Genome-scale metabolic modeling of responses to polymyxins in Pseudomonas aeruginosa

Metabolic model guided CRISPRi identifies a central role for phosphoglycerate mutase in Chlamydia trachomatis persistence

Upon nutrient starvation, Chlamydia trachomatis serovar L2 (CTL) shifts from its normal growth to a non-replicating form, termed persistence. It is unclear if persistence reflects an adaptive response or a lack thereof. To understand this, transcriptomics data were collected for CTL grown under nutrient-replete and nutrient-starved conditions. Applying K-means clustering on transcriptomics data revealed a global transcriptomic rewiring of CTL under stress conditions in the absence of any canonical global stress regulator. This is consistent with previous data that suggested that CTL's stress response is due to a lack of an adaptive response mechanism. To investigate the impact of this on CTL metabolism, we reconstructed a genome-scale metabolic model of CTL (iCTL278) and contextualized it with the collected transcriptomics data. Using the metabolic bottleneck analysis on contextualized iCTL278, we observed that phosphoglycerate mutase (pgm) regulates the entry of CTL to the persistence state. Our data indicate that pgm has the highest thermodynamics driving force and lowest enzymatic cost. Furthermore, CRISPRi-driven knockdown of pgm in the presence or absence of tryptophan revealed the importance of this gene in modulating persistence. Hence, this work, for the first time, introduces thermodynamics and enzyme cost as tools to gain a deeper understanding on CTL persistence.

IMPORTANCE

This study uses a metabolic model to investigate factors that contribute to the persistence of Chlamydia trachomatis serovar L2 (CTL) under tryptophan and iron starvation conditions. As CTL lacks many canonical transcriptional regulators, the model was used to assess two prevailing hypotheses on persistence-that the chlamydial response to nutrient starvation represents a passive response due to the lack of regulators or that it is an active response by the bacterium. K-means clustering of stress-induced transcriptomics data revealed striking evidence in favor of the lack of adaptive (i.e., a passive) response. To find the metabolic signature of this, metabolic modeling pin-pointed pgm as a potential regulator of persistence. Thermodynamic driving force, enzyme cost, and CRISPRi knockdown of pgm supported this finding. Overall, this work introduces thermodynamic driving force and enzyme cost as a tool to understand chlamydial persistence, demonstrating how systems biology-guided CRISPRi can unravel complex bacterial phenomena.

Preserving the efficacy of antibiotics to tackle antibiotic resistance

Different international agencies recognize that antibiotic resistance is one of the most severe human health problems that humankind is facing. Traditionally, the introduction of new antibiotics solved this problem but various scientific and economic reasons have led to a shortage of novel antibiotics at the pipeline. This situation makes mandatory the implementation of approaches to preserve the efficacy of current antibiotics. The concept is not novel, but the only action taken for such preservation had been the 'prudent' use of antibiotics, trying to reduce the selection pressure by reducing the amount of antibiotics. However, even if antibiotics are used only when needed, this will be insufficient because resistance is the inescapable outcome of antibiotics' use. A deeper understanding of the alterations in the bacterial physiology upon acquisition of resistance and during infection will help to design improved strategies to treat bacterial infections. In this article, we discuss the interconnection between antibiotic resistance (and antibiotic activity) and bacterial metabolism, particularly in vivo, when bacteria are causing infection. We discuss as well how understanding evolutionary trade-offs, as collateral sensitivity, associated with the acquisition of resistance may help to define evolution-based therapeutic strategies to fight antibiotic resistance and to preserve currently used antibiotics.

Antibacterial activity and mechanism of luteolin isolated from Lophatherum gracile Brongn. against multidrug-resistant Escherichia coli

Infections caused by multidrug-resistant (MDR) bacteria have become a major challenge for global healthcare systems. The search for antibacterial compounds from plants has received increasing attention in the fight against MDR bacteria. As a medicinal and edible plant, Lophatherum gracile Brongn. (L. gracile) has favorable antibacterial effect. However, the main antibacterial active compound and its antimicrobial mechanism are not clear. Here, our study first identified the key active compound from L. gracile as luteolin. Meanwhile, the antibacterial effect of luteolin was detected by using the broth microdilution method and time-kill curve analysis. Luteolin can also cause morphological structure degeneration and content leakage, cell wall/membrane damage, ATP synthesis reduction, and downregulation of mRNA expression levels of sulfonamide and quinolones resistance genes in multidrug-resistant Escherichia coli (MDR E. coli). Furthermore, untargeted UPLC/Q-TOF-MS-based metabolomics analysis of the bacterial metabolites revealed that luteolin significantly changed riboflavin energy metabolism, bacterial chemotaxis cell process and glycerophospholipid metabolism of MDR E. coli. This study suggests that luteolin could be a potential new food additive or preservative for controlling MDR E. coli infection and spread.

Translating eco-evolutionary biology into therapy to tackle antibiotic resistance

Antibiotic resistance is currently one of the most important public health problems. The golden age of antibiotic discovery ended decades ago, and new approaches are urgently needed. Therefore, preserving the efficacy of the antibiotics currently in use and developing compounds and strategies that specifically target antibiotic-resistant pathogens is critical. The identification of robust trends of antibiotic resistance evolution and of its associated trade-offs, such as collateral sensitivity or fitness costs, is invaluable for the design of rational evolution-based, ecology-based treatment approaches. In this Review, we discuss these evolutionary trade-offs and how such knowledge can aid in informing combination or alternating antibiotic therapies against bacterial infections. In addition, we discuss how targeting bacterial metabolism can enhance drug activity and impair antibiotic resistance evolution. Finally, we explore how an improved understanding of the original physiological function of antibiotic resistance determinants, which have evolved to reach clinical resistance after a process of historical contingency, may help to tackle antibiotic resistance.

