Network-Based Metabolism-Centered Screening of Potential Drug Targets in Klebsiella pneumoniae at Genome Scale

Unraveling bacterial stress responses: implications for next-generation antimicrobial solutions

The accelerated spread of antimicrobial-resistant bacteria has caused a serious health problem and rendered antimicrobial treatments ineffective. Innovative approaches are crucial to overcome the health threat posed by resistant pathogens and prevent the emergence of untreatable infections. Triggering stress responses in bacteria can diminish susceptibility to various antimicrobials by inducing resistance mechanisms. Therefore, a thorough understanding of stress response control, especially in relation to antimicrobial resistance, offers valuable perspectives for innovative and efficient therapeutic approaches to combat antimicrobial resistance. The aim of this study was to evaluate the stress responses of 8 different bacteria by analyzing reporter metabolites, around which significant alterations were observed, using a pathway-driven computational approach. For this purpose, the transcriptomic data that the bacterial pathogens were grown under 11 different stress conditions mimicking the human host environments were integrated with the genome-scale metabolic models of 8 pathogenic species (Enterococcus faecalis OG1R, Escherichia coli EPEC O127:H6 E2348/69, Escherichia coli ETEC H10407, Escherichia coli UPEC 536, Klebsiella pneumoniae MGH 78578, Pseudomonas aeruginosa PAO1, Staphylococcus aureus MRSA252, and Staphylococcus aureus MSSA476). The resulting reporter metabolites were enriched in multiple metabolic pathways, with cofactor biosynthesis being the most important. The results of this study will serve as a guide for the development of antimicrobial agents as they provide a first insight into potential drug targets.

Systematic analysis of microorganisms' metabolism for selective targeting

Selective drugs with a relatively narrow spectrum can reduce the side effects of treatments compared to broad-spectrum antibiotics by specifically targeting the pathogens responsible for infection. Furthermore, combating an infectious pathogen, especially a drug-resistant microorganism, is more efficient by attacking multiple targets. Here, we combined synthetic lethality with selective drug targeting to identify multi-target and organism-specific potential drug candidates by systematically analyzing the genome-scale metabolic models of six different microorganisms. By considering microorganisms as targeted or conserved in groups ranging from one to six members, we designed 665 individual case studies. For each case, we identified single essential reactions as well as double, triple, and quadruple synthetic lethal reaction sets that are lethal for targeted microorganisms and neutral for conserved ones. As expected, the number of obtained solutions for each case depends on the genomic similarity between the studied microorganisms. Mapping the identified potential drug targets to their corresponding pathways highlighted the importance of key subsystems such as cell envelope biosynthesis, glycerophospholipid metabolism, membrane lipid metabolism, and the nucleotide salvage pathway. To assist in the validation and further investigation of our proposed potential drug targets, we introduced two sets of targets that can theoretically address a substantial portion of the 665 cases. We expect that the obtained solutions provide valuable insights into designing narrow-spectrum drugs that selectively cause system-wide damage only to the target microorganisms.

Genomic and Transcriptomic Analyses Reveal Multiple Strategies for Vibrio parahaemolyticus to Tolerate Sub-Lethal Concentrations of Three Antibiotics

Vibrio parahaemolyticus can cause acute gastroenteritis, wound infections, and septicemia in humans. The overuse of antibiotics in aquaculture may lead to a high incidence of the multidrug-resistant (MDR) pathogen. Nevertheless, the genome evolution of V. parahaemolyticus in aquatic animals and the mechanism of its antibiotic tolerance remain to be further deciphered. Here, we investigated the molecular basis of the antibiotic tolerance of V. parahaemolyticus isolates (n = 3) originated from shellfish and crustaceans using comparative genomic and transcriptomic analyses. The genome sequences of the V. parahaemolyticus isolates were determined (5.0-5.3 Mb), and they contained 4709-5610 predicted protein-encoding genes, of which 823-1099 genes were of unknown functions. Comparative genomic analyses revealed a number of mobile genetic elements (MGEs, n = 69), antibiotic resistance-related genes (n = 7-9), and heavy metal tolerance-related genes (n = 2-4). The V. parahaemolyticus isolates were resistant to sub-lethal concentrations (sub-LCs) of ampicillin (AMP, 512 μg/mL), kanamycin (KAN, 64 μg/mL), and streptomycin (STR, 16 μg/mL) (p < 0.05). Comparative transcriptomic analyses revealed that there were significantly altered metabolic pathways elicited by the sub-LCs of the antibiotics (p < 0.05), suggesting the existence of multiple strategies for antibiotic tolerance in V. parahaemolyticus. The results of this study enriched the V. parahaemolyticus genome database and should be useful for controlling the MDR pathogen worldwide.

