Bacterial fitness shapes the population dynamics of antibiotic-resistant and -susceptible bacteria in a model of combined antibiotic and anti-virulence treatment

Identification of the elements of models of antimicrobial resistance of bacteria for assessing their usefulness and usability in One Health decision making: a protocol for scoping review

INTRODUCTION

Antimicrobial resistance (AMR) is a complex problem that requires the One Health approach, that is, a collaboration among various disciplines working in different sectors (animal, human and environment) to resolve it. Mathematical and statistical models have been used to understand AMR development, emergence, dissemination, prediction and forecasting. A review of the published models of AMR will help consolidate our knowledge of the dynamics of AMR and will also facilitate decision-makers and researchers in evaluating the credibility, generalisability and interpretation of the results and aspects of AMR models. The study objective is to identify and synthesise knowledge on mathematical and statistical models of AMR among bacteria in animals, humans and environmental compartments.

METHODS AND ANALYSIS

Eligibility criteria: Original research studies reporting mathematical and statistical models of AMR among bacteria in animal, human and environmental compartments that were published until 2022 in English, French and Spanish will be included in this study.

SOURCES OF EVIDENCE

Database of PubMed, Agricola (Ovid), Centre for Agriculture and Bioscience Direct (CABI), Web of Science (Clarivate), Cumulative Index to Nursing and Allied Health Literature (CINAHL) and MathScinet. Data charting: Metadata of the study, the context of the study, model structure, model process and reporting quality will be extracted. A narrative summary of this information, gaps and recommendations will be prepared and reported in One Health decision-making context.

ETHICS AND DISSEMINATION

Research ethics board approval was not obtained for this study as neither human participation nor unpublished human data were used in this study. The study findings will be widely disseminated among the One Health Modelling Network for Emerging Infections network and stakeholders by means of conferences, and publication in open-access peer-reviewed journals.

Filamentous Bacteriophages and the Competitive Interaction between Pseudomonas aeruginosa Strains under Antibiotic Treatment: a Modeling Study

Pseudomonas aeruginosa (Pa) is a major bacterial pathogen responsible for chronic lung infections in cystic fibrosis patients. Recent work has implicated Pf bacteriophages, nonlytic filamentous viruses produced by Pa, in the chronicity and severity of Pa infections. Pf phages act as structural elements in Pa biofilms and sequester aerosolized antibiotics, thereby contributing to antibiotic tolerance. Consistent with a selective advantage in this setting, the prevalence of Pf-positive (Pf+) bacteria increases over time in these patients. However, the production of Pf phages comes at a metabolic cost to bacteria, such that Pf+ strains grow more slowly than Pf-negative (Pf-) strains in vitro. Here, we use a mathematical model to investigate how these competing pressures might influence the relative abundance of Pf+ versus Pf- strains in different settings. Our model suggests that Pf+ strains of Pa cannot outcompete Pf- strains if the benefits of phage production falls onto both Pf+ and Pf- strains for a majority of parameter combinations. Further, phage production leads to a net positive gain in fitness only at antibiotic concentrations slightly above the MIC (i.e., concentrations for which the benefits of antibiotic sequestration outweigh the metabolic cost of phage production) but which are not lethal for Pf+ strains. As a result, our model suggests that frequent administration of intermediate doses of antibiotics with low decay rates and high killing rates favors Pf+ over Pf- strains. These models inform our understanding of the ecology of Pf phages and suggest potential treatment strategies for Pf+ Pa infections. IMPORTANCE Filamentous phages are a frontier in bacterial pathogenesis, but the impact of these phages on bacterial fitness is unclear. In particular, Pf phages produced by Pa promote antibiotic tolerance but are metabolically expensive to produce, suggesting that competing pressures may influence the prevalence of Pf+ versus Pf- strains of Pa in different settings. Our results identify conditions likely to favor Pf+ strains and thus antibiotic tolerance. This study contributes to a better understanding of the unique ecology of filamentous phages in both environmental and clinical settings and may facilitate improved treatment strategies for combating antibiotic tolerance.

