Multilayered VBC score predicts sgRNAs that efficiently generate loss-of-function alleles.
Journal
Nature methods
ISSN: 1548-7105
Titre abrégé: Nat Methods
Pays: United States
ID NLM: 101215604
Informations de publication
Date de publication:
07 2020
07 2020
Historique:
received:
19
04
2019
accepted:
27
04
2020
pubmed:
10
6
2020
medline:
21
10
2020
entrez:
10
6
2020
Statut:
ppublish
Résumé
CRISPR-Cas9 screens have emerged as a transformative approach to systematically probe gene functions. The quality and success of these screens depends on the frequencies of loss-of-function alleles, particularly in negative-selection screens widely applied for probing essential genes. Using optimized screening workflows, we performed essentialome screens in cancer cell lines and embryonic stem cells and achieved dropout efficiencies that could not be explained by common frameshift frequencies. We find that these superior effect sizes are mainly determined by the impact of in-frame mutations on protein function, which can be predicted based on amino acid composition and conservation. We integrate protein features into a 'Bioscore' and fuse it with improved predictors of single-guide RNA activity and indel formation to establish a score that captures all relevant processes in CRISPR-Cas9 mutagenesis. This Vienna Bioactivity CRISPR score (www.vbc-score.org) outperforms previous prediction tools and enables the selection of sgRNAs that effectively produce loss-of-function alleles.
Identifiants
pubmed: 32514112
doi: 10.1038/s41592-020-0850-8
pii: 10.1038/s41592-020-0850-8
doi:
Substances chimiques
RNA, Guide
0
CRISPR-Associated Protein 9
EC 3.1.-
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
708-716Références
Koike-Yusa, H., Li, Y., Tan, E.-P., Velasco-Herrera, M. D. C. & Yusa, K. Genome-wide recessive genetic screening in mammalian cells with a lentiviral CRISPR-guide RNA library. Nat. Biotechnol. 32, 267–273 (2013).
pubmed: 24535568
Wang, T., Wei, J. J., Sabatini, D. M. & Lander, E. S. Genetic screens in human cells using the CRISPR-Cas9 system. Science 343, 80–84 (2014).
pubmed: 24336569
Shalem, O. et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343, 84–87 (2014).
Sanjana, N. E., Shalem, O. & Zhang, F. Improved vectors and genome-wide libraries for CRISPR screening. Nat. Methods 11, 783–784 (2014).
pubmed: 25075903
pmcid: 25075903
Hart, T. et al. High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities. Cell 163, 1515–1526 (2015).
pubmed: 26627737
Wang, T. et al. Identification and characterization of essential genes in the human genome. Science 350, 1096–1101 (2015).
pubmed: 26472758
pmcid: 4662922
Shi, J. et al. Discovery of cancer drug targets by CRISPR–Cas9 screening of protein domains. Nat. Biotechnol. 33, 661–667 (2015).
pubmed: 25961408
pmcid: 4529991
Tzelepis, K. et al. A CRISPR dropout screen identifies genetic vulnerabilities and therapeutic targets in acute myeloid leukemia. Cell Rep. 17, 1193–1205 (2016).
pubmed: 27760321
pmcid: 5081405
Munoz, D. M. et al. CRISPR screens provide a comprehensive assessment of cancer vulnerabilities but generate false-positive hits for highly amplified genomic regions. Cancer Disco. 6, 900–913 (2016).
