Reconstruction of the metabolic network of Pseudomonas aeruginosa to interrogate virulence factor synthesis

Logic programming-based Minimal Cut Sets reveal consortium-level therapeutic targets for chronic wound infections

Minimal Cut Sets (MCSs) identify sets of reactions which, when removed from a metabolic network, disable certain cellular functions. The traditional search for MCSs within genome-scale metabolic models (GSMMs) targets cellular growth, identifies reaction sets resulting in a lethal phenotype if disrupted, and retrieves a list of corresponding gene, mRNA, or enzyme targets. Using the dual link between MCSs and Elementary Flux Modes (EFMs), our logic programming-based tool aspefm was able to compute MCSs of any size from GSMMs in acceptable run times. The tool demonstrated better performance when computing large-sized MCSs than the mixed-integer linear programming methods. We applied the new MCSs methodology to a medically-relevant consortium model of two cross-feeding bacteria, Staphylococcus aureus and Pseudomonas aeruginosa. aspefm constraints were used to bias the computation of MCSs toward exchanged metabolites that could complement lethal phenotypes in individual species. We found that interspecies metabolite exchanges could play an essential role in rescuing single-species growth, for instance inosine could complement lethal reaction knock-outs in the purine synthesis, glycolysis, and pentose phosphate pathways of both bacteria. Finally, MCSs were used to derive a list of promising enzyme targets for consortium-level therapeutic applications that cannot be circumvented via interspecies metabolite exchange.

Spatial transcriptome-guided multi-scale framework connects P. aeruginosa metabolic states to oxidative stress biofilm microenvironment

With the generation of spatially resolved transcriptomics of microbial biofilms, computational tools can be used to integrate this data to elucidate the multi-scale mechanisms controlling heterogeneous biofilm metabolism. This work presents a Multi-scale model of Metabolism In Cellular Systems (MiMICS) which is a computational framework that couples a genome-scale metabolic network reconstruction (GENRE) with Hybrid Automata Library (HAL), an existing agent-based model and reaction-diffusion model platform. A key feature of MiMICS is the ability to incorporate multiple -omics-guided metabolic models, which can represent unique metabolic states that yield different metabolic parameter values passed to the extracellular models. We used MiMICS to simulate Pseudomonas aeruginosa regulation of denitrification and oxidative stress metabolism in hypoxic and nitric oxide (NO) biofilm microenvironments. Integration of P. aeruginosa PA14 biofilm spatial transcriptomic data into a P. aeruginosa PA14 GENRE generated four PA14 metabolic model states that were input into MiMICS. Characteristic of aerobic, denitrification, and oxidative stress metabolism, the four metabolic model states predicted different oxygen, nitrate, and NO exchange fluxes that were passed as inputs to update the agent's local metabolite concentrations in the extracellular reaction-diffusion model. Individual bacterial agents chose a PA14 metabolic model state based on a combination of stochastic rules, and agents sensing local oxygen and NO. Transcriptome-guided MiMICS predictions suggested microscale denitrification and oxidative stress metabolic heterogeneity emerged due to local variability in the NO biofilm microenvironment. MiMICS accurately predicted the biofilm's spatial relationships between denitrification, oxidative stress, and central carbon metabolism. As simulated cells responded to extracellular NO, MiMICS revealed dynamics of cell populations heterogeneously upregulating reactions in the denitrification pathway, which may function to maintain NO levels within non-toxic ranges. We demonstrated that MiMICS is a valuable computational tool to incorporate multiple -omics-guided metabolic models to mechanistically map heterogeneous microbial metabolic states to the biofilm microenvironment.

Global stress response in Pseudomonas aeruginosa upon malonate utilization

Versatility in carbon source utilization assists Pseudomonas aeruginosa in its adaptation to various niches. Recently, we characterized the role of malonate, an understudied carbon source, in quorum sensing regulation, antibiotic resistance, and virulence factor production in P. aeruginosa . These results indicate that global responses to malonate metabolism remain to be uncovered. We leveraged a publicly available metabolomic dataset on human airway and found malonate to be as abundant as glycerol, a common airway metabolite and carbon source for P. aeruginosa . Here, we explored and compared adaptations of P. aeruginosa UCBPP-PA14 (PA14) in response to malonate or glycerol as a sole carbon source using transcriptomics and phenotypic assays. Malonate utilization activated glyoxylate and methylcitrate cycles and induced several stress responses, including oxidative, anaerobic, and metal stress responses associated with increases in intracellular aluminum and strontium. Some induced genes were required for optimal growth of P. aeruginosa in malonate. To assess the conservation of malonate-associated responses among P. aeruginosa strains, we compared our findings in strain PA14 with other lab strains and cystic fibrosis isolates of P. aeruginosa . Most strains grew on malonate as a sole carbon source as efficiently as or better than glycerol. While not all responses to malonate were conserved among strains, formation of biomineralized biofilm-like aggregates, increased tolerance to kanamycin, and increased susceptibility to norfloxacin were the most frequently observed phenotypes. Our findings reveal global remodeling of P. aeruginosa gene expression during its growth on malonate as a sole carbon source that is accompanied by several important phenotypic changes. These findings add to accumulating literature highlighting the role of different carbon sources in the physiology of P. aeruginosa and its niche adaptation.

Importance

Pseudomonas aeruginosa is a notorious pathogen that causes local and systemic infections in immunocompromised individuals. Different carbon sources can uniquely modulate metabolic and virulence pathways in P. aeruginosa , highlighting the importance of the environment that the pathogen occupies. In this work, we used a combination of transcriptomic analysis and phenotypic assays to determine how malonate utilization impacts P. aeruginosa, as recent evidence indicates this carbon source may be relevant to certain niches associated within the human host. We found that malonate utilization can induce global stress responses, alter metabolic circuits, and influence various phenotypes of P. aeruginosa that could influence host colonization. Investigating the metabolism of malonate provides insight into P. aeruginosa adaptations to specific niches where this substrate is abundant, and how it can be leveraged in the development of much-needed antimicrobial agents or identification of new therapeutic targets of this difficult-to-eradicate pathogen.

Genome-scale metabolic network model and phenome of solvent-tolerant Pseudomonas putida S12

BACKGROUND

Pseudomonas putida S12 is a gram-negative bacterium renowned for its high tolerance to organic solvents and metabolic versatility, making it attractive for various applications, including bioremediation and the production of aromatic compounds, bioplastics, biofuels, and value-added compounds. However, a metabolic model of S12 has yet to be developed.

RESULTS

In this study, we present a comprehensive and highly curated genome-scale metabolic network model of S12 (iSH1474), containing 1,474 genes, 1,436 unique metabolites, and 2,938 metabolic reactions. The model was constructed by leveraging existing metabolic models and conducting comparative analyses of genomes and phenomes. Approximately 2,000 different phenotypes were measured for S12 and its closely related KT2440 strain under various nutritional and environmental conditions. These phenotypic data, combined with the reported experimental data, were used to refine and validate the reconstruction. Model predictions quantitatively agreed well with in vivo flux measurements and the batch cultivation of S12, which demonstrated that iSH1474 accurately represents the metabolic capabilities of S12. Furthermore, the model was simulated to investigate the maximum theoretical metabolic capacity of S12 growing on toxic organic solvents.

CONCLUSIONS

iSH1474 represents a significant advancement in our understanding of the cellular metabolism of P. putida S12. The combined results of metabolic simulation and comparative genome and phenome analyses identified the genetic and metabolic determinants of the characteristic phenotypes of S12. This study could accelerate the development of this versatile organism as an efficient cell factory for various biotechnological applications.

Applications of genome-scale metabolic models to investigate microbial metabolic adaptations in response to genetic or environmental perturbations

Environmental perturbations are encountered by microorganisms regularly and will require metabolic adaptations to ensure an organism can survive in the newly presenting conditions. In order to study the mechanisms of metabolic adaptation in such conditions, various experimental and computational approaches have been used. Genome-scale metabolic models (GEMs) are one of the most powerful approaches to study metabolism, providing a platform to study the systems level adaptations of an organism to different environments which could otherwise be infeasible experimentally. In this review, we are describing the application of GEMs in understanding how microbes reprogram their metabolic system as a result of environmental variation. In particular, we provide the details of metabolic model reconstruction approaches, various algorithms and tools for model simulation, consequences of genetic perturbations, integration of '-omics' datasets for creating context-specific models and their application in studying metabolic adaptation due to the change in environmental conditions.