Genomic variations in polymyxin-resistant Pseudomonas aeruginosa clinical isolates and their effects on polymyxin resistance

Infection with P. aeruginosa, one of the most relevant opportunistic pathogens in hospital-acquired infections, can lead to high mortality due to its low antibiotic susceptibility to limited choices of antibiotics. Polymyxin as last-resort antibiotics is used in the treatment of systemic infections caused by multidrug-resistant P. aeruginosa strains, so studying the emergence of polymyxin-resistant was a must. The present study was designed to define genomic differences between paired polymyxin-susceptible and polymyxin-resistant P. aeruginosa strains and established polymyxin resistance mechanisms, and common chromosomal mutations that may confer polymyxin resistance were characterized. A total of 116 CRPA clinical isolates from patients were collected from three tertiary care hospitals in China during 2017-2021. Our study found that polymyxin B resistance represented 3.45% of the isolated carbapenem-resistant P. aeruginosa (CRPA). No polymyxin-resistant isolates were positive for mcr (1-8 and 10) gene and efflux mechanisms. Key genetic variations identified in polymyxin-resistant isolates involved missense mutations in parR, parS, pmrB, pmrA, and phoP. The waaL and PA5005 substitutions related to LPS synthesis were detected in the highest levels of resistant strain (R1). The missense mutations H398R in ParS (4/4), Y345H in PmrB (4/4), and L71R in PmrA (3/4) were the predominant. Results of the PCR further confirmed that mutation of pmrA, pmrB, and phoP individually or simultaneously did affect the expression level of resistant populations and can directly increase the expression of arnBCADTEF operon to contribute to polymyxin resistance. In addition, we reported 3 novel mutations in PA1945 (2129872_A < G, 2130270_A < C, 2130272_T < G) that may confer polymyxin resistance in P. aeruginosa. Our findings enriched the spectrum of chromosomal mutations, highlighted the complexity at the molecular level, and multifaceted interplay mechanisms underlying polymyxin resistance in P. aeruginosa.

Towards a hybrid user interface for the visual exploration of large biomolecular networks using virtual reality

Biomolecular networks, including genome-scale metabolic models (GSMMs), assemble the knowledge regarding the biological processes that happen inside specific organisms in a way that allows for analysis, simulation, and exploration. With the increasing availability of genome annotations and the development of powerful reconstruction tools, biomolecular networks continue to grow ever larger. While visual exploration can facilitate the understanding of such networks, the network sizes represent a major challenge for current visualisation systems. Building on promising results from the area of immersive analytics, which among others deals with the potential of immersive visualisation for data analysis, we present a concept for a hybrid user interface that combines a classical desktop environment with a virtual reality environment for the visual exploration of large biomolecular networks and corresponding data. We present system requirements and design considerations, describe a resulting concept, an envisioned technical realisation, and a systems biology usage scenario. Finally, we discuss remaining challenges.

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.

Integrated metabolomic and transcriptomic analyses of the synergistic effect of polymyxin-rifampicin combination against Pseudomonas aeruginosa

BACKGROUND

Understanding the mechanism of antimicrobial action is critical for improving antibiotic therapy. For the first time, we integrated correlative metabolomics and transcriptomics of Pseudomonas aeruginosa to elucidate the mechanism of synergistic killing of polymyxin-rifampicin combination.

METHODS

Liquid chromatography-mass spectrometry and RNA-seq analyses were conducted to identify the significant changes in the metabolome and transcriptome of P. aeruginosa PAO1 after exposure to polymyxin B (1 mg/L) and rifampicin (2 mg/L) alone, or in combination over 24 h. A genome-scale metabolic network was employed for integrative analysis.

RESULTS

In the first 4-h treatment, polymyxin B monotherapy induced significant lipid perturbations, predominantly to fatty acids and glycerophospholipids, indicating a substantial disorganization of the bacterial outer membrane. Expression of ParRS, a two-component regulatory system involved in polymyxin resistance, was increased by polymyxin B alone. Rifampicin alone caused marginal metabolic perturbations but significantly affected gene expression at 24 h. The combination decreased the gene expression of quorum sensing regulated virulence factors at 1 h (e.g. key genes involved in phenazine biosynthesis, secretion system and biofilm formation); and increased the expression of peptidoglycan biosynthesis genes at 4 h. Notably, the combination caused substantial accumulation of nucleotides and amino acids that last at least 4 h, indicating that bacterial cells were in a state of metabolic arrest.

CONCLUSION

This study underscores the substantial potential of integrative systems pharmacology to determine mechanisms of synergistic bacterial killing by antibiotic combinations, which will help optimize their use in patients.

In silico genome-scale metabolic modeling and in vitro static time-kill studies of exogenous metabolites alone and with polymyxin B against Klebsiella pneumoniae

Multidrug-resistant (MDR) Klebsiella pneumoniae is a top-prioritized Gram-negative pathogen with a high incidence in hospital-acquired infections. Polymyxins have resurged as a last-line therapy to combat Gram-negative “superbugs”, including MDR K. pneumoniae. However, the emergence of polymyxin resistance has increasingly been reported over the past decades when used as monotherapy, and thus combination therapy with non-antibiotics (e.g., metabolites) becomes a promising approach owing to the lower risk of resistance development. Genome-scale metabolic models (GSMMs) were constructed to delineate the altered metabolism of New Delhi metallo-β-lactamase- or extended spectrum β-lactamase-producing K. pneumoniae strains upon addition of exogenous metabolites in media. The metabolites that caused significant metabolic perturbations were then selected to examine their adjuvant effects using in vitro static time–kill studies. Metabolic network simulation shows that feeding of 3-phosphoglycerate and ribose 5-phosphate would lead to enhanced central carbon metabolism, ATP demand, and energy consumption, which is converged with metabolic disruptions by polymyxin treatment. Further static time–kill studies demonstrated enhanced antimicrobial killing of 10 mM 3-phosphoglycerate (1.26 and 1.82 log₁₀ CFU/ml) and 10 mM ribose 5-phosphate (0.53 and 0.91 log₁₀ CFU/ml) combination with 2 mg/L polymyxin B against K. pneumoniae strains. Overall, exogenous metabolite feeding could possibly improve polymyxin B activity via metabolic modulation and hence offers an attractive approach to enhance polymyxin B efficacy. With the application of GSMM in bridging the metabolic analysis and time–kill assay, biological insights into metabolite feeding can be inferred from comparative analyses of both results. Taken together, a systematic framework has been developed to facilitate the clinical translation of antibiotic-resistant infection management.

