Morten Otto Alexander Sommer

The shared antibiotic resistome of soil bacteria and human pathogens

From Farm to Clinic? Soil organisms have long been assumed to be an important source of antibiotic resistance genes, in part because of antibiotic-treated livestock and in part because of the natural ecology of antibiotic production in the soil. Forsberg et al. (p. 1107) developed a metagenomic protocol to assemble short-read sequence data after antibiotic selection experiments, using 12 different drugs in all antibiotic classes, and compared antibiotic resistance gene sequences between soil bacteria and clinically occurring pathogens. Sixteen sequences, representing seven gene products, were discovered in farmland soil bacteria within long stretches of perfect nucleotide identity with pathogenic proteobacteria. Perfect identity between antibiotic resistance genes in farmland soil bacteria and human pathogens suggests direct transfer. Soil microbiota represent one of the ancient evolutionary origins of antibiotic resistance and have been proposed as a reservoir of resistance genes available for exchange with clinical pathogens. Using a high-throughput functional metagenomic approach in conjunction with a pipeline for the de novo assembly of short-read sequence data from functional selections (termed PARFuMS), we provide evidence for recent exchange of antibiotic resistance genes between environmental bacteria and clinical pathogens. We describe multidrug-resistant soil bacteria containing resistance cassettes against five classes of antibiotics (β-lactams, aminoglycosides, amphenicols, sulfonamides, and tetracyclines) that have perfect nucleotide identity to genes from diverse human pathogens. This identity encompasses noncoding regions as well as multiple mobilization sequences, offering not only evidence of lateral exchange but also a mechanism by which antibiotic resistance disseminates.

Functional Characterization of the Antibiotic Resistance Reservoir in the Human Microflora

Hidden Pockets of Resistance Groups of bacteria indulge in gene swapping at frequencies correlated with prevailing selection pressures and phylogenetic relatedness. When assaulted by antibiotics, antibiotic resistance genes become favored currency for exchange among bacteria. During sequencing of human gut microflora, Sommer et al. (p. 1128) found a very large reservoir of distinct genes that, when put into Escherichia coli, conferred resistance to a wide range of drugs. By contrast, analysis of the culturable aerobic gut microbiome, which constitutes a tiny fraction of the entire gut flora, revealed resistance genes highly similar to those harbored by human pathogens. Although there is a risk of novel modes of antibiotic resistance emerging from this reservoir, because they are evolutionarily distant, gene transfer between pathogens and the poorly known majority of the microbiome might actually be quite restricted. Large numbers of previously unidentified antibiotic resistance genes occur in gut bacteria. To understand the process by which antibiotic resistance genes are acquired by human pathogens, we functionally characterized the resistance reservoir in the microbial flora of healthy individuals. Most of the resistance genes we identified using culture-independent sampling have not been previously identified and are evolutionarily distant from known resistance genes. By contrast, nearly half of the resistance genes we identified in cultured aerobic gut isolates (a small subset of the gut microbiome) are identical to resistance genes harbored by major pathogens. The immense diversity of resistance genes in the human microbiome could contribute to future emergence of antibiotic resistance in human pathogens.

Bacteria Subsisting on Antibiotics

Antibiotics are a crucial line of defense against bacterial infections. Nevertheless, several antibiotics are natural products of microorganisms that have as yet poorly appreciated ecological roles in the wider environment. We isolated hundreds of soil bacteria with the capacity to grow on antibiotics as a sole carbon source. Of 18 antibiotics tested, representing eight major classes of natural and synthetic origin, 13 to 17 supported the growth of clonal bacteria from each of 11 diverse soils. Bacteria subsisting on antibiotics are surprisingly phylogenetically diverse, and many are closely related to human pathogens. Furthermore, each antibiotic-consuming isolate was resistant to multiple antibiotics at clinically relevant concentrations. This phenomenon suggests that this unappreciated reservoir of antibiotic-resistance determinants can contribute to the increasing levels of multiple antibiotic resistance in pathogenic bacteria.

