Evidence of metabolic switching and implications for food safety from the phenome(s) of Salmonella enterica serovar Typhimurium DT104 cultured at selected points across the pork production food chain

Abstract

Salmonella enterica serovar Typhimurium DT104 is a recognized food-borne pathogen that displays a multidrug-resistant phenotype and that is associated with systemic infections. At one extreme of the food chain, this bacterium can infect humans, limiting the treatment options available and thereby contributing to increased morbidity and mortality. Although the antibiotic resistance profile is well defined, little is known about other phenotypes that may be expressed by this pathogen at key points across the pork production food chain. In this study, 172 Salmonella enterica serovar Typhimurium DT104/DT104b isolated from an extensive "farm-to-fork" surveillance study, focusing on the pork food chain, were characterized in detail. Isolates were cultured from environmental, processing, retail, and clinical sources, and the study focused on phenotypes that may have contributed to persistence/survival in these different niches. Molecular subtypes, along with antibiotic resistance profiles, tolerance to biocides, motility, and biofilm formation, were determined. As a basis for human infection, acid survival and the ability to utilize a range of energy sources and to adhere to and/or invade Caco-2 cells were also studied. Comparative alterations to biocide tolerance were observed in isolates from retail. l-Tartaric acid and d-mannose-1-phosphate induced the formation of biofilms in a preselected subset of strains, independent of their origin. All clinical isolates were motile and demonstrated an enhanced ability to survive in acidic conditions. Our data report on a diverse phenotype, expressed by S. Typhimurium isolates cultured from the pork production food chain. Extending our understanding of the means by which this pathogen adapts to environmental niches along the "farm-to-fork" continuum will facilitate the protection of vulnerable consumers through targeted improvements in food safety measures.

Fig 1

Study design. A total of 172 S. Typhimurium DT104 strains (collected from 2002 to 2010) originating from the pork food chain in Ireland were studied. Group A, isolates obtained from the environment (n = 15); group B, isolates obtained from the processing chain (n = 67); group C, isolates obtained from retail outlets (n = 60); group D, clinical isolates (n = 30).

Fig 2

(A) PFGE analysis of S. Typhimurium DT104 using XbaI and AvrII. Isolate groups were color identified for simplicity as follows, and the color codes were maintained in all of the assays: reference strain (white); group A, environmental (green); group B, processing (red); group C, retail (blue); and group D, clinical (yellow). Clonal relationships between the isolates were analyzed according to the PFGE patterns. A dendrogram was generated using UPGMA algorithms with Dice coefficients. Clusters (denoted I to IV) with >85% similarity were designated, three of which were major clonal groups (I, III, and IV). (B) MLVA analysis of S. Typhimurium DT104 isolates. MLVA analysis was based on five VNTR loci. The MLVA profiles were used for categorical clustering in BioNumerics, and a minimum-spanning tree was constructed. MLVA clustering was considered if neighbors differed in no more than one of the five VNTR loci. Groups were additionally colored based on their source: reference strain (white); group A, environmental (green); group B, processing (red); group C, Retail (blue); and group D, clinical (yellow). These color groups were maintained in all of the assays (PFGE dendrogram clustering and phenotypic microarrays). Based on the isolates distribution, four main clusters depicted in panel B were labeled A, B, C, and D.

Fig 3

Susceptibility profile of the isolates to antimicrobial compounds. A, ampicillin; C, chloramphenicol; Gm, gentamicin; Kan, kanamycin; NAL, nalidixic acid; S, streptomycin; Su, sulfonamides; T, tetracycline; Tm, trimethoprim.

Fig 4

Swim (A) and swarm (B) ability of the S. Typhimurium isolates. The reference strain showed an average swim ability of 65 mm. For plotting purposes, swarm motility was considered in the cases that confluence was achieved after 24 h of incubation.

Fig 5

Acid survival ability of S. Typhimurium strains. Statistical difference was obtained in the resistance to acid compared to the reference strain. The reference strain did not survived when exposed to the acidic conditions (2 h after exposure at pH 2.5).

Fig 6

(A) Phenotypic microarrays results showing the number of phenotypes lost (slower growth) by the tested isolates. The isolates (color coded according to their source) were grown in the presence of different conditions/substrates. (I) Carbon, phosphorus, and sulfur sources; (II) osmolytes; (III) pH. The plotted results are shown as difference (arbitrary units) in the growth of the isolates compared to the reference strain. (B) Phenotypic microarrays results showing the number of phenotypes gained (faster growth) by the tested isolates. The isolates were grown in the presence of different substrates, identified on the outside of the figure (clockwise orientation: pH, osmolytes and phosphorus and sulfur sources). All isolates were color coded (according to their source) for simplicity. Arrows represent the quantitative increase in growth units observed for the isolates when these were compared to the reference strain.

Fig 7

(A) Biofilm formation from Biolog plates (phenotypes lost). The ability of the isolates to form biofilms when in the presence of specific substrates was evaluated by processing the PM plates and using the CV-binding assay. (B) Biofilm formation from Biolog plates (phenotypes gained). The biofilm production was assessed after processing the PM plates and using the CV-binding assay.

Fig 8

Adhesion and invasion of the Salmonella isolates to Caco-2 cells. In this assay, the adherent bacteria are reported as the number of total bacteria (adhered and invading) minus the number of invading bacteria. The number of invading bacteria was estimated by plating serial dilutions. Adhesion and invasion assays were performed on three separate occasions, with four wells per assay used for each isolate.