Design considerations for representing systems biology information with the Systems Biology Graphical Notation

Visual representations are commonly used to explore, analyse, and communicate information and knowledge in systems biology and beyond. Such visualisations not only need to be accurate but should also be aesthetically pleasing and informative. Using the example of the Systems Biology Graphical Notation (SBGN) we will investigate design considerations for graphically presenting information from systems biology, in particular regarding the use of glyphs for types of information, the style of graph layout for network representation, and the concept of bricks for visual network creation.

Optimizing Antimicrobial Therapy by Integrating Multi-Omics With Pharmacokinetic/Pharmacodynamic Models and Precision Dosing

In the era of “Bad Bugs, No Drugs,” optimizing antibiotic therapy against multi-drug resistant (MDR) pathogens is crucial. Mathematical modelling has been employed to further optimize dosing regimens. These models include mechanism-based PK/PD models, systems-based models, quantitative systems pharmacology (QSP) and population PK models. Quantitative systems pharmacology has significant potential in precision antimicrobial chemotherapy in the clinic. Population PK models have been employed in model-informed precision dosing (MIPD). Several antibiotics require close monitoring and dose adjustments in order to ensure optimal outcomes in patients with infectious diseases. Success or failure of antibiotic therapy is dependent on the patient, antibiotic and bacterium. For some drugs, treatment responses vary greatly between individuals due to genotype and disease characteristics. Thus, for these drugs, tailored dosing is required for successful therapy. With antibiotics, inappropriate dosing such as insufficient dosing may put patients at risk of therapeutic failure which could lead to mortality. Conversely, doses that are too high could lead to toxicities. Hence, precision dosing which customizes doses to individual patients is crucial for antibiotics especially those with a narrow therapeutic index. In this review, we discuss the various strategies in optimizing antimicrobial therapy to address the challenges in the management of infectious diseases and delivering personalized therapy.

Dissecting carbon metabolism of Yarrowia lipolytica type strain W29 using genome-scale metabolic modelling

Yarrowia lipolytica is a widely-used chassis cell in biotechnological applications. It has recently gained extensive research interest owing to its extraordinary ability of producing industrially valuable biochemicals from a variety of carbon sources. Genome-scale metabolic models (GSMMs) enable analyses of cellular metabolism for engineering various industrial hosts. In the present study, we developed a high-quality GSMM iYli21 for Y. lipolytica type strain W29 by extensive manual curation with Biolog experimental data. The model showed a high accuracy of 85.7% in predicting nutrient utilization. Transcriptomics data were integrated to delineate cellular metabolism of utilizing six individual metabolites as sole carbon sources. Comparisons showed that 302 reactions were commonly used, including those from TCA cycle, oxidative phosphorylation, and purine metabolism for energy and material supply. Whereas glycolytic reactions were employed only when glucose and glycerol used as sole carbon sources, gluconeogenesis and fatty acid oxidation reactions were specifically employed when fatty acid, alkane and glycerolipid were the sole carbon sources. Further test of 46 substrates for generating 5 products showed that hexanoate outcompeted other compounds in terms of maximum theoretical yield owing to the lowest carbon loss for energy supply. This newly generated model iYli21 will be a valuable tool in dissecting metabolic mechanism and guiding metabolic engineering of this important industrial cell factory.

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.

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.

A Systematic Strategy to Find Potential Therapeutic Targets for Pseudomonas aeruginosa Using Integrated Computational Models

Pseudomonas aeruginosa is an opportunistic human pathogen that has been a constant global health problem due to its ability to cause infection at different body sites and its resistance to a broad spectrum of clinically available antibiotics. The World Health Organization classified multidrug-resistant Pseudomonas aeruginosa among the top-ranked organisms that require urgent research and development of effective therapeutic options. Several approaches have been taken to achieve these goals, but they all depend on discovering potential drug targets. The large amount of data obtained from sequencing technologies has been used to create computational models of organisms, which provide a powerful tool for better understanding their biological behavior. In the present work, we applied a method to integrate transcriptome data with genome-scale metabolic networks of Pseudomonas aeruginosa. We submitted both metabolic and integrated models to dynamic simulations and compared their performance with published in vitro growth curves. In addition, we used these models to identify potential therapeutic targets and compared the results to analyze the assumption that computational models enriched with biological measurements can provide more selective and (or) specific predictions. Our results demonstrate that dynamic simulations from integrated models result in more accurate growth curves and flux distribution more coherent with biological observations. Moreover, identifying drug targets from integrated models is more selective as the predicted genes were a subset of those found in the metabolic models. Our analysis resulted in the identification of 26 non-host homologous targets. Among them, we highlighted five top-ranked genes based on lesser conservation with the human microbiome. Overall, some of the genes identified in this work have already been proposed by different approaches and (or) are already investigated as targets to antimicrobial compounds, reinforcing the benefit of using integrated models as a starting point to selecting biologically relevant therapeutic targets.

