Polyploidy can drive rapid adaptation in yeast

Abstract

Polyploidy is observed across the tree of life, yet its influence on evolution remains incompletely understood1,2,3,4. Polyploidy, usually whole-genome duplication, is proposed to alter the rate of evolutionary adaptation. This could occur through complex effects on the frequency or fitness of beneficial mutations2,5,6,7. For example, in diverse cell types and organisms, immediately after a whole-genome duplication, newly formed polyploids missegregate chromosomes and undergo genetic instability8,9,10,11,12,13. The instability following whole-genome duplications is thought to provide adaptive mutations in microorganisms13,14 and can promote tumorigenesis in mammalian cells11,15. Polyploidy may also affect adaptation independently of beneficial mutations through ploidy-specific changes in cell physiology16. Here we perform in vitro evolution experiments to test directly whether polyploidy can accelerate evolutionary adaptation. Compared with haploids and diploids, tetraploids undergo significantly faster adaptation. Mathematical modelling suggests that rapid adaptation of tetraploids is driven by higher rates of beneficial mutations with stronger fitness effects, which is supported by whole-genome sequencing and phenotypic analyses of evolved clones. Chromosome aneuploidy, concerted chromosome loss, and point mutations all provide large fitness gains. We identify several mutations whose beneficial effects are manifest specifically in the tetraploid strains. Together, these results provide direct quantitative evidence that in some environments polyploidy can accelerate evolutionary adaptation.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Rapid spread of beneficial mutations in tetraploid yeast.
Figure 2: Tetraploid clones acquire frequent sequence variants, large-scale ploidy shifts and recurrent whole chromosome aneuploidy during adaptation.
Figure 3: Ploidy-specific fitness effects for certain beneficial mutations.
Figure 4: Rapid adaptation of tetraploids normalized for initial fitness.

Accession codes

Primary accessions

Gene Expression Omnibus

Sequence Read Archive

Data deposits

All aCGH data have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus database under accession number GSE51017 and all WGS data have been deposited in the National Center for Biotechnology Information Sequence Read Archive database under accession number SRP047435.

References

  1. 1

    Ohno, S., Wolf, U. & Atkin, N. B. Evolution from fish to mammals by gene duplication. Hereditas 59, 169–187 (1968)

    Article  CAS  PubMed  Google Scholar 

  2. 2

    Otto, S. P. & Whitton, J. Polyploid incidence and evolution. Annu. Rev. Genet. 34, 401–437 (2000)

    Article  CAS  PubMed  Google Scholar 

  3. 3

    Semon, M. & Wolfe, K. H. Consequences of genome duplication. Curr. Opin. Genet. Dev. 17, 505–512 (2007)

    Article  CAS  PubMed  Google Scholar 

  4. 4

    Hufton, A. L. & Panopoulou, G. Polyploidy and genome restructuring: a variety of outcomes. Curr. Opin. Genet. Dev. 19, 600–606 (2009)

    Article  CAS  PubMed  Google Scholar 

  5. 5

    Paquin, C. & Adams, J. Frequency of fixation of adaptive mutations is higher in evolving diploid than haploid yeast populations. Nature 302, 495–500 (1983)

    Article  ADS  CAS  PubMed  Google Scholar 

  6. 6

    Anderson, J. B., Sirjusingh, C. & Ricker, N. Haploidy, diploidy and evolution of antifungal drug resistance in Saccharomyces cerevisiae. Genetics 168, 1915–1923 (2004)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. 7

    Zorgo, E. et al. Ancient evolutionary trade-offs between yeast ploidy states. PLoS Genet. 9, e1003388 (2013)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. 8

    Mayer, V. W. & Aguilera, A. High levels of chromosome instability in polyploids of Saccharomyces cerevisiae. Mutat. Res. 231, 177–186 (1990)

    Article  CAS  PubMed  Google Scholar 

  9. 9

    Bennett, R. J. & Johnson, A. D. Completion of a parasexual cycle in Candida albicans by induced chromosome loss in tetraploid strains. EMBO J. 22, 2505–2515 (2003)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. 10

