Droplet Microfluidics for High-Throughput Analysis of Antibiotic Susceptibility in Bacterial Cells and Populations. - Related Documents




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428001.0000Droplet Microfluidics for High-Throughput Analysis of Antibiotic Susceptibility in Bacterial Cells and Populations. Antibiotic-resistant bacteria are an increasing concern both in everyday life and specialized environments such as healthcare. As the rate of antibiotic-resistant infections rises, so do complications to health and the risk of disability and death. Urgent action is required regarding the discovery of new antibiotics and rapid diagnosis of the resistance profile of an infectious pathogen as well as a better understanding of population and single-cell distribution of the resistance level. High-throughput screening is the major affordance of droplet microfluidics. Droplet screens can be exploited both to look for combinations of drugs that could stop an infection of multidrug-resistant bacteria and to search for the source of resistance via directed-evolution experiments or the analysis of various responses to a drug by genetically identical bacteria. In droplet techniques that have been used in this way for over a decade, aqueous droplets containing antibiotics and bacteria are manipulated both within and outside of the microfluidic devices. The diagnostics problem was approached by producing a series of microfluidic systems with integrated dilution modules for automated preparation of antibiotic concentration gradients, achieving the speed that allowed for high-throughput combinatorial assays. We developed a method for automated emulsification of a series of samples that facilitated measuring the resistance levels of thousands of individual cells encapsulated in droplets and quantifying the inoculum effect, the dependence of resistance level on bacterial cell count. Screening of single cells encapsulated in droplets with varying antibiotic contents has revealed a distribution of resistance levels within populations of clonally identical cells. To be able to screen bacteria from clinical samples, a study of fluorescent dyes in droplets determined that a derivative of a popular viability marker is more suitable for droplet assays. We have developed a detection system that analyzes the growth or death state of bacteria with antibiotics for thousands of droplets per second by measuring the scattering of light hitting the droplets without labeling the cells or droplets. The droplet-based microchemostats enabled long-term evolution of resistance experiments, which will be integrated with high-throughput single-cell assays to better understand the mechanism of resistance acquisition and loss. These techniques underlie automated combinatorial screens of antibiotic resistance in single cells from clinical samples. We hope that this Account will inspire new droplet-based research on the antibiotic susceptibility of bacteria.202235119826
406110.9998Beyond serial passages: new methods for predicting the emergence of resistance to novel antibiotics. Market launching of a new antibiotic requires knowing in advance its benefits and possible risks, and among them how rapidly resistance will emerge and spread among bacterial pathogens. This information is not only useful from a public health point of view, but also for pharmaceutical industry, in order to reduce potential waste of resources in the development of a compound that might be discontinued at the short term because of resistance development. Most assays currently used for predicting the emergence of resistance are based on culturing the target bacteria by serial passages in the presence of increasing concentrations of antibiotics. Whereas these assays may be valuable for identifying mutations that might cause resistance, they are not useful to establish how fast resistance might appear, neither to address the risk of spread of resistance genes by horizontal gene transfer. In this article, we review recent information pertinent for a more accurate prediction on the emergence and dispersal of antibiotic resistance.201121835695
426220.9998Fitness cost of antibiotic susceptibility during bacterial infection. Advances in high-throughput DNA sequencing allow for a comprehensive analysis of bacterial genes that contribute to virulence in a specific infectious setting. Such information can yield new insights that affect decisions on how to best manage major public health issues such as the threat posed by increasing antimicrobial drug resistance. Much of the focus has been on the consequences of the selective advantage conferred on drug-resistant strains during antibiotic therapy. It is thought that the genetic and phenotypic changes that confer resistance also result in concomitant reductions in in vivo fitness, virulence, and transmission. However, experimental validation of this accepted paradigm is modest. Using a saturated transposon library of Pseudomonas aeruginosa, we identified genes across many functional categories and operons that contributed to maximal in vivo fitness during lung infections in animal models. Genes that bestowed both intrinsic and acquired antibiotic resistance provided a positive in vivo fitness advantage to P. aeruginosa during infection. We confirmed these findings in the pathogenic bacteria Acinetobacter baumannii and Vibrio cholerae using murine and rabbit infection models, respectively. Our results show that efforts to confront the worldwide increase in antibiotic resistance might be exacerbated by fitness advantages that enhance virulence in drug-resistant microbes.201526203082