Path to improving the life cycle and quality of genome-scale models of metabolism

Genome-scale models of metabolism (GEMs) are key computational tools for the systems-level study of metabolic networks. Here, we describe the "GEM life cycle," which we subdivide into four stages: inception, maturation, specialization, and amalgamation. We show how different types of GEM reconstruction workflows fit in each stage and proceed to highlight two fundamental bottlenecks for GEM quality improvement: GEM maturation and content removal. We identify common characteristics contributing to increasing quality of maturing GEMs drawing from past independent GEM maturation efforts. We then shed some much-needed light on the latent and unrecognized but pervasive issue of content removal, demonstrating the substantial effects of model pruning on its solution space. Finally, we propose a novel framework for content removal and associated confidence-level assignment which will help guide future GEM development efforts, reduce duplication of effort across groups, potentially aid automated reconstruction platforms, and boost the reproducibility of model development.

Advances in Genome-Scale Metabolic Modeling toward Microbial Community Analysis of the Human Microbiome

A genome-scale metabolic model (GEM) represents metabolic pathways of an organism in a mathematical form and can be built using biochemistry and genome annotation data. GEMs are invaluable for understanding organisms since they analyze the metabolic capabilities and behaviors quantitatively and can predict phenotypes. The development of high-throughput data collection techniques led to an immense increase in omics data such as metagenomics, which expand our knowledge on the human microbiome, but this also created a need for systematic analysis of these data. In recent years, GEMs have also been reconstructed for microbial species, including human gut microbiota, and methods for the analysis of microbial communities have been developed to examine the interaction between the organisms or the host. The purpose of this review is to provide a comprehensive guide for the applications of GEMs in microbial community analysis. Starting with GEM repositories, automatic GEM reconstruction tools, and quality control of models, this review will give insights into microbe-microbe and microbe-host interaction predictions and optimization of microbial community models. Recent studies that utilize microbial GEMs and personalized models to infer the influence of microbiota on human diseases such as inflammatory bowel diseases (IBD) or Parkinson's disease are exemplified. Being powerful system biology tools for both species-level and community-level analysis of microbes, GEMs integrated with omics data and machine learning techniques will be indispensable for studying the microbiome and their effects on human physiology as well as for deciphering the mechanisms behind human diseases.

Synergy of the Polymyxin-Chloramphenicol Combination against New Delhi Metallo-β-Lactamase-Producing Klebsiella pneumoniae Is Predominately Driven by Chloramphenicol

Carbapenem-resistant Klebsiella pneumoniae has been classified as an Urgent Threat by the Centers for Disease Control and Prevention (CDC). The combination of two "old" antibiotics, polymyxin and chloramphenicol, displays synergistic killing against New Delhi metallo-β-lactamase (NDM)-producing K. pneumoniae. However, the mechanism(s) underpinning their synergistic killing are not well studied. We employed an in vitro pharmacokinetic/pharmacodynamic model to mimic the pharmacokinetics of the antibiotics in patients and examined bacterial killing against NDM-producing K. pneumoniae using a metabolomic approach. Metabolomic analysis was integrated with an isolate-specific genome-scale metabolic network (GSMN). Our results show that metabolic responses to polymyxin B and/or chloramphenicol against NDM-producing K. pneumoniae involved the inhibition of cell envelope biogenesis, metabolism of arginine and nucleotides, glycolysis, and pentose phosphate pathways. Our metabolomic and GSMN modeling results highlight the novel mechanisms of a synergistic antibiotic combination at the network level and may have a significant potential in developing precision antimicrobial chemotherapy in patients.

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.

Visual Exploration of Large Metabolic Models

MOTIVATION

Large metabolic models, including genome-scale metabolic models (GSMMs), are nowadays common in systems biology, biotechnology and pharmacology. They typically contain thousands of metabolites and reactions and therefore methods for their automatic visualisation and interactive exploration can facilitate a better understanding of these models.

RESULTS

We developed a novel method for the visual exploration of large metabolic models and implemented it in LMME (Large Metabolic Model Explorer), an add-on for the biological network analysis tool VANTED. The underlying idea of our method is to analyse a large model as follows. Starting from a decomposition into several subsystems, relationships between these subsystems are identified and an overview is computed and visualised. From this overview, detailed subviews may be constructed and visualised in order to explore subsystems and relationships in greater detail. Decompositions may either be predefined or computed, using built-in or self-implemented methods. Realised as add-on for VANTED, LMME is embedded in a domain-specific environment, allowing for further related analysis at any stage during the exploration. We describe the method, provide a use case, and discuss the strengths and weaknesses of different decomposition methods.

AVAILABILITY

The methods and algorithms presented here are implemented in LMME, an open-source add-on for VANTED. LMME can be downloaded from www.cls.uni-konstanz.de/software/lmme and VANTED can be downloaded from www.vanted.org. The source code of LMME is available from GitHub, at https://github.com/LSI-UniKonstanz/lmme.

Strategies for Enhancing in vitro Degradation of Linuron by Variovorax sp. Strain SRS 16 Under the Guidance of Metabolic Modeling

Phenyl urea herbicides are being extensively used for weed control in both agricultural and non-agricultural applications. Linuron is one of the key herbicides in this family and is in wide use. Like other phenyl urea herbicides, it is known to have toxic effects as a result of its persistence in the environment. The natural removal of linuron from the environment is mainly carried through microbial biodegradation. Some microorganisms have been reported to mineralize linuron completely and utilize it as a carbon and nitrogen source. Variovorax sp. strain SRS 16 is one of the known efficient degraders with a recently sequenced genome. The genomic data provide an opportunity to use a genome-scale model for improving biodegradation. The aim of our study is the construction of a genome-scale metabolic model following automatic and manual protocols and its application for improving its metabolic potential through iterative simulations. Applying flux balance analysis (FBA), growth and degradation performances of SRS 16 in different media considering the influence of selected supplements (potential carbon and nitrogen sources) were simulated. Outcomes are predictions for the suitable media modification, allowing faster degradation of linuron by SRS 16. Seven metabolites were selected for in vitro validation of the predictions through laboratory experiments confirming the degradation-promoting effect of specific amino acids (glutamine and asparagine) on linuron degradation and SRS 16 growth. Overall, simulations are shown to be efficient in predicting the degradation potential of SRS 16 in the presence of specific supplements. The generated information contributes to the understanding of the biochemistry of linuron degradation and can be further utilized for the development of new cleanup solutions without any genetic manipulation.

