A global interaction network maps a wiring diagram of cellular function

By M. Costanzo, Benjamin VanderSluis, Elizabeth N. Koch, Anastasia Baryshnikova, Carles Pons, Guihong Tan, Wen Wang, Matej Ušaj, J. Hanchard, Susan D. Lee and 44 more. 2016

1,113 citations recorded by the source corpus.

INTRODUCTION Genetic interactions occur when mutations in two or more genes combine to generate an unexpected phenotype. An extreme negative or synthetic lethal genetic interaction occurs when two mutations, neither lethal individually, combine to cause cell death. Conversely, positive genetic interactions occur when two mutations produce a phenotype that is less severe than expected. Genetic interactions identify functional relationships between genes and can be harnessed for biological discovery and therapeutic target identification. They may also explain a considerable component of the undiscovered genetics associated with human diseases. Here, we describe construction and analysis of a comprehensive genetic interaction network for a eukaryotic cell. RATIONALE Genome sequencing projects are providing an unprecedented view of genetic variation. However, our ability to interpret genetic information to predict inherited phenotypes remains limited, in large part due to the extensive buffering of genomes, making most individual eukaryotic genes dispensable for life. To explore the extent to which genetic interactions reveal cellular function and contribute to complex phenotypes, and to discover the general principles of genetic networks, we used automated yeast genetics to construct a global genetic interaction network. RESULTS We tested most of the ~6000 genes in the yeast Saccharomyces cerevisiae for all possible pairwise genetic interactions, identifying nearly 1 million interactions, including ~550,000 negative and ~350,000 positive interactions, spanning ~90% of all yeast genes. Essential genes were network hubs, displaying five times as many interactions as nonessential genes. The set of genetic interactions or the genetic interaction profile for a gene provides a quantitative measure of function, and a global network based on genetic interaction profile similarity revealed a hierarchy of modules reflecting the functional architecture of a cell. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections associated with defects in cell cycle progression or cellular proteostasis. Importantly, the global network illustrates how coherent sets of negative or positive genetic interactions connect protein complex and pathways to map a functional wiring diagram of the cell. CONCLUSION A global genetic interaction network highlights the functional organization of a cell and provides a resource for predicting gene and pathway function. This network emphasizes the prevalence of genetic interactions and their potential to compound phenotypes associated with single mutations. Negative genetic interactions tend to connect functionally related genes and thus may be predicted using alternative functional information. Although less functionally informative, positive interactions may provide insights into general mechanisms of genetic suppression or resiliency. We anticipate that the ordered topology of the global genetic network, in which genetic interactions connect coherently within and between protein complexes and pathways, may be exploited to decipher genotype-to-phenotype relationships. A global network of genetic interaction profile similarities. (Left) Genes with similar genetic interaction profiles are connected in a global network, such that genes exhibiting more similar profiles are located closer to each other, whereas genes with less similar profiles are positioned farther apart. (Right) Spatial analysis of functional enrichment was used to identify and color network regions enriched for similar Gene Ontology bioprocess terms. We generated a global genetic interaction network for Saccharomyces cerevisiae, constructing more than 23 million double mutants, identifying about 550,000 negative and about 350,000 positive genetic interactions. This comprehensive network maps genetic interactions for essential gene pairs, highlighting essential genes as densely connected hubs. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes and pathways, biological processes, and cellular compartments. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections among gene pairs, rather than shared functionality. The global network illustrates how coherent sets of genetic interactions connect protein complex and pathway modules to map a functional wiring diagram of the cell.

Subfields: Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Prioritizing network communities (2018, methodology): “For example, gene communities detected in a gene interaction network 11 provide predictions/hypotheses about disease pathways 2,3 , but to con ﬁ rm these predictions scientists have to test every detected community by performing experiments in a wet laboratory 3,8 .”
The BioGRID database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions (2020, background): “47 A substantial fraction of known human proteins remain poorly characterized, due in large part to a dearth of definitive genetic methods to assign function.”
The BioGRID interaction database: 2019 update (2018, background): “(SGA) screens were curated from two recent publications (34,35).”
OpenCell: proteome-scale endogenous tagging enables the cartography of human cellular organization (2022, background): “S9B), reflecting, as expected, that functional pathways are formed by groups of proteins that physically interact ( 60 , 61 ).”
Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens. (2016, methodology): “, 2002), as we have shown for both the perturbations and the gene targets; and (2) sparsity, such that themajority of gene pairs (or higher order combinations) do not manifest genetic interactions (supported by fitness studies of genetic interactions in yeast) (Costanzo et al., 2016).”
The genetic landscape of a physical interaction (2018, background): “In budding yeast, this has been undertaken on a genomic scale, with the resulting network of interactions referred to as the ‘genetic landscape’ of a cell (Costanzo et al., 2010, 2016; Tong et al., 2004).”
Using deep learning to model the hierarchical structure and function of a cell (2018, methodology): “Extensive training was made possible by an existing compendium of yeast growth phenotypes measured for single-and double-gene-deletion genotypes, comprising several million genotype–phenotype training examples 15,16 .”