Study of peripheral domains in structure-function of isocitrate lyase (ICL) from Pseudomonas aeruginosa

The capacity of Pseudomonas aeruginosa to assimilate nutrients is essential for niche colonization and contributes to its pathogenicity. Isocitrate lyase (ICL), the first enzyme of the glyoxylate cycle, redirects isocitrate from the tricarboxylic acid cycle to render glyoxylate and succinate. P. aeruginosa ICL (PaICL) is regarded as a virulence factor due to its role in carbon assimilation during infection. The AceA/ICL protein family shares the catalytic domain I, triosephosphate isomerase barrel (TIM-barrel). The carboxyl terminus of domain I is essential for Escherichia coli ICL (EcICL) of subfamily 1. PaICL, which belongs to subfamily 3, has domain II inserted at the periphery of domain I, which is believed to participate in enzyme oligomerization. In addition, PaICL has the α13-loop-α14 (extended motif), which protrudes from the enzyme core, being of unknown function. This study investigates the role of domain II, the extended motif, and the carboxyl-terminus (C-ICL) and amino-terminus (N-ICL) regions in the function of the PaICL enzyme, also as their involvement in the virulence of P. aeruginosa PAO1. Deletion of domain II and the extended motif results in enzyme inactivation and structural instability of the enzyme. The His6-tag fusion at the C-ICL protein produced a less efficient enzyme than fusion at the N-ICL, but without affecting the acetate assimilation or virulence. The PaICL homotetrameric structure of the enzyme was more stable in the N-His6-ICL than in the C-His6-ICL, suggesting that the C-terminus is critical for the ICL quaternary conformation. The ICL-mutant A39 complemented with the recombinant proteins N-His6-ICL or C-His6-ICL were more virulent than the WT PAO1 strain. The findings indicate that the domain II and the extended motif are essential for the ICL structure/function, and the C-terminus is involved in its quaternary structure conformation, confirming that in P. aeruginosa, the ICL is essential for acetate assimilation and virulence.

Bactabolize is a tool for high-throughput generation of bacterial strain-specific metabolic models

Metabolic capacity can vary substantially within a bacterial species, leading to ecological niche separation, as well as differences in virulence and antimicrobial susceptibility. Genome-scale metabolic models are useful tools for studying the metabolic potential of individuals, and with the rapid expansion of genomic sequencing there is a wealth of data that can be leveraged for comparative analysis. However, there exist few tools to construct strain-specific metabolic models at scale. Here, we describe Bactabolize, a reference-based tool which rapidly produces strain-specific metabolic models and growth phenotype predictions. We describe a pan reference model for the priority antimicrobial-resistant pathogen, Klebsiella pneumoniae, and a quality control framework for using draft genome assemblies as input for Bactabolize. The Bactabolize-derived model for K. pneumoniae reference strain KPPR1 performed comparatively or better than currently available automated approaches CarveMe and gapseq across 507 substrate and 2317 knockout mutant growth predictions. Novel draft genomes passing our systematically defined quality control criteria resulted in models with a high degree of completeness (≥99% genes and reactions captured compared to models derived from matched complete genomes) and high accuracy (mean 0.97, n=10). We anticipate the tools and framework described herein will facilitate large-scale metabolic modelling analyses that broaden our understanding of diversity within bacterial species and inform novel control strategies for priority pathogens.

Genome-scale metabolic modeling and in silico analysis of opportunistic skin pathogen Cutibacterium acnes

Cutibacterium acnes, one of the most abundant skin microbes found in the sebaceous gland, is known to contribute to the development of acne vulgaris when its strains become imbalanced. The current limitations of acne treatment using antibiotics have caused an urgent need to develop a systematic strategy for selectively targeting C. acnes, which can be achieved by characterizing their cellular behaviors under various skin environments. To this end, we developed a genome-scale metabolic model (GEM) of virulent C. acnes, iCA843, based on the genome information of a relevant strain from ribotype 5 to comprehensively understand the pathogenic traits of C. acnes in the skin environment. We validated the model qualitatively by demonstrating its accuracy prediction of propionate and acetate production patterns, which were consistent with experimental observations. Additionally, we identified unique biosynthetic pathways for short-chain fatty acids in C. acnes compared to other GEMs of acne-inducing skin pathogens. By conducting constraint-based flux analysis under endogenous carbon sources in human skin, we discovered that the Wood-Werkman cycle is highly activated under acnes-associated skin condition for the regeneration of NAD, resulting in enhanced propionate production. Finally, we proposed potential anti-C. acnes targets by using the model-guided systematic framework based on gene essentiality analysis and protein sequence similarity search with abundant skin microbiome taxa.

Understanding Antimicrobial Resistance Using Genome-Scale Metabolic Modeling

The urgent necessity to fight antimicrobial resistance is universally recognized. In the search of new targets and strategies to face this global challenge, a promising approach resides in the study of the cellular response to antimicrobial exposure and on the impact of global cellular reprogramming on antimicrobial drugs' efficacy. The metabolic state of microbial cells has been shown to undergo several antimicrobial-induced modifications and, at the same time, to be a good predictor of the outcome of an antimicrobial treatment. Metabolism is a promising reservoir of potential drug targets/adjuvants that has not been fully exploited to date. One of the main problems in unraveling the metabolic response of cells to the environment resides in the complexity of such metabolic networks. To solve this problem, modeling approaches have been developed, and they are progressively gaining in popularity due to the huge availability of genomic information and the ease at which a genome sequence can be converted into models to run basic phenotype predictions. Here, we review the use of computational modeling to study the relationship between microbial metabolism and antimicrobials and the recent advances in the application of genome-scale metabolic modeling to the study of microbial responses to antimicrobial exposure.

Interactions between Culturable Bacteria Are Predicted by Individual Species' Growth

Predicting interspecies interactions is a key challenge in microbial ecology given that interactions shape the composition and functioning of microbial communities. However, predicting microbial interactions is challenging because they can vary considerably depending on species' metabolic capabilities and environmental conditions. Here, we employ machine learning models to predict pairwise interactions between culturable bacteria based on their phylogeny, monoculture growth capabilities, and interactions with other species. We trained our models on one of the largest available pairwise interactions data set containing over 7,500 interactions between 20 species from two taxonomic groups that were cocultured in 40 different carbon environments. Our models accurately predicted both the sign (accuracy of 88%) and the strength of effects (R² of 0.87) species had on each other's growth. Encouragingly, predictions with comparable accuracy could be made even when not relying on information about interactions with other species, which are often hard to measure. However, species' monoculture growth was essential to the model, as predictions based solely on species' phylogeny and inferred metabolic capabilities were significantly less accurate. These results bring us one step closer to a predictive understanding of microbial communities, which is essential for engineering beneficial microbial consortia. IMPORTANCE In order to understand the function and structure of microbial communities, one must know all pairwise interactions that occur between the different species within the community, as these interactions shape the community's structure and functioning. However, measuring all pairwise interactions can be an extremely difficult task especially when dealing with big complex communities. Because of that, predicting interspecies interactions is a key challenge in microbial ecology. Here, we use machine learning models in order to accurately predict the type and strength of interactions. We trained our models on one of the largest available pairwise interactions data set, containing over 7,500 interactions between 20 different species that were cocultured in 40 different environments. Our results show that, in general, accurate predictions can be made, and that the ability of each species to grow on its own in the given environment contributes the most to predictions. Being able to predict microbial interactions would put us one step closer to predicting the functionality of microbial communities and to rationally microbiome engineering.

Genome-scale model of Pseudomonas aeruginosa metabolism unveils virulence and drug potentiation

Pseudomonas aeruginosa is one of the leading causes of hospital-acquired infections. To decipher the metabolic mechanisms associated with virulence and antibiotic resistance, we have developed an updated genome-scale model (GEM) of P. aeruginosa. The model (iSD1509) is an extensively curated, three-compartment, and mass-and-charge balanced BiGG model containing 1509 genes, the largest gene content for any P. aeruginosa GEM to date. It is the most accurate with prediction accuracies as high as 92.4% (gene essentiality) and 93.5% (substrate utilization). In iSD1509, we newly added a recently discovered pathway for ubiquinone-9 biosynthesis which is required for anaerobic growth. We used a modified iSD1509 to demonstrate the role of virulence factor (phenazines) in the pathogen survival within biofilm/oxygen-limited condition. Further, the model can mechanistically explain the overproduction of a drug susceptibility biomarker in the P. aeruginosa mutants. Finally, we use iSD1509 to demonstrate the drug potentiation by metabolite supplementation, and elucidate the mechanisms behind the phenotype, which agree with experimental results.

Systems-Wide Dissection of Organic Acid Assimilation in Pseudomonas aeruginosa Reveals a Novel Path To Underground Metabolism

The human pathogen Pseudomonas aeruginosa (Pa) is one of the most frequent and severe causes of nosocomial infection. This organism is also a major cause of airway infections in people with cystic fibrosis (CF). Pa is known to have a remarkable metabolic plasticity, allowing it to thrive under diverse environmental conditions and ecological niches; yet, little is known about the central metabolic pathways that sustain its growth during infection or precisely how these pathways operate. In this work, we used a combination of 'omics approaches (transcriptomics, proteomics, metabolomics, and ¹³C-fluxomics) and reverse genetics to provide systems-level insight into how the infection-relevant organic acids succinate and propionate are metabolized by Pa. Moreover, through structural and kinetic analysis of the 2-methylcitrate synthase (2-MCS; PrpC) and its paralogue citrate (CIT) synthase (GltA), we show how these two crucial enzymatic steps are interconnected in Pa organic acid assimilation. We found that Pa can rapidly adapt to the loss of GltA function by acquiring mutations in a transcriptional repressor, which then derepresses prpC expression. Our findings provide a clear example of how "underground metabolism," facilitated by enzyme substrate promiscuity, "rewires" Pa metabolism, allowing it to overcome the loss of a crucial enzyme. This pathogen-specific knowledge is critical for the advancement of a model-driven framework to target bacterial central metabolism. IMPORTANCE Pseudomonas aeruginosa is an opportunistic human pathogen that, due to its unrivalled resistance to antibiotics, ubiquity in the built environment, and aggressiveness in infection scenarios, has acquired the somewhat dubious accolade of being designated a "critical priority pathogen" by the WHO. In this work, we uncover the pathways and mechanisms used by P. aeruginosa to grow on a substrate that is abundant at many infection sites: propionate. We found that if the organism is prevented from metabolizing propionate, the substrate turns from being a convenient nutrient source into a potent poison, preventing bacterial growth. We further show that one of the enzymes involved in these reactions, 2-methylcitrate synthase (PrpC), is promiscuous and can moonlight for another essential enzyme in the cell (citrate synthase). Indeed, mutations that abolish citrate synthase activity (which would normally prevent the cell from growing) can be readily overcome if the cell acquires additional mutations that increase the expression of PrpC. This is a nice example of the evolutionary utility of so-called "underground metabolism."

