Development of super-specific epigenome editing by targeted allele-specific DNA methylation

doi:10.21203/rs.3.rs-3143155/v1

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Method Article

Development of super-specific epigenome editing by targeted allele-specific DNA methylation

https://doi.org/10.21203/rs.3.rs-3143155/v1

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Journal Publication

published 21 Oct, 2023

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Background

Epigenome editing refers to the targeted reprogramming of genomic loci using an EpiEditor which may consist of dCas9, DNMT3A/3L and sgRNA. Methylation of the locus can lead to a modulation of gene expression. Allele-specific DNA methylation (ASM) refers to the targeted methylation delivery only to one allele of a locus. In the context of diseases caused by a dominant mutation, the selective DNA methylation of the mutant allele could be used to repress its expression but retain the functionality of the normal gene.

Results

To set up allele-specific targeted DNA methylation, target regions were selected from hypomethylated CGIs bearing a SNP in their promoters in the HEK293 cell line. We aimed at delivering maximum DNA methylation with highest allelic specificity in the targeted regions. Placing SNPs in the PAM or seed regions of the sgRNA, we designed 24 different sgRNAs targeting single alleles. We achieved efficient ASM in multiple cases, such as ISG15, MSH6, GPD1L, MRPL52, PDE8A, NARF, DAP3, and GSPT1, which in best cases led to 5-10-fold stronger average DNA methylation at the on-target allele. This corresponds to average differences of the DNA methylation gain in on- and off-target alleles of > 50%. Maximum DNA methylation was observed on day 3 after transfection followed by a gradual decline. In selected cases ASM, was stable up to 11 day in HEK293 cells and it led to an up to 3.6 change in allelic expression ratios.

Conclusions

We successfully delivered ASM in multiple targets with high specificity, efficiency and stability. This form of super-specific epigenome editing could find applications in the treatment of diseases caused by dominant mutations, because it allows silencing of the mutant allele without repression of the expression of the normal allele thereby minimizing side-effects of the treatment.

Epigenome editing

targeted DNA methylation

dCas9

DNMT3A/3L

allele discrimination

specificity

The genetic material of human cells is stored in the cell nucleus in the form of chromatin comprising the DNA wrapped around histone proteins and a complex ensemble of accessory proteins. Chromatin plays structural and regulatory functions, which are defined by complex patterns of post-translational modifications of histone tails, methylation of DNA and non-coding RNAs. These chromatin signals together are often termed the epigenome (1). Dozens of chromatin-modifying and interacting enzymes and protein complexes, writers, erasers and readers, are involved in a setting of a cell-type specific epigenome, which controls gene activity and is essential for embryonic development, cellular differentiation, and adaptation of cells to environmental conditions (2–4).

The dynamic nature of the epigenome inspired the development of methods for targeted epigenome editing (5–9). The main aim of this technique is to set a desired chromatin state at a target genomic locus in an artificial manner to control gene expression or other chromatin functions. Writing repressive chromatin marks can lead to the inactivation of an actively transcribed target gene, and, vice versa, deposition of activating chromatin marks can trigger transcription of a silenced gene. Epigenome editing is currently used for fundamental research and is a promising direction for the development of targeted therapy in personalized medicine. The key tool of epigenome editing is an EpiEditor, a designed protein assembled from two functionally distinct units: a targeting and an editing unit. The targeting unit binds to the DNA sequence specifically allowing to deliver the EpiEditor to the targeted genomic locus. The editing unit possesses a catalytic activity required to deposit the desired chromatin mark or it recruits endogenous enzymes able to deposit the mark. Various chromatin modifying enzymes have been characterized and applied as editing units of EpiEditors. For example, initial work has used DNMT3A (10) and SUV39H1 (11) fused to zinc finger proteins to silence genes via DNA methylation and methylation of lysine 9 on histone 3, respectively. Later, a catalytically deactivated version of the Cas9 protein (dCas9) was introduced as targeting unit (12). It can recognize DNA sequence indirectly by forming Watson-Crick base pairs with a short 20-mer RNA, the singleguide RNA (sgRNA), generating an RNA-DNA hybrid at the target site. An additional requirement for the interaction of the sgRNA/dCas9 complex with the DNA is the presence of trinucleotide proto-spacer adjacent motif (PAM) directly at the 3' end of the target DNA sequence, which is NGG in the case of dCas9 (where N stands for any nucleotide). In the current work the dCas9 targeting unit was combined with a DNMT3A catalytic domain fused to a DNMT3L C-terminal domain as editing unit (13) which contained an R887E exchange in DNMT3A that was shown to increase the specificity of targeted DNA methylation by suppression of untargeted activity (14). Both entities were connected by a SunTag allowing to recruit up to 10 DNMT3A/3L units to each sgRNA/dCas9 complex (15). This leads to signal amplification and supports the dimerization of the DNMT3A/3L unit that is required for the formation of a catalytically active enzyme complex (16).

One of the key performance parameters of EpiEditors is the editing specificity and, in this context, both functional units contribute to the final outcome. The targeting unit has to be designed to bind only the desired genomic locus and the editing unit has to deliver the modification only at this particular site and nowhere else in the genome. The most commonly used epigenome editing scenario is the silencing or activation of a selected gene by setting corresponding chromatin marks at its promoter. Since most genes are present in two copies in the human genome, such an approach would result in simultaneous modulation of the expression of both alleles. However, there are cases where interrogation of only a single allele, i.e. an allele-specific epigenome editing (ASEE), might be beneficial. One potential clinical application could be an epigenetic inactivation of a dominant mutant allele in disorders such as Huntington's disease (17), Machado-Joseph disease (18), or Frontotemporal dementia (19), without affecting the second wild type allele. ASEE has two prerequisites, the presence of a sequence variation at the target site in one allele and the ability of the EpiEditor targeting unit to recognize one allele only. According to the International HapMap project, two human genomes differ from each other at every 1000 bases (20). The most frequent variations are single-nucleotide polymorphisms (SNPs), which occur on a population level with a frequency of one per 300 bases. Statistically, some SNPs are in heterozygous states in individuals and allow for the discrimination of alleles.

In this work we aimed to apply the sgRNA/dCas9 unit for the development of a universal ASEE system able to introduce allele-specific DNA methylation (ASM). Therefore, we systematically explored if SNPs in the seed region of the sg-dCas9 complex or in the PAM motif allow to deliver DNA methylation specifically to one allele. ASM was evaluated with respect to locus and allele-specificity, stability and its effects on allele-specific gene silencing. Our data show that strong ASM could be achieved in many of the test cases, where loci with the discriminatory SNP positioned in the PAM region in general showed higher success rate and better specificity. In selected cases, ASM was stable up to 11 day in HEK293 cells and it led up to a 3.6-fold change in allelic expression ratios. These results suggest that ASM might have clinical applications in future aiming towards the selective silencing of dominant disease alleles without affecting the expression of the second copy of the gene.