427130.9998Multi-step vs. single-step resistance evolution under different drugs, pharmacokinetics, and treatment regimens. The success of antimicrobial treatment is threatened by the evolution of drug resistance. Population genetic models are an important tool in mitigating that threat. However, most such models consider resistance emergence via a single mutational step. Here, we assembled experimental evidence that drug resistance evolution follows two patterns: (i) a single mutation, which provides a large resistance benefit, or (ii) multiple mutations, each conferring a small benefit, which combine to yield high-level resistance. Using stochastic modeling, we then investigated the consequences of these two patterns for treatment failure and population diversity under various treatments. We find that resistance evolution is substantially limited if more than two mutations are required and that the extent of this limitation depends on the combination of drug type and pharmacokinetic profile. Further, if multiple mutations are necessary, adaptive treatment, which only suppresses the bacterial population, delays treatment failure due to resistance for a longer time than aggressive treatment, which aims at eradication.202134001313
426940.9998Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in antibiotic responses. Antibiotic resistance poses mounting risks to human health, as current antibiotics are losing efficacy against increasingly resistant pathogenic bacteria. Of particular concern is the emergence of multidrug-resistant strains, which has been rapid among Gram-negative bacteria such as Escherichia coli. A large body of work has established that antibiotic resistance mechanisms depend on phenotypic heterogeneity, which may be mediated by stochastic expression of antibiotic resistance genes. The link between such molecular-level expression and the population levels that result is complex and multi-scale. Therefore, to better understand antibiotic resistance, what is needed are new mechanistic models that reflect single-cell phenotypic dynamics together with population-level heterogeneity, as an integrated whole. In this work, we sought to bridge single-cell and population-scale modeling by building upon our previous experience in "whole-cell" modeling, an approach which integrates mathematical and mechanistic descriptions of biological processes to recapitulate the experimentally observed behaviors of entire cells. To extend whole-cell modeling to the "whole-colony" scale, we embedded multiple instances of a whole-cell E. coli model within a model of a dynamic spatial environment, allowing us to run large, parallelized simulations on the cloud that contained all the molecular detail of the previous whole-cell model and many interactive effects of a colony growing in a shared environment. The resulting simulations were used to explore the response of E. coli to two antibiotics with different mechanisms of action, tetracycline and ampicillin, enabling us to identify sub-generationally-expressed genes, such as the beta-lactamase ampC, which contributed greatly to dramatic cellular differences in steady-state periplasmic ampicillin and was a significant factor in determining cell survival.202337327241
406050.9998Current status of antibiotic resistance in animal production. It is generally accepted that the more antibiotics we use, the faster bacteria will develop resistance. Further it has been more or less accepted that once an antibiotic is withdrawn from the clinic, the resistance genes will eventually disappear, [table: see text] since they will no more be of any survival value for the bacterial cell. However, recent research has shown that after a long time period of exposure to antibiotics, certain bacterial species may adapt to this environment in such a way that they keep their resistance genes stably also after the removal of antibiotics. Thus, there is reason to believe that once resistance has developed it will not even in the long term be eradicated. What then can we do not to increase further the already high level of antibiotic-resistant bacteria in animals? We should of course encourage a prudent use of these valuable drugs. In Sweden antibiotics are not used for growth promoting purposes and are available only after veterinary prescription on strict indications. Generally, antimicrobial treatment of animals on individual or on herd basis should not be considered unless in connection with relevant diagnostics. The amounts of antibiotics used and the development of resistance in important pathogens should be closely monitored. Furthermore, resistance monitoring in certain non-pathogenic intestinal bacteria, which may serve as a reservoir for resistance genes is probably more important than hitherto anticipated. Once the usage of or resistance to a certain antibiotic seems to increase in an alarming way, steps should be taken to limit the usage of the drug in order to prevent further spread of resistance genes in animals, humans and the environment. Better methods for detecting and quantifying antibiotic resistance have to be developed. Screening methods must be standardized and evaluated in order to obtain comparable and reliable results from different countries. The genetic mechanisms for development of resistance and spread of resistance genes should be studied in detail. Research in these areas will lead to new ideas on how to inhibit the resistance mechanisms. So far, it has been well established that a heavy antimicrobial drug selective pressure in overcrowded populations of production animals creates favourable environments both for the emergence and the spread of antibiotic resistance genes.199910783714
