Antibiotic cross-resistance in the lab and resistance co-occurrence in the clinic: Discrepancies and implications in E.coli

Circular and L50-like leaderless enterocins share a common ABC-transporter immunity gene

Microbes live within complex communities of interacting populations, either free-living in waters and soils or symbionts of animals and plants. Their interactions include the production of antimicrobial peptides (bacteriocins) to antagonize competitors, and these producers must carry their own immunity gene for self-protection. Whether other coexisting populations are sensitive or resistant to the bacteriocin producer will be key for the population dynamics within the microbial community. The immunity gene frequently consists of an ABC transporter to repel its own bacteriocin but rarely protects against a nonrelated bacteriocin. A case where this cross-resistance occurs mediated by a shared ABC transporter has been shown between enterocins MR10A/B and AS-48. The first is an L50-like leaderless enterocin, while AS-48 is a circular enterocin. In addition, L50-like enterocins such as MR10A/B have been found in E. faecalis and E. faecium, but AS-48 appears only in E. faecalis. Thus, using the ABC transporter of the enterocin MR10A/B gene cluster of Enterococcus faecalis MRR10-3 as a cross-resistance model, we aimed to unravel to what extent a particular ABC transporter can be shared across multiple bacteriocinogenic bacterial populations. To this end, we screened the MR10A/B-ABC transporters in available microbial genomes and analyzed their sequence homologies and distribution. Overall, our main findings are as follows: (i) the MR10A/B-ABC transporter is associated with multiple enterocin gene clusters; (ii) the different enterocins associated with this transporter have a saposin-like fold in common; (iii) the Mr10E component of the transporter is more conserved within its associated enterocin, while the Mr10FGH components are more conserved within the carrying species. This is the least known component of the transporter, but it has shown the greatest specificity to its corresponding enterocin. Bacteriocins are now being investigated as an alternative to antibiotics; hence, the wider or narrower distribution of the particular immunity gene should be taken into account for clinical applications to avoid the selection of resistant strains. Further research will be needed to investigate the mechanistic interactions between the Mr10E transporter component and the bacteriocin as well as the specific ecological and evolutionary mechanisms involved in the spread of the immunity transporter across multiple bacteriocins.

Bayesian network modeling of patterns of antibiotic cross-resistance by bacterial sample source

BACKGROUND

Antimicrobial resistance is a major healthcare burden, aggravated when it extends to multiple drugs. While cross-resistance is well-studied experimentally, it is not the case in clinical settings, and especially not while considering confounding. Here, we estimated patterns of cross-resistance from clinical samples, while controlling for multiple clinical confounders and stratifying by sample sources.

METHODS

We employed additive Bayesian network (ABN) modelling to examine antibiotic cross- resistance in five major bacterial species, obtained from different sources (urine, wound, blood, and sputum) in a clinical setting, collected in a large hospital in Israel over a 4-year period. Overall, the number of samples available were 3525 for E coli, 1125 for K pneumoniae, 1828 for P aeruginosa, 701 for P mirabilis, and 835 for S aureus.

RESULTS

Patterns of cross-resistance differ across sample sources. All identified links between resistance to different antibiotics are positive. However, in 15 of 18 instances, the magnitudes of the links are significantly different between sources. For example, E coli exhibits adjusted odds ratios of gentamicin-ofloxacin cross-resistance ranging from 3.0 (95%CI [2.3,4.0]) in urine samples to 11.0 (95%CI [5.2,26.1]) in blood samples. Furthermore, we found that for P mirabilis, the magnitude of cross-resistance among linked antibiotics is higher in urine than in wound samples, whereas the opposite is true for K pneumoniae and P aeruginosa.

CONCLUSIONS

Our results highlight the importance of considering sample sources when assessing likelihood of antibiotic cross-resistance. The information and methods described in our study can refine future estimation of cross-resistance patterns and facilitate determination of antibiotic treatment regimens.