Evolutionary dynamics of bacteria in a human host environment

Laboratory evolution experiments have led to important findings relating organism adaptation and genomic evolution. However, continuous monitoring of long-term evolution has been lacking for natural systems, limiting our understanding of these processes in situ. Here we characterize the evolutionary dynamics of a lineage of a clinically important opportunistic bacterial pathogen, Pseudomonas aeruginosa, as it adapts to the airways of several individual cystic fibrosis patients over 200,000 bacterial generations, and provide estimates of mutation rates of bacteria in a natural environment. In contrast to predictions based on in vitro evolution experiments, we document limited diversification of the evolving lineage despite a highly structured and complex host environment. Notably, the lineage went through an initial period of rapid adaptation caused by a small number of mutations with pleiotropic effects, followed by a period of genetic drift with limited phenotypic change and a genomic signature of negative selection, suggesting that the evolving lineage has reached a major adaptive peak in the fitness landscape. This contrasts with previous findings of continued positive selection from long-term in vitro evolution experiments. The evolved phenotype of the infecting bacteria further suggests that the opportunistic pathogen has transitioned to become a primary pathogen for cystic fibrosis patients.

Use of Collateral Sensitivity Networks to Design Drug Cycling Protocols That Avoid Resistance Development

Elucidation of complex collateral sensitivity networks provides rationale for new sequential drug deployment strategies that restrain antibiotic resistance development. Resistance is Futile In an emergency situation, people are often instructed to remain calm and proceed in an orderly fashion. The same advice may apply to the current antibiotic-resistance crisis. Imamovic and Sommer now show how collateral sensitivity profiles—deciphered by treating bacteria with multiple antibiotics—can help to order drug deployment in sequences that thwart the development of antibiotic resistance. Cells or organisms that have developed resistance to one drug sometimes display a greater sensitivity to a second drug often from a distinct structural class, a concept called collateral sensitivity. The authors tested whether application of this concept can aid in the management of bacterial infections by evolving resistance in Escherichia coli to 23 known antibiotics and then mapping the resulting collateral sensitivity and resistance profiles. On the basis of their derived collateral sensitivity network, the authors designed a new treatment framework—collateral sensitivity cycling—in which sequential treatment of E. coli cultures with antibiotics that display compatible collateral sensitivity profiles is predicted to select against drug-resistance development. The authors chronicled hundreds of such drug sets and validated their predictions E. coli and the antibiotics gentamicin and cefuroxime by showing that cyclical deployment of the drugs selected against resistance to either antibiotic. This proof of principle for collateral sensitivity cycling as a sustainable treatment regimen was then validated with two related bacterial pathogens. It remains to be seen whether deployment of cancer therapeutics in an orderly fashion also curbs drug resistance. New drug deployment strategies are imperative to address the problem of drug resistance, which is limiting the management of infectious diseases and cancers. We evolved resistance in Escherichia coli toward 23 drugs used clinically for treating bacterial infections and mapped the resulting collateral sensitivity and resistance profiles, revealing a complex collateral sensitivity network. On the basis of these data, we propose a new treatment framework—collateral sensitivity cycling—in which drugs with compatible collateral sensitivity profiles are used sequentially to treat infection and select against drug resistance development. We identified hundreds of such drug sets and demonstrated that the antibiotics gentamicin and cefuroxime can be deployed cyclically such that the treatment regimen selected against resistance to either drug. We then validated our findings with related bacterial pathogens. These results provide proof of principle for collateral sensitivity cycling as a sustainable treatment paradigm that may be generally applicable to infectious diseases and cancer.

The human microbiome harbors a diverse reservoir of antibiotic resistance genes.

