On the evolutionary ecology of multidrug resistance in bacteria

Predicting future hospital antimicrobial resistance prevalence using machine learning

BACKGROUND

Predicting antimicrobial resistance (AMR), a top global health threat, nationwide at an aggregate hospital level could help target interventions. Using machine learning, we exploit historical AMR and antimicrobial usage to predict future AMR.

METHODS

Antimicrobial use and AMR prevalence in bloodstream infections in hospitals in England were obtained per hospital group (Trust) and financial year (FY, April-March) for 22 pathogen-antibiotic combinations (FY2016-2017 to FY2021-2022). Extreme Gradient Boosting (XGBoost) model predictions were compared to the previous value taken forwards, the difference between the previous two years taken forwards and linear trend forecasting (LTF). XGBoost feature importances were calculated to aid interpretability.

RESULTS

Here we show that XGBoost models achieve the best predictive performance. Relatively limited year-to-year variability in AMR prevalence within Trust-pathogen-antibiotic combinations means previous value taken forwards also achieves a low mean absolute error (MAE), similar to or slightly higher than XGBoost. Using the difference between the previous two years taken forward or LTF performs consistently worse. XGBoost considerably outperforms all other methods in Trusts with a larger change in AMR prevalence from FY2020-2021 (last training year) to FY2021-2022 (held-out test set). Feature importance values indicate that besides historical resistance to the same pathogen-antibiotic combination as the outcome, complex relationships between resistance in different pathogens to the same antibiotic/antibiotic class and usage are exploited for predictions. These are generally among the top ten features ranked according to their mean absolute SHAP values.

CONCLUSIONS

Year-to-year resistance has generally changed little within Trust-pathogen-antibiotic combinations. In those with larger changes, XGBoost models can improve predictions, enabling informed decisions, efficient resource allocation, and targeted interventions.

Recent approaches in computational modelling for controlling pathogen threats

In this review, we assess the status of computational modelling of pathogens. We focus on three disparate but interlinked research areas that produce models with very different spatial and temporal scope. First, we examine antimicrobial resistance (AMR). Many mechanisms of AMR are not well understood. As a result, it is hard to measure the current incidence of AMR, predict the future incidence, and design strategies to preserve existing antibiotic effectiveness. Next, we look at how to choose the finite number of bacterial strains that can be included in a vaccine. To do this, we need to understand what happens to vaccine and non-vaccine strains after vaccination programmes. Finally, we look at within-host modelling of antibody dynamics. The SARS-CoV-2 pandemic produced huge amounts of antibody data, prompting improvements in this area of modelling. We finish by discussing the challenges that persist in understanding these complex biological systems.

Large-scale bacterial genomic and metagenomic analysis reveals Pseudomonas aeruginosa as potential ancestral source of tigecycline resistance gene cluster tmexCD-toprJ

BACKGROUND

The global dissemination of the multidrug resistance efflux pump gene cluster tmexCD-toprJ has greatly weakened the effects of multiple antibiotics, including tigecycline. However, the potential origin and transmission mechanisms of the gene cluster remain unclear.

METHODS

Here, we concluded a comprehensive bioinformatics analysis on integrated 73,498 bacterial genomes, including Pseudomonas spp., Klebsiella spp., Aeromonas spp., Proteus spp., and Citrobacter spp., along with 1,152 long-read metagenomic datasets to trace the origin and propagation of tmexCD-toprJ.

RESULTS

Our results demonstrated that tmexCD-toprJ was predominantly found in Pseudomonas aeruginosa sourced from human hosts in Asian countries and North American countries. Phylogenetic and genomic feature analyses showed that tmexCD-toprJ was likely evolved from mexCD-oprJ of some special clones of P. aeruginosa. Furthermore, metagenomic analysis confirmed that P. aeruginosa is the only potential ancestral bacterium for tmexCD-toprJ. A putative mobile genetic structure harboring tmexCD-toprJ, int-int-hp-hp-tnfxB-tmexCD-toprJ, was the predominant genetic context of tmexCD-toprJ across various bacterial genera, suggesting that the two integrase genes play a pivotal role in the horizontal transmission of tmexCD-toprJ.

CONCLUSIONS

Based on these findings, it is almost certain that the tmexCD-toprJ gene cluster was derived from P. aeruginosa and further spread to other bacteria.

Diagnostics for Public Health - Infectious Disease Surveillance and Control

AbstractAccurate diagnostics are critical in public health to ensure successful disease tracking, prevention, and control. Many of the same characteristics are desirable for diagnostic procedures in both medicine and public health: for example, low cost, high speed, low invasiveness, ease of use and interpretation, day-to-day consistency, and high accuracy. This review lays out five principles that are salient when the goal of diagnosis is to improve the overall health of a population rather than that of a particular patient, and it applies them in two important use cases: pandemic infectious disease and antimicrobial resistance.

Genetic insights of antibiotic resistance, pathogenicity (virulence) and phylogenetic relationship of Escherichia coli strains isolated from livestock, poultry and their handlers - a one health snapshot

BACKGROUND

Pathogenic and non-pathogenic strains of Escherichia coli harbouring antibiotic resistance genes (ARGs) from any source (clinical samples, animal settings, or environment) might be transmitted and contribute to the spread and increase of antibiotic resistance in the biosphere. The goal of this study was to investigate the genome to decipher the repertoire of ARGs, virulence genes carried by E. coli strains isolated from livestock, poultry, and their handlers (humans), and then unveil the genetic relatedness between the strains.

METHODS

Whole genome sequencing was done to investigate the genetic makeup of E. coli isolates (n = 20) [swine (n = 2), cattle (n = 2), sheep (n = 4), poultry (n = 7), and animal handlers (n = 5)] from southern India. The detection of resistome, virulome, biofilm forming genes, mobile genetic elements (MGE), followed by multilocus sequence typing (MLST) and phylogenetic analyses, were performed.

RESULTS

E. coli strains were found to be multi drug resistant, with a resistome encompassing > 20 ARGs, the virulome-17-22 genes, and > 20 key biofilm genes. MGE analysis showed four E. coli isolates (host: poultry, swine and cattle) harbouring composite transposons with ARGs/virulence genes (blaTEM, dfr, qnr/nleB, tir, eae,and esp) with the potential for horizontal transfer. MLST analyses revealed the presence of ST937 and ST3107 in both livestock/poultry and their handlers. Phylogenomic analyses with global E. coli isolates (human/livestock/poultry hosts) showed close relatedness with strains originating from different parts of the world (the United States, China, etc.).

CONCLUSION

The current study emphasizes the circulation of strains of pathogenic sequence types of clinical importance, carrying a diverse repertoire of genes associated with antibiotic resistance, biofilm formation and virulence properties in animal settings, necessitating immediate mitigation measures to reduce the risk of spread across the biosphere.

