CRISPR /Cas9 recombineering‐mediated deep mutational scanning of essential genes in Escherichia coli
2020; Springer Nature; Volume: 16; Issue: 3 Linguagem: Inglês
10.15252/msb.20199265
ISSN1744-4292
AutoresAlaksh Choudhury, Jacob Fenster, Reilly G. Fankhauser, Joel L. Kaar, Olivier Tenaillon, Ryan T. Gill,
Tópico(s)Bacterial Genetics and Biotechnology
ResumoMethod16 March 2020Open Access Transparent process CRISPR/Cas9 recombineering-mediated deep mutational scanning of essential genes in Escherichia coli Alaksh Choudhury Department of Chemical and Biological Engineering, University of Colorado, Boulder, CO, USA IAME, INSERM, Université de Paris, Paris, France Search for more papers by this author Jacob A Fenster Department of Chemical and Biological Engineering, University of Colorado, Boulder, CO, USA Search for more papers by this author Reilly G Fankhauser Renewable & Sustainable Energy Institute, University of Colorado, Boulder, CO, USA Search for more papers by this author Joel L Kaar Department of Chemical and Biological Engineering, University of Colorado, Boulder, CO, USA Search for more papers by this author Olivier Tenaillon Corresponding Author [email protected] IAME, INSERM, Université de Paris, Paris, France Search for more papers by this author Ryan T Gill Corresponding Author [email protected] orcid.org/0000-0003-2062-303X Department of Chemical and Biological Engineering, University of Colorado, Boulder, CO, USA Renewable & Sustainable Energy Institute, University of Colorado, Boulder, CO, USA Novo Nordisk Foundation Center for Biosustainability, Danish Technical University, Copenhagen, Denmark Search for more papers by this author Alaksh Choudhury Department of Chemical and Biological Engineering, University of Colorado, Boulder, CO, USA IAME, INSERM, Université de Paris, Paris, France Search for more papers by this author Jacob A Fenster Department of Chemical and Biological Engineering, University of Colorado, Boulder, CO, USA Search for more papers by this author Reilly G Fankhauser Renewable & Sustainable Energy Institute, University of Colorado, Boulder, CO, USA Search for more papers by this author Joel L Kaar Department of Chemical and Biological Engineering, University of Colorado, Boulder, CO, USA Search for more papers by this author Olivier Tenaillon Corresponding Author [email protected] IAME, INSERM, Université de Paris, Paris, France Search for more papers by this author Ryan T Gill Corresponding Author [email protected] orcid.org/0000-0003-2062-303X Department of Chemical and Biological Engineering, University of Colorado, Boulder, CO, USA Renewable & Sustainable Energy Institute, University of Colorado, Boulder, CO, USA Novo Nordisk Foundation Center for Biosustainability, Danish Technical University, Copenhagen, Denmark Search for more papers by this author Author Information Alaksh Choudhury1,2, Jacob A Fenster1,‡, Reilly G Fankhauser3,‡, Joel L Kaar1, Olivier Tenaillon *,2,‡ and Ryan T Gill *,1,3,4,‡ 1Department of Chemical and Biological Engineering, University of Colorado, Boulder, CO, USA 2IAME, INSERM, Université de Paris, Paris, France 3Renewable & Sustainable Energy Institute, University of Colorado, Boulder, CO, USA 4Novo Nordisk Foundation Center for Biosustainability, Danish Technical University, Copenhagen, Denmark ‡These authors contributed equally to this work ‡These authors contributed equally to this work *Corresponding author. Tel: +33 1 57 27 77 45; E-mail: [email protected] *Corresponding author. Tel: +45 93 51 19 29; E-mail: [email protected] Mol Syst Biol (2020)16:e9265https://doi.org/10.15252/msb.20199265 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract 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. Synopsis The study presents a CRISPR/Cas9-mediated genomic error-prone editing (CREPE) technology for generating a library of mutants within a targeted Escherichia coli gene in its native genomic context. In CREPE, Cas9-mediated recombineering is optimized to integrate a library of mutations made by error-prone PCR at a target locus on the chromosome. Using CREPE, high diversity libraries of mutants are generated, with 80% mutated clones for non-essential genes and 40–60% mutated clones for essential genes after genome editing. This allows deep mutational scanning of a gene of interest. CREPE is applied to scan mutations in rpoB that confer resistance to the drug rifampicin. Introduction The function of proteins, which drive all cellular processes, can be uncovered by studying mutations in their sequence. Historically, the analysis of the impact of a handful of mutations on protein stability and activity helped lay the foundations of protein science (Fersht et al, 1985; Wells, 1990; Clackson & Wells, 1995; Bloom et al, 2006; Romero & Arnold, 2009). More recently, the advent of next-generation DNA sequencing platforms has paved way for the emergence of high-throughput sequence-to-function mapping such as deep mutational scanning (DMS), which expands our access to previously unexplored areas of fitness landscapes for proteins (Araya & Fowler, 2011; Fowler & Fields, 2014). In DMS, activities of thousands of mutants in large libraries, covering most or potentially all possible substitutions in the protein sequence, are measured simultaneously using next-generation sequencing (Araya & Fowler, 2011; Fowler & Fields, 2014). This more complete picture of the protein sequence space has the potential to provide unprecedented mechanistic insights into protein structure and activity, evolution, epistasis (Sarkisyan et al, 2016; Kemble et al, 2019), intracellular behavior, and disease phenotypes (Araya & Fowler, 2011; Fowler & Fields, 2014). Essential bacterial genes are an especially interesting target for DMS because they play an important role in bacterial evolution (Long et al, 2015; Maddamsetti et al, 2017), the emergence of antibiotic resistance (Walsh, 2000; Allen et al, 2010), and strain engineering (Winkler et al, 2016; de Jong et al, 2017). It is important to modify the essential genes in their native genomic context. Expressing essential genes on plasmids alters the cellular fitness because of different expression levels due to copy number effects (Gibson et al, 2013) and the loss of epigenetic regulation. Current genome mutagenesis techniques suffer from low-editing efficiencies and mutational biasing, which greatly decrease the quality of the fitness data (i.e., due to the overabundance of wild type or a few over-represented members). As such, comprehensive DMS of essential genes using these approaches has remained elusive, especially in bacteria. Bacterial genome-editing technologies have advanced greatly in the past decade. One technology is multiplexed automated genome engineering (MAGE) that is based on lambda Red-mediated recombination of single-stranded oligos for desired allelic exchange on the genome or "Recombineering" (Wang et al, 2009). The efficiency of introducing a single nucleotide change using recombineering is often very low and context-dependent (Sharan et al, 2009). Using MAGE, efficiency and throughput are improved by re-transforming the same large pool of oligos over repeated cycles (Wang et al, 2009). This technique has been successfully used for several outstanding synthetic biology and metabolic engineering applications (Wang et al, 2009; Sandoval et al, 2012; Lajoie et al, 2013b; Raman et al, 2014; Amiram et al, 2015). However, it may be difficult to apply MAGE for DMS of essential genes. Due to the repeated transformation cycles during library construction, the mutations that are deleterious or even neutral to the host would be lost (Wang et al, 2009). Repeated heat shock and electroporation during the recombineering cycles in the MAGE protocol (Wang et al, 2009) would also add additional stress that would be detrimental to the diversity in essential genes. Additionally, in order to increase the efficiency of recombination in MAGE, significant modifications such as deletion of methyl-directed mismatch repair (MMR) and DNA primase (dnaG) are required, which change the native genetic context (Wang et al, 2009; Sawitzke et al, 2011). Deletion of mismatch repair systems increases the background mutation rate (Isaacs et al, 2011), which may also confound fitness estimates. Genome-editing technologies developed using CRISPR/Cas9-mediated recombineering have helped address several challenges with MAGE. A chimeric guide RNA (gRNA) programs the Cas9 endonuclease to induce a DNA double-strand break (DSB) at any genomic target upstream of a 5′-NGG-3′ PAM sequence and complementary to the 20-bp spacer sequence in the gRNA (Jinek et al, 2012). In several bacteria, including Escherichia coli, the Cas9:gRNA-mediated DNA DSB induces cell death due to a lack of adequate DSB repair pathways (Jiang et al, 2013). Therefore, Cas9:gRNA-induced DSBs can select for PAM substitutions introduced by recombineering (Cong et al, 2013; Jiang et al, 2013). Synonymous PAM-inactivating mutations (SPMs) can be coupled to other mutations in the same recombination template for precise genome manipulation with high efficiency using a single transformation step (Pyne et al, 2015; Reisch & Prather, 2015; Bassalo et al, 2016; Chung et al, 2017; Wang et al, 2018). High-throughput genome editing with Cas9-mediated recombineering was achieved recently using CRISPR-enabled trackable genome engineering (CREATE) (Garst et al, 2017). Using CREATE, the DNA encoding the gRNA expressed under a constitutive promoter was covalently linked to the DNA repair template