Extensive Salmonella nutrient utilization capabilities during infection

To characterize Salmonella metabolic capabilities during infection, we sorted Salmonella from infected mouse spleen and determined copy numbers of 477 metabolic enzymes (among 1182 identified proteins) using the well-established proteomics iBAQ label-free quantification approach [32] with 30 isotope labeled AQUA [33] peptides as internal standards (Table S1). This analysis extended our previous qualitative detection of 178 Salmonella enzymes in the same disease model [34] as a consequence of improved sorting and proteomics technologies.

The detected enzymes are known to catalyze 925 metabolic reactions, a remarkably high proportion of all known/inferred 2023 Salmonella metabolic reactions for which catalyzing enzymes have been annotated [31]. Interestingly, this included 102 transporters and enzymes involved in 77 reactions in 24 pathways for utilization of various carbohydrates, lipids, nucleosides, and amino acids (Figure 1). It is important to note that these enzyme numbers likely underrepresented the entire Salmonella in vivo proteome as limited material availability and mass spectrometry detection thresholds likely prevented identification of weakly expressed enzymes. These data suggested that during infection, Salmonella mobilized a large part of their diverse metabolic capabilities. In comparison, closely related E. coli requires only 293 reactions for optimal growth in a minimal in vitro medium [35]. However, even under such well-defined conditions, E. coli expresses more than 200 apparently not required enzymes suggesting that enzyme expression alone is not indicative of metabolic relevance [36] (see below).

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Figure 1. Nutrient utilization capabilities of Salmonella in infected mouse tissues.

Colored names represent transporters and enzymes that were detected in Salmonella purified from mouse spleen (Table S1). The color shows enzyme abundance in copies per Salmonella cell. Grey proteins were not detected. Arrows represent metabolic reactions. Transport reactions are labeled with cylinders. Arrow colors show maximal catalytic capacities calculated from enzyme abundance and reported turnover numbers (Table S2). Grey arrows represent reactions, for which enzymes were not detected and/or turnover numbers were unavailable. Tsx is an outer membrane general nucleoside channel; NupC is a high affinity transporter for all nucleosides except guanosine and deoxyguanosine. An interactive map with detailed description of all detected metabolic capabilities is available at http://www.biozentrum.unibas.ch/personal/bumann/steeb_et_al/index.html.

https://doi.org/10.1371/journal.ppat.1003301.g001

In addition to the qualitative identification of expressed Salmonella enzymes and associated metabolic reactions, our proteome data also provided quantitative data on Salmonella metabolic capabilities. We combined enzyme copy numbers with available turnover numbers to calculate maximal reaction rates for 469 reactions (Table S2). As an example, we detected 20’000±1000 copies per Salmonella cell of glycerol kinase GlpK that catalyzes MgATP-dependent phosphorylation of glycerol to yield sn-glycerol 3-phosphate. The closely related E. coli ortholog (95% amino acid identity) has v_max = 22 µmol min⁻¹ mg⁻¹ [37] equivalent to a turnover number of 21 s⁻¹. Based on these data, a single Salmonella cell would have the catalytic capacity to phosphorylate up to 420’000 glycerol molecules s⁻¹. Such results should be taken as approximate only since turnover numbers are usually determined for somewhat non-physiological in vitro conditions (e.g., glycerol kinase was assayed in low osmolarity buffer at 25°C). Moreover, these data were incomplete because of undetected enzymes with abundance below the proteomics detection threshold and missing kinetic data. Nevertheless, the data yielded an unprecedented large-scale overview of Salmonella catalytic capacities in an infected host tissue, and provided a unique quantitative basis for in-depth analysis of metabolic activities involved in Salmonella virulence (Figure S1; an interactive map with detailed descriptions is available at http://www.biozentrum.unibas.ch/personal/bumann/steeb_et_al/index.html).

As expected [38], central carbon metabolism had particularly high catalytic power in contrast to biosynthesis pathways for minor biomass components such as vitamins. Many nutrient utilization pathways had also substantial catalytic power with especially high values for glycerol utilization (Figure 1). Together, these data suggested that Salmonella maintained versatile catabolic capabilities for diverse nutrients during infection.

Functional relevance of diverse host nutrients

To determine the actual relevance of specific nutrients for supporting Salmonella host tissue colonization, we inactivated defined utilization pathways. We preferentially deleted transporters to prevent high-affinity nutrient uptake instead of inactivating degradation enzymes that could result in accumulation of toxic upstream metabolites such as phosphorylated carbohydrates, which can cause pleiotropic effects [39], [40]. Some nutrients can permeate membranes without a dedicated transporter (glycerol, short-chain fatty acids, myo-inositol, ethanolamine). In these cases, we inactivated enzymes that were unlikely to cause toxic intermediate accumulation based on available literature [41], [42], [43], [44].

Utilization defects have previously been used in several studies, for example to determine the relevance of several carbohydrates for E. coli growth in the intestinal lumen [2]. As a potential caveat, an excess supply of alternative nutrients may mask specific utilization defects. Moreover, some mutations might cause polar effects on the expression of downstream genes. In most of our mutants, this would only affect genes coding for subunits in the same transporters or enzymes involved in the same degradation pathways as the inactivated gene (Table S3). However, it was still possible that some such polar effects influenced mutant phenotypes.