427960.9998Simulation Model of Bacterial Resistance to Antibiotics Using Individual-Based Modeling. We designed and implemented simulation models of bacterial growth and antibiotic resistance to determine the appropriate antibiotics to use against antibiotic-resistant bacteria. Simulation models were designed using individual-based modeling, and a simulation tool, ARSim, was developed to conduct experiments using the models. Simulations of bacterial growth were conducted by virtually growing Klebsiella pneumoniae bacteria in a virtual environment with predefined parameters. Other experiments included predicting the effects of antibiotics when added to two different groups, one group of nonresistant bacteria and another group of both resistant and nonresistant bacteria. Carbapenem class antibiotics such as Imipenem were used for the simulation. The simulation results showed that the biological principles of bacteria and their antibiotic resistance mechanisms were correctly designed and implemented. Using the computational approaches developed in this study, we hope to provide researchers with a more effective method for finding new ways to fight antibiotic resistance.201829927616
429270.9998The impact of different antibiotic regimens on the emergence of antimicrobial-resistant bacteria. BACKGROUND: The emergence and ongoing spread of antimicrobial-resistant bacteria is a major public health threat. Infections caused by antimicrobial-resistant bacteria are associated with substantially higher rates of morbidity and mortality compared to infections caused by antimicrobial-susceptible bacteria. The emergence and spread of these bacteria is complex and requires incorporating numerous interrelated factors which clinical studies cannot adequately address. METHODS/PRINCIPAL FINDINGS: A model is created which incorporates several key factors contributing to the emergence and spread of resistant bacteria including the effects of the immune system, acquisition of resistance genes and antimicrobial exposure. The model identifies key strategies which would limit the emergence of antimicrobial-resistant bacterial strains. Specifically, the simulations show that early initiation of antimicrobial therapy and combination therapy with two antibiotics prevents the emergence of resistant bacteria, whereas shorter courses of therapy and sequential administration of antibiotics promote the emergence of resistant strains. CONCLUSIONS/SIGNIFICANCE: The principal findings suggest that (i) shorter lengths of antibiotic therapy and early interruption of antibiotic therapy provide an advantage for the resistant strains, (ii) combination therapy with two antibiotics prevents the emergence of resistance strains in contrast to sequential antibiotic therapy, and (iii) early initiation of antibiotics is among the most important factors preventing the emergence of resistant strains. These findings provide new insights into strategies aimed at optimizing the administration of antimicrobials for the treatment of infections and the prevention of the emergence of antimicrobial resistance.200819112501
427280.9998The hidden impact of antibacterial resistance in respiratory tract infection. Steering an appropriate course: principles to guide antibiotic choice. The prevalence and degree of antibacterial resistance in common respiratory pathogens are increasing worldwide. The health impact of resistance is not yet fully understood. However, once the impact of resistance becomes measurable, it may be too late to apply interventions to reduce resistance levels and regain previous quality and cost of care. We should address resistance now, before patient care is irreversibly compromised. The association between antibiotic consumption and the prevalence of resistance is widely assumed. However, evidence suggests that there is a more complex. multifactorial relationship between antibiotic use and resistance. It is also assumed that there is an adaptive fitness cost for bacterial resistance mutations. However, in some cases, bacteria are able to acquire 'compensatory genes' negating any negative impact of resistance mutations. Mathematical modeling indicates that the timescale for the emergence of resistance is typically shorter than the decay time following a decline in antibiotic consumption. Against this background, a general principle is proposed: to maximize patient outcome whilst minimizing the potential for selection and spread of resistance. This may be achieved through the use of agents that fulfill defined pharmacodynamic and pharmacokinetic parameters and elicit rapid eradication of the bacterial population, including emerging resistant mutants, from the site of infection. The choice of agent may not be the same in all regions, as selection will depend on local resistance patterns and disease etiology; however, the application of this principle may help to preserve the benefits of antibiotic therapy.200111419671