Systems Biology: New Insight into Antibiotic Resistance

Over the past few decades, antimicrobial resistance (AMR) has emerged as an important threat to public health, resulting from the global propagation of multidrug-resistant strains of various bacterial species. Knowledge of the intrinsic factors leading to this resistance is necessary to overcome these new strains. This has contributed to the increased use of omics technologies and their extrapolation to the system level. Understanding the mechanisms involved in antimicrobial resistance acquired by microorganisms at the system level is essential to obtain answers and explore options to combat this resistance. Therefore, the use of robust whole-genome sequencing approaches and other omics techniques such as transcriptomics, proteomics, and metabolomics provide fundamental insights into the physiology of antimicrobial resistance. To improve the efficiency of data obtained through omics approaches, and thus gain a predictive understanding of bacterial responses to antibiotics, the integration of mathematical models with genome-scale metabolic models (GEMs) is essential. In this context, here we outline recent efforts that have demonstrated that the use of omics technology and systems biology, as quantitative and robust hypothesis-generating frameworks, can improve the understanding of antibiotic resistance, and it is hoped that this emerging field can provide support for these new efforts.

Pf4 Phage Variant Infection Reduces Virulence-Associated Traits in Pseudomonas aeruginosa

Pf4 is a filamentous bacteriophage integrated as a prophage into the genome of Pseudomonas aeruginosa PAO1. Pf4 virions can be produced without killing P. aeruginosa. However, cell lysis can occur during superinfection when Pf virions successfully infect a host lysogenized by a Pf superinfective variant. We have previously shown that infection of P. aeruginosa PAO1 with a superinfective Pf4 variant abolished twitching motility and altered biofilm architecture. More precisely, most of the cells embedded into the biofilm were showing a filamentous morphology, suggesting the activation of the cell envelope stress response involving both AlgU and SigX extracytoplasmic function sigma factors. Here, we show that Pf4 variant infection results in a drastic dysregulation of 3,360 genes representing about 58% of P. aeruginosa genome; of these, 70% of the virulence factors encoding genes show a dysregulation. Accordingly, Pf4 variant infection (termed Pf4*) causes in vivo reduction of P. aeruginosa virulence and decreased production of N-acyl-homoserine lactones and 2-alkyl-4-quinolones quorum-sensing molecules and related virulence factors, such as pyocyanin, elastase, and pyoverdine. In addition, the expression of genes involved in metabolism, including energy generation and iron homeostasis, was affected, suggesting further relationships between virulence and central metabolism. Altogether, these data show that Pf4 phage variant infection results in complex network dysregulation, leading to reducing acute virulence in P. aeruginosa. This study contributes to the comprehension of the bacterial response to filamentous phage infection. IMPORTANCE Filamentous bacteriophages can become superinfective and infect P. aeruginosa, even though they are inserted in the genome as lysogens. Despite this productive infection, growth of the host is only mildly affected, allowing the study of the interaction between the phage and the host, which is not possible in the case of lytic phages killing rapidly their host. Here, we demonstrate by transcriptome and phenotypic analysis that the infection by a superinfective filamentous phage variant causes a massive disruption in gene expression, including those coding for virulence factors and metabolic pathways.

Pseudomonas aeruginosa reference strains PAO1 and PA14: A genomic, phenotypic, and therapeutic review

Pseudomonas aeruginosa is a ubiquitous, motile, gram-negative bacterium that has been recently identified as a multi-drug resistant pathogen in critical need of novel therapeutics. Of the approximately 5,000 strains, PAO1 and PA14 are common laboratory reference strains, modeling moderately and hyper-virulent phenotypes, respectively. PAO1 and PA14 have been instrumental in facilitating the discovery of novel drug targets, testing novel therapeutics, and supplying critical genomic information on the bacterium. While the two strains have contributed to a wide breadth of knowledge on the natural behaviors and therapeutic susceptibilities of P. aeruginosa, they have demonstrated significant deviations from observations in human infections. Many of these deviations are related to experimental inconsistencies in laboratory strain environment that complicate and, at times, terminate translation from laboratory results to clinical applications. This review aims to provide a comparative analysis of the two strains and potential methods to improve their clinical relevance.

Gregatins, a Group of Related Fungal Secondary Metabolites, Inhibit Aspects of Quorum Sensing in Gram-Negative Bacteria

Quorum sensing (QS) is a process that regulates gene expression based on cell density. In bacteria, QS facilitates collaboration and controls a large number of pathways, including biofilm formation and virulence factor production, which lead to lower sensitivity to antibiotics and higher toxicity in the host, respectively. Inhibition of QS is a promising strategy to combat bacterial infections. In this study, we tested the potential of secondary metabolites from fungi to inhibit bacterial QS using a library derived from more than ten thousand different fungal strains. We used the reporter bacterium, Chromobacterium violaceum, and identified 39 fungal strains that produced QS inhibitor activity. These strains expressed two QS inhibitors that had been described before and eight QS inhibitors that had not been described before. Further testing for QS inhibitor activity against the opportunistic pathogen Pseudomonas aeruginosa led to the identification of gregatins as an interesting family of compounds with QS inhibitor activity. Although various gregatins inhibited QS in P. aeruginosa, these gregatins did not inhibit virulence factor production and biofilm formation. We conclude that gregatins inhibit some, but not all aspects of QS.

Expanding the search for small-molecule antibacterials by multidimensional profiling

New techniques for systematic profiling of small-molecule effects can enhance traditional growth inhibition screens for antibiotic discovery and change how we search for new antibacterial agents. Computational models that integrate physicochemical compound properties with their phenotypic and molecular downstream effects can not only predict efficacy of molecules yet to be tested, but also reveal unprecedented insights on compound modes of action (MoAs). The unbiased characterization of compounds that themselves are not growth inhibitory but exhibit diverse MoAs, can expand antibacterial strategies beyond direct inhibition of core essential functions. Early and systematic functional annotation of compound libraries thus paves the way to new models in the selection of lead antimicrobial compounds. In this Review, we discuss how multidimensional small-molecule profiling and the ever-increasing computing power are accelerating the discovery of unconventional antibacterials capable of bypassing resistance and exploiting synergies with established antibacterial treatments and with protective host mechanisms.

Phenazines and toxoflavin act as interspecies modulators of resilience to diverse antibiotics

Bacterial opportunistic pathogens make diverse secondary metabolites both in the natural environment and when causing infections, yet how these molecules mediate microbial interactions and their consequences for antibiotic treatment are still poorly understood. Here, we explore the role of three redox-active secondary metabolites, pyocyanin, phenazine-1-carboxylic acid and toxoflavin, as interspecies modulators of antibiotic resilience. We find that these molecules dramatically change susceptibility levels of diverse bacteria to clinical antibiotics. Pyocyanin and phenazine-1-carboxylic acid are made by Pseudomonas aeruginosa, while toxoflavin is made by Burkholderia gladioli, organisms that infect cystic fibrosis and other immunocompromised patients. All molecules alter the susceptibility profile of pathogenic species within the "Burkholderia cepacia complex" to different antibiotics, either antagonizing or potentiating their effects, depending on the drug's class. Defense responses regulated by the redox-sensitive transcription factor SoxR potentiate the antagonistic effects these metabolites have against fluoroquinolones, and the presence of genes encoding SoxR and the efflux systems it regulates can be used to predict how these metabolites will affect antibiotic susceptibility of different bacteria. Finally, we demonstrate that inclusion of secondary metabolites in standard protocols used to assess antibiotic resistance can dramatically alter the results, motivating the development of new tests for more accurate clinical assessment.

Systems biology approach to functionally assess the Clostridioides difficile pangenome reveals genetic diversity with discriminatory power

SignificanceClostridioides difficile infections are the most common source of hospital-acquired infections and are responsible for an extensive burden on the health care system. Strains of the C. difficile species comprise diverse lineages and demonstrate genome variability, with advantageous trait acquisition driving the emergence of endemic lineages. Here, we present a systems biology analysis of C. difficile that evaluates strain-specific genotypes and phenotypes to investigate the overall diversity of the species. We develop a strain typing method based on similarity of accessory genomes to identify and contextualize genetic loci capable of discriminating between strain groups.

