The Resistome: A Comprehensive Database of Escherichia coli Resistance Phenotypes

Deep mutational scanning reveals the molecular determinants of RNA polymerase-mediated adaptation and tradeoffs

RNA polymerase (RNAP) is emblematic of complex biological systems that control multiple traits involving trade-offs such as growth versus maintenance. Laboratory evolution has revealed that mutations in RNAP subunits, including RpoB, are frequently selected. However, we lack a systems view of how mutations alter the RNAP molecular functions to promote adaptation. We, therefore, measured the fitness of thousands of mutations within a region of rpoB under multiple conditions and genetic backgrounds, to find that adaptive mutations cluster in two modules. Mutations in one module favor growth over maintenance through a partial loss of an interaction associated with faster elongation. Mutations in the other favor maintenance over growth through a destabilized RNAP-DNA complex. The two molecular handles capture the versatile RNAP-mediated adaptations. Combining both interaction losses simultaneously improved maintenance and growth, challenging the idea that growth-maintenance tradeoff resorts only from limited resources, and revealing how compensatory evolution operates within RNAP.

Integrating CRISPR-Enabled Trackable Genome Engineering and Transcriptomic Analysis of Global Regulators for Antibiotic Resistance Selection and Identification in Escherichia coli

The growing threat of antimicrobial resistance poses a serious threat to public health care and motivates efforts to understand the means by which resistance acquisition occurs and how this can be combatted. To address these challenges, we expedited the identification of novel mutations that enable complex phenotypic changes that result in improved tolerance to antibiotics by integrating CREATE and transcriptomic analysis of global regulators. The results give us a better understanding of the mechanisms of resistance to tetracycline antibiotics and aminoglycoside antibiotics and also indicate that the method may be used for quickly identifying resistance-related mutations.

It is important to expedite our understanding of antibiotic resistance to address the increasing numbers of fatalities and environmental pollution due to the emergence of antibiotic resistance and multidrug-resistant strains. Here, we combined the CRISPR-enabled trackable genome engineering (CREATE) technology and transcriptomic analysis to investigate antibiotic tolerance in Escherichia coli. We developed rationally designed site saturation mutagenesis libraries targeting 23 global regulators to identify fitness-conferring mutations in response to diverse antibiotic stresses. We identified seven novel mutations that confer resistance to the ribosome-targeting antibiotics doxycycline, thiamphenicol, and gentamicin in E. coli. To the best of our knowledge, these mutations that we identified have not been reported previously during treatment with the indicated antibiotics. Transcriptome sequencing-based transcriptome analysis was further employed to evaluate the genome-wide changes in gene expression in E. coli for SoxR G121P and cAMP receptor protein (CRP) V140W reconstructions, and improved fitness in response to doxycycline and gentamicin was seen. In the case of doxycycline, we speculated that SoxR G121P significantly increased the expression of genes involved in carbohydrate metabolism and energy metabolism to promote cell growth for improved adaptation. In the CRP V140W mutant with improved gentamicin tolerance, the expression of several amino acid biosynthesis genes and fatty acid degradation genes was significantly changed, and these changes probably altered the cellular energy state to improve adaptation. These findings have important significance for understanding such nonspecific mechanisms of antibiotic resistance and developing new antibacterial drugs.

IMPORTANCE The growing threat of antimicrobial resistance poses a serious threat to public health care and motivates efforts to understand the means by which resistance acquisition occurs and how this can be combatted. To address these challenges, we expedited the identification of novel mutations that enable complex phenotypic changes that result in improved tolerance to antibiotics by integrating CREATE and transcriptomic analysis of global regulators. The results give us a better understanding of the mechanisms of resistance to tetracycline antibiotics and aminoglycoside antibiotics and also indicate that the method may be used for quickly identifying resistance-related mutations.

CRISPR/Cas9 recombineering-mediated deep mutational scanning of essential genes in Escherichia coli

Deep mutational scanning can provide significant insights into the function of essential genes in bacteria. Here, we developed a high-throughput method for mutating essential genes of Escherichia coli in their native genetic context. We used Cas9-mediated recombineering to introduce a library of mutations, created by error-prone PCR, within a gene fragment on the genome using a single gRNA pre-validated for high efficiency. Tracking mutation frequency through deep sequencing revealed biases in the position and the number of the introduced mutations. We overcame these biases by increasing the homology arm length and blocking mismatch repair to achieve a mutation efficiency of 85% for non-essential genes and 55% for essential genes. These experiments also improved our understanding of poorly characterized recombineering process using dsDNA donors with single nucleotide changes. Finally, we applied our technology to target rpoB, the beta subunit of RNA polymerase, to study resistance against rifampicin. In a single experiment, we validate multiple biochemical and clinical observations made in the previous decades and provide insights into resistance compensation with the study of double mutants.