899990.9998Growth-Dependent Predation and Generalized Transduction of Antimicrobial Resistance by Bacteriophage. Bacteriophage (phage) are both predators and evolutionary drivers for bacteria, notably contributing to the spread of antimicrobial resistance (AMR) genes by generalized transduction. Our current understanding of this complex relationship is limited. We used an interdisciplinary approach to quantify how these interacting dynamics can lead to the evolution of multidrug-resistant bacteria. We cocultured two strains of methicillin-resistant Staphylococcus aureus, each harboring a different antibiotic resistance gene, with generalized transducing phage. After a growth phase of 8 h, bacteria and phage surprisingly coexisted at a stable equilibrium in our culture, the level of which was dependent on the starting concentration of phage. We detected double-resistant bacteria as early as 7 h, indicating that transduction of AMR genes had occurred. We developed multiple mathematical models of the bacteria and phage relationship and found that phage-bacteria dynamics were best captured by a model in which phage burst size decreases as the bacteria population reaches stationary phase and where phage predation is frequency-dependent. We estimated that one in every 10(8) new phage generated was a transducing phage carrying an AMR gene and that double-resistant bacteria were always predominantly generated by transduction rather than by growth. Our results suggest a shift in how we understand and model phage-bacteria dynamics. Although rates of generalized transduction could be interpreted as too rare to be significant, they are sufficient in our system to consistently lead to the evolution of multidrug-resistant bacteria. Currently, the potential of phage to contribute to the growing burden of AMR is likely underestimated. IMPORTANCE Bacteriophage (phage), viruses that can infect and kill bacteria, are being investigated through phage therapy as a potential solution to the threat of antimicrobial resistance (AMR). In reality, however, phage are also natural drivers of bacterial evolution by transduction when they accidentally carry nonphage DNA between bacteria. Using laboratory work and mathematical models, we show that transduction leads to evolution of multidrug-resistant bacteria in less than 8 h and that phage production decreases when bacterial growth decreases, allowing bacteria and phage to coexist at stable equilibria. The joint dynamics of phage predation and transduction lead to complex interactions with bacteria, which must be clarified to prevent phage from contributing to the spread of AMR.202235311576
9435100.9998Why are bacteria refractory to antimicrobials? The incidence of antibiotic resistance in pathogenic bacteria is rising. Antibiotic resistance can be achieved via three distinct routes: inactivation of the drug, modification of the target of action, and reduction in the concentration of drug that reaches the target. It has long been recognized that specific antibiotic resistance mechanisms can be acquired through mutation of the bacterial genome or by gaining additional genes through horizontal gene transfer. Recent attention has also brought to light the importance of different physiological states for the survival of bacteria in the presence of antibiotics. It is now apparent that bacteria have complex, intrinsic resistance mechanisms that are often not detected in the standard antibiotic sensitivity tests performed in clinical laboratories. The development of resistance in bacteria found in surface-associated aggregates or biofilms, owing to these intrinsic mechanisms, is paramount.200212354553
4270110.9998Antibiotic resistant bacteria survive treatment by doubling while shrinking. Many antibiotics that are used in healthcare, farming, and aquaculture end up in environments with different spatial structures that might promote heterogeneity in the emergence of antibiotic resistance. However, the experimental evolution of microbes at sub-inhibitory concentrations of antibiotics has been mainly carried out at the population level which does not allow capturing single-cell responses to antibiotics. Here, we investigate and compare the emergence of resistance to ciprofloxacin in Escherichia coli in well-mixed and structured environments using experimental evolution, genomics, and microfluidics-based time-lapse microscopy. We discover that resistance to ciprofloxacin and cross-resistance to other antibiotics is stronger in the well-mixed environment due to the emergence of target mutations, whereas efflux regulator mutations emerge in the structured environment. The latter mutants also harbor sub-populations of persisters that survive high concentrations of ciprofloxacin that inhibit bacterial growth at the population level. In contrast, genetically resistant bacteria that display target mutations also survive high concentrations of ciprofloxacin that inhibit their growth via population-level antibiotic tolerance. These resistant and tolerant bacteria keep doubling while shrinking in size in the presence of ciprofloxacin