Comparative Genome-Scale Metabolic Modeling of Metallo-Beta-Lactamase-Producing Multidrug-Resistant Klebsiella pneumoniae Clinical Isolates

Exploring the resistome, virulome, and mobilome of multidrug-resistant Klebsiella pneumoniae isolates: deciphering the molecular basis of carbapenem resistance

BACKGROUND

Klebsiella pneumoniae, a notorious pathogen for causing nosocomial infections has become a major cause of neonatal septicemia, leading to high morbidity and mortality worldwide. This opportunistic bacterium has become highly resistant to antibiotics due to the widespread acquisition of genes encoding a variety of enzymes such as extended-spectrum beta-lactamases (ESBLs) and carbapenemases. We collected Klebsiella pneumoniae isolates from a local tertiary care hospital from February 2019-February 2021. To gain molecular insight into the resistome, virulome, and genetic environment of significant genes of multidrug-resistant K. pneumoniae isolates, we performed the short-read whole-genome sequencing of 10 K. pneumoniae isolates recovered from adult patients, neonates, and hospital tap water samples.

RESULTS

The draft genomes of the isolates varied in size, ranging from 5.48 to 5.96 Mbp suggesting the genome plasticity of this pathogen. Various genes conferring resistance to different classes of antibiotics e.g., aminoglycosides, quinolones, sulfonamides, tetracycline, and trimethoprim were identified in all sequenced isolates. The highest resistance was observed towards carbapenems, which has been putatively linked to the presence of both class B and class D carbapenemases, blaNDM, and blaOXA, respectively. Moreover, the biocide resistance gene qacEdelta1 was found in 6/10 of the sequenced strains. The sequenced isolates exhibited a broad range of sequence types and capsular types. The significant antibiotic resistance genes (ARGs) were bracketed by a variety of mobile genetic elements (MGEs). Various spontaneous mutations in genes other than the acquired antibiotic-resistance genes were observed, which play an indirect role in making these bugs resistant to antibiotics. Loss or deficiency of outer membrane porins, combined with ESBL production, played a significant role in carbapenem resistance in our sequenced isolates. Phylogenetic analysis revealed that the study isolates exhibited evolutionary relationships with strains from China, India, and the USA suggesting a shared evolutionary history and potential dissemination of similar genes amongst the isolates of different origins.

CONCLUSIONS

This study provides valuable insight into the presence of multiple mechanisms of carbapenem resistance in K. pneumoniae strains including the acquisition of multiple antibiotic-resistance genes through mobile genetic elements. Identification of rich mobilome yielded insightful information regarding the crucial role of insertion sequences, transposons, and integrons in shaping the genome of bacteria for the transmission of various resistance-associated genes. Multi-drug resistant isolates that had the fewest resistance genes exhibited a significant number of mutations. K. pneumoniae isolate from water source displayed comparable antibiotic resistance determinants to clinical isolates and the highest number of virulence-associated genes suggesting the possible interplay of ARGs amongst bacteria from different sources.

A validated pangenome-scale metabolic model for the Klebsiella pneumoniae species complex

The Klebsiella pneumoniae species complex (KpSC) is a major source of nosocomial infections globally with high rates of resistance to antimicrobials. Consequently, there is growing interest in understanding virulence factors and their association with cellular metabolic processes for developing novel anti-KpSC therapeutics. Phenotypic assays have revealed metabolic diversity within the KpSC, but metabolism research has been neglected due to experiments being difficult and cost-intensive. Genome-scale metabolic models (GSMMs) represent a rapid and scalable in silico approach for exploring metabolic diversity, which compile genomic and biochemical data to reconstruct the metabolic network of an organism. Here we use a diverse collection of 507 KpSC isolates, including representatives of globally distributed clinically relevant lineages, to construct the most comprehensive KpSC pan-metabolic model to date, KpSC pan v2. Candidate metabolic reactions were identified using gene orthology to known metabolic genes, prior to manual curation via extensive literature and database searches. The final model comprised a total of 3550 reactions, 2403 genes and can simulate growth on 360 unique substrates. We used KpSC pan v2 as a reference to derive strain-specific GSMMs for all 507 KpSC isolates, and compared these to GSMMs generated using a prior KpSC pan-reference (KpSC pan v1) and two single-strain references. We show that KpSC pan v2 includes a greater proportion of accessory reactions (8.8 %) than KpSC pan v1 (2.5 %). GSMMs derived from KpSC pan v2 also generate more accurate growth predictions, with high median accuracies of 95.4 % (aerobic, n=37 isolates) and 78.8 % (anaerobic, n=36 isolates) for 124 matched carbon substrates. KpSC pan v2 is freely available at https://github.com/kelwyres/KpSC-pan-metabolic-model, representing a valuable resource for the scientific community, both as a source of curated metabolic information and as a reference to derive accurate strain-specific GSMMs. The latter can be used to investigate the relationship between KpSC metabolism and traits of interest, such as reservoirs, epidemiology, drug resistance or virulence, and ultimately to inform novel KpSC control strategies.

