Franco J. Vizeacoumar

The Genetic Landscape of a Cell

Making Connections Genetic interaction profiles highlight cross-connections between bioprocesses, providing a global view of cellular pleiotropy, and enable the prediction of genetic network hubs. Costanzo et al. (p. 425) performed a pairwise fitness screen covering approximately one-third of all potential genetic interactions in yeast, examining 5.4 million gene-gene pairs and generating quantitative profiles for ∼75% of the genome. Of the pairwise interactions tested, about 3% of the genes investigated interact under the conditions tested. On the basis of these data, a reference map for the yeast genetic network was created. A genome-wide interaction map of yeast identifies genetic interactions, networks, and function. A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for ~75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function. The global network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with a number of different gene attributes, which may be informative about genetic network hubs in other organisms. We also demonstrate that extensive and unbiased mapping of the genetic landscape provides a key for interpretation of chemical-genetic interactions and drug target identification.

Systematic exploration of essential yeast gene function with temperature-sensitive mutants

Conditional temperature-sensitive (ts) mutations are valuable reagents for studying essential genes in the yeast Saccharomyces cerevisiae. We constructed 787 ts strains, covering 497 (∼45%) of the 1,101 essential yeast genes, with ∼30% of the genes represented by multiple alleles. All of the alleles are integrated into their native genomic locus in the S288C common reference strain and are linked to a kanMX selectable marker, allowing further genetic manipulation by synthetic genetic array (SGA)–based, high-throughput methods. We show two such manipulations: barcoding of 440 strains, which enables chemical-genetic suppression analysis, and the construction of arrays of strains carrying different fluorescent markers of subcellular structure, which enables quantitative analysis of phenotypes using high-content screening. Quantitative analysis of a GFP-tubulin marker identified roles for cohesin and condensin genes in spindle disassembly. This mutant collection should facilitate a wide range of systematic studies aimed at understanding the functions of essential genes.

Transcriptome profiling to identify genes involved in peroxisome assembly and function.

Yeast cells were induced to proliferate peroxisomes, and microarray transcriptional profiling was used to identify PEX genes encoding peroxins involved in peroxisome assembly and genes involved in peroxisome function. Clustering algorithms identified 224 genes with expression profiles similar to those of genes encoding peroxisomal proteins and genes involved in peroxisome biogenesis. Several previously uncharacterized genes were identified, two of which, YPL112c and YOR084w, encode proteins of the peroxisomal membrane and matrix, respectively. Ypl112p, renamed Pex25p, is a novel peroxin required for the regulation of peroxisome size and maintenance. These studies demonstrate the utility of comparative gene profiling as an alternative to functional assays to identify genes with roles in peroxisome biogenesis.

Interaction landscape of membrane - protein complexes in Saccharomyces cerevisiae

Macromolecular assemblies involving membrane proteins (MPs) serve vital biological roles and are prime drug targets in a variety of diseases. Large-scale affinity purification studies of soluble-protein complexes have been accomplished for diverse model organisms, but no global characterization of MP-complex membership has been described so far. Here we report a complete survey of 1,590 putative integral, peripheral and lipid-anchored MPs from Saccharomyces cerevisiae, which were affinity purified in the presence of non-denaturing detergents. The identities of the co-purifying proteins were determined by tandem mass spectrometry and subsequently used to derive a high-confidence physical interaction map encompassing 1,726 membrane protein–protein interactions and 501 putative heteromeric complexes associated with the various cellular membrane systems. Our analysis reveals unexpected physical associations underlying the membrane biology of eukaryotes and delineates the global topological landscape of the membrane interactome.

Synthetic genetic array (SGA) analysis in Saccharomyces cerevisiae and Schizosaccharomyces pombe.

A genetic interaction occurs when the combination of two mutations leads to an unexpected phenotype. Screens for synthetic genetic interactions have been used extensively to identify genes whose products are functionally related. In particular, synthetic lethal genetic interactions often identify genes that buffer one another or impinge on the same essential pathway. For the yeast Saccharomyces cerevisiae, we developed a method termed synthetic genetic array (SGA) analysis, which offers an efficient approach for the systematic construction of double mutants and enables a global analysis of synthetic genetic interactions. In a typical SGA screen, a query mutation is crossed to an ordered array of ~5000 viable gene deletion mutants (representing ~80% of all yeast genes) such that meiotic progeny harboring both mutations can be scored for fitness defects. This approach can be extended to all ~6000 genes through the use of yeast arrays containing mutants carrying conditional or hypomorphic alleles of essential genes. Estimating the fitness for the two single mutants and their corresponding double mutant enables a quantitative measurement of genetic interactions, distinguishing negative (synthetic lethal) and positive (within pathway and suppression) interactions. The profile of genetic interactions represents a rich phenotypic signature for each gene and clustering genetic interaction profiles group genes into functionally relevant pathways and complexes. This array-based approach automates yeast genetic analysis in general and can be easily adapted for a number of different genetic screens or combined with high-content screening systems to quantify the activity of specific reporters in genome-wide sets of single or more complex multiple mutant backgrounds. Comparison of genetic and chemical-genetic interaction profiles offers the potential to link bioactive compounds to their targets. Finally, we also developed an SGA system for the fission yeast Schizosaccharomyces pombe, providing another model system for comparative analysis of genetic networks and testing the conservation of genetic networks over millions of years of evolution.

Global Map of SUMO Function Revealed by Protein-Protein Interaction and Genetic Networks

Systematic functional genomics approaches were used to map a network centered on the small ubiquitin-related modifier (SUMO) system. Over 250 physical interactions were identified using the SUMO protein as bait in affinity purification-mass spectrometry and yeast two-hybrid screens. More than 500 genes and 1400 synthetic genetic interactions were mapped by synthetic genetic array (SGA) analysis using eight different SUMO pathway query genes. The resultant global SUMO network highlights its role in 15 major biological processes and better defines functional relationships between the different components of the SUMO pathway. Using this information-rich resource, we have identified roles for the SUMO system in the function of the AAA ATPase Cdc48p, the regulation of lipid metabolism, localization of the ATP-dependent endonuclease Dna2p, and recovery from the DNA-damage checkpoint.

Integrating high-throughput genetic interaction mapping and high-content screening to explore yeast spindle morphogenesis

A combination of yeast genetics, synthetic genetic array analysis, and high-throughput screening reveals that sumoylation of Mcm21p promotes disassembly of the mitotic spindle.

YHR150w and YDR479c encode peroxisomal integral membrane proteins involved in the regulation of peroxisome number, size, and distribution in Saccharomyces cerevisiae

The peroxin Pex24p of the yeast Yarrowia lipolytica exhibits high sequence similarity to two hypothetical proteins, Yhr150p and Ydr479p, encoded by the Saccharomyces cerevisiae genome. Like YlPex24p, both Yhr150p and Ydr479p have been shown to be integral to the peroxisomal membrane, but unlike YlPex24p, their levels of synthesis are not increased upon a shift of cells from glucose- to oleic acid–containing medium. Peroxisomes of cells deleted for either or both of the YHR150w and YDR479c genes are increased in number, exhibit extensive clustering, are smaller in area than peroxisomes of wild-type cells, and often exhibit membrane thickening between adjacent peroxisomes in a cluster. Peroxisomes isolated from cells deleted for both genes have a decreased buoyant density compared with peroxisomes isolated from wild-type cells and still exhibit clustering and peroxisomal membrane thickening. Overexpression of the genes PEX25 or VPS1, but not the gene PEX11, restored the wild-type phenotype to cells deleted for one or both of the YHR150w and YDR479c genes. Together, our data suggest a role for Yhr150p and Ydr479p, together with Pex25p and Vps1p, in regulating peroxisome number, size, and distribution in S. cerevisiae. Because of their role in peroxisome dynamics, YHR150w and YDR479c have been designated as PEX28 and PEX29, respectively, and their encoded peroxins as Pex28p and Pex29p.

Genome-wide analysis of intracellular pH reveals quantitative control of cell division rate by pHc in Saccharomyces cerevisiae

BackgroundBecause protonation affects the properties of almost all molecules in cells, cytosolic pH (pHc) is usually assumed to be constant. In the model organism yeast, however, pHc changes in response to the presence of nutrients and varies during growth. Since small changes in pHc can lead to major changes in metabolism, signal transduction, and phenotype, we decided to analyze pHc control.ResultsIntroducing a pH-sensitive reporter protein into the yeast deletion collection allowed quantitative genome-wide analysis of pHc in live, growing yeast cultures. pHc is robust towards gene deletion; no single gene mutation led to a pHc of more than 0.3 units lower than that of wild type. Correct pHc control required not only vacuolar proton pumps, but also strongly relied on mitochondrial function. Additionally, we identified a striking relationship between pHc and growth rate. Careful dissection of cause and consequence revealed that pHc quantitatively controls growth rate. Detailed analysis of the genetic basis of this control revealed that the adequate signaling of pHc depended on inositol polyphosphates, a set of relatively unknown signaling molecules with exquisitely pH sensitive properties.ConclusionsWhile pHc is a very dynamic parameter in the normal life of yeast, genetically it is a tightly controlled cellular parameter. The coupling of pHc to growth rate is even more robust to genetic alteration. Changes in pHc control cell division rate in yeast, possibly as a signal. Such a signaling role of pHc is probable, and may be central in development and tumorigenesis.

Genetic interactions reveal the evolutionary trajectories of duplicate genes

The characterization of functional redundancy and divergence between duplicate genes is an important step in understanding the evolution of genetic systems. Large‐scale genetic network analysis in Saccharomyces cerevisiae provides a powerful perspective for addressing these questions through quantitative measurements of genetic interactions between pairs of duplicated genes, and more generally, through the study of genome‐wide genetic interaction profiles associated with duplicated genes. We show that duplicate genes exhibit fewer genetic interactions than other genes because they tend to buffer one another functionally, whereas observed interactions are non‐overlapping and reflect their divergent roles. We also show that duplicate gene pairs are highly imbalanced in their number of genetic interactions with other genes, a pattern that appears to result from asymmetric evolution, such that one duplicate evolves or degrades faster than the other and often becomes functionally or conditionally specialized. The differences in genetic interactions are predictive of differences in several other evolutionary and physiological properties of duplicate pairs.