Mouse Infections

6- to 8-week-old female 129/SvJ mice (Jackson Laboratories, Bar Harbor, ME) were used for oral infections. Mouse experiments in this study were performed in accordance with the Guide for the Care and Use of Laboratory Animals. The animal protocol was approved by the Oregon Health & Science University Institutional Animal Care and Use Committee (OHSU IACUC) under permit number A085/2011. All efforts were made to minimize animal suffering during the experiments. Animals were euthanized by carbon dioxide asphyxiation followed by cervical dislocation, either at the time point determined by the experiment or when they appeared moribund. The protocol from reference [11] was followed. Briefly, food was withheld from all mice overnight prior to infection. A final inoculum of 1.6×10⁸ CFU S. Typhimurium/mouse was delivered by oral gavage to 10 mice (two cages of five mice each=Salmonella-infected). An equal number of mock-infected animals (two cages of five mice each=control) were administered an equal volume of sterile saline. Our infecting dose (10⁸ CFU/mouse) aimed to establish a persistent infection [11] that would ensure observation of S. Typhimurium proteins in downstream analyses. On the day prior to infection, and at seven time points post-infection, mice from one cage were transferred to a clean cage and fecal samples produced at that time were collected, pooled, and immediately frozen at −80°C. Samples were collected on days −1, 1, 3, 6, 10, 14, 21, and 28. On day 28, mice were sacrificed as described above, and colonization of spleen and mesenteric lymph node were determined (Salmonella infected mice: 2.1–7.3×10³ CFU/spleen, 2.2–5.0×10³ CFU/MLN; mock-infected mice: not detected). Mouse deaths prior to day 28 occurred as follows (one death per day listed): experimental group 1 deaths on days 7, 25, and 26; experimental group 2 deaths on days 16, 17, 20, and 25. Incidence of mouse death was more frequent than previously documented [11], which was possibly due to subtle differences between the virulent strain S. Typhimurium 14028s (used here) and SL1344 (used in [11]). We used the 14028s strain because the genome sequence of this organism, but not SL1344, was available at the time for accurate proteomic analysis [13].

For competitive index measurements, three groups of 129/SvJ mice (3 mice/group) from the Jackson Laboratory were starved for 18 hours before oral inoculation. MA6054 was the WT reference strain that produces an arabinose-inducible β-galactosidase and can be distinguished from the test strain (Δfuc) on LB agar containing X-Gal (5-bromo-4-chloro-3-indolyl-D-galactopyranoside; 40 µg/ml) and arabinose (1 mM) [14]. The Δfuc and MA6054 strains were cultured individually in LB overnight prior to infection. Each strain was washed and diluted in PBS to 1×10⁸ CFU/ml. The two strains were mixed at a ratio of 11, and 100 µl (10⁷ CFU/mouse) of the mixture was used to orally infect the mice. The infecting inoculum was serially diluted in PBS and spread on LB agar plates with X-Gal and arabinose to determine the infecting dose of each strain. Fecal pellets were collected at the following time points and pooled by group: day −1, 1, 3, 6, 10, and 14 post-infection. For each time point, four fecal pellets were collected within 1 hour time period, and diluted in PBS before plating on Salmonella-Shigella selective agar plates. One hundred black colonies were picked from Salmonella-Shigella plates and sub-cultured on LB agar plates with X-Gal and arabinose. After overnight incubation at 37°C, colonies of the reference strain were blue and those of the test strain were white. The competitive index was then calculated as [number of test strains/number of reference strains] output/[number of test strains/number of reference strains] input [15].

Fecal Sample Preparation

Frozen fecal samples were thawed on ice, and starting material weight was measured. Samples ranged from four to 25 fecal pellets per pooled sample. After thawing, 150 mM ammonium bicarbonate buffer was added to the sample (between 1–2.5 ml based upon starting weight; volumes were recorded and used for downstream normalization), which was subsequently vortexed to disrupt fecal pellets. The resulting slurry was filtered through a 70 µm sieve to separate and remove large debris (mostly undigested food particles). Filtrate was centrifuged (900×g for 10 min), and the protein-rich pellet thought to contain cellular material was retained as “P1.” The supernatant was centrifuged to further clarify the sample (15,000×g for 10 min). The pellet was retained as “P2” and the supernatant retained as “SN2.” Preliminary analyses demonstrated that P2 samples contained prohibitively small protein yields; therefore, P2 samples were not investigated further. Proteomic and 16s rDNA sequencing analyses were performed using P1, and metabolite and glycan analyses were performed using SN2.