A curated collection of Klebsiella metabolic models reveals variable substrate usage and gene essentiality

The Klebsiella pneumoniae species complex (KpSC) is a set of seven Klebsiella taxa that are found in a variety of niches and are an important cause of opportunistic health care-associated infections in humans. Because of increasing rates of multi-drug resistance within the KpSC, there is a growing interest in better understanding the biology and metabolism of these organisms to inform novel control strategies. We collated 37 sequenced KpSC isolates isolated from a variety of niches, representing all seven taxa. We generated strain-specific genome-scale metabolic models (GEMs) for all 37 isolates and simulated growth phenotypes on 511 distinct carbon, nitrogen, sulfur, and phosphorus substrates. Models were curated and their accuracy was assessed using matched phenotypic growth data for 94 substrates (median accuracy of 96%). We explored species-specific growth capabilities and examined the impact of all possible single gene deletions using growth simulations in 145 core carbon substrates. These analyses revealed multiple strain-specific differences, within and between species, and highlight the importance of selecting a diverse range of strains when exploring KpSC metabolism. This diverse set of highly accurate GEMs could be used to inform novel drug design, enhance genomic analyses, and identify novel virulence and resistance determinants. We envisage that these 37 curated strain-specific GEMs, covering all seven taxa of the KpSC, provide a valuable resource to the Klebsiella research community.

Interrogation of Essentiality in the Reconstructed Haemophilus influenzae Metabolic Network Identifies Lipid Metabolism Antimicrobial Targets: Preclinical Evaluation of a FabH β-Ketoacyl-ACP Synthase Inhibitor

Expediting drug discovery to fight antibacterial resistance requires holistic approaches at system levels. In this study, we focused on the human-adapted pathogen Haemophilus influenzae, and by constructing a high-quality genome-scale metabolic model, we rationally identified new metabolic drug targets in this organism. Contextualization of available gene essentiality data within in silico predictions identified most genes involved in lipid metabolism as promising targets. We focused on the β-ketoacyl-acyl carrier protein synthase III FabH, responsible for catalyzing the first step in the FASII fatty acid synthesis pathway and feedback inhibition. Docking studies provided a plausible three-dimensional model of FabH in complex with the synthetic inhibitor 1-(5-(2-fluoro-5-(hydroxymethyl)phenyl)pyridin-2-yl)piperidine-4-acetic acid (FabHi). Validating our in silico predictions, FabHi reduced H. influenzae viability in a dose- and strain-dependent manner, and this inhibitory effect was independent of fabH gene expression levels. fabH allelic variation was observed among H. influenzae clinical isolates. Many of these polymorphisms, relevant for stabilization of the dimeric active form of FabH and/or activity, may modulate the inhibitory effect as part of a complex multifactorial process with the overall metabolic context emerging as a key factor tuning FabHi activity. Synergies with antibiotics were not observed and bacteria were not prone to develop resistance. Inhibitor administration during H. influenzae infection on a zebrafish septicemia infection model cleared bacteria without signs of host toxicity. Overall, we highlight the potential of H. influenzae metabolism as a source of drug targets, metabolic models as target-screening tools, and FASII targeting suitability to counteract this bacterial infection. IMPORTANCE Antimicrobial resistance drives the need of synergistically combined powerful computational tools and experimental work to accelerate target identification and drug development. Here, we present a high-quality metabolic model of H. influenzae and show its usefulness both as a computational framework for large experimental data set contextualization and as a tool to discover condition-independent drug targets. We focus on β-ketoacyl-acyl carrier protein synthase III FabH chemical inhibition by using a synthetic molecule with good synthetic and antimicrobial profiles that specifically binds to the active site. The mechanistic complexity of FabH inhibition may go beyond allelic variation, and the strain-dependent effect of the inhibitor tested supports the impact of metabolic context as a key factor driving bacterial cell behavior. Therefore, this study highlights the systematic metabolic evaluation of individual strains through computational frameworks to identify secondary metabolic hubs modulating drug response, which will facilitate establishing synergistic and/or more precise and robust antibacterial treatments.

In Silico Exploration of Mycobacterium tuberculosis Metabolic Networks Shows Host-Associated Convergent Fluxomic Phenotypes

Mycobacterium tuberculosis, the causative agent of tuberculosis, is composed of several lineages characterized by a genome identity higher than 99%. Although the majority of the lineages are associated with humans, at least four lineages are adapted to other mammals, including different M. tuberculosis ecotypes. Host specificity is associated with higher virulence in its preferred host in ecotypes such as M. bovis. Deciphering what determines the preference of the host can reveal host-specific virulence patterns. However, it is not clear which genomic determinants might be influencing host specificity. In this study, we apply a combination of unsupervised and supervised classification methods on genomic data of ~27,000 M. tuberculosis clinical isolates to decipher host-specific genomic determinants. Host-specific genomic signatures are scarce beyond known lineage-specific mutations. Therefore, we integrated lineage-specific mutations into the iEK1011 2.0 genome-scale metabolic model to obtain lineage-specific versions of it. Flux distributions sampled from the solution spaces of these models can be accurately separated according to host association. This separation correlated with differences in cell wall processes, lipid, amino acid and carbon metabolic subsystems. These differences were observable when more than 95% of the samples had a specific growth rate significantly lower than the maximum achievable by the models. This suggests that these differences might manifest at low growth rate settings, such as the restrictive conditions M. tuberculosis suffers during macrophage infection.

Quorum Sensing Promotes Phage Infection in Pseudomonas aeruginosa PAO1

Quorum sensing (QS) is used to coordinate social behaviors, such as virulence and biofilm formation, across bacterial populations. However, the role of QS in regulating phage-bacterium interactions remains unclear. Preventing phage recognition and adsorption are the first steps of bacterial defense against phages; however, both phage recognition and adsorption are a prerequisite for the successful application of phage therapy. In the present study, we report that QS upregulated the expression of phage receptors, thus increasing phage adsorption and infection rates in Pseudomonas aeruginosa. In P. aeruginosa PAO1, we found that las QS, instead of rhl QS, upregulated the expression of galU for lipopolysaccharide synthesis. Lipopolysaccharides act as the receptor of the phage vB_Pae_QDWS. This las QS-mediated phage susceptibility is a dynamic process, depending on host cell density. Our data suggest that inhibiting QS may reduce the therapeutic efficacy of phages. IMPORTANCE Phage resistance is a major limitation of phage therapy, and understanding the mechanisms by which bacteria block phage infection is critical for the successful application of phage therapy. In the present study, we found that Pseudomonas aeruginosa PAO1 uses las QS to promote phage infection by upregulating the expression of galU, which is necessary for the synthesis of phage receptor lipopolysaccharides. In contrast to the results of previous reports, we showed that QS increases the efficacy of phage-mediated bacterial killing. Since QS upregulates the expression of virulence factors and promotes biofilm development, which are positively correlated with lipopolysaccharide production in P. aeruginosa, increased phage susceptibility is a novel QS-mediated trade-off. QS inhibition may increase the efficacy of antibiotic treatment, but it will reduce the effectiveness of phage therapy.

Condensins are essential for Pseudomonas aeruginosa corneal virulence through their control of lifestyle and virulence programs

Pseudomonas aeruginosa is a significant opportunistic pathogen responsible for numerous human infections. Its high pathogenicity resides in a diverse array of virulence factors and an ability to adapt to hostile environments. We report that these factors are tied to the activity of condensins, SMC and MksBEF, which primarily function in structural chromosome maintenance. This study revealed that both proteins are required for P. aeruginosa virulence during corneal infection. The reduction in virulence was traced to broad changes in gene expression. Transcriptional signatures of smc and mksB mutants were largely dissimilar and non-additive, with the double mutant displaying a distinct gene expression profile. Affected regulons included those responsible for lifestyle control, primary metabolism, surface adhesion and biofilm growth, iron and sulfur assimilation, and numerous virulence factors, including type 3 and type 6 secretion systems. The in vitro phenotypes of condensin mutants mirrored their transcriptional profiles and included impaired production and secretion of multiple virulence factors, growth deficiencies under nutrient limiting conditions, and altered c-di-GMP signaling. Notably, c-di-GMP mediated some but not all transcriptional responses of the mutants. Thus, condensins are integrated into the control of multiple genetic programs related to epigenetic and virulent behavior of P. aeruginosa.

