A novel set of vectors for genome engineering of E. coli strains

AbstractSequence to activity mapping technologies are rapidly developing, enabling the isolation of mutations that confer novel phenotypes. Here we used the CRISPR EnAbled Trackable genome Engineering (CREATE) technology to investigate the inhibition of the essential IspC gene in Escherichia coli. IspC gene product, Deoxyxylulose Phosphate Reductoisomerase (DXR), converts 1-deoxy-D-xylulose 5-phosphate to 2-C-methyl-D-erythritol 4-phosphate in the DXP pathway. Since this pathway is shared with many pathogenic bacteria and protozoa and is missing in humans, it is an appealing target for inhibition. We created a full saturation library of 33 sites proximal to ligand binding and other sites and challenged it with the DXR-specific inhibitor, fosmidomycin. We identified several mutations that confer fosmidomycin resistance. All sites are highly conserved and also exist in pathogens including the malaria-inducing Plasmodium falciparum. These findings may have general implications on the isolation of resistance-conferring mutations and specifically, may affect the design of future generations of fosmidomycin-based drugs.SignificanceThe emergence of acquired drug resistance is a natural process that is likely to occur under most circumstances. Recently-developed technologies allow to map relative fitness contribution of multiple mutations in parallel. Such approaches may be used to predict which mutations are most likely to confer resistance, instead of waiting for them to evolve spontaneously. In this study, a rationally-designed IspC mutant library was generated genomically in E. coli. Mutants resistant to fosmidomycin, an antimalarial drug were identified, and most were in the highly conserved proline at position 274. These results may have implications on next-generation fosmidomycin drug design, and more broadly, this approach may be used for predicting mutational acquired resistance.

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CRISPR-nRAGE, a Cas9 nickase-reverse transcriptase assisted versatile genetic engineering toolkit for E. coli

10.1101/2020.09.02.279141 ◽

2020 ◽

Author(s):

Yaojun Tong ◽

Tue S. Jørgensen ◽

Christopher M. Whitford ◽

Tilmann Weber ◽

Sang Yup Lee

Keyword(s):

Reverse Transcriptase ◽

Genome Engineering ◽

Genetic Manipulation ◽

Fragment Size ◽

Leukemia Virus ◽

Strand Break ◽

Engineering System ◽

E Coli ◽

Dna Engineering ◽

Nucleotide Resolution

AbstractIn most prokaryotes, missing and poorly active non-homologous end joining (NHEJ) DNA repair pathways heavily restrict the direct application of CRISPR-Cas for DNA double-strand break (DSB)-based genome engineering without providing editing templates. CRISPR base editors, on the other hand, can be directly used for genome engineering in a number of bacteria, including E. coli, showing advantages over CRISPR-Cas9, since they do not require DSBs. However, as the current CRISPR base editors can only engineer DNA by A to G or C to T/G/A substitutions, they are incapable of mediating deletions, insertions, and combinations of deletions, insertions and substitutions. To address these challenges, we developed a Cas9 nickase (Cas9n)-reverse transcriptase (Moloney Murine Leukemia Virus, M-MLV) mediated, DSB-free, versatile, and single-nucleotide resolution genetic manipulation toolkit for prokaryotes, termed CRISPR-nRAGE (CRISPR-Cas9n Reverse transcriptase Assisted Genome Engineering) system. CRISPR-nRAGE can be used to introduce substitutions, deletions, insertions, and the combination thereof, both in plasmids and the chromosome of E. coli. Notably, small sized-deletion shows better editing efficiency compared to other kinds of DNA engineering. CRISPR-nRAGE has been used to delete and insert DNA fragments up to 97 bp and 33 bp, respectively. Efficiencies, however, drop sharply with the increase of the fragment size. It is not only a useful addition to the genome engineering arsenal for E. coli, but also may be the basis for the development of similar toolkits for other organisms.

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Assembly of Radically Recoded E. coli Genome Segments

10.1101/070417 ◽

2016 ◽

Cited By ~ 1

Author(s):

Julie E. Norville ◽

Cameron L. Gardner ◽

Eduardo Aponte ◽

Conor K. Camplisson ◽

Alexandra Gonzales ◽

...

