Deep scanning lysine metabolism in Escherichia coli

Application of modern synthetic biology technology in aromatic amino acids and derived compounds biosynthesis

Aromatic amino acids (AAA) and derived compounds have enormous commercial value with extensive applications in the food, chemical and pharmaceutical fields. Microbial production of AAA and derived compounds is a promising prospect for its environmental friendliness and sustainability. However, low yield and production efficiency remain major challenges for realizing industrial production. With the advancement of synthetic biology, microbial production of AAA and derived compounds has been significantly facilitated. In this review, a comprehensive overview on the current progresses, challenges and corresponding solutions for AAA and derived compounds biosynthesis is provided. The most cutting-edge developments of synthetic biology technology in AAA and derived compounds biosynthesis, including CRISPR-based system, genetically encoded biosensors and synthetic genetic circuits, were highlighted. Finally, future prospects of modern strategies conducive to the biosynthesis of AAA and derived compounds are discussed. This review offers guidance on constructing microbial cell factory for aromatic compound using synthetic biology technology.

Nitrogen assimilation by E. coli in the mammalian intestine

Nitrogen is an essential element for all living organisms, including Escherichia coli. Potential nitrogen sources are abundant in the intestine, but knowledge of those used specifically by E. coli to colonize remains limited. Here, we sought to determine the specific nitrogen sources used by E. coli to colonize the streptomycin-treated mouse intestine. We began by investigating whether nitrogen is limiting in the intestine. The NtrBC two-component system upregulates approximately 100 genes in response to nitrogen limitation. We showed that NtrBC is crucial for E. coli colonization, although most genes of the NtrBC regulon are not induced, which indicates that nitrogen is not limiting in the intestine. RNA-seq identified upregulated genes in colonized E. coli involved in transport and catabolism of seven amino acids, dipeptides and tripeptides, purines, pyrimidines, urea, and ethanolamine. Competitive colonization experiments revealed that L-serine, N-acetylneuraminic acid, N-acetylglucosamine, and di- and tripeptides serve as nitrogen sources for E. coli in the intestine. Furthermore, the colonization defect of a L-serine deaminase mutant was rescued by excess nitrogen in the drinking water but not by an excess of carbon and energy, demonstrating that L-serine serves primarily as a nitrogen source. Similar rescue experiments showed that N-acetylneuraminic acid serves as both a carbon and nitrogen source. To a minor extent, aspartate and ammonia also serve as nitrogen sources. Overall, these findings demonstrate that E. coli utilizes multiple nitrogen sources for successful colonization of the mouse intestine, the most important of which is L-serine.

IMPORTANCE

While much is known about the carbon and energy sources that are used by E. coli to colonize the mammalian intestine, very little is known about the sources of nitrogen. Interrogation of colonized E. coli by RNA-seq revealed that nitrogen is not limiting, indicating an abundance of nitrogen sources in the intestine. Pathways for assimilation of nitrogen from several amino acids, dipeptides and tripeptides, purines, pyrimidines, urea, and ethanolamine were induced in mice. Competitive colonization assays confirmed that mutants lacking catabolic pathways for L-serine, N-acetylneuraminic acid, N-acetylglucosamine, and di- and tripeptides had colonization defects. Rescue experiments in mice showed that L-serine serves primarily as a nitrogen source, whereas N-acetylneuraminic acid provides both carbon and nitrogen. Of the many nitrogen assimilation mutants tested, the largest colonization defect was for an L-serine deaminase mutant, which demonstrates L-serine is the most important nitrogen source for colonized E. coli.