Protein Extraction and Digestion

P1 samples (see Fecal sample preparation) were resuspended in lysis buffer (25 mM Tris-HCl, 5 mM EDTA, 0.05% Triton X-100, 50 µg/ml lysozyme, 3.3 U/ml mutanolysin) and incubated on ice for 15 min. An equal volume of 0.1 mm silica beads was added and the sample vortexed six times for 30 sec (3 min total); samples were incubated on ice between vortexing. The lysate was centrifuged (3500×g for 15 min) and the supernatant was used for further analysis.

Protein lysates were digested by trypsin (Promega). Briefly, protein concentration was determined by a BCA protein assay (Thermo Scientific, Rockford, IL). Of note, we tested whether overall protein load increased in infected animal feces. For each sample, the total µg of protein was divided by the starting weight of fecal matter, resulting in a µg protein/µg mass feces ratio. Values (averages) were as follows: 1.06×10⁻³ (control group 1), 1.17×10⁻³ (control group 2), 7.50×10⁻⁴ (infected group 1) and 8.27×10⁻⁴ (infected group 2), demonstrating that overall protein load did not increase significantly in infected samples. Samples were adjusted to equal protein concentration and combined with 7 M urea and 5 mM dithiothreitol (DTT). Samples were incubated at 60°C for 30 min with shaking. Trypsin was resuspended in 150 mM ammonium bicarbonate and warmed to 37°C for 10 min prior to use. Samples were diluted 10-fold with 150 mM ammonium bicarbonate, and CaCl₂ was added to a final concentration of 1 mM. Finally, trypsin was added to each sample (final concentration 1 unit trypsin:50 units protein) and incubated for 3 h at 37°C with gentle shaking. Peptide samples then underwent strong cation exchange solid phase extraction.

Protein Analysis: Mass Spectrometry

Peptide samples from both experimental groups were analyzed using a nano capillary liquid chromatography (LC) column [16], [17] coupled to an LTQ Orbitrap mass spectrometer (Thermo Fisher Scientific, San Jose, CA) via an in-house-manufactured interface. The heated capillary temperature and spray voltage were 200°C and 2.2 kV, respectively. For each cycle, the ten most abundant ions from MS analysis were selected for MS/MS analysis. Experimental group 2 samples also were analyzed using LC and an LTQ Orbitrap Velos (Thermo Fisher Scientific, San Jose, CA) for high resolution measurements (Group 1 samples were not subjected to high resolution measurements because of limited sample material). For this instrument, the heated capillary temperature and spray voltage were 300°C and 2.3 kV, respectively. For each cycle, the twenty most abundant ions from MS analysis were selected for MS/MS analysis. For all analyses, data acquisition occurred over an m/z range of 400 to 2000.

Protein Analysis: Data Analysis

Raw spectra were searched using SEQUEST ([18]) against three different protein databases: Mus musculus [19], Salmonella enterica serovar Typhimurium 14028 [13], and mouse gut commensal metagenome (Peterson C. et al., manuscript in preparation). Results were filtered using the Mass spectrum generating function (MS-GF [20]) SpecProb value as follows: Mus musculus (SpecProb ≤1×10⁻¹¹; false discovery rate (FDR)=1–1.5%), S. Typhimurium (SpecProb ≤5×10⁻¹¹; FDR=6.1–6.8%) and commensal microbiota (SpecProb ≤5×10⁻¹²; FDR=3.4%). Peptide observation counts and spectral counts were calculated using Microsoft Access. Proteins with a spectral count of one that were observed at only one time point in all LC-MS/MS analyses were removed. Spectral count and total number of protein observations are used as noted in text, figures, and tables. Statistical differences between control and infected spectral counts from mouse, Salmonella, and commensal proteins were identified using QuasiTel [21]. The analysis was conducted in the R software environment [22]. P-values adjusted for false discovery rate were used to filter the QuasiTel results. Early time points (days −1 and +1) were filtered out of the statistical analysis because minimal differences in protein abundance between control and treated mice were expected. These early time points were used as a negative control in the statistical analysis, to determine if differences in protein abundance could be detected in the absence of disease (no significant changes were found). In addition, the last time point, day 28, was removed due to several mice succumbing to disease. The QuasiTel analysis was performed to determine if there were significant differences between control and treated mice on days 3, 6, 10, 14 and 21.