Keyword(s):

Homologous Recombination ◽

Genome Engineering ◽

Next Generation ◽

Selective Marker ◽

E Coli ◽

Cassette Exchange ◽

Large Potential ◽

Attachment Sites

AbstractThe large potential of radically recoded organisms (RROs) in medicine and industry depends on improved technologies for efficient assembly and testing of recoded genomes for biosafety and functionality. Here we describe a next generation platform for conjugative assembly genome engineering, termed CAGE 2.0, that enables the scarless integration of large synthetically recoded E. coli segments at isogenic and adjacent genomic loci. A stable tdk dual selective marker is employed to facilitate cyclical assembly and removal of attachment sites used for targeted segment delivery by sitespecific recombination. Bypassing the need for vector transformation harnesses the multi Mb capacity of CAGE, while minimizing artifacts associated with RecA-mediated homologous recombination. Our method expands the genome engineering toolkit for radical modification across many organisms and recombinase-mediated cassette exchange (RMCE).

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Direct preparation of Cas9 ribonucleoprotein from E. coli for PCR-free seamless DNA assembly

10.1101/328468 ◽

2018 ◽

Cited By ~ 3

Author(s):

Wenqiang Li ◽

Shuntang Li ◽

Jie Qiao ◽

Fei Wang ◽

Yang Liu ◽

...

Keyword(s):

Large Scale ◽

Genome Engineering ◽

Restriction Enzymes ◽

General Strategy ◽

Dna Assembly ◽

E Coli ◽

Assembly Method ◽

Direct Preparation

AbstractCRISPR-Cas9 is a versatile and powerful genome engineering tool. Recently, Cas9 ribonucleoprotein (RNP) complexes have been used as promising biological tools with plenty of in vivo and in vitro applications, but there are by far no efficient methods to produce Cas9 RNP at large scale and low cost. Here, we describe a simple and effective approach for direct preparation of Cas9 RNP from E. coli by co-expressing Cas9 and target specific single guided RNAs. The purified RNP showed in vivo genome editing ability, as well as in vitro endonuclease activity that combines with an unexpected superior stability to enable routine uses in molecular cloning instead of restriction enzymes. We further develop a RNP-based PCR-free method termed Cas-Brick in a one-step or cyclic way for seamless assembly of multiple DNA fragments with high fidelity up to 99%. Altogether, our findings provide a general strategy to prepare Cas9 RNP and supply a convenient and cost-effective DNA assembly method as an invaluable addition to synthetic biological toolboxes.

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Improved bacterial recombineering by parallelized protein discovery

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2001588117 ◽

2020 ◽

Vol 117 (24) ◽

pp. 13689-13698 ◽

Cited By ~ 4

Author(s):

Timothy M. Wannier ◽

Akos Nyerges ◽

Helene M. Kuchwara ◽

Márton Czikkely ◽

Dávid Balogh ◽

...

Keyword(s):

Genome Engineering ◽

Human Pathogen ◽

Microbial Genomes ◽

E Coli ◽

Highly Active ◽

Recombination Processes ◽

High Throughput Method ◽

New Hosts ◽

Protein Discovery ◽

Genome Scale

Exploiting bacteriophage-derived homologous recombination processes has enabled precise, multiplex editing of microbial genomes and the construction of billions of customized genetic variants in a single day. The techniques that enable this, multiplex automated genome engineering (MAGE) and directed evolution with random genomic mutations (DIvERGE), are however, currently limited to a handful of microorganisms for which single-stranded DNA-annealing proteins (SSAPs) that promote efficient recombineering have been identified. Thus, to enable genome-scale engineering in new hosts, efficient SSAPs must first be found. Here we introduce a high-throughput method for SSAP discovery that we call “serial enrichment for efficient recombineering” (SEER). By performing SEER inEscherichia colito screen hundreds of putative SSAPs, we identify highly active variants PapRecT and CspRecT. CspRecT increases the efficiency of single-locus editing to as high as 50% and improves multiplex editing by 5- to 10-fold inE. coli, while PapRecT enables efficient recombineering inPseudomonas aeruginosa, a concerning human pathogen. CspRecT and PapRecT are also active in other, clinically and biotechnologically relevant enterobacteria. We envision that the deployment of SEER in new species will pave the way toward pooled interrogation of genotype-to-phenotype relationships in previously intractable bacteria.

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Cloning of Thalassiosira pseudonana’s Mitochondrial Genome in Saccharomyces cerevisiae and Escherichia coli

Biology ◽

10.3390/biology9110358 ◽

2020 ◽

Vol 9 (11) ◽

pp. 358

Author(s):

Ryan R. Cochrane ◽

Stephanie L. Brumwell ◽

Arina Shrestha ◽

Daniel J. Giguere ◽

Samir Hamadache ◽

...