Engineering of increased L-Threonine production in bacteria by combinatorial cloning and machine learning

The goal of this study is to develop a general strategy for bacterial engineering using an integrated synthetic biology and machine learning (ML) approach. This strategy was developed in the context of increasing L-threonine production in Escherichia coli ATCC 21277. A set of 16 genes was initially selected based on metabolic pathway relevance to threonine biosynthesis and used for combinatorial cloning to construct a set of 385 strains to generate training data (i.e., a range of L-threonine titers linked to each of the specific gene combinations). Hybrid (regression/classification) deep learning (DL) models were developed and used to predict additional gene combinations in subsequent rounds of combinatorial cloning for increased L-threonine production based on the training data. As a result, E. coli strains built after just three rounds of iterative combinatorial cloning and model prediction generated higher L-threonine titers (from 2.7 g/L to 8.4 g/L) than those of patented L-threonine strains being used as controls (4-5 g/L). Interesting combinations of genes in L-threonine production included deletions of the tdh, metL, dapA, and dhaM genes as well as overexpression of the pntAB, ppc, and aspC genes. Mechanistic analysis of the metabolic system constraints for the best performing constructs offers ways to improve the models by adjusting weights for specific gene combinations. Graph theory analysis of pairwise gene modifications and corresponding levels of L-threonine production also suggests additional rules that can be incorporated into future ML models.

Mass-Activated Droplet Sorting for the Selection of Lysine-Producing Escherichia coli

Synthetic biology relies on engineering cells to have desirable properties, such as the production of select chemicals. A bottleneck in engineering methods is often the need to screen and sort variant libraries for potential activity. Droplet microfluidics is a method for high-throughput sample preparation and analysis which has the potential to improve the engineering of cells, but a limitation has been the reliance on fluorescent analysis. Here, we show the ability to select cell variants grown in 20 nL droplets at 0.5 samples/s using mass-activated droplet sorting (MADS), a method for selecting droplets based on the signal intensity measured by electrospray ionization mass spectrometry (ESI-MS). Escherichia coli variants producing lysine were used to evaluate the applicability of MADS for synthetic biology. E. coli were shown to be effectively grown in droplets, and the lysine produced by these cells was detectable using ESI-MS. Sorting of lysine-producing cells based on the MS signal was shown, yielding 96-98% purity for high-producing variants in the selected pool. Using this technique, cells were recovered after screening, enabling downstream validation via phenotyping. The presented method is translatable to whole-cell engineering for biocatalyst production.

Mutation bias and the predictability of evolution

Predicting evolutionary outcomes is an important research goal in a diversity of contexts. The focus of evolutionary forecasting is usually on adaptive processes, and efforts to improve prediction typically focus on selection. However, adaptive processes often rely on new mutations, which can be strongly influenced by predictable biases in mutation. Here, we provide an overview of existing theory and evidence for such mutation-biased adaptation and consider the implications of these results for the problem of prediction, in regard to topics such as the evolution of infectious diseases, resistance to biochemical agents, as well as cancer and other kinds of somatic evolution. We argue that empirical knowledge of mutational biases is likely to improve in the near future, and that this knowledge is readily applicable to the challenges of short-term prediction. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.

FBA-PRCC. Partial Rank Correlation Coefficient (PRCC) Global Sensitivity Analysis (GSA) in Application to Constraint-Based Models

Background: Whole-genome models (GEMs) have become a versatile tool for systems biology, biotechnology, and medicine. GEMs created by automatic and semi-automatic approaches contain a lot of redundant reactions. At the same time, the nonlinearity of the model makes it difficult to evaluate the significance of the reaction for cell growth or metabolite production. Methods: We propose a new way to apply the global sensitivity analysis (GSA) to GEMs in a straightforward parallelizable fashion. Results: We have shown that Partial Rank Correlation Coefficient (PRCC) captures key steps in the metabolic network despite the network distance from the product synthesis reaction. Conclusions: FBA-PRCC is a fast, interpretable, and reliable metric to identify the sign and magnitude of the reaction contribution to various cellular functions.

Deep mutational scanning: A versatile tool in systematically mapping genotypes to phenotypes

Unveiling how genetic variations lead to phenotypic variations is one of the key questions in evolutionary biology, genetics, and biomedical research. Deep mutational scanning (DMS) technology has allowed the mapping of tens of thousands of genetic variations to phenotypic variations efficiently and economically. Since its first systematic introduction about a decade ago, we have witnessed the use of deep mutational scanning in many research areas leading to scientific breakthroughs. Also, the methods in each step of deep mutational scanning have become much more versatile thanks to the oligo-synthesizing technology, high-throughput phenotyping methods and deep sequencing technology. However, each specific possible step of deep mutational scanning has its pros and cons, and some limitations still await further technological development. Here, we discuss recent scientific accomplishments achieved through the deep mutational scanning and describe widely used methods in each step of deep mutational scanning. We also compare these different methods and analyze their advantages and disadvantages, providing insight into how to design a deep mutational scanning study that best suits the aims of the readers' projects.

