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Comparison of Cas9 activators in multiple species

Nature Methods volume 13, pages 563–567 (2016)Cite this article

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Abstract

Several programmable transcription factors exist based on the versatile Cas9 protein, yet their relative potency and effectiveness across various cell types and species remain unexplored. Here, we compare Cas9 activator systems and examine their ability to induce robust gene expression in several human, mouse, and fly cell lines. We also explore the potential for improved activation through the combination of the most potent activator systems, and we assess the role of cooperativity in maximizing gene expression.

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Figure 1: Initial tests of all second-generation activators on endogenous genes in HEK293T cells.
Figure 2: Activation of endogenous genes in HEK293T cells.
Figure 3: Evaluation of activator specificity by RNA sequencing.
Figure 4: Activation of endogenous genes in alternative human, mouse, and fly cell lines and effects of multiple guides.

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References

  1. Sander, J.D. & Joung, J.K. CRISPR-Cas systems for editing, regulating and targeting genomes. Nat. Biotechnol. 32, 347–355 (2014).

    Article  CAS  Google Scholar 

  2. Hsu, P.D., Lander, E.S. & Zhang, F. Development and applications of CRISPR-Cas9 for genome engineering. Cell 157, 1262–1278 (2014).

    Article  CAS  Google Scholar 

  3. Garneau, J.E. et al. The CRISPR/Cas bacterial immune system cleaves bacteriophage and plasmid DNA. Nature 468, 67–71 (2010).

    Article  CAS  Google Scholar 

  4. Jinek, M. et al. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337, 816–821 (2012).

    Article  CAS  Google Scholar 

  5. Mali, P. et al. RNA-guided human genome engineering via Cas9. Science 339, 823–826 (2013).

    Article  CAS  Google Scholar 

  6. Cong, L. et al. Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819–823 (2013).

    Article  CAS  Google Scholar 

  7. Mali, P. et al. CAS9 transcriptional activators for target specificity screening and paired nickases for cooperative genome engineering. Nat. Biotechnol. 31, 833–838 (2013).

    Article  CAS  Google Scholar 

  8. Sapranauskas, R. et al. The Streptococcus thermophilus CRISPR/Cas system provides immunity in Escherichia coli. Nucleic Acids Res. 39, 9275–9282 (2011).

    Article  CAS  Google Scholar 

  9. Gilbert, L.A. et al. CRISPR-mediated modular RNA-guided regulation of transcription in eukaryotes. Cell 154, 442–451 (2013).

    Article  CAS  Google Scholar 

  10. Perez-Pinera, P. et al. RNA-guided gene activation by CRISPR-Cas9-based transcription factors. Nat. Methods 10, 973–976 (2013).

    Article  CAS  Google Scholar 

  11. Maeder, M.L. et al. CRISPR RNA-guided activation of endogenous human genes. Nat. Methods 10, 977–979 (2013).

    Article  CAS  Google Scholar 

  12. Konermann, S. et al. Genome-scale transcriptional activation by an engineered CRISPR-Cas9 complex. Nature 517, 583–588 (2015).

    Article  CAS  Google Scholar 

  13. Chavez, A. et al. Highly efficient Cas9-mediated transcriptional programming. Nat. Methods 12, 326–328 (2015).

    Article  CAS  Google Scholar 

  14. Zalatan, J.G. et al. Engineering complex synthetic transcriptional programs with CRISPR RNA scaffolds. Cell 160, 339–350 (2015).

    Article  CAS  Google Scholar 

  15. Gilbert, L.A. et al. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159, 647–661 (2014).

    Article  CAS  Google Scholar 

  16. Tanenbaum, M.E., Gilbert, L.A., Qi, L.S., Weissman, J.S. & Vale, R.D. A protein-tagging system for signal amplification in gene expression and fluorescence imaging. Cell 159, 635–646 (2014).

    Article  CAS  Google Scholar 

  17. Chakraborty, S. et al. A CRISPR/Cas9-based system for reprogramming cell lineage specification. Stem Cell Rep. 3, 940–947 (2014).

    Article  CAS  Google Scholar 

  18. Hilton, I.B. et al. Epigenome editing by a CRISPR-Cas9-based acetyltransferase activates genes from promoters and enhancers. Nat. Biotechnol. 33, 510–517 (2015).

    Article  CAS  Google Scholar 

  19. Cheng, A.W. et al. Multiplexed activation of endogenous genes by CRISPR-on, an RNA-guided transcriptional activator system. Cell Res. 23, 1163–1171 (2013).

    Article  CAS  Google Scholar 

  20. Dominguez, A.A., Lim, W.A. & Qi, L.S. Beyond editing: repurposing CRISPR-Cas9 for precision genome regulation and interrogation. Nat. Rev. Mol. Cell Biol. 17, 5–15 (2016).

    Article  CAS  Google Scholar 

  21. La Russa, M.F. & Qi, L.S. The new state of the art: Cas9 for gene activation and repression. Mol. Cell. Biol. 35, 3800–3809 (2015).

