Epistasis, aneuploidy, and functional mutations underlie evolution of resistance to induced microtubule depolymerization
2021; Springer Nature; Volume: 40; Issue: 22 Linguagem: Inglês
10.15252/embj.2021108225
ISSN1460-2075
AutoresMattia Pavani, Paolo Bonaiuti, Elena Chiroli, Fridolin Groß, Federica Natali, Francesca Macaluso, Ádám Póti, Sebastiano Pasqualato, Zoltán Farkas, Simone Pompei, Marco Cosentino Lagomarsino, Giulia Rancati, Dávid Szüts, Andrea Ciliberto,
Tópico(s)Epigenetics and DNA Methylation
ResumoArticle4 October 2021free access Transparent process Epistasis, aneuploidy, and functional mutations underlie evolution of resistance to induced microtubule depolymerization Mattia Pavani Mattia Pavani orcid.org/0000-0003-1652-0038 IFOM, The Firc Institute of Molecular Oncology, Milano, Italy These authors contributed equally to this work Search for more papers by this author Paolo Bonaiuti Paolo Bonaiuti orcid.org/0000-0002-4446-9682 IFOM, The Firc Institute of Molecular Oncology, Milano, Italy These authors contributed equally to this work Search for more papers by this author Elena Chiroli Elena Chiroli orcid.org/0000-0001-8668-0657 IFOM, The Firc Institute of Molecular Oncology, Milano, Italy Search for more papers by this author Fridolin Gross Fridolin Gross orcid.org/0000-0003-4964-8062 IFOM, The Firc Institute of Molecular Oncology, Milano, Italy Search for more papers by this author Federica Natali Federica Natali orcid.org/0000-0001-7388-4570 Institute of Medical Biology (IMB), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore Search for more papers by this author Francesca Macaluso Francesca Macaluso orcid.org/0000-0002-1924-3761 IFOM, The Firc Institute of Molecular Oncology, Milano, Italy Search for more papers by this author Ádám Póti Ádám Póti Institute of Enzymology, Research Centre for Natural Sciences, Budapest, Hungary Search for more papers by this author Sebastiano Pasqualato Sebastiano Pasqualato orcid.org/0000-0002-9038-7768 IEO, European Institute of Oncology IRCCS, Milan, Italy Human Technopole, Milano, Italy Search for more papers by this author Zoltán Farkas Zoltán Farkas orcid.org/0000-0002-5085-3306 Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Szeged, Hungary Search for more papers by this author Simone Pompei Simone Pompei orcid.org/0000-0002-6673-7991 IFOM, The Firc Institute of Molecular Oncology, Milano, Italy Search for more papers by this author Marco Cosentino Lagomarsino Marco Cosentino Lagomarsino orcid.org/0000-0003-0235-0445 IFOM, The Firc Institute of Molecular Oncology, Milano, Italy Search for more papers by this author Giulia Rancati Giulia Rancati orcid.org/0000-0003-0835-0139 Institute of Medical Biology (IMB), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore Search for more papers by this author Dávid Szüts Dávid Szüts orcid.org/0000-0001-7985-0136 Institute of Enzymology, Research Centre for Natural Sciences, Budapest, Hungary Search for more papers by this author Andrea Ciliberto Corresponding Author Andrea Ciliberto [email protected] orcid.org/0000-0001-6078-8600 IFOM, The Firc Institute of Molecular Oncology, Milano, Italy Istituto di Genetica Molecolare, Consiglio Nazionale delle Ricerche (IGM-CNR), Pavia, Italy Search for more papers by this author Mattia Pavani Mattia Pavani orcid.org/0000-0003-1652-0038 IFOM, The Firc Institute of Molecular Oncology, Milano, Italy These authors contributed equally to this work Search for more papers by this author Paolo Bonaiuti Paolo Bonaiuti orcid.org/0000-0002-4446-9682 IFOM, The Firc Institute of Molecular Oncology, Milano, Italy These authors contributed equally to this work Search for more papers by this author Elena Chiroli Elena Chiroli orcid.org/0000-0001-8668-0657 IFOM, The Firc Institute of Molecular Oncology, Milano, Italy Search for more papers by this author Fridolin Gross Fridolin Gross orcid.org/0000-0003-4964-8062 IFOM, The Firc Institute of Molecular Oncology, Milano, Italy Search for more papers by this author Federica Natali Federica Natali orcid.org/0000-0001-7388-4570 Institute of Medical Biology (IMB), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore Search for more papers by this author Francesca Macaluso Francesca Macaluso orcid.org/0000-0002-1924-3761 