Multiple Antibiotic Resistance in Escherichia coli Isolates from Fecal and Water Sources in Laguna Lake, Philippines

Due to the misuse and overuse of antibiotics, antibiotic residues accumulate in natural environments, leading to the development of antibiotic-resistant bacteria (ARBs). The presence of ARBs in bodies of water poses health hazards to the surrounding community. This study focused on Laguna Lake, the largest lake in the Philippines, which serves as a water source for agriculture and domestic purposes. We aimed to detect the presence of antibiotic-resistant Escherichia coli from the lake waters and potential reservoirs of resistance as well as determine the multiple antibiotic resistance (MAR) indices of the isolates. E. coli (n = 450) was isolated from fecal-associated samples (chicken, cow, pig, human, sewage) and water samples (sites in Laguna Lake and selected river tributaries). The isolates were subjected to an antibiotic resistance assay using VITEK 2®. Among the 16 antibiotics tested, the isolates exhibited varying resistance to 14, but complete susceptibility to amikacin and tigecycline was observed. Isolates were most frequently resistant to ampicillin (196/450, 43.6%). Among fecal-associated samples, chicken isolates exhibited the highest MAR index (0.174), whereas samples from Pila River exhibited the highest MAR index (0.152) among water samples. The results of this study demonstrate the presence of multidrug-resistant E. coli in samples collected around Laguna Lake and reveal fecal and sewage sources as potential reservoirs of ARBs in the water body. With this information, the public is urged to use antibiotics responsibly to help mitigate the spread of antibiotic resistance.

Identification of antibiotic collateral sensitivity and resistance interactions in population surveillance data

BACKGROUND

Collateral effects of antibiotic resistance occur when resistance to one antibiotic agent leads to increased resistance or increased sensitivity to a second agent, known respectively as collateral resistance (CR) and collateral sensitivity (CS). Collateral effects are relevant to limit impact of antibiotic resistance in design of antibiotic treatments. However, methods to detect antibiotic collateral effects in clinical population surveillance data of antibiotic resistance are lacking.

OBJECTIVES

To develop a methodology to quantify collateral effect directionality and effect size from large-scale antimicrobial resistance population surveillance data.

METHODS

We propose a methodology to quantify and test collateral effects in clinical surveillance data based on a conditional t-test. Our methodology was evaluated using MIC data for 419 Escherichia coli strains, containing MIC data for 20 antibiotics, which were obtained from the Pathosystems Resource Integration Center (PATRIC) database.

RESULTS

We demonstrate that the proposed approach identifies several antibiotic combinations that show symmetrical or non-symmetrical CR and CS. For several of these combinations, collateral effects were previously confirmed in experimental studies. We furthermore provide insight into the power of our method for multiple collateral effect sizes and MIC distributions.

CONCLUSIONS

Our proposed approach is of relevance as a tool for analysis of large-scale population surveillance studies to provide broad systematic identification of collateral effects related to antibiotic resistance, and is made available to the community as an R package. This method can help mapping CS and CR, which could guide combination therapy and prescribing in the future.

A game theoretic approach reveals that discretizing clinical information can reduce antibiotic misuse

The overuse of antibiotics is exacerbating the antibiotic resistance crisis. Since this problem is a classic common-goods dilemma, it naturally lends itself to a game-theoretic analysis. Hence, we designed a model wherein physicians weigh whether antibiotics should be prescribed, given that antibiotic usage depletes its future effectiveness. The physicians' decisions rely on the probability of a bacterial infection before definitive laboratory results are available. We show that the physicians' equilibrium decision rule of antibiotic prescription is not socially optimal. However, we prove that discretizing the information provided to physicians can mitigate the gap between their equilibrium decisions and the social optimum of antibiotic prescription. Despite this problem's complexity, the effectiveness of the discretization solely depends on the type of information available to the physician to determine the nature of infection. This is demonstrated on theoretic distributions and a clinical dataset. Our results provide a game-theory based guide for optimal output of current and future decision support systems of antibiotic prescription.