Global surveillance of antimicrobial resistance in food animals using priority drugs maps

Antimicrobial resistance (AMR) in food animals is a growing threat to animal health and potentially to human health. In resource-limited settings, allocating resources to address AMR can be guided with maps. Here, we mapped AMR prevalence in 7 antimicrobials in Escherichia coli and nontyphoidal Salmonella species across low- and middle-income countries (LIMCs), using 1088 point-prevalence surveys in combination with a geospatial model. Hotspots of AMR were predicted in China, India, Brazil, Chile, and part of central Asia and southeastern Africa. The highest resistance prevalence was for tetracycline (59% for E. coli and 54% for nontyphoidal Salmonella, average across LMICs) and lowest for cefotaxime (33% and 19%). We also identified the antimicrobial with the highest probability of resistance exceeding critical levels (50%) in the future (1.7-12.4 years) for each 10 × 10 km pixel on the map. In Africa and South America, 78% locations were associated with penicillins or tetracyclines crossing 50% resistance in the future. In contrast, in Asia, 77% locations were associated with penicillins or sulphonamides. Our maps highlight diverging geographic trends of AMR prevalence across antimicrobial classes, and can be used to target AMR surveillance in AMR hotspots for priority antimicrobial classes.

Evolutionary Epidemiology Consequences of Trait-Dependent Control of Heterogeneous Parasites

AbstractDisease control can induce both demographic and evolutionary responses in host-parasite systems. Foreseeing the outcome of control therefore requires knowledge of the eco-evolutionary feedback between control and system. Previous work has assumed that control strategies have a homogeneous effect on the parasite population. However, this is not true when control targets those traits that confer to the parasite heterogeneous levels of resistance, which can additionally be related to other key parasite traits through evolutionary trade-offs. In this work, we develop a minimal model coupling epidemiological and evolutionary dynamics to explore possible trait-dependent effects of control strategies. In particular, we consider a parasite expressing continuous levels of a trait-determining resource exploitation and a control treatment that can be either positively or negatively correlated with that trait. We demonstrate the potential of trait-dependent control by considering that the decision maker may want to minimize both the damage caused by the disease and the use of treatment, due to possible environmental or economic costs. We identify efficient strategies showing that the optimal type of treatment depends on the amount applied. Our results pave the way for the study of control strategies based on evolutionary constraints, such as collateral sensitivity and resistance costs, which are receiving increasing attention for both public health and agricultural purposes.

Complete genome sequence of Halomonas alkaliantarctica MSP3 isolated from marine sediment, Jeju Island

As a moderate halophilic-heterotrophic bacterium, Halomonas alkaliantarctica MSP3 was isolated from marine sediment located in Jeju island, South Korea. The complete genome of strain MSP3 was sequenced and analyzed to reveal its genetic features and metabolic potential. The genome size of MSP3 was about 4.23 Mbp with 54.7% G + C content, and it contained 3811 protein-coding sequences and 79 RNA genes (61 tRNA and 18 rRNA). According to the genome annotation, it was revealed that the strain MSP3 harbors genes encoding for urease and urea transporters, which play a crucial role in the process of urea degradation and utilization. In addition, it is noteworthy that the MSP3 strain possesses genes encoding for both cytochrome c oxidase and cytochrome bd oxidase, thereby conferring upon it the ability to adapt to various levels of oxygen (oxic to microoxic) and to execute denitrification processes in the absence of oxygen. Moreover, it was observed that strain MSP3 had genes for the glyoxylate cycle, which is an alternative pathway to the TCA cycle. Furthermore, it was observed that the MSP3 strain exhibited the ability to thrive across a diverse spectrum of NaCl concentrations, ranging from 2% to 10% (w/v). Collectively, strain MSP3 may possess an advantage over competitors within the marine ecosystem, particularly in conditions where carbon substrates are restricted. The genomic-based assumption could potentially be substantiated by the presence of a multitude of transporter genes within the genome.

Estimating the fitness cost and benefit of antimicrobial resistance from pathogen genomic data

Increasing levels of antibiotic resistance in many bacterial pathogen populations are a major threat to public health. Resistance to an antibiotic provides a fitness benefit when the bacteria are exposed to this antibiotic, but resistance also often comes at a cost to the resistant pathogen relative to susceptible counterparts. We lack a good understanding of these benefits and costs of resistance for many bacterial pathogens and antibiotics, but estimating them could lead to better use of antibiotics in a way that reduces or prevents the spread of resistance. Here, we propose a new model for the joint epidemiology of susceptible and resistant variants, which includes explicit parameters for the cost and benefit of resistance. We show how Bayesian inference can be performed under this model using phylogenetic data from susceptible and resistant lineages and that by combining data from both we are able to disentangle and estimate the resistance cost and benefit parameters separately. We applied our inferential methodology to several simulated datasets to demonstrate good scalability and accuracy. We analysed a dataset of Neisseria gonorrhoeae genomes collected between 2000 and 2013 in the USA. We found that two unrelated lineages resistant to fluoroquinolones shared similar epidemic dynamics and resistance parameters. Fluoroquinolones were abandoned for the treatment of gonorrhoea due to increasing levels of resistance, but our results suggest that they could be used to treat a minority of around 10% of cases without causing resistance to grow again.

Mathematical Model of Antibiotic Resistance Determinants' Stability Under Space Flight Conditions

Increasing antibiotic resistance (AR) poses dangers of treatment complications and even treatment failure to astronauts. An AR determinant is a gene of resistance carried by bacteria. This article considers the issue of the stability of AR determinants and the influence of manned spaceflight conditions on this characteristic. A phenomenological model has been developed that makes it possible to evaluate the integral value of the stability of determinants of AR in bacteria as a function of time. Based on experimental results obtained during implementation of the SALYUT 7 space program, the stability of determinants of AR in Escherichia coli strains isolated before and after a spaceflight in 16 astronauts was evaluated. In addition, an assessment was made of the integral value of the stability of determinants of AR in bacteria during in vitro experiments, both in spaceflight and terrestrial conditions, after preincubation in space. The calculation using the developed phenomenological model showed that the stability of AR determinants in E. coli bacteria isolated from astronauts before the spaceflight is 33% higher than after the flight. The in vitro experiment carried out on board the International Space Station showed the opposite situation-an increase in the stability of AR determinants by 33% in cultures that have been in space compared with terrestrial control. This indicates an additional influence on the stability of determinants and of the astronaut's immune system, as well as space conditions. The common result in these two types of studies is the experimental fact that the largest number of bacteria, in space conditions, had two determinants of AR. The importance of fighting bacteria with two determinants is that at least three different antibiotics are required to have an effect. This circumstance makes it possible to predict a possible strategy for the use of antibiotics in autonomous spaceflights.