Combined analysis of metagenomic data revealed consistent changes of gut microbiome structure and function in inflammatory bowel disease

AIMS

To reveal the consistency and discrepancy in the gut microbial structure and function in inflammatory bowel disease (IBD) patients from different regions.

METHODS AND RESULTS

Gut microbes, antibiotic resistance genes (ARGs) and virulence factors genes (VFGs) were analysed using metagenome data from three cohorts. The abundance of Escherichia coli extensively increased in IBD patients, whereas Subdoligranulum unclassified decreased dramatically in IBD patients from three countries. Escherichia coli showed a positive correlation with multiple ARGs and VFGs in cohorts from China and the United States, including multidrug-related resistance genes and Capsule and LOS-related virulence factors genes. Escherichia coli biofilm synthesis pathways significantly enriched in IBD patients from three different regions. Notably, Subdoligranulum unclassified and Eubacterium hallii were negatively related to ARGs and VFGs.

CONCLUSIONS

Consistent changes of microbiome structure and function were observed in IBD patients from three different regions. As pathogenic bacteria, E. coli may accelerate IBD progression through encapsulation in biofilms by upregulating antibiotic resistance in Crohn's disease patients. Subdoligranulum unclassified and E. hallii may be beneficial for IBD patients and could serve as potential probiotics for IBD treatment.

SIGNIFICANCE AND IMPACT OF THE STUDY

This work dispels worries about the regional differences in gut microbial changes in IBD patients and provides useful guidance for more rational microbiome-based therapies.

Old problems and new solutions: antibiotic alternatives in food animal production

The antimicrobial resistance crisis is a Global Health challenge that impacts humans, animals, and the environment alike. In response to increased demands for animal protein and by-products, there has been a substantial increase in the use of antimicrobial agents in the animal industry. Indeed, they are extensively used to prevent, control, and (or) treat disease in animals. In addition to infection control, in-feed supplementation with antimicrobials became common practice for growth promotion of livestock. Unfortunately, the global overuse of antimicrobials has contributed to the emergence and spread of resistance. As such, many countries have implemented policies and approaches to eliminate the use of antimicrobials as growth promoters in food animals, which necessitates the need for alternate and One Health strategies to maintain animal health and welfare. This review summarizes the antimicrobial resistance crisis from Global Health and One Health perspectives. In addition, we outline examples of potential alternate strategies to circumvent antimicrobial use in animal husbandry practices, including antivirulence agents, bacteriophages, and nutritional measures to control bacterial pathogens. Overall, these alternate strategies require further research and development efforts, including assessment of efficacy and the associated development, manufacturing, and labor costs.

Combining antibiotics with antivirulence compounds can have synergistic effects and reverse selection for antibiotic resistance in Pseudomonas aeruginosa

Antibiotics are losing efficacy due to the rapid evolution and spread of resistance. Treatments targeting bacterial virulence factors have been considered as alternatives because they target virulence instead of pathogen viability, and should therefore exert weaker selection for resistance than conventional antibiotics. However, antivirulence treatments rarely clear infections, which compromises their clinical applications. Here, we explore the potential of combining antivirulence drugs with antibiotics against the opportunistic human pathogen Pseudomonas aeruginosa. We combined two antivirulence compounds (gallium, a siderophore quencher, and furanone C-30, a quorum sensing [QS] inhibitor) together with four clinically relevant antibiotics (ciprofloxacin, colistin, meropenem, tobramycin) in 9×9 drug concentration matrices. We found that drug-interaction patterns were concentration dependent, with promising levels of synergies occurring at intermediate drug concentrations for certain drug pairs. We then tested whether antivirulence compounds are potent adjuvants, especially when treating antibiotic resistant (AtbR) clones. We found that the addition of antivirulence compounds to antibiotics could restore growth inhibition for most AtbR clones, and even abrogate or reverse selection for resistance in five drug combination cases. Molecular analyses suggest that selection against resistant clones occurs when resistance mechanisms involve restoration of protein synthesis, but not when efflux pumps are up-regulated. Altogether, our work provides a first systematic analysis of antivirulence-antibiotic combinatorial treatments and suggests that such combinations have the potential to be both effective in treating infections and in limiting the spread of antibiotic resistance.

