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Siderophore biosynthesis governs the virulence of uropathogenic Escherichia coli by coordinately modulating the differential metabolism Qiao Su, Tianbing Guan, Yan He, and Haitao Lv J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00061 • Publication Date (Web): 08 Mar 2016 Downloaded from http://pubs.acs.org on March 8, 2016

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Journal of Proteome Research

Siderophore biosynthesis governs the virulence of uropathogenic Escherichia coli by coordinately modulating the differential metabolism

Running title: Siderophores govern the virulence of UPEC

Qiao Su, Tianbing Guan, Yan He, Haitao Lv*

The Laboratory for Functional Omics and Innovative Chinese Medicine, Innovative Drug Research Center, Chongqing University, Chongqing 401331, P.R. China

*Corresponding author: Haitao Lv, Ph.D., Professor Tel.: 86-23-65678464 Fax: 86-23-65678450 Email: [email protected]

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Abstract 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, and 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

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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. Keywords: siderophore biosynthesis, metabolomics, urinary tract infection, uropathogenic Escherichia coli, pathogen virulence

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Introduction Siderophores are chemically diverse secondary metabolites that are often produced by germs, fungi, plants and even viruses and often include iron chelators. Therefore, siderophores strongly influence the growth and development of these biological organisms 1-5. Urinary tract infections (UTIs) are among the most prevalent infectious diseases and are associated with high rates of recurrence and antibiotic resistance and thus severely degrade women’s health worldwide. Uropathogenic Escherichia coli (UPEC) is responsible for approximately 70-80% of clinical cases 6-12. A series of studies suggest that the metabolic characteristics of E. coli are the determinant foundation of the differentiation of UPEC and non-UPEC strains and are thus essential for the initiation and development of UTIs 9-12. Moreover, these metabolic characteristics also offer a novel opportunity for an improved understanding of disease pathogenesis. Siderophores are the key secondary metabolites that are biosynthesized by UPEC strains via a series of intermediate metabolites that are derived from primary metabolic pathways 13-16. Our previous data preliminarily indicated that the production of the siderophores yersiniabactin and salmochelin may govern the virulence of UPEC and that UPEC and non-UPEC strains may be significantly differential via observations of primary metabolism modulations 9, 11, 12. However, this previous study utilized a targeted metabolome method that covered only 44 hydrophilic metabolites of several central metabolic pathways 9, 11, 12, which was not sufficient to capture the virulent responses of the systemic metabolism of UPEC strains in relation to siderophore biosynthesis in a manner that clearly

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differentiated UPEC and non-UPEC strains. In the present study, we developed an untargeted metabolomic approach combined with a genetic strategy to mine the differential metabolomes of UPEC and non-UPEC strains and then systematically investigated the associated modulatory roles and siderophore biosynthesis patterns (aerobactin, salmochelin, yersiniabactin and enterobactin). Thus, we examined the aspects of the metabolome that could efficiently distinguish UPEC from non-UPEC strains in a novel effort to discover new functions of siderophores that extend beyond iron chelation to facilitate further advances in the understanding of the pathogenesis and the diagnosis and discovery of therapies for this disease from the metabolic perspective (Figure 1).

Experimental Procedures Chemicals and Reagents. High-purity D2O was purchased from Cambridge Isotope Laboratories (Cambridge, USA). Methanol (chromatographic grade) and acetonitrile (chromatographic grade) were purchased from Fisher Scientific (Loughborough, UK). LB broth and LB agar were purchased from Difco Laboratories (Franklin Lakes , USA). All other reagents were of analytical grade. Human Urine Collection and Preparation. Urine samples were donated by healthy adult volunteers (Table S1). This study was approved by the Institutional Review Board of Chongqing University and the Human Subjects Review Committee of Qianjiang Center for Disease Control and Prevention. The volunteers provided written informed consent for the collection of single specimens in the morning. The