Challenges in Forecasting Antimicrobial Resistance

Antimicrobial resistance is a major threat to human health. Since the 2000s, computational tools for predicting infectious diseases have been greatly advanced; however, efforts to develop real-time forecasting models for antimicrobial-resistant organisms (AMROs) have been absent. In this perspective, we discuss the utility of AMRO forecasting at different scales, highlight the challenges in this field, and suggest future research priorities. We also discuss challenges in scientific understanding, access to high-quality data, model calibration, and implementation and evaluation of forecasting models. We further highlight the need to initiate research on AMRO forecasting using currently available data and resources to galvanize the research community and address initial practical questions.

Epidemiological dynamics of bacteriocin competition and antibiotic resistance

Bacteriocins, toxic peptides involved in the competition between bacterial strains, are extremely diverse. Previous work on bacteriocin dynamics has highlighted the role of non-transitive 'rock-paper-scissors' competition in maintaining the coexistence of different bacteriocin profiles. The focus to date has primarily been on bacteriocin interactions at the within-host scale (i.e. within a single bacterial population). Yet in species such as Streptococcus pneumoniae, with relatively short periods of colonization and limited within-host diversity, ecological outcomes are also shaped by processes at the epidemiological (between-host) scale. Here, we first investigate bacteriocin dynamics and diversity in epidemiological models. We find that in these models, bacteriocin diversity is more readily maintained than in within-host models, and with more possible combinations of coexisting bacteriocin profiles. Indeed, maintenance of diversity in epidemiological models does not require rock-paper-scissors dynamics; it can also occur through a competition-colonization trade-off. Second, we investigate the link between bacteriocin diversity and diversity at antibiotic resistance loci. Previous work has proposed that bacterial duration of colonization modulates the fitness of antibiotic resistance. Due to their inhibitory effects, bacteriocins are a plausible candidate for playing a role in the duration of colonization episodes. We extend the epidemiological model of bacteriocin dynamics to incorporate an antibiotic resistance locus and demonstrate that bacteriocin diversity can indeed maintain the coexistence of antibiotic-sensitive and -resistant strains.

Identifying patient-level risk factors associated with non-β-lactam resistance outcomes in invasive MRSA infections in the United States using chain graphs

Background

MRSA is one of the most common causes of hospital- and community-acquired infections. MRSA is resistant to many antibiotics, including β-lactam antibiotics, fluoroquinolones, lincosamides, macrolides, aminoglycosides, tetracyclines and chloramphenicol.

Objectives

To identify patient-level characteristics that may be associated with phenotype variations and that may help improve prescribing practice and antimicrobial stewardship.

Methods

Chain graphs for resistance phenotypes were learned from invasive MRSA surveillance data collected by the CDC as part of the Emerging Infections Program to identify patient level risk factors for individual resistance outcomes reported as MIC while accounting for the correlations among the resistance traits. These chain graphs are multilevel probabilistic graphical models (PGMs) that can be used to quantify and visualize the complex associations among multiple resistance outcomes and their explanatory variables.

Results

Some phenotypic resistances had low connectivity to other outcomes or predictors (e.g. tetracycline, vancomycin, doxycycline and rifampicin). Only levofloxacin susceptibility was associated with healthcare-associated infections. Blood culture was the most common predictor of MIC. Patients with positive blood culture had significantly increased MIC of chloramphenicol, erythromycin, gentamicin, lincomycin and mupirocin, and decreased daptomycin and rifampicin MICs. Some regional variations were also observed.

Conclusions

The differences in resistance phenotypes between patients with previous healthcare use or positive blood cultures, or from different states, may be useful to inform first-choice antibiotics to treat clinical MRSA cases. Additionally, we demonstrated multilevel PGMs are useful to quantify and visualize interactions among multiple resistance outcomes and their explanatory variables.

Uses of mathematical modeling to estimate the impact of mass drug administration of antibiotics on antimicrobial resistance within and between communities

BACKGROUND

Antibiotics are a key part of modern healthcare, but their use has downsides, including selecting for antibiotic resistance, both in the individuals treated with antibiotics and in the community at large. When evaluating the benefits and costs of mass administration of azithromycin to reduce childhood mortality, effects of antibiotic use on antibiotic resistance are important but difficult to measure, especially when evaluating resistance that "spills over" from antibiotic-treated individuals to other members of their community. The aim of this scoping review was to identify how the existing literature on antibiotic resistance modeling could be better leveraged to understand the effect of mass drug administration (MDA) on antibiotic resistance.

MAIN TEXT

Mathematical models of antibiotic use and resistance may be useful for estimating the expected effects of different MDA implementations on different populations, as well as aiding interpretation of existing data and guiding future experimental design. Here, strengths and limitations of models of antibiotic resistance are reviewed, and possible applications of those models in the context of mass drug administration with azithromycin are discussed.

CONCLUSIONS

Statistical models of antibiotic use and resistance may provide robust and relevant estimates of the possible effects of MDA on resistance. Mechanistic models of resistance, while able to more precisely estimate the effects of different implementations of MDA on resistance, may require more data from MDA trials to be accurately parameterized.

Exploring the molecular interaction of Pheniramine with Enterococcus faecalis Homoserine Kinase: In-silico and in vitro studies

Infections caused by the bacteria Enterococcus faecalis (also known as E. faecalis) are common in hospitals. This bacterium is resistant to a wide range of medicines and causes a variety of nosocomial infections. An increase in the number of infections caused by multidrug-resistant (MDR) bacteria is causing substantial economic and health issues around the world. Consequently, new therapeutic techniques to tackle the growing threat of E. faecalis infections must be developed as soon as possible. In this regard, we have targeted a protein that is regarded to be critical for the survival of bacteria in this experiment. Homoserine kinase (HSK) is a threonine metabolism enzyme that belongs to the GHMP kinase superfamily. It is a crucial enzyme in threonine metabolism. This enzyme is responsible for a critical step in the threonine biosynthesis pathway. Given the important function that E. faecalis Homoserine Kinase (ESK) plays in bacterial metabolism, we proposed that E. faecalis HSK be cloned, overexpressed, purified, and subjected to structural analyses using homology modelling. In addition, we have reported on the model's molecular docking and Molecular Dynamic Stimulation (MD Stimulation) investigations to validate the results of the docking experiments. The results were promising. In silico investigations came up with the conclusion: pheniramine has good binding affinity for the E. faecalis HSK.

