Genetic instability, a heritable increase in the rate of genetic mutation, accelerates evolutionary adaptation1 and is widespread in cancer2,3. In mammals, instability can arise from damage to both copies of genes involved in DNA metabolism and cell cycle regulation4 or from inactivation of one copy of a gene whose product is present in limiting amounts (haploinsufficiency5); however, it has proved difficult to determine the relative importance of these two mechanisms. In Escherichia coli6, the application of repeated, strong selection enriches for genetic instability. Here we have used this approach to evolve genetic instability in diploid cells of the budding yeast Saccharomyces cerevisiae, and have isolated clones with increased rates of point mutation, mitotic recombination, and chromosome loss. We identified candidate, heterozygous, instability-causing mutations; engineering these mutations, as heterozygotes, into the ancestral diploid strain caused genetic instability. Mutations that inactivated one copy of haploinsufficient genes were more common than those that dominantly altered the function of the mutated gene copy. The mutated genes were enriched for genes functioning in transport, protein quality control, and DNA metabolism, and have revealed new targets for genetic instability7,8,9,10,11, including essential genes. Although only a minority (10 out of 57 genes with orthologues or close homologues) of the targets we identified have homologous human genes that have been implicated in cancer2, the remainder are candidates to contribute to human genetic instability. To test this hypothesis, we inactivated six examples in a near-haploid human cell line; five of these mutations increased instability. We conclude that single genetic events cause genetic instability in diploid yeast cells, and propose that similar, heterozygous mutations in mammalian homologues initiate genetic instability in cancer.
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The authors declare that the data supporting the findings of this study are available within the paper and its supplementary information files. Source data for all figures are provided with the paper (online). The genome sequence data (in short reads format) have been deposited in the NCBI Bioproject database under accession number PRJNA509936.
Swings, T. et al. Adaptive tuning of mutation rates allows fast response to lethal stress in Escherichia coli. eLife 6, e22939 (2017).
Lawrence, M. S. et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505, 495–501 (2014).
Drost, J. et al. Sequential cancer mutations in cultured human intestinal stem cells. Nature 521, 43–47 (2015).
Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).
Inoue, K. & Fry, E. A. Haploinsufficient tumor suppressor genes. Adv. Med. Biol. 118, 83–122 (2017).
Mao, E. F., Lane, L., Lee, J. & Miller, J. H. Proliferation of mutators in a cell population. J. Bacteriol. 179, 417–422 (1997).
Huang, M. E., Rio, A. G., Nicolas, A. & Kolodner, R. D. A genomewide screen in Saccharomyces cerevisiae for genes that suppress the accumulation of mutations. Proc. Natl Acad. Sci. USA 100, 11529–11534 (2003).
Yuen, K. W. et al. Systematic genome instability screens in yeast and their potential relevance to cancer. Proc. Natl Acad. Sci. USA 104, 3925–3930 (2007).
Stirling, P. C. et al. The complete spectrum of yeast chromosome instability genes identifies candidate CIN cancer genes and functional roles for ASTRA complex components. PLoS Genet. 7, e1002057 (2011).
Andersen, M. P., Nelson, Z. W., Hetrick, E. D. & Gottschling, D. E. A genetic screen for increased loss of heterozygosity in Saccharomyces cerevisiae. Genetics 179, 1179–1195 (2008).
Strome, E. D., Wu, X., Kimmel, M. & Plon, S. E. Heterozygous screen in Saccharomyces cerevisiae identifies dosage-sensitive genes that affect chromosome stability. Genetics 178, 1193–1207 (2008).
Knudson, A. G., Jr. Mutation and cancer: statistical study of retinoblastoma. Proc. Natl Acad. Sci. USA 68, 820–823 (1971).
Cho, A. et al. MUFFINN: cancer gene discovery via network analysis of somatic mutation data. Genome Biol. 17, 129 (2016).
Davoli, T. et al. Cumulative haploinsufficiency and triplosensitivity drive aneuploidy patterns and shape the cancer genome. Cell 155, 948–962 (2013).
Futreal, P. A. et al. A census of human cancer genes. Nat. Rev. Cancer 4, 177–183 (2004).
Tamborero, D. et al. Comprehensive identification of mutational cancer driver genes across 12 tumor types. Sci. Rep. 3, 2650 (2013).
Zehir, A. et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat. Med. 23, 703–713 (2017).
Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013).
Yin, Y. & Petes, T. D. Genome-wide high-resolution mapping of UV-induced mitotic recombination events in Saccharomyces cerevisiae. PLoS Genet. 9, e1003894 (2013).