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collection criteria were strictly defined to exclude subjects on antibiotic therapy, pregnant subjects, those with recent UTIs, and those with any kidney disease. The urine samples were initially pooled. Next, the pooled urine was centrifuged and sequentially sterilized consecutively with 0.45-µm and 0.22-µm filters for use as a nutritional medium for the culture of the strains.. Bacterial strains and human urinary culture. UPEC and associated mutant strains were selected in this study (see Table S2). The experimental samples were collected according to the following procedures, which were modified from our previous studies 9, 11. Briefly, all of the strains were statically incubated in LB agar plates for 16 h. Next a single colony was taken from each plate and transferred into 4 ml LB broth for 4 h and incubated with shaking at 37 ℃. Subsequently, 50 µl of culture solution was added to 50 ml of pooled urine and incubated with shaking for 18 h at 37 ℃. Finally, all of the samples were centrifuged to isolate the supernatants for NMR-based metabolomic assays. Construction of UPEC-associated mutant strains. E. coli 83972 iron acquisition genes were deleted using the previously described λ Red recombinase gene inactivation method 5. pKD4 plasmids were used as the templates for the siderophore biosynthetic gene deletion mutants, and the primers are listed in Table S3 9, 17

. The pCP20 plasmid was used to remove the antibiotic resistance cassette. Single,

double, triple and quadruple siderophore mutants were constructed according to the methods described in a previous study 5. All deletion mutants were verified by PCR and DNA sequencing, as described previously 5.

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Metabolite Extraction from the Bacterial Pellets. First, each E. coli pellet was dissolved in 2.4 ml of 80% cold methanol and vortexed for 30 seconds. Next, the solution was transferred into two individual grinding tubes with 0.5-ml mill beads. Second, the mixed sample underwent three cycles of freezing treatment in a -80 ℃ freezer with shaking for 15 minutes and grinding for 2 minutes. Third, the isolated supernatants were combined into a 10-ml tube, and 1.6 ml of cold acetonitrile was added and mixed to remove protein interference. Finally, the supernatant was harvested for lyophilization, and the freeze-dried samples were stored in a -80 ℃ freezer for further NMR assay. Sample preparation for the NMR-based metabolomics assay. Either the freeze-dried pellets or the supernatant samples were mixed with 500 µl of 100% D2O phosphate-buffered solution (DPBS) and subjected to ultra-sonication for 15 minutes. Next, the sample was utilized for NMR-based metabolic profiling in DPBS. Briefly, the DPBS was prepared by thoroughly mixing 3.635 g of Na2HPO4•12H2O, 0.338 g of NaH2PO4•2H2O and 1 mM trimethylsilyl propanoic acid (TSP) into 50 ml of D2O 18

. Nuclear Magnetic Resonance (NMR) spectroscopy. A DD2 nuclear magnetic

resonance (NMR) spectrometer (Agilent, USA) was utilized for the 1H NMR metabolomics assay. High-resolution NMR spectra were acquired at 298 K with an Agilent DD2 operating at 600 MHz for 1H and equipped with an actively shielded gradient unit with a maximum gradient strength output of 65 G cm-1. Standard 1-D 1H spectra were acquired using the pulse sequence –RD–90°–t1–90°–tm–90°–acquire–

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with a relaxation delay (RD) of 2 s, a mixing time (tm) of 100 ms and a fixed t1 delay of 3 ms. Water suppression was achieved via the pre-saturation of the water signal during the RD and mixing time. Each spectrum consisted of 256 free induction decays (FIDs) that were collected into 32 K complex data points with a spectral width of 9615.38 Hz and an acquisition time of 4.0 s. The metabolite concentrations of 5 samples were assessed via the integration of selected NMR resonances from the standard one-dimensional spectra relative to that of TSP following polynomial baseline correction using the same RD and acquisition parameters listed above. All of the samples were in the 64 scans per t1 increment mode. Basically, the acquisition of the 1H NMR spectra of metabolite features produced by each sample required 7.5 min. Chemometric analysis. The raw 1H NMR spectra were transformed into a three-dimensional data matrix using the MestReNova.9.0.1 software. Briefly, the raw 1H NMR spectra were firstly subjected to the suppression of H2O signal and further aligned for the shifted baseline and drifted chemical shift. Then the spectra with chemical shift (l< 0.00 ppm and > 10.00 ppm) were removed completely before the left was binned with 0.03 ppm and transformed into three-dimensional data matrix (Sample ID-Chemical Shift-Signal Abundance). To characterize and identify the differentiable metabolite features, the metabolometric analysis of the data matrix was performed using Metaboanalyst version 3.0 with supervised partial least-squares discriminate analysis (PLS-DA) 19-24. The metabolite features (spectral variables) were first normalized according to the factors of volume, CFUs/sample and the sum

