Enhanced killing of antibiotic-resistant bacteria enabled by massively parallel combinatorial genetics

Integrating bacterial molecular genetics with chemical biology for renewed antibacterial drug discovery

The application of dyes to understanding the aetiology of infection inspired antimicrobial chemotherapy and the first wave of antibacterial drugs. The second wave of antibacterial drug discovery was driven by rapid discovery of natural products, now making up 69% of current antibacterial drugs. But now with the most prevalent natural products already discovered, ∼107 new soil-dwelling bacterial species must be screened to discover one new class of natural product. Therefore, instead of a third wave of antibacterial drug discovery, there is now a discovery bottleneck. Unlike natural products which are curated by billions of years of microbial antagonism, the vast synthetic chemical space still requires artificial curation through the therapeutics science of antibacterial drugs - a systematic understanding of how small molecules interact with bacterial physiology, effect desired phenotypes, and benefit the host. Bacterial molecular genetics can elucidate pathogen biology relevant to therapeutics development, but it can also be applied directly to understanding mechanisms and liabilities of new chemical agents with new mechanisms of action. Therefore, the next phase of antibacterial drug discovery could be enabled by integrating chemical expertise with systematic dissection of bacterial infection biology. Facing the ambitious endeavour to find new molecules from nature or new-to-nature which cure bacterial infections, the capabilities furnished by modern chemical biology and molecular genetics can be applied to prospecting for chemical modulators of new targets which circumvent prevalent resistance mechanisms.

A high-throughput synthetic biology approach for studying combinatorial chromatin-based transcriptional regulation

The construction of synthetic gene circuits requires the rational combination of multiple regulatory components, but predicting their behavior can be challenging due to poorly understood component interactions and unexpected emergent behaviors. In eukaryotes, chromatin regulators (CRs) are essential regulatory components that orchestrate gene expression. Here, we develop a screening platform to investigate the impact of CR pairs on transcriptional activity in yeast. We construct a combinatorial library consisting of over 1,900 CR pairs and use a high-throughput workflow to characterize the impact of CR co-recruitment on gene expression. We recapitulate known interactions and discover several instances of CR pairs with emergent behaviors. We also demonstrate that supervised machine learning models trained with low-dimensional amino acid embeddings accurately predict the impact of CR co-recruitment on transcriptional activity. This work introduces a scalable platform and machine learning approach that can be used to study how networks of regulatory components impact gene expression.

Improved Combinatorial Assembly and Barcode Sequencing for Gene-Sized DNA Constructs

Synergistic and supportive interactions among genes can be incorporated in engineering biology to enhance and stabilize the performance of biological systems, but combinatorial numerical explosion challenges the analysis of multigene interactions. The incorporation of DNA barcodes to mark genes coupled with next-generation sequencing offers a solution to this challenge. We describe improvements for a key method in this space, CombiGEM, to broaden its application to assembling typical gene-sized DNA fragments and to reduce the cost of sequencing for prevalent small-scale projects. The expanded reach of the method beyond currently targeted small RNA genes promotes the discovery and incorporation of gene synergy in natural and engineered processes such as biocontainment, the production of desired compounds, and previously uncharacterized fundamental biological mechanisms.

Powering up antifungal treatment: using small molecules to unlock the potential of existing therapies

Fungal pathogens are increasingly appreciated as a significant infectious disease challenge. Compared to bacteria, fungal cells are more closely related to human cells, and few classes of antifungal drugs are available. Combination therapy offers a potential solution to reduce the likelihood of resistance acquisition and extend the lifespan of existing antifungals. There has been recent interest in combining first-line drugs with small-molecule adjuvants. In a recent article, Alabi et al. identified 1,4-benzodiazepines as promising molecules to enhance azole activity in pathogenic Candida spp. (P. E. Alabi, C. Gautier, T. P. Murphy, X. Gu, M. Lepas, V. Aimanianda, J. K. Sello, I. V. Ene, 2023, mBio https://doi.org/10.1128/mbio.00479-23). These molecules have no antifungal activity on their own but exhibited significant potentiation of fluconazole in azole-susceptible and -resistant isolates. Additionally, the 1,4-benzodiazepines increased the fungicidal activity of azoles that are typically fungistatic to Candida spp., inhibited filamentation (a virulence-associated trait), and accordingly increased host survival in Galleria mellonella. This research thus provides another encouraging step on the critical pathway toward reducing mortality due to antimicrobial resistance.