In a complementary second set of Salmonella mutants, we inactivated well-characterized biosynthesis pathways for essential biomass components. The resulting Salmonella auxotrophs were unable to grow unless the missing biomass components were provided externally (Table S4). Any growth of such mutants in infected spleen was, therefore, indicative of host supply of the respective supplement. Similar approaches have previously been used in various infection models.

To measure tissue colonization capabilities of the various mutants, we used competitive infections with mixtures of mutant and wildtype Salmonella (Figure 2; Table S3). Mutant fitness was measured as competitive indices (CI = output ratio (mutant/wildtype)/input ratio (mutant/wildtype)). A CI value of 1 (equivalent to log₂(CI) = 0) indicated that a mutant had equal colonization capabilities as wildtype Salmonella. Complementation of mutant alleles to verify mutation phenotypes was often difficult because most strains contained multiple mutations. However, we independently reconstructed the most attenuated mutant and confirmed the resulting colonization defect (Figure 2). In the statistical analysis we avoided the “multiple comparison problem” using the widely accepted Benjamini-Hochberg [45] “false discovery rate” (FDR) approach to identify the subset of attenuated mutants (Table S3).

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Figure 2. Mouse spleen colonization of Salmonella mutants with metabolic defects.

The data represent competitive indices (CI) of mutants vs. wildtype Salmonella in spleen of individual mice at three (open symbols) or four days (filled symbols) post infection (Table S3). A log₂(CI) value of 0 (equivalent to a CI of 1) represents full virulence. Down triangles represent mutants with utilization defects, up triangles represent auxotrophic mutants. Grey symbols represent data from a previous study [34] obtained in the same disease model. Red triangles represent data from an independently reconstructed glpFK gldA glpT ugpB mutant. The data provided evidence for access to a number of host nutrient which are shown in black (for detailed interpretation see Table S5). Nutrients with apparently low availability are shown in grey. Statistical analysis was carried out with the Benjamini-Hochberg false discovery rate (FDR) approach for multiple comparisons [45] (***, FDR<0.001; **, FDR<0.01; *, FDR<0.05).

https://doi.org/10.1371/journal.ppat.1003301.g002

Interestingly, several Salmonella mutants with nutrient utilization defects had significantly diminished colonization capabilities (Figure 2; for detailed interpretation see Table S5). This suggested that there was no large excess of nutrients that would mask any utilization defects, and no single major nutrient that alone could support full Salmonella virulence. Instead, Salmonella colonization depended on utilization of glycerol, fatty acids, N-acetylglucosamine, gluconate, glucose, lactate, and arginine. Glucose was the only nutrient that had previously been identified to contribute to systemic Salmonella infection [11].

All seven identified nutrients can serve as a sole carbon source for Salmonella growth [46] and can be interconverted into each other. It was thus unlikely that any of these nutrients was required because it provided a unique chemical structure. Instead, the seven metabolites seemed to supply individual small nutritional contributions that only together enabled normal Salmonella in vivo growth (see below). Other utilization mutants had non-significant colonization phenotypes suggesting limited contributions of the corresponding nutrients.

Most of our infection experiments used BALB/c mice that carry a dysfunctional Slc11a1 allele (see Introduction). Such mice are highly susceptible for systemic salmonellosis providing a useful model for severe human typhoid fever. For comparison, we also did some small-scale experiments in 129/Sv mice that carry a functional Slc11a1 allele and are therefore resistant to lethal salmonellosis. Competitive infections confirmed the importance of glycerol (or glycerol-3-phosphate) and N-acetyl-glucosamine for Salmonella growth (Figure S2) suggesting similarities of Salmonella nutrition in susceptible and resistant mice.

Additional evidence for nutrient availability came from the substantial colonization capabilities of most tested Salmonella auxotrophs (Figure 2; Tables S3, S5). In particular, Salmonella readily accessed sufficient quantities of several (pro-)vitamins and all tested amino acids (except proline). Similar colonization phenotypes were obtained for Salmonella mutants with utilization or biosynthesis defects in infected liver (Table S3) indicating that similar nutrients supported Salmonella growth in two different host organs.

Combination of our data with previously reported additional mutant virulence phenotypes indicated Salmonella access to a large set of at least 31 chemically diverse host nutrients in infected mouse spleen (Table S5). This analysis thus revealed a highly complex host/Salmonella nutritional interface, which is still likely incomplete because of limited mutant coverage and our inability to detect small colonization defects.

Salmonella virulence depends on parallel exploitation of diverse host nutrients

Our data suggested that Salmonella exploited a wide range of diverse host metabolites. This was initially surprising since most microorganisms utilize only a single preferred nutrient such as glucose when exposed to nutrient mixtures [47]. Other nutrients and their utilization pathways remain irrelevant as long as this preferred nutrient is available.

This was evidently not the case during infection, as glucose utilization only partially supported Salmonella growth in agreement with previous observations [11]. As one possible explanation, various nutrients including glucose might have been available in only limited amounts that together just supported Salmonella growth and tissue colonization. Indeed, colonization defects of Salmonella utilization mutants (Figure 2) suggested that Salmonella virulence depended on simultaneous effective exploitation of several nutrients instead of relying on only one preferred nutrient.