Brauer, M. J., Christianson, C. M., Pai, D. A. & Dunham, M. J. Mapping novel traits by array-assisted bulk segregant analysis in Saccharomyces cerevisiae. Genetics 173, 1813–1816 (2006).
Segrè, A. V., Murray, A. W. & Leu, J. Y. High-resolution mutation mapping reveals parallel experimental evolution in yeast. PLoS Biol. 4, e256 (2006).
Choy, J. S. et al. Genome-wide haploinsufficiency screen reveals a novel role for γ-TuSC in spindle organization and genome stability. Mol. Biol. Cell 24, 2753–2763 (2013).
Carette, J. E. et al. Ebola virus entry requires the cholesterol transporter Niemann-Pick C1. Nature 477, 340–343 (2011).
Huang, H. T., Chen, S. M., Pan, L. B., Yao, J. & Ma, H. T. Loss of function of SWI/SNF chromatin remodeling genes leads to genome instability of human lung cancer. Oncol. Rep. 33, 283–291 (2015).
Sniegowski, P. D., Gerrish, P. J. & Lenski, R. E. Evolution of high mutation rates in experimental populations of E. coli. Nature 387, 703–705 (1997).
Doench, J. G. et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat. Biotechnol. 34, 184–191 (2016).
Ran, F. A. et al. Genome engineering using the CRISPR-Cas9 system. Nat. Protocols 8, 2281–2308 (2013).
Duda, H. et al. A mechanism for controlled breakage of under-replicated chromosomes during mitosis. Dev. Cell 39, 740–755 (2016).
Jin, Y. H. et al. The multiple biological roles of the 3′→5′ exonuclease of Saccharomyces cerevisiae DNA polymerase delta require switching between the polymerase and exonuclease domains. Mol. Cell. Biol. 25, 461–471 (2005).
Chen, R. H., Shevchenko, A., Mann, M. & Murray, A. W. Spindle checkpoint protein Xmad1 recruits Xmad2 to unattached kinetochores. J. Cell Biol. 143, 283–295 (1998).
Rosenthal, G. A. The biological effects and mode of action of l-canavanine, a structural analogue of l-arginine. Q. Rev. Biol. 52, 155–178 (1977).
Toyn, J. H., Gunyuzlu, P. L., White, W. H., Thompson, L. A. & Hollis, G. F. A counterselection for the tryptophan pathway in yeast: 5-fluoroanthranilic acid resistance. Yeast 16, 553–560 (2000).
Chattoo, B. B. et al. Selection of LYS2 mutants of the yeast Saccharomyces cerevisiae by the utilization of α-aminoadipate. Genetics 93, 51–65 (1979).
Johnson, G. E. Mammalian cell HPRT gene mutation assay: test methods. Methods Mol. Biol. 817, 55–67 (2012).
Baym, M. et al. Inexpensive multiplexed library preparation for megabase-sized genomes. PLoS One 10, e0128036 (2015).
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).
Koschwanez, J. H., Foster, K. R. & Murray, A. W. Improved use of a public good selects for the evolution of undifferentiated multicellularity. eLife 2, e00367 (2013).
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, New York, 2009).
We thank F. Beça, D. Cabrera, B. Neugeboren and C. Pogliano for developing reagents and conducting preliminary experiments; J. Piper and the BauerCore facility at Harvard University for technical help; G. Wildenberg, J. Koschwanez, N. Wespe and M. Fumasoni for technical help and discussions; and J. Matos for the gift of the MUS81 deletion CRISPR cell lines used in Fig. 3c and Extended Data Fig. 8. V. Denic, B. Shraiman, M. Desai, M. Fumasoni and L. Bagamery provided comments on the manuscript. M.C.C. received a long-term fellowship from Human Frontiers Science Program (HFSP, LT 000694/2014-L). R.M.P. is a recipient of the Berman/Topper Family HD Career Development Fellowship from the Huntington’s Disease Society of America. The work was supported by an NIH/NIGMS award (R01/GM043987) to A.W.M.
Nature thanks J. DeGregori, S. Nijman and the other anonymous reviewer(s) for their contribution to the peer review of this work.
The authors declare no competing interests.