Exploring the expanse between theoretical questions and experimental approaches in the modern study of evolvability

Despite several decades of computational and experimental work across many systems, evolvability remains on the periphery with regards to its status as a widely accepted and regularly applied theoretical concept. Here we propose that its marginal status is partly a result of large gaps between the diverse but disconnected theoretical treatments of evolvability and the relatively narrower range of studies that have tested it empirically. To make this case, we draw on a range of examples-from experimental evolution in microbes, to molecular evolution in proteins-where attempts have been made to mend this disconnect. We highlight some examples of progress that has been made and point to areas where synthesis and translation of existing theory can lead to further progress in the still-new field of empirical measurements of evolvability.

On the incongruence of genotype-phenotype and fitness landscapes

The mapping from genotype to phenotype to fitness typically involves multiple nonlinearities that can transform the effects of mutations. For example, mutations may contribute additively to a phenotype, but their effects on fitness may combine non-additively because selection favors a low or intermediate value of that phenotype. This can cause incongruence between the topographical properties of a fitness landscape and its underlying genotype-phenotype landscape. Yet, genotype-phenotype landscapes are often used as a proxy for fitness landscapes to study the dynamics and predictability of evolution. Here, we use theoretical models and empirical data on transcription factor-DNA interactions to systematically study the incongruence of genotype-phenotype and fitness landscapes when selection favors a low or intermediate phenotypic value. Using the theoretical models, we prove a number of fundamental results. For example, selection for low or intermediate phenotypic values does not change simple sign epistasis into reciprocal sign epistasis, implying that genotype-phenotype landscapes with only simple sign epistasis motifs will always give rise to single-peaked fitness landscapes under such selection. More broadly, we show that such selection tends to create fitness landscapes that are more rugged than the underlying genotype-phenotype landscape, but this increased ruggedness typically does not frustrate adaptive evolution because the local adaptive peaks in the fitness landscape tend to be nearly as tall as the global peak. Many of these results carry forward to the empirical genotype-phenotype landscapes, which may help to explain why low- and intermediate-affinity transcription factor-DNA interactions are so prevalent in eukaryotic gene regulation.

Trade-offs predicted by metabolic network structure give rise to evolutionary specialization and phenotypic diversification

Mitigating trade-offs between different resource-utilization functions is key to an organism's ecological and evolutionary success. These trade-offs often reflect metabolic constraints with a complex molecular underpinning; therefore, their consequences for evolutionary processes have remained elusive. Here, we investigate how metabolic architecture induces resource utilization constraints and how these constraints, in turn, elicit evolutionary specialization and diversification. Guided by the metabolic network structure of the bacterium Lactococcus cremoris, we selected two carbon sources (fructose and galactose) with predicted co-utilization constraints. By evolving L. cremoris on either fructose, galactose or a mix of both sugars, we imposed selection favoring divergent metabolic specializations or co-utilization of both resources, respectively. Phenotypic characterization revealed the evolution of either fructose or galactose specialists in the single-sugar treatments. In the mixed sugar regime, we observed adaptive diversification: both specialists coexisted, and no generalist evolved. Divergence from the ancestral phenotype occurred at key pathway junctions in the central carbon metabolism. Fructose specialists evolved mutations in the fbp and pfk genes that appear to balance anabolic and catabolic carbon fluxes. Galactose specialists evolved increased expression of pgmA (the primary metabolic bottleneck of galactose metabolism) and silencing of ptnABCD (the main glucose transporter) and ldh (regulator/enzyme of downstream carbon metabolism). Overall, our study shows how metabolic network architecture and historical contingency serve to predict targets of selection and inform the functional interpretation of evolved mutations. The elucidation of the relationship between molecular constraints and phenotypic trade-offs contributes to an integrative understanding of evolutionary specialization and diversification.

