Abstract
Cas9 is commonly introduced into cell lines to enable CRISPR–Cas9-mediated genome editing. Here, we studied the genetic and transcriptional consequences of Cas9 expression itself. Gene expression profiling of 165 pairs of human cancer cell lines and their Cas9-expressing derivatives revealed upregulation of the p53 pathway upon introduction of Cas9, specifically in wild-type TP53 (TP53-WT) cell lines. This was confirmed at the messenger RNA and protein levels. Moreover, elevated levels of DNA repair were observed in Cas9-expressing cell lines. Genetic characterization of 42 cell line pairs showed that introduction of Cas9 can lead to the emergence and expansion of p53-inactivating mutations. This was confirmed by competition experiments in isogenic TP53-WT and TP53-null (TP53−/−) cell lines. Lastly, Cas9 was less active in TP53-WT than in TP53-mutant cell lines, and Cas9-induced p53 pathway activation affected cellular sensitivity to both genetic and chemical perturbations. These findings may have broad implications for the proper use of CRISPR–Cas9-mediated genome editing.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
All data sets are available within the article, its Supplementary Information, or from the corresponding authors upon request. DNA sequencing data were deposited to SRA (https://www.ncbi.nlm.nih.gov/sra) with BioProject accession number PRJNA545458. Gene expression data were uploaded to the following URL: https://clue.io/data/XPR_BASE#CAS9_BASELINE. Source Data of all immunostaining blots (in Fig. 1 and Extended Data Fig. 2) are available in the online version of this paper. Raw microscopy data are available at https://figshare.com/projects/Cas9_activates_the_p53_pathway_and_selects_for_p53-inactivating_mutations/81080.
Code availability
All of the code used to generate and/or analyze the data is publicly available.
Change history
25 June 2020
A Correction to this paper has been published: https://doi.org/10.1038/s41588-020-0663-9
References
Ben-David, U., Beroukhim, R. & Golub, T. R. Genomic evolution of cancer models: perils and opportunities. Nat. Rev. Cancer 19, 97–109 (2019).
Ben-David, U. et al. Genetic and transcriptional evolution alters cancer cell line drug response. Nature 560, 325–330 (2018).
Cong, L. et al. Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819–823 (2013).
Jinek, M. et al. RNA-programmed genome editing in human cells. eLife 2, e00471 (2013).
Mali, P. et al. RNA-guided human genome engineering via Cas9. Science 339, 823–826 (2013).
Subramanian, A. et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell 171, 1437–1452 e17 (2017).
Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Bunz, F. et al. Requirement for p53 and p21 to sustain G2 arrest after DNA damage. Science 282, 1497–1501 (1998).
Haapaniemi, E., Botla, S., Persson, J., Schmierer, B. & Taipale, J. CRISPR–Cas9 genome editing induces a p53-mediated DNA damage response. Nat. Med. 24, 927–930 (2018).
Ihry, R. J. et al. p53 inhibits CRISPR–Cas9 engineering in human pluripotent stem cells. Nat. Med. 24, 939–946 (2018).
Schiroli, G. et al. Precise gene editing preserves hematopoietic stem cell function following transient p53-mediated DNA damage response. Cell Stem Cell 24, 551–565.e8 (2019).
Wu, Y. et al. Highly efficient therapeutic gene editing of human hematopoietic stem cells. Nat. Med. 25, 776–783 (2019).
Elkon, R. et al. Dissection of a DNA-damage-induced transcriptional network using a combination of microarrays, RNA interference and computational promoter analysis. Genome Biol. 6, R43 (2005).
Wang, W., Mani, A. M. & Wu, Z. H. DNA damage-induced nuclear factor-kappa B activation and its roles in cancer progression. J. Cancer Metastasis Treat. 3, 45–59 (2017).
Sholl, L. M. et al. Institutional implementation of clinical tumor profiling on an unselected cancer population. JCI Insight 1, e87062 (2016).
Giacomelli, A. O. et al. Mutational processes shape the landscape of TP53 mutations in human cancer. Nat. Genet. 50, 1381–1387 (2018).
Doench, J. G. et al. Rational design of highly active sgRNAs for CRISPR–Cas9-mediated gene inactivation. Nat. Biotechnol. 32, 1262–1267 (2014).