Target selection and transient transfection of HEK293

HEK293 cells were chosen as the model cell line for allele-specific DNA methylation experiments because of their easy handling and good transfection yields. Information on SNPs in the genome were extracted from a public database (21). An initial estimation of DNA methylation levels was taken from the MBD2-pulldown sequencing data from a previous work (22). An in-silico search was performed to identify the SNPs in the unmethylated CGIs located in the promoter region of genes. Further, the search results were filtered regarding the presence of SNPs in the potential PAM site or next to the PAM site, in the seed region of a potential sgRNA. For 14 target genes containing suitable SNPs, up to 3 sgRNAs were designed to target only one allele as shown in Table 1. The detailed targeting strategy in each case is shown in Additional File 1. Based on the position of the SNP in the sgRNA, the 24 individual experiments could be divided into three categories, either with the SNP in the sgRNA seed region (12 experiments), at position 2 of the NGG PAM sequence (5 experiments), or at PAM position 3 (7 experiments) (Fig. 1, Table 1). SNPs in the PAM region leading to G > Y changes were preferred (where Y stands for C or T), as these changes were demonstrated to allow best discrimination of dCas9 binding (23, 24). Similarly, the position of the mismatch in the sgRNA has been shown to be very important for the binding specificity and single mismatches at the 3' end, within the so-called “seed” sequence, were used because these were shown to reduce Cas9 DNA binding most (25).

Table 1

**Compilation of ASM experiments conducted in this study.** “ASM (Δ%)” and “ASM (ratio)” refer to the difference and ratio of the increases in DNA methylation at the on- and off-target alleles. The detailed targeting strategy in each case including genome coordinates is shown in Additional File 1.
No.	Gene	SNP location	Experiment	SNP	Target allele	ASM (Δ%)	ASM ratio
1	GPD1L	PAM3	GPD1L - PAM3	G to T	G	50	7.6
2	GSPT1	PAM3	GSPT1 - PAM3	T to G	G	47	7.1
3	GSPT1	Seed	GSPT1 -Seed1	T to G	G	18	1.7
4	GSPT1	Seed	GSPT1 -Seed2	T to G	T	28	2.1
5	TTC41P	PAM3	TTC41P - PAM3	G to T	G	6	2.3
6	ISG15	PAM2	ISG15 – PAM2	C to G	G	63	8.5
7	ISG15	Seed	ISG15 - Seed2	C to G	C	34	1.8
8	ISG15	Seed	ISG15 - Seed1	C to G	G	36	2.3
9	MAPK1	Seed	MAPK1 - Seed1	T to G	T	19	1.4
10	MRPL52	PAM3	MRPL52 - PAM3	G to T	G	52	3.7
11	MSH6	PAM3	MSH6 - PAM3	T to G	G	46	10
12	MYH10	Seed	MYH10 - Seed1	G to A	A	16	2.6
13	NARF	PAM2	NARF -PAM2	G to T	G	45	5.6
14	NARF	Seed	NARF - Seed1	G to T	G	43	3.0
15	NARF	Seed	NARF - Seed 2	G to T	T	31	1.7
16	PDE8A	PAM2	PDE8A - PAM2	G to T	G	19	3.1
17	PDE8A	Seed	PDE8A - Seed1	G to T	G	26	1.9
18	PDE8A	Seed	PDE8A - Seed2	G to T	T	45	4.4
19	RAF1	PAM3	RAF1 - PAM3	G to A	G	4	1.7
20	RALB	PAM2	RALB - PAM2	G to C	G	8	2.6
21	TYK2	PAM2	TYK2 - PAM2	G to C	G	15	2.7
22	DAP3	PAM3	DAP3 - PAM3	T to G	G	44	4.4
23	DAP3	Seed	DAP3 - Seed1	T to G	G	15	1.4
24	DAP3	Seed	DAP3 - Seed2	T to G	T	27	5.6

To achieve targeted ASM, transient transfection of the dCas9-10x SunTag-BFP, scFv-DNMT3A/3L-sfGFP, and sgRNA-DsRed plasmids was performed in HEK293 cells. Control experiments were conducted with a scrambled sgRNA that does not have a binding site in the human genome (26). Initial studies showed that cells positive for all three plasmids exhibited highest fluorescence of the corresponding reporter proteins on day 3 post-transfection. Hence fluorescence activated cell sorting (FACS) of triple-positive cells was conducted at this time point. Genomic DNA was isolated from the FACS-sorted cells at day 3 after transfection and subjected to bisulfite treatment. Library preparation was performed using the bisulfite-converted samples, followed by NGS and data analysis (Fig. 2). Most DNA methylation experiments were conducted in three independent biological replicates.

Targeting ASM with a SNP in the seed region of the sgRNA

A total of 12 target genes were addressed by individual allele-specific sgRNAs with a SNP positioned in the seed region of the sgRNA (i.e. position 1–5 of the sgRNA). In each case, the targeted allele is referred to as the “on-target” allele, the second allele as “off-target”. Six of these experiments showed strong ASM as described in the next paragraphs (Fig. 3). In all cases, both alleles of the untransfected samples showed almost no DNA methylation, as expected after the pre-selection for unmethylated CGIs.

In the case of ISG15-Seed2 experiment, the transfected sample showed high DNA methylation at many CpG sites of the on-target allele. At CpG sites, 1, 2, 10, 11, 13 and 14, DNA methylation was delivered above 60% and the highest DNA methylation deposition of 80% was observed at CpG 13. The ISG15-Seed2 sgRNA binds to the CpG 16, 17, and 18 in the analyzed region and the DNA methylation data showed that binding of the dCas9 complex protected the sgRNA binding site from methylation deposition (Fig. 3) as observed in previous studies (13, 14). The off-target allele in this experiment also showed some DNA methylation deposition, but the methylation levels always remained below 25%. A scrambled sgRNA was used to further investigate the effect of the global untargeted DNA methylation activity of the EpiEditor. In this case, very low DNA methylation levels were observed at both alleles that were comparable. Interestingly, the methylation levels of the scrambled control samples were lower than that of the off-target allele of the sample transfected with allele-specific sgRNA, suggesting that the off-target allele DNA methylation observed with the ISG15-Seed2 sgRNA at least in part was caused by the undesired binding of the sgRNA/dCas9 complex to the off-target allele (Fig. 3).

