Interrogation of global mutagenesis data with a genome scale model of Neisseria meningitidis to assess gene fitness in vitro and in sera

Determinants of bacterial survival and proliferation in blood

Bloodstream infection is a major public health concern associated with high mortality and high healthcare costs worldwide. Bacteremia can trigger fatal sepsis whose prevention, diagnosis, and management have been recognized as a global health priority by the World Health Organization. Additionally, infection control is increasingly threatened by antimicrobial resistance, which is the focus of global action plans in the framework of a One Health response. In-depth knowledge of the infection process is needed to develop efficient preventive and therapeutic measures. The pathogenesis of bloodstream infection is a dynamic process resulting from the invasion of the vascular system by bacteria, which finely regulate their metabolic pathways and virulence factors to overcome the blood immune defenses and proliferate. In this review, we highlight our current understanding of determinants of bacterial survival and proliferation in the bloodstream and discuss their interactions with the molecular and cellular components of blood.

Transcriptome-guided metabolic network analysis reveals rearrangements of carbon flux distribution in Neisseria gonorrhoeae during neutrophil co-culture

The ability of bacterial pathogens to metabolically adapt to the environmental conditions of their hosts is critical to both colonization and invasive disease. Infection with Neisseria gonorrhoeae (the gonococcus, Gc) is characterized by the influx of neutrophils [polymorphonuclear leukocytes (PMNs)], which fail to clear the bacteria and make antimicrobial products that can exacerbate tissue damage. The inability of the human host to clear Gc infection is particularly concerning in light of the emergence of strains that are resistant to all clinically recommended antibiotics. Bacterial metabolism represents a promising target for the development of new therapeutics against Gc. Here, we generated a curated genome-scale metabolic network reconstruction (GENRE) of Gc strain FA1090. This GENRE links genetic information to metabolic phenotypes and predicts Gc biomass synthesis and energy consumption. We validated this model with published data and in new results reported here. Contextualization of this model using the transcriptional profile of Gc exposed to PMNs revealed substantial rearrangements of Gc central metabolism and induction of Gc nutrient acquisition strategies for alternate carbon source use. These features enhanced the growth of Gc in the presence of neutrophils. From these results, we conclude that the metabolic interplay between Gc and PMNs helps define infection outcomes. The use of transcriptional profiling and metabolic modeling to reveal new mechanisms by which Gc persists in the presence of PMNs uncovers unique aspects of metabolism in this fastidious bacterium, which could be targeted to block infection and thereby reduce the burden of gonorrhea in the human population. IMPORTANCE The World Health Organization designated Gc as a high-priority pathogen for research and development of new antimicrobials. Bacterial metabolism is a promising target for new antimicrobials, as metabolic enzymes are widely conserved among bacterial strains and are critical for nutrient acquisition and survival within the human host. Here we used genome-scale metabolic modeling to characterize the core metabolic pathways of this fastidious bacterium and to uncover the pathways used by Gc during culture with primary human immune cells. These analyses revealed that Gc relies on different metabolic pathways during co-culture with human neutrophils than in rich media. Conditionally essential genes emerging from these analyses were validated experimentally. These results show that metabolic adaptation in the context of innate immunity is important to Gc pathogenesis. Identifying the metabolic pathways used by Gc during infection can highlight new therapeutic targets for drug-resistant gonorrhea.

Architect: A tool for aiding the reconstruction of high-quality metabolic models through improved enzyme annotation

Constraint-based modeling is a powerful framework for studying cellular metabolism, with applications ranging from predicting growth rates and optimizing production of high value metabolites to identifying enzymes in pathogens that may be targeted for therapeutic interventions. Results from modeling experiments can be affected at least in part by the quality of the metabolic models used. Reconstructing a metabolic network manually can produce a high-quality metabolic model but is a time-consuming task. At the same time, current methods for automating the process typically transfer metabolic function based on sequence similarity, a process known to produce many false positives. We created Architect, a pipeline for automatic metabolic model reconstruction from protein sequences. First, it performs enzyme annotation through an ensemble approach, whereby a likelihood score is computed for an EC prediction based on predictions from existing tools; for this step, our method shows both increased precision and recall compared to individual tools. Next, Architect uses these annotations to construct a high-quality metabolic network which is then gap-filled based on likelihood scores from the ensemble approach. The resulting metabolic model is output in SBML format, suitable for constraints-based analyses. Through comparisons of enzyme annotations and curated metabolic models, we demonstrate improved performance of Architect over other state-of-the-art tools, notably with higher precision and recall on the eukaryote C. elegans and when compared to UniProt annotations in two bacterial species. Code for Architect is available at https://github.com/ParkinsonLab/Architect. For ease-of-use, Architect can be readily set up and utilized using its Docker image, maintained on Docker Hub.

