Category: Diet

Adaptive antimicrobial materials

Adaptive antimicrobial materials

Antimcirobial Reviewed: October 5, Source: Centers Hair growth tips Disease Matsrials and PreventionNational Center for Vegan energy solution and Zoonotic Infectious Adaptive antimicrobial materials NCEZID maerials, Division Hair growth tips Healthcare Quality Promotion DHQP. On the other hand, the control group had no sizeable amount of leakage protein from damaged cells after treatment. It is not known what causes compound 3 to permeabilize the membrane. Rybtke M. But then again, when the antibiotic is removed, the distribution eventually returns to its initial configuration. Mechanisms of antimicrobial resistance in bacteria. Adaptive antimicrobial materials


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Adaptive antimicrobial materials -

B Size of the population as a function of time for the same simulation as in A. After each antibiotic shock small black arrows the population size decreases exponentially and the recovery time becomes longer with each shock. After the antibiotic is removed tilted black arrow the population comes back again to its wild-type WT growth rate.

Note that the average increases while the shocks are applied and then gradually comes back to small values when the antibiotic is removed. Error bars indicate the standard deviation.

It can be observed that the standard deviation increases with the antibiotic stress. The panels below show the full distribution G μ β , σ β at three different times: before any antibiotic is introduced circle ; after several antibiotic shocks star ; after a long period of time without antibiotic line.

The only way for the population to truly reverse to its wild-type condition and become susceptible again is to return to their initial distribution G μ β , σ β , which is centered at low values of β 0. We expect this to happen because cells with a small β 0 duplicate faster than cells with large β 0 see S1 Text and S7 Fig.

Since the values of β 0 are correlated across generations, cells with faster division rates low β 0 will eventually dominate the population, shifting the distribution G μ β , σ β towards the low β 0 region.

Note that μ β increases as the antibiotic concentration is ramped higher. Then, when the external antibiotic is removed the average transcription rate across the population μ β decreases gradually, reaching the same value as in the original wild-type population.

The lower panels in Fig. But then again, when the antibiotic is removed, the distribution eventually returns to its initial configuration.

In our model the time-scale to produce a phenotypic change due to genetic mutations is one order of magnitude larger than that needed to produce a phenotypic change due to epigenetic modifications.

Thus, to observe any significant increase in resistance produced by changes in the pump efficiency we need to run the simulation for a longer time. Interestingly, by doing this we obtain a nonreversible resistance, first driven by our mechanism of epigenetic inheritance which is reversible , and then fixed by genetic variation and inheritance of the pump efficiency.

To observe this phenomenon, which can be considered analogous to genetic assimilation [ 39 , 40 , 41 ], we performed numerical experiments similar to the ones presented in the previous sections, where the population is first induced with M antibiotic shocks. The difference now is that we will let the population be in contact with the antibiotic for a very long time before removing it.

A similar measure was used in [ 3 ]. In each case, the arrows indicate the time at which the antibiotic is removed. The results depicted in Fig. The blue curve deserves special attention.

After this, the antibiotic concentration was kept constant until the time indicated by the blue arrow, at which the antibiotic was removed. Note that the RI keeps increasing even during the interval of steady antibiotic concentration.

Note also that the final RI stationary value reached after the antibiotic is removed is five times larger for the blue curve than for all the other curves. It is worth noting that the black curve, corresponding to a control population growing in the absence of antibiotic, remains close to the initial low basal level throughout the entire simulation.

Therefore, in our model antibiotic resistance occurs only as a response to the selective pressure imposed by the antibiotic and not by random genetic drift. A Resistance Index RI as a function of time for populations induced with M antibiotic shocks. The different curves correspond to different values of M , except by the black one which corresponds to a control population growing with no antibiotic.

B Blow up showing the first generations. For each curve, the corresponding arrow indicates the time at which the antibiotic is removed. In the case of the blue curve, the asterisk indicates the time at which the last antibiotic shock is applied, after which the antibiotic concentration is kept constant.

C Blow up of the last part of the simulation showing the point at which the antibiotic is removed from the population corresponding to the blue curve. It can be observed that in this case the final stationary value of the RI is about five times higher than that of the control population.

