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Programmable Nuclease-Based Integration into Novel Extragenic Genomic Safe Harbor Identified from Korean Population-Based CNV Analysis

Here, we found two genomic safe harbor (GSH) candidates from chromosomes 3 and 8, based on large-scale population-based cohort data from 4,694 Koreans by CNV analysis. Furthermore, estimated genotype of these CNVRs was validated by quantitative real-time PCR, and epidemiological data examined no significant genetic association between diseases or traits and two CNVRs. After screening the GSH candidates by in silico approaches, we designed TALEN pairs to integrate EGFP expression cassette into human cell lines in order to confirm the functionality of GSH candidates in an in vitro setting. As a result, transgene insertion into one of the two loci using TALEN showed robust transgene expression comparable to that with an AAVS1 site without significantly perturbing neighboring genes. Changing the promoter or cell type did not noticeably disturb this trend. Thus, we could validate two CNVRs as a site for effective and safe transgene insertion in human cells.


INTRODUCTION
One of the critical needs in the biomedical field is the ability to stably insert functional transgenes and other genetic elements into the human genome without disrupting genes or perturbing their transcription, which can potentially alter the biological properties of host cells. Several diseases have been successfully treated with stable insertion of therapeutic genes, such as Leber's congenital amaurosis, 1 adrenoleukodystrophy, 2 and Parkinson's disease. 3 Furthermore, this stable gene insertion can facilitate determination of transgene functions, labeling cells for tracking and lineage analysis through reporter gene insertion, and production of specific proteins from human cells.
In the meantime, random integration of transgenes may cause insertional mutagenesis, which can possibly alter expression levels of neighbor genes. The most common approach of integrating trans-genes in human cells is to use retroviral vector, which inserts transgenes into the human genome in a semi-random manner with a preference toward the vicinity of transcriptionally active genes. [4][5][6] This uncontrolled insertional mutagenesis can cause cancer such as leukemia 7 and lymphoma 8 by perturbing proto-oncogenes or tumor suppressors. Furthermore, when the transgenes are integrated into random regions of human chromosomes, the expression of these transgenes can be silenced or unpredictable depending on the integration site.
Instead of viral vectors, engineered nucleases such as zinc-finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and the CRISPR-Cas system have become more prospective approaches for the purpose of safety. These engineered nucleases induce site-specific double-stranded breaks into targeted sites and go through a highly precise genomic editing by the mechanism of homology-directed repair (HDR) in the presence of single-stranded oligodeoxynucleotide (ssODN) or a donor DNA. 9 Despite the advance in these engineered nucleases, there are only a few up-to-date identified and validated genomic safe harbors (GSHs). GSHs are intra-or extragenic regions that can support predictable transgene expression while minimizing neighboring gene perturbation. 10 The AAVS1 site on chromosome 19 is the most popular GSH, due to its ability to support transgene expression in multiple cell types, 11,12 yet at the same time, it was known that the AAVS1 locus could be silenced by the mechanism including DNA methylations. 13 A copy-number variation (CNV) is an insertion or deletion of DNA segment and is relatively common and widespread in the human genome. 14 There are different gene copy numbers in a particular The flowchart shows how various CNVs were screened to select two final GSH candidate regions. (B) Histograms and cluster plots illustrates two candidate regions of CNVR7 and CNVR22 generated by CNV tools. Like log 2 ratio plots, histograms and cluster plots show that CNV genotypes of these regions were clearly separated in to three groups (0 copies, 1 copy, and 2 copies). (C) Quantitative real-time PCR data show validation results on CNVR7 and CNVR22. We conducted a validation experiment for three CNV states. Samples in each state were randomly selected. Higher bars, lower bars, and no bar mean normal copy (2 copies), heterozygous deletion (1 copy), and homozygous deletion (0 copies), respectively. Blue bar represents CNV genotype of the reference sample (NA10851) used by comparative genomic hybridization array (aCGH). region among healthy individuals. We hypothesized that transgene insertion into the CNV region (CNVR), which has less association with genetic diseases, might not lead to any abnormal health problems. Accordingly, we performed the large-scale cohort studies through Korean CNV analysis to screen potential GSH candidates based on essential criteria. 10 We selected two possible chromosomal sites, chromosomes 3 and 8, further away from both gene-rich regions and genes implicated in cancer and microRNA (miRNA). Once these chromosomal sites satisfied the criteria for GSH, they were further investigated to rule out any disease correlation. We were able to achieve efficient site-specific integration and to measure the neighboring gene perturbation near the site of integration. Consequently, we proved that our CNVRs could derive robust transgene expression without significantly altering multiple neighboring genes.

