 31.8%), IIb (n = 28, 12.9%), and IIc (n = 25, 11.5%) sero-groups. The most abundant sequence types (STs) were ST204 (n = 32, 14.7%), ST2 (n = 30, 13.8%), ST1 (n = 25, 11.5%), ST9 (n = 24, 11.1%), and ST321 (n = 21, 9.7%). In addition, 14 clonal complex (CCs) were identified with over-representation of CC1, CC3, and CC121 in “Processed Meat-Beef”, “RTE-Poultry”, and “Raw-Lamb” meat categories, respectively. Listeria pathogenic islands were present in 7.4% (LIPI-1), 21.7% (LIPI-3), and 1.8% (LIPI-4) of the isolates. Mutation leading to premature stop codons was detected in inlA virulence genes across isolates identified as ST121 and ST321. The findings of this study demonstrated a high-level of genomic diversity among L. monocytogenes isolates recovered across the meat value chain control points in South Africa.


Introduction
The consumption of meat and meat-based products has increased in the last few years in South Africa (SA) [1]. This increase is primarily linked to human population growth, urbanization, higher disposable income, and a change in eating patterns as many people are adopting diets that contain high-quality animal proteins [2]. However, the chemical composition of meat predisposes it to bacterial

Sample Information
The isolates used in this study were obtained from samples submitted between 2014 and 2019 at Agriculture Research Council-Onderstepoort Veterinary Research (ARC-OVR): Feed and Food laboratory, SA, as part of the Department of Agriculture, Land Reform, and Rural Development (DALRRD) Pathogen Profiling project number 21.1.1/VPH-01/OVI. The samples (n = 217) included raw meat (n = 55), processed meat (n = 126), RTE meat products (n = 15) and environmental samples collected from commercial pig farm environment during a listeriosis outbreak (n = 21). The samples originated from different animal protein sources such as beef, poultry, lamb, and pork and various food establishments (farm environments, butcheries, abattoirs, retail outlets and cold stores).

Isolates Categorisation
Considering the diversity of the samples from which the isolates originated, the isolates were grouped according to different categories based on the origin of the sample and the establishment of origin. The sample origin was defined as a concatenation of the type of meat product and the animal from which it was produced from. Samples collected from the farm environment were labelled as environmental samples. The number of isolates for each category is shown in Table 1.

Bacterial Strains and DNA Isolation
The isolates were preserved as lyophilized and were revived by inoculation into brain heart infusion (BHI) broth then incubated at 37 • C for 18-24 h. DNA was extracted from BHI broth culture using a High Pure PCR template preparation kit (Roche, Potsdam, Germany) according to manufacturer's instructions.

Genome Sequencing, Quality Control and de novo Assembly
Whole-genome sequencing (WGS) of the isolates was performed at the Biotechnology Platform, Agricultural Research Council, Onderstepoort, SA. DNA libraries were prepared using TruSeq and Nextera DNA library preparation kits (Illumina, San Diego, CA, USA), followed by sequencing on HiSeq and MiSeq instruments (Illumina, San Diego, CA, USA). Quality control including adapter removal of the raw data was done using BBDuk (version 37.90; https://jgi.doe.gov/data-and-tools/ bbtools/bb-tools-user-guide/bbduk-guide/). SPAdes v.3.12.0 [23] was used to create a de novo assembly of each isolate.
Multi locus sequence type (MLST) profiles were obtained from the Listeria database hosted by the Pasteur Institute, France (http://bigsdb.pasteur.fr/listeria/) [24]. The MLST database contains 7 loci with a total of 2069 different alleles. A k-mer based mapping tool, stringMLST [25], was used to align reads against these profiles to determine the MLST for each sequenced sample using k-mers of length 21 and 35. To validate the k-mer based predictions, all de novo assembled isolates were analyzed using MLST v.2.18.0 [26]. Serotype determination was done in silico using stringMLST and validated with blastn v.2.10.0+.
Genomes of which the in silico determined sequence type and serogroup correlated with L. monocytogenes were annotated using Prokka v.1.14.0 [27]. The pan-genome composition was extracted using Roary [28] and a core genome phylogenetic tree constructed with IQ-TREE v.1.6.6 [29]. Pan-genome clusters were defined as follows: Core-genes present in all isolates; soft core-genes present in at least 95% of isolates; shell-genes present between 15% and 95% of isolates; cloud-genes in less than 15% of isolates. The core genome phylogenetic tree was visualized using ggtree v1.16.6 [30].

