Quantitative Genetic Interactions Reveal Biological Modularity
2010; Cell Press; Volume: 141; Issue: 5 Linguagem: Inglês
10.1016/j.cell.2010.05.019
ISSN1097-4172
AutoresPedro Beltrão, Gerard Cagney, Nevan J. Krogan,
Tópico(s)Genetics, Bioinformatics, and Biomedical Research
ResumoTraditionally, research has been reductionist, characterizing the individual components of biological systems. But new technologies have increased the size and scope of biological data, and systems approaches have broadened the view of how these components are interconnected. Here, we discuss how quantitative mapping of genetic interactions enhances our view of biological systems, allowing their deeper interrogation across different biological scales. Traditionally, research has been reductionist, characterizing the individual components of biological systems. But new technologies have increased the size and scope of biological data, and systems approaches have broadened the view of how these components are interconnected. Here, we discuss how quantitative mapping of genetic interactions enhances our view of biological systems, allowing their deeper interrogation across different biological scales. In 1977, Charles and Ray Eames produced a short movie entitled "Powers of Ten," taking viewers on a journey through space that spanned many orders of magnitude, from the atom to the outer universe (http://www.powersof10.com). The journey can be a humbling experience. Quoting Carl Sagan: "We find that we inhabit an insignificant planet of a hum-drum star lost in a galaxy tucked away in some forgotten corner of a universe in which there are far more galaxies than people." Biomedical research has focused on a subset of the orders of magnitude explored by Charles and Ray Eames, from ecosystems (106 meters) to the atomic structure of biomolecules (10−10 meters). Although each of these orders of magnitude is typically explored with different sets of experimental tools, in nature they are intricately connected. For example, point mutations in proteins can lead to changes in signaling circuitry that can change species behavior (de Bono and Bargmann, 1998de Bono M. Bargmann C.I. Cell. 1998; 94: 679-689Abstract Full Text Full Text PDF PubMed Scopus (559) Google Scholar) with a potential impact on interspecies interactions. Meanwhile behaviors like algal blooms that create phenotypes visible from space are likely to be under genetic control (Erdner and Anderson, 2006Erdner D.L. Anderson D.M. BMC Genomics. 2006; 7: 88Crossref PubMed Scopus (69) Google Scholar). Still, biological research has largely focused on characterizing the components that make up systems of interest. Only recently, with the advent of systems biology, has the emphasis shifted toward integrative studies that aim to describe how observed biological phenomena depend on the interplay of these components. An increase in quantitative data and improvements in computational methods have led to the rise of models that, to some extent, can predict the nonintuitive behavior of biological systems at different scales. Examples of these include models of protein-binding affinities (Chen et al., 2008Chen J.R. Chang B.H. Allen J.E. Stiffler M.A. MacBeath G. Nat. Biotechnol. 2008; 26: 1041-1045Crossref PubMed Scopus (118) Google Scholar), signaling events in cell decision making (Santos et al., 2007Santos S.D.M. Verveer P.J. Bastiaens P.I.H. Nat. Cell Biol. 2007; 9: 324-330Crossref PubMed Scopus (482) Google Scholar), development (Bergmann et al., 2007Bergmann S. Sandler O. Sberro H. Shnider S. Schejter E. Shilo B. Barkai N. PLoS Biol. 2007; 5: e46Crossref PubMed Scopus (147) Google Scholar), and homeostasis (Novák and Tyson, 2008Novák B. Tyson J.J. Nat. Rev. Mol. Cell Biol. 2008; 9: 981-991Crossref PubMed Scopus (705) Google Scholar). In this Essay, we discuss one such method, quantitative genetic interaction mapping, and its application to the study of different scales of biology. In a tribute to "Powers of Ten," we journey from the whole organism to the atomic resolution of single amino acids. The study of genetic interactions (or epistasis) has a strong theoretical basis in genetic linkage studies (Phillips, 2008Phillips P.C. Nat. Rev. Genet. 2008; 9: 855-867Crossref PubMed Scopus (912) Google Scholar). A genetic interaction between two genes implies that they impact each other's functions. Genetic interactions between two loci can be mapped by measuring how the phenotype of an organism lacking both genes (double mutant) differs from that expected when the phenotypes of the single mutations are combined (Figure 1A ) (Mani et al., 2008Mani R. St Onge R.P. Hartman J.L. Giaever G. Roth F.P. Proc. Natl. Acad. Sci. USA. 2008; 105: 3461-3466Crossref PubMed Scopus (301) Google Scholar, Phillips, 2008Phillips P.C. Nat. Rev. Genet. 