Metabolic Modeling to Interrogate Microbial Disease: A Tale for Experimentalists

The explosion of microbiome analyses has helped identify individual microorganisms and microbial communities driving human health and disease, but how these communities function is still an open question. For example, the role for the incredibly complex metabolic interactions among microbial species cannot easily be resolved by current experimental approaches such as 16S rRNA gene sequencing, metagenomics and/or metabolomics. Resolving such metabolic interactions is particularly challenging in the context of polymicrobial communities where metabolite exchange has been reported to impact key bacterial traits such as virulence and antibiotic treatment efficacy. As novel approaches are needed to pinpoint microbial determinants responsible for impacting community function in the context of human health and to facilitate the development of novel anti-infective and antimicrobial drugs, here we review, from the viewpoint of experimentalists, the latest advances in metabolic modeling, a computational method capable of predicting metabolic capabilities and interactions from individual microorganisms to complex ecological systems. We use selected examples from the literature to illustrate how metabolic modeling has been utilized, in combination with experiments, to better understand microbial community function. Finally, we propose how such combined, cross-disciplinary efforts can be utilized to drive laboratory work and drug discovery moving forward.

Genome-Scale Metabolic Modeling Reveals Metabolic Alterations of Multidrug-Resistant Acinetobacter baumannii in a Murine Bloodstream Infection Model

Multidrug-resistant (MDR) Acinetobacter baumannii is a critical threat to human health globally. We constructed a genome-scale metabolic model iAB5075 for the hypervirulent, MDR A. baumannii strain AB5075. Predictions of nutrient utilization and gene essentiality were validated using Biolog assay and a transposon mutant library. In vivo transcriptomics data were integrated with iAB5075 to elucidate bacterial metabolic responses to the host environment. iAB5075 contains 1530 metabolites, 2229 reactions, and 1015 genes, and demonstrated high accuracies in predicting nutrient utilization and gene essentiality. At 4 h post-infection, a total of 146 metabolic fluxes were increased and 52 were decreased compared to 2 h post-infection; these included enhanced fluxes through peptidoglycan and lipopolysaccharide biosynthesis, tricarboxylic cycle, gluconeogenesis, nucleotide and fatty acid biosynthesis, and altered fluxes in amino acid metabolism. These flux changes indicate that the induced central metabolism, energy production, and cell membrane biogenesis played key roles in establishing and enhancing A. baumannii bloodstream infection. This study is the first to employ genome-scale metabolic modeling to investigate A. baumannii infection in vivo. Our findings provide important mechanistic insights into the adaption of A. baumannii to the host environment and thus will contribute to the development of new therapeutic agents against this problematic pathogen.

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.

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.

The effect of Staphylococcus aureus on the antibiotic resistance and pathogenicity of Pseudomonas aeruginosa based on crc gene as a metabolism regulator: An in vitro wound model study

BACKGROUND

The cooperation of Pseudomonas aeruginosa and Staphylococcus aureus in various infections results in increased pathogenicity and antibiotic resistance. However, the mechanism controlling such a phenomenon is still unclear. In this study, the effects of S. aureus on the metabolism, antibiotic resistance, and pathogenicity of P. aeruginosa were investigated.

MATERIAL AND METHODS

The biofilm and the planktonic states of growth of P. aeruginosa and S. aureus were investigated using the co-culture method in the L929 cell line. Then, the antibiotic resistance and virulence factors production of the recovered colonies of P. aeruginosa were examined by phenotypic methods. Quantitative Real-Time PCR was used to determine the expression level of crc, lasI/R, and rhlI/R genes. Two way ANOVA test and student's t-test were used to analyze the effect of S.aureus on metabolism, virulence, and resistance of P.aeruginosa.

RESULTS

P. aeruginosa strains in a single-species planktonic culture on the L929 cell line indicated higher CFU counts than the biofilm. Conversely, in the biofilm state of co-culture, the CFU counts increased in comparison to the planktonic condition. Also, the expression level of crc increased two fold in the PA-1 and PA-2 strains compared to the single-species cultures on the L929 cell line. However, the PA-3 strain indicated a sharp decrease in the expression of crc (3 fold decrease). Besides, a 3-4 fold increase in susceptibility to amikacin was observed as the expression level of crc declined. The QS-regulated factors were diminished as rhlR and lasI were downregulated in both states of growth.

CONCLUSION

In polymicrobial wound infection, Staphylococcus aureus plays a vital role in the metabolic changes of Pseudomonas aeruginosa. However, the levels of antibiotic susceptibility and pathogenicity of Pseudomonas aeruginosa also changed due to metabolism.

