Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter
  • Published:

Hypo-osmotic-like stress underlies general cellular defects of aneuploidy

Abstract

Aneuploidy, which refers to unbalanced chromosome numbers, represents a class of genetic variation that is associated with cancer, birth defects and eukaryotic micro-organisms1,2,3,4. Whereas it is known that each aneuploid chromosome stoichiometry can give rise to a distinct pattern of gene expression and phenotypic profile4,5, it remains a fundamental question as to whether there are common cellular defects that are associated with aneuploidy. Here we show the existence in budding yeast of a common aneuploidy gene-expression signature that is suggestive of hypo-osmotic stress, using a strategy that enables the observation of common transcriptome changes of aneuploidy by averaging out karyotype-specific dosage effects in aneuploid yeast-cell populations with random and diverse chromosome stoichiometry. Consistently, aneuploid yeast exhibited increased plasma-membrane stress that led to impaired endocytosis, and this defect was also observed in aneuploid human cells. Thermodynamic modelling showed that hypo-osmotic-like stress is a general outcome of the proteome imbalance that is caused by aneuploidy, and also predicted a relationship between ploidy and cell size that was observed in yeast and aneuploid cancer cells. A genome-wide screen uncovered a general dependency of aneuploid cells on a pathway of ubiquitin-mediated endocytic recycling of nutrient transporters. Loss of this pathway, coupled with the endocytic defect inherent to aneuploidy, leads to a marked alteration of intracellular nutrient homeostasis.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Karyotype-independent transcriptomic response in heterogeneous aneuploid populations.
Fig. 2: Membrane stress and impaired endocytosis in aneuploid cells.
Fig. 3: Hypo-osmotic stress is a biophysical outcome of proteome imbalance.
Fig. 4: Dependency of aneuploid cells on the Art–Rsp5 pathway for fitness and nutrient homeostasis.

Similar content being viewed by others

References

  1. Chunduri, N. K. & Storchová, Z. The diverse consequences of aneuploidy. Nat. Cell Biol. 21, 54–62 (2019).

    Article  CAS  Google Scholar 

  2. Pavelka, N., Rancati, G. & Li, R. Dr Jekyll and Mr Hyde: role of aneuploidy in cellular adaptation and cancer. Curr. Opin. Cell Biol. 22, 809–815 (2010).

    Article  CAS  Google Scholar 

  3. Williams, B. R. et al. Aneuploidy affects proliferation and spontaneous immortalization in mammalian cells. Science 322, 703–709 (2008).

    Article  ADS  CAS  Google Scholar 

  4. Torres, E. M. et al. Effects of aneuploidy on cellular physiology and cell division in haploid yeast. Science 317, 916–924 (2007).

    Article  ADS  CAS  Google Scholar 

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

    Article  ADS  CAS  Google Scholar 

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

    Article  ADS  CAS  Google Scholar 

  7. 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  Google Scholar 

  8. Yona, A. H. et al. Chromosomal duplication is a transient evolutionary solution to stress. Proc. Natl Acad. Sci. USA 109, 21010–21015 (2012).

    Article  ADS  CAS  Google Scholar 

  9. Duncan, A. W. et al. Aneuploidy as a mechanism for stress-induced liver adaptation. J. Clin. Invest. 122, 3307–3315 (2012).

    Article  CAS  Google Scholar 

  10. Sunshine, A. B. et al. The fitness consequences of aneuploidy are driven by condition-dependent gene effects. PLoS Biol. 13, e1002155 (2015).

    Article  Google Scholar 

  11. Zhu, J., Tsai, H.-J., Gordon, M. R. & Li, R. Cellular stress associated with aneuploidy. Dev. Cell 44, 420–431 (2018).

