Methods for identifying epigenetic factors influencing gene editing and modulating gene editing outcome via epigenetic modulation

Abstract

Disclosed are methods for modulating gene editing efficiency of a target site in a genomic region of a cell. Modulating gene editing efficiency may include modulating chromatin accessibility of the genomic region and / or modulating transcriptional activity of a gene in the genomic region proximal to the gene editing target site. Also disclosed are methods for determining prime editing efficiencies across a genome, which may be used to identify epigenetic factors influencing gene editing such as by correlating the prime editing efficiencies with features of epigenetic environment. Reporter systems and related methods for mapping a genomic location of at least one modification in a genome of a cell are also disclosed.

Description

METHODS FOR IDENTIFYING EPIGENETIC FACTORS INFLUENCING GENE EDITING AND MODULATING GENE EDITING OUTCOME VIA EPIGENETIC MODULATION CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Application No. 63 / 495372, filed April 11, 2023. STATEMENT REGARDING SEQUENCE LISTING

[0002] The Sequence Listing XML associated with this application is provided in XML format and is hereby incorporated by reference into the specification. The name of the XML file containing the sequence listing is 3915-P1300WO- UW_Seq_Listing_ST26.xml. The XML file is 1,292,777 bytes; was created on March 28, 2024; and is being submitted electronically via Patent Center with the filing of the specification. STATEMENT OF GOVERNMENT LICENSE RIGHTS

[0003] This invention was made with government support under Grant Nos. R01HG010632, UM1HG011966, and F32HG011817, awarded by the National Institutes of Health (NIH). The government has certain rights in the invention. BACKGROUND

[0004] The development of prime editing has shifted the paradigm of genome manipulation from less controllable site-directed mutagenesis to more precise genome reprogramming. However, the efficiency of prime editing is still suboptimal and varies across genomic contexts, which limits its application in both basic and clinical research. The low efficiency can be attributed to inefficient generation of a single-strand DNA flap containing the edit and efficient removal of the intermediate by the mismatch repair (MMR) pathway. To overcome these issues, strategies such as improving the robustness of the prime editing core components and transiently dampening mismatch repair (MMR) response in cells have been developed and have shown promising enhancing effects on prime editing outcome. Additionally, several machine learning models have been generated to predict prime editing efficiency and guide experimental design. Like all other3915-P1300WO.UW () -1-DNA damage repair processes, prime editing, which involves the generation and repair of a single-strand DNA break, is very likely regulated by the local chromatin contexts. Nevertheless, these chromatin environmental factors remain largely uncharacterized. There remains a need for identifying epigenetic factors that govern prime editing outcomes and efficiencies to enhance its efficacy and broaden the therapeutic window of this promising genome editing tool. SUMMARY

[0005] This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This summary is not intended to identify key features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

[0006] In one aspect of the present disclosure, there is provided a reporter system for mapping a genomic location of at least one modification in a genome of a cell. In some embodiments, the reporter system comprises a vector comprising a promoter for in vitro transcription. In some embodiments, the vector is selected from a retroviral-based vector, a lentiviral-based vector, a transposon-based vector, and a serine integrase-based vector. In some embodiments, the transposon-based vector is a piggyBac-based vector. In certain embodiments, the vector is devoid of any cis-regulatory elements. In various embodiments, the promoter is selected from a bacteriophage T7 promoter, a T3 promoter, a Sp6 promoter and variants thereof.

[0007] In some embodiments, the reporter system disclosed herein further comprises a polynucleotide sequence linked to the promoter. In some embodiments, the polynucleotide sequence comprises an amplification primer binding domain / site. In some embodiments, the polynucleotide sequence further comprises a nucleic acid sequence for a target sequence. In some embodiments, the target sequence comprises a target sequence for a Cas9 guide RNA or a variant thereof. In some embodiments, the polynucleotide sequence further comprises a molecular identifier. In various embodiments, the polynucleotide sequence further comprises nucleic acid sequences selected from coding or non-coding transcripts, cis-regulatory elements, molecular identifier barcode, or functional or non-functional genomic elements.

[0008] In an embodiment, the reporter system disclosed herein comprises a vector comprising a promoter; and a polynucleotide sequence linked to the promoter. In some 3915-P1300WO.UW -2-embodiments, the polynucleotide sequence comprises: a target sequence; and a molecular identifier or a barcode. In some embodiments, the vector is selected from a retroviral-based vector, a lentiviral-based vector, a transposon-based vector, and a serine integrase-based vector. In some embodiments, the transposon-based vector is a piggyBac-based vector. In some embodiments, the vector is a transposon-based vector (e.g., a piggyBac-based vector) comprising an inverted terminal repeat (ITR) at each vector end. In certain embodiments, the vector is devoid of any cis-regulatory elements. In various embodiments, the promoter is selected from a bacteriophage T7 promoter, a T3 promoter, or a Sp6 promoter and variants thereof. In some embodiments, the target sequence comprises a target sequence for a Cas9 guide RNA or a variant thereof.

[0009] In another aspect, the present disclosure pertains to a method of mapping a genomic location of at least one reporter system disclosed herein in a genome of a cell. In certain embodiments of the present disclosure, the cell is selected from a prokaryotic cell or a eukaryotic cell. In certain embodiments, the cell is a eukaryotic cell, preferably a mammalian cell. In some embodiments, the method comprises integrating / inserting the at least one reporter system in the genome of the cell. In some embodiments, the method further comprises determining the insertion / integration site for the at least one reporter system within the genome of the cell.

[0010] In some embodiments, the step of integrating the at least one reporter system in the genome of the cell comprises transfecting the cell with the at least one reporter system. In an embodiment, the reporter system comprises a vector comprising: a promoter for in vitro transcription; and a polynucleotide sequence linked to the promoter. In certain embodiments, the polynucleotide sequence comprises: a target sequence; and a molecular identifier or a barcode. In some embodiments, the vector is selected from a retroviral-based vector, a lentiviral-based vector, a transposon-based-based vector, and a serine integrase- based vector. In some embodiments, the transposon-based vector is a piggyBac-based vector. In some embodiments, the vector is a transposon-based vector (e.g., a piggyBac- based vector) comprising an inverted terminal repeat (ITR) at each vector end. In certain embodiments, the vector is devoid of any cis-regulatory elements. In various embodiments, the promoter is selected from a bacteriophage T7 promoter, a T3 promoter, or a Sp6 promoter and variants thereof. In an embodiment, the step of integrating the at least one reporter system in the genome of the cell further comprises co-transfecting the cell with an expression construct capable of expressing a protein or an enzyme for excising a payload 3915-P1300WO.UW -3-of the vector and integrate the payload into the genome. In an embodiment, the protein or enzyme comprises transposase. In an embodiment, the payload of the vector comprises a promoter for in vitro transcription, a target sequence, and a molecular identifier. In an embodiment, the target sequence comprises an amplification primer binding domain / site. In an embodiment, the target sequence further comprises a target sequence for a Cas9 guide RNA or a variant thereof.

[0011] In various embodiments of the present disclosure, the step of determining the insertion site for the at least one reporter system within the genome of the cell comprises isolating genomic DNA of the cell comprising the at least one reporter system integrated in the genome of the cell. The method further comprises in vitro transcription of the isolated genomic DNA using the promoter of the reporter system to generate chimeric RNAs comprising a portion of the reporter system and of its neighboring genomic DNA. In an embodiment, the method comprises generating a cDNA library by reverse transcribing the chimeric RNAs followed by amplification of the cDNAs. In some embodiments, the cDNA library thus obtained is sequenced to associate the molecular identifiers with the insertion site to map the genomic location of the at least one reporter system in the genome of the cell. In various embodiments, the step of sequencing the library of cDNAs comprises high-throughput short- and long-read sequencing, and Sanger sequencing.

[0012] The reporter system and the methods disclosed herein may be used to map any number of genomic features in a genome of a cell including but not limited to expression cassettes of coding or non-coding transcripts, cis-regulatory elements (e.g., enhancers, silencers, and insulators), gene editing target sequences, recombination sites (e.g., loxP and FRT sites), and other functional or non-functional genomic elements.

[0013] Accordingly, in another aspect of the present disclosure, there is provided a method for determining prime editing efficiencies across a genome. In some embodiments, the method comprises generating a reporter pool of cells comprising a plurality of reporter systems, disclosed herein, integrated into genomes of a plurality of cells. In some embodiments, the step of generating a reporter pool of cells comprises transfecting the plurality of cells with the plurality of reporter systems. In some embodiments, the plurality of cells constitutively express Prime Editor (PE). In some embodiments, the PE comprises Cas9 Nickase and a Reverse transcriptase. 3915-P1300WO.UW -4-

[0014] In some embodiments, each of the plurality of the reporter system comprises a vector comprising a promoter for in vitro transcription; and a polynucleotide sequence linked to the promoter. In an embodiment, the polynucleotide sequence comprises: a target sequence; and a molecular identifier or a barcode. In some embodiments, the vector is selected from a retroviral-based vector, a lentiviral-based vector, a transposon-based vector (e.g., a piggyBac-based vector), and a serine integrase-based vector. In some embodiments, the vector comprises a transposon-based vector (e.g., a piggyBac-based vector) comprising an inverted terminal repeat (ITR) at each vector end. In certain embodiments, the method further comprises co-transfecting the plurality of cells with a transposase. In certain embodiments, the vector is devoid of any cis-regulatory elements. In various embodiments, the promoter is selected from a bacteriophage T7 promoter, a T3 promoter, or a Sp6 promoter and variants thereof. In some embodiments, the target sequence comprises a target sequence for a Cas9 guide RNA or a variant thereof.

[0015] In some embodiments, the method further comprises mapping insertion sites determining prime editing efficiency in the plurality of reporters in the genome of the plurality of cells by methods disclosed herein. In various embodiments, the method comprises transfecting the reporter pool of cells with pegRNAs comprising a sequence for introducing a desired edit at the target sequence of the plurality of reporter systems integrated into the genome of the plurality of cells, wherein each of the plurality of cells constitutively express a prime editor (PE). In various embodiments, the method comprises measuring the frequency of the desired edit in the genome of the cell. In some embodiments, the method further comprises correlating the prime editing efficiencies with features of epigenetic environment in the genome in about 100bp to about 2kb region centering at the edit sites of the target sequences. In various embodiments, the features of epigenetic environment are selected from DNA binding proteins, epigenetic modulators, histone modifications, DNase Hypersensitive sites, transposase-accessible chromatin (ATAC), and higher order chromatic structures. In some embodiments, the plurality of cells comprises eukaryotic cells, preferably mammalian cells. In some embodiments, the plurality of cells comprises stem cells (e.g., induced pluripotent stem cells (iPSC)).

[0016] In yet another aspect of the present disclosure there is provided a method for modulating gene editing efficiency of a target site in a genomic region of a cell. In some embodiments, the method comprises modulating chromatin accessibility of the genomic region and / or transcriptional activity of a gene in the genomic region proximal to 3915-P1300WO.UW -5-the gene editing target site. In some embodiments, gene editing comprises prime editing. In some embodiments, the prime editing target site is selected from an exon, an intron, a promoter, a 3’ UTR, a 5’ UTR, and intergenic sites in the genomic region. In some embodiments, the prime editing target site is downstream of a promoter of the gene. In some embodiments, the prime editing target site is downstream of a transcriptional start site (TSS) of the gene. In certain embodiments, the prime editing target site is upstream of a promoter of the gene. In various embodiments, modulating the prime editing efficiency comprises activating transcription of the gene proximal to the prime editing target site. In some embodiments, the step of activating transcription occurs before or during the step of prime editing of the target site. In some embodiments, modulating the prime editing comprises repressing transcription of the gene proximal to the prime editing target site. In various embodiments, the step of repressing transcription occurs before or during the step of prime editing of the target site.

[0017] The methods disclosed herein may be combined with other known methods of modulating prime editing efficiency of the target site. Accordingly, in some embodiments, the method further comprises modulating at least one DNA Damage Repair (DDR) gene in the cell. In some embodiments, the at least one DDR gene is a DNA mismatch repair (MMR) gene (e.g., an MMR gene selected from PMS2, MLH1, MLH2, MLH3, MLH6, EXO1, and FEN1). In some embodiments, the at least one DDR gene is HLTF. In other, non-mutually exclusive embodiments, the method further comprises use of a more robust or enhanced prime editing ribonucleoprotein (RNP) complex. In some such embodiments, the more robust or enhanced RNP complex comprises a prime editor selected from (a) a prime editor comprising an engineered reverse transcriptase (e.g., PE2), (b) a codon- and structure-optimized prime editor (PEmax), (c) a compact and / or specialized prime editor (e.g., a prime editor selected from PE6a, PE6b, PE6, PE6d, PE6e, PE6f, and PE6g), and (d) a prime editor fused to an RNA-binding protein (e.g., PE7). In other, non-mutually exclusive embodiments, the more robust or enhanced RNP complex comprises a structurally stabilized pegRNA (epegRNA) and / or a computationally optimized peg RNA).

[0018] In some embodiments of a method for modulating gene editing efficiency, the method further comprises inhibiting DNA mismatch repair (MMR). In some such embodiments, inhibiting MMR comprises use of a second-site nicking guide RNA targeting the non-edited strand. In other, non-mutually exclusive embodiments, inhibiting 3915-P1300WO.UW -6-MMR comprises inhibiting an MMR protein selected from the group consisting of MLH1, MSH2, MSH3, MSH6, PMS2, and EXO1. In more specific variations, inhibiting MMR comprises expressing a dominant negative MLH1 protein, RNAi-mediated knockdown of MMR protein expression, or targeted degradation of the MMR protein.

[0019] In various embodiments of a method for modulating gene editing efficiency, the cell is a eukaryotic cell, preferably a mammalian cell. In some embodiments, the cell is a stem cell (e.g., an induced pluripotent stem cell (iPSC)). DESCRIPTION OF THE DRAWINGS

[0020] The foregoing aspects and many of the attendant advantages of this disclosure will become more readily appreciated as the same become better understood by reference to the following detailed description, when taken in conjunction with the accompanying drawings.

[0021] FIGs 1A-1E show an example of efficient genome-wide mapping of the precise integration sites of a synHEK3 chromatin sensor, according to aspects of the disclosure. FIG.1A) Measuring cis-chromatin effects on prime editing efficiency. A library of synHEK3 reporters is randomly inserted throughout the genome. Genomic locations of individual reporters are determined with a T7-assisted reporter mapping method. The cis- chromatin contexts of mapped reporters are used to model prime editing outcomes as measured from each reporter. FIG.1B) Genome browser view of a read pileup pinpointing the precise coordinates of a synHEK3 reporter integration. Barcode sequence, orientation and coordinates of the reporter are annotated. FIG. 1C) Motif enrichment analysis of 20- bp windows surrounding synHEK3 integration sites. FIG. 1D) Coverage plot of unique synHEK3 reporters identified in the bottlenecked pool (n = 4,273). Vertical bar lengths correspond to read counts. FIG. 1E) UpSet plot of genomic annotations of synHEK3 integration sites.

[0022] FIGs 2A-2J show an example of characterization of synHEK3 insertion sites determined by T7-assisted reporter mapping assay, according to aspects of the disclosure. FIG. 2A) Schematic of the synHEK3 reporter construct and the T7-assisted reporter mapping method. FIG. 2B) Experimental workflow. A complex library of synHEK3 reporters was transfected into PE2(+) K562 cells along with the piggyBac transposase. After transposons were stably integrated, the population of cells was bottlenecked to ~500 clones. Cells derived from the expansion of this bottlenecked pool 3915-P1300WO.UW -7-were then subjected to T7-assisted reporter mapping. FIG.2C) Copy number of synHEK3 reporters was estimated by qPCR using that of SNRPB (3 copies) as a reference. FIG.2D) Histogram of T7 mapping counts of all synHEK3 reporters (n = 10,095). X-axis on log10 scale. FIG.2E) Scatter plot of number of integration sites recovered at different sequencing depths by subsampling the original dataset. Overlaid polynomial regression lines fit to all sites (top data) or unique (bottom data) sites. X- and y-axis on log10scale. FIG. 2F) Histogram of number of distinct genomic integration sites per synHEK3 barcode. Y-axis on log10scale. FIG. 2G) Log2enrichment of number of synHEK3 reporters across major genomic features relative to expected numbers based on feature sizes. FIG. 2H) Log2 enrichment of number of synHEK3 reporters across major genomic features relative to expected number of TTAA motifs in each feature. Black points: log2 enrichment calculated using 10 sets of randomly sampled TTAA motifs (n = 4,273). Error bar: standard deviation. FIG. 2I) Barplot showing fractions of genomic sites overlapping with the Roadmap annotations. For piggyBac integrations, the overlapping status of all integrations (n = 9,450, third bars from top) and integrations marked by unique barcodes (n = 4,273, fourth bars from top) are shown. For background, the mean fraction of 10 sets of TTAA sites (n = 9,450 each, black) and 10 sets of random 4-bp sites (n = 9,450 each, top bars) are shown. FIG. 2J) Density plots of chromatin feature scores of the selected genomic sites. For piggyBac integrations, distributions of all integrations (n = 9,450, dark gray, B) and integrations marked by unique barcodes (n = 4,273, medium gray, A) are plotted. For background, 10 sets of TTAA sites (n = 9,450 each, black, C) and 10 sets of random 4-bp sites (n = 9,450 each, light gray, D) are plotted.

[0023] FIGs 3A-3I show chromatin context has an impact on prime editing efficiency, according to aspects of the disclosure. FIG.3A) Left: workflow of experiment. Right: density plot of CTT insertion frequency in all uniquely barcoded synHEK3 reporters (n = 4,273). Black line indicates prime editing efficiency (17%) at the endogenous HEK3 locus in K562 cells. FIG. 3B) Heatmaps of fractions of highly editable (>25%) sites in synHEK3 sites stratified by chromatin feature scores. SynHEK3 reporters are binned into 10 equally sized bins with increasing chromatin feature scores. The chromatin features are ordered left-to-right by their correlation coefficient (Spearman’s ρ) with prime editing efficiency. FIG.3C) Scatter plot of observed vs. predicted prime editing efficiencies using reporters in a holdout test set. Points shaded by the number of neighboring points. FIG.3D) Scatter plot of Spearman’s ρ between chromatin feature scores and prime editing 3915-P1300WO.UW -8-efficiencies, calculated separately for intragenic and intergenic reporters. FIG. 3E) Prime editing efficiency for gene-proximal reporters. Distance was scaled by gene length and binned. Negative values refer to synHEK3 sites located upstream of TSS. Values >100% refer to synHEK3 sites located downstream of (transcription termination site) TTS. Points shaded based on expression levels (log10) of the genes. TPM, transcripts per million. FIG. 3F) Genome browser views of the 4 most highly editable sites. Sites of integration and measured editing efficiencies are shown as a dot plot at top and aligned with selected epigenetic tracks. For each synHEK3 insertion, editing efficiency, number of reads with edit (numerator), and total number of reads (denominator) are annotated. The dashed vertical lines mark locations of the insertions. FIG. 3G) Scatter plot of sequence-based prediction (DeepPrime, x-axis) vs. normalized editing rate (y-axis, log10 scale) for epegRNAs designed for prime editing at endogenous genomic sites. FIG.3H) Scatter plot of chromatin-based prediction (our model, x-axis) vs. normalized editing rate (y-axis, log10 scale). FIG.3I) Scatter plot of combined score (x-axis) vs. normalized editing rate (y-axis, log10 scale).

[0024] FIGs 4A-4K show chromatin context has a major impact on prime editing efficiency, according to aspects of the disclosure. FIG. 4A) Scatter plots of chromatin feature scores vs. prime editing efficiencies for individual synHEK3 reporters. Points are shaded by the number of neighboring points. The Spearman’s (ρ) and Pearson’s © correlation coefficients between the chromatin feature score and prime editing efficiency are annotated. FIG.4B) Boxplots of prime editing efficiency in synHEK3 sites, binned by chromatin feature scores. –1 - Q10 correspond to 10 equally sized bins of synHEK3 reporters with increasing chromatin feature scores. FIG.4C) Scatter plots of Spearman’s ρ of chromatin feature scores vs. prime editing efficiency observed in two independent pools of synHEK3 reporters in wild-type (x-axis) or PE2(+) K562 cells (y-axis). FIG.4D) Scatter plots of predicted (beta regression model) vs. observed prime editing efficiencies in two independent pools of synHEK3 reporters in wild-type K562 cells. Points are shaded by the number of neighboring points. FIG. 4E) Histogram of ChIP-seq signal of H3K79me2, H3K4me3, and H3K36me3 in an 10-kb window surrounding the TSS of genes that are expressed (n = 11,514, TPM > 3), top) or unexpressed (n = 10,506, TPM < 3, bottom) in K562 cells. FIG. 4F) Boxplot of prime editing efficiencies of gene-proximal synHEK3 reporters. Distances were calculated relative to the closest TSS, scaled by gene length and binned. Negative values refer to synHEK3 sites located upstream of TSS. Values >100% 3915-P1300WO.UW -9-refer to synHEK3 sites located downstream of TTS. The reporters were binned based on the TPM of the overlapping / nearest genes. Bin 1 contains unexpressed genes, while Bin 2- 5 are equally sized in terms of the number of assigned genes (though not necessarily in terms of the number of assigned synHEK3 reporters), sorted by increasing expression levels. FIG.4G) Boxplot of prime editing efficiencies of gene-proximal reporters. Same as panel F except that distance groupings are in terms of absolute rather than scaled distances from TSS. FIG. 4H) Cumulative distribution function (CDF) plot of prime editing efficiencies of intragenic synHEK3 reporters of different orientations with respect to the direction of transcription. “Opposite“ means synHEK3 reporter on the opposite strand of the coding strand, and “same“ means synHEK3 reporter on the same strand as the coding strand. Genes with detectable expression (TPM > 3) and synHEK3 reporters within 50 kb from the TSSs were selected for this analysis. P value: two-sided Kolmogorov–Smirnov test. FIG. 4I) Genome browser views of 4 poorly editable sites. Sites of integration and measured editing efficiencies are shown as a dot plot at the top and aligned with selected epigenetic tracks. For each synHEK3 insertion, editing efficiency, number of reads with edit (numerator), and total number of reads (denominator) are annotated. The dashed vertical lines mark locations of the insertions. FIG.4J) Boxplots of normalized editing rates (y-axis) for epegRNAs designed for prime editing at endogenous genomic sites, stratified by chromatin feature scores. The epegRNAs are grouped into quartiles based on each chromatin feature score (Q1 = lowest; Q4 = highest). The y-axis is in log10 scale. FIG.4K) Density plot of CTT insertion frequency of synHEK3 reporters in intragenic, proximal intergenic (<10 kb) and distal intergenic (>10 kb) regions.