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

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

A genome-scale metabolic model of the effect of dissolved oxygen on 1,3-propanediol fermentation by Klebsiella pneumoniae

Although 1,3-propanediol (1,3-PD) is usually considered an anaerobic fermentation product from glycerol by Klebsiella pneumoniae, microaerobic conditions proved to be more conducive to 1,3-PD production. In this study, a genome-scale metabolic model (GSMM) specific to K. pneumoniae KG2, a high 1.3-PD producer, was constructed. The iZY1242 model contains 2090 reactions, 1242 genes and 1433 metabolites. The model was not only able to accurately characterise cell growth, but also accurately simulate the fed-batch 1,3-PD fermentation process. Flux balance analyses by iZY1242 was performed to dissect the mechanism of stimulated 1,3-PD production under microaerobic conditions, and the maximum yield of 1,3-PD on glycerol was 0.83 mol/mol under optimal microaerobic conditions. Combined with experimental data, the iZY1242 model is a useful tool for establishing the best conditions for microaeration fermentation to produce 1,3-PD from glycerol in K. pneumoniae.

A contribution of metabolic engineering to addressing medical problems: Metabolic flux analysis

Metabolic engineering has served as a systematic discipline for industrial biotechnology as it has offered systematic tools and methods for strain development and bioprocess optimization. Because these metabolic engineering tools and methods are concerned with the biological network of a cell with emphasis on metabolic network, they have also been applied to a range of medical problems where better understanding of metabolism has also been perceived to be important. Metabolic flux analysis (MFA) is a unique systematic approach initially developed in the metabolic engineering community, and has proved its usefulness and potential when addressing a range of medical problems. In this regard, this review discusses the contribution of MFA to addressing medical problems. For this, we i) provide overview of the milestones of MFA, ii) define two main branches of MFA, namely constraint-based reconstruction and analysis (COBRA) and isotope-based MFA (iMFA), and iii) present successful examples of their medical applications, including characterizing the metabolism of diseased cells and pathogens, and identifying effective drug targets. Finally, synergistic interactions between metabolic engineering and biomedical sciences are discussed with respect to MFA.

Genomic Study of Chromosomally and Plasmid-Mediated Multidrug Resistance and Virulence Determinants in Klebsiella Pneumoniae Isolates Obtained from a Tertiary Hospital in Al-Kharj, KSA

Klebsiella pneumoniae is an emergent pathogen causing respiratory tract, bloodstream, and urinary tract infections in humans. This study defines the genomic sequence data, genotypic and phenotypic characterization of K. pneumoniae clinically isolated from Al-Kharj, KSA. Whole-genome analysis of four K. pneumoniae strains was performed, including de novo assembly, functional annotation, whole-genome-phylogenetic analysis, antibiotic-resistant gene identification, prophage regions, virulent factor, and pan-genome analysis. The results showed that K6 and K7 strains were MDR and ESBL producers, K16 was an ESBL producer, and K8 was sensitive to all tested drugs except ampicillin. K6 and K7 were identified with sequence type (ST) 23, while K16 and K8 were identified with STs 353 and 592, respectively. K6 and K7 were identified with the K1 (wzi1 genotype) capsule and O1 serotype, while K8 was identified with the K57 (wzi206 genotype) capsule and O3b. K6 isolates harbored 10 antimicrobial resistance genes (ARGs) associated with four different plasmids; the chloramphenicol acetyltransferase (catB3), bla_OXA-1 and aac(6')-Ib-cr genes were detected in plasmid pB-8922_OXA-48. K6 and K7 also carried a similar gene cassette in plasmid pC1K6P0122-2; the gene cassettes were the trimethoprim-resistant gene (dfrA14), integron integrase (IntI1), insertion sequence (IS1), transposase protein, and replication initiation protein (RepE). Two hypervirulent plasmids were reported in isolates K6 and K7 that carried synthesis genes (iucA, iucB, iucC, iucD, and iutA) and iron siderophore genes (iroB, iroC, iroD, and iroN). The presence of these plasmids in high-risk clones suggests their dissemination in our region, which represents a serious health problem.

