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Review

Dead Cas Systems: Types, Principles, and Applications

by
Sergey Brezgin
1,2,
Anastasiya Kostyusheva
1,
Dmitry Kostyushev
1,* and
Vladimir Chulanov
1,3,4
1
National Medical Research Center of Tuberculosis and Infectious Diseases, Ministry of Health, Moscow 127994, Russia
2
Institute of Immunology, Federal Medical Biological Agency, Moscow 115522, Russia
3
Sechenov First Moscow State Medical University, Moscow 119146, Russia
4
Central Research Institute of Epidemiology, Moscow 111123, Russia
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2019, 20(23), 6041; https://doi.org/10.3390/ijms20236041
Submission received: 31 October 2019 / Revised: 26 November 2019 / Accepted: 28 November 2019 / Published: 30 November 2019
(This article belongs to the Section Molecular Biology)

Abstract

:
The gene editing tool CRISPR-Cas has become the foundation for developing numerous molecular systems used in research and, increasingly, in medical practice. In particular, Cas proteins devoid of nucleolytic activity (dead Cas proteins; dCas) can be used to deliver functional cargo to programmed sites in the genome. In this review, we describe current CRISPR systems used for developing different dCas-based molecular approaches and summarize their most significant applications. We conclude with comments on the state-of-art in the CRISPR field and future directions.

1. Introduction

In recent years, precise genomic and epigenomic editing has transformed into a fast-growing area of research, with lucrative applications in medicine and biotechnology. Targeted modifications of genomes in various organisms, from bacteria to plants to mammals, can be applied to treating human diseases and to developing bacterial strains and genetically engineered organisms with desired properties. Discovery of site-specific CRISPR-Cas nucleases and adapting these bacterial tools for gene editing applications have revolutionized genetic engineering and molecular biology. Robust activity, easy design, and capacity to target virtually any DNA or RNA site has put CRISPR-Cas at the forefront of gene editing techniques, with yet-undiscovered potential applications of optimized CRISPR-Cas components and novel CRISPR-Cas systems. In simplest terms, CRISPR-Cas systems are based on nucleolytic activity of Cas9 protein guided by a chimeric RNA molecule (guide RNA; gRNA) to the desired site in the genome. An important property of CRISPR-Cas is the high specificity defined by gRNA sequences that recognize the nucleic acid target and the protospacer adjacent motif (PAM) sequence, adjacent to the target, and required for CRISPR-Cas activity.
Type II CRISPR-Cas systems are most commonly used, while CRISPR-Cas systems of other types (e.g., types V and VI) have also been leveraged for genomic and epigenomic editing. The Cas9 protein of the type II CRISPR-Cas system harbors two nucleolytic domains (RuvC and HNH) that cleave target DNA strands and generate double-stranded breaks (DSB) [1]. Introducing point mutations into each domain (D10A and H840A, correspondingly) blocks nucleolytic activity of Cas9 but does not impact its binding to its target [2]. This mutant protein, called dead Cas9 (dCas9), has significantly broadened the application of CRISPR-Cas9 technology. Chimeric dCas-X molecules, in which X is, in principle, any functionally active domain, can be used to deliver virtually any cargo (functionally active domains) to specific loci in the genome. Functionally active domains may include (a) epigenome remodeling factors for activating or suppressing gene expression; (b) domains for investigating chromatin structure; (c) domains for directly remodeling three-dimensional (3D) chromatin structure; and (d) base editing enzymes, among others (Figure 1).
In this review, we will discuss modifications of dCas proteins and the corresponding molecular techniques, principles of gRNA design, and applications of dCas-based technologies, as well as future developments.

2. Epigenomic Remodeling Using dCas-X

Activity of genes is mainly determined by chromatin architecture in regulatory regions (promoters and enhancers). Chromatin exists in two major forms: Transcriptionally inactive heterochromatin and active euchromatin. Epigenetically modifying DNA or histones alters chromatin state. Controlling the processes of epigenomic remodeling determines gene transcription. dCas proteins can be designed to directly modify DNA epigenetics by methylation or demethylation, as well as to change epigenetic marks on histones by acetylation/deacetylation, methylation/demethylation, or recruitment of transcription factors. Different types of dCas-X proteins and their potential applications are reviewed below. Manipulating epigenetics is needed to studying gene function and regulatory genetic elements, as well as for developing potential therapeutics that, for example, suppress viral genome activity, modulate immune responses, suppress oncogenes, or program stem cells.

2.1. Modifying the Methylation State of DNA

Methylation of DNA in regulatory regions of genes suppresses gene transcription [3]. DNA methyltransferase DNMT3A and its cofactor DNMT3L methylate DNA [4], whereas TET1 protein initiates removal of methyl residues from DNA [5].
dCas9-X technology has, for the first time, provided an opportunity to perform precise epigenetic DNA modifications. One of the first CRISPR-based tools to directly methylate DNA were chimeric proteins dCas9-DNMT3A and dCas9 linked to a catalytic domain of DNMT3A [6,7,8]. Efficacy of dCas9-DNMT3A was further enhanced by binding DNMT3A to a DNMT3L cofactor via a short peptide linker (dCas9-DNMT3A-3L). Adding DNMT3L to the system resulted in 4–5-fold increase in methylation efficacy and the size of the methylation editing window [9].
Another modification of dCas-based technology designed to increase methylation efficiency is coupling dCas-X with the SunTag system, which recruits multiple effector molecules (e.g., DNMT3A) to the desired site. The principle of dCas-SunTag is attachment of GCN4 peptide repeats to dCas9 and simultaneous intracellular production of effector molecules fused with single-chain variable fragments (scFv) that are prone to interacting with GCN4-dCas9. Several (up to 10 units) DNMT3A-scFv molecules bind to dCas9 via GCN4 repeats [10].
dCas9-SunTag-DNMT3A is highly specific, and does not seem to affect the overall methylation state of the genome [10]. It is appropriate for precisely methylating small portions of the genome to, for example, prevent interactions between genomic DNA and small interacting proteins. Systems linked to SunTag or DNMT3A-3L systems can be used to methylate large swaths of the genome and substantially suppress gene transcription. Whilst much more effective, the latter systems have multiple components and are very large, and thus can be difficult to accommodate into commonly used adeno-associated viral (AAV) vectors and other viral delivery tools.
In contrast, many research and potential therapeutic applications frequently require demethylation of genomic DNA, particularly to treat malignancies by activating tumor suppressors, to treat genetic diseases, or to generate or differentiate stem cells. Chimeric dCas9 proteins fused with a domain of TET1 DNA-demethylase (dCas9-TET1 or dCas9-SunTag-TET1) have been shown to effectively demethylate up to 90% of target DNA regions [8,11,12]. A different implementation of this technology includes three components: (1) dCas9 protein without additional domains; (2) gRNA with two special MS2-hairpins (aptamers); and (3) effector proteins linked to MCP proteins that interact with MS2. In this system, dCas9 protein in complex with a modified gRNA is first recruited to the target genomic region. TET1-MCP proteins then recognize and bind to MS2 hairpins within gRNA, enabling site-specific demethylation. Two molecules of MCP can interact with 1 MS2 hairpin, and a single gRNA harbors 2 MS2 hairpins, resulting in up to four units of TET1 attracted with a single modified gRNA [13].

2.2. Principles of gRNA Design for Genome Methylation or Demethylation

gRNA design is one of the most important considerations affecting CRISPR-Cas function. Several factors determine effective on-target mutagenesis induced by nucleolytic CRISPR-Cas systems, including availability of the PAM sequence, gRNA nucleotide (nt) composition, and the similarity between the gRNA and its DNA target. Additionally, designing gRNAs for methylation/demethylation applications requires several other parameters to consider:
  • Initial methylation of target DNA. Effective suppression of gene transcription by dCas9-DNMTs can be achieved if gRNAs target initially unmethylated or weakly methylated regions [8]. In contrast, dCas9-based systems of DNA demethylation are effective only if gRNAs target heavily methylated DNA regions. DNA methylation levels in different cell lines and tissues can be assessed using several databases, including ENCODE [14] and MethBase [15].
  • Methylation sites. Using dCas9-DNMT3A results in methylation of two regions. The first one lies within 27 nt in the 3ʹ-direction from the PAM sequence, and the second is within 27 nt from 5ʹ-end of the gRNA. The site of dCas9 binding (approximately 30 nt) is not methylated [6,9]. Methylation at the two sites occurs due to a flexible peptide linker between the dCas9 protein and the DNMT3A enzyme/catalytic subunit, providing mobility to the methyltransferase enzyme [6]. Peaks of DNA methylation vary upon introducing additional factors to the system, such as binding DNMT3A to DNMT3L [9].
  • Methylation window. Methylation of extensive DNA regions is mandatory for stable suppression of gene function. dCas9-DNMT3A methylates regions 25–35 nt in length [6], and thus can only be used for pinpoint methylation [8]. Methylating extensive DNA regions is possible when using multiple gRNAs annealing to proximal DNA regions with the dCas9-DNMT3A system, [6] or a single gRNA combined with dCas9-DNMT3A-3L systems (which can methylate up to 1000 nt) [9] or dCas9-SunTag-DNMT3A (which methylates up to 4500 nt) [10]. DNA demethylation can be induced by dCas9-SunTag-TET1 within 200 nt of dCas9 binding (±100 nt from the dCas9 binding site) [12].

2.3. Regulating DNA Methylation State by dCas-Based Tools: Practical Applications

dCas-DNMTs tools provide an opportunity to analyze gene function and the role of methylation in physiologic conditions and in disease. For instance, dCas9-based methylation tools helped to identify the association between promoter methylation and dysfunction of Desmoplakin gene expression in the pathogenesis of idiopathic lung fibrosis [16]. Moreover, methylation of enhancer regions regulating phospholipidphosphatases was shown to be associated with calcification of the aortic valve, a pathological condition leading to myocardial infarction [17]. dCas9-TET1 helped to identify the key role of TNFα promoter hypermethylation in the development of nephropathies in diabetes [18]. The role of multiple genes in the processes of cell transformation was investigated using the dCas9-DNMT3A system. Directly hypermethylating DNA regulatory regions of tumor suppressors, including CDKN2A, RASSF1, HIC1, PTEN, and SMARCA2, improved understanding of gene function in oncogenesis [19,20]. The results of CRISPR-mediated methylation of tumor-related genes are summarized in the MICMIC resource [21]. Generating dCas9-DNMT3A tools aided analysis of gene transcription regulation by a small CTCF factor [8]. dCas9-based methylation was also used to impair CTCF-linked interaction of the MYC proto-oncogene with its super-enhancer and thus was proposed as an effective approach to treat many oncological diseases [22]. Additionally, BRCA [11] and SARI [23] promoters were demethylated to normalize physiological expression of tumor suppressors.
dCas9-methylation and demethylation tools can be leveraged to develop new therapeutic approaches performed at the level of epigenetics. Demethylating FMR1 by dCas9-TET1, for example, was shown to correct the clinical manifestations of fragile X syndrome [24]. Moreover, hypermethylating the SNCA gene promoter led to decreased cell death in an in vitro model of Parkinson’s disease and thus can be potentially considered a new therapeutic approach [25].
The growing area of stem cell research and its application in regenerative medicine requires new, more advanced techniques to obtain pluripotent stem cells, maintain pluripotency, and differentiate stem cells into particular lineages. Recently, demethylation of Sox1 gene by dCas9-TET1 resulted in efficient reprogramming of neural stem progenitor cells [26].

3. Rewriting Histone Epigenetic Marks

In addition to DNA methylation, histones are another factor involved in transcriptional regulation. Heterochromatin formation includes several steps: (1) Deacetylation of H3K9 and H3K27 histone residues; (2) methylation of H3K9 and H3K27 (H3K9Me3 and H3K27Me3); and (3) methylation of DNA regions wrapped by histones [27]. Histone deacetylation is catalyzed by histone deacetylases, methylation of H3K9 is executed by proteins SUV39H1 and G9A [28,29], and H3K27 is methylated by EZH2 [30]. Histone deacetylation and methylation suppress gene transcription [31,32,33,34].
On the other hand, transcriptionally active chromatin (euchromatin) is characterized by methylated H3K4 and H3K79 histone residues and acetylated H3K9 and H3K27 residues [35,36]. Factors MLL and PRDM methylate H3K4, while histone methyltransferase DOT1L attaches methyl groups to H3K79 residues [37]. Histone demethylation is mediated by LSD1 [38]. Acetylation of H3K9 and H3K27 is carried out by CBP and p300 histone acetyltransferases [39].
Targeted modifications of epigenetics in regulatory regions or in-site recruitment of transcriptional factors is the basis of CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa) approaches. CRISPRi/a approaches rely on dCas proteins linked to functional activating or repressing domains.

3.1. CRISPRi

Current techniques enabling manipulation of gene activity include siRNA/shRNA approaches, which lead to degradation of transcribed mRNAs, and cDNA overexpression approaches. Several important drawbacks limit the application of these techniques. For example, siRNA/shRNA approaches frequently show significant off-target activity [40], and exogenous vectors have limited packaging capacity and can produce only a selected isoform of the gene of interest [41]. The latter may result in both qualitative and quantitative differences between the effects of a single gene isoform and many isoforms expressed from the genome.
Typically, CRISPRi is based on chimeric dCas-X proteins, where X is a repressive Krueppel-associated box (KRAB) domain [42] or enhancer of Zeste homolog 2 (EZH2) [34]. KRAB is a transcriptional repressor of eukaryotic genes; dCas9 molecules carrying KRAB target regulatory regions of genes (promoters or enhancers) [43] and attract histone deacetylases and methyltransferases that add epigenetic marks of inactive heterochromatin H3K9 and H3K27 (or H3K27 for dCas9-EZH2) [34], ultimately blocking mRNA synthesis [42,44].
Both dCas9-KRAB and dCas9-EZH2 affect genes transiently. Sustained suppression of transcription is possible if two systems (dCas9-KRAB/dCas9-EZH2) are combined with dCas DNA methylation systems (dCas9-DNMT3A-3L [34,45,46] or dCas9-SunTag-DNMT3A [10]). Alternatively, a repressive dCas9-KRAB-MeCP2 system can be used, as MeCP2 attracts DNMTs and histone deacetylases independently of KRAB. This combined system has 4-fold higher transcriptional repressor activity than KRAB system alone [47].
Lysine-specific demethylase LSD1 can be used for transcriptional repression as well. Gene silencing by dCas9-LSD1 is based on the demethylating active H3K4Me3 residues followed by H3K27 deacetylation [48]. LSD1-mediated regulation is enhancer-specific [48]. dCas tools fused with LSD1 are used to annotate unknown distal regulatory elements, as LSD1 activity is limited to enhancers. EZH2 and KRAB domains are comparable in repressive efficiency, but KRAB is more widely used and historically is one of the first transcriptional repressors adopted for precise epigenomic modifications.

