A scalable metabolite supplementation strategy against antibiotic resistant pathogen Chromobacterium violaceum induced by NAD+/NADH+ imbalance

Leveraging the microbiome to combat antibiotic resistant gynecological infections

The vaginal resistome can be considered a collection of the resistant determinants in the vaginal microbiome. Here we review the vaginal resistome including the microbes and resistant genes harbored in common gynecological infections, vaginal microbes that participate in horizontal gene transfer, host factors that contribute to the resistome, and common therapies. Finally, we provide perspective on technologies that can be leveraged to study the vaginal resistome and remaining challenges.

From bacteria to biomedicine: Developing therapies exploiting NAD+ metabolism

Nicotinamide adenine dinucleotide (NAD+) serves as a critical cofactor in cellular metabolism and redox reactions. Bacterial pathways rely on NAD+ participation, where its stability and concentration govern essential homeostasis and functions. This review delves into the role and metabolic regulation of NAD+ in bacteria, highlighting its influence on physiology and virulence. Notably, we explore enzymes linked to NAD+ metabolism as antibacterial drug targets and vaccine candidates. Moreover, we scrutinize NAD+'s medical potential, offering insights for its application in biomedicine. This comprehensive assessment informs future research directions in the dynamic realm of NAD+ and its biomedical significance.

Disrupting the ArcA Regulatory Network Amplifies the Fitness Cost of Tetracycline Resistance in Escherichia coli

There is an urgent need for strategies to discover secondary drugs to prevent or disrupt antimicrobial resistance (AMR), which is causing >700,000 deaths annually. Here, we demonstrate that tetracycline-resistant (TetR) Escherichia coli undergoes global transcriptional and metabolic remodeling, including downregulation of tricarboxylic acid cycle and disruption of redox homeostasis, to support consumption of the proton motive force for tetracycline efflux. Using a pooled genome-wide library of single-gene deletion strains, at least 308 genes, including four transcriptional regulators identified by our network analysis, were confirmed as essential for restoring the fitness of TetR E. coli during treatment with tetracycline. Targeted knockout of ArcA, identified by network analysis as a master regulator of this new compensatory physiological state, significantly compromised fitness of TetR E. coli during tetracycline treatment. A drug, sertraline, which generated a similar metabolome profile as the arcA knockout strain, also resensitized TetR E. coli to tetracycline. We discovered that the potentiating effect of sertraline was eliminated upon knocking out arcA, demonstrating that the mechanism of potential synergy was through action of sertraline on the tetracycline-induced ArcA network in the TetR strain. Our findings demonstrate that therapies that target mechanistic drivers of compensatory physiological states could resensitize AMR pathogens to lost antibiotics. IMPORTANCE Antimicrobial resistance (AMR) is projected to be the cause of >10 million deaths annually by 2050. While efforts to find new potent antibiotics are effective, they are expensive and outpaced by the rate at which new resistant strains emerge. There is desperate need for a rational approach to accelerate the discovery of drugs and drug combinations that effectively clear AMR pathogens and even prevent the emergence of new resistant strains. Using tetracycline-resistant (TetR) Escherichia coli, we demonstrate that gaining resistance is accompanied by loss of fitness, which is restored by compensatory physiological changes. We demonstrate that transcriptional regulators of the compensatory physiologic state are promising drug targets because their disruption increases the susceptibility of TetR E. coli to tetracycline. Thus, we describe a generalizable systems biology approach to identify new vulnerabilities within AMR strains to rationally accelerate the discovery of therapeutics that extend the life span of existing antibiotics.

Comparison of freeze-thaw and sonication cycle-based methods for extracting AMR-associated metabolites from Staphylococcus aureus

Emerging antimicrobial resistance (AMR) among Gram-positive pathogens, specifically in Staphylococcus aureus (S. aureus), is becoming a leading public health concern demanding effective therapeutics. Metabolite modulation can improve the efficacy of existing antibiotics and facilitate the development of effective therapeutics. However, it remained unexplored for drug-resistant S. aureus (gentamicin and methicillin-resistant), primarily due to the dearth of optimal metabolite extraction protocols including a protocol for AMR-associated metabolites. Therefore, in this investigation, we have compared the performance of the two most widely used methods, i.e., freeze-thaw cycle (FTC) and sonication cycle (SC), alone and in combination (FTC + SC), and identified the optimal method for this purpose. A total of 116, 119, and 99 metabolites were identified using the FTC, SC, and FTC + SC methods, respectively, leading to the identification of 163 metabolites cumulatively. Out of 163, 69 metabolites were found to be associated with AMR in published literature consisting of the highest number of metabolites identified by FTC (57) followed by SC (54) and FTC + SC (40). Thus, the performances of FTC and SC methods were comparable with no additional benefits of combining both. Moreover, each method showed biasness toward specific metabolite(s) or class of metabolites, suggesting that the choice of metabolite extraction method shall be decided based on the metabolites of interest in the investigation.