PCR Amplification and Sequencing Preparation

DNA was extracted from P1 samples (see Fecal sample preparation) using the MasterPure Gram Positive DNA Purification Kit (Epicentre Biotechnologies; Madison, WI). Quadruplicate 50 µl PCR reactions for each sample (50 ng of starting material) were assembled using Phusion Hot Start II DNA Polymerase (Thermo Scientific; Lafayette, CO) and a primer containing both the V3 to V5 variable region of the 16S rRNA and a unique barcode sequence (see Table S4). The samples were amplified using the following protocol: 98°C for 2 min, 40 cycles of 98°C for 10 s, 53°C for 30 s, and 72°C for 1 min, and 72°C for 5 min. The completed reactions were resolved on a 1% agarose gel, and the DNA band near 550 base pairs was excised and purified using the QIAquick Gel Extraction Kit (Qiagen; Valencia, CA). Equal amounts of each uniquely labeled DNA (calculated through serial dilution RT-PCR analysis) were combined, purified using the QIAquick PCR Cleanup Kit (Qiagen; Valencia, CA), and submitted for sequencing that followed the standard ABI Sanger protocol. Total number of 16 s reads ranged from 5128 to 52525, with a mean 23961 and median of 25404 reads.

RNA Extraction and RT-PCR Analysis

Total RNA of the mouse microbiome was extracted from mouse fecal matter using the RNA PowerSoil Total RNA Isolation Kit (MoBio Laboratories, Inc.; Carlsbad, CA) according to manufacturer’s protocol. The RNA was then purified using the RNEasy Mini Kit (Qiagen; Valencia, CA) according to the manufacturer’s protocol. 0.5 µg of total RNA from each sample was combined with random hexamers and SuperScript III reverse transcriptase (Invitrogen; Grand Island, NY) to create cDNA and subsequently cleaned up as previously described with the following modification: a 25 mM dNTP mix was used omitting the amino-allyl dUTP [23]. Quantitative RT-PCR was performed on the LightCycler 480 System (Roche Applied Sciences, Indianapolis, IN) using the LightCycler 480 SYBR Green I Master Mix with 1.25 µM final concentration of each primer. Quadruplicate reactions using 5.0 ng of cDNA were amplified with the following protocol: 95°C for 5 minutes and then 50 cycles of 95°C for 10 seconds, 56°C for 10 seconds, and 72°C for 10 seconds. Second derivative maximum and melting curve analysis was done in the LightCycler 480 software and an average of the quadruplicates was calculated for delta Ct comparisons.

Metabolite Extraction

All chemicals and reagents used in metabolomics analyses were purchased from Sigma-Aldrich (St. Louis, MO), except for ammonium bicarbonate (Merck, Darmstadt, Germany), mixture of fatty acid methyl esters (FAMEs) and deuterated myristic acid (Agilent Technologies, Santa Clara, CA). Deionized and purified water was used to prepare buffer and standard solutions (Nanopure Infinity ultrapure water system, Barnstead, Newton, WA). SN2 samples (see Fecal sample preparation) were transferred to 0.6 ml microcentrifuge tubes, and water soluble metabolites were extracted with four volumes of chilled (−20°C) chloroform:methanol mixture (21) [24]. After separating the two phases via centrifugation (12,000×g, 5 min), the upper aqueous layers were transferred to glass vials and dried under a vacuum concentrator (SpeedVac; Thermo Scientific, Waltham, MA). All extracted metabolites were subjected to chemical derivatization to enhance their stability and volatility during GC-MS analysis. Methoxyamine in pyridine (30 mg/ml) was added to each dried sample, and incubated at 37°C with shaking for 90 min to protect carbonyl groups and reduce the number of tautomeric peaks. N-methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA) with 1% trimethylchlorosilane (TMCS) was then added, followed by incubation at 37°C with shaking for 30 min to transform hydroxyl and amine groups to trimethylsilyated (TMS) forms [25]. The samples were then allowed to cool to room temperature and were analyzed using gas chromatography (GC)-MS.