    Gerstein, A. C., Chun, H. J., Grant, A. & Otto, S. P. Genomic convergence toward diploidy in Saccharomyces cerevisiae. PLoS Genet. 2, e145 (2006)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. 11

    Fujiwara, T. et al. Cytokinesis failure generating tetraploids promotes tumorigenesis in p53-null cells. Nature 437, 1043–1047 (2005)

    Article  ADS  CAS  PubMed  Google Scholar 

  12. 12

    Storchova, Z. et al. Genome-wide genetic analysis of polyploidy in yeast. Nature 443, 541–547 (2006)

    Article  ADS  CAS  PubMed  Google Scholar 

  13. 13

    Rancati, G. et al. Aneuploidy underlies rapid adaptive evolution of yeast cells deprived of a conserved cytokinesis motor. Cell 135, 879–893 (2008)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. 14

    Selmecki, A., Forche, A. & Berman, J. Aneuploidy and isochromosome formation in drug-resistant Candida albicans. Science 313, 367–370 (2006)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  15. 15

    Zack, T. I. et al. Pan-cancer patterns of somatic copy number alteration. Nature Genet. 45, 1134–1140 (2013)

    Article  CAS  Google Scholar 

  16. 16

    Chao, D. Y. et al. Polyploids exhibit higher potassium uptake and salinity tolerance in Arabidopsis. Science 341, 658–659 (2013)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  17. 17

    Hegreness, M., Shoresh, N., Hartl, D. & Kishony, R. An equivalence principle for the incorporation of favorable mutations in asexual populations. Science 311, 1615–1617 (2006)

    Article  ADS  CAS  PubMed  Google Scholar 

  18. 18

    Kao, K. C. & Sherlock, G. Molecular characterization of clonal interference during adaptive evolution in asexual populations of Saccharomyces cerevisiae. Nature Genet. 40, 1499–1504 (2008)

    Article  CAS  PubMed  Google Scholar 

  19. 19

    Haccou, P., Jagers, P., Vatutin, V. A. & International Institute for Applied Systems Analysis. Branching Processes: Variation, Growth, and Extinction of Populations 82–106 (Cambridge Univ. Press, 2005)

    Google Scholar 

  20. 20

    Barrett, R. D., M’Gonigle, L. K. & Otto, S. P. The distribution of beneficial mutant effects under strong selection. Genetics 174, 2071–2079 (2006)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. 21

    Lang, G. I. et al. Pervasive genetic hitchhiking and clonal interference in forty evolving yeast populations. Nature 500, 571–574 (2013)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  22. 22

    Brown, C. J., Todd, K. M. & Rosenzweig, R. F. Multiple duplications of yeast hexose transport genes in response to selection in a glucose-limited environment. Mol. Biol. Evol. 15, 931–942 (1998)

    Article  CAS  PubMed  Google Scholar 

  23. 23

    Gresham, D. et al. The repertoire and dynamics of evolutionary adaptations to controlled nutrient-limited environments in yeast. PLoS Genet. 4, e1000303 (2008)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. 24

    Kvitek, D. J. & Sherlock, G. Reciprocal sign epistasis between frequently experimentally evolved adaptive mutations causes a rugged fitness landscape. PLoS Genet. 7, e1002056 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. 25

    Ozcan, S., Dover, J., Rosenwald, A. G., Wolfl, S. & Johnston, M. Two glucose transporters in Saccharomyces cerevisiae are glucose sensors that generate a signal for induction of gene expression. Proc. Natl Acad. Sci. USA 93, 12428–12432 (1996)

    Article  ADS  CAS  PubMed  Google Scholar 

  26. 26

    Barrick, J. E., Kauth, M. R., Strelioff, C. C. & Lenski, R. E. Escherichia coli rpoB mutants have increased evolvability in proportion to their fitness defects. Mol. Biol. Evol. 27, 1338–1347 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. 27

    Kryazhimskiy, S., Rice, D. P., Jerison, E. R. & Desai, M. M. Global epistasis makes adaptation predictable despite sequence-level stochasticity. Science 344, 1519–1522 (2014)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  28. 28

    Sheltzer, J. M. et al. Aneuploidy drives genomic instability in yeast. Science 333, 1026–1030 (2011)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  29. 29