To summarize the data and compare the results of different experiments, DNA methylation levels of individual CpG sites were averaged. For this, CpG sites with DNA methylation levels of at least 50% of the highest methylation percentage delivered in the target allele were included (grey-shaded region in the DNA methylation profile graph of each target in Fig. 3). For uniformity in the analysis of the DNA methylation levels across different targets, this procedure was applied in each case. The same CpG sites were included to calculate the average DNA methylation levels of on-target allele and off-target allele DNA methylation levels in every sample. As the maximum DNA methylation at an individual CpG site in the case of ISG15 was 80%, CpG sites displaying more than 40% increase in methylation in the transfected on-target allele were included for average DNA methylation calculation (corresponding to CpG sites 1–3, 5, 7, and 10–14). Based on this, the average DNA methylation of the selected CpG sites in the ISG15 target region was 64%, while in the off-target allele only 27% average DNA methylation was observed at the same CpG sites.

In addition to ISG15-Seed2, PDE8A-Seed2, DAP3-Seed2, NARF-Seed1 and MYH10-Seed1 were successfully targeted allele-specifically with a discriminating SNP in the sgRNA seed region. Also in these cases, the DNA methylation levels of the on-target alleles of the transfected samples displayed higher DNA methylation than the respective off-target alleles of the transfected samples (Fig. 3). The DNA methylation profiles clearly showed the sgRNA binding region footprint in each case (MYH10-Seed1 CpG 6, NARF-Seed1 CpG 8–10, PDE8A-Seed2 CpG 9–10, DAP3-Seed2 CpG 3–5 (Fig. 3). The average DNA methylation levels in the on-target regions of NARF-Seed1, PDE8A-Seed2, DAP3-Seed2 and MYH10-Seed1 were 68%, 59%, 34% and 26%, respectively (Fig. 3). In the cases of PDE8A-Seed2 and DAP3-Seed2, the DNA methylation levels and profile of the off-target alleles from the transfected samples were comparable to the samples treated with scrambled sgRNA, indicating very high allele-specific binding of the allele-specific sgRNA. This observation suggests that the off-target allele methylation was due to an untargeted methylation activity of the DNMT3A/3L construct in these experiments, a problem that has been observed in previous work (14, 27, 28). In the case of NARF-Seed1 and MYH10-Seed1, a weaker allelic discrimination of the sgRNA/dCas9 complex was observed that was comparable to ISG15-Seed2. The DNA methylation profiles of the additional experiments using a SNP in the sgRNA guide region for allele discrimination which led to weaker ASM are shown in Additional File 2, Supplementary Fig. 1.

Targeting ASM with a SNP positioned at the second position of the PAM

In the cases of PDE8A, NARF, ISG15, TYK2, and RALB, allele-specific targeting could be designed by using an sgRNA that places the SNP in the second position of the PAM site. The on-targets of these genes contain the NGG PAM. The off-target allele of PDE8A-PAM2 and NARF-PAM2 contain an NTG sequence, the off-target alleles of RALB-PAM2, TYK2-PAM2, and ISG15-PAM2 contain an NCG sequence. ASM was successful in 3 cases, NARF-PAM2, PDE8A-PAM2 and ISG15-PAM2 (Fig. 4A, DNA methylation profiles are shown in Additional File 2, Supplementary Fig. 2). The average DNA methylation levels of the NARF-PAM2, PDE8A-PAM2, and ISG15-PAM2 on-target alleles were 58%, 28%, and 72%, respectively. In all three cases, the sgRNA/dCas9 complex showed a clear discrimination between the on-target and the off-target alleles and off-target DNA methylation levels were comparable to the scrambled sgRNA controls. In case of NARF-PAM2, the maximum DNA methylation deposited was 75% at CpG 13. The dCas9 footprint on CpG 7–9 was observed in the DNA methylation profile of the on-target allele while no footprint was observed in the off-target allele of the transfected sample as well as both alleles of the scrambled sample. In the case of the ISG15, high specificity and high on-target DNA methylation levels were observed (Fig. 4A). Multiple CpG sites (1, 2, 4, 11, 13, and 14) on the on-target allele showed over 70% DNA methylation deposition in the targeted sample. In the case of the sgRNA/dCas9 complex, targeting one allele of PDE8A-PAM2, DNA methylation was only observed on the on-target allele, although it was weaker, with maximum methylation of about 34% at CpG 5. The DNA methylation profiles of the additional experiments using a SNP at the second PAM position for allele discrimination which led to weaker ASM are shown in Additional File 2, Supplementary Fig. 3.

Targeting ASM with a SNP positioned at the third position of the PAM

In the case of the MSH6, GPD1L, MRPL52, DAP3, GSPT1, RAF1 and TTC41P gene loci, sgRNAs could be designed that place the SNP at the third position of the PAM site. ASM was successfully implemented in case of MSH6-PAM3, GPD1L-PAM3, MRPL52-PAM3, DAP3-PAM3, and GSPT1-PAM3 (Fig. 4B, DNA methylation profiles are shown in Additional File 2, Supplementary Fig. 4). In each case, high on-target DNA methylation and good ASM specificity was observed. The average DNA methylation delivered in the MSH6-PAM3, GPD1L-PAM3, MRPL52-PAM3, DAP3-PAM3, and GSPT1-PAM3 target alleles was 52%, 62%, 72%, 57% and 56%, respectively. The DNA methylation profiles of the additional experiments using a SNP at the third PAM position for allele discrimination which led to weaker ASM are shown in Additional File 2, Supplementary Fig. 5.

Comparison of ASM achieved at one genomic locus by different targeting methods

For the 5 loci, ISG15, NARF, PDE8A, DAP3 and GSPT1, multiple sgRNA could be designed that place the SNP for allele discrimination either in the PAM or sgRNA seed region. Of note, if the SNP is present in the seed region, two distinct sgRNAs can be designed to address both alleles of the target gene. This is not possible if the SNP is in the PAM region, where the non-G allele cannot be targeted. Hence in all cases, 3 different targeting constructs were used. The results of these experiments are presented in Fig. 5. Strikingly, in two examples, PDE8A and DAP3, efficient and opposite ASM was observed on both alleles of the target locus. In general, these data illustrate that ASM was most efficient when the SNP was present in the PAM region.