Protein Sequence of inlA Genes
Protein sequences for the inlA genes were extracted from the annotated assemblies and aligned using all-versus-all blastp with an e-value of 1 × 10 −30 . The results were filtered for 99% identify and clustered using the Markov clustering algorithm (MCL) [31] with an inflation parameter of 1.8. Protein sequences were inspected for truncation based on the reference protein length of 800 amino acids (AAs).

Data Analysis
Analysis was done using R v.3.6.0 [32]. Proportion and association testing were done using Chi-Square tests and over-representation was indicated by a Pearson residual value of larger than 2. Diversity analysis according to ST occurrence within categories was done using the R package vegan v2.5-6 [33]. A distance matrix based on the ST count matrix was produced using vegan with the "bray" method invoked. Principle coordinate analysis was done using ape v5.3 [34] with the distance matrix as input.
Analysis of CCs and over-representation in the various categories indicated 11 CCs, which were deemed to be associated with a certain category. In the sample origin category, the "Environmental samples" group displayed over-representation of various CCs, which were CC7, CC9, CC31, CC155, and CC288 (p-value = 2.222e-16). These CCs were further found to be over-represented in Farms (p-
Analysis of CCs and over-representation in the various categories indicated 11 CCs, which were deemed to be associated with a certain category. In the sample origin category, the "Environmental samples" group displayed over-representation of various CCs, which were CC7, CC9, CC31, CC155, and CC288 (p-value = 2.222 × 10 −16 ). These CCs were further found to be over-represented in Farms (p-value = 2.658 × 10 −8 ). The "Processed Meat-Beef" category had an over-representation of CC1; CC9 in Abattoirs; and in the "Processing Plant" establishment, CC2 was more than what was expected. In the "RTE-Poultry" and "Processing Plant" categories CC3 was over-represented, CC5 in the "Raw-Poultry" and C19 in "Raw-Beef" categories with CC121 significantly abundant in "Raw-Lamb", "RTE-Pork", and "Import Cold Stores". Over-representation of serogroups, STs, and CCs, indicated by a Pearson residual value larger than 2, for all categories, is presented in Figure 3. Poultry" and C19 in "Raw-Beef" categories with CC121 significantly abundant in "Raw-Lamb", "RTE-Pork", and "Import Cold Stores". Over-representation of serogroups, STs, and CCs, indicated by a Pearson residual value larger than 2, for all categories, is presented in Figure 3.

ST Diversity Analysis
The ST results were transformed into a count matrix and diversity analysis done according to the sample collection categories. The frequency of STs per category is displayed in Figure 4. The values for four different diversity indices (Richness, Simpson, Shannon, and Inverse Simpson) are presented in Table 2 for both the categories "Sample origin" and "Sample location". Samples from the "Processed meat-Beef" category displayed the highest ST diversity, as indicated by all the indices, with "Raw-Poultry" having the second highest diversity. With regards to the "Sample location" it was found that the "Butchery" category had the highest ST diversity, closely followed by the "Retail" category. Results of clustering analysis and Principle coordinates analysis (PCOA) of the sample categories and the ST occurrence are displayed in Figure 5. In general, the "Sample origin" categories "Processed Meat-Beef", "Raw-Poultry", and "Environmental sample" formed a cluster with "Processed meat-Mixed" and "Raw-Beef" grouping together. In the "Sample location" category, "Butchery" and "Retail" grouped closely together.

ST Diversity Analysis
The ST results were transformed into a count matrix and diversity analysis done according to the sample collection categories. The frequency of STs per category is displayed in Figure 4. The values for four different diversity indices (Richness, Simpson, Shannon, and Inverse Simpson) are presented in Table 2 for both the categories "Sample origin" and "Sample location". Samples from the "Processed meat-Beef" category displayed the highest ST diversity, as indicated by all the indices, with "Raw-Poultry" having the second highest diversity. With regards to the "Sample location" it was found that the "Butchery" category had the highest ST diversity, closely followed by the "Retail" category. Results of clustering analysis and Principle coordinates analysis (PCOA) of the sample categories and the ST occurrence are displayed in Figure 5. In general, the "Sample origin" categories "Processed Meat-Beef", "Raw-Poultry", and "Environmental sample" formed a cluster with "Processed meat-Mixed" and "Raw-Beef" grouping together. In the "Sample location" category, "Butchery" and "Retail" grouped closely together.

