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Provides evidence that `Salmonella typhimurium' responds during infection of macrophages by the increased synthesis of a number of bacterial proteins. Methods; Results; Discussion.

Abstract Background Complete and accurate genome annotation is crucial for comprehensive and systematic studies of biological systems. However, determining protein-coding genes for most new genomes is almost completely performed by inference using computational predictions with significant documented error rates (> 15%). Furthermore, gene prediction programs provide no information on biologically important post-translational processing events critical for protein function. Results We experimentally annotated the bacterial pathogen Salmonella Typhimurium 14028, using "shotgun" proteomics to accurately uncover the translational landscape and post-translational features. The data provide protein-level experimental validation for approximately half of the predicted protein-coding genes in Salmonella and suggest revisions to several genes that appear to have incorrectly assigned translational start sites, including a potential novel alternate start codon. Additionally, we uncovered 12 non-annotated genes missed by gene prediction programs, as well as evidence suggesting a role for one of these novel ORFs in Salmonella pathogenesis. We also characterized post-translational features in the Salmonella genome, including chemical modifications and proteolytic cleavages. We find that bacteria have a much larger and more complex repertoire of chemical modifications than previously thought including several novel modifications. Our in vivo proteolysis data identified more than 130 signal peptide and N-terminal methionine cleavage events critical for protein function. Conclusion This work highlights several ways in which application of proteomics data can improve the quality of genome annotations to facilitate novel biological insights and provides a comprehensive proteome map of Salmonella as a resource for systems analysis.

Abstract Background Despite the widespread usage of DNA microarrays, questions remain about how best to interpret the wealth of gene-by-gene transcriptional levels that they measure. Recently, methods have been proposed which use biologically defined sets of genes in interpretation, instead of examining results gene-by-gene. Despite a serious limitation, a method based on Fisher's exact test remains one of the few plausible options for gene set analysis when an experiment has few replicates, as is typically the case for prokaryotes. Results We extend five methods of gene set analysis from use on experiments with multiple replicates, for use on experiments with few replicates. We then use simulated and real data to compare these methods with each other and with the Fisher's exact test (FET) method. As a result of the simulation we find that a method named MAXMEAN-NR, maintains the nominal rate of false positive findings (type I error rate) while offering good statistical power and robustness to a variety of gene set distributions for set sizes of at least 10. Other methods (ABSSUM-NR or SUM-NR) are shown to be powerful for set sizes less than 10. Analysis of three sets of experimental data shows similar results. Furthermore, the MAXMEAN-NR method is shown to be able to detect biologically relevant sets as significant, when other methods (including FET) cannot. We also find that the popular GSEA-NR method performs poorly when compared to MAXMEAN-NR. Conclusion MAXMEAN-NR is a method of gene set analysis for experiments with few replicates, as is common for prokaryotes. Results of simulation and real data analysis suggest that the MAXMEAN-NR method offers increased robustness and biological relevance of findings as compared to FET and other methods, while maintaining the nominal type I error rate.

The alternative sigma factor σE functions to maintain bacterial homeostasis and membrane integrity in response to extracytoplasmic stress by regulating thousands of genes both directly and indirectly. The transcriptional regulatory network governed by σE in Salmonella and E. coli has been examined using microarray, however a genome-wide analysis of σE-binding sites in Salmonella has not yet been reported. We infected macrophages with Salmonella Typhimurium over a select time course. Using chromatin immunoprecipitation followed by high-throughput DNA sequencing (ChIP-seq), 31 σE-binding sites were identified. Seventeen sites were new, which included outer membrane proteins, a quorum-sensing protein, a cell division factor, and a signal transduction modulator. The consensus sequence identified for σE in vivo binding was similar to the one previously reported, except for a conserved G and A between the -35 and -10 regions. One third of the σE-binding sites did not contain the consensus sequence, suggesting there may be alternative mechanisms by which σE modulates transcription. By dissecting direct and indirect modes of σE-mediated regulation, we found that σE activates gene expression through recognition of both canonical and reversed consensus sequence. New σE regulated genes (greA, luxS, ompA and ompX) are shown to be involved in heat shock and oxidative stress responses.