Stability of ASM in HEK293 cells

The stability of the introduced targeted DNA methylation is a critical issue and previous work has provided examples of high and low stability (22, 29–32). Systematic studies showed that this depends, (among other factors) on the genomic locus (22, 33). In order to investigate the stability of the introduced ASM in our system, the DNA methylation of both alleles in the target regions in the ISG15-Seed2, MYH10-Seed1, PDE8A-PAM2, ISG15-PAM2, MRPL52-PAM3, MSH6-PAM3, and GPD1L-PAM3 experiments was studied at regular intervals after transfection. The day of transfection is noted as day 0 followed by the sorting of triple-positive cells on day 3. The sorted cells were seeded and the culture was maintained until day 11. A fraction of cells was collected on day 3, day 5, day 8, and day 11 and the DNA methylation was analyzed. Every target region showed a maximum of ASM on day 3 and a gradual decrease of DNA methylation until day 11 although the loss of DNA methylation showed different rates (Fig. 6, Additional file 2, Supplementary Fig. 6). The experiments GPD1L-PAM3 and ISG15-Seed2 delivered ASM with high stability of the DNA methylation, and on day 11 DNA methylation levels corresponding to 93% and 84%, respectively, of the methylation observed on day 3 were still present. In the experiments ISG15-PAM2, MRPL52-PAM3, and MYH10-Seed1 69%, 60% and 57%, repectively, of the DNA methylation deposited on day 3 was retained. Other experiments such as PDE8A-PAM3 and MSH6-PAM3 showed low stability of ASM and retained only about 30% of the DNA methylation observed on day 3 until day 11.

Modulation of allele-specific gene expression ratios by ASM

Next, we were interested to determine if the targeted ASM has the capacity to alter gene expression in an allele-specific manner. The genes ISG15, MSH6, MYH10, MRPL52, NARF and GPD1L contain SNPs in an exon allowing to discriminate the expression of both alleles. Therefore, the allelic expression ratios were analyzed in these genes with and without introduction of ASM. RNA was extracted from the cells sorted on day 3 post transfection and from this and untreated cells and cDNA was generated. A pair of primers was designed to amplify the corresponding exonic region containing the SNP followed by library generation and NGS. For each gene, the number of reads per allele was extracted from the sequencing data. The ratio of the reads of each allele was calculated for the untransfected and transfected samples. A change in the ratios between untransfected and the transfected samples indicates an alteration in the expression levels of the alleles of this gene. Among the genes tested, RNA of the transfected samples ISG15-Seed2 and MRPL52-PAM3 displayed a clear shift in the ratio of alleles after ASM when compared to the parental allele ratio (Fig. 7). The ratio of allele reads from ISG15-Seed2 in untransfected samples was 35:65, while after ASM establishment, a ratio of 66:34 was observed, corresponding to a 3.6-fold change of the expression ratio (p-value 4.8x10^− 4, based on two-sided t-test assuming equal variance). The allele reads ratio of MRPL52 before and after transfection was 66:34 and 49:51, respectively, corresponding to a 2-fold change (p-value 6.2x10^− 4, based on two-sided t-test assuming equal variance). The other studied genes did not show changes in the allelic read ratios (Fig. 7).

Previous work has demonstrated the ability of sgRNA/Cas9 complexes to interact with their genomic target sites in a locus specific manner if SNPs are present in critical parts of the target region and in such cases allele-specific genome editing could be achieved (34). In this work, we aimed to apply catalytically inactivated Cas9 (dCas9) (12) to develop and characterize highly specific EpiEditing systems which are able to deliver DNA methylation specifically to one allele of the target region and establish ASM. We used an improved DNMT3A/3L containing one mutation that reduced non-specific activity (14) as catalytic part. The sgRNA/dCas9 and DNMT3A/3L parts were connected by a SunTag (15) for additional signal amplification and to facilitate DNMT3A/3L dimerization. The design aimed to achieve allelic discrimination by targeting a heterozygous SNP either within the seed region of the sgRNA or in the PAM region of dCas9. In general, our data show that efficient ASM was achieved in many cases, though it did not work at some loci and with some of the sgRNAs. In the following, the performance of the newly designed EpiEditors for ASM will be discussed considering the most important properties: efficiency of targeted DNA methylation, specificity and stability of the ASM, and effects of ASM on allele-specific gene expression.

Efficiency of targeted DNA methylation

Average DNA methylation levels varied across the targets irrespective of the allele targeting strategy. However, most successful targets reached an average DNA methylation above 50% at the selected CpG sites indicating high efficiency of the targeted DNA methylation. DNA methylation variation among the targets could be explained either by the sgRNA properties or the inherent epigenetic regulation at the region. The sgRNA efficiency depends on the binding capacity of the sgRNA to the target region. As the allele discriminating SNP had to be positioned in the PAM or sgRNA seed region, some sgRNA with medium efficiency predicted by CRIPOR (http://crispor.tefor.net/crispor.py) (35) and CCTop (https://cctop.cos.uni-heidelberg.de/) (36, 37) also had to be used in our study. Among them, the RALB and RAF1 loci targeted sgRNAs had the poorest predicted efficiencies and they indeed did not lead to efficient DNA methylation. In addition, a condensed chromatin environment of the target region could have an impact on the efficiency of epigenome editing by restricting the access to the region. In this respect, previous untargeted DNA methylation experiments of CpG islands in HEK293 cells were inspected (22). Evidently, the promoter region of TTC41P and TYK2 remained unmethylated in the untargeted DNA methylation study as well, suggesting that local chromatin structure makes it inaccessible for DNA methylation.

Specificity of ASM

For the evaluation of the success of ASM, two parameters are critical: 1) the overall level of ASM determined by the difference of the DNA methylation gain in the on- and off-target allele, and 2) the ratio of DNA methylation gain at the on- and off-target allele. In a scatter plot with ASM difference on the x- and ASM ratio on the y-axis (Fig. 8), the most successful experiments are found in the upper right corner (quadrant I), while most unsuccessful ones appear in the lower left corner (quadrant III). Inspection of the data shown in Fig. 8 indicates that the most successful ASM experiments had the SNP placed in the PAM region. This observation agrees with the direct comparison of ASM levels achieved at loci which could be addressed in different ways (Fig. 5). Allele discrimination via a SNP in the sgRNA seed region can also be efficient, but in our data set, best results obtained with seed-SNPs were inferior to the best PAM based results, and the probability of inducing a strong ASM was also higher if the SNP was present in the PAM site.