[0025] FIGs 5A-5G show example comparisons between Cas9 and prime editing, leveraging a common set of integrated reporters, according to aspects of the disclosure. FIG.5A) Comparison of editing efficiencies for Cas9 (Day 1, 2 or 4 after transfection) vs. prime editing (Day 4 after transfection) at an identical set of synHEK3 reporters. Plots shaded based on the number of reporters assigned to each bin. 1-D histograms of editing efficiencies plotted at top and right. FIG.5B) Hierarchical clustering of synHEK3 reporters based on prime and Cas9 editing efficiencies. FIG. 5C) Density plot of prime and Cas9 editing efficiencies for 14 groups of synHEK3 reporters, ordered by mean prime editing efficiency. FIG.5D) Bar graph of the log2ratio between number of intragenic vs. intergenic sites in each of the 14 groups. FIGs 5E-5G) Comparison of properties of intragenic sites in selected groups. P-value: two-sided Kolmogorov–Smirnov test. FIG. 5E) Boxplot of 3915-P1300WO.UW -10-ATAC-seq scores of selected groups. FIG.5F) Expression levels of the overlapping genes in TPM of selected groups (x-axis; log10 scale). FIG. 5G) Distance (bp) between the synHEK3 reporters in selected groups and the nearest TSS (x-axis; log10 scale).

[0026] FIGs 6A-6J show example comparisons between prime editing and Cas9 editing, leveraging a common set of integrated editing reporters, according to aspects of the disclosure. FIG.6A) PCA plots of synHEK3 reporters generated using chromatin scores as features. The first two PCs are plotted (PC1: variance 62%; PC2: variance 9%). Points are shaded by prime editing efficiency at Day 4 and Cas9 indel frequency measured at Day 1, 2 and 4. FIG. 6B) Cas9 indel frequency measured at Day 1, 2 and 4 for gene-proximal reporters. Distance was calculated relative to the closest TSS, scaled by gene length and binned. Negative values refer to synHEK3 sites located upstream of TSS. Values >100% refer to synHEK3 sites located downstream of TTS. Points are shaded based on expression levels (log10) of the genes. FIG. 6C) PCA plots of synHEK3 reporters generated using chromatin scores as features. The first two PCs are plotted (PC1: variance 62%; PC2: variance 9%). Points are shaded by the 6 main groups as in FIG.5B. FIG.6D) Scatter plot of Cas9 editing (Day 1) and prime editing (Day 4) efficiency. Points are shaded by the 6 main groups as in panel C. FIG.6E) Scatter plot of Cas9 editing (Day 1) and prime editing (Day 4) efficiency, shaded by the 14 subgroups. FIG. 6F) Boxplot of H3K27me3 and H3K9me3 scores of synHEK3 reporters in Groups 1.0 and 2.0. P-value: two-sided Kolmogorov–Smirnov test. FIG.6G) The MMEJ / (MMEJ + NHEJ) ratio in all synHEK3 sites and in sites overlapping with H3K27me3 and H3K9me3 peaks. Black line: the median MMEJ / (MMEJ + NHEJ) ratio. P-value: two-sided Kolmogorov–Smirnov test. FIG.6H) The MMEJ / (MMEJ + NHEJ) ratio in the 6 groups of synHEK3 sites as in FIG.6C. Black line: the median MMEJ / (MMEJ + NHEJ) ratio. FIGs 6I-6J) Scatter plots of the MMEJ / (MMEJ + NHEJ) ratio or allele frequencies of intragenic synHEK3 reporters and their distance to corresponding TSSs (x-axis; log10 scale). Points are shaded based on expression levels (log10) of the genes. Black line: linear regression line with confidence interval in gray. FIG. 6I) The MMEJ / (MMEJ + NHEJ) ratio is plotted. FIG. 6J) Allele frequencies of the most frequent NHEJ (left) and MMEJ (right) alleles are plotted. The “NHEJ del / 0 / G“ allele contains a single-base deletion of G at the Cas9 cut site. The “MMEJ del / 5 / GCACGTGATG“ allele contains a bidirectional deletion of a 10-bp sequence (GCACGTGATG) around the cutsite. 3915-P1300WO.UW -11-

[0027] FIGs 7A-7E show an example of dissecting chromatin context-dependent regulation of prime editing using a modified sci-RNA-seq3 workflow, according to aspects of the disclosure. Experimental workflow of the pooled shRNA screen. FIG.7A) The two monoclonal K562 lines used in this example stably expressed PE2 and reverse tetracycline transactivator (rtTA), and together contained 50 unique synHEK3 reporters. Cells were transduced with the TRE-shRNA library at a high multiplicity of infection (MOI) and treated with doxycycline. On Day 2, cells were transfected with pegRNAs to introduce random 6-bp insertions at synHEK3 reporters. After 3-4 days, nuclei were extracted and fixed. TRE: tetracycline response element. FIG. 7B) Fixed nuclei were subjected to IST with T7 polymerase (gray circle) to produce transcripts from synHEK3 and shRNA constructs. FIG. 7C) Nuclei were distributed to 96-wells for indexed RT. In each well, a cocktail of three indexed RT primers were used: oligo-dT primers, and synHEK3- and shRNA-specific primers. FIG.7D) After RT, nuclei were pooled and redistributed into 96- well plates for indexed hairpin ligation. Then, they were pooled and split to final 96-well plates. After second-strand synthesis, nuclei were lysed and the resulting lysates were split to two plates. One plate underwent Tn5 tagmentation and indexed PCR to generate a transcriptome library. The other plate was used for indexed enrichment PCR targeting the synHEK3 and shRNA transcripts. FIG.7E) For each synHEK3 reporter, editing outcomes were computed and compared between cells with vs. without a specific shRNA.

[0028] FIGs 8A-8D show an example experimental setup of the pooled shRNA screen and its readout via T7 IST-assisted sci-RNA-seq3, according to aspects of the disclosure. FIG.8A) Correlation between efficiencies of random 3-bp insertions measured in the monoclonal lines versus efficiencies measured for the same reporters in the original polyclonal population. FIG. 8B) List of genes targeted by the shRNA library. Genes are grouped by pathways. Control genes are shown separately. FIG. 8C) Schematic of the lentiviral shRNA construct and the synHEK3 reporter, with key features relevant to the sci- RNA-seq3 workflow highlighted. TRE: tetracycline response element. FIG.8D) Schematic structures of the sci-RNA-seq3, shRNA and synHEK3 libraries. UMI: unique molecular identifier; RT PBS: reverse transcription primer binding site.

[0029] FIGs 9A-9D show example effects of perturbing MMR-related genes on prime editing, according to aspects of the disclosure. FIG. 9A) Q-Q plot of statistical significance (-log10) of synHEK3-shRNA pairs in clones 3 (left) and 5 (right). Candidate shRNAs (black) and control shRNAs (gray) are plotted separately. FIG. 9B) Plots of 3915-P1300WO.UW -12-adjusted p values (-log10) of all synHEK3-shRNA pairs. Target genes with high statistical significance are annotated. Points shaded by editing efficiency changes caused by corresponding shRNAs. FIG. 9C) Effects of shRNAs targeting MMR-related genes in clone 5. Log2fold-changes of prime editing efficiencies of synHEK3-shRNA pairs are plotted and shaded by their corresponding adjusted p values (-log10). FIG.9D) Effects of shRNAs against MLH1 and PMS2. Lines indicate: editing frequencies in cells with individual shRNAs, mean editing frequencies of the gene-targeting shRNAs, control editing frequencies for individual shRNAs (not visible because low variance relative to mean line), and mean control editing frequencies.

[0030] FIGs 10A-10J show an example pooled shRNA screen with sci-RNA- seq3 and effects of perturbing the MMR pathway on prime editing, according to aspects of the disclosure. FIG. 10A) Scatter plot of synHEK3 UMIs detected in single cells in sci- RNA-seq3 data. Cells assigned to the two clones are shaded (upper left grouping: clone 5; lower right grouping: clone 3). Mixed cells are in gray. FIG.10B) Histograms of cell count per shRNA (left), number of shRNAs captured per cell (middle), and number of synHEK3 reporters captured per cell (right) in the two clones. FIG.10C) Scatter plot of prime editing frequencies of synHEK3 reporters estimated with sci-RNA-seq3 vs. bulk amplicon sequencing. FIG.10D) Scatter plots of the number of cells per synHEK3-shRNA pair and corresponding adjusted p values (-log10). Candidate shRNAs (left) and control shRNAs (right) are plotted separately. FIG.10E) Effects of shRNAs targeting MMR-related genes in clone 3. Log2fold-changes of prime editing efficiencies of synHEK3-shRNA pairs are plotted and shaded by their corresponding adjusted p values (-log10). FIGs 10F-10G) Effects of shRNAs against FEN1 and EP300. Lines indicate: editing frequencies in cells with individual shRNAs, mean editing frequencies of the gene-targeting shRNAs, control editing frequencies for individual shRNAs (not visible because low variance relative to mean line), and mean control editing frequencies. FIG. 10H) Effects of shRNAs against DOT1L and KDM2B. Barcode sequences omitted but in the same order as FIGs 10F and 10G. Shading as per FIGs 10F and 10G. FIG. 10I) Volcano plots of gene expression changes in EPZ-5676 treated cells (5 μM for 6 days). Genes with synHEK3 insertions in clone 3 or clone 5 of the single cell screen are shaded in gray. FIG. 10J) Scatter plots of log2fold-change of prime editing efficiency vs. gene expression change (log2 fold; top) and H3K79me2 score (bottom) for a set of intragenic synHEK3 reporters with baseline efficiency > 20%. Black line: linear regression line with confidence interval in gray. 3915-P1300WO.UW -13-

[0031] FIGs 11A-11D show an example chromatin context-specific response to HLTF inhibition, according to aspects of the disclosure. FIG. 11A) Violin plot of fold- changes of editing efficiency of synHEK3 sites in response to inhibition of HLTF, MLH1 and PMS2. Points shaded by shRNA identity. FIG. 11B) Heatmap of synHEK3 reporters (row) and their responses to shRNAs against HLTF (left: clone 5; right: clone 3). Leftmost bar annotates the overlapping status of synHEK3 reporters with GRCh38 gene annotations. Second left bar indicates the expression status of the overlapping or nearest gene in TPM. Third left bar indicates distances (bp) to corresponding TSS of gene-overlapping synHEK3 reporters or nearest genes for synHEK3 reporters outside genes. Middle heatmap was generated using scaled chromatin feature scores of synHEK3 reporters and clustered by column. Right line plot shows effects of shRNAs against HLTF. Lines indicate: editing frequencies in cells with individual shRNAs, mean editing frequencies of the gene- targeting shRNAs, control editing frequencies for individual shRNAs (not visible because low variance relative to mean line), and mean control editing frequencies. Dashed lines: sites showing differential response to HLTF inhibition. FIG. 11C) Bar plot of synHEK3 reporter counts based on their responsiveness to HLTF inhibition and overlapping status with GRCh38 gene annotations. P value: Fisher’s exact test. FIG.11D) Expression of genes overlapping or proximal (within 10 kb) to a synHEK3 reporter. Genes above the dashed line (TPM = 3) are considered expressed. P value: two-sided Kolmogorov–Smirnov test.

[0032] FIGs 12A-12H show an example chromatin context-specific response to HLTF inhibition, according to aspects of the disclosure. FIG. 12A) Effects of shRNAs against HLTF on all synHEK3 reporters in clone 5 and clone 3. For every synHEK3- shRNA pair, editing frequencies in cells with candidate shRNA are plotted and shaded by their statistical significance. Control editing frequencies are shown as horizontal, flat data. Regions of the plot containing synHEK3 reporters that are significantly less responsive to HLTF inhibition are shaded gray. Regions of the plot containing synHEK3 reporters with low (<0.2) or very high (>0.9) editing frequencies are shaded light gray. Dashed horizontal lines indicate editing frequency of 0.2 and 0.9. FIG.12B) Effects of shRNAs against PMS2 on all synHEK3 reporters in clone 5 and clone 3. Layout as in FIG.12A. FIG.12C) Scatter plots of baseline editing efficiencies (x-axes) vs. fold-changes induced by shRNAs against HLTF, MLH1 and PMS2 (y-axes) across 50 synHEK3 reporters. Vertical lines correspond to baseline editing efficiencies of 0.2 and 0.9. FIG.12D) Cumulative distribution function (CDF) plots of log2 fold-change of mean editing frequency induced by shRNAs against 3915-P1300WO.UW -14-HLTF. synHEK3 reporters are colored by their responsiveness to HLTF inhibition based on the shRNA screen. P value: two-sided Kolmogorov–Smirnov test. FIG.12E) Validation of differential responsiveness to HLTF inhibition in cells transduced with individual shRNAs. CDF plots of log2fold-change of editing frequency induced by shRNAs against HLTF (shHLTF.2367: used in the shRNA screen; shHLTF.2623: an orthogonal shRNA) or MLH1 (shMLH1.1911). synHEK3 reporters are colored by their responsiveness to HLTF inhibition based on the shRNA screen. P value: two-sided Kolmogorov–Smirnov test. FIG. 12F) Scatter plot of RNA log2fold-changes induced by two shRNAs against HLTF. Genes overlapping or near shHLTF responsive sites are lower; genes overlapping or near shHLTF unresponsive sites are upper; other sites are in gray; HLTF is shown at bottom left corner; -1.5 and -2 log2 fold-change. FIG.12G) CDF plot of log2 fold-changes of ATAC-seq peak counts induced by shHLTF.2367. Peaks near synHEK3 sites were defined as those either: 1) within 5 kb of a synHEK3 site; and / or 2) in the promoter or body of a gene overlapping or proximal (within 10 kb) to a synHEK3 insertion. FIG. 12H) Western blot analysis of HLTF, PMS2 and MLH1. Clone 5 or a wild-type (WT) K562 line were transduced with rtTA and shRNAs targeting the candidate genes. Protein expression was compared to cells without doxycycline (Dox) or cells transduced with a shRNA targeting a different gene.

[0033] FIGs 13A-13H show an example of modulating prime editing outcomes by epigenetic conditioning, according to aspects of the disclosure. FIG.13A) Workflow of the CRISPRoff experiment. FIG. 13B) Scatter plots of mean prime editing efficiencies of synHEK3 reporters in cells transfected with CRISPRoff gRNAs targeting USP7, METTL2A and LRRC8C promoters. SynHEK3 reporters in corresponding target genes are labeled. Error bars correspond to standard deviation of measured editing efficiencies. FIG. 13C) Bar plot of prime editing efficiency changes of synHEK3 reporters in CRISPRoff experiment. Control editing efficiencies are predicted efficiencies using linear models trained in synHEK3 reporters that are not in the corresponding CRISPRoff target genes as shown in FIG. 13B. FIG. 13D) Workflow of the CRISPRa experiments. FIG. 13E) Prime editing efficiency (%) at endogenous gene targets in K562 cells, with or without epigenetic editing via CRISPRa. Dark gray bars show mean prime editing efficiencies in a wild-type K562 cell line which received only PEmax a©(e)pegRNA. Light gray bars show mean prime editing efficiencies when control promoters were activated via CRISPRa. Black bars show mean prime editing efficiencies when target gene promoters were activated. Fold- 3915-P1300WO.UW -15-changes are calculated between the CRISPRa and control groups. Inset shows a zoomed view of the first three genes. FIG. 13F) Prime editing efficiency (%) at endogenous gene targets in a human iPSC line, with or without epigenetic editing via CRISPRa. Gray bars show mean prime editing efficiencies when control promoters were activated via CRISPRa. Black bars show mean prime editing efficiencies when target gene promoters were activated. FIG. 13G) Scatter plots of mean prime editing efficiencies measured in control vs. CRISPRa cells in exons of IL2RB. Points shaded by edit Type. x- and y-axes in log10 scale. The dotted lines indicate 2-, 5- and 10-fold differences between the CRISPRa and control groups. FIG.13H) Boxplot of prime editing efficiency fold-change for all variants. The dashed line indicates a fold-change of 2.

[0034] FIGs 14A-14H show an example of modulating prime editing outcomes by epigenetic reprogramming, according to aspects of the disclosure. FIG.14A) Schematic diagrams of target genes in the CRISPRoff experiment, with the locations of the CRISPRoff gRNAs and synHEK3 reporters annotated. FIG. 14B) Scatter plots of mean normalized counts of gene expression in cells transfected with CRISPRoff gRNAs at Day 11 (2 replicates each). x- and y-axis on log10scale. Insets are bar plots of normalized counts of the CRISPRoff target genes. NTC: non-targeting control. FIG. 14C) Prime editing efficiency (%) at endogenous gene targets in K562 cells, with or without epigenetic editing via CRISPRa. Light gray bars show mean prime editing efficiencies when control promoters were activated via CRISPRa. Dark gray bars show mean prime editing efficiencies when target gene promoters were activated via CRISPRa. P value:’Welch‘s two sample t-test. FIG. 14D) mRNA fold-change of CRIPSRa target genes quantified by qPCR. FIG. 14E) Relative expression levels of selected CRISPRa target genes compared to a set of reference genes. Circle: reference genes; triangle: endogenous expression levels of the target genes; square: expression levels of the target genes after CRISPRa. y-axis on log10 scale. FIG.14F) Schematic of the IL2RB gene with locations of gRNA (for CRISPR© or (e)pegRNA targets (for prime editing) annotated. Lollipops indicate pegRNAs that were separately tested in experiments shown in FIG. 13E and FIG. 14C. Dashed lollipops indicate locations of the new epegRNA pools. FIG.14G) List of epegRNA edit types tested, including all possible single-nucleotide substitutions, small insertions of 1, 3 and 6 bp, insertion of a loxP site (34 bp), and small deletions of 1, 3 and 6 bp. FIG.14H) Total editing efficiencies measured at the individual IL2RB exons. Light gray bars show mean prime 3915-P1300WO.UW -16-editing efficiencies in control groups and dark gray bars show mean prime editing efficiencies when the IL2RB promoter was first activated via CRISPRa. DETAILED DESCRIPTION

[0035] The development of prime editing expands the repertoire of genome editing tools and allows for precise installation of genetic variants, including those that were inconvenient to introduce with conventional methods. Prime editor, consisting of a Cas9 nickase and a reverse transcriptase, searches for a genome engineering target and introduces a desired mutation, with both site specificity and edit identity pre-programmed in a prime editing guide RNA (pegRNA). The high programmability of this method has further allowed for repurposing the cellular genome as an information storage device, which serves as a means for deconvoluting regulatory events and lineage trajectories during the development of a highly complex multicellular organism. It also facilitates therapeutic genome editing and the high-throughput functional characterization of genetic variants.

[0036] Despite the promising new possibilities, efficiency of prime editing is generally low and highly variable across target sites, which can impede its broad application in both basic and clinical research. The low efficiency can be attributed to inefficient generation of a single-strand DNA flap containing the edit and efficient removal of the intermediate by the mismatch repair (MMR) pathway. Several strategies, such as improving the robustness of the prime editing core components and transiently dampening MMR response in cells have been developed, to overcome these issues, and show promising enhancing effects on prime editing outcome. Additionally, several machine learning models have been generated to predict prime editing efficiency and guide experimental design.

[0037] The variation in prime editing efficiency can be explained by differences in target identities as well as in local chromatin contexts. Chromatin compaction is known to physically shield DNA from damage caused by DNA damaging agents. Various epigenetic factors can interact with DNA repair factors to modulate the kinetics of DNA damage repair (DDR). In the context of genome editing, a recent study suggests that chromatin can alter the balance between the non-homologous end joining (NHEJ) and microhomology- mediated end joining (MMEJ) pathways in DNA double-strand break (DSB) repair induced by Cas9. Prime editing, which involves the generation and repair of a single-strand DNA break, like all other DNA damage repair processes, is very likely regulated by the local 3915-P1300WO.UW -17-chromatin contexts. Nevertheless, these chromatin environmental factors remain largely uncharacterized.

[0038] Provided herein are methods and reporter systems for identifying and modulating epigenetic factors influencing prime editing efficiencies. The inventors have developed a reporter system and developed an assay using this reporter system to resolve the complex and heterogeneous influence of chromatin landscape on the efficiency of prime editing. Utilizing this approach, highly variable prime editing efficiency was observed across genomic contexts and epigenetic features predictive of prime editing outcomes were identified. Furthermore, the inventors have demonstrated a link between transcriptional activity and prime editing efficiency and showed that prime editing outcome can be modulated via epigenetic editing. Based on the link between gene transcriptional activity and prime editing outcome, the feasibility of modulating prime editing outcome by modifying the epigenetic and expression status of the gene using dCas9-based epigenetic reprogramming methods was explored. CRISPRoff is a newly developed epigenetic reprogramming tool that has been shown to establish prolonged epigenetic silencing on target genes. Leveraging CRISPRoff, it was demonstrated that prime editing efficiency of intragenic targets can be attenuated after silencing of the underneath genes, corroborating the correlation between transcriptional activity and prime editing outcome. Surprisingly, transient gene activation by CRISPRa was shown to dramatically increase the efficiency of prime editing.

[0039] Coupling epigenetic reprogramming with prime editing highlights a new route of enhancing prime editing efficiency in addition to modulating the core components of the prime editor complex and the cellular DNA damage response. Specifically, it was demonstrated that transient gene activation before prime editing resulted in enhanced editing efficiency. This strategy will be extremely beneficial for stem-cell based gene therapy, in which the target gene remains transcriptionally silenced and only gets expressed in differentiated cell types.

[0040] The present disclosure thus highlights a new previously unknown route of enhancing prime editing efficiency via coupling epigenetic reprogramming with prime editing in addition to modulating the core components of the prime editor complex and the cellular DNA damage response. The reporter systems and methods disclosed herein, have revealed the tight regulation of prime editing outcome by the epigenome. This critical 3915-P1300WO.UW -18-understanding of epigenetic regulation of gene editing will facilitate better genome engineering strategy design for both basic research and gene therapy development.

