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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Cell. Author manuscript; available in PMC Nov 28, 2009.
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PMCID: PMC2776776

Aneuploidy and polyploidy underlie adaptive evolution of yeast cells deprived of a conserved cytokinesis motor


The ability to evolve is a fundamental feature of biological systems, but the mechanisms underlying this capacity and the evolutionary dynamics of conserved core processes remain elusive. We show here that yeast cells deleted of MYO1, encoding the only myosin-II normally required for cytokinesis, rapidly evolved divergent pathways to restore growth and cytokinesis. The evolved cytokinesis phenotypes correlated with specific changes in the transcriptome. Polyploidy and aneuploidy were common genetic alterations in the best evolved strains, and aneuploidy could account for gene expression changes at levels both correlated with and well beyond chromosome stoichiometry. The phenotypic effect of aneuploidy could be recapitulated with increased copy numbers of specific regulatory genes in myo1Δ cells. These results demonstrate the evolvability of even a well-conserved process and suggest that changes in chromosome stoichiometry provide a source of heritable variation driving the emergence of adaptive phenotypes when the cell division machinery is strongly perturbed.


Evolution is the creative force that shapes life on earth (Darwin, 1859). As such, evolvability, the ability to generate heritable variation to adjust to internal and external changes, should be a fundamental property of all living systems. It has been hypothesized that evolvability may be linked to the robustness and flexibility that characterize complex biological systems (Kirschner and Gerhart, 1998; Wagner and Altenberg, 1996). Whereas recent studies have shed light on the generation of robustness from complexity and redundancy in molecular pathways (Kitano, 2004; Wagner, 2005), it remains poorly understood how evolvability is related to these properties and, in turn, affects the dynamics of biological systems under varying physiological or pathological conditions. Experimental exploration into this relationship may bring fundamental insights into the design principles underlying biological systems and ultimately improve our ability to manipulate these systems for research and therapeutic goals.

Another important question in the understanding of cellular evolvability is the source of the heritable variation that could drive rapid phenotypic changes. At the molecular level, genetic changes can be generated through at least four different mechanisms: 1) point mutations in the nucleotide sequence of coding or non-coding regions; 2) amplification or deletion of chromosomal segments; 3) chromosomal translocation, inversion, and non-homologous end joining; and 4) whole chromosome aneuploidy. It is presently unclear how the different types of genetic changes may be particularly advantageous in evolutionary processes with different time scales, population sizes, and the degrees of the phenotypic leap under specific selective conditions. Another genomic change implicated in evolution is polyploidization, which is thought to facilitate evolution through increased gene dosage, the capacity to mask deleterious mutations, and elevated genomic instability (Otto, 2007).

In this study we have used cytokinesis in the budding yeast Saccharomyces cerevisiae as a model system to gain insights into cellular evolvability. Successful cytokinesis is accomplished through a fully integrated process of cell cycle regulation, spatial patterning, cytoskeletal rearrangements, force production and membrane trafficking (Balasubramanian et al., 2004; Eggert et al., 2006). In yeast and animal cells, cytokinesis relies on membrane constriction along an equatorial furrow, driven by a ring structure consisting of actin filaments, the non-muscle myosin-II and a number of other cytoskeleton-associated proteins. In budding yeast, Myo1, the only myosin-II, is required for ingression of the bud neck membrane and guides the production of a single primary septum by the transmembrane chitin synthase, Chs2 (Schmidt et al., 2002; Tolliday et al., 2003). Despite a high level of conservation, accumulating observations in yeast and other cell types suggest that the cytokinetic machinery may be highly adaptable (Kanada et al., 2005; Nagasaki et al., 2002; Schmidt et al., 2002; Tolliday et al., 2003). Here we took a systematic approach to investigate the evolvability of myo1Δ yeast cells. 45 lines of freshly isolated myo1Δ colonies, which initially divided poorly, were evolved independently over up to 20 passages. Through cell biological analyses, transcriptome profiling, and genome analyses we inquired into the divergent mechanisms that the cells evolved to restore cytokinesis in the absence of myosin-II and into the molecular basis of their emergence. Our results suggest that even conserved core processes can be highly evolvable, and aneuploidy can be a source of heritable variation driving the observed phenotypic evolution. Furthermore, we present evidence that polyploidy serves as a catalyst for this pathway of adaptive evolution.


Rapid adaptive evolution of myo1Δ yeast cells

At the start of the evolution experiment (Figure 1A), we dissected 24 tetrads sporulated from a MYO1/myo1Δ heterozygous diploid strain to isolate fresh myo1Δ haploid spores. After three days of growth, the tetrads exhibited the expected 2:2 segregation for growth between wild type (MYO1) and mutant (myo1Δ) spores (Figure 1B). After seven more days of incubation, 15 out of the 48 myo1Δ spores (31%) gave rise to small colonies, whereas 100% of the MYO1 spores grew to large colonies. Each of the 15 myo1Δ survivor colonies was streaked onto a new plate for growth into isolated colonies (P2 in Figure 1A and D). After three days, the three largest colonies from each plate were selected and streaked onto a new plate. Subsequently, the 45 myo1Δ strains were evolved separately by a repeated process of manually selecting the largest colony, which was then streaked onto a new plate for propagation (Figure 1A). At each passage, we compared size and number of viable colonies and cytokinesis efficiency (see Figure 1C for an example) with those of a wild type strain propagated in parallel. A strain was deemed to have evolved to a steady state when colony growth remained constant for at least four consecutive passages. This experiment ceased after the 20th passage (corresponding to ~400 generations for wild type) and resulted in 29 stably evolved strains (e-strains) with different evolutionary history and a variety of terminal phenotypes (Figure 1D).