In ASM, an undesired DNA methylation of the off-target allele can result from two scenarios, either binding of the sgRNA/dCas9 complex to the off-target allele or untargeted activity of the EpiEditor enzymatic part. In our study, the DNA methylation levels of many off-target alleles in transfected samples were comparable to the samples treated with scrambled sgRNA indicating that the off-target DNA methylation was due to untargeted activity of DNMT3A/3L. Of note, the untargeted activity still depends on the accessibility of the genomic loci, hence it is not expected to be equal at all loci. The problem of untargeted activity of the enzymatic parts of EpiEditors had been noted in previous studies (14, 27, 28) and the untargeted activity of the DNMT3A/3L construct used here had been reduced markedly by enzyme engineering (14). Our data demonstrate that untargeted activity is a limiting factor determining the specificity of ASM in these cases. Moreover, insufficient on-target DNA methylation in these cases could also be related to a weak overall performance of the sgRNA as described above. In some of the experiments, like ISG15-Seed2, NARF-Seed1, MYH10-Seed1, MRPL52-PAM3 or DAP3-Seed2, the DNA methylation levels of the off-target allele were markedly higher than the DNA methylation observed after treatment with scrambled sgRNA. In these cases, parts of the off-target allele methylation are expected to arise from the undesired binding of the sgRNA/dCas9 complex to the off-target allele, indicating that these EpiEditors were not ideal in allele discrimination.

There are several factors that were reported to affect the efficiency of sgRNA/dCas9 complexes: 1) SNPs in the PAM region leading to G to Y changes were shown to allow best discrimination of dCas9 binding (23). 2) Potential formation of quadruplex DNA in the sgRNA binding region has been reported to lower the efficiency of sgRNA/dCas9 binding (38). 3) The presence of multiple PAM sites in the seed region could reduce efficiency (39). It is unclear if these factors act in concert and if they are dependent on further properties of the sgRNA and PAM site. We inspected the 8 most unsuccessful experiments in our data set and observed that all of them contained one or several of these potentially negative features: TTC41P-PAM3, MAPK1-Seed1, MYH10-Seed1, RALB-PAM2, TYK2-PAM2 and DAP3-Seed1 contain multiple PAM sites and quadruplex binding sites either in the sense or antisense strand of the sgRNA binding site. RAF1-PAM3 carries a G-to-A exchange in the PAM site and it contains a quadruplex forming region in the antisense strand. GSPT1-Seed1 contains multiple PAM sites in the sgRNA binding site (in this case a G5 sequence). We conclude that avoiding these features as far as possible is advisable for the design of an ASM construct.

Stability of ASM

For any clinical application a durable reprogramming of the epigenome is desirable. Though this was not the main aim of our study, we analyzed the stability of the introduced ASM over 11 days. Irrespective of the target region, maximum DNA methylation was observed on day 3, which was expected as the highest expression of the EpiEditing components was observed on day 3 after transient transfection, which was followed by a decline in most cases. However, our data demonstrated that high ASM levels (~ 90%) were retained at some of the targets up to day 11. The variable stability of ASM could arise from the local chromatin nature of the loci (22, 33). In this respect, it is also noteworthy that HEK293 is a rapidly dividing cell line which accelerates the passive loss of DNA methylation. Hence, it is plausible to assume that ASM could be more stable in non-dividing or slowly dividing cells, like neurons. Future work may employ delivery of more than one EpiEditor which has been shown to improve durability of epigenome reprogramming (40–43).

Allele-specific expression changes

Due to the presence of an additional SNP in the exonic regions of the corresponding genes, we could investigate allelic expression ratios of six target loci after successful establishment of ASM. Among them, ISG15-Seed2 and MRPL52-PAM3 showed a noticeable variation in the allelic read ratios. This result is an indicator of varied transcription rates at each allele before and after EpiEditing, although we note a potential PCR bias as possible pitfall of this indirect analysis approach. We conclude that allele-specific epigenome editing can be applied to regulate the allele-specific gene expression. In other cases where allelic expression ratios did not change despite of the successful establishment of ASM, the region of ASM may not have covered the relevant gene regulatory elements, which would explain a lack of direct effects on allelic expression levels.

Other experimental approaches have been used for allele-specific silencing of dominant mutant alleles in diseases including short interfering RNA (siRNA) (44) and allele-specific silencing of poly-q containing alleles was achieved by miRNA targeting (45). Antisense oligonucleotides (ASOs) were used for allele-specific targeting of the Huntingtin gene (46, 47), and rhodopsin P23H gene in retinitis pigmentosa, where ASO mediated silencing led to long term effects in rodent models (48). However, one limiting factor of ASO is the protein level of RNaseH1 needed for the degradation of the mutant mRNA. Moreover, miRNA and ASO based approaches are transient by nature, and require regular application of the reagents. In contrast to this, genome and epigenome editing has the potential to cause durable effects. As an alternative, a permanent inactivation of Huntington's disease mutation had been achieved by allele-specific CRISPR/Cas9 (49), but this treatment is based on the generation of mutations. In contrast, epigenome editing has the advantage that no DNA mutations are introduced. Indeed, allele-specific transcriptional repression of the mutant Huntingtin gene had been achieved by zinc finger fused EpiEditors and it was shown to result in long term reduction of the expression of the disease allele (50, 51). Our study demonstrates that sgRNA/dCas9 complexes, which mediate flexible targeted epigenome editing based on Watson/Crick base pairing between the sgRNA and its target locus DNA, can be used in a similar way for ASEE, including ASM, which could increase the applicability of the ASEE approach in personalized medicine.

In this study, we demonstrated the targeted allele-specific deposition of DNA methylation in the promoter region of several genes in HEK293 cells using sgRNA/dCas9 complexes. Depending on the target locus and method of allele discrimination, we show high specificity and efficiency of ASM which was stable over several days in some cases. Moreover, ASM was shown to change the allelic ratio of gene expression at the target loci indicating that this technology could potentially be employed in the clinic to control the progression of diseases caused by the expression of a dominant disease allele.

sgRNA design and cloning

A single sgRNA expression vector was produced by replacement of a part of the backbone of the pMulti-sgRNA-LacZ-DsRed vector (a gift from Yujie Sun, Addgene plasmid #99914) (52) with the sgRNA expression cassette from the AIO-mCherry vector (a gift from Steve Jackson, Addgene plasmid #74120) (53). For targets with SNP in the PAM region, a genomic sequence of 20 nt upstream of the PAM was selected as sgRNA binding site. For targets with SNP in the sgRNA seed region, a 20 nt sequence upstream of the nearest PAM (containing the SNP) was used. The selected sgRNAs were evaluated for the efficiency and potential off-targets using CRIPOR (http://crispor.tefor.net/crispor.py) (35) and CCTop (https://cctop.cos.uni-heidelberg.de/) (36, 37). The potential formation of quadruplex DNA in the sgRNA region was analyzed by QGRS Mapper (54). All target regions and sgRNAs are schematically shown in Additional File 1.