3.2. CRISPRa

Similarly, CRISPRa approaches take advantage of dCas proteins to recruit activation domains to regulatory genomic elements and induce target gene transcription. First mammalian CRISPR activators were based on chimeric dCas9 proteins fused with p300 [49], p65 or p65 with heatshock factor 1 (HSF1) [50,51], or VP(16)n [50,52,53]. These CRISPRa systems function by acetylating histones in target regions (catalytic subunit of p300 histone acetyltransferase) or directly activate genes by recruiting transcription factors (endogenous transcription factors p65 (a subunit of NFkB) or p65-HSF1).
Another scenario is to utilize dCas to deliver herpesvirus factor VP(16)n (where n is the number of monomers) to regulatory elements, thereby recruiting preinitiation complex factors and activating target gene transcription. Gene activation by a VP16 monomer is very ineffective, so CRISPRa is usually coupled with multimers of VP16 (VP48, VP64, VP160, VP192) [54].
Robust induction of gene transcription by these systems requires multiplex gRNA targeting an extended genomic region. The VPR system relies on dCas9 protein linked to the protein complex VP64-p65-Rta, where Rta is an Epstein–Barr virus transcription factor. A three-component complex, VP64-p65-Rta has considerably higher efficacy than previous CRISPRa systems, potently activating target gene transcription [55,56].
CRISPRa systems can be coupled with affinity-binding technology that enables simultaneous recruitment of multiple domains to the target site. In this setting, different CRISPRa modifications can be introduced. These can modify Cas proteins (e.g., with SunTag technology), targeting gRNAs (e.g., Scaffold, Casilio), or both (SAM, TREE). These modified CRISPRa systems demonstrate high efficacy even with a single gRNA [57]. SunTag was described above; briefly, Cas9 is fused to a GCN4 peptide array that attracts scFv-linked pro-activation domains (VP64, p65-HSF1, p300, and others) [58,59] (Figure 2). Scaffold operates on a different principle; modified gRNAs carry aptamer sequences (MS2, PP7, or com) and attract aptamer-specific proteins (MCP, PCP, Com) fused to transcriptional activators [60] (Figure 2). The Casilio system is an upgrade of Scaffold and introduces the shorter Casilio aptamers into gRNA, improving gRNA stability and potency [61]. SAM combines dCas9 proteins linked to transcriptional activators and Scaffold technology with modified gRNAs, thus enabling transcriptional regulation both by transcriptional domains linked to dCas9 and domains recruited by gRNA aptamers [51,60] (Figure 2). The TREE system combines SunTag and Scaffold, enabling recruitment of up to 32 molecules of VP64 or p65-HSF1 [62].
Another implementation of CRISPRa is drawing an active cytomegalovirus (CMV) promoter to the target to activate regulatory regions of genes. This modification of CRISPRa is based on a hybrid gRNA molecule linked to a double-stranded DNA of the CMV promoter [63].
CRISPRa technologies are summarized in Table 1.

3.3. Principles of gRNA design for CRISPRa and CRISPRi

An important parameter defining the efficacy of CRISPRa/i systems is the epigenetic state of the target region and baseline gene expression levels. The following criteria should be taken into account when designing CRISPRa/i systems.

3.3.1. CRISPRi

  • Target region. CRISPRi approaches should primarily target proximal promoters or enhancers. gRNAs targeting promoters should be designed to anneal at –50 to +300 nt from transcription start site. Highest efficacy has been demonstrated for gRNAs targeting +50 to +100 nt [64]. Transcription start sites can be visualized using FANTOM5 [65] or GeneHancer databases [43].
  • Epigenetic state. The most effective binding of dCas proteins occurs in areas of open chromatin determined by peaks of DNase I sensitivity [66]. Moreover, effective interference is observed when using gRNAs targeting sites enriched with marks of active chromatin (H3K27Ac, H3K9Ac, H3K4Me3, H3K4Me2, H3K79Me2) [67]. Epigenetic marks and sites of DNase I hypersensitivity can be monitored using ENCODE database [14].

3.3.2. CRISPRa

  • Target region. CRISPRa should target proximal promoters, or, for some systems (e.g., dCas9-p300), distal enhancers. CRISPRa gRNAs to promoters should be designed to interact within −400 to −50 nt from the transcription start site [49].
  • Epigenetic state. The most effective activation of genes occurs when CRISPRa are recruited to the sites of DNase I hypersensitivity [66].
Design of gRNAs for many CRISPR application is made convenient by various online resources (reviewed in [68]).

3.4. Applications of CRISPRa/i

CRISPRa and CRISPRi are increasingly used in biological studies and in development of potential medications. These approaches have been extraordinarily effective for treating metabolic disorders and diseases associated with gene malfunction, including diabetes mellitus, Duchenne muscular dystrophy (DMD), and haploinsufficiency-related disorders, as well as for generating and differentiating stem cells.
As KRAB and p300 can repress and activate enhancers, these domains are used for dCas9-based screening of proposed distal regulatory elements and enhancers bound by transcriptional factors [69,70].
Shortly after they were described, CRISPRa approaches were used to activate endogenous antiviral factors APOBEC3B and APOBEC3G to combat human immunodeficiency virus (HIV) infection [71]. Another possible CRISPRa-based strategy to combat HIV infection is a “shock-and-kill” approach, which works by reactivating latent HIV using CRISPRa followed by death of infected cells by direct cytotoxic effects of the viral proteins or by immune clearance [72,73,74,75]. Most recently, a CRISPRa approach based on many CRISPRa systems was employed to activate APOBECs and destroy hepatitis B virus (HBV) genomes [76]; this method can be used in combination with Cas9 nucleases to treat HBV [77,78]. CRISPRs can also be used to identify new antiviral factors in cell models [79].
CRISPRa can help treat haploinsufficiency-induced diseases by overexpressing an intact copy of the insufficient gene. This method has been utilized in vivo to treat obesity [80] and Dravet syndrome [81].
CRISPR-mediated transcriptional regulation has great potential in oncology. CRISPRi approaches have been used to repress proto-oncogenes like Granulin [82], while activating tumor suppressors PTEN, DKK3, or CHEK2 greatly suppresses proliferation of cancer cells [83,84,85]. A recently described MAEGI approach overexpresses tumor antigens by CRISPRa to increase their presentation to the immune system resulting in efficient destruction of tumor cells by cytotoxic CD8+ lymphocytes [86].
The most important examples of CRISPRa/i applications are summarized in Table 2.

4. Analyzing Factors Involved in Chromatin Remodeling

Chromatin structure in regulatory regions determines transcriptional activity of genes. Chromatin state is regulated by complex interactions between DNA, transcription factors, and associated RNAs. Identifying factors implicated in these interactions is important for understanding fundamental aspects of transcriptional regulation.
Typically, factors interacting with chromatin at a given site are studied using the chromatin immunoprecipitation (ChIP) technique and its modifications. However, ChIP has several drawbacks. It can be used to capture pre-defined transcription factors using specific antibodies but identifying new proteins and RNAs is difficult. Moreover, antibodies to pre-defined factors are limited and very expensive. Therefore, new techniques are needed to identify interacting partners of chromatin-remodeling complexes.

4.1. dCas Technology for Analyzing Chromatin-Remodeling Factors

In recent years, three dCas-based methods (CAPTURE, CasID, and CASPEX) were devised to identify previously unknown factors interacting with chromatin by means of high-affinity extraction and proteomics.
In the CAPTURE method, a dCas9 protein contains a site for a biotintransferase enzyme that is added to cells to transfer biotin markers onto dCas9. Biotinylated dCas9 proteins complexed with adjacent proteins at the site of interest are then isolated using avidin-streptavidin interactions [117]. CasID technology relies on biotinylation of all proteins in the vicinity of a chimeric dCas9 protein linked to biotintransferase [118]. In both CAPTURE and in CasID methods, biotinylated proteins are isolated using avidin-streptavidin and assayed qualitatively and quantitatively by proteomics (high-performance liquid chromatography-mass spectrometry and Western blotting), whereas RNA and DNA interacting with the site of interest can be identified by next-generation sequencing [117,118]
CAPTURE and CasID require long incubation steps (several hours) to generate biotinylated targets, and thus cannot be used to analyze dynamic processes of chromatin remodeling, epigenetic modifications in response to exogenous stimuli, cell differentiation, and cell cycling, among others. This limitation can be overcome by using the CASPEX techniques (C-BERST and GLoPro protocols), in which a dCas9 protein linked to APEX2 peroxidase is added to cells with a reaction mixture composed of hydrogen peroxide and biotin-phenol. APEX2 induces oxidation of biotin-phenol, generating short-lived, highly reactive free radicals that biotinylate all factors in the direct vicinity of dCas9-APEX2 binding (±400 nt), which can be further analyzed by proteomics or sequencing [119,120]. CASPEX can be used to analyze transient chromatin interactions but should be tightly controlled by regulating intracellular concentrations of dCas9-APEX2 to avoid labeling off-target proteins. dCas9-APEX2 levels can be regulated by binding dCas9 to degrading domains FKBP and L106P and by using inducible promoters [120].

4.2. Principles of gRNA Design for CAPTURE, CasID, and CASPEX Methods

  • Target site. gRNAs should anneal at the most proximal area of the target region, but should not lie at sites bound by transcription factors to avoid impeding interactions between regulatory DNA elements and proteins [117].
  • Off-target interactions. For better consistency, proteome analysis of chromatin architecture should be performed with validated negative controls (cells without dCas9, and cells with dCas9 but without gRNA) and should consider endogenous and non-specific biotinylation [117,118,119,120]. Generating several gRNAs for each site and further comparing the data are strongly recommended to discern factors stably bound to the target region and those with rare and transient interactions [117,118].

5. dCas Systems for Shaping Three-dimensional Chromatin Architecture

Distal regulatory elements are located many thousands of nucleotides from gene promoters but strongly impact gene transcription when drawn close together. Dysregulation of these processes results in aberrant gene expression and is frequently observed in human diseases, including cancer [121]. Identifying distal regulatory elements and elucidating their function is necessary to understand many physiological and pathological processes and is critical in drug design.
The CLOuD9 method involves interactions between two orthologous dCas9 proteins fused to dimerizing domains PYL1 and ABI1. One dCas9 protein interacts with a distal region, while the other targets the promoter of the gene of interest. Adding the inducer (abscisic acid) promotes dimerization of dCas9 proteins carrying dimerization domains and interaction of the bound chromosomal regions. CLOuD9 can be devised to directly manipulate the 3D architecture of chromatin, analyze distal regulatory regions, and install new intra- and inter-chromosomal links [122].
Optimal sites of gRNA targeting for CLOuD9 can be selected in genomic browsers with the described considerations for distal regulatory regions annotated in FANTOM5 [65] and GeneHancer [43] databases.

6. Editing Nucleic Acids

Many hereditary diseases, cancers, and mutations resulting in drug resistance are associated with single-nucleotide polymorphisms (SNPs) [123]. Editing SNPs by classic nucleolytic Cas proteins and homologous templates depends on complex DNA repair pathways and appears to be inefficient [124]. Simply correcting SNPs has become possible with dCas systems fused to nucleic acid editing factors.

6.1. DNA Editing Using dCas Tools

Base-editing systems leverage dCas proteins fused to cytidine or adenosine deaminases. Cytidine deaminases convert cytosine to uracil (C→U) with the resulting U•G mismatches being resolved by DNA repair machinery to form thymine (C→T) on the target strand and guanine (G→A) on the complementary strand [125,126], while adenosine deaminases deaminate A yielding inosine (I). I preferentially base-pairs with cytidine in the context of a polymerase active site; in the third position of tRNAs, anticodon I base-pairs with either A, U, or C during mRNA translation [127]. Recently, evolved adenine base editors able to effectively convert A•T base pairs into G•C base pairs in DNA have been described [128]. rAPOBEC1 [128], APOBEC3A [129], AID, and its homologues [130,131,132,133] are among the most widely used cytidine deaminases. Adenine deamination is done by TadA adenosine deaminase [134].
Efficacy of cytosine base editors may be fairly low due to repair of edited nucleotides by endogenous DNA repair systems like the UNG factor. Improved efficacy of base editing was shown for nickase nCas9 (nCas9) proteins, which have a single mutated nucleolytic domain and one domain with preserved cleavage activity, linked to base-editing factors and co-expressed with UGI, an UNG inhibitor [128]. Blocking UNG by UGI transiently impairs DNA repair so that deaminated nucleotides are not corrected. The nCas9 protein fused to base editing factors deaminates nucleotides at the target DNA strand and at the same time makes single-stranded DNA cuts (nicks) on the complementary strand [128]. Next, a 2–12 nt site adjacent to the nick is incised from the DNA, removing the template for error-free repair of the deaminated nucleotide at the target DNA strand. Combining nCas9 with UGI provides a robust base-editing platform for efficient and site-specific introduction of single-nucleotide mutations [128,133].
Correcting virtually any mutation in any DNA region has become possible by using genetically engineered dCas proteins with optimized (shortened) PAM motifs. Relieving PAM restrictions broadens the range of potential sites edited by base editing factors and provides an opportunity to treat numerous genetic disorders [135,136].
The major consideration when using dCas base editors is potential off-target editing of RNA, as was unexpectedly described for rAPOBEC1, TadA, and APOBEC3A [137,138,139]. Direct mutagenesis of deaminases opens new avenues for improving their activity and making safer analogs of base-editing factors [137,138,139,140].
Among cytidine deaminases, rAPOBEC1 possesses the highest deamination activity [128]. However, cytosine editing in a GC-rich context by rAPOBEC1 is very limited [128]. Optimized rAPOBEC1 base editors evoAPOBEC1-BE4max and evoFERNY overcome this limitation [141]. Additionally, rAPOBEC1 and AID-based editors perform poorly on heavily methylated DNA. An alternative is APOBEC3A enzyme, which is less sensitive to methylated DNA bases and, consequently, can be utilized to target methylated sites [129].