Figure 1
Adaptive evolution and phenotypic characterization of myo1Δ e-strains

To obtain a quantitative phenotypic classification of the 29 myo1Δ e-strains, we determined their cytokinesis index (CI), rates of biomass and cell number increase, cell viability and DNA content over a time course of 10 hours. This dataset was subjected to a Principal Components Analysis (PCA). Projection of the phenotypic data in the space defined by the first two principal components, describing >80% of the total variance in the dataset, discriminated all wild type controls (Figure 1E, diamonds) from all the e-strains by a higher cell mortality in the latter. PCA further separated the 29 analyzed e-strains into distinct phenotypic classes. A cluster of ten strains was characterized by higher cytokinesis indexes, biomass increase rates and cell division rates in comparison to the remaining e-strains and were thus referred to as the “10 fittest e-strains” throughout this study (Figure 1E, large dark circles, and Figure S1). Even thought these strains divide and grow well under optimal conditions, competition experiments and growth assays under a variety of conditions showed that they were still less fit than wild-type cells (Figures S2).

Loss of ability to assemble the contractile ring in myo1Δ e-strains

As a first step to characterize the evolved cytokinesis in the e-strains, we analyzed the localization of actin and Chs2, known partners of Myo1 during normal cytokinesis. Both proteins join Myo1 in the cytokinetic ring at the bud neck in late anaphase (Balasubramanian et al., 2004). Compared to wild-type cells, the actin ring and Chs2-GFP ring were observed at drastically reduced frequencies in all ten fittest e-strains (Figure S3). In those cells that did show Chs2 localization the intensity of the signal was often dim and time-lapse microscopy data further showed that these rings often faded without evidence of constriction (Figure S3). Since these e-strains exhibited cytokinesis efficiencies comparable to wild type (Figure S1), these data indicated that it was unlikely that the evolved cytokinesis in the myo1Δ e-strains relied on formation of an actin or Chs2-containing cytokinetic ring.

We further tested if the e-strains retained the ability to assemble a myosin ring at the bud neck by re-introducing Myo1-GFP, carried on a centromeric plasmid under the control of its own promoter, into the wild type and the 10 fittest e-strains. Consistent with published observations, Myo1-GFP localizes at the bud neck in all budded wild-type cells, but most e-strains, with the exception of 12d-1 and 12d-2, exhibited drastically reduced ability to correctly localize Myo1 (Figure 2A–C), further suggesting that the e-strains were unlikely to perform cytokinesis through restoration of the previous cytokinetic apparatus.

Figure 2
Microscopy characterization of cytokinesis in 10 fittest myo1Δ e-strains

The fittest e-strains display three major mechanisms of septum formation

To gain further insights into how cytokinesis is achieved in the absence of MYO1, we carried out thin-section transmission electron microscopy (TEM) to examine septum morphologies of the 10 fittest e-strains. Wild-type cells formed a single, straight primary septum, which developed behind the cleavage furrow intruding symmetrically from opposite sides of the bud neck (Figure 2D). A secondary septum was then deposited along both sides of the primary septum. Four different septum morphologies were observed in dividing cells of myo1Δ e-strains: I) a primary and secondary septum that appeared morphologically similar to those in wild type cells, with only subtle differences such as occasional unilateral formation of a wavy primary septum; II) a single but slightly aberrant primary septum, frequently characterized by cytoplasmic entrapment; III) multiple randomly oriented septa, some of which emanated even from regions distant from the bud neck; IV) thickening of the cell wall around the bud neck without the formation of a primary septum (Figure 2D). Though none of the e-strains homogeneously displayed any single septum morphology, quantification of TEM images showed that one of three morphologies (I, III or IV) was predominant in each e-strain (Figure 2E). Serial sectioning TEM confirmed that the different septum morphologies observed were not simply due to sectioning through different planes of the bud neck (Figure S4).

We next used 3D time-lapse imaging to visualize plasma membrane dynamics during cytokinesis in e-strains representative of all septum morphologies. These e-strains expressed RAS2-GFP, used as a plasma membrane marker (Whistler and Rine, 1997), and GFP-TUB1, to visualize the mitotic spindle and mark cell cycle progression. In wild-type cells, closure of the mother-bud junction is achieved through bilateral invagination of the plasma membrane at the bud neck 4–6 min after spindle breakdown (Figure 2F and Movie S1). Seven time-lapse movies made of 12d-1 strain all showed behaviors consistent septum morphology I, four of which formed monolateral and three bilateral invaginations that extended across the bud neck to complete cytokinesis within 4–8.5 min after spindle breakdown (Figure 2F and Movie S2). Similar membrane closure dynamics were also observed in 7a-2 (Movie S3) and 24d-3 e-strains (Movie S4). Five of 9 movies of 13c-2 (representing morphology III) revealed formation of multiple, unstable invaginations from both sides of (and also far away from) the bud neck (Figure 2F and Movie S5). Cytoplasm separation was accomplished more slowly (6–30 min) than in the wild type. Movies of e-strains 7a-1 and 23a-1 (representing morphology IV) showed that in the majority of cells recorded (3/5 for 7a-1 and 5/6 for 23a-1), there was no evidence of furrowing, but membrane closure was achieved by a slow (19–33 min) inwardly pushing of the plasma membrane (Figure 2F and Movies S6-7), consistent with the idea that cytokinesis was achieved through gradual thickening of the cell wall around the bud neck. These results revealed the presence of three distinct physical mechanisms of membrane closure among the 10 fittest e-strains, each consistent with one of the three major septum morphologies observed by TEM.