Computer-Based Design of a Cell Factory for High-Yield Cytidine Production

Pyrimidine ribonucleotide de novo biosynthesis pathway (PRdnBP) is an important pathway to produce pyrimidine nucleosides. We attempted to systematically investigate PRdnBP in Escherichia coli with genome-scale metabolic models and utilized the models to guide strain design. The balance of central carbon metabolism and PRdnBP affected the production of cytidine from glucose. Using Bayesian metabolic flux analysis, the effect of modified PRdnBP on the metabolic network was analyzed. The acetate overflow became coupled with PRdnBP flux, while they were originally independent under oxygen-sufficient conditions. The coupling between cytidine production and acetate secretion in the modified strain was weakened by arcA deletion, which resulted in further improving the efficient accumulation of cytidine. In total, 1.28 g/L of cytidine with a yield of 0.26 g/g glucose was produced. The yield of cytidine produced by E. coli is higher than previous reports. Our strategy provides an effective attempt to find metabolic bottlenecks in genetically engineered bacteria by using flux coupling analysis.

Systems Biology: New Insight into Antibiotic Resistance

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

Genome-scale metabolic modeling in antimicrobial pharmacology

The increasing antimicrobial resistance has seriously threatened human health worldwide over the last three decades. This severe medical crisis and the dwindling antibiotic discovery pipeline require the development of novel antimicrobial treatments to combat life-threatening infections caused by multidrug-resistant microbial pathogens. However, the detailed mechanisms of action, resistance, and toxicity of many antimicrobials remain uncertain, significantly hampering the development of novel antimicrobials. Genome-scale metabolic model (GSMM) has been increasingly employed to investigate microbial metabolism. In this review, we discuss the latest progress of GSMM in antimicrobial pharmacology, particularly in elucidating the complex interplays of multiple metabolic pathways involved in antimicrobial activity, resistance, and toxicity. We also highlight the emerging areas of GSMM applications in modeling non-metabolic cellular activities (e.g., gene expression), identification of potential drug targets, and integration with machine learning and pharmacokinetic/pharmacodynamic modeling. Overall, GSMM has significant potential in elucidating the critical role of metabolic changes in antimicrobial pharmacology, providing mechanistic insights that will guide the optimization of dosing regimens for the treatment of antimicrobial-resistant infections.

Interventions in Nicotinamide Adenine Dinucleotide Metabolism, the Intestinal Microbiota and Microcin Peptide Antimicrobials

A proper NADH/NAD + balance allows for the flow of metabolic and catabolic activities determining cellular growth. In Escherichia coli, more than 80 NAD + dependent enzymes are involved in all major metabolic pathways, including the post-transcriptional build-up of thiazole and oxazole rings from small linear peptides, which is a critical step for the antibiotic activity of some microcins. In recent years, NAD metabolism boosting drugs have been explored, mostly precursors of NAD + synthesis in human cells, with beneficial effects on the aging process and in preventing oncological and neurological diseases. These compounds also enhance NAD + metabolism in the human microbiota, which contributes to these beneficial effects. On the other hand, inhibition of NAD + metabolism has been proposed as a therapeutic approach to reduce the growth and propagation of tumor cells and mitigating inflammatory bowel diseases; in this case, the activity of the microbiota might mitigate therapeutic efficacy. Antibiotics, which reduce the effect of microbiota, should synergize with NAD + metabolism inhibitors, but these drugs might increase the proportion of antibiotic persistent populations. Conversely, antibiotics might have a stronger killing effect on bacteria with active NAD + production and reduce the cooperation of NAD + producing bacteria with tumoral cells. The use of NADH/NAD + modulators should take into consideration the use of antibiotics and the population structure of the microbiota.

Multi-target drug with potential applications: violacein in the spotlight

The aim of the current review is to address updated research on a natural pigment called violacein, with emphasis on its production, biological activity and applications. New information about violacein's action mechanisms as antitumor agent and about its synergistic action in drug delivery systems has brought new alternatives for anticancer therapy. Thus, violacein is introduced as reliable drug capable of overcoming at least three cancer hallmarks, namely: proliferative signaling, cell death resistance and metastasis. In addition, antimicrobial effects on several microorganisms affecting humans and other animals turn violacein into an attractive drug to combat resistant pathogens. Emphasis is given to effects of violacein combined with different agents, such as antibiotics, anticancer agents and nanoparticles. Although violacein is well-known for many decades, it remains an attractive compound. Thus, research groups have been making continuous effort to help improving its production in recent years, which can surely enable its pharmaceutical and chemical application as multi-task compound, even in the cosmetics and food industries.

