https://doi.org/10.1038/s41429-020-00366-2 R E V I E W A R T I C L E
Novel antimicrobial development using genome-scale metabolic model of Gram-negative pathogens: a review
Wan Yean Chung1●Yan Zhu2●Mohd Hafidz Mahamad Maifiah3●Naveen Kumar Hawala Shivashekaregowda4● Eng Hwa Wong 5●Nusaibah Abdul Rahim6
Received: 29 July 2020 / Revised: 4 August 2020 / Accepted: 8 August 2020
© The Author(s), under exclusive licence to the Japan Antibiotics Research Association 2020
Abstract
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 bestfit 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.
Introduction
Antimicrobial resistance (AMR) is a major problem worldwide and it threatens antimicrobial development [1,2]. The overuse of existing antimicrobials has acceler- ated the spread of AMR [3]. The emergence of multidrug
resistant (MDR) bacteria has become a risk factor for increased mortality, prolonged length of hospital stays, and often associated with higher medical costs [4]. Hospital- acquired infection (HAI) also referred to as nosocomial infection is an infection acquired by patients during their hospital stay, which are typically not present at the time of admission [5]. HAI causes substantial health and economic burden to the healthcare system as well as to the patients and their families [5]. Nosocomial infections caused by Gram-negative bacteria are of particular concern as limited treatment options are available [6].
AMR has been addressed by the World Health Organi- zation (WHO) as one of the primary threats to global health due to its high and increasing spreading rate [7]. In June 2019, the WHOfirst announced the Access, Watch, Reserve classification of antibiotics database to be used as a tool to better support antibiotic monitoring, which is crucial to curb further emergence of AMR [8]. Carbapenem-resistant Pseudomonas aeruginosa, Enterobacteriaceae (including Klebsiella pneumoniae) and Acinetobacter baumannii (Table 1) have been prioritized by the WHO as critical pathogens that urgently requires the development of novel antimicrobial treatments [8,9]. It is necessary to understand the overall metabolic regulation mechanism of bacterial cells, thereby facilitating the development of effective therapies. Genome-scale metabolic models (GSMMs) serve as one of the major modeling approaches for system level
* Eng Hwa Wong
* Nusaibah Abdul Rahim
1 School of Pharmacy, Taylor’s University, 47500 Subang Jaya, Selangor, Malaysia
2 Biomedicine Discovery Institute, Infection and Immunity Program and Department of Microbiology, Monash University,
Melbourne 3800 VIC, Australia
3 International Institute for Halal Research and Training (INHART), International Islamic University Malaysia (IIUM), 53100 Jalan Gombak, Selangor, Malaysia
4 Center for Drug Discovery and Molecular Pharmacology (CDDMP), Faculty of Health and Medical Sciences, Taylor’s University, 47500 Subang Jaya, Selangor, Malaysia
5 School of Medicine, Taylor’s University, 47500 Subang Jaya, Selangor, Malaysia
6 Faculty of Pharmacy, University of Malaya, 50603 Kuala Lumpur, Malaysia
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metabolic studies. GSMMs provide valuable insight into the evolution of metabolic gene content by illustrating the mapping between genotype and phenotype for thousands of genes [10]. It is a stoichiometric model of metabolic net- work that describes the gene-protein-reaction (GPR) rela- tionships on the basis of genome annotation data [11].
These functional relationships allow models to be used as gene essentiality prediction where gene knockouts could be simulated and then cell survival assessed based on biomass end production, also known as flux balance analysis (FBA) [12].
With the rapid development of metabolic network model- ing, identification of novel antimicrobial drug targets using omics technologies become feasible [13–15]. Several studies have used GSMMs as systems platforms for integrative ana- lysis of multi-omics data in response to antibiotic treatments in MDR bacteria, includingP. aeruginosa[16–19],A. baumannii [20–22], and K. pneumoniae [23–26]. Advancements in metabolic network reconstructions will pave way for better understanding of adaptive resistance mechanisms in MDR bacteria and thus mitigate the treatment of infectious disease [27].
