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Bioinformatic Identification of Genes Suppressing Genome Instability

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Bioinformatic Identification of Genes Suppressing Genome Instability Bioinformatic identification of genes suppressing PNAS PLUS genome instability Christopher D. Putnama,b, Stephanie R. Allen-Solteroa,c, Sandra L. Martineza, Jason E. Chana,b, Tikvah K. Hayesa,1, and Richard D. Kolodnera,b,c,d,e,2 aLudwig Institute for Cancer Research, Departments of bMedicine and cCellular and Molecular Medicine, dMoores-University of California at San Diego Cancer Center, and eInstitute of Genomic Medicine, University of California School of Medicine at San Diego, La Jolla, CA 92093 Contributed by Richard D. Kolodner, September 28, 2012 (sent for review August 25, 2011) Unbiased forward genetic screens for mutations causing increased in concert to prevent genome rearrangements (reviewed in 12). gross chromosomal rearrangement (GCR) rates in Saccharomyces Modifications of the original GCR assay demonstrated that sup- cerevisiae are hampered by the difficulty in reliably using qualitative pression of GCRs mediated by segmental duplications and Ty GCR assays to detect mutants with small but significantly increased elements involves additional genes and pathways that do not GCR rates. We therefore developed a bioinformatic procedure using suppress single-copy sequence-mediated GCRs (13–15). Inter- genome-wide functional genomics screens to identify and prioritize estingly, homologs of some GCR-suppressing genes and pathways candidate GCR-suppressing genes on the basis of the shared drug suppress the development of cancer in mammals (16). Most of the sensitivity suppression and similar genetic interactions as known genes that suppress GCRs have been identified through a candi- GCR suppressors. The number of known suppressors was increased date gene approach. Some studies have screened collections of from 75 to 110 by testing 87 predicted genes, which identified un- arrayed S. cerevisiae mutants for mutations that cause increased anticipated pathways in this process. This analysis explicitly dealt GCR rates and have identified additional genes of interest with the lack of concordance among high-throughput datasets (17–20), although the mutations identified in each screen only to increase the reliability of phenotypic predictions. Additionally, had a small overlap with each other. Consequently, it is probable shared phenotypes in one assay were imperfect predictors for that not all the genes and pathways that suppress GCRs have shared phenotypes in other assays, indicating that although ge- been identified. nome-wide datasets can be useful in aggregate, caution and valida- The promise of genome-wide protein–protein interaction, ge- tion methods are required when deciphering biological functions via netic interaction, and drug sensitivity datasets developed using S. surrogate measures, including growth-based genetic interactions. cerevisiae is that these data can be used for predicting gene and gene product functions (e.g., ref. 21). Despite the fact that these datasets DNA damage | DNA repair | systems biology contain useful information, high-throughput methods are prone to both false-positive and false-negative errors. Consequently, differ- enetic instability is a characteristic of most cancers (1) that ent datasets generated using similar approaches to screen the same Gmay play a critical role in driving the accumulation of genetic mutant collection show a substantial lack of concordance (22). changes that underlie tumorigenesis (2). A number of observations Here, we show that combining these types of data identified addi- are consistent with this view, including the following: a number of tional genes involved in suppressing genome stability based on the cancer predisposition syndromes have been identified that are hypothesis that these additional genes will share aspects of their associated with inherited defects in genes involved in suppressing phenotypes with known genes. Using these data, we have generated genome instability, and inactivation of some of these genes has a set of 1,041 gene deletion mutations that have genetic interactions been observed in sporadic cancers (3, 4); p53, which promotes cell and drug sensitivity profiles matching those mutations known to cycle arrest or apoptosis in response to DNA damage, is inacti- affect the rate of accumulating GCRs; 787 of them are character- vated in roughly 50% of human cancers, and p53 defects allow cells ized by dense genetic interactions, and the remaining 254 have to tolerate the accumulation of genome rearrangements (5); and limited genetic interactions. To validate this approach, we in- genomic instability has been observed to precede the transition to vestigated a subset of the predicted genes and found that deletions the carcinogenic