Relevance of the Adjuvant Effect between Cellular Homeostasis and Resistance to Antibiotics in Gram-Negative Bacteria with Pathogenic Capacity: A Study of Klebsiella pneumoniae

Antibiotic resistance has become a global issue. The most significant risk is the acquisition of these mechanisms by pathogenic bacteria, which can have a severe clinical impact and pose a public health risk. This problem assumes that bacterial fitness is a constant phenomenon and should be approached from an evolutionary perspective to develop the most appropriate and effective strategies to contain the emergence of strains with pathogenic potential. Resistance mechanisms can be understood as adaptive processes to stressful conditions. This review examines the relevance of homeostatic regulatory mechanisms in antimicrobial resistance mechanisms. We focus on the interactions in the cellular physiology of pathogenic bacteria, particularly Gram-negative bacteria, and specifically Klebsiella pneumoniae. From a clinical research perspective, understanding these interactions is crucial for comprehensively understanding the phenomenon of resistance and developing more effective drugs and treatments to limit or attenuate bacterial sepsis, since the most conserved adjuvant phenomena in bacterial physiology has turned out to be more optimized and, therefore, more susceptible to alterations due to pharmacological action.

In-silico identification of potential inhibitors against FabI protein in Klebsiella pneumoniae

The development of new antimicrobial drugs is needed to combat multi-drug resistant and novel hypervirulent strains of Klebsiella pneumoniae (KPN) that are associated with increased morbidity and mortality globally. The FabI protein plays a crucial role in fatty acid biosynthesis and has been identified as an important target for in-silico, in-vitro, and in-vivo drug discovery. In this study we have used computer integrated-drug discovery approaches and binding-free energy calculations to identify three novel inhibitors (21272541, 67724550, and 67724551) of the FabI protein. All inhibitors showed strong affinity including van der Waals energy, electrostatic energy, polar and non-polar energies; however, the 21272541 compound was the most effective inhibitor and bound with the strongest affinity (ΔGbind -59.02 kcal/mol) to the FabI protein. Nevertheless, all three inhibitors are promising targets for new novel antimicrobial drugs that could contribute to the management of antimicrobial resistant KPN infections based on various computational analysis. Additional in-vitro and in-vivo clinical studies will be needed to confirm drug effectiveness for the treatment of KPN infections.

PBITV3 : A robust and comprehensive tool for screening pathogenic proteomes for drug targets and prioritizing vaccine candidates

Rise of life-threatening superbugs, pandemics and epidemics warrants the need for cost-effective and novel pharmacological interventions. Availability of publicly available proteomes of pathogens supports development of high-throughput discovery platforms to prioritize potential drug-targets and develop testable hypothesis for pharmacological screening. The pipeline builder for identification of target (PBIT) was developed in 2016 and updated in 2021, with the purpose of accelerating the search for drug-targets by integration of methods like comparative and subtractive genomics, essentiality/virulence and druggability analysis. Since then, it has been used for identification of drugs and vaccine targets, safety profiling of multiepitope vaccines and mRNA vaccine construction against a broad-spectrum of pathogens. This tool has now been updated with functionalities related to systems biology and immuno-informatics and validated by analyzing 48 putative antigens of Mycobacterium tuberculosis documented in literature. PBITv3 available as both online and offline tools will enhance drug discovery against emerging drug-resistant infectious agents. PBITv3 can be freely accessed at http://pbit.bicnirrh.res.in/.

Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases

The interconnectivity of advanced biological systems is essential for their proper functioning. In modern connectomics, biological entities such as proteins, genes, RNA, DNA, and metabolites are often represented as nodes, while the physical, biochemical, or functional interactions between them are represented as edges. Among these entities, metabolites are particularly significant as they exhibit a closer relationship to an organism's phenotype compared to genes or proteins. Moreover, the metabolome has the ability to amplify small proteomic and transcriptomic changes, even those from minor genomic changes. Metabolic networks, which consist of complex systems comprising hundreds of metabolites and their interactions, play a critical role in biological research by mediating energy conversion and chemical reactions within cells. This review provides an introduction to common metabolic network models and their construction methods. It also explores the diverse applications of metabolic networks in elucidating disease mechanisms, predicting and diagnosing diseases, and facilitating drug development. Additionally, it discusses potential future directions for research in metabolic networks. Ultimately, this review serves as a valuable reference for researchers interested in metabolic network modeling, analysis, and their applications.

Metabolic modeling predicts unique drug targets in Borrelia burgdorferi

IMPORTANCE

Lyme disease is often treated using long courses of antibiotics, which can cause side effects for patients and risks the evolution of antimicrobial resistance. Narrow-spectrum antimicrobials would reduce these risks, but their development has been slow because the Lyme disease bacterium, Borrelia burgdorferi, is difficult to work with in the laboratory. To accelerate the drug discovery pipeline, we developed a computational model of B. burgdorferi's metabolism and used it to predict essential enzymatic reactions whose inhibition prevented growth in silico. These predictions were validated using small-molecule enzyme inhibitors, several of which were shown to have specific activity against B. burgdorferi. Although the specific compounds used are not suitable for clinical use, we aim to use them as lead compounds to develop optimized drugs targeting the pathways discovered here.

Immunometabolic control by Klebsiella pneumoniae

Klebsiella pneumoniae is a common Gram-negative pathogen associated with community-acquired and healthcare-associated infections. Its ability to acquire genetic elements resulted in its rapid development of resistance to virtually all antimicrobial agents. Once infection is established, K. pneumoniae is able to evade the host immune response and perhaps more importantly, undergo metabolic rewiring to optimize its ability to maintain infection. K. pneumoniae lipopolysaccharide and capsular polysaccharide are central factors in the induction and evasion of immune clearance. Less well understood is the importance of immunometabolism, the intersection between cellular metabolism and immune function, in the host response to K. pneumoniae infection. Bacterial metabolism itself is perceived as a metabolic stress to the host, altering the microenvironment at the site of infection. In this review, we will discuss the metabolic responses induced by K. pneumoniae, particularly in response to stimulation with the metabolically active bacteria versus pathogen-associated molecular patterns alone, and their implications in shaping the nature of the immune response and the infection outcome. A better understanding of the immunometabolic response to K. pneumoniae may help identify new targets for therapeutic intervention in the treatment of multidrug-resistant bacterial infections.

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.

Genomic and Transcriptomic Analysis Reveal Multiple Strategies for the Cadmium Tolerance in Vibrio parahaemolyticus N10-18 Isolated from Aquatic Animal Ostrea gigas Thunberg

The waterborne Vibrio parahaemolyticus can cause acute gastroenteritis, wound infection, and septicemia in humans. Pollution of heavy metals in aquatic environments is proposed to link high incidence of the multidrug-resistant (MDR) pathogen. Nevertheless, the genome evolution and heavy metal tolerance mechanism of V. parahaemolyticus in aquatic animals remain to be largely unveiled. Here, we overcome the limitation by characterizing an MDR V. parahaemolyticus N10-18 isolate with high cadmium (Cd) tolerance using genomic and transcriptomic techniques. The draft genome sequence (4,910,080 bp) of V. parahaemolyticus N10-18 recovered from Ostrea gigas Thunberg was determined, and 722 of 4653 predicted genes had unknown function. Comparative genomic analysis revealed mobile genetic elements (n = 11) and heavy metal and antibiotic-resistance genes (n = 38 and 7). The bacterium significantly changed cell membrane structure to resist the Cd2+ (50 μg/mL) stress (p < 0.05). Comparative transcriptomic analysis revealed seven significantly altered metabolic pathways elicited by the stress. The zinc/Cd/mercury/lead transportation and efflux and the zinc ATP-binding cassette (ABC) transportation were greatly enhanced; metal and iron ABC transportation and thiamine metabolism were also up-regulated; conversely, propanoate metabolism and ribose and maltose ABC transportation were inhibited (p < 0.05). The results of this study demonstrate multiple strategies for the Cd tolerance in V. parahaemolyticus.

Metagenome Analysis of the Bacterial Characteristics in Invasive Klebsiella Pneumoniae Liver Abscesses

Background

Klebsiella pneumoniae liver abscess (KPLA) combined with extrahepatic migratory infection (EMI) is defined as invasive KPLA (IKPLA) and is associated with a poor prognosis. The mechanism of IKPLA formation is yet to be elucidated. In this study, metagenomic sequencing was used to compare the bacterial characteristics between IKPLA and KPLA to explore the underlying mechanism of invasiveness.

Methods

Clinical details, imaging, and microbial features were retrospectively evaluated by medical record review. Metagenomic sequencing was performed on the pus samples of liver abscesses whose culture results were indicative of monomicrobial Klebsiella pneumoniae (K. pneumoniae). Bacterial diversity and composition in IKPLA and KPLA were comparatively analyzed, and the key pathways and genes that may affect invasiveness were further explored.

Results

Sixteen patients were included in this study. Five patients with EMI were included in the IKPLA group, and the other eleven patients without EMI were assigned to the KPLA group. There was no statistical difference in the hypermucoviscous phenotype and serotype of K. pneumoniae between the two groups. The bacterial diversity of IKPLA was lower than that of KPLA. The abundant taxa in the IKPLA group were primarily species of unclassified Enterobacteriaceae and K. pneumoniae. The KPLA group had a high abundance of the genera Tetrasphaera and Leuconostoc. Metabolic pathway genes represented most of the enriched genes in IKPLA. Fourteen pathogenic genes with significant differences in abundance were identified between the two groups, including ybtS, fepC, phoQ, acrB, fimK, magA, entC, arnT, iucA, fepG, oqxB, entA, tonB, and entF (p < 0.001).

Conclusion

The diversity and bacterial composition of IKPLA were significantly different from those of KPLA. Microbiological changes in the abscess, activation of the related metabolic pathways, and the pathogenic gene expression may constitute a novel mechanism that regulates the invasiveness of KPLA.

Dual transcriptome based reconstruction of Salmonella-human integrated metabolic network to screen potential drug targets

Salmonella enterica serovar Typhimurium (S. Typhimurium) is a highly adaptive pathogenic bacteria with a serious public health concern due to its increasing resistance to antibiotics. Therefore, identification of novel drug targets for S. Typhimurium is crucial. Here, we first created a pathogen-host integrated genome-scale metabolic network by combining the metabolic models of human and S. Typhimurium, which we further tailored to the pathogenic state by the integration of dual transcriptome data. The integrated metabolic model enabled simultaneous investigation of metabolic alterations in human cells and S. Typhimurium during infection. Then, we used the tailored pathogen-host integrated genome-scale metabolic network to predict essential genes in the pathogen, which are candidate novel drug targets to inhibit infection. Drug target prioritization procedure was applied to these targets, and pabB was chosen as a putative drug target. It has an essential role in 4-aminobenzoic acid (PABA) synthesis, which is an essential biomolecule for many pathogens. A structure based virtual screening was applied through docking simulations to predict candidate compounds that eliminate S. Typhimurium infection by inhibiting pabB. To our knowledge, this is the first comprehensive study for predicting drug targets and drug like molecules by using pathogen-host integrated genome-scale models, dual RNA-seq data and structure-based virtual screening protocols. This framework will be useful in proposing novel drug targets and drugs for antibiotic-resistant pathogens.

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.