Metabolite Analysis

The GC-MS system used in this study consisted of an Agilent GC 7890A coupled with a single quadrupole MSD 5975C (Agilent Technologies, Santa Clara, CA). GC separations were performed on a HP-5MS column (30 m×0.25 mm×0.25 µm; Agilent Technologies). The injection mode was splitless and 1 µl of each sample was injected; the injection port temperature was held at 250°C throughout the analysis. The GC oven was initially maintained at 60°C for 1 min and then ramped to 325°C by 10°C/min, followed by a 5 min hold at 325°C [26]. GC-MS raw data files were deconvoluted using Metabolite Detector that has a format conversion to netCDF [27]. Retention indices were calculated based on the analysis of a mixture of FAMEs (C8–C30), and used for chromatographic alignment. Detected features were identified using the Agilent Fiehn GC/MS Metabolomics RTL Library [26] (that contains the mass spectra and retention indices of 700 metabolites), as well as the NIST08 GC-MS library. Finally, unidentified and identified features were transferred to DAnTE [28] for further visualization and statistical analysis after log transformation.

Glycan Analysis: Sample Prep

N-linked glycans were enzymatically released and purified according to an established method [29] with the following minor modifications. The sodium bicarbonate digestion buffer was replaced with sodium phosphate. Equal volumes of 100 mM sodium phosphate and SN2 sample (see Fecal sample preparation) were mixed to form a starting sample to which 10 mM dithiothreitol (DTT) was added and the sample was heated to 95°C in for 2 min to denature proteins. The pH for the PNGase glycan release was adjusted to 7.1 using 1.0 M HCL prior to microwave digestion for 40 min. The reaction was run at a constant microwave power of 20W. Digested samples were cold precipitated with ethanol to remove proteins and the samples were dried. Prior to purification, samples were reduced by treatment with sodium borohydride, desalted, and then concentrated using solid phase extraction (C8 and graphitized carbon cartridges in serial on a GX274 liquid handler; Gilson Inc., Middleton WI). Because the final samples would be separated again when analyzed using LC-MS, the elution fractions from the graphitized carbon cartridge were combined into a single fraction and dried completely. Dried samples were reconstituted in 20 µL of water prior to analysis. 5 µL of sample was used for each injection to the LC column.

Glycan Analysis: LC-MS

The glycan samples were analyzed using nanocapillary LC-MS (Exactive Orbitrap™; Thermo Scientific Inc., San Jose CA). The 75 µm i.d. ×45 cm long graphite carbon LC column was packed in-house with 3 µm porous Hypercarb particles (Thermo Scientific). A constant flow LC gradient was maintained using Agilent 1200 series nano pumps. The analytical gradient ramped from 1% B to 45% B in 90 min, and then to 95% B 5 min later. Mobile Phase A consisted of water acidified with 0.1% formic acid, and mobile Phase B, of acetonitrile acidified with 0.2% TFA. Gradients and valve switching were controlled by LCMSNet software (an in-house application coded with the.Net framework and built on a custom plug-in based automation framework). The software automated sample analysis and maintained mass spectrometer in continuous operation by alternating between two analytical columns on the LC system. The inlet capillary Exactive Orbitrap™ was heated to 250°C, and a 1.6 KV potential was used for the electrospray. A PAL auto sampler (controlled using LCMSNet software) loaded samples onto the column.

Glycan Analysis: Data Analysis

The overall pipeline for data analysis was to use DeconTools [30] for LC-MS data preprocessing, MultiAlign for retention time alignment and feature clustering, and Glycolyzer software for N-glycan annotation. Preprocessing involved peak picking, noise thresholding, and monoisotopic identification using THRASH [31], and is based off of the former Decon2LS engine [32]. The glycan averageose C_6.0000H_9.8124N_0.3733O_4.3470S_0.0 with an average mass of 156.64662 Da was used in place of the typical peptide averagine. MultiAlign performed retention time alignment and single linkage clustering of monoisotopic masses, and each time course study was aligned to the uninfected starting sample in the set. DeconTools and MultiAlign are open source programs developed at PNNL and freely available at http://omics.pnl.gov/. The Glycolyzer software used to find glycans from the features generated in MultiAlign was written with IgorPro (WaveMetrics, Portland OR). Previously developed theoretical retrosynthetic N-glycan libraries [33] were used to annotate features; a15 ppm root mean square (RMS) mass accuracy cutoff was used for annotation. All of the glycans reported were detected in families (that differed from other members by one monosaccharide) to improve assignment confidence. When glycan isomers were detected with the same mass and multiple retention times, the abundances from each isomer were added to give the reported abundance.