state or to be associated with the development of of 35 of the 87 genes selected from clusters containing known GCR- cancers in mouse model systems (6). suppressing genes for analysis increased the rate of accumulating The investigation of model systems in the study of genome in- GCRs, which represents a 200-fold higher efficiency for identifying stability has the potential to identify and understand novel genes new GCR-suppressing genes compared with that seen in genome- and pathways relevant to human cancer. A genetic assay developed wide screens. This experimental validation identified genes that had in the yeast Saccharomyces cerevisiae has been used to identify not been previously implicated in suppressing GCRs and demon- genes and pathways that suppress gross chromosomal rearrange- strated that components of the nuclear pore, the proteasome, and ments (GCRs) mediated by single-copy DNA sequences (7). In this the morphogenesis and septin checkpoint, as well as proper control assay, selection against two genetic markers, CAN1 and URA3, of the anaphase-promoting complex/cyclosome (APC/C), play roles placed on a nonessential end of the left arm of chromosome V in suppressing GCRs. Thus, the resulting gene lists are enriched for selects for the loss of these two genes that results as a consequence of the formation of GCRs that delete the left arm of chromosome V. The types of GCRs that have been observed with this assay GENETICS Author contributions: C.D.P. and R.D.K. designed research; C.D.P., S.R.A.-S., S.L.M., J.E.C., include terminal deletions healed by de novo telomere addition, and T.K.H. performed research; C.D.P. contributed new reagents/analytic tools; C.D.P. and translocations, isoduplications and other types of dicentric trans- R.D.K. analyzed data; and C.D.P. and R.D.K. wrote the paper. location chromosomes, interstitial deletions, circular chromosomes, The authors declare no conflict of interest. and complex GCRs resulting from multiple cycles of rearrange- 1Present address: Curriculum in Genetics and Molecular Biology and Lineberger Compre- ment, usually as a result of the formation of unstable dicentric hensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599. translocations (8–11). Using this assay, oxidative defense pathways, 2To whom correspondence should be addressed. E-mail: [email protected]. the replication machinery, DNA repair pathways, cell cycle check- See Author Summary on page 19055 (volume 109, number 47). point pathways, telomere maintenance pathways, and chromatin This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. modification and assembly pathways have been shown to function 1073/pnas.1216733109/-/DCSupplemental. www.pnas.org/cgi/doi/10.1073/pnas.1216733109 PNAS | Published online November 5, 2012 | E3251–E3259 Downloaded by guest on September 28, 2021 genes that function to suppress genome stability. Importantly, our tified in 119, 118, 106, 96, and 91 screens, respectively (Dataset S2). results indicate that identification of genes based on analysis of Over 60% of all mutations were identified in 5 or fewer screens, DNA damaging agent sensitivity and growth-based genetic in- and 16% were observed in only 1 screen. Using random computer teraction patterns was an imperfect predictor for identifying genes simulations (Materials and Methods), we calculated pnhit P values, that suppress GCRs, which has important implications for attempts which was the statistical significance of identifying a gene n times, to reconstruct pathways by computationally combining data from and found that mutations identified eight times (n =8)weresig- systematic genetic and physical interaction studies. nificant (pnhit < 0.01). We also analyzed mutations identified a statistically significant Results number of times (pnhit < 0.01) that caused sensitivity to specific Bioinformatic Identification of Candidate Genome Stability Genes. DNA damaging treatments using the program GOstat (23) to Genes identified as suppressing genome rearrangements. To identify identify statistically significant gene ontology terms (24). This candidate genes that suppress GCRs (Fig. 1), we first analyzed analysis primarily identified terms related to DNA repair, DNA over 700 published GCR rates of strains with single or multiple damage signaling, chromatin, and chromosome organization and mutations. This identified 75 mutations that increased GCR rates biogenesis (Dataset S3). Some unexpected pathways were also by fivefold or more as single mutants and/or caused synergistic identified: ubiquitin-dependent protein catabolism of the multi- increases in rate in combination with other mutations (SI Appen- vesicular body pathway [2-dimethylaminoethyl chloride (DMAEC), dix, Table S1 and Dataset S1) and 40 mutations that did not in- hydroxyurea (HU), mitomycin c, and tirapazamine]; osmotic stress crease GCR rates
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