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Enhancer RNAs (eRNAs) are non-coding RNAs produced from active enhancers. An optimal approach to study eRNA functions is to manipulate their levels in the native chromatin regions. Here we introduce a robust system for eRNA studies by using CRISPR-dCas9-fused transcriptional activators to induce the expression of eRNAs of interest.
Enhancers are pivotal genomic elements scattered through the mammalian genome and dictate tissue-specific gene expression programs. Increasing evidence has shown that enhancers not only provide DNA binding motifs for transcription factors (TFs) but also generate non-coding RNAs that are referred to as eRNAs. Studies have demonstrated that eRNA transcripts can play significant roles in gene regulation in both physiology and disease. Commonly used methods to investigate the function of eRNAs are constrained to βloss-of-functionβ approaches by knockdown of eRNAs, or by chemical inhibition of the enhancer transcription. There has not been a robust method to conduct βgain-of-functionβ studies of eRNAs to mimic specific disease conditions such as human cancer, where eRNAs are often overexpressed. Here, we introduce a method for precisely and robustly activating eRNAs for functional interrogation of their roles by applying the dCas9 mediated Synergistic Activation Mediators (SAM) system. We present the entire workflow of eRNA activation, from the selection of eRNAs, the design of gRNAs to the validation of eRNA activation by RT-qPCR. This method represents a unique approach to study the roles of a particular eRNA in gene regulation and disease development. In addition, this system can be employed for unbiased CRISPR screening to identify phenotype-driving eRNA targets in the context of a specific disease.
The human genome contains a constellation of regulatory elements1,2,3. Among these, enhancers emerge to be one of the most critical categories4,5,6. Enhancers play essential roles in regulating development, and are responsible for generating spatial-temporal gene expression programs to determine cell identity5,6,7. Conventionally, enhancers are only considered to be DNA elements that provide binding motifs for transcription factors (TFs), which then control target gene expression6,8. However, a series of studies found that many active enhancers also transcribe non-coding enhancer RNAs (i.e., eRNAs)4,9,10.
The level of eRNA transcription was found to correlate with the activity of an enhancer4,10. Active enhancers produce more eRNA transcripts and show higher levels of epigenome markers associated with active transcription, such as H3K27ac and H3K4me19,11,12. Some studies have demonstrated that eRNA transcripts can play important roles in transcriptional activation of target genes10,12. A large number of eRNAs were identified to be deregulated in human cancers13,14,15,16, many of which exhibited high cancer type specificity and clinical relevance. These findings bring opportunities that the elucidation of eRNAs that can drive/promote tumorigenesis may offer novel targets for therapeutic intervention13,15.Β
Current methods to study eRNA functions are almost exclusively based on knockdown strategies that used small interference RNAs (siRNA), short hairpin RNAs (shRNAs), or antisense oligonucleotides (ASOs, of which locked nucleic acids (LNAs) are the commonly used type in research)10,12,17. However, human diseases such as cancer predominantly show overexpression of eRNAs as compared to their adjacent normal tissue15, demanding tools to βoverexpressβ eRNAs to mimic their disease-relevant expression patterns for functional studies. To achieve this, a plasmid-based ectopic overexpression system is not optimal because the exact transcription start and termination sites of eRNAs remain largely unclear. In addition, a plasmid expression system may alter the location of eRNAs, causing potential artifacts of their functions18. Here we provide a detailed protocol to facilitate the functional characterization of eRNAs by enforcing their βoverexpressionβ in the native genomic locus of their production (i.e., in situ), which is based on the CRISPR/dCas9-Synergistic Activation Mediators System (SAM).
The SAM system was initially developed for activating coding genes and long intergenic non-coding RNAs (lincRNAs) associated with BRAF inhibitor resistance in melanoma cells19. Unlike other CRISPR activation (CRISPRa) technologies, the SAM system consists of a combination of transcription activators to confer robust transcriptional activation of target regions. These activators include: an enzymatically dead Cas9 (dCas9) fused with VP64 (i.e., dCas9-VP64); a guide RNA containing two MS2 RNA aptamers, and an MS2-p65-HSF1 fusion activator protein. The presence of the MS2 aptamers in the gRNA can recruit the MS2-p65-HSF1 fusion protein to the vicinity of dCas9/gRNA binding sites. Among these, VP64 is an engineered tetramer of the herpes simplex VP16 transcriptional activator domain, which has been shown to strongly activate gene transcription by recruiting general transcription factors20,21,22. The MS2-p65-HSF1 fusion protein consists of three parts. The first part, the MS2-N55K, is a mutant form of MS2 binding protein that has a stronger affinity23; the other two parts of this fusion protein are the transactivation domain of p65 and heat shock factor 1 (HSF1), both of which are transcription factors that possess strong transactivation domains and can induce robust transcription programs24,25. Therefore, the SAM system essentially created a highly potent activator complex to activate transcription of designated coding genes and lincRNAs19.
The entire workflow of this protocol is shown in Figure 1.
1. Enhancer RNA (eRNA) selection
2. gRNA design
3. Clone gRNAs into a lentiviral construct
4. gRNA efficiency testΒ
NOTE: Although it may not be necessary for every gRNA, it is recommended that researchers examine the quality of gRNA by performing Surveyor assay (i.e., mismatch cleavage assay) to detect indels or mutations that can only be efficiently generated by good quality gRNAs33,34. Other methods such as Tracking of Indels by Decomposition (TIDE) can also be used to determine gRNA efficiency30,35. Surveyor nuclease is a member of a family of mismatch-specific endonucleases that can cut double-strand DNA with mismatches (Figure 3A). The quality of gRNAs can be revealed by the efficacy of producing smaller DNA species. Practically, surveyor cutting efficacy can also be affected by the transfection efficiency of gRNAs and Cas9.Β
5. Lentivirus generation
6. Cell culture
7. Cell infection and selection
8. RNA extraction and quantitative RT-PCR to examine eRNA levels
9. dCas9 ChIP and qPCR
NOTE: This step is an optional experiment to validate the binding of dCas9/SAM-gRNA complex to the target enhancer by the specific gRNAs. While it is encouraged that users perform this step, it is not necessary to test every single gRNA. Refer to an example shown in Figure 5B. Refer to primers listed in Supplementary Table 1.