To further test the hypothesis of parallel utilization of different available nutrients, we used a cell culture infection model where Salmonella replicated intracellularly in macrophage-like RAW 264.7 cells mimicking conditions during systemic salmonellosis [48]. In this cell culture model, extracellular metabolites can reach intracellular Salmonella and contribute to their nutrition [49], [50], [51], [52]. To test the impact of nutrient availability, we added external glucose or mannitol at 4 h post infection when Salmonella had already established their intracellular niche (Figure 3A). Interestingly, both extracellular nutrients accelerated subsequent intracellular Salmonella growth (Figure 3B). This growth promoting effect was dependent on specific Salmonella glucose/mannitol utilization capabilities, suggesting that external glucose and mannitol directly contributed to Salmonella growth, whereas nutrient-induced changes in the host cell had negligible impact (e.g., moderate changes in osmolarity (2.7 mOsm per added nutrient, some 1% of the total osmolarity), glucose metabolization by host cells (mannitol cannot be metabolized by mammalian cells [53]), or modulation of host cell phagocytosis and oxidative bursting as observed at much higher mannitol doses [54]). These data indicated that intracellular Salmonella growth was limited by nutrient availability, and Salmonella exploited both a typically preferred (glucose) and a non-preferred carbon source (mannitol) when available thus supporting our nutrient limitation hypothesis.

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Figure 3. Nutrient limitation of intracellular Salmonella growth.

A) Schematic representation of external supplementation of intracellular Salmonella (red) in infected macrophages (grey). B) Increasing external nutrient availability accelerates intracellular Salmonella growth, and this depends on specific Salmonella nutrient utilization capabilities (open symbols, 0.5 g l⁻¹ glucose; filled black symbols, 1 g l⁻¹ glucose; filled grey symbols, 0.5 g l⁻¹ glucose 0.5 g l⁻¹ mannitol; circles, wildtype Salmonella; upward triangles, Salmonella ptsG manX galP mglB, deficient for high-affinity glucose transport; downward triangles, Salmonella mtlAD, deficient for high-affinity mannitol transport and degradation). Colony-forming units (CFU) at 10 h post infection for triplicate wells containing 300’000 RAW 264.7 cells are shown. C) Flux-balance analysis of nutrient excess scenarios. The computational model was set to incorporate various amounts of excess nutrients (beyond what was needed for cell maintenance and growth). Model parameters were adjusted to yield predictions that were consistent with experimental mutant and wildtype colonization data. Simulation of up to 18% nutrient excess was possible but required unrealistically high maintenance costs (shown in multiples of maintenance costs for axenic conditions). Simulated scenarios with nutrient excess beyond 18% were incompatible with experimental colonization data.

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Taken together, both mutant colonization defects and cell culture experiments were consistent with Salmonella growth being dependent on diverse scarce nutrients during infection.

Estimation of nutrient uptake rates

In addition to these qualitative results on nutrient-limited Salmonella growth, we were interested to obtain quantitative nutrient supply rates as a basis for comprehensive understanding and computational modeling of Salmonella nutrition, metabolism and growth. Quantitative nutrient supply rates have not yet been reported for any infection model, but the severity of mutant colonization defects could provide some hints. As an example, the strong colonization defect of Salmonella glpFK gldA glpT ugpB defective for glycerol utilization, compared to Salmonella manX nagE defective for GlcNAc utilization, could suggest that more glycerol was available as compared to GlcNAc. This rationale has previously been used to assess the relative relevance of various carbohydrates for E. coli gut colonization [2]. However, direct calculation of the respective nutrient supply rates from such mutant colonization defects was hampered by the parallel utilization of many diverse nutrients. Moreover, nutrients such as glycerol and GlcNAc differ in their nutritional value per molecule.

To quantitatively assess the availability of multiple host nutrients and their utilization by Salmonella, we therefore used a computational approach called Flux-Balance Analysis (FBA) [55]. This approach had been successfully applied to predict nutrient utilization and growth in a wide variety of organisms in excellent agreement with large-scale experimental data [56]. As a precondition for the application of FBA to Salmonella, we recently established together with more than 20 Salmonella experts an in silico reconstruction of the entire Salmonella metabolic network that contains all experimentally determined Salmonella metabolic activities, all enzymes with annotated metabolic activity encoded in the Salmonella genome, and their catalyzed reactions with all participating metabolites, stoichiometries, and information on reaction reversibility [31]. This consensus Salmonella metabolic reconstruction has been extensively documented and is continuously being updated by manual curation of newly available literature for Salmonella and closely related E. coli enzyme orthologs (reconstruction STMv1.1 with 1279 Salmonella enzymes, 1824 metabolites, 2573 reactions; Tables S6, S7, S8; the reconstruction is available in SBML format at http://www.biozentrum.unibas.ch/personal/bumann/steeb_et_al/index.html) and in the Supporting Information (Model S1).

Flux-balance analysis can be used to determine if the metabolic network is capable of producing all components required for Salmonella biomass generation. Importantly, biomass requirements can differ between growth conditions [57], [58]. To deduce Salmonella biomass requirements during infection, we analyzed published informative mutant virulence phenotypes and modified the biomass function accordingly (for detailed descriptions see Table S8; for limitations in the in vivo biomass definition see Discussion). Flux-balance analysis revealed that the metabolic reconstruction could generate all included biomass components in the correct stoichiometry under observance of fundamental thermodynamic laws such as preservation of mass and charge (“flux-balance”) [31]. In addition to biomass generation, all cells have growth-unrelated demands for survival and these are commonly accounted for as “maintenance requirements” [59]. Such demands could be especially important in pathogens during infection when they need resources to resist host antimicrobial defense.