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of all signals and then scaled to auto-scale the distribution to ensure the equal contributions of each metabolic variable to the models. Heatmaps were used to visually display the distributions of all of the data and to characterize the relative levels of the metabolite features of all sample groups. All of the differential metabolite features were recognized and characterized based on the loading plots combined with the VIP values (cut-off >1) that resulted from the PLS-DA analysis. Next, the features were provisionally identified by combining local and open-source databases that included the Human Metabolome Database (HMDB) (http://www.hmdb.ca) 25, the Madison Metabolomics Consortium Database (MMCD) (http://mmcd.nmrfam.wisc.edu) 26 and selected references 27-33. The metabolic pathways that hosted the differential metabolites were constructed by combining the utilization of the KEGG database (http://www.kegg.jp) 34 and the Metaboanalyst 3.0 database (http://www.metaboanalyst.ca/MetaboAnalyst) 22-24. Statistical analysis. The statistics and graphs were manipulated and prepared using the Microsoft Office Excel program.

Results Untargeted metabolomics reveals the distinctively differential metabolomics aspects of UPEC and non-UPEC strains. An NMR-based global metabolomics profiling approach was first applied to discover and phenotype the differential metabolomes of uropathogenic E. coli 83972 (UPEC) and non-uropathogenic E. coli MG1655 (non-UPEC) grown in human pooled urine. A PLS-DA assay of the global

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metabolic features identified the differential metabolomics aspects of UPEC and non-UPEC as this assay revealed marked shifts in the metabolic phenotypes of both bacterial strains (Figure 2). Furthermore, the heatmap illustration clearly demonstrates the group classification as visualized by a panel of differential metabolic features (Figure 3). Together, these differential metabolomic features efficiently distinguished the UPEC from the non-UPEC strains. Moreover, these differences may account for the virulence of UPEC that causes urinary tract infections. Rather than artificial cultural medium, we adopted human pooled urine as the culture medium to provides the E. coli with a real clinical growth environment that allowed us to acquire more robust results and discoveries in the present study. Siderophore biosynthesis modulated the differential metabolomic aspects of UPEC and non-UPEC in a coordinative manner. To investigate whether siderophore biosynthesis functionally modulated the differential metabolomes of UPEC and non-UPEC, we manipulated comparative metabolome assays of the UPEC, non-UPEC and UPEC mutant strains with different patterns of siderophore deletion. The pattern recognition analyses of the metabolic features are provided in Figure 3-6. Surprisingly, the deletion of any single siderophore from the UPEC elicited minimal modulatory influence on the differential metabolomic aspects of the UPEC and non-UPEC strains (Figure 3). Moreover, the double-siderophore-deletion mutations maintained their metabolic regulation, with the exceptions of small influences observed in the mutants with deletions of enterobactin and salmochelin (G9; Figure 4), compared with UPEC and non-UPEC. Nevertheless, the clusters of metabolic

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features were largely restored to that of non-UPEC in the triple-siderophore-deletion mutants, and the greatest restoration occurred in the triple-deletion aerobactin, yersiniabactin and salmochelin mutants (G13; Figure 5). Furthermore, the quadruple-siderophore-mutants had metabolically recovered to the non-UPEC state and were even more harmless than non-UPEC due to the production of enterobactin in non-UPEC (E. coli MG1655; Figure 6). These results suggested that siderophore biosynthesis was related to the virulence of the UPECs, particularly when the siderophores exhibited substantial cooperation. Moreover, salmochelin was again confirmed as the most functional siderophore because its biosynthesis exerted the strongest modulatory influence on the differential metabolome. Next, the heatmaps were overviewed to determine which aspects drove the conspicuous metabolic feature changes in terms of over- and under-expressions due to increasing numbers of siderophore mutations. The differential metabolome was discovered and visualized as the strains responded to noticeable metabolic switching from UPEC to non-UPEC status and were coordinately modulated by siderophore biosynthesis. The differential metabolome primarily involved the metabolic pathways that are the key effectors of siderophore biosynthesis and govern the virulence of UPECs. To explore and characterize the aspects of the differential metabolome that mostly accounted for the virulence of the UPECs as governed by siderophore biosynthesis, a total 23 differential metabolites were identified that were responsible for the metabolic classification of the strains as UPECs or non-UPECs, and these metabolites were coordinately modulated by siderophore biosynthesis. Twelve