10. Cell growth assay and other functional tests of eRNA over-activationΒ
Figure 1 illustrates the overall workflow of this protocol. Our focus was on a representative eRNA, NET1e15, which is overexpressed in breast cancer, for which SAM system was used to activate and study itβs biological role in regulating gene expression, cell proliferation and cancer drug response. For this NET1 enhancer, several p300 ChIP-Seq peaks, flanked by transcribed eRNA transcripts (Figure 2A,B
Based on our data, we conclude that the SAM system is suitable for studying the role of eRNAs in regulating cellular phenotypes, e.g., cell growth or drug resistance. However, careful gRNA designing is required for robust eRNA activation, due to the following reasons. First of all, the transcription start site (TSS) of eRNA in each specific cell lines/types remains less clearly annotated. Due to this, epigenomic information (e.g., ChIP-Seq of H3K27ac, of transcription factors, or of p300), transcriptional activity depict...
The content is solely the responsibility of the authors and does not necessarily represent the official views of the Cancer Prevention and Research Institute of Texas.
This work is supported by grants to W.L (Cancer Prevention and Research Institute of Texas, CPRIT RR160083 and RP180734; NCI K22CA204468; NIGMS R21GM132778; The University of Texas UT Stars Award; and the Welch foundation AU-2000-20190330) and a post-doctoral fellowship to J.L (UTHealth Innovation for Cancer Prevention Research Training Program Post-doctoral Fellowship, CPRIT RP160015). We acknowledge the original publicataion15 where some of our current figures were adopted from (with modifications), which follows the Creative Commons license (https://creativecommons.org/licenses/by/4.0/).
Name | Company | Catalog Number | Comments |
Blasticidin | Goldbio | B-800-100 | |
BsmBI restriction enzyme | New England BioLabs Inc. | R0580S | |
Cas9 mAb | Active Motif | 61757 | Lot: 10216001 |
Deoxynucleotide (dNTP) Solution Mix | New England BioLabs Inc. | N0447S | |
Dulbeccoβs Modified Eagle Medium | Corning | 10-013-CM | |
Dynabeads Protein G | Thermo Fisher Scientific | 65002 | |
EDTA | Thermo Fisher Scientific | BP118-500 | |
EGTA | Sigma | E3889 | |
Fetal Bovine Serum | GenDEPOT | F0900-050 | |
Glycogen | Thermo Fisher Scientific | 10814010 | |
Hepes-KOH | Thermo Fisher Scientific | BP310-100 | |
Hexadimethrine Bromide | Sigma | H9268 | |
Hygromycin B | Goldbio | H-270-25 | |
IGEPAL CA630 | Sigma | D6750 | |
IncuCyte live-cell imager | Essen BioScience | IncuCyte S3 Live-Cell Analysis System | |
lenti_dCAS-VP64_Blast | Addgene | 61425 | |
lenti_gRNA(MS2)_zeo backbone | Addgene | 61427 | |
lenti_MS2-p65-HSF1_Hygro | Addgene | 61426 | |
LiCL | Sigma | L9650 | |
Lipofectamine 2000 | Thermo Fisher Scientific | 11668-500 | |
NaCl | Sigma | S3014 | |
Na-Deoxycholate | Sigma | D6750 | |
NaHCO3 | Thermo Fisher Scientific | BP328-500 | |
N-lauroylsarcosine | Sigma | 97-78-9 | |
Opti-MEM Reduced Serum Medium | Thermo Fisher Scientific | 31985070 | |
PES syringe filter | BASIX | 13-1001-07 | |
Protease Inhibitor Cocktail Tablet | Roche Diagnostic | 11836145001 | |
pSpCas9(BB)-2A-Puro | Addgene | 62988 | |
Q5 High-Fidelity DNA Polymerase | New England BioLabs Inc. | M0491S | |
Q5 Reaction Buffer | New England BioLabs Inc. | B9027S | |
Quick-DNA Miniprep | ZYMO Research | D3025 | |
Quick-RNA Miniprep | ZYMO Research | R1054 | |
Restriction enzyme buffer | New England BioLabs Inc. | B7203S | |
RT-qPCR Detection System | Thermo Fisher Scientific | Quant Studio3 | |
SDS | Thermo Fisher Scientific | BP359-500 | |
Sonicator | Qsonica | Q800R2 | |
Sso Advanced Universal SYBR Green Supermix | Bio-Rad Laboratories | 1725274 | |
Stbl3 competent cell | Thermo Fisher Scientific | C7373-03 | |
Superscript IV reverse transcript | Thermo Fisher Scientific | 719000 | |
Surveyor Mutation Detection Kits | Integrated DNA Technologies | 706020 | |
T4 DNA Ligase | New England BioLabs Inc. | M0202S | |
T4 DNA Ligase Reaction Buffer | New England BioLabs Inc. | B0202S | |
TE buffer | Thermo Fisher Scientific | 46009CM | |
Thermal cycler | Bio-Rad Laboratories | T100 | |
Thermomixer | Sigma | 5384000020 | |
Zeocin | Thermo Fisher Scientific | ant-zn-1p |
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