[0041] Accordingly, in an aspect of the present disclosure, there is provided a reporter system for mapping a genomic location of at least one modification in a genome of a cell. In some embodiments, the reporter system comprises a vector comprising a promoter for in vitro transcription. For example, a vector may comprise polynucleotide sequences needed for RNA in vitro transcription, such as a promoter sequence, e.g., an RNA promoter sequence, preferably T3, T7 or SP6 RNA promoter sequences. Particularly preferred in the context of the present disclosure are vectors comprising promoters for DNA-dependent RNA polymerases such as T3, T7 and Sp6.

[0042] Generally, the vector can include any sequence that can integrate into the genome, whether randomly or by targeted integration. In some embodiments, the vector is selected from a transposon-based vector (e.g., a piggyBac-based vector) retroviral-based vector, a lentiviral-based vector, and a serine integrase-based vector.

[0043] The reporter system may further comprise a polynucleotide sequence linked to the promoter. The polynucleotide sequence may comprise an amplification primer binding domain / sequence. In some aspects of the disclosure the polynucleotide sequence may further comprise other nucleic acid sequences. For example, the polynucleotide may comprise nucleic acid sequences for coding or non-coding transcripts, cis-regulatory elements, molecular identifier barcode, or functional or non-functional genomic elements. In certain embodiments, the polynucleotide sequence comprises a target sequence and a molecular identifier. In some embodiments the target sequence comprises a target sequence for a Cas9 guide RNA or a variant thereof.

[0044] Molecular identifiers contemplated by the reporter systems and methods of this disclosure are well-known in the art and may comprise nucleic acid sequences or sequencing linkers functioning as molecular tags or barcodes. In an exemplary embodiment, the molecular barcode comprises at least about 5 (such as at least about any one of 10, 15, 20, or 25) preprogrammed, or randomly and / or degenerately designed nucleotides. In an exemplary embodiment the molecular identifier is 16bp.

[0045] In an exemplary embodiment, the reporter system comprises a vector comprising a polynucleotide sequence comprising: a promoter for in vitro transcription; a target sequence; and a molecular identifier. In an embodiment, the target sequence comprises a target sequence for a Cas9 guide RNA or a variant thereof. In various 3915-P1300WO.UW -19-embodiments, the reporter system is used to map a desired edit meditated by prime editing into a genome of a cell and / or determine prime editing efficiency in a genome of a cell. In some embodiments, the cell constitutively expresses Prime Editor (PE). Accordingly, in certain embodiments, the vector is devoid of any cis-regulatory elements.

[0046] Provided herein also is a method of mapping the genomic location of at least one reporter system disclosed herein in the genome of a cell. In yet another embodiment, the present disclosure provides a method for determining prime editing efficiencies across a genome using the reporter system of the present disclosure.

[0047] Also disclosed herein are methods for modulating gene editing efficiency of a target site in a genomic region of a cell. Such methods include modulating chromatin accessibility of the genomic region and / or transcriptional activity of a gene proximal to the gene editing target site. In some embodiments, gene editing comprises prime editing. The prime editing target site may be any site in the genome. Exemplary target sites include but are not limited to an exon, an intron, a promoter, 3’ UTR, 5’ UTR, and intergenic sites in the genomic region. The prime editing target site may be downstream or upstream of a promoter of the gene. In a preferred embodiment, the prime editing target site is downstream of a transcriptional start site (TSS) of the gene.

[0048] In various embodiments, modulating the prime editing efficiency comprises activating transcription of the gene proximal to the prime editing target site. For example, transcriptional activation of a gene may be mediated by CRISPRa system. The step of activating transcription may occur before, or during the step of prime editing of the target site in the genome of the cell. In a preferred embodiment, the step of activating transcription of the gene proximal to the prime editing target site occurs before the step of prime editing of the target site in the genome of the cell. In some embodiments, modulating the prime editing comprises repressing transcription of the gene proximal to the prime editing target site. For example, transcriptional repression of the gene proximal to the prime editing target site may be mediated by CRISPRoff system. The step of repressing transcription of the gene proximal to the prime editing target site occurs before, or during the step of prime editing of the target site.

[0049] The methods disclosed herein may be combined with other known methods of modulating prime editing efficiency of the target site. In this regard, factors impacting prime editing efficiency include 1) the properties of the prime editing ribonucleoprotein (RNP) complex itself; 2) the sequence of the target site and nature of the 3915-P1300WO.UW -20-programmed edit; 3) trans-acting factors such as the endogenous DNA repair proteins involved in the installation of prime edits; and 4) the cis-chromatin context of the target site (as addressed by the present disclosure). For the first factor, an engineered reverse transcriptase (PE2), a codon- and structure-optimized prime editor protein (PEmax), a set of more compact and functionally specialized prime editors (PE6a-g), a prime editor fused to a small RNA-binding protein to functionally interact with and stabilize pegRNAs (PE7), and a structurally stabilized pegRNA (epegRNA) have been developed. See Anzalone et al., Nature 576:149-157, 2019; Chen et al., Cell 184:5635-5652.e29, 2021; Doman et al., Cell 186:3983-4002.e26 (2023); Yan et al., Nature (2024) doi:10.1038 / s41586-024-07259- 6; Nelson et al., Nat. Biotechnol. (2021) doi:10.1038 / s41587-021-01039-7. For the second factor, the impact of the target and edit sequences have been studied and machine learning models have been constructed from the resulting data to facilitate pegRNA design. See Mathis et al., Nat. Biotechnol. (2023) doi:10.1038 / s41587-022-01613-7; Kim et al., Nat. Biotechnol. 39:198–206, 2021; Koeppel et al., Nat. Biotechnol. (2023) doi:10.1038 / s41587-023-01678-y; Yu et al., Cell 186:2256-2272.e23, 2023; Mathis et al., bioRxiv 2023.10.09.561414 (2023) doi:10.1101 / 2023.10.09.561414. For the third factor, a rate-limiter for prime editing is DNA mismatch repair (MMR), which detects the intermediate product of prime editing and efficiently repairs the edited strand. Through mechanistic understanding or screens of repair-relevant trans-acting factors, strategies that enhance prime editing efficiency, either by misleading the MMR machinery by nicking the non-edited strand (PE3) (see Anzalone et al., supra) or by transiently dampening the MMR pathway (see Chen et al., supra; Ferreira da Silva et al., Nat. Commun.13:760, 2022) have been developed. Example 1 (infra) demonstrates the effectiveness of the “epigenetic conditioning“ approach based on a combination of current strategies, including combinatorial strategies using PEmax, epegRNAs.

[0050] Accordingly, in some embodiments, the method further comprises modulating at least one DNA Damage Repair (DDR) gene in the cell. In a specific variation, the at least one DDR gene is HLTF. In other specific variations, the at least one DDR gene is a DNA mismatch repair (MMR) gene such as, for example, an MMR gene selected from PMS2, MLH1, MSH2, MSH3, MSH6, EXO1, and FEN1. In other, non- mutually exclusive embodiments, the method further comprises use of a more robust or enhanced prime editing ribonucleoprotein (RNP) complex, such as, for example, an RNP complex comprising (a) a prime editor comprising an engineered reverse transcriptase (e.g., 3915-P1300WO.UW -21-PE2), (b) a codon- and structure-optimized prime editor (PEmax), or (c) a compact and / or specialized prime editor (e.g., a prime editor selected from PE6a, PE6b, PE6, PE6d, PE6e, PE6f, and PE6g), (d) a prime editor fused to an RNA-binding protein (e.g., PE7), and / or (e) a structurally stabilized pegRNA (epegRNA) and / or a computationally optimized peg RNA).

[0051] In some embodiments, the method further comprises inhibiting DNA mismatch repair (MMR). In some such embodiments, inhibiting MMR comprises use of a second-site nicking guide RNA targeting the non-edited strand. In other, non-mutually exclusive embodiments, inhibiting MMR comprises inhibiting an MMR protein selected from MLH1, MSH2, MSH3, MSH6, PMS2, and EXO1. In more specific variations, inhibiting MMR comprises expressing a dominant negative MLH1 protein, RNAi- mediated knockdown of MMR protein expression, or targeted degradation of the MMR protein.

[0052] In various embodiments of a method as above, the cell is a eukaryotic cell, preferably a mammalian cell. In some embodiments, the cell is a stem cell (e.g., an induced pluripotent stem cell (iPSC)). In some embodiments, the target nucleotide site is a DNA sequence in a genome, e.g., a eukaryotic genome. In certain embodiments, the target site is a nucleotide sequence in a mammalian (e.g., a human) genome.

[0053] Unless defined otherwise, all technical and scientific terms used herein have the meaning commonly understood by a person skilled in the art to which this disclosure belongs. The following references provide one of skill with a general definition of many of the terms used in this disclosure: Singleton et al., Dictionary of Microbiology and Molecular Biology (2nd ed.1994); The Cambridge Dictionary of Science and Technology (Walker ed., 1988); The Glossary of Genetics, 5th Ed., R. Rieger et al. (eds.), Springer Verlag (1991); and Hale & Marham, The Harper Collins Dictionary of Biology (1991). As used herein, the following terms have the meanings ascribed to them unless specified otherwise.

[0054] A “vector“ is a composition of matter which comprises an isolated nucleic acid and which can be used to deliver the isolated nucleic acid to the interior of a cell. Numerous vectors are known in the art including, but not limited to, linear polynucleotides, polynucleotides associated with ionic or amphiphilic compounds, plasmids, and viruses. In accordance with the present disclosure, a vector can include any nucleic acid sequence that can integrate into the genome of a cell. In some embodiments, a vector comprises a 3915-P1300WO.UW -22-nucleic acid sequence that can integrate into the genome by random integration. In other embodiments, a vector comprises a nucleic acid sequence that can integrate into the genome by targeted integration.

[0055] In genetics, the “antisense“ strand of a segment within double-stranded DNA is the template strand, and which is considered to run in the 3′ to 5′ orientation. By contrast, the “sense“ strand is the segment within double-stranded DNA that runs from 5′ to 3′, and which is complementary to the antisense strand of DNA, or template strand, which runs from 3′ to 5′. In the case of a DNA segment that encodes a protein, the sense strand is the strand of DNA that has the same sequence as the mRNA, which takes the antisense strand as its template during transcription, and eventually undergoes (typically, not always) translation into a protein. The antisense strand is thus responsible for the RNA that is later translated to protein, while the sense strand possesses a nearly identical makeup to that of the mRNA. Note that for each segment of dsDNA, there will possibly be two sets of sense and antisense, depending on which direction one reads (since sense and antisense is relative to perspective). It is ultimately the gene product, or mRNA, that dictates which strand of one segment of dsDNA is referred to as sense or antisense.

[0056] The term “Cas9“ or “Cas9 nuclease“ refers to an RNA-guided nuclease comprising a Cas9 domain, or a fragment thereof (e.g., a protein comprising an active or inactive DNA cleavage domain of Cas9, and / or the gRNA binding domain of Cas9). A “Cas9 domain“ as used herein, is a protein fragment comprising an active or inactive cleavage domain of Cas9 and / or the gRNA binding domain of Cas9. A “Cas9 protein“ is a full length Cas9 protein. A Cas9 nuclease is also referred to sometimes as a casn1 nuclease or a CRISPR (Clustered Regularly Interspaced Short Palindromic Repeat)- associated nuclease. CRISPR is an adaptive immune system that provides protection against mobile genetic elements (viruses, transposable elements, and conjugative plasmids). CRISPR clusters contain spacers, sequences complementary to antecedent mobile elements, and target invading nucleic acids. CRISPR clusters are transcribed and processed into CRISPR RNA (crRNA). In type II CRISPR systems correct processing of pre-crRNA requires a trans-encoded small RNA (tracrRNA), endogenous ribonuclease 3 (rnc) and a Cas9 domain. The tracrRNA serves as a guide for ribonuclease 3-aided processing of pre-crRNA. Subsequently, Cas9 / crRNA / tracrRNA endonucleolytically cleaves linear or circular dsDNA target complementary to the spacer. The target strand not complementary to crRNA is first cut endonucleolytically, then trimmed 3′-5′ 3915-P1300WO.UW -23-exonucleolytically. In nature, DNA-binding and cleavage typically requires protein and both RNAs. However, single guide RNAs (“sgRNA,“ or simply “gRNA“) can be engineered so as to incorporate aspects of both the crRNA and tracrRNA into a single RNA species. See, e.g., Jinek M., Chylinski K., Fonfara I., Hauer M., Doudna J. A., Charpentier E. Science 337:816-821(2012), the entire contents of which are hereby incorporated by reference. Cas9 recognizes a short motif in the CRISPR repeat sequences (the PAM or protospacer adjacent motif) to help distinguish self versus non-self. Cas9 nuclease sequences and structures are well known to those of skill in the art (see, e.g., “Complete genome sequence of an M1 strain of Streptococcus pyogenes.“ Ferretti et al., J. J., McShan W. M., Ajdic D. J., Savic D. J., Savic G., Lyon K., Primeaux C., Sezate S., Suvorov A. N., Kenton S., Lai H. S., Lin S. P., Qian Y., Jia H. G., Najar F. Z., Ren Q., Zhu H., Song L., White J., Yuan X., Clifton S. W., Roe B. A., McLaughlin R. E., Proc. Natl. Acad. Sci. U.S.A.98:4658-4663(2001); “CRISPR RNA maturation by trans-encoded small RNA and host factor RNase III.“ Deltcheva E., Chylinski K., Sharma C. M., Gonzales K., Chao Y., Pirzada Z. A., Eckert M. R., Vogel J., Charpentier E., Nature 471:602-607(2011); and “A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity.“ Jinek M., Chylinski K., Fonfara I., Hauer M., Doudna J. A., Charpentier E. Science 337:816-821(2012), the entire contents of each of which are incorporated herein by reference). Cas9 orthologs have been described in various species, including, but not limited to, S. pyogenes and S. thermophilus. Additional suitable Cas9 nucleases and sequences will be apparent to those of skill in the art based on this disclosure, and such Cas9 nucleases and sequences include Cas9 sequences from the organisms and loci disclosed in Chylinski, Rhun, and Charpentier, “The tracrRNA and Cas9 families of type II CRISPR-Cas immunity systems“ (2013) RNA Biology 10:5, 726-737; the entire contents of which are incorporated herein by reference. In some embodiments, a Cas9 nuclease comprises one or more mutations that partially impair or inactivate the DNA cleavage domain.

[0057] As used herein the term “variant“ should be taken to mean the exhibition of qualities that have a pattern that deviates from what occurs in nature, e.g., a variant Cas9 is a Cas9 comprising one or more changes in amino acid residues as compared to a wild type Cas9 amino acid sequence. The term “variant“ encompasses homologous proteins having at least 75%, or at least 80%, or at least 85%, or at least 90%, or at least 95%, or at least 99% percent identity with a reference sequence and having the same or substantially the 3915-P1300WO.UW -24-same functional activity or activities as the reference sequence. The term also encompasses mutants, truncations, or domains of a reference sequence, and which display the same or substantially the same functional activity or activities as the reference sequence.

[0058] In some embodiments, proteins comprising Cas9 or fragments thereof are referred to as “Cas9 variants.“ A Cas9 variant shares homology to Cas9, or a fragment thereof. For example, a Cas9 variant is at least about 70% identical, at least about 80% identical, at least about 90% identical, at least about 95% identical, at least about 96% identical, at least about 97% identical, at least about 98% identical, at least about 99% identical, at least about 99.5% identical, at least about 99.8% identical, or at least about 99.9% identical to wild type Cas9. Exemplary Cas9 variants include but are not limited to High-fidelity Cas9, eSpCas9, HypaCas9, Sniper-Cas9, evoCas9 and Cas9 variants with broadened PAM compatibilities, include but are not limited to VQR, VRER, VRQR, QQR1, SpCas9-NG, xCas9. Also included are Cas9 from different species of bacteria, SpCas9, SaCas9, ScCas9, NmCas9, FnCas9, CjCas9, St1Cas9, and other DNA-targeting Cas proteins, including Cas12a (formerly Cpf1), Cas12b, CasX.

[0059] As used herein, the term “guide RNA“ is a particular type of guide nucleic acid which is mostly commonly associated with a Cas protein of a CRISPR-Cas9 and which associates with Cas9, directing the Cas9 protein to a specific sequence in a DNA molecule that includes complementarity to spacer sequence of the guide RNA. However, this term also embraces the equivalent guide nucleic acid molecules that associate with Cas9 equivalents, homologs, orthologs, or paralogs, whether naturally occurring or non-naturally occurring (e.g., engineered or recombinant), and which otherwise program the Cas9 equivalent to localize to a specific target nucleotide sequence. The Cas9 equivalents may include other napDNAbp from any type of CRISPR system (e.g., type II, V, VI), including Cpfl (a type-V CRISPR-Cas systems), C2cl (a type V CRISPR-Cas system), C2c2 (a type VI CRISPR-Cas system) and C2c3 (a type V CRISPR-Cas system). Further Cas- equivalents are described in Makarova et al, “C2c2 is a single-component programmable RNA-guided RNA-targeting CRISPR effector,“ Science 2016; 353(6299), the contents of which are incorporated herein by reference. As used herein, the “guide RNA“ may also be referred to as a “traditional guide RNA“ to contrast it with the modified forms of guide RNA termed “prime editing guide RNAs“ (or “pegRNAs“) which have been invented for the prime editing methods and composition disclosed herein. 3915-P1300WO.UW -25-

[0060] Guide RNAs or pegRNAs may comprise various structural elements that include, but are not limited to: Spacer sequence - the sequence in the guide RNA or pegRNA (having about 20 nts in length) which binds to the protospacer in the target sequence or DNA. The guide RNAs used in the present disclosure may be 15-1000 nucleotides in length and comprise a sequence of at least 10, at least 15, or at least 20 contiguous nucleotides that is complementary to a target nucleotide sequence. The guide RNA may comprise a sequence of 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, or 40 contiguous nucleotides that is complementary to a target nucleotide sequence. The guide RNA may be about 100 nucleotides or about ≥100 nucleotides in length.

[0061] The term “prime editor“ refers to the herein described fusion constructs comprising a napDNAbp (e.g., Cas9 nickase) and a reverse transcriptase and is capable of carrying out prime editing on a target nucleotide sequence in the presence of a PEgRNA (or “extended guide RNA“). The term “prime editor“ may refer to the fusion protein or to the fusion protein complexed with a PEgRNA, and / or further complexed with a second- strand nicking sgRNA. In some embodiments, the prime editor may also refer to the complex comprising a fusion protein (reverse transcriptase fused to a napDNAbp), a PEgRNA, and a regular guide RNA capable of directing the second-site nicking step of the non-edited strand as described herein. In other embodiments, the reverse transcriptase component of the “primer editor“ may be provided in trans.

[0062] The term “more robust or enhanced ribonucleotprotein (RNP) complex“ refers to later generations of prime editing systems (e.g., later than the original PE system (PE1)). More robust or enhanced RNP complexes can include, for example, enhanced expression level and stability (for either or both of prime editor protein and pegRNA), specialized performance (e.g., PE6 variants favoring different sequences), a more compact system to enable efficient packaging and in vivo delivery, and / or other derivative use of original PE systems (e.g., Prime-del, TwinPE, PEDAR, etc.).

[0063] As used herein, the “CRISPRa system or CRISPRa“ is a construct containing an appropriate expression cassette capable of targeting and activating the target promoter to enhance the expression of the target gene driven by the target promoter.

[0064] As used herein, the “CRISPRoff system or CRISPRoff“ is a construct containing an appropriate expression cassette capable of targeting, repressing and 3915-P1300WO.UW -26-methylating the target promoter and reducing the expression of the target gene driven by the target promoter.

[0065] The term “cDNA“ refers to a strand of DNA copied from an RNA template. cDNA is complementary to the RNA template.

[0066] As used herein, the terms “upstream“ and “downstream“ are terms of relativity that define the linear position of at least two elements located in a nucleic acid molecule (whether single or double-stranded) that is orientated in a 5′-to-3′ direction. In particular, a first element is upstream of a second element in a nucleic acid molecule where the first element is positioned somewhere that is 5′ to the second element.

[0067] As used herein, the term “complementary“ or “complementarity“ refers to the ability of a nucleic acid to form hydrogen bond(s) with another nucleic acid sequence by either traditional Watson-Crick or other non-traditional types. A percent complementarity indicates the percentage of residues in a nucleic acid molecule which can form hydrogen bonds (e.g., Watson-Crick base pairing) with a second nucleic acid sequence (e.g., 5, 6, 7, 8, 9, 10 out of 10 being 50%, 60%, 70%, 80%, 90%, and 100% complementary). “Perfectly complementary“ means that all the contiguous residues of a nucleic acid sequence will hydrogen bond with the same number of contiguous residues in a second nucleic acid sequence. “Substantially complementary“ as used herein refers to a degree of complementarity that is at least 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%. 97%, 98%, 99%, or 100% over a region of 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, 50, or more nucleotides, or refers to two nucleic acids that hybridize under stringent conditions.

[0068] While each of the elements of the present disclosure is described herein as containing multiple embodiments, it should be understood that, unless indicated otherwise, each of the embodiments of a given element of the present disclosure is capable of being used with each of the embodiments of the other elements of the present disclosure and each such use is intended to form a distinct embodiment of the present disclosure.

[0069] The referenced patents, patent applications, and scientific literature referred to herein are hereby incorporated by reference in their entirety as if each individual publication, patent or patent application were specifically and individually indicated to be incorporated by reference.

[0070] Without further elaboration, it is believed that one skilled in the art can, based on the above description, utilize the present disclosure to its fullest extent. As can 3915-P1300WO.UW -27-be appreciated from the disclosure above, the present disclosure has a wide variety of applications. The following specific embodiments are, therefore, to be construed as merely illustrative, and not limitative of the remainder of the disclosure in any way whatsoever. While illustrative embodiments have been illustrated and described, it will be appreciated that various changes can be made therein without departing from the spirit and scope of the invention.