Plantaricin A, Derived from Lactiplantibacillus plantarum, Reduces the Intrinsic Resistance of Gram-Negative Bacteria to Hydrophobic Antibiotics

The outer membrane of Gram-negative bacteria is one of the major factors contributing to the development of antibiotic resistance, resulting in a lack of effectiveness of several hydrophobic antibiotics. Plantaricin A (PlnA) intensifies the potency of antibiotics by increasing the permeability of the bacterial outer membrane. Moreover, it has been proven to bind to the lipopolysaccharide of Escherichia coli via electrostatic and hydrophobic interactions and to interfere with the integrity of the bacterial outer membrane. Based on this mechanism, we designed a series of PlnA1 analogs by changing the structure, hydrophobicity, and charge to enhance their membrane-permeabilizing ability. Subsequent analyses revealed that among the PlnA1 analogs, OP4 demonstrated the highest penetrating ability, weaker cytotoxicity, and a higher therapeutic index. In addition, it decelerated the development of antibiotic resistance when the E. coli cells were continuously exposed to sublethal concentrations of erythromycin and ciprofloxacin for 30 generations. Further in vivo studies in mice with sepsis showed that OP4 heightens the potency of erythromycin against E. coli and relieves inflammation. In summary, our results showed that the PlnA1 analogs investigated in the present study, especially OP4, reduce the intrinsic antibiotic resistance of Gram-negative pathogens and expand the antibiotic sensitivity spectrum of hydrophobic antibiotics in Gram-negative bacteria. IMPORTANCE Antibiotic resistance is a global health concern due to indiscriminate use of antibiotics, resistance transfer, and intrinsic resistance of certain Gram-negative bacteria. The asymmetric bacterial outer membrane prevents the entry of hydrophobic antibiotics and renders them ineffective. Consequently, these antibiotics could be employed to treat infections caused by Gram-negative bacteria, after increasing their outer membrane permeability. As PlnA reportedly penetrates outer membranes, we designed a series of PlnA1 analogs and proved that OP4, one of these antimicrobial peptides, effectively augmented the permeability of the bacterial outer membrane. Furthermore, OP4 effectively improved the potency of erythromycin and alleviated inflammatory responses caused by Escherichia coli infection. Likewise, OP4 curtailed antibiotic resistance development in E. coli, thereby prolonging exposure to sublethal antibiotic concentrations. Thus, the combined use of hydrophobic antibiotics and OP4 could be used to treat infections caused by Gram-negative bacteria by decreasing their intrinsic antibiotic resistance.

Inwardly rectifying potassium channels mediate polymyxin-induced nephrotoxicity

Polymyxin antibiotics are often used as a last-line defense to treat life-threatening Gram-negative pathogens. However, polymyxin-induced kidney toxicity is a dose-limiting factor of paramount importance and can lead to suboptimal treatment. To elucidate the mechanism and develop effective strategies to overcome polymyxin toxicity, we employed a whole-genome CRISPR screen in human kidney tubular HK-2 cells and identified 86 significant genes that upon knock-out rescued polymyxin-induced toxicity. Specifically, we discovered that knockout of the inwardly rectifying potassium channels Kir4.2 and Kir5.1 (encoded by KCNJ15 and KCNJ16, respectively) rescued polymyxin-induced toxicity in HK-2 cells. Furthermore, we found that polymyxins induced cell depolarization via Kir4.2 and Kir5.1 and a significant cellular uptake of polymyxins was evident. All-atom molecular dynamics simulations revealed that polymyxin B₁ spontaneously bound to Kir4.2, thereby increasing opening of the channel, resulting in a potassium influx, and changes of the membrane potential. Consistent with these findings, small molecule inhibitors (BaCl₂ and VU0134992) of Kir potassium channels reduced polymyxin-induced toxicity in cell culture and mouse explant kidney tissue. Our findings provide critical mechanistic information that will help attenuate polymyxin-induced nephrotoxicity in patients and facilitate the design of novel, safer polymyxins.

Enhanced electrochemical removal of sulfadiazine using stainless steel electrode coated with activated algal biochar

With the increasingly discharging and inappropriately disposing of antibiotics from human disease treatment and breeding industry, extensive development of antibiotic resistance in bacteria raised serious public health concern. In this work, algal biochar was coated onto the stainless steel mesh, and was employed as cathodic electrode for the degradation of sulfadiazine (SDZ) in an electro-Fenton (EF) system. It was found that algal biochar pyrolyzed at 600 °C with 1:1 KOH achieved best catalytic performance to generate H₂O₂ via oxygen reduction. Moreover, removal efficiency of SDZ reached 96.11% in 4 h with an initial concentration of 25 μg/mL, under the optimized condition as: initial pH at 3, 50 mM of Na₂SO₄ as electrolyte and an applied current of 20 mA/cm². In addition, it was found that the SDZ removal kept at about 96.99% even after four repeated degradation process. Moreover, four possible SDZ degradative pathways during the EF process were proposed according to determined intermediates, model optimization and density functional theory calculation. Finally, acute and chronic biotoxicity of the degradative products against fish and green algae was evaluated, to further elaborate the environmental impact of SDZ after electrochemical degradation.

Heavy metal-induced selection and proliferation of antibiotic resistance: A review

Antibiotic resistance is recognized as a global threat to public health. The selection and evolution of antibiotic resistance in clinical pathogens was believed to be majorly driven by the imprudent use of antibiotics. However, concerns regarding the same, through selection pressure by a multitude of other antimicrobial agents, such as heavy metals, are also growing. Heavy metal contamination co-selects antibiotic and metal resistance through numerous mechanisms, such as co-resistance and cross-resistance. Here, we have reviewed the role of heavy metals as antimicrobial resistance driving agents and the underlying concept and mechanisms of co-selection, while also highlighting the scarcity in studies explicitly inspecting the process of co-selection in clinical settings. Prospective strategies to manage heavy metal-induced antibiotic resistance have also been deliberated, underlining the need to find specific inhibitors so that alternate medicinal combinations can be added to the existing therapeutic armamentarium.

Pneumococcal within-host diversity during colonization, transmission and treatment

Characterizing the genetic diversity of pathogens within the host promises to greatly improve surveillance and reconstruction of transmission chains. For bacteria, it also informs our understanding of inter-strain competition and how this shapes the distribution of resistant and sensitive bacteria. Here we study the genetic diversity of Streptococcus pneumoniae within 468 infants and 145 of their mothers by deep sequencing whole pneumococcal populations from 3,761 longitudinal nasopharyngeal samples. We demonstrate that deep sequencing has unsurpassed sensitivity for detecting multiple colonization, doubling the rate at which highly invasive serotype 1 bacteria were detected in carriage compared with gold-standard methods. The greater resolution identified an elevated rate of transmission from mothers to their children in the first year of the child's life. Comprehensive treatment data demonstrated that infants were at an elevated risk of both the acquisition and persistent colonization of a multidrug-resistant bacterium following antimicrobial treatment. Some alleles were enriched after antimicrobial treatment, suggesting that they aided persistence, but generally purifying selection dominated within-host evolution. Rates of co-colonization imply that in the absence of treatment, susceptible lineages outcompeted resistant lineages within the host. These results demonstrate the many benefits of deep sequencing for the genomic surveillance of bacterial pathogens.