The alternative sigma factor E (σ(E)) is critical for response to extracytoplasmic stress in Salmonella. Extensive studies have been conducted on σ(E)-regulated gene expression, particularly at the transcriptional level. Increasing evidence suggests however that σ(E) may indirectly participate in post-transcriptional regulation. In this study, we conducted sample-matched global proteomic and transcriptomic analyses to determine the level of regulation mediated by σ(E) in Salmonella. Samples were analyzed from wild-type and isogenic rpoE mutant Salmonella cultivated in three different conditions: nutrient-rich and conditions that mimic early and late intracellular infection. We found that 30% of the observed proteome was regulated by σ(E) combining all three conditions. In different growth conditions, σ(E) affected the expression of a broad spectrum of Salmonella proteins required for miscellaneous functions. Those involved in transport and binding, protein synthesis, and stress response were particularly highlighted. By comparing transcriptomic and proteomic data, we identified genes post-transcriptionally regulated by σ(E) and found that post-transcriptional regulation was responsible for a majority of changes observed in the σ(E)-regulated proteome. Further, comparison of transcriptomic and proteomic data from hfq mutant of Salmonella demonstrated that σ(E)-mediated post-transcriptional regulation was partially dependent on the RNA-binding protein Hfq.

The extracytoplasmic functioning sigma factor σ(E) is known to play an essential role for Salmonella enterica serovar Typhimurium to survive and proliferate in macrophages and mice. However, its regulatory network is not well-characterized, especially during infection. Here we used microarray to identify genes regulated by σ(E) in Salmonella grown in three conditions: a nutrient-rich condition and two others that mimic early and late intracellular infection. We found that in each condition σ(E) regulated different sets of genes, and notably, several global regulators. When comparing nutrient-rich and infection-like conditions, large changes were observed in the expression of genes involved in Salmonella pathogenesis island (SPI)-1 type-three secretion system (TTSS), SPI-2 TTSS, protein synthesis, and stress responses. In total, the expression of 58% of Salmonella genes was affected by σ(E) in at least one of the three conditions. An important finding is that σ(E) up-regulates SPI-2 genes, which are essential for Salmonella intracellular survival, by up-regulating SPI-2 activator ssrB expression at the early stage of infection and down-regulating SPI-2 repressor hns expression at a later stage. Moreover, σ(E) is capable of countering the silencing of H-NS, releasing the expression of SPI-2 genes. This connection between σ(E) and SPI-2 genes, combined with the global regulatory effect of σ(E), may account for the lethality of rpoE-deficient Salmonella in murine infection.

Francisella tularensis is a facultative intracellular bacterium that causes the deadly disease tularemia. Most evidence suggests that Francisella is not well recognized by the innate immune system that normally leads to cytokine expression and cell death. In previous work, we identified new bacterial factors that were hyper-cytotoxic to macrophages. Four of the identified hyper-cytotoxic strains (lpcC, manB, manC, and kdtA) had an impaired lipopolysaccharide (LPS) synthesis and produced an exposed lipid A lacking the O-antigen. These mutants were not only hyper-cytotoxic but also were phagocytosed at much higher rates compared with the wild type parent strain. To elucidate the cellular signaling underlying this enhanced phagocytosis and cell death, we performed a large-scale comparative phosphoproteomic analysis of cells infected with wild-type and delta-lpcC F. novicida. Our data suggest that not only actin but also intermediate filaments and microtubules are important for F. novicida entry into the host cells. In addition, we observed differential phosphorylation of tristetraprolin, a key component of the mRNA-degrading machinery that controls the expression of a variety of genes including many cytokines. Infection with the delta-lpcC mutant induced the hyper-phosphorylation and inhibition of tristetraprolin, leading to the production of cytokines such as IL-1beta and TNF-alpha that may kill the host cells by triggering apoptosis. Together, our data provide new insights for Francisella invasion and a post-transcriptional mechanism that prevents the expression of host immune response factors that control infection by this pathogen.

Salmonella enterica serovar Typhimurium causes typhoid-like disease in mice and is a model of typhoid fever in humans. One of the hallmarks of typhoid is persistence, the ability of the bacteria to survive in the host weeks after infection. Virulence factors called effectors facilitate this process by direct transfer to the cytoplasm of infected cells thereby subverting cellular processes. Secretion of effectors to the cell cytoplasm takes place through multiple routes, including two separate type III secretion (T3SS) apparati as well as outer membrane vesicles. The two T3SS are encoded on separate pathogenicity islands, SPI-1 and -2, with SPI-1 more strongly associated with the intestinal phase of infection, and SPI-2 with the systemic phase. Both T3SS are required for persistence, but the effectors required have not been systematically evaluated. In this study, mutations in 48 described effectors were tested for persistence. We replaced each effector with a specific DNA barcode sequence by allelic exchange and co-infected with a wild-type reference to calculate the ratio of wild-type parent to mutant at different times after infection. The competitive index (CI) was determined by quantitative PCR in which primers that correspond to the barcode were used for amplification. Mutations in all but seven effectors reduced persistence demonstrating that most effectors were required. One exception was CigR, a recently discovered effector that is widely conserved throughout enteric bacteria. Deletion of cigR increased lethality, suggesting that it may be an anti-virulence factor. The fact that almost all Salmonella effectors are required for persistence argues against redundant functions. This is different from effector repertoires in other intracellular pathogens such as Legionella.