[0071] All publications cited herein are incorporated by reference for the purposes or subject matter referenced herein. Example 1. Chromatin context-dependent regulation and epigenetic manipulation of prime editing

[0072] It was sought to exhaustively characterize the impact of the cis-chromatin environment on prime editing, a precise genome engineering tool that exhibits widely varying efficiency. Using a highly sensitive method for mapping the genomic locations of randomly integrated reporters, position effects on prime editing are discovered (~0% to 94% efficiency for an identical target site and edit) and these are shown to correspond with chromatin marks including H3K79me2 (positively) and H3K9me3 (negatively). Next, a multiplex perturbational framework is developed to assess the interaction of trans-acting factors with the cis-chromatin environment on genome editing outcomes. Applying this framework to DNA repair factors, HLTF is identified as a context-dependent repressor of prime editing. Finally, it is proposed that active transcriptional elongation enhances prime editing. Consistent with this, it is shown that it is possible to robustly decrease or increase the efficiency of prime editing by preceding it with CRISPR-mediated silencing or activation, respectively. Introduction

[0073] Prime editing facilitates the precise installation of diverse genetic variants with minimal off-target effects. The prime editor includes a fusion of a Cas9 nickase and reverse transcriptase (RTase), while the prime editing guide RNA (pegRNA) specifies both the target site and desired edit. The high programmability and specificity of prime editing, together with its avoidance of double-stranded breaks (DSBs), offer advantages over alternatives in the context of therapeutic genome editing, molecular recording and the functional characterization of genetic variants. 3915-P1300WO.UW -28-

[0074] A counterpoint to this promise is that prime editing’s efficiency is variable and often low. Relevant factors likely include: 1) properties of the prime editing ribonucleoprotein (RNP) complex; 2) the target sequence and edit type; 3) trans-acting factors, e.g., endogenous DNA repair proteins; and 4) the cis-chromatin environment of the target site. The first three factors have been explored, resulting in developed and / or specialized prime editors, sequence-based machine learning models that facilitate pegRNA design, and MMR-targeting strategies that enhance prime editing efficiency, respectively. However, how the cis-chromatin environment impacts prime editing remains largely unknown.

[0075] Conventional Cas9-mediated genome editing is influenced by chromatin, both through the steric effects of nucleosomes and epigenetic effects on the balance of endogenous DNA repair pathways used to repair Cas9-mediated DSBs. However, as prime editing leverages a different set of endogenous DNA repair pathways than conventional Cas9-mediated genome editing, it is proposed herein that prime editing‘s outcome can also be influenced by the chromatin. Results Design and efficient mapping of prime editing reporters

[0076] To measure cis-chromatin effects, a strategy was developed for efficiently mapping the precise genomic locations of randomly integrated reporters (FIG. 1A). The method relies on linear amplification of insertion junctions by in vitro transcription (IVT) on genomic DNA (gDNA) from a bacteriophage T7 promoter embedded within the reporter. In vitro transcribed RNAs are then reverse transcribed (RT), amplified by semi- specific PCR, and sequenced to associate reporter-specific barcodes with neighboring genomic sequences. Sequencing reads span insertion junctions, such that integration sites are mapped at base-pair (bp) resolution (FIG.2A). T7-assisted reporter mapping offers two key advantages over conventional reporter mapping approaches. First, it eliminates digestion and ligation steps that are characterized by bias and / or suboptimal efficiency. Second, T7 polymerase linearly amplifies molecules carrying positional information before RT-PCR, which further increases sensitivity.

[0077] A compact piggyBac-based (PB) prime editing reporter bearing the T7 promoter was developed and included the target sequence for a highly efficient pegRNA (HEK3), and a 16-bp degenerate barcode (BC). The final construct was 358 bp and lacks 3915-P1300WO.UW -29-any known regulatory elements that could potentially interfere with the local chromatin environment (FIG. 2A). A complex library of these “synHEK3“ reporters were randomly integrated into a K562 cell line constitutively expressing the PE2 prime editor (FIG. 2B). After piggyBac transposase was diluted out and all synHEK3 integrations were stable, 15.5 reporter copies (mean) per cell were estimated by qPCR (FIG. 2C). These cells were bottlenecked to ~500 clones, each presumably containing a unique set of integrated reporters. Downstream work was performed on this bottlenecked population (FIG.2B).

[0078] To map the genomic locations of synHEK3 integrations, T7-assisted reporter mapping was performed. Subsets of aligned reads defined sharp boundaries when visualized on a genome browser, each corresponding to the precise insertion point of a synHEK3 reporter (FIG. 1B). After barcode error correction, 10,095 insertion sites were identified (FIG. 2D). Saturation analysis indicated the mapping was near-complete (FIG. 2E). Motif analysis of insertion junctions revealed the expected TTAA motif for piggyBac transposition (FIG. 1C). Sites lacking this motif (6.4%) were removed and the genomic coordinates of TTAA motifs assigned as locations of individual reporters. Of note, only 4,273 mapped sites (42.3%) bore a unique barcode (FIG. 1D), while the remaining 5,177 (51.3%) bore one of 880 barcodes that recurred at multiple integration sites (FIG. 2F). These recurrent barcodes are not due to limited barcode complexity in the plasmid library, but rather reflect piggyBac transposon excision and re-integration events occurring after DNA replication but before the transposase was fully diluted out.

[0079] SynHEK3 reporters were distributed across all major genomic annotations, with a bias towards genic regions (FIG. 1E; FIGs 2G and 2H). Compared to randomly selected genomic or TTAA sites, piggyBac integrations were most strongly enriched near active transcriptional start sites (TSS) and enhancers, and most strongly depleted from quiescent regions, which are mostly constitutive heterochromatin (FIG.2I). Given these biases, it was sought to assess how broad a range of epigenetic environments was sampled by integrated reporters. For this, epigenetic scores for 2-kb windows surrounding synHEK3 integrations were computed for various chromatin features in K562 cells, and these were compared against equivalent scores for randomly selected genomic and TTAA sites. While synHEK3 integration sites tended to have higher scores for markers of open chromatin due to piggyBac integration bias, they nonetheless sample the full dynamic ranges of the chromatin features surveyed, including those associated with poorly accessible chromatin environments (FIG.2J). 3915-P1300WO.UW -30-Impact of chromatin features on prime editing efficiency

[0080] To measure prime editing outcomes across different chromatin contexts, the bottlenecked cell population was transfected with a pegRNA designed to install a CTT insertion at the HEK3 target sequence. After 4 days, prime editing efficiencies for all uniquely barcoded reporters were measured (FIG. 3A, left). This pegRNA drove 17% editing at the endogenous HEK3 locus. However, a broad range of efficiencies (~0% to 94%, mean: 19.8%) was observed for the HEK3 target when integrated at different genomic locations via the synHEK3 reporter, demonstrating prime editing outcomes are influenced by position effects (FIG.3A, right).

[0081] Correlations between chromatin feature scores and prime editing efficiencies across synHEK3 genomic locations were examined. The chromatin feature scores of H3K79me2, POLR2AS2, H3K9ac and HDAC2 were most positively correlated, while H3K9me3 was most negatively correlated (FIGs 4A, 4B). When synHEK3 reporters were binned based on each chromatin feature score, the same positive features were strongly associated with the proportion of sites that were highly edited (>25%; FIG.3B).

[0082] Among all features examined, H3K79me2 levels were most strongly correlated with prime editing efficiency (Spearman’s ρ = 0.56; FIGs 4A, 4B). The lowest and highest deciles of H3K79me2 scores exhibited median efficiencies of 1.7% and 54.5% respectively, a 32-fold difference (FIG.4B). Upon training a beta regression model based on the chromatin feature scores, H3K79me2 (p = 1.47 x 10-12) and H3K9me3 (p = 1.15 x 10-14) were the best positive and negative predictors of efficiency, respectively. This model accurately predicted prime editing efficiencies on a holdout test set (Spearman’s ρ = 0.62, Pearson’s r= 0.67, p < 2.2 x 10-16for both; FIG.3C).

[0083] Surprisingly, a small fraction (11%) of H3K79me2-high sites (top 10%) had near-zero editing efficiencies (FIG.4A). Without wishing to be bound by any particular theory, it was surmised that some of these underedited outliers might be due to a technical artifact, wherein some clones lose expression of PE2 due to excision during synHEK3 integration, as both were integrated via piggyBac. To check this, two new pools of synHEK3 reporters were engineered in wild-type K562 cells, reporter locations were mapped, and editing efficiencies measured with transiently expressed PE2. Results show that a smaller fraction of H3K79me2-high sites had near-zero editing efficiencies (4.7% vs. 11%). Chromatin features predicting high prime editing efficiencies were similar, and the 3915-P1300WO.UW -31-beta regression model trained on the original data accurately predicted the editing efficiencies of these two independently generated sets of synHEK3 reporters (Spearman’s ρ = 0.71, Pearson’s r= 0.70, p < 2.2 x 10-16for both; FIGs 4C, 4D).

[0084] In mammalian cells, H3K79 methylation is solely deposited by DOT1L in a process coupled to RNA Pol II transcriptional elongation. As H3K79me2 is overwhelmingly intragenic, a global analysis might mask differences in the interaction of chromatin and prime editing in intragenic vs. intergenic regions. The correlation analysis and beta regression modeling was repeated on intragenic and intergenic synHEK3 reporters separately (FIG. 3D). These analyses showed that H3K79me2’s value as a positive predictor is restricted to intragenic regions of the genome, while H3K9me3 is a negative predictor of prime editing efficiency in both intragenic and intergenic regions.

[0085] The ChIP-seq signal of H3K79me2 is more spread out and downstream- biased than that of H3K4me3, a marker of promoters (FIG. 4E). Without wishing to be bound by any particular theory, it was hypothesized that prime editing is efficient in transcriptionally active regions, and particularly so immediately downstream of active TSS. To evaluate this, synHEK3 reporters were stratified by distance from the nearest TSS as well as the mRNA expression level of the overlapping or nearest gene. Prime editing efficiencies were indeed correlated with both expression levels and TSS proximity (FIG. 3E; FIGs 4F, 4G). In addition, a weak correlation was identified between prime editing efficiency and transcript orientation, as synHEK3 sites on the same strand as the coding strand of transcription exhibited slightly higher editing efficiencies (p = 0.05; FIG.4H).

[0086] To visualize the contrasting epigenetic environments, genome browser views of the top 4 highly editable synHEK3 sites (90-94% PE) and 4 poorly editable synHEK3 sites (~1% PE) were generated (FIG.3F; FIG. 4I). The top sites are intragenic, in highly expressed genes, and within 3.5 kb of the TSS. The poorly editable examples are also intragenic, but within unexpressed genes and in some cases hundreds of kilobases from the TSS. Impact of chromatin environment on prime editing in diverse endogenous target sites

[0087] To ask whether learnings based on synHEK3 reporters generalize, epegRNAs were designed with DeepPrime to install 3-bp CCT insertions at 121 endogenous genomic target sites and these were tested in K562 cells (predicted efficiencies 65-85%). Interestingly, the resulting editing efficiencies were uncorrelated with 3915-P1300WO.UW -32-DeepPrime scores, possibly because they were all designed for high efficiency (Spearman’s ρ = 0.03, p = 0.74, Pearson’s r = 0.05, p = 0.62; FIG. 3G). However, despite variation in guide and target sequences, editing efficiencies were well-predicted by the synHEK3- derived beta regression model (Spearman’s ρ = 0.56, p = 4.6 x 10-9, Pearson’s r = 0.52, p = 5.0 x 10-8; FIG. 3H; FIG. 4J). Combining the DeepPrime score with the model did not further increase prediction accuracy (Spearman’s ρ = 0.57, p = 1.2 x 10-9, Pearson’s r = 0.53, p = 2.0 x 10-8; FIG. 3I). It was concluded that the epigenetic factors shaping prime editing efficiencies at synHEK3 reporters can also apply to endogenous target sites. Furthermore, a prediction model based on chromatin features can be used to rank the outputs of sequence-based pegRNA design tools. Differential position effects on Cas9 vs. prime editing

[0088] Without wishing to be bound by any particular theory, to the extent that the wide range of prime editing efficiencies at integrated synHEK3 reporters is due to differential accessibility for the prime editor, one can expect Cas9 editing to be similarly affected. The K562 synHEK3 reporter pool was therefore transfected with Cas9 RNP bearing a gRNA with the same HEK3-targeting spacer, and editing outcomes compared for prime vs. Cas9 editing across an identical set of genomic locations. Cas9 editing was more efficient than prime editing. Frequent indels were observed as early as Day 1. 75% of reporters had indel frequencies >90% by Day 4, and Cas9 successfully edited synHEK3 reporters that were resistant to prime editing (FIG. 5A; FIG. 6A). As expected, synHEK3 reporters near highly expressed genes were more frequently edited by Cas9. Reporters immediately downstream of TSSs of highly expressed genes had higher indel frequencies at Day 1, but the differences were negligible at later time points (FIG.6B).

[0089] To explore differences, synHEK3 reporters were clustered into 6 groups based on measured efficiencies of prime and Cas9 editing (FIG.5B). The reporters in each group clustered in PCA plots of chromatin feature scores, suggesting that the epigenetic environment shapes the editing efficiencies exhibited by members of each group (FIGs 6C, 6D). The larger, more highly edited groups (Groups 3-6) were further clustered into subclusters, resulting in 14 clusters overall (FIG.6E).

[0090] While prime and Cas9 editing efficiencies were generally correlated, there were a few pairs of groups whose properties were informative beyond that correlation: 1) those highly edited by both prime and Cas9 editing (Groups 6.1 & 6.2); 2) those highly 3915-P1300WO.UW -33-edited by Cas9 but poorly by prime editing (Groups 4.2 & 4.3); and 3) those poorly edited by both prime and Cas9 editing (Groups 1.0 & 2.0) (Fig.3C).

[0091] Groups 6.1 and 6.2 exhibited similarly high Cas9 editing (mean: 96.1% vs. 96.8%, p = 0.1), but Group 6.2 exhibited significantly higher prime editing (mean: 69.3% vs. 82.5%, p = 6.6 x 10-16). Both Group 6.1 and Group 6.2 are overwhelmingly intragenic (FIG. 5D) and similarly accessible (p = 0.63; FIG. 5E). However, Group 6.2 sites are more highly expressed than Group 6.1 sites, suggesting that higher levels of transcription specifically promote prime editing (p = 2.8 x 10-3; FIG.5F).

[0092] Groups 4.2 and 4.3 exhibit high levels of Cas9 RNP editing (mean: 86.4% and 89.9%) but low levels of prime editing (mean: 17.1% and 2.2%). Many synHEK3 sites in Group 4.3 exhibited near-zero efficiency for prime editing, which was attributed to the technical artifact of piggyBac excision of PE2 construct, discussed above. However, Group 4.2 sites tended to be intragenic and accessible (FIGs. 5D, 5E), but lowly expressed (p = 8.3 x 10-14for Group 4.2 vs.6; FIG.5F) and far from any TSS (p = 4.1 x 10-11for Group 4.2 vs. 6; FIG. 5G). Without wishing to be bound by any particular theory, the fact that Group 4.2 sites are amenable to Cas9 editing but not prime editing suggests that both Cas9 and prime editing benefit from chromatin accessibility, but more active transcription specifically promotes prime editing.

[0093] The most poorly editable sites fell into Groups 1.0 and 2.0 (FIG.5C). Both exhibited very low prime editing efficiencies (mean: 0.8% vs. 2.2%, p = 9.3 x 10-9), but Group 1.0 had substantially lower Cas9 editing (mean: 25.8% vs.38.3%, p < 2.2 x 10-16). Without wishing to be bound by any particular theory, it was suspected that Group 1.0 corresponds to constitutive heterochromatin marked by higher H3K9me3 that remains silenced throughout the cell cycle and development, and in contrast, Group 2.0 is marked by higher H3K27me3 and might correspond to facultative heterochromatin, which is silenced upon differentiation (FIG.6F).

[0094] Finally, frequencies of alleles resulting from Cas9 editing inferred to derive from non-homologous end joining (NHEJ) vs. microhomology-mediated end joining (MMEJ) repair pathways were examined. It was found that MMEJ usage was significantly higher in synHEK3 sites near H3K27me3 and H3K9me3 (FIG. 6G). Moreover, MMEJ usage increased as editing efficiencies decreased, with Group 1.0 sites exhibiting the highest MMEJ / (MMEJ + NHEJ) ratios (median: 0.54; FIG. 6H). Finally, within genes, MMEJ usage steadily increased along the gene body (FIGs 6I, 6J), which 3915-P1300WO.UW -34-could suggest a gradient effect posed by chromatin on DNA break repair kinetics such that faster repair mechanisms are favored immediately downstream of TSSs. Without wishing to be bound by any particular theory, it was speculated that a similar TSS-distance gradient may affect which repair mechanisms ultimately resolve prime editing-induced single strand breaks (SSB) and intermediate structures. Investigating the cis-chromatin context-dependencies of trans-acting factors influencing prime editing

[0095] DNA damage repair (DDR) is tightly regulated by local chromatin context. To explore mechanisms mediating chromatin context-specific regulation of prime editing, it was sought to develop a multiplex perturbational framework with which one could capture differences in prime editing outcomes in various chromatin environments upon knocking down various DDR-relevant factors. Potential strategies for this goal include Repair-seq or well-based screening of pre-integrated DNA repair reporters. However, the former averages over genomic contexts, while the latter does not scale effectively. A DDR-focused genetic screen was therefore designed, in which perturbations are coupled to outcomes at pre-integrated synHEK3 reporters with single cell molecular profiling. To co-profile the perturbation(s) received by a given cell and prime editing outcomes at its synHEK3 reporters, it was sought to use T7 in situ transcription (IST) to drive RNA production from both perturbational and reporter constructs in fixed, permeabilized nuclei, and to then capture these molecules with sci-RNA-seq3, a scalable method for single cell transcriptional profiling.

[0096] For these experiments, 22 and 28 uniquely barcoded synHEK3 reporters in two monoclonal K562 lines derived from the original pool (‘clone 3’ and ‘clone 5’) were the initial focus; the integration sites for these spanned a range of chromatin environments (FIG.7A). Prime editing efficiencies in these monoclonal lines were highly correlated with those estimated for the same reporters in the original polyclonal population (FIG.8A).

[0097] To perturb trans-acting factors, a doxycycline-inducible shRNA system was chosen, which can trigger a potent knockdown effect at single copy and is orthogonal to prime editing. A library of 304 shRNAs against 76 genes was constructed, including 74 DDR-related genes (10 unexpressed) and 2 luciferase genes. The DDR-related, expressed genes comprised hits found by Repair-seq, genes in other major DDR pathways, and epigenetic factors involved in H3K79me2 metabolism (FIG. 8B). To enable post-fixation 3915-P1300WO.UW -35-enrichment and targeted capture of the shRNA transcripts, the lentiviral construct was modified to contain a T7 promoter upstream of the shRNA and a RT primer binding site (PBS) (FIG.8C).

[0098] It was decided to model 6-bp insertions for several reasons. First, random insertions minimized potential biases due to the insertion sequence. Second, 6-bp insertions achieved high baseline editing efficiencies (~20% across 50 synHEK3 sites), increasing signal-to-noise. Third, the random 6-mers serve as unique molecular identifiers (UMIs) for counting unique prime editing events at each integration site from sequencing data.

[0099] To co-capture perturbations and prime editing outcomes, cells were profiled via a modified sci-RNA-seq3 workflow (FIG.7A). The first modification occurred before the RT step, in that T7 IST was performed on methanol-fixed nuclei to drive RNA expression from both synHEK3 reporters and the shRNA construct (FIG. 7B). Second, at the RT step, a primer cocktail targeting mRNAs (oligo-dT), synHEK3 and shRNA transcripts was used to simultaneously capture the transcriptome, editing outcome, and perturbation in a given cell (FIG. 7C). Finally, towards the end of the protocol, nuclear lysate was split; half was subjected to conventional sci-RNA-seq3 library preparation, and half to enrichment PCR targeting the synHEK3 and shRNA-derived transcripts (FIG.7D).

[0100] For each experiment, three libraries were sequenced, corresponding to the cellular transcriptome, shRNA integrants and synHEK3 integrants (FIG. 8D). Cellular transcriptomes were used to determine high-quality cells, and captured shRNA and synHEK3 transcripts were assigned to these cells based on matching combinatorial indices. Thus, for each profiled cell, the identity of shRNA(s) present in that cell was obtained, as well as the editing outcome at each of its 22 (clone 3) or 28 (clone 5) synHEK3 sites. A quantitative trait locus-style analysis was then applied to this matrix, for each synHEK3- shRNA pair, comparing the editing frequency at the synHEK3 reporter for cells that did vs. did not receive the shRNA (FIG.7E). Identification of trans-acting regulatory factors of prime editing

[0101] As a result, 667,810 high-quality cells were recovered, as assessed by complexity of the cellular transcriptome. SynHEK3-derived reads assigned to the two clones were well-separated, indicating a low collision rate (FIG.10A). After quality control filters, 229,657 cells (105,583 and 124,074 for clones 3 and 5, respectively) were retrieved, for which at least one shRNA and one synHEK3 integrant were detected. For clones 3 and 3915-P1300WO.UW -36-5 respectively, the median numbers of: cells per shRNA were 1,604 and 2,208, shRNAs detected per cell were 4 and 5 and synHEK3 barcodes detected per cell were 7 and 11 (FIG. 10B). To determine whether one can reliably estimate editing efficiency with this approach, a small fraction of the cells in this screen were sampled and amplicon sequencing of the synHEK3 reporters was performed. Prime editing efficiencies estimated from single cell data were almost perfectly correlated with those estimated by bulk amplicon sequencing (FIG.10C).

[0102] To detect shRNA-mediated knockdown effects on prime editing outcomes, editing efficiencies were calculated and compared in cells with vs. without a given shRNA (binomial test). Across all synHEK3-shRNA combinations in the two monoclonal lines, a clear excess of significant synHEK3-shRNA pairs was observed compared to pairs involving control shRNAs (FIG. 9A). 337 / 7,168 and 250 / 5,632 synHEK3-shRNA pairs were significant at a false discovery rate (FDR) of 5% in clone 5 and clone 3, respectively. This excess of highly significant p values does not appear to be driven by differences in the number of cells associated with candidate-targeting vs. control shRNAs (FIG.10D).

