A computational model of antibiotic-resistance mechanisms in methicillin-resistant Staphylococcus aureus (MRSA)

A Review on Five and Six-Membered Heterocyclic Compounds Targeting the Penicillin-Binding Protein 2 (PBP2A) of Methicillin-Resistant Staphylococcus aureus (MRSA)

Staphylococcus aureus is a common human pathogen. Methicillin-resistant Staphylococcus aureus (MRSA) infections pose significant and challenging therapeutic difficulties. MRSA often acquires the non-native gene PBP2a, which results in reduced susceptibility to β-lactam antibiotics, thus conferring resistance. PBP2a has a lower affinity for methicillin, allowing bacteria to maintain peptidoglycan biosynthesis, a core component of the bacterial cell wall. Consequently, even in the presence of methicillin or other antibiotics, bacteria can develop resistance. Due to genes responsible for resistance, S. aureus becomes MRSA. The fundamental premise of this resistance mechanism is well-understood. Given the therapeutic concerns posed by resistant microorganisms, there is a legitimate demand for novel antibiotics. This review primarily focuses on PBP2a scaffolds and the various screening approaches used to identify PBP2a inhibitors. The following classes of compounds and their biological activities are discussed: Penicillin, Cephalosporins, Pyrazole-Benzimidazole-based derivatives, Oxadiazole-containing derivatives, non-β-lactam allosteric inhibitors, 4-(3H)-Quinazolinones, Pyrrolylated chalcone, Bis-2-Oxoazetidinyl macrocycles (β-lactam antibiotics with 1,3-Bridges), Macrocycle-embedded β-lactams as novel inhibitors, Pyridine-Coupled Pyrimidinones, novel Naphthalimide corbelled aminothiazoximes, non-covalent inhibitors, Investigational-β-lactam antibiotics, Carbapenem, novel Benzoxazole derivatives, Pyrazolylpyridine analogues, and other miscellaneous classes of scaffolds for PBP2a. Additionally, we discuss the penicillin-binding protein, a crucial target in the MRSA cell wall. Various aspects of PBP2a, bacterial cell walls, peptidoglycans, different crystal structures of PBP2a, synthetic routes for PBP2a inhibitors, and future perspectives on MRSA inhibitors are also explored.

Antimicrobial peptide GL13K immobilized onto SLA-treated titanium by silanization: antibacterial effect against methicillin-resistant Staphylococcus aureus (MRSA)

Infection is the main reason for implant failure, and the incidence of drug-resistant bacterial infection has increased in recent years. Further, methicillin-resistant Staphylococcus aureus (MRSA)-related implant infection has become a serious worldwide threat. New strategies, other than antibiotics, to tackle drug-resistance, are of high clinical significance. Antimicrobial peptides show clear superiority over conventional antibiotics in inhibiting drug-resistant bacteria. In the present study, we combined the antimicrobial peptide, GL13K, with sandblasting and acid-etching (SLA)-treated titanium using a silane coupling agent. Field emission scanning electron microscopy images showed the morphology of the coating. Attenuated total reflectance Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy results confirmed loading of GL13K, and the hydrophilicity of the SLA-GL13K coating was evaluated by water contact angle analysis. The releasing study of samples showed that the coating has a sustained releasing profile. SLA-GL13K coating exhibited strong contact- and release-killing abilities against MRSA, E. coli, and S. aureus. Meanwhile, Cell Counting Kit 8 analysis and examination of cell morphology demonstrated that the SLA-GL13K coating had good cytocompatibility at antibacterial concentrations. Overall, all these results suggest that SLA-GL13K coating can be successfully fabricated using silanization, and is a promising candidate for controlling MRSA-induced implant-related infection.

Towards a general model for predicting minimal metal concentrations co-selecting for antibiotic resistance plasmids