Selected CNVRs for GSH Site
Through a frequency analysis of CNV state, 290 CNVRs with 5%-25% frequency were selected from 3,601 CNVRs ( Figure 1A; Figure S1A). Among those, 30 CNVRs had no neighboring genes within 300 kb upstream and downstream of a CNVR (Table 1). Subsequently, two CNV regions with much lower disease association through statistical analysis assessing the disease association, CNVR7 (chr3:82951465-82955620, hg18) and CNVR22 (chr8:135127147-135140206, hg18), were selected as final candidates (Table 2; Figure S1B). Table 2 describes two selected regions. CNV genotypes of two regions were composed of homozygous deletion (0 copies), heterozygous deletion (1 copy), and normal copy (2 copies). Frequency of copy number deletion (cluster 1 + cluster 2) was 9.18% and 16.15% for CNVR 7 and CNVR22 (Table 2). Moreover, these regions overlapped From quantitative real-time PCR experiment results, we confirmed that two regions consisted of three copy-number classes and the CNV genotype of each individual was perfectly matched to those of our estimation ( Figure 1C). PPV of each candidate region was 1.

Disease-Association Results of Two Candidate Regions
Tables 3 and 4 are association analyses of CNVR7 and CNVR22, respectively. In the case of CNVR7, most diseases such as type 2 diabetes (T2D), hypertension, osteoporosis, obesity, dyslipidemia, and metabolic syndrome were not significantly associated with this region, except for high-density lipoprotein (HDL). The p value of HDL-CNV association analysis was p < 0.05 (p = 0.037) ( Table 3). Because the sample size of this case-control study might cause spurious results, we also checked statistical significance of association by conducting linear regression analysis. The results from linear regression analysis exhibited conflicting results (p = 0.178) compared to those from logistic regression. From this, we assumed that this discrepancy indicates no CNV association with HDL trait. In the case of CNVR22, we found that there was no statistical significance in association results (Table 4). Tables S1-S28 illustrate disease-association analysis of 28 CNVRs.
EGFP Cassette Integration and Expression into AAVS1, CNVR7, and CNVR22 We designed TALEN pairs to target AAVS1, CNVR22, and CNVR7 sites in human somatic cell lines and their corresponding EGFP expression cassettes, which were driven by the CMV early enhancer or chicken beta-actin (CAG) and viral origin SFFV (spleen focusforming virus) promoter terminating at the poly(A) site from the bovine growth hormone gene ( Figure 2; Figure S2B). To promote efficient homologous recombination at the required locus of AAVS1, CNVR7, and CNVR22, each of the targeting donor cassettes was constructed with homology arms of 800 bp (left arm) and 800 bp (right arm). The appropriate combination of TALENs and the EGFP expression cassette was transiently transfected into human K562 cells and Huh 7.5 cells by electroporation. We derived single clones from EGFP + sorted cells and performed site-specific integration PCR analysis to confirm transgene integration at the target site by homologydirected repair ( Figure S2C). From K562 cells, we selected a total of 252, 184, and 223 clones for AAVS1, CNVR7, and CNVR22, respectively, and sorted EGFP + clones ( Figures S2A and S2B). A similar degree of EGFP-expressing populations among three sites may validate the use of two extragenic GSH candidate regions. Then, target-integrated clones from EGFP + clones were represented by 78.6%, 73.1%, and 79% for AAVS1, CNVR7, and CNVR22 ( Figure 3A). As reported by Lombardo et al., 15 integration efficiency (percentage of EGFP + cells) is unaffected by the target site, and mean fluorescence intensity (MFI; a measure of the average expression per cell) of EGFP depends on both the promoter and the target locus. We also recorded a similar outcome (more than 70% of confirmed transgene integrated clones for each construct) 15 in that the MFI of EGFP showed no significance between AAVS1 and CNVR22 sites but outperforming CNVR7, with p = 0.0256 ( Figure 3B). Nonetheless, the integration efficiency was dependent on the TALEN activity (data not shown).
To validate that such findings are not strictly defined to one cell type, we tried a different cell line, Huh 7.5, that originated from a different human tissue. 10 Site-specific integration PCR analysis was performed on single cell-derived clones, and a similar finding was reported as that seen in K562 cells ( Figure S3B). Out of the EGFP + sorted clones, 11/23 for AAVS1, 4/18 for CNVR7, and 6/20 for CNVR22 were EGFP + -targeted integrations representing 47.8%, 17.8%, and 26.1% (Figures 3C; Figure S3A), respectively, as analyzed by flow cytometry. The MFI of EGFP + cells showed significance between AAVS1 and CNVR7 sites (p = 0.0032) and between CNVR7 and CNVR22 sites (p = 0.0092) ( Figure 3C).