[0103] shRNAs targeting several genes were both highly significant and reproducible across the two clones (FIG. 9B). The most prominent targets include major components of the MMR pathway (PMS2, MLH1), which can influence prime editing outcomes. To better visualize these effects, synHEK3-shRNA pairs targeting each MMR- related factor were consolidated (FIG. 9C; FIG. 10E). The strongest prime editing- promoting effects were observed for shRNAs against PMS2 and MLH1 (homologs of bacterial MutLα; FIG.9D), which form a heterodimer and coordinate multiple repair steps after mismatch recognition. Knocking down FEN1, a 5’ DNA flap endonuclease, led to strong suppression of prime editing (FIG. 10F). Perturbing the bacterial MutSα homologs MSH2 and MSH6 did not yield consistent effects, with MSH6 even leading to a slight decrease in prime editing efficiency in clone 5. MSH2 / MSH6 dimer recognizes 1-2-bp mismatches, while MSH2 / MSH3 dimer detects longer indels (>2 bp). Without wishing to be bound by any particular theory, it was speculated that knocking down MSH6 releases sequestered MSH2, allowing for more efficient detection and correction of the 6-bp insertions; the mild effect for MSH2 might suggest a different mechanism used in recognizing 6-bp insertions. Knocking down other MMR-related factors (e.g., EXO1, LIG1, SSBP1, MSH4) did not yield consistent effects (FIG.9C; FIG.10E). 3915-P1300WO.UW -37-

[0104] In addition to MMR genes, targeting EP300 decreased prime editing efficiencies across all sites (FIG.10G). EP300 encodes the p300 transcriptional coactivator and its direct involvement in DDR remains elusive. Without wishing to be bound by any particular theory, there is a possibility that its effects here result from downregulation of global transcriptional output including expression of the prime editor complex.

[0105] Surprisingly, knocking down the methyltransferase (DOT1L) and demethylase (KDM2B) of H3K79 did not lead to significant changes in the prime editing efficiencies (FIG. 10H). To confirm this result, both bulk RNA sequencing and measurements of prime editing efficiencies were performed in synHEK3-bearing K562 cells treated with EPZ-5676, a potent DOT1L inhibitor. Expression levels of genes containing synHEK3 insertions in the single cell screen were not significantly affected, despite massive expression changes induced by EPZ-5676 (FIG. 10I). There was a global reduction in prime editing efficiency, but no significant relationship between changes in prime editing efficiencies and baseline H3K79me2 levels at intragenic synHEK3 reporters nor gene expression changes (FIG. 10J). Without wishing to be bound by any particular theory, these results can suggest that H3K79me2 is a bystander of high prime editing efficiency, rather than playing a direct role. HLTF works as a chromatin context-dependent repressor of prime editing

[0106] Another strong hit in the screen was HLTF, a member of the SWI / SNF2 family that contains an E3 ubiquitin ligase domain. HLTF mediates polyubiquitination of PCNA, which promotes replication through DNA lesions in an error-free manner. Knocking down HLTF can lead to a slight decrease in the frequency of prime editing- mediated single-nucleotide substitutions. In contrast, strong upregulation of 6-bp insertions was observed, comparable to changes induced by shRNAs against MLH1 and PMS2 (FIG. 11A), and suggesting differences in mechanisms involving HLTF in the repair of short vs. longer mismatches.

[0107] Inspection of individual synHEK3 insertion sites revealed heterogeneous responses to HLTF inhibition (FIG. 11B; FIG. 12A). Due to editing frequencies being bounded on [0, 1], there is an inverse correlation between baseline editing efficiencies and fold-changes in responses to perturbations (FIG.12C). Therefore, synHEK3 reporters with intermediate baseline editing frequencies (0.2-0.9) was a focus. In clone 5, 4 / 21 of these sites showed much weaker increases in editing frequencies compared to reporters with 3915-P1300WO.UW -38-similar starting editing frequencies. In clone 3, reporters were overall less responsive to HLTF inhibition, but 5 / 14 sites showed stronger and statistically significant upregulation of editing frequencies (FIGs 12A, 12D). Sites that were less responsive to HLTF inhibition in clone 5 showed slightly higher response to shRNAs against PMS2 (FIG. 12B). The selected synHEK3 reporters were grouped based on their responsiveness to HLTF shRNA knockdown and their overlapping status counted with annotated genes. The responsive group was enriched for gene-overlapping sites (5.4-fold, Fisher’s exact test p = 0.033; FIG. 11C). Furthermore, when taking expression status of overlapping or nearby genes (within 10 kb) into consideration, responsive sites tended to be near actively transcribed genes, while the unresponsive sites were mostly in non-transcribing regions (9.2-fold mean gene expression difference; p = 0.021; FIG. 11D). These observations were validated in cells transduced with individual shRNAs (FIG.12E).

[0108] Without wishing to be bound by any particular theory, HLTF might suppress prime editing in transcribed regions through its role in epigenetic regulation or alternatively through its role in DNA repair. To distinguish between these possibilities, bulk RNA-seq and ATAC-seq were performed in cells expressing shRNAs targeting HLTF (shHLTF.2367, shHLTF.2623). However, minimal changes were observed upon HLTF knockdown, including at genes and ATAC peaks near HLTF-responsive synHEK3 sites (FIGs 12F, 12G). This result suggests that the context-specific effect of HLTF on prime editing is not mediated through its role in chromatin remodeling. Modulating prime editing outcomes by targeted epigenetic reprogramming

[0109] Without wishing to be bound by any particular theory, several lines of evidence presented in this example can suggest that active transcriptional elongation enhances prime editing. First, H3K79me2 is deposited by DOT1L, which is part of the Pol II transcriptional elongation complex. H3K79me2 is strongly predictive of intragenic prime editing efficiency. At the same time, shRNA-mediated knockdown of DOT1L failed to appreciably alter prime editing efficiencies, a result corroborated by pharmacological inhibition of DOT1L. Second, intragenic prime editing efficiencies were also correlated with transcription levels, proximity to TSS, and, more weakly, transcript orientation. Third, the analyses of the differential outcomes of prime and Cas9 editing on identical synHEK3 reporters suggested that while chromatin accessibility may enhance both prime and Cas9 3915-P1300WO.UW -39-editing, higher levels of transcription and TSS proximity appear to specifically promote prime editing.

[0110] It was next sought to exploit the apparent relationship between active transcriptional elongation and prime editing to modulate the efficiency of prime editing. Three intronic synHEK3 reporters identified in the monoclonal line, clone 5, used above, were chosen. These reporters were in the genes USP7, METTL2A and LRRC8C (FIG. 14A). These cells were transiently transfected with CRISPRoff-v2, an epigenetic editing tool and pairs of gRNAs to silence these genes’ promoters, and prime editing efficiency was subsequently measured across synHEK3 reporters (FIG. 13A). After calibrating to synHEK3 reporters outside of target genes (FIG. 13B), 39%, 26% and 47% reductions in editing frequencies of synHEK3 reporters were observed in USP7, METTL2A and LRRC8C, respectively (FIG.13C). Without wishing to be bound by any particular theory, these reductions are likely direct effects of gene silencing, as other synHEK3 reporters were not affected and RNA-seq indicated highly specific target gene silencing (FIG. 14B). Interestingly, the magnitude of editing efficiency reductions were on par with the reductions in gene expression induced by CRISPRoff (53%, 30% and 55%, respectively). Enhancing prime editing efficiency by transient gene activation

[0111] Encouraged by these results, the possibility of increasing intragenic prime editing efficiency by preceding it with transient gene activation by CRISPRa was examined. For these experiments, it was sought to enhance prime editing of the endogenous genome, rather than synHEK3 reporters. Four target genes were initially selected, CXCR4, IL2RB, EGFR and CDKL5, and prime editing used to introduce a different substitution per target, at least some being clinically relevant. A K562 cell line stably expressing the dCas9- VP64 fusion was transfected with a pair of gRNAs (2XMS2) targeting the target gene‘s promoter, along with an MCP-p65-Rta fusion protein. Then, 2 days later, the cells were transfected with PEmax and pegRNA to program the desired mutations (FIG. 13D). For three of the targets, increases in prime editing efficiency were observed, ranging from 40% (EGFR, p = 0.076) to 21.9-fold (CXCR4, p = 0.001) (FIGs 14C-14E).

[0112] A potential limitation is that these pegRNAs had low baseline efficiencies. Focusing on a subset of previous targets and some new targets that could be strongly upregulated by CRISPRa (FIG. 14D), new (e)pegRNAs were designed with DeepPrime. The newly designed (e)pegRNAs had baseline editing efficiencies ranging from 0.5% to 3915-P1300WO.UW -40-13.5%. After CRISPRa of corresponding promoters, 2.9- to 16.5-fold upregulation in prime editing efficiencies was observed (FIG.13E). The newly designed pegRNA for IL2RB had the highest baseline efficiency (13.5%), which was boosted to 40% (2.9-fold) by CRISPRa. A beneficial post-CRISPRa efficiency was for a target in CREB3L3, and was boosted from 4% to 66% (16.5-fold). Of note, editing efficiencies of the same (e)pegRNAs were measured in a wild-type K562 cell line and similar baselines observed, suggesting that prime editing is not suppressed by the CRISPRa machinery (FIG. 13E). To test whether this strategy was effective in a different cellular context, prime editing of CXCR4, WNT3A and EGFR was preceded with CRISPRa in human iPSCs, and 7.8-fold, 1.7-fold and 1.5- fold increases in editing were observed, respectively (FIG.13F).

[0113] To evaluate whether this strategy could facilitate various types of edits, 7 libraries of epegRNAs were designed to install 19 different variants within 7 different exons of IL2RB, including all possible substitutions, small insertions and deletions, and a long insertion (FIGs 14F, 14G). Preceding prime editing with CRISPRa resulted in 1.6- to 5.8-fold increases in editing efficiency for the 7 exons (FIG. 14H). The epegRNAs targeting exon 1 of IL2RB were the least responsive to CRISPRa, potentially due to the CRISPRa and editing target sites being only 300 bp apart. The installation of small insertions (1, 3, 6 bp) were most responsive to CRISPRa (median fold-changes ranging from 6.6- to 10.2-fold), followed by other edit types (FIGs 13G, 13H). Among single nucleotide substitutions, G→A and A→G edits were most responsive to CRISPRa (median fold-changes of 5.3-fold and 4.7-fold, respectively; FIG. 13H). It was concluded that epigenetic reprogramming strategy is effective for enhancing the prime editing efficiencies of all edit types.

[0114] Taken together, this example reinforces the link between transcriptional activity and prime editing outcome. It also demonstrates the feasibility of enhancing prime editing outcomes via transient activation of the gene to which edits are being introduced. Discussion

[0115] A part of this example is a T7 promoter-bearing reporter construct and protocol that leverages T7 IVT and IST to enable questions surrounding chromatin position effects to be tackled in either bulk or single-cell format. When applied in bulk, T7 IVT enabled near-complete mapping of densely integrated reporters and measurement of position-dependent regulatory effects on prime editing. When incorporated into single-cell 3915-P1300WO.UW -41-RNA-seq protocol, T7 IST enabled co-profiling of non-transcribing genomic constructs. By further incorporation into an shRNA genetic screen, the interaction between trans- acting factors and genome editing could be further stratified by chromatin context. Of note, the method is straightforward to adapt based on this disclosure, and can include, e.g., use of a 19-bp T7 promoter with any reporter construct, such that it has the ability to make capturing precise genomic coordinates a possibility for any bulk or single cell assay in which reporter or effector constructs can be randomly integrated.

[0116] Prime editing efficiency at over 4,000 genomic locations was measured, and the correlation of 23 chromatin features was quantified with those efficiencies. Through beta regression modeling, it is shown that H3K79me2 is an effective predictor of efficacious prime editing, but without wishing to be bound by any particular theory, multiple lines of evidence can suggest that this is due to its strong correlation with active transcriptional elongation rather than a direct effect. This same beta regression model, based entirely on position effects on prime editing of the synHEK3 reporter, successfully predicts the relative editing efficiencies of high-quality epegRNAs targeting endogenous genomic target sites, providing value that is orthogonal to sequence-based prediction tools.

[0117] Examining the intersection of trans-acting factors with the cis-chromatin environment in shaping prime editing, HLTF was identified as a context-dependent repressor of prime editing, as knocking it down preferentially enhanced prime editing at sites undergoing active transcription. Although a mechanism involving some unknown function of HLTF is possible, these observations more plausibly relate to HLTF’s role in DNA repair than its role in chromatin remodeling; HLTF represses PE3 but not PE2. Without wishing to be bound by any particular theory, it is hypothesized that HLTF might be unevenly distributed along chromatin or might preferentially recognize special SSB repair intermediates. Actively transcribed regions of the genome might be under intense scrutiny by HLTF such that DNA lesions can be corrected immediately by DDR to avoid accumulation of mutations in the transcribed genome over cell cycles. However, further studies, in particular biochemical profiling of HLTF’s transient interactions during prime editing of transcribed regions, will be necessary to elucidate the underlying mechanism.

[0118] The correlation between active transcriptional elongation and prime editing efficiency prompted to explore whether manipulating the epigenetic context of a target site could alter the efficiency with which it was edited. Indeed, with CRISPRoff, it is shown herein that targeted gene silencing reduces prime editing efficiency for intragenic 3915-P1300WO.UW -42-targets, with effect sizes on par with the fold-reduction in gene expression. Conversely, prime editing efficiencies of endogenous, intragenic targets can be markedly enhanced by first “conditioning“ the locus with CRISPRa. The effectiveness of this approach is validated for multiple loci, at a range of distances from the TSS, in both K562 and iPSC cell lines, and for all edit types.

[0119] Across target sites in 6 genes that were successfully upregulated with CRISPRa in K562 cells, a mean 10.2-fold (median 8.4-fold) increase in prime editing efficiency was observed (FIG.13E), while across 133 combinations of edit types and target sites within the IL2RB locus, a mean 4.1-fold (median 2.6-fold) increase was observed (FIG. 13G). These effect sizes are comparable to that of MMR inhibition (mean 7.7-fold increase). Of note, the effectiveness of MMR inhibition is highly mutation-dependent, with certain types of edits (e.g., longer indels) being less sensitive. While more experiments related to the edit biases of such a strategy can be performed, all prime editing-mediated edit types are robustly enhanced by CRISPRa conditioning, e.g., 6-bp insertions in IL2RB exhibited mean 10.0-fold (median 10.2-fold) enhancement, as shown herein. The strategies are thus complementary, but given that this approach may work through mechanisms orthogonal to MMR, they can also be combined.

[0120] In summary, it is shown that prime editing’s efficiency is strongly impacted by the cis-chromatin landscape, and is promoted by active transcription in particular. Leveraging that insight, it is further shown how CRISPRa conditioning can be used to enhance the efficiency of intragenic prime editing, a strategy that can be useful in both basic research and therapeutic contexts. Finally, it is noted that the methods described herein, in particular T7-assisted reporter mapping, can be generally useful for studying position effects on other chromatin-modulated processes. Supplemental Information Cell culture conditions

[0121] K562 cells (CCL-243) were purchased from ATCC and maintained in RPMI 1640 medium (Gibco) supplied with 10% FBS (Hyclone) and penicillin / streptomycin (Gibco, 100 U / mL). HEK293T cells were maintained in DMEM medium (Gibco) supplied with 10% FBS and penicillin / streptomycin.

[0122] The DHFR-dCas9-VPH (VP48-P65-HSF1) WTC11 (human iPSCs) line was generated by the Martin Kampmann lab. Cells were maintained on Geltrex (Gibco) 3915-P1300WO.UW -43-coated plates in the mTeSR Plus medium (STEMCELL Technologies) supplied with 10 μM Y-27632 (Stemgent).

[0123] All cells were kept in a humidified incubator at 37 °C, 5% CO2. Molecular cloning

[0124] PB-T7-HEK3-BC (synHEK3): First, a minimal piggyBac cargo construct was created by deleting all intervening sequences between the 5’ and 3’ terminal repeats (including core insulators) of the PB-CMV-MCS-EF1α-Puro vector (System Biosciences, PB510B-1). A gBlock (Integrated DNA Technologies, IDT) including a filler sequence and flanking scaffold sequences (from GFP) was inserted to create a shuttle vector. The filler sequence contains two divergent BsmBI recognition sites and can be removed scarlessly. Second, the shuttle vector was digested with BsmBI (New England Biolabs). An 87-bp region around the HEK3 gRNA target site was synthesized from IDT and amplified with a pair of primers to introduce a T7 promoter and a 16-bp barcode to its upstream and downstream, respectively. The resulting PCR product was inserted into the linearized shuttle vector using NEB HiFi assembly. Ligated products were cleaned up and concentrated with AMPure XP beads (Beckman Coulter), and electroporated into NEB 10- beta electrocompetent E. coli. Electroporation was performed in a 0.1 cm electroporation cuvette using a Bio-Rad GenePulser electroporator at 2.0 kV, 200 Omega, and 25 μF.

[0125] LT3-GFP-T7-miR-E-CS1-PGK-Neomycin: The LT3GEN vector was purchased from Addgene (#111173) and digested with I-SceI (New England Biolabs). A fragment containing a T7 promoter and homologous sequences was ordered from IDT and assembled into the backbone. The LT3-GFP-T7-PGK-Neomycin vector was digested with XhoI and EcoRI-HF (New England Biolabs). An shRNA targeting the Renilla luciferase (Ren713) was inserted along with a Capture Sequence 1 (CS1) after the EcoRI site. For shRNA cloning, the LT3-GFP-T7-miR-E-CS1-PGK-Neomycin construct was digested with XhoI and EcoRI-HF. shRNAs were ordered from IDT as 97-nt 4 nmole Ultramers (Table 2) or oPool and amplified with primers p1 (5’- ATTACTTCGACTTCTTAACCCAACAGAAGGCTCGAGAAGGTATATTGCTGTTG ACAGTGAGCG-3’; SEQ ID NO:1) and p2 (5’- AATTGCTCTTGCTAGGACCGGCCTTAAAGCGAATTCTAGCCCCTTGAAGTCCG AGGCAGTAGGCA-3’; SEQ ID NO:2). The PCR products were assembled into the backbone using NEB HiFi assembly and transformed into NEB Stable Competent E. coli 3915-P1300WO.UW -44-(single shRNA vectors) or electroporated into NEB 10-beta electrocompetent E. coli (library).

[0126] Lenti-rtTA-P2A-Blast: The Lenti-Cas9-P2A-Blast (Addgene: 52962) vector was digested with XbaI and BamHI (New England Biolabs) to create a backbone. rtTA was amplified from the LT3GEPIR vector (Addgene: 111177) and cloned into the backbone using NEB HiFi assembly.

[0127] pU6-Sp-pegRNA-HEK3-ins6N and pU6-Sp-pegRNA-HEK3-ins3N: the pU6-Sp-pegRNA-HEK3-insCTT vector (Addgene:132778)1 was linearized by PCR with 5’ phosphorylated oligos p3 (5’-TCTGCCATCANNNNNNCGTGCTCAGTCTGT TTTTTTAAGCTTG-3’, ins6N; SEQ ID NO:3) or p4 (5’-TCTGCCATCANNNC GTGCTCAGTCTGTTTTTTTAAGCTTG-3’, ins3N; SEQ ID NO:4) and p5 (5’- GGACCGACTCGGTCCCACTT-3’; SEQ ID NO:5) and ligated with T4 DNA ligase. Ligation product was concentrated and electroporated into NEB 10-beta electrocompetent E. coli.

[0128] pU6-Sp-dual-gRNA vectors: A pU6-Sp-dual-gRNA scaffold vector was generated by replacing the pegRNA expressing cassette of pU6-Sp-pegRNA-HEK3- insCTT vector with a dual U6-gRNA cassette from the PX333 vector (Addgene: 64073). The second gRNA cloning site (two BsaI sites) was modified to two BsmBI sites. Spacer sequences were cloned into this vector sequentially using an oligo annealing method.

[0129] pU6-Sp-gRNA-2XMS2 vectors: A pU6-Sp-gRNA-2XMS2 scaffold vector was generated by modifying the pU6-Sp-pegRNA-HEK3-insCTT backbone. The scaffold sequence is 5’-GTTTAAGAGCTAAGCCAACATGAGGATCACCCATGT CTGCAGGGCATAGCAAGTTTAAATAAGGCTAGTCCGTTATCAACTTGGCCAA CATGAGGATCACCCATGTCTGCAGGGCCAAGTGGCACCGAGTCGGTGCTTTTT TT-3’; SEQ ID NO:1370. Spacer sequences were cloned in between two BsmBI sites using the oligo annealing method.

[0130] PB-CMV-MCP-XTEN80-p65-Rta-3xNLS-P2A-T2A-mPlum: The MCP(N55K) sequence was synthesized as an IDT gBlock and amplified. XTEN80 and 3XNLS-P2A were amplified from TETv4 (Addgene: 167983). p65-Rta was amplified from sadCas9-VPR (Addgene: 188514). mPlum was amplified from mPlum-C1(Addgene: 54839). All PCR products were purified and cloned into the PB-CMV-MCS-EF1α-Puro vector between the NotI site and the SV40 polyA sequence using NEB HiFi assembly. Then, a T2A sequence was inserted next to P2A by NEB HiFi assembly. 3915-P1300WO.UW -45-

[0131] PB-CMV-PEmax-EF1a-Puro: PE2max was amplified from pCMV- PEmax-P2A-hMLH1dn (Addgene: 174828) and cloned into the PB-CMV-MCS-EF1α- Puro vector.

[0132] PB-UCOE-EF1a-PEmax-P2A-mCherry-PGK-Blast: The UCOE-EF1a was amplified from pMH0006 (Addgene: 135448). PE2max was amplified from pCMV- PEmax-P2A-hMLH1dn. The rest of the sequences were synthesized as IDT gblocks and amplified. All PCR products were cloned into an empty piggyBac transposon vector.

[0133] (e)pegRNAs (pU6-Sp-(e)pegRNA) used in this example were ordered as 4nm ultramers (IDT) or long primers containing the spacer and 3’ extension sequences and cloned into the backbone of pU6-Sp-pegRNA-HEK3-CTT using NEB HiFi assembly. The epegRNA libraries were ordered as individual IDT eBlocks, pooled and cloned into the same backbone by Golden Gate assembly (New England Biolabs). General transfection strategies

[0134] Transfections of K562 cells were performed using a Lonza Bioscience 4D-Nucleofector system and the SF Cell Line 4D-Nucleofector X kits (Lonza). For single nucleocuvettes (100 μL), 1-3 x 106cells were transfected with up to 9 μg DNA. For 96- well Nucleocuvette plates (20 μL), 2 x 105~ 4 x 105cells were transfected with up to 1.2 μg DNA. Program code was FF-120.