    Article  CAS  Google Scholar 

  12. Torres, E. M. et al. Identification of aneuploidy-tolerating mutations. Cell 143, 71–83 (2010).

    Article  CAS  Google Scholar 

  13. Sheltzer, J. M., Torres, E. M., Dunham, M. J. & Amon, A. Transcriptional consequences of aneuploidy. Proc. Natl Acad. Sci. USA 109, 12644–12649 (2012).

    Article  ADS  CAS  Google Scholar 

  14. Oromendia, A. B., Dodgson, S. E. & Amon, A. Aneuploidy causes proteotoxic stress in yeast. Genes Dev. 26, 2696–2708 (2012).

    Article  CAS  Google Scholar 

  15. Sheltzer, J. M. A transcriptional and metabolic signature of primary aneuploidy is present in chromosomally unstable cancer cells and informs clinical prognosis. Cancer Res. 73, 6401–6412 (2013).

    Article  CAS  Google Scholar 

  16. Stingele, S. et al. Global analysis of genome, transcriptome and proteome reveals the response to aneuploidy in human cells. Mol. Syst. Biol. 8, 608 (2012).

    Article  Google Scholar 

  17. Dürrbaum, M. et al. Unique features of the transcriptional response to model aneuploidy in human cells. BMC Genomics 15, 139 (2014).

    Article  Google Scholar 

  18. Ohashi, A. et al. Aneuploidy generates proteotoxic stress and DNA damage concurrently with p53-mediated post-mitotic apoptosis in SAC-impaired cells. Nat. Commun. 6, 7668 (2015).

    Article  ADS  Google Scholar 

  19. Santaguida, S., Vasile, E., White, E. & Amon, A. Aneuploidy-induced cellular stresses limit autophagic degradation. Genes Dev. 29, 2010–2021 (2015).

    Article  CAS  Google Scholar 

  20. Dodgson, S. E. et al. Chromosome-specific and global effects of aneuploidy in Saccharomyces cerevisiae. Genetics 202, 1395–1409 (2016).

    Article  CAS  Google Scholar 

  21. Gasch, A. P. et al. Genomic expression programs in the response of yeast cells to environmental changes. Mol. Biol. Cell 11, 4241–4257 (2000).

    Article  CAS  Google Scholar 

  22. Hohmann, S. Osmotic stress signaling and osmoadaptation in yeasts. Microbiol. Mol. Biol. Rev. 66, 300–372 (2002).

    Article  CAS  Google Scholar 

  23. Davenport, K. R., Sohaskey, M., Kamada, Y., Levin, D. E. & Gustin, M. C. A second osmosensing signal transduction pathway in yeast. Hypotonic shock activates the PKC1 protein kinase-regulated cell integrity pathway. J. Biol. Chem. 270, 30157–30161 (1995).

    Article  CAS  Google Scholar 

  24. Basu, R., Munteanu, E. L. & Chang, F. Role of turgor pressure in endocytosis in fission yeast. Mol. Biol. Cell 25, 679–687 (2014).

    Article  Google Scholar 

  25. Zwiewka, M., Nodzyński, T., Robert, S., Vanneste, S. & Friml, J. Osmotic stress modulates the balance between exocytosis and clathrin-mediated endocytosis in Arabidopsis thaliana. Mol. Plant 8, 1175–1187 (2015).

    Article  CAS  Google Scholar 

  26. Nikko, E. & Pelham, H. R. B. Arrestin-mediated endocytosis of yeast plasma membrane transporters. Traffic 10, 1856–1867 (2009).

    Article  CAS  Google Scholar 

  27. Lin, C. H., MacGurn, J. A., Chu, T., Stefan, C. J. & Emr, S. D. Arrestin-related ubiquitin-ligase adaptors regulate endocytosis and protein turnover at the cell surface. Cell 135, 714–725 (2008).

    Article  CAS  Google Scholar 

  28. Zielinski, D. C. et al. Systems biology analysis of drivers underlying hallmarks of cancer cell metabolism. Sci. Rep. 7, 41241 (2017).