Mechanism of Action of Isopropoxy Benzene Guanidine against Multidrug-Resistant Pathogens

The increasing emergence of antibiotic resistance is an urgent threat to global health care; thus, there is a need for new therapeutics. Guanidine is the preferred functional group for antimicrobial design and development. Herein, the potential antibacterial activity of the guanidine derivative isopropoxy benzene guanidine (IBG) against multidrug-resistant (MDR) bacteria was discovered. The synergistic antibacterial activity of IBG and colistin was determined by checkerboard assay, time-killing curve, and mouse experiments. The antibacterial mechanism of IBG was verified in fluorescent probe experiments, intracellular oxidative phosphorylation assays, and transcriptome analysis. The results showed that IBG displays efficient antibacterial activity against Gram-positive pathogens and Gram-negative pathogens with permeabilized outer membranes. Further mechanistic studies showed that IBG triggers cytoplasmic membrane damage by binding to phosphatidylglycerol and cardiolipin, leading to the dissipation of proton motive force and accumulation of intracellular ATP. IBG combined with low levels of colistin enhances bacterial outer membrane permeability and increases the accumulation of reactive oxygen species, as further evidenced by transcriptome analysis. Furthermore, the efficacy of IBG with colistin against MDR Escherichia coli in three infection models was demonstrated. Together, these results suggest that IBG is a promising adjuvant of colistin, providing an alternative approach to address the prevalent infections caused by MDR Gram-negative pathogens. IMPORTANCE As antibiotic discovery stagnates, the world is facing a growing menace from the emergence of bacteria that are resistant to almost all available antibiotics. The key to winning this race is to explore distinctive mechanisms of antibiotics. Thus, novel efficient antibacterial agents and alternative strategies are urgently required to fill the void in antibiotic development. Compared with the large amount of money and time required to develop new agents, the antibiotic adjuvant strategy is a promising approach to inhibit bacterial resistance and increase killing of bacteria. In this study, we found that the guanidine derivatives IBG not only displayed efficient antibacterial activities against Gram-positive bacteria but also restored colistin susceptibility of Gram-negative pathogens as an antibiotic adjuvant. More in-depth study showed that IBG is a potential lead to overcome antibiotic resistance, providing new insight into future antibiotic discovery and development.

Encoding Genetic Circuits with DNA Barcodes Paves the Way for Machine Learning-Assisted Metabolite Biosensor Response Curve Profiling in Yeast

Genetically encoded biosensors are valuable tools used in the precise engineering of metabolism. Although a large number of biosensors have been developed, the fine-tuning of their dose-response curves, which promotes the applications of biosensors in various scenarios, still remains challenging. To address this issue, we leverage a DNA trackable assembly method and fluorescence-activated cell sorting coupled with next-generation sequencing (FACS-seq) technology to set up a novel workflow for construction and comprehensive characterization of thousands of biosensors in a massively parallel manner. An FapR-fapO-based malonyl-CoA biosensor was used as proof of concept to construct a trackable combinatorial library, containing 5184 combinations with 6 levels of transcription factor dosage, 4 different operator positions, and 216 possible upstream enhancer sequence (UAS) designs. By applying the FACS-seq technique, the response curves of 2632 biosensors out of 5184 combinations were successfully characterized to provide large-scale genotype-phenotype association data of the designed biosensors. Finally, machine-learning algorithms were applied to predict the genotype-phenotype relationships of the uncharacterized combinations to generate a panoramic scanning map of the combinatorial space. With the assistance of our novel workflow, a malonyl-CoA biosensor with the largest dynamic response range was successfully obtained. Moreover, feature importance analysis revealed that the recognition sequence insertion scheme and the choice of UAS have a significant impact on the dynamic range. Taken together, our pipeline provides a platform for the design, tuning, and profiling of biosensor response curves and shows great potential to facilitate the rational design of genetic circuits.