S. Typhimurium Induces Inflammation in 129/SvJ mice, Including Potential Novel Host Immune Responses

The mouse model of systemic S. Typhimurium infection has provided much insight into the host response to Salmonella, mechanisms of pathogen clearance, and requirements for generation of protective immunity against subsequent infection [36]–[38]. However, the host responses that arise during gastrointestinal infection in a mouse are largely unknown. In humans, S. Typhimurium-induced inflammation is characterized by neutrophil transmigration into the intestinal lumen, fever, and complement activation [34]. To investigate the hypothesis that a similar inflammatory gut environment develops following S. Typhimurium infection in 129/SvJ mice, we identified host proteins in fecal samples through time (Figure 1A; Table S1).

The response to S. Typhimurium infection was dominated by known and potentially novel innate immune factors. We observed evidence of transmigration and activation of neutrophils, up-regulation of cell surface molecules used to coordinate immune responses, complement activation, and inflammatory antibacterial responses (Table S2). Interestingly, many proteins detected have not previously been associated with an anti-Salmonella response during infection in vivo. Proteins such as cell surface A33 antigen, deleted in malignant brain tumors 1 protein (DMBT1), lithostathine-1 and -2 (Reg-I and –II), and serum amyloid P component (SAP) may therefore represent novel parts of the response to natural S. Typhimurium infection (or, pathogen exposure in a more general sense) in a gut populated by commensal organisms. Host proteins from fecal samples highlight the inflammatory nature of the infected gut environment, including novel host factors in gastrointestinal S. Typhimurium infection.

Salmonella Alters the Native Gut Community

S. Typhimurium can withstand the antimicrobial activity of host inflammatory molecules like Reg-IIIβ [39], yet commensal organisms are generally more susceptible to such insults [39], [40]. The host inflammatory response to S. Typhimurium, as well as direct anti-commensal activities of S. Typhimurium that may exist, cause a striking disruption in the commensal population during gastrointestinal infection (Figure 1B, Table S2). We hypothesized that the inflammatory, infected gut environment may uniquely alter specific commensal organism lineages. To test this hypothesis, we performed 16 s rDNA sequencing to profile the microbial community structure during infection through time in 129/SvJ mice (for which the commensal population had not yet been characterized).

The initial microbial community of the mouse gut contained primarily Bacteroidetes and Firmicutes (Figure 2, day −1) [41]. Interestingly, the Barnesiella genus, which has recently been isolated from both chicken and human fecal samples [42]–[44], dominated the phylum Bacteroidetes population in all cases, with Blautia comprising abundant Firmicutes (Figure S2). Following overnight food withdrawal prior to oral infection, all mice exhibited a temporary increase in organisms of the Oscillibacter genus (Figures 2, S2, day 1). Control mice regained the initial microbial population balance by day 3–6 and maintained relatively stable composition thereafter (Figures 2A, S2A). Proteobacteria proliferated in two waves in Salmonella-infected mice (specifically due to increased Salmonellae; Figures 2B, S2B, S3), then ultimately decreased by day 28, presumably due to host- and potentially commensal-mediated clearance of the infection.

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Figure 2

Salmonella infection disrupts the commensal microbial community at the phylum level.

Phylum level 16 s rDNA analysis of cellular sample fractions revealed a pre-infection microbial population composed of primarily Bacteroidetes (green) and Firmicutes (teal) (left side), which fluctuates at day 1 in all experimental groups after withdrawal of food overnight prior to infection. (A) Control animals maintain relatively stable microbial populations, whereas the gut of (B) infected animals is taken over by Proteobacteria (orange; specifically Salmonella). By day 28 (right side), surviving animals are clearing S. Typhimurium from the intestinal lumen. Values represented are the percentage of each phylum; for figure clarity, only those present at >1% of the total population are included.