Genome-Scale Metabolic Modelling of Lifestyle Changes in Rhizobium leguminosarum

Biological nitrogen fixation in rhizobium-legume symbioses is of major importance for sustainable agricultural practices. To establish a mutualistic relationship with their plant host, rhizobia transition from free-living bacteria in soil to growth down infection threads inside plant roots and finally differentiate into nitrogen-fixing bacteroids. We reconstructed a genome-scale metabolic model for Rhizobium leguminosarum and integrated the model with transcriptome, proteome, metabolome, and gene essentiality data to investigate nutrient uptake and metabolic fluxes characteristic of these different lifestyles. Synthesis of leucine, polyphosphate, and AICAR is predicted to be important in the rhizosphere, while myo-inositol catabolism is active in undifferentiated nodule bacteria in agreement with experimental evidence. The model indicates that bacteroids utilize xylose and glycolate in addition to dicarboxylates, which could explain previously described gene expression patterns. Histidine is predicted to be actively synthesized in bacteroids, consistent with transcriptome and proteome data for several rhizobial species. These results provide the basis for targeted experimental investigation of metabolic processes specific to the different stages of the rhizobium-legume symbioses. IMPORTANCE Rhizobia are soil bacteria that induce nodule formation on plant roots and differentiate into nitrogen-fixing bacteroids. A detailed understanding of this complex symbiosis is essential for advancing ongoing efforts to engineer novel symbioses with cereal crops for sustainable agriculture. Here, we reconstruct and validate a genome-scale metabolic model for Rhizobium leguminosarum bv. viciae 3841. By integrating the model with various experimental data sets specific to different stages of symbiosis formation, we elucidate the metabolic characteristics of rhizosphere bacteria, undifferentiated bacteria inside root nodules, and nitrogen-fixing bacteroids. Our model predicts metabolic flux patterns for these three distinct lifestyles, thus providing a framework for the interpretation of genome-scale experimental data sets and identifying targets for future experimental studies.

Bacterial metabolism and pathogenesis intimate intertwining: time for metabolic modelling to come into action

We take a snapshot of the recent understanding of bacterial metabolism and the bacterial-host metabolic interplay during infection, and highlight key outcomes and challenges for the practical implementation of bacterial metabolic modelling computational tools in the pathogenesis field.

The Pseudomonas aeruginosa whole genome sequence: A 20th anniversary celebration

Toward the end of August 2000, the 6.3 Mbp whole genome sequence of Pseudomonas aeruginosa strain PAO1 was published. With 5570 open reading frames (ORFs), PAO1 had the largest microbial genome sequenced up to that point in time-including a large proportion of metabolic, transport and antimicrobial resistance genes supporting its ability to colonize diverse environments. A remarkable 9% of its ORFs were predicted to encode proteins with regulatory functions, providing new insight into bacterial network complexity as a function of network size. In this celebratory article, we fast forward 20 years, and examine how access to this resource has transformed our understanding of P. aeruginosa. What follows is more than a simple review or commentary; we have specifically asked some of the leaders in the field to provide personal reflections on how the PAO1 genome sequence, along with the Pseudomonas Community Annotation Project (PseudoCAP) and Pseudomonas Genome Database (pseudomonas.com), have contributed to the many exciting discoveries in this field. In addition to bringing us all up to date with the latest developments, we also ask our contributors to speculate on how the next 20 years of Pseudomonas research might pan out.

Periodically Disturbing the Spatial Structure of Biofilms Can Affect the Production of an Essential Virulence Factor in Pseudomonas aeruginosa

Understanding the environmental factors that affect the production of virulence factors has major implications in evolution and medicine. While spatial structure is important in virulence factor production, observations of this relationship have occurred in undisturbed or continuously disturbed environments. However, natural environments are subject to periodic fluctuations, including changes in physical forces, which could alter the spatial structure of bacterial populations and impact virulence factor production. Using Pseudomonas aeruginosa PA14, we periodically applied a physical force to biofilms and examined production of pyoverdine. Intermediate frequencies of disturbance reduced the amount of pyoverdine produced compared to undisturbed or frequently disturbed conditions. To explore the generality of this finding, we examined how an intermediate disturbance frequency affected pyoverdine production in 21 different strains of P. aeruginosa. Periodic disturbance increased, decreased, or did not change the amount of pyoverdine produced relative to undisturbed populations. Mathematical modeling predicts that interactions between pyoverdine synthesis rate and biofilm density determine the amount of pyoverdine synthesized. When the pyoverdine synthesis rates are high, depletion of the biofilm due to disturbance reduces the accumulation of pyoverdine. At intermediate synthesis rates, production of pyoverdine increases during disturbance as bacteria dispersed into the planktonic state enjoy increased growth and pyoverdine production rates. At low synthesis rates, disturbance does not alter the amount of pyoverdine produced since disturbance-driven access to nutrients does not augment pyoverdine synthesis. Our results suggest that environmental conditions shape robustness in the production of virulence factors and may lead to novel approaches to treat infections.

IMPORTANCE Virulence factors are required to cause infections. Previous work has shown that the spatial organization of a population, such as a biofilm, can increase the production of some virulence factors, including pyoverdine, which is produced by Pseudomonas aeruginosa. Pyoverdine is essential for the infection process, and reducing its production can limit infections. We have discovered that periodically changing the spatial structure of a biofilm of P. aeruginosa strain PA14 using a physical force can reduce the production of pyoverdine. A mathematical model suggests that this is due to the disruption of spatial organization. Using additional strains of P. aeruginosa isolated from patients and the environment, we use experiments and modeling to show that this reduction in pyoverdine is due to interactions between biofilm density and the synthesis rate of pyoverdine. Our results identify conditions where pyoverdine production is reduced and may lead to novel ways to treat infections.

An updated genome-scale metabolic network reconstruction of Pseudomonas aeruginosa PA14 to characterize mucin-driven shifts in bacterial metabolism

Mucins are present in mucosal membranes throughout the body and play a key role in the microbe clearance and infection prevention. Understanding the metabolic responses of pathogens to mucins will further enable the development of protective approaches against infections. We update the genome-scale metabolic network reconstruction (GENRE) of one such pathogen, Pseudomonas aeruginosa PA14, through metabolic coverage expansion, format update, extensive annotation addition, and literature-based curation to produce iPau21. We then validate iPau21 through MEMOTE, growth rate, carbon source utilization, and gene essentiality testing to demonstrate its improved quality and predictive capabilities. We then integrate the GENRE with transcriptomic data in order to generate context-specific models of P. aeruginosa metabolism. The contextualized models recapitulated known phenotypes of unaltered growth and a differential utilization of fumarate metabolism, while also revealing an increased utilization of propionate metabolism upon MUC5B exposure. This work serves to validate iPau21 and demonstrate its utility for providing biological insights.

Outer membrane permeability: Antimicrobials and diverse nutrients bypass porins in Pseudomonas aeruginosa

Novel antibiotics are urgently needed to resolve the current antimicrobial resistance crisis. For critical pathogens, drug entry through the cell envelope is one of the major challenges in the development of effective novel antibiotics. Envelope proteins forming water-filled channels, so-called porins, are commonly thought to be essential for entry of hydrophilic molecules, but we show here for the critical pathogen Pseudomonas aeruginosa that almost all antibiotics and diverse hydrophilic nutrients bypass porins and instead permeate directly through the outer membrane lipid bilayer. However, carboxylate groups hinder bilayer penetration, and Pseudomonas thus needs porins for efficient utilization of carboxylate-containing nutrients such as succinate. The major porin-independent entry route might open opportunities for facilitating drug delivery into bacteria.

Gram-negative bacterial pathogens have an outer membrane that restricts entry of molecules into the cell. Water-filled protein channels in the outer membrane, so-called porins, facilitate nutrient uptake and are thought to enable antibiotic entry. Here, we determined the role of porins in a major pathogen, Pseudomonas aeruginosa, by constructing a strain lacking all 40 identifiable porins and 15 strains carrying only a single unique type of porin and characterizing these strains with NMR metabolomics and antimicrobial susceptibility assays. In contrast to common assumptions, all porins were dispensable for Pseudomonas growth in rich medium and consumption of diverse hydrophilic nutrients. However, preferred nutrients with two or more carboxylate groups such as succinate and citrate permeated poorly in the absence of porins. Porins provided efficient translocation pathways for these nutrients with broad and overlapping substrate selectivity while efficiently excluding all tested antibiotics except carbapenems, which partially entered through OprD. Porin-independent permeation of antibiotics through the outer-membrane lipid bilayer was hampered by carboxylate groups, consistent with our nutrient data. Together, these results challenge common assumptions about the role of porins by demonstrating porin-independent permeation of the outer-membrane lipid bilayer as a major pathway for nutrient and drug entry into the bacterial cell.

NtrBC Selectively Regulates Host-Pathogen Interactions, Virulence, and Ciprofloxacin Susceptibility of Pseudomonas aeruginosa

Pseudomonas aeruginosa is a metabolically versatile opportunistic pathogen capable of infecting distinct niches of the human body, including skin wounds and the lungs of cystic fibrosis patients. Eradication of P. aeruginosa infection is becoming increasingly difficult due to the numerous resistance mechanisms it employs. Adaptive resistance is characterized by a transient state of decreased susceptibility to antibiotic therapy that is distinct from acquired or intrinsic resistance, can be triggered by various environmental stimuli and reverted by removal of the stimulus. Further, adaptive resistance is intrinsically linked to lifestyles such as swarming motility and biofilm formation, both of which are important in infections and lead to multi-drug adaptive resistance. Here, we demonstrated that NtrBC, the master of nitrogen control, had a selective role in host colonization and a substantial role in determining intrinsic resistance to ciprofloxacin. P. aeruginosa mutant strains (ΔntrB, ΔntrC and ΔntrBC) colonized the skin but not the respiratory tract of mice as well as WT and, unlike WT, could be reduced or eradicated from the skin by ciprofloxacin. We hypothesized that nutrient availability contributed to these phenomena and found that susceptibility to ciprofloxacin was impacted by nitrogen source in laboratory media. P. aeruginosa ΔntrB, ΔntrC and ΔntrBC also exhibited distinct host interactions, including modestly increased cytotoxicity toward human bronchial epithelial cells, reduced virulence factor production and 10-fold increased uptake by macrophages. These data might explain why NtrBC mutants were less adept at colonizing the upper respiratory tract of mice. Thus, NtrBC represents a link between nitrogen metabolism, adaptation and virulence of the pathogen P. aeruginosa, and could represent a target for eradication of recalcitrant infections in situ.

