Dual genome-wide CRISPR knockout and CRISPR activation screens identify common mechanisms that regulate the resistance to multiple ATR inhibitors

The ataxia telangiectasia and Rad3-related (ATR) protein kinase is a key regulator of the cellular response to DNA damage. Due to increased amount of replication stress, cancer cells heavily rely on ATR to complete DNA replication and cell cycle progression. Thus, ATR inhibition is an emerging target in cancer therapy, with multiple ATR inhibitors currently undergoing clinical trials. Here, we describe dual genome-wide CRISPR knockout and CRISPR activation screens employed to comprehensively identify genes that regulate the cellular resistance to ATR inhibitors. Specifically, we investigated two different ATR inhibitors, namely VE822 and AZD6738, in both HeLa and MCF10A cells. We identified and validated multiple genes that alter the resistance to ATR inhibitors. Importantly, we show that the mechanisms of resistance employed by these genes are varied, and include restoring DNA replication tract progression, and prevention of ATR inhibitor-induced apoptosis. Our dual genome-wide screen findings pave the way for personalized medicine by identifying potential biomarkers for ATR inhibitor resistance.


Introduction
Proper response to DNA damage and replication stress is critical for all organisms.
Replication stress occurs upon arrest of the DNA replication machinery at sites of DNA damage, or during replication of endogenous difficult to replicate DNA sequences such as microsatellite regions 1 . The ataxia-telangiectasia-mutated (ATM) and ataxia telangiectasia and Rad3-related (ATR) kinases are in primary control of the cellular responses to replication stress and DNA damage 2 . The activation of these kinases is critical to arrest the cell cycle and allow time for proper execution of DNA replication and repair prior to cell division 3 . ATR is activated by single-stranded DNA (ssDNA) formed upon replication fork arrest. ATR activation leads to downstream phosphorylation of Chk1, resulting in stabilization of the replication fork, suppression of origin firing, and cell cycle arrest. ATM is triggered by the presence of double-stranded DNA breaks, and phosphorylates P53 and Chk2 leading to cell cycle arrest to allow for the DNA to be repaired before proceeding through the cell cycle.
Cancer cells heavily rely on the replication stress response for viable cell division 4 . Many of the current cancer treatment agents are genotoxic compounds that lead to a variety of adverse side effects for patients, as non-tumor cells in the body can also be affected. One way to avoid these side effects is to enhance the specific targeting of cancer cells, by exploiting their reliance on the replication stress response. Thus, targeting ATR has been proposed as a potential cancer therapy 5 . ATR inhibitors (ATRi) could be an option for killing cancer cells with an inherently large amount of DNA damage, due to the pivotal role of ATR in the DNA damage response 6 .
Additionally, non-tumor cells in the body have little to no replication stress, and thus should not be affected by ATRi. ATR inhibitors may also be used in combination with DNA damaging agents as a therapeutic option 7,8 . Moreover, since ATR and ATM work together to manage DNA damage within the cell, ATR inhibitors have been shown to be particularly efficient in ATM/P53 deficient tumor cells 9-11 .
Since the development of ATR inhibitors, the effects of ATR/Chk1 pathway inhibition have been examined more closely. It was originally shown that inhibition of Chk1 caused a decrease in the inter-origin distance during DNA replication, accompanied by a decrease in replication fork progression 12 . Under normal conditions, Chk1 inhibits replication initiation by blocking CDC45 recruitment to the MCM2-7 complex, which is necessary for unwinding DNA at the replication fork 13 . Much like loss of Chk1, ATR inhibition has been shown to cause a decrease in replication fork speed as well as an increase in the amount of origins that are firing during DNA replication 14,15 .
AZD6738 inhibits both ATR kinase activity and Chk1 phosphorylation at an IC50 of 1nM and 74nM, respectively. Additionally, AZD6738 does not significantly inhibit other PI3K-like kinases such as ATM 11 . VE822 also blocks the phosphorylation of Chk1-Ser345 16 . VE822 and AZD6738 are currently in 11 phase I and phase II clinical trials and are being tested alone or in combination with other drugs such as Olaparib, Cisplatin, Paclitaxel, etc 17 . In addition, some of the current clinical trials focus on patients with particular tumor biomarkers such as DNA damage response pathway mutations, DNA damage, P53 mutations, or ATM deficiency 17 . Early results of these clinical trials show that both ATR inhibitors have low toxicity in patients and work best in combination with other drugs 18,19 . Specifically, VE822 was shown to be well tolerated in phase I trials with low toxicity and no dose-limiting affects 20 . Similarly, AZD6738 was shown to have reduced toxicity, both alone and in combination with a PARP inhibitor 20 .
Much like with other cancer therapies, identifying the subsets of tumors that respond well or are resistant to the drug will become paramount for efficient use of ATRi in the clinic. Genomewide screens are an effective technique to identify biomarkers of drug resistance and sensitivity.
genes that, when lost, caused sensitivity to AZD6738. In contrast, little is known about genes that cause resistance to ATR inhibitors when inactivated. Moreover, these screens only investigated one ATR inhibitor. Additionally, genome-wide identification of genes that alter the response to ATRi when overexpressed rather than suppressed, has not yet been addressed.
To comprehensively identify the genes regulating the resistance to ATR inhibitors, we employed a dual CRISPR screening approach wherein we investigated both loss and overexpression of the majority of genes in the human genome. We performed both knockout and activation screens in HeLa cancer cells. Additionally, we performed the activation screens in nontransformed, breast epithelial MCF10A cells. All screens were performed separately with two different ATR inhibitors, VE822 and AZD6738. This comprehensive approach allowed unbiased identification of genes that affect ATRi resistance.