6.2. Editing RNA with dCas Tools

Effector proteins of CRISPR-Cas type VI systems can directly interact with target RNAs independently of PAM, thus allowing deaminases binding to these proteins to edit RNA molecules.
The REPAIR RNA-editing system is based on a dCas13b protein linked to a mutant form of ADAR2, an enzyme that catalyzes adenine deamination in dsRNA (A→I conversion) [142]. Using the method of directed evolution, a new variant of ADAR2 protein with enhanced properties was created to become the basis of the RESCUE system. In addition to adenine deamination, this optimized ADAR2 modifies cytosine nucleotides (C→U conversion) as well [143]. The REPAIR system is rather specific and does not exhibit significant off-target binding or RNA editing [142], with a relatively low number of off-target sites [143].
Compared to DNA editing, RNA editing has several important advantages, including a wider range of potential sites due to PAM-independent functioning of CRISPR-Cas type VI proteins and direct RNA editing by deaminases without the assistance of endogenous repair systems [142]. DNA editing has PAM restrictions so that canonical SpCas9-derived base editors do not ensure targeting of even a quarter of all known pathogenic SNPs. Tethering base editors with orthologous Cas proteins or engineered Cas proteins with modified PAM compatibilities may solve this problem [133,135]. Another advantage of editing RNA instead of DNA is that DNA editing yields indels. Fusing cytosine editors to the bacteriophage Mu-derived GAM or inhibiting UNG reduces the rates of indel formation [133]. Proximal (within 200 bp of the target site), bystander editing executed by deaminase domains, single-stranded DNA and RNA editing by random encountering with deaminases and distal off-target edits related to off-target binding of Cas proteins is an important issue and an area of active investigation. Developing mutated deaminases with context-dependent activity and modified editing windows coupled with more specific Cas proteins has the potential to reduce off-target mutations. In particular, REPAIRv2 system had a 900-fold reduction in off-target editing, but at the expense of on-target editing efficiency (almost 2-fold decline) [142]. Improvements in CRISPR-Cas components and base editing enzymes will pave the way for developing safer and more accurate tools, but it is unlikely that the off-target mutagenesis can be avoided completely. A detailed review of base editors was provided in a brilliant paper by H. Rees and D. Liu [127].

6.3. Applications of dCas Base Editors

Site-specific base editors can be used to correct mutations associated with a disease phenotype or to introduce mutations to block or modify gene function.
Precise base editing using CRISPR-STOP and iSTOP has become a novel tool to knock out genes by editing four potential triplets of nucleotides to generate stop codons. Introducing stop codons into the early exons of genes leads to synthesis of short, non-functional mutant proteins [144,145]. Unprecedented efficacy was observed when using iSTOP technology coupled with the SunTag system [146]. CRISPR-STOP and iSTOP do not rely on nucleolytic cleavage of DNA and DSB generation, and thus can be considered a safer alternative for knocking out genes compared to canonic CRISPR-Cas cleavage tools. The database for iSTOP includes 3.4 million gRNAs targeting 97%–99% of genes in eight eukaryotic species [145]. These methods have been immensely effective in massive loss-of-function screens, in developing disease models, and as perspective therapeutic tools.
dCas base editors were used to create in vitro and in vivo models of different diseases associated with SNPs. For instance, base editors were used to create cell lines resistant to chemotherapeutic drugs [130,132,147] and models of hereditary diseases (including DMD, X-linked dilated cardiomyopathy, and albinism) [134,148,149,150,151,152] and chronic diseases [153], as well as to analyze SNPs associated with malignant cell transformation and cancer [147].
Base editors proved useful for creating new therapeutic approaches. dCas-guided base editors corrected SNPs linked to hereditary diseases, such as thalassemia [154,155], Marfan syndrome [156], and phenylketonuria [157]. Introducing inactivating mutations into the Pcsk9 gene was proposed as a therapeutic approach to treat atherosclerosis [158,159].
Conversely, an approach called CRISPR-Pass can be used to correct nonsense mutations by adenosine editors. Editing codons with nonsense mutations using CRISPR-Pass helps to recover production of active proteins. CRISPR-Pass was predicted to correct up to 95% mutations described in ClinVar database [160]. dCas-base editors were patented as antiviral tools capable of introducing mutations into viral genomes to block replication and protein synthesis of viruses including HIV, HBV, human papilloma virus, and Epstein–Barr virus.
CRISPR-SKIP method was created based on dCas base editors to introduce point mutations into splice acceptor sites. Generated mutations result in exon skipping and translation of new protein isoforms with altered properties [161]. This protocol can be used to investigate diseases linked to exon skipping or as therapeutic approaches for diseases like DMD. Most important applications and properties of dCas9-base editors are listed in Table 3 and Table 4, correspondingly.

6.4. Principles of gRNAs Design for Base Editing Applications

dCas base editing systems are characterized by a unique editing window. The majority of systems have a 5-nt window, which may vary if systems undergo modifications or are genetically optimized. Easy-to-use software, including BE-Designer (http://www.rgenome.net/be-designer/) and BE-Analyser (http://www.rgenome.net/be-analyzer), has been created to design gRNAs for base-editing applications [162]. For convenient design of gRNAs for correcting pathogenic T-to-C single nucleotide variations, BEable-GPS database (http://www.picb.ac.cn/rnomics/BEable-GPS) has been recently created. BEable-GPS enables design of gRNA for specific applications for almost every existing base editor system [163].
Importantly, designing gRNAs with a mismatched nucleotide complementary to the nucleotide mutated in the target (generated after base editing) increases efficacy of RNA editing by dCas13b-ADAR2 [142].

7. Conclusions and Perspectives

Many CRISPR-Cas systems have been employed by researchers to introduce various modifications into living organisms. Bioinformatics will allow discovery of numerous other systems in the coming years. Characterizing CRISPR-Cas and related systems has become the most important area of research in biology.
Genetic engineering has enabled directed modifications of CRISPR components to create optimized and highly effective gene editing approaches. Some of these tools were reviewed in this paper; many recent achievements, however, are outside the scope of this manuscript, the most notable of which includes creation of a multiplexed Cas12a-based approach for simultaneously modulating many genes to control complex biological processes with unprecedented accuracy. This multiplexing is done by a Cas12a protein that cuts a single RNA transcript into many gRNAs targeting individual targets [164]. Two groups have recently created a revolutionary approach based on dCas linked to transposases enabling highly efficient on-target integration of DNA sequences. Integrating desired DNA into the target site is a basic need of gene engineering necessary to meet many scientific and technological challenges [165,166].
New avenues in epitranscriptomics were opened by dCas tools empowered by m6A marker “writers” and m6A “erasers,” which modify the epigenetic state of RNA [167].
Visualizing target DNA and RNA sequences is another extraordinary achievement made possible by combining dCas with fluorescent proteins for in vitro and in vivo microscopy [168,169,170]. Creating cellular recorders with dCas systems like CAMERA [171] and DOMINO [172] allows writing incoming events and their parameters for detailed investigations of signaling cascades and other biological processes. CRISPR-driven evolution [132], CRISPR-diagnostics [173,174], CRISPR-biosensors [175], and many more create the foundations for the new technological era.
The utility of new CRISPR-Cas-based methods and therapeutics could be undermined by the potential off-target activity of Cas proteins, i.e., unintended binding/cutting at undesired sites. Off-target cleavage/binding may severely compromise the utility of CRISPR-Cas-based therapeutics, disrupting genes, introducing large mutations [176] and contributing to chromosome instability [177]. To reduce or even avoid potential off-target activity, a plethora of technical refinements has been made, including (a) advanced design of gRNAs using in situ CRISPR-Cas design tools (CRISPR design, E-Crisp, CROP-IT, Cas-OFFinder) [178], (b) modifications of gRNAs (truncations [179], introduction of secondary structures [180] etc.), (c) rationally engineered SpCas9 variants (eSpCas9 [181], Sp-HF1 [182], evoCas9 [183], Hypa-Cas9 [184]) with limited non-specific cleavage and off-target activity, (d) Cas proteins with altered PAM-specificity [185], (e) orthologous CRISPR-Cas systems [77,186,187], (f) engineered dCas proteins fused with FokI nuclease [188], and (g) delivery of CRISPR-Cas components as short-lived ribonucleoprotein complexes [189]. These technical achievements have minimized, but still not completely erased, potential off-target activity. Notably, off-target activity seems to be the most important issue for gene editing applications, e.g., by means of base editors (see above) or CRISPR-Cas nucleases. In contrast, off-target activity of CRISPR-Cas for epigenetic modifications does not appear to be a matter of significant concern. Off-target epigenetic modifications are usually transient and do not have an effect on transcriptional activity of off-target genes. In particular, Matharu et al. did not observe either off-target epigenetic modifications or non-target alterations in mRNA levels in mice stably transduced with AAV-CRISPRa [80]. Carefully designed gRNAs with the minimal number of predicted off-target sites and a highly specific Cas protein (rationally designed or orthologous with restrictive PAM) dramatically reduce the chances of undesired genome/epigenome modifications at non-specific loci.
To conclude, the field of CRISPR is currently evolving at a furious pace. Many years of research in biology, physics, and chemistry have been poured into the new CRISPR tools, allowing previously unfeasible biological manipulations and interventions. On the shoulders of giants, the CRISPR field is growing into one of the most powerful molecular tools. However, several major barriers stand before the CRISPR-Cas field, and the way these barriers are overcome will define the broadness and applicability of gene editing.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare that they have no potential conflicts of interests.

