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Gene activation by a CRISPR-assisted trans enhancer

  1. Xinhui Xu
  2. Jinliang Gao
  3. Wei Dai
  4. Danyang Wang
  5. Jian Wu
  6. Jinke Wang  Is a corresponding author
  1. Southeast University, China
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Cite this article as: eLife 2019;8:e45973 doi: 10.7554/eLife.45973

Abstract

The deactivated CRISPR/Cas9 (dCas9) is now the most widely used gene activator. However, current dCas9-based gene activators are still limited by their unsatisfactory activity. In this study, we developed a new strategy, the CRISPR-assisted trans enhancer, for activating gene expression at high efficiency by combining dCas9-VP64/sgRNA with the widely used strong CMV enhancer. In this strategy, CMV enhancer DNA was recruited to target genes in trans by two systems: dCas9-VP64/csgRNA-sCMV and dCas9-VP64-GAL4/sgRNA-UAS-CMV. The former recruited trans enhancer by annealing between two short complementary oligonucleotides at the ends of the sgRNA and trans enhancer. The latter recruited trans enhancer by binding between GAL4 fused to dCas9 and UAS sequence of trans enhancer. The trans enhancer activated gene transcription as the natural looped cis enhancer. The trans enhancer could activate both exogenous reporter genes and variant endogenous genes in various cells, with much higher activation efficiency than that of current dCas9 activators.

https://doi.org/10.7554/eLife.45973.001

Introduction

Clustered regularly interspaced short palindromic repeats (CRISPR) was originally identified in the immune system of bacteria, with the function of destroying the invading microphage DNA by enzymatic digestion. The system has been developed into a highly efficient gene editing tool (Doudna and Charpentier, 2014; Mali et al., 2013c), and also into new gene activators. For example, the dead Cas9 (dCas9) and its associated single guide RNA (sgRNA) have been widely used to regulate gene expression in recent years (Dominguez et al., 2016; Hilton et al., 2015; Jinek et al., 2012; Kiani et al., 2015; Mali et al., 2013b; Radzisheuskaya et al., 2016; Wang et al., 2016). Both dCas9 and sgRNA have been engineered for activating or repressing gene expression. For instance, the dCas9 protein has been fused with various gene activation or repression domains, such as VP48 (Cheng et al., 2013), VP160 (Perrin et al., 2017), VP64 (Maeder et al., 2013; Perez-Pinera et al., 2013), VPR (VP64-p65-Rta) (Chavez et al., 2015), and KRAB (Zheng et al., 2018). Additionally, the dCas9 protein has been fused with other functional domains with transcriptional regulatory functions, such as p300 (Hilton et al., 2015), LSD1 (Kearns et al., 2015), Dnmt3a (Liu et al., 2016a; Saunderson et al., 2017), and Tet1 (Choudhury et al., 2016; Liu et al., 2016a). Based on these domains, more elaborate activators have been developed for more potent activation of target genes in mammalian cells, such as SunTag (dcas9-GCN4/sgRNA plus scFV-VP64) (Tanenbaum et al., 2014) and SPH (dCas9-GCN4/sgRNA plus scFV-p65-HSF1) (Zhou et al., 2018). Furthermore, some inducible dCas9 systems have been developed to control activity of dCas9 activators in cells, such as light-activated CRISPR/Cas9 effector (Nihongaki et al., 2015; Polstein and Gersbach, 2015), hybrid drug inducible CRISPR/Cas9 technology (HIT) (Lu et al., 2018), and CRISPR activator gated by human antibody-based chemically induced dimerizers (AbCIDs) (Liu et al., 2018). However, it is difficult to package most of these dCas9 fusion proteins into adeno-associated virus (AAV) for in vivo application.

sgRNA has also been engineered to develop new dCas9-based activators. Compared with dCas9 engineering, sgRNA is more simple, flexible, and efficient to redesign. Moreover, the engineered sgRNA is more helpful for the in vivo application of dCas9-based activators because of its limited length for virus packaging. The most widely used sgRNA-based gene activator is the synergistic activation mediator (SAM) system, in which MS2 loops are fused to the 3′ end of sgRNA (Konermann et al., 2015; Liao et al., 2017). Similarly, Pumilio/FBF (PUF), modular scaffold RNAs (MS2, PP7, and com), and riboswitches have been fused to sgRNA (Cheng et al., 2016; Liu et al., 2016b; Zalatan et al., 2015). However, these chimeric sgRNA-based strategies were limited by their complicated RNA aptamers and the cognate RNA-binding fusion proteins.

Although variant dCas9-based activators have been developed (Chen and Qi, 2017), the current dCas9-based transcriptional activators are relatively inefficient in endogenous gene activation and cell reprogramming (Gao et al., 2014). By a systematic comparison of relative potency and effectiveness across various cell types and species (human, mouse, and fly) (Chavez et al., 2016), it was found that the majority of second-generation activators had higher activity than that of dCas9-VP64, with the three most potent activators being VPR, SAM, and Suntag. The three activators were consistently better than VP64 across a range of target genes and cellular environments. Moreover, the three activators showed a similar level of activity, and fusing their elements did not yield more potent activators (Chavez et al., 2016). Novel, more potent dCas9-based activators might be built by creating other architectures.

Almost three decades ago, the human cytomegalovirus (CMV) enhancer/promoter (referred to as CMV enhancer hereafter) was found. It is a natural mammalian promoter with high transcriptional activity (Boshart et al., 1985). Later studies showed the CMV enhancer to be a strong enhancer in various mammalian cells (Boshart et al., 1985; Foecking and Hofstetter, 1986; Ho et al., 2015; Kim et al., 1990). This enhancer has been widely used to drive ectopic expression of various genes in a wide range of mammalian cells, and to drive ectopic expression of exogenous genes in broad tissues in transgenic animals (Furth et al., 1991; Schmidt et al., 1990), protein production by gene engineering, and gene therapy. We have recently improved the transcriptional activity of the CMV enhancer by changing the natural NF-κB binding sites into artificially selected NF-κB binding sequences with high binding affinity (Wang et al., 2018). Therefore, we conceived that a unique architecture may be constructed to improve dCas9-based activators using the CMV enhancer.

In this study, mimicking the natural enhancer activating gene expression by a loop structure (Carter et al., 2002; Tolhuis et al., 2002), we developed a new dCas9-based activator by combining dCas9/sgRNA with CMV enhancer. The 3′ end of sgRNA was redesigned to contain a short capture sequence complementary to a stick-end of a double-stranded CMV enhancer. The CMV enhancer was anchored to the promoter region of a target gene by dCas9/sgRNA. The dCas9/sgRNA-recruited CMV enhancer thus functioned like a natural looped cis enhancer in a trans form. We found that the new activator could efficiently activate exogenous and endogenous genes in various cells. More importantly, the CMV enhancer could be also recruited to a target gene in trans using another system consisting of dCas9-VP64-GAL4/sgRNA and UAS-CMV.

Results

Principle of gene activation by a CRISPR-assisted trans enhancer

The principle of activating gene expression by a CRISPR-assisted trans enhancer is schematically illustrated in Figure 1a. A capture sgRNA (csgRNA) was produced by adding a capture sequence to the 3′ end of a normal sgRNA sequence. A linear stick-end CMV (sCMV) enhancer was produced by adding a 3′ end single-strand overhang. The overhang can anneal with the csgRNA capture sequence. When dCas9 protein was guided to the promoter of the target gene by csgRNA, sCMV could be recruited by csgRNA. The recruited sCMV may activate the transcription of the target gene like a natural looped cis enhancer. Because the dCas9/csgRNA-anchored sCMV functions as a transcription factors in trans, we named it a trans enhancer to distinguish it from the natural cis enhancer.

Principle of gene expression activation by the CRISPR-assisted trans enhancer and evaluation of designed csgRNAs.

(a) Schematic illustration of the principle of gene expression activation by the CRISPR-assisted trans enhancer. A capture sequence is added to the 3′ end of sgRNA, which is used to capture a trans CMV enhancer with a single-stranded overhang that can anneal with the capture sequence of sgRNA. The captured trans CMV enhancer may function like the natural looped cis enhancer to activate transcription of the gene of interest, including exogenous and endogenous genes. (b) In vitro target DNA cutting by the Cas9-csgRNA complex. DNA fragments (732 bp) amplified from the HNF4α promoter region were, respectively, cut by the Cas9/csgRNA and Cas9/sgRNA complexes. csgRNA1, csgRNA2 and csgRNA3 had the same target sequence but different capture sequences.

https://doi.org/10.7554/eLife.45973.002

Effect of capture sequence on the function of sgRNA

To determine whether the capture sequence affects the function of sgRNA, we prepared a normal sgRNA and three csgRNAs targeting the same site of the HNF4α promoter. The three csgRNAs had different capture sequences. We used these sgRNAs to associate with the Cas9 endonuclease to cut a 732 bp HNF4α promoter fragment. The results indicated that the target DNA could be digested by all sgRNAs (Figure 1b), indicating that the capture sequence did not affect the sgRNA function.