Pathway Driven Target Selection in Klebsiella pneumoniae: Insights Into Carbapenem Exposure

Carbapenem-resistant Klebsiella pneumoniae (CR-KP) represents an emerging threat to public health. CR-KP infections result in elevated morbidity and mortality. This fact, coupled with their global dissemination and increasingly limited number of therapeutic options, highlights the urgency of novel antimicrobials. Innovative strategies linking genome-wide interrogation with multi-layered metabolic data integration can accelerate the early steps of drug development, particularly target selection. Using the BioCyc ontology, we generated and manually refined a metabolic network for a CR-KP, K. pneumoniae Kp13. Converted into a reaction graph, we conducted topological-based analyses in this network to prioritize pathways exhibiting druggable features and fragile metabolic points likely exploitable to develop novel antimicrobials. Our results point to the aptness of previously recognized pathways, such as lipopolysaccharide and peptidoglycan synthesis, and casts light on the possibility of targeting less explored cellular functions. These functions include the production of lipoate, trehalose, glycine betaine, and flavin, as well as the salvaging of methionine. Energy metabolism pathways emerged as attractive targets in the context of carbapenem exposure, targeted either alone or in conjunction with current therapeutic options. These results prompt further experimental investigation aimed at controlling this highly relevant pathogen.

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.

Prioritization of biological processes based on the reconstruction and analysis of associative gene networks describing the response of plants to adverse environmental factors

Methods for prioritizing or ranking candidate genes according to their importance based on specif ic criteria via the analysis of gene networks are widely used in biomedicine to search for genes associated with diseases and to predict biomarkers, pharmacological targets and other clinically relevant molecules. These methods have also been used in other f ields, particularly in crop production. This is largely due to the development of technologies to solve problems in marker-oriented and genomic selection, which requires knowledge of the molecular genetic mechanisms underlying the formation of agriculturally valuable traits. A new direction for the study of molecular genetic mechanisms is the prioritization of biological processes based on the analysis of associative gene networks. Associative gene networks are heterogeneous networks whose vertices can depict both molecular genetic objects (genes, proteins, metabolites, etc.) and the higher-level factors (biological processes, diseases, external environmental factors, etc.) related to regulatory, physicochemical or associative interactions. Using a previously developed method, biological processes involved in plant responses to increased cadmium content, saline stress and drought conditions were prioritized according to their degree of connection with the gene networks in the SOLANUM TUBEROSUM knowledge base. The prioritization results indicate that fundamental processes, such as gene expression, post-translational modif ications, protein degradation, programmed cell death, photosynthesis, signal transmission and stress response play important roles in the common molecular genetic mechanisms for plant response to various adverse factors. On the other hand, a group of processes related to the development of seeds (“seeding development”) was revealed to be drought specif ic, while processes associated with ion transport (“ion transport”) were included in the list of responses specif ic to salt stress and processes associated with the metabolism of lipids were found to be involved specif ically in the response to cadmium.

Novel Drivers of Virulence in Clostridioides difficile Identified via Context-Specific Metabolic Network Analysis

The pathogen Clostridioides difficile causes toxin-mediated diarrhea and is the leading cause of hospital-acquired infection in the United States. Due to growing antibiotic resistance and recurrent infection, targeting C. difficile metabolism presents a new approach to combat this infection. Genome-scale metabolic network reconstructions (GENREs) have been used to identify therapeutic targets and uncover properties that determine cellular behaviors. Thus, we constructed C. difficile GENREs for a hypervirulent isolate (strain [str.] R20291) and a historic strain (str. 630), validating both with in vitro and in vivo data sets. Growth simulations revealed significant correlations with measured carbon source usage (positive predictive value [PPV] ≥ 92.7%), and single-gene deletion analysis showed >89.0% accuracy. Next, we utilized each GENRE to identify metabolic drivers of both sporulation and biofilm formation. Through contextualization of each model using transcriptomes generated from in vitro and infection conditions, we discovered reliance on the pentose phosphate pathway as well as increased usage of cytidine and N-acetylneuraminate when virulence expression is reduced, which was subsequently supported experimentally. Our results highlight the ability of GENREs to identify novel metabolite signals in higher-order phenotypes like bacterial pathogenesis. IMPORTANCE Clostridioides difficile has become the leading single cause of hospital-acquired infections. Numerous studies have demonstrated the importance of specific metabolic pathways in aspects of C. difficile pathophysiology, from initial colonization to regulation of virulence factors. In the past, genome-scale metabolic network reconstruction (GENRE) analysis of bacteria has enabled systematic investigation of the genetic and metabolic properties that contribute to downstream virulence phenotypes. With this in mind, we generated and extensively curated C. difficile GENREs for both a well-studied laboratory strain (str. 630) and a more recently characterized hypervirulent isolate (str. R20291). In silico validation of both GENREs revealed high degrees of agreement with experimental gene essentiality and carbon source utilization data sets. Subsequent exploration of context-specific metabolism during both in vitro growth and infection revealed consistent patterns of metabolism which corresponded with experimentally measured increases in virulence factor expression. Our results support that differential C. difficile virulence is associated with distinct metabolic programs related to use of carbon sources and provide a platform for identification of novel therapeutic targets.