Many antibiotic resistance genes co-occur with resistance genes for transition metals, such as copper, zinc, or mercury. In some environments, a positive correlation between high metal concentration and high abundance of antibiotic resistance genes has been observed, suggesting co-selection due to metal presence. Of particular concern is the use of copper and zinc in animal husbandry, leading to potential co-selection for antibiotic resistance in animal gut microbiomes, slurry, manure, or amended soils. For antibiotics, predicted no effect concentrations have been derived from laboratory measured minimum inhibitory concentrations and some minimal selective concentrations have been investigated in environmental settings. However, minimal co-selection concentrations for metals are difficult to identify. Here, we use mathematical modelling to provide a general mechanistic framework to predict minimal co-selective concentrations for metals, given knowledge of their toxicity at different concentrations. We apply the method to copper (Cu), zinc (Zn), mercury (Hg), lead (Pb) and silver (Ag), predicting their minimum co-selective concentrations in mg/L (Cu: 5.5, Zn: 1.6, Hg: 0.0156, Pb: 21.5, Ag: 0.152). To exemplify use of these thresholds, we consider metal concentrations from slurry and slurry-amended soil from a UK dairy farm that uses copper and zinc as additives for feed and antimicrobial footbath: the slurry is predicted to be co-selective, but not the slurry-amended soil. This modelling framework could be used as the basis for defining standards to mitigate risks of antimicrobial resistance applicable to a wide range of environments, including manure, slurry and other waste streams.

A generalised model for generalised transduction: the importance of co-evolution and stochasticity in phage mediated antimicrobial resistance transfer

Antimicrobial resistance is a major global challenge. Of particular concern are mobilizable elements that can transfer resistance genes between bacteria, leading to pathogens with new combinations of resistance. To date, mathematical models have largely focussed on transfer of resistance by plasmids, with fewer studies on transfer by bacteriophages. We aim to understand how best to model transfer of resistance by transduction by lytic phages. We show that models of lytic bacteriophage infection with empirically derived realistic phage parameters lead to low numbers of bacteria, which, in low population or localised environments, lead to extinction of bacteria and phage. Models that include antagonistic co-evolution of phage and bacteria produce more realistic results. Furthermore, because of these low numbers, stochastic dynamics are shown to be important, especially to spread of resistance. When resistance is introduced, resistance can sometimes be fixed, and at other times die out, with the probability of each outcome sensitive to bacterial and phage parameters. Specifically, that outcome most strongly depends on the baseline death rate of bacteria, with phage-mediated spread favoured in benign environments with low mortality over more hostile environments. We conclude that larger-scale models should consider spatial compartmentalisation and heterogeneous microenviroments, while encompassing stochasticity and co-evolution.

Minimal exposure of lipid II cycle intermediates triggers cell wall antibiotic resistance

Cell wall antibiotics are crucial for combatting the emerging wave of resistant bacteria. Yet, our understanding of antibiotic action is limited, as many strains devoid of all resistance determinants display far higher antibiotic tolerance in vivo than suggested by the antibiotic-target binding affinity in vitro. To resolve this conflict, here we develop a comprehensive theory for the bacterial cell wall biosynthetic pathway and study its perturbation by antibiotics. We find that the closed-loop architecture of the lipid II cycle of wall biosynthesis features a highly asymmetric distribution of pathway intermediates, and show that antibiotic tolerance scales inversely with the abundance of the targeted pathway intermediate. We formalize this principle of minimal target exposure as intrinsic resistance mechanism and predict how cooperative drug-target interactions can mitigate resistance. The theory accurately predicts the in vivo efficacy for various cell wall antibiotics in different Gram-positive bacteria and contributes to a systems-level understanding of antibiotic action.

Modelling environmental antibiotic-resistance gene abundance: A meta-analysis

The successful treatment of infectious diseases heavily relies on the therapeutic usage of antibiotics. However, the high use of antibiotics in humans and animals leads to increasing pressure on bacterial populations in favour of resistant phenotypes. Antibiotics reach the environment from a variety of emission sources and are being detected at relatively low concentrations. Given the possibility of selective pressure to occur at sub-inhibitory concentrations, the ecological impact of environmental antibiotic levels on microbial communities and resistance levels is vastly unknown. Quantification of antibiotic-resistance genes (ARG) and of antibiotic concentrations is becoming commonplace. Yet, these two parameters are often assessed separately and in a specific spatiotemporal context, thus missing the opportunity to investigate how antibiotics and ARGs relate. Furthermore, antibiotic (multi)resistance has been receiving ever growing attention from researchers, policy-makers, businesses and civil society. Our aim was to collect the limited data on antibiotic concentrations and ARG abundance currently available to explore if a relationship could be defined in surface waters, sediments and wastewaters. A metric of antibiotic selective pressure, i.e. the sum of concentrations corrected for microbial inhibition potency, was used to correlate the presence of antibiotics in the environment to total relative abundance of ARG while controlling for basic sources of non-independent variability, such as country, year, study, sample and antibiotic class. The results of this meta-analysis show a significant statistical effect of antibiotic pressure and type of environmental compartment on the increase of ARG abundance even at very low levels. If global environmental antibiotic pollution continues, ARG abundance is expected to continue as well. Moreover, our analysis emphasizes the importance of integrating existing information particularly when attempting to describe complex relationships with limited mechanistic understanding.