Activation of exogenous reporter gene by trans enhancer

To determine whether the CRISPR-assisted trans enhancer activates gene expression, we constructed a reporter construct of HNF4α promoter (pEZX-HP-ZsGreen). 293 T cells were then transfected with various vectors (Figure 2a, Figure 2—figure supplement 1). The transfection indicated that ZsGreen expression could be successfully activated by dCas9/csgRNA2-sCMV but not activated by dCas9/csgRNA2-blunt CMV (bCMV). Although the dCas9/csgRNA2-sCMV showed a similar activation level to Cas9-VP64/sgRNA, it was far inferior to cis CMV enhancer. To improve the performance of trans CMV, we tried transfecting 293 T cells with dCas9-VP64/csgRNA2-sCMV. The results indicated that ZsGreen expression was highly activated by the transfection. In contrast, the dCas9-VP64/csgRNA2-bCMV showed a similar activation level to dCas9-VP64/sgRNA. These data revealed that the trans CMV not only truly functioned in trans via dCas9/csgRNA, but also synergistically interacted with dCas9-fused VP64. Subsequent transfections indicated that ZsGreen expression could also be highly activated by combination of dCas9-VP64, sCMV and other two csgRNAs, csgRNA1 and csgRNA3.

Figure 2 with 4 supplements see all
Activation of an exogenous reporter gene ZsGreen under the control of a HNF4α promoter by the CRISPR-assisted trans enhancer in multiple cells.

(a) Transcriptional activation of reporter gene ZsGreen in various cells transfected by different vectors. The florescence intensity of cells was analyzed by flow cytometry and is shown as the mean fluorescence intensity (MFI). Transfections: DVS, dCas9-VP64/sgRNA; DSC, dCas9/csgRNA-sCMV; DVSC, dCas9-VP64/csgRNA-sCMV. (b) Comparison between trans enhancer and VPR. Cells were transfected with three different transcriptional activation systems to activate reporter gene ZsGreen. The florescence intensity of cells was analyzed by flow cytometry and the number of cells with certain fluorescence intensity was counted. Transfections: Lipo, lipofectin; DVS, dCas9-VP64/sgRNA; DVPRS; dCas9-VPR/csgRNA; DVSC, dCas9-VP64/csgRNA-sCMV.

https://doi.org/10.7554/eLife.45973.003

To further verify the function of CRISPR-assisted trans enhancer, we transfected six different cell lines with reporter construct and dCas9-VP64/csgRNA2-sCMV, dCas9/csgRNA2-sCMV, or dCas9-VP64/sgRNA (Figure 2b; Figure 2—figure supplement 24). The results revealed that dCas9-VP64/csgRNA2-sCMV always showed the highest gene activation efficiency in all cell lines. Additionally, dCas9/csgRNA2-sCMV always showed higher activity than dCas9-VP64/sgRNA. These results indicate that genes could be activated by the CRISPR-assisted trans enhancer. Importantly, the trans sCMV could synergistically function with dCas9-fused VP64 in gene activation.

Comparison of trans CMV enhancer with VPR

Having shown that VPR is a more potent transcriptional activation domain than VP64, we next compared the trans enhancer with VPR. 293T and HepG2 cells were, respectively, transfected with reporter construct and dCas9-VP64/csgRNA, dCas9-VPR/csgRNA, or dCas9-VP64/csgRNA-sCMV (Figure 2c). The results showed that dCas9-VPR/csgRNA had better activity than dCas9-VP64/csgRNA as previously reported. However, the dCas9-VP64/csgRNA-sCMV always showed significantly higher activity than dCas9-VPR/csgRNA.

Activation of endogenous genes by trans enhancer

To further evaluate the activity of CRISPR-assisted trans enhancer, we activated endogenous genes with trans sCMV. csgRNAs targeting ten different genes was designed and their linear expression vectors were produced. Seven different cell lines were transfected with dCas9-VP64/csgRNA2-sCMV, dCas9-VP64/sgRNA and dCas9/csgRNA2-sCMV (Figure 3—figure supplement 1). The quantitative PCR (qPCR) detection of gene expression revealed that almost all genes were most significantly activated by dCas9-VP64/csgRNA2-sCMV in all cells. Moreover, most genes were more significantly activated by dCas9/csgRNA-sCMV than dCas9-VP64/sgRNA in all cells. These results suggest that the CRISPR-assisted trans enhancer could be used to activate variant endogenous genes in various cells. In addition, by activating the HNF4α gene in 293 T cells, we found that dCas9-VP64/csgRNA-sCMV had better activity than dCas9-VPR/csgRNA-sCMV in activating endogenous genes (Figure 3—figure supplement 2).

It has been reported that the cancer cells HepG2 and PANC1 can be differentiated into normal liver- and pancreas-like cells by exogenously expressing transcription factor HNF4α and E47. In the above assays, we found that the endogenous HNF4α and E47 genes were highly activated by the CRISPR-assisted trans enhancer in HepG2 and PANC1 cells (Figure 3). To further confirm the cellular effects of HNF4α and E47 activation, we detected expressions of other genes related to the differentiation of the two cancer cells (Figure 4). The results indicated that the genes related to stemness (CD133 and CD90) and pluripotency (Oct3/4, Sox2, Nanog, c-Myc, LIN28, and Klf4) were down-regulated, but those related to normal liver (GS, BR, ALDOB, CYP1a2, PEPCK, APOCIII, G-6-P, and HPD) and pancreas (MIST1, PRSS2, CELA3A, and CPA2) functions were highly up-regulated in HepG2 and PANC1 cells. Additionally, the cell cycle arrest-related gene p21 (HepG2 and PANC1) and TP53INP1 (PANC1) were highly up-regulated.

Figure 3 with 2 supplements see all
Transcriptional activation of endogenous genes by the CRISPR-assisted trans enhancer.

294T, HepG2 and PANC1 cells were transfected with three different transcriptional activation systems to activate expression of 10 endogenous genes. Gene transcription was detected by qPCR and the expression level is shown as the relative RNA expression fold to house-keeping gene GAPDH. Data are shown as mean ± SD, n = 3. The statistical difference was analyzed using the Student’s t test. *, p<0.05; **, p<0.01; NS, no significant statistical difference. Transfection: DVS, dCas9-VP64/sgRNA; DSC, dCas9/csgRNA2-sCMV; DVSC, dCas9-VP64/csgRNA2-sCMV.

https://doi.org/10.7554/eLife.45973.008
Changes of gene expression in the HNF4α-activated HepG2 cells and E47-activated PANC-1 cells.

(a and b) Changes of gene expression in the HNF4α-activated HepG2 cells (a) and E47-activated PANC-1 cells (b). The gene transcription was detected by qPCR and the expression level is shown as the relative RNA expression fold to house-keeping gene GAPDH. Data are shown as mean ± SD, n = 3. The statistical difference was analyzed by Student’s t test. *, p<0.05; **, p<0.01; NS, no significant statistical difference. Transfection: Lipo, lipofectin; DVS, dCas9-VP64/sgRNA; DVSC, dCas9-VP64/csgRNA2-sCMV.

https://doi.org/10.7554/eLife.45973.011

Activation of genes by other trans enhancers

To explore whether other enhancers could be also used as trans enhancers, we fabricated the blunt- and stick-end versions of two other widely used promoters EF1a and PGK (bEF1a, bPGK, sEF1a, and sPGK). 293 T cells were co-transfected with these trans enhancers and dCas9-VP64/csgRNA and reporter construct. The results indicated that the ZsGreen expression was also activated by the two trans enhancers; however, the activation levels were lower than that of sCMV (Figure 5a, Figure 5—figure supplement 1). All other transfections as controls did not activate ZsGreen expression (Figure 5a, Figure 5—figure supplement 1). The qPCR detection of HNF4α expression in the same transfected 293 T cells revealed that the endogenous HNF4α gene expression was also significantly activated by three stick-end trans enhancers, but not activated by all blunt-ended trans enhancers (Figure 5b). Subsequent HepG2 cell transfections with the same trans enhancers and dCas9-VP64/csgRNA indicated that the endogenous HNF4α gene expression could also be significantly activated by all stick-ended trans enhancers, but not activated by all blunt-ended trans enhancers (Figure 5b). These results indicate that the variant enhancers could be used as the CRISPR-assisted trans enhancer.

Figure 5 with 1 supplement see all
Activation of exogenous and endogenous genes with other trans enhancers.