Different septum morphologies correlate with distinct patterns of gene expression

To identify molecular pathways underlying the evolved cytokinesis mechanisms, we performed a microarray-based mRNA profiling of the ten fittest e-strains (Supplemental Experimental Procedures and Spreadsheet S1). After filtering the data to remove non-changing genes, a statistical approach was applied to detect gene expression changes that significantly correlated with each of the four septum morphologies (Figure 3A). Gene expression changes detected by microarray analysis were validated by quantitative reverse transcription PCR (qRT-PCR) on 12 genes across all ten fittest e-strains (Figure S5).

Figure 3
Transcriptome changes underlying the evolved phenotypes

FatiScan analysis (Al-Shahrour et al., 2006) was applied to determine enrichment of Gene Ontology (GO) annotations among genes correlated with each of the septum morphologies (Figure 3B and Figure S6). While several GO Biological Process annotation terms that positively correlated with septum morphology I and II included ribosomal biogenesis, septum morphology IV was positively associated with cell wall biogenesis. Few biological processes were found to associate with septum morphology III possibly due to a small number of genes found to correlate with this morphology (data not shown). In addition, manual inspection of the microarray data revealed that both Hsp90 genes (HSP82 and HSC82) were more up-regulated in the e-strains that preferentially divided through morphology I (Figure 3C). Specific correlation of above pathways with septum morphology I and IV was further corroborated by a second analysis applied to the gene expression data of strains 7a-1, 7a-2 and 7a-3, which descended from the same spore but showed divergent septum morphologies (Figure S7).

Functional validation of pathways revealed by microarray analyses through genetic manipulation was challenging due to various genomic changes in the e-strains (see below) and their propensity to undergo adaptive evolution when perturbed. Chemical inhibitors were therefore used to acutely inhibit specific cellular pathways in the e-strains. We first used radicicol, an Hsp90 inhibitor (Roe et al., 1999), to test the requirement for Hsp90 in the growth of the e-strains. Consistent with the correlation of Hsp90 expression with septum morphology I, the five e-strains that displayed predominantly this morphology were hypersensitive to radicicol compared to wild type and the other 5 e-strains favoring morphology III or IV (Figure 3D). Liquid-culture experiments found that radicicol inhibited cytokinesis in the 5 e-strains that predominantly showed septum morphology I (Figure 3E). This provides a functional validation of a specific involvement of Hsp90 in the evolved cytokinesis represented by septum morphology I. Hypersensitivity to cycloheximide, an inhibitor of ribosome function, was also observed among the same group of e-strains (Figure 3F), consistent with a higher requirement for ribosome function in those e-strains. Since cycloheximide strongly inhibited budding and cell cycle progression of these e-strains (data not shown), we were unable to test specifically its effect on cytokinesis. Taken together, above results suggest that distinct cytokinesis mechanisms arose from expression changes of genes defining specific cellular functions.

Adaptive evolution of myo1Δ yeast correlates with and is enhanced by polyploidization

To identify the genetic changes that led to heritable phenotypic variation in the e-strains, we first used flow cytometry to determine the cellular DNA content of the 29 stably evolved myo1Δ e-strains (Figure S8). 20 of the 29 e-strains, including the 10 fittest e-strains, had increased their ploidy from the haploid state to a state ranging from diploid to tetraploid, and the rest remained haploid (Figure 4A). Cytokinesis failure in budding yeast normally results in multiple-budded, multi-nucleate cells, which could account for a ploidy increase. However, ~90% cells in each of the 10 fittest e-strain population were mononucleate (Figure S9), thus these myo1Δ e-strains had undergone mononucleate polyploidization (referred to hereafter as polyploidization). In contrast, 7 of 8 e-strains that were still defective in cytokinesis at the end of the evolution experiment were still haploid, showing that polyploidization was not an obligatory outcome of the cytokinesis defect. Instead, this analysis revealed a strong correlation between polyploidization and restoration of cytokinesis at the end of the evolution (Figure 4B and Table S3).

Figure 4
myo1Δ e-strains are characterized by polyploidy and aneuploidy

The above observation raised the possibility that polyploidization facilitates adaptive evolution of myo1Δ cells. To test this, a MYO1/MYO1/myo1Δ/myo1Δ tetraploid strain was used to generate fresh myo1Δ/myo1Δ diploid cells through sporulation. The adaptive evolution experiment was then repeated, comparing the rate of cytokinesis improvement between 7 myo1Δ/myo1Δ diploid spores and 22 myo1Δ haploid spores. Both haploid and diploid myo1Δ spores initially exhibited 100% cytokinesis defect (Figure 4C), but diploid strains showed significantly faster evolution during early passages (Figure 4C–D). Interestingly, the cytokinesis efficiency of myo1Δ haploid strains did reach that of diploid strains in later passages (Figure 4C), consistent with polyploidization in the haploids during earlier passages.

