Siderophore Biosynthesis Governs the Virulence of Uropathogenic Escherichia coli by Coordinately Modulating the Differential Metabolism

Urinary tract infections impose substantial health burdens on women worldwide. Urinary tract infections often incur a high risk of recurrence and antibiotic resistance, and uropathogenic E. coli accounts for approximately 80% of clinically acquired cases. The diagnosis of, treatment of, and drug development for urinary tract infections remain substantial challenges due to the complex pathogenesis of this condition. The clinically isolated UPEC 83972 strain was found to produce four siderophores: yersiniabactin, aerobactin, salmochelin, and enterobactin. The biosyntheses of some of these siderophores implies that the virulence of UPEC is mediated via the targeting of primary metabolism. However, the differential modulatory roles of siderophore biosyntheses on the differential metabolomes of UPEC and non-UPEC strains remain incompletely understood. In the present study, we sought to investigate how the differential metabolomes can be used to distinguish UPEC from non-UPEC strains and to determine the associated regulatory roles of siderophore biosynthesis. Our results are the first to demonstrate that the identified differential metabolomes strongly differentiated UPEC from non-UPEC strains. Furthermore, we performed metabolome assays of mutants with different patterns of siderophore deletions; the data revealed that the mutations of all four siderophores exerted a stronger modulatory role on the differential metabolomes of the UPEC and non-UPEC strains relative to the mutation of any single siderophore and that this modulatory role primarily involved amino acid metabolism, oxidative phosphorylation in the carbon fixation pathway, and purine and pyrimidine metabolism. Surprisingly, the modulatory roles were strongly dependent on the type and number of mutated siderophores. Taken together, these results demonstrated that siderophore biosynthesis coordinately modulated the differential metabolomes and thus may indicate novel targets for virulence-based diagnosis, therapeutics, and drug development related to urinary tract infections

Additional file 6 of Analysis of the ethanol stress response mechanism in Wickerhamomyces anomalus based on transcriptomics and metabolomics approaches

Additional file 6: Table S5

Additional file 2 of Analysis of the ethanol stress response mechanism in Wickerhamomyces anomalus based on transcriptomics and metabolomics approaches

Additional file 2: Table S1

Additional file 5 of Analysis of the ethanol stress response mechanism in Wickerhamomyces anomalus based on transcriptomics and metabolomics approaches

Additional file 5: Table S4

Additional file 7 of Analysis of the ethanol stress response mechanism in Wickerhamomyces anomalus based on transcriptomics and metabolomics approaches

Additional file 7: Table S6

Additional file 4 of Analysis of the ethanol stress response mechanism in Wickerhamomyces anomalus based on transcriptomics and metabolomics approaches

Additional file 4: Table S3

Additional file 3 of Analysis of the ethanol stress response mechanism in Wickerhamomyces anomalus based on transcriptomics and metabolomics approaches

Additional file 3: Table S2

Additional file 1 of Analysis of the ethanol stress response mechanism in Wickerhamomyces anomalus based on transcriptomics and metabolomics approaches

Additional file 1: FigureS1. Cells death determination under different concentrations of ethanoltreatment by methylene blue staining. A,0% ethanol treatment group; B, 3% ethanol treatment group; C, 6% ethanoltreatment group; D, 9% ethanol treatment group; E, 12% ethanol treatment group.Bar=100 μm.Figure S2.  Results of principalcomponent analysis (PCA) of the samples for transcriptome sequencing. FigureS3.  PCA score plots of the samples for metabolomicsanalysis in positive and negative ion modes. A, Positive ion mode; B, Negativeion mode. Table S7. Primersused in this study for real-time quantitative PCR detection