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. 2023 Oct 6:16:6567-6586.
doi: 10.2147/IDR.S411125. eCollection 2023.

The Resistance and Virulence Characteristics of Salmonella Enteritidis Strain Isolated from Patients with Food Poisoning Based on the Whole-Genome Sequencing and Quantitative Proteomic Analysis

Benjin Xu #  1   2   3 , Zhuru Hou #  2   4 , Ling Liu #  1   2   3 , Rongrong Yan  3 , Jinjing Zhang  3 , Jianhong Wei  4 , Miao Du  1   2 , Yan Xuan  1   2 , Lei Fan  2   4 , Zhuoxi Li  2   4
Affiliations

The Resistance and Virulence Characteristics of Salmonella Enteritidis Strain Isolated from Patients with Food Poisoning Based on the Whole-Genome Sequencing and Quantitative Proteomic Analysis

Benjin Xu et al. Infect Drug Resist. .

Abstract

Objective: This paper explores the drug resistance, genome and proteome expression characteristics of Salmonella from a food poisoning event.

Methods: A multidrug-resistant Salmonella Enteritidis strain, labeled as 27A, was isolated and identified from a food poisoning patient. Antimicrobial susceptibility testing determined the resistance of 27A strain to 14 antibiotics. Then, WGS analysis and comparative genomics analysis were performed on 27A, and the functional annotation of resistance genes, virulence genes were performed based on VFDB, ARDB, COG, CARD, GO, KEGG, and CAZY databases. Meanwhile, based on iTRAQ technology, quantitative proteomic analysis was conducted on 27A to analyze the functions and interactions of differentially expressed proteins related to bacterial resistance and pathogenicity.

Results: Strain 27A belonged to ST11 S. Enteritidis and was resistant to levofloxacin, ciprofloxacin, ampicillin, piperacillin, and ampicillin/sulbactam. There were 33 drug resistance genes, 384 virulence genes and 2 plasmid replicon, IncFIB(S) and IncFII(S), annotated by WGS. Proteomic analysis revealed significant changes in virulence and drug proteins, which were mainly involved in bacterial pathogenicity and metabolic processes. PPI prediction showed the relationship between virulence proteins and T3SS proteins, and PagN cooperated with proteins related to T3SS to jointly mediate the invasion of 27A strain on the human body. Phylogenetic analysis indicated that S. Enteritidis has potential transmission in humans, food, and animals.

Conclusion: This study comprehensively analyzed the drug resistance and virulence phenotypes of S. Enteritidis 27A using genomic and proteomic approaches. These helps reveal the drug resistance and virulence mechanisms of S. Enteritidis, and provides important information for the source tracing and the prevention of related diseases, which lays a foundation for research on food safety, public health monitoring, and the drug resistance and pathogenicity of S. Enteritidis.

Keywords: Salmonella; WGS; evolution; quantitative proteomics; resistance; virulence.

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Conflict of interest statement

The authors have no conflict of interest to declare.

Figures

Figure 1
Figure 1
Clinical Diagnosis, Treatment and Epidemiological Investigation of the Patient.
Figure 2
Figure 2
Genome Map of 27A. From outer to inner: 1, Genome Size; 2, Forward Strand Gene, colored according to cluster of orthologous groups (COG) classification; 3, Reverse Strand Gene, colored according to COG classification; 4, Forward Strand ncRNA; 5, Reverse Strand ncRNA; 6, repeat; 7, GC; 8, GC-SKEW. The circular representation of plasmid does not contain forward and reverse strand ncRNA and repeats.
Figure 3
Figure 3
Function Annotation Distribution Diagram of 27A. (A) A Venn diagram of genes annotated in different databases. (B) COG functional annotation. (C) GO functional annotation. (D) KEGG pathway annotation. (E) CAZY database annotation. (B–D) the vertical axis represented the annotation entry, and the horizontal axis represented the number of genes corresponding to the entry.
Figure 4
Figure 4
Resistance Genes and Virulence Genes Distribution Diagram of 27A. (A) Distribution of drug resistance and virulence genes on 27A plasmid. (B) Salmonella pathogenic island SPI-1 and SPI-2.
Figure 5
Figure 5
Core and Pan Genes Analysis. (A) Pan genes Venn graph. (B) Core genes and dispensable genes heatmap. (C) COG functional annotation of core genes and dispensable genes. (D) COG functional annotation of specific genes.
Figure 6
Figure 6
Phylogenetic Tree Based on CorePan Results and the Distribution of Resistance Genes, Plasmid Replicons.
Figure 7
Figure 7
Functional Annotation of the Proteins Based on Database Searches. (A) GO functional annotation. (B) KOG functional annotation. (C) KEGG pathway annotation.
Figure 8
Figure 8
Quantitative and Functional Enrichment of Differential Proteins. (A) volcano map of differential proteins. (B) Heatmaps of differential proteins related to drug resistance and virulence. (C) KOG functional annotation of differential proteins. (D) GO functional annotation of differential proteins. (E) KEGG pathway annotation.
Figure 9
Figure 9
Time Series Analysis of Differential Proteins. CG: The reference strain, EG: 27A. TheX-axis represents each time point, theY-axis represents the expression level after normalization.
Figure 10
Figure 10
Interaction Network of Differentially Expressed Proteins.
See this image and copyright information in PMC

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