As Salmonella survives and proliferates in the inflamed gut, resident organisms are largely out-competed; in experimental group 1 in particular, Bacteroidetes are nearly eliminated from the gut by day 10 (Figure 2B). Conversely, the Enterococci exhibit elevated fitness in the S. Typhimurium infected gut. Finally, by day 28 surviving mice appear to be reestablishing the original commensal population (Figure 2B; possibly from organisms retained in the gut or via another reservoir [45]). Extended time courses would likely reveal transient shedding of S. Typhimurium from the physiological reservoir (mesenteric lymph nodes) into the intestinal tract during persistent infection [11]. Although the infection kinetics demonstrated biological variability between experimental groups (Salmonellae increasing at day 6 for group 1 vs. day 3 for group 2), this timing was consistent within each group through the different omics analyses presented here. The combined impacts of host inflammatory molecules and direct mechanisms of S. Typhimurium effectively disrupt the commensal microbial population in the gut, thereby reducing Bacteroidetes and facilitating the outgrowth of the Firmicute, Enterococcus.

Salmonella Infection-induced Gut Metabolite Changes

Gut microbes perform many metabolic functions within the host, including breakdown of a diverse range of incompletely digested carbohydrates that pass through the mammalian digestive tract, which contributes to healthy digestion and nutrient absorption. Significant alterations in the gut environment upon S. Typhimurium infection, induced by the inflammatory host response (Tables S1, S2) and microbial population shift (Figure 2, Table S1B), suggested that the metabolite composition of the gut may be altered. Analysis of water-soluble metabolites derived from the gut revealed a mixture of candidate host-, microbe-, and/or diet-derived molecules. Infected animals demonstrated metabolite changes compared to control animals by day 3 post infection (Figures S4A–B), coinciding with observed changes in proteomic (Figure 1) and 16 s rDNA (Figure 2) analyses. Direct comparison of metabolites in control and Salmonella-infected animals (day 14, when S. Typhimurium is thriving in the infected gut; Figures 1C, S3) showed clear differences (Figures 3A, S4C), particularly for sugar moieties that accumulate in the infected gut (lactose, galactinol, melibiose, and raffinose; Figures 3B, S4C–D). Salmonella and murine host metabolic pathways lack the enzymes necessary to utilize most of these sugars; rather, commensal microorganisms, such as Bacteroidetes, express an extensive glycosidase repertoire [46]. Reduction of specific microbial lineages in infected animals (Figure 2B) leads to undigested carbohydrate accumulation (Figures 3, S4). Melibiose represents the exception, as S. Typhimurium can metabolize this sugar. However, catabolism of melibiose occurs only when other nutrient sources, such as glucose, are unavailable [47]. Because melibiose accumulates during S. Typhimurium-induced intestinal inflammation and we did not observe evidence of melibiose utilization in Salmonella proteomics analyses (see the following section; Table S1C), we speculate that the inflamed gut environment contains more energetically desirable sugars in the absence of commensal microorganisms, or Salmonella is utilizing this sugar at a level below our ability to detect. Metabolomic analysis supports the idea that changes during S. Typhimurium infection in the microbial population structure and composition (Figure 2), as well as the influence of inflammatory mediators (Table S2) alters the gut environment (Figures 3, S4).

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Figure 3

Environmental changes in the Salmonella-infected gut.

GC-MS metabolite analysis of soluble factors in fecal samples revealed that metabolites in infected animals are altered from those of control animals. A direct comparison of metabolite profiles at day 14 (A) shows the appearance of metabolites in infected samples (box). These peaks represent sugars which accumulate in the infected gut, in the absence of commensal microorganisms that normally metabolize these structures. (B) Intensity of selected peaks in infected animals is represented in a heat map (data shown are representative of one of two biological replicates (experimental group 1); experimental group 2 is presented in Figure S4). Additionally, N-linked glycan abundance increases with S. Typhimurium-induced gastroenteritis. Abundance of five fucosylated glycans (C) increases in infected animals (solid line with solid circles) over control animals (open circles with dashed line). In addition, (D) high mannose glycans were increased in infected animals (solid fill bars) over control animals (pattern filled bars). Values in (C) and (D) are averaged from both biological replicates (representing pooled samples from groups of mice in each case). Individual replicate data is provided in Figure S6. Heat map shows intensity data following log 2 and Z-score transformation, where red is more intense and blue is less intense.