D Evolution of the average transcription rate μ β and the average pump efficiency μ ε for the population corresponding to the blue curve. Notice that as soon as the antibiotic concentration is kept constant, μ β starts decreasing whereas μ ε keeps rising until the antibiotic is completely removed.

This shows that the evolutionary process does not reach a stationary state or fixed point in the presence of antibiotic. It is important to mention that the increase in the basal level of the RI shown in Fig.

Indeed, Fig. From Fig. However, as soon as the antibiotic concentration is kept constant, even at a high value, the average transcription rate μ β starts decreasing and reaches its initial low value at the end of the simulation. Contrary to this, the average pump efficiency μ ε keeps rising as long as there is antibiotic in the environment, reaching a steady value only when the antibiotic is removed.

Thus, exposing the population to a high antibiotic concentration for a long time produces a non-reversible shift in the pump efficiency distribution P ε , permanently increasing the level of resistance of the population.

It is also important to emphasize the difference between the survival rate SR and the resistance index RI. The former is defined as the fraction of cells that survive an induction, and this fraction ranges from 0 if no cell survives to 1 if all cells survive.

On the other hand, the RI is the value of the antibiotic concentration at which the SR is 0. Therefore, the RI does not have to be between 0 and 1. Actually, its value depends on the units used to measure the antibiotic concentration in our case we use arbitrary units and the capability of the population to resist the antibiotic.

This capability, in turn, depends on the way β 0 and ε I are distributed across the population. In each cell, these parameters determine the fixed points of the system only one fixed point exists for a given combination of β 0 and ε I in the range of concentrations explored in this work, see S1 Text and S9 Fig.

The results presented in Fig. Adaptive resistance in bacteria is observed after subjecting a population to gradual increments of antibiotic concentration.

Regardless of the level of resistance reached through this process, which can be very high , the resistance disappears after a few generations in the absence of antibiotic. Previous studies have independently identified epigenetic inheritance and phenotypic heterogeneity as important components involved in the emergence of adaptive resistance [ 1 , 3 , 4 , 6 , 7 , 8 , 11 ], but their role has never been evaluated quantitatively.

Additionally, the molecular origin of reversibility observed in adaptive resistance has remained unclear. In this study we present a theoretical framework that identifies the essential mechanisms for the emergence, evolution and reversibility of adaptive resistance. We constructed a single-cell dynamic model of a prototypic efflux pump regulatory network EPRN that incorporates the most updated information available in the literature.

We calibrated this model with experimental observations for wild type and mutant E. coli strains. We then grew a population of such single cells with growth dynamics obeying simple rules such as division, death, variability and inheritance of gene expression patterns.

For each cell in the population we compute their EPRN temporal dynamics. Through this model we demonstrate that heterogeneity and mother-daughter correlations affecting transcription rates, specifically those of the EPRN main regulators, can explain the gradual amplification of the multidrug resistant phenotype.

By contrast, mother-daughter correlations implemented in the pump efficiency, and developing at longer timescales, were not sufficient to make the population adapt and survive to successive antibiotic shocks but had a role in fixing resistance when the population had contact with antibiotics for a very long time.

We also found that introducing a cost associated with the functioning of the EPRN was enough to explain the observed reversibility to the susceptible non-resistant phenotype.

A previous report [ 11 ] proposed that adaptive resistance developed as a consequence of heterogeneity in gene expression because cells that randomly have a high production of efflux pumps survive, and those that did not, die.

Through our model, we were able to show that although heterogeneity in gene expression is necessary, it is not sufficient to explain the emergence of resistance, nor its gradual response, as epigenetic inheritance of gene expression patterns is also necessary.

Epigenetic modifications can change gene expression patterns at short time scales, providing a mechanism by which cells can adapt to changing environments quickly. At the same time, it allows for enough flexibility: if the environment returns to its earlier state, a population whose fitness is compromised by the new gene expression patterns can return to its previous state in a short time.

Based on several experimental observations, another report [ 1 ] suggested that DNA methylation is a plausible mechanism driving this epigenetic inheritance. Methylation can indeed produce both the heterogeneity and epigenetic inheritance of gene expression patterns required for adaptive resistance to occur.

Our results support this idea and specifically identify the regulatory regions of the main regulators of the EPRN as the most probable targets for the methylation process, as amplification of the antibiotic resistance do not occur without the mother-daughter correlations in gene transcription rates see Fig.

Consequently, the process of DNA methylation in bacteria is potentially an important target for the development of therapeutic treatments in preventing the emergence of adaptive resistance. It is important to stress that variability in gene expression is known to be essential for adaptive resistance to occur [ 11 ].

So, our model incorporates this variability with the additional feature that it has to be inherited. Whether or not this variability is caused by methylation is not the central point.

Nonetheless we propose DNA methylation of the marRAB operon as the possible cause of this variability because: i it can be inherited; ii mutant cells in which methylation is lacking are much more susceptible to antibiotics [ 1 ]; iii it provides the necessary variability in short periods of time required for adaptive resistance to emerge [ 1 ].

However, regardless of the precise mechanism behind this variability, the important point in our model is the existence of inheritable variability that can be quickly developed. For our results show that some heritable mechanism modifying the transcription rates of an efflux pump regulatory network must be present in order to observe adaptive resistance.

Another interesting observation is the emergence of a stable form of resistance when the population is left in a medium with high concentrations of antibiotic for very long times.

In our model this non-reversible resistance is produced by changes effectively improving the pump efficiency ε I , meaning that the pumps become better at distinguishing antibiotics from nutrients, so that they can pump out the former at a higher rate than the latter.

Although these genetic modifications are rare and insufficient to save the population initially, they become important at longer times, transforming into an alternate source of resistance without adverse effects.

Therefore, this heritable trait will, at longer time scales, permanently increase the basal levels of resistance of the population when it is under selective pressure see Fig.

In our model genetic changes at each generation are small and increase the pump efficiency gradually Fig. In reality, genetic changes, although rare, may produce abrupt changes in the level of resistance of the population. The important point is that the fast epigenetic changes occurring in the transcription rate allow the population to survive long enough as to develop more stable and efficient forms of resistance.

This behavior is consistent with experimental observations, showing that bacterial populations that have been continuously exposed to antibiotics are permanently much more resistant than populations that have been not [ 1 ].

We have based our simulations on the regulatory scheme of the widely known acrAB-tolC efflux pump system, for which many of the kinetic parameters are still unknown.

In our study, we aimed to identify the essential mechanisms that could explain and reproduce adaptive resistance and our results hold in a significant region of the parameter space and not only for the particular values presented in S1 Table see S4 and S5 Figs.

However, performing a deep search in the parameter space of the equations could reveal important constraints; such as the timescales at which epigenetic inheritance or genetic mutations must occur; or even the amount of pumps that the cell needs to produce which is to our current knowledge an unknown variable.

We also explored alternative mechanisms that could yield resistance, such as uneven pump distribution in each cell division one daughter cells takes the majority of the pumps and increased mutation rates which increases the variability σ ε in the efficiency of the efflux pumps.

The results, presented in the SI see S1 Text , S10 and S11 Figs. show that neither of these two mechanisms is able to produce adaptive resistance. Our model provides an explanation for the emergence of adaptive resistance based on the cost and benefit of the biological characteristics of an efflux pump system.

It does not only predict the behavior of populations subjected to different antibiotic shocks and at different time, but also a number of different phenomena observed experimentally in bacterial populations, such as phenotypic reversibility, genetic assimilation, and even the survival rates of populations that have been pre-induced with non-lethal antibiotic concentrations see S1 Text and S12 Fig.

Expression of the nodes corresponding to the activator MarA in the complete network and Activator in the simplified network and the pumps AcrAB-TolC in the complete network and Pumps in the simplified network. It can be observed that the curves for the complete and simplified networks are extremely similar in all cases.

Approximately, a twofold increase in the concentration of the activator in the mutant versus the wild type strains is observed in our simulations, which correspond to the experiments reported in [ 2 ]. In fact, this twofold increase was used to calibrate some of the parameters in the numerical simulation.

By moving the values of the degradation rates γ A and γ R of the activator and the repressor, respectively, along the curve, we obtain the same qualitative results for the induction experiments as the one shown in Fig. The triangles show the particular values used to generate the plots in S4 Fig.

The numbers between parentheses indicate the average increase of the antibiotic between two successive shocks. These results suggest that the conclusions of our model hold for a wide region in the parameter space and not just for the one particular point reported in S1 Table.

Tracking plots for the activator corresponding to the 1 st A , 2 nd B , 3 rd C and 5 th D points in S3 Fig.

Note that these plots are qualitatively similar to the one shown in the main text Fig. This plot shows the size of the population as a function of time for the case in which the value of β 0 for each cell in the population and for each generation is taken randomly with uniform probability from the interval [0, 10 ].

The upper arrow indicates the time at which the first antibiotic shock is applied, whereas the lower arrow indicates the application of the second antibiotic shock. Note that in this case in which there is no mother-daughter correlation in the value of β 0 , the population is not able to survive the second antibiotic induction, even though there is a relatively high variability in the population.

Each point is the average division time over cell division events The average is necessary because of the presence of noise. Note that the division time increases with both the concentration of external inducer I ext and the transcription rate β 0. In this case, in each replication the daughter cells can either acquire the same value of β 0 than the mother with probability 0.

The inset shows the distribution P β at the beginning of the simulation blue histogram and after several antibiotic shocks red histogram. Note that the behavior of the system is essentially the same as in Fig.

A Fixed point of the activator as a function of β 0 for a fixed concentration of external inducer. B -D Stream plots on the Activator-Repressor plane for different values of β 0 showing that there is only one fixed point in the range of parameters explored in this work.

A Increased mutation rates. B Inverted time-scales. We can observe that the population dies immediately after the first antibiotic shock, which supports the idea that mutations alone cannot explain adaptive resistance.

Time is measured in generations. Plot of the population size as a function of time when random pump segregation is implemented without genetic or epigenetic inheritance.

Each antibiotic shock is indicated by a change in color and by an arrow. Note that after the first shock just a few cells survive. These surviving cells are highly resistant because they can survive further antibiotic shocks. However, these cells cannot divide the population size remains constant.

This behavior is similar to the one observed experimentally in persistent cells [ 6 ]. Note that the survival ratio increases with the pre-induction concentration I pre. Values of the parameters that were used in the numerical simulations of both the single-cell and population models.

The second column shows the values used to obtain the results presented in the main text, while the third column shows the values used for the alternative scenarios presented in the SI. In the latter case the values that are different from those in the original model are shaded in gray.

This table lists all the parameters of the model and gives their biological interpretation. Values of the parameters used for the numerical simulations of the complete Mar system depicted in Fig. Conceived and designed the experiments: SSM PC MA.

Performed the experiments: SSM. Analyzed the data: SSM PC MA. Wrote the paper: SSM PC MA. Contributed to frame the work within the right biological context: PC.

Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Article Authors Metrics Comments Media Coverage Reader Comments Figures. Abstract Adaptive resistance emerges when populations of bacteria are subjected to gradual increases of antibiotics.

This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Data Availability: All relevant data are within the paper and its Supporting Information files.

Introduction It has been well-established that various species of bacteria, including E. Results Single Cell Efflux Pump Model. Download: PPT. Population model: Variability and inheritance. Population model: Cell duplication and death.

Emergence of the highly resistant phenotype. Genetic Assimilation. Discussion Adaptive resistance in bacteria is observed after subjecting a population to gradual increments of antibiotic concentration.

Supporting Information. S1 Fig. Comparison between the complete vs the simplified EPRN models. s TIF. S2 Fig. Dynamical behavior of the SC-EPRN. S3 Fig. Behavior of the SC-EPRN for wild-type and mutant strains.

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Issue 3, From the journal: Biomaterials Science. You have access to this article. Please wait while we load your content Something went wrong. Try again? Cited by. Download options Please wait aeruginosa , when the agent is used together with carbenicillin, an antibiotic that is substrate specific to MexAB-OprM.

Loss of potentiating activity of D occurred rapidly due to a FL substitution in mexB , which is known to play an important role in inhibitor binding Ranjitkar et al.

Polyamines are aliphatic carbon chains containing several amino groups and are essential organic polycations present in every form of life. Polyamines are implicated in cell maintenance and viability and in the functioning of a wide array of organ systems, including, the nervous and immune systems Sánchez-Jiménez et al.

Fleeman et al. identified a polyamine scaffold as a strong efflux pump inhibitor with no direct antimicrobial activity. Five lead agents were found to potentiate aztreonam, chloramphenicol and tetracycline by causing a 5- to 8-fold decrease in the MIC90 Fleeman et al.

In addition, the polyamine derivatives did not disrupt the bacterial membrane, unlike other polyamines, which can lead to the identification of false positives for EPIs Fleeman et al. Moreover, polyamines did not display toxicity to mammalian cell lines and did not inhibit calcium channel activity in human kidney cells Fleeman et al.

Phage therapy, the use of bacteriophages to infect and lyse bacterial cells, has been widely discussed Chan et al.

Traditional phage therapy involves the administration of one, or a mixture, of phages that will invade the bacterial cell and clear infection Waters et al. A different approach to phage therapy has been proposed, whereby phages would be used to steer antibiotic resistance evolution, selecting for phage resistance and antibiotic susceptibility.

For example, the lytic Myoviridae bacteriophage, OMKO1, utilizes OprM of the multidrug efflux systems MexAB and MexXY as a receptor-binding site. Selection for resistance to OMKO1 bacteriophage attack creates an evolutionary trade-off in MDR P. aeruginosa , by changing the efflux pump mechanism, leading to an increased sensitivity to ciprofloxacin, tetracycline, ceftazidime and erythromycin, four drugs from different antibiotic classes Chan et al.

Phage steering can be achieved when the binding receptor for the bacteriophage is implicated in both antibiotic resistance and phage resistance.

The advantage of this approach lies in the two distinct, and opposing, mechanisms leading to bacterial eradication Gurney et al. The prototypical example of successful anti-resistance therapeutics are the β-lactamase inhibitors.

β-lactamase inhibitors such as clavulanic acid, sulbactam and tazobactam are widely used to combat resistance mediated by β-lactamases Tooke et al. However, the majority of clinically used β-lactamase inhibitors have a limited spectrum and mainly target Ambler class A β-lactamases, excluding KPC-type β-lactamase.

Progress has been made in the development of novel β-lactamase inhibitors with a wider spectrum of activity. Three novel β-lactamase inhibitors, avibactam, vaborbactam and relebactam, function against Ambler class A, C and D β-lactamases Wong and Duin, However, only avibactam and relebactam are efficacious against P.

aeruginosa infection Aktaş et al. Lamut et al. designed 4,5,6,7-tetrahydrobenzo[d]thiazole-based DNA gyrase B inhibitors and incorporated these inhibitors with siderophore mimics. The siderophore mimic served as an inducer for increased uptake of the gyrase B inhibitors into the bacterial cytoplasm.

Out of the ten gyrase B inhibitors tested against P. Several more attempts have been made at designing broad-spectrum anti-bacterial and anti-biofilm therapies targeting DNA gyrase or topoisomerase but none have shown good activity for P.

aeruginosa Dubey et al. Murepavadin is a novel, non-lytic, species specific, outer-membrane protein targeting antibiotic for the treatment of P. aeruginosa infections, including those caused by MDR strains Dale et al. Murepavadin is derived from the β-hairpin host defense molecule protegrin 1 PG-1 and optimized to counteract unfavorable absorption, distribution, metabolism, excretion and toxicity ADMET properties normally associated with PG-1 Obrecht et al.

It is a macrocycle compound consisting of PG-1 loop sequences linked to a D-proline-L-proline sequence, the latter of which is important for its stability and subsequent strong antibacterial potential Srinivas et al. Murepavadin functions through binding to the LPS transport protein D LptD , an OMP necessary for LPS biogenesis in Gram-negative bacteria.

The interaction between murepavadin and LptD causes inhibition of LPS transport, which leads to alterations of the LPS on the bacterial OM and eventually, cell death Werneburg et al. Murepavadin derivatives have been screened for activity against Gram-negative ESKAPE pathogens, including P.

Therefore, compounds were generated consisting of β-hairpin macrocycles linked to the peptide macrocycle of polymyxin B. One of these compounds, compound 3, showed strong antimicrobial activity MIC 0. aeruginosa isolates , low toxicity to mammalian cells, low plasma protein binding, good human plasma stability and no lytic activity towards human red blood cells.