    Article  ADS  CAS  Google Scholar 

  29. Polo, S. Signaling-mediated control of ubiquitin ligases in endocytosis. BMC Biol. 10, 25 (2012).

    Article  CAS  Google Scholar 

  30. Dolfi, S. C. et al. The metabolic demands of cancer cells are coupled to their size and protein synthesis rates. Cancer Metab. 1, 20 (2013).

    Article  Google Scholar 

Download references

Acknowledgements

We thank S. Emr (Cornell University) and E. Spear (Johns Hopkins) for helpful suggestions and providing yeast strains, A. Narkar for assistance with chromosome counting from metaphase spreads, P. Iglesias (Johns Hopkins) for advice on mathematical modelling, B. Rubenstein (Stowers Institute) for advice on screen analysis, N. Chau and S. McCroskey for technical assistance, K. Staehling and members of Stowers Institute Molecular Biology core for assistance with the high-throughput screen, L. Kratz (Kennedy Krieger Institute) for amino acids analysis, H. Hao (Johns Hopkins Deep Sequencing and Microarray Core) for help with DNA and RNA sequencing, M. McCaffery (Johns Hopkins University Integrated Imaging Center) for electron microscopy, and A. Selmecki for comments on the primary manuscript. This work was supported by NIH grant R35-GM118172 to R.L., Prostate Cancer Foundation Young Investigator Award (16YOUN21) to H.-J.T., NSF award DBI-1350041 and NIH award R01-HG006677 to M.C.S., and NIH grant R01-GM114675 and U54-CA210173 to S.X.S.

Reviewer information

Nature thanks Stefan Hohmann and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Authors and Affiliations

Authors

Contributions

H.-J.T. and R.L. designed the experiments. H.-J.T., A.R.N., M.I.C., W.D.B. and J.K. performed the experiments. Data were analysed by H.-J.T., A.R.N., M.I.C., M.E.C. and D.B.M. M.C.S. supervised the genomic and transcriptome analyses. A.R.N. and A.K. implemented the mathematical and biophysical models (written descriptions of model details can be found in the Supplementary Methods) under the supervision of S.X.S. and R.L. The manuscript was primarily written by H.-J.T., A.R.N. and R.L. with contributions from other authors. R.L. conceived and supervised the project.

Corresponding author

Correspondence to Rong Li.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Generation of heterogeneous aneuploid populations without chromosome copy number bias for transcriptomic analyses.

a, Generation of aneuploid cells through random meiotic segregation from a homozygous triploid (3N) yeast strain that bears a centromeric plasmid that carries the selection marker spHIS5 under the MATa-specific promoter STE2pr. Coloured rods represent chromosomes. b, Left, DNA content of a cohort of random aneuploid spore colonies (each represented by a grey dot, n = 75; see Source Data) produced through triploid meiosis and analysed by FACS; box plot and violin plot (defined as in Fig. 2 and Supplementary Methods) show the distribution of ploidy levels. Right, 192 random aneuploid colonies produced by triploid meiosis were pooled, and the resulting population subjected to quantitative PCR-based karyotyping (representative results from two biological repeats). Y axis represents the relative copy number (mean of the two arms) of each chromosome (dots) to the reference haploid yeast. There was no significant difference in copy number between each chromosome (one-way analysis of variance, P value = 0.998). c, Principal component analysis of RNA sequencing (RNA-seq) results in different populations. PC1 (x axis) shows the apparent difference between haploid (n = 2) and aneuploid (n = 5) populations. PC2 (y axis) reflects a small difference in the two methods of generation of aneuploid populations. Each dot represents one population. d, Gene expression in heterogeneous aneuploid populations (n = 5) relative to haploid populations (n = 2) in MA plot. x axis represents basal mean expression in log10 scale; y axis represents differential expression changes between aneuploid and euploid populations in log2 scale. P values calculated on the Wald statistic were corrected for multiple comparisons (Benjamini–Hochberg), and the resulting false discovery rate was further corrected for variance underestimation using an empirical null model. CAGE genes were identified as genes with final false discovery rate < 0.05 (see details in Supplementary Methods; exact P values in Supplementary Table 1). Each dot represents 1 gene, and 222 significantly differentially expressed (CAGE) genes common to all 5 aneuploid cell populations are labelled in dark red. e, Quantitative PCR with reverse transcription (RT–qPCR) validations of four significantly differentially expressed genes from the RNA-seq analysis in a heterogeneous aneuploid population (n = 1), and randomly picked individual aneuploid clones (n = 12). Black circles, individual aneuploid populations; red dot: a heterogeneous aneuploid population; green dot, aneuploid populations in RNA-seq (d). For individual aneuploid colonies, the expression change of each gene was normalized by the gene copy number (determined by qPCR), and then normalized to that of a non-CAGE gene (ACT1). Average gene-expression changes in the population and in the individual aneuploid clones followed similar trends (up- or downregulated) to those of heterogeneous populations in RNA-seq. Box plot representation as in Fig. 2 for measurements from the 12 aneuploid clones. f, Correlation analysis of expression changes of the genes implicated in the CAGE signature, showing positive correlation between aneuploid populations and stable aneuploid strains5 (n = 5) (Spearman’s rank correlation). Green circles represent mean expression changes of genes within the CAGE signature (213 in common in the transcriptomic data between aneuploid populations and the individual strains). Error bars, s.e.m. Only eight genes across all five strains appear to show opposite expression trend to CAGE. The trend line was fitted by linear regression (red dashed line) with grey ribbon (95% CI). Significant positive correlation was observed between the average gene-expression changes in the five aneuploid strains, and CAGE observed with the heterogeneous aneuploid population subjected to RNA-seq (P value = 1.609 × 10−7).

Source Data

Extended Data Fig. 2 Comparisons of gene-expression patterns between CAGE and several stress responses.

a, Top, heat map showing negative correlation between CAGE and gene-expression changes of cells exposed to hyper-osmotic shock for different time periods. Colour gradient as in Fig. 1b. Scatter plot, genome-wide and CAGE correlation of gene-expression patterns between aneuploid versus haploid population (x axis) and cells exposed to hyper-osmotic stress for 15 min (y axis). Colour scheme as in Fig. 1c. Correlation scores from the Spearman’s rank correlation test and exact P value are in Supplementary Table 2; sample size (n) is documented in the Source Data for Fig. 1. Bottom, as for the top panel but for the correlation between CAGE and long-term DTT treatment (120 min). b, Heat map showing positive correlation of gene-expression changes in cells experiencing temperature down-shift (37 °C to 25 °C) with changes in cells experiencing hypo-osmotic shock (left), short-term DTT treatment (middle) or with CAGE (right). Correlation test and scores as in a; sample size (n) is documented in Source Data. c, The same MA plot as shown in Extended Data Fig. 1d with labels of environmental-stress response (ESR) genes21 (n = 857). Red and blue dots indicate ESR genes that were upregulated or downregulated, respectively, when cells were exposed to stress. d, Venn diagrams showing common genes between CAGE and ESR (left). No overlapping genes between upregulated genes in CAGE and ESR were found, and the same is true for downregulated genes in CAGE and ESR. By contrast, there are some overlaps between oppositely regulated CAGE and ESR genes. e, RT–qPCR monitoring expression changes of two CAGE genes that are also significantly differentially expressed under hypo- and hyper-osmotic shock conditions21 in a heterogeneous aneuploid population exposed to hyper-osmotic shock (0 to 1 M sorbitol). Multiple primer sets that target each gene were used. y axis indicates the ratio of gene expression of two CAGE genes between aneuploid and haploid populations over time, immediately after cells were shifted to medium with 1 M sorbitol (x axis). Note that one of the genes was upregulated (blue) in CAGE, and the other was downregulated (red). Plots show that these changes were alleviated by hyper-osmotic shock. f, Top, percentage of cells with nuclear-localized Hog1 as a function of time after different levels of acute hyper-osmotic exposure in haploid and aneuploid populations. y axis indicates the percentage of cells with nuclear-localized Hog1–GFP signal over time, immediately after cells were shifted to medium with 1 M sorbitol (x axis). Bottom, example images (two independent experiments) from two time points (t1 and t2, labelled with green lines in top panel) in haploid and aneuploid cells (n = 287 and 254; see Source Data for full information of sample size). Red arrows indicate diminished nuclear-localized Hog1. Note that Hog1 import occurred quickly, such that even at the first time point of measurement after osmotic shock, most cells displayed nuclear Hog1.

Source Data

Extended Data Fig. 3 Turgor-pressure measurements of cells in a hyper-osmotic environment or experiencing cell permeabilization, and biophysical model predictions.

a, Examples of force-displacement curves that show the difference of curve shapes from indented haploid and aneuploid cells in normal medium (YPD), or immediately after shift to medium containing 0.4 M sorbitol or pre-treated with 0.1% SDS (sample size (n) in Source Data). The pre-set force for this atomic force microscopy experiment was 1 nN. b, Box plots of slopes (nN nm−1) computed at two parts of the curves in a: the first 10 nm of z-piezo displacement, which mostly reflects cell-wall stiffness, and the third quartile (Q3) of the curve, which is expected to be a contribution from cellular turgor. The results show that that increasing extracellular osmolarity (sorbitol, middle) or cell permeabilization (SDS, right) led to a marked reduction of intracellular turgor, and diminished the increased turgor in aneuploid cells to the level of that in haploid cells. One-tailed t-test; sample size (n) in Source Data. In YPD, at 0–10 nm, P value = 0.241; at Q3, P value = 0.029. In sorbitol, at 0–10 nm, P value = 0.3299; at Q3, P value = 0.4765. In SDS, at 0–10 nm, P value = 0.9675; at Q3, P value = 0.5594. c, Model-simulated average turgor-pressure change (ratio between aneuploids and haploid cells) based on the combinations of two parameters—the abundance correlation of proteins that form large complexes (above 40 proteins per complex, such as ribosomes (x axis)) and the abundance correlation of proteins that form average complexes (base level (y axis)) across a reasonable range of values (0.7–0.95). Heat maps show the parameter scan without (left) and with (right) adjusting the abundance of large complexes to 20× the base concentration of complexes. d, Examples of box plots of simulated turgor pressure ratios (aneuploid and haploid populations, n = 18 and 2, respectively) using 0.85 abundance correlation of proteins that form base or large complexes, without (left) and with (right) the adjustment as in c. Each dot presents a simulated ratio of turgor based on experimental size measurements at each ploidy (see Fig. 3b). Box plots are as in Fig. 2.

Source Data

Extended Data Fig. 4 Aneuploid cells display cell-wall stress phenotypes owing to hypo-osmotic imbalance.

a, Representative electron micrographs of haploid (top) and aneuploid (bottom) cells, and box plots of measured cell-wall thickness showing no significant difference of cell-wall thickness between haploid (n = 15) and aneuploid (n = 15) cells (one-tailed t-test, P value = 0.152; measurements in Source Data). Scale bar, 1 μm. b. Mean intensity of fluorescently labelled concanavalin A on plasma membrane was measured in haploid and aneuploid cells at 0 and 20 min during zymolyase treatment (haploid cells, n = 50 and 31; aneuploids cells, n = 50 and 23, at 0 and 20 min, respectively). The fluorescence intensities were similar between haploid and aneuploid cells at these time points (one-tailed Mann–Whitney U test P value at 0 min = 0.1285 and at 20 min = 0.7286), which suggests rates of cell-wall removal by zymolyase were similar in the two populations. c, Haploid and aneuploid cells were treated with zymolyase to assess the tendency to burst by optical density measurement in normal or hyper-osmotic (0.8 M sorbitol) environments. y axis represents the percentage of intact cells relative to time zero of treatment; x axis indicates the time immediately after addition of zymolyase with or without 0.8 M sorbitol. Each dot represents one measurement at each time point (n = 3). Plots show that the hyper-osmotic environment rescued the hypersensitivity of aneuploid cells to zymolyase, as compared to haploid cells. d, Nine individual aneuploid clones were tested for their zymolyase sensitivity, as described in Fig. 2b. Haploid and aneuploid cells are indicated by blue and red, respectively; each dot represents one measurement at each time point (n = 3). The plots show that the zymolyase hypersensitivity was associated with most aneuploid clones. e, Extracellular glycerol concentration was measured to assess the exportation of glycerol (glycerol in the supernatant of the cultures) during a two-hour timespan. Each dot represents one measurement in each culture (n = 3 for both haploid and aneuploid populations; black bar, mean concentration). f, Immunoblots for phosphorylated Slt2, total Slt2 and Pgk1 showed an increased phosphorylation of Slt2 in aneuploid cells, as compared to that in haploid cells. Fold changes of quantified phospho-Slt2 and Slt2 signal intensity between haploid and aneuploid cells were normalized by the Pgk1 signal of each respective population. Representative blot from two independent experiments (Supplementary Fig. 1, raw gel images). Box plots in a and b are as in Fig. 2.

Source Data

Extended Data Fig. 5 Electron micrographs of haploid and aneuploid cells used for the quantification of cell-wall thickness.

Relates to cell-wall thickness measurements described in Extended Data Fig. 4a. Haploid and aneuploid cells were processed and imaged by using electron microscopy. Each electron microscopy panel was assembled as described in Supplementary Methods. Four measurements of cell-wall thickness were made from one cell in each image. Scale bars, 500 nm.

Source Data

Extended Data Fig. 6 Aneuploid cells show impaired endocytosis but not general cell-cycle or polarity defects.

a, b, Duration (left) and abundance (right, mean intensity of GFP signal) of Abp1 (a) and Sla2 (b) at endocytic patches were measured, comparing aneuploid and haploid cells. Duration and abundance of Abp1 signal are significantly greater in aneuploid cells, compared to haploid cells. Duration, endocytic patches in haploid and aneuploid cells, n = 146 and 255, respectively, P value < 2.94 × 10−11. Abundance, endocytic patches in haploid and aneuploid cells, n = 144 and 237, respectively, P value < 2.2 × 10−16 (one-tailed t-test). This is consistent with the delay in endocytic invagination observed in aneuploid cells, as the increase in abundance probably results from additional F-actin accumulation. Duration, but not abundance, of Sla2 signal was significantly greater in aneuploid cells. Duration, endocytic patches in haploid and aneuploid cells, n = 91 and 173, respectively, P value = 0.002758. Abundance, endocytic patches in haploid and aneuploid cells, n = 261 and 275, respectively, P value = 0.863 (one-tailed t-test). This is again consistent with the delay during endocytic invagination. These results also suggest that the endocytic defect of aneuploid cells was not due to a lack, or unstable localization, of these proteins to actin patches. c, Initial velocity of Abp1 during invagination was measured in single cells. Velocity of Abp1 inward movement in haploid cells is significantly faster than in aneuploid cells (haploid cells, n = 12; aneuploid cells, n = 20. P value = 2.237 × 10−6, one-tailed t-test). Box plots in ac are as in Fig. 2; full data are in Source Data. d, Average distance of Abp1 inward movement from cell cortex in individual aneuploid cells (n = 9; full data are in Source Data). The plots show average distance, with ribbons representing 95% CI. Abp1 in most aneuploid cells moves along the cortex. e, Net changes of FM4-64 signal intensity (arbitrary units) at plasma membrane and cytoplasm over time (related to Fig. 2e). Haploid cells, n = 205 and 206; aneuploid cells, n = 200 and 205 in left and right panels, respectively (full data are in Source Data for Fig. 2). Solid lines represent average fluorescent intensity; grey ribbons represent 95% CI. Bulk rate of FM4-64 internalization (mean ± s.d., per minute). Rate in haploid cells, 7.66 × 10−4 ± 1.49 × 10−3; rate in aneuploid cells, 1.87 × 10−4 ± 4.42 × 10−4; rate in haploid cells in sorbitol, 4.73 × 10−4 ± 9.35 × 10−4; rate in aneuploid cells in sorbitol, 9.73 × 10−4 ± 1.16 × 10−3. f, Correlation analysis between cell size (y axis) and cell-cycle profile. x axis, ratio of G1 and G2 peaks from FACS analysis (full data in Source Data) in aneuploid cells (red dots, n = 26) (from experimental results in Fig. 3b, bottom). No correlation was found (Spearman’s rank correlation score = −0.037, P value = 0.856), which indicates that variation in cell-cycle distribution was not associated with the increased cell size in aneuploid populations. The trend line (dashed line) was fitted by linear regression (red dashed line) with grey ribbon (95% CI). g, Representative images of polarized cells with Abp1–GFP as a marker of actin patches, showing the preferential localization at the bud cortex (red arrowheads) indicative of polarized cells. We found that 97.6% of haploid and 93.2% of aneuploid small budded cells (n = 42 and 44, respectively; bud size roughly equal to or smaller than 1/2 of the mother cell, data collected from three independent experiments) exhibited a polarized actin-patch distribution. Scale bars, 5 μm.

Source Data

Extended Data Fig. 7 Additional data showing that hypo-osmotic stress leads to increased cell size and endocytic slowdown in both haploid and aneuploid cells.

a, Left, average distance that Abp1 travelled away from cell cortex under acute exposure to medium containing 0.4 M sorbitol (grey ribbon, 95% confidence intervals; haploid cells, n = 150; aneuploid cells, n = 136). No significant difference in the distance travelled inwardly by Abp1 patches was observed between haploid cells (top) and aneuploid cells (bottom) in this hyper-osmotic environment (Tukey contrasts on generalized linear model (GLM), multiple-testing-corrected P value = 0.0968). Right, duration (top) and abundance (mean intensity of GFP signal, bottom) of Abp1 signal at endocytic patches under acute treatment of 0.4 M sorbitol were measured, comparing aneuploid and haploid cells. The duration of the Abp1 signal at cortex between haploid and aneuploid cells was similar, whereas the abundance of Abp1 immediately before inward traverse remained different between the two groups. Duration, haploid cells, n = 144; aneuploid cells, n = 169; P value = 0.731. Abundance, haploid cells, n = 140; aneuploid cells, n = 121; P value = 3.106 × 10−10 (one-tailed t-test). b, Haploid cells experiencing hypo-osmotic stress (20 min after shifting from growth medium (SC) containing 1 M sorbitol to SC without sorbitol) displayed increased cell size compared to cells continuously growing in medium with 1 M sorbitol (n = 120 in both sets; one-tailed t-test, P value = 3.437 × 10−8). c, Cell surface stiffness (y axis) was measured with atomic force microscopy in haploid cells under hyper- (left) and hypo-osmotic (right) shock within 30 min of osmotic shift. Box plots show that cell stiffness was significantly different between cells growing in normal (SC) and hyper-osmotic (1 M sorbitol) environments (P value = 0.028, one-tailed t-test; sample size (n) and full data in Source Data), and trended downward or upward after a hyper- or hypo-osmotic shift, respectively. d, Average distance travelled and velocity (centre) of Abp1 from cell cortex (y-axis; ribbon, 95% CI) was monitored over time (x axis) after haploid cells were exposed to hypo-osmotic shock as in a. When cells were transferred from high (1 M sorbitol) to low (SC) osmolarity environment, the average distance travelled and their velocity became shorter (Tukey contrasts on GLM, 1 M sorbitol to SC, P value = 0.00783) and slower (Tukey contrasts on GLM, 1 M sorbitol to SC, P value = 0.0134), respectively (see Source Data for sample size (n)). e, Cell size decreases in haploid and aneuploid cells exposed to hyper-osmotic stress. Cells in haploid (left) and aneuploid populations (right) significantly reduced in size after being shifted from SC to medium with 1 M sorbitol, compared to populations grown continuously in SC medium (for haploid cells, n = 300 and 321; one-tailed t-test, P value < 2.2 × 10−16; for aneuploid cells, n = 151 and 237; one-tailed t-test, P value < 2.2 × 10−16). Full data and exact P values for ae in Source Data. Box plots in ac, e are as in Fig. 2.

Source Data

Extended Data Fig. 8 Hypo-osmotic stress associated with aneuploidy impairs efficient endocytic internalization in human cells.

a, Left, distribution of chromosome counts from metaphase spreads in two different groups: MPS1-inhibitor-treated (red, aneuploid cells) and untreated (blue, diploid cells). The treated population is more-aneuploid than the untreated population (two-sided Fisher exact test, P value = 0.002688, n = 30 in both populations) (full data are in Source Data). Right, three example images from the metaphase spreads. Representative results from two independent experiments. b, FACS profiles in transferrin uptake assay (related to Fig. 2f). Three independent experiments were performed to assess uptake efficiency of transferrin in MPS1-inhibitor-treated (bottom, aneuploid) and untreated (top, diploid) cells. Histograms show the distribution of Alexa-Fluor-488-labelled transferrin signals (x axis) in the population (y axis). The same gate was applied to all three experiments to estimate the percentage of the population that was positive for Alexa Fluor 488.

Source Data

Extended Data Fig. 9 Genome-wide open reading frame deletion screen for mutations that reduce aneuploid cell fitness.

Schematic of the construction of yeast triploid homozygous-deletion collection, and the production of karyotypically heterogeneous aneuploid ORF-deletion populations (see detailed description in Supplementary Methods).

Extended Data Fig. 10 Aneuploidy prevents endocytic turnover of plasma-membrane transporters.

a, Representative images of results quantified in Fig. 4e. Haploid and aneuploid populations with Hxt3–GFP (left) or Hxt4–GFP (middle) were grown with or without glucose. Hxt3–GFP and Hxt4–GFP remained on the plasma membrane after 20 h of glucose depletion in aneuploid, but not haploid cells. Haploid and aneuploid populations with Can1–GFP (right) were grown with or without canavanine. Can1–GFP in aneuploid, but not haploid, cells remained on the plasma membrane after four hours of treatment. Two independent experiments. Scale bars, 5 μm. b, Representative images show Mig1–GFP translocation out of the nucleus after glucose depletion, and the return of Mig1–GFP after glucose repletion, which demonstrates that glucose sensing is not impaired in aneuploid cells compared to euploid cells (three independent images of each condition from two independent experiments). Scale bar, 5 μm.

Supplementary information

Supplementary Information

This file contains Supplementary Methods, Supplementary References, Supplementary Methods Figures 1-8 and a Supplementary Table List.

Reporting Summary

Supplementary Figure 1

This TIF file includes raw, uncropped images of western blots from two experimental replicates in Extended Data Fig. 4f.

Supplementary Tables

This zipped file contains Supplementary Tables 1-9 and a Supplementary Table guide.

Source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tsai, HJ., Nelliat, A.R., Choudhury, M.I. et al. Hypo-osmotic-like stress underlies general cellular defects of aneuploidy. Nature 570, 117–121 (2019). https://doi.org/10.1038/s41586-019-1187-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-019-1187-2

This article is cited by

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.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing