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• W1989161898 abstract "•Multiprocess screen for cell shape, microtubule, and cell-cycle progression genes•262 quantitatively annotated genes, 35% involved in multiple processes•Conserved role of DNA damage response in controlling microtubule stability found•Systems-level functional links across processes revealed Understanding cells as integrated systems requires that we systematically decipher how single genes affect multiple biological processes and how processes are functionally linked. Here, we used multiprocess phenotypic profiling, combining high-resolution 3D confocal microscopy and multiparametric image analysis, to simultaneously survey the fission yeast genome with respect to three key cellular processes: cell shape, microtubule organization, and cell-cycle progression. We identify, validate, and functionally annotate 262 genes controlling specific aspects of those processes. Of these, 62% had not been linked to these processes before and 35% are implicated in multiple processes. Importantly, we identify a conserved role for DNA-damage responses in controlling microtubule stability. In addition, we investigate how the processes are functionally linked. We show unexpectedly that disruption of cell-cycle progression does not necessarily affect cell size control and that distinct aspects of cell shape regulate microtubules and vice versa, identifying important systems-level links across these processes. Understanding cells as integrated systems requires that we systematically decipher how single genes affect multiple biological processes and how processes are functionally linked. Here, we used multiprocess phenotypic profiling, combining high-resolution 3D confocal microscopy and multiparametric image analysis, to simultaneously survey the fission yeast genome with respect to three key cellular processes: cell shape, microtubule organization, and cell-cycle progression. We identify, validate, and functionally annotate 262 genes controlling specific aspects of those processes. Of these, 62% had not been linked to these processes before and 35% are implicated in multiple processes. Importantly, we identify a conserved role for DNA-damage responses in controlling microtubule stability. In addition, we investigate how the processes are functionally linked. We show unexpectedly that disruption of cell-cycle progression does not necessarily affect cell size control and that distinct aspects of cell shape regulate microtubules and vice versa, identifying important systems-level links across these processes. In many ways, the genomes of most organisms remain as black boxes, with the function of the majority of genes and gene products still unknown. This is the case foremost in humans, where, a decade after publication of the human genome sequence, we still have no direct experimental evidence of the function of over half of all the proteins it encodes (http://www.ebi.ac.uk/QuickGO/GAnnotation). Yet this is just the tip of the iceberg because many genes and proteins play roles in multiple biological processes, themselves functionally linked, with most of those multiple roles and links awaiting discovery. Fission yeast (Schizosaccharomyces pombe) is excellently placed for that discovery, with a genome of ∼4,900 protein coding genes (26.1% essential), 40% of which have a function only inferred from homology and another 20% completely uncharacterized (Aslett and Wood, 2006Aslett M. Wood V. Gene Ontology annotation status of the fission yeast genome: preliminary coverage approaches 100%.Yeast. 2006; 23: 913-919Crossref PubMed Scopus (43) Google Scholar, Wood et al., 2002Wood V. Gwilliam R. Rajandream M.A. Lyne M. Lyne R. Stewart A. Sgouros J. Peat N. Hayles J. Baker S. et al.The genome sequence of Schizosaccharomyces pombe.Nature. 2002; 415: 871-880Crossref PubMed Scopus (1242) Google Scholar). Over the past four decades, classical genetic screening using S. pombe has allowed the discovery of numerous molecules and pathways controlling many essential eukaryotic processes thanks to the genetic tractability, simple morphology, and uniform growth and division pattern of S. pombe cells (Forsburg, 2003Forsburg S.L. Overview of Schizosaccharomyces pombe.Current Protocols in Molecular Biology. 