Quantitative Proteomics Charts the Landscape of Salmonella Carbon

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Quantitative proteomics charts the landscape of Salmonella carbon metabolism within host epithelial cells Yanhua Liu, Kaiwen Yu, Fan Zhou, Tao Ding, Yufei Yang, Mo Hu, and Xiaoyun Liu J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00793 • Publication Date (Web): 16 Nov 2016 Downloaded from http://pubs.acs.org on November 18, 2016

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Journal of Proteome Research

Quantitative proteomics charts the landscape of Salmonella carbon metabolism within host epithelial cells Yanhua Liu, †, # Kaiwen Yu, †, # Fan Zhou, † Tao Ding, † Yufei Yang, † Mo Hu, † and Xiaoyun Liu *, † †

Institute of Analytical Chemistry and Synthetic and Functional Biomolecules Center,

College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China

KEYWORDS: Salmonella metabolism, bacterial infection, proteomics, mass spectrometry

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ACS Paragon Plus Environment


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ABSTRACT We performed a proteomic survey of Salmonella enterica serovar Typhimurium during infection of host epithelial cells. Our data reveal substantial metabolic reshuffling of Salmonella in the host in addition to severe degeneration of bacterial flagella and chemotaxis systems. The large-scale quantitative data allowed us to chart an overview of intracellular Salmonella carbon metabolism. Notably, we found preferential utilization of glycolysis, the pentose phosphate pathway, mixed acid fermentation, and nucleotide metabolism. In contrast, the TCA cycle, aerobic and anaerobic respiration pathways were largely repressed. Furthermore, inactivation of glycolysis and purine biosynthesis led to severe growth defect, indicating important roles in intracellular Salmonella replication. In summary, we exploited quantitative proteomics for rational design of follow-up genetic studies and identified pathways important for bacterial fitness within host cells.

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INTRODUCTION Salmonella is a common food-borne bacterial pathogen that causes a range of illnesses from self-limited gastroenteritis to more severe systemic typhoid fever1. Its virulence highly depends on the type III secretion systems (T3SSs) that are encoded on two distinct Salmonella pathogenicity islands 1 and 2 (SPI-1 and SPI-2). These T3SSs can deliver bacterial virulence factors (effectors) directly into host cells, modulating various host cell processes to promote bacterial invasion as well as survival and proliferation in the host2,3. Till now, the vast majority of research in Salmonella pathogenesis focuses on elucidating the molecular mechanisms of bacterial virulence factors during infection. In contrast, the contribution of bacterial metabolism to pathogenesis remains largely unknown. To efficiently survive and/or multiply, bacterial pathogens must tailor their metabolic pathways to the host environment according to nutrient availability and other physical differences (e.g., oxygen tension). Indeed this aspect of bacterial pathogenesis has attracted increasing attention4,5,6,7,8,9 . Several studies utilized genetic approaches to define Salmonella nutritional and metabolic requirements during infection 10,11,12,13,14,15,16 . For example, glycolysis and glucose were shown to be necessary for intracellular Salmonella survival and proliferation in both murine macrophages and systemic infection of mice11. Rather, glycolysis was reported to be only partially required for Salmonella within infected epithelial cells and additional studies reported dispensability of both gluconeogenesis and the glyoxylate shunt for Salmonella infection12. Other than Salmonella, carbon metabolism pathways were also examined for pathogenic E. coli and Shigella flexneri during infection17,18,19,20. For instance, systematic 3



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disruption of E. coli central metabolic pathways showed that peptide uptake, gluconeogenesis, and tricarboxylic acid cycle are required for bacterial fitness during urinary tract infection whereas glycolysis, the pentose phosphate pathway, and the Entner-Doudoroff pathway are all dispensable17. In a similar report, Waligora et al studied the contribution of individual carbon metabolic pathways to Shigella flexneri virulence18. Furthermore, pyruvate is suggested to be a preferred carbon source for Shigella in epithelial cells, which is consistent with an independent study from Bumann and co-workers21. Other than random gene inactivation, high-throughput expression profiling data can be exploited to guide genetic testing of individual metabolic requirements 22 . Becker et al reported the first in vivo Salmonella proteome during systemic infection of mice and their exhaustive genetic inactivation suggested substantial robustness in Salmonella metabolism23. Nevertheless, qualitative measurement of metabolic enzymes (even in vivo) is often insufficient to implicate functional significance. Thus we hypothesize that highly quantitative expression data would be most useful in guiding subsequent genetic studies. Shi et al carried out the first quantitative studies of intracellular Salmonella and host protein expression during infection of macrophages24,25. We previously conducted proteomic analyses of intracellular Salmonella at 6 h postinfection (hpi) and found extensive bacterial adaptations to infected HeLa cells26. Yet our data were not informative in revealing Salmonella remodeling of carbon metabolism pathways, which may occur much later during infection (i.e., after multiple rounds of replication). Herein by extending our proteomic analyses to 18 hpi, we observed profound alterations in bacterial metabolism, indicating a unique physiological and metabolic state of intracellular 4


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Salmonella. The quantitative expression data allowed us to draft an overview of Salmonella carbon metabolism in the host and more importantly directed follow-up genetic studies via rational inactivation of specific metabolic pathways.

MATERIALS AND METHODS Bacterial strains and culture conditions. Bacterial strains and plasmids used in this study are shown in Table 1. All strains were routinely maintained at ‒80°C in LB broth supplemented with 25% (vol/vol) glycerol. The frozen bacteria were routinely grown on LB plates with 15% agar and 30 µg/mL streptomycin at 37°C. A single colony picked from the plate was inoculated into 3 mL LB medium with 30 µg/mL streptomycin, then the overnight culture was diluted 1:20 into 3 mL LB broth (supplemented with 0.3 M NaCl to induce expression of SPI-1 T3SS). The bacteria were harvested for infection assays when they grew to the mid-exponential phase (OD600 = 0.9). Bacterial growth was monitored by measuring optical density at 600 nm. Table 1. Strains and plasmids used in this study. Strains or plasmids

Description

Sources

SL1344

Wild-type strain

∆pfkAB ∆gnd ∆pflB ∆purM ∆deoC

SL1344∆pfkA ∆pfkB SL1344∆gnd SL1344∆pflB SL1344∆purM SL1344∆deoC

National Institute of Biological Sciences, Beijing This study This study This study This study This study

S. Typhimurium strains

E. coli strains DH5α-λPir

Donor in triparental mating 5


China Agricultural University


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DH5α (pRK600）

Helper in triparental mating


Plasmids pSR47s

Suicide plasmid in Salmonella


HeLa cell infection assays. HeLa cells were grown in Dulbecco’s Modified Eagle Medium (DMEM, Hyclone) supplemented with 10% fetal bovine serum (FBS, Gibco, Life Technologies, US) under an atmosphere of 5% CO2 at 37°C. Salmonella invasion of HeLa cells was performed when cell monolayers reached 70-85% confluence. For isolation of intracellular bacteria, infection was carried out for 45 min in Hanks’ balanced salt solution (HBSS) with a multiplicity of infection (MOI) of 100 or 30 depending on the sampling time (6 or 18 hpi). After infection, cell monolayers were washed with prewarmed HBSS (37°C) and incubated further for 2 h in prewarmed DMEM supplemented with 100 µg/mL gentamicin to kill extracellular bacteria. Subsequently, cell monolayers were washed again with prewarmed HBSS, and fresh DMEM supplemented with 10 µg/mL gentamicin was added. At 6 hpi and 18 hpi, cells were washed extensively with PBS and lysed in 20 mM Tris-HCl (pH 7.6) buffer containing 150 mM NaCl and 0.1% Triton X-100. To recover intracellular bacteria, collected cell lysates were centrifuged first at 600 × g for 5 min to remove nuclei and cell debris, and then the supernatant was centrifuged at 4000 × g for 20 min to pellet bacteria. The pellets were immediately washed with RIPA buffer (25 mM Tris-HCl (pH 7.6), 150 mM NaCl, 1% Triton X-100, 0.5% sodium deoxycholate, and 0.1% SDS) to remove residual host proteins. The final bacterial pellets were resuspended in the SDS-PAGE sample buffer containing 60 mM Tris-HCl (pH 6.8), 1.7% (w/v) SDS, 6% (v/v) glycerol, 100 mM dithiothreitol (DTT), and 0.002% (w/v) bromophenol blue, heated at 95°C for 5 min, and then kept frozen at ‒20°C for further analyses. 6


