Huiming Ding

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.

Functional dissection of protein complexes involved in yeast chromosome biology using a genetic interaction map

Defining the functional relationships between proteins is critical for understanding virtually all aspects of cell biology. Large-scale identification of protein complexes has provided one important step towards this goal; however, even knowledge of the stoichiometry, affinity and lifetime of every protein–protein interaction would not reveal the functional relationships between and within such complexes. Genetic interactions can provide functional information that is largely invisible to protein–protein interaction data sets. Here we present an epistatic miniarray profile (E-MAP) consisting of quantitative pairwise measurements of the genetic interactions between 743 Saccharomyces cerevisiae genes involved in various aspects of chromosome biology (including DNA replication/repair, chromatid segregation and transcriptional regulation). This E-MAP reveals that physical interactions fall into two well-represented classes distinguished by whether or not the individual proteins act coherently to carry out a common function. Thus, genetic interaction data make it possible to dissect functionally multi-protein complexes, including Mediator, and to organize distinct protein complexes into pathways. In one pathway defined here, we show that Rtt109 is the founding member of a novel class of histone acetyltransferases responsible for Asf1-dependent acetylation of histone H3 on lysine 56. This modification, in turn, enables a ubiquitin ligase complex containing the cullin Rtt101 to ensure genomic integrity during DNA replication.

Integration of chemical-genetic and genetic interaction data links bioactive compounds to cellular target pathways

Bioactive compounds can be valuable research tools and drug leads, but it is often difficult to identify their mechanism of action or cellular target. Here we investigate the potential for integration of chemical-genetic and genetic interaction data to reveal information about the pathways and targets of inhibitory compounds. Taking advantage of the existing complete set of yeast haploid deletion mutants, we generated drug-hypersensitivity (chemical-genetic) profiles for 12 compounds. In addition to a set of compound-specific interactions, the chemical-genetic profiles identified a large group of genes required for multidrug resistance. In particular, yeast mutants lacking a functional vacuolar H+-ATPase show multidrug sensitivity, a phenomenon that may be conserved in mammalian cells. By filtering chemical-genetic profiles for the multidrug-resistant genes and then clustering the compound-specific profiles with a compendium of large-scale genetic interaction profiles, we were able to identify target pathways or proteins. This method thus provides a powerful means for inferring mechanism of action.

A Snf2 Family ATPase Complex Required for Recruitment of the Histone H2A Variant Htz1

Deletions of three yeast genes, SET2, CDC73, and DST1, involved in transcriptional elongation and/or chromatin metabolism were used in conjunction with genetic array technology to screen approximately 4700 yeast deletions and identify double deletion mutants that produce synthetic growth defects. Of the five deletions interacting genetically with all three starting mutations, one encoded the histone H2A variant Htz1 and three encoded components of a novel 13 protein complex, SWR-C, containing the Snf2 family ATPase, Swr1. The SWR-C also copurified with Htz1 and Bdf1, a TFIID-interacting protein that recognizes acetylated histone tails. Deletions of the genes encoding Htz1 and seven nonessential SWR-C components caused a similar spectrum of synthetic growth defects when combined with deletions of 384 genes involved in transcription, suggesting that Htz1 and SWR-C belong to the same pathway. We show that recruitment of Htz1 to chromatin requires the SWR-C. Moreover, like Htz1 and Bdf1, the SWR-C promotes gene expression near silent heterochromatin.

The synthetic genetic interaction spectrum of essential genes

The nature of synthetic genetic interactions involving essential genes (those required for viability) has not been previously examined in a broad and unbiased manner. We crossed yeast strains carrying promoter-replacement alleles for more than half of all essential yeast genes to a panel of 30 different mutants with defects in diverse cellular processes. The resulting genetic network is biased toward interactions between functionally related genes, enabling identification of a previously uncharacterized essential gene (PGA1) required for specific functions of the endoplasmic reticulum. But there are also many interactions between genes with dissimilar functions, suggesting that individual essential genes are required for buffering many cellular processes. The most notable feature of the essential synthetic genetic network is that it has an interaction density five times that of nonessential synthetic genetic networks, indicating that most yeast genetic interactions involve at least one essential gene.

Quantitative analysis of fitness and genetic interactions in yeast on a genome scale

Global quantitative analysis of genetic interactions is a powerful approach for deciphering the roles of genes and mapping functional relationships among pathways. Using colony size as a proxy for fitness, we developed a method for measuring fitness-based genetic interactions from high-density arrays of yeast double mutants generated by synthetic genetic array (SGA) analysis. We identified several experimental sources of systematic variation and developed normalization strategies to obtain accurate single- and double-mutant fitness measurements, which rival the accuracy of other high-resolution studies. We applied the SGA score to examine the relationship between physical and genetic interaction networks, and we found that positive genetic interactions connect across functionally distinct protein complexes revealing a network of genetic suppression among loss-of-function alleles.

eSGA: E. coli Synthetic Genetic Array analysis

Physical and functional interactions define the molecular organization of the cell. Genetic interactions, or epistasis, tend to occur between gene products involved in parallel pathways or interlinked biological processes. High-throughput experimental systems to examine genetic interactions on a genome-wide scale have been devised for Saccharomyces cerevisiae, Schizosaccharomyces pombe, Caenorhabditis elegans and Drosophila melanogaster, but have not been reported previously for prokaryotes. Here we describe the development of a quantitative screening procedure for monitoring bacterial genetic interactions based on conjugation of Escherichia coli deletion or hypomorphic strains to create double mutants on a genome-wide scale. The patterns of synthetic sickness and synthetic lethality (aggravating genetic interactions) we observed for certain double mutant combinations provided information about functional relationships and redundancy between pathways and enabled us to group bacterial gene products into functional modules.NOTE: In the version of this article initially published online two author names (Gabriel Moreno-Hagelseib and Constantine Christopolous) were spelled incorrectly. The correct author names are Gabriel Moreno-Hagelsieb and Constantine Christopoulos. The error has been corrected for the print, PDF and HTML versions of this article.

DRYGIN: a database of quantitative genetic interaction networks in yeast

Genetic interactions are highly informative for deciphering the underlying functional principles that govern how genes control cell processes. Recent developments in Synthetic Genetic Array (SGA) analysis enable the mapping of quantitative genetic interactions on a genome-wide scale. To facilitate access to this resource, which will ultimately represent a complete genetic interaction network for a eukaryotic cell, we developed DRYGIN (Data Repository of Yeast Genetic Interactions)—a web database system that aims at providing a central platform for yeast genetic network analysis and visualization. In addition to providing an interface for searching the SGA genetic interactions, DRYGIN also integrates other data sources, in order to associate the genetic interactions with pathway information, protein complexes, other binary genetic and physical interactions, and Gene Ontology functional annotation. DRYGIN version 1.0 currently holds more than 5.4 million measurements of genetic interacting pairs involving ∼4500 genes, and is available at http://drygin.ccbr.utoronto.ca

Genetic Interaction Maps in Escherichia coli Reveal Functional Crosstalk among Cell Envelope Biogenesis Pathways

As the interface between a microbe and its environment, the bacterial cell envelope has broad biological and clinical significance. While numerous biosynthesis genes and pathways have been identified and studied in isolation, how these intersect functionally to ensure envelope integrity during adaptive responses to environmental challenge remains unclear. To this end, we performed high-density synthetic genetic screens to generate quantitative functional association maps encompassing virtually the entire cell envelope biosynthetic machinery of Escherichia coli under both auxotrophic (rich medium) and prototrophic (minimal medium) culture conditions. The differential patterns of genetic interactions detected among >235,000 digenic mutant combinations tested reveal unexpected condition-specific functional crosstalk and genetic backup mechanisms that ensure stress-resistant envelope assembly and maintenance. These networks also provide insights into the global systems connectivity and dynamic functional reorganization of a universal bacterial structure that is both broadly conserved among eubacteria (including pathogens) and an important target.

Identification of Saccharomyces cerevisiae spindle pole body remodeling factors.

The Saccharomyces cerevisiae centrosome or spindle pole body (SPB) is a dynamic structure that is remodeled in a cell cycle dependent manner. The SPB increases in size late in the cell cycle and during most cell cycle arrests and exchanges components during G1/S. We identified proteins involved in the remodeling process using a strain in which SPB remodeling is conditionally induced. This strain was engineered to express a modified SPB component, Spc110, which can be cleaved upon the induction of a protease. Using a synthetic genetic array analysis, we screened for genes required only when Spc110 cleavage is induced. Candidate SPB remodeling factors fell into several functional categories: mitotic regulators, microtubule motors, protein modification enzymes, and nuclear pore proteins. The involvement of candidate genes in SPB assembly was assessed in three ways: by identifying the presence of a synthetic growth defect when combined with an Spc110 assembly defective mutant, quantifying growth of SPBs during metaphase arrest, and comparing distribution of SPB size during asynchronous growth. These secondary screens identified four genes required for SPB remodeling: NUP60, POM152, and NCS2 are required for SPB growth during a mitotic cell cycle arrest, and UBC4 is required to maintain SPB size during the cell cycle. These findings implicate the nuclear pore, urmylation, and ubiquitination in SPB remodeling and represent novel functions for these genes.