Large-scale genomic analysis of antimicrobial resistance in the zoonotic pathogen Streptococcus suis

BACKGROUND

Antimicrobial resistance (AMR) is among the gravest threats to human health and food security worldwide. The use of antimicrobials in livestock production can lead to emergence of AMR, which can have direct effects on humans through spread of zoonotic disease. Pigs pose a particular risk as they are a source of zoonotic diseases and receive more antimicrobials than most other livestock. Here we use a large-scale genomic approach to characterise AMR in Streptococcus suis, a commensal found in most pigs, but which can also cause serious disease in both pigs and humans.

RESULTS

We obtained replicated measures of Minimum Inhibitory Concentration (MIC) for 16 antibiotics, across a panel of 678 isolates, from the major pig-producing regions of the world. For several drugs, there was no natural separation into 'resistant' and 'susceptible', highlighting the need to treat MIC as a quantitative trait. We found differences in MICs between countries, consistent with their patterns of antimicrobial usage. AMR levels were high even for drugs not used to treat S. suis, with many multidrug-resistant isolates. Similar levels of resistance were found in pigs and humans from regions associated with zoonotic transmission. We next used whole genome sequences for each isolate to identify 43 candidate resistance determinants, 22 of which were novel in S. suis. The presence of these determinants explained most of the variation in MIC. But there were also interesting complications, including epistatic interactions, where known resistance alleles had no effect in some genetic backgrounds. Beta-lactam resistance involved many core genome variants of small effect, appearing in a characteristic order.

CONCLUSIONS

We present a large dataset allowing the analysis of the multiple contributing factors to AMR in S. suis. The high levels of AMR in S. suis that we observe are reflected by antibiotic usage patterns but our results confirm the potential for genomic data to aid in the fight against AMR.

Screening of Epidemiologically Significant Mechanisms of Antibiotics to β-Lactams in Enterobacteriaceae - Pathogens of Zoonoses

Among the acquired mechanisms of resistance to antibiotics of microorganisms, the production of beta-lactamases, enzymes that inactivate penicillins, cephalosporins, carbapenems, and monobactams, is widespread. Most often, such beta-lactamases, in particular ESBL (extended-spectrum beta-lactamases), are capable of destroying III and IV generations of cephalosporins. One of the important ESBL producers is Escherichia coli and, to a lesser extent, Salmonella enteritidis, which are clinically significant in animals and humans. The purpose of the study was to screen ESBL DDM using cephalosporin markers and screening of mobile extrachromosomal factors of bacterial heredity – plasmids (potentially dangerous factors of genetic transport) in isolates of E. coli and S. enteritidis, polyresistant to aminoderms, from environmental objects, patho- and biological material, raw materials and products of animal origin. Results of our studies have shown the level of their distribution among animals, poultry, since from 13 field isolates of E. coli isolated from the milk of cows with mastitis and pathological material from pigs, ESBL production was found in 3 strains (23.1%) and from 18 field isolates of S. enteritidis isolated from pathological material from poultry, ESBL production was found in 2 strains (11.1%). Based on the results of molecular genetics studies, the presence of resistance plasmids (R-plasmids) in 9 field E. coli isolates was confirmed, 4 of which produced acquired beta-lactamases, incl. ESBL and 8 field isolates of S. enteritidis, 7 of which confirmed the presence of acquired carbapenemases.

The roles of antimicrobial resistance, phage diversity, isolation source and selection in shaping the genomic architecture of Bacillus anthracis

Bacillus anthracis, the causative agent of anthrax disease, is a worldwide threat to livestock, wildlife and public health. While analyses of genetic data from across the globe have increased our understanding of this bacterium’s population genomic structure, the influence of selective pressures on this successful pathogen is not well understood. In this study, we investigate the effects of antimicrobial resistance, phage diversity, geography and isolation source in shaping population genomic structure. We also identify a suite of candidate genes potentially under selection, driving patterns of diversity across 356 globally extant B. anthracis genomes. We report ten antimicrobial resistance genes and 11 different prophage sequences, resulting in the first large-scale documentation of these genetic anomalies for this pathogen. Results of random forest classification suggest genomic structure may be driven by a combination of antimicrobial resistance, geography and isolation source, specific to the population cluster examined. We found strong evidence that a recombination event linked to a gene involved in protein synthesis may be responsible for phenotypic differences between comparatively disparate populations. We also offer a list of genes for further examination of B. anthracis evolution, based on high-impact single nucleotide polymorphisms (SNPs) and clustered mutations. The information presented here sheds new light on the factors driving genomic structure in this notorious pathogen and may act as a road map for future studies aimed at understanding functional differences in terms of B. anthracis biogeography, virulence and evolution.

Modeling the effect of vaccination on selection for antibiotic resistance in Streptococcus pneumonia e

Vaccines against bacterial pathogens can protect recipients from becoming infected with potentially antibiotic-resistant pathogens. However, by altering the selective balance between antibiotic-sensitive and antibiotic-resistant bacterial strains, vaccines may also suppress-or spread-antibiotic resistance among unvaccinated individuals. Predicting the outcome of vaccination requires knowing what drives selection for drug-resistant bacterial pathogens and what maintains the circulation of both antibiotic-sensitive and antibiotic-resistant strains of bacteria. To address this question, we used mathematical modeling and data from 2007 on penicillin consumption and penicillin nonsusceptibility in Streptococcus pneumoniae (pneumococcus) invasive isolates from 27 European countries. We show that the frequency of penicillin resistance in S. pneumoniae can be explained by between-host diversity in antibiotic use, heritable diversity in pneumococcal carriage duration, or frequency-dependent selection brought about by within-host competition between antibiotic-resistant and antibiotic-sensitive S. pneumoniae strains. We used our calibrated models to predict the impact of non-serotype-specific pneumococcal vaccination upon the prevalence of S. pneumoniae carriage, incidence of disease, and frequency of S. pneumoniae antibiotic resistance. We found that the relative strength and directionality of competition between drug-resistant and drug-sensitive pneumococcal strains was the most important determinant of whether vaccination would promote, inhibit, or have little effect upon the evolution of antibiotic resistance. Last, we show that country-specific differences in pathogen transmission substantially altered the predicted impact of vaccination, highlighting that policies for managing antibiotic resistance with vaccines must be tailored to a specific pathogen and setting.

Understanding the evolution of multiple drug resistance in structured populations

The evolution of multidrug resistance (MDR) is a pressing public health concern. Yet many aspects, such as the role played by population structure, remain poorly understood. Here, we argue that studying MDR evolution by focusing upon the dynamical equations for linkage disequilibrium (LD) can greatly simplify the calculations, generate more insight, and provide a unified framework for understanding the role of population structure. We demonstrate how a general epidemiological model of MDR evolution can be recast in terms of the LD equations. These equations reveal how the different forces generating and propagating LD operate in a dynamical setting at both the population and metapopulation levels. We then apply these insights to show how the LD perspective: (i) explains equilibrium patterns of MDR, (ii) provides a simple interpretative framework for transient evolutionary dynamics, and (iii) can be used to assess the consequences of different drug prescription strategies for MDR evolution.