Extragenic Integration into CNVR7 and CNVR22 Did Not Regulate Nearby Genes as It Did into AAVS1
The AAVS1 site has already been reported as a possible safe harbor site, where integration into the AAVS1 site leads to stable expression with no upregulation of nearby genes. 15 We next investigated whether extragenic integration into CNVR7 and CNVR22 in K562 cells would lead to a stable and reliable expression with no neighboring gene perturbation beyond 300 kb up-and downstream of the target locus. We analyzed 9 genes for AAVS1 found within 300 kb upstream and downstream of the integration site by quantitative real-time PCR. 5 out of 9 genes in AAVS1-targeted cells demonstrated a significant downregulation, while PPPIR12C at the targeted integration point exhibited the steepest downregulation ( Figure 4A).
In order to satisfy the criteria for safe harbor ( Figure S4A), CNVR7 and CNVR22 were found to not contain any nearest gene within 300 kb up-and downstream of the integration site. For example, as to CNVR7 and CNVR22, the nearest genes to the target locus are GBE1 and ZFAT, respectively, whose distances from the target locus are 1.05 Mb and 426 kb ( Figure S4B). After integrating a CAG-promoter-driven EGFP cassette into CNVR7, we observed upregulation of CADM2 and substantial downregulation of ROBO1 ( Figure 4B). When we additionally analyzed 6 single-cell-derived clones with molecularly confirmed targeted integration in this locus, we could acquire similar outcome (data not shown). This demonstrates that transcriptional upregulation of the locus is independent of the extent of EGFP expression, as mentioned earlier. Performing the same analysis on single-cell-derived clones with molecularly confirmed targeted integration in CNVR22 resulted in no significant neighbor gene dysregulation ( Figure 4C). Therefore, a potency in stable and robust transgene expression followed by specific target integration in our safe-harbor candidates of CNVR7 and CNVR22 would be comparable to AAVS1.
To extend the generality of these findings, we assayed the impact of equivalent CAG-promoter-driven EGFP cassette integration on the three target loci in a different cell line, Huh 7.5 ( Figure 4D). We observed significant perturbation in 4 out of 9 genes in AAVS1targeted cells and 1 out of 5 genes in CNVR7-targeted cells. In CNVR22-targeted cells, single-cell-derived clones were not perturbed as much as in other loci, but we observed 13.5-fold downregulation of the flanking gene, TG. Again, even though we could observe one significant perturbation in both CNVR7 and CNVR22, it did not invoke the perturbations as much as seen in AAVS1, and this further supports that our GSH candidates are comparable to AAVS1.