2003; 64: 13.14.1-13.14.3Google Scholar). Recently, a genome-wide library of knockout (KO) haploid strains—where each of 3,004 nonessential genes across the S. pombe genome was systematically deleted—became commercially available (Kim et al., 2010Kim D.U. Hayles J. Kim D. Wood V. Park H.O. Won M. Yoo H.S. Duhig T. Nam M. Palmer G. et al.Analysis of a genome-wide set of gene deletions in the fission yeast Schizosaccharomyces pombe.Nat. Biotechnol. 2010; 28: 617-623Crossref PubMed Scopus (513) Google Scholar), opening the possibility to potentiate that discovery power using ultrasensitive image-based phenotypic screening strategies (Chia et al., 2012Chia J. Goh G. Racine V. Ng S. Kumar P. Bard F. RNAi screening reveals a large signaling network controlling the Golgi apparatus in human cells.Mol. Syst. Biol. 2012; 8: 629Crossref PubMed Scopus (98) Google Scholar, Collinet et al., 2010Collinet C. Stöter M. Bradshaw C.R. Samusik N. Rink J.C. Kenski D. Habermann B. Buchholz F. Henschel R. Mueller M.S. et al.Systems survey of endocytosis by multiparametric image analysis.Nature. 2010; 464: 243-249Crossref PubMed Scopus (345) Google Scholar, Cotta-Ramusino et al., 2011Cotta-Ramusino C. McDonald 3rd, E.R. Hurov K. Sowa M.E. Harper J.W. Elledge S.J. A DNA damage response screen identifies RHINO, a 9-1-1 and TopBP1 interacting protein required for ATR signaling.Science. 2011; 332: 1313-1317Crossref PubMed Scopus (153) Google Scholar, Laufer et al., 2013Laufer C. Fischer B. Billmann M. Huber W. Boutros M. Mapping genetic interactions in human cancer cells with RNAi and multiparametric phenotyping.Nat. Methods. 2013; 10: 427-431Crossref PubMed Scopus (98) Google Scholar, Mercer et al., 2012Mercer J. Snijder B. Sacher R. Burkard C. Bleck C.K. Stahlberg H. Pelkmans L. Helenius A. RNAi screening reveals proteasome- and Cullin3-dependent stages in vaccinia virus infection.Cell Reports. 2012; 2: 1036-1047Abstract Full Text Full Text PDF PubMed Scopus (111) Google Scholar, Neumann et al., 2010Neumann B. Walter T. Hériché J.K. Bulkescher J. Erfle H. Conrad C. Rogers P. Poser I. Held M. Liebel U. et al.Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes.Nature. 2010; 464: 721-727Crossref PubMed Scopus (651) Google Scholar, Rohn et al., 2011Rohn J.L. Sims D. Liu T. Fedorova M. Schöck F. Dopie J. Vartiainen M.K. Kiger A.A. Perrimon N. Baum B. Comparative RNAi screening identifies a conserved core metazoan actinome by phenotype.J. Cell Biol. 2011; 194: 789-805Crossref PubMed Scopus (48) Google Scholar, Simpson et al., 2012Simpson J.C. Joggerst B. Laketa V. Verissimo F. Cetin C. Erfle H. Bexiga M.G. Singan V.R. Hériché J.K. Neumann B. et al.Genome-wide RNAi screening identifies human proteins with a regulatory function in the early secretory pathway.Nat. Cell Biol. 2012; 14: 764-774Crossref PubMed Scopus (136) Google Scholar, Yin et al., 2013Yin Z. Sadok A. Sailem H. McCarthy A. Xia X. Li F. Garcia M.A. Evans L. Barr A.R. Perrimon N. et al.A screen for morphological complexity identifies regulators of switch-like transitions between discrete cell shapes.Nat. Cell Biol. 2013; 15: 860-871Crossref PubMed Scopus (128) Google Scholar). Here, we used fission yeast to carry out a 3D image-based genomic screen monitoring cell shape, microtubule organization, and cell-cycle progression to find genes involved in these processes, identify genes controlling multiple processes, and determine how processes are functionally linked. We describe the identification, large-scale validation and quantitative annotation of 262 putative regulators, with 62% newly implicated in the processes studied and 35% implicated in more than one. As a result of in-depth validation of one hit class, we identify a conserved role of the DNA damage response in controlling microtubule stability, revealing a link between those two therapeutically relevant cell biological machineries. Moreover, by exploiting the richness of the multidimensional feature sets obtained from the screen, we investigate statistically and in detail the functional links across processes. We show that disruption of cell-cycle progression does not necessarily affect cell size control and that the causal links between cell shape and microtubule regulation in S. pombe are directional and complex, with distinct cell shape and microtubule features