Antibiotic resistance: Time of synthesis in a post-genomic age

Antibiotic resistance has been highlighted by international organizations, including World Health Organization, World Bank and United Nations, as one of the most relevant global health problems. Classical approaches to study this problem have focused in infected humans, mainly at hospitals. Nevertheless, antibiotic resistance can expand through different ecosystems and geographical allocations, hence constituting a One-Health, Global-Health problem, requiring specific integrative analytic tools. Antibiotic resistance evolution and transmission are multilayer, hierarchically organized processes with several elements (from genes to the whole microbiome) involved. However, their study has been traditionally gene-centric, each element independently studied. The development of robust-economically affordable whole genome sequencing approaches, as well as other -omic techniques as transcriptomics and proteomics, is changing this panorama. These technologies allow the description of a system, either a cell or a microbiome as a whole, overcoming the problems associated with gene-centric approaches. We are currently at the time of combining the information derived from -omic studies to have a more holistic view of the evolution and spread of antibiotic resistance. This synthesis process requires the accurate integration of -omic information into computational models that serve to analyse the causes and the consequences of acquiring AR, fed by curated databases capable of identifying the elements involved in the acquisition of resistance. In this review, we analyse the capacities and drawbacks of the tools that are currently in use for the global analysis of AR, aiming to identify the more useful targets for effective corrective interventions.

The AraC-Type Transcriptional Regulator GliR (PA3027) Activates Genes of Glycerolipid Metabolism in Pseudomonas aeruginosa

Pseudomonas aeruginosa encodes a large set of transcriptional regulators (TRs) that modulate and manage cellular metabolism to survive in variable environmental conditions including that of the human body. The AraC family regulators are an abundant group of TRs in bacteria, mostly acting as gene expression activators, controlling diverse cellular functions (e.g., carbon metabolism, stress response, and virulence). The PA3027 protein from P. aeruginosa has been classified in silico as a putative AraC-type TR. Transcriptional profiling of P. aeruginosa PAO1161 overexpressing PA3027 revealed a spectacular increase in the mRNA levels of PA3026-PA3024 (divergent to PA3027), PA3464, and PA3342 genes encoding proteins potentially involved in glycerolipid metabolism. Concomitantly, chromatin immunoprecipitation-sequencing (ChIP-seq) analysis revealed that at least 22 regions are bound by PA3027 in the PAO1161 genome. These encompass promoter regions of PA3026, PA3464, and PA3342, showing the major increase in expression in response to PA3027 excess. In Vitro DNA binding assay confirmed interactions of PA3027 with these regions. Furthermore, promoter-reporter assays in a heterologous host showed the PA3027-dependent activation of the promoter of the PA3026-PA3024 operon. Two motifs representing the preferred binding sites for PA3027, one localized upstream and one overlapping with the -35 promoter sequence, were identified in PA3026p and our data indicate that both motifs are required for full activation of this promoter by PA3027. Overall, the presented data show that PA3027 acts as a transcriptional regulator in P. aeruginosa, activating genes likely engaged in glycerolipid metabolism. The GliR name, from a glycerolipid metabolism regulator, is proposed for PA3027 of P. aeruginosa.

Pseudomonas aeruginosa adaptation and evolution in patients with cystic fibrosis

Intense genome sequencing of Pseudomonas aeruginosa isolates from cystic fibrosis (CF) airways has shown inefficient eradication of the infecting bacteria, as well as previously undocumented patient-to-patient transmission of adapted clones. However, genome sequencing has limited potential as a predictor of chronic infection and of the adaptive state during infection, and thus there is increasing interest in linking phenotypic traits to the genome sequences. Phenotypic information ranges from genome-wide transcriptomic analysis of patient samples to determination of more specific traits associated with metabolic changes, stress responses, antibiotic resistance and tolerance, biofilm formation and slow growth. Environmental conditions in the CF lung shape both genetic and phenotypic changes of P. aeruginosa during infection. In this Review, we discuss the adaptive and evolutionary trajectories that lead to early diversification and late convergence, which enable P. aeruginosa to succeed in this niche, and we point out how knowledge of these biological features may be used to guide diagnosis and therapy.

Clinically Relevant Concentrations of Polymyxin B and Meropenem Synergistically Kill Multidrug-Resistant Pseudomonas aeruginosa and Minimize Biofilm Formation

The emergence of antibiotic resistance has severely impaired the treatment of chronic respiratory infections caused by multidrug-resistant (MDR) Pseudomonas aeruginosa. Since the reintroduction of polymyxins as a last-line therapy against MDR Gram-negative bacteria, resistance to its monotherapy and recurrent infections continue to be reported and synergistic antibiotic combinations have been investigated. In this study, comprehensive in vitro microbiological evaluations including synergy panel screening, population analysis profiling, time-kill kinetics, anti-biofilm formation and membrane damage analysis studies were conducted to evaluate the combination of polymyxin B and meropenem against biofilm-producing, polymyxin-resistant MDR P. aeruginosa. Two phylogenetically unrelated MDR P. aeruginosa strains, FADDI-PA060 (MIC of polymyxin B [MICpolymyxin B], 64 mg/L; MICmeropenem, 64 mg/L) and FADDI-PA107 (MICpolymyxin B, 32 mg/L; MICmeropenem, 4 mg/L) were investigated. Genome sequencing identified 57 (FADDI-PA060) and 50 (FADDI-PA107) genes predicted to confer resistance to a variety of antimicrobials, as well as multiple virulence factors in each strain. The presence of resistance genes to a particular antibiotic class generally aligned with MIC results. For both strains, all monotherapies of polymyxin B failed with substantial regrowth and biofilm formation. The combination of polymyxin B (16 mg/L)/meropenem (16 mg/L) was most effective, enhancing initial bacterial killing of FADDI-PA060 by ~3 log10 CFU/mL, followed by a prolonged inhibition of regrowth for up to 24 h with a significant reduction in biofilm formation (* p < 0.05). Membrane integrity studies revealed a substantial increase in membrane depolarization and membrane permeability in the surviving cells. Against FADDI-PA107, planktonic and biofilm bacteria were completely eradicated. In summary, the combination of polymyxin B and meropenem demonstrated synergistic bacterial killing while reinstating the efficacy of two previously ineffective antibiotics against difficult-to-treat polymyxin-resistant MDR P. aeruginosa.

Home, sweet home: how mucus accommodates our microbiota

As a natural environment for human-microbiota interactions, healthy mucus houses a remarkably stable and diverse microbial community. Maintaining this microbiota is essential to human health, both to support the commensal bacteria that perform a wide array of beneficial functions and to prevent the outgrowth of pathogens. However, how the host selects and maintains a specialized microbiota remains largely unknown. In this viewpoint, we propose several strategies by which mucus may regulate the composition and function of the human microbiota and discuss how compromised mucus barriers in disease can give rise to microbial dysbiosis.

Predicting changes in renal metabolism after compound exposure with a genome-scale metabolic model

The kidneys are metabolically active organs with importance in several physiological tasks such as the secretion of soluble wastes into the urine and synthesizing glucose and oxidizing fatty acids for energy in fasting (non-fed) conditions. Once damaged, the metabolic capability of the kidneys becomes altered. Here, we define metabolic tasks in a computational modeling framework to capture kidney function in an update to the iRno network reconstruction of rat metabolism using literature-based evidence. To demonstrate the utility of iRno for predicting kidney function, we exposed primary rat renal proximal tubule epithelial cells to four compounds with varying levels of nephrotoxicity (acetaminophen, gentamicin, 2,3,7,8-tetrachlorodibenzodioxin, and trichloroethylene) for six and twenty-four hours, and collected transcriptomics and metabolomics data to measure the metabolic effects of compound exposure. For the transcriptomics data, we observed changes in fatty acid metabolism and amino acid metabolism, as well as changes in existing markers of kidney function such as Clu (clusterin). The iRno metabolic network reconstruction was used to predict alterations in these same pathways after integrating transcriptomics data and was able to distinguish between select compound-specific effects on the proximal tubule epithelial cells. Genome-scale metabolic network reconstructions with coupled omics data can be used to predict changes in metabolism as a step towards identifying novel metabolic biomarkers of kidney function and dysfunction.