Recent advances in high-throughput metabolic engineering: Generation of oligonucleotide-mediated genetic libraries

The preparation of genetic libraries is an essential step to evolve microorganisms and study genotype-phenotype relationships by high-throughput screening/selection. As the large-scale synthesis of oligonucleotides becomes easy, cheap, and high-throughput, numerous novel strategies have been developed in recent years to construct high-quality oligo-mediated libraries, leveraging state-of-art molecular biology tools for genome editing and gene regulation. This review presents an overview of recent advances in creating and characterizing in vitro and in vivo genetic libraries, based on CRISPR/Cas, regulatory RNAs, and recombineering, primarily for Escherichia coli and Saccharomyces cerevisiae. These libraries' applications in high-throughput metabolic engineering, strain evolution and protein engineering are also discussed.

Engineered Halomonas spp. for production of l-Lysine and cadaverine

Cadaverine, a derivative of l-lysine, has been used as a monomer for the synthesis of bio-based nylon-5,6. This study engineered Halomonas bluephagenesis TD1.0 by blocking the feedback inhibition, overexpressing the key l-lysine synthesis genes, strengthening the l-lysine export system and increasing the supply of oxaloacetate for production of l-lysine in the supernatant and PHB in the cells. Subsequently, cadaverine biosynthetic pathway was constructed in H. campaniensis LC-9 to improve the efficiency of de novo cadaverine biosynthesis which combines l-lysine producing H. bluephagenesis TDL8-68-259 and cadaverine producing H. campaniensis LC-9-ldcC-lysP. When H. campaniensis LC-9-ldcC-lysP was used as a whole cell catalysis for cadaverine production, the conversion efficiency of l-lysine to cadaverine reached 100% in the presence of 0.05% Triton X-100 for cell membrane permeability enhancement, resulting in 118 g L^-1 cadaverine formed in the fermentor. Thus, Halomonas spp. have been successfully constructed for l-lysine and cadaverine production.

Oligo Pools as an Affordable Source of Synthetic DNA for Cost-Effective Library Construction in Protein- and Metabolic Pathway Engineering

The construction of custom libraries is critical for rational protein engineering and directed evolution. Array‐synthesized oligo pools of thousands of user‐defined sequences (up to ∼350 bases in length) have emerged as a low‐cost commercially available source of DNA. These pools cost ≤10 % (depending on error rate and length) of other commercial sources of custom DNA, and this significant cost difference can determine whether an enzyme engineering project can be realized on a given research budget. However, while being cheap, oligo pools do suffer from a low concentration of individual oligos and relatively high error rates. Several powerful techniques that specifically make use of oligo pools have been developed and proven valuable or even essential for next‐generation protein and pathway engineering strategies, such as sequence‐function mapping, enzyme minimization, or de‐novo design. Here we consolidate the knowledge on these techniques and their applications to facilitate the use of oligo pools within the protein engineering community.

Intelligent host engineering for metabolic flux optimisation in biotechnology

Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.

From genotypes to organisms: State-of-the-art and perspectives of a cornerstone in evolutionary dynamics

Understanding how genotypes map onto phenotypes, fitness, and eventually organisms is arguably the next major missing piece in a fully predictive theory of evolution. We refer to this generally as the problem of the genotype-phenotype map. Though we are still far from achieving a complete picture of these relationships, our current understanding of simpler questions, such as the structure induced in the space of genotypes by sequences mapped to molecular structures, has revealed important facts that deeply affect the dynamical description of evolutionary processes. Empirical evidence supporting the fundamental relevance of features such as phenotypic bias is mounting as well, while the synthesis of conceptual and experimental progress leads to questioning current assumptions on the nature of evolutionary dynamics-cancer progression models or synthetic biology approaches being notable examples. This work delves with a critical and constructive attitude into our current knowledge of how genotypes map onto molecular phenotypes and organismal functions, and discusses theoretical and empirical avenues to broaden and improve this comprehension. As a final goal, this community should aim at deriving an updated picture of evolutionary processes soundly relying on the structural properties of genotype spaces, as revealed by modern techniques of molecular and functional analysis.