Managing the emergence of pathogen resistance via spatially targeted antimicrobial use

From agriculture to public health to civil engineering, managing antimicrobial resistance presents a considerable challenge. The dynamics underlying resistance evolution reflect inherently spatial processes. Resistant pathogen strains increase in frequency when a strain that emerges in one locale can spread and replace pathogen subpopulations formerly sensitive to the antimicrobial agent. Moreover, the strength of selection for antimicrobial resistance is in part governed by the extent of antimicrobial use. Thus, altering how antimicrobials are used across a landscape can potentially shift the spatial context governing the dynamics of antimicrobial resistance and provide a potent management tool. Here, we model how the efficacy of adjusting antimicrobial use over space to manage antimicrobial resistance is mediated by competition among pathogen strains and the topology of pathogen metapopulations. For several pathogen migration scenarios, we derive critical thresholds for the spatial extent of antimicrobial use below which resistance cannot emerge, and relate these thresholds to (a) the ability to eradicate antimicrobial-sensitive pathogens locally and (b) the strength of the trade-off between resistance ability and competitive performance where antimicrobial use is absent. We find that in metapopulations where patches differ in connectedness, constraining antimicrobial use across space to mitigate resistance evolution only works if the migration of the resistant pathogen is modest; yet, this situation is reversed if the resistant strain has a high colonization rate, with variably connected metapopulations exhibiting less sensitivity to reducing antimicrobial use across space. Furthermore, when pathogens are alternately exposed to sites with and without the antimicrobial, bottlenecking resistant strains through sites without an antimicrobial is only likely to be effective under a strong competition-resistance trade-off. We therefore identify life-history constraints that are likely to suggest which pathogens can most effectively be controlled by a spatially targeted antimicrobial regime. We discuss implications of our results for managing and thinking about antimicrobial resistance evolution in spatially heterogeneous contexts.

Metal Resistance and Its Association With Antibiotic Resistance

Antibiotic resistance is recognised as a major global threat to public health by the World Health Organization. Currently, several hundred thousand deaths yearly can be attributed to infections with antibiotic-resistant bacteria. The major driver for the development of antibiotic resistance is considered to be the use, misuse and overuse of antibiotics in humans and animals. Nonantibiotic compounds, such as antibacterial biocides and metals, may also contribute to the promotion of antibiotic resistance through co-selection. This may occur when resistance genes to both antibiotics and metals/biocides are co-located together in the same cell (co-resistance), or a single resistance mechanism (e.g. an efflux pump) confers resistance to both antibiotics and biocides/metals (cross-resistance), leading to co-selection of bacterial strains, or mobile genetic elements that they carry. Here, we review antimicrobial metal resistance in the context of the antibiotic resistance problem, discuss co-selection, and highlight critical knowledge gaps in our understanding.

Syntheses, characterization, antimicrobial and cytotoxic activities of pyridine-2,5-dicarboxylate complexes with 1,10-phenanthroline

In this study, four mononuclear M(II)-pyridine-2,5-dicarboxylate (M = Co(II), Ni(II), Cu(II) and Zn(II) complexes with pyridine-2,5-dicarboxylic acid or isocinchomeronic acid, 1,10-phenanthroline (phen), [Co(Hpydc)(2)(phen)]·H(2)O (1), [Ni(pydc)(phen)(2)]·6.5H(2)O (2) [Cu(pydc)(phen)(H(2)O)(2)] (3) and [Zn(pydc)(phen)(H(2)O)(2)]·H(2)O (4) have been synthesized. Elemental, thermal and mass analyses, molar conductance, magnetic susceptibilities, IR and UV/vis spectroscopic studies have been performed to characterize the complexes. Subsequently, these ligands and complexes were tested for antimicrobial activity by disc diffusion method on Gram positive, negative bacteria and yeast. In addition, cytotoxic activity tests were performed on rat glioma (C6) cells by MTT viability assay for 24 and 48 h. Antimicrobial activity results demonstrated that when compared to the standard antibiotics, phen displayed the most effective antimicrobial effect. The effect of synthesized complexes was close to phen or less. Cytotoxic activity results showed that IC(50) value of phen was determined as 31 μM for 48 h. (1) and (2) compared to the alone ligand had less toxic activity. IC(50) values of (3) for 24 and 48 h treatments were 2.5 and 0.6 μM, respectively. IC(50) value of (4) for 48 h was 15 μM. In conclusion, phen, (3) and (4) may be useful as antibacterial and antiproliferative agents in the future.

Identification of Functional Regulatory Residues of the β -Lactam Inducible Penicillin Binding Protein in Methicillin-Resistant Staphylococcus aureus

Resistance to methicillin by Staphylococcus aureus is a persistent clinical problem worldwide. A mechanism for resistance has been proposed in which methicillin resistant Staphylococcus aureus (MRSA) isolates acquired a new protein called β-lactam inducible penicillin binding protein (PBP-2′). The PBP-2′ functions by substituting other penicillin binding proteins which have been inhibited by β-lactam antibiotics. Presently, there is no structural and regulatory information on PBP-2′ protein. We conducted a complete structural and functional regulatory analysis of PBP-2′ protein. Our analysis revealed that the PBP-2′ is very stable with more hydrophilic amino acids expressing antigenic sites. PBP-2′ has three striking regulatory points constituted by first penicillin binding site at Ser25, second penicillin binding site at Ser405, and finally a single metallic ligand binding site at Glu657 which binds to Zn²⁺ ions. This report highlights structural features of PBP-2′ that can serve as targets for developing new chemotherapeutic agents and conducting site direct mutagenesis experiments.

Agent-based modeling of competence phenotype switching in Bacillus subtilis

Background

It is a fascinating phenomenon that in genetically identical bacteria populations of Bacillus subtilis, a distinct DNA uptake phenotype called the competence phenotype may emerge in 10–20% of the population. Many aspects of the phenomenon are believed to be due to the variable expression of critical genes: a stochastic occurrence termed “noise” which has made the phenomenon difficult to examine directly by lab experimentation.

Methods

To capture and model noise in this system and further understand the emergence of competence both at the intracellular and culture levels in B. subtilis, we developed a novel multi-scale, agent-based model. At the intracellular level, our model recreates the regulatory network involved in the competence phenotype. At the culture level, we simulated growth conditions, with our multi-scale model providing feedback between the two levels.

Results

Our model predicted three potential sources of genetic “noise”. First, the random spatial arrangement of molecules may influence the manifestation of the competence phenotype. In addition, the evidence suggests that there may be a type of epigenetic heritability to the emergence of competence, influenced by the molecular concentrations of key competence molecules inherited through cell division. Finally, the emergence of competence during the stationary phase may in part be due to the dilution effect of cell division upon protein concentrations.

Conclusions

The competence phenotype was easily translated into an agent-based model – one with the ability to illuminate complex cell behavior. Models such as the one described in this paper can simulate cell behavior that is otherwise unobservable in vivo, highlighting their potential usefulness as research tools.

Escherichia coli adapts to tetracycline resistance plasmid (pBR322) by mutating endogenous potassium transport: in silico hypothesis testing

Antibiotic resistance exerts a metabolic cost on bacteria and presumably a fitness disadvantage in the absence of antibiotics. However, several studies have shown that bacteria can evolve to eliminate this cost. Escherichia coli can adapt to the plasmid pBR322 carrying the tetA tetracycline-resistance gene (codes for the TetA efflux pump) by a chromosome mutation, which requires an intact tetA gene on the plasmid. The TetA pump can mediate potassium uptake. Here, the hypothesis that TetA replaces the endogenous K(+) uptake system Trk is explored using a multi-level modeling approach that explicitly resolves relevant intracellular processes (e.g., metabolism and K(+) uptake) and simulates individual bacteria in competition. The general behavior of the model is consistent with observations from the literature (e.g., growth rate and K(+) limitation). In competition experiments without tetracycline, the model correctly predicts the fitness advantage of naive susceptible over naive resistant, evolved resistant over naive resistant and evolved resistant over evolved susceptible strains. Trk takes up about 10 times the K(+) required, which costs energy. TetA takes up less K(+) , which is more efficient and leads to the evolution of the Trk mutant. The evolved Trk mutant relies on TetA to take up K(+) , and thus, carrying the plasmid is advantageous even in the absence of the antibiotic.