Colistin resistance of carbapenem-resistant Klebsiella pneumoniae strains: molecular mechanisms and bacterial fitness

The increasing use of colistin in the clinic has led to the emergence and spread of colistin resistance. According to the literature, antibiotic resistance can have a metabolic cost, resulting in poor adaptation and survival, i.e. reduced bacterial fitness. The aim of this study was to investigate molecular mechanisms underlying resistance to colistin and their effect on the bacterial fitness of carbapenem-resistant (carba-R) strains of K. pneumoniae isolated from the patients of Moscow hospitals in 2012–2017. Of 159 analyzed carba-R isolates, 71 (45%) were resistant to colistin (minimum inhibitory concentration over 2 mg/L). By conducting Sanger sequencing, we were able to identify the mechanisms underlying colistin resistance in 26 (37%) isolates. Growth curves were constructed by measuring optical density at 600 nm wavelength for 15 hours. The competitive growth of colistin-resistant (col-R) K. pneumoniae isolates was assessed relative to the colistin-susceptible (col-S) isolate. Col-R and col-S cultures harvested in the exponential phase were combined at the ratio of 1:1, incubated in the Luria-Bertani medium and plated onto Luria-Bertani agar plates with 10 mg/L colistin and without it. The competition index was calculated as the ratio of grown col-R and col-S colonies. Resistance to colistin did not affect the growth kinetics of K. pneumoniae, but did reduce the competitive ability of the bacteria as compared to the col-S isolates. However, some col-R isolates were more competitive than the col-S strains of the same sequence type. Further research is needed to elucidate the effects of colistin resistance on bacterial fitness.

The effect of enrofloxacin on enteric Escherichia coli: Fitting a mathematical model to in vivo data

Antimicrobial drugs administered systemically may cause the emergence and dissemination of antimicrobial resistance among enteric bacteria. To develop logical, research-based recommendations for food animal veterinarians, we must understand how to maximize antimicrobial drug efficacy while minimizing risk of antimicrobial resistance. Our objective is to evaluate the effect of two approved dosing regimens of enrofloxacin (a single high dose or three low doses) on Escherichia coli in cattle. We look specifically at bacteria above and below the epidemiological cutoff (ECOFF), above which the bacteria are likely to have an acquired or mutational resistance to enrofloxacin. We developed a differential equation model for the antimicrobial drug concentrations in plasma and colon, and bacteria populations in the feces. The model was fit to animal data of drug concentrations in the plasma and colon obtained using ultrafiltration probes. Fecal E. coli counts and minimum inhibitory concentrations were measured for the week after receiving the antimicrobial drug. We predict that the antimicrobial susceptibility of the bacteria above the ECOFF pre-treatment strongly affects the composition of the bacteria following treatment. Faster removal of the antimicrobial drugs from the colon throughout the study leads to improved clearance of bacteria above the ECOFF in the low dose regimen. If we assume a fitness cost is associated with bacteria above the ECOFF, the increased fitness costs leads to reduction of bacteria above the ECOFF in the low dose study. These results suggest the initial E. coli susceptibility is a strong indicator of how steers respond to antimicrobial drug treatment.

A method to assess bioavailability of antibiotics in anthropogenic polluted ecosystems by using a bacterial fitness test

Antibiotics released in the environment exert a selective pressure on the resident microbiota. It is well accepted that the mere measurement of antibiotics does not reflect the actual bioavailability. In fact, antibiotics can be adsorbed or complexed to particles and/or chemicals in water and soil. Bioavailable concentrations of antibiotics in soil and water are subjected to great uncertainty, therefore biological assays are increasingly recognized as that allow an indirect determination of the residual antibiotic activity. Here we propose how a fitness test for bacteria can be used to qualitatively assess the bioavailability of a specific antibiotic in the environment. The findings show that by using a pair of resistant and sensitive bacterial strains, the resulting fitness can indirectly reflect antibiotic bioavailability. Hence, this test can be used as a complementary assay to other biological and chemical tests to assess bioavailability of antibiotics.