This compound was shown to perturb and permealise the bacterial membrane through interacting with the β-barrel domain of BamA in E. coli ATCC Luther et al.

BamA is part of the β-barrel assembly machinery BAM complex, which serves to fold and insert outer membrane proteins in the OM Gu et al. The binding interaction between BamA and compound 3 locks BamA in its closed state through changing the conformational composite in the β-barrel lateral gate between open and closed states.

It is not known what causes compound 3 to permeabilize the membrane. It may inhibit the folding activity of the BAM complex, leading to incorrectly folded proteins being misplaced in the inner membrane.

Alternatively, BamA may only serve as an extra binding site for compound 3, thereby evading the LPS-modification resistance mechanism of Gram-negative pathogens Luther et al. Quorum sensing regulates a wide range of genes involved in virulence and bacterial adaptation Kalia, For instance, QS is required for the surfing and swarming motility phenotypes associated with increased resistance to antimicrobials.

The surfing phenotype is regulated via three QS systems in P. aeruginosa ; Las, Rhl and Pqs Sun et al. In addition, QS has been found to influence tolerance to antibiotics in P. aeruginosa biofilms. QS provides structural rigidity through the regulation of Pel polysaccharides and eDNA release necessary for the extracellular polysaccharide matrix.

In addition, the production of rhamnolipids, surfactants important for the establishment and maintenance of biofilms, is controlled under QS de Kievit, Therefore, QS has been recognized as a significant potential target for developing anti-resistance therapies.

Strategies to combat antimicrobial resistance by targeting adaptive resistance mechanisms have significant potential for reversing antibiotic resistance in P. Adaptive resistance is often mediated through complex global regulatory systems, such as the QS system, and regulate an extensive set of genes involved in resistance.

Targeting these regulatory systems may prevent the activation of expression of these resistance genes that would normally be expressed under the environmental conditions of infection.

Ajoene is a natural sulphur-containing compound extracted from garlic Yoshida et al. rsmZ and rsmY bind the global regulatory protein RsmA, and unbound RsmA represses the translation of genes by preventing ribosome binding to the Shine-Dalgarno site. Several genes involved in QS are under RsmA regulation, and low expression of rsmZ and rsmY in the presence of ajoene promotes RsmA mediated repression of these target genes Jakobsen et al.

However, the therapeutic applicability of ajoene is limited due to availability, instability, hydrophobicity and relatively high MIC values. Efforts are being undertaken to overcome these issues through modification, the use novel delivery systems and a targeted route of administration and through the development of synthetic ajoene analogues Fong et al.

Another novel QS-inhibitor derived from a natural source is the plant flavonoid naringenin. Naringenin diminishes the production of QS-regulated virulence factors in P. aeruginosa by binding directly to LasR, thereby competing with the activator of LasR, N- 3-oxo-dodecanoyl -l-homoserine lactone HSL.

It is ineffective at outcompeting HSL when the activator is already bound to LasR. Thus, the QS-inhibitor will only sufficiently interfere with the QS response when administered during early exponential growth, when naringenin can compete with unbound HSL for LasR binding.

Naringenin is only suitable for combatting P. aeruginosa populations at low cellular densities, which often does not represent the clinical infection scenario. The full potential of QS-inhibition will only be realized if an inhibitor is developed that is capable of targeting P.

Bacterial biofilms pose a physical barrier for drug penetration, which is one of the reasons that bacteria in a biofilm mode of growth are more resistant to antimicrobials. This phenomenon may be subverted with the use of nanocarriers that encapsulate antimicrobials and facilitate drug diffusion through the bacterial biofilm.

In addition, nanocarriers can also protect drugs from degradation, ensure controlled drug release, and cause increased uptake by the drug target, leading to an overall higher efficiency of encapsulated drugs. Drug delivery methods can be diverse in chemical structure and nature Table 1.

Most published studies concur that encapsulated antibiotics are more effective at preventing or eradicating biofilm formation than their free drug counterpart Alhariri et al. Table 1 Recent efforts in the design of drug delivery methods for anti-pseudomonal therapies.