The potential for commensal microorganisms indigenous to a host (the 'microbiome' or 'microbiota') to alter infection outcome by influencing host-pathogen interplay is largely unknown. We used a multi-omics "systems" approach, incorporating proteomics, metabolomics, glycomics, and metagenomics, to explore the molecular interplay between the murine host, the pathogen Salmonella enterica serovar Typhimurium (S. Typhimurium), and commensal gut microorganisms during intestinal infection with S. Typhimurium. We find proteomic evidence that S. Typhimurium thrives within the infected 129/SvJ mouse gut without antibiotic pre-treatment, inducing inflammation and disrupting the intestinal microbiome (e.g., suppressing Bacteroidetes and Firmicutes while promoting growth of Salmonella and Enterococcus). Alteration of the host microbiome population structure was highly correlated with gut environmental changes, including the accumulation of metabolites normally consumed by commensal microbiota. Finally, the less characterized phase of S. Typhimurium's lifecycle was investigated, and both proteomic and glycomic evidence suggests S. Typhimurium may take advantage of increased fucose moieties to metabolize fucose while growing in the gut. The application of multiple omics measurements to Salmonella-induced intestinal inflammation provides insights into complex molecular strategies employed during pathogenesis between host, pathogen, and the microbiome.

Characterization of the mature protein complement in cells is crucial for a better understanding of cellular processes on a systems-wide scale. Toward this end, we used single-dimension ultra-high-pressure liquid chromatography mass spectrometry to investigate the comprehensive "intact" proteome of the Gram-negative bacterial pathogen Salmonella Typhimurium. Top-down proteomics analysis revealed 563 unique proteins including 1,665 proteoforms generated by posttranslational modifications (PTMs), representing the largest microbial top-down dataset reported to date. We confirmed many previously recognized aspects of Salmonella biology and bacterial PTMs, and our analysis also revealed several additional biological insights. Of particular interest was differential utilization of the protein S-thiolation forms S-glutathionylation and S-cysteinylation in response to infection-like conditions versus basal conditions. This finding of a S-glutathionylation-to-S-cysteinylation switch in a condition-specific manner was corroborated by bottom-up proteomics data and further by changes in corresponding biosynthetic pathways under infection-like conditions and during actual infection of host cells. This differential utilization highlights underlying metabolic mechanisms that modulate changes in cellular signaling, and represents a report of S-cysteinylation in Gram-negative bacteria. Additionally, the functional relevance of these PTMs was supported by protein structure and gene deletion analyses. The demonstrated utility of our simple proteome-wide intact protein level measurement strategy for gaining biological insight should promote broader adoption and applications of top-down proteomics approaches.

Salmonella enterica serovar Typhimurium (S. Typhimurium) is a facultative pathogen that uses complex mechanisms to invade and proliferate within mammalian host cells. To investigate possible contributions of metabolic processes to virulence in S. Typhimurium grown under conditions known to induce expression of virulence genes, we used a metabolomics-driven systems biology approach coupled with genome-scale modeling. First, we identified distinct metabolite profiles associated with bacteria grown in either rich or virulence-inducing media and report the most comprehensive coverage of the S. Typhimurium metabolome to date. Second, we applied an omics-informed genome-scale modeling analysis of the functional consequences of adaptive alterations in S. Typhimurium metabolism during growth under our conditions. Modeling efforts highlighted a decreased cellular capability to both produce and utilize intracellular amino acids during stationary phase culture in virulence conditions, despite significant abundance increases for these molecules as observed by our metabolomics measurements. Furthermore, analyses of omics data in the context of the metabolic model indicated rewiring of the metabolic network to support pathways associated with virulence. For example, cellular concentrations of polyamines were perturbed, as well as the predicted capacity for secretion and uptake.

Salmonella virulence is largely mediated by two type III secretion systems (T3SS) that deliver effector proteins from the bacterium to a host cell; however, the secretion signal is poorly defined. Effector N termini are thought to contain the signal, but they lack homology, possess no identifiable motif, and adopt intrinsically disordered structures. Alternative studies suggest that RNA-encoded signals may also be recognized and that they can be located in the 5' untranslated leader sequence. We began our study by establishing the minimum sequence required for reporter translocation. Untranslated leader sequences predicted from 42 different Salmonella effector proteins were fused to the adenylate cyclase reporter (CyaA'), and each of them was tested for protein injection into J774 macrophages. RNA sequences derived from five effectors, gtgA, cigR, gogB, sseL, and steD, were sufficient for CyaA' translocation into host cells. To determine the mechanism of signal recognition, we identified proteins that bound specifically to the gtgA RNA. One of the unique proteins identified was Hfq. Hfq had no effect upon the translocation of full-length CigR and SteD, but injection of intact GtgA, GogB, and SseL was abolished in an hfq mutant, confirming the importance of Hfq. Our results demonstrated that the Salmonella pathogenicity island 2 (SPI-2) T3SS assembled into a functional apparatus independently of Hfq. Since particular effectors required Hfq for translocation, Hfq-RNA complexes may participate in signal recognition.

Phage viruses that infect prokaryotes integrate their genome into the host chromosome; thus, microbial genomes typically contain genetic remnants of both recent and ancient phage infections. Often phage genes occur in clusters of atypical G+C content that reflect integration of the foreign DNA. However, some phage genes occur in isolation without other phage gene neighbors, probably resulting from horizontal gene transfer. In these cases, the phage gene product is unlikely to function as a component of a mature phage particle, and instead may have been co-opted by the host for its own benefit. The product of one such gene from Salmonella enterica serovar Typhimurium, STM3605, encodes a protein with modest sequence similarity to phage-like lysozyme (N-acetylmuramidase) but appears to lack essential catalytic residues that are strictly conserved in all lysozymes. Close homologs in other bacteria share this characteristic. The structure of the STM3605 protein was characterized by X-ray crystallography, and functional assays showed that it is a stable, folded protein whose structure closely resembles lysozyme. However, this protein is unlikely to hydrolyze peptidoglycan. Instead, STM3605 is presumed to have evolved an alternative function because it shows some lytic activity and partitions to micelles.

Salmonella and Yersinia are two distantly related genera containing species with wide host-range specificity and pathogenic capacity. The metabolic complexity of these organisms facilitates robust lifestyles both outside of and within animal hosts. Using a pathogen-centric systems biology approach, we are combining a multi-omics (transcriptomics, proteomics, metabolomics) strategy to define properties of these pathogens under a variety of conditions including those that mimic the environments encountered during pathogenesis. These high-dimensional omics datasets are being integrated in selected ways to improve genome annotations, discover novel virulence-related factors, and model growth under infectious states. We will review the evolving technological approaches toward understanding complex microbial life through multi-omic measurements and integration, while highlighting some of our most recent successes in this area.

Towards developing a systems-level pathobiological understanding of Salmonella enterica, we performed a subcellular proteomic analysis of this pathogen grown under standard laboratory and phagosome-mimicking conditions in vitro. Analysis of proteins from cytoplasmic, inner membrane, periplasmic, and outer membrane fractions yielded coverage of 25% of the theoretical proteome. Confident subcellular location could be assigned to over 1000 proteins, with good agreement between experimentally observed location and predicted/known protein properties. Comparison of protein location under the different environmental conditions provided insight into dynamic protein localization and possible moonlighting (multiple function) activities. Notable examples of dynamic localization were the response regulators of two-component regulatory systems (e.g., ArcB and PhoQ). The DNA-binding protein Dps that is generally regarded as cytoplasmic was significantly enriched in the outer membrane for all growth conditions examined, suggestive of moonlighting activities. These observations imply the existence of unknown transport mechanisms and novel functions for a subset of Salmonella proteins. Overall, this work provides a catalog of experimentally verified subcellular protein locations for Salmonella and a framework for further investigations using computational modeling.

Background: Complete and accurate genome annotation is crucial for comprehensive and systematic studies of biological systems. However, determining protein-coding genes for most new genomes is almost completely performed by inference using computational predictions with significant documented error rates (> 15%). Furthermore, gene prediction programs provide no information on biologically important post-translational processing events critical for protein function., Results: We experimentally annotated the bacterial pathogen Salmonella Typhimurium 14028, using "shotgun" proteomics to accurately uncover the translational landscape and post-translational features. The data provide protein-level experimental validation for approximately half of the predicted protein-coding genes in Salmonella and suggest revisions to several genes that appear to have incorrectly assigned translational start sites, including a potential novel alternate start codon. Additionally, we uncovered 12 non-annotated genes missed by gene prediction programs, as well as evidence suggesting a role for one of these novel ORFs in Salmonella pathogenesis. We also characterized post-translational features in the Salmonella genome, including chemical modifications and proteolytic cleavages. We find that bacteria have a much larger and more complex repertoire of chemical modifications than previously thought including several novel modifications. Our in vivo proteolysis data identified more than 130 signal peptide and N-terminal methionine cleavage events critical for protein function., Conclusion: This work highlights several ways in which application of proteomics data can improve the quality of genome annotations to facilitate novel biological insights and provides a comprehensive proteome map of Salmonella as a resource for systems analysis.

Background: Systemic bacterial infections are highly regulated and complex processes that are orchestrated by numerous virulence factors. Genes that are coordinately controlled by the set of regulators required for systemic infection are potentially required for pathogenicity., Results: In this study we present a systems biology approach in which sample-matched multi-omic measurements of fourteen virulence-essential regulator mutants were coupled with computational network analysis to efficiently identify Salmonella virulence factors. Immunoblot experiments verified network-predicted virulence factors and a subset was determined to be secreted into the host cytoplasm, suggesting that they are virulence factors directly interacting with host cellular components. Two of these, SrfN and PagK2, were required for full mouse virulence and were shown to be translocated independent of either of the type III secretion systems in Salmonella or the type III injectisome-related flagellar mechanism., Conclusions: Integrating multi-omic datasets from Salmonella mutants lacking virulence regulators not only identified novel virulence factors but also defined a new class of translocated effectors involved in pathogenesis. The success of this strategy at discovery of known and novel virulence factors suggests that the approach may have applicability for other bacterial pathogens.

Salmonella is a primary cause of enteric diseases in a variety of animals. During its evolution into a pathogenic bacterium, Salmonella acquired an elaborate regulatory network that responds to multiple environmental stimuli within host animals and integrates them resulting in fine regulation of the virulence program. The coordinated action by this regulatory network involves numerous virulence regulators, necessitating genome-wide profiling analysis to assess and combine efforts from multiple regulons. In this review we discuss recent high-throughput analytic approaches used to understand the regulatory network of Salmonella that controls virulence processes. Application of high-throughput analyses have generated large amounts of data and necessitated the development of computational approaches for data integration. Therefore, we also cover computer-aided network analyses to infer regulatory networks, and demonstrate how genome-scale data can be used to construct regulatory and metabolic systems models of Salmonella pathogenesis. Genes that are coordinately controlled by multiple virulence regulators under infectious conditions are more likely to be important for pathogenesis. Thus, reconstructing the global regulatory network during infection or, at the very least, under conditions that mimic the host cellular environment not only provides a bird's eye view of Salmonella survival strategy in response to hostile host environments but also serves as an efficient means to identify novel virulence factors that are essential for Salmonella to accomplish systemic infection in the host.

Salmonella enterica serovar Typhimurium, an intracellular pathogen and leading cause of food-borne illness, encodes a plethora of virulence effectors. Salmonella virulence factors are translocated into host cells and manipulate host cellular activities, providing a more hospitable environment for bacterial proliferation. In this study, we report a new set of virulence factors that is translocated into the host cytoplasm via bacterial outer membrane vesicles (OMV). PagK (or PagK1), PagJ, and STM2585A (or PagK2) are small proteins composed of ∼70 amino acids and have high sequence homology to each other (>85% identity). Salmonella lacking all three homologues was attenuated for virulence in a mouse infection model, suggesting at least partial functional redundancy among the homologues. While each homologue was translocated into the macrophage cytoplasm, their translocation was independent of all three Salmonella gene-encoded type III secretion systems (T3SSs)-Salmonella pathogenicity island 1 (SPI-1) T3SS, SPI-2 T3SS, and the flagellar system. Selected methods, including direct microscopy, demonstrated that the PagK-homologous proteins were secreted through OMV, which were enriched with lipopolysaccharide (LPS) and outer membrane proteins. Vesicles produced by intracellular bacteria also contained lysosome-associated membrane protein 1 (LAMP1), suggesting the possibility of OMV convergence with host cellular components during intracellular trafficking. This study identified novel Salmonella virulence factors secreted via OMV and demonstrated that OMV can function as a vehicle to transfer virulence determinants to the cytoplasm of the infected host cell.

Salmonella enterica serovar Typhimurium is a leading cause of acute gastroenteritis throughout the world. This pathogen has two type III secretion systems (TTSS) encoded in Salmonella pathogenicity islands 1 and 2 (SPI-1 and SPI-2) that deliver virulence factors (effectors) to the host cell cytoplasm and are required for virulence. While many effectors have been identified and at least partially characterized, the full repertoire of effectors has not been catalogued. In this proteomic study, we identified effector proteins secreted into defined minimal medium designed to induce expression of the SPI-2 TTSS and its effectors. We compared the secretomes of the parent strain to those of strains missing essential (ssaK::cat) or regulatory (ΔssaL) components of the SPI-2 TTSS. We identified 20 known SPI-2 effectors. Excluding the translocon components SseBCD, all SPI-2 effectors were biased for identification in the ΔssaL mutant, substantiating the regulatory role of SsaL in TTS. To identify novel effector proteins, we coupled our secretome data with a machine learning algorithm (SIEVE, SVM-based identification and evaluation of virulence effectors) and selected 12 candidate proteins for further characterization. Using CyaA' reporter fusions, we identified six novel type III effectors and two additional proteins that were secreted into J774 macrophages independently of a TTSS. To assess their roles in virulence, we constructed nonpolar deletions and performed a competitive index analysis from intraperitoneally infected 129/SvJ mice. Six mutants were significantly attenuated for spleen colonization. Our results also suggest that non-type III secretion mechanisms are required for full Salmonella virulence.

In this review, we provide an overview of the methods employed in four recent studies that described novel methods for computational prediction of secreted effectors from type III and IV secretion systems in Gram-negative bacteria. We present the results of these studies in terms of performance at accurately predicting secreted effectors and similarities found between secretion signals that may reflect biologically relevant features for recognition. We discuss the Web-based tools for secreted effector prediction described in these studies and announce the availability of our tool, the SIEVE server (http://www.sysbep.org/sieve). Finally, we assess the accuracies of the three type III effector prediction methods on a small set of proteins not known prior to the development of these tools that we recently discovered and validated using both experimental and computational approaches. Our comparison shows that all methods use similar approaches and, in general, arrive at similar conclusions. We discuss the possibility of an order-dependent motif in the secretion signal, which was a point of disagreement in the studies. Our results show that there may be classes of effectors in which the signal has a loosely defined motif and others in which secretion is dependent only on compositional biases. Computational prediction of secreted effectors from protein sequences represents an important step toward better understanding the interaction between pathogens and hosts.

Salmonella enterica serovar Typhimurium is an intracellular pathogen and a main cause of food-borne illness. In this study, a quantitative PCR (qPCR)-based competitive index (CI) method was developed to simultaneously compare the growth of multiple Salmonella strains. This method was applied to a mixture of 17 Salmonella mutants lacking regulator genes, and their survival ratios were compared based on expression of natural resistance-associated macrophage protein 1 (Nramp1). Nramp1, as a major host innate immune component, controls the intracellular replication of pathogens. Deletion strains containing unique DNA barcodes in place of regulator genes were mixed with the parental control, and the bacteria were inoculated into congenic mice differing only at Nramp1. Most of the deletion strains were outcompeted by wild-type bacteria in either mouse strain, and the lack of Nramp1 didn't increase the tested strain/parent control replication ratios. When the same collection of mutants was tested in congenic mouse-derived primary macrophages, a major Nramp1-expressing cell type, six strains (ΔhimD, ΔphoP/phoQ, ΔrpoE, ΔrpoS, ΔompR/envZ, and Δhfq strains) grew better in Nramp1(-/-) than in Nramp1(+/+) macrophages, suggesting that these six regulators may play roles in overcoming Nramp1-mediated bactericidal activity in primary macrophages. The discrepancy in survival of macrophages and that of mice suggests either that there are differences in macrophage populations or that other cell types expressing Nramp1 control Salmonella proliferation in the host. The method described allows competitive infection analysis to be carried out on complex mixtures of bacteria and provides high reproducibility from independent biological replicates.

Many pathogenic Gram-negative bacteria use a type III secretion system (T3SS) to deliver effector proteins into the host cell where they reprogram host defenses and facilitate pathogenesis. The first 20-30 N-terminal residues usually contain the 'secretion signal' that targets effector proteins for translocation, however, a consensus sequence motif has never been discerned. Recent machine-learning approaches, such as support vector machine (SVM)-based Identification and Evaluation of Virulence Effectors (SIEVE), have improved the ability to identify effector proteins from genomics sequence information. While these methods all suggest that the T3SS secretion signal has a characteristic amino acid composition bias, it is still unclear if the amino acid pattern is important and if there are any unifying structural properties that direct recognition. To address these issues a peptide corresponding to the secretion signal for Salmonella enterica serovar Typhimurium effector SseJ was synthesized (residues 1-30, SseJ) along with scrambled peptides of the same amino acid composition that produced high (SseJ-H) and low (SseJ-L) SIEVE scores. The secretion properties of these three peptides were tested using a secretion signal-CyaA fusion assay and their structural properties probed using circular dichroism, nuclear magnetic resonance, and ion mobility spectrometry-mass spectrometry. The secretion predictions from SIEVE matched signal-CyaA fusion experimental results with J774 macrophages suggesting that the SseJ secretion signal has some sequence order dependence. The structural studies showed that the SseJ, SseJ-H, and SseJ-L peptides were intrinsically disordered in aqueous solution with a small predisposition to adopt nascent helical structure only in the presence of structure stabilizing agents such as 1,1,1,3,3,3-hexafluoroisopropanol. Intrinsic disorder may be a universal feature of effector secretion signals as similar conclusions were reached following structural characterization of peptides corresponding to the N-terminal regions of the S. Typhimurium effectors SptP, SopD-2, GtgE, and the Yersinia pestis effector YopH.

Infection with the bacterial pathogen Francisella tularensis tularensis (F. tularensis) causes tularemia, a serious and debilitating disease. Francisella tularensis novicida strain U112 (abbreviated F. novicida), which is closely related to F. tularensis, is pathogenic for mice but not for man, making it an ideal model system for tularemia. Intracellular pathogens like Francisella inhibit the innate immune response, thereby avoiding immune recognition and death of the infected cell. Because activation of inflammatory pathways may lead to cell death, we reasoned that we could identify bacterial genes involved in inhibiting inflammation by isolating mutants that killed infected cells faster than the wild-type parent. We screened a comprehensive transposon library of F. novicida for mutant strains that increased the rate of cell death following infection in J774 macrophage-like cells, as compared to wild-type F. novicida. Mutations in 28 genes were identified as being hypercytotoxic to both J774 and primary macrophages of which 12 were less virulent in a mouse infection model. Surprisingly, we found that F. novicida with mutations in four genes (lpcC, manB, manC and kdtA) were taken up by and killed macrophages at a much higher rate than the parent strain, even upon treatment with cytochalasin D (cytD), a classic inhibitor of macrophage phagocytosis. At least 10-fold more mutant bacteria were internalized by macrophages as compared to the parent strain if the bacteria were first fixed with formaldehyde, suggesting a surface structure is required for the high phagocytosis rate. However, bacteria were required to be viable for macrophage toxicity. The four mutant strains do not make a complete LPS but instead have an exposed lipid A. Interestingly, other mutations that result in an exposed LPS core were not taken up at increased frequency nor did they kill host cells more than the parent. These results suggest an alternative, more efficient macrophage uptake mechanism for Francisella that requires exposure of a specific bacterial surface structure(s) but results in increased cell death following internalization of live bacteria.

Host-pathogen interactions are complex competitions during which both the host and the pathogen adapt rapidly to each other in order for one or the other to survive. Salmonella enterica serovar Typhimurium is a pathogen with a broad host range that causes a typhoid fever-like disease in mice and severe food poisoning in humans. The murine typhoid fever is a systemic infection in which S. typhimurium evades part of the immune system by replicating inside macrophages and other cells. The transition from a foodborne contaminant to an intracellular pathogen must occur rapidly in multiple, ordered steps in order for S. typhimurium to thrive within its host environment. Using S. typhimurium isolated from rich culture conditions and from conditions that mimic the hostile intracellular environment of the host cell, a native low molecular weight protein fraction, or peptidome, was enriched from cell lysates by precipitation of intact proteins with organic solvents. The enriched peptidome was analyzed by both LC-MS/MS and LC-MS-based methods, although several other methods are possible. Pre-fractionation of peptides allowed identification of small proteins and protein degradation products that would normally be overlooked. Comparison of peptides present in lysates prepared from Salmonella grown under different conditions provided a unique insight into cellular degradation processes as well as identification of novel peptides encoded in the genome but not annotated. The overall approach is detailed here as applied to Salmonella and is adaptable to a broad range of biological systems.

Motivation: Quantitative mass spectrometry-based proteomics requires protein-level estimates and associated confidence measures. Challenges include the presence of low quality or incorrectly identified peptides and informative missingness. Furthermore, models are required for rolling peptide-level information up to the protein level., Results: We present a statistical model that carefully accounts for informative missingness in peak intensities and allows unbiased, model-based, protein-level estimation and inference. The model is applicable to both label-based and label-free quantitation experiments. We also provide automated, model-based, algorithms for filtering of proteins and peptides as well as imputation of missing values. Two LC/MS datasets are used to illustrate the methods. In simulation studies, our methods are shown to achieve substantially more discoveries than standard alternatives., Availability: The software has been made available in the open-source proteomics platform DAnTE (http://omics.pnl.gov/software/).

To investigate the extent to which macrophages respond to Salmonella infection, we infected RAW 264.7 macrophages with Salmonella enterica serotype Typhimurium and analyzed macrophage proteins at various time points following infection by using a global proteomic approach. A total of 1,006 macrophage and 115 Salmonella proteins were identified with high confidence. Most of the Salmonella proteins were observed in the late stage of the infection time course, which is consistent with the fact that the bacterial cells proliferate inside RAW 264.7 macrophages. The peptide abundances of most of the identified macrophage proteins remained relatively constant over the time course of infection. Compared to those of the control, the peptide abundances of 244 macrophage proteins (i.e., 24% of the total identified macrophage proteins) changed significantly after infection. The functions of these Salmonella-affected macrophage proteins were diverse, including production of antibacterial nitric oxide (i.e., inducible nitric oxide synthase), production of prostaglandin H(2) (i.e., cyclooxygenase 2), and regulation of intracellular traffic (e.g., sorting nexin 5 [SNX5], SNX6, and SNX9). Diverse functions of the Salmonella-affected macrophage proteins demonstrate a global macrophage response to Salmonella infection. Western blot analysis not only confirmed the proteomic results for a selected set of proteins but also revealed that (i) the protein abundance of mitochondrial superoxide dismutase increased following macrophage infection, indicating an infection-induced oxidative stress in mitochondria, and (ii) in contrast to infection of macrophages by wild-type Salmonella, infection by the sopB deletion mutant had no negative impact on the abundance of SNX6, suggesting a role for SopB in regulating the abundance of SNX6.

The mechanism of iron transport in Francisella is still a puzzle since none of the sequenced Francisella strains appears to encode a TonB protein, the energy transducer of the proton motive force necessary to act on the bacterial outer membrane siderophore receptor to allow the internalization of iron. In this work we demonstrate using kinetic experiments of radioactive Fe(3+) utilization, that iron uptake in Francisella novicida, although with no recognizable TonB protein, is indeed dependent on energy generated by the proton motive force. Moreover, mutants of a predicted outer membrane receptor still transport iron and are sensitive to the iron dependent antimicrobial compound streptonigrin. Our studies suggest that alternative pathways to internalize iron might exist in Francisella.

The type III secretion system is an essential component for virulence in many Gram-negative bacteria. Though components of the secretion system apparatus are conserved, its substrates--effector proteins--are not. We have used a novel computational approach to confidently identify new secreted effectors by integrating protein sequence-based features, including evolutionary measures such as the pattern of homologs in a range of other organisms, G+C content, amino acid composition, and the N-terminal 30 residues of the protein sequence. The method was trained on known effectors from the plant pathogen Pseudomonas syringae and validated on a set of effectors from the animal pathogen Salmonella enterica serovar Typhimurium (S. Typhimurium) after eliminating effectors with detectable sequence similarity. We show that this approach can predict known secreted effectors with high specificity and sensitivity. Furthermore, by considering a large set of effectors from multiple organisms, we computationally identify a common putative secretion signal in the N-terminal 20 residues of secreted effectors. This signal can be used to discriminate 46 out of 68 total known effectors from both organisms, suggesting that it is a real, shared signal applicable to many type III secreted effectors. We use the method to make novel predictions of secreted effectors in S. Typhimurium, some of which have been experimentally validated. We also apply the method to predict secreted effectors in the genetically intractable human pathogen Chlamydia trachomatis, identifying the majority of known secreted proteins in addition to providing a number of novel predictions. This approach provides a new way to identify secreted effectors in a broad range of pathogenic bacteria for further experimental characterization and provides insight into the nature of the type III secretion signal.

We successfully modified an existing method to investigate protein-protein interactions in the pathogenic bacterium Salmonella enterica serovar Typhimurium (Salmonella Typhimurium). This method includes (i) addition of a histidine-biotin-histidine tag to the bait proteins via recombinant DNA techniques, (ii) in vivo cross-linking with formaldehyde, (iii) tandem affinity purification of bait proteins under fully denaturing conditions, and (iv) identification of the proteins cross-linked to the bait proteins by liquid-chromatography in conjunction with tandem mass-spectrometry. In vivo cross-linking stabilized protein interactions and permitted the subsequent two-step purification step conducted under denaturing conditions. The two-step purification greatly reduced nonspecific binding of noncross-linked proteins to bait proteins. Two different negative controls were employed to eliminate the possibility of identifying background and nonspecific proteins as interacting partners, especially those caused by nonspecific binding to the stationary phase used for protein purification. In an initial demonstration of this approach, we tagged three Salmonella proteinsHimD, PduB and PhoPwith known binding partners that ranged from stable (e.g., HimD) to transient (i.e., PhoP). Distinct sets of interacting proteins were identified for each bait protein, including the known binding partners such as HimA for HimD, as well as unexpected binding partners. Our results suggest that novel protein-protein interactions identified may be critical to pathogenesis by Salmonella.