Mathematical model predicts anti-adhesion-antibiotic-debridement combination therapies can clear an antibiotic resistant infection

As antimicrobial resistance increases, it is crucial to develop new treatment strategies to counter the emerging threat. In this paper, we consider combination therapies involving conventional antibiotics and debridement, coupled with a novel anti-adhesion therapy, and their use in the treatment of antimicrobial resistant burn wound infections. Our models predict that anti-adhesion–antibiotic–debridement combination therapies can eliminate a bacterial infection in cases where each treatment in isolation would fail. Antibiotics are assumed to have a bactericidal mode of action, killing bacteria, while debridement involves physically cleaning a wound (e.g. with a cloth); removing free bacteria. Anti-adhesion therapy can take a number of forms. Here we consider adhesion inhibitors consisting of polystyrene microbeads chemically coupled to a protein known as multivalent adhesion molecule 7, an adhesin which mediates the initial stages of attachment of many bacterial species to host cells. Adhesion inhibitors competitively inhibit bacteria from binding to host cells, thus rendering them susceptible to removal through debridement. An ordinary differential equation model is developed and the antibiotic-related parameters are fitted against new in vitro data gathered for the present study. The model is used to predict treatment outcomes and to suggest optimal treatment strategies. Our model predicts that anti-adhesion and antibiotic therapies will combine synergistically, producing a combined effect which is often greater than the sum of their individual effects, and that anti-adhesion–antibiotic–debridement combination therapy will be more effective than any of the treatment strategies used in isolation. Further, the use of inhibitors significantly reduces the minimum dose of antibiotics required to eliminate an infection, reducing the chances that bacteria will develop increased resistance. Lastly, we use our model to suggest treatment regimens capable of eliminating bacterial infections within clinically relevant timescales.

Since the development of the first antibiotics, bacteria have utilised and developed resistance mechanisms, helping them to avoid being eliminated and to survive within a host. Traditionally, the solution to this problem has been to treat with multiple antibiotics, switching to a new type when the one currently in use proves ineffective. However, the development of antibiotics has slowed significantly in the past two decades, while multi-drug resistant strains, otherwise known as ‘super bugs’, are on the rise. In answer to this challenge, alternative approaches, such as anti-adhesion therapy, are being developed as a complement or alternative to traditional antimicrobials. In this paper we formulate and analyse a mathematical model of a combination therapy, applied in the context of an infected burn wound, bringing together antibiotics, anti-adhesion therapy and debridement (the physical cleaning of a wound). We use our models to make sense of how these treatments interact to combat a bacterial infection, to predict treatment outcomes for a range of strategies and to suggest optimal treatment regimens. It is hoped that this study will guide future experimental and clinical research, helping biomedical researchers to identify the most promising approaches to treatment.

Computational Health Engineering Applied to Model Infectious Diseases and Antimicrobial Resistance Spread

Infectious diseases are the primary cause of mortality worldwide. The dangers of infectious disease are compounded with antimicrobial resistance, which remains the greatest concern for human health. Although novel approaches are under investigation, the World Health Organization predicts that by 2050, septicaemia caused by antimicrobial resistant bacteria could result in 10 million deaths per year. One of the main challenges in medical microbiology is to develop novel experimental approaches, which enable a better understanding of bacterial infections and antimicrobial resistance. After the introduction of whole genome sequencing, there was a great improvement in bacterial detection and identification, which also enabled the characterization of virulence factors and antimicrobial resistance genes. Today, the use of in silico experiments jointly with computational and machine learning offer an in depth understanding of systems biology, allowing us to use this knowledge for the prevention, prediction, and control of infectious disease. Herein, the aim of this review is to discuss the latest advances in human health engineering and their applicability in the control of infectious diseases. An in-depth knowledge of host–pathogen–protein interactions, combined with a better understanding of a host’s immune response and bacterial fitness, are key determinants for halting infectious diseases and antimicrobial resistance dissemination.

A systematic review of healthcare-associated infections in Africa: An antimicrobial resistance perspective

Background

Healthcare-associated infection (HCAI) is a global health challenge, not only as an issue of patient safety but also as a major driver of antimicrobial resistance (AMR). It is a major cause of morbidity and mortality with economic consequences.

Objective

This review provides an update on the occurrence of HCAI, as well as the contribution of emerging AMR on healthcare delivery in Africa.

