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Dual gene activation and knockout screen reveals directional dependencies in genetic networks

Nature Biotechnology volume 36, pages 170–178 (2018)Cite this article

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Abstract

Understanding the direction of information flow is essential for characterizing how genetic networks affect phenotypes. However, methods to find genetic interactions largely fail to reveal directional dependencies. We combine two orthogonal Cas9 proteins from Streptococcus pyogenes and Staphylococcus aureus to carry out a dual screen in which one gene is activated while a second gene is deleted in the same cell. We analyze the quantitative effects of activation and knockout to calculate genetic interaction and directionality scores for each gene pair. Based on the results from over 100,000 perturbed gene pairs, we reconstruct a directional dependency network for human K562 leukemia cells and demonstrate how our approach allows the determination of directionality in activating genetic interactions. Our interaction network connects previously uncharacterized genes to well-studied pathways and identifies targets relevant for therapeutic intervention.

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Figure 1: Ultra-complex CRISPRa screen identifies hundreds of genes involved in cancer signaling pathways.
Figure 2: The orthogonal CRISPR system.
Figure 3: Orthogonal CRISPR screens can quantify directional genetic interactions.
Figure 4: Validation of a directional Ras-centric genetic subnetwork.
Figure 5: Exploiting genetic dependencies for cancer therapy.

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Acknowledgements

Special thanks go to members of the McManus Lab who provided critical feedback during the course of this project and E. Cahill for excellent technical support. We also thank L.A. Gilbert and M.E. Tanenbaum for sharing the CRISPRa cell line ahead of its publication. M.T.M. was supported by NIH/CTD2 (U01CA168370) and IDG (1U01MH105028). M.K. was supported by NIH/NIGMS New Innovator Award DP2 GM119139, NIH/NCI K99/R00 CA181494, a Stand Up to Cancer Innovative Research Grant and the Chan Zuckerberg Biohub. J.A.B. was supported by NIH Training grant T32 GM00715 and an AFPE Predoctoral Fellowship. H.F. was supported by NIH/CTD2 (U01CA168449).

Author information

Authors and Affiliations

  1. Department of Microbiology and Immunology, University of California San Francisco Diabetes Center, WM Keck Center for Noncoding RNAs, University of California, San Francisco, San Francisco, California, USA.,

    Michael Boettcher, James A Blau, Ryan T Wagner, David Wu & Michael T McManus

  2. Department of Biochemistry and Biophysics, Institute for Neurodegenerative Diseases, University of California, San Francisco and Chan Zuckerberg Biohub, San Francisco, California, USA

    Ruilin Tian & Martin Kampmann

  3. Department of Microbiology and Immunology, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, USA.,

    Evan Markegard & Frank McCormick

  4. Department of Pharmacology and Emory Chemical Biology Discovery Center, Emory University School of Medicine, Atlanta, Georgia, USA

    Xiulei Mo & Haian Fu

  5. Department of Medicine, Lung Biology Center, University of California, San Francisco, San Francisco, California, USA.,

    Anne Biton & Noah Zaitlen

  6. Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI, USR 3756 Institut Pasteur et CNRS), Paris, France

    Anne Biton

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  1. Michael Boettcher
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Contributions

The project was conceived and directed by M.B. and M.T.M. Screen optimization was performed by M.B. and D.W. Libraries were designed by J.A.B. with guidance from M.B. and cloned by M.B. Orthogonal vectors and cell lines were created by M.B. All screens were performed by M.B., with A.B. assisting in CRISPRa screen analysis. R.T. developed the computational pipelines and the statistical framework for data analysis for screens with guidance from M.K. R.T. also selected the best-performing sgRNAs for arrayed validation. M.B. and R.T.W. conducted and analyzed arrayed validation experiments. E.M. and R.T.W. performed western blot analyses. X.M. and H.F. conducted and analyzed TR-FRET experiments. M.B. and M.T.M. wrote the manuscript with critical input from R.T., M.K., N.Z. and F.M. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Michael T McManus.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–15 (PDF 12455 kb)

Life Sciences Reporting Summary (PDF 4856 kb)

Supplementary Table 1

Read count values for each separate sgRNAs from primary CRISPRa screen together with sgRNA nucleotide sequences. (CSV 25544 kb)

Supplementary Table 2

Transcript level analysis of primary CRISPRa screen with τ and p-values as well as expression levels (FPKM) for each transcript are shown. Mann-Whitney U test was used to calculate p-values as described previously44. To correct for multiple hypothesis testing, we first performed random sampling with replacement among the set of values for nontargeting control sgRNAs and calculated p-values for each sampling. A total of 300 million cells and 26,718 transcripts were analysed. (CSV 1123 kb)

Supplementary Table 3

Arrayed validation of 20 candidate genes from primaryCRISPRa screen are shown. (CSV 10 kb)

Supplementary Table 4

Nucleotide sequences of sgRNAs used in the CRISPRaposition of the orthogonal library are shown together withnumbers of potential off-target sites as determined by CasOFFinder. (CSV 6 kb)

Supplementary Table 5

Nucleotide sequences for sgRNAs used in the SaCas9nuclease (knockout) position of the orthogonal library areshown. (CSV 568 kb)

Supplementary Table 6

For gene:gene combinations from the orthogonal screensingle activation and knockout τ values, expected andmeasured double perturbation τ values as well as calculated GI and Ψ scores are shown from each clonal replicate separately. (CSV 211 kb)

Supplementary Table 7

Reproducible gene:gene combinations that passed the filter criteria and that were used to construct the genetic interaction network in Figure 3h are shown. (CSV 14 kb)

Supplementary Table 8

Individual relative fitness values (τ) from arrayed validation of selected gene:gene interactions. Shown are enrichment values of cells expressing the indicated combination of sgRNAs in the absence (no drug) or presence of imatinib (IM) (PDF 40 kb)

Supplementary Table 9

GIv scores for each gene:gene combination and time point were calculated based on τ values in Extended Table 8. Correlation between GIv scores from each arrayed validation time point and the orthogonal screen in clonal replicate 2 are shown. For day 14 of the arrayed validation in the presence of imatinib, Ψ scores are shown which are also indicated in the genetic interaction network model in Figure 4c. (PDF 53 kb)

Supplementary Table 10

Orthogonal screen raw read counts from baseline and Day19 (imatinib treated) of clonal replicates 1 and 2. Dual sgRNA construct names are in the format: CRISPRa target gene symbol (two sgRNA per gene labelled gene-A and gene-B) followed by SaCas9 nuclease target gene symbol, RefSeq accession number and sgRNA sequence. All CRISPRa sgRNA sequences are shown in Extended Table 4. All SaCas9 nuclease sequences are shown in Extended Table 5. (XLS 162566 kb)

Supplementary Data 1

Map of sgLenti vector for CRISPRa screen sgRNAexpression. (TXT 20 kb)

Supplementary Data 2

Map sgLenti orthogonal vector for orthogonal screen sgRNA expression. (TXT 17 kb)

Supplementary Data 3

Map of S.aureus Cas9 nuclease vector. (TXT 22 kb)

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Boettcher, M., Tian, R., Blau, J. et al. Dual gene activation and knockout screen reveals directional dependencies in genetic networks. Nat Biotechnol 36, 170–178 (2018). https://doi.org/10.1038/nbt.4062

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