and regain their original size after antibiotic removal, which constitutes a newly discovered phenotypic response. This new knowledge sheds light on the diversity of strategies employed by bacteria to survive antibiotics and poses a stepping stone for understanding the link between mutations at the population level and phenotypic single-cell responses. IMPORTANCE: The evolution of antimicrobial resistance poses a pressing challenge to global health with an estimated 5 million deaths associated with antimicrobial resistance every year globally. Here, we investigate the diversity of strategies employed by bacteria to survive antibiotics. We discovered that bacteria evolve genetic resistance to antibiotics while simultaneously displaying tolerance to very high doses of antibiotics by doubling while shrinking in size.202439565111
9610120.9998The evolutionary rate of antibacterial drug targets. BACKGROUND: One of the major issues in the fight against infectious diseases is the notable increase in multiple drug resistance in pathogenic species. For that reason, newly acquired high-throughput data on virulent microbial agents attract the attention of many researchers seeking potential new drug targets. Many approaches have been used to evaluate proteins from infectious pathogens, including, but not limited to, similarity analysis, reverse docking, statistical 3D structure analysis, machine learning, topological properties of interaction networks or a combination of the aforementioned methods. From a biological perspective, most essential proteins (knockout lethal for bacteria) or highly conserved proteins (broad spectrum activity) are potential drug targets. Ribosomal proteins comprise such an example. Many of them are well-known drug targets in bacteria. It is intuitive that we should learn from nature how to design good drugs. Firstly, known antibiotics are mainly originating from natural products of microorganisms targeting other microorganisms. Secondly, paleontological data suggests that antibiotics have been used by microorganisms for million years. Thus, we have hypothesized that good drug targets are evolutionary constrained and are subject of evolutionary selection. This means that mutations in such proteins are deleterious and removed by selection, which makes them less susceptible to random development of resistance. Analysis of the speed of evolution seems to be good approach to test this hypothesis. RESULTS: In this study we show that pN/pS ratio of genes coding for known drug targets is significantly lower than the genome average and also lower than that for essential genes identified by experimental methods. Similar results are observed in the case of dN/dS analysis. Both analyzes suggest that drug targets tend to evolve slowly and that the rate of evolution is a better predictor of drugability than essentiality. CONCLUSIONS: Evolutionary rate can be used to score and find potential drug targets. The results presented here may become a useful addition to a repertoire of drug target prediction methods. As a proof of concept, we analyzed GO enrichment among the slowest evolving genes. These may become the starting point in the search for antibiotics with a novel mechanism.201323374913
3827130.9998The fitness cost of horizontally transferred and mutational antimicrobial resistance in Escherichia coli. Antimicrobial resistance (AMR) in bacteria implies a tradeoff between the benefit of resistance under antimicrobial selection pressure and the incurred fitness cost in the absence of antimicrobials. The fitness cost of a resistance determinant is expected to depend on its genetic support, such as a chromosomal mutation or a plasmid acquisition, and on its impact on cell metabolism, such as an alteration in an essential metabolic pathway or the production of a new enzyme. To provide a global picture of the factors that influence AMR fitness cost, we conducted a systematic review and meta-analysis focused on a single species, Escherichia coli. By combining results from 46 high-quality studies in a multilevel meta-analysis framework, we find that the fitness cost of AMR is smaller when provided by horizontally transferable genes such as those encoding beta-lactamases, compared to mutations in core genes such as those involved in fluoroquinolone and rifampicin resistance. We observe that the accumulation of acquired AMR genes imposes a much smaller burden on the host cell than the accumulation of AMR mutations, and we provide quantitative estimates of the additional cost of a new gene or mutation. These findings highlight that gene acquisition is more efficient than the accumulation of mutations to evolve multidrug resistance, which can contribute to the observed dominance of horizontally transferred genes in the current AMR epidemic.202337455716