Genome-wide CRISPR knockout screens identify genes regulating the resistance to multiple ATRi
A dual genome-wide CRISPR knockout and activation screening approach was designed to identify genes involved in the response to multiple ATR inhibitors ( Figure 1). First, to identify genes whose loss confers resistance to ATRi, the Brunello human CRISPR knockout lentiviral library was employed 23 . This library targets 19,114 genes with 76,441 unique guide RNAs, thus on average covering each gene with four different gRNAs. To maintain 250X library coverage, 20 million library-infected cells were treated with VE822 (1.5µM), AZD6738 (3.6µM), or DMSO control. In contrast to previously published screens which focused on ATRi sensitivity and thus used a relatively low dose, we chose these high ATRi dosages as we previously determined that they kill approximately 90% of cells over 108 hours of treatment, thus allowing us to specifically study resistance to the drugs. Surviving cells were collected and genomic DNA was extracted.
The sgRNA sequences were PCR-amplified and identified by Illumina sequencing (Figure 1A, C).
Bioinformatic analyses using the redundant siRNA activity (RSA) algorithm 24 was used to generate separate ranking lists of genes that were enriched in the VE822 and AZD6738 conditions compared to the control (Supplemental Table S1). This represents genes that, when inactivated, confer resistance to ATRi. Interestingly, there was large overlap between the VE822 and AZD6738 screen results, indicating common response mechanisms to the two different ATRi.
Of the top 500 hits of each of the two ATRi screens, 155 were present in both of them (Figure 2A, Supplemental Table S2), which is much higher than the random probability ( Figure 2B). Moreover, 7 genes were common within the top 40 hits of each ATRi screen. Biological pathway analysis of the top 500 hits of both screens revealed common biological processes, including DNA repair, translation, DNA replication, and sister chromatid cohesion ( Figure 2C). Notably, multiple components of the cell cycle, cell migration, and DNA repair biological processes were found in both ATRi screens ( Figure 2D).