Pathogenicity Islands
The actA, hly, and mpl genes, which form part of the LIPI-1 gene cluster were present in all the sequenced isolates. The complete gene cluster of LIPI-1 was present in 16 (7.4%) isolates all of which were found exclusively in "Raw-Poultry" and "Processed meat-Beef" categories obtained from "Butchery", "Cold Stores", and "Retail" Sample locations (Supplementary Table S1). These isolates presented CCs from lineage I (CC1, CC2 and CC5) and lineage II (CC9, CC121, CC155, and CC321). The complete LIPI-3 gene cluster was identified in 47 (21.7%) isolates (lineage I: 95.7%; lineage II: 4.3%), of which 35 (74.5%) originated from the "Processed meat-Beef" category. Four CCs (CC1, CC2, CC3, and CC288) belonged to lineage I and lineage II were represented only by CC204. A complete LIPI-4 gene cluster was detected in four isolates (1.8%) with the majority (75%) found in serogroup IVb (CC2 and CC87) and all in lineage I.

Pathogenicity Islands
The actA, hly, and mpl genes, which form part of the LIPI-1 gene cluster were present in all the sequenced isolates. The complete gene cluster of LIPI-1 was present in 16 (7.4%) isolates all of which were found exclusively in "Raw-Poultry" and "Processed meat-Beef" categories obtained from "Butchery", "Cold Stores", and "Retail" Sample locations (Supplementary Table S1). These isolates presented CCs from lineage I (CC1, CC2 and CC5) and lineage II (CC9, CC121, CC155, and CC321). The complete LIPI-3 gene cluster was identified in 47 (21.7%) isolates (lineage I: 95.7%; lineage II: 4.3%), of which 35 (74.5%) originated from the "Processed meat-Beef" category. Four CCs (CC1, CC2, CC3, and CC288) belonged to lineage I and lineage II were represented only by CC204. A complete LIPI-4 gene cluster was detected in four isolates (1.8%) with the majority (75%) found in serogroup IVb (CC2 and CC87) and all in lineage I.

Core Genome Phylogeny
In total, 22,790 genes were predicted across the 217 L. monocytogenes isolates. The partitioning of genes across the pan-genome was as follows: core -1029 genes; soft core -1141 genes; shell -1711 genes; cloud -18,909 genes. Phylogenetic analysis, based on the core genome, is displayed in Figure  7.

Discussion
To have a better understanding of population structure and genomic diversity of L. monocytogenes isolates in SA, a total of 217 isolates representing different meat and meat products as well as environmental samples were characterized using WGS. WGS is a very powerful tool for the characterization of L. monocytogenes as it allows an unprecedented subtyping resolution by using the entire genome to determine strain diversity and virulence traits [35][36][37]. The findings of the present study give a detailed overview into the genomic diversity of L. monocytogenes in the meat value chain that can inform food safety risk-based decisions and risk assessment.