Transmission of Klebsiella strains and plasmids within and between grey-headed flying fox colonies

The grey-headed flying fox (Pteropus poliocephalus) is an endemic Australian fruit bat, known to carry zoonotic pathogens. We recently showed they harbour bacterial pathogen Klebsiella pneumoniae and closely related species in the K. pneumoniae species complex (KpSC); however, the dynamics of KpSC transmission and gene flow within flying fox colonies are poorly understood. High-resolution genome comparisons of 39 KpSC isolates from grey-headed flying foxes identified five putative strain transmission clusters (four intra- and one inter-colony). The instance of inter-colony strain transmission of K. africana was found between two flying fox populations within flying distance, indicating either direct or indirect transmission through a common food/water source. All 11 plasmids identified within the KpSC isolates showed 73% coverage (mean) and ≥95% identity to human-associated KpSC plasmids, indicating gene flow between human clinical and grey-headed flying fox isolates. Along with strain transmission, inter-species horizontal plasmid transmission between K. pneumoniae and Klebsiella africana was also identified within a flying fox colony. Finally, genome-scale metabolic models were generated to predict and compare substrate usage to previously published KpSC models, from human and environmental sources. These models indicated no distinction on the basis of metabolic capabilities. Instead, metabolic capabilities were consistent with population structure and ST/lineage.

Metabolic modeling of the International Space Station microbiome reveals key microbial interactions

BACKGROUND

Recent studies have provided insights into the persistence and succession of microbes aboard the International Space Station (ISS), notably the dominance of Klebsiella pneumoniae. However, the interactions between the various microbes aboard the ISS and how they shape the microbiome remain to be clearly understood. In this study, we apply a computational approach to predict possible metabolic interactions in the ISS microbiome and shed further light on its organization.

RESULTS

Through a combination of a systems-based graph-theoretical approach, and a constraint-based community metabolic modeling approach, we demonstrated several key interactions in the ISS microbiome. These complementary approaches provided insights into the metabolic interactions and dependencies present amongst various microbes in a community, highlighting key interactions and keystone species. Our results showed that the presence of K. pneumoniae is beneficial to many other microorganisms it coexists with, notably those from the Pantoea genus. Species belonging to the Enterobacteriaceae family were often found to be the most beneficial for the survival of other microorganisms in the ISS microbiome. However, K. pneumoniae was found to exhibit parasitic and amensalistic interactions with Aspergillus and Penicillium species, respectively. To prove this metabolic prediction, K. pneumoniae and Aspergillus fumigatus were co-cultured under normal and simulated microgravity, where K. pneumoniae cells showed parasitic characteristics to the fungus. The electron micrography revealed that the presence of K. pneumoniae compromised the morphology of fungal conidia and degenerated its biofilm-forming structures.

CONCLUSION

Our study underscores the importance of K. pneumoniae in the ISS, and its potential positive and negative interactions with other microbes, including potential pathogens. This integrated modeling approach, combined with experiments, demonstrates the potential for understanding the organization of other such microbiomes, unravelling key organisms and their interdependencies. Video Abstract.

Metabolic modeling of the International Space Station microbiome reveals key microbial interactions

BACKGROUND

Recent studies have provided insights into the persistence and succession of microbes aboard the International Space Station (ISS), notably the dominance of Klebsiella pneumoniae. However, the interactions between the various microbes aboard the ISS and how they shape the microbiome remain to be clearly understood. In this study, we apply a computational approach to predict possible metabolic interactions in the ISS microbiome and shed further light on its organization.

RESULTS

Through a combination of a systems-based graph-theoretical approach, and a constraint-based community metabolic modeling approach, we demonstrated several key interactions in the ISS microbiome. These complementary approaches provided insights into the metabolic interactions and dependencies present amongst various microbes in a community, highlighting key interactions and keystone species. Our results showed that the presence of K. pneumoniae is beneficial to many other microorganisms it coexists with, notably those from the Pantoea genus. Species belonging to the Enterobacteriaceae family were often found to be the most beneficial for the survival of other microorganisms in the ISS microbiome. However, K. pneumoniae was found to exhibit parasitic and amensalistic interactions with Aspergillus and Penicillium species, respectively. To prove this metabolic prediction, K. pneumoniae and Aspergillus fumigatus were co-cultured under normal and simulated microgravity, where K. pneumoniae cells showed parasitic characteristics to the fungus. The electron micrography revealed that the presence of K. pneumoniae compromised the morphology of fungal conidia and degenerated its biofilm-forming structures.

CONCLUSION

Our study underscores the importance of K. pneumoniae in the ISS, and its potential positive and negative interactions with other microbes, including potential pathogens. This integrated modeling approach, combined with experiments, demonstrates the potential for understanding the organization of other such microbiomes, unravelling key organisms and their interdependencies. Video Abstract.

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

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

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

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

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.

Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data

Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data.

The Metabolome of Carbapenem-Resistant Klebsiella pneumoniae Infection in Plasma

AIM

Carbapenem-resistant Klebsiella pneumoniae- (CR-Kp-) mediated infections represent a challenge for clinical practitioners due to their expanding prevalence in hospital environments and antibiotic resistance. However, few studies have shown metabolic changes of carbapenem-resistant Klebsiella pneumoniae and CR-Kp-negative patients, and relevant studies are urgently needed.

METHODS

In this study, we comprehensively profile the metabolites of 20 CR-Kp-positive and 18 CR-Kp-negative patients in plasma by using 2D gas chromatography-time-of-flight mass spectrometry (GC×GC-TOFMS).

RESULTS

We identified 58 metabolites that were carbapenem-resistant Klebsiella pneumoniae-associated. N-Acetyl glucosamine, butanedioic acid, and myoinositol play a significant character in CR-Kp infection.

CONCLUSIONS

Our study provides valuable data to serve as potential targets for developing therapies against CR-Kp infection.

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.

Antimicrobial Resistance and Comparative Genome Analysis of Klebsiella pneumoniae Strains Isolated in Egypt

Klebsiella pneumoniae is an important human pathogen in both developing and industrialised countries that can causes a variety of human infections, such as pneumonia, urinary tract infections and bacteremia. Like many Gram-negative bacteria, it is becoming resistant to many frontline antibiotics, such as carbapenem and cephalosporin antibiotics. In Egypt, K. pneumoniae is increasingly recognised as an emerging pathogen, with high levels of antibiotic resistance. However, few Egyptian K. pneumoniae strains have been sequenced and characterised. Hence, here, we present the genome sequence of a multidrug resistant K. pneumoniae strain, KPE16, which was isolated from a child in Assiut, Egypt. We report that it carries multiple antimicrobial resistance genes, including a bla_NDM-1 carbapenemase and extended spectrum β-lactamase genes (i.e., bla_SHV-40, bla_TEM-1B, bla_OXA-9 and bla_CTX-M-15). By comparing this strain with other Egyptian isolates, we identified common plasmids, resistance genes and virulence determinants. Our analysis suggests that some of the resistance plasmids that we have identified are circulating in K. pneumoniae strains in Egypt, and are likely a source of antibiotic resistance throughout the world.

