Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2012 Jul;40(13):6367-79.
doi: 10.1093/nar/gks268. Epub 2012 Mar 29.

Chromosomal Context and Epigenetic Mechanisms Control the Efficacy of Genome Editing by Rare-Cutting Designer Endonucleases

Affiliations
Free PMC article

Chromosomal Context and Epigenetic Mechanisms Control the Efficacy of Genome Editing by Rare-Cutting Designer Endonucleases

Fayza Daboussi et al. Nucleic Acids Res. .
Free PMC article

Abstract

The ability to specifically engineer the genome of living cells at precise locations using rare-cutting designer endonucleases has broad implications for biotechnology and medicine, particularly for functional genomics, transgenics and gene therapy. However, the potential impact of chromosomal context and epigenetics on designer endonuclease-mediated genome editing is poorly understood. To address this question, we conducted a comprehensive analysis on the efficacy of 37 endonucleases derived from the quintessential I-CreI meganuclease that were specifically designed to cleave 39 different genomic targets. The analysis revealed that the efficiency of targeted mutagenesis at a given chromosomal locus is predictive of that of homologous gene targeting. Consequently, a strong genome-wide correlation was apparent between the efficiency of targeted mutagenesis (≤ 0.1% to ≈ 6%) with that of homologous gene targeting (≤ 0.1% to ≈ 15%). In contrast, the efficiency of targeted mutagenesis or homologous gene targeting at a given chromosomal locus does not correlate with the activity of individual endonucleases on transiently transfected substrates. Finally, we demonstrate that chromatin accessibility modulates the efficacy of rare-cutting endonucleases, accounting for strong position effects. Thus, chromosomal context and epigenetic mechanisms may play a major role in the efficiency rare-cutting endonuclease-induced genome engineering.