2008; 9: 855-867Crossref PubMed Scopus (912) Google Scholar). The most commonly used neutral model assumes that the fitness of the double mutant is equal to the product of individual single mutant fitness. For example, if loss of gene A results in a growth rate 0.9 times the wild-type growth rate, whereas loss of gene B results in a growth rate of 0.8, then the expected growth rate of the double mutant (lacking genes A and B) would be 0.72 times that of the wild-type (Figure 1A). This neutral model assumes that two genes do not normally impact each other, and in fact, experimental observations support the intuitive idea that most genes do not interact (i.e., strong genetic interactions are rare) (Tong et al., 2001Tong A.H. Evangelista M. Parsons A.B. Xu H. Bader G.D. Pagé N. Robinson M. Raghibizadeh S. Hogue C.W. Bussey H. et al.Science. 2001; 294: 2364-2368Crossref PubMed Scopus (1595) Google Scholar, Pan et al., 2004Pan X. Yuan D.S. Xiang D. Wang X. Sookhai-Mahadeo S. Bader J.S. Hieter P. Spencer F. Boeke J.D. Mol. Cell. 2004; 16: 487-496Abstract Full Text Full Text PDF PubMed Scopus (250) Google Scholar, Schuldiner et al., 2005Schuldiner M. Collins S.R. Thompson N.J. Denic V. Bhamidipati A. Punna T. Ihmels J. Andrews B. Boone C. Greenblatt J.F. et al.Cell. 2005; 123: 507-519Abstract Full Text Full Text PDF PubMed Scopus (673) Google Scholar). Cases where knocking out two genes causes a more deleterious effect than the fitness reduction expected from the combined loss of individual genes are referred to as negative or aggravating interactions (e.g., synthetic sickness) (Figure 1A) and often identify proteins that function in distinct but parallel pathways in a given process (Figure 1B). Alternatively, a double mutation can have a smaller than expected impact on fitness, and these cases represent positive or alleviating interactions (e.g., suppression) (Figure 1A). We have shown that pairs of yeast mutants that display positive genetic interactions often indicate two proteins that act in the same pathway or are physically associated (Figure 1B) (Roguev et al., 2008Roguev A. Bandyopadhyay S. Zofall M. Zhang K. Fischer T. Collins S.R. Qu H. Shales M. Park H. Hayles J. et al.Science. 2008; 322: 405-410Crossref PubMed Scopus (274) Google Scholar, Collins et al., 2007Collins S.R. Miller K.M. Maas N.L. Roguev A. Fillingham J. Chu C.S. Schuldiner M. Gebbia M. Recht J. Shales M. et al.Nature. 2007; 446: 806-810Crossref PubMed Scopus (708) Google Scholar). A possible explanation is that if removal of a component of a complex disables that complex, then deleting a second component would have no additional effect, resulting in an epistatic (i.e., positive) interaction (Figure 1A). Alternatively, deletion of one component of a complex could result in partial dysfunction of that complex with a detrimental effect on cell viability. If the removal of an additional component completely disabled this detrimental function, then the result would be a suppressive relationship, another type of positive interaction. Furthermore, if enough genetic interactions are collected for a set of genes, then each mutant engenders a genetic interaction profile, or phenotypic signature, representing how it genetically interacts with all other mutants tested. Comparison of these profiles is a powerful and unbiased way to identify genes that act in the same biochemical pathway (Figure 1C) (Pan et al., 2004Pan X. Yuan D.S. Xiang D. Wang X. Sookhai-Mahadeo S. Bader J.S. Hieter P. Spencer F. Boeke J.D. Mol. Cell. 2004; 16: 487-496Abstract Full Text Full Text PDF PubMed Scopus (250) Google Scholar, Schuldiner et al., 2005Schuldiner M. Collins S.R. Thompson N.J. Denic V. Bhamidipati A. Punna T. Ihmels J. Andrews B. Boone C. Greenblatt J.F. et al.Cell. 2005; 123: 507-519Abstract Full Text Full Text PDF PubMed Scopus (673) Google Scholar, Collins et al., 2007Collins S.R. Miller K.M. Maas N.L. Roguev A. Fillingham J. Chu C.S. Schuldiner M. Gebbia M. Recht J. Shales M. et al.Nature. 2007; 446: 806-810Crossref PubMed Scopus (708) Google Scholar, Tong et al., 2004Tong A.H.Y. Lesage G. Bader G.D. Ding H. Xu H. Xin X. Young J. Berriz G.F. Brost R.L. Chang M. et al.Science. 