Heterozygous screen in Saccharomyces cerevisiae identifies dosage-sensitive genes that affect chromosome stability

Abstract

Current techniques for identifying mutations that convey a small increased cancer risk or those that modify cancer risk in carriers of highly penetrant mutations are limited by the statistical power of epidemiologic studies, which require screening of large populations and candidate genes. To identify dosage-sensitive genes that mediate genomic stability, we performed a genomewide screen in Saccharomyces cerevisiae for heterozygous mutations that increase chromosome instability in a checkpoint-deficient diploid strain. We used two genome stability assays sensitive enough to detect the impact of heterozygous mutations and identified 172 heterozygous gene disruptions that affected chromosome fragment (CF) loss, 45% of which also conferred modest but statistically significant instability of endogenous chromosomes. Analysis of heterozygous deletion of 65 of these genes demonstrated that the majority increased genomic instability in both checkpoint-deficient and wild-type backgrounds. Strains heterozygous for COMA kinetochore complex genes were particularly unstable. Over 50% of the genes identified in this screen have putative human homologs, including CHEK2, ERCC4, and TOPBP1, which are already associated with inherited cancer susceptibility. These findings encourage the incorporation of this orthologous gene list into cancer epidemiology studies and suggest further analysis of heterozygous phenotypes in yeast as models of human disease resulting from haplo-insufficiency.

Figure 1.—

Screen strategy. (1) The rad9Δ/rad9Δ strain ES75 was mutagenized by transformation with the NotI-digested mTn-lacz/Leu2 insertional library and positive transformants were selected on leucine-deficient plates. (2) Approximately 185 colonies from each mutant strain were then struck out on low-adenine media and monitored for sectoring colonies as a measure of CF loss. Strains that met the sectoring threshold were restruck three times and strains that consistently showed increased CF loss were carried forward. (3) The transposon insertion site was then identified in mutagenized strains that showed increased CF loss. (4) The rate of chromosome V loss or recombination as quantitatively measured by conversion to canavanine resistance was estimated using fluctuation analysis for those strains that demonstrated increased CF loss.

Figure 2.—

Validation of chromosome V instability rate estimates and enrichment of increased CF loss strains for increases in chromosome V instability. (A) Fluctuation analysis experiments were performed on five wild-type and five rad9-deficient yeast strains to test reproducibility of the chromosome V instability rate estimations. These data are shown in a combined box and whisker plot where each box represents summary statistics of instability rate estimates in the 15 parallel cultures of each strain. The box has lines at the lower quartile, median, and upper quartile of the data. The whiskers are lines extending from each end of the box to show the extent of the rest of the data. The whiskers indicate the minimum and maximum data values, unless outliers (marked by “+”) are present in which case the whiskers extend to a maximum of 1.5 times the interquartile range. (B) We measured the chromsome V instability rates of 40 mutant strains from the increased CF loss category and 10 mutant strains from the nonsectoring category. One-sided unequal variance t-tests were performed on the estimated rate of chromosome V instability of each of the 50 strains compared to the rad9Δ/rad9Δ parental strain (3.37E-05 by summation of five separate rad9Δ/rad9Δ estimations). Strains that were statistically increased as determined by the t-test are shown as solid black diamonds; strains not significantly increased are shown as open gray diamonds. Seventy percent of the strains from the high CF loss group showed statistically increased chromosome V instability rates, while none of the strains from the no CF loss group were statistically different.

Figure 3.—

Gene ontology analysis of biological processes and cellular component categories for S. cerevisiae. This representation shows the percentage of usage in the gene ontology at the third level of both biological process (A) and cellular component (B). Solid bars show the observed percentage of representation in list of genes isolated in the screen; open bars show the representation observed in the yeast genome and demonstrate the expected rate in the list of genes if the list is random. The boxed ontologies of cell cycle, meiosis process, and chromosome (location) are statistically significantly overrepresented while the boxed ontology of protein biosynthesis is underrepresented.

Figure 4.—

Network interactome of 69 genes identified by coding region disruptions. This representation shows the 69 of 172 genes, identified by transposon insertion into their coding region, which had been previously identified to physically or genetically interact with another member of this group or with RAD9. The node color indicates the primary biological process gene ontology into which the gene is classified. The edge color indicates the identified physical or genetic interaction.

Figure 5.—

Altered benomyl sensitivity and resistance of a subset of mutant strains. Wild-type and rad9Δ/rad9Δ strains with heterozygous deletions in atg11, bfr2, elg1, mrc1, trx1, and ydr066c were grown on rich media (YPD) without or with 10 μg/ml benomyl. Although atg11Δ/ATG11, mrc1Δ/MRC1, trx1Δ/TRX1, and ydr066cΔ/YDR066C strains are sensitive to benomyl compared to wild-type strains, the heterozygous mutation confers relative resistance to benomyl in a rad9Δ/rad9Δ background. The rad9Δ/rad9Δ strain itself shows an increased sensitivity to benomyl vs. the wild-type counterpart.