Multilayer Regulation of Neisseria meningitidis NHBA at Physiologically Relevant Temperatures

Neisseria meningitidis colonizes the nasopharynx of humans, and pathogenic strains can disseminate into the bloodstream, causing septicemia and meningitis. NHBA is a surface-exposed lipoprotein expressed by all N. meningitidis strains in different isoforms. Diverse roles have been reported for NHBA in heparin-mediated serum resistance, biofilm formation, and adherence to host tissues. We determined that temperature controls the expression of NHBA in all strains tested, with increased levels at 30−32 °C compared to 37 °C. Higher NHBA expression at lower temperatures was measurable both at mRNA and protein levels, resulting in higher surface exposure. Detailed molecular analysis indicated that multiple molecular mechanisms are responsible for the thermoregulated NHBA expression. The comparison of mRNA steady-state levels and half-lives at 30 °C and 37 °C demonstrated an increased mRNA stability/translatability at lower temperatures. Protein stability was also impacted, resulting in higher NHBA stability at lower temperatures. Ultimately, increased NHBA expression resulted in higher susceptibility to complement-mediated killing. We propose that NHBA regulation in response to temperature downshift might be physiologically relevant during transmission and the initial step(s) of interaction within the host nasopharynx. Together these data describe the importance of NHBA both as a virulence factor and as a vaccine antigen during neisserial colonization and invasion.

Nucleoid-associated proteins shape chromatin structure and transcriptional regulation across the bacterial kingdom

Genome architecture has proven to be critical in determining gene regulation across almost all domains of life. While many of the key components and mechanisms of eukaryotic genome organization have been described, the interplay between bacterial DNA organization and gene regulation is only now being fully appreciated. An increasing pool of evidence has demonstrated that the bacterial chromosome can reasonably be thought of as chromatin, and that bacterial chromosomes contain transcriptionally silent and transcriptionally active regions analogous to heterochromatin and euchromatin, respectively. The roles played by histones in eukaryotic systems appear to be shared across a range of nucleoid-associated proteins (NAPs) in bacteria, which function to compact, structure, and regulate large portions of bacterial chromosomes. The broad range of extant NAPs, and the extent to which they differ from species to species, has raised additional challenges in identifying and characterizing their roles in all but a handful of model bacteria. Here we review the regulatory roles played by NAPs in several well-studied bacteria and use the resulting state of knowledge to provide a working definition for NAPs, based on their function, binding pattern, and expression levels. We present a screening procedure which can be applied to any species for which transcriptomic data are available. Finally, we note that NAPs tend to play two major regulatory roles - xenogeneic silencers and developmental regulators - and that many unrecognized potential NAPs exist in each bacterial species examined.

Construction of a complete set of Neisseria meningitidis mutants and its use for the phenotypic profiling of this human pathogen

The bacterium Neisseria meningitidis causes life-threatening meningitis and sepsis. Here, we construct a complete collection of defined mutants in protein-coding genes of this organism, identifying all genes that are essential under laboratory conditions. The collection, named NeMeSys 2.0, consists of individual mutants in 1584 non-essential genes. We identify 391 essential genes, which are associated with basic functions such as expression and preservation of genome information, cell membrane structure and function, and metabolism. We use this collection to shed light on the functions of diverse genes, including a gene encoding a member of a previously unrecognised class of histidinol-phosphatases; a set of 20 genes required for type IV pili function; and several conditionally essential genes encoding antitoxins and/or immunity proteins. We expect that NeMeSys 2.0 will facilitate the phenotypic profiling of a major human bacterial pathogen.

Genetic incorporation of non-canonical amino acid photocrosslinkers in Neisseria meningitidis: New method provides insights into the physiological function of the function-unknown NMB1345 protein

Although whole-genome sequencing has provided novel insights into Neisseria meningitidis, many open reading frames have only been annotated as hypothetical proteins with unknown biological functions. Our previous genetic analyses revealed that the hypothetical protein, NMB1345, plays a crucial role in meningococcal infection in human brain microvascular endothelial cells; however, NMB1345 has no homology to any identified protein in databases and its physiological function could not be elucidated using pre-existing methods. Among the many biological technologies to examine transient protein-protein interaction in vivo, one of the developed methods is genetic code expansion with non-canonical amino acids (ncAAs) utilizing a pyrrolysyl-tRNA synthetase/tRNAPyl pair from Methanosarcina species: However, this method has never been applied to assign function-unknown proteins in pathogenic bacteria. In the present study, we developed a new method to genetically incorporate ncAAs-encoded photocrosslinking probes into N. meningitidis by utilizing a pyrrolysyl-tRNA synthetase/tRNAPyl pair and elucidated the biological function(s) of the NMB1345 protein. The results revealed that the NMB1345 protein directly interacts with PilE, a major component of meningococcal pili, and further physicochemical and genetic analyses showed that the interaction between the NMB1345 protein and PilE was important for both functional pilus formation and meningococcal infectious ability in N. meningitidis. The present study using this new methodology for N. meningitidis provides novel insights into meningococcal pathogenesis by assigning the function of a hypothetical protein.