Another promising antibiotic adjuvant targeting biofilms is the non-bactericidal, inhaled adjuvant, nitric oxide NO. Exposure of P. aeruginosa biofilms to low-dose NO has been shown to cause dispersal of biofilms, rendering the infection susceptible to subsequent antibiotic treatment Cai, NO functions by increasing bacterial phosphodiesterase activity which, in turn, leads to a reduction in the vital secondary signaling messenger, cyclic di-GMP.

Cyclic di-GMP is vital for intracellular regulation of biofilm formation. Howlin et al. carried out in vitro biofilm studies using CF sputum clinical samples. Biofilms treated with NO showed a relative decrease in biofilm biomass and surface bound thickness in comparison to the untreated control.

There is some evidence for the safety of NO administration in CF patients in vivo and NO is currently undergoing clinical trials to measure clinical efficacy Howlin et al. Poly-acetyl-arginyl-glucosamine PAAG , also called SNSP, is a novel inhaled adjuvant therapy currently undergoing phase one clinical trials.

PAAG is a polycationic glycoprotein that functions by permeabilizing the bacterial membrane and is active against methicillin resistant Staphylococcus aureus MRSA , Burkholderia spp.

and E. PAAG has been shown to effectively disperse Burkholderia cepacia complex biofilm structures extracted from the CF lung Narayanaswamy et al.

aeruginosa , PAAG is has been shown to effectively eradicate persister cells, which is important for the prevention of recurrent P.

aeruginosa infections and subsequent exacerbations in people with CF Narayanaswamy et al. In addition to serving as an effective antibiotic adjuvant, PAAG also reduces inflammation and promotes viscoelasticity and mucociliary clearance, making it a suitable drug candidate to improve the quality of life for patients with a variety of mucus diseases Fernandez-Petty et al.

The global overuse and misuse of antibiotics during the last 80 years has led to a profound increase in antimicrobial resistance. AMR is a complex, One Health issue, involving human, animal and environmental factors.

The solution to AMR is therefore also likely to be a complex one, involving multiple strategies; maintaining AMR surveillance, containing AMR transmission, reducing selection pressure, developing novel antimicrobials or reverting antibiotic resistant microbes back to the susceptible phenotype with the use of antibiotic adjuvants Hernando-Amado et al.

Although progress in the development of naturally derived and peptide-based antimicrobials has been made Mok et al. The conservation of existing antibiotics through careful stewardship is paramount to help mitigate the gap between the demand for new drugs and the diminishing supply pipeline.

Antibiotic adjuvants will also play an important role in extending the shelf life of our existing antimicrobial therapeutic agents. Adjuvant strategies targeting resistance mechanisms in P.

aeruginosa could rejuvenate traditional antibiotic therapy by potentiating drug activity as well as slowing the development of antibiotic resistance. As described in this review, antimicrobial resistance in P.

aeruginosa is regulated through a complex interplay of mechanisms. Resistance encoded in the core genome of P. aeruginosa , such as low outer membrane permeability, Mex-type efflux pumps and AmpC β-lactamase amount to the basal level of resistance against antimicrobials.

This intrinsic resistance is present in the all P. aeruginosa strains and serves as a foundational level, which can be expanded upon. This expansion can be induced by environmental influences, such as host factors and signalling molecules, that switch on adaptive resistance mechanisms.

Acquired resistance mechanisms, such as antibiotic target modifications generated via mutation, and antibiotic modifying enzymes or resistance plasmids, acquired by gene transfer, may serve as additional building blocks to expand the arsenal of resistance mechanisms a particular strain might carry.

Several novel therapeutic strategies, targeting one or more of these mechanisms, have been described in this review. In light of recent findings, OM perturbants capable of sensitizing the Gram-negative bacterial membrane to previously non-active antibiotics seem an opportune strategy to combat resistance.

OM perturbants can by-pass intrinsic as well as acquired and spontaneous resistance mechanisms, making them highly promising drug candidates, for which the development of resistance would be unlikely.

However, efforts to finding perturbants suitable for targeting the P. aeruginosa membrane must be increased. A second promising strategy is phage steering, which uses the natural predators of bacteria and the forces of evolutionary pressure to our advantage. Counterbalancing antibiotic resistance with phage susceptibility creates a double edged sword to circumvent key AMR mechanisms.