IFOM, The Firc Institute of Molecular Oncology, Milano, Italy Search for more papers by this author Ádám Póti Ádám Póti Institute of Enzymology, Research Centre for Natural Sciences, Budapest, Hungary Search for more papers by this author Sebastiano Pasqualato Sebastiano Pasqualato orcid.org/0000-0002-9038-7768 IEO, European Institute of Oncology IRCCS, Milan, Italy Human Technopole, Milano, Italy Search for more papers by this author Zoltán Farkas Zoltán Farkas orcid.org/0000-0002-5085-3306 Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Szeged, Hungary Search for more papers by this author Simone Pompei Simone Pompei orcid.org/0000-0002-6673-7991 IFOM, The Firc Institute of Molecular Oncology, Milano, Italy Search for more papers by this author Marco Cosentino Lagomarsino Marco Cosentino Lagomarsino orcid.org/0000-0003-0235-0445 IFOM, The Firc Institute of Molecular Oncology, Milano, Italy Search for more papers by this author Giulia Rancati Giulia Rancati orcid.org/0000-0003-0835-0139 Institute of Medical Biology (IMB), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore Search for more papers by this author Dávid Szüts Dávid Szüts orcid.org/0000-0001-7985-0136 Institute of Enzymology, Research Centre for Natural Sciences, Budapest, Hungary Search for more papers by this author Andrea Ciliberto Corresponding Author Andrea Ciliberto [email protected] orcid.org/0000-0001-6078-8600 IFOM, The Firc Institute of Molecular Oncology, Milano, Italy Istituto di Genetica Molecolare, Consiglio Nazionale delle Ricerche (IGM-CNR), Pavia, Italy Search for more papers by this author Author Information Mattia Pavani1, Paolo Bonaiuti1, Elena Chiroli1, Fridolin Gross1, Federica Natali2, Francesca Macaluso1, Ádám Póti3, Sebastiano Pasqualato4,5, Zoltán Farkas6, Simone Pompei1, Marco Cosentino Lagomarsino1, Giulia Rancati2, Dávid Szüts3 and Andrea Ciliberto *,1,7 1IFOM, The Firc Institute of Molecular Oncology, Milano, Italy 2Institute of Medical Biology (IMB), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore 3Institute of Enzymology, Research Centre for Natural Sciences, Budapest, Hungary 4IEO, European Institute of Oncology IRCCS, Milan, Italy 5Human Technopole, Milano, Italy 6Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Szeged, Hungary 7Istituto di Genetica Molecolare, Consiglio Nazionale delle Ricerche (IGM-CNR), Pavia, Italy *Corresponding author. Tel: +39 02574303253; E-mail: [email protected] The EMBO Journal (2021)40:e108225https://doi.org/10.15252/embj.2021108225 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Cells with blocked microtubule polymerization are delayed in mitosis, but eventually manage to proliferate despite substantial chromosome missegregation. While several studies have analyzed the first cell division after microtubule depolymerization, we have asked how cells cope long-term with microtubule impairment. We allowed 24 clonal populations of yeast cells with beta-tubulin mutations preventing proper microtubule polymerization, to evolve for ˜150 generations. At the end of the laboratory evolution experiment, cells had regained the ability to form microtubules and were less sensitive to microtubule-depolymerizing drugs. Whole-genome sequencing identified recurrently mutated genes, in particular for tubulins and kinesins, as well as pervasive duplication of chromosome VIII. Recreating these mutations and chromosome VIII disomy prior to evolution confirmed that they allow cells to compensate for the original mutation in beta-tubulin. Most of the identified mutations did not abolish function, but rather restored microtubule functionality. Analysis of the temporal order of resistance development in independent populations repeatedly revealed the same series of events: disomy of chromosome VIII followed by a single additional adaptive mutation in either tubulins or kinesins. Since tubulins are highly conserved among eukaryotes, our results have implications for understanding resistance to microtubule-targeting drugs widely used in cancer therapy. SYNOPSIS Cells with blocked microtubule polymerization undergo massive, death-inducing chromosome missegregation, but may eventually restore microtubule functionality. Here, yeast laboratory evolution shows