Genetic interaction analysis in microbial pathogens: unravelling networks of pathogenesis, antimicrobial susceptibility and host interactions

Genetic interaction (GI) analysis is a powerful genetic strategy that analyzes the fitness and phenotypes of single- and double-gene mutant cells in order to dissect the epistatic interactions between genes, categorize genes into biological pathways, and characterize genes of unknown function. GI analysis has been extensively employed in model organisms for foundational, systems-level assessment of the epistatic interactions between genes. More recently, GI analysis has been applied to microbial pathogens and has been instrumental for the study of clinically important infectious organisms. Here, we review recent advances in systems-level GI analysis of diverse microbial pathogens, including bacterial and fungal species. We focus on important applications of GI analysis across pathogens, including GI analysis as a means to decipher complex genetic networks regulating microbial virulence, antimicrobial drug resistance and host-pathogen dynamics, and GI analysis as an approach to uncover novel targets for combination antimicrobial therapeutics. Together, this review bridges our understanding of GI analysis and complex genetic networks, with applications to diverse microbial pathogens, to further our understanding of virulence, the use of antimicrobial therapeutics and host-pathogen interactions. .

Comprehensive genetic analysis of adhesin proteins and their role in virulence of Candida albicans

Candida albicans is a microbial fungus that exists as a commensal member of the human microbiome and an opportunistic pathogen. Cell surface-associated adhesin proteins play a crucial role in C. albicans' ability to undergo cellular morphogenesis, develop robust biofilms, colonize, and cause infection in a host. However, a comprehensive analysis of the role and relationships between these adhesins has not been explored. We previously established a CRISPR-based platform for efficient generation of single- and double-gene deletions in C. albicans, which was used to construct a library of 144 mutants, comprising 12 unique adhesin genes deleted singly, and every possible combination of double deletions. Here, we exploit this adhesin mutant library to explore the role of adhesin proteins in C. albicans virulence. We perform a comprehensive, high-throughput screen of this library, using Caenorhabditis elegans as a simplified model host system, which identified mutants critical for virulence and significant genetic interactions. We perform follow-up analysis to assess the ability of high- and low-virulence strains to undergo cellular morphogenesis and form biofilms in vitro, as well as to colonize the C. elegans host. We further perform genetic interaction analysis to identify novel significant negative genetic interactions between adhesin mutants, whereby combinatorial perturbation of these genes significantly impairs virulence, more than expected based on virulence of the single mutant constituent strains. Together, this study yields important new insight into the role of adhesins, singly and in combinations, in mediating diverse facets of virulence of this critical fungal pathogen.

Carbapenemases in Enterobacteriaceae: Detection and Antimicrobial Therapy

Carbapenem-resistant Enterobacteriaceae (CRE) have spread rapidly around the world in the past few years, posing great challenges to human health. The plasmid-mediated horizontal transmission of carbapenem-resistance genes is the main cause of the surge in the prevalence of CRE. Therefore, the timely and accurate detection of CRE, especially carbapenemase-producing Enterobacteriaceae, is very important for the clinical prevention and treatment of these infections. A variety of methods for the rapid detection of CRE phenotypes and genotypes have been developed for use in clinical microbiology laboratories. To overcome the lack of efficient antibiotics, CRE infections are often treated with combination therapies. Moreover, novel drugs and emerging strategies appeared successively and in various stages of development. In this article, we summarized the global distribution of various carbapenemases. And we focused on summarizing and comparing the advantages and limitations of the detection methods and the therapeutic strategies of CRE primarily.