Whole chromosome aneuploidy occurred in all 10 fittest myo1Δ e-strains

Flow cytometry data indicated that the DNA content of some e-strains was between that of a pure diploid and a pure tetraploid, suggesting the possibility of aneuploidy (Figure S8). Array-based Comparative Genomic Hybridization (aCGH) indicated that all 10 fittest e-strains, but not the wild type strain propagated in parallel, carried at least one chromosome in aneuploidy (Figure 4E). The frequency at which individual chromosomes were observed in aneuploidy was not evenly distributed: some chromosomes (e.g. II, III, XIII and XVI) were in aneuploidy across the majority of the e-strains, while others (i.e. V, VII and XIV) were always found in euploid number. The karyotype of the e-strains was stable during the time frame of this work, as shown by replicate aCGH on three independent cultures of strain 7a-1 and on two independent cultures of strain 7a-2 (data not shown).

The spontaneous rate of chromosome missegregation is related to chromosome size in both yeast (Murray et al., 1986) and vertebrate cells (Spence et al., 2006). However, aneuploidization frequencies in the e-strains were uncorrelated with either chromosome length or chromosomal gene content (Figure S10). In contrast, shared aneuploid chromosomes can be found among e-strains that preferentially divided with the septum morphology (Figure 4F). To further determine the correlation between karyotype and evolved phenotypes, aCGH was carried out on four additional e-strains, each sharing an ancestral spore with one or more of the 10 fittest e-strains but resulting in less fit evolved phenotypes. In every case, the poorly evolved e-strains exhibited a drastically different aneuploidy pattern from their well evolved relatives (Figure S11).

Besides the expected deletion of the MYO1 locus and the presence of the TRP1 marker used for generating the deletion, close inspection of the aCGH data across all the chromosomes indicated absence of sub-chromosomal amplifications or deletions in all ten fittest myo1Δ e-strains (Figure 4E). The only local changes in hybridization intensity mapped exclusively to highly repetitive sequences, such as transposons and telomeric repeats (Figure 4E), for which aCGH data cannot be interpreted (Pinkel and Albertson, 2005). Pulse-field gel electrophoresis revealed no gross chromosomal rearrangements or non-homologous end joining (Figure S12).

Identification of point mutations in e-strains by Illumina whole-genome re-sequencing

Phenotypic adaptation during experimental evolution can often be attributed to point mutations in DNA sequences that result in changes in protein activities or expression levels (Elena and Lenski, 2003). However, given the fact that all of the e-strains have become polyploid and aneuploid, it would be difficult to identify mutations in these strains by commonly used genetic mapping techniques. We therefore used the Illumina sequencing system to obtain full genomic sequences from five strains: the parental MYO1/myo1Δ diploid, the wild type strain at passage 1 and at passage 20, and two e-strains, 7a-1 and 7a-2, which exhibited the same basal ploidy, similar but non-identical karyotypes, and different predominant septum morphologies.

For all five strains we obtained high genome coverage, high sequencing depth and quality, within or above the expected performance of this technology (Tables S4 and S5). Within the non-repetitive genome, 820 single nucleotide polymorphisms (SNPs) were called as homozygous mutations in the parental diploid strain, compared to the published yeast genome sequence (Spreadsheet S2). 803 (~98%) of these SNPs were also identified in 3 of the 4 remaining strains and were therefore likely to be true mutations present in the parental genetic background. Of the 33 SNPs predicted in the parental diploid strain as heterozygous, 23 (~70%) were also detected in either the wild type haploid strains or the 7a e-strains or both (Spreadsheet S2). Six of these predicted heterozygous mutations were validated by Sanger sequencing (data not shown), further confirming the ability of this technology to identify heterozygous mutations.

Nine and twelve mutations were predicted to be unique to 7a-1 and 7a-2, respectively. These SNPs, if confirmed, could contribute to phenotypic evolution; however, SNPs predicted by a high-throughput sequencing technology to be unique to a single strain are more prone to bear false positives compared to SNPs consistently identified across several strains. Indeed, Sanger sequencing showed that all 9 mutations predicted to be unique to e-strain 7a-1 were false positives, whereas only a single mutation was confirmed in e-strain 7a-2. This mutation mapped to the coding sequence of YGR130C, encoding a protein of unknown function, and causes a lysine to asparagine change that was predicted by the SIFT software (Ng and Henikoff, 2001) not to affect protein function (Spreadsheet S2). Taken together, within the limitation of available technology, current evidence has not indicated point mutations to be the primary genetic changes accounting for the divergent cytokinesis mechanisms observed in myo1Δ e-strains.