9611140.9998Parallel evolution of Pseudomonas aeruginosa phage resistance and virulence loss in response to phage treatment in vivo and in vitro. With rising antibiotic resistance, there has been increasing interest in treating pathogenic bacteria with bacteriophages (phage therapy). One limitation of phage therapy is the ease at which bacteria can evolve resistance. Negative effects of resistance may be mitigated when resistance results in reduced bacterial growth and virulence, or when phage coevolves to overcome resistance. Resistance evolution and its consequences are contingent on the bacteria-phage combination and their environmental context, making therapeutic outcomes hard to predict. One solution might be to conduct 'in vitro evolutionary simulations' using bacteria-phage combinations from the therapeutic context. Overall, our aim was to investigate parallels between in vitro experiments and in vivo dynamics in a human participant. Evolutionary dynamics were similar, with high levels of resistance evolving quickly with limited evidence of phage evolution. Resistant bacteria-evolved in vitro and in vivo-had lower virulence. In vivo, this was linked to lower growth rates of resistant isolates, whereas in vitro phage resistant isolates evolved greater biofilm production. Population sequencing suggests resistance resulted from selection on de novo mutations rather than sorting of existing variants. These results highlight the speed at which phage resistance can evolve in vivo, and how in vitro experiments may give useful insights for clinical evolutionary outcomes.202235188102
3829150.9998Associations among Antibiotic and Phage Resistance Phenotypes in Natural and Clinical Escherichia coli Isolates. The spread of antibiotic resistance is driving interest in new approaches to control bacterial pathogens. This includes applying multiple antibiotics strategically, using bacteriophages against antibiotic-resistant bacteria, and combining both types of antibacterial agents. All these approaches rely on or are impacted by associations among resistance phenotypes (where bacteria resistant to one antibacterial agent are also relatively susceptible or resistant to others). Experiments with laboratory strains have shown strong associations between some resistance phenotypes, but we lack a quantitative understanding of associations among antibiotic and phage resistance phenotypes in natural and clinical populations. To address this, we measured resistance to various antibiotics and bacteriophages for 94 natural and clinical Escherichia coli isolates. We found several positive associations between resistance phenotypes across isolates. Associations were on average stronger for antibacterial agents of the same type (antibiotic-antibiotic or phage-phage) than different types (antibiotic-phage). Plasmid profiles and genetic knockouts suggested that such associations can result from both colocalization of resistance genes and pleiotropic effects of individual resistance mechanisms, including one case of antibiotic-phage cross-resistance. Antibiotic resistance was predicted by core genome phylogeny and plasmid profile, but phage resistance was predicted only by core genome phylogeny. Finally, we used observed associations to predict genes involved in a previously uncharacterized phage resistance mechanism, which we verified using experimental evolution. Our data suggest that susceptibility to phages and antibiotics are evolving largely independently, and unlike in experiments with lab strains, negative associations between antibiotic resistance phenotypes in nature are rare. This is relevant for treatment scenarios where bacteria encounter multiple antibacterial agents.IMPORTANCE Rising antibiotic resistance is making it harder to treat bacterial infections. Whether resistance to a given antibiotic spreads or declines is influenced by whether it is associated with altered susceptibility to other antibiotics or other stressors that bacteria encounter in nature, such as bacteriophages (viruses that infect bacteria). We used natural and clinical isolates of Escherichia coli, an abundant species and key pathogen, to characterize associations among resistance phenotypes to various antibiotics and bacteriophages. We found associations between some resistance phenotypes, and in contrast to past work with laboratory strains, they were exclusively positive. Analysis of bacterial genome sequences and horizontally transferred genetic elements (plasmids) helped to explain this, as well as our finding that there was no overall association between antibiotic resistance and bacteriophage resistance profiles across isolates. This improves our understanding of resistance evolution in nature, potentially informing new rational therapies that combine different antibacterials, including bacteriophages.201729089428
4276160.9998Phages limit the evolution of bacterial antibiotic resistance in experimental microcosms. The evolution of multi-antibiotic resistance in bacterial pathogens, often resulting from de novo mutations, is creating a public health crisis. Phages show promise for combating antibiotic-resistant bacteria, the efficacy of which, however, may also be limited by resistance evolution. Here, we suggest that phages may be used as supplements to antibiotics in treating initially sensitive bacteria to prevent resistance evolution, as phages are unaffected by most antibiotics and there should be little cross-resistance to antibiotics and phages. In vitro experiments using the bacterium Pseudomonas fluorescens, a lytic phage, and the antibiotic kanamycin supported this prediction: an antibiotic-phage combination dramatically decreased the chance of bacterial population survival that indicates resistance evolution, compared with antibiotic treatment alone, whereas the phage alone did not affect bacterial survival. This effect of the combined treatment in preventing resistance evolution was robust to immigration of bacteria from an untreated environment, but not to immigration from environment where the bacteria had coevolved with the phage. By contrast, an isogenic hypermutable strain constructed from the wild-type P. fluorescens evolved resistance to all treatments regardless of immigration, but typically suffered very large fitness costs. These results suggest that an antibiotic-phage combination may show promise as an antimicrobial strategy.201223028398