Core Genome Phylogeny
In total, 22,790 genes were predicted across the 217 L. monocytogenes isolates. The partitioning of genes across the pan-genome was as follows: core -1029 genes; soft core -1141 genes; shell -1711 genes; cloud -18,909 genes. Phylogenetic analysis, based on the core genome, is displayed in Figure 7. The primary and universally acceptable method for characterization of L. monocytogenes isolates has been serotyping [38]. Serotyping has been used as a rapid tool for epidemiological investigations of listeriosis outbreaks and to understand the importance of certain serotypes in causing listeriosis in humans [39]. Analysis of the serotypes in this study revealed that all isolates belonged to four major serogroups IIa, IVb, IIb, and IIc (43.8%, 31.8%, 12.9%, and 11.5%, respectively). This is in general agreement with observations made in other countries where serogroup IIa, IIb, and IVb isolates were found frequently while serogroup IIc isolates were rarely found [39][40][41]. In Ireland, O'Connor et al. [42] analyzed 5869 of L. monocytogenes isolates from different foods and found that the most common serogroup was IIa (43.9%), followed by IVb (27.5%), IIb (16.1%), and IIc (12.2%).
In a comparative analysis of serogroups, a hierarchy among isolates was observed with IIa and IVb found to be over-represented. The high presence of serogroup IIa in this study was expected, as IIa has been previously identified as over-represented in food sources and environmental samples in different countries [22,41,43]. Although serogroup IIa is highly associated with contamination of food, it is important to mention that they can cause human infection in certain countries with a high number of susceptible individuals such as SA [44]. Over-representation of serogroup IVb in the present study is concerning as more than 80% of human infections globally are caused by L. monocytogenes strains in this serogroup [22,45,46]. Further analysis of serogroup distribution based on sample origin indicated over-representation (p-value = 0.003) of IIb in "RTE-poultry", IVb in "Processed meat", and IIc in "Environmental samples". This distribution provides critical information on the meat products that are prone to contamination by certain serogroups of L. monocytogenes and may subsequently help in good agricultural and hygiene practices, policy formation, and control measures of this bacterium in South Africa. For instance, implementing proper biosecurity and biosafety measures as good agriculture practice at farm level can play a critical role in minimizing the introduction and spread of different serogroups of L. monocytogenes on downstream processing steps across meat value chain.