Aneuploidy affects gene expression at and beyond the chromosomal average level

Above findings left us with the unexpected possibility that aneuploidy, observed in all e-strains, was the primary genetic change that could account for the heritable phenotypic variation. To test this, we first asked whether aneuploidy could account for the global transcriptome changes that occurred in the myo1Δ e-strains. Consistent with a global effect of aneuploidy on transcriptome, the two pairs of e-strains with identical karyotypes (i.e. 7a-2/7a-3 and 23a-1/23b-1) displayed the highest overall transcriptome similarity: the microarray data of these pairs were in fact as similar to each other as any two biological replicates of a same strain (Figure S13). Previous studies showed that genes encoded on extranumerary chromosomes tend to be more highly expressed than genes on euploid chromosomes (Hughes et al., 2000; Torres et al., 2007). Above observation was confirmed by directly comparing our aCGH data with our microarray gene expression data in the myo1Δ e-strains (Figure 5A). In addition, there was a direct proportionality between chromosome average gene expression change and the chromosome copy number stoichiometry (Figure 5B): i) gaining two extra copies of a given chromosome produced a chromosomal average expression change roughly twice as high as that caused by gaining one extra copy; and ii) gaining an extra chromosome in a triploid background had a smaller effect on the chromosomal average gene expression level than gaining the same chromosome in a diploid. These data demonstrate that chromosome stoichiometry quantitatively determined the chromosomal average level of gene expression.

Figure 5
Effects of aneuploidy on gene expression

Besides the above effect, careful inspection of gene-specific expression changes in each e-strain revealed the presence of genes whose expression change deviated considerably from the chromosome average change (Figure 5C). Whereas most genes indeed changed their expression within one standard deviation from the chromosome average change (referred to as inlier genes), a small number of genes changed their expression level more than three standard deviations away from the chromosome average change (referred to as outlier genes). This small number was nonetheless significantly higher than the number expected by chance (Figure S14).

Since a subset of the outlier genes encompassed some of the genes identified to be correlated with specific cytokinesis phenotypes (Spreadsheet S1), we investigated if and how aneuploidy might be able to cause such gene expression changes. Two possibilities were considered (Figure 5D). First, karyotype changes could directly give rise to outlier gene expression changes, if some genes were intrinsically more sensitive or more resistant to gene copy number changes. If this was correct, outlier genes should be preferentially located on aneuploid chromosomes; however, they were found to be evenly distributed across the whole genome (Figure 5E). The second possibility was that outlier gene expression changes might be an indirect consequence of karyotype changes, mediated by inlier gene expression changes. In support of this hypothesis, e-strains with similar inlier gene expression also displayed similar outlier gene expression and vice versa, indicating that outlier gene expression changes correlated with inlier gene expression changes (Figure 5F).

A possible explanation for this observation is that altered expression of transcription factors (TFs) due to their gene location on aneuploid chromosomes could cause expression changes of target genes not necessarily encoded on aneuploid chromosomes. To test this, we took advantage of a recently published yeast transcriptional regulatory network, reconstructed based on functional identification of direct and indirect targets of 268 yeast TFs (Hu et al., 2007). Although the genome coverage of this network is incomplete, 2570 (~40%) of yeast genes are represented as potential targets of the analyzed TFs (P ≤ 0.001). Moreover, both the TFs and their targets are evenly distributed across all chromosomes (Figure S15). Based on this data, we found that 43–78% of the outlier genes found in the myo1Δ e-strains were downstream targets of TFs encoded on aneuploid chromosomes (Figure 5G, black bars). In contrast, only 8–31% of non-outlier genes had predicted TFs on aneuploid chromosomes (Figure 5G, white bars). This indicates that a specific pattern of aneuploidy might be sufficient to predict gene expression changes both at and beyond chromosome average levels. Interestingly, a specific pattern of aneuploidy was not required to cause a specific set of outlier genes. In fact, for a given set of outlier genes in an e-strain, their upstream regulators were not biased toward those carried on aneuploid chromosomes in that particular e-strain (Figure 5H). This implies that similar patterns of outlier gene expression may be achieved through different patterns of aneuploidy, supporting the observation that e-strains with different karyotypes can converge to a similar cytokinesis phenotype.

Phenocopying myo1Δ e-strains by mimicking gene-dosage increase due to aneuploidy

To experimentally test the hypothesis that aneuploidy can contribute to phenotypic evolution, we extended the transcription network analysis to specific target genes implicated in achieving septum morphology I or IV. First, we analyzed how aneuploidy could contribute to expression changes in the Hsp90 genes, which correlated with septum morphology I. HSC82 and HSP82 are located on chromosome XIII and XVI, respectively. Either or both of these chromosomes were gained in all five e-strains that favored morphology I (Figure 4E–F), suggesting that aneuploidy could directly account for increased expression of Hsp90 in these strains. Note that HSC82 and HSP82 share ~97% sequence identity and it is therefore challenging to design microarray probes that would clearly distinguish the two isoforms without any cross-hybridization. This might explain why strain 24d-3 also showed increased expression of HSC82 even though chromosome XIII was in euploid number.