Complete genome sequence and genome-scale metabolic modelling of Acinetobacter baumannii type strain ATCC 19606

Multidrug-resistant (MDR) Acinetobacter baumannii is a critical threat to global health. The type strain ATCC 19606 has been widely used in studying the virulence, pathogenesis and mechanisms of antimicrobial resistance in A. baumannii. However, the lack of a complete genome sequence is a hindrance towards detailed bioinformatic studies. Here we report the generation of a complete genome for ATCC 19606 using PacBio sequencing. ATCC 19606 genome consists of a 3,980,848-bp chromosome and a 9,450-bp plasmid pMAC, and harbours a chromosomal dihydropteroate synthase gene sul2 conferring resistance to sulphonamides and a plasmid-borne ohr gene conferring resistance to peroxides. The genome also contains 69 virulence genes involved in surface adherence, biofilm formation, extracellular phospholipase, iron uptake, immune evasion and quorum sensing. Insertion sequences ISCR2 and ISAba11 are embedded in a 36.1-Kb genomic island, suggesting an IS-mediated large-scale DNA recombination. Furthermore, a genome-scale metabolic model (GSMM) iATCC19606v2 was constructed using the complete genome annotation. The model iATCC19606v2 incorporated a periplasmic compartment, 1,422 metabolites, 2,114 reactions and 1,009 genes, and a set of protein crowding constraints taking into account enzyme abundance limitation. The prediction of bacterial growth on 190 carbon and 95 nitrogen sources achieved a high accuracy of 85.6% compared to Biolog experiment results. Based upon two transposon mutant libraries of AB5075 and ATCC 17978, the predictions of essential genes reached the accuracy of 87.6% and 82.1%, respectively. Together, the complete genome sequence and high-quality GSMM iATCC19606v2 provide valuable tools for antimicrobial systems pharmacological investigations on A. baumannii.

Systems-level analysis of NalD mutation, a recurrent driver of rapid drug resistance in acute Pseudomonas aeruginosa infection

Pseudomonas aeruginosa, a main cause of human infection, can gain resistance to the antibiotic aztreonam through a mutation in NalD, a transcriptional repressor of cellular efflux. Here we combine computational analysis of clinical isolates, transcriptomics, metabolic modeling and experimental validation to find a strong association between NalD mutations and resistance to aztreonam-as well as resistance to other antibiotics-across P. aeruginosa isolated from different patients. A detailed analysis of one patient's timeline shows how this mutation can emerge in vivo and drive rapid evolution of resistance while the patient received cancer treatment, a bone marrow transplantation, and antibiotics up to the point of causing the patient's death. Transcriptomics analysis confirmed the primary mechanism of NalD action-a loss-of-function mutation that caused constitutive overexpression of the MexAB-OprM efflux system-which lead to aztreonam resistance but, surprisingly, had no fitness cost in the absence of the antibiotic. We constrained a genome-scale metabolic model using the transcriptomics data to investigate changes beyond the primary mechanism of resistance, including adaptations in major metabolic pathways and membrane transport concurrent with aztreonam resistance, which may explain the lack of a fitness cost. We propose that metabolic adaptations may allow resistance mutations to endure in the absence of antibiotics and could be targeted by future therapies against antibiotic resistant pathogens.

The CELLmicrocosmos Tools: A Small History of Java-Based Cell and Membrane Modelling Open Source Software Development

For more than one decade, CELLmicrocosmos tools are being developed. Here, we discus some of the technical and administrative hurdles to keep a software suite running so many years. The tools were being developed during a number of student projects and theses, whereas main developers refactored and maintained the code over the years. The focus of this publication is laid on two Java-based Open Source Software frameworks. Firstly, the CellExplorer with the PathwayIntegration combines the mesoscopic and the functional level by mapping biological networks onto cell components using database integration. Secondly, the MembraneEditor enables users to generate membranes of different lipid and protein compositions using the PDB format. Technicalities will be discussed as well as the historical development of these tools with a special focus on group-based development. In this way, university-associated developers of Integrative Bioinformatics applications should be inspired to go similar ways. All tools discussed in this publication can be downloaded and installed from https://www.CELLmicrocosmos.org.

Comparative metabolomics shows the metabolic profiles fluctuate in multi-drug resistant Escherichia coli strains

In this study, two susceptible strains and two multi-drug resistant clinical Escherichia coli strains were obtained by Kirby-Bauer method, and then a GC-MS-based metabolomics method was used to compare the differential expression of metabolites between two drug sensitive (CK1 and CK2) and two multidrug-resistant (MDR1 and MDR2) clinical strains of E. coli. We characterized a total of 273 metabolites, including 77 commonly altered metabolites, between MDR vs. antibiotic sensitive strains. Interestingly, the PCA score plot clearly discriminated drug sensitive and MDR strains. The following bioinformatics analysis showed that biosynthesis of amino acids, biosynthesis of phenylpropanoids and purine metabolism were commonly enriched in MDR strains. Moreover, microbial metabolism in diverse environments, carbon metabolism，and pyrimidine metabolism pathways were more likely to be enriched MDR1 strain, while ABC transporters, and cysteine and methionine metabolism pathways were enriched in MDR2 strains. The enzyme activities in several involved metabolic pathways were further measured and metabolite candidates were validated by GC-MS-SIM method. These results indicated that antibiotic resistance affects the metabolite profiles of bacteria. In general, our study provides evidence on the study and prediction of MDR characteristics and mechanisms in bacteria at the metabolite level. BIOLOGICAL SIGNIFICANCE: Overuse and abuse of antibiotics has led to the emergence of antibiotic-resistant strains of bacteria; however, relatively little is known about their resistance mechanisms. In this study, metabolomics method was used to compare the differential expression of metabolites between sensitive and multidrug-resistant clinical strains of E. coli. Results show that the PCA score plot clearly discriminated sensitive and MDR strains, indicating that they had different metabolic profiles. Further, bioinformatics analysis showed that biosynthesis of amino acids, biosynthesis of phenylpropanoids and purine metabolism may be related to resistance. Finally, the enzyme activities in several involved metabolic pathways were further measured and metabolite candidates were validated by GC-MS-SIM method. In general, our study provides evidence on the study and prediction of MDR characteristics and mechanisms in bacteria at the metabolite level.

Seawater salt-trapped Pseudomonas aeruginosa survives for years and gets primed for salinity tolerance

Background

In nature, microorganisms have to adapt to long-term stressful conditions often with growth limitations. However, little is known about the evolution of the adaptability of new bacteria to such environments. Pseudomonas aeruginosa, an opportunistic pathogen, after natural evaporation of seawater, was shown to be trapped in laboratory-grown halite crystals and to remain viable after entrapment for years. However, how this bacterium persists and survives in such hypersaline conditions is not understood.