Essential gene prediction using limited gene essentiality information-An integrative semi-supervised machine learning strategy

Essential gene prediction helps to find minimal genes indispensable for the survival of any organism. Machine learning (ML) algorithms have been useful for the prediction of gene essentiality. However, currently available ML pipelines perform poorly for organisms with limited experimental data. The objective is the development of a new ML pipeline to help in the annotation of essential genes of less explored disease-causing organisms for which minimal experimental data is available. The proposed strategy combines unsupervised feature selection technique, dimension reduction using the Kamada-Kawai algorithm, and semi-supervised ML algorithm employing Laplacian Support Vector Machine (LapSVM) for prediction of essential and non-essential genes from genome-scale metabolic networks using very limited labeled dataset. A novel scoring technique, Semi-Supervised Model Selection Score, equivalent to area under the ROC curve (auROC), has been proposed for the selection of the best model when supervised performance metrics calculation is difficult due to lack of data. The unsupervised feature selection followed by dimension reduction helped to observe a distinct circular pattern in the clustering of essential and non-essential genes. LapSVM then created a curve that dissected this circle for the classification and prediction of essential genes with high accuracy (auROC > 0.85) even with 1% labeled data for model training. After successful validation of this ML pipeline on both Eukaryotes and Prokaryotes that show high accuracy even when the labeled dataset is very limited, this strategy is used for the prediction of essential genes of organisms with inadequate experimentally known data, such as Leishmania sp. Using a graph-based semi-supervised machine learning scheme, a novel integrative approach has been proposed for essential gene prediction that shows universality in application to both Prokaryotes and Eukaryotes with limited labeled data. The essential genes predicted using the pipeline provide an important lead for the prediction of gene essentiality and identification of novel therapeutic targets for antibiotic and vaccine development against disease-causing parasites.

The Evanescent GacS Signal

The GacS histidine kinase is the membrane sensor of the major upstream two-component system of the regulatory Gac/Rsm signal transduction pathway. This pathway governs the expression of a wide range of genes in pseudomonads and controls bacterial fitness and motility, tolerance to stress, biofilm formation, and virulence or plant protection. Despite the importance of these roles, the ligands binding to the sensor domain of GacS remain unknown, and their identification is an exciting challenge in this domain. At high population densities, the GacS signal triggers a switch from primary to secondary metabolism and a change in bacterial lifestyle. It has been suggested, based on these observations, that the GacS signal is a marker of the emergence of nutritional stress and competition. Biochemical investigations have yet to characterize the GacS signal fully. However, they portray this cue as a low-molecular weight, relatively simple and moderately apolar metabolite possibly resembling, but nevertheless different, from the aliphatic organic acids acting as quorum-sensing signaling molecules in other Proteobacteria. Significant progress in the development of metabolomic tools and new databases dedicated to Pseudomonas metabolism should help to unlock some of the last remaining secrets of GacS induction, making it possible to control the Gac/Rsm pathway.

Genome-Scale Metabolic Modeling for Unraveling Molecular Mechanisms of High Threat Pathogens

Pathogens give rise to a wide range of diseases threatening global health and hence drawing public health agencies' attention to establish preventative and curative solutions. Genome-scale metabolic modeling is ever increasingly used tool for biomedical applications including the elucidation of antibiotic resistance, virulence, single pathogen mechanisms and pathogen-host interaction systems. With this approach, the sophisticated cellular system of metabolic reactions inside the pathogens as well as between pathogen and host cells are represented in conjunction with their corresponding genes and enzymes. Along with essential metabolic reactions, alternate pathways and fluxes are predicted by performing computational flux analyses for the growth of pathogens in a very short time. The genes or enzymes responsible for the essential metabolic reactions in pathogen growth are regarded as potential drug targets, as a priori guide to researchers in the pharmaceutical field. Pathogens alter the key metabolic processes in infected host, ultimately the objective of these integrative constraint-based context-specific metabolic models is to provide novel insights toward understanding the metabolic basis of the acute and chronic processes of infection, revealing cellular mechanisms of pathogenesis, identifying strain-specific biomarkers and developing new therapeutic approaches including the combination drugs. The reaction rates predicted during different time points of pathogen development enable us to predict active pathways and those that only occur during certain stages of infection, and thus point out the putative drug targets. Among others, fatty acid and lipid syntheses reactions are recent targets of new antimicrobial drugs. Genome-scale metabolic models provide an improved understanding of how intracellular pathogens utilize the existing microenvironment of the host. Here, we reviewed the current knowledge of genome-scale metabolic modeling in pathogen cells as well as pathogen host interaction systems and the promising applications in the extension of curative strategies against pathogens for global preventative healthcare.

Elucidating potential molecular signatures through host-microbe interactions for reactive arthritis and inflammatory bowel disease using combinatorial approach

Reactive Arthritis (ReA), a rare seronegative inflammatory arthritis, lacks exquisite classification under rheumatic autoimmunity. ReA is solely established using differential clinical diagnosis of the patient cohorts, where pathogenic triggers linked to enteric and urogenital microorganisms e.g. Salmonella, Shigella, Yersinia, Campylobacter, Chlamydia have been reported. Inflammatory Bowel Disease (IBD), an idiopathic enteric disorder co-evolved and attuned to present gut microbiome dysbiosis, can be correlated to the genesis of enteropathic arthropathies like ReA. Gut microbes symbolically modulate immune system homeostasis and are elementary for varied disease patterns in autoimmune disorders. The gut-microbiota axis structured on the core host-microbe interactions execute an imperative role in discerning the etiopathogenesis of ReA and IBD. This study predicts the molecular signatures for ReA with co-evolved IBD through the enveloped host-microbe interactions and microbe-microbe 'interspecies communication', using synonymous gene expression data for selective microbes. We have utilized a combinatorial approach that have concomitant in-silico work-pipeline and experimental validation to corroborate the findings. In-silico analysis involving text mining, metabolic network reconstruction, simulation, filtering, host-microbe interaction, docking and molecular mimicry studies results in robust drug target/s and biomarker/s for co-evolved IBD and ReA. Cross validation of the target/s or biomarker/s was done by targeted gene expression analysis following a non-probabilistic convenience sampling. Studies were performed to substantiate the host-microbe disease network consisting of protein-marker-symptom/disease-pathway-drug associations resulting in possible identification of vital drug targets, biomarkers, pathways and inhibitors for IBD and ReA.Our study identified Na(+)/H(+) anti-porter (NHAA) and Kynureninase (KYNU) to be robust early and essential host-microbe interacting targets for IBD co-evolved ReA. Other vital host-microbe interacting genes, proteins, pathways and drugs include Adenosine Deaminase (ADA), Superoxide Dismutase 2 (SOD2), Catalase (CAT), Angiotensin I Converting Enzyme (ACE), carbon metabolism (folate biosynthesis) and methotrexate. These can serve as potential prognostic/theranostic biomarkers and signatures that can be extrapolated to stratify ReA and related autoimmunity patient cohorts for further pilot studies.

Novel antimicrobial development using genome-scale metabolic model of Gram-negative pathogens: a review

Antimicrobial resistance (AMR) threatens the effective prevention and treatment of a wide range of infections. Governments around the world are beginning to devote effort for innovative treatment development to treat these resistant bacteria. Systems biology methods have been applied extensively to provide valuable insights into metabolic processes at system level. Genome-scale metabolic models serve as platforms for constraint-based computational techniques which aid in novel drug discovery. Tools for automated reconstruction of metabolic models have been developed to support system level metabolic analysis. We discuss features of such software platforms for potential users to best fit their purpose of research. In this work, we focus to review the development of genome-scale metabolic models of Gram-negative pathogens and also metabolic network approach for identification of antimicrobial drugs targets.

Integrated Metabolic Modeling, Culturing, and Transcriptomics Explain Enhanced Virulence of Vibrio cholerae during Coinfection with Enterotoxigenic Escherichia coli

Most studies proposing new strategies to manage and treat infections have been largely focused on identifying druggable targets that can inhibit a pathogen's growth when it is the single cause of infection. In vivo, however, infections can be caused by multiple species. This is important to take into account when attempting to develop or use current antibacterials since their efficacy can change significantly between single infections and coinfections. In this study, we used genome-scale metabolic models (GEMs) to interrogate the growth capabilities of Vibrio cholerae in single infections and coinfections with enterotoxigenic E. coli (ETEC), which cooccur in a large fraction of diarrheagenic patients. Coinfection model predictions showed that V. cholerae growth capabilities are enhanced in the presence of ETEC relative to V. cholerae single infection, through cross-fed metabolites made available to V. cholerae by ETEC. In vitro, cocultures of the two enteric pathogens further confirmed model predictions showing an increased growth of V. cholerae in coculture relative to V. cholerae single cultures while ETEC growth was suppressed. Dual RNAseq analysis of the cocultures also confirmed that the transcriptome of V. cholerae was distinct during coinfection compared to single-infection scenarios where processes related to metabolism were significantly perturbed. Further, in silico gene-knockout simulations uncovered discrepancies in gene essentiality for V. cholerae growth between single infections and coinfections. Integrative model-guided analysis thus identified druggable targets that would be critical for V. cholerae growth in both single infections and coinfections; thus, designing inhibitors against those targets would provide a broader spectrum of coverage against cholera infections.

