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. 2022 Mar 8;88(5):e0214921.
doi: 10.1128/aem.02149-21. Epub 2022 Jan 12.

Genomic Epidemiology of Salmonella enterica Circulating in Surface Waters Used in Agriculture and Aquaculture in Central Mexico

Affiliations

Genomic Epidemiology of Salmonella enterica Circulating in Surface Waters Used in Agriculture and Aquaculture in Central Mexico

N E Ballesteros-Nova et al. Appl Environ Microbiol. .

Abstract

Salmonella enterica can survive in surface waters (SuWa), and the role of nonhost environments in its transmission has acquired increasing relevance. In this study, we conducted comparative genomic analyses of 172 S. enterica isolates collected from SuWa across 3 months in six states of central Mexico during 2019. S. enterica transmission dynamics were assessed using 87 experimental and 112 public isolates from Mexico collected during 2002 through 2019. We also studied genetic relatedness between SuWa isolates and human clinical strains collected in North America during 2005 through 2020. Among experimental isolates, we identified 41 S. enterica serovars and 56 multilocus sequence types (STs). Predominant serovars were Senftenberg (n = 13), Meleagridis, Agona, and Newport (n = 12 each), Give (n = 10), Anatum (n = 8), Adelaide (n = 7), and Infantis, Mbandaka, Ohio, and Typhimurium (n = 6 each). We observed a high genetic diversity in the sample under study, as well as clonal dissemination of strains across distant regions. Some of these strains are epidemiologically important (ST14, ST45, ST118, ST132, ST198, and ST213) and were genotypically close to those involved in clinical cases in North America. Transmission network analysis suggests that SuWa are a relevant source of S. enterica (0.7 source/hub ratio) and contribute to its dissemination as isolates from varied sources and clinical cases have SuWa isolates as common ancestors. Overall, the study shows that SuWa act as reservoirs of various S. enterica serovars of public health significance. Further research is needed to better understand the mechanisms involved in SuWa contamination by S. enterica, as well as to develop interventions to contain its dissemination in food production settings. IMPORTANCE Surface waters are heavily used in food production worldwide. Several human pathogens can survive in these waters for long periods and disseminate to food production environments, contaminating our food supply. One of these pathogens is Salmonella enterica, a leading cause of foodborne infections, hospitalizations, and deaths in many countries. This research demonstrates the role of surface waters as a vehicle for the transmission of Salmonella along food production chains. It also shows that some strains circulating in surface waters are very similar to those implicated in human infections and harbor genes that confer resistance to multiple antibiotics, posing a risk to public health. This study contributes to expand our current knowledge on the ecology and epidemiology of Salmonella in surface waters.

Keywords: Mexico; Salmonella; aquaculture; irrigation; phylogenetic analysis; serotyping; surface water; transmission dynamics; whole-genome sequencing.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

FIG 1
FIG 1
Genotypic antimicrobial resistance (AMR) profile of 172 Salmonella isolates from surface waters (SuWa). Antibiotic classes are color-coded, and cells filled with the corresponding antibiotic class color indicate that the AMR gene is present. The rightmost columns report the presence of point mutations in the listed genes (dark gray cells) and the occurrence of multidrug-resistant (MDR) genotypes (black cells). Blank cells indicate the absence of AMR genes, mutations, or MDR genotypes. The results are summarized considering the number of isolates of the same serovar with the same AMR profile. Individual results and isolate metadata are provided in Table S1 in and Fig. 2.
FIG 2
FIG 2
ML tree based on single-nucleotide polymorphism (SNP) analysis of 172 Salmonella isolates from SuWa. Tip labels show NCBI assembly accessions, Mexican state, serovar, sequence types (STs), and type of SuWa body. AMR genotypes are mapped onto the tree. Black cells correspond to isolates that carried at least one variant of the indicated AMR alleles, while gray cells indicate absence of that gene. Clade support is indicated in the branches as bootstrap values, unless it is less than 0.7.
FIG 3
FIG 3
Transmission network of 199 Salmonella isolates collected in Mexico from SuWa (n = 105), human clinical cases (n = 6), animals (n = 28), vegetables (n = 55), and the environment (n = 5). The network was generated for the source/hub ratio. Each circle corresponds to a specific isolate source, and the source/hub ratio is indicated inside the circle. The values for centrality metrics (degree, indegree, outdegree, betweenness, closeness, and source/hub ratio) are reported below the network. The accession numbers and metadata of isolates used in this analysis are provided in Fig. 4.
FIG 4
FIG 4
Reconstruction of character states at ancestral nodes in a phylogenetic tree of 199 public Salmonella isolates recovered from multiples sources in Mexico during 2002 through 2019. Isolates from this research are highlighted with bold type. Character states are color-coded according to isolation source, while the summary of changes in character states is summarized next to the tree. NCBI assembly accession, serovar, isolation source, and Mexican state of origin (if available) are indicated at tip labels. BC, Baja California; CAM, Campeche; CDMX, Mexico City; COL, Colima; GTO, Guanajuato; HGO, Hidalgo; MEX: State of Mexico; MOR, Morelos; NAY, Nayarit; SIN, Sinaloa; SON, Sonora; TLAX, Tlaxcala; VER, Veracruz; YUC, Yucatán.
FIG 5
FIG 5
ML tree based on SNP analysis of 33 public Salmonella isolates collected from SuWa in Mexico and from human clinical cases in Mexico (MX), Canada (CA), and the United States (US). Tip labels show NCBI accessions, serovar, isolation source, country of origin, and collection year, unless there was no data (ND) recorded for collection year. Clade support is indicated in the branches as bootstrap values, unless it is less than 0.7. SNP clusters are color-coded and numbered in bold text to facilitate their visualization.
FIG 6
FIG 6
Examples of selected sampling sites nearby food production areas and their global positioning system (GPS) coordinates. (Left) River used by local sheep producers (19.29425, −98.875394). (Center) Irrigation canal used in sugarcane fields (18.78898, −99.221747). (Right) Tilapia production in a pond (20.436595, −99.36901).
FIG 7
FIG 7
Overview of sample collection points across the six Mexican states that were included in the survey. For full details, refer to the interactive map at: https://www.google.com/maps/d/edit?mid=1dScbM7__NgBr6eoybRmPImb1qey2GcbK&usp=sharing.

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