Sinha, S. et al. Integrated computational and experimental identification of p53, KRAS and VHL mutant selection associated with CRISPR-Cas9 editing. Preprint at bioRxiv https://doi.org/10.1101/407767 (2019).
Piras, F. et al. Lentiviral vectors escape innate sensing but trigger p53 in human hematopoietic stem and progenitor cells. EMBO Mol. Med. 9, 1198–1211 (2017).
Zacharias, J., Romanova, L. G., Menk, J. & Philpott, N. J. p53 inhibits adeno-associated viral vector integration. Hum. Gene Ther. 22, 1445–1451 (2011).
Ben-David, U. et al. The landscape of chromosomal aberrations in breast cancer mouse models reveals driver-specific routes to tumorigenesis. Nat. Commun. 7, 12160 (2016).
Meyers, R. M. et al. Computational correction of copy number effect improves specificity of CRISPR–Cas9 essentiality screens in cancer cells. Nat. Genet. 49, 1779–1784 (2017).
Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).
McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).
McLaren, W. et al. Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics 26, 2069–2070 (2010).
Bamford, S. et al. The COSMIC (Catalogue of Somatic Mutations in Cancer) database and website. Br. J. Cancer 91, 355–358 (2004).
Karczewski, K.J. et al. Variation across 141,456 human exomes and genomes reveals the spectrum of loss-of-function intolerance across human protein-coding genes. Preprint at bioRxiv https://doi.org/10.1101/531210 (2019).
Dempster, J.M. et al. Extracting biological insights from the Project Achilles genome-scale CRISPR screens in cancer cell lines. Preprint at bioRxiv https://doi.org/10.1101/720243 (2019).
McFarland, J. M. et al. Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration. Nat. Commun. 9, 4610 (2018).
Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576.e16 (2017).
Huang, D. W. et al. The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol. 8, R183 (2007).
Kung, C. P., Khaku, S., Jennis, M., Zhou, Y. & Murphy, M. E. Identification of TRIML2, a novel p53 target, that enhances p53 SUMOylation and regulates the transactivation of proapoptotic genes. Mol. Cancer Res. 13, 250–262 (2015).
Enache, O. M. et al. The GCTx format and cmap{Py, R, M, J} packages: resources for optimized storage and integrated traversal of annotated dense matrices. Bioinformatics 35, 1427–1429 (2019).
R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2010).
Acknowledgements
We thank A. Subramanian, D. Root, T. Natoli, C. Kadoch, S. Cassel and C. Collings for helpful discussions; X. Lu, A. Giacomelli, K. Labella, K. Sanson and W. Hahn for contributing reagents; M. Ducar and S. Drinan for assistance with the OncoPanel assay; and F. Piccioni for assistance with Cas9 cell line generation. This work was supported by the NIH (R01 CA18828, CA215489 and CA219943 to R.B.), the Gray Matters Brain Cancer Foundation (R.B.), Pediatric Brain Tumor Foundation (R.B.), HHMI (T.R.G.) and HFSP (U.B.-D.). Research in the the laboratory of U.B.-D. is supported by the Azrieli Foundation, the Richard Eimert Research Fund on Solid Tumors, the Tel-Aviv University Cancer Biology Research Center, and the Israel Cancer Association.
Author information
Authors and Affiliations
Contributions
U.B.-D. conceived and supervised the project; O.M.E. and U.B.-D. collected the data and performed the computational analyses; V.R., M.A. and U.B.-D. carried out the experiments; D.L. and D.D. assisted with the L1000 assay; S.P. and N.C. assisted with western blots; J.H. assisted with the mutation data analysis; S.P., J.G.D. and F.V. contributed the matched parental and Cas9-expressing cell lines; A.N. and A.T. assisted with the Oncopanel assay and analysis. O.M.E., V.R., R.B., T.R.G. and U.B.-D. analyzed the data and wrote the manuscript. R.B., T.R.G. and U.B.-D. directed the project.
Corresponding author
Ethics declarations
Competing interests
J.G.D. consults for Tango Therapeutics, Foghorn Therapeutics, and Pfizer. T.R.G. is a paid advisor to GlaxoSmithKline and Sherlock Biosciences. R.B. owns shares in Ampressa and receives grant funding from Novartis. D.D. is an employee of Cellarity.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data
Extended Data Fig. 1 Cas9 introduction activates the p53 pathway (related to Fig. 1).
a, Unsupervised hierarchical clustering of 165 WT/Cas9 cell line pairs, based on their median L1000 transcriptional profiles (landmark space, n = 978 genes). Cell line pairs are colored in red and black, alternately, to highlight that all Cas9 lines cluster together with their parental WT lines. b, Transcriptional activity scores (TAS)6 comparison of technical replicates of 165 parental lines, 165 technical replicates of Cas9 lines, 165 Cas9 lines vs. parental lines, or 22 control vector lines vs. parental cell lines. *, P < 2 × 10–16, P < 2 ×10–16 and P = 2.5 × 10–7, two-tailed paired t test. Data points represent cell line pairs. c, Lack of correlation between Cas9 activity levels (measured by GFP levels; see Online Methods) and the strength of the transcriptional response (measured by TAS). P = 0.68, two-tailed test for association using Spearman’s rho. 158 lines are colored by their TP53 mutation status; 7 lines excluded due to lack of Cas9 activity data. d, The proportion of lines (n = 165) with an activated p53 pathway activity following Cas9 introduction, in TP53-WT vs. TP53-mutant cell lines. *, P = 0.0007, two-tailed Fisher’s exact Test. e, The proportion of TP53-WT lines (n = 61) with an activated p53 pathway activity following Cas9 or empty/reporter vector introduction. *, P = 0.006, two-tailed Fisher’s exact Test. f, The degree and significance of enrichment of the 50 MSigDB Hallmark biological pathways, following the introduction of empty vectors, reporter vectors and Cas9 into TP53-WT cell lines, and the introduction of Cas9 into TP53-mutant cell lines. Black, significantly enriched (GSEA enrichment score with multiple hypotheses correction; q < 0.05) pathways. Orange, the p53 pathway. Each plot represents the results of one Meta expression signature (see Online Methods). g, Comparison of Cas9 activity levels and TAS, as in (d), but only 40 available TP53-WT lines are presented. Cell lines are colored by whether their gene expression profiles were enriched for the p53 Hallmark gene set (and in which direction). P = 0.30, two-tailed test for association using Spearman’s rho.
Extended Data Fig. 2 Confirmation of p53 activation following Cas9 introduction (related to Fig. 1).
a, Left: confirmation of p53 pathway activation in BT159 cell lines by RT-qPCR analysis of 7 transcriptional targets of p53. *, P = 0.017, **, P = 0.0065, ****, P < 0.0001, one-tailed t test. Data values represent the means of 3 replicates, with error bars corresponding to S.D. Right: the average activation of p53 transcriptional targets. P = 0.08, two-tailed one-sample t test. Data values represent the means of the 7 targets, with error bars corresponding to S.D. b, Left: RT-qPCR analysis of 7 transcriptional targets of p53 in A549 (TP53-WT) before and after its transduction with Cas9 or with three control vectors: luciferase, GFP or DNA barcode. *, P = 0.048, one-tailed t test. Data values represent the means of the 3 control vectors and of 3 biological replicates of Cas9, with error bars corresponding to S.D. Right: the average activation of p53 transcriptional targets. *, P < 0.05, two-tailed one-sample t test. Data values represent the means of the 7 targets, with error bars corresponding to S.D. c, Protein levels of Cas9, p53, p21 and a housekeeping protein in HCT116 cells transfected with GFP, Cas9 or a backbone-matched empty vector (EV). Results represent a single experiment. d, Protein levels of Cas9, p53, p21 and a housekeeping protein in isogenic TP53-WT (P) and TP53-null HCT116 cells before and after transduction of Cas9 (C) or of a backbone-matched control vector (EV). Results represent a single experiment. e, Left: RT-qPCR analysis of 7 transcriptional targets of p53 shows p53 pathway activation specifically in the Cas9-expressing TP53-WT HCT116 cells. Data values represent the means of 2 replicates, with error bars corresponding to S.D. Right: the average activation of p53 transcriptional targets. *, P = 0.028, ***, P = 0.0004, ****, P < 0.0001, two-tailed one-sample t test. Data values represent the means of the 7 targets, with error bars corresponding to S.D.
Extended Data Fig. 3 Cas9 introduction activates the DNA damage response (related to Fig. 2).
a, The proportion of cell lines (n = 165) with a positively enriched DNA damage transcriptional signature, following Cas9 introduction. *, P = 0.07; two-tailed Fisher’s exact Test. b, Fluorescent microscopy images of 𝛾H2AX foci (green) and DAPI (blue) in parental TP53-WT HCT116 cells and following Cas9 transduction. Cells with > 5 foci have been marked in white. Scale bar represents 10 µm. c, Quantification of 𝛾H2AX foci from three independent repeats; n = 1,765 and n = 2,523, for WT and Cas9 HCT116 cells, respectively. **, P = 0.0095; one-tailed t test. Data show means, with error bars corresponding to S.D.
Extended Data Fig. 4 Cas9 introduction selects for inactivating TP53 mutations (related to Fig. 3).
a, Unsupervised hierarchical clustering of 42 WT/Cas9 cell line pairs across 40 independent cell lines, based on their genetic profiles. Cell line pairs are colored in red and black, alternately, to highlight that all Cas9 lines cluster together with their parental WT lines. b, The count of overall mutations detected across the 42 WT/Cas9 cell line pairs. c, The number of recurrent COSMIC mutations that differ between the Cas9 lines and their matched WT lines (that is, detected either in the parental or in the Cas9 line, but not in both). Emerging mutations are shown in black, disappearing mutations in gray, for the 25 cell lines with any COSMIC mutations present. *, P = 0.027, one-tailed paired t test. d, Sequencing coverage of the TP53 exons in the three cell line pairs in which emergence or expansion of TP53 mutations were detected. e, Cancer genes ranked by their tendency to acquire mutations in the Cas9 lines. Emerging mutations are shown in black, disappearing mutations in gray. TP53 is highlighted in orange. f, The number of non-silent mutations that differ between WT lines and their reported or barcoded derivatives. No mutation in TP53 was observed in 9 independent experiments across three TP53-WT cell lines. g, Cancer genes ranked by the proportion of silent mutations out of all emerging (silent and non-silent) mutations. TP53 is highlighted in orange, and is among the top ~1% of genes (out of 128 genes with a non-silent mutation present).
Extended Data Fig. 5 Proposed workflow for Cas9-related laboratory experiments.
When conducting systematic CRISPR/Cas9-mediated screens or focused studies in TP53-WT cancer cell lines, we recommend determining the basal activation level of the p53 pathway in the Cas9-expressing line. If there is p53 activation, it is recommended to assess Cas9-derived ongoing DNA damage accumulation as well. Finally, as continuous Cas9 expression poses a selection pressure that over time may be reflected in the emergence or expansion of p53-inactivating mutations, it is recommended to avoid extensive passaging and culture bottlenecks that may accelerate this process.
Supplementary information
Supplementary Information
Supplementary Notes 1–9 and Fig. 1
Supplementary Data
Supplementary Data 1–7
Rights and permissions
About this article
Cite this article
Enache, O.M., Rendo, V., Abdusamad, M. et al. Cas9 activates the p53 pathway and selects for p53-inactivating mutations. Nat Genet 52, 662–668 (2020). https://doi.org/10.1038/s41588-020-0623-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41588-020-0623-4
This article is cited by
-
Precise genome-editing in human diseases: mechanisms, strategies and applications
Signal Transduction and Targeted Therapy (2024)
-
Translating p53-based therapies for cancer into the clinic
Nature Reviews Cancer (2024)
-
CRISPR technologies for genome, epigenome and transcriptome editing
Nature Reviews Molecular Cell Biology (2024)
-
Doxycycline-dependent Cas9-expressing pig resources for conditional in vivo gene nullification and activation
Genome Biology (2023)
-
Optimization of base editors for the functional correction of SMN2 as a treatment for spinal muscular atrophy
Nature Biomedical Engineering (2023)