on 250-bp editing cassettes (Garst et al, 2017). Over 100,000 editing cassettes can be synthesized on microarray chips and subsequently cloned in high-throughput into cells with active Cas9 and lambda Red recombination to generate genome-wide mutation libraries. The editing cassettes on the plasmid also serve as the barcode to track the mutations before and after selection to assign fitness scores to each mutation (Garst et al, 2017). The technology has been used for directed evolution of E. coli proteins, pathways, and strains (Shalem et al, 2014; Cobb et al, 2015; Cho et al, 2017; Liang et al, 2017; Liu et al, 2017; Lu et al, 2017; Wu et al, 2017a,b; Zhu et al, 2017; Bassalo et al, 2018). However, applying CREATE for DMS proved to be challenging. Anywhere between 10 and 60% of randomly chosen gRNA targeting different genomic loci have been shown not to induce Cas9:gRNA-induced cell death (Cui & Bikard, 2016; Zerbini et al, 2017). Consequently, due to variable selection, editing efficiency can vary between 0 and 100% across gRNAs (Garst et al, 2017; Zerbini et al, 2017). Cells with gRNAs that fail to induce DSB-mediated cell death can grow significantly faster than cells with active gRNAs undergoing DSBs and editing (Jiang et al, 2015; Cui & Bikard, 2016). Consequently, in high-throughput non-DSB-inducing gRNAs, with low-editing efficiency, take over the population and reduce overall editing efficiency to only ~ 1–4% (Bassalo et al, 2018). Several gRNAs also cause unintended mutations on the genome that are not encoded in the repair template (Cui & Bikard, 2016; Zerbini et al, 2017). Consequently, cells with no edits and unintended mutations can be falsely tracked as beneficial mutations. Finally, each gRNA is coupled to a different synonymous PAM mutation (SPM) and synonymous mutations can lead to significant fitness effects, especially in essential genes (Lind et al, 2010; Agashe et al, 2013; Lajoie et al, 2013a). Because of these limitations, CREATE experiments have largely been limited to finding mutants with large fitness effects in the presence of strong selective pressures (Bassalo et al, 2018; Pines et al, 2018). We posited that in order to target a single genomic locus, we could use a single pre-screened gRNA and synonymous PAM-inactivating mutations (SPM). In this study, we discuss the CRISPR/Cas9-mediated genomic error-prone editing (CREPE) technology. As opposed to other Cas9-mediated high-throughput technologies in E. coli, in the CREPE protocol we use a single gRNA to integrate an error-prone PCR library of the target with the SPM on the genome (Fig 1). Recently, a similar technology, CASPER, was reported in yeast (Jakočiūnas et al, 2018). However, yeast has a significantly higher recombination efficiency than bacteria such as E. coli. Recombination efficiency with linear dsDNA templates is very low in E. coli (Murphy et al, 2000), and recombineering using dsDNA template with limited single nucleotide changes is poorly understood. Therefore, we varied the homology arm length and the Cas9 recombineering system to improve recombination and our understanding of recombination using a repair template with single nucleotide changes. We successfully developed a platform that efficiently generates unbiased and diverse genomic mutant libraries with > 80% editing efficiency for non-essential genes and > 55% efficiency for essential genes. Additionally, while CASPER was used for directed evolution, we adapted CREPE for use as a DMS platform to study essential E. coli genes in their native genomic context. Using CREPE, we scored the fitness of naturally accessible mutations in the RNA polymerase beta subunit that confer resistance to rifampicin. Figure 1. CREPE strategyGeneral workflow for the CREPE protocol. Download figure Download PowerPoint Results CREPE protocol In the CREPE workflow (Fig 1), we initially screened for a gRNA centered around the genomic target of interest that enables over 95% editing efficiency for replacing the NGG PAM with the synonymous PAM mutation (SPM) to be used in the repair template. We also ensured that the SPM does not affect the fitness of the cells and that their growth is comparable to wild-type E. coli. In order to use a single gRNA to incorporate multiple mutations, we link the synonymous PAM mutation and secondary targeted mutations on the same repair template. Since we wanted to target as large a window as possible (300–400 bp long) for mutagenesis using a single gRNA, a dsDNA repair template as opposed to single-stranded oligos to overcome the length limitations of oligo synthesis was used. We amplified and cloned the target region with the SPM and sufficient unmutated end homology, which was used for recombination, into a plasmid and develop error-prone