Synthetic Genome Defenses against Selfish DNA Elements Stabilize Engineered Bacteria against Evolutionary Failure

Mobile genetic elements drive evolution by disrupting genes and rearranging genomes. Eukaryotes have evolved epigenetic mechanisms, including DNA methylation and RNA interference, that silence mobile elements and thereby preserve the integrity of their genomes. We created an artificial reprogrammable epigenetic system based on CRISPR interference to give engineered bacteria a similar line of defense against transposons and other selfish elements in their genomes. We demonstrate that this CRISPR interference against mobile elements (CRISPRi-ME) approach can be used to simultaneously repress two different transposon families in Escherichia coli, thereby increasing the evolutionary stability of costly protein expression. We further show that silencing a transposon in Acinetobacter baylyi ADP1 reduces mutation rates by a factor of 5, nearly as much as deleting all copies of this element from its genome. By deploying CRISPRi-ME on a broad-host-range vector, we have created a generalizable platform for stabilizing the genomes of engineered bacterial cells for applications in metabolic engineering and synthetic biology.

Chemogenomic model identifies synergistic drug combinations robust to the pathogen microenvironment

Antibiotics need to be effective in diverse environments in vivo. However, the pathogen microenvironment can have a significant impact on antibiotic potency. Further, antibiotics are increasingly used in combinations to combat resistance, yet, the effect of microenvironments on drug-combination efficacy is unknown. To exhaustively explore the impact of diverse microenvironments on drug-combinations, here we develop a computational framework—Metabolism And GENomics-based Tailoring of Antibiotic regimens (MAGENTA). MAGENTA uses chemogenomic profiles of individual drugs and metabolic perturbations to predict synergistic or antagonistic drug-interactions in different microenvironments. We uncovered antibiotic combinations with robust synergy across nine distinct environments against both E. coli and A. baumannii by searching through 2556 drug-combinations of 72 drugs. MAGENTA also accurately predicted the change in efficacy of bacteriostatic and bactericidal drug-combinations during growth in glycerol media, which we confirmed experimentally in both microbes. Our approach identified genes in glycolysis and glyoxylate pathway as top predictors of synergy and antagonism respectively. Our systems approach enables tailoring of antibiotic therapies based on the pathogen microenvironment.

The antibiotic resistance epidemic has created a pressing need to understand factors that influence antibiotic efficacy. An often-overlooked factor in the search for new treatments is the pathogen environment. Understanding the differences in pathogen sensitivity to antibiotics in lab conditions versus inside the host is necessary for translating new discoveries into the clinic. Hence, we experimentally measured the sensitivity of E. coli to drugs and drug combinations in different metabolic conditions. Our data revealed that the environment dramatically changes treatment potency. Each antibiotic class was affected uniquely by each metabolic condition. The large number of metabolic conditions inside the host greatly complicates the identification of effective therapies. To address this challenge, we present a computational approach called MAGENTA that accurately predicted efficacy of antibiotic regimens in different conditions, which we confirmed experimentally. Furthermore, we show that MAGENTA can be applied to other bacterial pathogens such as A. baumannii and M. tuberculosis without the need for generating expensive data in each organism. MAGENTA accurately predicted efficacy in the pathogen A. baumannii using data from E. coli by identifying genes that are common between the two bacteria. Our study revealed the significant yet predictable impact of environment on drug combination potency.

Genomic Deoxyxylulose Phosphate Reductoisomerase (DXR) Mutations Conferring Resistance to the Antimalarial Drug Fosmidomycin in E. coli

Sequence to activity mapping technologies are rapidly developing, enabling the generation and isolation of mutations conferring novel phenotypes. Here we used the CRISPR enabled trackable genome engineering (CREATE) technology to investigate the inhibition of the essential ispC gene in its native genomic context in Escherichia coli. We created a full saturation library of 33 sites proximal to the ligand binding pocket and challenged this library with the antimalarial drug fosmidomycin, which targets the ispC gene product, DXR. This selection is especially challenging since it is relatively weak in E. coli, with multiple naturally occurring pathways for resistance. We identified several previously unreported mutations that confer fosmidomycin resistance, in highly conserved sites that also exist in pathogens including the malaria-inducing Plasmodium falciparum. This approach may have implications for the isolation of resistance-conferring mutations and may affect the design of future generations of fosmidomycin-based drugs.

Improving the success and impact of the metabolic engineering design, build, test, learn cycle by addressing proteins of unknown function

Rational, predictive metabolic engineering of organisms requires an ability to associate biological activity to the corresponding gene(s). Despite extensive advances in the 20 years since the Escherichia coli genome was published, there are still gaps in our knowledge of protein function. The substantial amount of data that has been published, such as: omics-level characterization in a myriad of conditions; genome-scale libraries; and evolution and genome sequencing, provide means of identifying and prioritizing proteins for characterization. This review describes the scale of this knowledge gap, demonstrates the benefit of addressing the knowledge gap, and demonstrates the availability of interesting candidates for characterization.