Salmonella Protein Expression in the Mouse Gut

Introduction of the pathogen S. Typhimurium into the gut environment initiates the inflammatory host response and disrupts metabolites and commensal microorganisms. Our study provides a global perspective on the gastrointestinal lifestyle of Salmonella. Proteomic analysis revealed nearly 140 S. Typhimurium proteins expressed during infection (Figures 1C, S1C, and Table S1C). Identification of several stress response-related proteins and outer membrane proteins and lipoproteins during infection suggests that S. Typhimurium likely responds to various host environments, and may promote surface modification mechanisms to resist host immune insults [48]. In the inflammatory gut, S. Typhimurium respires anaerobically to out-compete fermentative commensal microbes by using tetrathionate as an alternative terminal electron acceptor for metabolism of compounds such as ethanolamine [49]. Ethanolamine utilization by S. Typhimurium is important during oral infection, but not when S. Typhimurium is delivered intraperitoneally, which stresses its unique role during pathogenesis in the gut environment [50]. We observed EutM (a protein involved in ethanolamine use [49]) and the two tetrathionate utilization proteins TtrS and TtrA (although detected at low relative abundance), suggesting that this metabolic strategy may be used by S. Typhimurium during infection not only in streptomycin-treated mice, but when microbiota are present.

In the intestinal lumen, S. Typhimurium expressed sugar utilization proteins, including those involved in galactose, glucose, maltose, and mannitol metabolism (Table S1C). Interestingly, we observed expression of three fucose utilization proteins (FucI, FucU, FucA), as well as expression of fuc genes by qRT-PCR (Figure S5). FucA and FucU have rarely, if ever, been observed in our extensive proteomic studies of S. Typhimurium grown in multiple in vitro growth conditions (Table S1C; these and other infrequent protein identifications in the gut environment are noted), suggesting S. Typhimurium modulates its protein expression in the intestinal environment in response to substrates such as fucose to promote growth.

Glycan Content of the Infected Gut Suggests Salmonella may Respond to Increased Fucose

Observation of fucose utilization proteins in the Salmonella-infected samples suggested that S. Typhimurium might respond to and metabolize fucose in a gut environment absent of extensive commensal competition. Glycosylation of abundant gastrointestinal proteins (such as epithelial surface glycoproteins, mucin, and immunoglobulins) often contain fucose moieties [51], and serve as a nutrient source for commensal organisms [46], [52], [53]. To determine whether protein fucosylation was altered in the infected gut, we analyzed the N-linked glycan profile of our samples through time while tracking the abundance of a select family of five fucosylated glycans detected at all time points (Figure S6A). The total fucosylated glycan content increased with infection, likely induced by a combination of the following non-mutually exclusive factors: 1) loss of commensal organisms responsible for metabolizing fucose moieties in the healthy gut that results in accumulation, 2) part of an increased host immune response, as the kinetics of glycan increase and host protein increase coincide (Figures 1A, ​,3C,3C, S6B–C), or 3) intestinal cell damage during infection. In addition, we observed a significant increase in the entire high mannose glycan series during infection (Figures 3D, S6D–G). High mannose glycans (Man4-Man9) are modified in the endoplasmic reticulum and represent relatively minimally processed glycans. Detection of increased high mannose glycans suggests that glycoproteins are being trafficked outside of cells prior to additional glycan processing in the Golgi apparatus [54]. The source of these glycans is currently unknown, and further targeted investigation will help to elucidate this observation. Our data support the hypothesis that S. Typhimurium metabolizes fucose in the gut as part of its gastrointestinal lifestyle, and suggests that glycan release might be an underappreciated immune response mechanism of the host.

The authors would like to thank the following individuals for their involvement in this study: Karla Thrall for providing samples for preliminary experiments; Anil Shukla, Nathan Jones, and Erika Zink for LC-MS analysis of protein samples; Ronald Moore, Danny Orton, Thomas Fillmore, Ioan Marginean, and Rui Zhao for LC-MS analysis of the glycomics samples; Gordon Slysz, Brian LaMarche, and Matt Monroe for data analysis; Penny Colton for manuscript input. Christine Peterson is a pre-doctoral student in the Immunology & Microbiology Program of the Institute for Biomedical Sciences at the George Washington University and this publication partially contributes toward the fulfillment of the requirements for the Ph.D. degree.