(a) Activation of exogenous reporter gene ZsGreen. The fluorescence intensity of cells was analyzed with flow cytometry. (b) Activation of endogenous HNF4α gene in 293T and HepG2 cells with two new trans enhancers, sEF1a and sPGK. The sCMV was used as a positive control for comparison. The blunt-end trans enhancers (bEF1a and bPGK) were also used as controls. Data are shown as mean ± SD, n = 3. The statistical difference was analyzed using Student’s t test. *, p<0.05; **, p<0.01.

https://doi.org/10.7554/eLife.45973.012

Activation of genes by the GAL4/UAS-based trans enhancer

To further improve in vivo application of the CRISPR-assisted trans enhancer, we tried realizing the trans enhancer with the GAL4-UAS system. A dCas9-VP64-GAL4 expression vector and a UAS-CMV trans enhancer was constructed. Two forms of trans UAS-CMV enhancers, linear UAS-CMV (LUAS-CMV) and circular UAS-CMV (CUAS-CMV), were expected to be recruited to the target gene by the dCas9-VP64-fused GAL4 (Figure 6a). By transfecting 293 T cells with dCas9-VP64-GAL4/sgRNA-LUAS-CMV/CUAS-CMV and reporter construct, the ZsGreen expression of the exogenous reporter gene was significantly activated by both LUAS-CMV and CUAS-CMV, but not activated by all transfections as controls (Figure 6b, Figure 6—figure supplement 1). By transfecting 293T and HepG2 cells with dCas9-VP64-GAL4/sgRNA-LUAS-CMV/CUAS-CMV, the expression of endogenous HNF4α gene was highly activated in the two cells (Figure 6c). More importantly, both trans LUAS-CMV and CUAS-CMV enhancers showed significantly higher activity than the trans sCMV (Figure 6c). In contrast, all transfections as controls did not activate the expression of endogenous HNF4α gene in the two cells (Figure 6c). These results reveal that the CRISPR-assisted trans enhancer could be better realized with the GAL4-UAS system.

Figure 6 with 1 supplement see all
Activation of exogenous and endogenous genes with the GAL4-UAS-based trans enhancer.

(a) Schematic show of gene activation using the GAL4-UAS-based CRISPR-assisted trans enhancer. (b) Activation of exogenous reporter gene ZsGreen. The fluorescence intensity of cells was analyzed with flow cytometry. (c) Activation of endogenous HNF4α gene in 293T and HepG2 cells with the GAL4-UAS-based CRISPR-assisted trans enhancer. The sCMV was used as a positive control for comparison. Three other transfections were used as controls. LUAS-CMV, linear UAS-CMV; CUAS-CMV, circular UAS-CMV. Data are shown as mean ± SD, n = 3. The statistical difference was analyzed by Student’s t-test. *, p<0.05; **, p<0.01.

https://doi.org/10.7554/eLife.45973.014

Discussion

In this study, we developed a new dCas9-based gene activation strategy, the CRISPR-assisted trans enhancer, in which a trans enhancer could be recruited to target promoters by dCas9-VP64/csgRNA or dCas9-VP64-GAL4/sgRNA. The results revealed that expression of variant exogenous and endogenous genes could be highly activated by CRISPR-assisted trans enhancers in various mammalian cells, more efficiently than with current widely used dCas9-VP64 and dCas9-VPR. This strategy has unique advantages over the current dCas9-based gene activation systems.

First, only one csgRNA was used in activating all target genes in various cells with the CRISPR-assisted trans enhancer. However, in current dCas9-based gene activations, multiple sgRNAs are used. In general, three or more sgRNAs are used to activate a gene of interest (Cheng et al., 2013; Maeder et al., 2013; Mali et al., 2013a; Perez-Pinera et al., 2013). In many assays with various numbers of sgRNAs, one sgRNA often produced very low or undetectable expression. Using multiple sgRNAs, each sgRNA has to be independently transcribed by a long U6 promoter. Second, csgRNA is the simplest sgRNA used in dCas9-based gene activators, which only extended a 24 bp short sequence at the 3′ end of normal sgRNA. However, current dCas9/sgRNA activators often use long complex chimeric sgRNAs that harbor multiple tandem aptamers of various RNA-binding proteins, such as SAM sgRNA (MS2) (Konermann et al., 2015; Liao et al., 2017), Casilio sgRNA (Pumilio/FBF) (Cheng et al., 2016), and scaffold RNAs (MCP, PCP, and Com) (Zalatan et al., 2015).

The capture sequences of csgRNA can be easily designed. We originally designed three different capture sequences. All functioned in the CRISPR-assisted trans enhancer; however, csgRNA2 showed the best performance. The capture sequences were artificially designed short sequences, they have no complementary sequences in human cells, which is important for their specific annealing with sCMV at high efficiency. This study demonstrated that sCMV could be efficiently captured by csgRNA in the nucleus of human cells. To our knowledge, this is the first report of a gene being activated by an artificial DNA in trans.

In this study, we realized the CRISPR-assisted trans enhancer with two forms: csgRNA-sCMV and GAL4-UAS. Two forms can be easily used to activate genes in in vitro cultivated cells. As to the in vivo applications, the csgRNA-sCMV-based trans enhancer can be used via nanoparticle gene carriers, while the GAL4-UAS-based trans enhancer can be easily transferred by virus vectors such as AAV, with AAV already being approved for use as a gene vector in clinics. We found that the GAL4-UAS-based trans enhancer had better performance than the csgRNA-sCMV-based trans enhancer, especially the linear UAS-CMV. In in vivo applications, the linear UAS-CMV can be easily transferred by AAV vector.

As a typical application, dCas9-based transcriptional activators are used to reprogram cells in vitro and in vivo for biomedical applications by activating endogenous genes. For example, fibroblasts were reprogramed into induced pluripotent stem (iPS) cells by endogenous activation of the Oct4 and Sox2 genes with dCas9-SunTag-VP64 (Liu et al., 2018). Mouse embryonic fibroblasts were converted into neuronal cells by endogenous activation of the Brn2, Ascl1, and Myt1l genes with VP64dCas9VP64 (Black et al., 2016). In vivo target genes were activated by MPH to ameliorate disease phenotypes in mouse models of type I diabetes, acute kidney injury, and muscular dystrophy (Liao et al., 2017). Brain astrocytes were converted into functional neurons in vivo by activating the Ascl1, Neurog2 and Neurod1 genes with SPH (Zhou et al., 2018). These studies make CRISPR therapies the grade not the cut (Burgess, 2018).

In this study, we selected 10 endogenous genes to be activated by the CRISPR-assisted trans enhancer. Most of these genes code transcription factors, including HNF4α, E47, Ascl1, Ngn2, Sox2, Oct4, and Nanog. Ascl1, Ngn2, and Sox2 are used to directly reprogram fibroblasts into nerve cells (Zhao et al., 2015). Oct4, Sox2, and Nanog are widely used to reprogram fibroblasts into iPS cells (Takahashi et al., 2007; Takahashi and Yamanaka, 2016; Yu et al., 2007). HNF4α and E47 are used to differentiate liver and panaceas cancer cells into normal cells (Kim et al., 2015; Yin et al., 2008). TNFAIP3 is a well-known natural NF-κB inhibitor (Cooper et al., 1996), having the potential to treat NF-κB-overactivated diseases such as inflammation and cancers. Caspase9 is a key gene making cell apoptosis (Li et al., 2017). CSF3 codes granulocyte-colony stimulating factor (G-CSF), a glycoprotein that stimulates the bone marrow to produce granulocytes and stem cells and release them into bloodstream (Cetean et al., 2015), and is widely used in chemotherapy to enhance the immunity of cancer patients. We selected these genes for exploring the future in vitro and in vivo applications of the CRISPR-assisted trans enhancer.

Materials and methods

Vector construction

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A lac operon fragment was amplified from pEASY-Blunt-simple (Transgen) using primers Lac-px-F and Lac-px-R. The product was ligated into px458 (Addgene) to construct px458-lac using BbsI (NEB) and BsaI (NEB). The U6-sgRNA-lac fragment was amplified from px458-lac using primers U6-F and U6-R/U6-1-R/U6-2-R/U6-3-R. The products were cloned into the pEASY-Blunt-simple to produce pEASY-sgRNA and pEASY-csgRNA (Supplementary file 2), which were used to construct particular sgRNA/csgRNA expressing plasmids. The sgRNAs targeting genes of interest were designed by CHOPCHOP. Chemically synthesized complementary oligonucleotides containing sgRNA/csgRNA targets were annealed and ligated into pEASY-sgRNA/pEASY-csgRNA. The ligation reaction consisted of 10 U BbsI (NEB), 120 U T4 DNA ligase (NEB), 1 × T4 DNA ligase buffer, 0.1 mg/mL bovine serum albumin, and 50 ng plasmid pEASY-csgRNA. The reaction was run as follows: 10 rounds of 37 °C5 min and 16°C 10 min, 37°C 30 min, and 80°C 5 min. The reaction was then used to transfect DH5α competent cells. The white clones were screened by blue-white screening on LB agar plates with 100 µg/mL ampicillin, 40 µL of 20 mg/mL X-gal, and 40 µL of 0.1 M IPTG. The vectors were validated by sequencing, then the linear sgRNA/csgRNA expression vectors were amplified from the validated pEASY-sgRNA/pEASY-csgRNAs using primers U6-F and U6-R/U6-1-R/U6-2-R/U6-3-R. The primer U6-R was used to amplify the normal sgRNA expression template (named as U6-sgRNA) from pEASY-sgRNA. The primer U6-1/2/3 R was used to amplify the csgRNA expression template (named as U6-csgRNA) from pEASY-csgRNA. The PCR products were purified with PCR clean kit (Axygen) and used to transfect cells as sgRNA/csgRNA expression vector.