Cysteine biosynthesis in Neisseria species

The principal mechanism of reducing sulfur into organic compounds is via the synthesis of l-cysteine. Cysteine is used for protein and glutathione synthesis, as well as being the primary sulfur source for a variety of other molecules, such as biotin, coenzyme A, lipoic acid and more. Glutathione and other cysteine derivatives are important for protection against the oxidative stress that pathogenic bacteria such as Neisseria gonorrhoeae and Neisseria meningitidis encounter during infection. With the alarming rise of antibiotic-resistant strains of N. gonorrhoeae, the development of inhibitors for the future treatment of this disease is critical, and targeting cysteine biosynthesis enzymes could be a promising approach for this. Little is known about the transport of sulfate and thiosulfate and subsequent sulfate reduction and incorporation into cysteine in Neisseria species. In this review we investigate cysteine biosynthesis within Neisseria species and examine the differences between species and with other bacteria. Neisseria species exhibit different arrangements of cysteine biosynthesis genes and have slight differences in how they assimilate sulfate and synthesize cysteine, while, most interestingly, N. gonorrhoeae by virtue of a genome deletion, lacks the ability to reduce sulfate to bisulfide for incorporation into cysteine, and as such uses the thiosulfate uptake pathway for the synthesis of cysteine.

Neisseria genomics: current status and future perspectives

High-throughput whole genome sequencing has unlocked a multitude of possibilities enabling members of the Neisseria genus to be examined with unprecedented detail, including the human pathogens Neisseria meningitidis and Neisseria gonorrhoeae. To maximise the potential benefit of this for public health, it is becoming increasingly important to ensure that this plethora of data are adequately stored, disseminated and made readily accessible. Investigations facilitating cross-species comparisons as well as the analysis of global datasets will allow differences among and within species and across geographic locations and different times to be identified, improving our understanding of the distinct phenotypes observed. Recent advances in high-throughput platforms that measure the transcriptome, proteome and/or epigenome are also becoming increasingly employed to explore the complexities of Neisseria biology. An integrated approach to the analysis of these is essential to fully understand the impact these may have in the Neisseria genus. This article reviews the current status of some of the tools available for next generation sequence analysis at the dawn of the ‘post-genomic’ era.

Peripheral blood vessels are a niche for blood-borne meningococci

Neisseria meningitidis is the causative agent of cerebrospinal meningitis and that of a rapidly progressing fatal septic shock known as purpura fulminans. Meningococcemia is characterized by bacterial adhesion to human endothelial cells of the microvessels. Host specificity has hampered studies on the role of blood vessels colonization in N. meningitidis associated pathogenesis. In this work, using a humanized model of SCID mice allowing the study of bacterial adhesion to human cells in an in vivo context we demonstrate that meningococcal colonization of human blood vessels is a prerequisite to the establishment of sepsis and lethality. To identify the molecular pathways involved in bacterial virulence, we performed transposon insertion site sequencing (Tn-seq) in vivo. Our results demonstrate that 36% of the genes that are important for growth in the blood of mice are dispensable when bacteria colonize human blood vessels, suggesting that human endothelial cells lining the blood vessels are feeding niches for N. meningitidis in vivo. Altogether, our work proposes a new paradigm for meningococcal virulence in which colonization of blood vessels is associated with metabolic adaptation and sustained bacteremia responsible for sepsis and subsequent lethality.

Genomic, Transcriptomic, and Phenotypic Analyses of Neisseria meningitidis Isolates from Disease Patients and Their Household Contacts

Neisseria meningitidis causes meningococcal disease but is frequently carried in the throats of healthy individuals; the factors that determine whether invasive disease develops are not completely understood. We carried out detailed studies of isolates, collected from patients and their household contacts, to identify differences between commensal throat isolates and those that caused invasive disease. Though isolates were identical by laboratory typing methods, we uncovered many differences in their genomes, in gene expression, and in their interactions with host cells. In particular, we found that several carriage isolates had lost their type IV pili, a surprising finding since pili are often described as essential for colonization. However, loss of type IV pili correlated with reduced secretion of a proinflammatory cytokine, TNF-α, when meningococci were cocultured with human bronchial epithelial cells; hence, the loss of pili could provide an advantage to meningococci, by resulting in a dampened localized host immune response.

Neisseria meningitidis (meningococcus) can cause meningococcal disease, a rapidly progressing and often fatal disease that can occur in previously healthy children. Meningococci are found in healthy carriers, where they reside in the nasopharynx as commensals. While carriage is relatively common, invasive disease, associated with hypervirulent strains, is a comparatively rare event. The basis of increased virulence in some strains is not well understood. New Zealand suffered a protracted meningococcal disease epidemic, from 1991 to 2008. During this time, a household carriage study was carried out in Auckland: household contacts of index meningococcal disease patients were swabbed for isolation of carriage strains. In many households, healthy carriers harbored strains identical, as determined by laboratory typing, to the ones infecting the associated patient. We carried out more-detailed analyses of carriage and disease isolates from a select number of households. We found that isolates, although indistinguishable by laboratory typing methods and likely closely related, had many differences. We identified multiple genome variants and transcriptional differences between isolates. These studies enabled the identification of two new phase-variable genes. We also found that several carriage strains had lost their type IV pili and that this loss correlated with reduced tumor necrosis factor alpha (TNF-α) expression when cultured with epithelial cells. While nonpiliated meningococcal isolates have been previously found in carriage strains, this is the first evidence of an association between type IV pili from meningococci and a proinflammatory epithelial response. We also identified potentially important metabolic differences between carriage and disease isolates, including the sulfate assimilation pathway.

IMPORTANCENeisseria meningitidis causes meningococcal disease but is frequently carried in the throats of healthy individuals; the factors that determine whether invasive disease develops are not completely understood. We carried out detailed studies of isolates, collected from patients and their household contacts, to identify differences between commensal throat isolates and those that caused invasive disease. Though isolates were identical by laboratory typing methods, we uncovered many differences in their genomes, in gene expression, and in their interactions with host cells. In particular, we found that several carriage isolates had lost their type IV pili, a surprising finding since pili are often described as essential for colonization. However, loss of type IV pili correlated with reduced secretion of a proinflammatory cytokine, TNF-α, when meningococci were cocultured with human bronchial epithelial cells; hence, the loss of pili could provide an advantage to meningococci, by resulting in a dampened localized host immune response.

Unraveling Neisseria meningitidis pathogenesis: from functional genomics to experimental models

Neisseria meningitidis is a harmless commensal bacterium finely adapted to humans. Unfortunately, under “privileged” conditions, it adopts a “devious” lifestyle leading to uncontrolled behavior characterized by the unleashing of molecular weapons causing potentially lethal disease such as sepsis and acute meningitis. Indeed, despite the lack of a classic repertoire of virulence genes in N. meningitidis separating commensal from invasive strains, molecular epidemiology and functional genomics studies suggest that carriage and invasive strains belong to genetically distinct populations characterized by an exclusive pathogenic potential. In the last few years, “omics” technologies have helped scientists to unwrap the framework drawn by N. meningitidis during different stages of colonization and disease. However, this scenario is still incomplete and would benefit from the implementation of physiological tissue models for the reproduction of mucosal and systemic interactions in vitro. These emerging technologies supported by recent advances in the world of stem cell biology hold the promise for a further understanding of N. meningitidis pathogenesis.

An integrated computational-experimental approach reveals Yersinia pestis genes essential across a narrow or a broad range of environmental conditions

Background

The World Health Organization has categorized plague as a re-emerging disease and the potential for Yersinia pestis to also be used as a bioweapon makes the identification of new drug targets against this pathogen a priority. Environmental temperature is a key signal which regulates virulence of the bacterium. The bacterium normally grows outside the human host at 28 °C. Therefore, understanding the mechanisms that the bacterium used to adapt to a mammalian host at 37 °C is central to the development of vaccines or drugs for the prevention or treatment of human disease.

Results

Using a library of over 1 million Y. pestis CO92 random mutants and transposon-directed insertion site sequencing, we identified 530 essential genes when the bacteria were cultured at 28 °C. When the library of mutants was subsequently cultured at 37 °C we identified 19 genes that were essential at 37 °C but not at 28 °C, including genes which encode proteins that play a role in enabling functioning of the type III secretion and in DNA replication and maintenance. Using genome-scale metabolic network reconstruction we showed that growth conditions profoundly influence the physiology of the bacterium, and by combining computational and experimental approaches we were able to identify 54 genes that are essential under a broad range of conditions.

Conclusions

Using an integrated computational-experimental approach we identify genes which are required for growth at 37 °C and under a broad range of environments may be the best targets for the development of new interventions to prevent or treat plague in humans.

Electronic supplementary material

The online version of this article (doi:10.1186/s12866-017-1073-8) contains supplementary material, which is available to authorized users.

The Hfq regulon of Neisseria meningitidis

The conserved RNA‐binding protein, Hfq, has multiple regulatory roles within the prokaryotic cell, including promoting stable duplex formation between small RNAs and mRNAs, and thus hfq deletion mutants have pleiotropic phenotypes. Previous proteome and transcriptome studies of Neisseria meningitidis have generated limited insight into differential gene expression due to Hfq loss. In this study, reversed‐phase liquid chromatography combined with data‐independent alternate scanning mass spectrometry (LC‐MS^E) was utilized for rapid high‐resolution quantitative proteomic analysis to further elucidate the differentially expressed proteome of a meningococcal hfq deletion mutant. Whole‐cell lysates of N. meningitidis serogroup B H44/76 wild‐type (wt) and H44/76Δhfq (Δhfq) grown in liquid growth medium were subjected to tryptic digestion. The resulting peptide mixtures were separated by liquid chromatography (LC) prior to analysis by mass spectrometry (MS^E). Differential expression was analyzed by Student's t‐test with control for false discovery rate (FDR). Reliable quantitation of relative expression comparing wt and Δhfq was achieved with 506 proteins (20%). Upon FDR control at q ≤ 0.05, 48 up‐ and 59 downregulated proteins were identified. From these, 81 were identified as novel Hfq‐regulated candidates, while 15 proteins were previously found by SDS/PAGE/MS and 24 with microarray analyses. Thus, using LC‐MS^E we have expanded the repertoire of Hfq‐regulated proteins. In conjunction with previous studies, a comprehensive network of Hfq‐regulated proteins was constructed and differentially expressed proteins were found to be involved in a large variety of cellular processes. The results and comparisons with other gram‐negative model systems, suggest still unidentified sRNA analogs in N. meningitidis.

Transcriptomic buffering of cryptic genetic variation contributes to meningococcal virulence

Background

Commensal bacteria like Neisseria meningitidis sometimes cause serious disease. However, genomic comparison of hyperinvasive and apathogenic lineages did not reveal unambiguous hints towards indispensable virulence factors. Here, in a systems biological approach we compared gene expression of the invasive strain MC58 and the carriage strain α522 under different ex vivo conditions mimicking commensal and virulence compartments to assess the strain-specific impact of gene regulation on meningococcal virulence.

Results

Despite indistinguishable ex vivo phenotypes, both strains differed in the expression of over 500 genes under infection mimicking conditions. These differences comprised in particular metabolic and information processing genes as well as genes known to be involved in host-damage such as the nitrite reductase and numerous LOS biosynthesis genes. A model based analysis of the transcriptomic differences in human blood suggested ensuing metabolic flux differences in energy, glutamine and cysteine metabolic pathways along with differences in the activation of the stringent response in both strains. In support of the computational findings, experimental analyses revealed differences in cysteine and glutamine auxotrophy in both strains as well as a strain and condition dependent essentiality of the (p)ppGpp synthetase gene relA and of a short non-coding AT-rich repeat element in its promoter region.

Conclusions

Our data suggest that meningococcal virulence is linked to transcriptional buffering of cryptic genetic variation in metabolic genes including global stress responses. They further highlight the role of regulatory elements for bacterial virulence and the limitations of model strain approaches when studying such genetically diverse species as N. meningitidis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3616-7) contains supplementary material, which is available to authorized users.

Integration of Biomass Formulations of Genome-Scale Metabolic Models with Experimental Data Reveals Universally Essential Cofactors in Prokaryotes

The composition of a cell in terms of macromolecular building blocks and other organic molecules underlies the metabolic needs and capabilities of a species. Although some core biomass components such as nucleic acids and proteins are evident for most species, the essentiality of the pool of other organic molecules, especially cofactors and prosthetic groups, is yet unclear. Here we integrate biomass compositions from 71 manually curated genome-scale models, 33 large-scale gene essentiality datasets, enzyme-cofactor association data and a vast array of publications, revealing universally essential cofactors for prokaryotic metabolism and also others that are specific for phylogenetic branches or metabolic modes. Our results revise predictions of essential genes in Klebsiella pneumoniae and identify missing biosynthetic pathways in models of Mycobacterium tuberculosis. This work provides fundamental insights into the essentiality of organic cofactors and has implications for minimal cell studies as well as for modeling genotype-phenotype relations in prokaryotic metabolic networks.

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Seventy one biomass equations of manually curated genome-scale metabolic models are compared.

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Eight classes of universally essential prokaryotic organic cofactors are proposed.

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Conditionally essential organic cofactors are presented and discussed.

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Gene essentiality predictions for Klebsiella pneumoniae are revised.

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A missing essential pathway in models of Mycobacterium tuberculosis is predicted.

MUFINS: multi-formalism interaction network simulator

Systems Biology has established numerous approaches for mechanistic modeling of molecular networks in the cell and a legacy of models. The current frontier is the integration of models expressed in different formalisms to address the multi-scale biological system organization challenge. We present MUFINS (MUlti-Formalism Interaction Network Simulator) software, implementing a unique set of approaches for multi-formalism simulation of interaction networks. We extend the constraint-based modeling (CBM) framework by incorporation of linear inhibition constraints, enabling for the first time linear modeling of networks simultaneously describing gene regulation, signaling and whole-cell metabolism at steady state. We present a use case where a logical hypergraph model of a regulatory network is expressed by linear constraints and integrated with a Genome-Scale Metabolic Network (GSMN) of mouse macrophage. We experimentally validate predictions, demonstrating application of our software in an iterative cycle of hypothesis generation, validation and model refinement. MUFINS incorporates an extended version of our Quasi-Steady State Petri Net approach to integrate dynamic models with CBM, which we demonstrate through a dynamic model of cortisol signaling integrated with the human Recon2 GSMN and a model of nutrient dynamics in physiological compartments. Finally, we implement a number of methods for deriving metabolic states from ~omics data, including our new variant of the iMAT congruency approach. We compare our approach with iMAT through the analysis of 262 individual tumor transcriptomes, recovering features of metabolic reprogramming in cancer. The software provides graphics user interface with network visualization, which facilitates use by researchers who are not experienced in coding and mathematical modeling environments.