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metabolites, including methylhistidine, acetone, phenylacetylglycine, hypoxanthine, guanine, L-cystine, thymidine, indoleacetate, 4-hydroxyphenylacetate, creatine, fumarate, and phenylalanine, were markedly up-regulated, and 11, including NAD+, adenine, alanine, formate, L-cysteine, leucine, guanidinoacetate, 5-aminovalerate, threonine, lactate and glycerate, were markedly down-regulated (Figures 7-8). These differential metabolites were further annotated and incorporated into 19 metabolic pathways (Figure 9); 7 of these metabolic pathways were primarily subordinate to amino acid metabolism, 7 primarily belonged to carbohydrate and energy metabolism, and 3 pathways were allocated to nucleotide metabolism. Additionally, we found that four differential metabolites were related to ABC transporters and that one was evidently correlated with siderophore transportation. Another 4 metabolites were directly related to aminoacyl-tRNA biosynthesis.

Discussion As a human plague that has been maintained for centuries, urinary tract infections have continuously been spread broadly for decades. Because E. coli has been suspected of being the main causative pathogen of UTIs in over 80% of clinical cases, we proposed the hypothesis that the siderophore-associated metabolic phenotypes of uropathogenic E. coli strains represent a novel opportunity to characterize the virulences of UTIs 8, 35. In the present study, we explored a combined metabolomic-genetic strategy for delineating the differential metabolomic aspects of UPEC and non-UPEC strains, with a particular focus on annotating the modulatory

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roles of siderophore biosynthesis. Our data revealed the distinctly differential metabolomes of UPEC and non-UPEC strains and consequently further elucidated the modulatory influence of siderophore biosynthesis. These findings indicated that the coordination of the four siderophores rather than the contribution of any single siderophore produced the modulations that were critical to the differential metabolome of UPEC. As expected, salmochelin was again verified as the most functional siderophore in terms of the modulatory process, which is consistent with our previous finding. Many differential metabolites were identified in the UPEC strains both with and without siderophores that mimicked their modulatory roles in relation to virulence These metabolites included 12 that were up-regulated in the UPEC strains compared with the mutants with different patterns of siderophore deletion and the non-UPEC strains and 11 other metabolites that were down-regulated. Some of the metabolites were involved in amino acid metabolism, and we noted the potential metabolic contributions of amino acids to the differentiations of the virulences of the UPEC and non-UPEC strains (Figure 9A). Specifically, the opposite expression patterns of L-cysteine and L-cystine and the opposite expression patterns of creatine and guanidinoacetate (GAA) suggested that cysteine and methionine metabolism and arginine and proline metabolism should be the focus of much attention to assist with improvements in the understanding of the differential metabolomic aspects of UPEC and non-UPEC. To the best of our knowledge, cysteine sulfur chemistry is essential for transcriptional regulators at the host-bacterial pathogen interface 36. Additionally,

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the L-cysteine/L-cystine system plays important roles in oxidative stress and tolerance by providing a reducing equivalent to the periplasm in E. coli 37 that enables UPEC strains to exhibit much greater virulence during urinary tract infections. The cysteine-cystine pair can also provoke significant ferric iron reduction 38 and may thus promote the iron chelation triggered by siderophore biosynthesis in UPEC. Creatinine is a key metabolite of creatine and has been defined as a typical biomarker for nephropathy diagnoses 39, 40. The up-regulation of creatine in UPEC may also be developed as a biomarker for UTI diagnoses. Indoleacetate, L-cystine, threonine and leucine are strongly associated with the ABC transporter, and indoleacetate was up-regulated in the UPEC compared with the non-UPEC strains and the UPEC strains with siderophore deletions. These findings suggested that indoleacetate production was likely affected by siderophore biosynthesis. Additionally, phenylalanine, L-cysteine, threonine and leucine have been confirmed to participate in aminoacyl-tRNA biosynthesis, which is an essential process for protein expression 41, 42

and thus offers a novel point for an improved understanding of functional

metabolite-RNA interactions. Our data indicated that siderophore biosynthesis markedly modulated the expressions of these amino acids in different manners that were observed to significantly differentiate the UPEC from the non-UPEC strains. The findings discussed above may suggest that siderophore biosynthesis was capable of promoting the virulence of UPEC during infection partially by modulating amino acid metabolism.