The Expanding Computational Toolbox for Engineering Microbial Phenotypes at the Genome Scale

Microbial strains are being engineered for an increasingly diverse array of applications, from chemical production to human health. While traditional engineering disciplines are driven by predictive design tools, these tools have been difficult to build for biological design due to the complexity of biological systems and many unknowns of their quantitative behavior. However, due to many recent advances, the gap between design in biology and other engineering fields is closing. In this work, we discuss promising areas of development of computational tools for engineering microbial strains. We define five frontiers of active research: (1) Constraint-based modeling and metabolic network reconstruction, (2) Kinetics and thermodynamic modeling, (3) Protein structure analysis, (4) Genome sequence analysis, and (5) Regulatory network analysis. Experimental and machine learning drivers have enabled these methods to improve by leaps and bounds in both scope and accuracy. Modern strain design projects will require these tools to be comprehensively applied to the entire cell and efficiently integrated within a single workflow. We expect that these frontiers, enabled by the ongoing revolution of big data science, will drive forward more advanced and powerful strain engineering strategies.

High-Quality Genome-Scale Models From Error-Prone, Long-Read Assemblies

Advances in nanopore-based sequencing techniques have enabled rapid characterization of genomes and transcriptomes. An emerging application of this sequencing technology is point-of-care characterization of pathogenic bacteria. However, genome assessments alone are unable to provide a complete understanding of the pathogenic phenotype. Genome-scale metabolic reconstruction and analysis is a bottom-up Systems Biology technique that has elucidated the phenotypic nuances of antimicrobial resistant (AMR) bacteria and other human pathogens. Combining these genome-scale models (GEMs) with point-of-care nanopore sequencing is a promising strategy for combating the emerging health challenge of AMR pathogens. However, the sequencing errors inherent to the nanopore technique may negatively affect the quality, and therefore the utility, of GEMs reconstructed from nanopore assemblies. Here we describe and validate a workflow for rapid construction of GEMs from nanopore (MinION) derived assemblies. Benchmarking the pipeline against a high-quality reference GEM of Escherichia coli K-12 resulted in nanopore-derived models that were >99% complete even at sequencing depths of less than 10× coverage. Applying the pipeline to clinical isolates of pathogenic bacteria resulted in strain-specific GEMs that identified canonical AMR genome content and enabled simulations of strain-specific microbial growth. Additionally, we show that treating the sequencing run as a mock metagenome did not degrade the quality of models derived from metagenome assemblies. Taken together, this study demonstrates that combining nanopore sequencing with GEM construction pipelines enables rapid, in situ characterization of microbial metabolism.

Reconstructing organisms in silico: genome-scale models and their emerging applications

Escherichia coli is considered to be the best-known microorganism given the large number of published studies detailing its genes, its genome and the biochemical functions of its molecular components. This vast literature has been systematically assembled into a reconstruction of the biochemical reaction networks that underlie E. coli's functions, a process which is now being applied to an increasing number of microorganisms. Genome-scale reconstructed networks are organized and systematized knowledge bases that have multiple uses, including conversion into computational models that interpret and predict phenotypic states and the consequences of environmental and genetic perturbations. These genome-scale models (GEMs) now enable us to develop pan-genome analyses that provide mechanistic insights, detail the selection pressures on proteome allocation and address stress phenotypes. In this Review, we first discuss the overall development of GEMs and their applications. Next, we review the evolution of the most complete GEM that has been developed to date: the E. coli GEM. Finally, we explore three emerging areas in genome-scale modelling of microbial phenotypes: collections of strain-specific models, metabolic and macromolecular expression models, and simulation of stress responses.

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.

Leveraging genome-scale metabolic models for human health applications

Genome-scale metabolic modeling is a scalable and extensible computational method for analyzing and predicting biological function. With the ongoing improvements in computational methods and experimental capabilities, genome-scale metabolic models (GEMs) are demonstrating utility in addressing human health applications. The initial areas of highest impact are likely to be health applications where disease states involve metabolic changes. In this review, we focus on recent application of GEMs to studying cancer and the human microbiome by describing the enabling methodologies and outcomes of these studies. We conclude with proposing some areas of research that are likely to arise as a result of recent methodological advances.

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.

Sewer biofilm microbiome and antibiotic resistance genes as function of pipe material, source of microbes, and disinfection: field and laboratory studies

Wastewater systems are recognized pathways for the spread of antibiotic resistant bacteria, but relatively little is known about the microbial ecology of the sewer environment. Sewer biofilm colonization by antibiotic resistance gene (ARG) carrying bacteria may impact interpretations of sewage epidemiology data, water quality during sewer overflows, and hazard to utility workers. The objectives of this research were to evaluate the (1) microbiome of real and simulated sewer biofilms and their potential to accumulate ARGs and (2) susceptibility of simulated sewer biofilms to bleach disinfection. First, biofilm samples were collected from sewer municipal systems. Next, an annular biofilm reactor was used to simulate the sewer environment while controlling the pipe material (concrete vs. PVC). The reactor was operated either as fed semi-batch with sewer sediment and synthetic wastewater (Sed-SB) or fed with a continuous flow of raw sewage (WW-CF). The abundance of ARGs, human fecal marker HF183, and 16S rRNA gene copies in these biofilm samples was measured with qPCR. Amplicon sequencing was performed to compare the prokaryotic diversity between samples. Finally, the susceptibility of reactor biofilm to a 4.6% bleach disnfection protocol was evaluated using viability qPCR and amplicon sequencing. Field and WW-CF biofilms contained the most ARG copies and the microbial community compositions varied between the different biofilm samples (field, Sed-SB, and WW-CF). Pipe material did not affect the abundance of ARGs in the reactor samples. However, log removal following bleach treatment suggested that the biofilm grown on PVC surface was primarily dislodged from the surface by the bleach treatment whereas more bacteria were lysed within the biofilm that remained on the concrete surface. Viable bacteria carrying ARGs were observed following 10 minutes of treatment. This study showed that sewer biofilms can accumulate bacteria carrying ARGs and that while bleach can reduce sewer biofilm density, the protocol tested here will not completely remove the biofilms.

BiGG Models 2020: multi-strain genome-scale models and expansion across the phylogenetic tree

The BiGG Models knowledge base (http://bigg.ucsd.edu) is a centralized repository for high-quality genome-scale metabolic models. For the past 12 years, the website has allowed users to browse and search metabolic models. Within this update, we detail new content and features in the repository, continuing the original effort to connect each model to genome annotations and external databases as well as standardization of reactions and metabolites. We describe the addition of 31 new models that expand the portion of the phylogenetic tree covered by BiGG Models. We also describe new functionality for hosting multi-strain models, which have proven to be insightful in a variety of studies centered on comparisons of related strains. Finally, the models in the knowledge base have been benchmarked using Memote, a new community-developed validator for genome-scale models to demonstrate the improving quality and transparency of model content in BiGG Models.

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

Klebsiella pneumoniae is an opportunistic bacterial pathogen leading to life-threatening nosocomial infections. Emergence of highly resistant strains poses a major challenge in the management of the infections by healthcare-associated K. pneumoniae isolates. Thus, despite intensive efforts, the current treatment strategies remain insufficient to eradicate such infections. Failure of the conventional infection-prevention and treatment efforts explicitly indicates the requirement of new therapeutic approaches. This prompted us to systematically analyze the K. pneumoniae metabolism to investigate drug targets. Genome-scale metabolic networks (GMNs) facilitating the systematic analysis of the metabolism are promising platforms. Thus, we used a GMN of K. pneumoniae MGH 78578 to determine putative targets through gene- and metabolite-centric approaches. To develop more realistic infection models, we performed the bacterial growth simulations within different host-mimicking media, using an improved biomass formation reaction. We selected more suitable targets based on several property-based prioritization procedures. KdsA was identified as the high-ranked putative target satisfying most of the target prioritization criteria specified under the gene-centric approach. Through a structure-based virtual screening protocol, we identified potential KdsA inhibitors. In addition, the metabolite-centric approach extended the drug target list based on synthetic lethality. This revealed the importance of combined metabolic analyses for a better understanding of the metabolism. To our knowledge, this is the first comprehensive effort on the investigation of the K. pneumoniae metabolism for drug target prediction through the constraint-based analysis of its GMN in conjunction with several bioinformatic approaches. This study can guide the researchers for the future drug designs by providing initial findings regarding crucial components of the Klebsiella metabolism.