PCR libraries (Fig 1). We amplified and co-transformed the linear donor error-prone PCR library with the gRNA-encoding plasmid in cells with active Cas9 and lambda Red recombination proteins, encoded by a single plasmid (preprint: Morgenthaler et al, 2019), to integrate the mutated donor onto the genome. The plasmid encodes cas9 expressed under the constitutive Pro1 promoter (Davis et al, 2011) and the lambda Red recombination genes exo, beta, and gam expressed using the heat-inducible pL promoter, induced by heat shock at 42°C (Yu et al, 2000). Similar to several lambda Red recombination systems (Sharan et al, 2009), the plasmid also has the temperature curable pSC101 origin of replication, which replicates at 30°C and is cured from the cells at 37°C (Phillips, 1999). In standard lambda Red recombineering protocols, the lambda Red recombination system is induced for 15 min prior to recombination (Sharan et al, 2009). Since the induction time is shorter than the replication time, the plasmid should be retained in most cells. The plasmid is cured prior to selection to remove the bulky plasmid that could impact cellular fitness and also to avoid fitness changes due to off-target effects of Cas9 (Hsu et al, 2013; Fig 1). We then sequenced the target region directly from the genome using deep sequencing before and after selection to quantify the frequency of mutations and estimate the distribution of fitness for the mutants in the library (Fig 1). There is a strong preference for integration of low-diversity sequences with PAM-proximal mutations Cas9:gRNA-induced DSBs increase editing efficiency primarily by selecting for edited cells with the SPM introduced by recombineering (Cong et al, 2013; Jiang et al, 2013). Initially, we assumed that recombineering using a dsDNA substrate with single nucleotide changes may follow the same mechanism proposed for dsDNA-mediated gene replacement (Fig 2A). The lambda-Exo protein processes the dsDNA template into a single-stranded intermediate, which anneals to the Okazaki fragment using both ends by lambda-beta protein, and the gene replacement is completed by the native replication machinery (Mosberg et al, 2010; Fig 2A). Figure 2. Impact of donor diversity and homology arm length on mutation efficiency Proposed mechanism for dsDNA-mediated gene replacement using recombineering occurs via a single-stranded intermediate (Mosberg et al, 2010) (top). We assume that lambda-mediated recombination of the CREPE substrate with limited mutations may also follow this proposed mechanism (bottom). In our experiments to optimize CREPE, we target a 330-bp-long region within the galK gene. Initial tests were performed using high-diversity (mean 3–4 mutations per donor sequence) and low-diversity (mean 1–2 mutations per donor sequence) donors (Materials and Methods) with 50-bp-long unmutated end homologies. A comparison of percentage sequence variants categorized by the number of mutations (x-axis) between the high-diversity donors before (red) and after (blue) integration on the genome. #Mutations = 1 corresponds to sequences with only the SPM. The experiments were performed in biological replicates. The trends for the replicates are represented by solid (—) and dashed (--) lines, respectively. Change in mutation frequency per base (%) and percentage of sequences with a mutation at the position, using the high-diversity donor, are represented as rolling mean over 10 bases versus the distance from the PAM measured in base pairs. The experiments were performed in biological replicates. The trends for the replicates are represented by solid (—) and dashed (--) lines, respectively. Comparison of % mutation efficiency determined by deep sequencing the genome after integrating the high-diversity donor using end homology of lengths of 50, 150, and 250 bp. Significant changes, determined as P-value < 0.05 for 1-tailed Student's t-test, are demonstrated using the *. Each value represents the mean, and error bars represent standard deviation for biological replicates. Comparison of change in mutation frequency per base (%) and percentage of sequences with a mutation at each position, using the high-diversity donor, are represented as rolling mean over 10 bases versus the distance from the PAM (base pairs) for recombination of the high-diversity donor using 50- and 250-bp end homology. The dashed lines represent an exponential decay model fitted to quantify the decrease in mutation frequency with distance from PAM. The equations on top represent the fitted equation, and the R-squared value is the lower R-squared value of the 2 fits. A comparison of percentage sequence variants categorized by the number of mutations (x-axis) for the high-diversity donor before (red) and after genome integration using 