A Stronger Expression Promoter Did Not Affect the Perturbation
To test whether these findings were not promoter dependent, we designed an EGFP cassette with a stronger promoter, SFFV. Again, site-specific integration PCR analysis was performed on single-cellderived clones in K562 cells ( Figure S5B). Out of the EGFP + sorted clones, 29/45 for AAVS1, 9/23 for CNVR7, and 39/80 for CNVR22 were EGFP + -targeted integrations representing 64.4%, 39.1%, and 48.8% ( Figure 5A), respectively, as analyzed by flow cytometry. While the type of promoter did not affect target integration efficiency, MFI was higher in the SFFV promoter group than in the CAG promoter group ( Figure S5A). The MFI of EGFP + cells showed significance between AAVS1 and CNVR7 sites (p = 0.0079) and between CNVR7 and CNVR22 sites (p = 0.0464).
Similar to the results shown in CAG-promoter-driven integration, AAVS1-targeted cells exhibited three significant gene perturbations ( Figure 5B), whereas neither CNVR7 nor CNVR22 demonstrated significant gene dysregulation, except for the CNVR22 group's flanking gene, TG, which showed a similar degree of downregulation (13.1fold). Therefore, changing the promoter type did not induce any detectable trend in neighboring gene perturbation in the three groups.

DISCUSSION
The integration of transgene into human chromosomes should be very careful, because it may cause unpredictable adverse effects, depending on the integration site. Hence, it is very important to check whether the integration leads to not only unpredictable effects on the cell but also undesirable outcomes for the human phenotype. Here, in order to find the GSH candidate region, we used real-world data based on already known CNVRs and concurrent disease statuses from cohort participants. Then, we performed the experimental analysis on transgene insertion into GSH candidates and validated their suitability by comparison to AAVS1.
We first proceeded with genomic approaches to find appropriate safeharbor candidates, especially from the Korean population, as described in Figure S1A. 10,15 Depending on target cell type, transgene, or disease type, safe harbors may be sub-categorized into specific types, yet we aimed to identify universal GSHs for the general Korean population. Throughout the disease-association analysis in 4,694  Korean adults aged 40 to 69, we could select two safe-harbor candidates of CNVR7 and CNVR22. Since this in silico analysis preconfirmed that extragenic insertion into two selected regions barely influenced copy number change, we experimentally inserted the EGFP donor cassette by TALEN pairs into AAVS1, CNVR7, and CNVR22 to observe whether site-specific extragenic expression would incur any significant endogenous gene perturbation.
Our in vitro data demonstrate that both CNVR7 and CNVR22 are comparable to AAVS1. Especially, CNVR22 can be more feasibly utilized as a new GSH in human cells, in that neighboring gene expression beyond at least 426 kb on either side of the insertion site was not significantly expressed as maintaining adequate EGFP intensity. CNVR7 was enough to be categorized into GSH, yet less neighboring gene perturbation unwaveringly supports CNVR22 as a more suitable GSH candidate. After transfecting the EGFP donor cassette into CNVR7 and CNVR22 target loci, integrated clones were thoroughly analyzed to observe the neighbor gene expressions. Since CNVR7 and CNVR22 were chosen by safe-harbor criteria of excluding the genomic region within 300 kb of any miRNA and 500 kb of oncogenes, there was no genome within this range, unlike AAVS1, adjacent to the immediate vicinity within 100 kb. While the most common insertional oncogenesis comes from transactivation of neighboring oncogenes, 16 transgene insertion at CNVR7 and CNVR22 will not suffer from undesirable transcription caused by unwanted normal gene dysregulation, which tends to occur up to a distance of $275 kb from the vector insertion site. 17 Although AAVS1 is known to resist neighboring gene perturbation due to the presence of chromatin insulator preventing enhancer-stimulated gene expression, 18 the AAVS1 locus was still exposed to uncontrolled gene dysregulation, as shown in our data. This further supports the suitability of CNVR7 and CNVR22 as comparable safe-harbor candidates to AAVS1.
When it comes to a broader application in clinical therapeutics, CNVR22 is well suited to the definition of universal GSH, owing to its appropriate expression independent of cell type and promoter. However, a reason for conspicuous TG downregulation upon altering the cell line and promoter is not clearly assumed, albeit TG-adjacent genes were not significantly perturbed. In domain-wide regulation spanning megabases, the activation or silencing of genes can be often accompanied by changing histone code or DNA methylation that can spread over considerable genomic distances, and this can disturb chromatin condensation. 13 Since TALEN binds to 5-methylated cytosine in its endogenous cognate target, 19 this could influence epigenetic modification, which may disturb endogenous TG expression. This phenomenon may also raise safety issues with regard to target locus containing transgene integration. In this sense, the CRISPR-Cas9 system can be adapted to improve targeting efficiency and safety, and optimal design of the transgene cassette would be another alternative strategy to diminish endogenous transcription around the insertion site.
Throughout our GSH screening strategy, combining both genomic data with disease association in a general Korean population and experimental analysis on transgene insertion, we could discover the new GSH candidate of CNVR22 in the human genome and characterize putative universal GSHs preventing genotoxicity and achieving stable extragenic insertion near endogenous neighboring genes, which was less significantly disrupted than those of AAVS1. While such a rapid advance in genome editing field challenges us to constantly focus on seeking novel techniques for effective clinical studies, our findings on new GSHs specialized to Korean populations will broaden the horizon to search for prospective therapeutics by sustainable gene transfer.