The CMV enhancer fragment was amplified from pEGFP-N1 using primers CMV-F and CMV-1-R/CMV-2-R/CMV-3-R. The PCR products were purified with PCR clean kit and used as linear blunt-end CMV (bCMV). To prepare stick-end CMV (sCMV), the PCR products were firstly digested with Nt.BbvCI and then added with complementary oligonucleotide CS-1-R/CS-2-R/CS-3-R. The PCR products were denatured at 85°C for 10 min and then naturally cooled to room temperature. The PCR products was purified with PCR clean kit and used as linear sCMV. The blunt-end EF1α/PGK promoters were all amplified from pEF1a-FB-dCas9-puro (Addgene) using primers EF1-a-F/R and PGK-F/R. The stick-end EF1α/PGK promoters were similarly prepared by treating blunt-end EF1α/PGK promoters.

A 1000 bp HNF4α promoter sequence was amplified from the genomic DNA of HepG2 cells using primers HNF4α-P-F and HNF4α-P-R. The amplified promoter fragment was ligated into pEZX-ZsGreen, producing an HNF4α promoter reporter construct pEZX-HP-ZsGreen. The VP64 sequence was deleted from pcDNA-dCas9-VP64 (Addgene) to construct pcDNA-dCas9. The VPR sequence was cloned into pcDNA-dCas9 to construct pcDNA-dCas9-VPR.

The GAL4 fragment was amplified from pGBKT7 (MiaoLing Plasmid Sharing Platform) using primers GAL4-BsiEW-F and GAL4-BspE-R, and was then ligated into pcDNA-dCas9-VP64 using BsiWI and BspEI to prepare pcDNA-dCas9-VP64-GAL4. A chemically synthesized 5 × UAS fragment was ligated into pEASY-Blunt using BglII and HindIII to obtain pEASY-Blunt-UAS. A CMV fragment was amplified from pEGFP-C1 using primers UAS-CMV-Bgl-F and UAS-CMV-Hind-R. The CMV fragment was then ligated into pEASY-Blunt-UAS to obtain pEASY-Blunt-UAS-CMV, which was used as circular UAS-CMV (CUAS-CMV). The linear UAS-CMV (LUAS-CMV) fragment was amplified from pEASY-Blunt-UAS-CMV using primers CMV-UAS-Bgl-F and UAS-CMV-R.

The sequences of all PCR primers used in the above vector construction are shown in the Supplementary file 1–Table 1. The chemically synthesized complementary oligonucleotides used to construct pEASY-sgRNA/pEASY-csgRNA are shown in the Supplementary file 1–Table 2. The functional sequences of all linear and plasmid vectors are provided as Supplementary file 3.

DNA cutting with Cas9-csgRNA

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A sgRNA targeting the HNF4α promoter sequence was selected. The sgRNAs were prepared by in vitro transcription using T7 RNA polymerase (NEB). The sgRNA transcription template was amplified from pEASY-csgRNA using primers HNF4α-T7-F and U6-R/U6-1-R/U6-2-R/U6-3-R. A normal sgRNA (HNF4α-sgRNA) and three csgRNAs (HNF4α-csgRNAs) were prepared. A 732 bp HNF4α promoter fragment was amplified from pEZX-HP-ZsGreen using primers HNF4α-sP-F and HNF4α-sP-R. The sequences of PCR primers are shown in the Supplementary file 1-Table 1. The Cas9 digestion reaction (30 μL) consisted of 1 × Cas9 Nuclease Reaction Buffer, 1 µM Cas9 Nuclease (NEB), and 300 nM HNF4α-sgRNA or HNF4α-csgRNA. The reaction was incubated at 25°C for 10 min. Then 400 ng of purified 732 bp HNF4α promoter fragment was added to the reaction and incubated at 37°C for 15 min. Finally, the Cas9 nuclease was inactivated at 65°C for 10 min. The reaction was run with 1.5% agarose gel electrophoresis.

Cell lines

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All cells including 293T, HepG2, PANC1, A549, HeLa, SKOV3, and HT29 were obtained from the Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences. The identity was authenticated by STR profiling. Mycoplasma contamination testing was performed and no mycoplasma contamination was ensured.

Cell culture and transfection

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Cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM) or Roswell Park Memorial Institute (RPMI) 1640 medium supplemented with 10% FBS, 100 units/mL penicillin, and 100 µg/mL streptomycin. Cells at >70% confluence in each well of a 12-well plate were transfected with various combinations (see figures) of linear or plasmid vectors, including pcDNA-dCas9, pcDNA-dCas9-VP64, pcDNA-dCas9-VPR, U6-sgRNA, U6-csgRNA, sCMV, bCMV, and pEZX-HP-ZsGreen, using Lipofectamine 2000 (ThermoFisher Scientific) according to the manufacturer’s instructions. The transfected cells were incubated at 37°C and 5% CO2 for 36 h. Cells were then imaged with a fluorescence microscope (Olympus) at 200 × magnification.

Flow cytometry

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The fluorescence intensity of cells was quantified with flow cytometry (Calibur). Ten-thousand cells were measured for each transfection. Flow cytometry data analysis and figure preparation were done using BD software.

Quantitative PCR

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The total RNA was extracted from cells using TRIzol (Invitrogen). The complementary DNA (cDNA) was synthesized with 3 μg of total RNA using the Hifair III SuperMix (Yeasen). Gene transcription was detected with quantitative PCR (qPCR) using the Hieff qPCR SYBR Green Master Mix (Yeasen) according to the manufacturer’s instructions. GAPDH was used as an internal reference to analyze the relative mRNA expression of different genes. The sequences of PCR primers are shown in the Supplementary file 1-Table 3 and 4. The qPCR programs were run on StepOne Plus (Applied Biosystems). Each qPCR detection was performed in at least three technical replicates. Melting curve analysis was performed. Data analysis was performed using the Applied Biosystems StepOne software v2.3, and Ct values were normalized with that of GAPDH. The relative expression level of target mRNAs was calculated as relative quantity (RQ) according to the equation: RQ = 2-ΔΔCt.

Statistical analyses

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Each cell transfection for detecting gene expression activation by trans enhancer was performed in three biological replicates. In each biological replicate, at least three technical replicates (three replicate wells) were performed. In qPCR detection of gene expression, the mean RQ value of technical replicates was used as the RQ value of one biological replicate. The mean RQ value of three biological replicates was used to calculate the final mean and standard deviation (SD). Data were analyzed by Student’s t test when comparing two groups. Data are shown as mean ± SD, and differences were considered significant at p<0.05.

References

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Decision letter

  1. Irwin Davidson
    Reviewing Editor; Institut de Génétique et de Biologie Moléculaire et Cellulaire, France
  2. Kevin Struhl
    Senior Editor; Harvard Medical School, United States
  3. Irwin Davidson
    Reviewer; Institut de Génétique et de Biologie Moléculaire et Cellulaire, France

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

[Editors’ note: a previous version of this study was rejected after peer review, but the authors submitted for reconsideration. The first decision letter after peer review is shown below.]

Thank you for submitting your work entitled "Gene activation by a CRISPR-assistant trans enhancer" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Irwin Davidson as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor.

Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in eLife.

While the referees found the concept of tethering an enhancer element in trans as a novel and innovative approach to activate expression of chosen endogenous cellular genes, they were unanimously concerned by the lack of information on the reproducibility of the data, the absence of appropriate statistical tests and important missing controls. The presentation of the data made it difficult to assess the efficiency of the system and the many of the experiments will have to be repeated again and the data presented in a much more rigorous fashion. The referees would encourage the authors to take note of the many suggestions and comments that would have to be addressed in any future manuscript.

Reviewer #1:

This paper describes a novel system to tether an CMV enhancer element in trans to selected regions of the genome using CRISPR/CAS9 allowing the selective activation of chosen target genes. They show that activation by this method is more efficient that CAS9-VP64 and that the two systems can be used cooperatively.

Major issues:

To demonstrate the specificity of the technique, the authors should perform experiments with a version of the CMV enhancer in which key bases have been mutated to impair the binding of transcription factors. This would demonstrate the importance of using a functional enhancer. The authors may also consider experiments using cell-specific enhancers and demonstrating that the system can used in a cell-type specific manner. This is not essential for the message of the paper but may give added value to the study.

In Figure 2 and Figure 3 as well as Figure 2—figure supplement 2, Figure 2—figure supplement 3, Figure 2—figure supplement 4 the results of the ZsGreen assays are absolutely not clear. What are we supposed to compare, how by looking at the figures can we assess the activation in the different conditions? Also, in the text, there are no precise figures given concerning the fold activation in each condition. In Figure 4A, there are only 2 transfections so there are no robust statistics concerning activation. The reasons for assaying ZsGreen rather measuring RNA output directly are not well justified. All of these figures have to be modified such that the reader can readily assess the differences in each experimental condition. RNA should be measured by RT-qPCR and fold changes and statistics from replicate experiments should be provided.

In Figure 5 and Figure 6 the number of biological replicates and the statistical tests used should both be indicated. The text describing these figures is somewhat oversimplified as they claim that activation of HNF4A results in strong induction of liver function genes. In fact, some are strongly induced, but for others the effect is very low and this is not recognised in the text. Statistics should be shown for the cell cycle analyses in Figure 6C number of replicates and statistical test used.

Figure S9. There is no obvious difference in the number of cells in the different conditions. This must be quantitated and analysed to provide reliable statistics.

In summary, while this paper describes an interesting and novel technique, the data as they are presented require additional replicates, statistical test and clearer presentation to be acceptable.

Additional data files and statistical comments:

The number of replicates and the statistical tests used are not indicated and more rigorous analyses have to be performed and present in any revised version. The present version is way below acceptable standards.

Reviewer #2:

Here, Xu et al., describe a transcriptional activation strategy using dCas9-VP64 together with a modified gRNA scaffold (csgRNA) that can anneal to a CMV enhancer DNA sequence (sCMV) provided in trans. They show that dCas9/csgRNA/sCMV (in the absence of a transactivation domain) is capable of inducing transcription of a reporter construct and that the transcriptional activity of the complex is enhanced by the addition of a transactivation domain (VP64) to dCas9 (dCas9-VP64). They also show that transfecting dCas9-VP64/csgRNA/sCMV is capable of inducing transcription of the reporter in 7 different cell lines. They also claim that dCas9-VP64/csgRNA/sCMV is a little bit more efficient that dCas9-VPR in inducing transcription. Finally, they show that dCas9-VP64/csgRNA/sCMV can induce transcription of endogenous genes in two different cancer cell lines and that this can trigger differentiation.

Although the strategy proposed here is original, the sCMV component needs to be provided in trans (co-transfected), therefore application of this system is limited to cells in culture and would be very challenging to use this strategy in vivo.

A major flaw of this study is that transfection efficiency cannot be determined. It is impossible to know the percentage of cells that are expressing dCas9-VP64 and the proportion of these in which transcriptional activation has occurred. Therefore, the transcriptional effect observed is either under- or over-estimated.

There are many variables in the experiment, as 4 constructs need to be co-transfected (reporter, dCas9-VP64, csgRNA and sCMV). The experiments should have been done in a cell line stably transfected with the reporter construct and using a single plasmid co-expressing the csgRNA together with mCherry-dCas9-VP64 (or mCherry-dCas9-VPR). This would not only reduce the number of variables but would allow the authors to: (1) Determine the transfection efficiency and the frequency of mCherry+ cells (expressing dCas9-VP64) in which transcriptional activation has occurred (ZsGreen+) and (2) Determine the transactivation level by scoring the MFI of ZsGreen in the mCherry+ ZsGreen+ population.

The FACS data should be presented as dot plots (FSC vs. ZsGreen). This allows to visualize the ZsGreen+ population, which is not clear at all (given the low percentage of ZsGreen+ cells) from the histograms.

The manuscript is poorly written and is full of strange words and expressions and should be proof-read by an English-speaking person.

Figure legends do not describe the figures and should be re-written. Legend for Figure 1B is missing.

On all FACS data shown, the gating for ZsGreen is not the same throughout (i.e. compare panels in Figure 2).

It is not clear how the MFI intensity in Figure 4 was determined. Therefore, it is impossible to assess the validity of the comparisons.

In Figure 1B, the percentage of cleavage should be displayed, as it is clear that the csgRNA is not as efficient as the unmodified scaffold. Also, the input is missing.

In Figure 5, a negative control without the csgRNA is missing. The relative mRNA induction should be calculated relative to this control, as it would directly show the fold induction triggered by dCas9-VP64/csgRNA/sCMV. Also, the y axis should be labelled relative mRNA (and not RNA) expression.

In the qPCR data shown in Figure 6A and 6B, it is not indicated what was used to determine the relative expression. Same as in Figure 5, the negative control (without the csgRNA) is missing and should be performed in order to determine the relative expression of each gene. Also, the expression level of the target gene should be shown.

For clarity, a diagram of the reporter construct should be shown.

Additional data files and statistical comments:

Throughout the manuscript, it is not clear how many times the experiments were done. For example, the qPCR data shown in Figure 5, do the error bars correspond to technical or biological replicates? Is a representative experiment shown? Also, in several figures it is not indicated which samples are compared when the * is shown for statistical significance.

Reviewer #3:

In their study, Xu et al., developed a novel approach using CRISPR/dCas9 to activate gene expression. They modified the guide RNA scaffold as such that a CMV enhancer DNA fragment was recruited to the target gene by the dCas9-VP64/sgRNA complex. The authors argue that previously published approaches have limitation regarding the strength of transcriptional activation and their approach is superior in this regard.

The approach by Xu et al., is novel and innovative having potential beyond the use case presented in the current study. Based on the data presented I have the following comments/questions.

1) The authors initially validate their approach (Figure 2) in 293T cells by co-transfecting an exogenous GFP-based reporter construct. Transient transfection efficiencies of 293T cells are known to be extremely high. Why does e.g. dCas9-VP64/csgRNA2-sCMV achieve only 11% of GFP+ cells, assuming that >90% of 293T are transfected?

2) Regarding Figure 5 and along the line of my previous comment. How do we have to interpret the fold-changes in expression of endogenous genes having seen that in Figure 2 only a comparably small proportion of cells activated the reporter construct? Based on the methods description, all endogenous gene expression analyses were done using the bulk cell cultures. Are the changes in gene expression seen in the bulk culture caused by extremely high transactivation in a small fraction of cells or by homogenous induction across all cells? At least the first scenario is suggested by the pilot experiments shown in Figure 2. Thus, single cell-based approaches could clarify this for the endogenous target genes, e.g. immunofluorescence/FACS for the proteins encoded by the transactivated target genes.

3) If transcriptional activation is achieved only in a fraction (approximately 10%) of the cells despite high transfection efficiencies, then the approach by Xu et al., would have some limitations, at least for bulk cell culture analyses, in comparison to SAM and other methods, which can be also efficiently delivered to target cells by viruses. Where do the authors then see the advantage of their approach, as the requirement for transient transfection remains a drawback, in particular in cell models that are difficult to transfect?

[Editors’ note: what now follows is the decision letter after the authors submitted for further consideration.]

Thank you for resubmitting your work entitled "Gene activation by a CRISPR-assisted trans enhancer" for further consideration at eLife. Your revised article has been favorably evaluated by Kevin Struhl (Senior Editor), a Reviewing Editor, and two reviewers.

The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:

In this new version, the authors addressed many of the issues raised on the previous versions. They have provided data on the reproducibility of the results with the appropriate statistics. They have used alternative regulatory elements in place of CMV and they have developed an alternative strategy for recruiting the trans-acting element using the GAL4 DNA binding domain. These new innovations have strengthened the study. However, the paper is now too long with too many figures. The paper would be more appropriate if it was shortened to a Tools and Resources article rather than a full manuscript. This can be done by reorganizing the figures. For example, rather than showing all of the fluorescence images and sorting results, this could all be shown in graphical form as is already the case for Figures 8C, D and E and Figure 9C, D and E. This would save a lot of space and condense all the information into a single figure, showing perhaps a single representative sorting experiment and cell images as supplemental data. Also Figure 5 could be shortened to show only 1 or 2 cell types with the rest shown as supplemental data. I also propose that the data on cell cycle changes and migration be removed to shorten the text. This data is not central to the main message of the paper. On the other hand, supplemental figure 11 should be shown in the main figures possibly as an addition to Figure 1.

Finally, the writing is poor and the meaning of what is written is in several places very difficult to understand. Mitigating this will require a major effort.

The authors are invited to submit a shortened revised version based on the above suggestions.

https://doi.org/10.7554/eLife.45973.021

Author response

[Editors’ note: the author responses to the first round of peer review follow.]

Reviewer #1:

[…] To demonstrate the specificity of the technique, the authors should perform experiments with a version of the CMV enhancer in which key bases have been mutated to impair the binding of transcription factors. This would demonstrate the importance of using a functional enhancer. The authors may also consider experiments using cell-specific enhancers and demonstrating that the system can used in a cell-type specific manner. This is not essential for the message of the paper but may give added value to the study.

In order to obtain the significant gene activation, we used a widely used mammalian enhancer/promoter, CMV enhancer/promoter. This was described in detail in the Introduction:

“Almost three decades ago, the human cytomegalovirus (CMV) enhancer/promoter (referred to as CMV enhancer hereafter) was found as a natural mammalian promoter with high transcriptional activity (Boshart et al. 1985). The late studies gradually found that the CMV enhancer is the known strongest promoter in various mammalian cells (Boshart et al., 1985; Foecking and Hofstetter, 1986; Ho et al., 2015; Kim et al., 1990). Therefore, this enhancer has been widely used to drive the ectopic expression of various genes in wide range of mammalian cells. For example, the CMV enhancer is also used to drive the ectopic expressions of exogenous genes in broad tissues in transgenic animals (Furth et al., 1991; Schmidt et al., 1990), protein production by gene engineering, and human gene therapy. We have recently further improved the transcriptional activity of the CMV enhancer by changing the natural NF-κB binding sites in this enhancer into artificially selected NF-κB binding sequences with high binding affinity (Wang et al., 2018). Therefore, we conceived that an unique architecture may be constructed to further improve dCas9-based activators by using the CMV enhancer.”

CMV enhancer/promoter is a known strong natural mammalian enhancer/promoter. Therefore, it was widely employed in researches of biological sciences and biomedicine. Its strongest transcriptional activity was found to be dependent on many binding sites of multiple transcription factors. For example, there are four binding sites of NF-κB. We have recently published a paper about CMV enhancer/promoter in which we discussed in detail the CMV enhancer/promoter and further improved the transcriptional activity of the CMV enhancer by changing the natural NF-κB binding sites in this enhancer into artificially selected NF-κB binding sequences with high binding affinity (Wang et al., 2018). We have already cited the study in the paragraph above. In this study, we found that changing any one of the NF-κB binding sites had no significant effect on the transcriptional activity of CMV enhancer/promoter. Maybe this is the reason why the CMV enhancer/promoter always shows high transcriptional activity in various cell lines. Different transcription factor binding sites and transcription factors show synergistic reaction, which jointly contributed to the strong transcriptional activity of CMV enhancer/promoter. Due to its harbouring of many binding sites of multiple transcription factors, it is very difficult to create a mutation of this enhancer/promoter to get a mutant CMV enhancer/promoter that has significant impaired transcriptional activity relative to the wild-type CMV enhancer/promoter.

In this study, we focused on verifying the feasibility and reliability of activating expression of both exogenous and endogenous genes by CRISPR-based trans-enhancer, not focused one verifying a function of a known or potential enhancer. Therefore, we did not used a mutated CMV enhancer/promoter.

When transfecting cells, we included many necessary transfections as controls. All transfections of a cell line were performed simultaneously at the same condition for the sake of comparison.

We are sorry that we have not used any enhancer fragments other than CMV due to the reasons described above. However, your suggestion is very good. We expect to measure other enhancer fragments in future study.

In Figure 2 and Figure 3 as well as Figure 2—figure supplement 2, Figure 2—figure supplement 3, Figure 2—figure supplement 4 the results of the ZsGreen assays are absolutely not clear. What are we supposed to compare, how by looking at the figures can we assess the activation in the different conditions? Also, in the text, there are no precise figures given concerning the fold activation in each condition. In Figure 4A, there are only 2 transfections so there are no robust statistics concerning activation. The reasons for assaying ZsGreen rather measuring RNA output directly are not well justified. All of these figures have to be modified such that the reader can readily assess the differences in each experimental condition. RNA should be measured by RT-qPCR and fold changes and statistics from replicate experiments should be provided.

As suggested by another reviewer, all FACS data were presented as dot plots (FSC vs. ZsGreen) as suggested. Please see the revised Figure 2 and Figure 3 as well as Figure 2—figure supplement 2, Figure 2—figure supplement 3, Figure 2—figure supplement 4. An important sentence was added in the legends of these figures: “The reporter gene activation efficiency was indicated by the percentage of cells with green fluorescence over the threshold (cells in Q1-UR quadrant).” In these experiments, we aimed at evaluating the gene activation capability by using a reporter construct, ZsGreen.

Figure 4A was revised, a further biological replicate was performed and the statistics were added. Please see the revised Figure 4.

Because the expression of reporter gene of ZsGreen can be measured at protein level by detecting fluorescence, we showed gene activation by fluorescence images and fluorescence intensity analyzed by flow cytometry. Therefore, we did not measured expression of reporter gene ZsGreen at mRNA level.

However, in activating ten endogenous genes in seven different cell lines, we detected the gene expression at mRNA level by using qPCR. In all activation of endogenous genes, the fold changes and statistics from three biological replicates were provided. Please the revised Figure 5 and Figure 6.

In Figure 5 and Figure 6 the number of biological replicates and the statistical tests used should both be indicated. The text describing these figures is somewhat oversimplified as they claim that activation of HNF4A results in strong induction of liver function genes. In fact, some are strongly induced, but for others the effect is very low and this is not recognised in the text. Statistics should be shown for the cell cycle analyses in Figure 6C number of replicates and statistical test used.

As to Figure 5 and Figure 6, the experiments were re-performed in the past three months. Three biological replicates were performed for the transfection of each cell line. The statistical difference was analyzed by Student t test. The new results were showed as the revised Figure 5 and Figure 6. The figure legends were also revised. Please see the revised manuscript. Please note that Figure 6C is now Figure 7A.

The content of subsection “Statistical analyses” was also revised as follows:

“Each cell transfection for detecting gene expression activation by trans-enhancer was performed in three biological replicates. In each biological replicate, at least three technical replicates were performed. The mean RQ value of technical replicates was used as the RQ value of one biological replicate. The mean RQ value of three biological replicates were used to calculate the final mean and standard deviation (SD). Data were analyzed by Student t test when comparing 2 groups. Data were expressed as mean ± SD and differences were considered significant at P < 0.05.”

Figure 6C is now Figure 7A. Statistics was shown for the cell cycle analyses in this figure. Number of replicates and statistical test used were added.

Figure S9. There is no obvious difference in the number of cells in the different conditions. This must be quantitated and analysed to provide reliable statistics.

The images of acridine orange-stained cells were counted with ImageJ software. The difference among different treatments were tested with Students t test. There is a significant difference between treated cells and control cells. Please see the added Figure S9C.

In summary, while this paper describes an interesting and novel technique, the data as they are presented require additional replicates, statistical test and clearer presentation to be acceptable.

Yes, three biological replicates were performed for the transfection of each cell line. The statistical difference was analyzed by Student’s t-test. The new results were showed as the revised Figure 4, Figure 5, Figure 6 and Figure 7. The figure legends were also revised. Please see the revised manuscript.

Additional data files and statistical comments:

The number of replicates and the statistical tests used are not indicated and more rigorous analyses have to be performed and present in any revised version. The present version is way below acceptable standards.

Each cell transfection for detecting gene expression activation by trans-enhancer was performed in three biological replicates. In each biological replicate, at least three technical replicates were performed. In the detection of gene expression by qPCR, the mean RQ value of technical replicates was used as the RQ value of one biological replicate. The mean RQ value of three biological replicates were used to calculate the final mean and standard deviation (SD). Data were analyzed by Student’s t-test when comparing 2 groups. Data were shown as mean ± SD and differences were considered significant at P < 0.05.

Reviewer #2:

[…] Although the strategy proposed here is original, the sCMV component needs to be provided in trans (co-transfected), therefore application of this system is limited to cells in culture and would be very challenging to use this strategy in vivo.

This is a good point. It is clear that the csgRNA-sCMV-based trans enhancer technique is helpful for in vitro application, such as in vitro cell reprogramming and gene activation for gain-of function. However, it’s true that the csgRNA-sCMV-based trans enhancer still faces difficulty in in vivo application. The trans enhancer used a linear CMV enhancer DNA fragment that has a single-stranded overhang complementary to the 3′ end of csgRNA. It is difficult to produce this kind of trans enhancer DNA in the in vivo cells unless transfecting the in vitro pre-prepared trans enhancer with nanoparticle gene carriers together with expression vectors of dCas9-VP64 and csgRNA. However, the current trans enhancer can’t be brought into the in vivo cells by the current most effective in vivo transgenic vector virus (e.g. AAV) that has been approved by FDA to clinical application.

To address the problem, we also developed a new strategy by using a GAL4-UAS system, in which dCas9-VP64-GAL4, sgRNA and UAS-CMV were used. The UAS-CMV was recruited to target gene by dCas9-VP64-GAL4 via the interaction between GAL4 and UAS. We showed that UAS-CMV in both linear and circular forms could be recruited by dCas9-VP64-GAL4 and functioned. This system allows easy in vivo application of the CRISPR-assisted trans enhancer technique. For example, all components including dCas9-VP64-GAL4, sgRNA and LUAS-CMV could be easily provided to in vivo cells as Adeno-associated virus (AAV), a current widely used safe virus vector of human gene therapy.

Thanks for your comments.

A major flaw of this study is that transfection efficiency cannot be determined. It is impossible to know the percentage of cells that are expressing dCas9-VP64 and the proportion of these in which transcriptional activation has occurred. Therefore, the transcriptional effect observed is either under- or over-estimated.

All FACS data were presented as dot plots (FSC vs. ZsGreen) as suggested. Please see the revised figures. All FACS data presented as dot plots of a particular cell line simultaneously transfected by various vectors used the same gating for comparing. Maybe these dot plots tell something about transfection efficiency. It was clear the ZsGreen activation rate is low in our transfection. The main reason for this is the close relationship between the co-transfection and the three independent vectors, a csgRNA expression vector, a dCas9-VP64/VPR expression vector, and a reporter gene ZsGreen expression vector.