References

  1. Jinek, M.; Chylinski, K.; Fonfara, I.; Hauer, M.; Doudna, J.A.; Charpentier, E. A Programmable Dual-RNA–Guided DNA Endonuclease in Adaptive Bacterial Immunity. Science 2012, 337, 816–821. [Google Scholar] [CrossRef] [PubMed]
  2. Qi, L.S.; Larson, M.H.; Gilbert, L.A.; Doudna, J.A.; Weissman, J.S.; Arkin, A.P.; Lim, W.A. Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell 2013, 152, 1173–1183. [Google Scholar] [CrossRef] [PubMed]
  3. Jaenisch, R.; Bird, A. Epigenetic regulation of gene expression: How the genome integrates intrinsic and environmental signals. Nat. Genet. 2003, 33, 245–254. [Google Scholar] [CrossRef] [PubMed]
  4. Chedin, F.; Lieber, M.R.; Hsieh, C.L. The DNA methyltransferase-like protein DNMT3L stimulates de novo methylation by Dnmt3a. Proc. Natl. Acad. Sci. USA 2002, 99, 16916–16921. [Google Scholar] [CrossRef]
  5. Guo, J.U.; Su, Y.; Zhong, C.; Ming, G.; Song, H. Hydroxylation of 5-Methylcytosine by TET1 Promotes Active DNA Demethylation in the Adult Brain. Cell 2011, 145, 423–434. [Google Scholar] [CrossRef]
  6. Vojta, A.; Dobrinic, P.; Tadic, V.; Bockor, L.; Korac, P.; Julg, B.; Klasic, M.; Zoldos, V. Repurposing the CRISPR-Cas9 system for targeted DNA methylation. Nucleic Acids Res. 2016, 44, 5615–5628. [Google Scholar] [CrossRef]
  7. McDonald, J.I.; Celik, H.; Rois, L.E.; Fishberger, G.; Fowler, T.; Rees, R.; Kramer, A.; Martens, A.; Edwards, J.R.; Challen, G.A. Reprogrammable CRISPR/Cas9-based system for inducing site-specific DNA methylation. Biol. Open 2016, 5, 866–874. [Google Scholar] [CrossRef]
  8. Liu, X.S.; Wu, H.; Ji, X.; Stelzer, Y.; Wu, X.; Czauderna, S.; Shu, J.; Dadon, D.; Young, R.A.; Jaenisch, R. Editing DNA methylation in the mammalian genome. Cell 2016, 167, 233–247. [Google Scholar] [CrossRef]
  9. Stepper, P.; Kungulovski, G.; Jurkowska, R.Z.; Chandra, T.; Krueger, F.; Reinhardt, R.; Reik, W.; Jeltsch, A.; Jurkowski, T.P. Efficient targeted DNA methylation with chimeric dCas9–Dnmt3a–Dnmt3L methyltransferase. Nucleic Acids Res. 2016, 45, 1703–1713. [Google Scholar] [CrossRef]
  10. Huang, Y.-H.; Su, J.; Lei, Y.; Brunetti, L.; Gundry, M.C.; Zhang, X.; Jeong, M.; Li, W.; Goodell, M.A. DNA epigenome editing using CRISPR-Cas SunTag-directed DNMT3A. Genome Biol. 2017, 18, 176. [Google Scholar] [CrossRef]
  11. Choudhury, S.R.; Cui, Y.; Lubecka, K.; Stefanska, B.; Irudayaraj, J. CRISPR-dCas9 mediated TET1 targeting for selective DNA demethylation at BRCA1 promoter. Oncotarget 2016, 7, 46545. [Google Scholar] [CrossRef] [PubMed]
  12. Morita, S.; Noguchi, H.; Horii, T.; Nakabayashi, K.; Kimura, M.; Okamura, K.; Sakai, A.; Nakashitna, H.; Hata, K.; Nakashima, K.; et al. Targeted DNA demethylation in vivo using dCas9-peptide repeat and scFv-TET1 catalytic domain fusions. Nat. Biotechnol. 2016, 34, 1060–1065. [Google Scholar] [CrossRef] [PubMed]
  13. Xu, X.; Tao, Y.; Gao, X.; Zhang, L.; Li, X.; Zou, W.; Ruan, K.; Wang, F.; Xu, G.; Hu, R. A CRISPR-based approach for targeted DNA demethylation. Cell Discov. 2016, 2, 16009. [Google Scholar] [CrossRef] [PubMed]
  14. Dunham, I.; Kundaje, A.; Aldred, S.F.; Collins, P.J.; Davis, C.; Doyle, F.; Epstein, C.B.; Frietze, S.; Harrow, J.; Kaul, R.; et al. An integrated encyclopedia of DNA elements in the human genome. Nature 2012, 489, 57–74. [Google Scholar]
  15. Song, Q.; Decato, B.; Hong, E.E.; Zhou, M.; Fang, F.; Qu, J.; Garvin, T.; Kessler, M.; Zhou, J.; Smith, A.D. A Reference Methylome Database and Analysis Pipeline to Facilitate Integrative and Comparative Epigenomics. PLoS ONE 2013, 8, e81148. [Google Scholar] [CrossRef]
  16. Qu, J.; Zhu, L.; Zhou, Z.; Chen, P.; Liu, S.; Locy, M.L.; Thannickal, V.J.; Zhou, Y. Reversing Mechanoinductive DSP Expression by CRISPR/dCas9-mediated Epigenome Editing. Am. J. Respir. Crit. Care Med. 2018, 198, 599–609. [Google Scholar] [CrossRef]
  17. Mkannez, G.; Gagne-Ouellet, V.; Nsaibia, M.J.; Boulanger, M.-C.; Rosa, M.; Argaud, D.; Hadji, F.; Gaudreault, N.; Rheaume, G.; Bouchard, L.; et al. DNA methylation of a PLPP3 MIR transposon-based enhancer promotes an osteogenic programme in calcific aortic valve disease. Cardiovasc. Res. 2018, 114, 1525–1535. [Google Scholar] [CrossRef]
  18. Park, J.; Guan, Y.; Sheng, X.; Gluck, C.; Seasock, M.J.; Hakimi, A.A.; Qiu, C.; Pullman, J.; Verma, A.; Li, H.; et al. Functional methylome analysis of human diabetic kidney disease. JCI Insight 2019. [Google Scholar] [CrossRef]
  19. Saunderson, E.A.; Stepper, P.; Gomm, J.J.; Hoa, L.; Morgan, A.; Allen, M.D.; Jones, J.L.; Gribben, J.G.; Jurkowski, T.P.; Ficz, G. Hit-and-run epigenetic editing prevents senescence entry in primary breast cells from healthy donors. Nat. Commun. 2017, 8, 1450. [Google Scholar] [CrossRef]
  20. Wu, J.; He, K.; Zhang, Y.; Song, J.; Shi, Z.; Chen, W.; Shao, Y. Inactivation of SMARCA2 by promoter hypermethylation drives lung cancer development. Gene 2019, 687, 193–199. [Google Scholar] [CrossRef]
  21. Tong, Y.; Sun, J.; Wong, C.F.; Kang, Q.; Ru, B.; Wong, C.N.; Chan, A.S.; Leung, S.Y.; Zhang, J. MICMIC: Identification of DNA methylation of distal regulatory regions with causal effects on tumorigenesis. Genome Biol. 2018, 19, 73. [Google Scholar] [CrossRef] [PubMed]
  22. Schuijers, J.; Manteiga, J.C.; Weintraub, A.S.; Day, D.S.; Zamudio, A.V.; Hnisz, D.; Lee, T.I.; Young, R.A. Transcriptional Dysregulation of MYC Reveals Common Enhancer-Docking Mechanism. Cell Rep. 2018, 23, 349–360. [Google Scholar] [CrossRef] [PubMed]
  23. Wang, Q.; Dai, L.; Wang, Y.; Deng, J.; Lin, Y.; Wang, Q.; Fang, C.; Ma, Z.; Wang, H.; Shi, G.; et al. Targeted demethylation of the SARI promotor impairs colon tumour growth. Cancer Lett. 2019, 448, 132–143. [Google Scholar] [CrossRef] [PubMed]
  24. Liu, X.S.; Wu, H.; Krzisch, M.; Wu, X.; Graef, J.; Muffat, J.; Hnisz, D.; Li, C.H.; Yuan, B.; Xu, C.; et al. Rescue of Fragile X Syndrome Neurons by DNA Methylation Editing of the FMR1 Gene. Cell 2018, 172, 979. [Google Scholar] [CrossRef] [PubMed]
  25. Kantor, B.; Tagliafierro, L.; Gu, J.; Zamora, M.E.; Ilich, E.; Grenier, C.; Huang, Z.Y.; Murphy, S.; Chiba-Falek, O. Downregulation of SNCA Expression by Targeted Editing of DNA Methylation: A Potential Strategy for Precision Therapy in PD. Mol. Ther. 2018, 26, 2638–2649. [Google Scholar] [CrossRef]
  26. Baumann, V.; Wiesbeck, M.; Breunig, C.T.; Braun, J.M.; Koeferle, A.; Ninkovic, J.; Goetz, M.; Stricker, S.H. Targeted removal of epigenetic barriers during transcriptional reprogramming. Nat. Commun. 2019, 10, 2119. [Google Scholar] [CrossRef] [PubMed]
  27. Barski, A.; Cuddapah, S.; Cui, K.; Roh, T.-Y.; Schones, D.E.; Wang, Z.; Wei, G.; Chepelev, I.; Zhao, K. High-resolution profiling of histone methylations in the human genome. Cell 2007, 129, 823–837. [Google Scholar] [CrossRef]
  28. Shinkai, Y.; Tachibana, M. H3K9 methyltransferase G9a and the related molecule GLP. Genes Dev. 2011, 25, 781–788. [Google Scholar] [CrossRef]
  29. Rea, S.; Eisenhaber, F.; O’Carroll, N.; Strahl, B.D.; Sun, Z.W.; Schmid, M.; Opravil, S.; Mechtler, K.; Ponting, C.P.; Allis, C.D.; et al. Regulation of chromatin structure by site-specific histone H3 methyltransferases. Nature 2000, 406, 593–599. [Google Scholar] [CrossRef]
  30. Cao, R.; Zhang, Y. SUZ12 is required for both the histone methyltransferase activity and the silencing function of the EED-EZH2 complex. Mol. Cell 2004, 15, 57–67. [Google Scholar] [CrossRef]
  31. Vire, E.; Brenner, C.; Deplus, R.; Blanchon, L.; Fraga, M.; Didelot, C.; Morey, L.; Van Eynde, A.; Bernard, D.; Vanderwinden, J.M.; et al. The Polycomb group protein EZH2 directly controls DNA methylation. Nature 2006, 439, 871–874. [Google Scholar] [CrossRef] [PubMed]
  32. Fuks, F.; Hurd, P.J.; Deplus, R.; Kouzarides, T. The DNA methyltransferases associate with HP1 and the SUV39H1 histone methyltransferase. Nucleic Acids Res. 2003, 31, 2305–2312. [Google Scholar] [CrossRef] [PubMed]
  33. Lachner, M.; O’Carroll, N.; Rea, S.; Mechtler, K.; Jenuwein, T. Methylation of histone H3 lysine 9 creates a binding site for HP1 proteins. Nature 2001, 410, 116–120. [Google Scholar] [CrossRef] [PubMed]
  34. O’Geen, H.; Bates, S.L.; Carter, S.S.; Nisson, K.A.; Halmai, J.; Fink, K.D.; Rhie, S.K.; Farnham, P.J.; Segal, D.J. Ezh2-dCas9 and KRAB-dCas9 enable engineering of epigenetic memory in a context-dependent manner. Epigenet. Chromatin 2019, 12, 26. [Google Scholar] [CrossRef] [PubMed]
  35. Schneider, R.; Bannister, A.J.; Myers, F.A.; Thorne, A.W.; Crane-Robinson, C.; Kouzarides, T. Histone H3 lysine 4 methylation patterns in higher eukaryotic genes. Nat. Cell Biol. 2004, 6, 73–77. [Google Scholar] [CrossRef] [PubMed]
  36. Creyghton, M.P.; Cheng, A.W.; Welstead, G.G.; Kooistra, T.; Carey, B.W.; Steine, E.J.; Hanna, J.; Lodato, M.A.; Frampton, G.M.; Sharp, P.A.; et al. Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proc. Natl. Acad. Sci. USA 2010, 107, 21931–21936. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Steger, D.J.; Lefterova, M.I.; Ying, L.; Stonestrom, A.J.; Schupp, M.; Zhuo, D.; Vakoc, A.L.; Kim, J.-E.; Chen, J.; Lazar, M.A.; et al. DOT1L/KMT4 recruitment and H3K79 methylation are ubiquitously coupled with gene transcription in mammalian cells. Mol. Cell Biol. 2008, 28, 2825–2839. [Google Scholar] [CrossRef] [Green Version]
  38. Shi, Y.J.; Lan, F.; Matson, C.; Mulligan, P.; Whetstine, J.R.; Cole, P.A.; Casero, R.A.; Shi, Y. Histone demethylation mediated by the nuclear arnine oxidase homolog LSD1. Cell 2004, 119, 941–953. [Google Scholar] [CrossRef] [Green Version]
  39. Pasini, D.; Malatesta, M.; Jung, H.R.; Walfridsson, J.; Willer, A.; Olsson, L.; Skotte, J.; Wutz, A.; Porse, B.; Jensen, O.N.; et al. Characterization of an antagonistic switch between histone H3 lysine 27 methylation and acetylation in the transcriptional regulation of Polycomb group target genes. Nucleic Acids Res. 2010, 38, 4958–4969. [Google Scholar] [CrossRef]
  40. Jackson, A.L.; Bartz, S.R.; Schelter, J.; Kobayashi, S.V.; Burchard, J.; Mao, M.; Li, B.; Cavet, G.; Linsley, P.S. Expression profiling reveals off-target gene regulation by RNAi. Nat. Biotechnol. 2003, 21, 635–637. [Google Scholar] [CrossRef]
  41. Kampmann, M. CRISPRi and CRISPRa Screens in Mammalian Cells for Precision Biology and Medicine. ACS Chem. Biol. 2018, 13, 406–416. [Google Scholar] [CrossRef] [PubMed]
  42. Friedman, J.R.; Fredericks, W.J.; Jensen, D.E.; Speicher, D.W.; Huang, X.P.; Neilson, E.G.; Rauscher, F.J. KAP-1, a novel corepressor for the highly conserved KRAB repression domain. Genes Dev. 1996, 10, 2067–2078. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Thakore, P.I.; D’ippolito, A.M.; Song, L.; Safi, A.; Shivakumar, N.K.; Kabadi, A.M.; Reddy, T.E.; Crawford, G.E.; Gersbach, C.A. Highly specific epigenome editing by CRISPR-Cas9 repressors for silencing of distal regulatory elements. Nat. Methods 2015, 12, 1143. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Schultz, D.C.; Friedman, J.R.; Rauscher, F.J. Targeting histone deacetylase complexes via KRAB-zinc finger proteins: The PHD and bromodomains of KAP-1 form a cooperative unit that recruits a novel isoform of the Mi-2 alpha subunit of NuRD. Genes Dev. 2001, 15, 428–443. [Google Scholar] [CrossRef] [Green Version]
  45. O’Geen, H.; Ren, C.; Nicolet, C.M.; Perez, A.A.; Halmai, J.; Le, V.M.; Mackay, J.P.; Farnham, P.J.; Segal, D.J. dCas9-based epigenome editing suggests acquisition of histone methylation is not sufficient for target gene repression. Nucleic Acids Res. 2017, 45, 9901–9916. [Google Scholar] [CrossRef] [Green Version]
  46. Amabile, A.; Migliara, A.; Capasso, P.; Biffi, M.; Cittaro, D.; Naldini, L.; Lombardo, A. Inheritable silencing of endogenous genes by hit-and-run targeted epigenetic editing. Cell 2016, 167, 219–232. [Google Scholar] [CrossRef] [Green Version]
  47. Yeo, N.C.; Chavez, A.; Lance-Byrne, A.; Chan, Y.; Menn, D.; Milanova, D.; Kuo, C.-C.; Guo, X.; Sharma, S.; Tung, A.; et al. An enhanced CRISPR repressor for targeted mammalian gene regulation. Nat. Methods 2018, 15, 611. [Google Scholar] [CrossRef]