Gene essentiality is altered during polymicrobial infections. Nevertheless, most studies rely on single-species infections to assess pathogen gene essentiality. Here, we use genome-scale metabolic models (GEMs) to explore the effect of coinfection of the diarrheagenic pathogen Vibrio cholerae with another enteric pathogen, enterotoxigenic Escherichia coli (ETEC). Model predictions showed that V. cholerae metabolic capabilities were increased due to ample cross-feeding opportunities enabled by ETEC. This is in line with increased severity of cholera symptoms known to occur in patients with dual infections by the two pathogens. In vitro coculture systems confirmed that V. cholerae growth is enhanced in cocultures relative to single cultures. Further, expression levels of several V. cholerae metabolic genes were significantly perturbed as shown by dual RNA sequencing (RNAseq) analysis of its cocultures with different ETEC strains. A decrease in ETEC growth was also observed, probably mediated by nonmetabolic factors. Single gene essentiality analysis predicted conditionally independent genes that are essential for the pathogen’s growth in both single-infection and coinfection scenarios. Our results reveal growth differences that are of relevance to drug targeting and efficiency in polymicrobial infections.

IMPORTANCE Most studies proposing new strategies to manage and treat infections have been largely focused on identifying druggable targets that can inhibit a pathogen's growth when it is the single cause of infection. In vivo, however, infections can be caused by multiple species. This is important to take into account when attempting to develop or use current antibacterials since their efficacy can change significantly between single infections and coinfections. In this study, we used genome-scale metabolic models (GEMs) to interrogate the growth capabilities of Vibrio cholerae in single infections and coinfections with enterotoxigenic E. coli (ETEC), which cooccur in a large fraction of diarrheagenic patients. Coinfection model predictions showed that V. cholerae growth capabilities are enhanced in the presence of ETEC relative to V. cholerae single infection, through cross-fed metabolites made available to V. cholerae by ETEC. In vitro, cocultures of the two enteric pathogens further confirmed model predictions showing an increased growth of V. cholerae in coculture relative to V. cholerae single cultures while ETEC growth was suppressed. Dual RNAseq analysis of the cocultures also confirmed that the transcriptome of V. cholerae was distinct during coinfection compared to single-infection scenarios where processes related to metabolism were significantly perturbed. Further, in silico gene-knockout simulations uncovered discrepancies in gene essentiality for V. cholerae growth between single infections and coinfections. Integrative model-guided analysis thus identified druggable targets that would be critical for V. cholerae growth in both single infections and coinfections; thus, designing inhibitors against those targets would provide a broader spectrum of coverage against cholera infections.

Novel therapeutic strategies for treating Pseudomonas aeruginosa infection

INTRODUCTION

Persistent infections caused by the superbug Pseudomonas aeruginosa and its resistance to multiple antimicrobial agents are huge threats to patients with cystic fibrosis as well as those with compromised immune systems. Multidrug-resistant P. aeruginosa has posed a major challenge to conventional antibiotics and therapeutic approaches, which show limited efficacy and cause serious side effects. The public demand for new antibiotics is enormous; yet, drug development pipelines have started to run dry with limited targets available for inventing new antibacterial drugs. Consequently, it is important to uncover potential therapeutic targets.

AREAS COVERED

The authors review the current state of drug development strategies that are promising in terms of the development of novel and potent drugs to treat P. aeruginosa infection.

EXPERT OPINION

The prevention of P. aeruginosa infection is increasingly challenging. Furthermore, targeting key virulence regulators has great potential for developing novel anti-P. aeruginosa drugs. Additional promising strategies include bacteriophage therapy, immunotherapies, and antimicrobial peptides. Additionally, the authors believe that in the coming years, the overall network of molecular regulatory mechanism of P. aeruginosa virulence will be fully elucidated, which will provide more novel and promising drug targets for treating P. aeruginosa infections.

Pseudomonas aeruginosa core metabolism exerts a widespread growth-independent control on virulence

To assess the role of core metabolism genes in bacterial virulence - independently of their effect on growth - we correlated the genome, the transcriptome and the pathogenicity in flies and mice of 30 fully sequenced Pseudomonas strains. Gene presence correlates robustly with pathogenicity differences among all Pseudomonas species, but not among the P. aeruginosa strains. However, gene expression differences are evident between highly and lowly pathogenic P. aeruginosa strains in multiple virulence factors and a few metabolism genes. Moreover, 16.5%, a noticeable fraction of the core metabolism genes of P. aeruginosa strain PA14 (compared to 8.5% of the non-metabolic genes tested), appear necessary for full virulence when mutated. Most of these virulence-defective core metabolism mutants are compromised in at least one key virulence mechanism independently of auxotrophy. A pathway level analysis of PA14 core metabolism, uncovers beta-oxidation and the biosynthesis of amino-acids, succinate, citramalate, and chorismate to be important for full virulence. Strikingly, the relative expression among P. aeruginosa strains of genes belonging in these metabolic pathways is indicative of their pathogenicity. Thus, P. aeruginosa strain-to-strain virulence variation, remains largely obscure at the genome level, but can be dissected at the pathway level via functional transcriptomics of core metabolism.

Understanding the metabolism of the tetralin degrader Sphingopyxis granuli strain TFA through genome-scale metabolic modelling

Sphingopyxis granuli strain TFA is an α-proteobacterium that belongs to the sphingomonads, a group of bacteria well-known for its degradative capabilities and oligotrophic metabolism. Strain TFA is the only bacterium in which the mineralisation of the aromatic pollutant tetralin has been completely characterized at biochemical, genetic, and regulatory levels and the first Sphingopyxis characterised as facultative anaerobe. Here we report additional metabolic features of this α-proteobacterium using metabolic modelling and the functional integration of genomic and transcriptomic data. The genome-scale metabolic model (GEM) of strain TFA, which has been manually curated, includes information on 743 genes, 1114 metabolites and 1397 reactions. This represents the largest metabolic model for a member of the Sphingomonadales order thus far. The predictive potential of this model was validated against experimentally calculated growth rates on different carbon sources and under different growth conditions, including both aerobic and anaerobic metabolisms. Moreover, new carbon and nitrogen sources were predicted and experimentally validated. The constructed metabolic model was used as a platform for the incorporation of transcriptomic data, generating a more robust and accurate model. In silico flux analysis under different metabolic scenarios highlighted the key role of the glyoxylate cycle in the central metabolism of strain TFA.

Allelic polymorphism shapes community function in evolving Pseudomonas aeruginosa populations

Pseudomonas aeruginosa is an opportunistic pathogen that chronically infects the lungs of individuals with cystic fibrosis (CF) by forming antibiotic-resistant biofilms. Emergence of phenotypically diverse isolates within CF P. aeruginosa populations has previously been reported; however, the impact of heterogeneity on social behaviors and community function is poorly understood. Here we describe how this heterogeneity impacts on behavioral traits by evolving the strain PAO1 in biofilms grown in a synthetic sputum medium for 50 days. We measured social trait production and antibiotic tolerance, and used a metagenomic approach to analyze and assess genomic changes over the duration of the evolution experiment. We found that (i) evolutionary trajectories were reproducible in independently evolving populations; (ii) over 60% of genomic diversity occurred within the first 10 days of selection. We then focused on quorum sensing (QS), a well-studied P. aeruginosa trait that is commonly mutated in strains isolated from CF lungs. We found that at the population level, (i) evolution in sputum medium selected for decreased the production of QS and QS-dependent traits; (ii) there was a significant correlation between lasR mutant frequency, the loss of protease, and the 3O-C12-HSL signal, and an increase in resistance to clinically relevant β-lactam antibiotics, despite no previous antibiotic exposure. Overall, our findings provide insights into the effect of allelic polymorphism on community functions in diverse P. aeruginosa populations. Further, we demonstrate that P. aeruginosa population and evolutionary dynamics can impact on traits important for virulence and can lead to increased tolerance to β-lactam antibiotics.

Selection or drift: The population biology underlying transposon insertion sequencing experiments

Transposon insertion sequencing methods such as Tn-seq revolutionized microbiology by allowing the identification of genomic loci that are critical for viability in a specific environment on a genome-wide scale. While powerful, transposon insertion sequencing suffers from limited reproducibility when different analysis methods are compared. From the perspective of population biology, this may be explained by changes in mutant frequency due to chance (drift) rather than differential fitness (selection). Here, we develop a mathematical model of the population biology of transposon insertion sequencing experiments, i.e. the changes in size and composition of the transposon-mutagenized population during the experiment. We use this model to investigate mutagenesis, the growth of the mutant library, and its passage through bottlenecks. Specifically, we study how these processes can lead to extinction of individual mutants depending on their fitness and the distribution of fitness effects (DFE) of the entire mutant population. We find that in typical in vitro experiments few mutants with high fitness go extinct. However, bottlenecks of a size that is common in animal infection models lead to so much random extinction that a large number of viable mutants would be misclassified. While mutants with low fitness are more likely to be lost during the experiment, mutants with intermediate fitness are expected to be much more abundant and can constitute a large proportion of detected hits, i.e. false positives. Thus, incorporating the DFEs of randomly generated mutations in the analysis may improve the reproducibility of transposon insertion experiments, especially when strong bottlenecks are encountered.