    Dewhurst, S. M. et al. Tolerance of whole-genome doubling propagates chromosomal instability and accelerates cancer genome evolution. Cancer Disc. 4, 175–185 (2014)

    Article  CAS  Google Scholar 

  30. 30

    Ezov, T. K. et al. Molecular-genetic biodiversity in a natural population of the yeast Saccharomyces cerevisiae from “Evolution Canyon”: microsatellite polymorphism, ploidy and controversial sexual status. Genetics 174, 1455–1468 (2006)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. 31

    Durrett, R., Foo, J., Leder, K., Mayberry, J. & Michor, F. Intratumor heterogeneity in evolutionary models of tumor progression. Genetics 188, 461–477 (2011)

    Article  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  32. 32

    Zhu, Y. O., Siegal, M. L., Hall, D. W. & Petrov, D. A. Precise estimates of mutation rate and spectrum in yeast. Proc. Natl Acad. Sci. USA 111, E2310–E2318 (2014)

    Article  ADS  CAS  PubMed  Google Scholar 

  33. 33

    Barbera, M. A. & Petes, T. D. Selection and analysis of spontaneous reciprocal mitotic cross-overs in Saccharomyces cerevisiae. Proc. Natl Acad. Sci. USA 103, 12819–12824 (2006)

    Article  ADS  CAS  PubMed  Google Scholar 

  34. 34

    Rouzine, I. M., Wakeley, J. & Coffin, J. M. The solitary wave of asexual evolution. Proc. Natl Acad. Sci. USA 100, 587–592 (2003)

    Article  ADS  CAS  PubMed  Google Scholar 

  35. 35

    Desai, M. M., Fisher, D. S. & Murray, A. W. The speed of evolution and maintenance of variation in asexual populations. Curr. Biol. 17, 385–394 (2007)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. 36

    Fogle, C. A., Nagle, J. L. & Desai, M. M. Clonal interference, multiple mutations and adaptation in large asexual populations. Genetics 180, 2163–2173 (2008)

    Article  PubMed  PubMed Central  Google Scholar 

  37. 37

    Vetterling, W. T. Numerical Recipes Example Book 2nd edn, Ch. 7 (Cambridge Univ. Press, 1992)

    Google Scholar 

  38. 38

    Wakeley, J. Coalescent Theory: An Introduction (Roberts, 2009)

    Google Scholar 

  39. 39

    Moura de Sousa, J. A., Campos, P. R. & Gordo, I. An ABC method for estimating the rate and distribution of effects of beneficial mutations. Genome Biol. Evol. 5, 794–806 (2013)

    Article  PubMed  PubMed Central  Google Scholar 

  40. 40

    Goyal, S. et al. Dynamic mutation-selection balance as an evolutionary attractor. Genetics 191, 1309–1319 (2012)

    Article  PubMed  PubMed Central  Google Scholar 

  41. 41

    Ewens, W. J. Mathematical Population Genetics 2nd edn (Springer, 2004)

    Google Scholar 

  42. 42

    Efron, B. & Tibshirani, R. An Introduction to the Bootstrap (Chapman & Hall, 1993)

    Google Scholar 

  43. 43

    Frenkel, E. M., Good, B. H. & Desai, M. M. The fates of mutant lineages and the distribution of fitness effects of beneficial mutations in laboratory budding yeast populations. Genetics 196, 1217–1226 (2014)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. 44

    Longtine, M. S. et al. Additional modules for versatile and economical PCR-based gene deletion and modification in Saccharomyces cerevisiae. Yeast 14, 953–961 (1998)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. 45

    Mumberg, D., Muller, R. & Funk, M. Yeast vectors for the controlled expression of heterologous proteins in different genetic backgrounds. Gene 156, 119–122 (1995)

    Article  CAS  PubMed  Google Scholar 

  46. 46

    Cross, F. R. ‘Marker swap’ plasmids: convenient tools for budding yeast molecular genetics. Yeast 13, 647–653 (1997)

    Article  CAS  PubMed  Google Scholar 

  47. 47

    Goldstein, A. L. & McCusker, J. H. Three new dominant drug resistance cassettes for gene disruption in Saccharomyces cerevisiae. Yeast 15, 1541–1553 (1999)