50-bp (green)- and 250-bp (blue)-long end homology. The inset highlights percent of sequences with 3–9 mutations in addition to the SPM. #Mutations = 1 corresponds to sequences with only the SPM. The experiments were performed in biological replicates. The trends for the replicates are represented by solid (—) and dashed (--) lines, respectively. Download figure Download PowerPoint Beta can stably anneal DNA at both ends of the single-stranded repair intermediate with 1- to 2-kb-long non-homologous region using only 50 bp of flanking homology (Fig 2A; Maresca et al, 2010; Mosberg et al, 2010). Therefore, we expected that using a recombination template with single nucleotide changes, interactions between the annealed flanking homology may not have a significant impact on recombination, and the efficiency of recombination would be similar regardless of the number of mutations in the donor sequence. We used a 330-bp region in the galK gene as the target and developed two error-prone PCR donor libraries with high diversity and low diversity that contained 92 and 66% sequences with one or more mutations in addition to the synonymous PAM mutation (SPM), respectively (Fig 2B and Appendix Fig S1). The high-diversity donor contained a mean of 3–4 mutations per donor sequence, and the low-diversity donor contained a mean of 1–2 mutations per donor sequence (Appendix Fig S1). Hereon, we refer to the percentage of sequences with mutations in addition to the SPM as the mutation efficiency. As expected, after integration on the genome mutation efficiency with the high-diversity donor library was higher than the low-diversity donor (Fig EV1A). While in the high-diversity donor the sequences with 1–5 mutations were uniformly distributed, we observed a substantial bias toward sequences with 1 (only SPM) and 2 (1 mutation in addition to the SPM) on the genome (Fig 2C). Biased preference for sequences with fewer mutations was also observed with the low-diversity library (Fig EV1B). Contrary to our expectations, the efficiency of recombination decreased with an increasing number of mutations. Click here to expand this figure. Figure EV1. Impact of donor diversity and homology arm length on mutation efficiency using a low-diversity donor Comparison of mutation efficiency between the high- and low-diversity donors before (black) and after (blue) integration on the genome. The lighter colors represent the fraction of sequences with only the SPM. Each value represents the mean, and error bars represent the standard deviation for two biological replicates of deep sequencing data. A comparison of percentage sequence variants categorized by the number of mutations (x-axis) between the low-diversity donor before (red) and after (blue) integration on the genome. #Mutations = 1 corresponds to sequences with only the SPM. The experiments were performed in biological replicates. The trends for the replicates are represented by solid (—) and dashed (--) lines, respectively. Change in mutation frequency per base (%) and percentage of sequences with a mutation at the position, using the low-diversity donor, are represented as the rolling mean over 10 bases versus the distance from the PAM measured in base pairs. Comparison of % mutation efficiency determined by deep sequencing the genome after integrating the low-diversity donor using end homology of lengths of 50, 150, and 250 bp. Each value represents the mean, and error bars represent the standard deviation for two biological replicates. Comparison of the change in mutation frequency per base (%) and percentage of sequences with a mutation at each position, using the high-diversity donor, are represented as the rolling mean over 10 bases versus the distance from the PAM (base pairs) for recombination of the low-diversity donor using 50- and 250-bp end homology. A comparison of percentage sequence variants categorized by the number of mutations (x-axis) for the low-diversity donor before (red) and after genome integration using 50-bp (green)- and 250-bp (blue)-long end homology. The trends for the replicates are represented by solid (—) and dashed (--) lines, respectively. Download figure Download PowerPoint Additionally, if the end homology is sufficient for beta-mediated annealing (Fig 2A), the position of mutations within the target should not impact recombination efficiency (Li et al, 2013). While the mutation frequency in the donor was consistently high, we observed a decrease in the mutation frequency per residue with increasing distance from PAM on the genome (Figs 2D and EV1C). Similar observations have been made when using double-stranded plasmid donors (Garst et al, 2017). With the high-diversity donor, the mutation frequency per residue exceeded the error frequency observed