Combinatorial mutagenesis en masse optimizes the genome editing activities of SpCas9

The combined effect of multiple mutations on protein function is hard to predict; thus, the ability to functionally assess a vast number of protein sequence variants would be practically useful for protein engineering. Here we present a high-throughput platform that enables scalable assembly and parallel characterization of barcoded protein variants with combinatorial modifications. We demonstrate this platform, which we name CombiSEAL, by systematically characterizing a library of 948 combination mutants of the widely used Streptococcus pyogenes Cas9 (SpCas9) nuclease to optimize its genome-editing activity in human cells. The ease with which the editing activities of the pool of SpCas9 variants can be assessed at multiple on- and off-target sites accelerates the identification of optimized variants and facilitates the study of mutational epistasis. We successfully identify Opti-SpCas9, which possesses enhanced editing specificity without sacrificing potency and broad targeting range. This platform is broadly applicable for engineering proteins through combinatorial modifications en masse.

Multiplexed deactivated CRISPR-Cas9 gene expression perturbations deter bacterial adaptation by inducing negative epistasis

The ever-increasing threat of multi-drug resistant bacteria, a shrinking antibiotic pipeline, and the innate ability of microorganisms to adapt necessitates long-term strategies to slow the evolution of antibiotic resistance. Here we develop an approach, dubbed Controlled Hindrance of Adaptation of OrganismS or CHAOS, involving induction of epistasis between gene perturbations to deter adaption. We construct a combinatorial library of multiplexed, deactivated CRISPR-Cas9 devices to systematically perturb gene expression in Escherichia coli. While individual perturbations improved fitness during antibiotic exposure, multiplexed perturbations caused large fitness loss in a significant epistatic fashion. Strains exhibiting epistasis adapted significantly more slowly over three to fourteen days, and loss in adaptive potential was shown to be sustainable. Finally, we show that multiplexed peptide nucleic acids increase the antibiotic susceptibility of clinically isolated Carbapenem-resistant E. coli in an epistatic fashion. Together, these results suggest a new therapeutic strategy for restricting the evolution of antibiotic resistance.

Peter Otoupal et al. present CHAOS, an approach for preventing the development of antibiotic resistance in E. coli through CRISPR-Cas9-based perturbation of gene expression. They show that multiplexed perturbations decrease fitness of clinically-isolated Carbapenem-resistant E. coli upon antibiotic exposure.

Construction of an overexpression library for Mycobacterium tuberculosis

There is a pressing need to develop novel anti-tubercular drugs. High-throughput phenotypic screening yields chemical series that inhibit bacterial growth. Target identification for such series is challenging, but necessary for optimization of target engagement and the development of series into clinical drugs. We constructed a library of recombinant Mycobacterium tuberculosis strains each expressing a single protein from an inducible promoter as a tool for target identification. The library of 1733 clones was arrayed in 96-well plates for rapid screening and monitoring growth. The library contains the majority of the annotated essential genes as well as genes involved in cell wall and fatty acid biosynthesis, virulence factors, regulatory proteins, efflux, and respiration pathways. We evaluated the growth kinetics and plasmid stability over three passages for each clone in the library. We determined expression levels (mRNA and/or protein) in 396 selected clones. We screened the entire library and identified the Alr-expressing clone as the only recombinant strain, which grew in the presence of d-cycloserine (DCS). We confirmed that the Alr-expressing clone was resistant to DCS (7-fold shift in minimum inhibitory concentration). The library represents a new tool that can be used to screen for compound resistance and other phenotypes.

