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. 2023 Dec 20;40(2):48.
doi: 10.1007/s11274-023-03857-0.

Genomic and proteomic analysis of Salmonella Enteritidis isolated from a patient with foodborne diarrhea

Affiliations

Genomic and proteomic analysis of Salmonella Enteritidis isolated from a patient with foodborne diarrhea

Benjin Xu et al. World J Microbiol Biotechnol. .

Abstract

Salmonella is a major cause of foodborne diseases and clinical infections worldwide. This study aimed to investigate the drug resistance, genomic characteristics, and protein expression of foodborne Salmonella in Shanxi Province. We isolated a strain of Salmonella Enteritidis from patient feces and designated it 31A. The drug resistance of 31A against 14 antibiotics was determined using an antimicrobial susceptibility test. Whole-genome sequencing and quantitative proteomic analysis were performed on the 31A strain. Functional annotation of drug resistance genes/proteins and virulence genes/proteins was conducted using various databases, such as VFDB, ARDB, CAZY, COG, KOG, CARD, GO, and KEGG. The focus of this study was understanding the mechanisms related to food poisoning, and the genetic evolution of 31A was analyzed through comparative genomics. The 31A strain belonged to ST11 Salmonella Enteritidis and showed resistance to β-lactam and quinolone antibiotics. The genome of 31A had 70 drug resistance genes, 321 virulence genes, 12 SPIs, and 3 plasmid replicons. Functional annotation of these drug resistance and virulence genes revealed that drug resistance genes were mainly involved in defense mechanisms to confer resistance to antibiotics, while virulence genes were mainly associated with cellular motility. There were extensive interactions among the virulence genes, which included SPI-1, SPI-2, flagella, fimbriae, capsules and so on. The 31A strain had a close relationship with ASM2413794v1 and ASM130523v1, which were also ST11 Salmonella Enteritidis strains from Asia and originated from clinical patients, animals, and food. These results suggested minimal genomic differences among strains from different sources and the potential for interhost transmission. Differential analysis of the virulence and drug resistance-related proteins revealed their involvement in pathways related to human diseases, indicating that these proteins mediated bacterial invasion and infection. The integration of genomic and proteomic information led to the discovery that Salmonella can survive in a strong acid environment through various acid resistance mechanisms after entering the intestine with food and then invade intestinal epithelial cells to exert its effects. In this study, we comprehensively analyzed the drug resistance and virulence characteristics of Salmonella Enteritidis 31A using a combination of genomic and proteomic approaches, focusing on the pathogenic mechanism of Salmonella Enteritidis in food poisoning. We found significant fluctuations in various virulence factors during the survival, invasion, and infection of Salmonella Enteritidis, which collectively contributed to its pathogenicity. These results provide important information for the source tracing, prevention, and treatment of clinical infections caused by Salmonella Enteritidis.

Keywords: Drug resistance; Proteome; Salmonella; Traceability; Virulence; Whole-genome.

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References

    1. Alcock BP, Raphenya AR, Lau TTY, Tsang KK, Bouchard M, Edalatmand A et al (2020) CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database. Nucleic Acids Res 48(D1):D517–D525. https://doi.org/10.1093/nar/gkz935 - DOI - PubMed
    1. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM et al (2000) Gene ontology: tool for the unification of biology. Nat Genet 25(1):25 - DOI - PubMed - PMC
    1. Barilleau E, Vedrine M, Koczerka M, Burlaud-Gaillard J, Kempf F, Grepinet O et al (2021) Investigation of the invasion mechanism mediated by the outer membrane protein PagN of Salmonella typhimurium. BMC Microbiol 21(1):153. https://doi.org/10.1186/s12866-021-02187-1 - DOI - PubMed - PMC
    1. Brown E, Dessai U, McGarry S, Gerner-Smidt P (2019) Use of whole-genome sequencing for food safety and public health in the United States. Foodborne Pathog Dis 16(7):441–450. https://doi.org/10.1089/fpd.2019.2662 - DOI - PubMed - PMC
    1. Chen L, Zheng D, Liu B, Yang J, Jin Q (2016) VFDB 2016: hierarchical and refined dataset for big data analysis—10 years on. Nucleic Acids Res 44(D1):D694-697. https://doi.org/10.1093/nar/gkv1239 - DOI - PubMed

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