Genome-scale metabolic model (GSMM) reconstruction
Metabolic network reconstruction represents structured knowledge bases that capture crucial information about all known biochemical transformations taking place within specific target organisms [28]. Data of known enzymes
including metabolites and its substrates, genes encoding these enzymes and the stoichiometric reactions that are catalyzed by these enzymes are assembled piece-by-piece to build a functional metabolic model [29]. GSMMs can be used as a powerful tool for integration of multi-omics data.
GSMMs serve as an in silico platform that enable compu- tational prediction in drug target prediction [29]. It enables the elucidation of metabolic changes upon antibiotic treat- ment, thereby identifying key metabolic pathways and metabolites involved in antibacterial activity and emergence of resistance [19, 22]. The reconstruction steps are cate- gorized into five stages: (1) draft reconstruction, (2) refinement of reconstruction, (3) conversion into compu- table format, (4) network evaluation, and lastly (5) data assembly and dissemination (Fig. 1).
At the first stage of draft reconstruction, an initial first draft can be automatically developed using software tools such as Pathway tools [30] or metaSHARK [31] to obtain genome annotation. This draft is further optimized by adding metabolic reactions catalyzed by identified gene products, which can be retrieved from Kyoto Encyclopedia of Genes and Genomes [32] and Biocyc [33] databases. The second stage of the refinement reconstruction process involves further curation of the model. The draft recon- struction is evaluated against the organism-specific litera- ture to add information of genes and to calculate reaction stoichiometry. Tools such as Model SEED [34], Pathway tools [30], and CarveMe [35] can be used in conjunction with each other for verification of GPR associations and assessment of biomass composition. A confidence scoring system is employed to rate reactions with the amount of information available for a metabolic function, pathway or the entire reconstruction [36]. After that, determination of growth medium requirements is needed prior to the conversion stage.
The third stage involves conversion of the reconstruction to computable format and defining system boundaries [36].
During this stage, general reconstruction is converted into a Table 1 List of pathogens declared by WHO which require urgent
antibiotic development. Table is reproduced from WHO publication [9]
Priority Pathogens
Critical Acinetobacter baumannii, carbapenem-resistant Pseudomonas aeruginosa, carbapenem-resistant Enterobacteriaceaea, carbapenem-resistant, 3rd generation cephalosporin-resistant
High Enterococcus faecium, vancomycin-resistant
Staphylococcus aureus, methicillin-resistant, vancomycin intermediate and resistant
Helicobacter pylori, clarithromycin-resistant Campylobacter,fluoroquinolone-resistant Salmonellaspp.,fluoroquinolone-resistant
Neisseria gonorrhoeae, 3rd generation cephalosporin- resistant,fluoroquinolone-resistant
Medium Streptococcus pneumoniae, penicillin-nonsusceptible Haemophilus influenzae, ampicillin-resistant Shigellaspp.,fluoroquinolone-resistant Table is reproduced from WHO publication [9]
aEnterobacteriaceae include:Klebsiella pneumoniae,Escherichia coli, Enterobacterspp.,Serratiaspp.,Proteusspp.,Providenciaspp., and Morganellaspp.
Fig. 1 Five stages of genome-scale metabolic models reconstruction
condition-specific model by setting simulation constraints [36]. The network evaluation and debugging in the fourth stage includes various quality control and quality assurance tests, such as network mass and charge balancing, identifi- cation of metabolic dead-ends, and gap analysis [36]. Gap analysis can be challenging as it requires intensive literature searches to re-evaluate genomic and experimental data. This is a critical step to determine whether gap filling for a particular reaction is necessary for the model functionality [36]. Other evaluation steps may involve block reaction identification, biomass precursor production, and assess- ment of growth rate accuracy [37].
The final stage in reconstruction is data assembly and dissemination [36]. This step is to make the final recon- struction available to the research community by exporting models in System Biology Markup Language (SBML) format and providing a spreadsheet containing essential reconstruction information [36].