Validation of hits from the knockout screen
Seven common genes were found within the top 40 genes in both ATRi resistance CRISPR knockout screens; these genes were: KNTC1, EEF1B2, LUC7L3, SOD2, MED12, RETSAT, and LIAS ( Figure 3A, B). None of these genes were previously shown to induce ATRi resistance, to our knowledge. Thus, we sought to directly confirm that these seven genes alter the ATRi response. First, we tested these genes in HeLa cells, in which the screen was originally performed. We employed siRNA to knockdown each of these genes. The knockdown efficiency was confirmed by western blot for siSOD2, siMED12, siLUC7L3, and siEEF1B2, for which antibodies are available (Supplemental Figure S1). We performed clonogenic assays by incubating siRNA-treated HeLa cells with 1µM AZD6738 or 0.5µM VE822. After three days the media was replaced and colonies were allowed to grow for two weeks. HeLa cells with knockdown of each of the seven hits presented more colonies compared to control, indicating that they are more resistant to ATR inhibitors ( Figure 3C). Moreover, we also validated the seven top hits by measuring cellular proliferation. HeLa cells were treated with siRNA and incubated for three days with 0.5µM VE822, 1µM VE822, 0.5µM AZD6738, or 1µM AZD6738. Cellular proliferation was determined using the CellTiterGlo reagent. Knockdown of each of the 7 top hits resulted in increased cell survival after ATR inhibitor treatment compared to control ( Figure 3D). These findings show that all seven hits investigated confer resistance to both ATRi tested, thus validating our screen results.
We next sought to investigate if these findings are restricted to HeLa cells, or in fact these hits also regulate ATRi resistance in other cell lines. To address this, we repeated the cellular proliferation experiments in MCF10A normal breast epithelial and 8988T pancreatic cancer cell lines. Both of these cell lines were slightly less sensitive to ATR inhibitors, so higher concentrations were used. Cells treated with 1µM VE822, 2µM VE822, 1µM AZD6738, and 2µM AZD6738 were analyzed for cellular proliferation after 3 days. In MCF10A cells, knockdown of all hits with the exception of SOD2 resulted in resistance to either ATR inhibitor ( Figure 3E). In 8988T cells, knockdown of each of the 7 hits resulted in significant ATRi resistance ( Figure 3F). These findings indicate that the genes identified by screening HeLa cells control ATRi resistance across multiple cell lines.

Loss of the top hits does not restore ATR catalytic activity
One potential mechanism of ATRi resistance is the restoration of ATR catalytic activity in the presence of ATRi. Chk1 is the main component of the ATR signaling cascade that is activated by DNA damage 25 . Once ATR is activated, its kinase activity phosphorylates Chk1 leading to all of the downstream effects. As a result, Chk1 phosphorylation at Serine 317 and Serine 345 represent markers of ATR cascade activation and activity. Thus, we knocked down the top hits and analyzed the levels of phosphorylated Chk1 by western blot, under no treatment conditions, hydroxyurea treatment, ATRi treatment, and a combination of hydroxyurea and ATRi. Hydroxyurea depletes the cellular dNTP pools, thereby stimulating the replication stress response and activating ATR. We found that after knockdown of our top hits, there was no difference in Chk1 phosphorylation by any of the hits (Supplemental Figure S2). This indicates that these genes do not act by restoring ATR activity in the presence of ATRi.