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Extended data figures and tables
Extended Data Fig. 1 The genetic instability reporter responds to perturbations that induce point mutations, recombination/truncation events and chromosome loss.
a, Cells were treated with three agents: ethyl methanesulfonate (EMS) a chemical mutagen that primarily induces point mutations; ultraviolet radiation (UV), which induces a mixture of events, and benomyl; a microtubule depolymerizing drug, which primarily induces chromosome loss. The graph shows the frequency with which URA3 was inactivated or lost from our ancestral diploid strain upon each of the treatments. We show the global rate of loss or inactivation of URA3 as well as the rates for point mutation, mitotic recombination/chromosome truncation, and chromosome loss. b, Genetic perturbations were applied to a diploid strain carrying the reporter construct: a proof-reading mutation in polymerase delta (POL3-L523D/POL3) primarily induces point mutations, removing RAD27, which encodes a 5′ flap endonuclease, primarily elevates mitotic recombination (rad27∆/rad27∆); and removing MAD2, which encodes a component of the spindle checkpoint, induces chromosome loss (mad2∆/mad2∆). As a control, inactivation of a single copy of LYS2, using the Kanamycin resistance cassette (KanR) did not result in increased instability (lys2∆/LYS2). The graph shows the frequency with which URA3 was inactivated or lost from each of the genetically engineered strains. We show the global rate of loss or inactivation of URA3 as well as the rates for point mutation, mitotic recombination/chromosome truncation, and chromosome loss (n represents biologically independent experiments per condition, bars represent mean and error bars represent s.e.m.). Boxplots display centre line (median), upper and lower limits (quartiles) and whiskers are 1.5× interquartile range. Datapoints = 0 were not plotted, but were used to determine mean and s.e.m.
Extended Data Fig. 2 Triple selection evolved higher levels of genetic instability than single selection and many evolved strains show a correlated increase in recombination and chromosome loss frequencies.
a, URA3 mutation frequency in evolved clones after a single selection step for resistance to 5-FAA (n = 18 biologically independent samples), α-AA (n = 29 biologically independent samples), or canavanine (CAN, n = 18 biologically independent experiments), and after triple, sequential selection (n = 93 biologically independent experiments) as assayed by testing clones for the frequency with which they gave rise to resistance to 5FOA normalized by the ancestor (mean ± s.e.m.). A single selection step does not promote as much instability as the triple selection (P values for one sided Student’s t-test comparing each single to triple selection pair: P5FAA = 0.004, Pα-AA = 0.009, PCAN = 0.002). b, Correlation between recombination/truncation (RT) and chromosome loss (CL) mutation frequency for n = 77 mutants that did not show a specific elevation in one class of mutational event (linear quadratic regression). c, Correlation between rates of the three classes of mutational events and global mutation rates (nPM = 51, nRT = 54, nCL = 80 biologically independent samples, linear quadratic regression). R value, Pearson correlation coefficient.
a, Possible scenarios for the evolution of genetic instability. In diploids, a recessive mutation can cause genetic instability only if both alleles of the gene are inactivated (two-hit diploid), whereas a dominant mutation need occur only in one copy of the gene (single-hit diploid) and some mutators require heterozygous mutations in two or more genes (multi-hit diploid). The number and nature of causative mutations will be reflected in the meiotic segregation pattern, assuming the mutations also cause instability in haploids. b, Meiotic segregation for the maximum level of genetic instability (measured as the mutation frequency to 5FOA resistance) in haploid spores derived from different evolved diploid clones. Genetically unstable spores are shown in magenta.
Extended Data Fig. 4 Heterozygous deletion of frequently mutated genes affects individual genetic instability types.
Heterozygous deletions (n = 25) were engineered in the ancestral strains (Δ/WT) carrying our reporter construct and used to measure instability. a–c, Rates of point mutation (a), recombination/truncation (b) and chromosome loss (c) in heterozygous deletion mutants. d, Mutation rates for global and individual instability types in strains carrying heterozygous deletions (∆/+) of genes whose candidate genetic-instability-causing mutations were nonsense mutations leading to the production of truncated proteins, which are likely to be non-functional. Filled circles, strains whose instability exceeds that of the ancestor by 2 s.d. (n = 3 biologically independent experiments).
a, Frequency distribution of synonymous mutations at 795 loci, summed across multiple sequenced pools of segregants (n = 48 biologically independent samples). Ninety-five per cent of synonymous mutations have read frequencies of less than 0.67, allowing us to define this as the cutoff for putative causative mutations. Only those loci with 20 or more reads were included in the analysis. b, Mutant read frequencies for lineage-specific, putative causative mutations for genetic instability (magenta). The dotted line represents the cutoff frequency (0.67) and the magenta line represents the average frequency of the 74 mutant alleles identified (magenta box shows ± 2 s.d.). Three genes (RAD1, TCB2, and CIN1) that have values slightly below the cutoff were also tested, and in all three cases, reconstruction demonstrated that mutations in these genes increased genetic instability.