The Tolerome: A Database of Transcriptome-Level Contributions to Diverse Escherichia coli Resistance and Tolerance Phenotypes

Tolerance and resistance are complex biological phenotypes that are desirable bioengineering goals for those seeking to design industrial strains or prevent the spread of antibiotic resistance. Over decades of research, a wealth of information has been generated to attempt to decode a molecular basis for tolerance, but to fully achieve the goal of engineering tolerance, researchers must be able to easily learn from a variety of data sources. To this end, we here describe a resource designed to enable scrutiny of diverse tolerance phenotypes. We have curated hundreds of gene expression studies exploring the response of Escherichia coli to chemical and environmental perturbations, from antibiotics to biofuels and solvents and more. Overall, our efforts give rise to a database encompassing more than 56 000 gene expression changes across 89 different stress conditions. This resource is designed for compatibility with the Resistome database, which includes more than 5000 strains with mutations conferring resistance or sensitivity but no transcriptomic data. Thus, the work here results in the first combined resource specialized to tolerance and resistance in E. coli that supports investigations across genomic, transcriptomic, and phenotypic levels. We leverage the database to identify promising bioengineering targets by searching globally across multiple stress conditions as well as by narrowing the focus to fewer conditions of interest, such as biofuel stress and antibiotic stress. We discuss some of the most frequently differentially expressed or coexpressed genes, and predict which transcription factors and sigma factors most likely contribute to gene expression profiles in a wide array of conditions. We also compare profiles from sensitive and resistant strains, gaining knowledge of how responses differ per overrepresented gene ontology terms. Finally, we search for genes that are frequently differentially expressed but not mutated, with the expectation that these may present interesting targets for future engineering efforts. The curated data presented here is publicly available, and should be advantageous to those studying a variety of bacterial tolerance phenotypes.

Refactoring the Genetic Code for Increased Evolvability

The standard genetic code is robust to mutations during transcription and translation. Point mutations are likely to be synonymous or to preserve the chemical properties of the original amino acid. Saturation mutagenesis experiments suggest that in some cases the best-performing mutant requires replacement of more than a single nucleotide within a codon. These replacements are essentially inaccessible to common error-based laboratory engineering techniques that alter a single nucleotide per mutation event, due to the extreme rarity of adjacent mutations. In this theoretical study, we suggest a radical reordering of the genetic code that maximizes the mutagenic potential of single nucleotide replacements. We explore several possible genetic codes that allow a greater degree of accessibility to the mutational landscape and may result in a hyperevolvable organism that could serve as an ideal platform for directed evolution experiments. We then conclude by evaluating the challenges of constructing such recoded organisms and their potential applications within the field of synthetic biology.

The conservative nature of the genetic code prevents bioengineers from efficiently accessing the full mutational landscape of a gene via common error-prone methods. Here, we present two computational approaches to generate alternative genetic codes with increased accessibility. These new codes allow mutational transitions to a larger pool of amino acids and with a greater extent of chemical differences, based on a single nucleotide replacement within the codon, thus increasing evolvability both at the single-gene and at the genome levels. Given the widespread use of these techniques for strain and protein improvement, along with more fundamental evolutionary biology questions, the use of recoded organisms that maximize evolvability should significantly improve the efficiency of directed evolution, library generation, and fitness maximization.

Insights on Osmotic Tolerance Mechanisms in Escherichia coli Gained from an rpoC Mutation

An 84 bp in-frame duplication (K370_A396dup) within the rpoC subunit of RNA polymerase was found in two independent mutants selected during an adaptive laboratory evolution experiment under osmotic stress in Escherichia coli, suggesting that this mutation confers improved osmotic tolerance. To determine the role this mutation in rpoC plays in osmotic tolerance, we reconstructed the mutation in BW25113, and found it to confer improved tolerance to hyperosmotic stress. Metabolite analysis, exogenous supplementation assays, and cell membrane damage analysis suggest that the mechanism of improved osmotic tolerance by this rpoC mutation may be related to the higher production of acetic acid and amino acids such as proline, and increased membrane integrity in the presence of NaCl stress in exponential phase cells. Transcriptional analysis led to the findings that the overexpression of methionine related genes metK and mmuP improves osmotic tolerance in BW25113. Furthermore, deletion of a stress related gene bolA was found to confer enhanced osmotic tolerance in BW25113 and MG1655. These findings expand our current understanding of osmotic tolerance in E. coli, and have the potential to expand the utilization of high saline feedstocks and water sources in microbial fermentation.

Toward a quantitative understanding of antibiotic resistance evolution

The rising prevalence of antibiotic resistant bacteria is an increasingly serious public health challenge. To address this problem, recent work ranging from clinical studies to theoretical modeling has provided valuable insights into the mechanisms of resistance, its emergence and spread, and ways to counteract it. A deeper understanding of the underlying dynamics of resistance evolution will require a combination of experimental and theoretical expertise from different disciplines and new technology for studying evolution in the laboratory. Here, we review recent advances in the quantitative understanding of the mechanisms and evolution of antibiotic resistance. We focus on key theoretical concepts and new technology that enables well-controlled experiments. We further highlight key challenges that can be met in the near future to ultimately develop effective strategies for combating resistance.