Aneuploidy, referring here to genome contents characterized by abnormal numbers of chromosomes, has been associated with developmental defects, cancer and adaptive evolution in experimental organisms1,2,3,4,5,6,7,8,9. 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.
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Microarray data are deposited in ArrayExpress under accession numbers E-MTAB-318 and E-MTAB-325. Sequencing data are deposited in the NCBI SRA database under accession number SRP003582.
We thank C. W. Seidel for assistance with microarray data analysis, B. Fleharty and A. Peak for technical assistance with microarray hybridization, A. Perera and K. Walton for assistance in genome resequencing, W. McDowell for technical assistance with qPCR, J. Haug for technical support with flow cytometry experiments, G. Chen for technical suggestions, and A. Paulson for assistance with the submission of microarray and sequencing data to public repositories. This work was performed to fulfil, in part, requirements for J. Zhu’s PhD thesis research as a student registered with the Open University. This work was supported by NIH grant RO1GM059964 to R.L.
This file contains detailed peptide and spectral counts for proteins detected by MudPIT analysis of whole-cell lysates from Saccharomyces cerevisiae strains with different chromosome copy numbers.
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International Journal of Cancer (2019)