On the other hand, septum morphology IV was associated with up-regulation of genes involved in cell wall biogenesis (Figure 3B), consistent with the slow closure of the mother-bud junction through cell wall thickening (Figure 2D and 2F). We noticed that in e-strains 23a-1 and 23b-1, which show the highest preference for morphology IV (Figure 2E), chromosome XVI was the only aneuploid chromosome. Moreover, chromosome XVI gain was the only aneuploidy shared among all e-strains preferentially dividing by morphology IV (Figure 4F). E-strains 23a-1 and 23b-1 shared 58 outlier genes, 11 of which are known targets of RLM1, a TF encoded on chromosome XVI and implicated in cell wall remodeling. Most of these 11 RLM1 targets encode either cell wall proteins or proteins involved in cell wall biogenesis. Microarray and qRT-PCR analyses showed that 9 of these 11 genes were up-regulated in 23a-1 and 23b-1 up to 16 fold compared to wild type (Figure 6A). In addition, a signaling molecule that positively regulates Rlm1, the MAP kinase kinase Mkk2 (Levin, 2005), is also encoded on chromosome XVI. These observations provided an opportunity to test if modulating copy numbers of specific genes on chromosome XVI, such as RLM1 and MKK2, could bring about the evolved phenotype displayed by e-strains 23a-1 and 23b-1.

Figure 6
Effects of increased copy numbers of RLM1 and MKK2 in myo1Δ cells

We introduced two extra copies of RLM1 and of MKK2 into the MYO1/myo1Δ heterozygous diploid strain at the RLM1 and MKK2 locus, respectively. After sporulation, tetrads were dissected and spores were genotyped retrospectively after the colonies grew up. The genotype of most dead spores could be inferred assuming Mendelian segregation. First, we observed that gaining both genes, but not either gene alone, significantly improved the initial spore viability (Figure 6B). Assessment at passage 1 confirmed that gaining both genes led to significantly improved cytokinesis compared to control myo1Δ spores (Figure 6C). The morphology of cytokinesis in the resulting strains was subsequently analyzed by TEM (Figure 6D). We found that whereas among myo1Δ spores <50% of the cells exhibited septum morphology IV, this morphology was the predominant phenotype (~75%) among myo1Δ cells carrying extra copies of RLM1 and MKK2 (Figure 6E). These observations indicated that gaining extra copies of RLM1 and MKK2 not only improved the initial survival and cytokinesis but also biased the phenotype toward morphology IV.


Deletion of a conserved core component leads to divergent adaptive evolution

It has been postulated that the evolvability of highly conserved core processes may be limited due to constraints resulting from their strong internal linkages (Kirschner and Gerhart, 1998; Wagner, 2005). Our results suggest that, under certain circumstances, even a core process can be highly evolvable when a major structural component is eliminated entirely. The majority of the fittest myo1Δ e-strains appeared to show diminished ability to correctly localize F-actin and Chs2, normal partners of Myo1, or even Myo1 itself, when it was reintroduced. This suggests that the evolved cytokinesis mechanisms were unlikely to result from a simple repair of the broken cytokinetic machinery. Instead, characterization through EM and time-lapse 3D microscopy identified three major modes of cell division, at least two of which were morphologically distinct from that occurring in wild-type budding yeast. This study might thus reveal an example where adaptive evolution is achieved by abandoning a damaged functional module and accomplishing the same task through creative tinkering with other modules.

An open question is whether the evolved mechanisms of cytokinesis were pre-existing as back-up pathways or were invented de novo. Septum morphology IV was observed even in 5% of wild-type cells, suggesting that this could be a pre-existing but poorly utilized pathway; however, the majority of the evolved strains did not resort to this mechanism. Morphology I, and to a much lesser degree, morphology III, showed some resemblance to wild-type cytokinesis because dynamic membrane furrowing was observed. Even though the contractile machinery could not have driven these furrows, membrane-based processes, such as local delivery or synthesis of new membranes (Balasubramanian et al., 2004), which normally contribute to cleavage furrow progression and closure, could have been enhanced or altered to accomplish the alternative mechanisms of cytokinesis.

Divergent evolution of cytokinesis correlates with broad but specific changes in gene expression

Gene expression profiling has provided some initial clues as to how molecular networks might have been altered to bring about the evolved phenotypes. An up-regulation of cell wall genes is a logical explanation for septum morphology IV, characterized by cell wall thickening, but how increased expression of Hsp90 genes and genes involved in ribosome biogenesis might contribute to type I cytokinesis remains unknown. Hsp90 chaperones facilitate evolutionary processes by buffering genetic variation, hence allowing robust and uniform phenotypic expression under distinct selective conditions (Rutherford and Lindquist, 1998). In addition to this general role, the increased requirement of Hsp90 activity in a specific set of evolved myo1Δ e-strains suggests that Hsp90 may contribute directly to type I cytokinesis. A recent study showed that Hsp90 has diverse targets in membrane trafficking (McClellan et al., 2007), which could contribute to the growth of a membrane furrow.

Morphology I also correlated with an increase in the expression of genes involved in ribosome biogenesis. A previous study showed that up-regulation of stress response genes and genes encoding ribosomal proteins are common to yeast strains carrying aneuploid chromosomes (Torres et al., 2007). However, these classes of genes were not commonly up-regulated across the ten fittest e-strains (Figure S16) and ribosome biogenesis genes were specifically up-regulated in e-strains preferring type I cytokinesis (Figures 3B, S6 and S7). The discrepancy between the two studies might be the result of adaption to aneuploidy-induced stress, which might have occurred during the evolutionary process. Intriguingly, mutations in some ribosome biogenesis genes correlated with septum morphology I or II (i.e. NOP15, PWP2 or SDA1) have previously been shown to cause cytokinesis defects in budding yeast (Buscemi et al., 2000; Oeffinger and Tollervey, 2003; Shafaatian et al., 1996) (Figure S5 and Spreadsheet S1). Studies in other organisms also reported that depletion of certain ribosome proteins resulted in cytokinesis failure (Eggert et al., 2004; Squirrell et al., 2006; Wilker et al., 2007). How these proteins participate in normal and evolved cytokinesis remains to be elucidated, but their implication further highlights the complexity of the cell division system and provides a glimpse of how this complexity contributes to the system’s evolvability.