Samples and CNVRs
We first used the 3,601 CNVRs from the previous Korean CNV study using 4,694 individuals. 20 These were part of 8,842 Korea Association Resource (KARE) Project genome-wide association study (GWAS) subjects who satisfied quality control criteria, including exclusion of any kind of cancer samples (n = 101) from 10,038 subjects ( Figure S1A). 21 Table 5 shows a summary of the 4,694 participants' characteristics. Moreover, the Supplemental Materials and Methods show CNV detection methods such as characteristics of genotyping array and CNV detection tools or parameters in detail. All procedures were in accordance with the ethical standards of the responsible committee on human experimentation (approved IRB number of Korea Centers for Disease Control and Prevention in Korea: 2016-02-20-T-A). Informed written consent was obtained from all participants.
Many of the copy number duplications are tandem, and this means that duplicated regions are located very near to each other. However, a significant number of copy number duplications are located far from the original locus in humans. 22 Moreover, these dispersed duplications appear randomly distributed among the genome. 23,24 A current CNV detection approach that we chose enabled us to discover copy number duplication but cannot determine the location of duplication. In this region of the initial set of CNVRs, we excluded copy number duplication regions.

CNVR Selection Criteria for GSH Candidate Regions
We postulated that a certain genome region could be a GSH candidate as long as deletion of a corresponding region does not lead to any abnormal health problems, including neoplasm. As some adverse phenotypes of genetic difference can be observed as the individuals become old, we analyzed the genome sequences from 4,964 of men and women who do not have a history of cancer. To fulfill the safety issue, we set the criteria for safe-harbor candidates to be more stringent than previously proposed 10 ( Figure S1A). Then, we mainly considered well-genotyped common CNVRs (>5% frequency rate) to be 2-or 3-class CNV genotypes, with the distribution of minor CNV state ranging from 5% to 25%, because it is known that the common CNVs have a much lower effect on disease than rare CNVs. 24 Moreover, disease-association analysis with well-genotyped CNVRs is more reliable than that with poorly genotyped CNVRs. Regarding CNV with 3 classes, we calculated minor CNV state as the sum of two minor states. For example, if frequencies of CNV state with 3 classes were 85%, 12%, and 3%, we calculated minor CNV state as 15%.
To investigate CNVs on a gene-poor region, we used an annotation script of the PennCNV using refGene annotation of the NCBI Reference Sequence Database for human hg18 genome build. 25 We examined whether identified candidate CNV regions are correlated to disease-related genetic components or not. To do this, we selected CNVs with a neighboring distance of >300 kb from the 5 0 end of any genes, including cancer-related genes, and a distance of >100 kb from any miRNA. We also selected CNVs outside of a gene transcription unit and ultra-conserved regions. Each CNV region was assessed for the existence of protein-coding genes and also non-coding RNA elements on the GENCODE database (human genome build HG38).
We also examined cancer-related elements near GSH candidate regions using the COSMIC genome browser of the COSMIC data- base (http://cancer.sanger.ac.uk/cancergenome/ projects/cosmic/). Genomic locations for GSH candidate regions based on human genome assembly hg18 were converted to those based on hg19 by the liftOver tool from the UCSC genome browser. Cancer-related elements with a neighboring distance of >300 kb from each GSH candidate region were investigated.
To observe potential regulatory elements in regions around CNVs (±300 kb), we assessed common DNase I hypersensitivity regions (Broad ChromHMM, UW DNaseI DGF, and UW DNaseI HS) and transcription factor binding sites (Yale TFBS) among multiple cell types on the UCSC database.

