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

  1. Anne Ramsay Bowden
  2. David A Morales-Juarez
  3. Matylda Sczaniecka-Clift
  4. Maria Martin Agudo
  5. Natalia Lukashchuk
  6. John Christopher Thomas  Is a corresponding author
  7. Stephen P Jackson  Is a corresponding author
  1. Wellcome/Cancer Research UK Gurdon Institute, University of Cambridge, United Kingdom
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Cite this article as: eLife 2020;9:e55325 doi: 10.7554/eLife.55325

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.

eLife digest

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.

Introduction

CRISPR-Cas9 genome engineering technologies have transformed cell biology, particularly high throughput functional genomic screens (Wang et al., 2015; Shalem et al., 2014; Shalem et al., 2015Smith et al., 2017) . Pooled CRISPR-Cas9 cell viability screens have been successfully employed in determining gene essentiality (Hart et al., 2015), identifying genetic interactions (Chan et al., 2019) and assessing drug sensitivities across various genetic backgrounds (Han et al., 2017). A number of factors influence CRISPR-Cas9 screen performance, including cellular background. In particular, recent reports concerning technical difficulties in CRISPR-Cas9 genome editing in p53-proficient cells, have brought into question the suitability of p53-proficient cell lines for high throughput CRISPR-Cas9 genetic screens (Haapaniemi et al., 2018; Ihry et al., 2018).

TP53, encoding p53, acts as a master regulator of cell-cycle checkpoint activation (Kastan et al., 1991), cellular senescence (Shay et al., 1991) and induction of apoptosis in response to DNA damage (Clarke et al., 1993Lowe et al., 1993; Lakin and Jackson, 1999). TP53 is arguably the most important tumour suppressor gene, with loss of function mutations in up to 50% of human cancers (Bouaoun et al., 2016). Consequently, the p53 status of a cell line, either wild-type (proficient) or mutant (deficient), can be an important factor in determining the suitability of a cellular model, and hence is an important consideration in design of high throughput genetic screens.

Generation of DNA double strand breaks (DSBs) induces p53-dependent cell-cycle arrest in normal fibroblasts (Di Leonardo et al., 1994), and most CRISPR-Cas9 genome editing approaches rely on DSB generation to achieve efficient editing (Jinek et al., 2012). Recent work has shown that CRISPR-Cas9-associated DSBs in hPSCs (human pluripotent stem cells) induce a p53-mediated apoptotic response, leading to high levels of toxicity and reduced editing efficiency in this background (Ihry et al., 2018). Furthermore, a similar p53-mediated DSB response in wild-type retinal pigment epithelial (RPE-1) cells reportedly severely impaired identification of essential genes in a CRISPR-Cas9 screen when compared to RPE-1 TP53 knockout (TP53KO) cells (Haapaniemi et al., 2018). In contrast, analysis of data from a small number of additional screens in p53 wild-type RPE-1 cells has shown that performance of successful CRISPR screens, as determined by essential gene identification and enrichment of expected targets, is possible in this cellular background (Brown et al., 2019). This controversy is confounded by the complexity of variation in experimental design between screens with a lack of controlled parallel experiments. To provide more definitive insights into this important debate, we performed parallel CRISPR-Cas9 screens in paired wild-type and TP53KO cell lines, thereby minimising additional confounding factors that can preclude accurate screen comparisons.

Results and discussion

We carried out parallel screens, in wild-type and TP53KO RPE-1 cells with two independent Cas9-expressing monoclonal populations for each genetic background, selected based on p53 status and high Cas9 cutting efficiency (Figure 1—figure supplement 1). To facilitate high screen sensitivity and in-depth interrogation of p53-mediated responses to CRISPR-Cas9-associated DSBs, we designed a bespoke dual guide RNA library targeting 852 DDR-related genes, with 112 olfactory receptor genes included as non-essential gene controls and 14 sequence-scrambled negative controls (Supplementary file 1). The library was manually curated to include established DDR components, putatively DDR related interactors, and a considerable number of bioinformatically-associated DDR factors. Moreover, the smaller size of this library compared to a whole genome library enabled high guide representation (>1000 x) to be maintained throughout the screen, minimising the impact of this key factor on screen sensitivity (Miles et al., 2016). In addition, our library incorporated a dual guide RNA vector design (Erard et al., 2017) to increase the frequency of functional knockout events in transduced cells compared to the canonical single guide RNA (sgRNA) approach. We reasoned that a vector generating two DSBs per cell may increase detection of differences in screen sensitivity due to variation in DSB responses between genetic backgrounds. Thus, the custom DDR library enables interrogation of p53-mediated DDR events, a cell’s overall responses to DSBs, and the fitness effects of inactivating DDR-related genes. Screens were executed as depicted in Figure 1, and relative enrichments and depletions of gene knockouts in the edited cell populations were determined from guide read counts generated by next-generation Illumina DNA sequencing (Supplementary file 2) using the program MAGeCK (Li et al., 2014; Supplementary file 3).

Figure 1 with 1 supplement see all
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.

In our screens, depletion of core essential genes (as defined by Hart et al., 2017) was clearly evident in both wild-type and TP53KO backgrounds (Figure 2A and Figure 2—figure supplement 1A). Due to the conservative nature of this essential gene list, additional genes with significant depletions were also identified in both cell lines (Supplementary file 3). A receiver operating characteristic (ROC) curve showing the classification of essential versus non-essential genes by gene depletion p-value ranks (calculated by MAGeCK) (Figure 2B) demonstrated good performance of both screens. Nevertheless, the TP53KO screen slightly outperformed the wild-type screen at both harvesting timepoints in terms of detection of essential genes by rank.

Figure 2 with 2 supplements see all
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).

When the significance of gene depletions was considered, we found that essential genes were much more likely to have low adjusted p-values (q-values) in the TP53KO background, compared to wild-type. In addition, we observed that the day 19 timepoint outperformed the day 15 timepoint, detecting increased numbers of essential genes at a given significance threshold (Figure 2C and Figure 2—figure supplement 1B). The underlying basis behind this differential sensitivity to identifying essential genes lies in the magnitude of the phenotypic effect observed for each guide. While log fold changes (LFCs) across non-core essential (‘not essential’) genes were not significantly different between the two genetic backgrounds (p=0.60), LFCs for core essential genes were significantly lower in the TP53KO screens compared to screens in TP53 wild-type settings (p=0.0010) (Figure 2D), consistent with wild-type cells initiating a p53-mediated response to Cas9-induced DSBs. This would inhibit the proliferation rates of all transduced cells during the course of the screens, leading to smaller LFCs and a narrower distribution of guides within the population, with a consequent reduction in genes with significant depletion scores. Similar results were seen in our analyses of day 15 samples (Figure 2—figure supplement 1C).

The impact of the p53-mediated response is also evident when comparing screen results from differential enrichment and depletion of genes between the two genetic backgrounds (Figure 2E). As expected, in TP53 wild-type cells, guides targeting TP53 were the most significantly enriched, with guides targeting other components of the p53 pathway showing the most significant differences between the two genetic backgrounds. Guides significantly enriched in the TP53 wild-type background included those targeting CDKN1A that encodes p21, the major downstream mediator of p53-mediated cell cycle arrest (el-Deiry et al., 1993), and those targeting USP28 that encodes a deubiquitylating enzyme that acts to stabilise p53 (Zhang et al., 2006; Cuella-Martin et al., 2016). In contrast, guides targeting genes that were significantly depleted in the wild-type but not the TP53 knockout background included MDM2 and MDM4, which act as negative regulators of p53. MDM2 is an E3 ubiquitin ligase that targets p53 for degradation (Haupt et al., 1997), while MDM4 inhibits p53-dependent transcriptional activity (Francoz et al., 2006). SETDB1, which acts via MDM2, was also enriched in the TP53 wild-type background. This protein forms a complex with p53 and catalyses p53 K370 di-methylation. Attenuation of SETDB1 reduces the level of di-methylation at this site, leading to increased recognition and degradation of p53 by MDM2 (Fei et al., 2015). Furthermore, when we assessed the enrichment/depletion of specific biological pathways between the wild-type and TP53KO backgrounds, cell cycle and p53 signalling were the two pathways that were enriched (Figure 2—figure supplement 2 and Supplementary file 4).

Genes that are not acting in the p53 pathway were also identified as significantly enriched (e.g. EP300) or depleted (e.g. CCNA2) at a FDR < 0.1 (Supplementary file 3). EP300 was enriched on both genetic backgrounds and has an established role as a tumour suppressor through the regulation of the G1/S cell-cycle transition (Ait-Si-Ali et al., 2000). CCNA2, or cyclin A2, was depleted on both genetic backgrounds as it interacts with both CDK1 and CDK2 to drive S-phase progression and regulate the G1/S and G2/M phases of the cell-cycle (Pagano et al., 1992). Altogether, these results demonstrate that despite reduced screen sensitivity in p53-proficient cells, biologically meaningful enrichment and depletion analyses at the individual gene and pathway levels can, when required, still be performed in TP53 wild-type settings.

To further contextualise the feasibility of performing CRISPR-Cas9 screens in a p53-proficient background, we analysed our screens with five others performed in TP53 wild-type RPE-1 cells. When we performed a comparative ROC curve analysis to assess the screens’ abilities to discriminate between core essential genes and other genes (Figure 3A), this established that the performance of all screens was similar, with the exception of Haapaniemi et al. (2018) data which underperformed in the ability to distinguish essential genes. We then examined the distribution of normalised LFCs for each screen (Figure 3B). This revealed that the core essential genes formed distributions distinct from those of olfactory receptors and other non-essential genes in all wild-type screens, with the exception of the Haapaniemi et al. screen where the separation was minimal (the smaller median LFC in our screen compared to the other four successful screens did not notably hinder our ability to distinguish essential genes). Taken together, these analyses provide further evidence that CRISPR-Cas9 screens can be performed successfully in a p53-proficient background. It appears that the Haapaniemi et al. screen is an outlier in its inability to robustly detect essential genes, possibly due to differences in experimental design and execution, and perhaps reflecting relatively low editing efficiency of the polyclonal RPE-1 population used in this screen. This factor strengthens the importance of carefully selecting clones with high Cas9 editing efficiency and also for the use of biological replicates, to enable recognition of common screen results that are independent of clonal background.

We noted that while the median LFC is higher in the LTRI/MDACC, Hart, UBC and MSKCC screens, the variance is also increased when compared to ours. Consequently, we interrogated the relationship between the standard deviation (SD) of the LFCs and the mean LFC values for each of the wild-type screens. Figure 3C shows that the variance in LFC between guides targeting the same gene is less in our screen than in these other screens. We speculate that this decrease in variance is linked to the much higher gRNA representation kept throughout our screen (>1000 x mean gRNA representation) than in these other screens, although we cannot discard the possibility that the dual-sgRNA system we used is the cause of this effect. High gRNA representation is relevant for the success and reliability of CRISPR-Cas9 screens, with most published recommendations suggesting screening to at least 200x gRNA representation (Aregger et al., 2019) but ideally >500 x (Joung et al., 2017). Importantly, high representation must be maintained throughout cell culture and also in the PCR amplification steps. Sufficient sequencing depth is also essential to maintain the sensitivity achieved through high gRNA representation. Figure 3D demonstrates the variability in guide abundance determined by sequencing reads across the screens analysed. The MSKCC screen is the only dataset to show a distribution with a substantial number of zero reads in the final samples, which accounts for the decreased variance at more negative LFCs in this screen (Figure 3—figure supplement 1). Through modelling the effect of decreased sequencing depth in our data, we demonstrate that low read counts can notably decrease screen sensitivity (Figure 3—figure supplement 2).

Figure 3 with 2 supplements see all
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.

Conclusions

In summary, we present data from parallel screens in TP53 wild-type and TP53KO RPE-1 cells, which demonstrate that a p53-mediated response does negatively impact the sensitivity of CRISPR-Cas9 screens. The extent of the impact of TP53 status on CRISPR-Cas9 screens might vary depending on the cell type being studied, including those with loss-of-function mutations in TP53 without being fully TP53 null. It remains to be established precisely how and to what extent different TP53 mutations, including ‘hotspot’ mutations, might influence CRISPR-Cas9 screen performance. However, we anticipate that most or all cell lines with an intact TP53 pathway and proper cell-cycle checkpoint activation would likely recapitulate our findings. Other important factors impacting sensitivity include the guide RNA library used, the magnitude of guide effects, adequate gRNA representation and sufficient sequencing depth. Selection of high-editing efficiency Cas9-expressing cells is also highly recommended and use of biological replicates enables identification of clonal variation. Considering these factors in screen design and execution allows successful CRISPR-Cas9 screens to be carried out in both p53-proficient and p53-deficient cells, thereby fostering new biological insights.

Materials and methods

Dual-sgRNA library design

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A custom dual-sgRNA library was designed to target 852 genes related to the DNA damage response, 112 olfactory-receptor genes, and 14 sequence scrambled negative controls with a total of 3404 dual-sgRNAs. The genes targeted by this library include a total of 95 core essential genes. The sgRNA sequences and pairwise scores were determined using the Croatan scoring algorithm (Erard et al., 2017). Transomic Technologies selected the top pairs of sgRNAs for each gene and assigned a distinct barcode to each pair, cloned them into the pCLIP-dual-SFFV-ZsGreen vector, and packaged them into lentiviral particles ready for transduction. For pooled screening, the viral titre was determined by exposing cells to a 6-point dose response of the lentiviral stock. The optimal concentration of virus to achieve a multiplicity of infection (MOI) of 0.3 was determined by linear regression analysis.

CRISPR-Cas9 screens

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CRISPR-Cas9 screens were performed using the custom dual-sgRNA DNA damage response library outlined above. Biological duplicates (two independently isolated Cas9-expressing clones) of wild-type and TP53KO RPE-1 cells were transduced at a MOI of 0.3 and >1,000 fold coverage of the library. The following day, cells were cultured with puromycin to select for the transductants for 12 additional days. Surviving cells from each biological replicate were harvested prior to puromycin selection (day 3), and at day 15 and day 19 after initial transduction. Subsequently, the genomic DNA (gDNA) was isolated using TAIL buffer (17 mM Tris pH 7.5, 17 mM EDTA, 170 mM NaCl, 0.85% SDS, and 1 mg/mL Proteinase K) and subjected to 24 PCR reactions with custom indexed primers designed to amplify the barcode within the lentiviral backbone and append Illumina adapter sequences. Finally, the PCR products were purified (QIAquick PCR Purification kit, Qiagen), multiplexed, and sequenced on an Illumina HiSeq1500 system. Genes enriched or depleted in the day 15 and day 19 samples compared to the day 3 samples were determined using MAGeCK v0.5.9.2 (Li et al., 2014).

Cell culture

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RPE-1 TP53 wild-type and TP53KO cells were cultured in DMEM/F-12 media (Dulbecco’s Modified Eagle Medium: Nutrient Mixture Ham’s F-12, Sigma-Aldrich) supplemented with 17 mL of 7.5% NaHCO3 (Sigma-Aldrich) per 500 mL, 10% (v/v) foetal bovine serum (FBS, BioSera), 100 U/mL penicillin, 100 µg/mL streptomycin (Sigma-Aldrich), 2 mM L-glutamine, and 10 μg/mL blasticidin (Sigma-Aldrich) to select for Cas9 expressing cells. Cells were additionally cultured with 1.5 µg/mL puromycin during selection of the transductants.

Western blot

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RPE-1 TP53 wild-type and TP53KO cells were harvested in 100–200 uL of Laemmli buffer (120 mM Tris 6.8 pH, 4%SDS, 20% glycerol). Protein concentrations were determined using a NanoDrop spectrophotometer (Thermo Scientific) at A280 nm. SDS-PAGE was performed with 35 µg of protein lysates, the proteins were resolved on a precast NuPAGE Novex 4–12% Bis/Tris gradient gel (Invitrogen). Resolved proteins were transferred to a nitrocellulose membrane (GE Healthcare) and immunoblotted with the following antibodies at a 1/1,000 dilution: p53 (#554293, BD Biosciences) and GAPDH (#MAB374, Merck Millipore).

Human cell line generation

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RPE-1 wild-type cells were originally obtained from the ATCC cell repository by Professor Jonathon Pines. They were routinely tested for mycoplasma and were authenticated using Affymetrix SNP6 copy number analysis. RPE-1 TP53KO cells were generated as described previously (Chiang et al., 2016). The TP53 wild-type and TP53KO RPE-1 cells were transduced with a lentiviral vector encoding Cas9 and a blasticidin resistance cassette to facilitate the isolation of Cas9-expressing clones. Limiting dilution of the transduced population enabled isolation of monoclonal cell lines. Cas9 expression was validated by western blot and Cas9 editing efficiency was assayed by transducing clones with a lentiviral vector encoding GFP, BFP, and a sgRNA for GFP (obtained from Dr Emmanouil Metzakopian, UK Dementia Research Institute, Cambridge, UK). Transduced and non-transduced cells were subjected to FACS sorting using an LSRFortessa (BD Biosciences) flow cytometer. The Cas9 editing efficiency for each clone was calculated by comparing the percentage of BFP+ (i.e. edited) cells to the GFP/BFP+ cells (i.e. total transduced population) using FlowJo.

Statistical software used

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Statistical analyses were performed in Python (3.7.5), using the following packages in particular:

  • MAGeCK (0.5.9.2)

  • jupyterlab (1.1.4)

  • matplotlib (3.1.1)

  • seaborn (0.9.0)

  • pandas (0.25.0)

  • numpy (1.16.4)

  • scipy (for t-tests & Fisher’s exact test, 1.3.0)

  • scikit-learn (for PCA, 0.21.2)

  • statsmodels (for linear regression and multiple testing correction, 0.10.1)

CRISPR screen re-analyses

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Data files containing guide abundances were downloaded from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE128210.

Supplementary file 5 lists the origins of the data. Where multiple timepoints were available, the day 18 timepoint was used. Guides targeting genes not present in our DDR library were removed from the abundance tables, and MAGeCK (0.5.9.2) was used to obtain significance values for depletion and enrichment of genes. The command line arguments remove-zero-threshold=10 and remove-zero=control were used.

LFC normalisation

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LFCs were normalised by subtracting the mean of the olfactory receptor (OR) genes from all values, and then dividing all values by the SD of the OR genes.

Resampling

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To simulate smaller sequencing runs, guide abundances were resampled by N random draws using the initial abundances as weights. N was set to yield expected median abundances ranging between 10 and 1000. MAGeCK was used to obtain significance values as above. five replicate draws were performed per sample.

Pathway analysis

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Genes within the library were annotated according to KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway. Selection of relevant pathways within the library was based on classifications by Pearl et al. (2015). The enrichment of genes with p<0.05 in these pathways was evaluated using Fisher’s exact test. Genes that were depleted over time, or enriched, were tested separately.

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Decision letter

  1. Jean-Christophe Marine
    Reviewing Editor; Vlaams Instituut voor Biotechnologie, Belgium
  2. Kathryn Song Eng Cheah
    Senior Editor; The University of Hong Kong, Hong Kong
  3. Andrea Ventura
    Reviewer; Memorial Sloan-Kettering, United States
  4. Oscar Fernandez-Capetillo
    Reviewer; Karolinska Institute, Sweden

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

All reviewers have pointed out the importance, relevance and timely nature of the work. The effect of p53 status on the performance of CRISPR-Cas9 screens has indeed been debated and we believe that your study makes a significant contribution in resolving this issue.

Decision letter after peer review:

Thank you for submitting your work entitled "Parallel CRISPR-Cas9 screens clarify impacts of p53 on screen performance" for consideration by eLife. Your article has been reviewed by three reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by a Senior Editor.

All three reviewers have pointed out the importance, relevance and timely nature of the work. The effect of p53 status on the performance of CRISPR-Cas9 screens has indeed been debated and we believe that your study makes a significant contribution in resolving this issue.

They raised, however, several issues that need to be addressed before acceptance, as outlined below:

1) Please address the textual changes proposed by reviewer 1.

2) Indicate which genes -in addition to those highlighted- are substantially enriched or depleted in the WT screen and whether they belong to specific functional groups. Please discuss/speculate on why these genes were selected in this screen.

3) Provide a rationale for choosing DDR as the targeted screen (reviewer 2).

Please find below all the reviewers' comments:

Reviewer #1:

This is an interesting and timely piece of work.

I have two remarks:

1) The screen is performed in one model cell type/system (retinal pigment epithelial cells). Knowing that the type (G1 arrest, senescence, apoptosis,..) and extend of the p53 response can vary dramatically from one cell type to another, one question that emerges is that how can one generalise the findings? The authors may add a statement in the Discussion about this. Similarly the control line is p53 KO. As stated in the Introduction, 50% of human cancers (cell lines) carry TP53 loss of function mutations (but are not fully KO). It maybe interesting to introduce some of the hotspot mutations in the authors' preferred model system and ask how much this would influence the results.

2) The authors argue that biologically meaningful enrichment and depletion analyses can still be performed in TP53 WT cells. This is correct if one is interested in p53 biology. It maybe also important to highlight enrichment or depletion of essential genes that do not act in the p53 pathway, when describing this part of the data.

Reviewer #2:

In this brief report, Bowden, Juarez and colleague address an important issue in the genome editing field: the effect of p53 status on the performance of CRISPR-Cas9 screens. Previous studies had reported that activation of the p53 response by the DSBs induced by CRISPR-Cas9 negatively impacted the outcome of genome wide and targeted screens, presumably by causing the elimination of gene-edited cells.

To directly test this hypothesis, the authors performed pooled, dual guides, targeted CRISPR-Cas9 screens in isogenic WT and p53-KO RPE-1 cells using 852 gRNAs targeting DNA damage response genes, 112 control gRNAs targeting olfactory receptor genes, and 14 scrambled gRNAs.

The experiments were performed in two independent clones per genotype, and at two timepoints (15 and 19 days).

The authors convincingly show that the screens can efficiently identify core essential genes in both genotypes, although the screens performed better in the KO clones: lower q-values and greater average gRNA depletion (log2 Fold change) for essential genes. Reassuringly, known p53 regulators score positive for enrichment and depletion selectively in the WT background.

Finally, their screen in WT RPE-1 cells shows good correlation with similar screens performed in the same cell line by other groups, with the significant exception of Haapaniemi et al., whose screen appears to be an outlier.

Collectively, these results are of substantial interest to the scientific community and will guide the optimal design of future screens. The experiments are well performed, the results are clearly described, and their interpretation seems accurate. The relevant scientific literature is cited. The computational approaches used seem appropriate to me.

I only have a few comments:

1) The rationale for choosing DDR for the targeted screen is not entirely clear and should be discussed.

2) The authors should explicitly indicate how many of the genes targeted by the screen are considered "Core essential".

3) In Figure 2E it appears that several other genes in addition to those highlighted are substantially enriched or depleted in the WT screen. Can the authors comment on those as well?

4) The authors interpret the lower depletion of core essential genes detected in the WT background compared to the KO background (Figure 2D) as likely resulting from the general DDR response induced by the gRNAs in the WT cells. This is certainly a plausible explanation, but another trivial possibility is that the KO clones simply have a faster population doubling compared to WT cells, even in the absence of an exogenous source of DNA damage. To exclude this possibility it would be important to directly compare the growth rate of the 4 RPE-1 clones.

Reviewer #3:

In the present manuscripts the authors compare the differences that might arise in defining essential genes when performing CRISPR screens in p53WT vs p53 deficient cells. This has been somewhat confusing due to reports for instance from the Taipale lab claiming that P53 proficient cells were not suitable to conduct such screens. The reasoning behind is that the breaks generated by the CAS9 would activate a P53-dependent toxic response in cells which can mask the results of these studies and reduce the window to identify bona-fide essential genes.

To address this, the authors have generated a novel CRISPR library for DNA damage response genes, based on dual sgRNAs to favour gene deletion events. The study was conducted by comparing essential genes in P53 WT and KO RPE human cells.

The study is well performed, and clearly shows that gene essentiality screens can in fact be conducted in P53 WT cells. The authors show a few examples of genes that are preferentially lost in both backgrounds, which make sense.

In my opinion, the work is well done and the data provided support the main claim of the authors. Namely, that gene essentiality screens can be conducted in P53 WT cells. Since this has been somewhat confusing in the field of screens, I guess it will be clarifying to have it published.

That being said, I somewhat guess that the initial objective of this study was not to enter into this dispute, but rather a more exciting experiment in the look for genes that are selectively essential for P53-KO cells.

Due to the covid crisis, I sincerely feel in no position to ask the authors for more experiments. But perhaps, they could still work a bit more on their existing data and make a better paper if they do a bit more analysis and representations of their data. For instance; they could provide a comprehensive list of all the genes that are selectively lost in P53-KO cells, and discuss/speculate on why this could be. In addition, they could also provide a list of genes that are essential in P53 WT cells, but not in P53-KO ones. This is relevant as many DDR genes are essential in mice, in a manner that is rescued by P53 loss. Dissecting which ones behave in this manner and which ones not, might turn out to be informative.

In summary, I think the data provided is enough to end the discussion on whether gene essentiality screens can be done in P53 WT cells. Before publication, I would suggest the authors work a bit more in the analysis of their data and the presentation, as the same data can make an even nicer paper for their readers.

https://doi.org/10.7554/eLife.55325.sa1

Author response

Reviewer #1:

This is an interesting and timely piece of work.

I have two remarks:

1) The screen is performed in one model cell type/system (retinal pigment epithelial cells). Knowing that the type (G1 arrest, senescence, apoptosis,..) and extend of the p53 response can vary dramatically from one cell type to another, one question that emerges is that how can one generalise the findings? The authors may add a statement in the Discussion about this. Similarly the control line is p53 KO. As stated in the Introduction, 50% of human cancers (cell lines) carry TP53 loss of function mutations (but are not fully KO). It maybe interesting to introduce some of the hotspot mutations in the authors' preferred model system and ask how much this would influence the results.

In response to these relevant and important comments, we have added the following statements to the conclusion section of our paper:

“The extent of the impact of TP53 status on CRISPR-Cas9 screens might vary depending on the cell type being studied, including those with loss-of-function mutations in TP53 without being fully TP53 null. It remains to be established precisely how and to what extent different TP53 mutations, including “hotspot” mutations, might influence CRISPR screen performance. However, we anticipate that most or all cell lines with an intact TP53 pathway and proper cell-cycle checkpoint activation would likely recapitulate our findings.”

2) The authors argue that biologically meaningful enrichment and depletion analyses can still be performed in TP53 WT cells. This is correct if one is interested in p53 biology. It maybe also important to highlight enrichment or depletion of essential genes that do not act in the p53 pathway, when describing this part of the data.

While we understand the reasoning for this comment, our conclusions on the negative effects of an active p53-mediated DDR response in CRISPR-Cas9 screens would still hold regardless of whether one’s interest lies in p53 biology. The data presented in this paper (in Figure 2E and Figure 2—figure supplement 2) intend to highlight the key differences amongst the WT and TP53KO samples. Nevertheless, we are now discussing additional genes that are significantly depleted and enriched that are not acting in the p53 pathway. The revised manuscript now includes the following text:

“Genes that are not acting in the p53 pathway were also identified as significantly enriched (e.g. EP300) or depleted (e.g. CCNA2) at a FDR<0.1 (Supplementary file 5). EP300 was enriched on both genetic backgrounds and has an established role as a tumour suppressor through the regulation of the G1/S cell-cycle transition (Ait-Si-Ali et al., 2000). CCNA2, or cyclin A2, was depleted on both genetic backgrounds as it interacts with both CDK1 and CDK2 to drive S-phase progression and regulate the G1/S and G2/M phases of the cell-cycle (Pagano et al., 1992).”

Additionally, we are including a table of the genes significantly (FDR<0.1) enriched and depleted for both genetic backgrounds as Supplementary file 5.

Indicate which genes – in addition to those highlighted – are substantially enriched or depleted in the WT screen and whether they belong to specific functional groups. Please discuss/speculate on why these genes were selected in this screen.

In this paper, we were interested in analysing the differences between the WT and TP53KO samples (as highlighted in Figure 2E and Figure 2—figure supplement 2) but, as the reviewers highlight, it might be interesting to look into functional clustering of the individual samples. Therefore, we performed a functional clustering analysis with GO terms of the substantially enriched/depleted genes. There was no significant enrichment for functional groups at FDR < 0.05 and so we have not shown this data. All the results indicating enrichments and depletions from the individual samples are available in supplementary data.

Reviewer #2:

[…]

I only have a few comments:

1) The rationale for choosing DDR for the targeted screen is not entirely clear and should be discussed.

The bespoke custom library allowed us to interrogate aspects of p53 biology and the DDR, the latter being the main focus of our research. Additionally, the size of our focused library compared to a whole genome library facilitated library construction and logistical considerations of performing parallel CRISPR-Cas9 screens with a thorough gRNA representation (1,000x) in our lab setting. To address these points, we include the following text in the Results and Discussion sections of our paper:

“The library was manually curated to include established DDR components, putative DDR-related interactors, and a considerable number of bioinformatically-associated DDR factors.”

“Moreover, the smaller size of this library compared to a whole genome library enabled high guide representation (>1000x) to be maintained throughout the screen, minimising the impact of this key factor on screen sensitivity (Miles, Garippa and Poirier, 2016).”

“Thus, the custom DDR library enables interrogation of p53-mediated DDR events, a cell’s overall responses to DSBs, and the fitness effects of inactivating DDR-related genes.”

2) The authors should explicitly indicate how many of the genes targeted by the screen are considered "Core essential".

There are 95 core essential genes in our DDR library. We have now added this information in the Materials and methods section concerning the dual-sgRNA library design.

3) In Figure 2E it appears that several other genes in addition to those highlighted are substantially enriched or depleted in the WT screen. Can the authors comment on those as well?

The genes highlighted in Figure 2E are those that show significant differences between the two genetic backgrounds (as calculated by MAGeCK). The list of other substantially enriched or depleted genes for both the wild-type and the TP53KO are provided in supplementary information.

4) The authors interpret the lower depletion of core essential genes detected in the WT background compared to the KO background (Figure 2D) as likely resulting from the general DDR response induced by the gRNAs in the WT cells. This is certainly a plausible explanation, but another trivial possibility is that the KO clones simply have a faster population doubling compared to WT cells, even in the absence of an exogenous source of DNA damage. To exclude this possibility it would be important to directly compare the growth rate of the 4 RPE-1 clones.

We had considered this possibility and experimentally determined that the clones behaved similarly in terms of growth rates. The reviewer correctly points out that the growth rates of the clones in the absence of DNA damage were not presented. In Author response image 1, we provide a graph representing the unperturbed growth rates of all the RPE-1 clones used in our CRISPR-Cas9 screens.

Author response image 1

Reviewer #3:

[…]

That being said, I somewhat guess that the initial objective of this study was not to enter into this dispute, but rather a more exciting experiment in the look for genes that are selectively essential for P53-KO cells.

Due to the covid crisis, I sincerely feel in no position to ask the authors for more experiments. But perhaps, they could still work a bit more on their existing data and make a better paper if they do a bit more analysis and representations of their data. For instance; they could provide a comprehensive list of all the genes that are selectively lost in P53-KO cells, and discuss/speculate on why this could be. In addition, they could also provide a list of genes that are essential in P53 WT cells, but not in P53-KO ones. This is relevant as many DDR genes are essential in mice, in a manner that is rescued by P53 loss. Dissecting which ones behave in this manner and which ones not, might turn out to be informative.

Figure 2E shows all the genes that are significantly differentially essential in the two cell backgrounds. Comparing lists of genes that pass some arbitrary threshold in one cell background but not another could possibly confuse the issue, as gene essentiality is not black and white. A small difference in phenotype could result in a significant dropout in one cellular background and a not-quite-significant dropout in the other. For this reason, we only discuss genes where the difference between cell backgrounds is statistically significant.

https://doi.org/10.7554/eLife.55325.sa2

Article and author information

Author details

  1. Anne Ramsay Bowden

    Wellcome/Cancer Research UK Gurdon Institute, University of Cambridge, Cambridge, United Kingdom
    Contribution
    Conceptualization, Resources, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing
    Contributed equally with
    David A Morales-Juarez
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1138-4452
  2. David A Morales-Juarez

    Wellcome/Cancer Research UK Gurdon Institute, University of Cambridge, Cambridge, United Kingdom
    Contribution
    Conceptualization, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing
    Contributed equally with
    Anne Ramsay Bowden
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5370-5512
  3. Matylda Sczaniecka-Clift

    Wellcome/Cancer Research UK Gurdon Institute, University of Cambridge, Cambridge, United Kingdom
    Contribution
    Investigation, Contributed substantially to the collection of data
    Competing interests
    No competing interests declared
  4. Maria Martin Agudo

    Wellcome/Cancer Research UK Gurdon Institute, University of Cambridge, Cambridge, United Kingdom
    Contribution
    Investigation, Contributed substantially to the collection of data
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3605-9963
  5. Natalia Lukashchuk

    Wellcome/Cancer Research UK Gurdon Institute, University of Cambridge, Cambridge, United Kingdom
    Present address
    Oncology Research & Early Development, AstraZeneca, Cambridge, United Kingdom
    Contribution
    Resources, Investigation
    Competing interests
    No competing interests declared
  6. John Christopher Thomas

    Wellcome/Cancer Research UK Gurdon Institute, University of Cambridge, Cambridge, United Kingdom
    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Visualization, Methodology, Writing - original draft, Writing - review and editing
    For correspondence
    jct61@cam.ac.uk
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2425-8412
  7. Stephen P Jackson

    Wellcome/Cancer Research UK Gurdon Institute, University of Cambridge, Cambridge, United Kingdom
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Project administration, Writing - review and editing
    For correspondence
    s.jackson@gurdon.cam.ac.uk
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9317-7937

Funding

Cancer Research UK (C6/A18796)

  • Anne Ramsay Bowden
  • David A Morales Juarez
  • Matylda Sczaniecka-Clift
  • Maria Martin Agudo
  • John Christopher Thomas
  • Stephen P Jackson

Wellcome (206388/Z/17/Z)

  • Anne Ramsay Bowden
  • David A Morales Juarez
  • Matylda Sczaniecka-Clift
  • Maria Martin Agudo
  • John Christopher Thomas
  • Stephen P Jackson

Cancer Research UK (C6946/A24843)

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

Wellcome (WT203144)

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

Cancer Research UK (C6/A11224)

  • Matylda Sczaniecka-Clift
  • Natalia Lukashchuk
  • Stephen P Jackson

Consejo Nacional de Ciencia y Tecnología (304302648)

  • David A Morales Juarez

Wellcome (205253/Z/16/Z)

  • Anne Ramsay Bowden

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank all Steve Jackson lab members for support and advice, particularly K Dry who provided editorial assistance with the manuscript. The GFP-BFP-sgRNA vector used for determining Cas9-editing efficiency was provided by Dr Emmanouil Metzakopian (UK Dementia Research Institute, Cambridge, UK).

Research in the SPJ laboratory is funded by Cancer Research UK (programme grant C6/A18796) and Wellcome Investigator Award (206388/Z/17/Z). Institute core infrastructure funding is provided by Cancer Research UK (C6946/A24843) and Wellcome (WT203144). SPJ receives salary from the University of Cambridge. This work was funded by Cancer Research UK programme grants C6/A18796 and C6/A11224 (MS-C and NL, respectively), Wellcome Investigator Award 206388/Z/17/Z (JT and MMA). ARB. is funded by a Wellcome Clinical Fellowship. DMJ is funded by a CONACYT-Cambridge scholarship.

Senior Editor

  1. Kathryn Song Eng Cheah, The University of Hong Kong, Hong Kong

Reviewing Editor

  1. Jean-Christophe Marine, Vlaams Instituut voor Biotechnologie, Belgium

Reviewers

  1. Andrea Ventura, Memorial Sloan-Kettering, United States
  2. Oscar Fernandez-Capetillo, Karolinska Institute, Sweden

Publication history

  1. Received: January 20, 2020
  2. Accepted: April 23, 2020
  3. Version of Record published: May 22, 2020 (version 1)

Copyright

© 2020, Bowden et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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