Lysine and Threonine Biosynthesis from Aspartate Contributes to Staphylococcus aureus Growth in Calf Serum

Staphylococcus aureus is a human pathogen, and S. aureus bacteremia can cause serious problems in humans. To identify the genes required for bacterial growth in calf serum (CS), a library of S. aureus mutants with randomly inserted transposons were analyzed for growth in CS, and the aspartate semialdehyde dehydrogenase (asd)-inactivated mutant exhibited significantly reduced growth in CS compared with the wild type (WT). The mutant also exhibited significantly reduced growth in medium, mimicking the concentrations of amino acids and glucose in CS. Asd is an essential enzyme for the biosynthesis of lysine, methionine, and threonine from aspartate. We constructed inactivated mutants of the genes for lysine (lysA), methionine (metE), and threonine (thrC) biosynthesis and found that the inactivated mutants of lysA and thrC exhibited significantly lower growth in CS than the WT, but the growth of the metE mutant was similar to that of the WT. The reduced growth of the asd mutant was recovered by addition of 100 μg/ml lysine and threonine in CS. These results suggest that S. aureus requires lysine and threonine biosynthesis to grow in CS. On the other hand, the asd-, lysA-, metE-, and thrC-inactivated mutants exhibited significantly reduced growth in mouse serum compared with the WT. In mouse bacteremia experiments, the asd-, lysA-, metE-, and thrC-inactivated mutants exhibited attenuated virulence compared with WT infection. In conclusion, our results suggest that the biosynthesis of de novo aspartate family amino acids, especially lysine and threonine, is important for staphylococcal bloodstream infection.

IMPORTANCE

Studying the growth of bacteria in blood is important for understanding its pathogenicity in the host. Staphylococcus aureus sometimes causes bacteremia or sepsis. However, the factors responsible for S. aureus growth in the blood are not well understood. In this study, using a library of 2,914 transposon-insertional mutants in the S. aureus MW2 strain, we identified the factors responsible for bacterial growth in CS. We found that inactivation of the lysine and threonine biosynthesis genes led to deficient growth in CS. However, the inactivation of these genes did not affect S. aureus growth in general medium. Because the concentration of amino acids in CS is low compared to that in general bacterial medium, our results suggest that lysine and threonine biosynthesis is important for the growth of S. aureus in CS. Our findings provide new insights for S. aureus adaptation in the host and for understanding the pathogenesis of bacteremia.

Comprehensive Identification of Meningococcal Genes and Small Noncoding RNAs Required for Host Cell Colonization

Neisseria meningitidis is a leading cause of bacterial meningitis and septicemia, affecting infants and adults worldwide. N. meningitidis is also a common inhabitant of the human nasopharynx and, as such, is highly adapted to its niche. During bacteremia, N. meningitidis gains access to the blood compartment, where it adheres to endothelial cells of blood vessels and causes dramatic vascular damage. Colonization of the nasopharyngeal niche and communication with the different human cell types is a major issue of the N. meningitidis life cycle that is poorly understood. Here, highly saturated random transposon insertion libraries of N. meningitidis were engineered, and the fitness of mutations during routine growth and that of colonization of endothelial and epithelial cells in a flow device were assessed in a transposon insertion site sequencing (Tn-seq) analysis. This allowed the identification of genes essential for bacterial growth and genes specifically required for host cell colonization. In addition, after having identified the small noncoding RNAs (sRNAs) located in intergenic regions, the phenotypes associated with mutations in those sRNAs were defined. A total of 383 genes and 8 intergenic regions containing sRNA candidates were identified to be essential for growth, while 288 genes and 33 intergenic regions containing sRNA candidates were found to be specifically required for host cell colonization.

Meningococcal meningitis is a common cause of meningitis in infants and adults. Neisseria meningitidis (meningococcus) is also a commensal bacterium of the nasopharynx and is carried by 3 to 30% of healthy humans. Under some unknown circumstances, N. meningitidis is able to invade the bloodstream and cause either meningitis or a fatal septicemia known as purpura fulminans. The onset of symptoms is sudden, and death can follow within hours. Although many meningococcal virulence factors have been identified, the mechanisms that allow the bacterium to switch from the commensal to pathogen state remain unknown. Therefore, we used a Tn-seq strategy coupled to high-throughput DNA sequencing technologies to find genes for proteins used by N. meningitidis to specifically colonize epithelial cells and primary brain endothelial cells. We identified 383 genes and 8 intergenic regions containing sRNAs essential for growth and 288 genes and 33 intergenic regions containing sRNAs required specifically for host cell colonization.

Integrating Kinetic Model of E. coli with Genome Scale Metabolic Fluxes Overcomes Its Open System Problem and Reveals Bistability in Central Metabolism

An understanding of the dynamics of the metabolic profile of a bacterial cell is sought from a dynamical systems analysis of kinetic models. This modelling formalism relies on a deterministic mathematical description of enzyme kinetics and their metabolite regulation. However, it is severely impeded by the lack of available kinetic information, limiting the size of the system that can be modelled. Furthermore, the subsystem of the metabolic network whose dynamics can be modelled is faced with three problems: how to parameterize the model with mostly incomplete steady state data, how to close what is now an inherently open system, and how to account for the impact on growth. In this study we address these challenges of kinetic modelling by capitalizing on multi-‘omics’ steady state data and a genome-scale metabolic network model. We use these to generate parameters that integrate knowledge embedded in the genome-scale metabolic network model, into the most comprehensive kinetic model of the central carbon metabolism of E. coli realized to date. As an application, we performed a dynamical systems analysis of the resulting enriched model. This revealed bistability of the central carbon metabolism and thus its potential to express two distinct metabolic states. Furthermore, since our model-informing technique ensures both stable states are constrained by the same thermodynamically feasible steady state growth rate, the ensuing bistability represents a temporal coexistence of the two states, and by extension, reveals the emergence of a phenotypically heterogeneous population.