    Article  CAS  Google Scholar 

  22. Thakore, P.I., Black, J.B., Hilton, I.B. & Gersbach, C.A. Editing the epigenome: technologies for programmable transcription and epigenetic modulation. Nat. Methods 13, 127–137 (2016).

    Article  CAS  Google Scholar 

  23. Wu, X. et al. Genome-wide binding of the CRISPR endonuclease Cas9 in mammalian cells. Nat. Biotechnol. 32, 670–676 (2014).

    Article  CAS  Google Scholar 

  24. Maeder, M.L. et al. Robust, synergistic regulation of human gene expression using TALE activators. Nat. Methods 10, 243–245 (2013).

    Article  CAS  Google Scholar 

  25. Perez-Pinera, P. et al. Synergistic and tunable human gene activation by combinations of synthetic transcription factors. Nat. Methods 10, 239–242 (2013).

    Article  CAS  Google Scholar 

  26. Oehler, S. & Müller-Hill, B. High local concentration: a fundamental strategy of life. J. Mol. Biol. 395, 242–253 (2010).

    Article  CAS  Google Scholar 

  27. Gori, J.L. et al. Delivery and specificity of CRISPR/Cas9 genome editing technologies for human gene therapy. Hum. Gene Ther. 26, 443–451 (2015).

    Article  CAS  Google Scholar 

  28. Yu, K., Liu, C., Kim, B.-G. & Lee, D.-Y. Synthetic fusion protein design and applications. Biotechnol. Adv. 33, 155–164 (2015).

    Article  CAS  Google Scholar 

  29. Poss, Z.C., Ebmeier, C.C. & Taatjes, D.J. The Mediator complex and transcription regulation. Crit. Rev. Biochem. Mol. Biol. 48, 575–608 (2013).

    Article  CAS  Google Scholar 

  30. Spitz, F. & Furlong, E.E.M. Transcription factors: from enhancer binding to developmental control. Nat. Rev. Genet. 13, 613–626 (2012).

    Article  CAS  Google Scholar 

  31. Jaenisch, R. & Bird, A. Epigenetic regulation of gene expression: how the genome integrates intrinsic and environmental signals. Nat. Genet. 33, 245–254 (2003).

    Article  CAS  Google Scholar 

  32. Chari, R., Mali, P., Moosburner, M. & Church, G.M. Unraveling CRISPR-Cas9 genome engineering parameters via a library-on-library approach. Nat. Methods 12, 823–826 (2015).

    Article  CAS  Google Scholar 

  33. Doench, J.G. et al. Rational design of highly active sgRNAs for CRISPR-Cas9-mediated gene inactivation. Nat. Biotechnol. 32, 1262–1267 (2014).

    Article  CAS  Google Scholar 

  34. Gibson, D.G. Enzymatic assembly of overlapping DNA fragments. Methods Enzymol. 498, 349–361 (2011).

    Article  CAS  Google Scholar 

  35. Gibson, D.G. et al. Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat. Methods 6, 343–345 (2009).

    Article  CAS  Google Scholar 

  36. Port, F., Chen, H.-M., Lee, T. & Bullock, S.L. Optimized CRISPR/Cas tools for efficient germline and somatic genome engineering in Drosophila. Proc. Natl. Acad. Sci. USA 111, E2967–E2976 (2014).

    Article  CAS  Google Scholar 

  37. Li, B. & Dewey, C.N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

    Article  CAS  Google Scholar 

  38. Law, C.W., Chen, Y., Shi, W. & Smyth, G.K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).

    Article  Google Scholar 

  39. Smyth, G.K. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, 1–25 (2004).

    Article  Google Scholar 

  40. Lin, S., Ewen-Campen, B., Ni, X., Housden, B.E. & Perrimon, N. In vivo transcriptional activation using CRISPR/Cas9 in Drosophila. Genetics 201, 433–442 (2015).

    Article  CAS  Google Scholar 

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Acknowledgements

We would like to thank S. Vora, A. Tung, M.K. Cromer, and all the members of the Church and Collins labs for helpful discussions and technical assistance. G.C. acknowledges support from US National Institutes of Health (NIH) National Human Genome Research Institute grant P50 HG005550 and from the Wyss Institute for Biologically Inspired Engineering. In addition, A.C. was funded by National Cancer Institute grant 5T32CA009216-34, R.C. was funded by a Banting postdoctoral fellowship from the Canadian Institutes of Health Research, and J.J.C. was supported by Defense Threat Reduction Agency grant HDTRA1-14-1-0006. B.E.-C. acknowledges funding from the NIH under Ruth L. Kirschstein National Research Service Award F32GM113395 from the NIH General Medical Sciences Division. We would also like to thank J. Lee (Cold Spring Harbor, Cold Spring Harbor, NY), P. Mali (UCSD, La Jolla, CA), and S. Shipman (Harvard Medical School, Boston, MA) for gifting us cell lines.