Reconstruction of a Context-Specific Model Based on Genome-Scale Metabolic Simulation for Identification of Prochloraz Resistance Mechanisms in Penicillium digitatum

Penicillium digitatum is the most destructive postharvest pathogen of citrus fruits, causing substantial economic losses. Prochloraz-resistant strains have emerged due to overuse of imidazole fungicides in agriculture. To study the prochloraz resistance mechanisms at the system level, a genome-scale metabolic model (GEM, iPD1512) of P. digitatum was reconstructed and constrained based on context-specific transcriptome data of the prochloraz-resistant strain, PdF6, from our previous work, a newly sequenced, context-specific transcriptome result of the major facilitator superfamily transporter-encoding gene mfs2 knockout mutant PdF6Δmfs2, and experimentally derived growth rate data. Through the model, iPD1512, the processes of prochloraz resistance in P. digitatum were well simulated. In detail, the growth rates of both wild-type and mutant P. digitatum under different prochloraz concentrations were simulated using constraint-based reconstruction and analysis. The growth rates of the mutant strains (sterol regulatory element-binding protein-encoding gene sreA knockout mutant PdF6ΔsreA and PdF6Δmfs2) were calculated and confirmed to be consistent with the simulation results. Furthermore, correlations between genes and prochloraz resistance were predicted and showed a great difference when compared with correlation results based on p-values from the hypothesis testing used by comparative transcriptomics. To sum up, in contrast to traditional transcriptome analysis, the GEM provides a systemic and dynamic drug resistance mechanism, which might help to detect some key upstream regulatory genes, but with small expression changes, and might provide more efficient targets to control prochloraz-resistant P. digitatum.

Constraints-based analysis identifies NAD+ recycling through metabolic reprogramming in antibiotic resistant Chromobacterium violaceum

In the post genomic era, high throughput data augment stoichiometric flux balance models to compute accurate metabolic flux states, growth and energy phenotypes. Investigating altered metabolism in the context of evolved resistant genotypes potentially provide simple strategies to overcome drug resistance and induce susceptibility to existing antibiotics. A genome-scale metabolic model (GSMM) for Chromobacterium violaceum, an opportunistic human pathogen, was reconstructed using legacy data. Experimental constraints were used to represent antibiotic susceptible and resistant populations. Model predictions were validated using growth and respiration data successfully. Differential flux distribution and metabolic reprogramming were identified as a response to antibiotics, chloramphenicol and streptomycin. Streptomycin resistant populations (StrpR) redirected tricarboxylic acid (TCA) cycle flux through the glyoxylate shunt. Chloramphenicol resistant populations (ChlR) resorted to overflow metabolism producing acetate and formate. This switch to fermentative metabolism is potentially through excess reducing equivalents and increased NADH/NAD ratios. Reduced proton gradients and changed Proton Motive Force (PMF) induced by antibiotics were also predicted and verified experimentally using flow cytometry based membrane potential measurements. Pareto analysis of NADH and ATP maintenance showed the decoupling of electron transfer and ATP synthesis in StrpR. Redox homeostasis and NAD+ cycling through rewiring metabolic flux was implicated in re-sensitizing antibiotic resistant C. violaceum. These approaches can be used to probe metabolic vulnerabilities of resistant pathogens. On the verge of a post-antibiotic era, we foresee a critical need for systems level understanding of pathogens and host interaction to extend shelf life of antibiotics and strategize novel therapies.

Integrated Experimental and Computational Analyses Reveal Differential Metabolic Functionality in Antibiotic-Resistant Pseudomonas aeruginosa

Metabolic adaptations accompanying the development of antibiotic resistance in bacteria remain poorly understood. To study this relationship, we profiled the growth of lab-evolved antibiotic-resistant lineages of the opportunistic pathogen Pseudomonas aeruginosa across 190 unique carbon sources. Our data revealed that the evolution of antibiotic resistance resulted in systems-level changes to growth dynamics and metabolic phenotype. A genome-scale metabolic network reconstruction of P. aeruginosa was paired with whole-genome sequencing data to predict genes contributing to observed changes in metabolism. We experimentally validated computational predictions to identify mutations in resistant P. aeruginosa affecting loss of catabolic function. Finally, we found a shared metabolic phenotype between lab-evolved P. aeruginosa and clinical isolates with similar mutational landscapes. Our results build upon previous knowledge of antibiotic-induced metabolic adaptation and provide a framework for the identification of metabolic limitations in antibiotic-resistant pathogens.