[0135] Transfections of WTC11 cells were performed using a Lonza Bioscience 4D-Nucleofector system and the P3 Primary Cell 96-well Nucleofector Kit (Lonza) . For each transfection, 2 x 105cells were transfected with up to 3.5 μg DNA. Program code was CB-150.

[0136] Transfections of the HEK293T cell line were performed using Lipofectamine 3000 (Invitrogen) following manufacturer’s instructions. Measuring prime editing efficiency in the 500-cell K562 synHEK3 pools

[0137] 3 x 106PE2(+) or wild-type K562 cells were transfected with synHEK3 and the pCMV-HyPBase plasmid at a 3:1 ratio (total 9 μg) in a 100 μL nucleofection reaction. Cells were cultured in a large flask to maintain complexity. After 10 days, gDNA was extracted to estimate synHEK3 copy number by qPCR. Cells were counted and 500 cells were seeded to a new dish. gDNA were extracted from a portion of the expansion of these cells and used for T7-assisted reporter mapping (described below). 3915-P1300WO.UW -46-

[0138] For measuring prime editing efficiency, 1 x 106PE2(+) or wild-type K562 cells with integrated synHEK3 reporters from the bottlenecked 500-cell pools generated above were transfected with 2 μg pU6-Sp-pegRNA-HEK3-insCTT or 1 μg pU6-Sp- pegRNA-HEK3-insCTT with 1 μg PB-CMV-PE2-EF1a-Puro, respectively. Wild-type K562 cells were selected with 2 μg / mL puromycin for 2 days 24 hours after transfection. Cells were lysed 4 days after transfection and subjected to amplicon sequencing (described below). Generation of the K562 PEmax(+) cell line

[0139] Wild-type K562 cells were transfected with PB-UCOE-EF1a-PEmax- P2A-mCherry-PGK-Blast and pCMV-HyPBase at a ratio of 3:1 in a 100 μL nucleofection reaction. Cells were selected with 10 μg / mL blasticidin (Gibco) for 7 days. Monoclonal lines were isolated and the clone with the brightest mCherry fluorescent signal was used for following experiments. Design and test of epegRNAs for the endogenous target sites

[0140] SpCas9 spacers of the pegRNAs were searched in a set of 1-kb windows 500-bp away from a different set of randomly integrated synHEK3 reporters in K562 cells (n = 3,634) using FlashFry. gRNAs were filtered by specificity (specificity score > 80) and ranked by cutting efficiency, and best gRNAs were selected. This resulted in 3,157 gRNAs. DeepPrime was used to predict best pegRNAs or epegRNAs for installing 3-bp insertions (CCT) at the cut sites (the HEK293T PE2max models). The predicted scores were highly correlated for each spacer between the pegRNA and epegRNA (Pearson’s r = 0.83). Spacers scored highly in both pegRNAs (>40%) and epegRNAs (>65%) were selected (n = 121). Eventually, the library was synthesized in the form of epegRNAs (with the evopreQ1 hairpin; Table 1).

[0141] The epegRNAs were grouped into 2 separate pools (N = 60 and 61, respectively) with 2% spike-in of the pU6-Sp-pegRNA-HEK3-insCTT vector. The abundance of pegRNAs were measured by sequencing of the plasmid libraries. The plasmid pools (2 μg) were then transfected into 1 x 106PEmax(+) K562 cells in duplicates. Editing rates were measured at the 121 sites and normalized using the abundance of the HEK3 plasmid and editing efficiency at the HEK3 loci in the individual pools (Table 1). 3915-P1300WO.UW -47-Cas9 RNP editing experiment

[0142] Alt-R sgRNAs were ordered from IDT and resuspended in TE buffer to 100 μM. The RNP complex was assembled by combining 2.1 μL PBS, 1.2 μL Alt-R sgRNA (100 μM) and 1.7 μL Alt-R Cas9 Nuclease (61 μM, IDT) and incubating at room temperature for 10-20 min. The RNP mixture was added to 1 x 106PE2(+) K562500-cell pool resuspended in nucleofection solution along with 0.5 μg pmax-GFP (Lonza) and 1 μL electroporation enhancer (100 μM, IDT). gDNA was extracted at Day 1, 2 and 4 after transfection for amplicon sequencing of synHEK3 reporter. CRISPRoff experiment

[0143] On Day 0, 2 x 106clone 5 cells were transfected with CRISPRoff-v2.1 (3 μg), pU6-Sp-dual-gRNA (1 μg) and pmax-GFP (500 ng, Lonza) using the SF Cell Line 4D-Nucleofector X kit L (Lonza). On Day 2, cells were sorted based on high GFP expression (top 20%) on a flow cytometer and expanded. On Day 11, 4 x 105cells were transfected with the pU6-Sp-pegRNA-HEK3-insCTT plasmid (1 μg). Cells were lysed on Day 15 for amplicon sequencing of the synHEK3 reporter. A portion of cells were corrected on Day 11 for total RNA extraction and RNA sequencing (described below). CRISPRa experiments

[0144] K562 experiments: On Day 0, 4 x 105K562 dCas9-VP64 cells were transfected with PB-CMV-MCP-XTEN80-p65-Rta-3xNLS-P2A-T2A-mPlum (600 ng) and paired pU6-Sp-gRNA-2XMS2 (200 ng each) plasmids targeting the same promoter. On Day 2, 4 x 105cells from the previous transfection were transfected with pCMV-PEmax (Addgene: 174820) or PB-CMV-PEmax-EF1a-Puro (800 ng) and the pU6-Sp-(e)pegRNA plasmids (400 ng). Cells transfected with PB-CMV-PEmax-EF1a-Puro were selected with 2 μg / mL puromycin 24 hours after transfection for 2 days. Cells were lysed at Day 5 or 6 for amplicon sequencing.

[0145] iPSCs experiments: On Day 0, 2 x 105the DHFR-dCas9-VPH WTC11 cells were transfected with PB-CMV-MCP-XTEN80-p65-Rta-3xNLS-P2A-T2A-mPlum (2.1 μg) and paired pU6-Sp-gRNA-2XMS2 (700 ng each) plasmids targeting the same promoter.20 μM Trimethoprim (Sigma-Aldrich) was added to induce dCas9-VPH. On Day 3, 2 x 105cells from the previous transfection were transfected with PB-CMV-PEmax- EF1a-Puro (2 μg) and the pU6-Sp-(e)pegRNA plasmids (1 μg). Cells were continuously 3915-P1300WO.UW -48-treated with 20 μM Trimethoprim and were selected with 2 μg / mL puromycin 24 hours after transfection for 1 day. Cells were lysed at Day 5 or 6 for amplicon sequencing. Design and test of IL2RB epegRNA libraries

[0146] SpCas9 spacers for the selected exons of human IL2RB (Exons 1, 2, 4, 5, 8, 9 and 10) were generated using GuideScan2, sorted by the number of off target and cutting efficiency, and filtered for spacers with all of A, T, C, G bases within the first 7 base pairs from the cut sites (which allowed for modeling all 12 possible substitutions in close proximity of the cut sites). For substitutions, the first occurrences of the A, T, C, G bases were converted to the rest 3 bases. For 1-bp insertions, insertions reverse complement to the first base after the cut sites were modeled. For 3- and 6-bp insertions, insertions of CCT and CGTCAT were modeled at the cut sites. For long insertions, the 34-bp loxP sequences were inserted at the cut sites. For deletions, sequences of 1-, 3- and 6-bp were deleted after the cut sites. epegRNAs for substitutions, and insertions and deletions ≤ 3bps were designed using DeepPrime. The resulting epegRNAs for each exon had a median DeepPrime score > 40%. For 6-bp insertions and deletions, and insertions of loxP, the epegRNAs were designed manually with the length of primer binding sites being 13 bp and length of template (excluding the inserted or deleted sequences) being 10 bp. Table 1 shows sequences of the epegRNAs.

[0147] 19 epegRNAs derived from the same spacer were synthesized as a pool (IDT oPool) and cloned into the pU6-Sp-pegRNA-HEK3-CTT backbone as a library. The 7 epegRNAs libraries were tested following the CRISPRa experiment procedure in the dCas9-VP64 K562 cells as described above. Quantitative PCR (qPCR) analysis

[0148] qPCRs were performed on purified gDNA or cDNA reverse transcribed from total RNAs using SuperScript IV reverse transcriptase (200 U / μL, Invitrogen) following manufacturer’s instructions. qPCRs were performed with Power SYBR Green PCR Master Mix (Invitrogen) or KAPA2G Robust HotStart ReadyMix (Roche) supplied with SYBR Green (Invitrogen). For copy number estimation of synHEK3 reporters, Cq values of synHEK3 were normalized to those of SNRPB (3 copies per genome). Table 1 shows the list of primers used. 3915-P1300WO.UW -49-T7-assisted reporter mapping

[0149] gDNA of K562 cells was purified using the DNeasy Blood & Tissue Kit (QIAGEN) and in vitro transcribed with the HiScribe T7 High Yield RNA Synthesis Kit (New England Biolabs). Briefly, each reaction (20 μL) contained 0.3~1 μg gDNA, NTPs (10 mM each) and 2 μl T7 RNA Polymerase Mix. The reaction mixture was incubated at 37 °C for 16 hours. Then, gDNA was digested with 2.5 μL DNase (QIAGEN) in a 100 μL reaction at room temperature for 30 min. RNA was extracted with TRIzol LS Reagent (Invitrogen), and aqueous phase was precipitated with 1 volume of isopropanol and 5 μg Glycogen (Invitrogen) at -80 °C for 1 hour. RNA pellet was collected by centrifugation at 21,000 x g at 4 °C for 1 hour. The pellet was washed with 80% ice-cold ethanol and resuspended in 11.5 μL nuclease-free water.

[0150] For reverse transcription, RNA was incubated with 0.5 μL 100 μM RT primer p6 (5’-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGNNNNNNNN-3’; SEQ ID NO:6) and 1 μL 10 mM dNTP at 65 °C for 5 min and cooled on ice. Then, 4 μL 5X RT buffer, 1 μL 100 mM DTT, 1 μL RNaseOUT (40 U / μL) and 1 μL SuperScript IV RT (200 U / μL) were added and the reaction mixture was incubated at 23 °C for 10 min, 50 °C for 15 min and 80 °C for 10 min.

[0151] cDNA library was amplified with KAPA2G Robust HotStart polymerase, using primers p7 (5’- GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGAAAGG AAGCCCTGCTTCCTCCAGAGGG-3’, 0.5 μM; SEQ ID NO:7) and p8 (5’- TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG-3’, 0.5 μM; SEQ ID NO:8). PCR reaction was performed as follows: 95 °C 3 min; 95 °C 15 s, 65 °C 15 s, 72 °C 30 s, 16~18 cycles; and 72 °C 1 min. The PCR product was subjected to double-sided size selection (0.5X, 0.9X) and cleaned up with AMPure XP beads. The resulting product ranged from 200 to 1000 bp. To prepare Illumina sequencing libraries, 5-10 ng PCR product was re- amplified with the Nextera P5 and TruSeq P7 library index primers as shown in Table 1 for 5 cycles. The final PCR product underwent another round of double-sided size selection (0.5X, 0.9X) and clean-up with AMPure XP beads. The library was sequenced on an Illumina MiSeq in paired-end mode (Read 1: 254 bp; Read2: 55bp). HEK3 editing library preparation

[0152] gDNA of K562 cells was purified using the DNeasy Blood & Tissue Kit. Alternatively, cells were lysed in a lysis buffer [10 mM Tris-HCl pH8.0, 0.05% SDS and 3915-P1300WO.UW -50-0.04 mg / mL proteinase K(Thermo Scientific)], and incubated at 50 °C 60 min and 80 °C 30 min.100~250 ng gDNA or cell lysates were amplified using KAPA2G Robust HotStart ReadyMix with primers p9 (5’-GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CTACCCCGACCACATGAAGCAGC-3’, 0.5 μM; SEQ ID NO:9) and p10 (5’- TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGNNNNNNNNNNGACCATGTC ATCGCGCTTCTCGT-3’, 0.5 μM; SEQ ID NO:10). PCR reaction was performed as follows: 95 °C 3 min; 95 °C 15 s, 68-N °C 15 s, 72 °C 30 s, for 9 cycles (N was cycle number); 95 °C 15 s, 65 °C 15 s, 72 °C 30 s, for 11 cycles; and 72 °C 1 min. To ensure enough coverage and accurate measurement of editing efficiencies, for the K562 synHEK3 pool, products from at least 16 PCR reactions were pooled. The PCR product was purified with AMPure XP beads. 5 ng PCR product was re-amplified with the Nextera P5 and TruSeq P7 library index primers in Table 1 for 5 cycles. The final libraries were cleaned up with AMPure XP beads and sequenced on an Illumina NextSeq 500 or an Illumina NextSeq 2000 sequencer. Lentivirus production and transduction

[0153] 1.6 x 107HEK293T cells were seeded in a 10 cm2dish the day before transfection. 5.6 μg lentiviral vector and 14.4 μg ViraPower Lentiviral Packaging Mix (Invitrogen) were mixed and transfected using Lipofectamine 3000 (Invitrogen) following manufacturer’s instructions. Medium was changed at 24 hours post transfection. Viruses were collected at 48 and 72 hours post transfection, filtered using 45 μm filters and concentrated 100-fold using PEG-it Virus Precipitation Solution (System Biosciences).

[0154] In general, K562 cells were transduced with lentivirus in the presence of 8 μg / mL polybrene (Millipore). Medium was replaced after 24 hours. Pooled shRNA screen and T7 IST-assisted sci-RNA-seq3

[0155] To prepare for the pooled shRNA screening, two monoclonal lines (clone 3 and clone 5) from the original 500-cell synHEK3 pool were isolated and genotyped. There were 22 and 28 unique synHEK3 reporters in these two clones. These cells were transduced with Lenti-rtTA-P2A-Blast viruses and selected in 10 μg / mL blasticidin for 7 days. rtTA (+) monoclonal lines were then generated for downstream experiments.

[0156] The shRNA lentiviral library was first titrated, and mixed with clone 3 and 5 rtTA(+) cells at an MOI of 10 and a >1000X coverage. Transduced cells were selected in 3915-P1300WO.UW -51-800 μg / mL Geneticin (Invitrogen) for 7 days. 1 x 106cells were treated with 1 μg / mL doxycycline for 2 days to induce shRNA expression. Then 1.5 x 106- 2.5 x 106cells were transfected with 4.5-6.0 μg pU6-Sp-pegRNA-HEK3-ins6N (plasmid to introduce 6-bp insertion in HEK3). After 3-4 days, cells from the two clones were collected and mixed at a 1:1 ratio for sci-RNA-seq3 library preparation.

[0157] The sci-RNA-seq3 libraries were prepared by modifying a recently published protocol. Briefly, K562 cells were counted, washed with PBS and lysed in 5 mL Hypotonic lysis buffer B with DPEC (Sigma) for 10 min on ice. Nuclei were collected by centrifugation at 500 xg, 4 °C for 3 min and resuspended in 1 mL 0.3 M SPBSTM buffer with DEPC. Nuclei were fixed with 4 mL ice-cold methanol for 15 min on ice, swirled occasionally. After rehydration by adding 10 mL SPBSTM, nuclei were collected by centrifugation and washed once with 1 mL 0.3 M SPBSTM. For T7 in situ transcription, fixed nuclei resuspended in 171 μL SPBSTM were mixed with 99 μL NTP buffer and 30 μL T7 polymerase mix (New England Biolabs) and incubated in a 1.5 mL LoBind tube (Eppendorf) at 37 °C for 30 min. Afterwards, nuclei were washed with 1 mL SPBSTM, sonicated using a Diagenode sonicator for 12 seconds. At this point, nuclei were stained with YOYO-1 Iodide (Invitrogen) and counted on a Countess automated cell counter.

[0158] Counted nuclei were resuspended in SPBSTM at 4 x 106 / mL. For eachRT plate, 500 μL nuclei were combined with 56 μL 10 mM dNTPs (New England Biolabs) and distributed to a low-bind 96-well plate (5 μL per well).1 μL indexed oligo-dT, HEK3 and CS1 primers (10 μM) were added to each well. The plate was incubated at 55 °C for 5 min and cooled on ice. RT mixture was prepared by combining 240 μL 5X SuperScript IV Buffer, 60 μL SuperScript IV and 60 μL water. 3 μL RT mixture was added to each well. The RT plate was incubated at 55 °C for 10 min and cooled on ice.5 μL ice-cold SPBSTM was added to each well and all nuclei were pooled in pre-chilled LoBind tubes. Nuclei were washed once with 1 mL SPBSTM and resuspended in 1,200 μL SPBSTM (per ligation plate).

[0159] 11 μL nuclei were distributed to each well of a new 96-well plate and mixed with 2 μL 10 μM ligation primers. For each ligation plate, 195 μL 10X T4 Ligation Buffer was mixed with 65 μL T4 DNA Ligase, and 2 μL of the ligation mixture was added to each well. Ligation was performed at room temperature for 20 min on the bench. For the shRNA screen, nuclei were distributed into 4 ligation plates (384 ligation indices) to increase cell index complexity. The ligation plate was then cooled on ice and 10 μL ice- 3915-P1300WO.UW -52-cold SPBSTM was added to pool nuclei from all wells. Nuclei were washed twice with 1 mL SPBSTM.

[0160] Nuclei were resuspended in 1X Second Strand Synthesis Buffer (New England Biolabs), counted with YOYO-1 Iodide and diluted to 1.3 x 105- 2.5 x 105per 400 μL. 4 μL nuclei were distributed to multiple 96-well plates based on the number of nuclei retrieved at this step. Extra plates with nuclei were stored at -80 °C. Second strand synthesis mixture was prepared by combining 10.5 μL 10X Second Strand Synthesis Buffer, 35 μL 20X Second Strand Synthesis Enzyme with 94.5 μL water. 1 μL of second strand synthesis mixture was added to each well. The plate was incubated at 16 °C for 2.5 hours. To lyse nuclei, 1 μL ~1.07 AU / mL protease (QIAGEN) was added to each well and incubated at 37 °C for 40 min. Protease was inactivated at 75 °C for 20 min.5 μL EB buffer (QIAGEN) was added to each well and mixed.5 μL was taken out from each well and used for enrichment PCR, while the rest was used for Tn5 tagmentation and transcriptome library preparation.

[0161] To prepare the transcriptomic library, Tn5-N7 and mosaic end oligos were resuspended to 100 μM in annealing buffer (50 mM NaCl, 40 mM Tris-HCl pH8.0), mixed at a 1:1 ratio and annealed on a thermocycler using the following program: 95°C 5 min, cool to 65°C (0.1°C / s), 65°C 5 min, cool to 4°C (0.1°C / s). 20 μL Tagmentase (Tn5 transposase - unloaded, Diagenode) was mixed with 20 μL annealed oligos and incubated on a thermomixer at 350 rpm, 23°C for 30 min. 20 μL glycerol was added to the loaded Tn5 before storage at -20 °C. For tagmentation, 13.75 μL N7-loaded Tn5 (Diagenode) was mixed with 550 μL TD buffer and 5 μL of this mixture was added to each well. The plate was incubated at 55 °C for 5 min. To remove Tn5 transposase, 50 μL 1% SDS, 50 μL BSA (New England Biolabs) and 225 μL water were mixed, and 2.6 μL of the mixture was added to each well and incubated at 55 °C for 15 min. Then, SDS was quenched by adding 2 μL 10% Tween-20 to each well. For PCR, 96 indexed P5 primers were used with constant or indexed P7 primers (See Table 1). A PCR master mixture was prepared by combining 2,200 μL 2x NEBNext High-Fidelity 2X PCR Master Mix (New England Biolabs), 22 μL common P7 primer (100 μM) and 352 μL water.2 μL indexed TruSeq P5 primer and 23.4 μL PCR mixture were added to each well. If using indexed P7 primers, 2 μL of 10 μM primer was added to each well. PCR reaction was performed as follows: 70 °C 3 min; 98 °C 30 s; 98 °C 10 s, 63 °C 30 s, 72 °C 60 s, 16 cycles; and 72 °C 5 min.3 μL of each well was pooled and cleaned up with 0.8X AMPure XP beads. The library was resolved on a 1% 3915-P1300WO.UW -53-agarose gel and the smear between 300-600 bp was extracted using Monarch DNA Gel Extraction Kit (New England Biolabs).

[0162] For HEK3 and shRNA libraries, enrichment PCRs were directly performed on protease-treated cDNAs. To match PCR indices with those of the transcriptome library, the same indexed TruSeq P5 primers were used. The enrichment PCR master mixture contained 2,200 OneTaq 2X Master Mix (New England Biolabs), 16.5 μL synHEK3 P7 enrichment primer (100 μM), 16.5 μL shRNA P7 enrichment primer (100 μM), 44 μL 100X SYBR green (Invitrogen) and 1,353 μL water. 2 μL indexed P5 primer and 33 μL PCR mixture were added to each well. PCR reaction was performed as follows: 95 °C 3 min; 95 °C 15 s, 68-N °C 15 s, 72 °C 30 s, for 9 cycles (N was cycle number); 95 °C 15 s, 65 °C 15 s, 72 °C 30 s, for M cycles (decided by qPCR); and 72 °C 1 min. We monitored the reaction on a real-time qPCR machine and terminated the reaction at the log phase. All PCR products were pooled and concentrated using 0.9X AMPure XP beads. The library was resolved on a 1% agarose gel and the two discrete bands corresponding to HEK3 and shRNA constructs were extracted using Monarch DNA Gel Extraction Kit.