Keyword(s):

Escherichia Coli ◽

Saccharomyces Cerevisiae ◽

Mitochondrial Genome ◽

Phaeodactylum Tricornutum ◽

Genome Engineering ◽

Genome Instability ◽

Thalassiosira Pseudonana ◽

Genome Integrity ◽

Mitochondrial Genomes ◽

E Coli

Algae are attractive organisms for biotechnology applications such as the production of biofuels, medicines, and other high-value compounds due to their genetic diversity, varied physical characteristics, and metabolic processes. As new species are being domesticated, rapid nuclear and organelle genome engineering methods need to be developed or optimized. To that end, we have previously demonstrated that the mitochondrial genome of microalgae Phaeodactylum tricornutum can be cloned and engineered in Saccharomyces cerevisiae and Escherichia coli. Here, we show that the same approach can be used to clone mitochondrial genomes of another microalga, Thalassiosira pseudonana. We have demonstrated that these genomes can be cloned in S. cerevisiae as easily as those of P. tricornutum, but they are less stable when propagated in E. coli. Specifically, after approximately 60 generations of propagation in E. coli, 17% of cloned T. pseudonana mitochondrial genomes contained deletions compared to 0% of previously cloned P. tricornutum mitochondrial genomes. This genome instability is potentially due to the lower G+C DNA content of T. pseudonana (30%) compared to P. tricornutum (35%). Consequently, the previously established method can be applied to clone T. pseudonana’s mitochondrial genome, however, more frequent analyses of genome integrity will be required following propagation in E. coli prior to use in downstream applications.

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A highly precise and portable genome engineering method allows comparison of mutational effects across bacterial species

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1520040113 ◽

2016 ◽

Vol 113 (9) ◽

pp. 2502-2507 ◽

Cited By ~ 93

Author(s):

Ákos Nyerges ◽

Bálint Csörgő ◽

István Nagy ◽

Balázs Bálint ◽

Péter Bihari ◽

...

Keyword(s):

Genome Engineering ◽

Bacterial Species ◽

Bacterial Genome ◽

Dominant Negative ◽

Laboratory Model ◽

Dominant Negative Mutant ◽

Mutant Library ◽

E Coli ◽

Multiple Loci ◽

Wide Range

Currently available tools for multiplex bacterial genome engineering are optimized for a few laboratory model strains, demand extensive prior modification of the host strain, and lead to the accumulation of numerous off-target modifications. Building on prior development of multiplex automated genome engineering (MAGE), our work addresses these problems in a single framework. Using a dominant-negative mutant protein of the methyl-directed mismatch repair (MMR) system, we achieved a transient suppression of DNA repair inEscherichia coli, which is necessary for efficient oligonucleotide integration. By integrating all necessary components into a broad-host vector, we developed a new workflow we term pORTMAGE. It allows efficient modification of multiple loci, without any observable off-target mutagenesis and prior modification of the host genome. Because of the conserved nature of the bacterial MMR system, pORTMAGE simultaneously allows genome editing and mutant library generation in other biotechnologically and clinically relevant bacterial species. Finally, we applied pORTMAGE to study a set of antibiotic resistance-conferring mutations inSalmonella entericaandE. coli. Despite over 100 million y of divergence between the two species, mutational effects remained generally conserved. In sum, a single transformation of a pORTMAGE plasmid allows bacterial species of interest to become an efficient host for genome engineering. These advances pave the way toward biotechnological and therapeutic applications. Finally, pORTMAGE allows systematic comparison of mutational effects and epistasis across a wide range of bacterial species.

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A versatile genetic engineering toolkit for E. coli based on CRISPR-prime editing

Nature Communications ◽

10.1038/s41467-021-25541-3 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Yaojun Tong ◽

Tue S. Jørgensen ◽

Christopher M. Whitford ◽

Tilmann Weber ◽

Sang Yup Lee

Keyword(s):

Genome Engineering ◽

Genetic Manipulation ◽

Nucleotide Substitutions ◽

Single Nucleotide ◽

Base Editing ◽

Guide Rna ◽

E Coli ◽

Potential Basis ◽

Low Efficiency ◽

Nucleotide Resolution

AbstractCRISPR base editing is a powerful method to engineer bacterial genomes. However, it restricts editing to single-nucleotide substitutions. Here, to address this challenge, we adapt a CRISPR-Prime Editing-based, DSB-free, versatile, and single-nucleotide resolution genetic manipulation toolkit for prokaryotes. It can introduce substitutions, deletions, insertions, and the combination thereof, both in plasmids and the chromosome of E. coli with high fidelity. Notably, under optimal conditions, the efficiency of 1-bp deletions reach up to 40%. Moreover, deletions of up to 97 bp and insertions up to 33 bp were successful with the toolkit in E. coli, however, efficiencies dropped sharply with increased fragment sizes. With a second guide RNA, our toolkit can achieve multiplexed editing albeit with low efficiency. Here we report not only a useful addition to the genome engineering arsenal for E. coli, but also a potential basis for the development of similar toolkits for other bacteria.

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Rapid and scalable characterization of CRISPR technologies using an E. coli cell-free transcription-translation system

10.1101/169441 ◽

2017 ◽

Cited By ~ 4

Author(s):

Ryan Marshall ◽

Colin S. Maxwell ◽

Scott P. Collins ◽

Michelle L. Luo ◽

Thomas Jacobsen ◽

...

Keyword(s):

Protein Purification ◽

Dna Cleavage ◽

Genome Engineering ◽

Gene Repression ◽

Live Cells ◽

Translation System ◽

Guide Rna ◽

E Coli ◽

Translation Systems

ABSTRACTCRISPR-Cas systems have offered versatile technologies for genome engineering, yet their implementation has been outpaced by the ongoing discovery of new Cas nucleases and anti-CRISPR proteins. Here, we present the use of E. coli cell-free transcription-translation systems (TXTL) to vastly improve the speed and scalability of CRISPR characterization and validation. Unlike prior approaches that require protein purification or live cells, TXTL can express active CRISPR machinery from added plasmids and linear DNA, and TXTL can output quantitative dynamics of DNA cleavage and gene repression. To demonstrate the applicability of TXTL, we rapidly measure guide RNA-dependent DNA cleavage and gene repression for single- and multi-effector CRISPR-Cas systems, accurately predict the strength of gene repression in E. coli, quantify the inhibitory activity of anti-CRISPR proteins, and develop a fast and scalable high-throughput screen for protospacer-adjacent motifs. These examples underscore the potential of TXTL to facilitate the characterization and application of CRISPR technologies across their many uses.

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A Framework for Predicting Design Failures in Engineered Genetic Codes

10.1101/363812 ◽

2018 ◽

Author(s):

Bea Yu ◽

Matthew Murphy ◽

Peter A. Carr

Keyword(s):

Machine Learning ◽

Codon Usage ◽

Genetic Code ◽

Genome Engineering ◽

Machine Learning Algorithms ◽

Entire Genome ◽

Genetic Incompatibility ◽

E Coli ◽

Genetic Codes ◽

Data Driven Approach

AbstractExtreme engineering of an organism’s genetic code could impart true genetic incompatibility, even blocking effects of horizontal gene transfer and viral infection. Recent experiments exploring this possibility demonstrate that such radical genome engineering achievements are plausible. However, it is unclear when the modifications will compromise the fitness of an organism. Efforts to reformat an entire genome are difficult and expensive; computational methods predicting fruitful experimental trajectories could play a pivotal role in advancing such efforts. We present a framework for building in silico models to assist genome-scale engineering. Genetic code engineering requires choosing from many possible codon-usage schemes, to find a design that is viable and effective. We use machine learning to identify which alternative codon-usage schemes are likely to result in no observed viable cells. Our data-driven approach employs observations of how modifying codon usage in individual genes impacted observed viability in E. coli, revealing salient features for early identification of problematic genetic code designs. We achieved an average area under the receiver operating characteristic of 0.72 on out-ofsample data.Author SummaryAs machine learning and artificial intelligence play an increasingly central role in science and engineering, it will be important to establish standardized techniques that facilitate the dialogue between experimentation and modeling. Biological experimental techniques are concurrently evolving at a rapid pace, providing unique opportunities to collect high-quality, novel information that was previously unobtainable. This work navigates the landscape of this vast, new territory, identifies interesting landmarks for exploration and posits new approaches towards advancing our research efforts in these areas. In this work, we show that, using a small dataset of 47 observations and rigorous nested cross validation techniques, we can build a model that makes better-than-random predictions of how codon usage changes in essential genes influence viability in E. coli. These predictions can be used to inform experimental trajectories in both genetic code and codon optimization experiments. We discuss ways to improve this model, iteratively, by performing high value experiments that decrease uncertainty in predictions and extrapolation error. Finally, we present novel visualization methods to aid in developing intuitions for how re-coding impacts groups of genes. These methods are also useful tools in building important insights into how well machine learning algorithms can generalize to new data.

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