A simplified strategy for titrating gene expression reveals new relationships between genotype, environment, and bacterial growth

A lack of high-throughput techniques for making titrated, gene-specific changes in expression limits our understanding of the relationship between gene expression and cell phenotype. Here, we present a generalizable approach for quantifying growth rate as a function of titrated changes in gene expression level. The approach works by performing CRISPRi with a series of mutated single guide RNAs (sgRNAs) that modulate gene expression. To evaluate sgRNA mutation strategies, we constructed a library of 5927 sgRNAs targeting 88 genes in Escherichia coli MG1655 and measured the effects on growth rate. We found that a compounding mutational strategy, through which mutations are incrementally added to the sgRNA, presented a straightforward way to generate a monotonic and gradated relationship between mutation number and growth rate effect. We also implemented molecular barcoding to detect and correct for mutations that 'escape' the CRISPRi targeting machinery; this strategy unmasked deleterious growth rate effects obscured by the standard approach of ignoring escapers. Finally, we performed controlled environmental variations and observed that many gene-by-environment interactions go completely undetected at the limit of maximum knockdown, but instead manifest at intermediate expression perturbation strengths. Overall, our work provides an experimental platform for quantifying the phenotypic response to gene expression variation.

Riboswitches as Drug Targets for Antibiotics

Riboswitches reside in the untranslated region of RNA and regulate genes involved in the biosynthesis of essential metabolites through binding of small molecules. Since their discovery at the beginning of this century, riboswitches have been regarded as potential antibacterial targets. Using fragment screening, high-throughput screening and rational ligand design guided by X-ray crystallography, lead compounds against various riboswitches have been identified. Here, we review the current status and suitability of the thiamine pyrophosphate (TPP), flavin mononucleotide (FMN), glmS, guanine, and other riboswitches as antibacterial targets and discuss them in a biological context. Further, we highlight challenges in riboswitch drug discovery and emphasis the need to develop riboswitch specific high-throughput screening methods.

Machine learning for metabolic engineering: A review

Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth illustrative examples leveraging omics data and improving production. We also include practical advice for the practitioner in terms of data management, algorithm libraries, computational resources, and important non-technical issues. A variety of applications ranging from pathway construction and optimization, to genetic editing optimization, cell factory testing, and production scale-up are discussed. Moreover, the promising relationship between machine learning and mechanistic models is thoroughly reviewed. Finally, the future perspectives and most promising directions for this combination of disciplines are examined.

High-Throughput Functional Genomics for Energy Production

Functional genomics remains a foundational field for establishing genotype-phenotype relationships that enable strain engineering. High-throughput (HTP) methods accelerate the Design-Build-Test-Learn cycle that currently drives synthetic biology towards a forward engineering future. Trackable mutagenesis techniques including transposon insertion sequencing and CRISPR-Cas-mediated genome editing allow for rapid fitness profiling of a collection, or library, of mutants to discover beneficial mutations. Due to the relative speed of these experiments compared to adaptive evolution experiments, iterative rounds of mutagenesis can be implemented for next-generation metabolic engineering efforts to design complex production and tolerance phenotypes. Additionally, the expansion of these mutagenesis techniques to novel bacteria are opening up industrial microbes that show promise for establishing a bio-based economy.

Mutation bias interacts with composition bias to influence adaptive evolution

Mutation is a biased stochastic process, with some types of mutations occurring more frequently than others. Previous work has used synthetic genotype-phenotype landscapes to study how such mutation bias affects adaptive evolution. Here, we consider 746 empirical genotype-phenotype landscapes, each of which describes the binding affinity of target DNA sequences to a transcription factor, to study the influence of mutation bias on adaptive evolution of increased binding affinity. By using empirical genotype-phenotype landscapes, we need to make only few assumptions about landscape topography and about the DNA sequences that each landscape contains. The latter is particularly important because the set of sequences that a landscape contains determines the types of mutations that can occur along a mutational path to an adaptive peak. That is, landscapes can exhibit a composition bias—a statistical enrichment of a particular type of mutation relative to a null expectation, throughout an entire landscape or along particular mutational paths—that is independent of any bias in the mutation process. Our results reveal the way in which composition bias interacts with biases in the mutation process under different population genetic conditions, and how such interaction impacts fundamental properties of adaptive evolution, such as its predictability, as well as the evolution of genetic diversity and mutational robustness.