Validation of Estimated CNV Genotype on Two Candidate Regions by Experiment
Estimated CNV genotypes had three copynumber classes, composed of homozygous deletion (0 copies), heterozygous deletion (1 copy), and normal copy (2 copies) (Figure 1B). To validate whether the estimated CNV genotype is true or not, we carried out quantitative realtime PCR using the TaqMan Copy Number Assay (Life Technologies, Foster City, CA, USA) according to the manufacturer's protocols. Two pre-designed TaqMan probes, Hs03453765_cn and Hs_06238257_cn, were used to validate the genotype of the two CNVs at chromosomes 3 and 8, respectively. All experiments were replicated three times to increase the validation accuracy. Moreover, validation samples were randomly selected from each CNV region, and genotypes of the reference DNA (NA10851) were observed together. CopyCaller v.2.0 (Life Technologies, Foster City, CA, USA) was used to analyze data generated by the TaqMan Copy Number Assay. www.moleculartherapy.org

Statistical Analysis of Disease/Trait Association Using Epidemiology Data
Subsequently, statistical analyses were conducted to find a correlation between candidate CNV regions and diseases or traits. Using the epidemiological information of 4,694 individuals, we grouped datasets for each trait. In total, 23 diseases and traits, including T2D, hypertension, and obesity, were considered as well (Table 6). We did not conduct an association study for cancer, because 101 cancer patients and/or individuals with a medical history of cancer were already excluded. Logistic or linear regression analysis adjusting for gender and age as covariates was used to calculate statistical significance.

Flow Cytometry
Suspension cells were collected while adherent cells were trypsinized and resuspended in PBS. Single-cell suspensions were analyzed and sorted using the FACSAria II (BD Biosciences).

Sorting Strong EGFP + Cells Containing TALEN-Induced Knockin
Untransfected cells were used as controls. Sorted cells were then used to obtain single-cell-derived clones by limiting dilution (0.25 cells per well of a 96-well plate). After 2 weeks, wells with cell populations from    www.moleculartherapy.org a single clone (round colony) were selected and expanded to perform flow cytometry using the BD FACSCanto system (MFI) and molecular analysis.

Clonal Analysis of Single Cells and Colonies
Before and after cell sorting, single cells were isolated using a mouth pipette under a microscope and transferred to PCR tubes. To obtain clonal populations of cells, sorted and unsorted cells were plated at a density of 1,000 cells per 100-mm plate, and colonies were manually picked after 2 weeks. For site-specific PCR analysis, the same donorspecific primer was used commonly among cells with locus-specific primer (AAVS1, CNVR7, and CNVR22). Then, PCR amplicons were cast on agarose gel and visualized by ethidium bromide staining.

Gene Expression Analysis
For gene expression analysis, total RNA was extracted from 1 Â 10 6 cells using the TRIzol-chloroform method and reverse-transcribed with random primers according to the RT-&GO Mastermix (MP Biomedicals Asia Pacific, RTRAG100) manufacturer's protocol. We analyzed 200 ng cDNA from K562 or Huh 7.5 cells, respectively, in triplicate with TOPreal qRT-PCR 2X PreMIX (Enzynomics, RT500M) in a CFX96 real-time PCR detection system (Bio-Rad, C1000 real-time PCR thermal cycler). The relative expression level of each gene was B2M and YWHAZ expression (housekeeping gene controls) and represented as fold change relative to the mocktreated samples (calibrator).

CONFLICTS OF INTEREST
The authors declare no competing interests.