Current GSMM reconstruction tools
There are several tools developed for automated recon- struction of GSMMs and these include Model SEED [34], RAVEN toolbox [38], Pathway tools [30], merlin [39], AuReMe [40], CarveMe [35], and MetaDraft [41]. The great evolution of automated GSMM reconstruction tools in recent years has markedly improved the iterative process of metabolic network reconstruction from months to a few minutes [42].
In particular, Model SEED and CarveMe have a higher degree of automation in most of the reconstruction process while others may require further manual curation to obtain a quality GSMM [42]. Notably, merlin, and Pathway tools provide workspace and assistance for manual refinement and enable traceability to track changes for the whole process.
Metabolic models are required to be coded in latest SBML standard, which is SBML Level 3 [43]. This is to meet the purpose of interoperability among software users in accordance with the FAIR guiding principles [44].
Comparing this feature of all tools, most of them meet the standard and are able to export network in SBML Level 3, except for Model SEED and Pathway tools, in which the output in SBML is Level 2 [42].
Automated gapfilling is essential for model evaluation as manual gap filling can be cumbersome. All tools (except MetaDraft and merlin) are evaluated as outstanding with respect to the feature of performing automatic gap-filling analysis [38]. Although MetaDraft and merlin lack this particular feature, the latter provides a tool to detect unconnected reactions in facilitating the process [37].
Overall, Model SEED and CarveMe are recommended for users who are new to network reconstruction as both are template-based reconstruction tools and featured with high degree of automation [42]. In spite of that, manual inspec- tion is required to dismiss any potential false result. Several comprehensive reviews on genome-scale metabolic recon- struction tools have been published [37,42,45].
GSMMs developed for Gram-negative pathogens
Gram-negative pathogens are particularly worrisome as studies found that A. baumannii, P. aeruginosa, and K.
pneumoniae are becoming increasingly prevalent in the community [46]. Gram-negative pathogens differ from Gram-positive pathogens by having an outer cell membrane that function as a permeability barrier. This outer membrane limits the penetration of certain drugs and antibiotics into the cell, which is attributed to the intrinsic antibiotic resis- tance of Gram-negative pathogens [47]. GSMMs that have been developed for these pathogens throughout the years are listed in Table2.
Klebsiella pneumoniae
In year 2011, an experimentally validated GSMM recon- struction of K. pneumoniae strain MGH 78578 was first published, namely iYL1228 [23]. This model composed of 1970 reactions with 1228 genes encoding 1188 enzymes and growth simulation accuracy was 84% of the substrate tested [23]. The model provides an experimentally validated in silico platform for further studies of closely related members of the family Enterobacteriaceae and also as a predictive model for gene essentiality analysis. This GSMM has been used as a platform for genomic analysis [26, 48], aid in screening potential drug targets [25,49] and reveal essential Table 2 List of genome-scale metabolic models developed forK. pneumoniae,A. baumannii, andP. aeruginosa
Pathogens Genome-scale metabolic models
K. pneumoniae iYL1228 (MGH 78578) [23], iKp1289 (KPPR1) [24], Kp13-MN (Kp13) [25], 22K. pneumoniaemodels [26]
A. baumannii AbyMBEL891 (AYE) [57], iPL844 (ATCC 19606) [20], iCN718 (AYE) [21], iATCC19606 (ATCC 19606) [22]
P. aeruginosa iMO1056 (PAO1) [16], Opt208964 (PAO1) [34], iMO1086 (PAO1) [17], iPau1129 (PAO1) [18], iPAO1 (PAO1) [19]
Bacterial strains are written in brackets
and conserved metabolism in prokaryotes (such as archaea, cyanobacteria, actinobacteria, and proteobacteria) [50].
Later a significant paper by Henry et al. published a comprehensive GSMM ofK. pneumoniae strain KPPR1, a highly virulent strain that is rifampin-resistant derivative of ATCC 43816 [24]. The authors developed a metabolic model, iKp1289 on the basis of iYL1228 [23] by adding metabolic genes in KPPR1 [24]. KPPR1 strain has been demonstrated to have greater metabolic capabilities on lar- ger carbohydrates, uptake nutrients more robustly, and invoke more extensive stress responses. This is owing to the gene traits of KPPR1 that induce significant tissue damage during respiratory infection compared to MGH 78578 [24].
Thymidylate kinase and lipid A disaccharide synthase were identified as essential genes and have been further investi- gated as antimicrobial drug targets [24]. Model iKp1289 has been used to study the interactions betweenK. pneumoniae and the host environment (lung) [51]. Furthermore, the high metabolicflexibility of KPPR1 impacts the bacterialfitness through reduction of metabolic capacity (through deletion of citrate synthase) of K. pneumoniae strain. This may confer the KPPR1 strain virulence and drastically modify fitness on different host sites [52].
A metabolic network reconstruction of Kp13, Kp13-MN was built to study the prioritization ofK. pneumoniaedrug targets [25]. This model aimed to identify metabolic com- partment and enzymatic activities by Kp13 and related species organism, as well as topological metrics in the network [25]. Model Kp13-MN contained a total of 1847 enzymes assigned to 1969 reactions and forming part of 321 predicted metabolic pathways. A total of 1523 enzymes play a part in these transformations [25]. The authors, Ramos et al. [25] also determined choke-points (CPs) pro- teins, which are important from a metabolic perspective in the network. The identified CPs are mainly involved in transformation of life-and-death compounds, which make them attractive drug targets [25]. In addition, network centrality was analyzed as a result of topological metrics calculation that relates to network node importance. Larger centrality nodes (higher betweenness centrality) indicate higher network cohesiveness of that particular reaction hub [25]. After incorporation of multi-omics data, a list of 29 proteins with desirable druggability traits were shortlisted.
These prioritized protein targets participate in fundamental processes such as lipid synthesis, cofactor production, and core metabolism [25].
The emergence of metallo-beta-lactamase-producing pathogens such as Imipenemase and Verona integron- encoded metallo-β-lactamase, and New Delhi metallo-β- lactamase poses a serious threat to public health owing to their capacity to hydrolyze all β-lactams, including carba- penems [53–55]. Recently, a comparative genomics analysis of 22K. pneumoniaestrains (Table3) with different levels
of resistance was investigated to determine the differences in catabolic capabilities and correlate with the AMR pheno- types of the strains [26]. The models’in silico growth cap- abilities were predicted by simulating biomass production in minimal media conditions and alternating the carbon, phosphorus, nitrogen, and sulfur sources [26]. Interestingly, the results indicated that the 22 strains could be classified into resistant, intermediate, or susceptible to different drugs based on model predicted growth results [26].
In a recent study, a comprehensive effort on screening of potential drug targets via network-based metabolism-cen- tered approaches has been developed [49]. An improved model of iYL1228 [23] was used by incorporating different host-mimicking media and using an improved biomass formation reaction in model growth simulations [49]. In this study, 2-dehydro-3-deoxyphosphooctonate aldolase (KdsA) was putatively identified as high-ranked target through constraint-based analysis coupled with bioinformatics prioritization steps [49]. In addition, an extended list of drug targets was presented based on synthetic lethality [49]. This result led to further research effort on KdsA inhibitors and highlighted the importance of combined metabolic analyses to enable a better understanding of K. pneumoniae meta- bolism [49].
Acinetobacter baumannii
A. baumannii has emerged as a highly problematic Gram- negative pathogen and has been identified as one of the six significant ESKAPE superbugs [56]. Thefirst GSMM ofA.
baumanniistrain AYE, AbyMBEL891 was reconstructed in year 2011, which identified essential genes and metabolites critical to the cell growth [57]. Since then, several studies on genome analysis of A. baumannii were published in later years [58,59]. Undoubtedly, AbyMBEL891 was used as a valuable foundation for their development. Nevertheless, the lack of standardization in identifiers for metabolites and reactions has limited the use of this model and other reconstructions [22].
Following a tremendous rise in reported outbreak of infections by pan-resistant A. baumannii, a system-level study of antibiotic response in A. baumannii is urgently Table 3 List ofK. pneumoniaestrains analyzed by Norsigian et al.
with varied resistance characteristics [26]
K. pneumoniaestrains Resistance characteristics
SF MDR and colistin resistant
SK
HM MDR but noncolistin resistant
SP Not highly resistant
needed [60]. One of the remarkable findings of model iLP844, developed in year 2017, was the revelation of metabolic reprogramming of A. baumannii strain ATCC 19606 against the exposure of colistin [20]. Colistin is one of last-line resorts for treating such MDR pathogen [61].
Colistin, a positively charged molecule, is bactericidal against Gram-negative bacteria, interacting with the lipid A moiety of lipopolysaccharide (LPS) to cause disorganiza- tion of the outer membrane [62]. The metabolic response of pathogen toward colistin was delineated by establishing a stress condition such as the presence of colistin to the strain [20]. Flux distributions for untreated and treated (with colistin) models were compared and determined whether reactions are turned on and off using MADE [63]. It was reported that there was an increase influx in most metabolic reactions of biosynthetic pathways including fatty acid, peptidoglycan, and lysine [20]. Conversely, under the same condition (colistin exposure), there was an increase influx in some catabolic pathways such as sugars and nucleotide metabolism. This was inferred as bacterial resistance mechanism by redirecting a certain amount of LPS com- ponent to catabolic processes, a sign of rearrangement of the external membrane layer [20]. Interestingly, FBA allows simulationflow of metabolites in a metabolic network and thus essential genes may be identified through gene dele- tion. This led to the identification of 67 essential genes in this study that may be novel potential drug targets [20].
Model iLP844 was anticipated as a ready-to-use blueprint metabolic network for A. baumannii for future drug development [20].
Model iCN718 is a high-quality GSMM known to be an improved version of model AbyMBEL891 ofA. baumannii strain AYE [21]. It achieved 80% accuracy in gene
essentiality prediction and accuracy as high as 89% in phenotypic microarray data matched with experimental dataset [21]. The model was reconstructed against AbyM- BEL891 as reference then further manually curated with the current literature on this organism. Model iCN718 com- posed of 718 genes, 1016 reactions, and 890 metabolites compared to AbyMBEL891, which contained 650 genes, 891 reactions, and 770 metabolites. In addition to standar- dization of reaction and metabolite identifiers of AbyM- BEL891, iCN718 also has been refined in transport processes and mass and charge balance of metabolic reac- tions [21]. The authors also demonstrated the capability of this GSMM across 74 other A. baumannii strains. All the updated features enhanced this model that lends itself to further use by the community studying this “superbug”as well as in future multi-strain reconstructions of diverse A.
baumanniispecies [21].
A multi-omics integrated GSMM forA. baumanniistrain ATCC 19606, namely iATCC19606 was constructed to delineate the metabolic mechanism of this MDR pathogen upon colistin treatment [22]. Numerous studies had focused effort on polymyxin-resistance mechanism of ATCC 19606 [64,65] hence an ATCC 19606-specific model was devel- oped in this study. The reconstruction was an integrated model of transcriptomics and metabolomics data to eluci- date the cellular response of A. baumannii [22]. In turn, model iLP844 integrated solely transcriptomic data using MADE algorithm without further looking into metabo- lomics data [22]. Modeling with multi-omics data using iATCC19606 revealed multiple metabolic pathways including pentose phosphate pathway, glyoxylate shunt, arginine biosynthesis, LPS, and peptidoglycan biosynthesis that played major roles in bactericidal activity upon colistin Fig. 2 Metabolic pathways ofA.
baumanniiaffected when treated with colistin
treatment (Fig.2) [22]. Combined with antibiotic pharma- cokinetics/pharmacodynamics, iATCC19606 provides novel mechanistic insights into developing synergistic polymyxin combination drug therapy [22].
Pseudomonas aeruginosa
P. aeruginosa is a common nosocomial pathogen that causes healthcare-associated infections such as pneumonia, urinary tract infection, bloodstream infection, and surgical site infection especially in immunocompromised patients [66].P. aeruginosapossesses a large and complex genome network [67] and is capable of surviving under hostile conditions such as prolonged antibiotic exposure [68]. P.
aeruginosa demonstrates resistance to multiple classes of antibiotics including β-lactams, fluoroquinolones and ami- noglycosides antibiotics (Fig. 3) [69]. Antibiotics are expelled from the inner membrane through the resistance- nodulation-division transport inP. aeruginosa(Fig.4) [70].
GSMM iMO1056 was amongst the earliest reconstruction built forP. aeruginosaPAO1 used to explore metabolism [16] and also to elucidate the mechanism of biofilm for- mation [71, 72]. Furthermore, GSMM Opt208964 was reconstructed during the introduction of Model SEED tool [34]. Opt208964 was generated through optimization of iMO1056 using the auto-completion feature in Model SEED as the reconstruction tool [34].
The lack of standardization in the metabolic reconstruc- tion process has limited the comparative genome analysis of different organism species. A novel method, termed meta- bolic network reconciliation was developed to tackle this issue [17]. GSMM reconciliation is a process of eliminating imprecise differences between two existing GSMMs [17].
Two well-studied and phylogenetically related bacteria, P. aeruginosa and Pseudomonas putida were compared such that differences between the two models that were not biologically important were removed [17]. Two new GSMMs have been developed for P. aeruginosa and P. putidadesignated as iMO1086 and iJP962, respectively.
The reconciliation process yielded two greatly aligned GSMMs of these two bacteria by sharing more similar reactions compared to original reconstructions [17]. As a result of reconciliation, P. aeruginosa was observed to be more flexible in sulfur-related metabolic pathways than P. putida[17].P. aeruginosademonstrated ability to utilize lung mucin as a sole sulfur source where cysticfibrosis lung is a sulfur rich environment [73]. The ability to extract sulfur from complex organic sources may contribute to the P. aeruginosa virulence factor [17]. Model reconciliation enables true differences in two GSMMs to be identified above the noise from the respective reconstruction processes [17]. Thorough genome-scale comparison across multiple- species is vitally important for the interpretation of cellular networks evolution and the underlying genotype–phenotype relationships [17].
AMR is the biggest stumbling block to the antibiotic development pipeline. To address this concern, a novel approach was raised by Bartell et al., through diverting focus of drug target development from microbial growth resistance to microbial infection inhibition [18]. Focusing on the inhibition of virulence-linked genes synthesis can Fig. 3 Antibiotic resistance rates ofP. aeruginosaacross high-income
countries (e.g. United Kingdom and United States) and low-income and middle-income countries (e.g., India and Thailand). Resistance
data adapted from ResistanceMap [69] Fig. 4 Resistance-nodulation-division (RND) transporter inP. aeru- ginosa. Antibiotics first pass through OM via porin channels (P), specific protein channels, or passive diffusion. The RND system is composed of three membrane proteins: the cytoplasmic membrane protein (CMP) recognizes the substrate and expels it by outer motive force to the outer membrane protein (OMP), through a membrane fusion protein (MFP). Figure is reproduced from Mesaros et al. [70], with permission
result in hindrance of infection mechanism, which may be a promising novel therapeutic strategy that improves patient outcomes and curbs the emergence of antibiotic resistance [18]. In contrast to the traditional targets that directly impact growth-essential pathway, this was rationalized by wea- kened selection pressure [18]. The authors presented a new GSMM of P. aeruginosa strain PA14 (iPau1129) and an updated GSMM of reference strain P. aeruginosa PAO1 (iPae1146) [18]. Model iPau1129 provided a quantitative measure to the impact of genetic targets in growth versus virulence instead of binary categorization (essential/non- essential) [18]. Genes have been identified into growth essential, virulence-linked essential, and essential for both.
Gene essentiality was further assessed based on their functional impact on growth, virulence factor synthesis or a combination of roles highlighting a higher degree of con- nection [18]. Association of gene essentiality with survival fitness in varied environmental conditions by understanding their impact without the pressure of survival in a host will greatly help in metabolic gene distribution work in future [18]. This analysis provides an important expansion of virulence-linked genes to consider when studying virulence factor during adaptation [18].