Results

In this study, we aimed to understand the basis of survival, and to characterise the physiological changes required to develop salt tolerance using P. aeruginosa as a model. Several clones of P. aeruginosa were rescued after 14 years in naturally evaporated marine salt crystals. Incubation of samples in nutrient-rich broth allowed re-growth and subsequent plating yielded observable colonies. Whole genome sequencing of the P. aeruginosa isolates confirmed the recovery of the original strain. The re-grown strains, however, showed a new phenotype consisting of an enhanced growth in growing salt concentration compared to the ancestor strain. The intracellular accumulation of K⁺ was elicited by high concentration of Na⁺ in the external medium to maintain the homeostasis. Whole transcriptomic analysis by microarray indicated that 78 genes had differential expression between the parental strain and its derivative clones. Sixty-one transcripts were up-regulated, while 17 were down-regulated. Based on a collection of single-gene knockout mutants and gene ontology analysis, we suggest that the adaptive response in P. aeruginosa to hyper-salinity relies on multiple gene product interactions.

Conclusions

The individual gene contributions build up the observed phenotype, but do not ease the identification of salinity-related metabolic pathways. The long-term inclusion of P. aeruginosa in salt crystals primes the bacteria, mediating a readjustment of the bacterial physiology to growth in higher salt concentrations. Our findings provide a starting point to understand how P. aeruginosa, a relevant environmental and pathogenic bacterium, survives to long-term salt stress.

Electronic supplementary material

The online version of this article (10.1186/s12866-019-1499-2) contains supplementary material, which is available to authorized users.

Genome-Scale Metabolic Model of a Microbial Cell Factory (Brevibacillus thermoruber 423) with Multi-Industry Potentials for Exopolysaccharide Production

Brevibacillus thermoruber 423 is a thermophilic bacterium capable of producing high levels of exopolysaccharide (EPS) that has broad applications in nutrition, feed, cosmetics, pharmaceutical, and chemical industries, not to mention in health and bionanotechnology sectors. EPS is a natural, nontoxic, and biodegradable polymer of sugar residues and plays pivotal roles in cell-to-cell interactions, adhesion, biofilm formation, and protection of cell against environmental extremes. This bacterium is a thermophilic EPS producer while exceeding other thermophilic producers by virtue of high level of polymer synthesis. Recently, B. thermoruber 423 was noted for relevance to multiple industry sectors because of its capacity to use xylose, and produce EPS, isoprenoids, ethanol/butanol, lipases, proteases, cellulase, and glucoamylase enzymes as well as its resistance to arsenic. A key step in understanding EPS production with a systems-based approach is the knowledge of microbial genome sequence. To speed biotechnology and industrial applications, this study reports on a genome-scale metabolic model (GSMM) of B. thermoruber 423, constructed using the recently available high-quality genome sequence that we have subsequently validated using physiological data on batch growth and EPS production on seven different carbon sources. The model developed contains 1454 reactions (of which 1127 are assigned an enzyme commission number) and 1410 metabolites from 925 genes. This GSMM offers the promise to enable and accelerate further systems biology and industrial scale studies, not to mention the ability to calculate metabolic flux distribution in large networks and multiomic data integration.

Comparative Metabolomics and Transcriptomics Reveal Multiple Pathways Associated with Polymyxin Killing in Pseudomonas aeruginosa

Pseudomonas aeruginosa has been highlighted by the recent WHO Global Priority Pathogen List due to multidrug resistance. Without new antibiotics, polymyxins remain a last-line therapeutic option for this difficult-to-treat pathogen. The emergence of polymyxin resistance highlights the growing threat to our already very limited antibiotic armamentarium and the urgency to understand the exact mechanisms of polymyxin activity and resistance. Integration of the correlative metabolomics and transcriptomics results in the present study discovered that polymyxin treatment caused significant perturbations in the biosynthesis of lipids, lipopolysaccharide, and peptidoglycan, central carbon metabolism, and oxidative stress. Importantly, lipid A modifications were surprisingly rapid in response to polymyxin treatment at clinically relevant concentrations. This is the first study to reveal the dynamics of polymyxin-induced cellular responses at the systems level, which highlights that combination therapy should be considered to minimize resistance to the last-line polymyxins. The results also provide much-needed mechanistic information which potentially benefits the discovery of new-generation polymyxins.

Polymyxins are a last-line therapy against multidrug-resistant Pseudomonas aeruginosa; however, resistance to polymyxins has been increasingly reported. Therefore, understanding the mechanisms of polymyxin activity and resistance is crucial for preserving their clinical usefulness. This study employed comparative metabolomics and transcriptomics to investigate the responses of polymyxin-susceptible P. aeruginosa PAK (polymyxin B MIC, 1 mg/liter) and its polymyxin-resistant pmrB mutant PAKpmrB6 (MIC, 16 mg/liter) to polymyxin B (4, 8, and 128 mg/liter) at 1, 4, and 24 h, respectively. Our results revealed that polymyxin B at 4 mg/liter induced different metabolic and transcriptomic responses between polymyxin-susceptible and -resistant P. aeruginosa. In strain PAK, polymyxin B significantly activated PmrAB and the mediated arn operon, leading to increased 4-amino-4-deoxy-L-arabinose (L-Ara4N) synthesis and the addition to lipid A. In contrast, polymyxin B did not increase lipid A modification in strain PAKpmrB6. Moreover, the syntheses of lipopolysaccharide and peptidoglycan were significantly decreased in strain PAK but increased in strain PAKpmrB6 due to polymyxin B treatment. In addition, 4 mg/liter polymyxin B significantly perturbed phospholipid and fatty acid levels and induced oxidative stress in strain PAK, but not in PAKpmrB6. Notably, the increased trehalose-6-phosphate levels indicate that polymyxin B potentially caused osmotic imbalance in both strains. Furthermore, 8 and 128 mg/liter polymyxin B significantly elevated lipoamino acid levels and decreased phospholipid levels but without dramatic changes in lipid A modification in wild-type and mutant strains, respectively. Overall, this systems study is the first to elucidate the complex and dynamic interactions of multiple cellular pathways associated with the polymyxin mode of action against P. aeruginosa.

IMPORTANCEPseudomonas aeruginosa has been highlighted by the recent WHO Global Priority Pathogen List due to multidrug resistance. Without new antibiotics, polymyxins remain a last-line therapeutic option for this difficult-to-treat pathogen. The emergence of polymyxin resistance highlights the growing threat to our already very limited antibiotic armamentarium and the urgency to understand the exact mechanisms of polymyxin activity and resistance. Integration of the correlative metabolomics and transcriptomics results in the present study discovered that polymyxin treatment caused significant perturbations in the biosynthesis of lipids, lipopolysaccharide, and peptidoglycan, central carbon metabolism, and oxidative stress. Importantly, lipid A modifications were surprisingly rapid in response to polymyxin treatment at clinically relevant concentrations. This is the first study to reveal the dynamics of polymyxin-induced cellular responses at the systems level, which highlights that combination therapy should be considered to minimize resistance to the last-line polymyxins. The results also provide much-needed mechanistic information which potentially benefits the discovery of new-generation polymyxins.

Efflux pumps and antimicrobial resistance: Paradoxical components in systems genomics

Efflux pumps play a major role in the increasing antimicrobial resistance rendering a large number of drugs of no use. Large numbers of pathogens are becoming multidrug resistant due to inadequate dosage and use of the existing antimicrobials. This leads to the need for identifying new efflux pump inhibitors. Design of novel targeted therapies using inherent complexity involved in the biological network modeling has gained increasing importance in recent times. The predictive approaches should be used to determine antimicrobial activities with high pathogen specificity and microbicidal potency. Antimicrobial peptides, which are part of our innate immune system, have the ability to respond to infections and have gained much attention in making resistant strain sensitive to existing drugs. In this review paper, we outline evidences linking host-directed therapy with the efflux pump activity to infectious disease.

Genome-scale metabolic modeling of responses to polymyxins in Pseudomonas aeruginosa

Background

Pseudomonas aeruginosa often causes multidrug-resistant infections in immunocompromised patients, and polymyxins are often used as the last-line therapy. Alarmingly, resistance to polymyxins has been increasingly reported worldwide recently. To rescue this last-resort class of antibiotics, it is necessary to systematically understand how P. aeruginosa alters its metabolism in response to polymyxin treatment, thereby facilitating the development of effective therapies. To this end, a genome-scale metabolic model (GSMM) was used to analyze bacterial metabolic changes at the systems level.

Findings

A high-quality GSMM iPAO1 was constructed for P. aeruginosa PAO1 for antimicrobial pharmacological research. Model iPAO1 encompasses an additional periplasmic compartment and contains 3022 metabolites, 4265 reactions, and 1458 genes in total. Growth prediction on 190 carbon and 95 nitrogen sources achieved an accuracy of 89.1%, outperforming all reported P. aeruginosa models. Notably, prediction of the essential genes for growth achieved a high accuracy of 87.9%. Metabolic simulation showed that lipid A modifications associated with polymyxin resistance exert a limited impact on bacterial growth and metabolism but remarkably change the physiochemical properties of the outer membrane. Modeling with transcriptomics constraints revealed a broad range of metabolic responses to polymyxin treatment, including reduced biomass synthesis, upregulated amino acid catabolism, induced flux through the tricarboxylic acid cycle, and increased redox turnover.

Conclusions

Overall, iPAO1 represents the most comprehensive GSMM constructed to date for Pseudomonas. It provides a powerful systems pharmacology platform for the elucidation of complex killing mechanisms of antibiotics.

Metabolic Analyses Revealed Time-Dependent Synergistic Killing by Colistin and Aztreonam Combination Against Multidrug-Resistant Acinetobacter baumannii

Background: Polymyxins are a last-line class of antibiotics against multidrug-resistant Acinetobacter baumannii; however, polymyxin resistance can emerge with monotherapy. Therefore, synergistic combination therapy is a crucial strategy to reduce polymyxin resistance.

Methods: This study conducted untargeted metabolomics to investigate metabolic responses of a multidrug-resistant (MDR) A. baumannii clinical isolate, AB090342, to colistin and aztreonam alone, and their combination at 1, 4, and 24 h. Metabolomics data were analyzed using univariate and multivariate statistics; metabolites showing ≥ 2-fold changes were subjected to bioinformatics analysis.

Results: The synergistic action of colistin-aztreonam combination was initially driven by colistin via significant disruption of bacterial cell envelope, with decreased phospholipid and fatty acid levels at 1 h. Cell wall biosynthesis was inhibited at 4 and 24 h by aztreonam alone and the combination as shown by the decreased levels of two amino sugars, UDP-N-acetylglucosamine and UDP-N-acetylmuramate; these results suggested that aztreonam was primarily responsible for the synergistic killing at later time points. Moreover, aztreonam alone and the combination significantly depleted pentose phosphate pathway, amino acid, peptide and nucleotide metabolism, but elevated fatty acid and key phospholipid levels. Collectively, the combination synergy between colistin and aztreonam was mainly due to the inhibition of cell envelope biosynthesis via different metabolic perturbations.

Conclusion: This metabolomics study is the first to elucidate multiple cellular pathways associated with the time-dependent synergistic action of colistin-aztreonam combination against MDR A. baumannii. Our results provide important mechanistic insights into optimizing synergistic colistin combinations in patients.