Loss of LUC7L3 causes resistance to ATRi by suppressing ATRi-induced apoptosis through splicing of BCL-X
Treatment of HeLa cells with VE822 for three days resulted in induction of apoptosis, as measured by detecting Annexin-V expression ( Figure 4A). Thus, we sought to employ this assay as another readout of resistance to ATR inhibitors. Knockdown of each of the 7 top hits significantly reduced the amount of apoptosis induced by treatment with 0.5µM VE822 for three days ( Figure 4B), further validating that their loss confers resistance to ATRi.
One of the top hits, namely LUC7L3, was previously identified as an interactor of RBM25, an mRNA splicing factor which regulates the splicing of the apoptosis regulator BCL-X 26 . The long isoform of BCL-X is anti-apoptotic, while the short isoform is pro-apoptotic 27 . Loss of RBM25 causes a shift in the splicing of BCL-X from the short isoform to the long isoform. Thus, we hypothesized that loss of LUC7L3 may cause a decrease in the pro-apoptotic BCL-Xshort, which could help explain the resistance to ATRi-induced apoptosis. To test this, we knocked-down LUC7L3 in HeLa cells and treated these cells with 0.25µM or 0.5µM VE822 for 24 hours. We then collected the cells and investigated BCL-Xshort levels at both the protein and mRNA levels.
Western blots using an antibody specific to BCL-Xshort showed a reduction in the levels of this isoform in LUC7L3-depleted cells ( Figure 4C). Moreover, using reverse transcriptase PCR we observed a decrease in BCLXshort mRNA levels in the cells treated with siRNA targeting LUC7L3 compared to control ( Figure 4D). These results indicate that the loss of LUC7L3 causes a decrease in the pro-apoptotic isoform of BCL-X, therefore potentially explaining the resistance to ATR inhibitor treatment.

Loss of MED12 and LIAS stabilizes the replication fork in response to ATRi
A major role of ATR is to stabilize replication forks upon genotoxic insults 28 . ATR inhibitors have been shown to cause a decrease in replication tract length 15 . This replication fork slowing is detrimental to the cell and may eventually result in replication deficiency and cell cycle arrest. We sought to investigate if any of the hits described above regulate ATRi resistance by suppressing the impact of ATRi on fork slowing. For this, we employed the DNA fiber assay. We incubated cells consecutively with two thymidine analogs to detect active DNA replication forks. IdU was added first for 30 minutes followed by CldU for 30 minutes. VE822 was added concomitantly with CldU. With increasing amounts of ATRi (2µM VE822, 4µM VE822, or 6µM VE822), the CldU replication tract was significantly decreased ( Figure 5A), thereby confirming that ATR inhibition causes a decrease in replication fork progression.
Next, we investigated the impact of the top hits described above, by performing the DNA fiber combing assay with cells depleted of each of these genes by siRNA. Cells were treated with IdU for 30 minutes followed by co-treatment with CldU and 2µM VE822 for 30 minutes. The effect of loss of the top hits was investigated by calculating the ratio of CldU tract length to IdU tract length. Strikingly, knockdowns of MED12 and LIAS significantly increased the CldU-to-IdU ratio, indicating that fork slowing in the presence of ATRi is suppressed ( Figure 5B, C). The other hits did not affect the CldU-to-IdU ratio. Therefore, the ATRi resistance observed upon loss of MED12 and LIAS may involve restoration of replication fork speed in the presence of ATR inhibitors.

Genome-wide CRISPR activation screens identify genes whose overexpression causes resistance to multiple ATRi
To evaluate genes whose overexpression leads to resistance to ATR inhibitors, we performed CRISPR activation screens in HeLa cells as well as MCF10A cells. The Calabrese CRISPR activation library was employed for these experiments 29 . This library targets 18,885 genes with 56,762 sgRNAs, for an average of 3 guides per gene. First, HeLa and MCF10A cells were infected with a lentivirus containing the dCas9 construct necessary for transcriptional activation (Supplemental Figure S3A). Expression of dCas9 was confirmed by Western blot (Supplemental Figure S3B). These cells were then infected with the activation library. Next, 30 million library-infected cells (for 500X library coverage) were treated with ATRi using the same conditions as for the knockout screens ( Figure 1). Surviving cells were collected and genomic DNA was extracted. The sgRNA sequences were PCR-amplified and identified by Illumina sequencing ( Figure 1B, C). Using the RSA algorithm, we generated separate lists of genes that were enriched in the VE822 and AZD6738 conditions compared to the control (Supplemental Table S3). This represents genes that, when overexpressed, confer resistance to ATRi. Similar to the knockout screen, there was large overlap between the VE822 and AZD6738 conditions.
Within the top 500 genes, 99 genes were common for the two ATRi screens in HeLa cells ( Figure   6A, Supplemental Table S4), and 115 genes were common in MCF10A screens ( Figure 6B, Supplemental Table S4). The number of common hits is much higher than expected from a random distribution ( Figure 6C). Moreover, within the top 40 genes, 7 hits were common in HeLa cells ( Figure 6D) and 6 were common in MCF10A cells ( Figure 6E). Importantly, all seven top hits from the HeLa knockout screen, were ranked towards the bottom in the HeLa overexpression screens ( Figure 7A), indicating that their overexpression promotes ATRi sensitivity and thus further highlighting the relevance of our screening strategy.
Interestingly, when comparing between the HeLa and MCF10A screens, there was no overlap of top hits (Supplemental Table S3). This surprising finding perhaps reflects the inherent differences between these cell lines, as one is cancer-derived and p53 deficient, while the other is non-transformed with wild-type p53 function. To confirm these findings, we chose to validate two hits that were common to both ATR inhibitors, but cell line specific. RPL35A was a top hit for both ATRi screens in HeLa cells, but was not a top hit in the MCF10A screens ( Figure 7A, B). On the other hand, IRF2BP2 was a top hit for both ATRi screens in the MCF10A cells, but not in the HeLa screens ( Figure 7A, B). We overexpressed these two genes in the HeLa dCas9 cells by CRISPR activation using the MS2-P65-HSF1 (MPH) activator complex, to induce transcription.
Western blots confirmed the overexpression of both IRF2BP2 ( Figure 7C) and RPL35A ( Figure   7D). Cellular proliferation of the overexpression cell lines was analyzed upon incubation with VE822 for three days. RPL35A overexpression resulted in ATRi resistance compared to control cells, while IRF2BP2 overexpression did not show any difference to control cells ( Figure 7E).
These results are in line with the screen data that identified RPL35A as a top hit in HeLa cells but not in MCF10A cells ( Figure 7A, B), thus validating the activation screens. These findings suggest that different mechanisms may regulate ATRi resistance in HeLa compared to MCF10A cells.

Discussion
Detailed information on the genetic make-up of tumors will help to better treat patients on a personalized basis. Identification of markers that lead to resistance to ATRi is critical to advance the use of ATRi in cancer therapy. With the emerging use of ATRi in clinical trials, there has been renewed interest in determining predictors to ATRi response. Moreover, beyond the treatment of patients, the effects of ATRi on cell biology and pathways that regulate responses to ATRi are still not well understood.
Here, we present dual-genome wide CRISPR knockout and activation screens to identify predictors of ATRi resistance in HeLa and MCF10A cells. Specifically, two of the main ATRi that are currently in clinical trials, VE822 and AZD6738 were investigated. Our CRISPR screening strategy provides an unbiased approach to identify genes that, when altered, cause resistance to ATRi. First, we performed a CRISPR knockout screen in HeLa cells to identify genes that, when knocked out, caused resistance to VE822 and AZD6738. When comparing the results for the two ATRi screens, we observed a significant overlap of the top hits. 155 of the top 500 genes were common between the two ATRi, as were 7 of the top 40. This high number of identical hits, much higher than expected from a random distribution, validates our screening strategy and shows that common pathways are involved in the response to different ATRi.
We validated that loss of these seven top-ranked common gene hits causes resistance to both VE822 and AZD6738, in three different cell lines using siRNA knockdown. Mechanistically, we show that none of these cases involve restoration of ATR activity in the presence of the inhibitors. Instead, we identified two different mechanisms through which some of these genes regulate ATRi resistance. We found that LUC7L3 regulates the splicing of the BCL-X apoptosis regulator. Upon loss of LUC7L3, the level of the pro-apoptotic isoform of BCL-X decreases, resulting in reduced ability to undergo apoptosis, potentially explaining the ATRi inhibitor resistance of these cells.
Another mechanism of resistance we uncovered involves restoration of replication fork protection. It was previously shown that ATR inhibition causes a decrease in fork progression and an increase in origin firing 28 . Under normal conditions, ATR is responsible for suppression of local origin firing, therefore when ATR is inhibited, origin firing increases 30 . As there is an inverse correlation between DNA tract length and number of origins firing, the more origins that fire, the slower the fork progression rate is 31 . However, in the case of ATRi it is still unknown whether replication slowing causes more origins to fire, or if more origins firing cause the replication forks to slow 32 . We found that loss of either MED12 or LIAS causes a restoration of fork stability as the decrease in fork progression upon ATRi treatment is not seen under these conditions. This result suggests that mechanisms countering fork slowing or origin firing in the presence of ATRi, may in fact promote cellular viability under these conditions. Finally, we completed the first (to our knowledge) genome-wide CRISPR activation screen to identify genes that cause resistance to ATRi when overexpressed. This activation screen was performed in both HeLa and MCF10A cells. Validating the screening strategy, top hits from the knockout screen ranked very low on the list of hits for the activation screens. Similar to the HeLa knockout screen, there was a high number of identical top hits between the VE822 and AZD6738 screens for each cell line. However, there was almost no overlap between the two cell lines. We Our studies employed a unique combination of genome-wide CRISPR-based screening approaches to comprehensively identify genes that, when altered, cause resistance to ATRi. We analyzed two separate ATRi using both knockout and activation screens, in both cancer cells and non-transformed cells. Our findings could provide biomarkers to ultimately help create effective treatment plan for cancer therapy with ATR inhibitors.
Gene knockdown was performed using Lipofectamine RNAiMAX transfection reagent.   were determined using the user guide provided with the CRISPR libraries (https://media.addgene.org/cms/filer_public/61/16/611619f4-0926-4a07-b5c7-e286a8ecf7f5/broadgpp-sequencing-protocol.pdf). The PCR cycled as follows: 98˚C for 2min before cycling, then 98˚C for 10sec, 60˚C for 15sec, and 72˚C for 45sec, for 30 cycles, and finally 72˚C for 5min. After PCR purification, the final product was Sanger sequenced to confirm that the guide region is present, followed by qPCR to determine the exact amount of PCR product present.
The purified PCR product was then sequenced with Illumina HiSeq 2500 single read for 50 cycles, targeting 10M reads.
Next, the sequencing results were analyzed bioinformatically. First, the sgRNA representation was analyzed using the custom python script provided (count_spacers.py) 34 . The difference between the number of guides present in each ATRi condition compared to control condition was then determined. Specifically, one read count was added to each sgRNA, and then the treatment reads were normalized to no treatment. Finally, the values found were used as input in the Redundant siRNA Activity (RSA) algorithm 35  rev: TGGGAGGGTAGAGTGGATGGT) 39 . The PCR cycled as follows: 95°C for 2min to start, followed by 95°C for 30sec, 45°C for 1min, and 68°C for 1min (cycled 30 times), with a final 5minute incubation at 68°C. The PCR product was then run on a 2% agarose gel and imaged with a ChemiDoc Gel Imager (Bio-Rad).
Statistical Analysis. The Mann-Whitney statistical test was performed for the DNA fiber assay.

Legends to Supplemental Tables
Supplemental Table S1 -Lists of all genes in the CRISPR knockout screens ranked by pvalue (2 tabs).
Supplemental Table S2 -List of common hits from the knockout screen (1 tab).
Supplemental Table S3 -Lists of all genes in the CRISPR activation screens ranked by pvalue (4 tabs).
Supplemental Table S4 -List of common hits from the activation screens (2 tabs).

Legends to Supplemental Figures
Supplemental Figure S1.