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A Global Genetic Network Charts the Functional Map of a Cell

A genetic map of a cell begins to explain how genes work together to coordinate cellular life.


Genome sequencing projects are providing an unprecedented view of genetic variation. However, our ability to interpret genetic information to understand cell function and predict phenotypes, including disease, remains limited, in large part to the extensive buffering of genomes that makes most individual genes dispensable for life. Recent studies applied automated genetics to construct a global genetic interaction network for a model cell to explore the extent to which genetic interactions reveal cellular function and contribute to complex inherited traits, as well as to discover the general principles of genetic networks.


Like most eukaryotic organisms, only a small subset of yeast genes (~1,000 out of 6,000) are essential for viability and disrupting one of these genes leads to cell death. On the other hand, the remaining ~5,000 genes are individually dispensable such that a yeast cell can tolerate and survive the loss of any one of these genes. While most yeast genes are not required for viability, it does not mean that these genes are not important. Rather, the large fraction of nonessential genes is a reflection of the extensive buffering or backup systems cells have evolved to ensure survival in response to genetic perturbations and environmental insults.

Identification and dissection of the genetic systems that back up critical cellular processes are important to our understanding of the basic components of the cell and how the genes encoding these different parts work together to coordinate fundamental functions essential for cellular life. One way to identify these backup systems involves systematically mutating two genes at a time and examining the effect of the mutated gene pair on cell growth and proliferation. An unexpected change in cell growth or fitness resulting from disruption of a pair of genes is known as a genetic interaction. Extreme negative genetic interactions, referred to as “synthetic lethal” interactions, describe instances where two mutations, each causing little or no growth defect on their own, result in cell death when combined in the same genome. These interactions are particularly interesting because they identify genes that impinge on or buffer the same essential biological function. As a result, large-scale screening for genetic interactions provides a means of exploring the buffering capacity and creating a diagram of the functional wiring of a cell.

Mutant combinations affecting different genes are not always associated with detrimental effects. In some instances, deleterious consequences associated with a mutation in one gene can be overcome or avoided by a second mutation in a different gene. These so-called positive or suppression interactions represent another means to understand and potentially treat genetic disorders since they may provide insight into to why some people remain healthy despite carrying genetic mutations that are known to cause debilitating diseases.


A recent study describing 15 years of research and culminating in the first complete genetic network map of a yeast cell revealed that thousands of individual genes interact with one another to support cellular life.

The researchers constructed and analyzed more than 23 million pairs of mutant gene combinations to map an intricate network of close to 1 million genetic interactions, identifying connections between most of the 6,000 genes in yeast. They discovered thousands of synthetic lethal interactions identifying gene pairs that must be carrying out a similar function in the cell and highlighted the extent and complexity of genetic backup systems at work in even the simplest of eukaryotic organisms. Moreover, the study confirmed that genetic networks are hierarchical and composed of groups of interacting genes that correspond to specific biological pathways, processes and cellular compartments hence providing a complete picture of the functional architecture of a cell and a powerful tool for discovering gene function.

In a second study, the same research group focused on a subset of positive genetic interactions to complete a comprehensive analysis of suppressor mutations for the same yeast cell. By combining an analysis of all published suppression interactions with an unbiased experimental approach, this study measured how well cells grew when they carried a damaging mutation on their own, or in combination with another mutation. Since harmful mutations adversely affect cell growth, any improvement in growth rate must be attributed to a suppression mutation in a second gene. These experiments revealed hundreds of suppressor mutations for the known damaging mutations and succeeded in revealing a set of general properties that can be used to predict suppressive interactions. Notably, the data revealed properties generally associated with suppressor interactions. For example, these interactions often involve pairs of genes that share similar roles in the cell. This may suggest that if we hope to find these relationships in more complex species (e.g. humans), we don’t need to look far from genes with damaging mutations to identify second suppressor mutations.


The researchers previously developed Synthetic Genetic Array (SGA) technology – an automated method that combines high density arrays of yeast gene mutants with robotic manipulations for highthroughput construction of double mutant strains. SGA methodology was applied to construct ~23 million double mutant strains. Arrayed plates of yeast mutant colonies derived from SGA were photographed and the resulting images were processed using customdeveloped image processing software identifying and measuring colonies and their pixel sizes. To identify negative and positive genetic interactions, yeast colony size pixel data were subjected to a series of normalization steps to correct for systematic experimental effects, after which genetic interactions were measured by comparing corrected double mutant colony size to the colony sizes of the corresponding single mutants. Employing this combined experimental and computational approach led to the construction of a comprehensive yeast genetic interaction network.

The same SGA approach was combined with next generation DNA sequencing in a second study to map the genome location and identity of spontaneously arising mutations that were found to suppress the growth defects associated with other defined mutations.


It is becoming clear that human genes have many functional backup systems, suggesting that most hereditary diseases will likely not be traced to variation in a single causal gene. The prevalence of genetic interactions in yeast emphasize the importance of developing new strategies to expand our focus beyond single genes to explore combinations of genes with the potential to underlie or modify diseases and exploit such genetic interactions for therapeutic benefit. The concept of synthetic lethality is already changing cancer treatment because synthetic lethal relationships pinpoint genetic vulnerabilities specific to tumor cells that can be targeted for drug therapy. Mapping similar genetic interactions in humans is a daunting challenge given the ~200 million possible gene pairs in the human genome. Indeed, finding synthetic lethal or suppressor mutations in humans is akin to searching for a needle in a haystack. However, the fundamental principles emerging from genetic interaction studies using genetically tractable model organisms, such as yeast, may help to simplify the problem by providing clues about how genetic mutations combine to manifest inherited traits, including disease.


Costanzo et al. (2016). A global genetic interaction network maps a wiring diagram of cellular function. Science 353: aaf1420.

van Leeuwen et al. (2016). Exploring genetic suppression interactions on a global scale. Science 354: aag0839.

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