For cloning of sgRNA expression vectors, the top and bottom strand oligonucleotides designed for each target region were mixed (5 µM) in annealing buffer (NEB buffer2: 50 mM NaCl,10 mM Tris-HCl, 10 mM MgCl₂ and 1 mM DTT). All sgRNA sequences are listed in Additional File 3, Supplementary Table 1. Annealing was carried out in a thermocycler using the program: heat to 90°C, followed by cooling to 20°C with 1°C per minute. Annealed oligos were cloned into the single sgRNA expression vector by Golden Gate Assembly. Kanamycin resistant positive clones were sequenced to confirm the sgRNA expression cassette. Afterwards, four to six sg-RNAs were cloned into each multi-sgRNA vector. For this, the region covering the U6 promoter, inserted target guide and the gRNA scaffold from the single sgRNA vector was amplified using the primers provided in the Additional File 3, Supplementary Table 2. The primers were designed with suitable overhangs for ligation after BbsI digestion. The amplified regions were cloned into the pMulti-sgRNA-LacZ-DsRed vector (a gift from Yujie Sun, Addgene plasmid # 99914) (52) by Golden Gate Assembly.

Cell culture and transfection

HEK293 cells (RRID: CVCL_0045) were cultivated in Dulbecco’s modified Eagle’s Medium (D5671, Sigma) supplemented with 10% Fetal Bovine Serum (F7524, Sigma), L-Glutamine (G7513, Sigma) and 1X PenStrep (Sigma, P0781). For subculture and harvest, the cells were washed with PBS (D802, Sigma) and treated with trypsin (T3924, Sigma). For transfections, 1.4 million cells were seeded in 100 mm dish the day prior to transfection. The cells were transfected with a mixture of three constructs: the dCas9-10x SunTag (Addgene #174141) (14) (6 µg), scFv-DNMT3A(R887E)-DNMT3L (Addgene #154141) (14) (3 µg) and the multi-sgRNA expression vector (0.5 g). Every plasmid additionally expressed a fluorescent marker, namely the plasmids encoding dCas9-10x SunTag BFP, scFv-DNMT3A/3L sfGFP, and sgRNA DsRed, respectively. FuGENE® HD was used as the transfection reagent according to the manufacturer’s guideline. The growth medium was replaced the next day. Three days after transfection, the cells were trypsinised and passed through a pre-separation filter of 30 µm (Cat no:130-041-407, Miltenyi Biotec). Filtered cells were sorted by SH800S Cell Sorter (Sony Biotechnology) to obtain the triple-positive population, using the gating strategy shown in Additional File 2, Supplementary Fig. 7. About 0.5 million triple-positive cells were obtained for every sample and used for further experiments.

DNA methylation analysis

Genomic DNA was extracted using QIAmp DNA Mini Kit (Qiagen). A total of 500 ng genomic DNA was subjected to overnight digestion with EcoRV, a non-cutter for all the target amplicons. Zymo EZ DNA Methylation-Lightning Kit (D5030-E) was used for bisulfite conversion. The library for NGS was prepared by two consecutive PCR reactions (55). Firstly, bisulfite converted genomic DNA of each sample was amplified with target gene specific primers provided in Additional File 3, Supplemental Table 3. The gene specific optimized amount of a product from the first PCR was used as a template for the second PCR to add the Illumina TruSeq sequencing adapters (Additional File 3, Supplemental Table 4). Final products were quantified, pooled in equimolar amounts and purified using SPRIselect beads (Beckman Coulter). Ready-to-use pools of libraries were sequenced on NovaSeq 6000 using a PE250 flow cell (Novogene). NGS data were obtained in the form of FASTQ files, which were processed on the local instance of Galaxy server as described earlier (56) with some modifications. Briefly, an adaptor and low-quality trimming was conducted using Trim Galore! (https://github.com/FelixKrueger/TrimGalore). The two associated paired reads were merged using PEAR (57). Experiment specific combinations of Illumina indices and barcodes were used to extract reads for the individual experiments from the pool of reads. Reads corresponding to different alleles of the same target gene were identified based on the presence of the SNP. Two files of reads corresponding to alleles were generated and their DNA methylation level was analyzed independently. First, reads were mapped against a reference sequence of the target region using bwameth (58) and then DNA methylation of individual CpG sites was computed using MethylDackel (https://github.com/dpryan79/MethylDackel). Final visualization and statistics were prepared using Microsoft Excel.

RNA isolation and expression analysis

RNA was extracted from sorted cells using Qiagen RNeasy extraction kit (Cat. No. 74034). 500 ng of the purified RNA was used for the cDNA synthesis with the Applied Biosystems- High-Capacity cDNA Reverse Transcription Kit (Cat No 4368814). The expression analysis was performed for the genes with additional SNPs in one of the exons. For each gene, a pair of primers was designed to amplify the exonic regions with SNP. Library preparation for this region was performed in by two step PCR. In the first PCR, cDNA was amplified with the target specific primers provided in Additional File 3, Supplemental Table 5 using Hot start Taq Polymerase (95 ℃ for 15 min, 20 cycles of 94 ℃ − 30s, T_A − 30s and 72 ℃ for 60 s, and 72 ℃ for 10 min). Second PCR for library generation was performed using the undiluted product from the previous PCR amplified with Illumina library specific primers provided in Additional File 3, Supplemental Table 4. Q5 polymerase was used for amplification (98 ℃ for 30s, 15 cycles of 98 ℃ for 10s and 72 for 40s, and 72 ℃ for 2min). The allelic expression studies were conducted in two independent biological replicates.

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Availability of data and materials

Primary sequencing data are published in Darus (https://doi.org/10.18419/darus-3581). All analyzed data are included in this published article and its supplementary information files.

Competing interests

The authors declare that they have no competing interests

Funding

This work has been supported by the Baden-Württemberg Stiftung gGmbH (AllEpi, ID09 to AJ and SB). The funder had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Authors' contributions

PB and AJ devised the study. NR conducted all experiments. NR, PB, and AJ were involved in data analysis. PB and AJ supervised the work. AK and SB provided resources. NR, PB and AJ wrote the draft article and prepared the figures. AJ and SB acquired funding. All authors were involved in preparing the final manuscript.