Revealing antibiotic cross-resistance patterns in hospitalized patients through Bayesian network modelling

OBJECTIVES

Microbial resistance exhibits dependency patterns between different antibiotics, termed cross-resistance and collateral sensitivity. These patterns differ between experimental and clinical settings. It is unclear whether the differences result from biological reasons or from confounding, biasing results found in clinical settings. We set out to elucidate the underlying dependency patterns between resistance to different antibiotics from clinical data, while accounting for patient characteristics and previous antibiotic usage.

METHODS

Additive Bayesian network modelling was employed to simultaneously estimate relationships between variables in a dataset of bacterial cultures derived from hospitalized patients and tested for resistance to multiple antibiotics. Data contained resistance results, patient demographics and previous antibiotic usage, for five bacterial species: Escherichia coli (n = 1054), Klebsiella pneumoniae (n = 664), Pseudomonas aeruginosa (n = 571), CoNS (n = 495) and Proteus mirabilis (n = 415).

RESULTS

All links between resistance to the various antibiotics were positive. Multiple direct links between resistance of antibiotics from different classes were observed across bacterial species. For example, resistance to gentamicin in E. coli was directly linked with resistance to ciprofloxacin (OR = 8.39, 95% credible interval 5.58-13.30) and sulfamethoxazole/trimethoprim (OR = 2.95, 95% credible interval 1.97-4.51). In addition, resistance to various antibiotics was directly linked with previous antibiotic usage.

CONCLUSIONS

Robust relationships among resistance to antibiotics belonging to different classes, as well as resistance being linked to having taken antibiotics of a different class, exist even when taking into account multiple covariate dependencies. These relationships could help inform choices of antibiotic treatment in clinical settings.

Assessment of Phenotype Microarray plates for rapid and high-throughput analysis of collateral sensitivity networks

The crisis of antimicrobial resistance is driving research into the phenomenon of collateral sensitivity. Sometimes, when a bacterium evolves resistance to one antimicrobial, it becomes sensitive to others. In this study, we have investigated the utility of Phenotype Microarray (PM) plates for identifying collateral sensitivities with unprecedented throughput. We assessed the relative resistance/sensitivity phenotypes of nine strains of Staphylococcus aureus (two laboratory strains and seven clinical isolates) towards the 72 antimicrobials contained in three PM plates. In general, the PM plates reported on resistance and sensitivity with a high degree of reproducibility. However, a rigorous comparison of PM growth phenotypes with minimum inhibitory concentration (MIC) measurements revealed a trade-off between throughput and accuracy. Small differences in PM growth phenotype did not necessarily correlate with changes in MIC. Thus, we conclude that PM plates are useful for the rapid and high-throughput assessment of large changes in collateral sensitivity phenotypes during the evolution of antimicrobial resistance, but more subtle examples of cross-resistance or collateral sensitivity cannot be identified reliably using this approach.

Application of dynamic modelling techniques to the problem of antibacterial use and resistance: a scoping review

Selective pressure exerted by the widespread use of antibacterial drugs is accelerating the development of resistant bacterial populations. The purpose of this scoping review was to summarise the range of studies that use dynamic models to analyse the problem of bacterial resistance in relation to antibacterial use in human and animal populations. A comprehensive search of the peer-reviewed literature was performed and non-duplicate articles (n = 1486) were screened in several stages. Charting questions were used to extract information from the articles included in the final subset (n = 81). Most studies (86%) represent the system of interest with an aggregate model; individual-based models are constructed in only seven articles. There are few examples of inter-host models outside of human healthcare (41%) and community settings (38%). Resistance is modelled for a non-specific bacterial organism and/or antibiotic in 40% and 74% of the included articles, respectively. Interventions with implications for antibacterial use were investigated in 67 articles and included changes to total antibiotic consumption, strategies for drug management and shifts in category/class use. The quality of documentation related to model assumptions and uncertainty varies considerably across this subset of articles. There is substantial room to improve the transparency of reporting in the antibacterial resistance modelling literature as is recommended by best practice guidelines.

