Christian Munck

Limited dissemination of the wastewater treatment plant core resistome.

Horizontal gene transfer is a major contributor to the evolution of bacterial genomes and can facilitate the dissemination of antibiotic resistance genes between environmental reservoirs and potential pathogens. Wastewater treatment plants (WWTPs) are believed to play a central role in the dissemination of antibiotic resistance genes. However, the contribution of the dominant members of the WWTP resistome to resistance in human pathogens remains poorly understood. Here we use a combination of metagenomic functional selections and comprehensive metagenomic sequencing to uncover the dominant genes of the WWTP resistome. We find that this core resistome is unique to the WWTP environment, with <10% of the resistance genes found outside the WWTP environment. Our data highlight that, despite an abundance of functional resistance genes within WWTPs, only few genes are found in other environments, suggesting that the overall dissemination of the WWTP resistome is comparable to that of the soil resistome.

Prediction of resistance development against drug combinations by collateral responses to component drugs

Collateral sensitivity profiles of drug-evolved lineages can be used to predict drug combinations that suppress evolution of resistance. It Takes Two—But Which Two? Long-term treatment regimens—such as those prescribed for patients with tuberculosis or cancer—often drive development of drug resistance. Treatment with two or more drugs reduces resistance evolution, but not all drug pairs are created equally. Predictive models are needed to permit the rational design of resistance-limiting therapeutic regimens. To design such models, Munck et al. used adaptive evolution and genomic analysis to decipher the resistance response of pathogenic Escherichia coli to 5 different single antibiotics and ten different antibiotic drug pairs. Resistance mutations often occur in key genes influencing the overall homeostasis of the cell. As a result, organisms that develop resistance to one drug sometimes display a greater sensitivity to a second drug (collateral sensitivity). The authors analyzed the genomes of evolved resistant E. coli lineages and pinpointed the mutational events that gave rise to differences in drug-resistance levels and collateral sensitivity and illuminated mechanisms that underlie the development of resistance to drug combinations. Furthermore the authors demonstrate that this effect can be exploited to design drug combinations that limit drug resistance and even select against resistance mutations. The resulting framework sets the stage for rational selection of drug combinations that limit resistance development. Resistance arises quickly during chemotherapeutic selection and is particularly problematic during long-term treatment regimens such as those for tuberculosis, HIV infections, or cancer. Although drug combination therapy reduces the evolution of drug resistance, drug pairs vary in their ability to do so. Thus, predictive models are needed to rationally design resistance-limiting therapeutic regimens. Using adaptive evolution, we studied the resistance response of the common pathogen Escherichia coli to 5 different single antibiotics and all 10 different antibiotic drug pairs. By analyzing the genomes of all evolved E. coli lineages, we identified the mutational events that drive the differences in drug resistance levels and found that the degree of resistance development against drug combinations can be understood in terms of collateral sensitivity and resistance that occurred during adaptation to the component drugs. Then, using engineered E. coli strains, we confirmed that drug resistance mutations that imposed collateral sensitivity were suppressed in a drug pair growth environment. These results provide a framework for rationally selecting drug combinations that limit resistance evolution.

Survival and evolution of a large multidrug resistance plasmid in new clinical bacterial hosts

Large conjugative plasmids are important drivers of bacterial evolution and contribute significantly to the dissemination of antibiotic resistance. Although plasmid borne multidrug resistance is recognized as one of the main challenges in modern medicine, the adaptive forces shaping the evolution of these plasmids within pathogenic hosts are poorly understood. Here we study plasmid–host adaptations following transfer of a 73u2009kb conjugative multidrug resistance plasmid to naïve clinical isolates of Klebsiella pneumoniae and Escherichia coli. We use experimental evolution, mathematical modelling and population sequencing to show that the long-term persistence and molecular integrity of the plasmid is highly influenced by multiple factors within a 25u2009kb plasmid region constituting a host-dependent burden. In the E. coli hosts investigated here, improved plasmid stability readily evolves via IS26 mediated deletions of costly regions from the plasmid backbone, effectively expanding the host-range of the plasmid. Although these adaptations were also beneficial to plasmid persistence in a naïve K. pneumoniae host, they were never observed in this species, indicating that differential evolvability can limit opportunities of plasmid adaptation. While insertion sequences are well known to supply plasmids with adaptive traits, our findings suggest that they also play an important role in plasmid evolution by maintaining the plasticity necessary to alleviate plasmid–host constrains. Further, the observed evolutionary strategy consistently followed by all evolved E. coli lineages exposes a trade-off between horizontal and vertical transmission that may ultimately limit the dissemination potential of clinical multidrug resistance plasmids in these hosts.

Functional metagenomic investigations of the human intestinal microbiota.

The human intestinal microbiota encode multiple critical functions impacting human health, including metabolism of dietary substrate, prevention of pathogen invasion, immune system modulation, and provision of a reservoir of antibiotic resistance genes accessible to pathogens. The complexity of this microbial community, its recalcitrance to standard cultivation, and the immense diversity of its encoded genes has necessitated the development of novel molecular, microbiological, and genomic tools. Functional metagenomics is one such culture-independent technique, used for decades to study environmental microorganisms, but relatively recently applied to the study of the human commensal microbiota. Metagenomic functional screens characterize the functional capacity of a microbial community, independent of identity to known genes, by subjecting the metagenome to functional assays in a genetically tractable host. Here we highlight recent work applying this technique to study the functional diversity of the intestinal microbiota, and discuss how an approach combining high-throughput sequencing, cultivation, and metagenomic functional screens can improve our understanding of interactions between this complex community and its human host.

Prediction of antibiotic resistance: time for a new preclinical paradigm?