Methods

We searched PubMed, Cochrane database, African Journals Online and Google Scholar for relevant articles on HCAI in Africa between 2010 and 2017. Preferred reporting items of systematic reviews and meta-analyses guidelines were followed for selection. Thirty-five eligible articles were considered for the qualitative synthesis.

Results

Of the 35 eligible articles, more than half (n = 21, 60%) were from East Africa. Klebsiella spp., Staphylococcus aureus, Escherichia coli and Pseudomonas spp. were the common pathogens reported in bloodstream infection, (catheter-associated) urinary tract infection, surgical site infection and healthcare-associated pneumonia. Among these various subtypes of HCAI, methicillin-resistant S. aureus (3.9% – 56.8%) and extended-spectrum beta-lactamase producing Gram-negative bacilli (1.9% – 53.0%) were the most reported antimicrobial resistant pathogens.

Conclusion

This review shows a paucity of HCAI surveillance in Africa and an emergence of AMR priority pathogens. Hence, there is a need for a coordinated national and regional surveillance of both HCAI and AMR in Africa.

An ecosystem framework for understanding and treating disease

Pathogens and cancers are pervasive health risks in the human population. I argue that if we are to better understand disease and its treatment, then we need to take an ecological perspective of disease itself. I generalize and extend an emerging framework that views disease as an ecosystem and many of its components as interacting in a community. I develop the framework for biological etiological agents (BEAs) that multiply within humans—focusing on bacterial pathogens and cancers—but the framework could be extended to include other host and parasite species. I begin by describing why we need an ecosystem framework to understand disease, and the main components and interactions in bacterial and cancer disease ecosystems. Focus is then given to the BEA and how it may proceed through characteristic states, including emergence, growth, spread and regression. The framework is then applied to therapeutic interventions. Central to success is preventing BEA evasion, the best known being antibiotic resistance and chemotherapeutic resistance in cancers. With risks of evasion in mind, I propose six measures that either introduce new components into the disease ecosystem or manipulate existing ones. An ecosystem framework promises to enhance our understanding of disease, BEA and host (co)evolution, and how we can improve therapeutic outcomes.

THE MATHEMATICAL ANALYSIS OF THE MODEL OF ANTIBIOTIC-RESISTANT BACTERIA AND THE IMMUNE CELLS

Currently, World Health Organization (WHO) report confirms that the antibiotic-resistant infections are the greatest threat to health. Despite the new therapeutic strategies, the bacteria develop mechanisms to defend themselves against antibiotics. In this paper, we propose a mathematical model describing the dynamics of resistant bacteria, non-resistant bacteria and immune cells exposed to an antibiotic. The global stability of equilibria is performed by using Lyapunov functions.

Predictive modelling of a novel anti-adhesion therapy to combat bacterial colonisation of burn wounds

As the development of new classes of antibiotics slows, bacterial resistance to existing antibiotics is becoming an increasing problem. A potential solution is to develop treatment strategies with an alternative mode of action. We consider one such strategy: anti-adhesion therapy. Whereas antibiotics act directly upon bacteria, either killing them or inhibiting their growth, anti-adhesion therapy impedes the binding of bacteria to host cells. This prevents bacteria from deploying their arsenal of virulence mechanisms, while simultaneously rendering them more susceptible to natural and artificial clearance. In this paper, we consider a particular form of anti-adhesion therapy, involving biomimetic multivalent adhesion molecule 7 coupled polystyrene microbeads, which competitively inhibit the binding of bacteria to host cells. We develop a mathematical model, formulated as a system of ordinary differential equations, to describe inhibitor treatment of a Pseudomonas aeruginosa burn wound infection in the rat. Benchmarking our model against in vivo data from an ongoing experimental programme, we use the model to explain bacteria population dynamics and to predict the efficacy of a range of treatment strategies, with the aim of improving treatment outcome. The model consists of two physical compartments: the host cells and the exudate. It is found that, when effective in reducing the bacterial burden, inhibitor treatment operates both by preventing bacteria from binding to the host cells and by reducing the flux of daughter cells from the host cells into the exudate. Our model predicts that inhibitor treatment cannot eliminate the bacterial burden when used in isolation; however, when combined with regular or continuous debridement of the exudate, elimination is theoretically possible. Lastly, we present ways to improve therapeutic efficacy, as predicted by our mathematical model.