Potential therapeutic targets of Klebsiella pneumoniae: a multi-omics review perspective

The multidrug resistance developed in many organisms due to the prolonged use of antibiotics has been an increasing global health crisis. Klebsiella pneumoniae is a causal organism for various infections, including respiratory, urinary tract and biliary diseases. Initially, immunocompromised individuals are primarily affected by K. pneumoniae. Due to the emergence of hypervirulent strains recently, both healthy and immunocompetent individuals are equally susceptible to K. pneumoniae infections. The infections caused by multidrug-resistant and hypervirulent K. pneumoniae strains are complicated to treat, illustrating an urgent need to develop novel and more practical approaches to combat the pathogen. We focused on the previously performed high-throughput analyses by other groups to discover several novel enzymes that may be considered attractive drug targets of K. pneumoniae. These targets qualify most of the selection criteria for drug targeting, including an absence of its homolog's gene in the host. The capsule, lipopolysaccharide, fimbriae, siderophores and essential virulence factors facilitate the pathogen entry, infection and survival inside the host. This review discusses K. pneumoniae pathophysiology, including its virulence determinants and further the potential drug targets that might facilitate the discovery of novel drugs and effective treatment regimens shortly.

Key Technologies for Progressing Discovery of Microbiome-Based Medicines

A growing number of experimental and computational approaches are illuminating the “microbial dark matter” and uncovering the integral role of commensal microbes in human health. Through this work, it is now clear that the human microbiome presents great potential as a therapeutic target for a plethora of diseases, including inflammatory bowel disease, diabetes and obesity. The development of more efficacious and targeted treatments relies on identification of causal links between the microbiome and disease; with future progress dependent on effective links between state-of-the-art sequencing approaches, computational analyses and experimental assays. We argue determining causation is essential, which can be attained by generating hypotheses using multi-omic functional analyses and validating these hypotheses in complex, biologically relevant experimental models. In this review we discuss existing analysis and validation methods, and propose best-practice approaches required to enable the next phase of microbiome research.

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 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.

Identification of Growth Inhibitors of the Fish Pathogen Saprolegnia parasitica Using in silico Subtractive Proteomics, Computational Modeling, and Biochemical Validation

Many Stramenopile species belonging to oomycetes from the genus Saprolegnia infect fish, amphibians, and crustaceans in aquaculture farms and natural ecosystems. Saprolegnia parasitica is one of the most severe fish pathogens, responsible for high losses in the aquaculture industry worldwide. Most of the molecules reported to date for the control of Saprolegnia infections either are inefficient or have negative impacts on the health of the fish hosts or the environment resulting in substantial economic losses. Until now, the whole proteome of S. parasitica has not been explored for a systematic screening of novel inhibitors against the pathogen. The present study was designed to develop a consensus computational framework for the identification of potential target proteins and their inhibitors and subsequent experimental validation of selected compounds. Comparative analysis between the proteomes of Saprolegnia, humans and fish species identified proteins that are specific and essential for the survival of the pathogen. The DrugBank database was exploited to select food and drug administration (FDA)-approved inhibitors whose high binding affinity to their respective protein targets was confirmed by computational modeling. At least six of the identified compounds significantly inhibited the growth of S. parasitica in vitro. Triclosan was found to be most effective with a minimum inhibitory concentration (MIC₁₀₀) of 4 μg/ml. Optical microscopy showed that the inhibitors affect the morphology of hyphal cells, with hyper-branching being commonly observed. The inhibitory effects of the compounds identified in this study on Saprolegnia’s mycelial growth indicate that they are potentially usable for disease control against this class of oomycete pathogens. Similar approaches can be easily adopted for the identification of potential inhibitors against other plant and animal pathogenic oomycete infections.

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.