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Furthermore, our data illustrated that most of the identified differential metabolites that were down-regulated (Figure 9B) in UPEC compared with non-UPEC and UPEC with different siderophore deletion patterns were related to carbohydrate and energy metabolism, which suggested that UPEC incurred much higher metabolic costs that should be related to virulence. For example, carbon fixation pathways in bacteria and decreases in NAD+ in oxidative phosphorylation indicate a greater capacity for survival because carbon fixation pathways have been known to play a vital role in carbon supplementation in prokaryotes for centuries 43. Therefore, we could argue that UPEC strains are much more efficient in switching metabolic modes to maintain normal growth by consuming considerably less energy by enhancing their carbon fixation capability to complement the reduction in oxidative phosphorylation. Additionally, siderophore biosynthesis likely played a vital modulatory role during this process in the UPEC strains compared with the non-UPEC and siderophore-mutant UPEC strains. These findings may provide new evidence that siderophore biosynthesis is essential for pathogen virulence via modulation of the metabolic pathways that are associated with the energy utilization patterns required for pathogen growth. Moreover, we found that many identified differential metabolites related to purine and pyrimidine metabolism were up-regulated (Figure 9C) in the UPEC strains compared with the non-UPEC and siderophore-mutant UPEC strains. Moreover, guanine and hypoxanthine have been reported to be critical for the virulence of uropathogens 44-46 and thus may have biomarker potential in the diagnosis

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of UTIs. Our data demonstrated that siderophore biosynthesis substantially modulated the expressions of the intermediate metabolites related to purine and pyrimidine metabolism and that these metabolites were among the key effectors for differentiating the UPEC from the non-UPEC strains because they are the virulent foundation by which UPEC causes infection. In summary, for the first time, our combined metabolomic-genetic approach verified the coordinated contribution of siderophore biosynthesis to significantly modulating the differential metabolomes implicated in pathogen virulence and has the capacity to distinguish UPEC from non-UPEC strains. These findings enable us to confidently confirm that siderophore biosynthesis and the associated modulatory metabolic pathways (i.e., the differential metabolome) ought to be novel targets for virulence-based diagnoses, therapeutic discoveries and the development of antibiotics against the UPEC strains that cause UTI. Subsequently, we will focus an intensive effort on delineating the molecular mechanisms that coordinate the modulation of siderophore biosynthesis in the virulence-associated differential metabolome in an effort to interrogate the pathogenesis of UTI and to facilitate the development of novel siderophore-based antibiotics.

ACKNOWLEDGEMENTS This work was supported by a National Natural Science Foundation of China Grant (grant no. 81274175), the Fundamental Research Funds for the Central Universities (grant no. 106112015CDJZR468808 and CSTC2014JCYIA10109), the Start-up Fund

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for the “Hundred Young-Talent Scheme” Professorship provided by Chongqing University in China (grant no. 0236011104401), a Queensland University of Technology Vice Chancellor’s Research Fellowship Grant (grant no. 150410-0070/08), and Open Grants for Key Instrumental Platform Usage Provided by Chongqing University in China (grant nos. 2013121564, 201406150008 and 201412150114). We also thank Prof. Mark A. Schembri from The University of Queensland for providing the UPEC 83972 and mutant strains.

NOTES The authors declare that no conflicts of interest exist.

SUPPORTING INFORMATION The Supporting Information is available free of charge on the ACS Publications website at DOI: Table S1-Personal Characteristics for the healthy volunteers; Table S2-The selected UPEC and associated mutant strains; Table S3-Primers used in this study for the deletion of single, double, triple and quadruple siderophores from UPEC strain involving aerobactin, salmochelin, yerisniabactin and enterobactin.

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Table 1. Identities of differential metabolite features were recognized and characterized based on the loading plots combined with the VIP values (cut-off >1) Chemical Shift (ppm)

Expressional Tendency

Metabolites

References

Leucine 0.96

Down-regulated

1.33

Down-regulated

32, 47-53

Lactate 32, 47, 51-53

Thymidine 1.87

54

Up-regulated Acetone

2.24

Up-regulated

2.24

Down-regulated

3.04

Up-regulated

25

5-Aminovalerate 52

Creatine 32, 47, 51-53, 55-58

L-Cysteine 3.06

54

Down-regulated L-Cystine

3.67

54

Up-regulated Alanine

3.79

25

Down-regulated Guanidinoacetate (GAA)