    Article  CAS  Google Scholar 

  48. 48

    Storici, F., Lewis, L. K. & Resnick, M. A. In vivo site-directed mutagenesis using oligonucleotides. Nature Biotechnol. 19, 773–776 (2001)

    Article  CAS  Google Scholar 

  49. 49

    Storici, F. & Resnick, M. A. The delitto perfetto approach to in vivo site-directed mutagenesis and chromosome rearrangements with synthetic oligonucleotides in yeast. Methods Enzymol. 409, 329–345 (2006)

    Article  CAS  PubMed  Google Scholar 

  50. 50

    Pavelka, N. et al. Aneuploidy confers quantitative proteome changes and phenotypic variation in budding yeast. Nature 468, 321–325 (2010)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  51. 51

    Selmecki, A., Bergmann, S. & Berman, J. Comparative genome hybridization reveals widespread aneuploidy in Candida albicans laboratory strains. Mol. Microbiol. 55, 1553–1565 (2005)

    Article  CAS  PubMed  Google Scholar 

  52. 52

    Saldanha, A. J. Java Treeview—extensible visualization of microarray data. Bioinformatics 20, 3246–3248 (2004)

    Article  CAS  Google Scholar 

  53. 53

    Wenger, J. W. et al. Hunger artists: yeast adapted to carbon limitation show trade-offs under carbon sufficiency. PLoS Genet. 7, e1002202 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. 54

    Hittinger, C. T. et al. Remarkably ancient balanced polymorphisms in a multi-locus gene network. Nature 464, 54–58 (2010)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  55. 55

    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nature Methods 9, 357–359 (2012)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. 56

    Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009)

    PubMed  PubMed Central  Google Scholar 

  57. 57

    McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. 58

    DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nature Genet. 43, 491–498 (2011)

    Article  CAS  PubMed  Google Scholar 

  59. 59

    Thorvaldsdottir, H., Robinson, J. T. & Mesirov, J. P. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief. Bioinform. 14, 178–192 (2013)

    Article  CAS  PubMed  Google Scholar 

  60. 60

    Anders, S., Pyl, P. T. & Huber, W. HTSeq-a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. 61

    Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. 62

    Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. 63

    Novocraft. Novocraft short read alignment package. http://www.novocraft.com (2009)

  64. 64

    Homer, N., Merriman, B. & Nelson, S. F. BFAST: an alignment tool for large scale genome resequencing. PLoS ONE 4, e7767 (2009)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  65. 65

    Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. 66

    Homer, N. & Nelson, S. F. Improved variant discovery through local re-alignment of short-read next-generation sequencing data using SRMA. Genome Biol. 11, R99 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. 67

    Dalca, A. V., Rumble, S. M., Levy, S. & Brudno, M. VARiD: a variation detection framework for color-space and letter-space platforms. Bioinformatics 26, i343–i349 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. 68

    Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by the Howard Hughes Medical Institute, the National Institutes of Health (R37 GM61345), the G. Harold & Leila Y. Mathers Charitable Foundation, the Dana-Farber Cancer Institute Physical Sciences-Oncology Center (U54CA143798), the Boettcher Foundation’s Webb-Waring Biomedical Research Program, the National Science Foundation (NSF 1350915), the National Institutes of Health (R01 GM081617), and an American Cancer Society Postdoctoral Fellowship.

Author information

Affiliations

Authors

Contributions

A.M.S., M.G., N.S., R.K., and D.P. contributed to the overall study design. A.M.S. and M.G. performed the experiments. Y.E.M. implemented the mathematical modelling with contributions from N.S., R.K., and F.M. A.M.S., P.A.R., and A.L.S. generated WGS libraries; P.A.R. developed the sequencing pipeline and analysed the WGS data with help from A.M.S. and A.L.S, under the supervision of R.D.D. Data were analysed by A.M.S., Y.E.M., P.A.R., M.G., N.S., A.L.S., S.D., F.M., and D.P. The manuscript was written primarily by A.M.S., Y.E.M., and D.P. with contributions from the other authors.

Corresponding authors

Correspondence to Anna M. Selmecki or David Pellman.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Schematic representation of the construction of isogenic haploid, diploid, and tetraploid strains used in this study.

