Polyploidy can drive rapid adaptation in yeast (original) (raw)

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Gene Expression Omnibus

Sequence Read Archive

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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.

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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

Author notes

  1. Anna M. Selmecki
    Present address: Present address: Department of Medical Microbiology and Immunology, Creighton University School of Medicine, 2500 California Plaza, Omaha, Nebraska 68178, USA.,

Authors and Affiliations

  1. Department of Pediatric Oncology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, Massachusetts 02215, USA,
    Anna M. Selmecki, Marie Guillet & David Pellman
  2. Department of Cell Biology, Harvard Medical School, 25 Shattuck Street, Boston, Massachusetts 02215, USA,
    Anna M. Selmecki, Marie Guillet & David Pellman
  3. Howard Hughes Medical Institute, 4000 Jones Bridge Road, Chevy Chase, 20815, Maryland, USA
    Anna M. Selmecki, Marie Guillet & David Pellman
  4. Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue Boston, 02215, Massachusetts, USA
    Yosef E. Maruvka & Franziska Michor
  5. Department of Biostatistics, Harvard School of Public Health, 158 Longwood Avenue, Boston, Massachusetts 02215, USA,
    Yosef E. Maruvka & Franziska Michor
  6. BioFrontiers Institute, University of Colorado at Boulder, 3415 Colorado Avenue, Boulder, Colorado 80303, USA,
    Phillip A. Richmond, Amber L. Sorenson & Robin Dowell
  7. Department of Molecular, Cellular and Developmental Biology, University of Colorado at Boulder, 347 UCB, Boulder, Colorado 80309, USA,
    Phillip A. Richmond, Amber L. Sorenson & Robin Dowell
  8. Broad Institute, 415 Main Street, Cambridge, 02142, Massachusetts, USA
    Noam Shoresh
  9. Department of Medicine, University of Colorado School of Medicine, 13001 East 17th Place, Aurora, Colorado 80045, USA,
    Subhajyoti De
  10. Department of Biostatistics and Informatics, Colorado School of Public Health, 13001 East 17th Place, Aurora, Colorado 80045, USA,
    Subhajyoti De
  11. Molecular Oncology Program, University of Colorado Cancer Center, 13001 East 17th Place, Aurora, Colorado 80045, USA,
    Subhajyoti De
  12. Department of Systems Biology, Harvard Medical School, 200 Longwood Ave, Boston, Massachusetts 02115, USA,
    Roy Kishony
  13. Department of Biology, Technion - Israel Institute of Technology, Haifa, 32000, Israel,
    Roy Kishony
  14. Department of Pediatric Hematology/Oncology, Children’s Hospital, 300 Longwood Avenue, Boston, Massachusetts 02115, USA,
    David Pellman

Authors

  1. Anna M. Selmecki
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  2. Yosef E. Maruvka
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  3. Phillip A. Richmond
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  4. Marie Guillet
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  5. Noam Shoresh
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  6. Amber L. Sorenson
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  7. Subhajyoti De
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  8. Roy Kishony
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  9. Franziska Michor
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  10. Robin Dowell
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  11. David Pellman
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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 toAnna M. Selmecki or David Pellman.

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Extended data figures and tables

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 (_U_b) and deleterious (_U_d) 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 (Nμ > 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, p_STE5_, 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 Table 1 Yeast strains and plasmids used in this study

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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

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