[0163] Eventually, all libraries were sequenced on an Illumina NextSeq 500 or an Illumina NextSeq 2000 sequencer. For HEK3 and shRNA libraries, custom Index 1 primer and Read 2 primer were used. Usually, 34 cycles were allocated to Read 1 for reading the RT index, ligation index, and unique molecular identifier (UMI). Amplicon sequencing for endogenous genes

[0164] Cells were lysed in a lysis buffer (10 mM Tris-HCl pH8.0, 0.05% SDS and 0.04 mg / mL proteinase K), and incubated at 50 °C 60 min and 80 °C 30 min. Lysates were directly used for PCR with the KAPA2G Robust HotStart ReadyMix with primers (0.5 μM each) designed for the endogenous targets. PCR reaction was performed as follows: 95 °C 3 min; 95 °C 15 s, 66-N °C 15 s, 72 °C 40 s, for 8 cycles (N was cycle number); 95 °C 15 s, 60 °C 15 s, 72 °C 40 s, for M cycles (decided by qPCR); and 72 °C 1 min. The PCR product was purified with AMPure XP beads. 5 ng PCR product was re- amplified with the Nextera P5 and TruSeq P7 library index primers in Table 1 for 5 cycles. The final libraries were cleaned up with AMPure XP beads and sequenced on an Illumina Miseq, an Illumina NextSeq 500 or an Illumina NextSeq 2000 sequencer. 3915-P1300WO.UW -54-

[0165] Sequencing reads were demultiplexed using the bcl2fastq software (Illumina). A custom script was used to determine frequencies of the introduced substitutions. RNA sequencing library preparation and processing

[0166] For bulk RNA sequencing of K562 PE2(+) cells and EPZ-5676 treated cells, total RNAs were purified from cells using TRIzol LS Reagent (Invitrogen) following manufacturer’s instructions, treated with RNase-free DNase (QIAGEN) and cleaned up using RNeasy Mini kit (QIAGEN). 1 μg total RNA (RNA integrity >= 9.7) were used for unstranded mRNA library preparation using the TruSeq RNA Library Prep Kit v2. For the CRISPRoff experiment and the HLTF knockdown experiment, total RNAs were purified from cells using TRIzol LS Reagent, treated with Turbo DNase (Invitrogen) and cleaned up using the RNeasy Mini kit. 1 μg total RNA (RNA integrity >= 9.9) were used for stranded mRNA library preparation using the Illumina TruSeq Stranded mRNA Library Prep kit. The RNA libraries were indexed using the Illumina TruSeq RNA UD Indexes. All libraries were sequenced on an Illumina NextSeq 500 or an Illumina NextSeq 2000 sequencer in a paired end mode.

[0167] Sequencing reads were demultiplexed using the bcl2fastq software (Illumina). For K562 PE2(+) and EPZ-5676 treated cells, an average of 34 million 75-bp paired-end reads were obtained. For calculating TPM of genes, sequencing reads from the unstranded RNA libraries were aligned to the GRCh38 reference genome (Gencode V43) using Salmon (v1.9.0). For the CRISPRoff experiment, on average, 55 million 50-bp paired-end reads were obtained per sample. For the HLTF knockdown experiment, 20 million 59-bp paired-end reads were obtained per sample. Reads were aligned to the GRCh38 reference genome and counted against all Ensembl genes using STAR (2.7.6a). Raw counts were analyzed with DESeq2. ATAC sequencing library preparation and processing

[0168] 1 x 105cells were collected, washed with PBS, and pelleted by centrifugation at 500 xg, 4 °C for 5 min. Cells were then lysed with 50 μL lysis buffer (10 mM Tris-HCl pH7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% NP-40, 0.1% Tween-20, 0.01% Digitonin) on ice for 3 min and neutralize with 250 μL RSB buffer with Tween-20 (10 mM Tris-HCl pH7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% Tween-20). Nuclei were counted on a 3915-P1300WO.UW -55-hemocytometer and 5 x 104nuclei underwent tagmentation. For tagmentation, per 50 μL contained 25 μL 2x TD Buffer, 8.25 μL PBS, 0.5 μL 1% Digitonin, 0.5 μL 10% Tween- 20, 2.5 μL Tn5 enzyme (Illumina, 2.5 μM) and 13.25 μL water. The reaction was incubated at 37 °C for 30 min. DNA was purified using the Clean and Concentrate kit (Zymo) and eluted in 10-20 μL EB Buffer (QIAGEN). All or half of the eluted DNA was used for qPCR. In each reaction, 10 μL tagmented DNA was mixed with 25 μL 2x NEBNext High- Fidelity 2X PCR Master Mix (NEB), 2.5 μL i5 primer, 2.5 μL i7 primer (Table 1), 0.25 μL 100X SYBR Green (Invitrogen) and 9.75 μL water. The qPCR reaction was performed as follows: 72 °C 5 min, 98 °C 30 s; 98 °C 10 s, 63 °C 30 s, 72 °C 1 min. The reaction was stopped after 7 cycles. Libraries were pooled, purified with AMPure XP beads, and sequenced on an Illumina NextSeq 2000 sequencer.

[0169] On average, 34 million 59-bp paired-end reads were obtained per sample. Reads were processed using the ENCODE ATAC-seq pipeline. Filtered peaks were sorted and merged across samples using bedops (v2.4.35) and were used to generate a peak count table using featureCounts (v2.0.2). Raw counts were analyzed with DESeq2. T7-assisted reporter mapping library processing

[0170] Sequencing reads were first demultiplexed using the bcl2fastq software. Under the current library design and sequencing scheme, Read1 started from genomic sequence and extended into the integrated synHEK3 construct, while Read2 contained reporter barcode information. Processing was as follows: 1) for each sequencing read pair, the 16-bp reporter barcode was extracted from Read 2 and attached to the read name of Read 1.2) Read1 was trimmed using cutadapt (v4.1) with following parameters: --cores=4 --discard-untrimmed -e 0.2 -m 10 -O 8 -a CCCTAGAAAGATAGTCTGCGTAAAATTGACGCATG (SEQ ID NO:1371). The adapter corresponded to the 3’ITR of piggyBac transposon. Since parameter “--discard- untrimmed“ was used, only reads spanning insertion junctions were kept and trimmed. 3) Trimmed sequences were aligned to the GRCh38 reference genome using bwa mem (v0.7.17). The “-Y“ option was used to enable soft clipping for supplementary alignments. 4) Reads uniquely (without XA:Z tag) and contiguously mapped near putative piggyBac landing pads (TTAA motifs) were kept using samtools (v1.9) and a custom script. 5) Aligned reads were converted to BED format using the sam2bed function in bedops (v2.4.35). Reads aligned to standard chromosomes were kept. 6) Insertion points were 3915-P1300WO.UW -56-calculated for all reads based on strand of alignment and reads were sorted by the insertion coordinates. 7) The first 8-bp of reads were used as UMIs. A custom script was used to collapse reads at a per-location, per-barcode, per UMI basis. A barcode-location-UMI count table was generated.8) synHEK3 barcodes <3 Levenshtein Distances at each location were collapsed and barcodes with > 3 UMIs were kept. 9) The count table was converted to a GenomicRanges object in R. Coordinates of the last 4 base pairs of aligned reads were designated as genomic locations of the inserted synHEK3 reporters.10) “Landing pads“ of the mapped synHEK3 reporters were retrieved using the getSeq() function in the BSgenome package. Reporters that didn’t have a TTAA sequence (which could be due to PCR error, mapping error, or the use of non-canonical landing pads) were removed. 11) SynHEK3 barcodes mapped to more than one location were removed.

[0171] Aligned reads were visualized on the Integrated Genome Browser (IGV; FIG.1B). Motif enrichment and visualization (FIG.1C) was performed using WebLogo 3. Genomic coverage analysis and annotation of the synHEK3 reporters (FIGs 1D, 1E) were performed using the ChIPseeker package in R. Enrichment of synHEK3 reporters across genomic features (FIGs 2G, 2H) were performed using the annotatePeaks.pl function in Homer. A custom script was used to find overlapping genes and calculate distance to closest TSSs. Analysis of ENCODE datasets

[0172] Accessions of ENCODE datasets (bigwig files) used in this study can be obtained. The GenomicRanges object containing all unique synHEK3 reporters was resized to 2 kb in width. The R package Genomation89 was used to extract values within the 2-kb windows from the bigwig files of the chromatin features. For a specific dataset, scores of the synHEK3 reporters were calculated as ^^ ^^^ ∑ଶ^^^^^^^^ ^^ ^, where ^^^was the value at base i, and c was the minimum non-zero value of the chromatin feature score in a base within all the windows surveyed. For the generation of heatmap in FIG. 11B, chromatin feature scores were scaled using the scale() function in R. Clustering and visualization of synHEK3 reporters was performed using the ComplexHeatmap package in R. Visualization of the epigenetic tracks was done by Gviz in R (FIG.3F; FIG.4I). 3915-P1300WO.UW -57-synHEK3 editing library analysis

[0173] Sequencing reads from the Illumina NextSeq platforms were first demultiplexed using the bcl2fastq software. The 16-bp reporter barcodes were extracted from read and attached to its read name. Sequences around the CRISPR cut site were extracted for all reads. A barcode-editing outcome table was generated. For prime editing experiments, a custom script was used to align sequences to a reference sequence and count mutation frequency for every barcode. For Cas9 mutagenesis analysis, all sequences were aggregated and editing outcomes (alleles) with the highest number of counts were selected. These most frequent alleles were then aligned to reference sequences using needleall with the following parameters: -gapopen 20 -gapextend 0.5 -endopen 20. A custom script was used to annotate the mutational events. MMEJ alleles were selected based on the following criteria: 1) microhomology sequences being at least 2 bp; 2) observed allele frequencies being 6-fold higher than expected frequencies. The 9 most frequent non-wild-type alleles were used for calculating the MMEJ / (MMEJ+NHEJ) ratio in FIGs 6G-6I. sci-RNA-seq3 transcriptome library processing

[0174] Sequencing reads from the Illumina NextSeq platforms were first demultiplexed based on PCR index using the bcl2fastq software. Reads were filtered based on RT and ligation index, by allowing <2 Hamming distance from reference. Filtered reads were trimmed with TrimGalore (v0.6.6) with parameters: -a AAAAAAAA -- three_prime_clip_R11. Reads were then aligned to the GRCh38 reference genome using STAR (v2.7.6a). PCR duplicates were collapsed using the RT index, ligation index, UMI sequence and end coordinate of reads. Reads were further demultiplexed based on the combination of the RT, ligation and PCR index and split into files for individual cells. To generate gene expression count matrices, reads were assigned to the exonic and intronic region of closest genes with HTseq (v.2.0.2). Reads with ambiguous assignments were discarded. Cells were further filtered based on total UMI (> 100) and mitochondrial reads percentage (<10%). Cells with the number of features detected between 10% and 90% percentile of all cells were kept and considered high-quality cells. The single cell analysis was performed using the Seurat (v4.0.0) package in R. 3915-P1300WO.UW -58-Pooled shRNA screen analysis

[0175] Sequencing reads from the Illumina NextSeq platforms were first demultiplexed based on PCR index using the bcl2fastq software. A custom script was used to further demultiplex and filter the synHEK3 and shRNA libraries based on the combination of RT, ligation and PCR index (cell ID), by allowing <2 Hamming distance from reference. For the synHEK3 library, reporter barcodes (<2 Hamming distance to the reference barcode set), prime editing outcomes and read UMIs were extracted. For the shRNA library, shRNA sequences (<2 Hamming distance to the reference set) and read UMIs were extracted. UMIs with <3 Hamming distances were collapsed.

[0176] A series of pre-processing steps were applied to the data.1) Cell IDs were matched between the sci-RNA-seq3 transcriptome libraries and the capture libraries, and only kept high-quality cells (number of features between 10% and 90% percentile).2) For the shRNA library, cells with <3 or >200 UMIs or >20 shRNA were removed. 3) For the synHEK3 library, cells with <4 UMIs were first removed. UMIs were counted for synHEK3 reporters belonging to the two clones and calculated a clone UMI / total UMI ratio. If the ratio was >80% for a specific clone, the cell is assigned to the corresponding clone. Cells with extremely high UMIs (cutoff between 100 and 250 UMIs, depending on library complexity and sequencing depth of the plate) were also removed before downstream analysis. 4) For a cell-synHEK3 barcode combination, if multiple insertion sequences were observed, sequences with <3 Hamming distance were collapsed. Cell- synHEK3 barcode pairs having conflicting editing outcomes were discarded. 5) A cell- synHEK3 barcode-editing outcome-shRNA matrix was generated for a common set of cells identified by the shRNA and synHEK3 libraries.

[0177] In general, the prime editing efficiency of a specific synHEK3 reporter was estimated in all cells containing the reporter by collapsing reads on a per-cell, per- barcode basis. To assess the effect of an shRNA on a specific synHEK3 reporter, editing efficiency of this reporter was calculated in cells without the specific shRNA, which was defined as the hypothesized probability of editing. Then, by treating prime editing outcomes as binary events (unedited vs. insertion), the total number of cells containing the shRNA and number of cells with 6-bp insertions were counted. A p value was computed using the function binom.test() in R. For each clone, all synHEK3-shRNA pairs were assessed by binomial tests. Q-Q plots in FIG. 9A were generated using uncorrected p values. The p values were further corrected using the function p.adjust() in R and the 3915-P1300WO.UW -59-Benjamini-Hochberg method was used. Empirical p values of candidate synHEK3-shRNA pairs were calculated as (n_lower + 1) / (N + 1), where n_low was the number of control tests with a raw p value lower than the candidate test‘s raw p value, and N was the total number of control tests. Empirical p values were then Benjamini-Hochberg corrected, and those with eFDR < 5% were reported. Processed screen results for clone 3 and clone 5 are provided in Table 2. Quantification and Statistical Analysis

[0178] The statistical tests for each experiment are described in the text, figure legends or STAR methods. In FIG.3C, the beta regression model was performed using the function betareg() in R package betareg using default parameters. In FIGs 6I, 6J, and 10J, the fit with confidence intervals was produced using function ‘geom_smooth()’ in the R package ggplot using parameter ‘method = ‘‘lm’’’ and all other parameters being default. In FIGs 9A-9D, binomial tests were performed to measure the effects of shRNAs and the resulting p values were corrected for multiple hypothesis testing using the Benjamini & Hochberg method. In FIGs 10I, 10J, 12F, 12G, and 14B, differential gene expression or accessibility analyses were performed using the DESeq2 package in R (Wald tests). In FIG. 11C, Fisher’s exact test was used to compare the differential enrichment of gene- overlapping synHEK3 reporters in the two groups. In FIGs 4H, 5E-5G, 6F-6G, 11D, 12D, and 12E, p values were calculated using two-sided Kolmogorov–Smirnov tests. In FIG. 14C, welch’s two sample t-tests were used to compare prime editing efficiency of genes before and after CRISPR activation. Table 1: List of oligos and nucleic acid sequences used in this study (part 1 of 6; oligos). SEQ ID Name Note NO - A y,3915-P1300WO.UW -60-SEQ ID Name Note NO: F i f lif i h HEK R 2 er er w N3915-P1300WO.UW -61-SEQ ID Name Note NO: XL R i IL2RB E 4 R 2, pegRNA, shRNA sequences). SEQ ID Name Backbone Target Note NO: he3915-P1300WO.UW -62-SEQ ID Name Backbone Target Note NO: XL R A R2F1 RI PR R A1Table 1: List of oligos and nucleic acid sequences used in this study (part 3 of 6; IL2RB epegRNA pools). SEQ ID NO: Pool name3915-P1300WO.UW -63-SEQ ID NO: Pool name 766 Il2RB Exon 13915-P1300WO.UW -64-SEQ ID NO: Pool name 811 Il2RB Exon 53915-P1300WO.UW -65-SEQ ID NO: Pool name 856 Il2RB Exon 9Table 1: List of oligos and nucleic acid sequences used in this study (part 4 of 6; 121 epegRNA sequence). ID pegRNA SE ID NO SE ID NO3915-P1300WO.UW -66-ID pegRNA (SEQ ID NO:) (SEQ ID NO:)3915-P1300WO.UW -67-ID pegRNA (SEQ ID NO:) (SEQ ID NO:)3915-P1300WO.UW -68-ID pegRNA (SEQ ID NO:) (SEQ ID NO:)Table 1: List of oligos and nucleic acid sequences used in this study (part 5 of 6; epegRNA PCR primers (Plate 1)). Well Position SEQ ID Sequence Name3915-P1300WO.UW -69-Well Position SEQ ID Sequence Name NO: A 114 T A TT TT TTAA AIDTf3915-P1300WO.UW -70-Well Position SEQ ID Sequence Name NO: 11 A TT TT T IDTf3915-P1300WO.UW -71-Well Position SEQ ID Sequence Name NO: E 121 A A TAT A AIDTf3915-P1300WO.UW -72-Well Position SEQ ID Sequence Name NO: 12 AT ATTAT TTA ATIDTf3915-P1300WO.UW -73-Table 1: List of oligos and nucleic acid sequences used in this study (part 6 of 6; epegRNA PCR primers (Plate 2)). Well Position SEQ ID Sequence Name NO:3915-P1300WO.UW -74-Well Position SEQ ID Sequence Name NO: B 1 TT AA T AAT IDTl screen. (shRNA library (part 1 of 2)); index values correspond among table parts. Index shRNA name Symbol Entrez ID SplashRNA 22-mer guide score (SEQ ID NO)3915-P1300WO.UW -75-Index shRNA name Symbol Entrez ID SplashRNA 22-mer guide score (SEQ ID NO) 34 EP300 2033 514 EP300 2033 1813751 943915-P1300WO.UW -76-Index shRNA name Symbol Entrez ID SplashRNA 22-mer guide score (SEQ ID NO) 82 LIG1 3978 3050 LIG1 3978 1794108 1423915-P1300WO.UW -77-Index shRNA name Symbol Entrez ID SplashRNA 22-mer guide score (SEQ ID NO) 127 PMS2 5395 1847 PMS2 5395 1774557 1873915-P1300WO.UW -78-Index shRNA name Symbol Entrez ID SplashRNA 22-mer guide score (SEQ ID NO) 172 HLTF 6596 1722 HLTF 6596 1859193 2323915-P1300WO.UW -79-Index shRNA name Symbol Entrez ID SplashRNA 22-mer guide score (SEQ ID NO) 220 CCNA1 8900 1369 CCNA1 8900 1692802 2803915-P1300WO.UW -80-Index shRNA name Symbol Entrez ID SplashRNA 22-mer guide score (SEQ ID NO) 264 H2AFX 3014 1543 H2AFX 3014 09422031 3243915-P1300WO.UW -81-Table 2: The DDR-focused shRNA library and result of the single-cell screen. (shRNA library (part 2 of 2)); index values correspond among table parts. Index 97-mer note (SEQ ID NO)3915-P1300WO.UW -82-Index 97-mer note (SEQ ID NO) 42 43915-P1300WO.UW -83-Index 97-mer note (SEQ ID NO) 43915-P1300WO.UW -84-Index 97-mer note (SEQ ID NO) 1 4 43915-P1300WO.UW -85-Index 97-mer note (SEQ ID NO) 1 43915-P1300WO.UW -86-Index 97-mer note (SEQ ID NO) 21 2 l3915-P1300WO.UW -87-Index 97-mer note (SEQ ID NO) 22 23915-P1300WO.UW -88-Table 3: Lists of synHEK3 reporters and relevant features used in this example (synHEK3 reporters in shRNA screen (synHEK3 Clone 3 and Clone 5)); index values correspond among portions of the table. In- Seq start end width strand pos Barcode dex name (SEQ ID NO)3915-P1300WO.UW -89-45 chr11 13743779 13743782 4 + 13743782 713 46 chr3 97940151 97940154 4 - 97940150 714 47 chr11 69179059 69179062 4 + 69179062 715 6 3 2 9 4 7 8 8 0 2 5 3 9 23915-P1300WO.UW -90-15 1275 FALS clone - - - - - E 3 1.165065 0.501867 0.173779 0.4695468 1.237681 2 5 4 5 8 1 6 3 7 2 0 4 7 4 0 6 0 1 3 1 13915-P1300WO.UW -91-34 2266 FALS clone 1.490454 - 0.738548 0.3081472 1.714882 E 5 99 0.068618 2 6 62 2 8 2 7 8 5 5 6 2 4 8 9 6 8 6 0 63915-P1300WO.UW -92-In- H3K9a H3K9 H3K9 H4K20 BRD4 EZH2 H2AFZ POLR2 dex c me3 me1 me1 AS2 1 - - 067102 0304124 -0269533 - - 264972 8 0 9 1 7 4 0 3 6 2 3 8 0 9 83915-P1300WO.UW -93-17 2.04944 0.19683 - - 1.101177 0.42157 1.950988 0.12904 582 001 0.47765 0.374822 57 555 12 573 99 7 1 4 3 7 0 54 5 7 2 1 4 7 5 3 4 8 03915-P1300WO.UW -94-35 - - 0.69916 0.268582 - - - 0.50313 0.01578 0.62235 353 9 0.000871 0.49963 0.437963 683 67 39 3 24 6 7 2 7 8 3 1 5 7 9 2 9 0 4 1 13915-P1300WO.UW -95-In- SMC3 HDAC1 HDAC HDAC3 H3K4 H3K4 H3K4m H3K27m dex 2 me3 me2 e1 e3 1 030731 0589003 095206 - - - - - 4 6 0 9 9 1 2 7 1 1 1 0 1 7 7 5 53915-P1300WO.UW -96-18 0.47865 0.534159 0.93933 - - 0.38218 1.20441 - 883 74 22 0.14325 0.60602 185 816 0.536267 37 38 7 7 0 4 1 7 8 8 2 6 3 9 5 4 1 1 33915-P1300WO.UW -97-35 1.43677 0.161590 0.52974 0.78059 - - 0.46840 - 785 92 783 771 0.79608 0.28290 317 1.248641 49 99 7 1 9 0 6 1 1 0 8 7 5 0 1 5 6 23915-P1300WO.UW -98-In- H3K27a H3K36 txdb_ distanc scaled_d gene TPM gene_ dex c me3 over- e istance pro- lap ximal E E E E E E3915-P1300WO.UW -99-19 - - FALS 5138 1.73933 ENSG000 0 TRUE 0.51050 0.901985 E 649 00267133 46 9 E E E3915-P1300WO.UW -100-38 - - TRUE 57357 0.25329 ENSG000 4.268352 TRUE 0.38984 0.901386 777 00164756 33 12 2 ENon-limiting Embodiments

[0179] While general features of the disclosure are described and shown and particular features of the disclosure are set forth in the claims, the following non-limiting embodiments relate to features, and combinations of features, that are explicitly envisioned as being part of the disclosure. The following non-limiting Embodiments contain elements that are modular and can be combined with each other in any number, order, or combination to form a new non-limiting Embodiment, which can itself be further combined with other non-limiting Embodiments. 3915-P1300WO.UW -101-

[0180] Embodiment 1. A method for modulating gene editing efficiency of a target site in a genomic region of a cell comprising: modulating chromatin accessibility of the genomic region and / or transcriptional activity of a gene in the genomic region proximal to the gene editing target site.