Discussion
To have a better understanding of population structure and genomic diversity of L. monocytogenes isolates in SA, a total of 217 isolates representing different meat and meat products as well as environmental samples were characterized using WGS. WGS is a very powerful tool for the characterization of L. monocytogenes as it allows an unprecedented subtyping resolution by using the entire genome to determine strain diversity and virulence traits [35][36][37]. The findings of the present study give a detailed overview into the genomic diversity of L. monocytogenes in the meat value chain that can inform food safety risk-based decisions and risk assessment.
The primary and universally acceptable method for characterization of L. monocytogenes isolates has been serotyping [38]. Serotyping has been used as a rapid tool for epidemiological investigations of listeriosis outbreaks and to understand the importance of certain serotypes in causing listeriosis in humans [39]. Analysis of the serotypes in this study revealed that all isolates belonged to four major serogroups IIa, IVb, IIb, and IIc (43.8%, 31.8%, 12.9%, and 11.5%, respectively). This is in general agreement with observations made in other countries where serogroup IIa, IIb, and IVb isolates were found frequently while serogroup IIc isolates were rarely found [39][40][41]. In Ireland, O'Connor et al. [42] analyzed 5869 of L. monocytogenes isolates from different foods and found that the most common serogroup was IIa (43.9%), followed by IVb (27.5%), IIb (16.1%), and IIc (12.2%).
In a comparative analysis of serogroups, a hierarchy among isolates was observed with IIa and IVb found to be over-represented. The high presence of serogroup IIa in this study was expected, as IIa has been previously identified as over-represented in food sources and environmental samples in different countries [22,41,43]. Although serogroup IIa is highly associated with contamination of food, it is important to mention that they can cause human infection in certain countries with a high number of susceptible individuals such as SA [44]. Over-representation of serogroup IVb in the present study is concerning as more than 80% of human infections globally are caused by L. monocytogenes strains in this serogroup [22,45,46]. Further analysis of serogroup distribution based on sample origin indicated over-representation (p-value = 0.003) of IIb in "RTE-poultry", IVb in "Processed meat", and IIc in "Environmental samples". This distribution provides critical information on the meat products that are prone to contamination by certain serogroups of L. monocytogenes and may subsequently help in good agricultural and hygiene practices, policy formation, and control measures of this bacterium in South Africa. For instance, implementing proper biosecurity and biosafety measures as good agriculture practice at farm level can play a critical role in minimizing the introduction and spread of different serogroups of L. monocytogenes on downstream processing steps across meat value chain.
MLST is a technique used to analyze nucleotide sequence data from a number of conserved (usually 7) housekeeping genes to derive a combination of alleles known as a ST. The application of this technique in the present study has served as tool also to determine the lineages and CCs of L. monocytogenes. CCs are defined as a group of STs differing by no more than one allele from at least one other ST in the group, regardless of its involvement in outbreaks [22]. Analysis of MLST data revealed the distribution of 20 different STs among all the sample isolates that belong to two main lineages, I or II. Similar descriptive differentiation of lineage I and lineage II isolates has been recorded in previous studies [22,47]. This suggests that lineage I and II isolates are important etiological agents common in the South African red meat and poultry value chain. The five largest ST groupings identified in this study, ST204 and ST321 (serogroup IIa), ST1 and ST2 (serogroup IVb), and ST9 (serogroup IIa), have previously been isolated from meat, meat products and production environments around the world [40,[47][48][49]. However, this is the first detailed report on the distribution of STs along the livestock value chain in SA and as such it provides contemporary and applicable data. The predominant ST in the current study, ST204, has also been reported by Kwong et al. [50] and Ebner et al. [51], as the most common ST in meat-associated products in Australia and France. Other studies reported ST204 as a common persisting strain of L. monocytogenes that has been isolated from various sources such as food processing facilities [52], non-clinical isolates [22], and RTE food products [53]. ST1 and ST2 are regarded as the most common STs associated with food contamination and causing infection of humans and animal globally [54][55][56]. In a survey of food-producing facilities between 1996 and 2003 in Austria, ST1 and ST2 were the most predominant in meat-based products as cited by Ebner et al., [51]. Data on the occurrence and distribution of ST9 and ST321 in meat and meat products are lacking globally.
In comparison to the STs in the meat value chain, the non-ST6 sequence types reported from molecular epidemiology of human cases in South Africa are ST1, ST2, ST5, ST54, ST204, ST876, ST7, ST219, Unknown ST, Novel ST, ST101, ST1039, ST224, ST3, ST554, ST8, ST808, ST88, and ST87 in order of frequency [57]. ST6, ST132, ST155, ST2, ST204, ST3, ST5, ST533, ST602, and ST9 have been reported as the common environmental STs in SA can food production facilities [9,57]. Some of these STs (ST2, ST3, ST5, ST9, ST155, and ST204) have been reported in the present study and are induced with mechanisms that allow them to survive in food production environment and keep contaminating food products [5]. Therefore, there is need to link clinical isolates to food samples, since such epidemiological linkages are known to help further understand the key transmission routes and high-risk foods [22].
The absence of highly hypervirulent strains of L. monocytogenes ST6 was observed in the present study. In SA, ST6 strains have been associated with RTE products as samples cultured from a meat production facility's food contact and non-contact environmental surfaces yielded ST6 isolates, which, together with the isolates from the human patients, belonged to the same core-genome MLST cluster with no more than four allelic differences [9]. The absence of ST6 in the current study and the rapid decline in the incidence of L. monocytogenes ST6 infections in humans soon after a recall of the implicated RTE processed meat products suggests that polony (Bologna sausage) produced at a single facility was highly likely to be the outbreak source with the primary contamination originating from a confined primary source [9].