Mutation is often depicted as a random process due its unpredictable nature. However, such randomness does not imply uniformly distributed outcomes, because some DNA sequence changes happen more frequently than others. Mutation bias can be an orienting factor in adaptive evolution, influencing the mutational trajectories populations follow toward higher-fitness genotypes. Because these trajectories are typically just a small subset of all possible mutational trajectories, they can exhibit composition bias—an enrichment of a particular kind of DNA sequence change, such as transition or transversion mutations. Here, we use empirical data from eukaryotic transcriptional regulation to study how mutation bias and composition bias interact to influence adaptive evolution.

Applications of CRISPR in a Microbial Cell Factory: From Genome Reconstruction to Metabolic Network Reprogramming

The well-designed microbial cell factory finds wide applications in chemical, pharmaceutical, and food industries due to its sustainable and environmentally friendly features. Recently, the clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated proteins (CRISPR-Cas) systems have been developed into powerful tools to perform genome editing and transcriptional regulation in prokaryotic and eukaryotic cells. Accordingly, these tools are useful to build microbial cell factories not only by reconstructing the genome but also by reprogramming the metabolic network. In this review, we summarize the recent significant headway and potential uses of the CRISPR technology in the construction of efficient microbial cell factories. Moreover, the future perspectives on the improvement and upgradation of CRISPR-based tools are also discussed.

Unlocking nature's biosynthetic potential by directed genome evolution

Microorganisms have been increasingly explored as microbial cell factories for production of fuels, chemicals, drugs, and materials. Among the various metabolic engineering strategies, directed genome evolution has emerged as one of the most powerful tools to unlock the full biosynthetic potential of microorganisms. Here we summarize the directed genome evolution strategies that have been developed in recent years, including adaptive laboratory evolution and various targeted genome-scale engineering strategies, and discuss their applications in basic and applied biological research.

Targeted mutagenesis of multiple chromosomal regions in microbes

Directed evolution allows the effective engineering of proteins, biosynthetic pathways, and cellular functions. Traditional plasmid-based methods generally subject one or occasionally multiple genes-of-interest to mutagenesis, require time-consuming manual interventions, and the genes that are subjected to mutagenesis are outside of their native genomic context. Other methods mutagenize the whole genome unselectively which may distort the outcome. Recent recombineering- and CRISPR-based technologies radically change this field by allowing exceedingly high mutation rates at multiple, predefined loci in their native genomic context. In this review, we focus on recent technologies that potentially allow accelerated tunable mutagenesis at multiple genomic loci in the native genomic context of these target sequences. These technologies will be compared by four main criteria, including the scale of mutagenesis, portability to multiple microbial species, off-target mutagenesis, and cost-effectiveness. Finally, we discuss how these technical advances open new avenues in basic research and biotechnology.

Development and Application of CRISPR/Cas in Microbial Biotechnology

The clustered regularly interspaced short palindromic repeats (CRISPR)-associated (Cas) system has been rapidly developed as versatile genomic engineering tools with high efficiency, accuracy and flexibility, and has revolutionized traditional methods for applications in microbial biotechnology. Here, key points of building reliable CRISPR/Cas system for genome engineering are discussed, including the Cas protein, the guide RNA and the donor DNA. Following an overview of various CRISPR/Cas tools for genome engineering, including gene activation, gene interference, orthogonal CRISPR systems and precise single base editing, we highlighted the application of CRISPR/Cas toolbox for multiplexed engineering and high throughput screening. We then summarize recent applications of CRISPR/Cas systems in metabolic engineering toward production of chemicals and natural compounds, and end with perspectives of future advancements.

Determinants for Efficient Editing with Cas9-Mediated Recombineering in Escherichia coli

In E. coli, editing efficiency with Cas9-mediated recombineering varies across targets due to differences in the level of Cas9:gRNA-mediated DNA double-strand break (DSB)-induced cell death. We found that editing efficiency with the same gRNA and repair template can also change with target position, cas9 promoter strength, and growth conditions. Incomplete editing, off-target activity, nontargeted mutations, and failure to cleave target DNA even if Cas9 is bound also compromise editing efficiency. These effects on editing efficiency were gRNA-specific. We propose that differences in the efficiency of Cas9:gRNA-mediated DNA DSBs, as well as possible differences in binding of Cas9:gRNA complexes to their target sites, account for the observed variations in editing efficiency between gRNAs. We show that editing behavior using the same gRNA can be modified by mutating the gRNA spacer, which changes the DNA DSB activity. Finally, we discuss how variable editing with different gRNAs could limit high-throughput applications and provide strategies to overcome these limitations.

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.

High-throughput enrichment of temperature-sensitive argininosuccinate synthetase for two-stage citrulline production in E. coli

Controlling metabolism of engineered microbes is important to modulate cell growth and production during a bioprocess. For example, external parameters such as light, chemical inducers, or temperature can act on metabolism of production strains by changing the abundance or activity of enzymes. Here, we created temperature-sensitive variants of an essential enzyme in arginine biosynthesis of Escherichia coli (argininosuccinate synthetase, ArgG) and used them to dynamically control citrulline overproduction and growth of E. coli. We show a method for high-throughput enrichment of temperature-sensitive ArgG variants with a fluorescent TIMER protein and flow cytometry. With 90 of the thus derived ArgG variants, we complemented an ArgG deletion strain showing that 90% of the strains exhibit temperature-sensitive growth and 69% of the strains are auxotrophic for arginine at 42 °C and prototrophic at 30 °C. The best temperature-sensitive ArgG variant enabled precise and tunable control of cell growth by temperature changes. Expressing this variant in a feedback-dysregulated E. coli strain allowed us to realize a two-stage bioprocess: a 33 °C growth-phase for biomass accumulation and a 39 °C stationary-phase for citrulline production. With this two-stage strategy, we produced 3 g/L citrulline during 45 h cultivation in a 1-L bioreactor. These results show that temperature-sensitive enzymes can be created en masse and that they may function as metabolic valves in engineered bacteria.

•

Method to enrich temperature-sensitive enzymes en masse.

•

Temperature-sensitive enzymes function as metabolic valve.

•

Temperature controlled two-stage production of citrulline.

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 chimeric nucleases function for efficient genome editing

CRISPR-Cas systems have revolutionized genome editing across a broad range of biotechnological endeavors. Many CRISPR-Cas nucleases have been identified and engineered for improved capabilities. Given the modular structure of such enzymes, we hypothesized that engineering chimeric sequences would generate non-natural variants that span the kinetic parameter landscape, and thus provide for the rapid selection of nucleases fit for a particular editing system. Here, we design a chimeric Cas12a-type library with approximately 560 synthetic chimeras, and select several functional variants. We demonstrate that certain nuclease domains can be recombined across distantly related nuclease templates to produce variants that function in bacteria, yeast, and human cell lines. We further characterize selected chimeric nucleases and find that they have different protospacer adjacent motif (PAM) preferences and the M44 chimera has higher specificity relative to wild-type (WT) sequences. This demonstration opens up the possibility of generating nuclease sequences with implications across biotechnology.

MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect

Multiplex assays of variant effect (MAVEs), such as deep mutational scans and massively parallel reporter assays, test thousands of sequence variants in a single experiment. Despite the importance of MAVE data for basic and clinical research, there is no standard resource for their discovery and distribution. Here, we present MaveDB ( https://www.mavedb.org ), a public repository for large-scale measurements of sequence variant impact, designed for interoperability with applications to interpret these datasets. We also describe the first such application, MaveVis, which retrieves, visualizes, and contextualizes variant effect maps. Together, the database and applications will empower the community to mine these powerful datasets.