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Proteomic analysis of intracellular Salmonella. Bacterial samples were prefractionated by 10% SDS-PAGE, processed into 8 gel bands, and subjected to in-gel trypsin digestion as previously described27. The final peptide samples were reconstituted in HPLC-grade water for LC-MS/MS analyses, which were performed on a hybrid LTQ-Orbitrap Velos mass spectrometer equipped with nanoflow reversed-phase liquid chromatography (EASY-nLC 1000, Thermo Scientific). The capillary column (75 µm × 150 mm) with a laser-pulled electrospray tip (Model P-2000, Sutter instruments) was home-packed with 4 µm, 100 Å Magic C18AQ silica-based particles (Michrom BioResources Inc., Auburn, CA). The mobile phase was comprised of solvent A (97% H2O, 3% acetonitrile (ACN), and 0.1% formic acid) and solvent B (100% ACN and 0.1% formic acid). The LC separation was carried out with the following gradient: solvent B was started at 7% for 3 min, and then raised to 35% in 40 min; subsequently, solvent B was rapidly increased to 90% in 2 min and maintained for 10 min before 100% solvent A was used for column equilibration. Eluted fractions from the capillary column were electrosprayed directly onto the mass spectrometer for MS and MS/MS analyses with the data-dependent acquisition mode. One full MS scan (m/z 350-1500) was acquired and then MS/MS analyses of 10 most intense ions were performed. Dynamic exclusion was set with repeat duration of 24 seconds and exclusion duration of 12 seconds. We analyzed three biological replicates of intracellular Salmonella, and in total we carried out 48 LC-MS/MS experiments. Proteomic data processing. MaxQuant (http://maxquant.org/, Version 1.5.3.30) was used to generate peak lists from raw files, and Andromeda was used to search the protein sequence database using the following parameters: carbamidomethyl (C) was set as a fixed 7



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modification, while oxidation (M) was set as a variable modification. Acquired MS/MS spectra were searched against a merged human (20195 sequences) and S. enterica serovar Typhimurium LT2 (5,199 sequences, downloaded from UniProt) database augmented with the reversed sequence of each entry in the forward database. The precursor mass tolerance was set at 20 ppm, and the fragment mass tolerance for CID MS/MS spectra was set at 0.8 Da. Trypsin was selected as the digestive enzyme with two potential missed cleavages. The minimum ratio counts were set at 2. The false discovery rate (FDR) of peptides and proteins was controlled at < 1%. The MaxQuant software was used to calculate the label-free quantitation (LFQ) intensity for each protein. The Salmonella protein LFQ intensity lists of the samples were processed using the Perseus software (version 1.5.4.1). Logarithmic values (Log2) of the LFQ intensity were used and the missing values were replaced with random numbers from a normal distribution (width = 0.3, shift = 1.8). The p value of each protein between the 6 h and 18 h groups was obtained by using the two-tailed Student’s t-test. Only the proteins detected in at least three individual samples either from 6 h or from 18 h groups were selected as quantified proteins. Filtered proteins with average fold changes > 2 or < 0.5 and p value < 0.05 were considered up- or down-regulation. Construction and phenotypic characterization of bacterial mutant strains. S. Typhimurium mutant strains and 3 × FLAG-tagged strains were constructed using the standard homologous recombination method as previously described26. For chromosomal gene tagging, the sequences encoding the 3 × FLAG epitope were inserted in-frame at the C termini of targeted bacterial genes right before the stop codons. All primers are listed in supplemental Table S1. Successful deletion or tagging of the target gene was confirmed by 8


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both sequencing and PCR analyses. For the intracellular replication assay, HeLa cells in 6-well plates were infected with either the wild-type or mutant strains with an MOI of 10. At 1 and 18 hpi, infected cells were lysed and viable intracellular bacteria were numerated by colony forming unit (CFU) assays. Western blot analyses. The 3 × FLAG-tagged Salmonella strains were used to infect HeLa cells in 6-well plates with an MOI of 100 or 30. At the appropriate times after infection (6 hpi and 18 hpi), mammalian cells were lysed and crude fractions of intracellular bacteria were obtained by differential centrifugation. Isolated bacterial pellets were resuspended in the SDS loading buffer and run by SDS-PAGE. Gel-separated bacterial proteins were further transferred to polyvinylidene diﬂuoride (PVDF) membranes. Immunoblotting analyses were carried out with primary antibodies speciﬁc for Salmonella DnaK (Enzo Life Sciences) (1:5000) or FLAG (Cwbio, China) (1:2000) and horseradish peroxidase (HRP)-conjugated secondary antibodies (Cwbio, China) (1:5000).