Predicting the future is difficult, especially for evolutionary processes that are influenced by numerous unknown factors. Still, this is what is required of drug developers when they assess the risk of resistance arising against a new antibiotic candidate during preclinical development. In this Opinion article, we argue that the traditional procedures that are used for the prediction of antibiotic resistance today could be markedly improved by including a broader analysis of bacterial fitness, infection dynamics, horizontal gene transfer and other factors. This will lead to more informed preclinical decisions for continuing or discontinuing the development of drug candidates.

Collateral Resistance and Sensitivity Modulate Evolution of High-Level Resistance to Drug Combination Treatment in Staphylococcus aureus

As drug-resistant pathogens continue to emerge, combination therapy will increasingly be relied upon to treat infections and to help combat further development of multidrug resistance. At present a dichotomy exists between clinical practice, which favors therapeutically synergistic combinations, and the scientific model emerging from in vitro experimental work, which maintains that this interaction provides greater selective pressure toward resistance development than other interaction types. We sought to extend the current paradigm, based on work below or near minimum inhibitory concentration levels, to reflect drug concentrations more likely to be encountered during treatment. We performed a series of adaptive evolution experiments using Staphylococcus aureus. Interestingly, no relationship between drug interaction type and resistance evolution was found as resistance increased significantly beyond wild-type levels. All drug combinations, irrespective of interaction types, effectively limited resistance evolution compared with monotreatment. Cross-resistance and collateral sensitivity were found to be important factors in the extent of resistance evolution toward a combination. Comparative genomic analyses revealed that resistance to drug combinations was mediated largely by mutations in the same genes as single-drug-evolved lineages highlighting the importance of the component drugs in determining the rate of resistance evolution. Results of this work suggest that the mechanisms of resistance to constituent drugs should be the focus of future resistance evolution work.

Dissemination of antibiotic resistance genes from antibiotic producers to pathogens

It has been hypothesized that some antibiotic resistance genes (ARGs) found in pathogenic bacteria derive from antibiotic-producing actinobacteria. Here we provide bioinformatic and experimental evidence supporting this hypothesis. We identify genes in proteobacteria, including some pathogens, that appear to be closely related to actinobacterial ARGs known to confer resistance against clinically important antibiotics. Furthermore, we identify two potential examples of recent horizontal transfer of actinobacterial ARGs to proteobacterial pathogens. Based on this bioinformatic evidence, we propose and experimentally test a ‘carry-back mechanism for the transfer, involving conjugative transfer of a carrier sequence from proteobacteria to actinobacteria, recombination of the carrier sequence with the actinobacterial ARG, followed by natural transformation of proteobacteria with the carrier-sandwiched ARG. Our results support the existence of ancient and, possibly, recent transfers of ARGs from antibiotic-producing actinobacteria to proteobacteria, and provide evidence for a defined mechanism.

Smoking Cessation and the Microbiome in Induced Sputum Samples from Cigarette Smoking Asthma Patients.

Asthma is a common disease causing cough, wheezing and shortness of breath. It has been shown that the lung microbiota in asthma patients is different from the lung microbiota in healthy controls suggesting that a connection between asthma and the lung microbiome exists. Individuals with asthma who are also tobacco smokers experience more severe asthma symptoms and smoking cessation is associated with improved asthma control. In the present study we investigated if smoking cessation in asthma patients is associated with a change in the bacterial community in the lungs, examined using induced sputum. We found that while tobacco smokers with asthma have a greater bacterial diversity in the induced sputum compared to non-smoking healthy controls, smoking cessation does not lead to a change in the microbial diversity.

Simulating Serial-Target Antibacterial Drug Synergies Using Flux Balance Analysis

Flux balance analysis (FBA) is an increasingly useful approach for modeling the behavior of metabolic systems. However, standard FBA modeling of genetic knockouts cannot predict drug combination synergies observed between serial metabolic targets, even though such synergies give rise to some of the most widely used antibiotic treatments. Here we extend FBA modeling to simulate responses to chemical inhibitors at varying concentrations, by diverting enzymatic flux to a waste reaction. This flux diversion yields very similar qualitative predictions to prior methods for single target activity. However, we find very different predictions for combinations, where flux diversion, which mimics the kinetics of competitive metabolic inhibitors, can explain serial target synergies between metabolic enzyme inhibitors that we confirmed in Escherichia coli cultures. FBA flux diversion opens the possibility for more accurate genome-scale predictions of drug synergies, which can be used to suggest treatments for infections and other diseases.

Adaptive Laboratory Evolution of Antibiotic Resistance Using Different Selection Regimes Lead to Similar Phenotypes and Genotypes

Antibiotic resistance is a global threat to human health, wherefore it is crucial to study the mechanisms of antibiotic resistance as well as its emergence and dissemination. One way to analyze the acquisition of de novo mutations conferring antibiotic resistance is adaptive laboratory evolution. However, various evolution methods exist that utilize different population sizes, selection strengths, and bottlenecks. While evolution in increasing drug gradients guarantees high-level antibiotic resistance promising to identify the most potent resistance conferring mutations, other selection regimes are simpler to implement and therefore allow higher throughput. The specific regimen of adaptive evolution may have a profound impact on the adapted cell state. Indeed, substantial effects of the selection regime on the resulting geno- and phenotypes have been reported in the literature. In this study we compare the geno- and phenotypes of Escherichia coli after evolution to Amikacin, Piperacillin, and Tetracycline under four different selection regimes. Interestingly, key mutations that confer antibiotic resistance as well as phenotypic changes like collateral sensitivity and cross-resistance emerge independently of the selection regime. Yet, lineages that underwent evolution under mild selection displayed a growth advantage independently of the acquired level of antibiotic resistance compared to lineages adapted under maximal selection in a drug gradient. Our data suggests that even though different selection regimens result in subtle genotypic and phenotypic differences key adaptations appear independently of the selection regime.