To model Salmonella nutrition and growth in infected spleen, we provided the in silico reconstruction with all experimentally identified nutrients and used FBA to compute the resulting Salmonella biomass generation (which we used as an approximation for growth throughout this study). We adjusted nutrient uptake rates to reproduce our experimental Salmonella mutant phenotypes (for a detailed description of our approach, see Material and Methods and Figure S3). This yielded supply rates for 31 organic nutrients (Table S9), as well as 13 inorganic ions (Table S9). To obtain consistent data we needed to assume enhanced maintenance requirements (145±20% of the value for axenic in vitro cultures). Such enhanced maintenance costs could reflect defense against hostile host environments (see Discussion).

We also explored scenarios of excess nutrient supply (see Materials and Methods). The results revealed that the computation model could accommodate only modest nutrient excess up to 118% of the minimal nutrient supply values, and this would require improbably high maintenance costs for consistency with our experimental colonization data (Figure 3C). These data provided additional in silico support for nutrient-limited Salmonella growth (see above).

It is important to note that our computational approach had several caveats (see Discussion). On the other hand, the resulting model provided a first comprehensive quantitative approximation to the host nutritional landscape and its exploitation by Salmonella that could serve as a basis for subsequent improvements (Figure 4; the model is available in SBML format at http://www.biozentrum.unibas.ch/personal/bumann/steeb_et_al/index.html) and in the Supporting Information (Model S1).

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Figure 4. A quantitative genome-scale model of Salmonella nutrition, metabolism, and growth in infected mouse spleen.

This schematic map shows available host nutrients, their respective uptake rates represented by color and font size, and their conversion to new Salmonella biomass through the Salmonella metabolic network (see text and Tables S6, S7, S8, S9 for detailed explanation and quantitative values). Symbols represent metabolites (squares, carbohydrates; pointing up triangles, amino acids; vertical ellipses, purines; horizontal ellipses, pyrimidines; pointing down triangles, cofactors; tees, tRNAs; circles, other metabolites; filled symbols, phosphorylated metabolites) and proteins (diamonds). The connecting lines present metabolic reactions. The brown lines represent the inner and outer membranes. An interactive map with detailed annotation of all reactions and the computational model in SBML format are available at http://www.biozentrum.unibas.ch/personal/bumann/steeb_et_al/index.html. The model is also available in the supporting information (Model S1).

https://doi.org/10.1371/journal.ppat.1003301.g004

Experimental validation of the model of Salmonella metabolism

To assess how well the current computational model captured relevant aspects of Salmonella nutrition and growth during infection, we compared model predictions with large-scale experimental data sets on Salmonella mutant phenotypes, enzyme expression, and metabolic capabilities.

To validate functional aspects of the computational model, we systematically predicted in vivo growth phenotypes for all 1279 model enzymes, and compared these predictions to reported experimental Salmonella mutant colonization phenotypes (Table S10; Figure 5A; interactive maps for predicted and experimental mutant phenotypes are available at http://www.biozentrum.unibas.ch/personal/bumann/steeb_et_al/index.html).

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Figure 5. Large-scale experimental data are consistent with computational model predictions.

A) Validation of mutant phenotype predictions. The colors show the predicted gene relevance for spleen colonization (red, essential; orange, contributing; blue, non-detectable; see text for definitions). Comparison of model predictions with 738 experimental Salmonella mutant phenotypes revealed 92% prediction accuracy (inner dark colors) but also 61 discrepancies (pale outer colors). Numbers (correct/total number of experimentally validated predictions) are also given. B) Potential reasons for inaccurate phenotype predictions (redu, unrealistic redundancy; biom, incomplete biomass/maintenance issues; part, partially contributing functions; toxic, accumulation of toxic upstream metabolites; gap, missing enzyme; or exp, possibly inaccurate experimental data). For detailed descriptions see Table S10. C) Detection of enzymes with predicted differential relevance for optimal Salmonella in vivo growth. Enzyme relevance was classified by parsimonious enzyme usage flux-balance analysis (pFBA) (ess, essential enzymes; optima, enzymes predicted to be used for optimal in vivo growth; ELE, enzymatically less efficient enzymes that will increase flux if used; MLE, metabolically less efficient enzymes that will impair growth rate if used; zeroFlux, enzymes that cannot be not used in vivo). Filled bars represent enzymes that were detected by Salmonella ex vivo proteomics, open bars represent enzymes that were not detected. Statistical significance of the relationship between enzyme classes and the proportion of detected proteins was determined using the Chi square trend test. D) Feasibility of predicted reaction rates. For each reaction, the range of flux rates compatible with full Salmonella growth was determined using Flux-Variability Analysis. The circles represent the most economical state with minimal total flux (see text). Predicted reaction rates are compared to corresponding catalytic capacities calculated form experimental enzyme abundance and turnover numbers (Table S2). The reddish area represents infeasible fluxes. Reactions with substantial infeasible fluxes in the most economic simulated state are labeled (1, formyltetrahydrofolate dehydrogenase; 2, phosphoserine aminotransferase; 3, glycerol dehydrogenase). E) Predicted flux ranges and corresponding catalytic capacities after constraining all reactions to feasible fluxes (except for the three aminoacyl tRNA ligations mentioned in the text). F) Relative flux ranges of the initial unrestrained (straight line) and the enzyme capacity-restrained (dotted line) models. For each reaction, the flux range was divided by the respective flux value in the most economical state. Reactions that carried no flux in the most economical state were not considered. Statistical significance of the difference between both distributions was tested using the Mann-Whitney U test.