2004; 303: 808-813Crossref PubMed Scopus (1609) Google Scholar). This multiplicative model is useful for quantitative measures such as growth rate but less so for complex phenotypes like cell morphology, necessitating alternative models of epistatic behavior (Mani et al., 2008Mani R. St Onge R.P. Hartman J.L. Giaever G. Roth F.P. Proc. Natl. Acad. Sci. USA. 2008; 105: 3461-3466Crossref PubMed Scopus (301) Google Scholar). Here, our focus is on high-throughput quantification of genetic interactions, analysis methods, and their applications across different species and scales of biological organization. Genetic studies are traditionally subdivided into forward and reverse genetics. Forward genetics often defines a phenotype of interest and then identifies mutants that contribute to this phenotype. In contrast, reverse genetics starts with genes of interest and attempts to define their function through mutational analysis. In this context, genetic interaction screening can be defined as a form of reverse genetics. The development of high-throughput genetic interaction screening was made possible by the creation of deletion libraries for single nonessential genes in the budding yeast Saccharomyces cerevisiae (reviewed in Boone et al., 2007Boone C. Bussey H. Andrews B.J. Nat. Rev. Genet. 2007; 8: 437-449Crossref PubMed Scopus (430) Google Scholar). An important landmark was the first implementation, termed synthetic genetic array (SGA), where each S. cerevisiae single gene deletion strain was mated to produce arrays of double-mutant strains (Tong et al., 2001Tong A.H. Evangelista M. Parsons A.B. Xu H. Bader G.D. Pagé N. Robinson M. Raghibizadeh S. Hogue C.W. Bussey H. et al.Science. 2001; 294: 2364-2368Crossref PubMed Scopus (1595) Google Scholar). This enabled the rapid qualitative assessment of synthetic lethal interactions for many thousands of gene pair combinations. An alternative approach, dSLAM (diploid-based synthetic lethal analysis with microarrays), detects genetic interactions using pools of barcoded yeast mutants (Pan et al., 2004Pan X. Yuan D.S. Xiang D. Wang X. Sookhai-Mahadeo S. Bader J.S. Hieter P. Spencer F. Boeke J.D. Mol. Cell. 2004; 16: 487-496Abstract Full Text Full Text PDF PubMed Scopus (250) Google Scholar). In this approach, genetic interactions are determined by the differential enrichment of double mutants growing in competitive culture as measured using barcode microarrays. Although in principle both methods can measure a range of epistatic effects, in practice they were used to identify synthetic sick or lethal (i.e., negative) interactions. The E-MAP (epistatic mini-array profile) strategy enabled colony size to be measured in an array format, thus quantifying genetic interactions in a high-throughput fashion (Collins et al., 2006Collins S.R. Schuldiner M. Krogan N.J. Weissman J.S. Genome Biol. 2006; 7: R63Crossref PubMed Scopus (241) Google Scholar, Schuldiner et al., 2005Schuldiner M. Collins S.R. Thompson N.J. Denic V. Bhamidipati A. Punna T. Ihmels J. Andrews B. Boone C. Greenblatt J.F. et al.Cell. 2005; 123: 507-519Abstract Full Text Full Text PDF PubMed Scopus (673) Google Scholar). The barcode approach has been adapted to provide a quantitative genetic score (Decourty et al., 2008Decourty L. Saveanu C. Zemam K. Hantraye F. Frachon E. Rousselle J. Fromont-Racine M. Jacquier A. Proc. Natl. Acad. Sci. USA. 2008; 105: 5821-5826Crossref PubMed Scopus (92) Google Scholar), and a flow cytometry device has been developed that can quantify precisely very small epistatic effects (Breslow et al., 2008Breslow D.K. Cameron D.M. Collins S.R. Schuldiner M. Stewart-Ornstein J. Newman H.W. Braun S. Madhani H.D. Krogan N.J. Weissman J.S. Nat. Methods. 2008; 5: 711-718Crossref PubMed Scopus (348) Google Scholar). In parallel with genetic interaction screening for S. cerevisiae, screening methods using knock down of gene expression by RNA interference (RNAi) have been developed for the worm Caenorhabditis elegans. In this case, worm strains carrying a specific mutation (gene knockout) are fed bacteria expressing different microRNAs in a 96-well assay (Byrne et al., 2007Byrne A. Weirauch M. Wong V. Koeva M. Dixon S. Stuart J. Roy P. J. Biol. 2007; 6: 8Crossref PubMed Scopus (130) Google Scholar, Lehner et al., 2006Lehner B. Crombie C. Tischler J. Fortunato A. Fraser A.G. Nat. Genet. 2006; 38: 896-903Crossref PubMed Scopus (367) Google Scholar). RNAi mutant combinations that differ from the expected phenotypes of the combined single perturbations are defined as synthetic sick/lethal. Quantifying genetic interactions in C. elegans is more challenging than in S. cerevisiae given the added complexity of multicellularity. Nevertheless, a semiquantitative measure of genetic interactions in the worm has been obtained by scoring phenotypes visually (Byrne et al., 2007Byrne A. Weirauch M. Wong V. Koeva M. Dixon S. Stuart J. Roy P. J. Biol. 2007; 6: 8Crossref PubMed Scopus (130) Google Scholar). Recently, protocols have been developed to assay genetic interactions in the bacterium Escherichia coli (Typas et al., 2008Typas A. Nichols R.J. Siegele D.A. Shales M. Collins S.R. Lim B. Braberg H. Yamamoto N. Takeuchi R. Wanner B.L. et al.Nat. Methods. 