Additional material

Additional file 1: File type pdf, Schematic images showing the target loci, allele discrimination strategy and sgRNA binding sites within the analyzed DNA region
Additional file 2: File type pdf, Supplementary Figures 1-7
Additional file 3: File type pdf, Supplementary Tables 1-5

Allis CD, Jenuwein T. The molecular hallmarks of epigenetic control. Nat Rev Genet. 2016;17(8):487-500.
Luo C, Hajkova P, Ecker JR. Dynamic DNA methylation: In the right place at the right time. Science. 2018;361(6409):1336-40.
Chen Z, Zhang Y. Role of Mammalian DNA Methyltransferases in Development. Annual review of biochemistry. 2020;89:135-58.
Jambhekar A, Dhall A, Shi Y. Roles and regulation of histone methylation in animal development. Nat Rev Mol Cell Biol. 2019;20(10):625-41.
Kungulovski G, Jeltsch A. Epigenome Editing: State of the Art, Concepts, and Perspectives. Trends Genet. 2016;32(2):101-13.
Stolzenburg S, Goubert D, Rots MG. Rewriting DNA Methylation Signatures at Will: The Curable Genome Within Reach? Adv Exp Med Biol. 2016;945:475-90.
Holtzman L, Gersbach CA. Editing the Epigenome: Reshaping the Genomic Landscape. Annu Rev Genomics Hum Genet. 2018;19:43-71.
Gjaltema RAF, Rots MG. Advances of epigenetic editing. Curr Opin Chem Biol. 2020;57:75-81.
Sgro A, Blancafort P. Epigenome engineering: new technologies for precision medicine. Nucleic Acids Res. 2020;48(22):12453-82.
Li F, Papworth M, Minczuk M, Rohde C, Zhang Y, Ragozin S, et al. Chimeric DNA methyltransferases target DNA methylation to specific DNA sequences and repress expression of target genes. Nucleic Acids Res. 2007;35(1):100-12.
Snowden AW, Gregory PD, Case CC, Pabo CO. Gene-specific targeting of H3K9 methylation is sufficient for initiating repression in vivo. Curr Biol. 2002;12(24):2159-66.
Qi LS, Larson MH, Gilbert LA, Doudna JA, Weissman JS, Arkin AP, et al. Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell. 2013;152(5):1173-83.
Stepper P, Kungulovski G, Jurkowska RZ, Chandra T, Krueger F, Reinhardt R, et al. Efficient targeted DNA methylation with chimeric dCas9-Dnmt3a-Dnmt3L methyltransferase. Nucleic Acids Res. 2017;45(4):1703-13.
Hofacker D, Broche J, Laistner L, Adam S, Bashtrykov P, Jeltsch A. Engineering of Effector Domains for Targeted DNA Methylation with Reduced Off-Target Effects. Int J Mol Sci. 2020;21(2).
Tanenbaum ME, Gilbert LA, Qi LS, Weissman JS, Vale RD. A protein-tagging system for signal amplification in gene expression and fluorescence imaging. Cell. 2014;159(3):635-46.
Gowher H, Jeltsch A. Mammalian DNA methyltransferases: new discoveries and open questions. Biochem Soc Trans. 2018;46(5):1191-202.
Lee JM, Ramos EM, Lee JH, Gillis T, Mysore JS, Hayden MR, et al. CAG repeat expansion in Huntington disease determines age at onset in a fully dominant fashion. Neurology. 2012;78(10):690-5.
Bettencourt C, Lima M. Machado-Joseph Disease: from first descriptions to new perspectives. Orphanet J Rare Dis. 2011;6:35.
Young JJ, Lavakumar M, Tampi D, Balachandran S, Tampi RR. Frontotemporal dementia: latest evidence and clinical implications. Ther Adv Psychopharmacol. 2018;8(1):33-48.
International HapMap C. The International HapMap Project. Nature. 2003;426(6968):789-96.
Lin YC, Boone M, Meuris L, Lemmens I, Van Roy N, Soete A, et al. Genome dynamics of the human embryonic kidney 293 lineage in response to cell biology manipulations. Nat Commun. 2014;5:4767.
Broche J, Kungulovski G, Bashtrykov P, Rathert P, Jeltsch A. Genome-wide investigation of the dynamic changes of epigenome modifications after global DNA methylation editing. Nucleic Acids Res. 2021;49(1):158-76.
Tang L, Yang F, He X, Xie H, Liu X, Fu J, et al. Efficient cleavage resolves PAM preferences of CRISPR-Cas in human cells. Cell Regen. 2019;8(2):44-50.
Gleditzsch D, Pausch P, Muller-Esparza H, Ozcan A, Guo X, Bange G, et al. PAM identification by CRISPR-Cas effector complexes: diversified mechanisms and structures. RNA Biol. 2019;16(4):504-17.
Cong L, Ran FA, Cox D, Lin S, Barretto R, Habib N, et al. Multiplex genome engineering using CRISPR/Cas systems. Science. 2013;339(6121):819-23.
Lawhorn IE, Ferreira JP, Wang CL. Evaluation of sgRNA target sites for CRISPR-mediated repression of TP53. PLoS One. 2014;9(11):e113232.
Galonska C, Charlton J, Mattei AL, Donaghey J, Clement K, Gu H, et al. Genome-wide tracking of dCas9-methyltransferase footprints. Nat Commun. 2018;9(1):597.
Pflueger C, Tan D, Swain T, Nguyen T, Pflueger J, Nefzger C, et al. A modular dCas9-SunTag DNMT3A epigenome editing system overcomes pervasive off-target activity of direct fusion dCas9-DNMT3A constructs. Genome Res. 2018;28(8):1193-206.
Stolzenburg S, Rots MG, Beltran AS, Rivenbark AG, Yuan X, Qian H, et al. Targeted silencing of the oncogenic transcription factor SOX2 in breast cancer. Nucleic Acids Res. 2012;40(14):6725-40.
Kungulovski G, Nunna S, Thomas M, Zanger UM, Reinhardt R, Jeltsch A. Targeted epigenome editing of an endogenous locus with chromatin modifiers is not stably maintained. Epigenetics Chromatin. 2015;8:12.
Vojta A, Dobrinic P, Tadic V, Bockor L, Korac P, Julg B, et al. Repurposing the CRISPR-Cas9 system for targeted DNA methylation. Nucleic Acids Res. 2016;44(12):5615-28.
O'Geen H, Ren C, Nicolet CM, Perez AA, Halmai J, Le VM, et al. dCas9-based epigenome editing suggests acquisition of histone methylation is not sufficient for target gene repression. Nucleic Acids Res. 2017;45(17):9901-16.
O'Geen H, Tomkova M, Combs JA, Tilley EK, Segal DJ. Determinants of heritable gene silencing for KRAB-dCas9 + DNMT3 and Ezh2-dCas9 + DNMT3 hit-and-run epigenome editing. Nucleic Acids Res. 2022;50(6):3239-53.
Wu J, Tang B, Tang Y. Allele-specific genome targeting in the development of precision medicine. Theranostics. 2020;10(7):3118-37.