Extended Data Fig. 6 Effect of engineering causative mutations into the ancestral diploid on point mutation, recombination/truncation and chromosome loss frequencies.
Putative causative mutations from genes identified by bulk segregant analysis were engineered into the ancestral diploid strains as heterozygotes by replacing one of the wild type alleles (mut/+) and our reporter construct was used to measure instability compared to that of the evolved clone and a heterozygous deletion for the gene that harboured the putative causative mutation (∆/+). We also tested a putative causative mutation in PAC10, a gene that was frequently mutated. a–c, Rates of point mutation (a), recombination/truncation (b) and chromosome loss (c). d, Global mutation rates for strains in which the causative heterozygous allele (black circle) was replaced with the wild-type allele (green circle, rescue). Filled circles, strains whose instability exceeds that of the ancestor by 2 s.d. (n = 3 biologically independent experiments).
Extended Data Fig. 7 Enrichment in protein quality control and transport genes as heterozygous drivers of instability.
a, Frequency of functional classes within genes where we identified mutations that caused genetic instability. Probabilities were calculated from the binomial distribution,∗∗ P < 0.05 with Bonferroni correction, 10 hypotheses tested, ∗significant at 2.5% false discovery rate using the Benjamini–Hochberg procedure (left, frequency in yeast genome; right, genes whose mutation we identified as leading to genetic instability, n = 92 independent genes). Blue represents the fraction of essential genes, magenta represents the fraction of non-essential genes. b, Overlap between genes in which we selected mutations that produced genetic instability (evolved, green), yeast genes homologous to human cancer genes (cancer, magenta), random subsets of yeast genes (yeast, black), and randomly selected subsets of genes previously identified in yeast screens for genetic instability (CIN, grey). c–f, Quantification of global (c), point mutation (d), recombination/truncation (e) and chromosome loss rates (f) in heterozygous point mutants or heterozygous deletion mutants for yeast genetic instability genes with cancer driver homologues. Data for the mut/+ reconstructed strain shown for four strains (MSH2, PAC10, RAD1 and TEL1). The data for five of these genes (MSH2, PAC10, SCC2, SNF2 and TOR1) are also shown in Extended Data Fig. 4. Filled circles in c–f correspond to strains whose instability exceeds that of the ancestor by 2 s.d. (n = 3 biologically independent experiments).
Extended Data Fig. 8 Mutation rates of HPRT1 in HAP1 human cells carrying inactivated homologues of genes mutated during yeast evolution to trigger genetic instability.
a, Targets selected from yeast evolution experiments were tested in human HAP1 cells, by using CRISPR-mediated gene inactivation of the corresponding homologues. HAP1 knockout cell lines were incubated with 6-TG (after treatment with HAT medium to eliminate jackpots) and colony formation was quantified and compared to the wild type. b, Inactivation frequency of HPRT1, normalized to the wild type for different homologue deletions. HAP1 #1 and HAP1 #2 are the parental wild type; non-edited corresponds to a cell line that went through the transformation protocol for ATG2B editing, but did not acquire mutations in ATG2B; ATG2B#1 and ATG2B#2 did not show increased instability. SMARCA4 was known to increase HPRT1 inactivation frequency and was used as a positive control for our assay. Values are normalized by average HPRT1 inactivation rate of wild-type HAP1 cells; filled circles correspond to strains whose instability exceeds that of the ancestor by 2 s.d. (n = 3 biologically independent experiments). c, Guide RNA target sequences and sequences of the mutated regions of genes after CRISPR-mediated gene inactivation for each of the cell lines tested (fs, frameshift; del., deletion; ins., insertion; red, deleted sequence; green, inserted sequence). Corresponding DNA sequences were cloned into p458x-GFP.
This file contains Table 1: Mutation rates in evolved clones and ancestor, consisting of global, point mutation (PM), recombination or truncation (RT) or chromosome loss (CL) rates.
This file contains Table 2: List of causative mutations found in our evolved lines, presented as frequently mutated or found by bulk segregant analysis.
This file contains Table 3: Lists of genes used for comparison between our evolved gene hits and the yeast genome, yeast CIN genes and homologs of human cancer driver genes.
List of yeast strains produced in this study.
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Coelho, M.C., Pinto, R.M. & Murray, A.W. Heterozygous mutations cause genetic instability in a yeast model of cancer evolution. Nature 566, 275–278 (2019). https://doi.org/10.1038/s41586-019-0887-y
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