2008; 5: 781-787Crossref PubMed Scopus (182) Google Scholar, Butland et al., 2008Butland G. Babu M. Díaz-Mejía J.J. Bohdana F. Phanse S. Gold B. Yang W. Li J. Gagarinova A.G. Pogoutse O. et al.Nat. Methods. 2008; 5: 789-795Crossref PubMed Scopus (193) Google Scholar), the fission yeast Schizosaccharomyces pombe (Dixon et al., 2008Dixon S.J. Fedyshyn Y. Koh J.L.Y. Prasad T.S.K. Chahwan C. Chua G. Toufighi K. Baryshnikova A. Hayles J. Hoe K. et al.Proc. Natl. Acad. Sci. USA. 2008; 105: 16653-16658Crossref PubMed Scopus (140) Google Scholar, Roguev et al., 2007Roguev A. Wiren M. Weissman J.S. Krogan N.J. Nat. Methods. 2007; 4: 861-866Crossref PubMed Scopus (114) Google Scholar), and cell lines derived from the fruit fly Drosophila melanogaster (Bakal et al., 2008Bakal C. Linding R. Llense F. Heffern E. Martin-Blanco E. Pawson T. Perrimon N. Science. 2008; 322: 453-456Crossref PubMed Scopus (103) Google Scholar). These will increase our capacity to probe for epistatic effects across different species. Although methods to assay genetic interactions differ in implementation and have different advantages and disadvantages, they are all able to quantify genetic interactions on a large scale. Next, we describe how genetic interaction screening can be used to study biological systems across different scales of space and time. We start our journey at the millimeter scale (10−3 m), the length of a C. elegans worm. From the whole organism viewpoint, the phenotypes of interest relate to survival and development. Quantitatively measuring epistatic effects in a multicellular organism is difficult, but genetic interactions can be used to predict the effects of single-gene perturbations on the whole organism (Lee et al., 2008Lee I. Lehner B. Crombie C. Wong W. Fraser A.G. Marcotte E.M. Nat. Genet. 2008; 40: 181-188Crossref PubMed Scopus (239) Google Scholar). Genetic interaction data in combination with additional information (e.g., mRNA coexpression and protein-protein interactions) have defined a network of functional interactions in C. elegans that enables predictions to be made about phenotypes visible in the whole organism. For example, this approach has identified genes that suppress the ectopic vulval phenotype associated with inactivation of the synMuv (synthetic multivulva) A protein (Lee et al., 2008Lee I. Lehner B. Crombie C. Wong W. Fraser A.G. Marcotte E.M. Nat. Genet. 2008; 40: 181-188Crossref PubMed Scopus (239) Google Scholar). Although this study was based on a predicted network of functional interactions, it clearly shows the potential for understanding genetic interactions at an organismal level. Next, we zoom down two to three orders of magnitude to about 5 × 10−6 meters, or the average diameter of a yeast cell (Figure 2). Although we leave the complexities of multicellularity behind, we now face the myriad functions required for the cell to survive and replicate. To illustrate the power of quantitative genetic interaction information at different scales, we use data derived from a single E-MAP study of chromatin functions, including chromosome segregation, transcriptional regulation, DNA repair and replication, chromatin modifications, and remodeling (Collins et al., 2007Collins S.R. Miller K.M. Maas N.L. Roguev A. Fillingham J. Chu C.S. Schuldiner M. Gebbia M. Recht J. Shales M. et al.Nature. 2007; 446: 806-810Crossref PubMed Scopus (708) Google Scholar). At the cellular and subcellular level (10−6 to 10−7 meters), quantitative genetic interactions reveal how different biological processes are functionally connected. For example, there is a strong propensity for negative genetic interactions between DNA replication and DNA recombination/repair genes as well as between transcription and chromatin modification/remodeling genes (Figure 2), arguing that significant redundancy exists among pathways in these processes. A decade ago, Hartwell stated: "Cell biology is in transition from a science that was preoccupied with assigning functions to individual proteins or genes, to one that is now trying to cope with the complex sets of molecules that interact to form functional modules" (Hartwell et al., 1999Hartwell L.H. Hopfield J.J. Leibler S. Murray A.W. Nature. 1999; 402: C47-C52Crossref PubMed Scopus (2668) Google Scholar). Similarly, Alberts called for molecular biologists to change their view of the cell from a bag of proteins to a collection of machines (Alberts, 1998Alberts B. Cell. 1998; 92: 291-294Abstract Full Text Full Text PDF PubMed Scopus (958) Google Scholar). One clear example of this modular organization is the assembly of protein macromolecular structures (complexes) from smaller modular groups of proteins that cooperate to carry out biochemical tasks (Krogan et al., 2006Krogan N.J. Cagney G. Yu H. Zhong G. Guo X. Ignatchenko A. Li J. Pu S. Datta N. Tikuisis A.P. et al.Nature. 