Balancing gene expression without library construction via a reusable sRNA pool

Balancing protein expression is critical when optimizing genetic systems. Typically, this requires library construction to vary the genetic parts controlling each gene, which can be expensive and time-consuming. Here, we develop sRNAs corresponding to 15nt ‘target’ sequences that can be inserted upstream of a gene. The targeted gene can be repressed from 1.6- to 87-fold by controlling sRNA expression using promoters of different strength. A pool is built where six sRNAs are placed under the control of 16 promoters that span a ∼10³-fold range of strengths, yielding ∼10⁷ combinations. This pool can simultaneously optimize up to six genes in a system. This requires building only a single system-specific construct by placing a target sequence upstream of each gene and transforming it with the pre-built sRNA pool. The resulting library is screened and the top clone is sequenced to determine the promoter controlling each sRNA, from which the fold-repression of the genes can be inferred. The system is then rebuilt by rationally selecting parts that implement the optimal expression of each gene. We demonstrate the versatility of this approach by using the same pool to optimize a metabolic pathway (β-carotene) and genetic circuit (XNOR logic gate).

Quorum-Quenching Human Designer Cells for Closed-Loop Control of Pseudomonas aeruginosa Biofilms

Current antibiotics gradually lose their efficacy against chronic Pseudomonas aeruginosa infections due to development of increased resistance mediated by biofilm formation, as well as the large arsenal of microbial virulence factors that are coordinated by the cell density-dependent phenomenon of quorum sensing. Here, we address this issue by using synthetic biology principles to rationally engineer quorum-quencher cells with closed-loop control to autonomously dampen virulence and interfere with biofilm integrity. Pathogen-derived signals dynamically activate a synthetic mammalian autoinducer sensor driving downstream expression of next-generation anti-infectives. Engineered cells were able to sensitively score autoinducer levels from P. aeruginosa clinical isolates and mount a 2-fold defense consisting of an autoinducer-inactivating enzyme to silence bacterial quorum sensing and a bipartite antibiofilm effector to dissolve the biofilm matrix. The self-guided cellular device fully cleared autoinducers, potentiated bacterial antibiotic susceptibility, substantially reduced biofilms, and alleviated cytotoxicity to lung epithelial cells. We believe this strategy of dividing otherwise coordinated pathogens and breaking up their shielded stronghold represents a blueprint for cellular anti-infectives in the postantibiotic era.

Metabolic constraints on the evolution of antibiotic resistance

Despite our continuous improvement in understanding antibiotic resistance, the interplay between natural selection of resistance mutations and the environment remains unclear. To investigate the role of bacterial metabolism in constraining the evolution of antibiotic resistance, we evolved Escherichia coli growing on glycolytic or gluconeogenic carbon sources to the selective pressure of three different antibiotics. Profiling more than 500 intracellular and extracellular putative metabolites in 190 evolved populations revealed that carbon and energy metabolism strongly constrained the evolutionary trajectories, both in terms of speed and mode of resistance acquisition. To interpret and explore the space of metabolome changes, we developed a novel constraint‐based modeling approach using the concept of shadow prices. This analysis, together with genome resequencing of resistant populations, identified condition‐dependent compensatory mechanisms of antibiotic resistance, such as the shift from respiratory to fermentative metabolism of glucose upon overexpression of efflux pumps. Moreover, metabolome‐based predictions revealed emerging weaknesses in resistant strains, such as the hypersensitivity to fosfomycin of ampicillin‐resistant strains. Overall, resolving metabolic adaptation throughout antibiotic‐driven evolutionary trajectories opens new perspectives in the fight against emerging antibiotic resistance.

Current Trends in Multidrug Optimization: An Alley of Future Successful Treatment of Complex Disorders

The identification of effective and long-lasting cancer therapies still remains elusive, partially due to patient and tumor heterogeneity, acquired drug resistance, and single-drug dose-limiting toxicities. The use of drug combinations may help to overcome some limitations of current cancer therapies by challenging the robustness and redundancy of biological processes. However, effective drug combination optimization requires the careful consideration of numerous parameters. The complexity of this optimization problem is clearly nontrivial and likely requires the assistance of advanced heuristic optimization techniques. In the current review, we discuss the application of optimization techniques for the identification of optimal drug combinations. More specifically, we focus on the application of phenotype-based screening approaches in the field of cancer therapy. These methods are divided into three categories: (1) modeling methods, (2) model-free approaches based on biological search algorithms, and (3) merged approaches, particularly phenotypically driven network biology methods and computation network models relying on phenotypic data. In addition to a brief description of each approach, we include a critical discussion of the advantages and disadvantages of each method, with a strong focus on the limitations and considerations needed to successfully apply such methods in biological research.