3.80

51

Down-regulated Glycerate

3.86

49, 58

Down-regulated

29



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Threonine 4.24

25

Down-regulated Fumarate

6.54

47, 52

Up-regulated 1-Methylhistidine O

7.02

Up-regulated

59

N OH NH2

N

Indoleacetate 7.13

Up-regulated

7.14

Up-regulated

56

4-Hydroxyphenylacetate 32

Phenylalanine 7.40

7.45

47, 48, 51, 53

Up-regulated

Up-regulated

Phenylacetylglycine (PAG)

51, 52, 57, 60, 61

Guanine 7.72

52, 56

Up-regulated Hypoxanthine

8.16

54

Up-regulated Adenine

8.18

62

Down-regulated Formate

8.43

29, 47, 52, 53

Down-regulated NAD+ O

8.83

O

Down-regulated

H N 14

H2N14C

C N

O

C14C

N14C H

OH N

HO

O-

NH2

O

P

O

14

P O

O-

O

O

+

N +

NH4 HO OH

30


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Note: The expressional tendency of the differential metabolites were down-regulated or up-regulated in the strain with siderophore deletion when compared to the strains of wild-type UPEC and non-UPEC.

Figure Legends Figure 1. Schematic illustration of the combined metabolomic-genetic strategy explored in the present study. Different strains of siderophore mutants were cultured in human pooled urine and further metabolically characterized with an NMR-based combined metabolomic-genetic strategy to explore the modulatory roles of siderophore biosynthesis on the differential metabolomic aspects of the UPEC and non-UPEC strains. Excitingly, siderophore biosynthesis coordinately modulated the virulence-associated differential metabolomes of the UPEC and non-UPEC strains. Therefore, siderophores may be novel targets for facilitating diagnosis, therapeutic discovery, and drug development related to the treatment of urinary tract infections.

Figure 2. Untargeted metabolomics reveal the distinctively differential metabolomes of UPEC and non-UPEC strains. (A) PLS-DA-based group classification of the detectable metabolic features of the UPEC and non-UPEC groups. (B) Heatmap overview of the detectable metabolic features in the UPEC and non-UPEC groups.

31



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Figure 3. Any single mutation of a siderophore exerted a minimal modulatory effect on the differential metabolomes of the UPEC and non-UPEC strains. (A) PLS-DA-based group classification of the detectable metabolic features of the UPEC, non-UPEC and UPEC-mutant strains with deletions of single siderophores. (B) Heatmap overview of the detectable metabolic features of the UPEC, non-UPEC and UPEC mutants with single-siderophore deletions.

Figure 4. Double-siderophore mutants exhibited slight modulations of the differential metabolomes of the UPEC and non-UPEC strains. (A) PLS-DA-based group classification of the detectable metabolic features of the UPEC, non-UPEC and UPEC mutants with double-siderophore deletions. (B) Heatmap overview of the detectable metabolic features of the UPEC, non-UPEC and UPEC mutants with double-siderophore deletions.

Figure 5. Triple-siderophore mutants largely removed the differential aspects of the metabolomes of the UPEC and non-UPEC strains. (A) PLS-DA-based group classification of the detectable metabolic features of the UPEC, non-UPEC and UPEC mutants with triple-siderophore deletions. (B) Heatmap overview of the detectable metabolic features of the UPEC, non-UPEC and UPEC mutants with triple-siderophore deletions.

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Figure 6. The complete quadruple-siderophore mutant almost recreated the metabolome of the non-UPEC strain. (A) PLS-DA-based group classification of the detectable metabolic features of the UPEC, non-UPEC and UPEC mutants with quadruple-siderophore deletions. (B) Heatmap overview of the detectable metabolic features of the UPEC, non-UPEC and UPEC mutants with quadruple-siderophore deletions.

Figure 7. The up-regulated metabolites hosted by the identified differentiable metabolome were characterized by their chemical structures and relative levels in UPEC (blue bar) compared to non-UPEC (green bar) and UPEC with the complete deletion of quadruple siderophores (red bar).

Figure 8. The down-regulated metabolites hosted by the identified differentiable metabolome were characterized by their chemical structures and relative levels in UPEC (blue bar) compared to non-UPEC (green bar) and UPEC with the complete deletion of quadruple siderophores (red bar).