having defined epistatic relationships in this species. The multiprocess screen images and gene annotations are available online as a resource for the community at http://www.sysgro.org as well as linked to the centralized fission yeast repository PomBase http://www.pombase.org. To carry out a multiprocess phenotypic screen in fission yeast, we developed a live cell, 3D fluorescence image-based phenotypic profiling pipeline combining automated high-resolution spinning disk confocal microscopy and large-scale, quantitative multiparametric image analysis. We used confocal microscopy and 3D (xyz) imaging to extract high-resolution subcellular information from individual yeast cells, allowing us to screen with high sensitivity and to obtain refined phenotypic cell biological annotations. Details of the experimental and computational implementation of the pipeline are described in the Experimental Procedures. We chose to screen for genes controlling cell shape, microtubules, and cell-cycle progression because they are fundamental, well-studied processes for which an extensive, yet likely not exhaustive, list of regulators is known. In addition, all three processes can be monitored simultaneously in live cells expressing only fluorescently labeled tubulin, minimizing manipulation of their genetic background. Indeed, microtubules can be used as bona fide reporters of the cell-cycle state, as they take defined stereotypical patterns across the cell cycle (Hagan, 1998Hagan I.M. The fission yeast microtubule cytoskeleton.J. Cell Sci. 1998; 111: 1603-1612PubMed Google Scholar); in turn, cell shape can be simply monitored using extracellular fluorescent dyes (see below). Thus, we generated a version of the genome-wide KO library expressing GFP-tagged endogenous alpha tubulin 2 (GFP-Atb2; Figure 1 and Figure S1A available online), allowing us to visualize microtubules and cell-cycle stage “live” in all mutants. Because the different KO mutants arrayed in 96-well plates had different growth proficiencies compared to the wild-type (Kim et al., 2010Kim D.U. Hayles J. Kim D. Wood V. Park H.O. Won M. Yoo H.S. Duhig T. Nam M. Palmer G. et al.Analysis of a genome-wide set of gene deletions in the fission yeast Schizosaccharomyces pombe.Nat. Biotechnol. 2010; 28: 617-623Crossref PubMed Scopus (513) Google Scholar), prior to imaging we used a serial dilution and manual repooling strategy to ensure all mutants grew exponentially and were hence physiologically comparable (Figure S1B; Protocol). Then, in preparation for high-throughput imaging, cells were immersed in Cascade blue dextran-containing fluorescent growth medium. This allowed visualization of live cell morphology without the need to express a cytoplasmic fluorophore (Figure 1). Thereafter, mutants in the 96-well plates were filmed with two-color (405 nm, 488 nm) automated high-throughput confocal microscopy at high magnification (60× 1.2NA) and in 3D (xy and 16 z planes), and their images computationally analyzed and phenotyped using custom-made image analysis software. First, we segmented images in the Cascade blue channel, and extracted 57 shape and gray-level features from each 2D cell object (length, width, area, convexity, concavity, topological skeleton, fluorescence intensity along the object’s contour, etc.; Figures 1B and 2A ; Figure S2 and Table S1). Then, using Machine Learning (Jones et al., 2009Jones T.R. Carpenter A.E. Lamprecht M.R. Moffat J. Silver S.J. Grenier J.K. Castoreno A.B. Eggert U.S. Root D.E. Golland P. Sabatini D.M. Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning.Proc. Natl. Acad. Sci. USA. 2009; 106: 1826-1831Crossref PubMed Scopus (282) Google Scholar, Sommer and Gerlich, 2013Sommer C. Gerlich D.W. Machine learning in cell biology - teaching computers to recognize phenotypes.J. Cell Sci. 2013; 126: 5529-5539Crossref PubMed Scopus (221) Google Scholar), specifically a Random Forest classifier trained with both wild-type shaped and strongly misshapen mutant cells, we identified and rejected poorly segmented objects and kept only well-segmented cells for further analysis (9.28% out-of-bag error rate). Subsequently, we detected microtubules in the GFP channel xyz image stacks, reconstructed microtubule orientation within every cell in 3D, and extracted 24 geometrical and grey level microtubule features (number, length, fluorescence intensity, orientation, etc.; Figures 1C, 2B, and S3A–S3D; Table S2). Finally, we identified the cell-cycle stage for each cell based on 3D microtubule pattern, using a four-class support vector machine classifier (Jones et al., 2009Jones T.R. Carpenter A.E. Lamprecht M.R. Moffat J. Silver S.J. Grenier J.K. Castoreno A.B. Eggert U.S. Root D.E. Golland P. Sabatini D.M. Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning.Proc. Natl. Acad. Sci. USA. 2009; 106: 1826-1831Crossref PubMed Scopus (282) Google Scholar, Sommer and Gerlich, 2013Sommer C. Gerlich D.W. Machine learning in cell biology - teaching computers to recognize phenotypes.J. Cell Sci. 2013; 126: 5529-5539Crossref PubMed Scopus (221) Google Scholar),. The classifier, trained with wild-type cells and cells from four known microtubule mutants (lacking Tip1/CLIP170, Brunner and Nurse, 2000Brunner D. Nurse P. CLIP170-like tip1p spatially organizes microtubular dynamics in fission yeast.Cell. 2000; 102: 695-704Abstract Full Text Full Text PDF PubMed Scopus (240) Google Scholar; Mto1/Centrosomin, Sawin et al., 2004Sawin K.E. Lourenco P.C. Snaith H.A. Microtubule nucleation at non-spindle pole body microtubule-organizing centers requires fission yeast centrosomin-related protein mod20p.Current biology: CB. 2004; 14: 763-775Abstract Full Text Full Text PDF PubMed Scopus (147) Google Scholar; Ase1/PRC1, Loïodice et al., 2005Loïodice I. Staub J. Setty T.G. Nguyen N.P. Paoletti A. Tran P.T. Ase1p organizes antiparallel microtubule arrays during interphase and mitosis in fission yeast.Mol. Biol. Cell. 2005; 16: 1756-1768Crossref PubMed Scopus (146) Google Scholar; and Pkl1/Kinesin-14A, Troxell et al., 2001Troxell C.L. Sweezy M.A. West R.R. Reed K.D. Carson B.D. Pidoux A.L. Cande W.Z. McIntosh J.R. pkl1(+)and klp2(+): Two kinesins of the Kar3 subfamily in fission yeast perform different functions in both mitosis and meiosis.Mol. Biol. Cell. 2001; 12: 3476-3488Crossref PubMed Scopus (94) Google Scholar) distinguished four cell-cycle-related microtubule patterns: interphase microtubule array (IP; characteristic of G2 phase in our exponential growth conditions), metaphase spindle (SP; characteristic of M phase), postanaphase array (PAA; reflective of G1 phase), and postmitotic interphase microtubule array (called IP2 for “interphase2”; likely reflective of G1-S phases). The classifier accuracy was 93.78% across the wild-type and four mutants combined, indicating that we could achieve robust cell-cycle stage assignment even when microtubules had an abnormal phenotype. This was partly thanks to the use of 3D microtubule features, which allowed a more accurate assignment of cell-cycle stage than 2D features (see Supplemental Experimental Procedures). The output was a signature of four scores reflecting the proportion of cells assigned to each cell-cycle stage, for each wild-type (typically 70% IP, 10% SP, 10% PAA, and 10% IP2) and mutant cell population, indicative of their cell-cycle progression characteristics (Figures 1D, S3E, and S3F). We used two complementary strategies for detecting KO mutants with aberrant cell shape or microtubules (hits; Figures 1B, 1C, and S4). The first strategy identified mutants with a prominent alteration in a single feature (p value; Figure 1). The second strategy identified mutants with multiple subtle feature alterations (multiparametric profile scoring, Figure 1). In proof-of-principle experiments prior to screening, the use of both strategies combined led to highly consistent detection of the wild-type and of four known microtubule mutants within and across 96-well microplates (Figures 2C–2F), validating the quality and reproducibility of our hit detection strategy. The combined hit detection procedure was optimized independently for cell shape and microtubules based on the results of visual screening by a human observer of one genomic image data set (Figures S5 and S6). To detect KO mutants (hits) with altered cell-cycle progression, we used bootstrap statistics to estimate the typical proportions of wild-type cells in each cell-cycle stage, scoring as hits KOs where at least two cell-cycle stages were statistically disproportionate with respect to the wild-type (i.e., under- or overrepresented; Figure 1D). That