2006; 440: 637-643Crossref PubMed Scopus (2264) Google Scholar, Gavin et al., 2006Gavin A. Aloy P. Grandi P. Krause R. Boesche M. Marzioch M. Rau C. Jensen L.J. Bastuck S. Dümpelfeld B. et al.Nature. 2006; 440: 631-636Crossref PubMed Scopus (2067) Google Scholar). As we delve deeper toward the 10−8 meter scale, we begin to see these individual complexes. Although datasets comprising only physical protein interactions tend to arrange into distinct (albeit modular) complexes, these in turn are often connected by negative epistatic interactions (Kelley and Ideker, 2005Kelley R. Ideker T. Nat. Biotechnol. 2005; 23: 561-566Crossref PubMed Scopus (356) Google Scholar). In fact, using quantitative genetic interactions, we can identify pathways where sets of physically distinct complexes are acting together in linear pathways (Figure 2). Segrè and colleagues first noted that the genetic interactions between genes acting in the same cellular process tended to be predominantly negative or predominantly positive (Segrè et al., 2005Segrè D. Deluna A. Church G.M. Kishony R. Nat. Genet. 2005; 37: 77-83PubMed Google Scholar). Moreover, if the complex or pathway is nonessential, the components tend to be enriched for positive genetic interactions among themselves and have very similar genetic interaction profiles (Collins et al., 2007Collins S.R. Miller K.M. Maas N.L. Roguev A. Fillingham J. Chu C.S. Schuldiner M. Gebbia M. Recht J. Shales M. et al.Nature. 2007; 446: 806-810Crossref PubMed Scopus (708) Google Scholar, Roguev et al., 2008Roguev A. Bandyopadhyay S. Zofall M. Zhang K. Fischer T. Collins S.R. Qu H. Shales M. Park H. Hayles J. et al.Science. 2008; 322: 405-410Crossref PubMed Scopus (274) Google Scholar). For example, quantitative genetic interactions revealed a pathway required for efficient transcriptional elongation by RNA polymerase II comprising three complexes: the Rad6 histone H2B ubiquitination complex (Osley, 2004Osley M.A. Biochim. Biophys. Acta. 2004; 1677: 74-78Crossref PubMed Scopus (96) Google Scholar), the Paf1 complex (Jaehning, 2010Jaehning J.A. Biochim. Biophys. Acta. 2010; 1799: 379-388Crossref PubMed Scopus (169) Google Scholar), and COMPASS, an eight subunit complex that methylates lysine 4 of histone H3 (Shilatifard, 2008Shilatifard A. Curr. Opin. Cell Biol. 2008; 20: 341-348Crossref PubMed Scopus (350) Google Scholar) (Figure 2). Interestingly, genetic interaction information alone cannot distinguish among mutated components of the complexes in this pathway as they all share similar profiles as well as positive genetic interactions (Figures 1B, 1C, and 2). Further analysis of genes associated with this pathway revealed additional stable, stoichiometric complexes in which all of the components act in a concerted and coherent fashion, including Rpd3C(L), the histone deacetylation complex responsible for regulating gene expression at the promoters of many genes (Keogh et al., 2005Keogh M. Kurdistani S.K. Morris S.A. Ahn S.H. Podolny V. Collins S.R. Schuldiner M. Chin K. Punna T. Thompson N.J. et al.Cell. 2005; 123: 593-605Abstract Full Text Full Text PDF PubMed Scopus (590) Google Scholar, Carrozza et al., 2005Carrozza M.J. Li B. Florens L. Suganuma T. Swanson S.K. Lee K.K. Shia W. Anderson S. Yates J. Washburn M.P. et al.Cell. 2005; 123: 581-592Abstract Full Text Full Text PDF PubMed Scopus (927) Google Scholar) (Figure 2). Such success has spurred the development of unsupervised machine-learning approaches that use genetic and physical interaction information to provide more accurate predictions of protein modules (Ulitsky et al., 2008Ulitsky I. Shlomi T. Kupiec M. Shamir R. Mol. Syst. Biol. 2008; 4: 209Crossref PubMed Scopus (59) Google Scholar, Bandyopadhyay et al., 2008Bandyopadhyay S. Kelley R. Krogan N.J. Ideker T. PLoS Comput. Biol. 2008; 4: e1000065Crossref PubMed Scopus (121) Google Scholar). Unsupervised machine-learning methods aim to find how the data is organized without any prior knowledge of the system. The accuracy of these methods is in itself evidence that modularity is a property of cellular networks as postulated by Alberts, Hartwell, and colleagues (Alberts, 1998Alberts B. Cell. 1998; 92: 291-294Abstract Full Text Full Text PDF PubMed Scopus (958) Google Scholar, Hartwell et al., 1999Hartwell L.H. Hopfield J.J. Leibler S. Murray A.W. Nature. 