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Inactivation of most enzymes had no impact on predicted growth rate. Only few, mostly biosynthetic, enzymes were essential for Salmonella virulence (predicted mutant growth rate below 60% of wildtype ), while some genes contributed to virulence (predicted mutant growth between 60% and 98% of wildtype), and the vast majority of enzymes had non-detectable effects (mutant growth rate higher than 98% of wildtype) in agreement with our previous experimental data [34]. Detailed analysis of 738 single mutants with available experimental data revealed an overall prediction accuracy of 92% (Figure 5A; Table S10) similar to accuracies achieved for the best computational models for E. coli in vitro cultures [59]. This analysis included 14 mutant phenotypes that we had used to deduce the biomass function (Table S8) and 69 mutation phenotypes that we had used to deduce nutrient supply (Table S5). Consistency of model predictions for these mutants and additional mutants with linked phenotypes (such as enzymes in the same pathways) was, therefore, unsurprising. Moreover, gene selection for mutant testing in our and other labs was likely influenced by previous knowledge. Mutant phenotypes thus did not provide truly independent validation, but they demonstrated that the model yielded consistent quantitative explanations for the function of hundreds of Salmonella genes during infection. On the other hand, there were 61 discrepancies between computational predictions and experimental data that could help to identify remaining errors and knowledge gaps (Figure 5B; for detailed analysis, see Table S10 and Discussion).

We also compared model predictions with our protein identification data. Specifically, we used a recently developed approach called parsimonious enzyme usage FBA (pFBA) [36] to predict enzyme classes with differential functional relevance for efficient Salmonella biomass generation. These enzyme classes included, in order of decreasing relevance, (i) essential enzymes, (ii) enzymes required for optimal growth with minimal overall flux (“optima”), (iii) enzymatically less efficient enzymes (which could sustain optimal growth but would require more total flux), (iv) metabolically less efficient enzymes (which could sustain only suboptimal growth), and (v) genes with no contribution to Salmonella growth (zero flux in the associated reactions). Comparison with our proteome data for ex vivo sorted Salmonella revealed that there was a statistically highly significant relationship between relevance and the proportion and abundance of detected Salmonella enzymes in the various classes (Figure 5C; Figure S4), similar to what has been observed for computational models of well-characterized E. coli in vitro cultures [36]. On the other hand, we still detected only some 50% of the relevant enzymes (classes “essential” and “optima”). Many non-detected enzymes were associated with rather low predicted reaction rates (Figure S5), suggesting that these enzymes might have been present in small quantities below our ex vivo proteome detection threshold. Incomplete proteome coverage of important enzymes has also been observed for E. coli in vitro cultures [36].

On the other hand, we detected several enzymes that were predicted to mediate no flux, again similar to observations for in vitro cultures [36]. Many such enzymes were involved in amino acid biosynthesis, nutrient utilization, gluconeogenesis, glycogen metabolism, and other pathways that all had experimentally non-detectable mutant phenotypes, consistent with their predicted non-functionality. It is possible, however, that these pathways were actually active, but accounted for minor contributions to Salmonella virulence that were undetectable with current in vivo methods. Alternatively, Salmonella might have prepared themselves for subsequent environments in their life cycle where these pathways would offer fitness benefits. Finally, Salmonella might be unable to optimally regulate its enzyme expression to shut down all dispensable enzymes (as it is likely the case in E. coli in vitro cultures). Further research is required to test these and other hypotheses.

We also compared our in vivo model with a model for Salmonella growth in minimal medium with glucose as sole source of carbon and energy. Interestingly, there was a large overlap between enzymes that were important for optimal growth of Salmonella under these two conditions. We detected 30 proteins that were predicted to be specifically required in vivo but not in vitro, providing some support for our in vivo model. On the other hand, we also detected 15 proteins that should be required only in the in vitro minimal medium but not in vivo. Interestingly, eleven of these 15 proteins were involved in amino acid biosynthesis suggesting that Salmonella maintained such biosynthetic capabilities in vivo despite access to host amino acids (see above). It is possible that the amino acid supply was just marginally sufficient and Salmonella prepared itself for future amino acid starvation. Further work is required to clarify this issue.

In addition to predicting enzyme relevance, the model also provided quantitative predictions for fluxes through hundreds of metabolic reactions. For some reactions, a large range of reaction rates was possible whereas others had more restricted rates (Figure 5D) as previously observed in other systems (“flux variability” [60]). We determined the state with the lowest overall metabolic activity corresponding to economical use of costly enzymes. Such states have shown to correspond well with experimental flux data in other systems [36], [61].

To determine the feasibility of these predicted reaction rates, we compared them to Salmonella catalytic capacities calculated from experimentally determined enzyme concentrations and turnover numbers (see above; Table S2; Figure S1; http://www.biozentrum.unibas.ch/personal/bumann/steeb_et_al/index.html). Interestingly, 459 out of 469 analyzed reactions had feasible predicted rates (Figure 5D). Three reactions had clearly infeasible reactions rates in the most economical computational state with lowest overall metabolic activity (>3 fold above the corresponding catalytic capacities; these reactions are labeled in Figure 5D: 1, formyltetrahydrofolate dehydrogenase; 2, phosphoserine aminotransferase; 3, glycerol dehydrogenase). However, all these reactions could be restrained to feasible rates without compromising predicted Salmonella growth or making other reactions infeasible. All seven other reactions had only moderate discrepancies between simulated and feasible rates, and four of them could again be restrained without compromising growth.