Combined Effects and Cross-Interactions of Different Antibiotics and Polypeptides in Salmonella bredeney

Salmonella spp. are health-threatening foodborne pathogens. The increasingly common spread of antibiotic-resistant Salmonella spp. is a major public healthcare issue worldwide. In this study, we wished to explore (1) antibiotic or polypeptide combinations to inhibit multidrug-resistant Salmonella bredeney and (2) the regulation of cross-resistance and collateral sensitivity of antibiotics and polypeptides. We undertook a study to select antibiotic combinations. Then, we promoted drug-resistant strains of S. bredeney after 15 types of antibiotic treatment. From each evolving population, the S. bredeney strain was exposed to a particular single drug. Then, we analyzed how the evolved S. bredeney strains acquired resistance or susceptibility to other drugs. A total of 105 combinations were tested against S. bredeney following the protocols of CLSI-2016 and EUCAST-2017. The synergistic interactions between drug pairings were diverse. Notably, polypeptides were more likely to be linked to synergistic combinations: 56% (19/34) of the synergistic pairings were relevant to polypeptides. Simultaneously, macrolides demonstrated antagonism toward polypeptides. The latter were more frequently related to collateral sensitivity than the other drugs because the other 13 drugs sensitized S. bredeney to polypeptides. In an experimental evolution involving 15 drugs, single drug-evolved strains were examined against the other 14 drugs, and the results were compared with the minimal inhibitory concentration of the ancestral strain. Single drug-evolved S. bredeney strains could alter the sensitivity to other drugs, and S. bredeney evolution against antibiotics could sensitize it to polypeptides.

Vaccination can drive an increase in frequencies of antibiotic resistance among nonvaccine serotypes of Streptococcus pneumoniae

The bacterial pathogen Streptococcus pneumoniae is a major public health concern, being responsible for more than 1.5 million deaths annually through pneumonia, meningitis, and septicemia. Available vaccines target only a subset of serotypes, so vaccination is often accompanied by a rise in the frequency of nonvaccine serotypes. Epidemiological studies suggest that such a change in serotype frequencies is often coupled with an increase of antibiotic resistance among nonvaccine serotypes. Building on previous multilocus models for bacterial pathogen population structure, we have developed a theoretical framework incorporating variation of serotype and antibiotic resistance to examine how their associations may be affected by vaccination. Using this framework, we find that vaccination can result in a rapid increase in the frequency of preexisting resistant variants of nonvaccine serotypes due to the removal of competition from vaccine serotypes.

Key issues review: evolution on rugged adaptive landscapes

Adaptive landscapes represent a mapping between genotype and fitness. Rugged adaptive landscapes contain two or more adaptive peaks: allele combinations with higher fitness than any of their neighbors in the genetic space. How do populations evolve on such rugged landscapes? Evolutionary biologists have struggled with this question since it was first introduced in the 1930s by Sewall Wright. Discoveries in the fields of genetics and biochemistry inspired various mathematical models of adaptive landscapes. The development of landscape models led to numerous theoretical studies analyzing evolution on rugged landscapes under different biological conditions. The large body of theoretical work suggests that adaptive landscapes are major determinants of the progress and outcome of evolutionary processes. Recent technological advances in molecular biology and microbiology allow experimenters to measure adaptive values of large sets of allele combinations and construct empirical adaptive landscapes for the first time. Such empirical landscapes have already been generated in bacteria, yeast, viruses, and fungi, and are contributing to new insights about evolution on adaptive landscapes. In this Key Issues Review we will: (i) introduce the concept of adaptive landscapes; (ii) review the major theoretical studies of evolution on rugged landscapes; (iii) review some of the recently obtained empirical adaptive landscapes; (iv) discuss recent mathematical and statistical analyses motivated by empirical adaptive landscapes, as well as provide the reader with instructions and source code to implement simulations of evolution on adaptive landscapes; and (v) discuss possible future directions for this exciting field.