Aguirre, A. J. et al. Genomic copy number dictates a gene-independent cell response to CRISPR/Cas9 targeting. Cancer Disco. 6, 914–929 (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).
pubmed: 29083409
pmcid: 5709193
Wang, T. et al. Gene essentiality profiling reveals gene networks and synthetic lethal interactions with oncogenic ras. Cell 168, 890–903.e15 (2017).
pubmed: 28162770
pmcid: 5445660
Sanson, K. R. et al. Optimized libraries for CRISPR–Cas9 genetic screens with multiple modalities. Nat. Commun. 9, 5416 (2018).
pubmed: 30575746
pmcid: 6303322
Steinhart, Z. et al. Genome-wide CRISPR screens reveal a Wnt-FZD5 signaling circuit as a druggable vulnerability of RNF43-mutant pancreatic tumors. Nat. Med. 23, 60–68 (2017).
pubmed: 27869803
Martin, T. D. et al. A role for mitochondrial translation in promotion of viability in K-Ras mutant cells. Cell Rep. 20, 427–438 (2017).
pubmed: 28700943
pmcid: 5553568
Behan, F. M. et al. Prioritization of cancer therapeutic targets using CRISPR–Cas9 screens. Nature 568, 511–516 (2019).
pubmed: 30971826
Hart, T. et al. Evaluation and design of genome-wide CRISPR/SpCas9 knockout screens. Genes Genome. Genet. 7, 2719–2727 (2017).
Hart, T., Brown, K. R., Sircoulomb, F., Rottapel, R. & Moffat, J. Measuring error rates in genomic perturbation screens: gold standards for human functional genomics. Mol. Syst. Biol. 10, 733–733 (2014).
pubmed: 24987113
pmcid: 4299491
van Overbeek, M. et al. DNA repair profiling reveals nonrandom outcomes at Cas9-mediated breaks. Mol. Cell 63, 633–646 (2016).
pubmed: 27499295
Allen, F. et al. Predicting the mutations generated by repair of Cas9-induced double-strand breaks. Nat. Biotechnol. 498, 349 (2018).
Shen, M. W. et al. Predictable and precise template-free CRISPR editing of pathogenic variants. Nature 563, 646–651 (2018).
pubmed: 30405244
pmcid: 6517069
Chakrabarti, A. M. et al. Target-specific precision of CRISPR-mediated genome editing. Mol. Cell 73, 699–713 (2018).
pubmed: 30554945
Smits, A. H. et al. Biological plasticity rescues target activity in CRISPR knock outs. Nat. Methods 346, 1258096–1258097 (2019).
Anderson, J. L. et al. mRNA processing in mutant zebrafish lines generated by chemical and CRISPR-mediated mutagenesis produces unexpected transcripts that escape nonsense-mediated decay. PLoS Genet. 13, e1007105 (2017).
pubmed: 29161261
pmcid: 5716581
Mou, H. et al. CRISPR/Cas9-mediated genome editing induces exon skipping by alternative splicing or exon deletion. Genome Biol. 18, 108–108 (2017).
pubmed: 28615073
pmcid: 5470253
Tuladhar, R. et al. CRISPR–Cas9-based mutagenesis frequently provokes on-target mRNA misregulation. Nat. Commun. 10, 4010–4056 (2019).
Schoonenberg, V. A. C. et al. CRISPRO: identification of functional protein coding sequences based on genome editing dense mutagenesis. Genome Biol. 19, 169 (2018).
pubmed: 30340514
pmcid: 6195731
Tarumoto, Y. et al. LKB1, Salt-inducible kinases, and MEF2C are linked dependencies in acute myeloid leukemia. Mol. Cell 69, 1017–1027.e6 (2018).
pubmed: 29526696
pmcid: 5856641
Michlits, G. et al. CRISPR-UMI: single-cell lineage tracing of pooled CRISPR–Cas9 screens. Nat. Methods 14, 1191–1197 (2017).
pubmed: 29039415
Doench, J. G. et al. Rational design of highly active sgRNAs for CRISPR–Cas9-mediated gene inactivation. Nat. Biotechnol. 32, 1262–1267 (2014).
pubmed: 25184501
pmcid: 4262738
Andersson, B. S. et al. KBM-7, a human myeloid leukemia cell line with double Philadelphia chromosomes lacking normal c-ABL and BCR transcripts. Leukemia 9, 2100–2108 (1995).
pubmed: 8609723
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).
pubmed: 10527618
Doench, J. G. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR–Cas9. Nat. Biotechnol. 34, 184–191 (2016).
pubmed: 26780180
pmcid: 4744125
Chen, B. et al. Dynamic imaging of genomic loci in living human cells by an optimized CRISPR/Cas system. Cell 155, 1479–1491 (2013).
pubmed: 24360272
pmcid: 3918502
Elling, U. et al. Forward and reverse genetics through derivation of haploid mouse embryonic stem cells. Cell. Stem Cell 9, 563–574 (2011).
pubmed: 22136931
pmcid: 4008724
Elling, U. et al. A reversible haploid mouse embryonic stem cell biobank resource for functional genomics. Nature 550, 114–118 (2017).
pubmed: 28953874
pmcid: 6235111
Allen, F. et al. Predicting the mutations generated by repair of Cas9-induced double-strand breaks. Nat. Biotechnol. 37, 64–72 (2019).
He, W. et al. De novo identification of essential protein domains from CRISPR–Cas9 tiling-sgRNA knockout screens. Nat. Commun. 10, 4510–4541 (2019).
Blanchette, M. et al. Aligning multiple genomic sequences with the threaded blockset aligner. Genome Res. 14, 708–715 (2004).
pubmed: 15060014
pmcid: 383317
Ma, J. et al. CRISPR-DO for genome-wide CRISPR design and optimization. Bioinformatics 32, 3336–3338 (2016).
pubmed: 27402906
pmcid: 6095119
Adzhubei, I. A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).
pubmed: 20354512
pmcid: 2855889
Sim, N.-L. et al. SIFT web server: predicting effects of amino acid substitutions on proteins. Nucleic Acids Res. 40, W452–W457 (2012).
pubmed: 22689647
pmcid: 3394338
Chari, R., Yeo, N. C., Chavez, A. & Church, G. M. sgRNA Scorer 2.0: a species-independent model to predict CRISPR/Cas9 activity. ACS Synth. Biol. 6, 902–904 (2017).
pubmed: 28146356
pmcid: 5793212
Labuhn, M. et al. Refined sgRNA efficacy prediction improves large- and small-scale CRISPR-Cas9 applications. Nucleic Acids Res. 46, 1375–1385 (2018).
pubmed: 29267886
Chuai, G. et al. DeepCRISPR: optimized CRISPR guide RNA design by deep learning. Genome Biol. 19, 18–80 (2018).
Blomen, V. A. et al. Gene essentiality and synthetic lethality in haploid human cells. Science 350, 1092–1096 (2015).
pubmed: 26472760
Ashburner, M. et al. Gene ontology: tool for the unification of biology. The gene ontology consortium. Nat. Genet. 25, 25–29 (2000).
pubmed: 10802651
pmcid: 3037419
Michlits, G., Burkard, T. R., Novatchkova, M. & Elling, U. CRISPR-UMI step by step: a protocol for robust CRISPR screening. Protoc. Exch. https://doi.org/10.1038/protex.2017.111 (2017).
doi: 10.1038/protex.2017.111
Capra, J. A. & Singh, M. Predicting functionally important residues from sequence conservation. Bioinformatics 23, 1875–1882 (2007).
pubmed: 17519246
Rauscher, B., Heigwer, F., Breinig, M., Winter, J. & Boutros, M. GenomeCRISPR—a database for high-throughput CRISPR/Cas9 screens. Nucleic Acids Res. 45, D679–D686 (2017).
pubmed: 27789686
Finn, R. D. et al. The pfam protein families database: towards a more sustainable future. Nucleic Acids Res. 44, D279–D285 (2016).
pubmed: 26673716
pmcid: 26673716
El-Gebali, S. et al. The Pfam protein families database in 2019. Nucleic Acids Res. 47, D427–D432 (2019).
Rosenbloom, K. R. et al. The UCSC genome browser database: 2015 update. Nucleic Acids Res. 43, D670–D681 (2015).
pubmed: 25428374