[0181] Embodiment 2. The method of Embodiment 1, wherein gene editing comprises prime editing.

[0182] Embodiment 3. The method of Embodiment 1 or 2, wherein the target site is downstream of a promoter of the gene.

[0183] Embodiment 4. The method of any one of Embodiments 1 to 3, wherein the target site is downstream of a transcriptional start site (TSS) of the gene.

[0184] Embodiment 5. The method of Embodiment 1 or 2, wherein the target site is upstream of a promoter of the gene.

[0185] Embodiment 6. The method of any one of Embodiments 1 to 5, wherein the target site is selected from the group consisting of an exon, an intron, a promoter, a 3’ UTR, a 5’ UTR, and intergenic sites in the genomic region.

[0186] Embodiment 7. The method of any one of Embodiments 1 to 6, wherein modulating the gene editing efficiency comprises activating transcription of the gene proximal to the gene editing target site.

[0187] Embodiment 8. The method of Embodiment 7, wherein activating transcription of the gene proximal to the gene editing target site comprises CRISPRa- mediated transcriptional activation.

[0188] Embodiment 9. The method of Embodiment 7, wherein the step of activating transcription occurs before, or during the step of prime editing of the target site.

[0189] Embodiment 10. The method of any one of Embodiments 1 to 6, wherein modulating the gene editing efficiency comprises repressing transcription of the gene proximal to the prime editing target site.

[0190] Embodiment 11. The method of claim 10, wherein repressing transcription of the gene proximal to the gene editing target site comprises CRISPRoff- mediated transcriptional repression.

[0191] Embodiment 12. The method of any one of Embodiments 1 to 11, wherein the method further comprises modulating at least one DNA damage repair (DDR) gene in the cell. 3915-P1300WO.UW -102-

[0192] Embodiment 13. The method of Embodiment 12, wherein the at least one DDR gene is a DNA mismatch repair (MMR) gene.

[0193] Embodiment 14. The method of Embodiment 13, wherein the MMR gene is selected from the group consisting of PMS2, MLH1, MLH2, MLH3, MLH6, EXO1, and FEN1.

[0194] Embodiment 15. The method of any one of Embodiments 2 to 14, wherein the method further comprises use of a more robust or enhanced prime editing ribonucleoprotein (RNP) complex.

[0195] Embodiment 16. The method of Embodiment 15, wherein the more robust or enhanced prime editing RNP complex comprises a prime editor selected from the group consisting of (a) a prime editor comprising an engineered reverse transcriptase, (b) a codon- and structure-optimized prime editor, (c) a compact and / or specialized prime editor, and (d) a prime editor fused to an RNA-binding protein.

[0196] Embodiment 17. The method of Embodiment 16, wherein (a) the prime editor comprising an engineered reverse transcriptase is PE2, (b) the codon- and structure-optimized primer editor is PEmax, (c) the compact and / or specialized prime editor is selected from PE6a, PE6b, PE6c, PE6d, PE6e, PE6f, and PE6g, or (d) the prime editor fused to an RNA-binding protein is PE7.

[0197] Embodiment 18. The method of any one of Embodiments 15 to 17, wherein the more robust or enhanced prime editing RNP complex comprises a structurally stabilized pegRNA (epegRNA).

[0198] Embodiment 19. The method of any one of Embodiments 15 to 18, wherein the more robust or enhanced prime editing RNP complex comprises a computationally optimized pegRNA.

[0199] Embodiment 20. The method of any one of Embodiments 1 to 11, wherein the method further comprises inhibiting DNA mismatch repair (MMR).

[0200] Embodiment 21. The method Embodiment 20, wherein inhibiting MMR comprises use of a second-site nicking guide RNA targeting the non-edited strand.

[0201] Embodiment 22. The method of Embodiment 20, wherein inhibiting MMR comprises inhibiting an MMR protein selected from the group consisting of MLH1, MLH2, MLH3, MLH6, PMS2, and EXO1.

[0202] Embodiment 23. The method of Embodiment 22, wherein the MMR protein is MLH1. 3915-P1300WO.UW -103-

[0203] Embodiment 24. The method of Embodiment 23, wherein inhibiting MLH1 comprises expressing a dominant negative MLH1 protein.

[0204] Embodiment 25. The method of Embodiment 22, wherein inhibiting the MMR protein comprises RNAi-mediated knockdown of MMR protein gene expression.

[0205] Embodiment 26. The method of Embodiment 22, wherein inhibiting the MMR protein comprises targeted degradation of the MMR protein.

[0206] Embodiment 27. The method of any one of Embodiments 1 to 26, wherein the cell is a eukaryotic cell, preferably a mammalian cell.

[0207] Embodiment 28. The method of any one of Embodiments 1 to 27, wherein the cell is a stem cell.

[0208] Embodiment 29. The method of Embodiment 28, wherein the stem cell is an induced pluripotent stem cell (iPSC).

[0209] Embodiment 30. The method of Embodiment 29, wherein the iPSC is a human iPSC.

[0210] Embodiment 31. A method for determining prime editing efficiencies across a genome, the method comprising: (i) generating a reporter pool of cells comprising a plurality of reporter systems integrated into genomes of a plurality of cells, wherein each of the plurality of reporter systems comprise a vector comprising: (a) a promoter for in vitro transcription; (b) a target sequence for a pegRNA; and (c) a molecular identifier barcode; (ii) mapping insertion sites of the plurality of reporters in the genome of the plurality of cells; (iii) transfecting the reporter pool of cells with pegRNAs comprising a sequence for introducing a desired edit at the target sequence of the plurality of reporter systems integrated into the genome of the plurality of cells, wherein each of the plurality of cells constitutively express a prime editor (PE); and (iv) measuring the frequency of the desired edit in the genome of the cell.

[0211] Embodiment 32. The method of Embodiment 31, wherein the step of generating a reporter pool of cells comprises transfecting the plurality of cells with the plurality of reporter systems. 3915-P1300WO.UW -104-

[0212] Embodiment 33. The method of Embodiment 31 or 32, wherein the step of measuring the frequency of the desired edit in the genome of the cell by prime editing comprises: (i) isolating genomic DNA from the plurality of cells; (ii) amplifying reporter systems integrated in the genome of the plurality of cells to obtain a library of amplified DNAs; (iii) sequencing the library of amplified DNAs; (iv) associating the molecular identifier barcode with the edit site in the target sequence of the plurality of reporters; and (v) computationally determining frequency of edits in the target sequence of the plurality of reporter systems associated with the molecular identifier barcode.

[0213] Embodiment 34. The method of Embodiment 33, wherein the step of sequencing the library of amplified DNAs comprises high-throughput short- and long-read sequencing.

[0214] Embodiment 35. The method of any one of Embodiments 31 to 34, wherein the prime editor (PE) comprises a Cas9 nickase and a reverse transcriptase.

[0215] Embodiment 36. The method of Embodiment 31, further comprising correlating the prime editing efficiencies with features of epigenetic environment in the genome in about 100bp to about 2kb region centering at the edit sites of the target sequences.

[0216] Embodiment 37. The method of Embodiment 36, wherein the features of epigenetic environment are selected from DNA binding proteins, epigenetic modulators, histone modifications, DNase Hypersensitive sites, transposase-accessible chromatin (ATAC), and higher order chromatic structures.

[0217] Embodiment 38. The method of any one of Embodiments 31 to 37, wherein the plurality of cells comprises eukaryotic cells, preferably mammalian cells.

[0218] Embodiment 39. A method of mapping a genomic location of at least one reporter system in a genome of a cell comprising: (i) integrating the at least one reporter system in the genome of the cell; and (ii) determining an insertion / integration site for the at least one reporter system within the genome of the cell. 3915-P1300WO.UW -105-

[0219] Embodiment 40. The method of Embodiment 39, wherein the reporter system comprises a vector comprising a promoter for in vitro transcription.

[0220] Embodiment 41. The method of Embodiment 40, wherein the vector further comprises a polynucleotide sequence linked to the promoter.

[0221] Embodiment 42. The method of any one of Embodiments 39 to 41, wherein the polynucleotide sequence comprises an amplification primer binding domain.

[0222] Embodiment 43. The method of Embodiment 42, wherein the polynucleotide sequence further comprises nucleic acid sequences selected from coding or non-coding transcripts, cis-regulatory elements, molecular identifier barcode, or functional or non-functional genomic elements.

[0223] Embodiment 44. The method of any one of Embodiments 39 to 43, wherein the polynucleotide sequence comprises a target sequence and a molecular identifier.

[0224] Embodiment 45. The method of Embodiment 39, wherein the reporter system comprises a vector comprising: (a) a promoter for in vitro transcription; (b) a target sequence; and (c) a molecular identifier.

[0225] Embodiment 46. The method of Embodiment 45, wherein the vector is devoid of any cis-regulatory elements.

[0226] Embodiment 47. The method of any one of Embodiments 39 to 46, wherein the vector is selected from a transposon-based vector, a retroviral-based vector, a lentiviral-based vector, and a serine integrase-based vector.

[0227] Embodiment 48. The method of Embodiment 47, wherein the vector is a transposon-based vector, and wherein the vector comprises an inverted terminal repeat (ITR) at each vector end.

[0228] Embodiment 49. The method of Embodiment 47 or 48, wherein the transposon-based vector is a piggyBac-based vector.

[0229] Embodiment 50. The method of Embodiment 48 or 49, wherein the cell is co-transfected with an expression construct capable of expressing transposase.

[0230] Embodiment 51. The method of any one of Embodiments 40, 41, 45, and 46, wherein the promoter is selected from a bacteriophage T7 promoter, a T3 promoter, a Sp6 promoter, and variants thereof.

[0231] Embodiment 52. The method of any one of Embodiments 45 to 51, wherein the target sequence is a target sequence for a Cas9 guide RNA or a variant thereof. 3915-P1300WO.UW -106-

[0232] Embodiment 53. The method of any one of Embodiments 39 to 52, wherein the step of integrating the at least one reporter system in the genome of the cell comprises transfecting the cell with the at least one reporter system.

[0233] Embodiment 54. The method of any one of Embodiments 39 to 53, wherein the step of determining the insertion site for the at least one reporter system within the genome of the cell comprises: (i) isolating genomic DNA of the cell comprising the at least one reporter system integrated in the genome of the cell; (ii) in vitro transcription of the isolated genomic DNA using the promoter of the reporter system to generate chimeric RNAs comprising a portion of the reporter system and of its neighboring genomic DNA; (iii) reverse transcribing the chimeric RNAs; (iv) amplifying cDNAs to generate a cDNA library comprising the reporter system integrated into the genome of the cell; (v) sequencing the cDNA library; and (vi) associating the molecular identifier barcode with insertion site in the genomic DNA.

[0234] Embodiment 55. The method of Embodiment 53, wherein the step of sequencing the library of cDNAs comprises high-throughput short- and long-read sequencing, and Sanger sequencing.

[0235] Embodiment 56. The method of any one of Embodiments 45 to 55, wherein the cell constitutively expresses a prime editor (PE), wherein the prime editor (PE) comprises a Cas9 nickase and a reverse transcriptase.

[0236] Embodiment 57. The method of Embodiment 56, wherein the method further comprises transfecting the cell with at least one pegRNA.

[0237] Embodiment 58. The method of Embodiment 57, wherein the at least one pegRNA comprises a sequence complementary to the target sequence of the reporter system and comprises a desired edit for editing the target sequence.

[0238] Embodiment 59. The method of Embodiment 58, wherein the method determines editing status and frequency of a desired edit in the genome of the cell.

[0239] Embodiment 60. The method of any one of Embodiments 39 to 59, wherein the insertion site comprises an exon, an intron, a promoter, a 3’ UTR, a 5’ UTR, or an intergenic site in the genomic region. 3915-P1300WO.UW -107-

[0240] Embodiment 61. The method of Embodiment 12, wherein the at least one DDR gene is HLTF.

[0241] Embodiment 62. The method of any one of Embodiments 12 to 14 and 61, wherein modulating the at least one DDR gene comprises inhibiting the at least one DDR gene.

[0242] While illustrative embodiments have been illustrated and described, it will be appreciated that various changes can be made therein without departing from the spirit and scope of the disclosure. 3915-P1300WO.UW -108-

Claims

CLAIMS The embodiments of the invention in which an exclusive property or privilege is claimed are defined as follows:

1. A method for modulating gene editing efficiency of a target site in a genomic region of a cell comprising: modulating chromatin accessibility of the genomic region and / or transcriptional activity of a gene in the genomic region proximal to the gene editing target site.

2. The method of claim 1, wherein gene editing comprises prime editing.

3. The method of claim 1, wherein the target site is downstream of a promoter of the gene.

4. The method of claim 1, wherein the target site is downstream of a transcriptional start site (TSS) of the gene.

5. The method of claim 1, wherein the target site is upstream of a promoter of the gene.

6. The method of claim 1, wherein the target site is selected from the group consisting of an exon, an intron, a promoter, a 3’ UTR, a 5’ UTR, and intergenic sites in the genomic region.

7. The method of claim 1, wherein modulating the gene editing efficiency comprises activating transcription of the gene proximal to the gene editing target site.

8. The method of claim 7, wherein the step of activating transcription occurs before, or during the step of prime editing of the target site.

9. The method of claim 1, wherein modulating the gene editing efficiency comprises repressing transcription of the gene proximal to the prime editing target site. 3915-P1300WO.UW -109-10. The method of any one of claims 1 to 9, wherein the method further comprises modulating at least one DNA damage repair (DDR) gene in the cell.

11. The method of claim 10, wherein the at least one DDR gene is a DNA mismatch repair (MMR) gene.

12. The method of claim 11, wherein the MMR gene is selected from the group consisting of PMS2, MLH1, MSH2, MSH3, MSH6, and EXO1.

13. The method of claim 2, wherein the method further comprises use of a more robust or enhanced prime editing ribonucleoprotein (RNP) complex.

14. The method of claim 13, wherein the more robust or enhanced prime editing RNP complex comprises a prime editor selected from the group consisting of (a) a prime editor comprising an engineered reverse transcriptase, (b) a codon- and structure-optimized prime editor, (c) a compact and / or specialized prime editor, and (d) a prime editor fused to an RNA-binding protein.

15. The method of claim 14, wherein (a) the prime editor comprising an engineered reverse transcriptase is PE2, (b) the codon- and structure-optimized primer editor is PEmax, (c) the compact and / or specialized prime editor is selected from the group consisting of PE6a, PE6b, PE6c, PE6d, PE6e, PE6f, and PE6g, or (d) the prime editor fused to an RNA-binding protein is PE7.

16. The method of any one of claims 13 to 15, wherein the more robust or enhanced prime editing RNP complex comprises a structurally stabilized pegRNA (epegRNA).

17. The method of any one of claims 13 to 15, wherein the more robust or enhanced prime editing RNP complex comprises a computationally optimized pegRNA. 3915-P1300WO.UW -110-18. The method of any one of claims 1 to 9, wherein the method further comprises inhibiting DNA mismatch repair (MMR).

19. The method of claim 18, wherein inhibiting MMR comprises use of a second-site nicking guide RNA targeting the non-edited strand.

20. The method of claim 18, wherein inhibiting MMR comprises inhibiting an MMR protein selected from the group consisting of MLH1, MSH2, MSH3, MSH6, PMS2, and EXO1.

21. The method of claim 20, wherein the MMR protein is MLH1.

22. The method of claim 21, wherein inhibiting MLH1 comprises expressing a dominant negative MLH1 protein.

23. The method of claim 20, wherein inhibiting the MMR protein comprises RNAi-mediated knockdown of MMR protein gene expression.

24. The method of claim 20, wherein inhibiting the MMR protein comprises targeted degradation of the MMR protein.

25. The method of any one of claims 1 to 9, wherein the cell is a eukaryotic cell, preferably a mammalian cell.

26. The method of any one of claims 1 to 9, wherein the cell is a stem cell.

27. The method of claim 26, wherein the stem cell is an induced pluripotent stem cell (iPSC).

28. The method of claim 27, wherein the iPSC is a human iPSC.

29. A method for determining prime editing efficiencies across a genome, the method comprising: 3915-P1300WO.UW -111-(i) generating a reporter pool of cells comprising a plurality of reporter systems integrated into genomes of a plurality of cells, wherein each of the plurality of reporter systems comprise a vector comprising: (a) a promoter for in vitro transcription; (b) a target sequence for a pegRNA; and (c) a molecular identifier barcode; (ii) mapping insertion sites of the plurality of reporters in the genome of the plurality of cells; (iii) transfecting the reporter pool of cells with pegRNAs comprising a sequence for introducing a desired edit at the target sequence of the plurality of reporter systems integrated into the genome of the plurality of cells, wherein each of the plurality of cells constitutively express a prime editor (PE); and (iv) measuring the frequency of the desired edit in the genome of the cell.

30. The method of claim 29, wherein the step of generating a reporter pool of cells comprises transfecting the plurality of cells with the plurality of reporter systems.

31. The method of claim 29, wherein the step of measuring the frequency of the desired edit in the genome of the cell by prime editing comprises: (i) isolating genomic DNA from the plurality of cells; (ii) amplifying reporter systems integrated in the genome of the plurality of cells to obtain a library of amplified DNAs; (iii) sequencing the library of amplified DNAs; (iv) associating the molecular identifier barcode with the edit site in the target sequence of the plurality of reporters; and (v) computationally determining frequency of edits in the target sequence of the plurality of reporter systems associated with the molecular identifier barcode.

32. The method of claim 31, wherein the step of sequencing the library of amplified DNAs comprises high-throughput short- and long-read sequencing.

33. The method of any one of claims 29 to 32, wherein the prime editor (PE) comprises a Cas9 nickase and a reverse transcriptase. 3915-P1300WO.UW -112-34. The method of claim 29, further comprising correlating the prime editing efficiencies with features of epigenetic environment in the genome in about 100bp to about 2kb region centering at the edit sites of the target sequences.

35. The method of claim 34, wherein the features of epigenetic environment are selected from DNA binding proteins, epigenetic modulators, histone modifications, DNase Hypersensitive sites, transposase-accessible chromatin (ATAC), and higher order chromatic structures.

36. The method of any one of claims 29 to 32, 34, and 35, wherein the plurality of cells comprises eukaryotic cells, preferably mammalian cells.

37. A method of mapping a genomic location of at least one reporter system in a genome of a cell comprising: (i) integrating the at least one reporter system in the genome of the cell; and (ii) determining an insertion / integration site for the at least one reporter system within the genome of the cell.

38. The method of claim 37, wherein the reporter system comprises a vector comprising a promoter for in vitro transcription.

39. The method of claim 38, wherein the vector further comprises a polynucleotide sequence linked to the promoter.

40. The method of claim 37, wherein the polynucleotide sequence comprises an amplification primer binding domain.

41. The method of claim 40, wherein the polynucleotide sequence further comprises nucleic acid sequences selected from coding or non-coding transcripts, cis- regulatory elements, molecular identifier barcode, or functional or non-functional genomic elements. 3915-P1300WO.UW -113-42. The method of claim 37, wherein the polynucleotide sequence comprises a target sequence and a molecular identifier.

43. The method of claim 37, wherein the reporter system comprises a vector comprising: (a) a promoter for in vitro transcription; (b) a target sequence; and (c) a molecular identifier.

44. The method of claim 43, wherein the vector is devoid of any cis-regulatory elements.

45. The method of claim 37, wherein the vector is selected from a transposon- based vector, a retroviral-based vector, a lentiviral-based vector, and a serine integrase- based vector.

46. The method of claim 45, wherein the vector is a transposon-based vector, and wherein the vector comprises an inverted terminal repeat (ITR) at each vector end.

47. The method of claim 46, wherein the cell is co-transfected with an expression construct capable of expressing transposase.

48. The method of any one of claims 38, 39, and 43, wherein the promoter is selected from a bacteriophage T7 promoter, a T3 promoter, a Sp6 promoter, and variants thereof.

49. The method of claim 43, wherein the target sequence is a target sequence for a Cas9 guide RNA or a variant thereof.

50. The method of any one of claims 37 to 49, wherein the step of integrating the at least one reporter system in the genome of the cell comprises transfecting the cell with the at least one reporter system. 3915-P1300WO.UW -114-51. The method of any one of claims 37 to 50, wherein the step of determining the insertion site for the at least one reporter system within the genome of the cell comprises: (i) isolating genomic DNA of the cell comprising the at least one reporter system integrated in the genome of the cell; (ii) in vitro transcription of the isolated genomic DNA using the promoter of the reporter system to generate chimeric RNAs comprising a portion of the reporter system and of its neighboring genomic DNA; (iii) reverse transcribing the chimeric RNAs; (iv) amplifying cDNAs to generate a cDNA library comprising the reporter system integrated into the genome of the cell; (v) sequencing the cDNA library; and (vi) associating the molecular identifier barcode with insertion site in the genomic DNA.

52. The method of claim 50, wherein the step of sequencing the library of cDNAs comprises high-throughput short- and long-read sequencing, and Sanger sequencing.

53. The method of any one of claims 43 to 52, wherein the cell constitutively expresses a prime editor (PE), wherein the prime editor (PE) comprises a Cas9 nickase and a reverse transcriptase.

54. The method of claim 53, wherein the method further comprises transfecting the cell with at least one pegRNA.

55. The method of claim 54, wherein the at least one pegRNA comprises a sequence complementary to the target sequence of the reporter system and comprises a desired edit for editing the target sequence.

56. The method of claim 55, wherein the method determines editing status and frequency of a desired edit in the genome of the cell. 3915-P1300WO.UW -115-57. The method of any one of claims 37 to 56, wherein the insertion site comprises an exon, an intron, a promoter, a 3’ UTR, a 5’ UTR, or an intergenic site in the genomic region.

58. The method of claim 10, wherein the at least one DDR gene is HLTF. 3915-P1300WO.UW -116-

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