Biology Reference
In-Depth Information
fitness defect associated with most genes cannot be
explained entirely by functional redundancy between non-
essential gene pairs. A comprehensive understanding of
gene function and genetic interaction networks therefore
requires further analysis in a variety of conditions that
depend on the activity of otherwise dispensable genes.
One of the first studies to explore condition-specific
genetic interactions in a quantitative manner examined all
possible pairwisemutant combinations of a panel of 26 genes
in the absence and presence of the DNA damaging agent
methyl methanesulfonate (MMS) [19] . Although genes
implicated in DNA replication and repair pathways tend to
show many genetic interactions under standard laboratory
conditions [2] , exposure to MMS uncovered twice as many
interactions, highlighting the potential of condition-specific
genetic networks [19] . More recently, condition-specific
genetic interaction screens have been extended to larger sets
of DNA replication and repair genes [99] . Bandyopadhyay
and co-authors analyzed all pairwise genetic interactions
among 418 yeast genes, covering DNA repair proteins as
well as a biased set of signalingmolecules and transcriptional
factors. Similarly to previous studies [19] , a significant
increase in genetic interaction density was observed in
response to MMS treatment [99] . Substantial rewiring of the
genetic network was also observed when cells were chemi-
cally challenged, indicating that, in addition to uncovering
new interactions, condition-specific studies also reveal
changes in network structure and topology [99] . Exploring
genetic network dynamics in response to different experi-
mental conditions will further the functional characterization
of the cell as well as our understanding of how cells adapt to
cope with environmental stress.
receptor endocytosis by measuring the efficiency of protein
internalization in an array of single and double mutants
[102] . Internalization efficiency was quantified using an
enzymatic assay based on the cell surface localization of
Snc1, the yeast vesicle-associated membrane protein
(VAMP)/synaptobrevin homologue. Quantitative negative
and positive genetic interactions derived from Snc1 local-
ization signals were used to cluster genes into functional
modules identifying 20 novel genes involved in Snc1
uptake at the plasma membrane [102] . Using SGA meth-
odologies to introduce a green-fluorescent protein (GFP)-
reporter gene into single and double deletion mutants,
another group examined genetic interactions among genes
implicated in the yeast unfolded protein response (UPR)
[103] . Finally, fluorescent reporters have also been intro-
duced into the yeast deletion collection using SGA to
examine cell cycle-dependent transcription on a genome-
wide scale [104,105] .
The spectrum of phenotypic traits amenable to genome-
scale mapping of genetic networks has expanded immea-
surably with technological advances in high-throughput
microscopy and imaging tools [106] . By combining cyto-
logical reporters with high-content screening it is now
possible to classify and measure a variety of morphological
and protein localization phenotypes in a large-scale manner
[100,107] . For example, morphological data pertaining to
the cell wall, actin cytoskeleton, and nuclear DNA have
been systematically collected and analyzed for the entire set
of S. cerevisiae non-essential gene deletion mutants [100] .
Similar approaches have been applied in mammalian cells
to characterize genes regulating various cellular processes,
including cell morphology and cell cycle progression [108] .
Combining high content screening with SGA ( Box 6.2 )
enabled genome-wide screening of single and double
mutants for phenotypic defects associated with mitotic
spindle morphogenesis [109] . This endeavor generated
a ~fourfold increase over the fitness-based genetic interac-
tion network derived for the microtubule-binding protein
Bim1 by identifying 100 novel genetic interactions that
impinge on spindle morphology [109] . Thus, integrating
high-throughput technologies for combining mutations,
such as SGA, with diverse and quantitative phenotypic
assays should lead to the construction of high-resolution
networks that provide comprehensive genome coverage and
the potential to incorporate temporal and environmental
influences to accurately reflect global cellular functions.
Quantitative Phenotypes to Measure Genetic
Interactions
Owing to ease of measurement and amenability to high-
throughput applications, most genome-scale studies con-
ducted to date have focused on cell fitness as the phenotypic
readout for genetic interactions [2,3,25,27] .However,itis
becoming increasingly evident that more subtle and
specific phenotypes harbor as much functional information
as fitness. For example, nearly 50% of non-essential yeast
deletion mutants, despite having no fitness defect, exhibit
a number of morphological defects [100] . This finding
suggests that investigation of complex phenotypes will
uncover functional connections between genes that may not
be apparent from growth data alone.
A variety of quantitative phenotypic assays have been
developed to map the genetic networks that underlie
specific biological processes, including yeast filamentous
growth [20] as well as protein kinase A [101] and RAS
signaling [67] . One study investigated the mechanisms of
GENETIC INTERACTIONS AND GENOME-
WIDE ASSOCIATION STUDIES
In the past decade much progress has been made in our
understanding of genetic interactions. Enabled by devel-
opments in experimental
technology,
it
is possible to
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