Figure 9. Metabolic pathways were primarily affected by the differential metabolomes of the UPEC and non-UPEC strains, which were coordinately modulated by siderophore biosynthesis. (A) Amino acid metabolism and the associated metabolic pathways. (B) Carbohydrate and energy metabolism-associated metabolic pathways. (C) Nucleotide metabolism-associated metabolic pathways.

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TOC

The virulence associated differential metabolome between UPEC and non-UPEC was discovered and identified that was coordinately modulated by siderophore biosynthesis.

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Figure 1. Schematic illustration of the combined metabolomic-genetic strategy explored in the present study. Different strains of siderophore mutants were cultured in human pooled urine and further metabolically characterized with an NMR-based combined metabolomic-genetic strategy to explore the modulatory roles of siderophore biosynthesis on the differential metabolomic aspects of the UPEC and non-UPEC strains. Excitingly, siderophore biosynthesis coordinately modulated the virulence-associated differential metabolomes of the UPEC and non-UPEC strains. Therefore, siderophores may be novel targets for facilitating diagnosis, therapeutic discovery, and drug development related to the treatment of urinary tract infections. 865x1152mm (96 x 96 DPI)


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Figure 2. Untargeted metabolomics reveal the distinctively differential metabolomes of UPEC and non-UPEC strains. (A) PLS-DA-based group classification of the detectable metabolic features of the UPEC and non-UPEC groups. (B) Heatmap overview of the detectable metabolic features in the UPEC and non-UPEC groups. 844x526mm (96 x 96 DPI)



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 3. Any single mutation of a siderophore exerted a minimal modulatory effect on the differential metabolomes of the UPEC and non-UPEC strains. (A) PLS-DA-based group classification of the detectable metabolic features of the UPEC, non-UPEC and UPEC-mutant strains with deletions of single siderophores. (B) Heatmap overview of the detectable metabolic features of the UPEC, non-UPEC and UPEC mutants with single-siderophore deletions. 860x633mm (96 x 96 DPI)


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Figure 4. Double-siderophore mutants exhibited slight modulations of the differential metabolomes of the UPEC and non-UPEC strains. (A) PLS-DA-based group classification of the detectable metabolic features of the UPEC, non-UPEC and UPEC mutants with double-siderophore deletions. (B) Heatmap overview of the detectable metabolic features of the UPEC, non-UPEC and UPEC mutants with double-siderophore deletions. 856x686mm (96 x 96 DPI)



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 5. Triple-siderophore mutants largely removed the differential aspects of the metabolomes of the UPEC and non-UPEC strains. (A) PLS-DA-based group classification of the detectable metabolic features of the UPEC, non-UPEC and UPEC mutants with triple-siderophore deletions. (B) Heatmap overview of the detectable metabolic features of the UPEC, non-UPEC and UPEC mutants with triple-siderophore deletions. 858x648mm (96 x 96 DPI)


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Figure 6. The complete quadruple-siderophore mutant almost recreated the metabolome of the non-UPEC strain. (A) PLS-DA-based group classification of the detectable metabolic features of the UPEC, non-UPEC and UPEC mutants with quadruple-siderophore deletions. (B) Heatmap overview of the detectable metabolic features of the UPEC, non-UPEC and UPEC mutants with quadruple-siderophore deletions. 856x578mm (96 x 96 DPI)



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Figure 7. The up-regulated metabolites hosted by the identified differentiable metabolome were characterized by their chemical structures and relative levels in UPEC (blue bar) compared to non-UPEC (green bar) and UPEC with the complete deletion of quadruple siderophores (red bar). 819x438mm (96 x 96 DPI)


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Figure 8. The down-regulated metabolites hosted by the identified differentiable metabolome were characterized by their chemical structures and relative levels in UPEC (blue bar) compared to non-UPEC (green bar) and UPEC with the complete deletion of quadruple siderophores (red bar). 808x434mm (96 x 96 DPI)



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Figure 9. Metabolic pathways were primarily affected by the differential metabolomes of the UPEC and nonUPEC strains, which were coordinately modulated by siderophore biosynthesis. (A) Amino acid metabolism and the associated metabolic pathways. (B) Carbohydrate and energy metabolism-associated metabolic pathways. (C) Nucleotide metabolism-associated metabolic pathways. 685x1158mm (96 x 96 DPI)


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Siderophore Biosynthesis Governs the Virulence of ... - ACS Publications

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