Analysis of antibiotic resistance patterns and detection of mecA gene in Staphylococcus aureus isolated from packaged hamburger

Presence of Staphylococcus aureus, antibiotic resistance pattern and PCR detection of mecA gene in isolated strains were investigated in total of 256 packaged hamburgers in Iran-Tehran. For this purpose we used standard disk-diffusion method and sensitive and specific PCR technique, respectively. Results showed that 25% of samples were positive for S. aureus. Resistance to meticillin, erythromycin, penicillin G, cefazolin, ciprofloxasin, vacomycin and amoxiclave was determined 89%, 20.3%, 18.7%, 15.6%, 14%, 26.6% and 12.5%, respectively. According to the obtained results from PCR analysis of methicillin-resistant S. aureus (MRSA), mecA gene was present in 100% of the resistant isolates, 0% of intermediate-resistance isolates and 25% of susceptible isolates. The results obtained from PCR detection of mecA gene showed high correlation with standard disk diffusion test.

Contribution of mathematical modeling to the fight against bacterial antibiotic resistance

PURPOSE OF REVIEW

Modeling of antibiotic resistance in pathogenic bacteria responsible for human disease has developed considerably over the last decade. Herein, we summarize the main published studies to illustrate the contribution of models for understanding both within-host and population-based phenomena. We then suggest possible topics for future studies.

RECENT FINDINGS

Model building of bacterial resistance has involved epidemiologists, biologists and modelers with two different objectives. First, modeling has helped largely in identifying and understanding the factors and biological phenomena responsible for the emergence and spread of resistant strains. Second, these models have become important decision support tools for medicine and public health.

SUMMARY

Major improvements of models in the coming years should take into account specific pathogen characteristics (resistance mechanisms, multiple colonization phenomena, cooperation and competition among species) and better description of the contacts associated with transmission risk within populations.

A theoretical analysis of the prodrug delivery system for treating antibiotic-resistant bacteria

Simulations were carried out to analyze a promising new antimicrobial treatment strategy for targeting antibiotic-resistant bacteria called the β-lactamase-dependent prodrug delivery system. In this system, the antibacterial drugs are delivered as inactive precursors that only become activated after contact with an enzyme characteristic of many species of antibiotic-resistant bacteria (β-lactamase enzyme). The addition of an activation step contributes an extra layer of complexity to the system that can lead to unexpected emergent behavior. In order to optimize for treatment success and minimize the risk of resistance development, there must be a clear understanding of the system dynamics taking place and how they impact on the overall response. It makes sense to use a systems biology approach to analyze this method because it can facilitate a better understanding of the complex emergent dynamics arising from diverse interactions in populations. This paper contains an initial theoretical examination of the dynamics of this system of activation and an assessment of its therapeutic potential from a theoretical standpoint using an agent-based modeling approach. It also contains a case study comparison with real-world results from an experimental study carried out on two prodrug candidate compounds in the literature.

Modeling of the bacterial mechanism of methicillin-resistance by a systems biology approach

Background

A microorganism is a complex biological system able to preserve its functional features against external perturbations and the ability of the living systems to oppose to these external perturbations is defined “robustness”. The antibiotic resistance, developed by different bacteria strains, is a clear example of robustness and of ability of the bacterial system to acquire a particular functional behaviour in response to environmental changes. In this work we have modeled the whole mechanism essential to the methicillin-resistance through a systems biology approach. The methicillin is a β-lactamic antibiotic that act by inhibiting the penicillin-binding proteins (PBPs). These PBPs are involved in the synthesis of peptidoglycans, essential mesh-like polymers that surround cellular enzymes and are crucial for the bacterium survival.

Methodology

The network of genes, mRNA, proteins and metabolites was created using CellDesigner program and the data of molecular interactions are stored in Systems Biology Markup Language (SBML). To simulate the dynamic behaviour of this biochemical network, the kinetic equations were associated with each reaction.

Conclusions

Our model simulates the mechanism of the inactivation of the PBP by methicillin, as well as the expression of PBP2a isoform, the regulation of the SCCmec elements (SCC: staphylococcal cassette chromosome) and the synthesis of peptidoglycan by PBP2a. The obtained results by our integrated approach show that the model describes correctly the whole phenomenon of the methicillin resistance and is able to respond to the external perturbations in the same way of the real cell. Therefore, this model can be useful to develop new therapeutic approaches for the methicillin control and to understand the general mechanism regarding the cellular resistance to some antibiotics.