1999; 402: C47-C52Crossref PubMed Scopus (2668) Google Scholar). As we zoom to 10−8 meters, we reach the individual protein. Genetic interactions can provide valuable insight into the functions of individual proteins and how they relate to other proteins, complexes, or pathways. The discovery that the histone H2A variant Htz1 gets incorporated into chromatin via the SWR-C complex, an event that facilitates transcription, chromosome segregation, replication, and DNA repair, relied on quantitative genetic interactions (Korber and Hörz, 2004Korber P. Hörz W. Cell. 2004; 117: 5-7Abstract Full Text Full Text PDF PubMed Scopus (51) Google Scholar). Close inspection of these interactions allows us to piece together pathway architecture (Figures 1B and 2). Redundancy exists with respect to Htz1 incorporation by the SWR-C complex and histone deacetylation by Rpd3C(L) as there are strong negative genetic interactions among these complexes (compared to positive genetic interactions between the subunits of each complex). Finally, we reach a resolution of 10−10 meters and ask whether genetic information allows us to make functional inferences about protein structure. Until this point, we have discussed experiments in which wild-type genes were either knocked down or knocked out, that is, a gene's function is perturbed in its entirety. These same methods can be used to study other mutants, including overexpression alleles or mutations disrupting specific gene functions, enabling structure-function relationships to be analyzed. For example, alanine mutation variants in the actin gene have been used to test genetic interactions among genes already known to interact genetically with haplo-insufficient actin (Haarer et al., 2007Haarer B. Viggiano S. Hibbs M.A. Troyanskaya O.G. Amberg D.C. Genes Dev. 2007; 21: 148-159Crossref PubMed Scopus (74) Google Scholar). Different mutations seemed to recapitulate subsets of the phenotypes previously indentified using the null allele. Interestingly, mutations that were near each other on the protein surface tended to share genetic interactions, consistent with the concept of individual functions mapping to local regions of domains within protein sequences. Using E-MAP (Collins et al., 2007Collins S.R. Miller K.M. Maas N.L. Roguev A. Fillingham J. Chu C.S. Schuldiner M. Gebbia M. Recht J. Shales M. et al.Nature. 2007; 446: 806-810Crossref PubMed Scopus (708) Google Scholar), we analyzed PCNA (Pol30), an essential protein involved in DNA repair, chromatin assembly, and DNA replication (Zhang et al., 2000Zhang Z. Shibahara K. Stillman B. Nature. 2000; 408: 221-225Crossref PubMed Scopus (367) Google Scholar, Eissenberg et al., 1997Eissenberg J.C. Ayyagari R. Gomes X.V. Burgers P.M. Mol. Cell. Biol. 1997; 17: 6367-6378Crossref PubMed Google Scholar). As this protein is multifunctional, we predicted that different parts of the protein will be linked to different processes. Indeed, one specific mutation (pol30-79) results in an E-MAP profile resembling E-MAP profiles for a hypomorphic allele of POL30 (pol30-DAmP) (Schuldiner et al., 2005Schuldiner M. Collins S.R. Thompson N.J. Denic V. Bhamidipati A. Punna T. Ihmels J. Andrews B. Boone C. Greenblatt J.F. et al.Cell. 2005; 123: 507-519Abstract Full Text Full Text PDF PubMed Scopus (673) Google Scholar) and for several canonical replication mutants including RAD27 and POL32 null alleles (Figure 2). Based on these profiles, we speculate that the pol30-79 mutation could destabilize the PCNA protein. However, another mutation located in a different region of Pol30 (pol30-8 in Figure 2) engenders a strikingly different profile, resembling those seen when the CAC2, RLF2, and MSL1 genes (encoding three components of the CAF chromatin assembly complex) are deleted. Further evidence that Pol30 cooperates with CAF-1 came from the fact that the pol30-8 mutation displays positive genetic interactions with components of the complex (Figure 2). These proof-of-principle experiments demonstrate that it should be possible to use quantitative genetic interaction screening to probe the relationship between structure and function in a high-throughput, systematic manner. We have discussed the same chromosome biology dataset across different scales, but other studies have analyzed different functional aspects of yeast biology. For instance, Lin et al., 2008Lin Y. Qi Y. Lu J. Pan X. Yuan D.S. Zhao Y. Bader J.S. Boeke J.D. Genes Dev. 2008; 22: 2062-2074Crossref PubMed Scopus (125) Google Scholar surveyed histone acetylating and deacetylating enzyme complexes in yeast, discovering their overall organization and that the NuA4 complex is involved in DNA double-strand break repair. Similarly, the SGA method showed how diverse cellular modules such as cell polarity, the mitotic microtubule complex, and DNA synthesis and repair are integrated into higher-order pathways (Tong et al., 2001Tong A.H. Evangelista M. Parsons A.B. Xu H. Bader G.D. Pagé N. Robinson M. Raghibizadeh S. Hogue C.W. Bussey H. et al.Science. 