  48. Kearns, N.A.; Pham, H.; Tabak, B.; Genga, R.M.; Silverstein, N.J.; Garber, M.; Maehr, R. Functional annotation of native enhancers with a Cas9-histone demethylase fusion. Nat. Methods 2015, 12, 401–403. [Google Scholar] [CrossRef] [Green Version]
  49. Hilton, I.B.; Vockley, C.M.; Pratiksha, I.; Crawford, G.E.; Reddy, T.E.; Gersbach, C.A. CRISPR Acetyltransferase Activates Genes From Promoters and Enhancers. Nat. Biotechnol. 2015, 33, 510–517. [Google Scholar] [CrossRef] [Green Version]
  50. Gilbert, L.A.; Larson, M.H.; Morsut, L.; Liu, Z.; Brar, G.A.; Torres, S.E.; Stern-Ginossar, N.; Brandman, O.; Whitehead, E.H.; Doudna, J.A. CRISPR-mediated modular RNA-guided regulation of transcription in eukaryotes. Cell 2013, 154, 442–451. [Google Scholar] [CrossRef] [Green Version]
  51. Konermann, S.; Brigham, M.D.; Trevino, A.E.; Joung, J.; Abudayyeh, O.O.; Barcena, C.; Hsu, P.D.; Habib, N.; Gootenberg, J.S.; Nishimasu, H. Genome-scale transcriptional activation by an engineered CRISPR-Cas9 complex. Nature 2015, 517, 583. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Perez-Pinera, P.; Kocak, D.D.; Vockley, C.M.; Adler, A.F.; Kabadi, A.M.; Polstein, L.R.; Thakore, P.I.; Glass, K.A.; Ousterout, D.G.; Leong, K.W.; et al. RNA-guided gene activation by CRISPR-Cas9-based transcription factors. Nat. Methods 2013, 10, 973. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Maeder, M.L.; Linder, S.J.; Cascio, V.M.; Fu, Y.; Ho, Q.H.; Joung, J.K. CRISPR RNA—guided activation of endogenous human genes. Nat. Methods 2013, 10, 977. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Cheng, A.W.; Wang, H.; Yang, H.; Shi, L.; Katz, Y.; Theunissen, T.W.; Rangarajan, S.; Shivalila, C.S.; Dadon, D.B.; Jaenisch, R. Multiplexed activation of endogenous genes by CRISPR-on, an RNA-guided transcriptional activator system. Cell Res. 2013, 23, 1163. [Google Scholar] [CrossRef] [PubMed]
  55. Chavez, A.; Scheiman, J.; Vora, S.; Pruitt, B.W.; Tuttle, M.; Iyer, E.P.R.; Lin, S.; Kiani, S.; Guzman, C.D.; Wiegand, D.J. Highly efficient Cas9-mediated transcriptional programming. Nat. Methods 2015, 12, 326. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Fang, L.; Hung, S.S.C.; Yek, J.; El Wazan, L.; Nguyen, T.; Khan, S.; Lim, S.Y.; Hewitt, A.W.; Wong, R.C.B. A Simple Cloning-free Method to Efficiently Induce Gene Expression Using CRISPR/Cas9. Mol. Ther. 2019, 14, 184–191. [Google Scholar] [CrossRef] [Green Version]
  57. Chavez, A.; Tuttle, M.; Pruitt, B.W.; Ewen-Campen, B.; Chari, R.; Ter-Ovanesyan, D.; Haque, S.J.; Cecchi, R.J.; Kowal, E.J.K.; Buchthal, J. Comparison of Cas9 activators in multiple species. Nat. Methods 2016, 13, 563. [Google Scholar] [CrossRef] [Green Version]
  58. Zhou, H.; Liu, J.; Zhou, C.; Gao, N.; Rao, Z.; Li, H.; Hu, X.; Li, C.; Yao, X.; Shen, X.; et al. In vivo simultaneous transcriptional activation of multiple genes in the brain using CRISPR-dCas9-activator transgenic mice. Nat. Neurosci. 2018, 21, 440. [Google Scholar] [CrossRef]
  59. Tanenbaum, M.E.; Gilbert, L.A.; Qi, L.S.; Weissman, J.S.; Vale, R.D. A Protein-Tagging System for Signal Amplification in Gene Expression and Fluorescence Imaging. Cell 2014, 159, 635–646. [Google Scholar] [CrossRef] [Green Version]
  60. Zalatan, J.G.; Lee, M.E.; Almeida, R.; Gilbert, L.A.; Whitehead, E.H.; La Russa, M.; Tsai, J.C.; Weissman, J.S.; Dueber, J.E.; Qi, L.S. Engineering complex synthetic transcriptional programs with CRISPR RNA scaffolds. Cell 2015, 160, 339–350. [Google Scholar] [CrossRef] [Green Version]
  61. Cheng, A.W.; Jillette, N.; Lee, P.; Plaskon, D.; Fujiwara, Y.; Wang, W.; Taghbalout, A.; Wang, H. Casilio: A versatile CRISPR-Cas9-Pumilio hybrid for gene regulation and genomic labeling. Cell Res. 2016, 26, 254–257. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  62. Kunii, A.; Hara, Y.; Takenaga, M.; Hattori, N.; Fukazawa, T.; Ushijima, T.; Yamamoto, T.; Sakuma, T. Three-Component Repurposed Technology for Enhanced Expression: Highly Accumulable Transcriptional Activators via Branched Tag Arrays. CRISPR J. 2018, 1, 337–347. [Google Scholar] [CrossRef] [PubMed]
  63. Xu, X.; Gao, J.; Dai, W.; Wang, D.; Wu, J.; Wang, J. Gene activation by a CRISPR-assisted trans enhancer. Elife 2019, 8, e45973. [Google Scholar] [CrossRef] [PubMed]
  64. Gilbert, L.A.; Horlbeck, M.A.; Adamson, B.; Villalta, J.E.; Chen, Y.; Whitehead, E.H.; Guimaraes, C.; Panning, B.; Ploegh, H.L.; Bassik, M.C.; et al. Genome-Scale CRISPR-Mediated Control of Gene Repression and Activation. Cell 2014, 159, 647–661. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Andersson, R.; Gebhard, C.; Miguel-Escalada, I.; Hoof, I.; Bornholdt, J.; Boyd, M.; Chen, Y.; Zhao, X.; Schmidl, C.; Suzuki, T.; et al. An atlas of active enhancers across human cell types and tissues. Nature 2014, 507, 455. [Google Scholar] [CrossRef] [PubMed]
  66. Kuscu, C.; Arslan, S.; Singh, R.; Thorpe, J.; Adli, M. Genome-wide analysis reveals characteristics of off-target sites bound by the Cas9 endonuclease. Nat. Biotechnol. 2014, 32, 677. [Google Scholar] [CrossRef] [PubMed]
  67. Radzisheuskaya, A.; Shlyueva, D.; Muller, I.; Helin, K. Optimizing sgRNA position markedly improves the efficiency of CRISPR/dCas9-mediated transcriptional repression. Nucleic Acids Res. 2016, 44, e141. [Google Scholar] [CrossRef] [Green Version]
  68. Cui, Y.; Xu, J.; Cheng, M.; Liao, X.; Peng, S. Review of CRISPR/Cas9 sgRNA Design Tools. Interdiscip. Sci. 2018, 10, 455–465. [Google Scholar] [CrossRef]
  69. Gasperini, M.; Hill, A.J.; McFaline-Figueroa, J.L.; Martin, B.; Kim, S.; Zhang, M.D.; Jackson, D.; Leith, A.; Schreiber, J.; Noble, W.S.; et al. A Genome-wide Framework for Mapping Gene Regulation via Cellular Genetic Screens. Cell 2019, 176, 377. [Google Scholar] [CrossRef] [Green Version]
  70. Klann, T.S.; Black, J.B.; Chellappan, M.; Safi, A.; Song, L.; Hilton, I.B.; Crawford, G.E.; Reddy, T.E.; Gersbach, C.A. CRISPR-Cas9 epigenome editing enables high-throughput screening for functional regulatory elements in the human genome. Nat. Biotechnol. 2017, 35, 561. [Google Scholar] [CrossRef]
  71. Bogerd, H.P.; Kornepati, A.V.R.; Marshall, J.B.; Kennedy, E.M.; Cullen, B.R. Specific induction of endogenous viral restriction factors using CRISPR/Cas-derived transcriptional activators. Proc. Natl. Acad. Sci. USA 2015, 112, E7249–E7256. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  72. Saayman, S.M.; Lazar, D.C.; Scott, T.A.; Hart, J.R.; Takahashi, M.; Burnett, J.C.; Planelles, V.; Morris, K.V.; Weinberg, M.S. Potent and Targeted Activation of Latent HIV-1 Using the CRISPR/dCas9 Activator Complex. Mol. Ther. 2016, 24, 488–498. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  73. Limsirichai, P.; Gaj, T.; Schaffer, D.V. CRISPR-mediated Activation of Latent HIV-1 Expression. Mol. Ther. 2016, 24, 499–507. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  74. Zhang, Y.; Yin, C.; Zhang, T.; Li, F.; Yang, W.; Kaminski, R.; Fagan, P.R.; Putatunda, R.; Young, W.-B.; Khalili, K.; et al. CRISPR/gRNA-directed synergistic activation mediator (SAM) induces specific, persistent and robust reactivation of the HIV-1 latent reservoirs. Sci. Rep. 2015, 5, 16277. [Google Scholar] [CrossRef] [PubMed]
  75. Ji, H.; Jiang, Z.; Lu, P.; Ma, L.; Liz, C.; Pan, H.; Fill, Z.; Qui, X.; Wang, P.; Deng, J.; et al. Specific Reactivation of Latent HIV-1 by dCas9-SunTag-VP64-mediated Guide RNA Targeting the HIV-1 Promoter. Mol. Ther. 2016, 24, 508–521. [Google Scholar] [CrossRef] [Green Version]
  76. Kostiushev, D.; Brezgin, S.; Kostyusheva, A.; Zarifyan, D.; Chulanov, V. A novel CRISPR/Cas9-based approach to transient activation of intracellular host restriction factors results in strong suppression of hepatitis B virus and degradation of cccDNA. J. Viral Hepat. 2018, 25, 16–17. [Google Scholar]
  77. Kostyushev, D.; Brezgin, S.; Kostyusheva, A.; Zarifyan, D.; Goptar, I.; Chulanov, V. Orthologous CRISPR/Cas9 systems for specific and efficient degradation of covalently closed circular DNA of hepatitis B virus. Cell Mol. Life Sci. 2019, 76, 1779–1794. [Google Scholar] [CrossRef]
  78. Kostyushev, D.; Kostyusheva, A.; Brezgin, S.; Zarifyan, D.; Utkina, A.; Goptar, I.; Chulanov, V. Suppressing the NHEJ pathway by DNA-PKcs inhibitor NU7026 prevents degradation of HBV cccDNA cleaved by CRISPR/Cas9. Sci. Rep. 2019, 9, 1847. [Google Scholar] [CrossRef] [Green Version]
  79. Orchard, R.; Sullender, M.E.; Dunlap, B.F.; Balce, D.R.; Doench, J.G.; Virgin, H.W. Identification of anti-norovirus genes in mouse and human cells using genome-wide CRISPR activation screening. BioRxiv 2018, 350090. [Google Scholar] [CrossRef] [Green Version]
  80. Matharu, N.; Rattanasopha, S.; Tamura, S.; Maliskova, L.; Wang, Y.; Bernard, A.; Hardin, A.; Eckalbar, W.L.; Vaisse, C.; Ahituv, N. CRISPR-mediated activation of a promoter or enhancer rescues obesity caused by haploinsufficiency. Science 2019, 363, 246. [Google Scholar] [CrossRef]
  81. Colasante, G.; Lignani, G.; Brusco, S.; Di Berardino, C.; Carpenter, J.; Giannelli, S.; Valassina, N.; Bido, S.; Ricci, R.; Castoldi, V. dCas9-based Scn1a gene activation restores inhibitory interneuron excitability and attenuates seizures in Dravet syndrome mice. Mol. Ther. 2019. [Google Scholar] [CrossRef] [PubMed]
  82. Wang, H.; Guo, R.; Du, Z.; Bai, L.; Li, L.; Cui, J.; Li, W.; Hoffman, A.R.; Hu, J.-F. Epigenetic Targeting of Granulin in Hepatoma cells by Synthetic CRISPR dCas9 Epi-suppressors. Mol. Ther. Nucl. Acids 2018, 11, 23–33. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  83. Moses, C.; Nugent, F.; Waryah, C.B.; Garcia-Bloj, B.; Harvey, A.R.; Blancafort, P. Activating PTEN Tumor Suppressor Expression with the CRISPR/dCas9 System. Mol. Ther. Nucl. Acids 2019, 14, 287–300. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  84. Braun, C.J.; Bruno, P.M.; Horlbeck, M.A.; Gilbert, L.A.; Weissman, J.S.; Hemann, M.T. Versatile in vivo regulation of tumor phenotypes by dCas9-mediated transcriptional perturbation. Proc. Natl. Acad. Sci. USA 2016, 113, E3892–E3900. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  85. Kardooni, H.; Gonzalez-Gualda, E.; Stylianakis, E.; Saffaran, S.; Waxman, J.; Kypta, R.M. CRISPR-Mediated Reactivation of DKK3 Expression Attenuates TGF-beta Signaling in Prostate Cancer. Cancers 2018, 10, 165. [Google Scholar] [CrossRef] [Green Version]
  86. Wang, G.; Chow, R.D.; Bai, Z.; Zhu, L.; Errami, Y.; Dai, X.; Dong, M.B.; Ye, L.; Zhang, X.; Renauer, P.A. Multiplexed activation of endogenous genes by CRISPRa elicits potent antitumor immunity. Nat. Immunol. 2019, 20, 1494–1505. [Google Scholar] [CrossRef]
  87. Carleton, J.B.; Berrett, K.C.; Gertz, J. Multiplex Enhancer Interference Reveals Collaborative Control of Gene Regulation by Estrogen Receptor alpha-Bound Enhancers. Cell Syst. 2017, 5, 333. [Google Scholar] [CrossRef] [Green Version]
  88. Zheng, Y.; Shen, W.; Zhang, J.; Yang, B.; Liu, Y.-N.; Qi, H.; Yu, X.; Lul, S.-Y.; Chen, Y.; Xu, Y.-Z.; et al. CRISPR interference-based specific and efficient gene inactivation in the brain. Nat. Neurosci. 2018, 21, 447. [Google Scholar] [CrossRef]
  89. Savell, K.E.; Bach, V.S.; Zipperly, M.E.; Revanna, J.S.; Goska, N.A.; Tuscher, J.J.; Duke, C.G.; Sultan, F.A.; Burke, J.N.; Williams, D.; et al. A Neuron-Optimized CRISPR/dCas9 Activation System for Robust and Specific Gene Regulation. eNeuro 2019, 6. [Google Scholar] [CrossRef] [Green Version]
  90. Baeumler, T.A.; Ahmed, A.A.; Fulga, T.A. Engineering Synthetic Signaling Pathways with Programmable dCas9-Based Chimeric Receptors. Cell Rep. 2017, 20, 2639–2653. [Google Scholar] [CrossRef] [Green Version]
  91. Kipniss, N.H.; Dingal, P.C.D.P.; Abbott, T.R.; Gao, Y.; Wang, H.; Dominguez, A.A.; Labanieh, L.; Qi, L.S. Engineering cell sensing and responses using a GPCR-coupled CRISPR-Cas system. Nat. Commun. 2017, 8, 2212. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  92. 92. Sanson, K.R.; Hanna, R.E.; Hegde, M.; Donovan, K.F.; Strand, C.; Sullender, M.E.; Vaimberg, E.W.; Goodale, A.; Root, D.E.; Piccioni, F.; et al. Optimized libraries for CRISPR-Cas9 genetic screens with multiple modalities. Nat. Commun. 2018, 9, 5416. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  93. Law, C.-T.; Wei, L.; Tsang, F.H.-C.; Chan, C.Y.-K.; Xu, I.M.-J.; Lai, R.K.-H.; Ho, D.W.-H.; Lee, J.M.-F.; Wong, C.C.-L.; Ng, I.O.-L.; et al. HELLS Regulates Chromatin Remodeling and Epigenetic Silencing of Multiple Tumor Suppressor Genes in Human Hepatocellular Carcinoma. Hepatology 2019, 69, 2013–2030. [Google Scholar] [CrossRef]