Current Trends in Multidrug Optimization

The identification of effective and long-lasting cancer therapies still remains elusive, partially due to patient and tumor heterogeneity, acquired drug resistance, and single-drug dose-limiting toxicities. The use of drug combinations may help to overcome some limitations of current cancer therapies by challenging the robustness and redundancy of biological processes. However, effective drug combination optimization requires the careful consideration of numerous parameters. The complexity of this optimization problem is clearly nontrivial and likely requires the assistance of advanced heuristic optimization techniques. In the current review, we discuss the application of optimization techniques for the identification of optimal drug combinations. More specifically, we focus on the application of phenotype-based screening approaches in the field of cancer therapy. These methods are divided into three categories: (1) modeling methods, (2) model-free approaches based on biological search algorithms, and (3) merged approaches, particularly phenotypically driven network biology methods and computation network models relying on phenotypic data. In addition to a brief description of each approach, we include a critical discussion of the advantages and disadvantages of each method, with a strong focus on the limitations and considerations needed to successfully apply such methods in biological research.

The rapid spread of carbapenem-resistant Enterobacteriaceae

Carbapenems, our one-time silver bullet for multidrug resistant bacterial infections, are now threatened by widespread dissemination of carbapenem-resistant Enterobacteriaceae (CRE). Successful expansion of Enterobacteriaceae clonal groups and frequent horizontal gene transfer of carbapenemase expressing plasmids are causing increasing carbapenem resistance. Recent advances in genetic and phenotypic detection facilitate global surveillance of CRE diversity and prevalence. In particular, whole genome sequencing enabled efficient tracking, annotation, and study of genetic elements colocalized with carbapenemase genes on chromosomes and on plasmids. Improved characterization helps detail the co-occurrence of other antibiotic resistance genes in CRE isolates and helps identify pan-drug resistance mechanisms. The novel β-lactamase inhibitor, avibactam, combined with ceftazidime or aztreonam, is a promising CRE treatment compared to current colistin or tigecycline regimens. To halt increasing CRE-associated morbidity and mortality, we must continue quality, cooperative monitoring and urgently investigate novel treatments.

The analysis of the antibiotic resistome offers new opportunities for therapeutic intervention

Most efforts in the development of antimicrobials have focused on the screening of lethal targets. Nevertheless, the constant expansion of antimicrobial resistance makes the antibiotic resistance determinants themselves suitable targets for finding inhibitors to be used in combination with antibiotics. Among them, inhibitors of antibiotic inactivating enzymes and of multidrug efflux pumps are suitable candidates for improving the efficacy of antibiotics. In addition, the application of systems biology tools is helping to understand the changes in bacterial physiology associated to the acquisition of resistance, including the increased susceptibility to other antibiotics displayed by some antibiotic-resistant mutants. This information is useful for implementing novel strategies based in metabolic interventions or combination of antibiotics for improving the efficacy of antibacterial therapy.

Corynebacterium glutamicum Metabolic Engineering with CRISPR Interference (CRISPRi)

Corynebacterium glutamicum is an important organism for the industrial production of amino acids. Metabolic pathways in this organism are usually engineered by conventional methods such as homologous recombination, which depends on rare double-crossover events. To facilitate the mapping of gene expression levels to metabolic outputs, we applied CRISPR interference (CRISPRi) technology using deactivated Cas9 (dCas9) to repress genes in C. glutamicum. We then determined the effects of target repression on amino acid titers. Single-guide RNAs directing dCas9 to specific targets reduced expression of pgi and pck up to 98%, and of pyk up to 97%, resulting in titer enhancement ratios of l-lysine and l-glutamate production comparable to levels achieved by gene deletion. This approach for C. glutamicum metabolic engineering, which only requires 3 days, indicates that CRISPRi can be used for quick and efficient metabolic pathway remodeling without the need for gene deletions or mutations and subsequent selection.