2001; 294: 2364-2368Crossref PubMed Scopus (1595) Google Scholar). E-MAP has enabled study of diverse processes such as the early secretory pathway (Schuldiner et al., 2005Schuldiner M. Collins S.R. Thompson N.J. Denic V. Bhamidipati A. Punna T. Ihmels J. Andrews B. Boone C. Greenblatt J.F. et al.Cell. 2005; 123: 507-519Abstract Full Text Full Text PDF PubMed Scopus (673) Google Scholar), kinase signaling systems (Fiedler et al., 2009Fiedler D. Braberg H. Mehta M. Chechik G. Cagney G. Mukherjee P. Silva A.C. Shales M. Collins S.R. van Wageningen S. et al.Cell. 2009; 136: 952-963Abstract Full Text Full Text PDF PubMed Scopus (205) Google Scholar), RNA processing (Wilmes et al., 2008Wilmes G.M. Bergkessel M. Bandyopadhyay S. Shales M. Braberg H. Cagney G. Collins S.R. Whitworth G.B. Kress T.L. Weissman J.S. et al.Mol. Cell. 2008; 32: 735-746Abstract Full Text Full Text PDF PubMed Scopus (199) Google Scholar), and protein folding in the endoplasmic reticulum (Jonikas et al., 2009Jonikas M.C. Collins S.R. Denic V. Oh E. Quan E.M. Schmid V. Weibezahn J. Schwappach B. Walter P. Weissman J.S. et al.Science. 2009; 323: 1693-1697Crossref PubMed Scopus (485) Google Scholar). Having zoomed in on amino acids in individual protein molecules (10−10 meters), we now consider another important dimension, time. Like space, the analysis of time-dependent changes in biology spans many orders of magnitude, from the picosecond molecular dynamics of single proteins to evolutionary changes over millions of years. Again, quantitative genetic interaction studies shed light on time-dependent biological processes (Figure S1 available online), such as the cellular response to signaling inputs. The cell has many pathways and genes that enable it to sense and adapt to changes in external conditions, but only in the presence of specific external conditions will some functions and genetic interactions become apparent (Figure S1A). The tools described above could be used to study genetic interactions before and after specific changes in external conditions. For example, a DNA-damaging agent (MMS) has been used to search for changes in genetic interactions in the presence of this environmental stress (St Onge et al., 2007St Onge R.P. Mani R. Oh J. Proctor M. Fung E. Davis R.W. Nislow C. Roth F.P. Giaever G. Nat. Genet. 2007; 39: 199-206Crossref PubMed Scopus (248) Google Scholar). By studying gene pairs that elicited alleviating genetic interactions in the presence of MMS, the investigators identified interactions that are important for the DNA-damage response. Another study showed that instead of colony size or relative growth rate, the pathway activity of a fly double mutant could be used as a measure of fitness (Bakal et al., 2008Bakal C. Linding R. Llense F. Heffern E. Martin-Blanco E. Pawson T. Perrimon N. Science. 2008; 322: 453-456Crossref PubMed Scopus (103) Google Scholar). In Drosophila, simultaneous repression of combinations of genes by RNAi revealed regulators of Jun NH2-kinase (JNK) activity measured by FRET. These studies show that genetic interaction assays can be used to elucidate condition- and time-dependent cellular functions, suggesting that increasing the number and variety of phenotypic readouts (e.g., pathway activity, transcriptional output, etc.) will enable analysis of cellular pathways in an unprecedented manner. Evolutionary changes are an important source of time-dependent variation, casting light on how nature uses genes and proteins to solve a variety of biological problems. After speciation events, species diverge over time as they adapt to their specific niches, resulting in differences in their genome organization and cellular interaction networks (Figure S1B). Studies of genetic interactions in different species have begun to elucidate how DNA mutations generate phenotypic variation across species. Using RNAi, Tischler et al., 2008Tischler J. Lehner B. Fraser A.G. Nat. Genet. 2008; 40: 390-391Crossref PubMed Scopus (106) Google Scholar perturbed 837 gene pairs in C. elegans that were orthologous to synthetic lethal gene pairs in S. cerevisiae. They estimated that, at most, 5% of the synthetic lethal interactions are conserved between these two species, in contrast to the marked conservation of essential genes (Tischler et al., 2008Tischler J. Lehner B. Fraser A.G. Nat. Genet. 2008; 40: 390-391Crossref PubMed Scopus (106) Google Scholar). Meanwhile, Roguev et al., 2007Roguev A. Wiren M. Weissman J.S. Krogan N.J. Nat. Methods. 