Relevant strain numbers are indicated for the CFP- and YFP-containing ancestors.

Extended Data Figure 2 Estimates from our mathematical modelling of the best-fit value of the beneficial mutation rate (μ) and the selection coefficient (s) of each ploidy evolution experiment.

a, Table of μ and s values that had the best-fit between the simulations and the experimental data; brackets, 95% confidence intervals. Values were determined on the basis of different assumptions about the underlying distribution of beneficial mutations, which included (b) uniform, (c) delta, and (d) exponential distributions. Estimates of μ and s were also obtained with (e) the equivalence principle model17, which assumes a delta distribution of beneficial mutations. Each two-dimensional plot includes the error range obtained by parametric bootstrap of 1,000 independent simulated data sets (Methods). The 95% confidence intervals of μ and s from those 1,000 data sets were defined as the error ranges. f, Schematic diagram of the three distributions of fitness effects that we used in our mathematical modelling: exponential (red), uniform (black), and delta (green) distributions. For illustration, we also provide a narrow Gaussian distribution (blue) that is close to a delta function. The real distribution of fitness effects probably has a more complex structure than any of the examples shown. The diagram illustrates the fact that the shape of the assumed distribution mandates differences in mutation rates. For example, if the mutations that mainly drive adaptation fall within the region of the double arrow, only a small proportion of the mutations from the exponential distribution will fall within this range, necessitating a much higher mutation rate to generate mutations in this region. By contrast, the delta distribution lies in the middle of the double arrow range; therefore, all of the mutations that arise from this distribution are strong enough to contribute to adaptation, resulting in a relatively lower mutation rate. The uniform distribution is intermediate between these two extremes. Only a small portion of the mutations of the uniform distribution is within the double arrow region, but the probability of these mutations is orders of magnitude larger than the exponential. Therefore, the mutation rate of the uniform is closer to the delta than to the exponential distribution. The values used to generate this figure are the best-fit values of μ and s of the haploid populations in the different three distributions. See Methods for more details.

Extended Data Figure 3 Experimental and computational analyses of the noise in our experimental measurements and of the methods used in our mathematical modelling (see Supplementary Information).

a, Three different methods were used to determine the percentage of YFP-expressing cells in mixtures of the 1N, 2N, and 4N CFP and YFP ancestor strains. Cells were analysed by flow cytometry (10,000 cells) and fluorescence microscopy (300 cells), and by single colony analysis (96 colonies) of the mixture before galactose induction. The percentage YFP determined by all three methods was highly correlated (Pearson correlation coefficient = 0.98). b, Table showing variation in flow cytometry replicate measurements: the standard deviation of the percentage YFP obtained from 48 replicate populations of six different CFP:YFP ratios, for each ploidy type. c, Table showing the average and standard deviation of the best-fit values for different ratios between beneficial (Ub) and deleterious (Ud) mutations, obtained from 100 independent data sets. d, Evaluation of different summary statistics by calculating the distribution of best-fit values from 1,000 replicate simulations. Four different summary statistics were used to analyse 1,000 replicates of a parameter pair, s and μ (see Methods). The summary statistic using ten bins has the highest mode and no outliers, and was used to generate our best-fit values. e, Criteria for exclusion of near-neutral mutations for implementation of the branching evolutionary model with an exponential distribution of mutations. Shown is the average deviation from equal percentages of YFP and CFP-expressing cells with different thresholds for neutral mutations. The threshold (Tr) represents the fraction of the average fitness effect (s), meaning every mutation whose absolute value was smaller than Tr × s was excluded. For this scenario (with parameters μ = 2 × 10−5 and s = 0.08), we can exclude every mutation with a fitness effect smaller than s (that is, Tr = 1, light blue) without changing the outcome relative to excluding no mutations (Tr = 0). However, when excluding all mutations with fitness effects smaller than 10× s (Tr = 10, dark green), the result changes substantially. Thus, for high mutation rates ( > 1), we can exclude weak mutations20.

Extended Data Figure 4 aCGH karyotype of the ancestor strains used in this study.