Concordet JP, Haeussler M. CRISPOR: intuitive guide selection for CRISPR/Cas9 genome editing experiments and screens. Nucleic Acids Res. 2018;46(W1):W242-W5.
Labuhn M, Adams FF, Ng M, Knoess S, Schambach A, Charpentier EM, et al. Refined sgRNA efficacy prediction improves large- and small-scale CRISPR-Cas9 applications. Nucleic Acids Res. 2018;46(3):1375-85.
Stemmer M, Thumberger T, Del Sol Keyer M, Wittbrodt J, Mateo JL. CCTop: An Intuitive, Flexible and Reliable CRISPR/Cas9 Target Prediction Tool. PLoS One. 2015;10(4):e0124633.
Balci H, Globyte V, Joo C. Targeting G-quadruplex Forming Sequences with Cas9. ACS Chem Biol. 2021;16(4):596-603.
Malina A, Cameron CJF, Robert F, Blanchette M, Dostie J, Pelletier J. PAM multiplicity marks genomic target sites as inhibitory to CRISPR-Cas9 editing. Nat Commun. 2015;6:10124.
Amabile A, Migliara A, Capasso P, Biffi M, Cittaro D, Naldini L, et al. Inheritable Silencing of Endogenous Genes by Hit-and-Run Targeted Epigenetic Editing. Cell. 2016;167(1):219-32 e14.
Mlambo T, Nitsch S, Hildenbeutel M, Romito M, Muller M, Bossen C, et al. Designer epigenome modifiers enable robust and sustained gene silencing in clinically relevant human cells. Nucleic Acids Res. 2018;46(9):4456-68.
Nakamura M, Gao Y, Dominguez AA, Qi LS. CRISPR technologies for precise epigenome editing. Nat Cell Biol. 2021;23(1):11-22.
O'Geen H, Bates SL, Carter SS, Nisson KA, Halmai J, Fink KD, et al. Ezh2-dCas9 and KRAB-dCas9 enable engineering of epigenetic memory in a context-dependent manner. Epigenetics Chromatin. 2019;12(1):26.
Monga I, Qureshi A, Thakur N, Gupta AK, Kumar M. ASPsiRNA: A Resource of ASP-siRNAs Having Therapeutic Potential for Human Genetic Disorders and Algorithm for Prediction of Their Inhibitory Efficacy. G3 (Bethesda). 2017;7(9):2931-43.
Ciesiolka A, Stroynowska-Czerwinska A, Joachimiak P, Ciolak A, Kozlowska E, Michalak M, et al. Artificial miRNAs targeting CAG repeat expansion in ORFs cause rapid deadenylation and translation inhibition of mutant transcripts. Cell Mol Life Sci. 2021;78(4):1577-96.
Southwell AL, Skotte NH, Kordasiewicz HB, Ostergaard ME, Watt AT, Carroll JB, et al. In vivo evaluation of candidate allele-specific mutant huntingtin gene silencing antisense oligonucleotides. Mol Ther. 2014;22(12):2093-106.
Ostergaard ME, Southwell AL, Kordasiewicz H, Watt AT, Skotte NH, Doty CN, et al. Rational design of antisense oligonucleotides targeting single nucleotide polymorphisms for potent and allele selective suppression of mutant Huntingtin in the CNS. Nucleic Acids Res. 2013;41(21):9634-50.
Murray SF, Jazayeri A, Matthes MT, Yasumura D, Yang H, Peralta R, et al. Allele-Specific Inhibition of Rhodopsin With an Antisense Oligonucleotide Slows Photoreceptor Cell Degeneration. Invest Ophthalmol Vis Sci. 2015;56(11):6362-75.
Shin JW, Kim KH, Chao MJ, Atwal RS, Gillis T, MacDonald ME, et al. Permanent inactivation of Huntington's disease mutation by personalized allele-specific CRISPR/Cas9. Hum Mol Genet. 2016;25(20):4566-76.
Fink KD, Deng P, Gutierrez J, Anderson JS, Torrest A, Komarla A, et al. Allele-Specific Reduction of the Mutant Huntingtin Allele Using Transcription Activator-Like Effectors in Human Huntington's Disease Fibroblasts. Cell Transplant. 2016;25(4):677-86.
Zeitler B, Froelich S, Marlen K, Shivak DA, Yu Q, Li D, et al. Allele-selective transcriptional repression of mutant HTT for the treatment of Huntington's disease. Nat Med. 2019;25(7):1131-42.
Shao S, Chang L, Sun Y, Hou Y, Fan X, Sun Y. Multiplexed sgRNA Expression Allows Versatile Single Nonrepetitive DNA Labeling and Endogenous Gene Regulation. ACS Synth Biol. 2018;7(1):176-86.
Chiang TW, le Sage C, Larrieu D, Demir M, Jackson SP. CRISPR-Cas9(D10A) nickase-based genotypic and phenotypic screening to enhance genome editing. Sci Rep. 2016;6:24356.
Kikin O, D'Antonio L, Bagga PS. QGRS Mapper: a web-based server for predicting G-quadruplexes in nucleotide sequences. Nucleic Acids Res. 2006;34(Web Server issue):W676-82.
Leitao E, Beygo J, Zeschnigk M, Klein-Hitpass L, Bargull M, Rahmann S, et al. Locus-Specific DNA Methylation Analysis by Targeted Deep Bisulfite Sequencing. Methods Mol Biol. 2018;1767:351-66.
Bashtrykov P, Jeltsch A. DNA Methylation Analysis by Bisulfite Conversion Coupled to Double Multiplexed Amplicon-Based Next-Generation Sequencing (NGS). Methods Mol Biol. 2018;1767:367-82.
Zhang J, Kobert K, Flouri T, Stamatakis A. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics. 2014;30(5):614-20.
Pedersen JS, Valen E, Velazquez AM, Parker BJ, Rasmussen M, Lindgreen S, et al. Genome-wide nucleosome map and cytosine methylation levels of an ancient human genome. Genome Res. 2014;24(3):454-66.

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Development of super-specific epigenome editing by targeted allele-specific DNA methylation

Status:

Journal Publication

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Abstract

Background

Results

Conclusions

Figures

Background

Results

Target selection and transient transfection of HEK293

Targeting ASM with a SNP in the seed region of the sgRNA

Targeting ASM with a SNP positioned at the second position of the PAM

Targeting ASM with a SNP positioned at the third position of the PAM

Comparison of ASM achieved at one genomic locus by different targeting methods

Stability of ASM in HEK293 cells

Modulation of allele-specific gene expression ratios by ASM

Discussion

Efficiency of targeted DNA methylation

Specificity of ASM

Stability of ASM

Allele-specific expression changes

Conclusions

Methods

sgRNA design and cloning

Cell culture and transfection

DNA methylation analysis

RNA isolation and expression analysis

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1