EmrR-Dependent Upregulation of the Efflux Pump EmrCAB Contributes to Antibiotic Resistance in Chromobacterium violaceum

Chromobacterium violaceum is an environmental Gram-negative bacterium that causes infections in humans. Treatment of C. violaceum infections is difficult and little is known about the mechanisms of antibiotic resistance in this bacterium. In this work, we identified mutations in the MarR family transcription factor EmrR and in the protein GyrA as key determinants of quinolone resistance in C. violaceum, and we defined EmrR as a repressor of the MFS-type efflux pump EmrCAB. Null deletion of emrR caused increased resistance to nalidixic acid, but not to other quinolones or antibiotics of different classes. Moreover, the ΔemrR mutant showed decreased production of the purple pigment violacein. Importantly, we isolated C. violaceum spontaneous nalidixic acid-resistant mutants with a point mutation in the DNA-binding domain of EmrR (R92H), with antibiotic resistance profile similar to that of the ΔemrR mutant. Other spontaneous mutants with high MIC values for nalidixic acid and increased resistance to fluoroquinolones presented point mutations in the gene gyrA. Using DNA microarray, Northern blot and EMSA assays, we demonstrated that EmrR represses directly a few dozen genes, including the emrCAB operon and other genes related to transport, oxidative stress and virulence. This EmrR repression on emrCAB was relieved by salicylate. Although mutation of the C. violaceum emrCAB operon had no effect in antibiotic susceptibility or violacein production, deletion of emrCAB in an emrR mutant background restored antibiotic susceptibility and violacein production in the ΔemrR mutant. Using a biosensor reporter strain, we demonstrated that the lack of pigment production in ΔemrR correlates with the accumulation of quorum-sensing molecules in the cell supernatant of this mutant strain. Therefore, our data revealed that overexpression of the efflux pump EmrCAB via mutation and/or derepression of EmrR confers quinolone resistance and alters quorum-sensing signaling in C. violaceum, and that point mutation in emrR can contribute to emergence of antibiotic resistance in bacteria.

Integrated constraints based analysis of an engineered violacein pathway in Escherichia coli

Strategies towards optimal violacein biosynthesis, a potential drug molecule, need systems level coordination of enzymatic activities of individual genes in a multigene operon vioABCDE. Constraints-based flux balance analysis of an extended iAF1260 model (iAF1260vio) with a reconstructed violacein module predicted growth and violacein yields in Escherichia coli accurately. Shadow price (SP) analysis identified tryptophan metabolism and NADPH as limiting. Increased tryptophan levels in Δpgi & ΔpheA were validated using in silico gene deletion analysis. Phenotypic phase plane (PhPP) analysis highlighted sensitivity between tryptophan and NADPH for violacein synthesis at molar growth yields. A synthetic VioABCDE operon (SYNO) sequence was designed to maximize Codon Adaptive Index (CAI: 0.9) and tune translation initiation rates (TIR: 2-50 fold higher) in E. coli. All pSYN E. coli transformants produced higher violacein, with a maximum six-fold increase in yields. The rational design E. coli: ΔpheA SYN: gave the highest violacein titers (33.8 mg/l). Such integrated approaches targeting multiple molecular hierarchies in the cell can be extended further to increase violacein yields.

Biomedical applications of genome-scale metabolic network reconstructions of human pathogens

The growing global threat of antibiotic resistant human pathogens has coincided with improved methods for developing and using genome-scale metabolic network reconstructions. Consequently, there has been an increase in the number of high-quality reconstructions of relevant human and zoonotic pathogens. Novel biomedical applications of pathogen reconstructions focus on three key aspects of pathogen behavior: the evolution of antibiotic resistance, virulence factor production, and host-pathogen interactions. New methods using these reconstructions aim to improve understanding of microbe pathogenicity and guide the development of new therapeutic strategies. This review summarizes the latest ways that genome-scale metabolic network reconstructions have been used to study human pathogens and suggests future applications with the potential to mitigate infectious disease.

Computational Systems Biology of Metabolism in Infection

A systems approach to elucidate the effect of infection on cell metabolism provides several opportunities from a better understanding of molecular mechanisms to the identification of potential biomarkers and drug targets. This is obvious from the fact that we have witnessed the accelerated use of computational systems biology in the last five years to study metabolic changes in pathogen and/or host cells in response to infection. In this chapter, we aim to present a comprehensive review of the recent research by focusing on genome-scale metabolic network models of pathogen-host systems and genome-wide metabolomics and fluxomics analysis of infected cells.