that this recurrently involves chromosome VIII disomy followed by mutually exclusive mutations in either tubulins or kinesin. After ˜150 generations, budding yeast cells impaired in microtubule formation recover the ability to polymerize tubulin. Genetically-identical cell populations evolved in parallel exhibit similar evolutionary paths. Evolved cells first become disomic for chromosome VIII, and then acquire mutations in either tubulins or the kinesin KIP3. Disomy of chromosome VIII is present in the large majority of evolved populations, while mutations are mutually exclusive. Introduction Microtubules are essential components of the cytoskeleton, formed by alpha- and beta-tubulins (Tub1/3 and Tub2 in budding yeast) (Desai & Mitchison, 1997; Brouhard & Rice, 2018). Early screens in yeast identified a wide array of temperature-sensitive mutations that either depolymerize or hyperstabilize microtubules (Richards et al, 2000). Cells expressing these alleles are typically very sick and delayed in mitosis. During this phase of the cell cycle, microtubules form the mitotic spindle, which is responsible for the segregation of sister chromatids to the daughter cells. Microtubules of the mitotic spindle via a process of "search and capture" interact with chromosomes at specialized proteinaceous structures called kinetochores (Musacchio & Desai, 2017). As long as there are unattached or improperly attached kinetochores, cells are arrested in pro-metaphase by a signaling pathway called the mitotic checkpoint or spindle assembly checkpoint (SAC) (Musacchio, 2015). After all chromosomes are properly attached, the checkpoint is silenced, and cells transit into anaphase. Besides the SAC, chromosome segregation also requires proper positioning of the mitotic spindle, so that sister chromatids are segregated along the mother–daughter axis. The spindle position checkpoint (SPOC) delays mitosis when the spindle is not oriented properly (Pereira et al, 2000; Caydasi & Pereira, 2012). Microtubule dynamics plays a key role both for the search and capture of chromosomes, and for segregating them to the opposite poles of the cell. Not surprisingly, microtubule dynamics is controlled by several different factors. Tubulins can polymerize or shrink, the alternation between the two being heavily affected by the status of the GTP bound to beta-tubulin on the plus-end of the filament. At centrosomes, gamma-tubulins contribute to polymerization by nucleating new filaments. In budding yeast, this requires the gamma-tubulin small complex (gamma-TuSC), which is formed by gamma-tubulin, Tub4 in budding yeast, and two co-factors called Spc98 and Spc97 (Lin et al, 2015). Tub4 also requires GTP for microtubules polymerization (Gombos et al, 2013). Besides tubulins themselves, other proteins interact with microtubules and control their polymerization. Among them are kinesins, specialized motors that move along filaments, some of which can also depolymerize microtubules (Akhmanova & Steinmetz, 2015). In budding yeast, Kip3, which belongs to the kinesin-8 family, is such a kinesin with depolymerization activity (Su et al, 2012; Arellano-Santoyo et al, 2017). Laboratory evolution experiments have been used to explore how cells recover from the impairment of essential functions. This approach, together with next-generation sequencing (NGS), allows deciphering the dynamics of genetic changes underlying the emergence of adaptation. "Repair evolution experiments" have shown that cells can recover from the absence of essential components, among them those required for cytokinesis, budding, DNA replication, and many genes originally deemed essential (Rancati et al, 2008; Selmecki et al, 2009; Laan et al, 2015; Fumasoni & Murray, 2020; LaBar et al, 2020). Similarly, it was shown that cells adapt and recover growth following deletions of non-essential genes which come with a strong reduction in fitness (Szamecz et al, 2014). The mechanisms at place to compensate for growth impairment are quite diverse and occur via genetic changes that often do not restore the function of the inactivated proteins. Rather, they act indirectly, either by inactivating regulators of the impaired proteins, or by re-purposing cellular components (Liu & Rancati, 2016). Aneuploidy often plays a key role in these processes, especially as a first quick evolutionary response to environmental stress, later followed by mutations (Rancati et al, 2008; Chen et al, 2012; Yona et al, 2012; Ravichandran et al, 2018). To date, no study addressed the mechanisms of compensatory evolution to the inactivation of tubulins. Given the essential role of these proteins, it is not even clear whether cells can recover at all from their inactivation; and if they do, whether this happens via divergent evolutionary paths or through few reproducible series of genetic changes; whether recovery occurs via the development of alternative means for chromosome segregation or via restoration of tubulin activity; whether compensatory mutations are mostly loss of function; and whether aneuploidy plays any role in this process. Tubulins are coded by genes highly conserved among eukaryotes. Thus, answering these questions may be also potentially relevant for understanding how cells develop resistance to drugs targeting microtubules, either stabilizers, e.g., taxanes, or destabilizers, e.g., vinca alkaloids. Both types of drugs affect chromosome-microtubule attachment, activate the mitotic checkpoint, and arrest cells before anaphase. By delaying cell cycle progression, microtubule drugs can promote apoptosis in transformed cells (Taylor & McKeon, 1997). In the long-term, however, the effects of antimitotics are jeopardized by the emergence of resistance. Here, we performed a laboratory evolution experiment to study how cells react to forced microtubule depolymerization. We used haploid yeast cells expressing an allele of beta-tubulin (tub2-401), which carries four-point mutations resulting in three amino acid changes (M233V, Y242C, Q245L, Fig EV1A). This is a cold-sensitive allele, which cannot polymerize microtubules when grown at low temperature (Huffaker et al, 1988; Sullivan & Huffaker, 1992). Several other alleles are available, but tub2-401 is the one showing the most penetrant phenotype. We opted for a mutation mimicking the effect of drugs, rather than drugs themselves (e.g., benomyl, nocodazole), to avoid generic and well-characterized multidrug mechanisms of resistance (Prasad & Goffeau, 2012). Click here to expand this figure. Figure EV1. Characterization of evolved cells The three amino acid changes of the tub2-401 allele. Serial 5-fold dilution of cells of the indicated genotypes spotted on YPD and incubated at the indicated temperatures. Different fates of tub2-401 cells growing at 18°C (correct chromosome segregation after rebudding, missegregation after rebudding, or death before rebudding). Cells were synchronized in G1, released at 18°C to express the tub2-401 phenotype, and imaged every hour for 41 h in a microfluidic chamber. The events of segregation were identified by following GFP-tagged chromosome V. Among the 21.5% of cells correctly segregating, 44.4% died before the end of the movie and 55.5% did not. Among the 50.2% of cell missegregating, 48.5% died before the end of the movie and 51.4% did not. Cells that neither died nor rebudded by the end of the movie (12 out of 221 cells from 2 biological replicates) are not included in this analysis. Scale bars are 5 μm long. Every image, except the leftmost "death", have the same contrast settings. See Movie EV1. Normalized and corrected depth of coverage for the two ancestral strains. Each dot represents the median depth over a 10,000 bp window. Ancestral cells and selected Gf populations (G5, D5 and H6) were synchronized in G1 at 30°C and released at 18°C, as explained in Materials and Methods. Cells were collected 3.5 and 5 h after G1 release. DNA content was assessed by Sytox Green staining. Selected Gf populations (G5, D4, E5, H6, D5, and B4) and ancestors were synchronized in G1 at 30°C and released at 18°C, as explained in Materials and Methods. The fraction of cells with a large bud was monitored every 30 min from 3 to 5 h after G1 release. Black dots identify the timepoints in which spindle lengths were measured. The kinetics is representative of one biological replicate. Representative spindles in Ancestor and selected Gf cells. Tub1 was stained for immunofluorescence, while nuclei were stained with DAPI. Images are examples from one biological replicate. mad2Δ, ancestral cells, and selected Gf populations (the same as in Fig 1D and E) were synchronized in G1 at 30°C and released in nocodazole 15 μg/ml. Cells were collected 1, 3, and 4 h after nocodazole addition. DNA content was assessed by Sytox Green staining. Download figure Download PowerPoint We grew cells for more than one hundred generations at the semi-restrictive temperature, until they recovered growth. We confirmed that evolved cells regained the ability to assemble regular spindles, and we identified two pathways through which cells can acquire resistance. In both of them, disomy of chromosome VIII (chrVIII 2X) is the likely initial step. Our results may be relevant for understanding principles underlying the emergence of resistance in the context of cancer treatment. Results Yeast cells become resistant to stimuli inducing microtubules depolymerization In order to investigate how yeast cells cope on the long term with tubulin defects, we used a conditional allele of TUB2, tub2-401. At low temperature, it is non-functional and results in improper microtubule polymerization (Huffaker et al, 1988; Sullivan & Huffaker, 1992). This leads to the activation of both the mitotic checkpoint (Corno et al, 2019) and the spindle position checkpoint, since deletion of two of their essential genes (MAD2 and BUB2, respectively) impairs growth even at the semi-permissive temperature of 23°C (Fig EV1B). By using live-cell imaging at the restrictive temperature (18°C), we confirmed that cells were large and budded, a phenotype typical of mitotic arrest (Movie EV1). Regardless of the prolonged mitotic delay, many cells slipped through the arrest and continued proliferating, as shown previously (Corno et al, 2019). Following the inheritance of GFP-tagged chromosome V after growing cells at the restrictive temperature, we observed high rates of chromosome missegregation. A large fraction of cells (˜60%) died either before or immediately after segregating chromosomes (Fig EV1C). We next performed a laboratory evolution experiment at 18°C using multiple parallel lines of both the tub2-401 mutant strain (N = 24) and the TUB2 wild-type (N = 8). Each population started from an individual clone of the same euploid ancestor (Figs 1A and EV1D). Measurements of growth started after 1 day at 18°C (Generation 0 or G0—Fig 1B). Cells expressing tub2-401 grew slower than control cells expressing wild-type TUB2. After ˜20 generations, the apparent growth rate (cell division subtracted of cell death) started to increase, and after ˜45 generations (Generation recovery or Gr), growth rate stabilized without ever reaching the wild type. It fluctuated around the same value for another ˜100 generations, when we stopped the experiment (final Generation or Gf). Based on the data gathered from the movie (Fig EV1C and Movie EV1), we interpreted the initial slow growth rate as a consequence of both prolonged activation of the mitotic checkpoints and cell death caused by massive chromosome missegregation. Figure 1. Cells impaired for microtubule polymerization increase their growth rate during a laboratory evolution experiment Scheme of the evolution experiment. We isolated the two ancestral strains to obtain 8 clonal TUB2 populations and 24 clonal tub2-401 populations. The first measurement of growth rate was done after 1 day at 18°C (generation 0 or G0). The last measurement was done after 42 days, at generation final (Gf). Between the two, we also analyzed by NGS cells immediately after recovery of growth (generation recovery or Gr). Growth rate was measured every 3–4 days by fitting the dynamics of optical density with an exponential (see Materials and Methods). Thus, growth rate is the net output between cell division and cell death. Thick lines mark the medians, and shadowed areas mark the interquartile ranges for the eight wild-type and 24 tub2-401 populations that we evolved. Evolving cells were treated with the nucleic acid stain Sytox Green, and DNA content was measured with flow cytometry. For each evolving population, the G2 mode of the signal distribution was used to evaluate DNA content (Zhu et al, 2012). DNA content was normalized on the mean value of the TUB2 evolving populations. Thick lines mark the medians, and shadowed areas mark the interquartile ranges for the eight wild-type and 24 tub2-401 populations that we evolved. 1.04 is the expected DNA content of a strain disomic for chromosome VIII, since this chromosome accounts for ˜4% of the total DNA of a haploid euploid cell. Ancestral cells (Anc) and selected evolved cells collected at the end of the experiment (Gf) were synchronized in G1 at 30°C and released at 18°C. Spindle lengths were measured on Tub1 immunodecorated microtubules across 3 timepoints centered on the maximum proportion of large-budded cells (dumbell) (see Fig EV1F). Selected evolved populations were chosen among those carrying only one mutation with high frequency in the three most frequently mutated genes—KIP3, TUB2, or TUB1 (populations G5, D4, B4, D5, H6, E5) For each sample, the number of cells (n) and the number of biological replicates (N) is stated. Pairwise strain comparisons were made using a linear model, adjusting for batch effects for experiments performed on different days. Symbols refer to the P-values of the strain comparison (****P-value < 10−4). The boxes span the interquartile range (IQR, from the 25th to the 75th percentiles), and the central band represents the median. The lower (upper) whisker extends from the box to the smallest (largest) value no further than 1.5*IQR from the box. Individual measures are plotted as dots. Ancestral cells and selected Gf populations (the same as in panel (D)) were synchronized in G1 at 30°C and released in nocodazole 2 μg/ml. 1 and 2 h after release cells were collected and DNA content was assessed by Sytox Green staining. Download figure Download PowerPoint The dynamics of DNA content confirmed high levels of chromosome instability in the early stages of tub2-401 evolution (Fig 1C). The DNA content of TUB2 and tub2-401 populations initially overlapped, but quickly diverged. It stayed constant in TUB2 cells, while it rapidly increased in tub2-401, reaching its peak at around generation 25. After that point, ploidy decreased and kept a constant value, higher than the euploid control. This result is consistent with decreased chromosome missegregation at the end of the experiment. To explain how cells recovered growth and stabilized their DNA content, we hypothesized that some of them acquired mutations allowing the assembly of more structured mitotic spindles that segregated chromosomes more efficiently. As such, the mitotic checkpoint and the SPOC were lifted, and fitter cells progressed more rapidly. To test this interpretation, we analyzed by FACS the cell cycle dynamics of evolved cells. A subset of Gf populations was synchronized in G1 by α-factor and released at 18°C. We observed that, compared to the ancestors, evolved cells spent less time with 2C DNA content, in agreement with a shorter mitotic delay (Fig EV1E). We also analyzed mitotic spindles in cells carrying the tub2-401 mutations at Gf. The length of spindles across three different timepoints, centered on the time with the highest fraction of cells with a large bud (a feature of mitotic arrest; Fig EV1F), was longer in evolved cells than in ancestors (Figs 1D and EV1G). This result is in agreement with increased ability of evolved cells to polymerize microtubules. This feature of evolved cells was not uniquely related to overcoming the tub2-401 mutations, but was confirmed also upon treatment with the microtubules depolymerizing drug nocodazole. After synchronization in G1 and release in low concentration of nocodazole at 30°C, wild-type cells did not arrest, whereas tub2-401 cells were delayed in mitosis due to increased sensitivity to microtubule depolymerization. Evolved tub2-401 showed less sensitivity to nocodazole, in-between ancestors and wild-type cells (Fig 1E). Under higher concentration of nocodazole, all strains mounted an efficient checkpoint response (Fig EV1H). In conclusion, we showed that evolved cells became less sensitive to induced microtubule depolymerization, either caused by the tub2-401 allele or nocodazole. Evolved strains mutate recurrently a small set of genes With the aim of understanding the evolutionary dynamics, we addressed the genetic basis of resistance. We sequenced all populations at the final generation and looked for genes that were mutated more than once in different populations with unique mutations. We then collected all mutations occurring in these recurrently mutated genes. Hereafter, we focused our attention only on these mutations (Table EV1). Control cells did not experience impairment of microtubule polymerization, yet they were under stress due to the low temperature. In these cells, genes of the PHO pathway (PHO4 and PHO81, Fig EV2A and B) were recurrently mutated. We did not observe any change of ploidy (Fig EV2C). In cells expressing tub2-401, we identified recurrently mutated genes that were related to microtubules dynamics, and primarily tubulins themselves. Several mutations affected TUB2 (Fig 2A). They were all missense mutations (neither nonsense nor frameshift), in agreement with TUB2 being essential in budding yeast. None of them were reversions to the original sequence. We also observed mutations in TUB1 (alpha-tubulin) and components of the gamma-tubulin complex (SPC98). Like TUB2, these essential genes had missense mutations (Fig 2A). Besides tubulins, we found the gene coding for the kinesin-8 Kip3 mutated multiple times. Here, mutations were not only missense, but also nonsense and frameshifts, spread all over the gene (Fig 2B). Finally, we observed disomy of chromosome VIII in the large majority of tub2-401 populations (Figs 2C and EV2D). In only two populations, we observed a slight disomy of chromosome III. This result is in agreement with tub2-401 having a higher DNA content than TUB2 cells at the end of the experiment (Fig 1C). We did not identify recurrent structural variants. Click here to expand this figure. Figure EV2. Recurrent amino acid changes and disomies in evolved TUB2 A, B. Amino acid changes caused by mutations occurring independently multiple times in PHO81 and PHO4. They were detected only in TUB2 cells (Fig 3A) C. Lack of disomic populations among the eight evolving TUB2 populations. Empty dots are used to mark chromosomes that are monosomic in every population. Chromosome copy numbers were determined by coverage analysis (an example in Fig EV2D). D. Normalized and corrected depth of coverage for two representative samples (TUB2, population A1; tub2-401, population A4). Each dot represents the median depth over a 10,000 bp window. Download figure Download PowerPoint Figure 2. Amino acid changes in recurrently mutated genes Amino acid changes caused by mutations occurring independently multiple times in alpha-, beta-, and gamma-tubulin genes. In the analysis, we sum up all mutations that affect the same amino acid residue. Only missense mutations were found. Red stripes in TUB2 mark the three residues mutated in tub2-401 allele. Amino acid changes caused by mutations occurring independently multiple times in KIP3. Number of disomic populations among the 24 evolving tub2-401. Empty dots are used to mark chromosomes that are monosomic in every population. Chromosome copy number was determined by coverage analysis (an example in Fig EV2D). All data were collected at the end of the evolution experiment (Gf in Fig 1B). Download figure Download PowerPoint Some genes originally identified by our pipeline were not followed up in our analysis. Among them, genes of the ADE pathway, which have already been reported, mutated in evolution experiments of yeast with the same genetic background (W303) but independently from impaired microtubule polymerization and low temperature (Kaya et al, 2020). We also did not follow up the two genes, which have not been characterized yet (YHR033W, YJL070C) and PRR2, as they are present with very low frequency in two populations only (Table EV1). Instead, we followed up TUB4, which is mutated twice with high frequency but always with the same mutation, since its protein product interacts directly with Spc98. In summary, tub2-401 cells recurrently mutated genes belonging to two classes: (i) tubulins and members of gamma-TuSC (TUB1, TUB2, TUB4, SPC98); and (ii) KIP3. Moreover, we showed that the large majority of evolved strains were disomic for chromosome VIII. Disomy of chromosome VIII precedes acquisition of a singl
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