In addition to these strategies, adjuvants targeting adaptive resistance mechanisms are worthy of consideration, due to the potential to disrupt multiple bacterial resistance and virulence processes with agents targeting a single regulator.

Targeting global regulatory systems that would normally control the expression of resistance genes under infection conditions will prevent the activation of those genes with potential knock-on advantages in inhibition of virulence mechanisms.

QS and two-component signaling systems are particularly attractive targets from this perspective, as are the regulators of biofilm formation. There are several challenges in developing resistance-breaking therapy for P.

aeruginosa infection. Firstly, due to its comparatively large genome and highly adaptive nature, a plethora of regulatory systems, as well as limited drug penetration and active efflux, many antibiotic adjuvants designed for Gram-negative pathogens do not show efficacy against P.

Secondly, toxicity has been proven to be the major hurdle for adjuvants designed against P. aeruginosa , leading to many being abandoned at early phases of development.

Drug safety assessment is a long, expensive, but crucial process and toxicity is most likely where drug targets share structural similarity with human proteins. In this respect, bacterial signaling systems are good candidates, as prokaryotic and eukaryotic signaling systems are highly divergent, with eukaryotes lacking TCSS or phosphorelay systems.

As with all newly developed drugs designed to be used as combination therapy, care must be taken in determining the correct dosing and investigating clear synergy profiles.

Drug levels necessary for synergy in vitro may not be achievable in vivo. Synergy in vivo may be affected by failure to obtain desired levels of drugs in the target tissue, drug metabolism or plasma protein binding.

In addition, it is of paramount importance to evaluate drugs in relevant models that reflect the environmental conditions of infection. This will increase the predictive power of preclinical testing, reducing the costly progression of unpromising agents to clinical trials.

The lack of well-validated in vivo models for testing CF anti-infective therapeutics limits the speed of development of new drugs. Murine models using cystic fibrosis transmembrane conductance regulator CFTR knockout animals, or transgenics in which the severe gut phenotype associated with loss of CFTR has been corrected are available, and have proved useful, but do not develop the characteristic features of acute and chronic P.

aeruginosa infection seen in those with CF Bragonzi, ; Semaniakou et al. Progress on the use of ferret and porcine infection models with mutated CFTR has been made, although these are limited by the availability of suitable immunobiology reagents Keiser and Engelhardt, Intranasal administration of P.

aeruginosa into the healthy murine lung often leads to either rapid clearance or sepsis. To create a model of persistent infection, it is usually necessary to immobilize P.

This can be achieved by encapsulating the bacteria into agar or alginate beads, where the bacteria are protected from clearance by immune effectors and where their mode of living more closely mimics bacterial biofilms present in chronic infection Cigana et al. However, this bead model is technically demanding and requires surgical transtracheal instillation of the bead suspension, leading to additional complications and mortality not representative of bacterial infections in CF Van Heeckeren and Schluchter, Alternatively, long term lung infection can be achieved using P.

aeruginosa isolates from CF, some of which naturally establish chronic infection in mice, without the need for implantation into beads Fothergill et al. This model has the advantages of using a natural infection route and having no requirement for surgical intervention, and offers opportunity to study lung infection over prolonged periods.

However, the density of infection achieved in the lung is low, making some analyses challenging. Questions remain regarding the commitment of governments and pharmaceutical manufacturers to ongoing investment in antibacterial drug development, particularly as financial and research priorities are reshuffled by the ongoing SARS-CoV2 crisis.

Despite the understandable current emphasis on anti-viral agents and vaccines, it is important that we do not lose ground in the fight against AMR. Indeed, emerging evidence suggests that antibiotic use has increased dramatically in the COVID era Hsu, On top of this, increased usage of sanitizers and disinfectants globally may induce the development of cross-resistance to antibiotics.

The ESKAPE pathogens, for which new medicines are urgently needed, continue to cause serious community-acquired and nosocomial infections, and if investment into research and drug development for these bacterial pathogens is diminished, it will exacerbate the global health and economic costs associated with the ongoing pandemic.

FL wrote the manuscript with supervision and input from all others. All authors contributed to the article and approved the submitted version. FL is supported by a PhD studentship from the Rosetrees Trust M The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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