Evolutionary mechanisms that determine which bacterial genes are carried on plasmids

The evolutionary pressures that determine the location (chromosomal or plasmid‐borne) of bacterial genes are not fully understood. We investigate these pressures through mathematical modeling in the context of antibiotic resistance, which is often found on plasmids. Our central finding is that gene location is under positive frequency‐dependent selection: the higher the frequency of one form of resistance compared to the other, the higher its relative fitness. This can keep moderately beneficial genes on plasmids, despite occasional plasmid loss. For these genes, positive frequency dependence leads to a priority effect: whichever form is acquired first—through either mutation or horizontal gene transfer—has time to increase in frequency and thus becomes difficult to displace. Higher rates of horizontal transfer of plasmid‐borne than chromosomal genes therefore predict moderately beneficial genes will be found on plasmids. Gene flow between plasmid and chromosome allows chromosomal forms to arise, but positive frequency‐dependent selection prevents these from establishing. Further modeling shows that this effect is particularly pronounced when genes are shared across a large number of species, suggesting that antibiotic resistance genes are often found on plasmids because they are moderately beneficial across many species. We also revisit previous theoretical work—relating to the role of local adaptation in explaining gene location and to plasmid persistence—in light of our findings.

Effects of healthcare failure mode and effect analysis on the prevention of multi-drug resistant organisms infections in oral and maxillofacial surgery

OBJECTIVE

Care models of Healthcare Failure Mode and Effect Analysis (FMEA) were evaluated for the prevention of multi-drug resistant organisms (MDRO) infections in oral and maxillofacial surgery.

METHODS

Two hundred patients who received oral and maxillofacial surgery from January to December 2017 were enrolled as the control group, and another 200 patients who received oral and maxillofacial surgery from January to December 2018 were enrolled as the FMEA group. The incidence of MDRO, the implementation of preventive and control measures, the mastery of preventive and control knowledge, and oral self-care ability were compared between the two groups. Risk Priority Number (RPN) and behavioral changes of health care personnel were observed in FMEA group.

RESULTS

The FMEA group had a lower incidence of MDRO (2.00%) than the control group (6.00%) and a higher rate of acquisition of prevention and control knowledge (93.00%) than the control group (84.50%) (P < 0.05). Patients in FMEA group were higher than those in the control group in terms of compliance towards isolation signs and precautions, appropriate use of PPE, implementation of disinfection measures, hand hygiene and exercise of self-care agency (ESCA) scale scores (P < 0.05). The total RPN score of the FMEA group before and after management was 1384 and 180, respectively, and the reduction rate of total RPN scores was 86.99%. Scores with regard to knowledge, attitude, and behavior of health care personnel were increased after FMEA treatment (P < 0.05).

CONCLUSION

The nursing model of FMEA for oral and maxillofacial surgery can prevent MDRO infections, reduce RPN, improve the implementation of preventive and control measures as well as oral self-care ability and the acquisition of knowledge.

Macrolide and Nonmacrolide Resistance with Mass Azithromycin Distribution

BACKGROUND

Mass distribution of azithromycin to preschool children twice yearly for 2 years has been shown to reduce childhood mortality in sub-Saharan Africa but at the cost of amplifying macrolide resistance. The effects on the gut resistome, a reservoir of antimicrobial resistance genes in the body, of twice-yearly administration of azithromycin for a longer period are unclear.

METHODS

We investigated the gut resistome of children after they received twice-yearly distributions of azithromycin for 4 years. In the Niger site of the MORDOR trial, we enrolled 30 villages in a concurrent trial in which they were randomly assigned to receive mass distribution of either azithromycin or placebo, offered to all children 1 to 59 months of age every 6 months for 4 years. Rectal swabs were collected at baseline, 36 months, and 48 months for analysis of the participants' gut resistome. The primary outcome was the ratio of macrolide-resistance determinants in the azithromycin group to those in the placebo group at 48 months.

RESULTS

Over the entire 48-month period, the mean (±SD) coverage was 86.6±12% in the villages that received placebo and 83.2±16.4% in the villages that received azithromycin. A total of 3232 samples were collected during the entire trial period; of the samples obtained at the 48-month monitoring visit, 546 samples from 15 villages that received placebo and 504 from 14 villages that received azithromycin were analyzed. Determinants of macrolide resistance were higher in the azithromycin group than in the placebo group: 7.4 times as high (95% confidence interval [CI], 4.0 to 16.7) at 36 months and 7.5 times as high (95% CI, 3.8 to 23.1) at 48 months. Continued mass azithromycin distributions also selected for determinants of nonmacrolide resistance, including resistance to beta-lactam antibiotics, an antibiotic class prescribed frequently in this region of Africa.

CONCLUSIONS

Among villages assigned to receive mass distributions of azithromycin or placebo twice yearly for 4 years, antibiotic resistance was more common in the villages that received azithromycin than in those that received placebo. This trial showed that mass azithromycin distributions may propagate antibiotic resistance. (Funded by the Bill and Melinda Gates Foundation and others; ClinicalTrials.gov number, NCT02047981.).

Tackling Multidrug Resistance in Streptococci - From Novel Biotherapeutic Strategies to Nanomedicines

The pyogenic streptococci group includes pathogenic species for humans and other animals and has been associated with enduring morbidity and high mortality. The main reason for the treatment failure of streptococcal infections is the increased resistance to antibiotics. In recent years, infectious diseases caused by pyogenic streptococci resistant to multiple antibiotics have been raising with a significant impact to public health and veterinary industry. The rise of antibiotic-resistant streptococci has been associated to diverse mechanisms, such as efflux pumps and modifications of the antimicrobial target. Among streptococci, antibiotic resistance emerges from previously sensitive populations as result of horizontal gene transfer or chromosomal point mutations due to excessive use of antimicrobials. Streptococci strains are also recognized as biofilm producers. The increased resistance of biofilms to antibiotics among streptococci promote persistent infection, which comprise circa 80% of microbial infections in humans. Therefore, to overcome drug resistance, new strategies, including new antibacterial and antibiofilm agents, have been studied. Interestingly, the use of systems based on nanoparticles have been applied to tackle infection and reduce the emergence of drug resistance. Herein, we present a synopsis of mechanisms associated to drug resistance in (pyogenic) streptococci and discuss some innovative strategies as alternative to conventional antibiotics, such as bacteriocins, bacteriophage, and phage lysins, and metal nanoparticles. We shall provide focused discussion on the advantages and limitations of agents considering application, efficacy and safety in the context of impact to the host and evolution of bacterial resistance.