Multiplexed barcoded CRISPR-Cas9 screening enabled by CombiGEM

The orchestrated action of genes controls complex biological phenotypes, yet the systematic discovery of gene and drug combinations that modulate these phenotypes in human cells is labor intensive and challenging to scale. Here, we created a platform for the massively parallel screening of barcoded combinatorial gene perturbations in human cells and translated these hits into effective drug combinations. This technology leverages the simplicity of the CRISPR-Cas9 system for multiplexed targeting of specific genomic loci and the versatility of combinatorial genetics en masse (CombiGEM) to rapidly assemble barcoded combinatorial genetic libraries that can be tracked with high-throughput sequencing. We applied CombiGEM-CRISPR to create a library of 23,409 barcoded dual guide-RNA (gRNA) combinations and then perform a high-throughput pooled screen to identify gene pairs that inhibited ovarian cancer cell growth when they were targeted. We validated the growth-inhibiting effects of specific gene sets, including epigenetic regulators KDM4C/BRD4 and KDM6B/BRD4, via individual assays with CRISPR-Cas-based knockouts and RNA-interference-based knockdowns. We also tested small-molecule drug pairs directed against our pairwise hits and showed that they exerted synergistic antiproliferative effects against ovarian cancer cells. We envision that the CombiGEM-CRISPR platform will be applicable to a broad range of biological settings and will accelerate the systematic identification of genetic combinations and their translation into novel drug combinations that modulate complex human disease phenotypes.

Engineering Synthetic Gene Circuits in Living Cells with CRISPR Technology

One of the goals of synthetic biology is to build regulatory circuits that control cell behavior, for both basic research purposes and biomedical applications. The ability to build transcriptional regulatory devices depends on the availability of programmable, sequence-specific, and effective synthetic transcription factors (TFs). The prokaryotic clustered regularly interspaced short palindromic repeat (CRISPR) system, recently harnessed for transcriptional regulation in various heterologous host cells, offers unprecedented ease in designing synthetic TFs. We review how CRISPR can be used to build synthetic gene circuits and discuss recent advances in CRISPR-mediated gene regulation that offer the potential to build increasingly complex, programmable, and efficient gene circuits in the future.

Synthetic biology for pharmaceutical drug discovery

Synthetic biology (SB) is an emerging discipline, which is slowly reorienting the field of drug discovery. For thousands of years, living organisms such as plants were the major source of human medicines. The difficulty in resynthesizing natural products, however, often turned pharmaceutical industries away from this rich source for human medicine. More recently, progress on transformation through genetic manipulation of biosynthetic units in microorganisms has opened the possibility of in-depth exploration of the large chemical space of natural products derivatives. Success of SB in drug synthesis culminated with the bioproduction of artemisinin by microorganisms, a tour de force in protein and metabolic engineering. Today, synthetic cells are not only used as biofactories but also used as cell-based screening platforms for both target-based and phenotypic-based approaches. Engineered genetic circuits in synthetic cells are also used to decipher disease mechanisms or drug mechanism of actions and to study cell-cell communication within bacteria consortia. This review presents latest developments of SB in the field of drug discovery, including some challenging issues such as drug resistance and drug toxicity.

A Barcoding Strategy Enabling Higher-Throughput Library Screening by Microscopy

Dramatic progress has been made in the design and build phases of the design-build-test cycle for engineering cells. However, the test phase usually limits throughput, as many outputs of interest are not amenable to rapid analytical measurements. For example, phenotypes such as motility, morphology, and subcellular localization can be readily measured by microscopy, but analysis of these phenotypes is notoriously slow. To increase throughput, we developed microscopy-readable barcodes (MiCodes) composed of fluorescent proteins targeted to discernible organelles. In this system, a unique barcode can be genetically linked to each library member, making possible the parallel analysis of phenotypes of interest via microscopy. As a first demonstration, we MiCoded a set of synthetic coiled-coil leucine zipper proteins to allow an 8 × 8 matrix to be tested for specific interactions in micrographs consisting of mixed populations of cells. A novel microscopy-readable two-hybrid fluorescence localization assay for probing candidate interactions in the cytosol was also developed using a bait protein targeted to the peroxisome and a prey protein tagged with a fluorescent protein. This work introduces a generalizable, scalable platform for making microscopy amenable to higher-throughput library screening experiments, thereby coupling the power of imaging with the utility of combinatorial search paradigms.