The remaining three reactions had simulated reaction rates that remained slightly infeasible in all states (simulated fluxes 1.2 to 2.5 fold too high). Interestingly, all three reactions were aminoacyl tRNA ligations (for proline, alanine, and threonine). Possible causes for these discrepancies included inaccurate biomass assumptions for proline, alanine, and threonine protein content, experimental errors in protein quantification, and/or suboptimal assay conditions for tRNA ligase turnover number measurements. Moreover, the computational model disregards important processes outside metabolism such as macromolecular expression [58], which could contribute to discrepancies between feasible and simulated reaction rates.

Despite these three minor discrepancies, the overwhelming feasibility of reaction rates indicated that Salmonella had sufficient in vivo enzyme amounts and catalytic power to mediate nutrient utilization, metabolization, and biomass generation as predicted by the computational model.

Although almost all reactions had entirely plausible reaction rates in the computational state with lowest overall metabolic activity, the entire flux solution space also included many reaction fluxes that exceeded plausible rates. In a next step, we prevented such implausible fluxes by setting upper/lower limits according to the maximum experimental enzyme capacities (except for the problematic three tRNA ligations, see above). Interestingly, these large-scale constraints still allowed normal Salmonella in silico growth, but resulted in a dramatically reduced flux solution space (Figure 5E, F). Specifically, the vast majority (80%) of reactions had narrowly defined flux ranges (relative flux variability below 10%), whereas in the initial unrestrained model only a small minority (16%) had such narrowly defined reaction rates (Figure 5F). This enzyme capacity-based model might thus provide a much better defined approximation to the actual in vivo flux state.

Together, these data revealed that the model predicted (i) largely correct mutant virulence phenotypes, (ii) predicted enzyme relevance that correlated with experimental protein detection, and (iii) predicted reaction rates that were biologically plausible. This large-scale consistency with experimental data suggested that the computational model captured major aspects of Salmonella nutrition, metabolism, and growth in infected host tissues.

A common nutritional signature for mammalian pathogens

To investigate if Salmonella conditions during mouse typhoid fever might be generally representative for pathogen nutrition in infected host tissues, we compared pathogen metabolic networks based on genome pathway annotations [62]. We analyzed 154 different mammalian pathogens (Table S11) for presence of 254 nutrient utilization pathways and 118 biosynthetic pathways (Figure 6A). Most pathogens shared the capability to utilize glycerol, fatty acids, various carbohydrates, nucleosides, and amino acids that could serve as N-sources (such as arginine), suggesting a general preference for nutrients that Salmonella used in the mouse typhoid fever model. Additional genome comparisons for 316 non-pathogenic species revealed that they might also preferentially utilize similar nutrients (Figure S6).

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Figure 6. A common nutritional pattern for mammalian pathogens.

A) Presence of 254 nutrient utilization pathways in genomes of 153 mammalian pathogens (excluding all Salmonella serovars). Data were based on pathway annotations available in MetaCyc [62]. Degradation pathways for nutrients that support Salmonella in mouse spleen were highly overrepresented among pathogen genomes (P<0.001; Mann-Whitney U test) suggesting similar nutritional preferences (filled circles; 1, purine nucleosides; 2, pyrimidine nucleosides; 3, fatty acids; 4, glycerol; 5, arginine; 6, N-Acetylglucosamine; 7, glucose; 8, gluconate). B) Depletion frequency of 118 biosynthesis pathways in mammalian pathogens. The values represent differences in pathway frequency in sets of 153 pathogens and 316 environmental bacteria (see text for explanation). Biosynthesis pathways for biomass components that Salmonella could obtain from the host were selectively depleted among pathogen genomes (P<0.0001; Mann-Whitney U test) suggesting similar host supplementation patterns (filled circles; 1, tyrosine; 2, histidine; 3, arginine; 4, cysteine; 5, methionine; 6, tryptophan; 7, threonine; 8, valine; 9 leucine; 10, isoleucine; 11, proline; 12, pyridoxal; 13, purine nucleosides; 14, pyrimidine nucleosides; 15, glutamine; 16, thiamin; 17, pantothenate).

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On the other hand, many pathogens have smaller genomes compared to related non-pathogenic species as a result of reductive genome evolution [63] resulting in loss of many pathways. To identify biosynthesis pathways that were commonly lost during this process, we determined a “biosynthesis depletion frequency” (DF) as follows. For each biomass component, we determined the fractions of pathogenic (P) and non-pathogenic (NP) species that encoded corresponding biosynthesis pathways in their genomes. The “biosynthesis depletion frequency” was calculated as the difference of the respective frequencies DF = NP−P. As an example, 89% of environmental bacteria but only 47% of mammalian pathogens had an apparently functional tyrosine biosynthesis pathway yielding a “depletion frequency” of 89%−47% = 42%. The results revealed that many pathogens lost biosynthesis pathways for several amino acids, nucleosides, and (pro)vitamins indicating that these pathogens - like Salmonella - might obtain such biomass components from their respective host environments (Fig. 6B).

Together, these genome comparisons suggested that many pathogens share with Salmonella access to a common large set of diverse host metabolites in infected mammalian tissues.

Ethics statement

All animal experiments were approved by Kantonales Veterinäramt Basel-Stadt (license 2239) and performed according to local guidelines (Tierschutz-Verordnung, Basel-Stadt) and the Swiss animal protection law (Tierschutz-Gesetz).

Bacterial genetics

Salmonella mutants were constructed by lambda red-recombinase mediated allelic replacement [92] followed by general transduction using phage P22 int [93]. In multiple mutants, usage of the same resistance cassettes was enabled by FLP recombinase-mediated excision of the first cassette [92]. Strains were cultivated on Lennox LB medium containing 90 µg ml⁻¹ streptomycin, 50 µg ml⁻¹ kanamycin, 20 µg ml⁻¹ chloramphenicol, and/or 100 µg ml⁻¹ ampicillin. All auxotrophs required supplementation for growth as expected (Table S4).

Mouse infections

We infected female, 8 to 12 weeks old BALB/c mice intravenously with 500–2000 CFU Salmonella from late exponential LB cultures. For some experiments, we used female, 8–12 weeks old 129/Sv mice. Three to four days post-infection (or five days for 129/Sv), mice were sacrificed and bacterial loads in spleen and liver were determined by plating of tissue homogenates treated with 0.3% Triton Tx-100. In competitive infections, wildtype and mutant Salmonella carrying different antibiotic resistance markers were mixed before administration. Individual strain tissue loads were determined by replica plating on selective media and competitive indices (CI = output ratio/input ratio) were calculated. Statistical significance was analyzed using t-test of log-transformed CI values (a parametric test was appropriate based on the normal distribution of such values [34]). Our experiments involved a large set of strains. To avoid the multiple comparison problem, we used the Benjamini-Hochberg false discovery rate (FDR) approach [45].

Flow cytometry

For Salmonella ex vivo purification, Salmonella sifB::gfp [94] were sorted from infected mouse spleen as described [34] using a FACSAria III sorter (BD Biosciences). We used optical emission filters (green fluorescence, 499–529 nm; orange fluorescence, 564–606 nm) that optimally separated Salmonella GFP fluorescence from host cell autofluorescence. Proteome changes were minimized by preventing de novo synthesis with 170 µM chloramphenicol and delaying proteolysis by maintaining the samples at 0–4°C. Our previous results suggested that these conditions were effective to largely preserve the in vivo Salmonella proteome during sorting [34].

Enzyme quantification using mass spectrometry-based proteomics

Preparation of tryptic peptides and analysis by LC-MS/MS was done essentially as described [95] with some modifications. Protein LoBind tubes and pipette tips (Axygen) were used throughout the procedure to minimize protein loss through adsorption. Frozen FACS sorted Salmonella pellets were resuspended in 15 µl lysis buffer (100 mM ammonium bicarbonate, 8 M urea, 0.1% RapiGest) and sonicated for 2× 30 seconds. Released proteins were reduced and alkylated, and first digested for 4 hrs with sequencing grade LysC peptidase (10 ng/µl; Promega) before overnight trypsin digestion. The detergent was cleaved by adding 2 M HCL and 5% TFA to final concentrations of 50 mM and 0.5% respectively, and incubating for 45 min at 37°C. Prior to centrifugation to remove the cleaved detergent (14,000×g, 10 min, 4°C), a mixture containing 32 heavy labeled reference peptides were added to the samples (5×10⁻⁵ fmoles per Salmonella for expected “high” abundance proteins, 5×10⁻⁶ fmoles per Salmonella for expected “low” abundance proteins; Table S12). The recovered peptides were desalted on C18 reverse-phase spin columns (Macrospin columns, Harvard apparatus), dried under vacuum and subjected to LC-MS/MS using an LTQ-Orbitrap-Velos instrument (Thermo-Fischer Scientific). Between 5×10⁵ and 2×10⁶ Salmonella sorted from individual mice were analyzed in replicate LC-MS/MS runs. In order to increase the number of Salmonella protein identifications, MS-sequencing was partially focused on previously identified Salmonella peptides using the recently developed inclusion list driven workflow [95]. Peptides and proteins were database searched against a decoy database consisting of the SL1344 genome sequence (ftp://ftp.sanger.ac.uk/pub/pathogens/Salmonella/), GFP, 204 frequently observed contaminants, all mouse entries from SwissProt (Version 57.12), and all sequences in reversed order (total 42502 entries) using the Mascot search algorithm. The search criteria were set as follows: full tryptic specificity was required (cleavage after lysine or arginine residues); 2 missed cleavages were allowed; carbamidomethylation (C) was set as fixed modification; oxidation (M) as variable modification. The mass tolerance was set to 10 ppm for precursor ions and 0.5 Da for fragment ions. The false discovery rate was set to 1% for protein and peptide identifications. In addition to Salmonella proteins a substantial number of contaminating mouse proteins were identified in the samples as previously noted [34]. Absolute quantities were determined for those 18–20 “anchor” Salmonella proteins that were detected along with a corresponding labeled AQUA peptide (Table S12) using the Trans-Proteomic Pipeline (TPP,V4.4.0). We then used the iBAQ method to establish absolute quantities of all remaining protein identifications, with a linear model error of between 47 and 60%. Comparison of samples from four independently infected mice revealed good reproducibility (Table S1). The data associated with this manuscript may be downloaded from ProteomeCommons.org Tranche using the following hash: HaSHrE4Paqa3Io3NARhJsV/7XeqYsNNvHYX3tt++xYcVYOf47nChKFB9E/PCD+j+xt5meJ1+4ytJIHVUeXx9Xb+ohBEAAAAAAAACZw =  = 

Enzyme abundance was combined with reported turnover numbers for the respective Salmonella enzymes (or closely related E. coli orthologs) to calculate maximal feasible reaction rates (Table S2). Data were visualized using the pathway tools package [96].

Macrophage-like cell culture infection

Raw 264.7 macrophage-like cells were cultured in DMEM cell culture medium containing 10% serum and 0.5 g l⁻¹ glucose. Cells were infected with Salmonella from stationary cultures at a multiplicity of infection of 30 for 30 min with an initial 5 min 1100×g centrifugation step. Medium was exchanged against DMEM containing 0.5 g l⁻¹ glucose and 50 mg l⁻¹ gentamycin. At 4 hours post infection, medium was exchanged with DMEM containing 0.5 g l⁻¹ glucose, 1 g l⁻¹ glucose, or 0.5 g l⁻¹ glucose and 0.5 g l⁻¹ mannitol. Cells were washed and lysed 10 h after infection, and aliquots were plated to determine CFU numbers.

Computational modeling of Salmonella metabolism

The consensus genome-scale metabolism reconstruction STMv1 [31] was updated to STMv1.1 based on recent literature (Tables S6, S7). For in vivo modeling, we modified biomass requirements based on published mutant virulence phenotypes in infected host tissues. As an example, the high virulence of Salmonella mutants rfbH, rfbJ, rfbV, rfbF, rfbG [97] suggested that lipopolysaccharide with O-sidechains containing the carbohydrate abequose was not required in vivo. In total, these biomass modifications accounted for 14 mutant phenotypes (for detailed descriptions of all modifications see Table S8).

We generated an in vivo model using Flux-Balance Analysis (FBA) with the COBRA toolbox [98] in a MATLAB environment. Nutrient uptake rates were adjusted to yield consistent results with experimental competitive indices of Salmonella mutants and reported phenotypes (Tables S3, S5, S9) as well as the experimentally determined Salmonella wildtype in vivo generation time of 6.4 h [34] using the new MATLAB function nutrientUtilization() (Script S1). Specifically, uptake of each nutrient was varied and glucose was added to achieve the wildtype growth rate (in case of glucose as the nutrient of interest, we compensated with glycerol; in case of arginine that we modeled as a nitrogen source, we used ammonium for compensation). We then determined the nutrient uptake rate that matched the Competitive Index of a mutant that was unable to utilize this specific nutrient. After completing this procedure for each nutrient, we incorporated all first round nutrient uptake rates in an updated model. We then adjusted the maintenance costs to ensure a normal wildtype growth rate. We repeated this procedure a few times until values converged. We report these as final uptake rates in Table S9. We determined simulation error margins by analyzing error propagation from the experimental data (for examples, see Fig. S3). We used the calculated median uptake rate for 14 amino acids to estimate uptake of amino acids alanine, asparagine, aspartate, glutamate, glycine, and serine, for which we lacked informative mutant data. Biomass requirements suggested uptake rates for additional 13 inorganic components (Table S9).

We also explored the possibility of Salmonella access to excess nutrients using the new MATLAB function excess() (Script S2). Specifically, we increased the growth rate to higher values than experimentally observed. For these scenarios, we determined uptake rates for the six major carbon/energy sources and adjusted maintenance costs as described above. To calculate the corresponding nutrient excess, we then compared the total nutrient uptake for these scenarios to what would be needed for normal growth at the experimentally determined rate.

We predicted flux states with “minimal total flux” at maximal rates for biomass generation (“objective function”) using the respective options in the optimize() function. We determined flux variability in alternative solutions using the fluxVariability() function. This flux variability analysis was performed without assuming lowest overall metabolic activity to obtain the full range of possible flux states compatible with optimal Salmonella growth. We predicted biomass generation (which we used as an approximation for growth throughout this study) for all single gene deletions using the deleteModelGenes() function. Genes were defined as essential if predicted mutant growth rates were below 60% of wildtype (based on experimental growth data [34] for the avirulent aroA mutant [65]), contributing if mutants growth rates were between 60% and 98%, and non-detectable if mutants had growth rates higher than 98% of wildtype. We performed parsimonious FBA using the pFBA() function of the COBRA toolbox.

To validate these predictions, we examined reported experimental Salmonella colonization phenotypes and classified genes again as essential (lethal dose 1000fold higher than wildtype, or CI after four days below 0.005), contributing (significant colonization defect below thresholds for essential genes), or non-detectable (no significant difference to wildtype). We also used large-scale mutant phenotypes from two recent high-throughput studies [97], [99]. In these cases, we converted the reported mutant phenotype scores to growth rates and estimated confidence intervals based on the data provided (their Table S3 [97]; their Table S3 [99]) and the Salmonella in vivo generation time of 6.4 h in susceptible mice [34]. In cases where conflicting data had been reported, we preferentially used data from studies with low infection dose.

Metabolic network comparisons

Metabolic Pathway predictions for 909 genomes were generated by the MetaCyc consortium [62] and kindly by provided Tomer Altman and Peter Karp on November 22, 2010. We identified 287 mammalian pathogens and 367 environmental organisms in this data set. We merged multiple strains belonging to the same species resulting in data for 154 pathogen species and 316 environmental species (Table S11). We then determined how many organisms in each group were capable to degrade a specific nutrient, or to synthesize a certain biomass component.