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Data from: Genome-wide CRISPR synthetic lethality screen identifies a role for the ADP-ribosyltransferase PARP14 in replication fork stability controlled by ATR

Citation

George-Lucian, Moldovan (2020), Data from: Genome-wide CRISPR synthetic lethality screen identifies a role for the ADP-ribosyltransferase PARP14 in replication fork stability controlled by ATR, Dryad, Dataset, https://doi.org/10.5061/dryad.p8cz8w9mj

Abstract

The DNA damage response is essential to maintain genomic stability, suppress replication stress, and protect against carcinogenesis. The ATR-CHK1 pathway is an essential component of this response, which regulates cell cycle progression in the face of replication stress. PARP14 is an ADP-ribosyltransferase with multiple roles in transcription, signaling, and DNA repair. To understand the biological functions of PARP14, we catalogued the genetic components that impact cellular viability upon loss of PARP14 by performing an unbiased, comprehensive, genome-wide CRISPR knockout genetic screen in PARP14-deficient cells. We uncovered the ATR-CHK1 pathway as essential for viability of PARP14-deficient cells, and identified regulation of DNA replication dynamics as an important mechanistic contributor to the synthetic lethality observed. Our work shows that PARP14 is an important modulator of the response to ATR-CHK1 pathway inhibitors.

Methods

CRISPR screens methods. For CRISPR knockout screens, the Brunello Human CRISPR knockout pooled lentiviral library (Addgene 73179) was used. This library targets 19,114 genes with 76,411 guide RNA (gRNA) sequences. 100 million 8988T (wildype and PARP14KO6) cells were infected with this library at a multiplicity of infection (MOI) of 0.4 to achieve 500x coverage and selected for 4 days with 1.25 µg/mL puromycin. The same lentiviral preparation of the library was used to infect both cell lines, to ensure similar guide representation. For each condition, 20 million cells freshly infected with the library (to maintain 250x coverage) were seeded and allowed to grow for two weeks. Genomic DNA was isolated using the DNeasy Blood and Tissue Kit (Qiagen 69504) per the manufacturer’s instructions. The gRNA sequences were amplified using PCR primers with Illumina adapters. Genomic DNA from 20 million cells (250-fold library coverage) was used as template for PCR. The PCR reaction contained 10µg of gDNA, with 20µl 5X HiFi Reaction Buffer, 4µl of P5 primer, 4µl of P7 primer, 3µl of Radiant HiFi Ultra Polymerase (Stellar Scientific), and water. The P5 and P7 primers used 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 purified PCR product was sequenced with Illumina HiSeq 2500 single read for 50 cycles. The percentage of undetected guides was 0.5% for wildtype cells and 0.4% for PARP14-knockout cells, respectively. The skew ratio of top 10% to bottom 10% guides was 7.3 for wildtype cells and 10.7 for PARP14-knockout cells, respectively. Both parameters are within the recommended range, indicating an appropriate library coverage.

For bioinformatic analysis of the screen results, the custom python script provided (count_spacers.py) was used to calculate gRNA representation. The difference between the number of guides present in the PARP14-knockout condition compared to the wildtype condition was determined. Specifically, one read count was added to each gRNA, and then the reads from the PARP14-knockout condition were normalized to the wildtype condition. The values obtained were then used as input in the Redundant siRNA Activity (RSA) algorithm, which takes into consideration the ranking of each individual gRNA targeting a gene to rank all genes. For RSA, the Bonferroni option was used and guides that were at least 2-fold enriched in the PARP14-knockout condition compared to the wildtype condition were considered hits. The p-values are determined by the RSA algorithm for the genes that are most enriched in the PARP14-knockout condition compared to the wildtype condition. Separately from the RSA analyses, we also analyzed the screen results using MAGeCK, which takes into consideration raw gRNA read counts to test if individual guides vary significantly between the conditions. The MAGeCK software and instructions on running it were obtained from https://sourceforge.net/p/mageck/wiki/libraries/.

Table S1. List of all genes and gRNAs in the PARP14 synthetic lethality CRISPR screen ranked by RSA. The “Gene Rank RSA” tab lists all genes ranked by p-value as obtained from the RSA analyses. The “gRNA List” tab lists all gRNA sequences and indicates the read count for each of them.

Table S2. List of all genes and gRNAs in the PARP14 synthetic lethality CRISPR screen ranked by MAGeCK. The “Gene Rank MAGeCK” tab lists all genes ranked by p-value as obtained from the MAGeCK analyses. The “gRNA List” tab lists all gRNA sequences and indicates the normalized read count for each of them as obtained from the MAGeCK analyses.

Funding

National Institutes of Health, Award: R01ES026184