The clustering observed between "Processed Meat-Beef/Raw-Poultry" and "Butchery/Retail" categories based on the ST occurrence is highly comparative with several previous studies that recorded more diversity in isolates from RTE products [20,58]. Although, human infections caused by L. monocytogenes are commonly linked to RTE products, the findings of the present study are important in the South African context as raw-meat and processed-meat products are part of the raw materials for RTE.
The isolates in the present study were also classified into 14 CCs that represent two typical groups of L. monocytogenes CCs. The first group (infection-associated isolates) includes isolates (CC1, CC2, CC4, and CC6) that belong to lineage I and have a strong link to clinical cases (also known as hypervirulent strains) while the second group (food-associated isolates) represent isolates (CC7, CC9, CC121, CC155, and CC204), which belong to lineage II and are predominantly found in the food production environment [58]. The distribution of infection-associated isolates revealed the presence of CC1 and CC2, which were found to be over-represented in the "Raw-Beef" and "Processing Plant" categories, respectively. This over-representation of CC1 and CC2 clones in meat samples has been reported in different studies globally, which suggest their adaptation to diverse food products [25,59]. The distribution of food-associated isolates of L. monocytogenes CCs revealed a significant over-representation of CC7, CC155, CC9, CC121, and CC204, which all belong to lineage II. CC7 and CC155 were mostly found in isolates recovered from farm and environmental samples. CC7 isolates have been globally reported from diverse sources such as animal (wild, poultry, ruminants and fish), abattoir floor, compost, animal food products (milk, cheese, meat), and animal feeds (hay, silage) suggesting the possibility that it might persist in varying environments [39,[60][61][62]. CC155 isolates were frequently detected in food samples in Eastern Asia [63], animals in Switzerland [64], and to a lesser extent in clinical cases in France, New Zealand, Greece, and Netherlands [65]. In the present study, CC9 and CC121 were significantly abundant in samples from "Raw-Lamb" and "Cold Stores (raw imported meat samples) categories and over-represented in "RTE-Pork" meat. Other studies also reported CC9 and CC121 as being significantly over-represented in food of animal origin and food-processing facilities around the world [47,52,66,67]; however, in this study, no significant association of CC204 isolates were observed with respect to environmental samples as well as meat and meat products isolates.
In the current study, although there was a bias towards specific lineages and CCs, there was considerable variation on pathogenic islands known to contribute to L. monocytogenes virulence among the isolates. The complete LIPI-1 gene cluster was detected in 16 isolates, recovered exclusively from "Raw-Poultry" and "Processed meat-Beef" categories. The presence of LIPI-1 in these meat products indicate an increased potential risk to cause infection in humans as LIPI-1 harbor a cluster of genes, prfA, plcA plcB, hly, mpl, and actA, that are very important in the infectious cycle of L. monocytogenes [68]. LIPI-3 consists of eight genes (llsAGHXBYDP) and this complete gene set was detected in 47 isolates, of which the majority (95.7%) were from lineage I (CC1, CC2, CC3, and CC228) and originating from the "Processed meat-Beef" category. This island encodes for hemolysin listeriolysin S, which is known to contribute to the survival of L. monocytogenes in human polymorphonuclear neutrophils [69]. Therefore, the presence of LIPI-1 and LIPI-3 islands in isolates from the "Processed meat-Beef" category, increases the risk for certain members of the population, such as the elderly, acquiring listeriosis in SA. LIPI-4 island is often implicated in placental and central nervouss system infections [69,70] and was found in four isolates in the present study. LIPI-4 has previously been identified mostly in CC4 isolates, however results of this study indicate its presence in CC2 and CC87 isolates, which is consistent with a recent report of this island in CC2 and CC87 isolates cultured in China [70]. The presence of LIPI-4 islands in hypervirulent CC2 and CC87 L. monocytogenes strains in beef and pork products must be a consideration in public health risk management.
The inlA gene encodes a surface protein that is responsible to facilitate the invasion of human intestinal epithelial cells by L. monocytogenes [71]. However, truncation of the inlA gene due to premature stop codons (PMSCs) has been associated with reduced invasiveness in some L. monocytogenes STs that possess them [71]. Analysis of the inlA protein sequence from isolates in this study identified 18 isolates, all from ST121 (n = 17) and ST321 (n = 1), having PMSCs, both of which were lineage II STs.
Although the ST121 and ST321 PMSC mutation has previously been reported [22,64], this is the first report in SA.

Conclusions
Characterization of L. monocytogenes isolates from 2014-2019 using WGS has provided valuable insights into strain diversity and virulence potent of isolates found in meat products consumed in SA. This is also the largest study to report baseline data on the presence of L. monocytogenes serogroups, lineages, STs, and CCs across meat value chain in SA. This study confirmed the heterogeneous distribution of L. monocytogenes CCs across different meat and meat products with evidence of over-representation of certain CCs, which share similarities with those previously linked with human listeriosis outbreaks in other geographical areas. This study again illustrated meat products which are prone to contamination by diverse strains of L. monocytogenes within a specific point in the value chain. This study highlights the association of multiple STs of L. monocytogenes to different meat products in SA and identifies virulence traits as well as genetic mutations of certain subgroups found in food products. Therefore, the information generated here can be used in food safety risk assessment, management and protecting public health.
Future work is still required to compare the WGS dataset produced from this study with clinical isolates from the same timeframe and geographic regions, to identify clusters and determine potential linkages to human listeriosis cases and outbreaks, taking into consideration temporal, microbiological, and epidemiological evidence.