9569170.9998The global epidemic nature of antimicrobial resistance and the need to monitor and manage it locally. An antimicrobial agent may be used for years before a gene expressing resistance to it emerges in a strain of bacteria somewhere. Progeny of that strain, or of others to which the gene is transferred, may then disseminate preferentially through global networks of bacterial populations on people or animals treated with that agent or with other agents as the gene becomes linked to genes expressing resistance to them. Over 100 resistance genes-varying in their frequency of emergence, vectors, linkages, and pathways-have thus emerged, reemerged, converged, and disseminated irregularly through the world's bacterial ecosystems over the last 60 years to reach infecting strains and block treatment of infection. We may delay emergence by using agents less and retard dissemination by good hygiene, infection control measures, and avoidance of agents that select for resistance genes in contiguous populations. Local monitoring and management of resistance appear essential because of the intricacies of tracing and targeting the problems at each place and because national or global surveillance and strategy develop from local information and understanding.19978994775
9438180.9998The challenge of antibiotic resistance: need to contemplate. "Survival of the fittest " holds good for men and animals as also for bacteria. A majority of bacteria in nature are nonpathogenic, a large number of them, live as commensals on our body leading a symbiotic existence. A limited population of bacteria which has became pathogenic was also sensitive to antibiotics to begin with. It is the man made antibiotic pressure, which has led to the emergence and spread of resistant genes amongst bacteria. Despite the availability of a large arsenal of antibiotics, the ability of bacteria to become resistant to antibacterial agents is amazing. This is more evident in the hospital settings where the antibiotic usage is maximum. The use of antibiotics is widespread in clinical medicine, agriculture, aquaculture, veterinary practice, poultry and even in household products. The major reason for this is the inappropriate use of antibiotics due to a lack of uniform policy and disregard to hospital infection control practices. The antibiotic cover provided by newer antibiotics has been an important factor responsible for the emergence of multi-drug resistant bacteria. Bacterial infections increase the morbidity and mortality, increase the cost of treatment, and prolong hospital stay adding to the economical burden on the nation. The problem is further compounded by the lack of education and " over the counter " availability of antibiotics in developing countries. Antibiotic resistance is now all pervasive with the developed world as much vulnerable to the problem. Despite advancement in medical technology for diagnosis and patient care, a person can still die of an infection caused by a multi-drug resistant bacteria. It is time to think, plan and formulate a strong antibiotic policy to address the burgeoning hospital infection.200515756040
9678190.9997Molecular basis of bacterial disinfectant resistance. Antibiotic resistance could accelerate humanity towards an already fast-approaching post-antibiotic era, where disinfectants and effective biosecurity measures will be critically important to control microbial diseases. Disinfectant resistance has the potential to change our way of life from compromising food security to threatening our medical health systems. Resistance to antimicrobial agents occurs through either intrinsic or acquired resistance mechanisms. Acquired resistance occurs through the efficient transfer of mobile genetic elements, which can carry single, or multiple resistance determinants. Drug resistance genes may form part of integrons, transposons and insertions sequences which are capable of intracellular transfer onto plasmids or gene cassettes. Thereafter, resistance plasmids and gene cassettes mobilize by self-transmission between bacteria, increasing the prevalence of drug resistance determinants in a bacterial population. An accumulation of drug resistance genes through these mechanisms gives rise to multidrug resistant (MDR) bacteria. The study of this mobility is integral to safeguard current antibiotics, disinfectants and other antimicrobials. Literature evidence, however, indicates that knowledge regarding disinfectant resistance is severly limited. Genome engineering such as the CRISPR-Cas system, has identified disinfectant resistance genes, and reversed resistance altogether in certain prokaryotes. Demonstrating that these techniques could prove invaluable in the combat against disinfectant resistance by uncovering the secrets of MDR bacteria.202031830738