Figures

Figure 1.
Figure 1.
Genome-wide distribution of the targets of the MNs used in this study. (a) Schematic representation of the distribution per chromosome. Thirty-seven MNs cleaving 39 targets were used in this study, including 36 MNs cleaving each single target (represented by filled-in triangles) and 1 MN (CLS3902m), cleaving 3 (represented by open triangles). Target sequences and MN properties are described in Table 1. (b) Distribution per type of genomic sequence, per GC content and per CpG number. Distributions for target locations (top row) are compared with the distribution found for the whole genome (bottom row, on assembly GRCh37/hg19). For the type of genomic sequences, two MN target sites are overlapping with an intron/exon boundary and a special class was made for them. For GC content and CpG number, windows of 1 kb are taken around the recognition site (top row) or successively on the whole genome (bottom row) and the percentage of GC or the number of CpG dinucleotides are computed. Percentages in each class are displayed. The estimation of the percentage of sequences in exon, intron and intergenic regions on the whole genome is based on available annotations. The human genome version we used was GRCh37/hg19.
Figure 2.
Figure 2.
Characterization of MNs properties using cell-based assays. (a) An extrachromosomal assay for the characterization of MN activity in mammalian cells. Briefly, MN expressing vectors and reporter vector are co-transfected into CHO-KI or 293-H cells. Upon cleavage of the target site, tandem repeat recombination by SSA between two truncated copies of the LacZ gene restores a functional β-galactosidase gene, which can be monitored by standard assays. For each MN, a dose response is performed and AUC is used as a quantitative measure of MN activity. (b) Example of read out of the extrachromosomal assay described in panel. The activity of the CLS4076m, I-SceIm and Rag1m MNs is featured as an example. AUC is used as a quantitative measure of MN activity in this type of extrachromosomal assay. For CLS4076, AUC is the area in grey. (c) Monitoring of MN expression by western blotting. 293-H cells and CHO-K1 were transfected with a plasmid-encoding MN. The MN expression level was monitored by western blot. (d) Quantification of MN expression level. The MN expression level from western blot (c) was quantified and normalized to β-tubulin signal. These ratios were plotted for both cell lines and statistical analysis was performed. r, Pearson coefficient (linear correlation coefficient); ρ, Spearman coefficient (non-linear correlation coefficient); N, sample size; P, probability of finding a given correlation when the underlying variables are not correlated. (e) Comparison between the extra-chromosomal SSA assays in CHO-KI and HEK293 cells. Sixteen MNs were tested in both assays (Table 1). Dotted grey curves represent 95% confidence interval for the regression line. r, Pearson coefficient (linear correlation coefficient); ρ, Spearman coefficient (non-linear correlation coefficient); N, sample size; P, probability of finding a given correlation when the underlying variables are not correlated.
Figure 3.
Figure 3.
Characterization of MNs efficacy on their chromosomal cognate target. (a) A chromosomal assay for the characterization of MN-induced TM. MN expressing vectors are transfected into 293-H cells; the targeted locus is amplified by PCR and 10 000 molecules (on average) are sequenced to detect indels. (b) A chromosomal test for the detection of MN-induced HGT. MN-expressing vectors are co-transfected into 293-H cells together with a repair matrix; cells are grown in 96-well plates, and targeted alleles are detected by PCR. (c) Comparison between the TM and HGT assays in 293-H cells. Eighteen MNs were characterized in both assays (Table 1). Dotted grey curves represent 95% confidence interval for the regression line. Linear regression fits the formula y = 5.46x − 0.66. r, Pearson coefficient (linear correlation coefficient); ρ, Spearman coefficient (non-linear correlation coefficient); N, sample size; P: probability of finding a given correlation when the underlying variables are not correlated. (d) Comparison between the TM and HGT frequencies measured simultaneously by deep sequencing. Sixteen independent experiments were conducted, using different MNs (Rag1m, ADCY9m, CLS3676m, CLS3759m, CLS3894m, CLS4076m and CLS4607m), promoters (SV40 or CMV) and vector concentrations, all described in Supplementary Table S2. Dotted grey curves represent 95% confidence interval for the regression line. Linear regression fits the formula y = 1.71x + 0.20. r, ρ, P and N have the same meaning as on the previous panel.
Figure 4.
Figure 4.
The efficacy of MNs on their chromosomal target is poorly correlated with the activity of the nucleases in extrachromosomal assays. Throughout the figure, r, Pearson coefficient (linear correlation coefficient); ρ, Spearman coefficient (non-linear correlation coefficient); N, sample size; P, probability of finding a given correlation when the underlying variables are not correlated. (a) Comparison between the extrachromosomal SSA assay in CHO-KI and the TM assay in HEK293 cells. Thirty-seven MNs cleaving 39 targets were tested in both assays (Table 1). Correlation was also made with a smaller sample of 18 MNs (black circles), which are the same as the 18 MNs characterized in the HGT assay (Figure 3b), and displayed on Figure 3c. Vertical grey lines represent the activity levels of I-SceIm (left line) and Rag1m (right line). (b) Correlation between the extrachromosomal SSA assays in 293-H cells and the TM assay in 293-H cells. (c) Correlation between the extrachromosomal SSA assays in CHO cells and the HGT assay in 293-H cells. (d) Correlation between the extrachromosomal SSA assays in 293-H cells and the HGT assay in 293-H cells.
Figure 5.
Figure 5.
Impact of position effect on MNs efficacy. (a) The Rag1m MNs as well as Iso1Rag1m and Iso2Rag1m, two isoschizomers were tested for activity in our extrachromosomal assay in CHO-KI cells, for toxicity, and for their ability to induce targeted insertion at the endogenous Rag1 locus. Activity, efficacy and toxicity assays were conducted as described in Figures 2 and 3. (b) Similar studies were conducted with the DMD21m MN and Iso1DMDm and Iso2DMDm cleaving the DMD21t target. (c) The CLS3902m MN cleaves a sequence found in four copies in the human. CLS3902m was tested for activity, toxicity and efficacy of targeted mutagenesis at three of its cognate targets, CLS3902t_5, CLS3902t_7 and CLS3902t_14. In A, B and C, each value is the average of two independent experiments but for Rag1m (n = 7) and DMD21 (n = 3). (d) CHART-PCR assay for micrococcal nuclease accessibility of the Rag1t, DMD21t, CLS3902t_5, CLS3902t_7, CLS3902t_14, CLS4076t, CAPNS1t, ADCY9t, CLS3759t and XPC4t loci. The GAPDH and PAX7 loci are used as controls for accessible and non accessible chromatin, respectively (37). Nuclei were isolated and treated with various amounts of micrococcal nuclease, and for each locus, the amount of non digested DNA was monitored by Q-PCR as described in materials and methods. Data are normalized to the value obtained in absence of nuclease, and each point is the average of four measurements from two independent experiments. (e) Comparison between the micrococcal nuclease accessibility assay and the TM assay at Day 2. The values of panel d were used to construct a curve, and a nuclease resistance index (AUC) was calculated as the AUC, based on the same principle as activity and cell survival index calculations (Figure 2b and h). Efficacy of TM at Day 2 was plotted against this resistance index, using a semi-logarithmic scale. Here, r represents the linear correlation coefficient between log(TM) and AUC. r, Pearson coefficient (linear correlation coefficient); ρ, Spearman coefficient (non-linear correlation coefficient); N, sample size; P, probability of finding a given correlation when the underlying variables are not correlated. (f) Comparison between the micrococcal nuclease accessibility assay and the TM assay at Day 7. Here, r represents the linear correlation coefficient between log(TM) and AUC.

Similar articles

See all similar articles

Cited by 24 articles

See all "Cited by" articles

References

    1. Silva G, Poirot L, Galetto R, Smith J, Montoya G, Duchateau P, Paques F. Meganucleases and other tools for targeted genome engineering: perspectives and challenges for gene therapy. Curr. Gene Ther. 2011;11:11–27. - PMC - PubMed
    1. Pingoud A, Silva GH. Precision genome surgery. Nat. Biotechnol. 2007;25:743–744. - PubMed
    1. Pattanayak V, Ramirez CL, Joung JK, Liu DR. Revealing off-target cleavage specificities of zinc-finger nucleases by in vitro selection. Nat. Methods. 2011;8:765–770. - PMC - PubMed
    1. Urnov FD, Rebar EJ, Holmes MC, Zhang HS, Gregory PD. Genome editing with engineered zinc finger nucleases. Nat. Rev. Genet. 2010;11:636–646. - PubMed
    1. Lombardo A, Cesana D, Genovese P, Di Stefano B, Provasi E, Colombo DF, Neri M, Magnani Z, Cantore A, Lo Riso P, et al. Site-specific integration and tailoring of cassette design for sustainable gene transfer. Nat. Methods. 2011;8:861–869. - PubMed

Publication types

Substances

Feedback