The aforementioned four GSMMs developed for P.
aeruginosa PAO1, are in small-medium size, which had compromised their modeling function and limited their modeling capacity. Most importantly, none of them incor- porated the key cellular compartment, the periplasmic space. Periplasmic space is where the changes occur in the cell envelope when polymyxins initially target LPS in the outer membrane [74–76]. iPAO1 [19] is a comprehensive metabolic network developed forP. aeruginosaPAO1 with intensive manual curation based on iMO1056 [16] and Opt208964 [34]. This model was composed of 1458 genes, covering ~25.8% of the PAO1 genome [19]. Metabolic simulation of the model discovered that lipid A modifica- tion, a resistance mechanism of Gram-negative bacteria to polymyxins, exert minimal effect on bacterial growth but substantially alter physicochemical properties of outer membrane ofP. aeruginosa[19]. The results revealed that Gram-negative bacteria: (1) reduced negative charge of lipid A via addition of L-Ara4N, which caused decrease hydro- phobicity of outer membrane; (2) hydroxylation on acyl chains of lipid A; and (3) addition of acyl chains and thus enhanced molecular polarity of lipid A in response to polymyxin treatment [19]. Notably, none of the three aforementioned changes produced a significant impact on bacterial growth [19]. Furthermore, integration of tran- scriptomics and metabolomics data demonstrated that polymyxin treatment leads to reduced biomass synthesis, reduced growth, decreased energy turnover, and increased redox turnover [19]. These were detailed in cross-membrane transport reaction, glycerophospholipid metabolism, and
LPS biosynthesis [19]. In summary, iPAO1 model provided better pharmacological understanding and bactericidal responses prediction in the context of complex interplay of signaling, transcriptional regulation, and metabolism [19].
Conclusion and future directions
GSMMs have been established as one of the major com- putational approaches to comprehend in-depth under- standing on microbial genomics and metabolic changes to fight against antibiotic resistance crisis. Advances in tech- nology, spurred by interest in developing a high-quality GSMM as a tool for discovering novel antibiotic treatment, have led to a significant expansion in the use of multi-omics data in reconstruction. While most GSMMs are mainly focused on the use of transcriptomic data, it is critical to incorporate multi-omics in designing the model to bridge the gap between transcriptomics and metabolomics. Major gaps exist between transcriptomics and metabolomics such as: (1) changes in gene expression do not necessarily cause metabolic activity perturbations; and (2) changes in meta- bolite intensity do not necessarily indicate the result of transcriptional regulation [22]. Recently metabolomics study has a significant rise in analysis of abundance level of metabolites as a function of antibiotic treatment [22,74,75]. Building a multi-omics metabolic network by employing system biology approaches will present a com- prehensive and realistic tool that simulates interactions between biological components and predict physiological behaviors.
As we are standing on the edge of a postantibiotic era, one potential approach for antibiotic adjuvant development is utilizing systems biology to discover interrelation of antibiotic mode of action and antimicrobial death physiol- ogy [77,78]. To thrive into metabolism-centered focus for new drug targets instead of essential target focused, it is crucial to develop a new design of GSMM on the basis of predictive understanding of how bacterial metabolism interrelated with antibiotic efficacy [78]. This will enable us to establish a framework to develop a genome metabolic model that integrates disease biomarkers discovery and metabolic modulation [79]. This led to new drug develop- ment paradigm shift by revealing novel mechanisms of antibiotic adjuvants and allow for synergistic therapeutic combinations. Overall, application of GSMMs has opened up opportunities for investigating host–pathogen interac- tions and provide insight into bacterial metabolism of metabolic modulation in novel drug development.
Acknowledgements The authors acknowledge thefinancial support by the Fundamental Research Grant Scheme, Ministry of Higher Educa- tion, Malaysia (FRGS/1/2019/SKK11/TAYLOR/03/1).
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict of interest.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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