2007; 4: 861-866Crossref PubMed Scopus (114) Google Scholar, Roguev et al., 2008Roguev A. Bandyopadhyay S. Zofall M. Zhang K. Fischer T. Collins S.R. Qu H. Shales M. Park H. Hayles J. et al.Science. 2008; 322: 405-410Crossref PubMed Scopus (274) Google Scholar developed a strategy for mapping genetic interactions in fission yeast and used it to quantitatively measure pair-wise interactions among 550 genes. Analyzing orthologous gene pairs in budding and fission yeast, the authors found that ∼17% of negative interactions and ∼10% of positive interactions were conserved across species. A similar observation has been reported by Dixon et al., 2008Dixon S.J. Fedyshyn Y. Koh J.L.Y. Prasad T.S.K. Chahwan C. Chua G. Toufighi K. Baryshnikova A. Hayles J. Hoe K. et al.Proc. Natl. Acad. Sci. USA. 2008; 105: 16653-16658Crossref PubMed Scopus (140) Google Scholar, with 23% to 29% of negative genetic interactions conserved between fission and budding yeast orthologous pairs. This marked divergence is unlikely to reflect the assay used as these same genetic interaction scores show high reproducibility in biological replicates within each of the species. Interestingly, in contrast to the high divergence of genetic interactions observed for average gene pairs, positive interactions in gene pairs encoding the same complex subunits show much higher levels of conservation (Roguev et al., 2008Roguev A. Bandyopadhyay S. Zofall M. Zhang K. Fischer T. Collins S.R. Qu H. Shales M. Park H. Hayles J. et al.Science. 2008; 322: 405-410Crossref PubMed Scopus (274) Google Scholar). These early cross-species genetic interaction studies have shown us that genetic interactions diverge quickly. However, despite this high divergence rate, within-module genetic interactions (i.e., within protein complexes) exhibit marked conservation. These genetic results are consistent with data from other experimental methods showing that protein complexes are highly conserved across species (van Dam and Snel, 2008van Dam T.J.P. Snel B. PLoS Comput. Biol. 2008; 4: e1000132Crossref PubMed Scopus (35) Google Scholar), whereas their regulation by gene expression or by posttranslational modifications appears to change quickly (Jensen et al., 2006Jensen L.J. Jensen T.S. de Lichtenberg U. Brunak S. Bork P. Nature. 2006; 443: 594-597PubMed Google Scholar, Holt et al., 2009Holt L.J. Tuch B.B. Villén J. Johnson A.D. Gygi S.P. Morgan D.O. Science. 2009; 325: 1682-1686Crossref PubMed Scopus (625) Google Scholar, Tan et al., 2009Tan C.S.H. Bodenmiller B. Pasculescu A. Jovanovic M. Hengartner M.O. Jørgensen C. Bader G.D. Aebersold R. Pawson T. Linding R. Sci. Signal. 2009; 2: ra39Crossref PubMed Scopus (155) Google Scholar, Beltrao et al., 2009Beltrao P. Trinidad J.C. Fiedler D. Roguev A. Lim W.A. Shokat K.M. Burlingame A.L. Krogan N.J. PLoS Biol. 2009; 7: e1000134Crossref PubMed Scopus (160) Google Scholar). Overall, these results are consistent with the notion that modularity increases evolutionary plasticity by allowing the cell to reuse modules to adapt to changing environments (Figure S1B). Given new methods for assaying other species, these evolutionary studies are sure to be the first of many (Typas et al., 2008Typas A. Nichols R.J. Siegele D.A. Shales M. Collins S.R. Lim B. Braberg H. Yamamoto N. Takeuchi R. Wanner B.L. et al.Nat. Methods. 2008; 5: 781-787Crossref PubMed Scopus (182) Google Scholar, Bakal et al., 2008Bakal C. Linding R. Llense F. Heffern E. Martin-Blanco E. Pawson T. Perrimon N. Science. 2008; 322: 453-456Crossref PubMed Scopus (103) Google Scholar). The mapping of genetic interaction networks for multiple species will enable comparative studies that promise to advance our understanding of cellular networks in much the same way as comparative genomics advanced our knowledge of genome architecture. We have described how technological developments in systematic genetic interaction screening can be used to gain insights at different scales of biological organization. Other methods are also under development; for example, the yeast two-hybrid assay has been adapted to identify the protein domains most likely to be responsible for a given interaction (Boxem et al., 2008Boxem M. Maliga Z. Klitgord N. Li N. Lemmens I. Mana M. de Lichtervelde L. Mul J.D. van de Peut D. 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These technological developments will improve our ability to measure the effects of changes (i.e., mutations) across many layers of biological organization (interactions, cells, tissues, etc.). In turn, this will help us to formulate unified models of biological systems that could, for example, be used to better understand how mutations result in disease.
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