Systems biology perspectives on minimal and simpler cells

The concept of the minimal cell has fascinated scientists for a long time, from both fundamental and applied points of view. This broad concept encompasses extreme reductions of genomes, the last universal common ancestor (LUCA), the creation of semiartificial cells, and the design of protocells and chassis cells. Here we review these different areas of research and identify common and complementary aspects of each one. We focus on systems biology, a discipline that is greatly facilitating the classical top-down and bottom-up approaches toward minimal cells. In addition, we also review the so-called middle-out approach and its contributions to the field with mathematical and computational models. Owing to the advances in genomics technologies, much of the work in this area has been centered on minimal genomes, or rather minimal gene sets, required to sustain life. Nevertheless, a fundamental expansion has been taking place in the last few years wherein the minimal gene set is viewed as a backbone of a more complex system. Complementing genomics, progress is being made in understanding the system-wide properties at the levels of the transcriptome, proteome, and metabolome. Network modeling approaches are enabling the integration of these different omics data sets toward an understanding of the complex molecular pathways connecting genotype to phenotype. We review key concepts central to the mapping and modeling of this complexity, which is at the heart of research on minimal cells. Finally, we discuss the distinction between minimizing the number of cellular components and minimizing cellular complexity, toward an improved understanding and utilization of minimal and simpler cells.

Critical assessment of genome-scale metabolic networks: the need for a unified standard

Genome-scale metabolic networks have been reconstructed for several organisms. These metabolic networks provide detailed information about the metabolism inside the cells, coupled with the genomic, proteomic and thermodynamic information. These networks are widely simulated using 'constraint-based' modelling techniques and find applications ranging from strain improvement for metabolic engineering to prediction of drug targets in pathogenic organisms. Components of these metabolic networks are represented in multiple file formats and also using different markup languages, with varying levels of annotations; this leads to inconsistencies and increases the complexities in comparing and analysing reconstructions on multiple platforms. In this work, we critically examine nearly 100 published genome-scale metabolic networks and their corresponding constraint-based models and discuss various issues with respect to model quality. One of the major concerns is the lack of annotations using standard identifiers that can uniquely describe several components such as metabolites, genes, proteins and reactions. We also find that many models do not have complete information regarding constraints on reactions fluxes and objective functions for carrying out simulations. Overall, our analysis highlights the need for a widely acceptable standard for representing constraint-based models. A rigorous standard can help in streamlining the process of reconstruction and improve the quality of reconstructed metabolic models.

A statistical framework for improving genomic annotations of transposon mutagenesis (TM) assigned essential genes

Whole-genome transposon mutagenesis (TM) experiment followed by sequence-based identification of insertion sites is the most popular genome-wise experiment to identify essential genes in Prokaryota. However, due to the limitation of high-throughput technique, this approach yields substantial systematic biases resulting in the incorrect assignments of many essential genes. To obtain unbiased and accurate annotations of essential genes from TM experiments, we developed a novel Poisson model based statistical framework to refine these TM assignments. In the model, first we identified and incorporated several potential factors such as gene length and TM insertion information which may cause the TM assignment biases into the basic Poisson model. Then we calculated the conditional probability of an essential gene given the observed TM insertion number. By factorizing this probability through introducing a latent variable the real insertion number, we formalized the statistical framework. Through iteratively updating and optimizing model parameters to maximize the goodness-of-fit of the model to the observed TM insertion data, we finalized the model. Using this model, we are able to assign the probability score of essentiality to each individual gene given its TM assignment, which subsequently correct the experimental biases. To enable our model widely useable, we established a user-friendly Web-server that is accessible to the public: http://research.cchmc.org/essentialgene/.

Metabolism and virulence in Neisseria meningitidis

A longstanding question in infection biology addresses the genetic basis for invasive behavior in commensal pathogens. A prime example for such a pathogen is Neisseria meningitidis. On the one hand it is a harmless commensal bacterium exquisitely adapted to humans, and on the other hand it sometimes behaves like a ferocious pathogen causing potentially lethal disease such as sepsis and acute bacterial meningitis. Despite the lack of a classical repertoire of virulence genes in N. meningitidis separating commensal from invasive strains, molecular epidemiology suggests that carriage and invasive strains belong to genetically distinct populations. In recent years, it has become increasingly clear that metabolic adaptation enables meningococci to exploit host resources, supporting the concept of nutritional virulence as a crucial determinant of invasive capability. Here, we discuss the contribution of core metabolic pathways in the context of colonization and invasion with special emphasis on results from genome-wide surveys. The metabolism of lactate, the oxidative stress response, and, in particular, glutathione metabolism as well as the denitrification pathway provide examples of how meningococcal metabolism is intimately linked to pathogenesis. We further discuss evidence from genome-wide approaches regarding potential metabolic differences between strains from hyperinvasive and carriage lineages and present new data assessing in vitro growth differences of strains from these two populations. We hypothesize that strains from carriage and hyperinvasive lineages differ in the expression of regulatory genes involved particularly in stress responses and amino acid metabolism under infection conditions.