However, we think that transfection efficiency do not affect our conclusion, that is, the trans-enhancer itself can activate the reporter gene expression by complexing with dCas9/csgRNA and dCas9-VP64/csgRNA (please see Figure 2 and Figure 3, Figure 2—figure supplement, Figure 2—figure supplement3, Figure 2—figure supplement 4, Figure 3—figure supplement 1, and Figure 3—figure supplement 2). Moreover, trans-enhancer had significant higher gene activation efficiency by complexing with dCas9-VP64/csgRNA than dCas9-VP64/sgRNA (please see Figure 2 and Figure 3, Figure 2—figure supplement, Figure 2—figure supplement3, Figure 2—figure supplement 4, Figure 3—figure supplement 1, and Figure 3—figure supplement 2). Because each cell was simultaneously transfected by various vectors at the same condition and many necessary controls were performed at the same time, the transfection efficiency is not a variable to affect the results of comparison of various transfections. Additionally, in flow cytometry assay, as many as ten thousand cells were measured for each transfection.

Moreover, the reporter gene is only one way to evaluate the gene activation capability. Our major focus is to activate endogenous genes by CRISPR-assistant trans-enhancer. We activated as many as ten endogenous genes in as many as seven different cell lines from various tissues. By comparing with dCas9-VP64/sgRNA in all transfections, it is clear that the trans-enhancer effectively activated these endogenous genes by complexing with dCas9-VP64/csgRNA, with significant high activation efficiency in all transfections than dCas9-VP64/sgRNA. Additionally, dCas9-VP64/sgRNA system included a two vector co-transfection, but the trans-enhancer system included three vectors. The former should have higher transfection efficiency than the latter; however, it still showed significant lower activation efficiency than trans-enhancer.

There are many variables in the experiment, as 4 constructs need to be co-transfected (reporter, dCas9-VP64, csgRNA and sCMV). The experiments should have been done in a cell line stably transfected with the reporter construct and using a single plasmid co-expressing the csgRNA together with mCherry-dCas9-VP64 (or mCherry-dCas9-VPR). This would not only reduce the number of variables but would allow the authors to: (1) Determine the transfection efficiency and the frequency of mCherry+ cells (expressing dCas9-VP64) in which transcriptional activation has occurred (ZsGreen+) and (2) Determine the transactivation level by scoring the MFI of ZsGreen in the mCherry+ ZsGreen+ population.

Yes, this is a good point and if we performed the experiments as suggested, the results should be better. However, as we described above, the transfection efficiency does not affect our conclusion. We focused on determining the gene activation efficiency by comparing various transfection obtained at the same condition. We did not focus on investigating transfection efficiency of different vectors. We think that at the same transfection conditions, the transfection efficiency did not affect our results and conclusion. Also, as we described above, the reporter gene is only one way to evaluate the gene activation capability. Our major focus is to activate endogenous genes by CRISPR-assistant trans-enhancer. We activated as many as ten endogenous genes in as many as seven different cell lines from various tissues. By comparing with dCas9-VP64/sgRNA in all transfections, it is clear that the trans-enhancer effectively activated these endogenous genes by complexing with dCas9-VP64/csgRNA, with significant high activation efficiency in all transfections than dCas9-VP64/sgRNA.

The FACS data should be presented as dot plots (FSC vs.ZsGreen). This allows to visualize the ZsGreen+ population, which is not clear at all (given the low percentage of ZsGreen+ cells) from the histograms.

Yes, all FACS data were presented as dot plots (FSC vs. ZsGreen) as suggested. Please see the revised figures.

The manuscript is poorly written and is full of strange words and expressions and should be proof-read by an English-speaking person.

The whole manuscript was carefully revised to improve the writing.

Figure legends do not describe the figures and should be re-written. Legend for Figure 1B is missing.

The figure legends were carefully revised to describe the figures. Legend for Figure 1B was added. Thank you.

On all FACS data shown, the gating for ZsGreen is not the same throughout (i.e. compare panels in Figure 2).

Yes, all FACS data were presented as dot plots (FSC vs. ZsGreen) as suggested. In all FACS data of a cell, the same gating was used for comparing.

It is not clear how the MFI intensity in Figure 4 was determined. Therefore, it is impossible to assess the validity of the comparisons.

Figure 4 was revised. The MFI intensity was determined by flow cytometry. Please see the revised Figure 4 legends.

In Figure 1B, the percentage of cleavage should be displayed, as it is clear that the csgRNA is not as efficient as the unmodified scaffold. Also, the input is missing.

The legend for Figure 1B and the input band were added, thank you. Yes, it seems that csgRNA is not as efficient as the unmodified scaffold. This is not a quantitative assay. This assay just showed that csgRNA is functional.

In Figure 5, a negative control without the csgRNA is missing. The relative mRNA induction should be calculated relative to this control, as it would directly show the fold induction triggered by dCas9-VP64/csgRNA/sCMV. Also, the y axis should be labelled relative mRNA (and not RNA) expression.

In the qPCR data shown in Figure 6A and 6B, it is not indicated what was used to determine the relative expression. Same as in Figure 5, the negative control (without the csgRNA) is missing and should be performed in order to determine the relative expression of each gene. Also, the expression level of the target gene should be shown.

In the transfection of reporter construct, we used a negative control without the csgRNA which revealed that no target gene was activated. Therefore, in the latter transfections to activate endogenous genes (Figure 5), we did not include this control. We focused on comparing target activation efficiency of three different transfection to show the effectiveness of dCas9-VP64/csgRNA/sCMV.

As to Figures 5 and 6, the experiments were re-performed in the past three months. Three biological replicates were performed for the transfection of each cell line. The statistical difference was analyzed by Student’s t-test. The new results were showed as the revised Figure 5 and Figure 6. The figure legends were also revised. Please see the revised manuscript.

The content of subsection “Statistical analyses” was also revised as follows:

Each cell transfection for detecting gene expression activation by trans-enhancer was performed in three biological replicates. In each biological replicate, at least three technical replicates were performed. The mean RQ value of technical replicates was used as the RQ value of one biological replicate. The mean RQ value of three biological replicates were used to calculate the final mean and standard deviation (SD). Data were analyzed by Student t test when comparing 2 groups. Data were expressed as mean ± SD and differences were considered significant at P < 0.05.

For clarity, a diagram of the reporter construct should be shown.

The whole sequences of HNF4a promoter report vector have been provided and clearly show the reporter construct.

Additional data files and statistical comments:

Throughout the manuscript, it is not clear how many times the experiments were done. For example, the qPCR data shown in Figure 5, do the error bars correspond to technical or biological replicates? Is a representative experiment shown? Also, in several figures it is not indicated which samples are compared when the * is shown for statistical significance.

Figures 4, 5, 6, and 7 were revised by performing more biological replicates. In the figure legends, the numbers of biological replicates were given.

Reviewer #3:

[…] 1) The authors initially validate their approach (Figure 2) in 293T cells by co-transfecting an exogenous GFP-based reporter construct. Transient transfection efficiencies of 293T cells are known to be extremely high. Why does e.g. dCas9-VP64/csgRNA2-sCMV achieve only 11% of GFP+ cells, assuming that >90% of 293T are transfected?

In our lab, we often transfected 293T cells by a single plasmid expressing EGFP under the control of strong CMV promoter, in which we found that the EGFP activation in cells is about 20~30. In comparison, transfection of dCas9-VP64/csgRNA2-sCMV achieved 11% of GFP+ cells. It is ideal in our opinion. The decreased transient transfection efficiencies of 293T cells in this study have a close relationship with the co-transfection with four independent vectors: a csgRNA expression vector, a dCas9-VP64/VPR expression vector, a reporter gene ZsGreen expression vector, and sCMV. Co-transfection can’t achieve high transfection efficiencies. By combining dCas9-VP64 and csgRNA into one vector, the gene activation efficiency can be further improved in the future.

However, we think that transfection efficiency do not affect our conclusion, that is, the trans-enhancer itself can activate the reporter gene expression by complexing with dCas9/csgRNA and dCas9-VP64/csgRNA (please see the Figure 2 and Figure 3, Figure 2—figure supplement, Figure 2—figure supplement3, Figure 2—figure supplement 4, Figure 3—figure supplement 1, and Figure 3—figure supplement 2). Moreover, trans-enhancer had significant higher gene activation efficiency by complexing with dCas9-VP64/csgRNA than dCas9-VP64/sgRNA (please see the Figure 2 and Figure 3, Figure 2—figure supplement, Figure 2—figure supplement3, Figure 2—figure supplement 4, Figure 3—figure supplement 1, and Figure 3—figure supplement 2). Because each cell was simultaneously transfected by various vectors at the same condition and many necessary controls were performed at the same time, the transfection efficiency is not a variable to affect the results of comparison of various transfections. Additionally, in flow cytometry assay, as many as ten thousand cells were measured for each transfection.

Moreover, the reporter gene is only one way to evaluate the gene activation capability. Our major focus is to activate endogenous genes by CRISPR-assistant trans-enhancer. We activated as many as ten endogenous genes in as many as seven different cell lines from various tissues. By comparing with dCas9-VP64/sgRNA in all transfections, it is clear that the trans-enhancer effectively activated these endogenous genes by complexing with dCas9-VP64/csgRNA, with significant high activation efficiency in all transfections than dCas9-VP64/sgRNA. Additionally, dCas9-VP64/sgRNA system included a two vector co-transfection, but the trans-enhancer system included three vectors. The former should have higher transfection efficiency than the latter; however, it still showed significant lower activation efficiency than trans-enhancer.