Aneuploidy was not detected in the parental 1N, 2N, or 4N strains. Genomic DNA from each strain was compared with that of an isogenic ancestor PY3295 (BY4741 MATa ura3 his3 trp1 leu2 LYS2) and log2 DNA copy number ratios were plotted using a custom Matlab script. To account for regions of complete deletion, the data were cropped at log2 ratios of ±2.0 and averaged across each chromosome using a sliding window of nine oligonucleotides. A log2 ratio of zero is indicated by the red line. Loci altered during strain construction are indicated (TRP1, pSTE5, URA3, STE4). Strain ploidy, determined by flow cytometry, is indicated on the right.

Extended Data Figure 5 aCGH karyotype of haploid- and diploid-evolved clones at generation 250.

a, aCGH of eight haploid-evolved clones. Data are displayed as in Extended Data Fig. 4. No aneuploidy was detected. Clone 1N_131 acquired the HXT6/7 amplification (arrow). b, aCGH of eight diploid-evolved clones. No aneuploidy was detected, but all clones except 2N_233 acquired the HXT6/7 amplification. Log2 ratios were averaged across each chromosome using a sliding window of 29 oligonucleotides. The ploidy of the evolved clone, determined by flow cytometry, is indicated on the right.

Extended Data Figure 6 aCGH karyotype for 20 tetraploid-evolved clones at generation 250.

aCGH data are displayed as in Extended Data Fig. 4. Note that whole chromosome or large segmental chromosome gain and loss events are observed in all clones except clone 4N_337. Ploidy of the evolved clone, determined by flow cytometry, is indicated on the right, with +/− indicating chromosome aneuploidy. Some highly aneuploid clones had widely different chromosome copy numbers for different chromosomes (for example, some chromosomes were disomic, others trisomic and tetrasomic).

Extended Data Figure 7 Analysis of recurrent and concerted chromosome loss events in the tetraploid-evolved clones.

a, Evolved tetraploids acquired large segmental aneuploidies (regions greater than the 7 kb HXT6/7 amplification); aCGH data for individual chromosomes with large segmental aneuploidies in 4N-evolved clones (plotted using Treeview52). All breakpoints occurred at or near Ty sequences (arrowheads). b, The pairwise patterns (Pearson correlation) of all chromosome copy number alterations in the 4N-evolved clones at generation 250 (n = 30, Supplementary Table 2). The copy numbers of some chromosomes were correlated (for example, chromosome XV and chromosome XVI), whereas others were anti-correlated (for example, chromosome VIII and chromosome IX), possibly reflecting the need for gene expression balance. c, Hierarchical clustering showing the copy number relationship among the chromosomes. d, Proportion of all chromosomes in the evolved tetraploid clones with the indicated copy number (black). The copy number of chromosome XIII (grey) in the 4N-evolved clones at generation 250 was significantly different from that of all other aneuploid chromosomes (Cochran–Armitage test, P < 1 × 10−7).

Extended Data Figure 8 aCGH karyotype for tetraploid-evolved clones at generations 35, 55, and 500.

All 4N-evolved clones at (a) generations 35 and 55 and (b) generation 500 were aneuploid for multiple chromosomes or carried large segmental chromosome aneuploidies, except for clone 4N_gen35_503, which remained tetraploid. Data are displayed as in Extended Data Fig. 4. Ploidy of the evolved clone, determined by flow cytometry, is indicated on the right, with +/− indicating chromosome aneuploidy.

Extended Data Figure 9 aCGH from isogenic 2N and 4N strains with an extra copy of chromosome XIII or chromosome XII.

Data are displayed as in Extended Data Fig. 5b.

Extended Data Table 1 Yeast strains and plasmids used in this study

Supplementary information

Supplementary Data

This file contains Supplementary Table 1. (XLSX 55 kb)

Supplementary data

This file contains Supplementary Table 2. (XLSX 47 kb)

Supplementary Data

This file contains the computer code used in the modelling. (TXT 17 kb)

PowerPoint slides

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Selmecki, A., Maruvka, Y., Richmond, P. et al. Polyploidy can drive rapid adaptation in yeast. Nature 519, 349–352 (2015). https://doi.org/10.1038/nature14187

Download citation

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.