Humankind is engaged in an arms race; one we are in danger of losing. Since the development and application of the first antibiotics, resistant strains of bacteria have steadily emerged. As the rate of discovery of new antibiotics slows, the threat increases. At present, 700,000 individuals globally die each year due to antimicrobial resistance and this number is predicted to rise to 10 million per year by 2050 unless fresh action is taken. It is important, therefore, that we explore alternative treatment strategies to replace or complement traditional antimicrobials. Here we use mathematical models to explain and predict the effects of a novel anti-adhesion therapy applied to infected burn wounds. This theoretically resistance-proof therapy operates by impeding bacteria from binding to host cells by blocking the host cell binding sites. This prevents bacteria from accessing nutrients and renders them susceptible to artificial clearance. Fitting our model to experimental data, we identify a number of valid parameter sets, and predict the conditions under which treatment will be effective for each set. These predictions are experimentally testable, and could be used to guide the development and application of anti-adhesion treatments in a clinical setting.

Mathematical modelling of the antibiotic-induced morphological transition of Pseudomonas aeruginosa

Here we formulate a mechanistic mathematical model to describe the growth dynamics of P. aeruginosa in the presence of the β-lactam antibiotic meropenem. The model is mechanistic in the sense that carrying capacity is taken into account through the dynamics of nutrient availability rather than via logistic growth. In accordance with our experimental results we incorporate a sub-population of cells, differing in morphology from the normal bacillary shape of P. aeruginosa bacteria, which we assume have immunity from direct antibiotic action. By fitting this model to experimental data we obtain parameter values that give insight into the growth of a bacterial population that includes different cell morphologies. The analysis of two parameters sets, that produce different long term behaviour, allows us to manipulate the system theoretically in order to explore the advantages of a shape transition that may potentially be a mechanism that allows P. aeruginosa to withstand antibiotic effects. Our results suggest that inhibition of this shape transition may be detrimental to bacterial growth and thus suggest that the transition may be a defensive mechanism implemented by bacterial machinery. In addition to this we provide strong theoretical evidence for the potential therapeutic strategy of using antimicrobial peptides (AMPs) in combination with meropenem. This proposed combination therapy exploits the shape transition as AMPs induce cell lysis by forming pores in the cytoplasmic membrane, which becomes exposed in the spherical cells.

Antimicrobial resistance is an urgent global health threat and it is critical that we formulate alternative treatment strategies to combat bacterial infections. To do this we must understand how bacteria respond to currently used antibiotics. Pseudomonas aeruginosa is the leading cause of death among cystic fibrosis patients, a top cause of hospital-acquired infections in the UK and is currently listed as a critical priority in a list of antibiotic-resistant bacteria produced by the World Health Organisation. P. aeruginosa can change shape in the presence of certain antibiotics that work by targeting cell wall synthesis. The bacteria make the reversible transition from the native rod shape to a fragile spherical shape by shedding the cell wall and in doing so they evade the effects of the antibiotic. We formulate a system of equations that describes the growth of the bacteria including the shape transition we witness when we add antibiotic. Fitting this model to experimental data, we obtain parameter values that we then vary to make predictions on how inhibiting the shape transition or increasing the death rate of spherical cells would affect the overall bacterial growth. These predictions can support suitable combination therapies and hint towards alternative treatment strategies.

Co-selection of antibiotic resistance via copper shock loading on bacteria from a drinking water bio-filter