  94. Bester, A.C.; Lee, J.D.; Chavez, A.; Lee, Y.-R.; Nachmani, D.; Vora, S.; Victor, J.; Sauvageau, M.; Monteleone, E.; Rinn, J.L.; et al. An Integrated Genome-wide CRISPRa Approach to Functionalize lncRNAs in Drug Resistance. Cell 2018, 173, 649. [Google Scholar] [CrossRef] [PubMed]
  95. Joung, J.; Engreitz, J.M.; Konermann, S.; Abudayyeh, O.O.; Verdine, V.K.; Aguet, F.; Gootenberg, J.S.; Sanjana, N.E.; Wright, J.B.; Fulco, C.P. Genome-scale activation screen identifies a lncRNA locus regulating a gene neighbourhood. Nature 2017, 548, 343. [Google Scholar] [CrossRef] [PubMed]
  96. Liu, S.J.; Horlbeck, M.A.; Cho, S.W.; Birk, H.S.; Malatesta, M.; He, D.; Attenello, F.J.; Villalta, J.E.; Cho, M.Y.; Chen, Y. CRISPRi-based genome-scale identification of functional long noncoding RNA loci in human cells. Science 2017, 355, eaah7111. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  97. Wangensteen, K.J.; Wang, Y.J.; Dou, Z.; Wang, A.W.; Mosleh-Shirazi, E.; Horlbeck, M.A.; Gilbert, L.A.; Weissman, J.S.; Berger, S.L.; Kaestner, K.H. Combinatorial genetics in liver repopulation and carcinogenesis with a in vivo CRISPR activation platform. Hepatology 2018, 68, 663–676. [Google Scholar] [CrossRef] [Green Version]
  98. Lou, K.; Steri, V.; Ge, A.Y.; Hwang, Y.C.; Yogodzinski, C.H.; Shkedi, A.R.; Choi, A.L.M.; Mitchell, D.C.; Swaney, D.L.; Hann, B.; et al. KRAS(G12C) inhibition produces a driver-limited state revealing collateral dependencies. Sci. Signal 2019, 12, eaaw9450. [Google Scholar] [CrossRef]
  99. Zhang, Y.; Ozono, S.; Yao, W.; Tobiume, M.; Yamaoka, S.; Kishigami, S.; Fujita, H.; Tokunaga, K. CRISPR-mediated activation of endogenous BST-2/tetherin expression inhibits wild-type HIV-1 production. Sci. Rep. 2019, 9, 3134. [Google Scholar] [CrossRef] [Green Version]
  100. Farhang, N.; Brunger, J.M.; Stover, J.D.; Thakore, P.I.; Lawrence, B.; Guilak, F.; Gersbach, C.A.; Setton, L.A.; Bowles, R.D. CRISPR-Based Epigenome Editing of Cytokine Receptors for the Promotion of Cell Survival and Tissue Deposition in Inflammatory Environments. Tissue Eng. Part A 2017, 23, 738–749. [Google Scholar] [CrossRef]
  101. Farhang, N.; Ginley-Hidinger, M.; Berrett, K.C.; Gertz, J.; Lawrence, B.; Bowles, R.D. Lentiviral CRISPR Epigenome Editing of Inflammatory Receptors as a Gene Therapy Strategy for Disc Degeneration. Hum. Gene Ther. 2019. [Google Scholar] [CrossRef] [PubMed]
  102. Stover, J.D.; Farhang, N.; Lawrence, B.; Bowles, R.D. Multiplex Epigenome Editing of Dorsal Root Ganglion Neuron Receptors Abolishes Redundant Interleukin 6, Tumor Necrosis Factor Alpha, and Interleukin 1 beta Signaling by the Degenerative Intervertebral Disc. Hum. Gene Ther. 2019. [Google Scholar] [CrossRef] [PubMed]
  103. Chen, C.-D.; Zeldich, E.; Li, Y.; Yuste, A.; Abraham, C.R. Activation of the Anti-Aging and Cognition-Enhancing Gene Klotho by CRISPR-dCas9 Transcriptional Effector Complex. J. Mol. Neurosci. 2018, 64, 175–184. [Google Scholar] [CrossRef] [PubMed]
  104. Thakore, P.I.; Kwon, J.B.; Nelson, C.E.; Rouse, D.C.; Gemberling, M.P.; Oliver, M.L.; Gersbach, C.A. RNA-guided transcriptional silencing in vivo with S. aureus CRISPR-Cas9 repressors. Nat. Commun. 2018, 9, 1674. [Google Scholar] [CrossRef] [PubMed]
  105. Liao, H.-K.; Hatanaka, F.; Araoka, T.; Reddy, P.; Wu, M.-Z.; Sui, Y.; Yamauchi, T.; Sakurai, M.; O’Keefe, D.D.; Nunez-Delicado, E.; et al. In Vivo Target Gene Activation via CRISPR/Cas9-Mediated Trans-epigenetic Modulation. Cell 2017, 171, 1495. [Google Scholar] [CrossRef] [Green Version]
  106. Gimenez, C.A.; Ielpi, M.; Mutto, A.; Grosembacher, L.; Argibay, P.; Pereyra-Bonnet, F. CRISPR-on system for the activation of the endogenous human INS gene. Gene Ther. 2016, 23, 543–547. [Google Scholar] [CrossRef]
  107. Pinto, B.S.; Saxena, T.; Oliveira, R.; Mendez-Gomez, H.R.; Cleary, J.D.; Denes, L.T.; McConnell, O.; Arboleda, J.; Xia, G.; Swanson, M.S.; et al. Impeding Transcription of Expanded Microsatellite Repeats by Deactivated Cas9. Mol. Cell 2017, 68, 479. [Google Scholar] [CrossRef] [Green Version]
  108. Kemaladewi, D.U.; Bassi, P.S.; Erwood, S.; Al-Basha, D.; Gawlik, K.I.; Lindsay, K.; Hyatt, E.; Kember, R.; Place, K.M.; Marks, R.M.; et al. A mutation-independent approach for muscular dystrophy via upregulation of a modifier gene. Nature 2019, 572, 125. [Google Scholar] [CrossRef]
  109. Dai, W.; Xu, X.; Wang, D.; Wu, J.; Wang, J. Cancer therapy with a CRISPR-assisted telomerase-activating gene expression system. Oncogene 2019, 38, 4110–4124. [Google Scholar] [CrossRef]
  110. Xiong, K.; Zhou, Y.; Blichfeld, K.A.; Hyttel, P.; Bolund, L.; Freude, K.K.; Luo, Y. RNA-Guided Activation of Pluripotency Genes in Human Fibroblasts. Cell. Reprogram. 2017, 19, 189–198. [Google Scholar] [CrossRef]
  111. Hu, J.; Lei, Y.; Wong, W.-K.; Liu, S.; Lee, K.-C.; He, X.; You, W.; Zhou, R.; Guo, J.-T.; Chen, X.; et al. Direct activation of human and mouse Oct4 genes using engineered TALE and Cas9 transcription factors. Nucleic Acids Res. 2014, 42, 4375–4390. [Google Scholar] [CrossRef] [PubMed]
  112. Weltner, J.; Balboa, D.; Katayama, S.; Bespalov, M.; Krjutskov, K.; Jouhilahti, E.-M.; Trokovic, R.; Kere, J.; Otonkoski, T. Human pluripotent reprogramming with CRISPR activators. Nat. Commun. 2018, 9, 2643. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  113. Guo, J.; Ma, D.; Huang, R.; Ming, J.; Ye, M.; Kee, K.; Xie, Z.; Na, J. An inducible CRISPR-ON system for controllable gene activation in human pluripotent stem cells. Protein Cell 2017, 8, 379–393. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  114. Furuhata, Y.; Nihongaki, Y.; Sato, M.; Yoshimoto, K. Control of Adipogenic Differentiation in Mesenchymal Stem cells via Endogenous Gene Activation Using CRISPR-Cas9. ACS Synth. Biol. 2017, 6, 2191–2197. [Google Scholar] [CrossRef]
  115. Liu, Y.; Yu, C.; Daley, T.P.; Wang, F.; Cao, W.S.; Bhate, S.; Lin, X.; Still, I.I.C.; Liu, H.; Zhao, D.; et al. CRISPR Activation Screens Systematically Identify Factors that Drive Neuronal Fate and Reprogramming. Cell Stem Cell 2018, 23, 758. [Google Scholar] [CrossRef] [Green Version]
  116. Balboa, D.; Weltner, J.; Eurola, S.; Trokovic, R.; Wartiovaara, K.; Otonkoski, T. Conditionally Stabilized dCas9 Activator for Controlling Gene Expression in Human Cell Reprogramming and Differentiation. Stem Cell Rep. 2015, 5, 448–459. [Google Scholar] [CrossRef] [Green Version]
  117. Liu, X.; Zhang, Y.; Chen, Y.; Li, M.; Zhou, F.; Li, K.; Cao, H.; Ni, M.; Liu, Y.; Gu, Z.; et al. In Situ Capture of Chromatin Interactions by Biotinylated dCas9. Cell 2017, 170, 1028. [Google Scholar] [CrossRef] [Green Version]
  118. Schmidtmann, E.; Anton, T.; Rombaut, P.; Herzog, F.; Leonhardt, H. Determination of local chromatin composition by CasID. Nucleus 2016, 7, 476–484. [Google Scholar] [CrossRef] [Green Version]
  119. Myers, S.A.; Wright, J.; Peckner, R.; Kalish, B.T.; Zhang, F.; Carr, S.A. Discovery of proteins associated with a predefined genomic locus via dCas9-APEX-mediated proximity labeling. Nat. Methods 2018, 15, 437. [Google Scholar] [CrossRef]
  120. Gao, X.D.; Tu, L.-C.; Mir, A.; Rodriguez, T.; Ding, Y.; Leszyk, J.; Dekker, J.; Shaffer, S.A.; Zhu, L.J.; Wolfe, S.A.; et al. C-BERST: Defining subnuclear proteomic landscapes at genomic elements with dCas9-APEX2. Nat. Methods 2018, 15, 433. [Google Scholar] [CrossRef]
  121. Drier, Y.; Cotton, M.J.; Williamson, K.E.; Gillespie, S.M.; Ryan, R.J.H.; Kluk, M.J.; Carey, C.D.; Rodig, S.J.; Sholl, L.M.; Afrogheh, A.H.; et al. An oncogenic MYB feedback loop drives alternate cell fates in adenoid cystic carcinoma. Nat. Genet. 2016, 48, 265–272. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  122. Morgan, S.L.; Mariano, N.C.; Bermudez, A.; Arruda, N.L.; Wu, F.; Luo, Y.; Shankar, G.; Jia, L.; Chen, H.; Hu, J.-F.; et al. Manipulation of nuclear architecture through CRISPR-mediated chromosomal looping. Nat. Commun. 2017, 8, 15993. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  123. Daub, H.; Specht, K.; Ullrich, A. Strategies to overcome resistance to targeted protein kinase inhibitors. Nat. Rev. Drug Discov. 2004, 3, 1001–1010. [Google Scholar] [CrossRef]
  124. Wang, H.; Yang, H.; Shivalila, C.S.; Dawlaty, M.M.; Cheng, A.W.; Zhang, F.; Jaenisch, R. One-step generation of mice carrying mutations in multiple genes by CRISPR/Cas-mediated genome engineering. Cell 2013, 153, 910–918. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  125. Nabel, C.S.; Jia, H.; Ye, Y.; Shen, L.; Goldschmidt, H.L.; Stivers, J.T.; Zhang, Y.; Kohli, R.M. AID/APOBEC deaminases disfavor modified cytosines implicated in DNA demethylation. Nat. Chem. Biol. 2012, 8, 751–758. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  126. Navaratnam, N.; Sarwar, R. An overview of cytidine deaminases. Int. J. Hematol. 2006, 83, 195–200. [Google Scholar] [CrossRef] [PubMed]
  127. Rees, H.A.; Liu, D.R. Base editing: Precision chemistry on the genome and transcriptome of living cells. Nat. Rev. Genet. 2018, 19, 770–788. [Google Scholar] [CrossRef]
  128. Komor, A.C.; Kim, Y.B.; Packer, M.S.; Zuris, J.A.; Liu, D.R. Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage. Nature 2016, 533, 420. [Google Scholar] [CrossRef] [Green Version]
  129. Wang, X.; Li, J.; Wang, Y.; Yang, B.; Wei, J.; Wu, J.; Wang, R.; Huang, X.; Chen, J.; Yang, L. Efficient base editing in methylated regions with a human APOBEC3A-Cas9 fusion. Nat. Biotechnol. 2018, 36, 946. [Google Scholar] [CrossRef]
  130. Ma, Y.; Zhang, J.; Yin, W.; Zhang, Z.; Song, Y.; Chang, X. Targeted AID-mediated mutagenesis (TAM) enables efficient genomic diversification in mammalian cells. Nat. Methods 2016, 13, 1029. [Google Scholar] [CrossRef]
  131. Nishida, K.; Arazoe, T.; Yachie, N.; Banno, S.; Kakimoto, M.; Tabata, M.; Mochizuki, M.; Miyabe, A.; Araki, M.; Hara, K.Y. Targeted nucleotide editing using hybrid prokaryotic and vertebrate adaptive immune systems. Science 2016, 353, aaf8729. [Google Scholar] [CrossRef] [PubMed]
  132. Hess, G.T.; Frésard, L.; Han, K.; Lee, C.H.; Li, A.; Cimprich, K.A.; Montgomery, S.B.; Bassik, M.C. Directed evolution using dCas9-targeted somatic hypermutation in mammalian cells. Nat. Methods 2016, 13, 1036. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  133. Komor, A.C.; Zhao, K.T.; Packer, M.S.; Gaudelli, N.M.; Waterbury, A.L.; Koblan, L.W.; Kim, Y.B.; Badran, A.H.; Liu, D.R. Improved base excision repair inhibition and bacteriophage Mu Gam protein yields C: G-to-T: A base editors with higher efficiency and product purity. Sci. Adv. 2017, 3, eaao4774. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  134. Gaudelli, N.M.; Komor, A.C.; Rees, H.A.; Packer, M.S.; Badran, A.H.; Bryson, D.I.; Liu, D.R. Programmable base editing of A• T to G• C in genomic DNA without DNA cleavage. Nature 2017, 551, 464. [Google Scholar] [CrossRef]
  135. Kim, Y.B.; Komor, A.C.; Levy, J.M.; Packer, M.S.; Zhao, K.T.; Liu, D.R. Increasing the genome-targeting scope and precision of base editing with engineered Cas9-cytidine deaminase fusions. Nat. Biotechnol. 2017, 35, 371. [Google Scholar] [CrossRef]
  136. Nishimasu, H.; Shi, X.; Ishiguro, S.; Gao, L.; Hirano, S.; Okazaki, S.; Noda, T.; Abudayyeh, O.O.; Gootenberg, J.S.; Mori, H.; et al. Engineered CRISPR-Cas9 nuclease with expanded targeting space. Science 2018, 361, 1259–1262. [Google Scholar] [CrossRef]
  137. Zhou, C.; Sun, Y.; Yan, R.; Liu, Y.; Zuo, E.; Gu, C.; Han, L.; Wei, Y.; Hu, X.; Zeng, R. Off-target RNA mutation induced by DNA base editing and its elimination by mutagenesis. Nature 2019, 1, 275–278. [Google Scholar] [CrossRef]