Author information

Author notes

  1. Alejandro Chavez and Marcelle Tuttle: These authors contributed equally to this work.

Authors and Affiliations

  1. Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, Massachusetts, USA

    Alejandro Chavez, Marcelle Tuttle, Benjamin W Pruitt, Dmitry Ter-Ovanesyan, Ryan J Cecchi, Emma J K Kowal, Joanna Buchthal, James J Collins & George Church

  2. Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts, USA

    Alejandro Chavez

  3. Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA

    Alejandro Chavez, Ben Ewen-Campen, Raj Chari, Dmitry Ter-Ovanesyan, Benjamin E Housden, Norbert Perrimon & George Church

  4. Department of Molecular Biology and Biochemistry, Middlebury College, Middlebury, Vermont, USA

    Sabina J Haque

  5. Department of Mathematics, Middlebury College, Middlebury, Vermont, USA

    Sabina J Haque

  6. Howard Hughes Medical Institute, Harvard Medical School, Boston, Massachusetts, USA

    Norbert Perrimon

  7. Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    James J Collins

  8. Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    James J Collins

  9. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    James J Collins

  10. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

    James J Collins

Authors
  1. Alejandro Chavez
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Contributions

A.C. and M.T. conceived of the study. A.C., M.T., B.W.P., and R.C. designed and performed experiments. S.J.H., R.J.C., and J.B. performed experiments. B.E.-C., B.E.H., and N.P. designed and performed all experiments in Drosophila melanogaster. D.T.-O. and E.J.K.K. performed RNA-seq experiments and analyzed data. J.J.C. and G.C. supervised the study. A.C. and M.T. wrote the manuscript with support from all authors.

Corresponding authors

Correspondence to Alejandro Chavez or George Church.

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Competing interests

G.C. has equity in Editas and Caribou Biosciences.

Integrated supplementary information

Supplementary Figure 1 Additional tests of activators on endogenous genes in HEK293T cells.

Data represents the mean + s.e.m. (n = 2 independent transfections).

Source data

Supplementary Figure 2 Effect of Baseline Level of Expression on Activation.

Data represents the mean activation from Fig. 2b and Supplementary Fig. 1. Baseline expression data calculated from the Puc19 control sample for each experiment.

Source data

Supplementary Figure 3 Multiplexed activation of two sets of three endogenous human genes.

Data indicate the mean + s.e.m (n = 2 independent transfections).

Source data

Supplementary Figure 4 Additional tests of activators on endogenous genes in Hela, U-2 OS, MCF7, N2A, NIH-3T3, and S2R+ cells.

(a) Each human cell line was transfected with the indicated activators and guides. Data indicate the mean + s.e.m (n = 2 independent transfections) (b) Activation of endogenous genes in mouse and fly. Data indicate the mean + s.e.m (n = 2 independent transfections).

Source data

Supplementary Figure 5 Combinations of different activator components.

Samples were tested on both a single gene and a panel of multiplexed genes. Data represents the mean + s.e.m. (n = 2 independent transfections) See Supplementary Note 1 for more explanation on the canonical activator components. For the purposes of this figure, dCas9-10xGCN4 + normal guide + scFV-VP64 represents the canonical Suntag activator and dCas9-VP64 + SAM guide + ms2-p65-hsf1 represents the canonical SAM activator.

Source data

Supplementary Figure 6 Recruitment of different activation domains to SAM and Suntag.

dCas9-VP64 denotes the SAM version of VP64. Data represents the mean + s.e.m. (n = 2 independent transfections). See Supplementary Note 1 for more explanation on the canonical activator components. For the purposes of this figure, dCas9-VP64 + SAM guide + ms2-p65-hsf1 represents the canonical SAM activator.

Source data

Supplementary Figure 7 SAM and Scaffold gRNA chimeras.

All samples contain dCas9 recruiting MCP-p65-hsf1 via different hairpin designs. All chimeras represent the SAM gRNA with the Scaffold tail appended on it with various parts disabled by either point mutation or deletion. Chimera 1 has the first MS2 extension of the SAM gRNA deleted. Chimera 2 has the second MS2 extension of the SAM gRNA deleted. Chimera 3 has the MS2 loop of the scaffold gRNA disabled via point mutation while Chimera 4 has the F6 loop of the scaffold gRNA disabled via point mutation. For full chimera gRNA tail sequences, refer to the Plasmids section of the supplement. Addition of the Scaffold tail to the end of the SAM gRNA resulted in worse activation than each system alone and there was no method of disabling any part of the hybrid hairpin which led greater activation. Data represents the mean + s.e.m. (n = 2 independent transfections).

Source data

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7, Supplementary Tables 1 and 2, and Supplementary Notes 1 and 2 (PDF 1029 kb)

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Chavez, A., Tuttle, M., Pruitt, B. et al. Comparison of Cas9 activators in multiple species. Nat Methods 13, 563–567 (2016). https://doi.org/10.1038/nmeth.3871

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