Aneuploidy as a source of heritable variation in adaptive evolution of myo1Δ cells

All our experimental data has pointed to the conclusion that aneuploidy is a main driving force for the observed adaptive evolution. One line of support came from a lack of evidence for other possible mechanisms. In contrast to what observed during adaptive evolution of budding yeast to glucose-limiting conditions (Dunham et al., 2002), aCGH and PFGE did not reveal any segmental amplifications or deletions, chromosome breakages or non-homologous end joining in the 10 fittest e-strains. Whole genome re-sequencing of two e-strains, 7a-1 and 7a-2, which descended from the same ancestor spore but evolved divergent cytokinesis mechanisms, did not find any unique SNPs that might underlie the divergent cytokinesis phenotypes. It is important to stress, however, that above conclusions are drawn within the limitations of current genome analysis tools.

The positive line of evidence came from the observed links from phenotype to gene expression, and from gene expression to karyotype. For example, while the five e-strains favoring type I cytokinesis showed an elevated requirement of Hsp90 activity for efficient cytokinesis (Figure 3C–E), one or both of the Hsp90 genes were present on aneuploid chromosomes in each of these e-strains (Figure 4F). Globally, aneuploidy not only imposed a direct effect on average gene expression along affected chromosomes but could also account for the expression of genes that were several standard deviations away from the chromosomal average change. This may be due to synergistic effects of relatively small changes in gene expression of certain regulatory proteins (e.g. TFs or signaling molecules) encoded directly on aneuploid chromosomes. A functional validation of this idea came from the experiment where increased copy numbers of a TF (Rlm1) and a kinase (Mkk2), which control the expression of genes associated with septum morphology IV and are present on an aneuploid chromosome shared among strains favoring this morphology, led to significantly improved cytokinesis with primarily the same morphology.

The neutral or near-neutral theory of molecular evolution assumes the collective benefit of slowly accumulating, individually neutral, small-effect mutations (Gillespie, 1984; Ohta, 2002). However, this assumption imposes a limit on the size of the leap on the fitness landscape that can be achieved with each mutation event. Theoretically, large populations may be able to cross large fitness valleys by simultaneous fixation of jointly beneficial mutations (Carter and Wagner, 2002; Weinreich and Chao, 2005), but this does not apply to the present experimental design, which involved small population sizes. Aneuploidy, on the other hand, alters the expression of many genes by a small extent (direct effect) and a few genes by a large extent (indirect effect) and can thus be viewed as a large-effect mutation. Given the presence of 16 chromosomes in yeast, if each chromosome is allowed to vary between 1 to 4 copies, there are in theory billions of possible aneuploid karyotypes, causing a large variation in gene expression stoichiometry and regulation, thus creating the opportunity for the emergence of adaptive phenotypes. This means of achieving phenotypic variation may be particularly effective for evolutionary processes that rely on rapid improvement of fitness and small population sizes.

Polyploidy as a catalyst and buffer for adaptive evolution through aneuploidy

There is strong evidence that polyploidization occurred once or multiple times during the evolution of vertebrates, plants, fungi and ciliates, and gave rise to species-rich groups (Aury et al., 2006; Brunet et al., 2006; Dehal and Boore, 2005; Otto and Whitton, 2000; Wolfe and Shields, 1997). However, these evolutionary processes occurred on very long time scales. In this work, we found that polyploidization occurred and correlated strongly with the evolvability of myo1Δ cells, which were allowed to evolve on time scales negligible compared to speciation. These results suggest that polyploidy can be a catalyst for the observed experimental evolution through aneuploidy, consistent with a recent work showing that higher ploidy results in higher chromosome missegregation rates in budding yeast (Storchova et al., 2006). In addition, polyploidy could fine-tune the incremental changes in gene expression by aneuploidy (Figure 5B), not only allowing higher precision in exploring the fitness landscape but possibly also buffering deleterious effects due to imbalances in gene expression.

Model and perspective

Our working model on the adaptive evolution of myo1Δ yeast cells is depicted in Figure 7. Cytokinesis failure due to lack of MYO1 creates a strong selective pressure, setting the stage for the emergence of adaptive phenotypes. Even though cytokinesis failure in budding yeast results in multi-nucleate cells rather than mononucleate polyploids, polyploidization might result from either nuclear fusion or endomitosis. Polyploidization could subsequently catalyze and buffer aneuploidy, as discussed above. Aneuploid yeast cells are known to have reduced fitness compared to wild type cells (Torres et al., 2007), but under the strong selective conditions set by MYO1 deletion any fitness improvement caused by karyotype-driven gene expression changes could lead to positive selection of aneuploid clones. It is important to emphasize that this model does not rule out contributions from other types of genetic aberrations, such as point mutations, which could fine-tune the large leaps of change caused by aneuploidy.

