Biological plasticity rescues target activity in CRISPR knock outs

Abstract

Gene knock outs (KOs) are efficiently engineered through CRISPR–Cas9-induced frameshift mutations. While the efficiency of DNA editing is readily verified by DNA sequencing, a systematic understanding of the efficiency of protein elimination has been lacking. Here we devised an experimental strategy combining RNA sequencing and triple-stage mass spectrometry to characterize 193 genetically verified deletions targeting 136 distinct genes generated by CRISPR-induced frameshifts in HAP1 cells. We observed residual protein expression for about one third of the quantified targets, at variable levels from low to original, and identified two causal mechanisms, translation reinitiation leading to N-terminally truncated target proteins or skipping of the edited exon leading to protein isoforms with internal sequence deletions. Detailed analysis of three truncated targets, BRD4, DNMT1 and NGLY1, revealed partial preservation of protein function. Our results imply that systematic characterization of residual protein expression or function in CRISPR–Cas9-generated KO lines is necessary for phenotype interpretation.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Residual transcript and protein expression in 193 HAP1 cell lines harboring frameshift KO mutations.
Fig. 2: Residual protein expression owing to exon skipping or translation reinitiation.
Fig. 3: Residual protein expression results in retained activity of DNMT1 in one of the two KO lines.
Fig. 4: NGLY1 frameshift mutation in K562 cells results in a partially functional NGLY1 truncation.
Fig. 5: Consequences of CRISPR–Cas9-generated frameshift mutations.

Data availability

The transcriptomics and proteomics data of the 19 KO lines shown in Fig. 1b and the NGLY1 KO lines were deposited to publicly available repositories. The mass spectrometry proteomics data were deposited to the ProteomeXchange Consortium via the PRIDE57 partner repository with the dataset identifier PXD010335. RNA-seq data were deposited in the ArrayExpress database at EMBL-EBI (www.ebi.ac.uk/arrayexpress) under accession number E-MTAB-7061. The data for the remaining 174 KO lines shown in Fig. 1a are available from the corresponding author upon request.

References

  1. 1.

    Doudna, J. A. & Charpentier, E. The new frontier of genome engineering with CRISPR–Cas9. Science 346, 1258096 (2014).

    Google Scholar 

  2. 2.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Jinek, M. et al. RNA-programmed genome editing in human cells. eLife 2, e00471 (2013).

    PubMed  PubMed Central  Google Scholar 

  6. 6.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Lieber, M. R. The mechanism of double-strand DNA break repair by the nonhomologous DNA end-joining pathway. Annu. Rev. Biochem. 79, 181–211 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Lykke-Andersen, J. & Bennett, E. J. Protecting the proteome: eukaryotic cotranslational quality control pathways. J. Cell Biol. 204, 467–476 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Fu, Y. et al. High-frequency off-target mutagenesis induced by CRISPR–Cas nucleases in human cells. Nat. Biotechnol. 31, 822–826 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Hsu, P. D. et al. DNA targeting specificity of RNA-guided Cas9 nucleases. Nat. Biotechnol. 31, 827–832 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Pattanayak, V. et al. High-throughput profiling of off-target DNA cleavage reveals RNA-programmed Cas9 nuclease specificity. Nat. Biotechnol. 31, 839–843 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Cho, S. W. et al. Analysis of off-target effects of CRISPR/Cas-derived RNA-guided endonucleases and nickases. Genome Res. 24, 132–141 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Tsai, S. Q. et al. GUIDE-seq enables genome-wide profiling of off-target cleavage by CRISPR–Cas nucleases. Nat. Biotechnol. 33, 187–198 (2015).

    CAS  PubMed  Google Scholar 

  14. 14.

    Ran, F. A. et al. Double nicking by RNA-guided CRISPR Cas9 for enhanced genome editing specificity. Cell 154, 1380–1389 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Shen, B. et al. Efficient genome modification by CRISPR–Cas9 nickase with minimal off-target effects. Nat. Methods 11, 399–402 (2014).

    CAS  PubMed  Google Scholar 

  16. 16.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Doench, J. G. et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR–Cas9. Nat. Biotechnol. 34, 184–191 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Kleinstiver, B. P. et al. High-fidelity CRISPR–Cas9 nucleases with no detectable genome-wide off-target effects. Nature 529, 490–495 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Slaymaker, I. M. et al. Rationally engineered Cas9 nucleases with improved specificity. Science 351, 84–88 (2016).

    CAS  PubMed  Google Scholar 

  20. 20.

    Kotecki, M., Reddy, P. S. & Cochran, B. H. Isolation and characterization of a near-haploid human cell line. Exp. Cell Res. 252, 273–280 (1999).

    CAS  PubMed  Google Scholar 

  21. 21.

    Carette, J. E. et al. Haploid genetic screens in human cells identify host factors used by pathogens. Science 326, 1231–1235 (2009).

    CAS  PubMed  Google Scholar 

  22. 22.

    Lackner, D. H et al. A generic strategy for CRISPR–Cas9-mediated gene tagging. Nat. Commun. 6, 10237 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Moll, P., Ante, M., Seitz, A. & Reida, T. QuantSeq 3′ mRNA sequencing for RNA quantification. Nat. Methods 11, 972 (2014).

    Google Scholar 

  24. 24.

    Lindeboom, R. G. H., Supek, F. & Lehner, B. The rules and impact of nonsense-mediated mRNA decay in human cancers. Nat. Genet. 48, 1112–1118 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Ting, L., Rad, R., Gygi, S. P. & Haas, W. MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics. Nat. Methods 8, 937–940 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Grossmann, J. et al. Implementation and evaluation of relative and absolute quantification in shotgun proteomics with label-free methods. J. Proteom. 73, 1740–1746 (2010).

    CAS  Google Scholar 

  27. 27.

    Zhang, J. & Maquat, L. E. Evidence that translation reinitiation abrogates nonsense-mediated mRNA decay in mammalian cells. EMBO J. 16, 826–833 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Makino, S., Fukumura, R. & Gondo, Y. Illegitimate translation causes unexpected gene expression from on-target out-of-frame alleles created by CRISPR–Cas9. Sci. Rep. 6, 39608 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Filippakopoulos, P. et al. Histone recognition and large-scale structural analysis of the human bromodomain family. Cell 149, 214–231 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Bechtel, S et al. The full-ORF clone resource of the German cDNA Consortium. BMC Genomics 8, 399 (2007).

    PubMed  PubMed Central  Google Scholar 

  31. 31.

    Filippakopoulos, P. et al. Selective inhibition of BET bromodomains. Nature 468, 1067–1073 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Dawson, M. A. et al. Inhibition of BET recruitment to chromatin as an effective treatment for MLL-fusion leukaemia. Nature 478, 529–533 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Zerbino, D. R. et al. Ensembl 2018. Nucleic Acids Res. 46, D754–D761 (2017).

    PubMed Central  Google Scholar 

  34. 34.

    Kar, S. et al. An insight into the various regulatory mechanisms modulating human DNA methyltransferase 1 stability and function. Epigenetics 7, 994–1007 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Tomlin, F. M. et al. Inhibition of NGLY1 inactivates the ranscription factor Nrf1 and potentiates proteasome inhibitor cytotoxicity. ACS Cent. Science 3, 1143–1155 (2017).

    CAS  Google Scholar 

  36. 36.

    Misaghi, S., Pacold, M. E., Blom, D., Ploegh, H. L. & Korbel, G. A. Using a small molecule inhibitor of peptide: N-glycanase to probe its role in glycoprotein turnover. Chem. Biol. 11, 1677–1687 (2004).

    CAS  PubMed  Google Scholar 

  37. 37.

    Enns, G. M. et al. Mutations in NGLY1 cause an inherited disorder of the endoplasmic reticulum-associated degradation pathway. Genet. Med. 16, 751 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Kapahnke, M., Banning, A. & Tikkanen, R. Random splicing of several exons caused by a single base hange in the target exon of CRISPR/Cas9 mediated gene knockout. Cells 5, 45 (2016).

    PubMed Central  Google Scholar 

  39. 39.

    Mou, H et al. CRISPR/Cas9-mediated genome editing induces exon skipping by alternative splicing or exon deletion. Genome Biol. 18, 108 (2017).

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    Mitchell, A. L. et al. InterPro in 2019: improving coverage, classification and access to protein sequence annotations. Nucleic Acids Res. 47, D351–D360 (2019).

    CAS  PubMed  Google Scholar 

  41. 41.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Hahne, F. & Ivanek, R. in Statistical Genomics: Methods and Protocols (Eds. Mathé, E & Davis, S) 335–351 (Humana Press, 2016).

  45. 45.

    Durinck, S., Spellman, P. T., Birney, E. & Huber, W. Mapping identifiers for the integration of genomic datasets with the R/ Bioconductor package biomaRt. Nat. Protoc. 4, 1184–1191 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Moggridge, S., Sorensen, P. H., Morin, G. B. & Hughes, C. S. Extending the compatibility of the SP3 paramagnetic bead processing approach for proteomics. J. Proteome Res. 17, 1730–1740 (2018).

    CAS  PubMed  Google Scholar 

  47. 47.

    Werner, T. et al. High-resolution enabled TMT 8-plexing. Anal. Chem. 84, 7188–7194 (2012).

    CAS  PubMed  Google Scholar 

  48. 48.

    Werner, T. et al. Ion coalescence of neutron encoded TMT 10-plex reporter ions. Anal. Chem. 86, 3594–3601 (2014).

    CAS  PubMed  Google Scholar 

  49. 49.

    Savitski, M. M. et al. Multiplexed proteome dynamics profiling reveals mechanisms controlling protein homeostasis. Cell 173, 260–274 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Savitski, M. M. et al. Targeted data acquisition for improved reproducibility and robustness of proteomic mass spectrometry assays. J. Am. Soc. Mass Spectrom. 21, 1668–1679 (2010).

    CAS  PubMed  Google Scholar 

  51. 51.

    Savitski, M. M. et al. Measuring and managing ratio compression for accurate iTRAQ/TMT quantification. J. Proteome Res. 12, 3586–3598 (2013).

    CAS  PubMed  Google Scholar 

  52. 52.

    Franken, H. et al. Thermal proteome profiling for unbiased identification of direct and indirect drug targets using multiplexed quantitative mass spectrometry. Nat. Protoc. 10, 1567–1593 (2015).

    CAS  PubMed  Google Scholar 

  53. 53.

    Huber, W. et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat. Methods 12, 115–121 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Wickham, H. Elegant Graphics for Data Analysis (Springer-Verlag, New York, 2009).

  55. 55.

    Fortin, J.-P., Triche, T. J. Jr & Hansen, K. D. Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi. Bioinformatics 33, 558–560 (2016).

    PubMed Central  Google Scholar 

  56. 56.

    Sanjana, N. E., Shalem, O. & Zhang, F. Improved vectors and genome-wide libraries for CRISPR screening. Nat. Methods 11, 783–784 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Vizcaino, J. A. et al. 2016 update of the PRIDE database and its related tools. Nucleic Acids Res. 44, D447–D456 (2015).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We would like to thank D. Pavlinic, F. Jung and V. Benes for RNA-seq; J. Stuhlfauth and team for cell banking; M. Boesche and team for mass spectrometry analyses; S. Shimamura for cell culture and sample preparation; and the microarray unit of the DKFZ Genomics and Proteomics Core Facility for providing the Illumina Human Methylation arrays and related services. A.H.S. was supported by a fellowship from the EMBL Interdisciplinary Postdoc (EIPOD) Programme under a grant from the Marie Sklodowska-Curie Actions COFUND (664726). The NGLY1 work was supported by the Grace Science Foundation.

Author information

Affiliations

Authors

Contributions

A.H.S. and F.Z. analyzed the data. A.H.S. and D.E. performed transcriptomics experiments. N.Z. performed mass spectrometry measurements. G.J. resequenced cell lines and analyzed BRD4 and DNMT1 truncation data. W.F.M., K.T. and H.S. performed all NGLY1 KO experiments and initial analyses. P.J. and S.C.-J. created and validated the NGLY1 KO cell lines and established the functional reporter in K562 to test NGLY1 functionality. A.-M.M. performed the BRD4 functional experiments. P.G., T.B., M.F.S., M.B., L.M.S., W.H. and G.D. supervised the work. A.H.S., F.Z., W.H. and G.D. wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Lars M. Steinmetz or Gerard Drewes or Wolfgang Huber.

Ethics declarations

Competing interests

G.J., N.Z., D.E., M.F.S., P.G., M.B., G.D. are employees and/or shareholders of Cellzome and GlaxoSmithKline. T.B. was an employee of Horizon Genomics GmbH.

Additional information

Peer review information Nicole Rusk was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–6 and Supplementary Tables 1 and 2

Reporting Summary

Data of Figure 1

Residual RNA and protein levels for 193 KO cell lines.

Data of Figure 2

Residual peptide levels aggregated per exon of four BRD4 KO replicates.

Data of Figure 3

Residual peptide levels aggregated per exon for two DNMT1 KO lines.

Data of Figure 4

Residual mRNA and protein levels, and deglycosylation quantification of two NGLY1-KO clones.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Smits, A.H., Ziebell, F., Joberty, G. et al. Biological plasticity rescues target activity in CRISPR knock outs. Nat Methods 16, 1087–1093 (2019). https://doi.org/10.1038/s41592-019-0614-5

Download citation

Further reading

Search

Quick links

Sign up for the Nature Briefing newsletter for a daily update on COVID-19 science.
Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing