Aneuploidy confers quantitative proteome changes and phenotypic variation in budding yeast

Abstract

Aneuploidy, referring here to genome contents characterized by abnormal numbers of chromosomes, has been associated with developmental defects, cancer and adaptive evolution in experimental organisms. However, it remains unresolved how aneuploidy impacts gene expression and whether aneuploidy could directly bring about phenotypic variation and improved fitness over that of euploid counterparts. Here we show, using quantitative mass spectrometry-based proteomics and phenotypic profiling, that levels of protein expression in aneuploid yeast strains largely scale with chromosome copy numbers, following the same trend as that observed for the transcriptome, and that aneuploidy confers diverse phenotypes. We designed a novel scheme to generate, through random meiotic segregation, 38 stable and fully isogenic aneuploid yeast strains with distinct karyotypes and genome contents between 1N and 3N without involving any genetic selection. Through quantitative growth assays under various conditions or in the presence of a panel of chemotherapeutic or antifungal drugs, we found that some aneuploid strains grew significantly better than euploid control strains under conditions suboptimal for the latter. These results provide strong evidence that aneuploidy directly affects gene expression at both the transcriptome and proteome levels and can generate significant phenotypic variation that could bring about fitness gains under diverse conditions. Our findings suggest that the fitness ranking between euploid and aneuploid cells is dependent on context and karyotype, providing the basis for the notion that aneuploidy can directly underlie phenotypic evolution and cellular adaptation.

Figure 1. Generation of aneuploid yeast strains

(a) Sporulation of a homozygous triploid strain followed by karyotype stability tests of the meiotic progenies. (b–f) Karyotypes of the five aneuploid strains used in Fig. 3, determined by qPCR (white bars; mean ± s.d.) and aCGH (black bars). (g) Distribution of aneuploid chromosomes; p-values were calculated from a binomial distribution; the horizontal line represents the expectation number assuming uniform representation (h) Karyotype (total chromosome number) distribution across the aneuploid strain collection. Black lines: observed distribution (binned every two chromosomes); blue and red dashed lines: expected binomial distributions from random homolog segregation during triploid and pentaploid sporulation, respectively.

Figure 2. Phenotypic profiling of aneuploid strains

(a–b, e–h) Representative images (left) and growth curves (right) under indicated conditions. U1–U3: haploid, diploid and triploid euploid control strains, respectively. (c) Strain positions. A1–A38: aneuploid strains (see Supplementary Figure 4 for their karyotype). P1–4: four petite strains not further studied. (d) One-copy number increase of ATR1 is required and sufficient to confer resistance to 0.4μg/ml 4-NQO. (i) Clustering of strains based on karyotypic similarity. White: euploid chromosome number; red: gain over euploid number; blue: chromosome loss. (j) Clustering of conditions used in phenotypic profiling based on the fitness relative to U1. White: growth similar to U1; red: fitness gain over U1; blue: fitness loss. The strains were ordered as in (i). Scale bar applies to both (i) and (j). Analysis details in Supplementary Methods.

Figure 3. Effects of aneuploidy on the proteome

(a–c) Heat maps of chromosome stoichiometry (a, aCGH data, Fig. 1b–f), average mRNA level (b, microarray data) and average protein level (c, proteomics data; see Supplementary Fig. 9) per chromosome of the five aneuploid strains compared to U1. (d) A correlation between protein expression and gene expression changes relative to haploid euploid strain U1 (see Supplementary Fig. 12). Outlier mRNAs and proteins (defined as in Supplementary Information) are highlighted in red and blue, respectively. (e–f) Subset of GO-Slim analysis applied to outlier genes from microarray (e) and proteomics (f) datasets (see Supplementary Methods for details). Complete results in Supplementary Fig. 12a–b. P-values were calculated from hypergeometric tests.