criterion ensured only detection of hits where general cell-cycle progression was affected. In particular, this allowed us to screen for genes distinct from classical cell-cycle regulators which, when mutated, often lead to checkpoint-mediated delay in just one cell-cycle stage or transition. We grew, imaged, and computationally screened the entire library twice independently (Figures S1C–S1F), analyzing 1,880,064 images and making and analyzing 1,707,870 cell, 5,597,165 microtubule, and 1,607,406 cell-cycle stage assignments. This identified 372 cell shape hits, 449 microtubule hits, and 199 cell-cycle progression hits (note: hit identification for each process was independent of the others). To generate a high-confidence hit list, we then rescreened all hits at large-scale to obtain ten biologically independent screening rounds (also see the Protocol online) and ranked hits according to the fraction of repeats in which they were detected (confidence value; Figure 1E). Only hits with ≥35% confidence—the percentage corresponding to a well-established but weak phenotype hit (tea1Δ; Figure 2F) added as positive control in all repeats—were selected for further analysis. Altogether, this led to identification of 143 cell shape, 186 microtubules, and 35 cell-cycle progression high-confidence hit genes (Table S3) described next. Genes whose KO affected cell shape (cell shape genes; Figure 1E) included expected regulators of cell morphology, cell polarity and growth (Tea3, Pom1, Arf6, Rga2, Tea2, Sla2/End4, Myo1, Efc25, Scd2; for genes’ annotations see http://www.pombase.org/) but also many genes related to a wide range of other processes such as trafficking or cell-cycle control, and 17 altogether unannotated genes. Forty-one percent (58/143) of genes implicated in cell shape regulation had not been previously reported as such, to our knowledge. Importantly, they were not identified in a recent visual screen of the S. pombe KO library (Hayles et al., 2013Hayles J. Wood V. Jeffery L. Hoe K.L. Kim D.U. Park H.O. Salas-Pino S. Heichinger C. Nurse P. A genome-wide resource of cell cycle and cell shape genes of fission yeast.Open biology. 2013; 3: 130053Crossref PubMed Scopus (108) Google Scholar). This is likely due to the very different physiological conditions used in that study (nonexponential growth on solid medium) and our use of sensitive computational hit detection. Similar to previously published studies (Bakal et al., 2007Bakal C. Aach J. Church G. Perrimon N. Quantitative morphological signatures define local signaling networks regulating cell morphology.Science. 2007; 316: 1753-1756Crossref PubMed Scopus (246) Google Scholar, Fuchs et al., 2010Fuchs F. Pau G. Kranz D. Sklyar O. Budjan C. Steinbrink S. Horn T. Pedal A. Huber W. Boutros M. Clustering phenotype populations by genome-wide RNAi and multiparametric imaging.Mol. Syst. Biol. 2010; 6: 370Crossref PubMed Scopus (118) Google Scholar), cell shape hit classification was done using eight support vector machine classifiers trained to recognize eight basic phenotypic classes on an individual cell basis (Figures 3A, 3B, and S4A): stubby (wide), banana (curved), orb (round), kinky (S-shaped), long (elongated), skittle (with one side wider than the other), and T-shaped (branched). Classically, cell shape mutants are thought to display only one aberrant shape phenotype, such as being round or curved (Hayles et al., 2013Hayles J. Wood V. Jeffery L. Hoe K.L. Kim D.U. Park H.O. Salas-Pino S. Heichinger C. Nurse P. A genome-wide resource of cell cycle and cell shape genes of fission yeast.Open biology. 2013; 3: 130053Crossref PubMed Scopus (108) Google Scholar). Surprisingly we found that, instead, all strains including the wild-type did not display only a single shape phenotype but rather could be defined as a mixture of those eight phenotypes (Figures 3A, 3B, and S4A). Thus, even within a genotypically uniform cell population, the genome allows S. pombe cells to explore multiple morphogenetic states. These might be brought about by cell-to-cell differences in the content of key shape-controlling proteins due, for example, to nonexact equipartition of cellular material—polarity landmarks, secretory machinery, cell wall composition/properties, etc.—between daughter cells at cell division or from stochastic gene expression. Quantitatively, the most common aberrant cell phenotype was stubby (Figure 3C), indicating it may be the most general manifestation of compromised cell shape; conversely the least common was orb (i.e., completely nonpolarized), consistent with the finding that known genes whose disruption leads to complete rounding are essential (Hayles et al., 2013Hayles J. Wood V. Jeffery L. Hoe K.L. Kim D.U. Park H.O. Salas-Pino S. Heichinger C. Nurse P. A genome-wide resource of cell cycle and cell shape genes of fission yeast.Open biology. 2013; 3: 130053Crossref PubMed Scopus (108) Google Scholar) and with the notion that complete loss of polarity may be incompatible with viability. We clustered mutants based on their shape phenoprint and found that KOs of specific pathways shared characteristic morphological signatures (Figure 3D). One major cluster of predominantly stubby mutants comprised regulators of endocytosis and exocytosis (Vps25, Vps32, Vps36, Shd1, Dip1, Did4, Sla2/End4, and Sft1; likely involved in apical restriction of cellular growth zones), genes involved in ubiquitin/COP9 signalosome-mediated protein degradation (Csn1, Csn2, Pub1, and Ubi1), and several uncharacterized factors. Another major cluster comprised significantly longer mutants (note: cell elongation, usually associated with cell-cycle deregulation, was scored in our screen as a cell shape defect), corresponding to factors involved in the DNA damage response (DDR; Mre11, Rad50, Rad55, Set1, Ccq1, Cdt2, and Ctp1; the DDR leads to cell-cycle delay and cell elongation), transcriptional regulators (Cuf1 and Rep2), elongator complex subunits (Elp3, Elp4, Elp6, and Dph3; this complex has been involved in negatively regulating exocytosis), histone modifiers (Brl2, Cph1, Cph2, Dep1, and Rtx2), and other putative regulators. We next asked whether, given their geometrical disruption, cell shape mutants properly control cell size. S. pombe cells are thought to need to reach a critical cell size at the G1/S and, most importantly, at the G2/M cell-cycle transition, when cells engage in cell division only after reaching twice their original size at birth (Mitchison, 2003Mitchison J.M. Growth during the cell cycle.Int. Rev. Cytol. 2003; 226: 165-258Crossref PubMed Scopus (131) Google Scholar). We calculated the average cell area (as a proxy for size) at mitosis by looking at cells containing a mitotic spindle and plotted distributions of the average area at mitosis for all hits (Figure 3E, top) and its coefficient of variation (Figure 3E, bottom). Ninety percent of cell shape hits had an area at division lower or higher than wild-type cells, which divide at an area of ∼48 μm2. This included KOs of factors known to be involved in cell size control such as Pom1 (Martin and Berthelot-Grosjean, 2009Martin S.G. Berthelot-Grosjean M. Polar gradients of the DYRK-family kinase Pom1 couple cell length with the cell cycle.Nature. 2009; 459: 852-856Crossref PubMed Scopus (234) Google Scholar, Moseley et al., 2009Moseley J.B. Mayeux A. Paoletti A. Nurse P. A spatial gradient coordinates cell size and mitotic entry in fission yeast.Nature. 2009; 459: 857-860Crossref PubMed Scopus (285) Google Scholar). Strikingly, 30% had a higher coefficient of variation of the cell area than the 0.12–0.22 coefficient of the wild-type (gray, Figure 3E), indicative of lack of precision in cell size control at division. Interestingly, the latter was enriched for mutants in the ubiquitin/COP9 signalosome complex (implicated in cell-cycle and cell size control in Drosophila melanogaster; Björklund et al., 2006Björklund M. Taipale M. Varjosalo M. Saharinen J. Lahdenperä J. Taipale J. Identification of pathways regulating cell size and cell-cycle progression by RNAi.Nature. 2006; 439: 1009-1013Crossref PubMed Scopus (222) Google Scholar), DDR regulators, and various factors involved in intracellular protein transport. Because the COP9 complex regulates cullin activity in mammals and cullin (Cul-4) has been implicated in both cell-cycle control and the DDR (Hu et al., 2004Hu J. McCall C.M. Ohta T. Xiong Y. Targeted ubiquitination of CDT1 by the DDB1-CUL4A-ROC1 ligase in response to DNA damage.Nat. Cell Biol. 2004; 6: 1003-1009Crossref PubMed Scopus (295) Google Scholar), one possibility is that ubiquitin/COP9 and the DDR act on cell size control via the same pathway, possibly via their role in cell-cycle regulation. Alternatively, each may play a distinct role that needs to be further explored. Similarly, the role in size control of other factors identified needs to be clarified. Genes whose KO affected microtubules (microtubule genes; Figure 1E) included known microtubule regulators (Tea2, Tip1, and Mal3), mitochondrial factors, trafficking-related genes, and 19 altogether unannotated factors. Notably, 93.5% (174/186) of the genes implicated in microtubule regulation had not, to our knowledge, been previously reported as such. Mutants in those genes primarily led to deregulation of microtubule number, length, or orientation, with most KOs affecting several features simultaneously albeit in different proportions (Figure 4A). Microtubule length (encompassing the features: length, length variance, occupancy, occupancy variance; Figures 4B and 4C) was by far the most common quantitatively affected microtubule property, demonstrating that microtubule length per se is not essential for cell viability. In contrast, low microtubule number was a very infrequent feature, consistent with the fact that microtubule nucleation is essential for cell viability. Clustering of microtubule hits was done using a subset of 12 features selected by visual quality control to optimize for high interclass variability and low intraclass variability (i.e., to optimally group together KOs judged visually to have the same phenotype and assign to separate groups KOs with visually different phenotypes) and identified various pathways, each associated with a specific microtubule phenoprint (Figure 4D). Among the most prominent pathways we found were cytoskeleton/cell polarity (Tea2, Tip1, Tea4, Mal3; whose KO leads to short, disoriented microtubules), DDR (Mre11, Rad50, Rad51, Rad55, Mcl1, Ccq1, Cdt2, Ctp1; slightly elongated, hyperoriented microtubules), transport/vesicles and mitochondria (Vps25, Vps66, Tlg2, Ryh1, SPAC823.10c, Tom7, SPAC1F3.03, Sat1, and Rrf1, SPAC823.10c, SPAC1610.02c, Cys11, SPBC106.07c, Coq5; slightly more microtubules), and tubulin folding (the Prefoldin complex subunits SPBC1D7.01, Pac10, SPAC227.10, Bob1; fewer microtubules). We next assessed whether differences in tubulin content could account for the mutants’ microtubule feature signatures by quantitating their intracellular GFP-Atb2 fluorescence. We found that, although many mutants displayed substantial differences in tubulin content compared to the wild-type, there was no obvious correlation between their microtubule feature signatures and GFP-tubulin fluorescence, suggesting that their microtubule phenotype arises from deregulation of microtubule assembly rather than tubulin content (Figure 4E). Interestingly, analysis of the hits’ microtubule length in interphase versus mitosis revealed a correlation between the two in ∼80% of cases (Figure 4F), indicating that many genes identified may also play a role in mitotic spindle control. Genes whose KO affected cell-cycle progression (cell-cycle progression genes; Figure 1E) comprised a diverse range of factors and, as expected, did not include classical cell-cycle regulators. To look for interesting functional groups, we measured the cell-cycle duration of all cell-cycle progression hits. This allowed us to convert for each KO the proportions of cells in each cell-cycle stage into average times spent in each stage (Figures 5A and 5B ). We then calculated the Z score of all four stage times (durations) with respect to the wild-type, for all hits (Figure 5C). Subsequently, we used ergodic rate analysis (ERA; Kafri et al., 2013Kafri R. Levy J. Ginzberg M.B. Oh S. Lahav G. Kirschner M.W. Dynamics extracted from fixed cells reveal feedback linking cell growth to cell cycle.Nature. 2013; 494: 480-483Crossref PubMed Scopus (147) Google Scholar) to estimate the average rate of progression from each c" @default.
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• W1989161898 date "2014-10-01" @default.
• W1989161898 modified "2023-10-11" @default.
• W1989161898 title "A Genomic Multiprocess Survey of Machineries that Control and Link Cell Shape, Microtubule Organization, and Cell-Cycle Progression" @default.
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