  138. Grunewald, J.; Zhou, R.; Garcia, S.P.; Iyer, S.; Lareau, C.A.; Aryee, M.J.; Joung, J.K. Transcriptome-wide off-target RNA editing induced by CRISPR-guided DNA base editors. Nature 2019, 569, 433. [Google Scholar] [CrossRef]
  139. Grünewald, J.; Zhou, R.; Iyer, S.; Lareau, C.A.; Garcia, S.P.; Aryee, M.J.; Joung, J.K. CRISPR DNA base editors with reduced RNA off-target and self-editing activities. Nat. Biotechnol. 2019, 37, 1–8. [Google Scholar] [CrossRef]
  140. Rees, H.A.; Wilson, C.; Doman, J.L.; Liu, D.R. Analysis and minimization of cellular RNA editing by DNA adenine base editors. Sci. Adv. 2019, 5, eaax5717. [Google Scholar] [CrossRef] [Green Version]
  141. Thuronyi, B.W.; Koblan, L.W.; Levy, J.M.; Yeh, W.-H.; Zheng, C.; Newby, G.A.; Wilson, C.; Bhaumik, M.; Shubina-Oleinik, O.; Holt, J.R. Continuous evolution of base editors with expanded target compatibility and improved activity. Nat. Biotechnol. 2019, 1, 1070–1079. [Google Scholar] [CrossRef] [PubMed]
  142. Cox, D.B.T.; Gootenberg, J.S.; Abudayyeh, O.O.; Franklin, B.; Kellner, M.J.; Joung, J.; Zhang, F. RNA editing with CRISPR-Cas13. Science 2017, 358, 1019–1027. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  143. Abudayyeh, O.O.; Gootenberg, J.S.; Franklin, B.; Koob, J.; Kellner, M.J.; Ladha, A.; Joung, J.; Kirchgatterer, P.; Cox, D.B.T.; Zhang, F. A cytosine deaminase for programmable single-base RNA editing. Science 2019, 365, 382–386. [Google Scholar] [CrossRef] [PubMed]
  144. Kuscu, C.; Parlak, M.; Tufan, T.; Yang, J.; Szlachta, K.; Wei, X.; Mammadov, R.; Adli, M. CRISPR-STOP: Gene silencing through base-editing-induced nonsense mutations. Nat. Methods 2017, 14, 710. [Google Scholar] [CrossRef] [PubMed]
  145. Billon, P.; Bryant, E.E.; Joseph, S.A.; Nambiar, T.S.; Hayward, S.B.; Rothstein, R.; Ciccia, A. CRISPR-Mediated Base Editing Enables Efficient Disruption of Eukaryotic Genes through Induction of STOP Codons. Mol. Cell 2017, 67, 1068. [Google Scholar] [CrossRef] [Green Version]
  146. Jiang, W.; Feng, S.; Huang, S.; Yu, W.; Li, G.; Yang, G.; Liu, Y.; Zhang, Y.; Zhang, L.; Hou, Y.; et al. BE-PLUS: A new base editing tool with broadened editing window and enhanced fidelity. Cell Res. 2018, 28, 855–861. [Google Scholar] [CrossRef] [Green Version]
  147. Zafra, M.P.; Schatoff, E.M.; Katti, A.; Foronda, M.; Breinig, M.; Schweitzer, A.Y.; Simon, A.; Han, T.; Goswami, S.; Montgomery, E.; et al. Optimized base editors enable efficient editing in cells, organoids and mice. Nat. Biotechnol. 2018, 36, 888. [Google Scholar] [CrossRef]
  148. Kim, K.; Ryu, S.-M.; Kim, S.-T.; Baek, G.; Kim, D.; Lim, K.; Chung, E.; Kim, S.; Kim, J.-S. Highly efficient RNA-guided base editing in mouse embryos. Nat. Biotechnol. 2017, 35, 435. [Google Scholar] [CrossRef]
  149. Liang, P.; Sun, H.; Zhang, X.; Xie, X.; Zhang, J.; Bai, Y.; Ouyang, X.; Zhi, S.; Xiong, Y.; Ma, W. Effective and precise adenine base editing in mouse zygotes. Protein Cell 2018, 9, 808–813. [Google Scholar] [CrossRef] [Green Version]
  150. Ryu, S.-M.; Koo, T.; Kim, K.; Lim, K.; Baek, G.; Kim, S.-T.; Kim, H.S.; Kim, D.; Lee, H.; Chung, E.; et al. Adenine base editing in mouse embryos and an adult mouse model of Duchenne muscular dystrophy. Nat. Biotechnol. 2018, 36, 536–539. [Google Scholar] [CrossRef]
  151. Park, D.-S.; Yoon, M.; Kweon, J.; Jang, A.-H.; Kim, Y.; Choi, S.-C. Targeted Base Editing via RNA-Guided Cytidine Deaminases in Xenopus laevis Embryos. Mol. Cells 2017, 40, 823–827. [Google Scholar] [PubMed]
  152. Liu, Z.; Chen, M.; Chen, S.; Deng, J.; Song, Y.; Lai, L.; Li, Z. Highly efficient RNA-guided base editing in rabbit. Nat. Commun. 2018, 9, 2717. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  153. Sasaguri, H.; Nagata, K.; Sekiguchi, M.; Fujioka, R.; Matsuba, Y.; Hashimoto, S.; Sato, K.; Kurup, D.; Yokota, T.; Saido, T.C. Introduction of pathogenic mutations into the mouse Psen1 gene by Base Editor and Target-AID. Nat. Commun. 2018, 9, 2892. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  154. Gehrke, J.M.; Cervantes, O.; Clement, M.K.; Wu, Y.; Zeng, J.; Bauer, D.E.; Pinello, L.; Joung, J.K. An APOBEC3A-Cas9 base editor with minimized bystander and off-target activities. Nat. Biotechnol. 2018, 36, 977. [Google Scholar] [CrossRef] [PubMed]
  155. Liang, P.; Ding, C.; Sun, H.; Xie, X.; Xu, Y.; Zhang, X.; Sun, Y.; Xiong, Y.; Ma, W.; Liu, Y.; et al. Correction of beta-thalassemia mutant by base editor in human embryos. Protein Cell 2017, 8, 811–822. [Google Scholar] [CrossRef] [PubMed]
  156. Zeng, Y.; Li, J.; Li, G.; Huang, S.; Yu, W.; Zhang, Y.; Chen, D.; Chen, J.; Liu, J.; Huang, X. Correction of the Marfan Syndrome Pathogenic FBN1 Mutation by Base Editing in Human Cells and Heterozygous Embryos. Mol. Ther. 2018, 26, 2631–2637. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  157. Villiger, L.; Grisch-Chan, H.M.; Lindsay, H.; Ringnalda, F.; Pogliano, C.B.; Allegri, G.; Fingerhut, R.; Haberle, J.; Matos, J.; Robinson, M.D.; et al. Treatment of a metabolic liver disease by in vivo genome base editing in adult mice. Nat. Med. 2018, 24, 1519. [Google Scholar] [CrossRef]
  158. Rossidis, A.C.; Stratigis, J.D.; Chadwick, A.C.; Hartman, H.A.; Ahn, N.J.; Li, H.; Singh, K.; Coons, B.E.; Li, L.; Lv, W.; et al. In utero CRISPR-mediated therapeutic editing of metabolic genes. Nat. Med. 2018, 24, 1513. [Google Scholar] [CrossRef]
  159. Chadwick, A.C.; Wang, X.; Musunuru, K. In Vivo Base Editing of PCSK9 (Proprotein Convertase Subtilisin/Kexin Type 9) as a Therapeutic Alternative to Genome Editing. Arterioscler. Thromb. Vasc. Biol. 2017, 37, 1741. [Google Scholar] [CrossRef] [Green Version]
  160. Lee, C.; Jo, D.H.; Hwang, G.-H.; Yu, J.; Kim, J.H.; Park, S.; Kim, J.-S.; Kim, J.H.; Bae, S. CRISPR-pass: Gene rescue of nonsense mutations using adenine base editors. Mol. Ther. 2019. [Google Scholar] [CrossRef] [Green Version]
  161. Gapinske, M.; Luu, A.; Winter, J.; Woods, W.S.; Kostan, K.A.; Shiva, N.; Song, J.S.; Perez-Pinera, P. CRISPR-SKIP: Programmable gene splicing with single base editors. Genome Biol. 2018, 19, 107. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  162. Hwang, G.-H.; Park, J.; Lim, K.; Kim, S.; Yu, J.; Yu, E.; Kim, S.-T.; Eils, R.; Kim, J.-S.; Bae, S. Web-based design and analysis tools for CRISPR base editing. BMC Bioinformatics 2018, 19, 542. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  163. Wang, Y.; Gao, R.; Wu, J.; Xiong, Y.-C.; Wei, J.; Zhang, S.; Yang, B.; Chen, J.; Yang, L. Comparison of cytosine base editors and development of the BEable-GPS database for targeting pathogenic SNVs. Genome Biol. 2019, 20, 1–7. [Google Scholar] [CrossRef] [PubMed]
  164. Campa, C.C.; Weisbach, N.R.; Santinha, A.J.; Incarnato, D.; Platt, R.J. Multiplexed genome engineering by Cas12a and CRISPR arrays encoded on single transcripts. Nat. Methods 2019, 16, 887. [Google Scholar] [CrossRef] [PubMed]
  165. Klompe, S.E.; Vo, P.L.H.; Halpin-Healy, T.S.; Sternberg, S.H. Transposon-encoded CRISPR–Cas systems direct RNA-guided DNA integration. Nature 2019, 571, 219–225. [Google Scholar] [CrossRef]
  166. Strecker, J.; Ladha, A.; Gardner, Z.; Schmid-Burgk, J.L.; Makarova, K.S.; Koonin, E.V.; Zhang, F. RNA-guided DNA insertion with CRISPR-associated transposases. Science 2019, 365, 48–53. [Google Scholar] [CrossRef]
  167. Liu, X.-M.; Zhou, J.; Mao, Y.; Ji, Q.; Qian, S.-B. Programmable RNA N-6-methyladenosine editing by CRISPR-Cas9 conjugates. Nat. Chem. Biol. 2019, 15, 865. [Google Scholar] [CrossRef]
  168. Ma, H.; Tu, L.-C.; Naseri, A.; Huisman, M.; Zhang, S.; Grunwald, D.; Pederson, T. Multiplexed labeling of genomic loci with dCas9 and engineered sgRNAs using CRISPRainbow. Nat. Biotechnol. 2016, 34, 528. [Google Scholar] [CrossRef] [Green Version]
  169. Duan, J.; Lu, G.; Hong, Y.; Hu, Q.; Mai, X.; Guo, J.; Si, X.; Wang, F.; Zhang, Y. Live imaging and tracking of genome regions in CRISPR/dCas9 knock-in mice. Genome Biol. 2018, 19, 192. [Google Scholar] [CrossRef]
  170. Nelles, D.A.; Fang, M.Y.; O’Connell, M.R.; Xu, J.L.; Markmiller, S.J.; Doudna, J.A.; Yeo, G.W. Programmable RNA Tracking in Live cells with CRISPR/Cas9. Cell 2016, 165, 488–496. [Google Scholar] [CrossRef] [Green Version]
  171. Tang, W.; Liu, D.R. Rewritable multi-event analog recording in bacterial and mammalian cells. Science 2018, 360, eaap8992. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  172. Farzadfard, F.; Gharaei, N.; Higashikuni, Y.; Jung, G.; Cao, J.; Lu, T.K. Single-Nucleotide-Resolution Computing and Memory in Living Cells. Mol. Cell 2019, 75, 769. [Google Scholar] [CrossRef] [PubMed]
  173. Zhang, Y.; Qian, L.; Wei, W.; Wang, Y.; Wang, B.; Lin, P.; Liu, W.; Xu, L.; Li, X.; Liu, D.; et al. Paired Design of dCas9 as a Systematic Platform for the Detection of Featured Nucleic Acid Sequences in Pathogenic Strains. ACS Synth. Biol. 2017, 6, 211–216. [Google Scholar] [CrossRef] [PubMed]
  174. Qiu, X.-Y.; Zhu, L.-Y.; Zhu, C.-S.; Ma, J.-X.; Hou, T.; Wu, X.-M.; Xie, S.-S.; Min, L.; Tan, D.-A.; Zhang, D.-Y.; et al. Highly Effective and Low-Cost MicroRNA Detection with CRISPR-Cas9. ACS Synth. Biol. 2018, 7, 807–813. [Google Scholar] [CrossRef] [PubMed]
  175. Koo, B.; Kim, D.; Kweon, J.; Jin, C.E.; Kim, S.-H.; Kim, Y.; Shin, Y. CRISPR/dCas9-mediated biosensor for detection of tick-borne diseases. Sens. Actuators B Chem. 2018, 273, 316–321. [Google Scholar] [CrossRef]
  176. Kosicki, M.; Tomberg, K.; Bradley, A. Repair of double-strand breaks induced by CRISPR-Cas9 leads to large deletions and complex rearrangements. Nat. Biotechnol. 2018, 36, 765. [Google Scholar] [CrossRef] [PubMed]
  177. Ihry, R.J.; Worringer, K.A.; Salick, M.R.; Frias, E.; Ho, D.; Theriault, K.; Kommineni, S.; Chen, J.; Sondey, M.; Ye, C.; et al. p53 inhibits CRISPR-Cas9 engineering in human pluripotent stem cells. Nat. Med. 2018, 24, 939. [Google Scholar] [CrossRef]
  178. Tycko, J.; Myer, V.E.; Hsu, P.D. Methods for optimizing CRISPR-Cas9 genome editing specificity. Mol. Cell 2016, 63, 355–370. [Google Scholar] [CrossRef] [Green Version]
  179. Ren, X.; Yang, Z.; Xu, J.; Sun, J.; Mao, D.; Hu, Y.; Yang, S.-J.; Qiao, H.-H.; Wang, X.; Hu, Q. Enhanced specificity and efficiency of the CRISPR/Cas9 system with optimized sgRNA parameters in Drosophila. Cell Rep. 2014, 9, 1151–1162. [Google Scholar] [CrossRef] [Green Version]
  180. Kocak, D.D.; Josephs, E.A.; Bhandarkar, V.; Adkar, S.S.; Kwon, J.B.; Gersbach, C.A. Increasing the specificity of CRISPR systems with engineered RNA secondary structures. Nat. Biotechnol. 2019, 37, 657. [Google Scholar] [CrossRef]
  181. Slaymaker, I.M.; Gao, L.; Zetsche, B.; Scott, D.A.; Yan, W.X.; Zhang, F. Rationally engineered Cas9 nucleases with improved specificity. Science 2016, 351, 84–88. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  182. Kleinstiver, B.P.; Pattanayak, V.; Prew, M.S.; Tsai, S.Q.; Nguyen, N.T.; Zheng, Z.; Keith Joung, J. High-fidelity CRISPR–Cas9 nucleases with no detectable genome-wide off-target effects. Nature 2016, 529, 490–495. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  183. Casini, A.; Olivieri, M.; Petris, G.; Montagna, C.; Reginato, G.; Maule, G.; Lorenzin, F.; Prandi, D.; Romanel, A.; Demichelis, F. A highly specific SpCas9 variant is identified by in vivo screening in yeast. Nat. Biotechnol. 2018, 36, 265. [Google Scholar] [CrossRef] [PubMed]
  184. Chen, J.S.; Dagdas, Y.S.; Kleinstiver, B.P.; Welch, M.M.; Sousa, A.A.; Harrington, L.B.; Sternberg, S.H.; Joung, J.K.; Yildiz, A.; Doudna, J.A. Enhanced proofreading governs CRISPR–Cas9 targeting accuracy. Nature 2017, 550, 407. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  185. Kleinstiver, B.P.; Prew, M.S.; Tsai, S.Q.; Topkar, V.V.; Nguyen, N.T.; Zheng, Z.; Gonzales, A.P.W.; Li, Z.; Peterson, R.T.; Yeh, J.-R.J.; et al. Engineered CRISPR-Cas9 nucleases with altered PAM specificities. Nature 2015, 523, 481–485. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  186. Agudelo, D.; Carter, S.; Velimirovic, M.; Duringer, A.; Levesque, S.; Rivest, J.-F.; Loehr, J.; Mouchiroud, M.; Cyr, D.; Waters, P.J. Versatile and robust genome editing with Streptococcus thermophilus CRISPR1-Cas9. BioRxiv 2019, 321208. [Google Scholar] [CrossRef] [Green Version]
  187. Ran, F.A.; Cong, L.; Yan, W.X.; Scott, D.A.; Gootenberg, J.S.; Kriz, A.J.; Zetsche, B.; Shalem, O.; Wu, X.; Makarova, K.S.; et al. In vivo genome editing using Staphylococcus aureus Cas9. Nature 2015, 520, 186–191. [Google Scholar] [CrossRef]
  188. Tsai, S.Q.; Wyvekens, N.; Khayter, C.; Foden, J.A.; Thapar, V.; Reyon, D.; Goodwin, M.J.; Aryee, M.J.; Joung, J.K. Dimeric CRISPR RNA-guided FokI nucleases for highly specific genome editing. Nat. Biotechnol. 2014, 32, 569. [Google Scholar] [CrossRef] [Green Version]
  189. Zuris, J.A.; Thompson, D.B.; Shu, Y.; Guilinger, J.P.; Bessen, J.L.; Hu, J.H.; Maeder, M.L.; Joung, J.K.; Chen, Z.-Y.; Liu, D.R. Cationic lipid-mediated delivery of proteins enables efficient protein-based genome editing in vitro and in vivo. Nat. Biotechnol. 2015, 33, 73. [Google Scholar] [CrossRef] [Green Version]
Figure 1. Types and applications of dCas-based molecular tools. (A) Investigation of chromatin structure. dCas proteins tethered with specific enzymes (e.g., peroxidase) enable inducible marking (biotinylation) of chromatin factors in the vicinity of the target site. These factors can be subsequently analyzed by proteomics to study chromatin organization. (B) Base editing. dCas proteins coupled with base editing enzymes (cytidine or adenine deaminases) can be used to modify RNA or DNA, correct genetic mutations, or knock-out genes. (C) Epigenetic remodeling. dCas-based epigenome modifiers can directly alter epigenetic state at a given locus, which is frequently used to annotate gene regulatory elements. Red and green spheres indicate heterochromatin and euchromatin marks, correspondingly. (D) Programming 3D chromatin interactions. Using two dCas proteins targeting defined genomic loci can program 3D chromatin interactions. A chemical inducer stimulates dimerization of dCas proteins fused with dimerization domains building long-range connections between genomic elements. (E) Transcriptional regulation. Control of gene expression by dCas proteins tethered to transcriptional suppressors (red) or activators (green). PAM—protospacer adjacent motif; H840A and D10A are point mutations inactivating catalytic residues RuvC and HNH, correspondingly. This picture was created in BioRender software.
Figure 1. Types and applications of dCas-based molecular tools. (A) Investigation of chromatin structure. dCas proteins tethered with specific enzymes (e.g., peroxidase) enable inducible marking (biotinylation) of chromatin factors in the vicinity of the target site. These factors can be subsequently analyzed by proteomics to study chromatin organization. (B) Base editing. dCas proteins coupled with base editing enzymes (cytidine or adenine deaminases) can be used to modify RNA or DNA, correct genetic mutations, or knock-out genes. (C) Epigenetic remodeling. dCas-based epigenome modifiers can directly alter epigenetic state at a given locus, which is frequently used to annotate gene regulatory elements. Red and green spheres indicate heterochromatin and euchromatin marks, correspondingly. (D) Programming 3D chromatin interactions. Using two dCas proteins targeting defined genomic loci can program 3D chromatin interactions. A chemical inducer stimulates dimerization of dCas proteins fused with dimerization domains building long-range connections between genomic elements. (E) Transcriptional regulation. Control of gene expression by dCas proteins tethered to transcriptional suppressors (red) or activators (green). PAM—protospacer adjacent motif; H840A and D10A are point mutations inactivating catalytic residues RuvC and HNH, correspondingly. This picture was created in BioRender software.
Ijms 20 06041 g001
Figure 2. Modification of CRISPR components for improved epigenetic regulation. (A) SunTag technique. dCas is fused with GCN4 peptide array, which attracts any effector molecule containing single-chain variable fragments (scFV). Multiple GCN4-scFV interactions ensure efficient recruitment of many effector molecules to the dCas-programmed genomic site. (B) Scaffold technique. In Scaffold, effector molecules are recruited to the target site via the interaction of MCP aptamer-specific protein with a short synthetic gRNA containing MS2 aptamer. gRNA protrudes out of the Cas-gRNA complex, so that chimeric gRNA-MS2 transcripts efficiently recruit effectors carrying MCP molecules. (C) TREE combines SunTag and Scaffold techniques, providing additional recruitment of effector molecules. (D) SAM is based on a two-component transcriptional effector (p65-HSF1) recruited to the target via MS2-MCP interaction. Additionally, dCas protein is tethered to a transcriptional regulator (VP64) to increase potency of the effect. This picture was created in BioRender software.
Figure 2. Modification of CRISPR components for improved epigenetic regulation. (A) SunTag technique. dCas is fused with GCN4 peptide array, which attracts any effector molecule containing single-chain variable fragments (scFV). Multiple GCN4-scFV interactions ensure efficient recruitment of many effector molecules to the dCas-programmed genomic site. (B) Scaffold technique. In Scaffold, effector molecules are recruited to the target site via the interaction of MCP aptamer-specific protein with a short synthetic gRNA containing MS2 aptamer. gRNA protrudes out of the Cas-gRNA complex, so that chimeric gRNA-MS2 transcripts efficiently recruit effectors carrying MCP molecules. (C) TREE combines SunTag and Scaffold techniques, providing additional recruitment of effector molecules. (D) SAM is based on a two-component transcriptional effector (p65-HSF1) recruited to the target via MS2-MCP interaction. Additionally, dCas protein is tethered to a transcriptional regulator (VP64) to increase potency of the effect. This picture was created in BioRender software.
Ijms 20 06041 g002
Table 1. Epigenetic regulation by dCas-based tools. The number of (+) symbols indicates the potency of a particular dCas tool.
Table 1. Epigenetic regulation by dCas-based tools. The number of (+) symbols indicates the potency of a particular dCas tool.
TargetModificationEffect on Gene TranscriptionSystemEfficacy
DNAMethylationSupressionDNMT3A [6,7,8]+
DNMT3A-3L [9]+++
SunTag-DNMT3A [10];+++
DemethylationActivationdCas9-TET1 [8,11]+
dCas9-SunTag-TET1 [12]+++
dCas9/MS2/MCP-TET1 [13]+++
ChromatinHistone demethylationSupressiondCas9-LSD1 [48]++
Histone acetylationActivationp300Core [49]++
Transcriptional factor recruitmentActivationVP64 [50,52,53]+
VP160/VP192 [54]++
p65/p65-HSF1 [50,51]+/++
SunTag-VP64 [59]+++
VPR [55,56]+++
SunTag-p65-HSF1 [58]++++
SAM [51,60]++++
TREE [62]++++
Casilio [61]+++
Scaffold [60]+++
SupressiondCas9-KRAB [43]++
dCas9-KRAB-MeCP2 [47]+++
dCas9-EZH2 [34]++
Exogenic promoter recruitmentActivationCMV [63]++++
Table 2. Applications of CRISPRi and CRISPRa tools in different areas of research and manufacture.
Table 2. Applications of CRISPRi and CRISPRa tools in different areas of research and manufacture.
Fundamental Studies
ApplicationCRISPR toolTarget
Annotating regulatory elementsdCas9-KRAB
dCas9-p300
Distal regulatory elements [69,70]
dCas9-KRABEstrogen receptor enhancers [87]
Analyzing gene functiondCas9-KRAB/
dCas9-VPR
Function of Syt1 [88], Bdnf, and Reln [89] in mammalian brain
Analyzing cell signalingdCas9-VPR
Scaffolds
SAM
Generating chimeric receptors [90,91]
Identifying antiviral factorsSAMNorovirus infection [79]
Analyzing human genomedCas9-KRABCRISPRi gRNA libraries [92]
SAMCRISPRa gRNA libraries [92]
Annotating tumor-related factorsSAM
SunTag-VP64
dCas9-KRAB
Genes involved in cancer: Hells [93], Egfr [51], lncRNAs [94,95,96], Myc [97], Kras-dependent genes [98]
Creating Therapeutic Approaches
ApplicationCRISPR toolTarget
Treating infectious diseasesSAM;
Scaffold (MCP-p65-HSF1)
HIV therapy by activating BST2/tetherin [99], APOBEC3B [71], and APOBEC3G [71]
dCas9-p300HBV therapy by activating APOBEC3A, APOBEC3B, APOBEC3G, AID [76]
SunTag-VP64;
dCas9-VPR;
SAM
Reactivating HIV in a “shock-and-kill” therapeutic approach [72,73,74,75]
Treating metabolic and inflammatory diseasesdCas9-KRABRepressing TNFR1, IL1R1, IL6st [100,101,102]
SAMNeuro- and nephroprotection by activating Klotho gene [103]
dCas9-KRABRepressing Pcsk9 to reduce serum cholesterol levels [104]
SAM
dCas9-VP160
Generating insulin-producing cells by upregulating Pdx1 or Ins [105,106]
Treating genetic disordersSAMTreating DMD by activating Utrophin gene [105]
dCas9-VP64Treating obesity by upregulating Sim1 [80]
dCas9-VP64Treating Dravet syndrome by upregulating Scn1a [81]
dCas9Correcting myotonic dystrophy types 1 and 2 by blocking transcription of expanded microsatellite repeats [107]
dCas9-VP64Treating congenital muscular dystrophy type 1A by upregulating Lama1 [108]
Treating cancerdCas9-DNMT3A
dCas9-KRAB
dCas9-Ezh2
Repressing Granulin proto-oncogene [82]
dCas9-VP64
dCas9-VPR
Activating tumor suppressors PTEN [83], CHEK2 [84], DKK3 [85]
dCas9-VP64Activating telomere-targeting Cas9 nuclease in cancer cells [109]
SAMIncreased presentation of tumor antigens to immune cells [86]
Stem cell fielddCas9-VP64
SAM
dCas9-p300
Generating iPSCs by inducing KLF4, LIN28, MYC, OCT4, SOX2 [110,111,112]
dCas9-VPRUpregulating NANOG to maintain pluripotency [113]
SAM
SunTag-VP64
Differentiating stem cells into adipocytes [114], neural cells [115], pancreatic cells [116]
SAM
SunTag-p65-HSF1
Neural reprogramming by activating Neurog2, Ascl1, Neurod1, Dkk1, etc. [58]
Table 3. Applications of dCas-based editors in different areas of research and manufacture.
Table 3. Applications of dCas-based editors in different areas of research and manufacture.
AimDeaminase DomainApplications
Disease modelingAIDMutating Bcr-Abl gene resulting in imatinib resistance [130]
CRISPR-X (dCas/MCP-AID)Mutating Psmb5 resulting in bortezomib resistance [132]
rAPOBEC1Mutating Ctnnb1, Apc, and Pi3kca genes as cancer models [147]
TadAIntroducing SNPs to model hereditary persistence of fetal hemoglobin syndrome and hereditary haemochromatosis [134]
rAPOBEC1
TadA
Modeling DMD and albinism by mutating Dmd and Tyr genes [148,149,150]
Target-AID
rAPOBEC1
Modeling amyloidosis by mutating Psen1 gene [153]
rAPOBEC1
TadA
Modeling hereditary diseases by mutating Tia1, Lmna, and Dmd genes [152]
Developing new therapiesAPOBEC3A
rAPOBEC1
Correcting β-thalassemia-linked mutations [154,155]
rAPOBEC1Correcting phenylketonuria-linked mutations [157]
rAPOBEC1Introducing stop codons in Pcsk9 gene to treat atherosclerosis [158,159]
ADAR2Correcting mutations in Avpr2 and Fancc mRNAs to treat X-linked nephrogenic diabetes and Fanconi anemia [142]
rAPOBEC1Treating Marfan syndrome by correcting pathogenic mutation Fbn1T7489C [156]
Table 4. Properties of different dCas base editing systems.
Table 4. Properties of different dCas base editing systems.
SystemChangeActivity at Methylated SitesTargetBase Editing Window
dCas9-rAPOBEC1C→TWeakDNA13–17 nt from PAM [128]
dCas9-APOBEC3AC→TPotentDNA13–18 nt from PAM [129]
dCas9-AIDC→TWeakDNA16–19 nt from PAM [131]
dCas9-TadAA→G-DNA14–16 nt from PAM [134]
dCas13b-ADAR2 (RESCUE)A→I
C→U
-RNA-
dCas13b-ADAR2
(REPAIR)
A→I-RNA-

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Brezgin, S.; Kostyusheva, A.; Kostyushev, D.; Chulanov, V. Dead Cas Systems: Types, Principles, and Applications. Int. J. Mol. Sci. 2019, 20, 6041. https://doi.org/10.3390/ijms20236041

AMA Style

Brezgin S, Kostyusheva A, Kostyushev D, Chulanov V. Dead Cas Systems: Types, Principles, and Applications. International Journal of Molecular Sciences. 2019; 20(23):6041. https://doi.org/10.3390/ijms20236041

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Brezgin, Sergey, Anastasiya Kostyusheva, Dmitry Kostyushev, and Vladimir Chulanov. 2019. "Dead Cas Systems: Types, Principles, and Applications" International Journal of Molecular Sciences 20, no. 23: 6041. https://doi.org/10.3390/ijms20236041

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