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RESULTS Proteomic snapshots of intracellular Salmonella within infected epithelial cells To further understand the metabolic features that underlie Salmonella survival and replication during infection, we extended our previous proteomic studies of intracellular bacteria from 6 hpi (the onset of Salmonella replication) to 18 hpi (~10-fold increase in bacterial number). In total, we identified 1957 bacterial proteins from 220, 646 MS/MS spectra. In addition, 1520 proteins of human origin were also assigned from 74, 206 MS/MS spectra, contributing to ~25% of total spectral counts. By using label-free quantitation (LFQ), we found alteration of 252 Salmonella proteins. Figure 1a shows the protein-level volcano plot of log2 ratio and p values from the bacterial samples at 6 and 18 hpi. With a fold change cutoff > 2 (p value < 0.05), 119 proteins were up-regulated (denoted by the red dots) and 133 proteins were down-regulated (the green dots). A complete list of all protein identifications as well as altered proteins is provided as supplemental Table S2. In comparison to the proteomic data at 1 and 6 hpi, many PhoPQ-regulated proteins (e.g., PgtE, PqaA, PhoN, and STM3595) and metal transporters such as SitAB were further up-regulated from 6 to 18 hpi, while in contrast the levels of proteins that are associated with iron-uptake fell significantly at 18 hpi relative to those at 6 hpi (e.g., IroB, IroC, EntA, EntB, EntC, and EntE). To confirm some of the proteomic changes, we constructed Salmonella strains chromosomally expressing 3 × FLAG tagged proteins (PhoN, STM3595, and EntB) and assayed their expression levels upon infection of HeLa cells by Western blot analyses. As seen in Figure 1b, the immunoblotting data clearly demonstrated the increased levels of PhoN and STM3595 and the decreased level of EntB, which agreed well with our proteomic results. 10


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In addition, the molybdate transporter ModA and magnesium transporter MgtABC were markedly induced. Though host magnesium starvation was well documented28,29,30, we are the first to report Mo limitation in host epithelial cells. Next we will discuss in more details those differentially regulated Salmonella proteins that fall into distinct biological processes or pathways.

a.

-Log10p-value

6

ArtJ

CheA

5

SpaO CheY FliG CheZ NuoI InvH CheW CadA STM3138 EntB PrgH

TalA YihX

4

MgtC

STM3036 SsaN PqaA STM1940 SsaJ STM3595 PhoN

3

OrgB SrfB

2

PipB STM4505 1

0 -6

-4

-2

0

2

4

6

Log2ratio(18h/6h) PhoN

15 10 5 0

6h

18 h

4

STM3595

3 2 1 0

6h

LFQ intensity (108)

20

LFQ intensity (109)

b. LFQ intensity (109)

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18 h

α-DnaK α-FLAG 11


15

EntB

10 5 0

6h

18 h


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Figure 1. (a) The volcano plot of detected intracellular Salmonella proteins by LC-MS/MS. The fold changes were calculated by dividing LFQ intensity values at 18 hpi by those at 6 hpi. The logarithmic ratios of average fold changes are reported on the x-axis. The y-axis plots negative logarithmic p values from the t-test performed on three biological replicates. Dotted lines denote 2-fold (vertical) and p < 0.05 cut-off (horizontal). The up- or down-regulated proteins are denoted by the red and green dots, respectively. (b) Western blot analyses of representative Salmonella proteins at 6 and 18 hpi (the lower panels) as well as their corresponding LFQ intensity values derived from LC-MS/MS measurements of three biological replicates (the upper panels). Degeneration of Salmonella flagellar and chemotaxis systems in host epithelial cells At 18 hpi, one of the most striking changes was substantial down-regulation of Salmonella flagellar proteins (Table 2). The flagellin protein FliC was one of the most abundant proteins expressed by Salmonella both extracellularly and early during infection, whereas its abundance was significantly reduced at 18 hpi (a 4.2-fold decrease relative to 6 hpi). We also observed down-regulation of other flagellar proteins including FliF, FliG, FliL, FliM, FliN, and FlgI, supporting a non-motile state of intracellular Salmonella. In addition to reduced motility, Salmonella residing in the host also seemed to lose chemotactic capabilities as the levels of several chemotaxis proteins (e.g., CheA, CheW, CheZ, and CheY) were substantially lower (> 4 fold) at 18 hpi (Table 2). Furthermore, methyl-accepting chemotaxis proteins were repressed as well including Tsr, Tcp, Tar, and Trg. Taken together, these findings suggest the loss of motility and chemotaxis capabilities of Salmonella within infected HeLa cells. Table 2. Differentially expressed proteins that are associated with Salmonella flagellar and chemotaxis systems. Gene fliC fliG

Abundance1 6 h2 18 h3 1.3E9 3.1E8 8.6E7 0

Protein description Flagellin Flagellar motor switch protein 12


Fold4 -4.2 n.a6

p value5 0.024

Quantitative Proteomics Charts the Landscape of Salmonella Carbon

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