Heavy metal contamination of source water frequently occurred in developing countries as a result of accidents. To address the problems, most of the previous studies have focused on engineering countermeasures. In this study, we investigated the effects of heavy metals, particularly copper, on the development of antibiotic resistance by establishing a copper shock loading test. Results revealed that co-selection occurred rapidly within 6 h. Copper, at the levels of 10 and 100 mg/L, significantly increased bacterial resistance to the antibiotics tested, including rifampin, erythromycin, kanamycin, and a few others. A total of 117 antimicrobial-resistance genes were detected from 12 types of genes, and the relative abundance of most genes (particularly mobile genetic elements intⅠand transposons) was markedly enriched by at least one fold. Furthermore, the copper shock loading altered the bacterial community. Numerous heavy metal and antibiotic resistant strains were screened out and enriched. These strains are expected to enhance the overall level of resistance. More noticeably, the majority of the co-selected antibiotic resistance could sustain for at least 20 h in the absence of copper and antimicrobial drugs. Resistance to vancomycin, erythromycin and lincomycin even could remain for 7 days. The prominent selection pressure by the copper shock loading implies that a real accident most likely poses similar impacts on the water environment. An accidental release of heavy metals would not only cause harm to the ecological environment, but also contribute to the development of bacterial antibiotic resistance. Broader concerns should be raised about the biological risks caused by sudden releases of pollutants by accidents.

Against the tide: the role of bacterial adhesion in host colonization

Evolving under the constant exposure to an abundance of diverse microbial life, the human body has developed many ways of defining the boundaries between self and non-self. Many physical and immunological barriers to microbial invasion exist, and yet bacteria have found a multitude of ways to overcome these, initiate interactions with and colonize the human host. Adhesion to host cells and tissues is a key feature allowing bacteria to persist in an environment under constant flux and to initiate transient or permanent symbioses with the host. This review discusses reasons why adhesion is such a seemingly indispensable requirement for bacteria–host interactions, and whether bacteria can bypass the need to adhere and still persist. It further outlines open questions about the role of adhesion in bacterial colonization and persistence within the host.

A unified inter-host and in-host model of antibiotic resistance and infection spread in a hospital ward

As the battle continues against hospital-acquired infections and the concurrent rise in antibiotic resistance among many of the major causative pathogens, there is a dire need to conduct controlled experiments, in order to compare proposed control strategies. However, cost, time, and ethical considerations make this evaluation strategy either impractical or impossible to implement with living patients. This paper presents a multi-scale model that offers promise as the basis for a tool to simulate these (and other) controlled experiments. This is a "unified" model in two important ways: (i) It combines inter-host and in-host dynamics into a single model, and (ii) it links two very different modeling approaches - agent-based modeling and differential equations - into a single model. The potential of this model as an instrument to combat antibiotic resistance in hospitals is demonstrated with numerical examples.

Bacterial resistance to antibodies: a model evolutionary study

The tangled nature model of evolution (reviewed in the main text) is adapted for use in the study of antibody resistance acquired by horizontal gene transfer. Exchanges of DNA and the acquisition of resistant gene sequences are considered. For the parameters used, resistant strains rapidly proliferate and dominate, although initial intense antibiotic treatment can occasionally prevent this. Variation in genome distribution appears to be long tailed. If this is reflected in nature, the occurrence of resistant bacterial strains can be expected, as well as considerable variation in patient outcomes.

Small Molecules That Sabotage Bacterial Virulence

The continued rise of antibiotic-resistant bacterial infections has motivated alternative strategies for target discovery and treatment of infections. Antivirulence therapies function through inhibition of in vivo required virulence factors to disarm the pathogen instead of directly targeting viability or growth. This approach to treating bacteria-mediated diseases may have advantages over traditional antibiotics because it targets factors specific for pathogenesis, potentially reducing selection for resistance and limiting collateral damage to the resident microbiota. This review examines vulnerable molecular mechanisms used by bacteria to cause disease and the antivirulence compounds that sabotage these virulence pathways. By expanding the study of antimicrobial targets beyond those that are essential for growth, antivirulence strategies offer new and innovative opportunities to combat infectious diseases.