Disruption of Mycobacterium avium subsp. paratuberculosis-specific genes impairs in vivo fitness

Background

Mycobacterium avium subsp. paratuberculosis (MAP) is an obligate intracellular pathogen that infects many ruminant species. The acquisition of foreign genes via horizontal gene transfer has been postulated to contribute to its pathogenesis, as these genetic elements are absent from its putative ancestor, M. avium subsp. hominissuis (MAH), an environmental organism with lesser pathogenicity. In this study, high-throughput sequencing of MAP transposon libraries were analyzed to qualitatively and quantitatively determine the contribution of individual genes to bacterial survival during infection.

Results

Out of 52384 TA dinucleotides present in the MAP K-10 genome, 12607 had a MycoMarT7 transposon in the input pool, interrupting 2443 of the 4350 genes in the MAP genome (56%). Of 96 genes situated in MAP-specific genomic islands, 82 were disrupted in the input pool, indicating that MAP-specific genomic regions are dispensable for in vitro growth (odds ratio = 0.21). Following 5 independent in vivo infections with this pool of mutants, the correlation between output pools was high for 4 of 5 (R = 0.49 to 0.61) enabling us to define genes whose disruption reproducibly reduced bacterial fitness in vivo. At three different thresholds for reduced fitness in vivo, MAP-specific genes were over-represented in the list of predicted essential genes. We also identified additional genes that were severely depleted after infection, and several of them have orthologues that are essential genes in M. tuberculosis.

Conclusions

This work indicates that the genetic elements required for the in vivo survival of MAP represent a combination of conserved mycobacterial virulence genes and MAP-specific genes acquired via horizontal gene transfer. In addition, the in vitro and in vivo essential genes identified in this study may be further characterized to offer a better understanding of MAP pathogenesis, and potentially contribute to the discovery of novel therapeutic and vaccine targets.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-415) contains supplementary material, which is available to authorized users.

Parallel exploitation of diverse host nutrients enhances Salmonella virulence

Pathogen access to host nutrients in infected tissues is fundamental for pathogen growth and virulence, disease progression, and infection control. However, our understanding of this crucial process is still rather limited because of experimental and conceptual challenges. Here, we used proteomics, microbial genetics, competitive infections, and computational approaches to obtain a comprehensive overview of Salmonella nutrition and growth in a mouse typhoid fever model. The data revealed that Salmonella accessed an unexpectedly diverse set of at least 31 different host nutrients in infected tissues but the individual nutrients were available in only scarce amounts. Salmonella adapted to this situation by expressing versatile catabolic pathways to simultaneously exploit multiple host nutrients. A genome-scale computational model of Salmonella in vivo metabolism based on these data was fully consistent with independent large-scale experimental data on Salmonella enzyme quantities, and correctly predicted 92% of 738 reported experimental mutant virulence phenotypes, suggesting that our analysis provided a comprehensive overview of host nutrient supply, Salmonella metabolism, and Salmonella growth during infection. Comparison of metabolic networks of other pathogens suggested that complex host/pathogen nutritional interfaces are a common feature underlying many infectious diseases.

A statistical framework for improving genomic annotations of prokaryotic essential genes

Large-scale systematic analysis of gene essentiality is an important step closer toward unraveling the complex relationship between genotypes and phenotypes. Such analysis cannot be accomplished without unbiased and accurate annotations of essential genes. In current genomic databases, most of the essential gene annotations are derived from whole-genome transposon mutagenesis (TM), the most frequently used experimental approach for determining essential genes in microorganisms under defined conditions. However, there are substantial systematic biases associated with TM experiments. In this study, we developed a novel Poisson model–based statistical framework to simulate the TM insertion process and subsequently correct the experimental biases. We first quantitatively assessed the effects of major factors that potentially influence the accuracy of TM and subsequently incorporated relevant factors into the framework. Through iteratively optimizing parameters, we inferred the actual insertion events occurred and described each gene’s essentiality on probability measure. Evaluated by the definite mapping of essential gene profile in Escherichia coli, our model significantly improved the accuracy of original TM datasets, resulting in more accurate annotations of essential genes. Our method also showed encouraging results in improving subsaturation level TM datasets. To test our model’s broad applicability to other bacteria, we applied it to Pseudomonas aeruginosa PAO1 and Francisella tularensis novicida TM datasets. We validated our predictions by literature as well as allelic exchange experiments in PAO1. Our model was correct on six of the seven tested genes. Remarkably, among all three cases that our predictions contradicted the TM assignments, experimental validations supported our predictions. In summary, our method will be a promising tool in improving genomic annotations of essential genes and enabling large-scale explorations of gene essentiality. Our contribution is timely considering the rapidly increasing essential gene sets. A Webserver has been set up to provide convenient access to this tool. All results and source codes are available for download upon publication at http://research.cchmc.org/essentialgene/.