Massively parallel high-order combinatorial genetics in human cells

The systematic functional analysis of combinatorial genetics has been limited by the throughput that can be achieved and the order of complexity that can be studied. To enable massively parallel characterization of genetic combinations in human cells, we developed a technology for rapid, scalable assembly of high-order barcoded combinatorial genetic libraries that can be quantified with high-throughput sequencing. We applied this technology, combinatorial genetics en masse (CombiGEM), to create high-coverage libraries of 1,521 two-wise and 51,770 three-wise barcoded combinations of 39 human microRNA (miRNA) precursors. We identified miRNA combinations that synergistically sensitize drug-resistant cancer cells to chemotherapy and/or inhibit cancer cell proliferation, providing insights into complex miRNA networks. More broadly, our method will enable high-throughput profiling of multifactorial genetic combinations that regulate phenotypes of relevance to biomedicine, biotechnology and basic science.

Tackling Drug Resistant Infection Outbreaks of Global Pandemic Escherichia coli ST131 Using Evolutionary and Epidemiological Genomics

High-throughput molecular screening is required to investigate the origin and diffusion of antimicrobial resistance in pathogen outbreaks. The most frequent cause of human infection is Escherichia coli, which is dominated by sequence type 131 (ST131)-a set of rapidly radiating pandemic clones. The highly infectious clades of ST131 originated firstly by a mutation enhancing conjugation and adhesion. Secondly, single-nucleotide polymorphisms occurred enabling fluoroquinolone-resistance, which is near-fixed in all ST131. Thirdly, broader resistance through beta-lactamases has been gained and lost frequently, symptomatic of conflicting environmental selective effects. This flexible approach to gene exchange is worrying and supports the proposition that ST131 will develop an even wider range of plasmid and chromosomal elements promoting antimicrobial resistance. To stop ST131, deep genome sequencing is required to understand the origin, evolution and spread of antimicrobial resistance genes. Phylogenetic methods that decipher past events can predict future patterns of virulence and transmission based on genetic signatures of adaptation and gene exchange. Both the effect of partial antimicrobial exposure and cell dormancy caused by variation in gene expression may accelerate the development of resistance. High-throughput sequencing can decode measurable evolution of cell populations within patients associated with systems-wide changes in gene expression during treatments. A multi-faceted approach can enhance assessment of antimicrobial resistance in E. coli ST131 by examining transmission dynamics between hosts to achieve a goal of pre-empting resistance before it emerges by optimising antimicrobial treatment protocols.

Engineering allostery

Allosteric proteins have great potential in synthetic biology, but our limited understanding of the molecular underpinnings of allostery has hindered the development of designer molecules, including transcription factors with new DNA-binding or ligand-binding specificities that respond appropriately to inducers. Such allosteric proteins could function as novel switches in complex circuits, metabolite sensors, or as orthogonal regulators for independent, inducible control of multiple genes. Advances in DNA synthesis and next-generation sequencing technologies have enabled the assessment of millions of mutants in a single experiment, providing new opportunities to study allostery. Using the classic LacI protein as an example, we describe a genetic selection system using a bidirectional reporter to capture mutants in both allosteric states, allowing the positions most crucial for allostery to be identified. This approach is not limited to bacterial transcription factors, and could reveal new mechanistic insights and facilitate engineering of other major classes of allosteric proteins such as nuclear receptors, two-component systems, G protein-coupled receptors, and protein kinases.