2) Regarding Figure 5 and along the line of my previous comment. How do we have to interpret the fold-changes in expression of endogenous genes having seen that in Figure 2 only a comparably small proportion of cells activated the reporter construct? Based on the methods description, all endogenous gene expression analyses were done using the bulk cell cultures. Are the changes in gene expression seen in the bulk culture caused by extremely high transactivation in a small fraction of cells or by homogenous induction across all cells? At least the first scenario is suggested by the pilot experiments shown in Figure 2. Thus, single cell-based approaches could clarify this for the endogenous target genes, e.g. immunofluorescence/FACS for the proteins encoded by the transactivated target genes.

As we described above, the decreased transient transfection efficiencies of 293T cells in this study (Figure 2) have a close relationship with the co-transfection with four independent vectors. Co-transfection can’t achieve very high transfection efficiencies. However, in Figure 5, cells were only transfected by three vectors, a csgRNA expression vector, a dCas9-VP64/VPR expression vector, and sCMV. The transfection was enhanced. Moreover, in Figure 2, we detected reporter gene expression at protein level, but in Figure 5 and Figure 6 we detected gene expression at transcription level by qPCR that has high detection sensitivity due to amplification. The reporter gene expression at protein level was quantitatively detected by flow cytometry, which has relative low detection sensitivity in comparison with qPCR because it has relative low fluorescence detection limitation.

We detected gene expression of target endogenous genes by qPCR. This is a standard approach for detecting gene expression. In many other previous reports of activation gene expression by CRISPR-based methods, the same qPCR detection of gene expression in the bulk culture were widely used. The related references were systematically cited in the Introduction. We found no reported single cell-based approaches were used in this aspect.

Based on the qPCR detection, all endogenous gene expression analyses were done using the bulk cell cultures. “Are the changes in gene expression seen in the bulk culture caused by extremely high transactivation in a small fraction of cells or by homogenous induction across all cells?”. This is a good question. We reduced that this is the changes in gene expression seen in the bulk culture should be caused by the enhanced transactivation in increased fraction of cells. Please see Figure 4B, in which we found that in comparison with other transfections, the transfection of dCas9-VP64/csgRNA and sCMV (DVSC) significantly increased numbers of cells with certain fluorescence intensity threshold. At the highest fluorescence intensity threshold (6.9), no cells were in other transfection, however, there are still about 2% of cells with this highest fluorescence. In flow cytometry, we measured 10000 cells for each transfection, 2% means that as many 200 cells had the highest fluorescence (i.e. target gene ZsGreen expression at protein level). This is very useful. For example, in cell reprogramming, high target gene activation can increase the efficiency of cell reprogramming.

Of course, as suggested by reviewer, it is better to characterize the endogenous target gene activation with single cell-based approaches, e.g. immunofluorescence/FACS for the proteins encoded by the transactivated target genes.

3) If transcriptional activation is achieved only in a fraction (approximately 10%) of the cells despite high transfection efficiencies, then the approach by Xu et al., would have some limitations, at least for bulk cell culture analyses, in comparison to SAM and other methods, which can be also efficiently delivered to target cells by viruses. Where do the authors then see the advantage of their approach, as the requirement for transient transfection remains a drawback, in particular in cell models that are difficult to transfect?

Yes, this study tested the idea that the exogenous and endogenous genes could be efficiently activated by a CRISPR-assistant trans-enhancer in many cells. To our knowledge, this is the first time it’s been shown that genes in cells could be activated by an exogenous enhancer in trans format. We show that genes in cells could be activated by the interaction between genomic DNA and exogenous DNA via protein mediation. This technique has potential application in vitro such as cell reprogramming and cell-based gene therapy.

It is clear that the csgRNA-sCMV-based trans enhancer technique is helpful for in vitro application, such as in vitro cell reprogramming and gene activation for gain-of function. However, it is true to say that the csgRNA-sCMV-based trans enhancer still faces difficulty in in vivo application. The trans enhancer used a linear CMV enhancer DNA fragment that has a single-stranded overhang complementary to the 3′ end of csgRNA. It is difficult to produce this kind of trans enhancer DNA in the in vivo cells unless transfecting the in vitro pre-prepared trans enhancer with nanoparticle gene carriers together with expression vectors of dCas9-VP64 and csgRNA. However, the current trans enhancer can’t be brought into the in vivo cells by the most effective current in vivo transgenic vector virus (e.g. AAV) that has been approved by FDA to clinical application.

To address the problem, we also developed a new strategy by using GAL4-UAS system, in which the dCas9-VP64-GAL4, sgRNA and UAS-CMV were used. The UAS-CMV was recruited to target gene by dCas9-VP64-GAL4 via the interaction between GAL4 and UAS. We should that UAS-CMV in both linear and circular forms could be recruited by dCas9-VP64-GAL4 and functioned. This system allows easy in vivo application of the CRISPR-assisted trans enhancer technique. For example, all components including dCas9-VP64-GAL4, sgRNA and LUAS-CMV could be easily provided to in vivo cells as Adeno-associated virus (AAV), a current widely used safe virus vector of human gene therapy.

Thanks for your comments.

[Editors' note: the author responses to the re-review follow.]

The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:

In this new version, the authors addressed many of the issues raised on the previous versions. They have provided data on the reproducibility of the results with the appropriate statistics. They have used alternative regulatory elements in place of CMV and they have developed an alternative strategy for recruiting the trans-acting element using the GAL4 DNA binding domain. These new innovations have strengthened the study. However, the paper is now too long with too many figures. The paper would be more appropriate if it was shortened to a Tools and Resources article rather than a full manuscript. This can be done by reorganizing the figures. For example, rather than showing all of the fluorescence images and sorting results, this could all be shown in graphical form as is already the case for Figures 8C, D and E and Figure 9C, D and E. This would save a lot of space and condense all the information into a single figure, showing perhaps a single representative sorting experiment and cell images as supplemental data. Also Figure 5 could be shortened to show only 1 or 2 cell types with the rest shown as supplemental data. I also propose that the data on cell cycle changes and migration be removed to shorten the text. This data is not central to the main message of the paper. On the other hand, supplemental figure 11 should be shown in the main figures possibly as an addition to Figure 1.

The paper was thoroughly and carefully revised, significantly shortened (from 9952 words to 6753 words including references). The text of revised manuscript (all contents except references) has 4293 words. After revision, only 6 figures were kept in text due to their central importance to the main message of the paper. The figures were also reorganized.

Finally, the writing is poor and the meaning of what is written is in several places very difficult to understand. Mitigating this will require a major effort.

The paper was thoroughly and carefully revised.

https://doi.org/10.7554/eLife.45973.022

Article and author information

Author details

  1. Xinhui Xu

    State Key Laboratory of Bioelectronics, Southeast University, Nanjing, China
    Contribution
    Methodology, Writing—original draft, Writing—review and editing, Performed the csgRNA-sCMV-based experiments
    Competing interests
    No competing interests declared
  2. Jinliang Gao

    State Key Laboratory of Bioelectronics, Southeast University, Nanjing, China
    Contribution
    Methodology, Writing—review and editing, Performed the GAL4-UAS-based experiments
    Competing interests
    No competing interests declared
  3. Wei Dai

    State Key Laboratory of Bioelectronics, Southeast University, Nanjing, China
    Contribution
    Methodology, Writing—review and editing, Helped to prepare solutions and culture cells
    Competing interests
    No competing interests declared
  4. Danyang Wang

    State Key Laboratory of Bioelectronics, Southeast University, Nanjing, China
    Contribution
    Methodology, Writing—review and editing, Helped to construct vectors
    Competing interests
    No competing interests declared
  5. Jian Wu

    State Key Laboratory of Bioelectronics, Southeast University, Nanjing, China
    Contribution
    Data curation, Software, Validation, Writing—review and editing, Helped to analyze data
    Competing interests
    No competing interests declared
  6. Jinke Wang

    State Key Laboratory of Bioelectronics, Southeast University, Nanjing, China
    Contribution
    Conceptualization, Supervision, Funding acquisition, Writing—original draft, Writing—review and editing, Conceptualized, designed and supervised the research, Wrote the manuscript and provided financial support for the project
    For correspondence
    wangjinke@seu.edu.cn
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3352-4690

Funding

National Natural Science Foundation of China (61571119)

  • Jinke Wang

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Senior Editor

  1. Kevin Struhl, Harvard Medical School, United States

Reviewing Editor

  1. Irwin Davidson, Institut de Génétique et de Biologie Moléculaire et Cellulaire, France

Reviewer

  1. Irwin Davidson, Institut de Génétique et de Biologie Moléculaire et Cellulaire, France

Publication history

  1. Received: February 10, 2019
  2. Accepted: April 10, 2019
  3. Accepted Manuscript published: April 11, 2019 (version 1)
  4. Version of Record published: April 23, 2019 (version 2)

Copyright

© 2019, Xu et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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