Figure 7
Conceptual model of myo1Δ adaptive evolution

An important question to ask is whether this adaptive evolution pathway may be applied to selective conditions other than a cytokinesis failure. It is reasonable to propose that blockage at any point during the cell cycle after genome duplication could lead to the same path of adaptive evolution, since failure in any of the mitotic events can potentially result in aneuploidy and/or polyploidy. In this regard, the findings of this study may be particularly relevant to approaches in cancer chemotherapy, which often involves inhibitors that impair mitotic processes. Whereas the goal of these treatments is to block cell division, a potentially undesirable result is enhanced rates of polyploidy and aneuploidy, which could stimulate cancer somatic evolution to escape the inhibitory effects and develop drug resistance. In addition to cell division failure, various stress conditions may also trigger polyploidization (Storchova and Pellman, 2004), which would then promote rapid adaptive evolution through aneuploidy.

Experimental Procedures

Yeast culture and genetic manipulations

Yeast cell culture and genetic manipulations were performed as described (Sherman et al., 1974). All yeast strains were derivatives of the S288c background (Table S1). Unless otherwise stated, all experiments were performed on exponentially growing cultures in YEPD at 23 °C. A BamH1 fragment containing the MKK2 coding region plus ~500 bp of upstream and ~285 bp of downstream sequence was cloned into the BamH1 site of pRS305. The resulting pGR78 was integrated at the MKK2 locus by Stu1 digestion. An Xba1 fragment containing the RLM1 coding region plus ~450 bp of upstream and ~250 bp of downstream sequence was cloned into the Xba1 site of pRS306. The resulting pGR55 was integrated at RLM1 locus by Hpa1 digestion. Double integration was confirmed by Southern analysis. Drop test analyses are described in Supplemental Experimental Procedures.

Quantitative characterization of stable e-strains

Samples of the e-strains were collected from OD600=0.1 (~1.6×106 cells/ml) exponentially growing cultures in YEPD at 23 °C over a time-course of 10 h. Zymolyase treatment to assess the cytokinesis index was performed as described (Tolliday et al., 2003). Cytokinesis index was defined as the ratio of the number of cell clusters over the number of cell bodies (buds or mothers). Analysis of DNA content was performed as described (Camahort et al., 2007) using a CyAn ADP flow cytometer (Dako Cytomation). Biomass increase rate was assessed using a spectrophotometer (SmartSpec 3000, BioRad) reading at 600nm. Cell division rate and cell viability were determined by counting propidium iodide-stained (10μg/ml) live cells on a Coulter Counter (Cell Lab Quanta SC, Beckman Coulter) using an excitation wavelength of λ=488nm. Data analysis was performed using Cell Lab Quanta SC MPL Analysis software (Beckman Coulter).

Electron and fluorescence microscopy

Logarithmically growing cells were harvested and processed for EM as described (Tolliday et al., 2003). Serial sectioning EM was performed on 80nm thick serially cut sections. EM micrographs were acquired at J.E.O.L. 100CXII TEM or at FEI Technai Spirit Biotwin using a Gatan Model 894 US1000 (2K × 2K) camera. Staining of cells with rhodamine phalloidin (Molecular Probes) and 4′-6-Diamidino-2-phenylindole (DAPI) were performed as described (Adams and Pringle, 1991). For live cell imaging, relevant strains were grown logarithmically in appropriate synthetic complete media and imaged using a GFP filter set (Chroma Technology). Data on Chs2-GFP and Myo1-GFP were acquired on a wide-field fluorescence microscope (Nikon E1000), and images acquired with a Hamamatsu ORCA ER c4742-80 camera. Time-lapse imaging of Ras2-GFP was performed on a spinning disk confocal microscope (Yokagawa spinning disk with a Hamamatsu EM-CCD camera), Z-series of 0.2μm optical sections. All time-lapse images were acquired every 30 sec using MetaMorph software (Molecular Devices). Image data analysis is described in Supplemental Experimental Procedures.

Expression profiling and genome analyses

Details on gene expression microarray, qRT-PCR and aCGH experiments and whole-genome re-sequencing can be found in Supplemental Experimental Procedures.

Statistical Analysis

All statistical analyses were performed in the R environment (Ihaka and Gentleman, 1996) using standard packages and custom scripts.

Supplementary Material











Authors thank W. Bosl, O. Rando and B. Rubinstein for stimulating discussions, J. Haug and J. Wunderlich for technical assistance with flow cytometry, F. Guo for technical assistance with electron microscopy, K. Zueckert-Gaudenz and A. Peak for suggestions and assistance in the preparation of genomic DNA samples and in microarray hybridizations, G. Hattem for help with bioinformatics analysis, A. Paulson for assistance in microarray data submission to public repositories, P. Baumann and X. Wang for suggestions and assistance with pulse-field gel electrophoresis, G. Chen for help with strain construction, T. Nichols and M. Toth for media preparation and laboratory management, J. Rine for plasmids, and A. Murray, B. Slaughter and S. Wai for critical reading of the manuscript. This paper is dedicated to the memory of Karen A. Pavelka. This work was supported by NIH GM058864 to RL.


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