Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 May 22:9:e55325.
doi: 10.7554/eLife.55325.

Parallel CRISPR-Cas9 screens clarify impacts of p53 on screen performance

Anne Ramsay Bowden #  1 , David A Morales-Juarez #  1 , Matylda Sczaniecka-Clift  1 , Maria Martin Agudo  1 , Natalia Lukashchuk  1 , John Christopher Thomas  1 , Stephen P Jackson  1
Affiliations

Parallel CRISPR-Cas9 screens clarify impacts of p53 on screen performance

Anne Ramsay Bowden et al. Elife. .

Abstract

CRISPR-Cas9 genome engineering has revolutionised high-throughput functional genomic screens. However, recent work has raised concerns regarding the performance of CRISPR-Cas9 screens using TP53 wild-type human cells due to a p53-mediated DNA damage response (DDR) limiting the efficiency of generating viable edited cells. To directly assess the impact of cellular p53 status on CRISPR-Cas9 screen performance, we carried out parallel CRISPR-Cas9 screens in wild-type and TP53 knockout human retinal pigment epithelial cells using a focused dual guide RNA library targeting 852 DDR-associated genes. Our work demonstrates that although functional p53 status negatively affects identification of significantly depleted genes, optimal screen design can nevertheless enable robust screen performance. Through analysis of our own and published screen data, we highlight key factors for successful screens in both wild-type and p53-deficient cells.

Keywords: CRISPR screening; DNA damage response; TP53; genetics; genomics; human.

Plain language summary

The invention of CRISPR-Cas9 genome editing has unlocked a greater understanding of the human genome. Researchers can use this system to make targeted cuts in any gene in the genome, forcing the cell to perform a rapid repair at the cut site. These repairs often introduce mutations into the damaged area, adding or removing DNA letters and disrupting the gene. This allows researchers to study what happens to cells when specific genes are missing, which can help to uncover what each gene is for. One of the most comprehensive ways to use this technique is to perform a CRISPR-Cas9 screen, which disrupts each gene in the genome one by one. For a CRISPR-Cas9 screen to work well, a cell needs to survive the cuts to its genome. But there is a crucial gene that can stop this happening. Often described as the 'guardian of the genome', this gene codes for a protein called p53, a tumour suppressor that helps to stop a cell turning cancerous when its DNA becomes damaged. This protein activates when the cell senses a cut in its genetic material and can kill the cell if it fails to make a successful repair. Recent work has shown that the presence of a working copy of the gene for the p53 protein might limit the ability of CRISPR-Cas9 to edit genes. But the evidence was inconclusive. So, Bowden, Morales-Juarez et al. performed two parallel CRISPR-Cas9 screens in human cells with and without p53 to find out more. This revealed that CRISPR-Cas9 can inactivate genes in both normal cells and cells lacking the p53 protein, but that it works better in cells without p53. This was because, when p53 was active, the cells initiated a protective response against the CRISPR-Cas9 cuts. This changed the patterns of genes successfully inactivated by the screen, but it did not make the results unusable. Careful experimental design and thorough data analysis made it possible to get useful results even in cells with functional p53 protein. The gene for p53 has mutations in around half of human cancers. So, understanding how it affects CRISPR-Cas9 screens could influence the design of future experiments. It is possible that the effects of the p53 protein could vary from cell type to cell type, and with different p53 mutations. Comparisons like the one performed here could help to further unpick how the cell's DNA repair systems might interfere with future CRISPR experiments.

PubMed Disclaimer

Conflict of interest statement

AB, DM, MS, MA, NL, JT, SJ No competing interests declared

Figures

Figure 1.
Figure 1.. Experimental set-up of parallel CRISPR-Cas9 screens in wild-type (WT) and TP53 knockout(TP53KO) RPE-1 cells.
Cells were infected at a low multiplicity of infection (MOI=0.3). An initial sample was harvested 48 hours after infection. Subsequently, transduced cells were selected with puromycin and harvested at days 15 and 19. Guide RNA (gRNA) representations were evaluated by extraction of genomic DNA from surviving cells, PCR amplification of barcodes, and next-generation sequencing. MAGeCK (Li et al., 2014) was used to determine the relative depletion and enrichment of genes in later samples compared to the 48-hour samples.
Figure 1—figure supplement 1.
Figure 1—figure supplement 1.. Validation of RPE-1 clones used in the screens.
(A) Western Blot of p53 and GAPDH with the RPE-1 wild-type and TP53KO clones used in the screens. (B) Cas9 editing efficiency assayed by FACS. Non-infected samples were used for gating purposes. Cells with no Cas9 expression were used as negative controls. Editing efficiency of Cas9-expressing clones was calculated by comparing the percentage of BFP+ (i.e. edited) cells to the GFP/BFP+ (i.e. total transduced population) using FlowJo. Editing efficiencies of Cas9-expressing clones are displayed in red.
Figure 2.
Figure 2.. Comparison of CRISPR-Cas9 screens in wild-type (WT) and TP53 knockout(TP53KO) RPE-1 cells demonstrates the impact of p53 on screen performance.
(A) Mean log2 fold change (LFC) in guide abundance per gene, and significance of this change, from day 3 to day 19 of the experiment. The q-values are false discovery rates (FDR) given by MAGeCK. (B) Receiver operating characteristic curves of MAGeCK p-values, discriminating between genes classified as core essential by Hart et al. (2017) and other genes. (C) Number of core essential genes with q-value less than the range of values given on the x-axis. (D) Mean LFC of guides targeting core essential and not core essential genes (Day 19 samples). Paired t-tests were used to test core essential or not essential genes between cell lines, unpaired t-tests were used within a cell line. (E) Mean LFC of guides targeting core essential and not core essential genes (Day 19 samples).
Figure 2—figure supplement 1.
Figure 2—figure supplement 1.. Additional comparisons between wild-type and TP53KO CRISPR-Cas9 screens.
(A) Mean log2 fold change (LFC) in guide abundance per gene, and significance of this change, from day 3 to day 15 of the experiment. The q-values were calculated using MAGeCK. (B) Number of core essential genes with p-value less than the range of values given on the x-axis. (C) Mean LFC of guides targeting core essential and not core essential genes (day 15 samples). Paired t-tests were used to test core essential or not essential genes between cell lines, unpaired t-tests were used within a cell line.
Figure 2—figure supplement 2.
Figure 2—figure supplement 2.. Biological pathway analysis identifies cell-cycle and p53 signalling as the pathways showing enrichment in the wild-type (WT) compared to TP53KO screens.
Genes were categorised according to KEGG pathways and significance of enrichment and depletion values were determined by Fisher’s exact test.
Figure 3.
Figure 3.. Comparison of wild-type (WT) RPE-1 CRISPR-Cas9 screens highlights important factors in screen design.
(A) Receiver operating characteristic curves of MAGeCK p-values, discriminating between core essential and not core essential genes in TP53 WT cells. (B) Distribution of normalised log2 fold changes (LFCs). The solid lines give kernel density estimates for each distribution, and the dashed line shows the median LFC of the core essential genes. (C) Mean LFC vs standard deviation (SD) per gene for genes with mean LFC < 0. As the SD is expected to scale with mean LFC, and the LFC distributions vary between experiments, ordinary least squares regressions were performed to determine the size of the variance across the range of LFCs. The dashed line shows the line of best fit and the equation for each line is given in the chart. (D) Log2 guide abundance across all screens. Box plots give median and quartile values.
Figure 3—figure supplement 1.
Figure 3—figure supplement 1.. Reduced variance at higher Log Fold Change is attributable to decreased sequencing reads across multiple guides.
Mean and standard deviation (SD) of LFC per gene in the MSKCC data are shown. Points are coloured by the number of guides targeting a gene that have abundance equal to zero in both end point replicates.
Figure 3—figure supplement 2.
Figure 3—figure supplement 2.. The effect on detection of core essential genes at different sequencing read depths in our screens.
The read abundances of our screens were resampled to different levels and analysed with MAGeCK. Cumulative proportion of core essential genes with depletion -log10(p) greater than values given on the y-axis. The mean proportions across 5-replicate sampling are given.
Author response image 1.
Author response image 1.
See this image and copyright information in PMC

References

    1. Ait-Si-Ali S, Polesskaya A, Filleur S, Ferreira R, Duquet A, Robin P, Vervish A, Trouche D, Cabon F, Harel-Bellan A. CBP/p300 histone acetyl-transferase activity is important for the G1/S transition. Oncogene. 2000;19:2430–2437. doi: 10.1038/sj.onc.1203562. - DOI - PubMed
    1. Aregger M, Chandrashekhar M, Tong AHY, Chan K, Moffat J. Pooled lentiviral CRISPR-Cas9 screens for functional genomics in mammalian cells. Methods in Molecular Biology. 2019;1869:169–188. doi: 10.1007/978-1-4939-8805-1_15. - DOI - PubMed
    1. Bouaoun L, Sonkin D, Ardin M, Hollstein M, Byrnes G, Zavadil J, Olivier M. TP53 variations in human cancers: new lessons from the IARC TP53 database and genomics data. Human Mutation. 2016;37:865–876. doi: 10.1002/humu.23035. - DOI - PubMed
    1. Brown KR, Mair B, Soste M, Moffat J. CRISPR screens are feasible in TP53 wild‐type cells. Molecular Systems Biology. 2019;15:e71. doi: 10.15252/msb.20188679. - DOI - PMC - PubMed
    1. Chan EM, Shibue T, McFarland JM, Gaeta B, Ghandi M, Dumont N, Gonzalez A, McPartlan JS, Li T, Zhang Y, Bin Liu J, Lazaro JB, Gu P, Piett CG, Apffel A, Ali SO, Deasy R, Keskula P, Ng RWS, Roberts EA, Reznichenko E, Leung L, Alimova M, Schenone M, Islam M, Maruvka YE, Liu Y, Roper J, Raghavan S, Giannakis M, Tseng YY, Nagel ZD, D'Andrea A, Root DE, Boehm JS, Getz G, Chang S, Golub TR, Tsherniak A, Vazquez F, Bass AJ. WRN helicase is a synthetic lethal target in microsatellite unstable cancers. Nature. 2019;568:551–556. doi: 10.1038/s41586-019-1102-x. - DOI - PMC - PubMed

Publication types

Substances