The effect of growth medium on an Escherichia coli pathway mirrored into GC/MS profiles

Escherichia coli (E. coli) is a Gram-negative coliform bacterium that is commonly found in the lower intestine of warm-blooded organisms. Most of the strains are harmless but some serotypes are pathogenic, meaning they can cause illness, either diarrhea or illness outside the intestinal tract. The aim of this work is to assess which components are generated for the purpose of E. coli target analysis. In this study, we intend to emphasize the importance of cultivability and to prove that growth media plays a crucial role in bacteria growth. To do this, E. coli was cultivated in three different growth mediums: (a) trypcase soy broth (TSB), (b) Mueller Hinton (MH), and (c) minimal salts (M9) enriched with glucose, respectively. Solid phase micro extraction was used as a sampling method, followed by gas chromatography-mass spectrometry for subsequent analysis. The relevant microbial volatile organic compounds (MVOCs) released in the headspace over the cultures of the E. coli bacteria and the afferent metabolic processes that occur in order to generate these compounds are presented in this work. The characteristic volatile compounds found in E. coli strain emissions were indole, phenylethyl alcohol and a series of esters when it was grown in TSB. Different pyrazines were found (pyrazine, 2-ethyl-3,5-dimethyl-, pyrazine, 2,5-dimethyl- and pyrazine, trimethyl-) when it was cultivated in MH. Long-chain alcohols such as 2-pentadecanol, 9-tetradecen-1-ol and 11-hexadecenol occurred in M9. Dimethyl disulfide, dimethyl trisulfide and a consistent number of alcohols and ketones were observed for E. coli cultivated in all three growth mediums. The occurrence and biosynthesis of these MVOCs clearly denote that the growth media used plays a crucial role in bacterial cultivation. The biomarker chemicals documented from this work may ultimately be used to identify bacterial infections by analyzing exhaled breath.

With a little help from my friends: cooperation can accelerate the rate of adaptive valley crossing

Background

Natural selection favors changes that lead to genotypes possessing high fitness. A conflict arises when several mutations are required for adaptation, but each mutation is separately deleterious. The process of a population evolving from a genotype encoding for a local fitness maximum to a higher fitness genotype is termed an adaptive peak shift.

Results

Here we suggest cooperative behavior as a factor that can facilitate adaptive peak shifts. We model cooperation in a public goods scenario, wherein each individual contributes resources that are later equally redistributed among all cooperating individuals. We use mathematical modeling and stochastic simulations to study the effect of cooperation on peak shifts in both panmictic and structured populations. Our results show that cooperation can substantially affect the rate of complex adaptation. Furthermore, we show that cooperation increases the population diversity throughout the peak shift process, thus increasing the robustness of the population to sudden environmental changes.

Conclusions

We provide a new explanation to adaptive valley crossing in natural populations and suggest that the long term evolution of a species depends on its social behavior.

Electronic supplementary material

The online version of this article (doi:10.1186/s12862-017-0983-2) contains supplementary material, which is available to authorized users.

A new look at the drug-resistance investigation of uropathogenic E. coli strains

Bacterial drug resistance and uropathogenic tract infections are among the most important issues of current medicine. Uropathogenic Escherichia coli strains are the primary factor of this issue. This article is the continuation of the previous study, where we used Kohonen relations to predict the direction of drug resistance. The characterized collection of uropathogenic E. coli strains was used for microbiological (the disc diffusion method for antimicrobial susceptibility testing), chemical (ATR/FT-IR) and mathematical (artificial neural networks, Ward's hierarchical clustering method, the analysis of distributions of inhibition zone diameters for antibiotics, Cohen's kappa measure of agreement) analysis. This study presents other potential tools for the epidemiological differentiation of E. coli strains. It is noteworthy that ATR/FT-IR technique has turned out to be useful for the quick and simple identification of MDR strains. Also, diameter zones of resistance of this E. coli population were compared to the population of E. coli strains published by EUCAST. We observed the bacterial behaviors toward particular antibiotics in comparison to EUCAST bacterial collections. Additionally, we used Cohen's kappa to show which antibiotics from the same class are closely related to each other and which are not. The presented associations between antibiotics may be helpful in selecting the proper therapy directions. Here we present an adaptation of interdisciplinary studies of drug resistance of E. coli strains for epidemiological and clinical investigations. The obtained results may be some indication in deciding on antibiotic therapy.