The increasing levels of multi-drug resistance in human pathogenic bacteria are compromising our ability to treat infectious disease. Since antibiotic resistance determinants are readily exchanged between bacteria through lateral gene transfer, there is an increasing interest in investigating reservoirs of antibiotic resistance accessible to pathogens. Due to the high likelihood of contact and genetic exchange with pathogens during disease progression, the human microflora warrants special attention as perhaps the most accessible reservoir of resistance genes. Indeed, numerous previous studies have demonstrated substantial antibiotic resistance in cultured isolates from the human microflora. By applying metagenomic functional selections, we recently demonstrated that the functional repertoire of resistance genes in the human microbiome is much more diverse than suggested using previous culture-dependent methods. We showed that many resistance genes from cultured proteobacteria from human fecal samples are identical to resistance genes harbored by human pathogens, providing strong support for recent genetic exchange of this resistance machinery. In contrast, most of the resistance genes we identified with culture independent metagenomic sampling from the same samples were novel when compared to all known genes in public databases. While this clearly demonstrates that the antibiotic resistance reservoir of the large fraction of the human microbiome recalcitrant to culturing is severely under sampled, it may also suggest that barriers exist to lateral gene transfer between these bacteria and readily cultured human pathogens. If we hope to turn the tide against multidrug resistant infections, we must urgently commit to quantitatively characterizing the resistance reservoirs encoded by our diverse human microbiomes, with a particular focus on routes of exchange of these reservoirs with other microbial communities.

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.

CRMAGE: CRISPR Optimized MAGE Recombineering

A bottleneck in metabolic engineering and systems biology approaches is the lack of efficient genome engineering technologies. Here, we combine CRISPR/Cas9 and λ Red recombineering based MAGE technology (CRMAGE) to create a highly efficient and fast method for genome engineering of Escherichia coli. Using CRMAGE, the recombineering efficiency was between 96.5% and 99.7% for gene recoding of three genomic targets, compared to between 0.68% and 5.4% using traditional recombineering. For modulation of protein synthesis (small insertion/RBS substitution) the efficiency was increased from 6% to 70%. CRMAGE can be multiplexed and enables introduction of at least two mutations in a single round of recombineering with similar efficiencies. PAM-independent loci were targeted using degenerate codons, thereby making it possible to modify any site in the genome. CRMAGE is based on two plasmids that are assembled by a USER-cloning approach enabling quick and cost efficient gRNA replacement. CRMAGE furthermore utilizes CRISPR/Cas9 for efficient plasmid curing, thereby enabling multiple engineering rounds per day. To facilitate the design process, a web-based tool was developed to predict both the λ Red oligos and the gRNAs. The CRMAGE platform enables highly efficient and fast genome editing and may open up promising prospective for automation of genome-scale engineering.

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.

Evolution of Escherichia coli to 42°C and Subsequent Genetic Engineering Reveals Adaptive Mechanisms and Novel Mutations

Adaptive laboratory evolution (ALE) has emerged as a valuable method by which to investigate microbial adaptation to a desired environment. Here, we performed ALE to 42 °C of ten parallel populations of Escherichia coli K-12 MG1655 grown in glucose minimal media. Tightly controlled experimental conditions allowed selection based on exponential-phase growth rate, yielding strains that uniformly converged toward a similar phenotype along distinct genetic paths. Adapted strains possessed as few as 6 and as many as 55 mutations, and of the 144 genes that mutated in total, 14 arose independently across two or more strains. This mutational recurrence pointed to the key genetic targets underlying the evolved fitness increase. Genome engineering was used to introduce the novel ALE-acquired alleles in random combinations into the ancestral strain, and competition between these engineered strains reaffirmed the impact of the key mutations on the growth rate at 42 °C. Interestingly, most of the identified key gene targets differed significantly from those found in similar temperature adaptation studies, highlighting the sensitivity of genetic evolution to experimental conditions and ancestral genotype. Additionally, transcriptomic analysis of the ancestral and evolved strains revealed a general trend for restoration of the global expression state back toward preheat stressed levels. This restorative effect was previously documented following evolution to metabolic perturbations, and thus may represent a general feature of ALE experiments. The widespread evolved expression shifts were enabled by a comparatively scant number of regulatory mutations, providing a net fitness benefit but causing suboptimal expression levels for certain genes, such as those governing flagellar formation, which then became targets for additional ameliorating mutations. Overall, the results of this study provide insight into the adaptation process and yield lessons important for the future implementation of ALE as a tool for scientific research and engineering.