The Complex Relationship between Virulence and Antibiotic Resistance

Antibiotic resistance, prompted by the overuse of antimicrobial agents, may arise from a variety of mechanisms, particularly horizontal gene transfer of virulence and antibiotic resistance genes, which is often facilitated by biofilm formation. The importance of phenotypic changes seen in a biofilm, which lead to genotypic alterations, cannot be overstated. Irrespective of if the biofilm is single microbe or polymicrobial, bacteria, protected within a biofilm from the external environment, communicate through signal transduction pathways (e.g., quorum sensing or two-component systems), leading to global changes in gene expression, enhancing virulence, and expediting the acquisition of antibiotic resistance. Thus, one must examine a genetic change in virulence and resistance not only in the context of the biofilm but also as inextricably linked pathologies. Observationally, it is clear that increased virulence and the advent of antibiotic resistance often arise almost simultaneously; however, their genetic connection has been relatively ignored. Although the complexities of genetic regulation in a multispecies community may obscure a causative relationship, uncovering key genetic interactions between virulence and resistance in biofilm bacteria is essential to identifying new druggable targets, ultimately providing a drug discovery and development pathway to improve treatment options for chronic and recurring infection.

Harnessing Intra-Host Strain Competition to Limit Antibiotic Resistance: Mathematical Model Results

Antibiotic overuse has promoted the spread of antibiotic resistance. To compound the issue, treating individuals dually infected with antibiotic-resistant and antibiotic-vulnerable strains can make their infections completely resistant through competitive release. We formulate mathematical models of transmission dynamics accounting for dual infections and extensions accounting for lag times between infection and treatment or between cure and ending treatment. Analysis using the Next-Generation Matrix reveals how competition within hosts and the costs of resistance determine whether vulnerable and resistant strains persist, coexist, or drive each other to extinction. Invasion analysis predicts that treatment of dually infected cases will promote resistance. By varying antibiotic strength, the models suggest that physicians have two ways to achieve a particular resistance target: prescribe relatively weak antibiotics to everyone infected with an antibiotic-vulnerable strain or give more potent prescriptions to only those patients singly infected with the vulnerable strain after ruling out the possibility of them being dually infected with resistance. Through selectivity and moderation in antibiotic prescription, resistance might be limited.

Exploiting Interkingdom Interactions for Development of Small-Molecule Inhibitors of Candida albicans Biofilm Formation

A rapid decline in the development of new antimicrobial therapeutics has coincided with the emergence of new and more aggressive multidrug-resistant pathogens. Pathogens are protected from antibiotic activity by their ability to enter an aggregative biofilm state. Therefore, disrupting this process in pathogens is a key strategy for the development of next-generation antimicrobials. Here, we present a suite of compounds, based on the Pseudomonas aeruginosa 2-heptyl-4(1H)-quinolone (HHQ) core quinolone interkingdom signal structure, that exhibit noncytotoxic antibiofilm activity toward the fungal pathogen Candida albicans In addition to providing new insights into what is a clinically important bacterium-fungus interaction, the capacity to modularize the functionality of the quinolone signals is an important advance in harnessing the therapeutic potential of signaling molecules in general. This provides a platform for the development of potent next-generation small-molecule therapeutics targeting clinically relevant fungal pathogens.

Mathematical modelling of bacterial resistance to multiple antibiotics and immune system response

Resistance of developed bacteria to antibiotic treatment is a very important issue, because introduction of any new antibiotic is after a little while followed by the formation of resistant bacterial isolates in the clinic. The significant increase in clinical resistance to antibiotics is a troubling situation especially in nosocomial infections, where already defenseless patients can be unsuccessful to respond to treatment, causing even greater health issue. Nosocomial infections can be identified as those happening within 2 days of hospital acceptance, 3 days of discharge or 1 month of an operation. They influence 1 out of 10 patients admitted to hospital. Annually, this outcomes in 5000 deaths only in UK with a cost to the National Health Service of a billion pounds. Despite these problems, antibiotic therapy is still the most common method used to treat bacterial infections. On the other hand, it is often mentioned that immune system plays a major role in the progress of infections. In this context, we proposed a mathematical model defining population dynamics of both the specific immune cells produced according to the properties of bacteria by host and the bacteria exposed to multiple antibiotics synchronically, presuming that resistance is gained through mutations due to exposure to antibiotic. Qualitative analysis found out infection-free equilibrium point and other equilibrium points where resistant bacteria and immune system cells exist, only resistant bacteria exists and sensitive bacteria, resistant bacteria and immune system cells exist. As a result of this analysis, our model highlights the fact that when an individual’s immune system weakens, he/she suffers more from the bacterial infections which are believed to have been confined or terminated. Also, these results was supported by numerical simulations.