Antibiotic Resistance: Moving From Individual Health Norms to Social Norms in One Health and Global Health

Antibiotic resistance is a problem for human health, and consequently, its study had been traditionally focused toward its impact for the success of treating human infections in individual patients (individual health). Nevertheless, antibiotic-resistant bacteria and antibiotic resistance genes are not confined only to the infected patients. It is now generally accepted that the problem goes beyond humans, hospitals, or long-term facility settings and that it should be considered simultaneously in human-connected animals, farms, food, water, and natural ecosystems. In this regard, the health of humans, animals, and local antibiotic-resistance-polluted environments should influence the health of the whole interconnected local ecosystem (One Health). In addition, antibiotic resistance is also a global problem; any resistant microorganism (and its antibiotic resistance genes) could be distributed worldwide. Consequently, antibiotic resistance is a pandemic that requires Global Health solutions. Social norms, imposing individual and group behavior that favor global human health and in accordance with the increasingly collective awareness of the lack of human alienation from nature, will positively influence these solutions. In this regard, the problem of antibiotic resistance should be understood within the framework of socioeconomic and ecological efforts to ensure the sustainability of human development and the associated human-natural ecosystem interactions.

Mass drug administration with azithromycin for trachoma elimination and the population structure of Streptococcus pneumoniae in the nasopharynx

Objective

Mass drug administration (MDA) with azithromycin for trachoma elimination reduces nasopharyngeal carriage of Streptococcus pneumoniae in the short term. We evaluated S. pneumoniae carried in the nasopharynx before and after a round of azithromycin MDA to determine whether MDA was associated with changes in pneumococcal population structure and resistance.

Methods

We analysed 514 pneumococcal whole genomes randomly selected from nasopharyngeal samples collected in two Gambian villages that received three annual rounds of MDA for trachoma elimination. The 514 samples represented 293 participants, of which 75% were children aged 0–9 years, isolated during three cross-sectional surveys (CSSs) conducted before the third round of MDA (CSS-1) and at 1 (CSS-2) and 6 (CSS-3) months after MDA. Bayesian Analysis of Population Structure (BAPS) was used to cluster related isolates by capturing variation in the core genome. Serotype and multilocus sequence type were inferred from the genotype. Antimicrobial resistance determinants were identified from assemblies, including known macrolide resistance genes.

Results

Twenty-seven BAPS clusters were assigned. These consisted of 81 sequence types (STs). Two BAPS clusters not observed in CSS-1 (n = 109) or CSS-2 (n = 69), increased in frequency in CSS-3 (n = 126); BAPS20 (8.73%, p 0.016) and BAPS22 (7.14%, p 0.032) but were not associated with antimicrobial resistance. Macrolide resistance within BAPS17 increased after treatment (CSS-1 n = 0/6, CSS-2/3 n = 5/5, p 0.002) and was carried on a mobile transposable element that also conferred resistance to tetracycline.

Discussion

Limited changes in pneumococcal population structure were observed after the third round of MDA, suggesting treatment had little effect on the circulating lineages. An increase in macrolide resistance within one BAPS highlights the need for antimicrobial resistance surveillance in treated villages.

Factors favouring the evolution of multidrug resistance in bacteria

The evolution of multidrug antibiotic resistance in commensal bacteria is an important public health concern. Commensal bacteria such as Escherichia coli, Streptococcus pneumoniae or Staphylococcus aureus, are also opportunistic pathogens causing a large fraction of the community-acquired and hospital-acquired bacterial infections. Multidrug resistance (MDR) makes these infections harder to treat with antibiotics and may thus cause substantial additional morbidity and mortality. Here, we develop an evolutionary epidemiology model to identify the factors favouring the evolution of MDR in commensal bacteria. The model describes the evolution of antibiotic resistance in a commensal bacterial species evolving in a host population subjected to multiple antibiotic treatments. We combine statistical analysis of a large number of simulations and mathematical analysis to understand the model behaviour. We find that MDR evolves more readily when it is less costly than expected from the combinations of single resistances (positive epistasis). MDR frequently evolves when bacteria are in contact with multiple drugs prescribed in the host population, even if individual hosts are only treated with a single drug at a time. MDR is favoured when the host population is structured in different classes that vary in their rates of antibiotic treatment. However, under most circumstances, recombination between loci involved in resistance does not meaningfully affect the equilibrium frequency of MDR. Together, these results suggest that MDR is a frequent evolutionary outcome in commensal bacteria that encounter the variety of antibiotics prescribed in the host population. A better characterization of the variability in antibiotic use across the host population (e.g. across age classes or geographical location) would help predict which MDR genotypes will most readily evolve.

Targeted surveillance strategies for efficient detection of novel antibiotic resistance variants

Genotype-based diagnostics for antibiotic resistance represent a promising alternative to empiric therapy, reducing inappropriate antibiotic use. However, because such assays infer resistance based on known genetic markers, their utility will wane with the emergence of novel resistance. Maintenance of these diagnostics will therefore require surveillance to ensure early detection of novel resistance variants, but efficient strategies to do so remain undefined. We evaluate the efficiency of targeted sampling approaches informed by patient and pathogen characteristics in detecting antibiotic resistance and diagnostic escape variants in Neisseria gonorrhoeae, a pathogen associated with a high burden of disease and antibiotic resistance and the development of genotype-based diagnostics. We show that patient characteristic-informed sampling is not a reliable strategy for efficient variant detection. In contrast, sampling informed by pathogen characteristics, such as genomic diversity and genomic background, is significantly more efficient than random sampling in identifying genetic variants associated with resistance and diagnostic escape.

Horizontal gene transfer rate is not the primary determinant of observed antibiotic resistance frequencies in Streptococcus pneumoniae

The extent to which evolution is constrained by the rate at which horizontal gene transfer (HGT) allows DNA to move between genetic lineages is an open question, which we address in the context of antibiotic resistance in Streptococcus pneumoniae. We analyze microbiological, genomic, and epidemiological data from the largest-to-date sequenced pneumococcal carriage study in 955 infants from a refugee camp on the Thailand-Myanmar border. Using a unified framework, we simultaneously test prior hypotheses on rates of HGT and a key evolutionary covariate (duration of carriage) as determinants of resistance frequencies. We conclude that in this setting, there is little evidence of HGT playing a major role in determining resistance frequencies. Instead, observed resistance frequencies are best explained as the outcome of selection acting on a pool of variants, irrespective of the rate at which resistance determinants move between genetic lineages.

Comparative Genomic Analysis Provides Insights into the Phylogeny, Resistome, Virulome, and Host Adaptation in the Genus Ewingella

Ewingella americana is a cosmopolitan bacterial pathogen that has been isolated from many hosts. Here, we sequenced a high-quality genome of E. americana B6-1 isolated from Flammulina filiformis, an important cultivated mushroom, performed a comparative genomic analysis with four other E. americana strains from various origins, and tested the susceptibility of B6-1 to antibiotics. The genome size, predicted genes, and GC (guanine-cytosine) content of B6-1 was 4.67 Mb, 4301, and 53.80%, respectively. The origin of the strains did not significantly affect the phylogeny, but mobile genetic elements shaped the evolution of the genus Ewingella. The strains encoded a set of common genes for type secretion, virulence effectors, CAZymes, and toxins required for pathogenicity in all hosts. They also had antibiotic resistance, pigments to suppress or evade host defense responses, as well as genes for adaptation to different environmental conditions, including temperature, oxidation, and nutrients. These findings provide a better understanding of the virulence, antibiotic resistance, and host adaptation strategies of Ewingella, and they also contribute to the development of effective control strategies.

Systematic comparison of coexistence in models of drug-sensitive and drug-resistant pathogen strains

A number of mathematical models have recently been proposed to explain empirical trends of pathogen diversity. In particular, long-term coexistence of both drug-sensitive and drug-resistant variants of a single pathogen is something of a mystery, given that simple models of pathogens competing for the same ecological niche predict competitive exclusion, and more complex models admitting coexistence require assumptions that may not be justified. Coinfection is among the candidate mechanisms to generate coexistence, as it occurs in many pathogens and provides the opportunity for strains to interact directly. Recently, coinfection and competitive release have been described as creating a form of negative frequency-dependent selection that promotes coexistence, and a range of models containing coinfection have been proposed as having generic stable coexistence of multiple strains. This abundance of new models presents the challenge of comparison and interpretation. To this end, we describe a dimensionless quantity that can be used to compare the amount of coexistence generated by different models. We focus on models that include coinfection, although this framework could be generalized to a larger class of structured models.

Genome-wide epistasis and co-selection study using mutual information

Covariance-based discovery of polymorphisms under co-selective pressure or epistasis has received considerable recent attention in population genomics. Both statistical modeling of the population level covariation of alleles across the chromosome and model-free testing of dependencies between pairs of polymorphisms have been shown to successfully uncover patterns of selection in bacterial populations. Here we introduce a model-free method, SpydrPick, whose computational efficiency enables analysis at the scale of pan-genomes of many bacteria. SpydrPick incorporates an efficient correction for population structure, which adjusts for the phylogenetic signal in the data without requiring an explicit phylogenetic tree. We also introduce a new type of visualization of the results similar to the Manhattan plots used in genome-wide association studies, which enables rapid exploration of the identified signals of co-evolution. Simulations demonstrate the usefulness of our method and give some insight to when this type of analysis is most likely to be successful. Application of the method to large population genomic datasets of two major human pathogens, Streptococcus pneumoniae and Neisseria meningitidis, revealed both previously identified and novel putative targets of co-selection related to virulence and antibiotic resistance, highlighting the potential of this approach to drive molecular discoveries, even in the absence of phenotypic data.

Case-based surveillance of antimicrobial resistance with full susceptibility profiles

Surveillance of antimicrobial resistance (AMR) is essential for clinical decision-making and for public health authorities to monitor patterns in resistance and evaluate the effectiveness of interventions and control measures. Existing AMR surveillance is typically based on reports from hospital laboratories and public health laboratories, comprising reports of pathogen frequencies and resistance frequencies among each species detected. Here we propose an improved framework for AMR surveillance, in which the unit of surveillance is patients with specific conditions, rather than biological samples of a particular type. In this 'case-based' surveillance, denominators as well as numerators will be clearly defined with clinical relevance and more comparable at the local, national and international level. In locations with sufficient resources, individual-based data on patient characteristics and full antibiotic susceptibility profiles would provide high-quality evidence for monitoring resistant pathogens of clinical importance, clinical treatment of infections and public health responses to outbreaks of infections with resistant bacteria.

Mathematical modelling for antibiotic resistance control policy: do we know enough?

BACKGROUND

Antibiotics remain the cornerstone of modern medicine. Yet there exists an inherent dilemma in their use: we are able to prevent harm by administering antibiotic treatment as necessary to both humans and animals, but we must be mindful of limiting the spread of resistance and safeguarding the efficacy of antibiotics for current and future generations. Policies that strike the right balance must be informed by a transparent rationale that relies on a robust evidence base.

MAIN TEXT

One way to generate the evidence base needed to inform policies for managing antibiotic resistance is by using mathematical models. These models can distil the key drivers of the dynamics of resistance transmission from complex infection and evolutionary processes, as well as predict likely responses to policy change in silico. Here, we ask whether we know enough about antibiotic resistance for mathematical modelling to robustly and effectively inform policy. We consider in turn the challenges associated with capturing antibiotic resistance evolution using mathematical models, and with translating mathematical modelling evidence into policy.

CONCLUSIONS

We suggest that in spite of promising advances, we lack a complete understanding of key principles. From this we advocate for priority areas of future empirical and theoretical research.

Levels of outpatient prescribing for four major antibiotic classes and rates of septicemia hospitalization in adults in different US states - a statistical analysis

BACKGROUND

Rates of sepsis/septicemia hospitalization in the US have risen significantly during recent years. Antibiotic resistance and use may contribute to those rates through various mechanisms, including lack of clearance of resistant infections following antibiotic treatment, with some of those infections subsequently devolving into sepsis. At the same time, there is limited information on the effect of prescribing of certain antibiotics vs. others on the rates of septicemia and sepsis-related hospitalizations and mortality.

METHODS

We used multivariable linear regression to relate state-specific rates of outpatient prescribing overall for oral fluoroquinolones, penicillins, macrolides, and cephalosporins between 2011 and 2012 to state-specific rates of septicemia hospitalization (ICD-9 codes 038.xx present anywhere on a discharge diagnosis) in each of the following age groups of adults: (18-49y, 50-64y, 65-74y, 75-84y, 85 + y) reported to the Healthcare Cost and Utilization Project (HCUP) between 2011 and 2012, adjusting for additional covariates, and random effects associated with the ten US Health and Human Services (HHS) regions.

RESULTS

Increase in the rate of prescribing of oral penicillins by 1 annual dose per 1000 state residents was associated with increases in annual septicemia hospitalization rates of 0.19 (95% CI (0.02,0.37)) per 10,000 persons aged 50-64y, of 0.48(0.12,0.84) per 10,000 persons aged 65-74y, and of 0.81(0.17,1.40) per 10,000 persons aged 74-84y. Increase by 1 in the percent of African Americans among state residents in a given age group was associated with increases in annual septicemia hospitalization rates of 2.3(0.32,4.2) per 10,000 persons aged 75-84y, and of 5.3(1.1,9.5) per 10,000 persons aged over 85y. Average minimal daily temperature was positively associated with septicemia hospitalization rates in persons aged 18-49y, 50-64y, 75-84y and over 85y.

CONCLUSIONS

Our results suggest positive associations between the rates of prescribing for penicillins and the rates of hospitalization with septicemia in US adults aged 50-84y. Further studies are needed to better understand the potential effect of antibiotic replacement in the treatment of various syndromes, including the potential impact of the recent US FDA guidelines on restriction of fluoroquinolone use, as well as the potential effect of changes in the practices for prescribing of penicillins on the rates of sepsis-related hospitalization and mortality.