in unmutated regions across the entire target. However, with the low-diversity donor, high mutation frequencies per residue were observed predominantly within a 100-bp region around the PAM (Fig EV1C). Mutations closer to the PAM had higher chances of being integrated on the genome as opposed to ones further from the PAM. Increasing end-homology length improves mutation efficiency for high-diversity donor We posited that the PAM-proximal mutation bias was likely due to better annealing of sequences with mutations closer to the PAM because of longer uninterrupted end homology versus sequences with mutations distal to the PAM. Therefore, we evaluated whether the PAM-proximal bias could be alleviated by increasing end-homology length. With the high-diversity donor, we observed a 11.4 ± 2.1% increase in mutation efficiency by increasing the end homology from 50 to 250 bp (Fig 2E). For each HA length, the donor libraries were prepared by PCR amplification using the same error-prone PCR plasmid library, but with different primers to obtain different homology arm lengths. We used very high template concentrations, a high-fidelity polymerase, and low and same number of amplification cycles for each PCR. Therefore, the mutation efficiency of each donor library was expected to be consistent and similar to that of the plasmid error-prone PCR library. Therefore, we assumed that the variation in mutation efficiency on the genome due to variation in mutation efficiency of the donor was unlikely. To test whether the PAM-proximal mutational bias reduced with an increase in end-homology length, we quantified the decrease in mutation frequency with increasing distance from PAM as an exponential decay: where μ is the mutation frequency, μo is the maximum mutation frequency, λ is the decay rate by position, and x is the distance from PAM. The λ after genome integration with 50-bp-long end homology with the high-diversity donor was significantly higher than that of the donor library itself. This demonstrated that the transformation led to a biased distribution of mutations as a function of distance from the PAM site. For the high-diversity donor library with an increase in end-homology length, there was a significant increase in μo (ANOVA P-value of interaction = 10−16) and a slight but significant change in λ as well (ANOVA P-value of interaction = 0.005) (Fig 2F). We observed a significant increase in the percentage of sequences with higher diversity (number of mutations in addition to the SPM > 2) on the genome (Fig 2G) (P-value for chi-squared test < 10−16). Increasing the HA length did not substantially reduce the PAM-proximal mutation bias but improved the mutation efficiency by improving recombination of donor sequences with a higher number of mutations per sequence on the genome. This was corroborated by the observation that for the low-diversity library, which lacked high-diversity sequences in the donor to begin with (Appendix Fig S1), the mutation efficiency and per-base mutation frequency did not change with the increase in the HA length (Fig EV1D and E). Inhibiting mutL improved mutation efficiency Replication forks with beta-annealed ssDNA are usually resolved by native DNA polymerases and ligases (Sawitzke et al, 2011; Li et al, 2013; Fig 2A). Mismatches between the wild-type sequences and the recombination substrates are corrected by methyl-directed mismatch repair (MMR) to reduce recombination efficiency (Costantino & Court, 2003; Sawitzke et al, 2011). Therefore, deleting mutL or mutS genes improves recombineering efficiency with mutagenic single-stranded oligos (Costantino & Court, 2003; Sawitzke et al, 2011). However, the background mutation rates can increase significantly in ΔmutS and ΔmutL strains (Isaacs et al, 2011; Nyerges et al, 2014). Recently, the background mutation rate was significantly reduced by temporarily co-expressing a dominant-negative allele of MutL, MutL-E32K, with the lambda Red recombination proteins by heat shock at 42°C using the pL promoter (Nyerges et al, 2016). We cloned the mutL-E32K gene similarly in the plasmid encoding cas9 and lambda Red recombination proteins (Fig 3A) and compared the mutation efficiency in the absence and presence of MutL-E32K using the high-diversity library with 250 bp HA. In the presence of MutL-E32K, the non-PAM editing efficiency improved by 24.2 ± 0.8%, (Fig 3B) and we also observed an increase in mutation frequency per position across the target (Figs 3C and 2E). Expression of MutL-E32K significantly increased the maximum mutation frequency (μo, ANOVA P-value of interaction = 10−16 Fig 3C) and decreased the PAM-proximal positional bias of mutations (reduction in λ, ANOVA P-value of interaction = 10−16 Fig 3C). Figure 3. Impact of blo
Referência(s)